From 02810c7aa89b8100b90b7b0f5e96bc55aafd3d0a Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Tue, 16 Jun 2026 11:52:38 +0200
Subject: [PATCH 001/292] Fix and restrict NVFP4 edge-cases in llama-graph
 (#24331)

* Move post-GEMM MUL required for dequant b4 lora and bias add

see https://github.com/ggml-org/llama.cpp/pull/23484 :
1. For lora, I would presume we want fully dequantized values before
   doing the residuals, but this depends on how the LORAs were
generated. Literature tells me LORA happens post-mul but pre-bias add https://github.com/ggml-org/llama.cpp/pull/8332
2. For ModelOPT, bias-add should happen on [fully-dequantized
   values](https://github.com/NVIDIA/Model-Optimizer/blob/b49f9b9e2d747af992d78a3aa7f10efe5a8847e1/modelopt/torch/quantization/backends/nvfp4_gemm.py#L59-L64)

* Restrict build_ffn for NVFP4 to supported combinations
---
 src/llama-graph.cpp | 103 +++++++++++++++++++++++++-------------------
 src/llama-graph.h   |   5 ++-
 2 files changed, 61 insertions(+), 47 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 7468bd9b7..68c9e606c 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1088,6 +1088,10 @@ ggml_tensor * llm_graph_context::build_lora_mm(
           ggml_tensor * w_s) const {
     ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
 
+    if (w_s) {
+        res = ggml_mul(ctx0, res, w_s);
+    }
+
     for (const auto & lora : *loras) {
         llama_adapter_lora_weight * lw = lora.first->get_weight(w);
         if (lw == nullptr) {
@@ -1106,18 +1110,24 @@ ggml_tensor * llm_graph_context::build_lora_mm(
         res = ggml_add(ctx0, res, ab_cur);
     }
 
-    if (w_s) {
-        res = ggml_mul(ctx0, res, w_s);
-    }
-
     return res;
 }
 
 ggml_tensor * llm_graph_context::build_lora_mm_id(
           ggml_tensor * w,   // ggml_tensor * as
           ggml_tensor * cur, // ggml_tensor * b
-          ggml_tensor * ids) const {
+          ggml_tensor * ids,
+          ggml_tensor * w_s) const {
     ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
+
+    if (w_s) {
+        const int64_t n_expert = w_s->ne[0];
+        const int64_t n_tokens = cur->ne[2];
+        ggml_tensor * s = ggml_reshape_3d(ctx0, w_s, 1, n_expert, 1);
+        s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
+        s = ggml_get_rows(ctx0, s, ids);
+        res = ggml_mul(ctx0, res, s);
+    }
     for (const auto & lora : *loras) {
         llama_adapter_lora_weight * lw = lora.first->get_weight(w);
         if (lw == nullptr) {
@@ -1269,6 +1279,29 @@ ggml_tensor * llm_graph_context::build_ffn(
      llm_ffn_op_type   type_op,
    llm_ffn_gate_type   type_gate,
                  int   il) const {
+    // NVFP4 support is currently restricted to
+    // 1) LORA absence (*_s would be applied after LORA residual, which is incorrect)
+    // 2) bias absense (*_s would be applied after bias addition, which is incorrect)
+    // TODO: disambiguate LLM-architectural scales (which use *_s) from NVFP4 scale_2 (which also uses *_s currently)
+    auto has_lora = [this](ggml_tensor * w) {
+        if (!w) {
+            return false;
+        }
+        for (const auto & lora : *loras) {
+            if (lora.first->get_weight(w) != nullptr) {
+                return true;
+            }
+        }
+        return false;
+    };
+
+    GGML_ASSERT(!up_s   || !up_b   || !up   || up->type   != GGML_TYPE_NVFP4);
+    GGML_ASSERT(!gate_s || !gate_b || !gate || gate->type != GGML_TYPE_NVFP4);
+    GGML_ASSERT(!down_s || !down_b || !down || down->type != GGML_TYPE_NVFP4);
+    GGML_ASSERT(!up_s   || !up   || up->type   != GGML_TYPE_NVFP4 || !has_lora(up));
+    GGML_ASSERT(!gate_s || !gate || gate->type != GGML_TYPE_NVFP4 || !has_lora(gate));
+    GGML_ASSERT(!down_s || !down || down->type != GGML_TYPE_NVFP4 || !has_lora(down));
+
     ggml_tensor * tmp = up ? build_lora_mm(up, cur) : cur;
     cb(tmp, "ffn_up", il);
 
@@ -1627,23 +1660,18 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
     if (gate_up_exps) {
         // merged gate_up path: one mul_mat_id, then split into gate and up views
-        ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens]
+        ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts, up_exps_s); // [n_ff*2, n_expert_used, n_tokens]
         cb(gate_up, "ffn_moe_gate_up", il);
 
+        if (up_exps_s) {
+            cb(gate_up, "ffn_moe_gate_up_scaled", il);
+        }
+
         if (gate_up_exps_b) {
             gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts);
             cb(gate_up, "ffn_moe_gate_up_biased", il);
         }
 
-        // apply per-expert scale2 to merged gate_up (use up_exps_s since gate and up are fused)
-        if (up_exps_s) {
-            ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
-            s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
-            s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
-            gate_up = ggml_mul(ctx0, gate_up, s);
-            cb(gate_up, "ffn_moe_gate_up_scaled", il);
-        }
-
         const int64_t n_ff = gate_up->ne[0] / 2;
         cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
         cb(cur, "ffn_moe_gate", il);
@@ -1651,43 +1679,33 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
         cb(up, "ffn_moe_up", il);
     } else {
         // separate gate and up path
-        up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+        up = build_lora_mm_id(up_exps, cur, selected_experts, up_exps_s); // [n_ff, n_expert_used, n_tokens]
         cb(up, "ffn_moe_up", il);
 
+        if (up_exps_s) {
+            cb(up, "ffn_moe_up_scaled", il);
+        }
+
         if (up_exps_b) {
             up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
             cb(up, "ffn_moe_up_biased", il);
         }
 
-        // apply per-expert scale2 to up
-        if (up_exps_s) {
-            ggml_tensor * s = ggml_reshape_3d(ctx0, up_exps_s, 1, n_expert, 1);
-            s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
-            s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
-            up = ggml_mul(ctx0, up, s);
-            cb(up, "ffn_moe_up_scaled", il);
-        }
-
         if (gate_exps) {
-            cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+            cur = build_lora_mm_id(gate_exps, cur, selected_experts, gate_exps_s); // [n_ff, n_expert_used, n_tokens]
             cb(cur, "ffn_moe_gate", il);
         } else {
             cur = up;
         }
 
+        if (gate_exps_s) {
+            cb(cur, "ffn_moe_gate_scaled", il);
+        }
+
         if (gate_exps_b) {
             cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
             cb(cur, "ffn_moe_gate_biased", il);
         }
-
-        // apply per-expert scale2 to gate
-        if (gate_exps_s) {
-            ggml_tensor * s = ggml_reshape_3d(ctx0, gate_exps_s, 1, n_expert, 1);
-            s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
-            s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
-            cur = ggml_mul(ctx0, cur, s);
-            cb(cur, "ffn_moe_gate_scaled", il);
-        }
     }
 
     const bool has_gate = gate_exps || gate_up_exps;
@@ -1759,23 +1777,18 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
             GGML_ABORT("fatal error");
     }
 
-    experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
+    experts = build_lora_mm_id(down_exps, cur, selected_experts, down_exps_s); // [n_embd, n_expert_used, n_tokens]
     cb(experts, "ffn_moe_down", il);
 
+    if (down_exps_s) {
+        cb(experts, "ffn_moe_down_scaled", il);
+    }
+
     if (down_exps_b) {
         experts = ggml_add_id(ctx0, experts, down_exps_b, selected_experts);
         cb(experts, "ffn_moe_down_biased", il);
     }
 
-    // apply per-expert scale2 to down
-    if (down_exps_s) {
-        ggml_tensor * s = ggml_reshape_3d(ctx0, down_exps_s, 1, n_expert, 1);
-        s = ggml_repeat_4d(ctx0, s, 1, n_expert, n_tokens, 1);
-        s = ggml_get_rows(ctx0, s, selected_experts); // [1, n_expert_used, n_tokens]
-        experts = ggml_mul(ctx0, experts, s);
-        cb(experts, "ffn_moe_down_scaled", il);
-    }
-
     if (!weight_before_ffn) {
         experts = ggml_mul(ctx0, experts, weights);
         cb(experts, "ffn_moe_weighted", il);
diff --git a/src/llama-graph.h b/src/llama-graph.h
index cc5cfe51d..5e8a65835 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -853,11 +853,12 @@ struct llm_graph_context {
               ggml_tensor * cur,
               ggml_tensor * w_s = nullptr) const;
 
-    // do mat_mul_id, while optionally apply lora
+    // do mat_mul_id, while optionally apply lora and per-expert scale
     ggml_tensor * build_lora_mm_id(
               ggml_tensor * w,   // ggml_tensor * as
               ggml_tensor * cur, // ggml_tensor * b
-              ggml_tensor * ids) const;
+              ggml_tensor * ids,
+              ggml_tensor * w_s = nullptr) const;
 
     ggml_tensor * build_norm(
              ggml_tensor * cur,

From c1304d7b28e14380dbb90252c92aa2798db60185 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Tue, 16 Jun 2026 14:14:22 +0200
Subject: [PATCH 002/292] ui: add source toggle to mermaid and svg blocks
 (#24652)

* ui: add source toggle to mermaid and svg blocks

Add a toggle button next to copy and preview that switches a rendered
mermaid or svg block to its source code and back. The button is shared by
both block types and the rendered view stays the default.

The source view reuses the code block scroll container and the highlighted
code element captured at transform time, so it matches the app code blocks
without highlighting again.

Make tall diagrams scroll like text code blocks: safe centering keeps the
diagram centered when it fits and falls back to start alignment when it
overflows, so the top stays reachable instead of clipping above.

Keep the block header opaque and layered above the scrolled diagram, and
ignore header clicks in the zoom handler, so a button click never falls
through to the zoom dialog.

* ui: transparent diagram block header, address review from @allozaur
---
 .../MarkdownContent/MarkdownContent.svelte    | 29 +++++++-
 .../MarkdownContent/markdown-content.css      | 48 +++++++++++--
 .../plugins/rehype/code-block-utils.ts        | 70 +++++++++++++++++--
 .../plugins/rehype/enhance-mermaid-blocks.ts  | 16 ++++-
 .../plugins/rehype/enhance-svg-blocks.ts      | 24 +++++--
 .../plugins/rehype/pre-transform.ts           | 14 +++-
 tools/ui/src/lib/constants/diagram-blocks.ts  |  9 +++
 tools/ui/src/lib/constants/icons.ts           |  2 +
 tools/ui/src/lib/constants/index.ts           |  1 +
 9 files changed, 192 insertions(+), 21 deletions(-)
 create mode 100644 tools/ui/src/lib/constants/diagram-blocks.ts

diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
index 7139d2e63..8ac7f9448 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/MarkdownContent.svelte
@@ -41,6 +41,7 @@
 		DATA_ERROR_HANDLED_ATTR,
 		BOOL_TRUE_STRING,
 		SETTINGS_KEYS,
+		CODE_BLOCK_HEADER_CLASS,
 		MERMAID_WRAPPER_CLASS,
 		MERMAID_BLOCK_CLASS,
 		MERMAID_LANGUAGE,
@@ -53,7 +54,11 @@
 		SVG_TAG_PREFIX,
 		SVG_SOURCE_ATTR,
 		SVG_RENDERED_ATTR,
-		SVG_INLINE_SHADOW_STYLE
+		SVG_INLINE_SHADOW_STYLE,
+		TOGGLE_SOURCE_BTN_CLASS,
+		DIAGRAM_VIEW_MODE_ATTR,
+		DIAGRAM_VIEW_RENDERED,
+		DIAGRAM_VIEW_SOURCE
 	} from '$lib/constants';
 	import { ColorMode, UrlProtocol } from '$lib/enums';
 	import { FileTypeText } from '$lib/enums/files.enums';
@@ -501,6 +506,23 @@
 	async function handleMermaidClick(event: MouseEvent) {
 		const target = event.target as HTMLElement;
 
+		// Toggle a diagram block between its rendered view and its source view.
+		// Shared by mermaid and svg, css drives the visibility from the wrapper mode.
+		const toggleBtn = target.closest(`.${TOGGLE_SOURCE_BTN_CLASS}`);
+		if (toggleBtn) {
+			event.preventDefault();
+			event.stopPropagation();
+
+			const wrapper = toggleBtn.closest(`.${MERMAID_WRAPPER_CLASS}, .${SVG_WRAPPER_CLASS}`);
+			if (!wrapper) return;
+
+			const isSource = wrapper.getAttribute(DIAGRAM_VIEW_MODE_ATTR) === DIAGRAM_VIEW_SOURCE;
+			const next = isSource ? DIAGRAM_VIEW_RENDERED : DIAGRAM_VIEW_SOURCE;
+			wrapper.setAttribute(DIAGRAM_VIEW_MODE_ATTR, next);
+			toggleBtn.setAttribute('aria-pressed', String(!isSource));
+			return;
+		}
+
 		// Check if clicking on copy or preview button in mermaid block
 		const copyBtn = target.closest(`.${MERMAID_WRAPPER_CLASS} .copy-code-btn`);
 		const previewBtn = target.closest(`.${MERMAID_WRAPPER_CLASS} .preview-code-btn`);
@@ -573,6 +595,11 @@
 			}
 		}
 
+		// A click on the header chrome targets the action buttons, never the
+		// diagram. Guard so a header click can not fall through to the click to
+		// zoom branches below, whatever the scroll position or stacking.
+		if (target.closest(`.${CODE_BLOCK_HEADER_CLASS}`)) return;
+
 		// Open preview when clicking the svg block itself. A final block carries its
 		// source, a streaming block does not and is mirrored live into the dialog.
 		const svgEl = target.closest(`.${SVG_BLOCK_CLASS}`);
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css
index 072db383d..b0e04ca62 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/markdown-content.css
@@ -300,7 +300,8 @@ div.markdown-user-content :global(.table-wrapper) {
 }
 
 .markdown-content :global(.copy-code-btn),
-.markdown-content :global(.preview-code-btn) {
+.markdown-content :global(.preview-code-btn),
+.markdown-content :global(.toggle-source-btn) {
 	display: flex;
 	align-items: center;
 	justify-content: center;
@@ -312,15 +313,22 @@ div.markdown-user-content :global(.table-wrapper) {
 }
 
 .markdown-content :global(.copy-code-btn:hover),
-.markdown-content :global(.preview-code-btn:hover) {
+.markdown-content :global(.preview-code-btn:hover),
+.markdown-content :global(.toggle-source-btn:hover) {
 	transform: scale(1.05);
 }
 
 .markdown-content :global(.copy-code-btn:active),
-.markdown-content :global(.preview-code-btn:active) {
+.markdown-content :global(.preview-code-btn:active),
+.markdown-content :global(.toggle-source-btn:active) {
 	transform: scale(0.95);
 }
 
+/* Pressed state marks the source view as active */
+.markdown-content :global(.toggle-source-btn[aria-pressed='true']) {
+	color: var(--primary);
+}
+
 .markdown-content :global(.code-block-wrapper pre) {
 	background: transparent;
 	margin: 0;
@@ -629,8 +637,8 @@ div.markdown-user-content :global(.table-wrapper) {
 	overflow-y: auto;
 	overflow-x: auto;
 	display: flex;
-	align-items: center;
-	justify-content: center;
+	align-items: safe center;
+	justify-content: safe center;
 	padding: 3rem 1rem 1rem;
 }
 
@@ -645,7 +653,9 @@ div.markdown-user-content :global(.table-wrapper) {
 	overflow-y: visible;
 }
 
-/* Diagram block uses same header styling as code blocks */
+/* Diagram block uses same header styling as code blocks. The header floats over
+   scrollable diagram content and stays transparent, so the overflow shows up to
+   the box edge. It keeps a z-index so it stays the click target above content. */
 .markdown-content :global(.mermaid-block-wrapper .code-block-header),
 .markdown-content :global(.svg-block-wrapper .code-block-header) {
 	display: flex;
@@ -657,6 +667,7 @@ div.markdown-user-content :global(.table-wrapper) {
 	top: 0;
 	left: 0;
 	right: 0;
+	z-index: 2;
 }
 
 .markdown-content :global(.mermaid-block-wrapper .code-block-actions),
@@ -683,6 +694,31 @@ div.markdown-user-content :global(.table-wrapper) {
 	padding: 3rem 1rem;
 }
 
+/* Source view stays hidden while the block renders, css swaps the two views
+   from the wrapper mode so the click handler only flips one attribute. The view
+   reuses the code block scroll container, so it matches the app code blocks. */
+.markdown-content :global(.diagram-source) {
+	display: none;
+	text-align: left;
+}
+
+.markdown-content :global(.diagram-source pre) {
+	background: transparent;
+	margin: 0;
+	border-radius: 0;
+	border: none;
+	font-size: 0.875rem;
+}
+
+.markdown-content :global([data-view-mode='source'] .mermaid-scroll-container),
+.markdown-content :global([data-view-mode='source'] .svg-scroll-container) {
+	display: none;
+}
+
+.markdown-content :global([data-view-mode='source'] .diagram-source) {
+	display: block;
+}
+
 /* Streaming mermaid block - empty preview box */
 .mermaid-streaming-block {
 	min-height: 300px;
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/code-block-utils.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/code-block-utils.ts
index 732315464..f1dd867e8 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/code-block-utils.ts
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/code-block-utils.ts
@@ -7,12 +7,16 @@ import type { Element, ElementContent } from 'hast';
 import {
 	CODE_BLOCK_HEADER_CLASS,
 	CODE_BLOCK_ACTIONS_CLASS,
+	CODE_BLOCK_SCROLL_CONTAINER_CLASS,
 	CODE_LANGUAGE_CLASS,
 	COPY_CODE_BTN_CLASS,
 	PREVIEW_CODE_BTN_CLASS,
+	TOGGLE_SOURCE_BTN_CLASS,
+	DIAGRAM_SOURCE_CLASS,
 	RELATIVE_CLASS,
 	COPY_ICON_SVG,
-	PREVIEW_ICON_SVG
+	PREVIEW_ICON_SVG,
+	CODE_ICON_SVG
 } from '$lib/constants';
 
 export interface BlockIdGenerator {
@@ -32,14 +36,16 @@ export function createIconElement(svg: string): Element {
 }
 
 /**
- * Creates a button element with icon.
+ * Creates a button element with icon. Extra properties merge onto the button,
+ * which lets a stateful button carry attributes like aria-pressed.
  */
 export function createButton(
 	className: string,
 	title: string,
 	iconSvg: string,
 	id: string,
-	idAttribute: string
+	idAttribute: string,
+	extraProperties: Record<string, string> = {}
 ): Element {
 	return {
 		type: 'element',
@@ -48,7 +54,8 @@ export function createButton(
 			className: [className],
 			[idAttribute]: id,
 			title,
-			type: 'button'
+			type: 'button',
+			...extraProperties
 		},
 		children: [createIconElement(iconSvg)]
 	};
@@ -72,6 +79,52 @@ export function createPreviewButton(
 	return createButton(PREVIEW_CODE_BTN_CLASS, title, PREVIEW_ICON_SVG, id, idAttribute);
 }
 
+/**
+ * Creates a button that toggles a diagram block between its rendered view and
+ * its source view. aria-pressed starts false, the rendered view is the default.
+ */
+export function createToggleSourceButton(
+	id: string,
+	idAttribute: string,
+	title: string = 'Toggle source'
+): Element {
+	return createButton(TOGGLE_SOURCE_BTN_CLASS, title, CODE_ICON_SVG, id, idAttribute, {
+		'aria-pressed': 'false'
+	});
+}
+
+/**
+ * Creates a source view for a diagram block. It reuses the code block scroll
+ * container so it matches the app code blocks, and wraps the highlighted code
+ * element captured at transform time. A missing code element falls back to a
+ * plain code node built from the raw source.
+ */
+export function createSourceView(
+	codeElement: Element | undefined,
+	source: string,
+	language: string
+): Element {
+	const code: Element = codeElement ?? {
+		type: 'element',
+		tagName: 'code',
+		properties: { className: ['hljs', `language-${language}`] },
+		children: [{ type: 'text', value: source }]
+	};
+	return {
+		type: 'element',
+		tagName: 'div',
+		properties: { className: [DIAGRAM_SOURCE_CLASS, CODE_BLOCK_SCROLL_CONTAINER_CLASS] },
+		children: [
+			{
+				type: 'element',
+				tagName: 'pre',
+				properties: {},
+				children: [code]
+			}
+		]
+	};
+}
+
 /**
  * Creates a block header with language label and action buttons.
  */
@@ -116,14 +169,17 @@ export function createScrollContainer(preElement: Element, scrollContainerClass:
 }
 
 /**
- * Creates a wrapper element with header and scroll container.
+ * Creates a wrapper element with header and scroll container. Extra children
+ * append after the scroll container, which lets a block carry a source view
+ * alongside its rendered output.
  */
 export function createWrapper(
 	header: Element,
 	preElement: Element,
 	wrapperClass: string,
 	scrollContainerClass: string,
-	additionalAttributes?: Record<string, string>
+	additionalAttributes?: Record<string, string>,
+	extraChildren: Element[] = []
 ): Element {
 	return {
 		type: 'element',
@@ -132,7 +188,7 @@ export function createWrapper(
 			className: [wrapperClass, RELATIVE_CLASS],
 			...additionalAttributes
 		} as Element['properties'],
-		children: [header, createScrollContainer(preElement, scrollContainerClass)]
+		children: [header, createScrollContainer(preElement, scrollContainerClass), ...extraChildren]
 	};
 }
 
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-mermaid-blocks.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-mermaid-blocks.ts
index f9decf206..4007c20a1 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-mermaid-blocks.ts
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-mermaid-blocks.ts
@@ -19,12 +19,17 @@ import {
 	MERMAID_BLOCK_CLASS,
 	MERMAID_LANGUAGE,
 	MERMAID_SYNTAX_ATTR,
-	MERMAID_ID_ATTR
+	MERMAID_ID_ATTR,
+	DIAGRAM_VIEW_MODE_ATTR,
+	DIAGRAM_VIEW_RENDERED
 } from '$lib/constants';
+import type { DiagramPreData } from './pre-transform';
 import {
 	createBlockHeader,
 	createCopyButton,
 	createPreviewButton,
+	createToggleSourceButton,
+	createSourceView,
 	createWrapper,
 	generateBlockId
 } from './code-block-utils';
@@ -75,16 +80,23 @@ export const rehypeEnhanceMermaidBlocks: Plugin<[], Root> = () => {
 
 			const actions = [
 				createCopyButton(mermaidId, MERMAID_ID_ATTR, 'Copy mermaid syntax'),
+				createToggleSourceButton(mermaidId, MERMAID_ID_ATTR, 'Toggle mermaid source'),
 				createPreviewButton(mermaidId, MERMAID_ID_ATTR, 'Preview diagram')
 			];
 
 			const header = createBlockHeader(MERMAID_LANGUAGE, mermaidId, MERMAID_ID_ATTR, actions);
+			const preservedCode = (node.data as DiagramPreData | undefined)?.sourceCode;
+			const sourceView = createSourceView(preservedCode, diagramText, MERMAID_LANGUAGE);
 			const wrapper = createWrapper(
 				header,
 				node,
 				MERMAID_WRAPPER_CLASS,
 				MERMAID_SCROLL_CONTAINER_CLASS,
-				{ [MERMAID_ID_ATTR]: mermaidId }
+				{
+					[MERMAID_ID_ATTR]: mermaidId,
+					[DIAGRAM_VIEW_MODE_ATTR]: DIAGRAM_VIEW_RENDERED
+				},
+				[sourceView]
 			);
 
 			// Replace pre with wrapper in parent
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-svg-blocks.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-svg-blocks.ts
index e5e7514ed..55bcb6065 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-svg-blocks.ts
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/enhance-svg-blocks.ts
@@ -18,12 +18,17 @@ import {
 	SVG_BLOCK_CLASS,
 	SVG_LANGUAGE,
 	SVG_SOURCE_ATTR,
-	SVG_ID_ATTR
+	SVG_ID_ATTR,
+	DIAGRAM_VIEW_MODE_ATTR,
+	DIAGRAM_VIEW_RENDERED
 } from '$lib/constants';
+import type { DiagramPreData } from './pre-transform';
 import {
 	createBlockHeader,
 	createCopyButton,
 	createPreviewButton,
+	createToggleSourceButton,
+	createSourceView,
 	createWrapper,
 	generateBlockId
 } from './code-block-utils';
@@ -65,13 +70,24 @@ export const rehypeEnhanceSvgBlocks: Plugin<[], Root> = () => {
 
 			const actions = [
 				createCopyButton(svgId, SVG_ID_ATTR, 'Copy svg source'),
+				createToggleSourceButton(svgId, SVG_ID_ATTR, 'Toggle svg source'),
 				createPreviewButton(svgId, SVG_ID_ATTR, 'Preview svg')
 			];
 
 			const header = createBlockHeader(SVG_LANGUAGE, svgId, SVG_ID_ATTR, actions);
-			const wrapper = createWrapper(header, node, SVG_WRAPPER_CLASS, SVG_SCROLL_CONTAINER_CLASS, {
-				[SVG_ID_ATTR]: svgId
-			});
+			const preservedCode = (node.data as DiagramPreData | undefined)?.sourceCode;
+			const sourceView = createSourceView(preservedCode, svgSource, SVG_LANGUAGE);
+			const wrapper = createWrapper(
+				header,
+				node,
+				SVG_WRAPPER_CLASS,
+				SVG_SCROLL_CONTAINER_CLASS,
+				{
+					[SVG_ID_ATTR]: svgId,
+					[DIAGRAM_VIEW_MODE_ATTR]: DIAGRAM_VIEW_RENDERED
+				},
+				[sourceView]
+			);
 
 			// Replace pre with wrapper in parent
 			(parent.children as ElementContent[])[index] = wrapper;
diff --git a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/pre-transform.ts b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/pre-transform.ts
index 06848eb26..7aa967bb8 100644
--- a/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/pre-transform.ts
+++ b/tools/ui/src/lib/components/app/content/MarkdownContent/plugins/rehype/pre-transform.ts
@@ -2,6 +2,15 @@ import type { Plugin } from 'unified';
 import type { Root, Element, ElementContent, Text } from 'hast';
 import { visit } from 'unist-util-visit';
 
+/**
+ * Metadata a diagram pre carries on its unist data field. The source code holds
+ * the highlighted code element captured before the pre became a render target,
+ * which the enhancer reuses to build a matching source view.
+ */
+export interface DiagramPreData {
+	sourceCode: Element;
+}
+
 /**
  * Recursively extracts all text content from a HAST node.
  * Handles nested elements (e.g., span wrappers from syntax highlighting).
@@ -69,7 +78,10 @@ export function createPreTransform(
 					properties: {
 						className: [targetClass]
 					},
-					children: [{ type: 'text', value: text } as Text]
+					children: [{ type: 'text', value: text } as Text],
+					// Keep the highlighted code element so the block can offer a source
+					// view that matches the app code blocks without re highlighting.
+					data: { sourceCode: codeElement } satisfies DiagramPreData
 				};
 
 				(parent.children as ElementContent[])[index] = pre;
diff --git a/tools/ui/src/lib/constants/diagram-blocks.ts b/tools/ui/src/lib/constants/diagram-blocks.ts
new file mode 100644
index 000000000..caeb6b5b3
--- /dev/null
+++ b/tools/ui/src/lib/constants/diagram-blocks.ts
@@ -0,0 +1,9 @@
+// Shared constants for diagram blocks (mermaid and svg) that toggle between a
+// rendered view and a source view. The wrapper carries the active mode, css
+// drives the visibility, the click handler only flips the attribute.
+
+export const DIAGRAM_VIEW_MODE_ATTR = 'data-view-mode';
+export const DIAGRAM_VIEW_RENDERED = 'rendered';
+export const DIAGRAM_VIEW_SOURCE = 'source';
+export const DIAGRAM_SOURCE_CLASS = 'diagram-source';
+export const TOGGLE_SOURCE_BTN_CLASS = 'toggle-source-btn';
diff --git a/tools/ui/src/lib/constants/icons.ts b/tools/ui/src/lib/constants/icons.ts
index a9448c2a6..6ef02c4cb 100644
--- a/tools/ui/src/lib/constants/icons.ts
+++ b/tools/ui/src/lib/constants/icons.ts
@@ -39,3 +39,5 @@ export const MODALITY_LABELS = {
 export const COPY_ICON_SVG = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-copy-icon lucide-copy"><rect width="14" height="14" x="8" y="8" rx="2" ry="2"/><path d="M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2"/></svg>`;
 
 export const PREVIEW_ICON_SVG = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-eye lucide-eye-icon"><path d="M2.062 12.345a1 1 0 0 1 0-.69C3.5 7.73 7.36 5 12 5s8.5 2.73 9.938 6.655a1 1 0 0 1 0 .69C20.5 16.27 16.64 19 12 19s-8.5-2.73-9.938-6.655"/><circle cx="12" cy="12" r="3"/></svg>`;
+
+export const CODE_ICON_SVG = `<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-code lucide-code-icon"><path d="m16 18 6-6-6-6"/><path d="m8 6-6 6 6 6"/></svg>`;
diff --git a/tools/ui/src/lib/constants/index.ts b/tools/ui/src/lib/constants/index.ts
index 07e441112..c51d84cdc 100644
--- a/tools/ui/src/lib/constants/index.ts
+++ b/tools/ui/src/lib/constants/index.ts
@@ -30,6 +30,7 @@ export * from './literal-html';
 export * from './markdown';
 export * from './mermaid-blocks';
 export * from './svg-blocks';
+export * from './diagram-blocks';
 export * from './max-bundle-size';
 export * from './mcp';
 export * from './mcp-form';

From 94653a9be4c66cf1263514cc96466075e7673584 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 16 Jun 2026 23:23:36 +0800
Subject: [PATCH 003/292] change test prompt for macos

---
 .github/workflows/kcpp-build-release-macos.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/kcpp-build-release-macos.yaml b/.github/workflows/kcpp-build-release-macos.yaml
index bfed75028..61e7ea90d 100644
--- a/.github/workflows/kcpp-build-release-macos.yaml
+++ b/.github/workflows/kcpp-build-release-macos.yaml
@@ -43,7 +43,7 @@ jobs:
         id: test
         run: |
           wget https://huggingface.co/concedo/koboldcpp/resolve/main/baby_llama.gguf
-          dist/koboldcpp-mac-arm64 --model baby_llama.gguf --gpulayers 99 --benchmark --prompt 'Hi, my name is'
+          dist/koboldcpp-mac-arm64 --model baby_llama.gguf --gpulayers 99 --benchmark --prompt 'Once upon a'
 
       - name: Save artifact
         uses: actions/upload-artifact@v6

From 74ade52741203e5c8f81eaf06a96cb1cfe15f2a3 Mon Sep 17 00:00:00 2001
From: "Alessandro de Oliveira Faria (A.K.A.CABELO)" <cabelo@opensuse.org>
Date: Tue, 16 Jun 2026 15:24:28 -0300
Subject: [PATCH 004/292] vendor : update BoringSSL to 0.20260616.0 (#24693)

---
 vendor/cpp-httplib/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index 5fb3cf8d5..408e06151 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -41,7 +41,7 @@ if (LLAMA_BUILD_BORINGSSL)
     set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
 
     set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
-    set(BORINGSSL_VERSION "0.20260526.0" CACHE STRING "BoringSSL version")
+    set(BORINGSSL_VERSION "0.20260616.0" CACHE STRING "BoringSSL version")
 
     message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
 

From 9b260fc9efff574394bd4948ffaf533ef1d00082 Mon Sep 17 00:00:00 2001
From: Francois Dugast <francois.dugast@intel.com>
Date: Wed, 17 Jun 2026 07:54:21 +0200
Subject: [PATCH 005/292] sycl: Add optional USM system allocations (#22526)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This introduces an optional feature to allocate large GPU buffers (≥ 1GB)
using USM system allocations if supported by the device. It allows using
buffers from the system allocator then letting the system manage memory
migrations between host and device as necessary.

This feature is disabled by default and requires the GGML_SYCL_USM_SYSTEM
environment variable to enable. If USM system allocations are not supported
by the device or the system, we fallback to regular allocations.

This feature can allow VRAM overcommit. For example, the test below fails
on B580 due to lack of memory for allocation, but it passes when enabling
USM system allocations:

  ./examples/sycl/test.sh -m Qwen3.5-27B-Q3_K_M.gguf -lv 4

Signed-off-by: Francois Dugast <francois.dugast@intel.com>
---
 docs/backend/SYCL.md             |  1 +
 ggml/src/ggml-sycl/common.hpp    |  1 +
 ggml/src/ggml-sycl/ggml-sycl.cpp | 92 +++++++++++++++++++++++---------
 3 files changed, 69 insertions(+), 25 deletions(-)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 18307d170..97d4b5216 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -720,6 +720,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Allow SYCL/Unified Runtime Level Zero device allocations larger than 4 GiB. llama.cpp's direct Level Zero allocation path requests the relaxed maximum-size limit itself when GGML_SYCL_ENABLE_LEVEL_ZERO=1. |
+| GGML_SYCL_USM_SYSTEM | 0 (default) or 1 | Enable experimental support for [USM system allocations](https://github.khronos.org/SYCL_Reference/iface/usm_basic_concept.html#system-allocations) for large GPU buffers. This requires enough host memory for model weights and caches, an Intel Xe2+ GPU such as BMG or newer and supported on Linux only, with CONFIG_DRM_XE_GPUSVM enabled. |
 
 ## Compile-time Flags
 
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 5fb1a1d6b..9ec94464b 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -230,6 +230,7 @@ struct sycl_device_info {
     size_t  total_vram;
     sycl_hw_info hw_info;
     optimize_feature opt_feature;
+    bool    usm_system_support; // support for USM system allocations
 };
 
 
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 0900fade6..f029f6325 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -72,6 +72,9 @@
 #include "ggml-sycl/gated_delta_net.hpp"
 #include "ggml-sycl/pool.hpp"
 
+#define MEM_SIZE_2M	0x00200000
+#define MEM_SIZE_1G	0x40000000
+
 static bool g_sycl_loaded = false;
 int g_ggml_sycl_debug = 0;
 int g_ggml_sycl_disable_optimize = 0;
@@ -83,7 +86,7 @@ int g_ggml_sycl_use_async_mem_op = 0;
 int g_ggml_sycl_use_async_mem_op_requested = 1;
 int g_ggml_sycl_enable_level_zero = 0;
 int g_ggml_sycl_enable_flash_attention = 1;
-
+int g_ggml_sycl_usm_system = 0;
 
 static ggml_sycl_device_info ggml_sycl_init() {
     ggml_sycl_device_info info = {};
@@ -137,6 +140,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
         info.devices[i].opt_feature.reorder = device.ext_oneapi_architecture_is(syclex::arch_category::intel_gpu);
         info.devices[i].smpbo = prop.get_local_mem_size();
         info.devices[i].warp_size = WARP_SIZE;
+        info.devices[i].usm_system_support = device.has(sycl::aspect::usm_system_allocations);
 
         info.max_work_group_sizes[i] = prop.get_max_work_group_size();
         info.devices[i].max_wg_per_cu = info.max_work_group_sizes[i] / prop.get_max_compute_units();
@@ -274,6 +278,8 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_enable_flash_attention = 0;
 #endif
 
+        g_ggml_sycl_usm_system = ggml_sycl_get_env("GGML_SYCL_USM_SYSTEM", 0);
+
         GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n");
 
         GGML_LOG_INFO("Build with Macros:\n");
@@ -342,6 +348,8 @@ static void ggml_check_sycl() try {
             g_ggml_sycl_enable_flash_attention);
 #endif
 
+        GGML_LOG_INFO("  GGML_SYCL_USM_SYSTEM: %d\n", g_ggml_sycl_usm_system);
+
 /* NOT REMOVE, keep it for next optimize for XMX.
 #if defined(SYCL_USE_XMX)
         fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__);
@@ -417,6 +425,14 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+inline void free_aligned_mem_host(void * memblock) {
+#ifdef _WIN32
+    _aligned_free(memblock);
+#else
+    free(memblock);
+#endif
+}
+
 // sycl buffer
 
 struct ggml_backend_sycl_buffer_context {
@@ -426,9 +442,10 @@ struct ggml_backend_sycl_buffer_context {
     std::string name;
     optimize_feature opt_feature;
     std::vector<ggml_tensor_extra_gpu *> tensor_extras;
+    bool is_usm_system;
 
-    ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) :
-        device(device), dev_ptr(dev_ptr), stream(stream) {
+    ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream, bool is_usm_system) :
+        device(device), dev_ptr(dev_ptr), stream(stream), is_usm_system(is_usm_system) {
             check_allow_gpu_index(device);
             name = (GGML_SYCL_NAME + std::to_string(device));
             opt_feature = ggml_sycl_info().devices[device].opt_feature;
@@ -437,7 +454,10 @@ struct ggml_backend_sycl_buffer_context {
     ~ggml_backend_sycl_buffer_context() {
         if (dev_ptr != nullptr) {
             ggml_sycl_set_device(device);
-            SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(dev_ptr, *stream)));
+            if (is_usm_system)
+                free_aligned_mem_host(dev_ptr);
+            else
+                SYCL_CHECK(CHECK_TRY_ERROR(ggml_sycl_free_device(dev_ptr, *stream)));
         }
 
         //release extra used by tensors
@@ -759,21 +779,59 @@ static const char * ggml_backend_sycl_buffer_type_get_name(ggml_backend_buffer_t
     return ctx->name.c_str();
 }
 
+static bool check_usm_system(int device, size_t size) {
+    bool use_usm_system = g_ggml_sycl_usm_system && size >= MEM_SIZE_1G;
+
+    if (use_usm_system && !ggml_sycl_info().devices[device].usm_system_support) {
+        GGML_LOG_INFO("Device does not support USM system allocations\n");
+        use_usm_system = false;
+    }
+
+    return use_usm_system;
+}
+
+inline void * aligned_malloc_host(size_t alignment, size_t size) {
+#ifdef _WIN32
+    return _aligned_malloc(size, alignment);
+#else
+    return aligned_alloc(alignment, size);
+#endif
+}
+
 static ggml_backend_buffer_t
 ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft,
                                            size_t size) try {
+    ggml_check_sycl();
+
     ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context;
     ggml_sycl_set_device(buft_ctx->device);
     const queue_ptr stream = buft_ctx->stream;
     size = std::max(size, (size_t)1); // syclMalloc returns null for size 0
+    /*
+    Alignment below ensures best performance. While in theory it could lead to
+    wasting memory, this is acceptable because in practice only few buffers are
+    allocated and even less exceed the minimum size accepted here for USM system
+    allocations.
+     */
+    size_t alignment = MEM_SIZE_2M;
+    size_t aligned_size = ((size + alignment - 1) / alignment) * alignment;
+    bool use_usm_system = check_usm_system(buft_ctx->device, aligned_size);
 
     void * dev_ptr;
-    SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)ggml_sycl_malloc_device(size, *stream)));
-    if (!dev_ptr) {
-      GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device\n", __func__, size);
-      return nullptr;
+    if (use_usm_system) {
+        dev_ptr = (void *)aligned_malloc_host(alignment, aligned_size);
+        if (!dev_ptr) {
+            GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on host\n", __func__, size);
+            return nullptr;
+        }
+    } else {
+        SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)ggml_sycl_malloc_device(size, *stream)));
+        if (!dev_ptr) {
+          GGML_LOG_ERROR("%s: can't allocate %lu Bytes of memory on device\n", __func__, size);
+          return nullptr;
+        }
     }
-    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream);
+    ggml_backend_sycl_buffer_context * ctx = new  ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream, use_usm_system);
     return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size);
 }
 catch (sycl::exception const &exc) {
@@ -1300,22 +1358,6 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
     GGML_UNUSED(buft);
 }
 
-inline void * aligned_malloc_host(size_t alignment, size_t size) {
-#ifdef _WIN32
-    return _aligned_malloc(size, alignment);
-#else
-    return aligned_alloc(alignment, size);
-#endif
-}
-
-inline void free_aligned_mem_host(void * memblock) {
-#ifdef _WIN32
-    _aligned_free(memblock);
-#else
-    free(memblock);
-#endif
-}
-
 static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     free_aligned_mem_host((void *)buffer->context);
 }

From ebbc1e51c136093f887bfc36e337520da352ee22 Mon Sep 17 00:00:00 2001
From: Alexey Kopytko <alexey@kopytko.com>
Date: Wed, 17 Jun 2026 14:57:29 +0900
Subject: [PATCH 006/292] SYCL: fix use-after-free bug with async memcpy in MoE
 prefill (#24676)

* SYCL: fix a bug with async memcpy

* make mmid_row_mapping_host persistent

* comment on stream->wait

* Apply suggestion from @sanmai

* Apply suggestion from @sanmai

* Apply suggestion from @sanmai
---
 ggml/src/ggml-sycl/common.hpp    | 7 +++++++
 ggml/src/ggml-sycl/ggml-sycl.cpp | 9 +++------
 2 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 9ec94464b..96586ea46 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -324,6 +324,11 @@ void ggml_sycl_free_device(void *ptr, sycl::queue &q);
 
 void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> streams={});
 
+struct mmid_row_mapping {
+    int32_t i1;
+    int32_t i2;
+};
+
 namespace sycl_ex = sycl::ext::oneapi::experimental;
 struct ggml_backend_sycl_context {
     int device;
@@ -421,6 +426,8 @@ struct ggml_backend_sycl_context {
 
     std::unique_ptr<ggml_sycl_pool> host_pools[GGML_SYCL_MAX_DEVICES];
 
+    std::vector<mmid_row_mapping> mmid_row_mapping_host;
+
     static std::unique_ptr<ggml_sycl_pool> new_pool_for_device(queue_ptr qptr, int device);
 
     static std::unique_ptr<ggml_sycl_pool> new_pool_for_host(queue_ptr qptr, int device);
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index f029f6325..376d4376f 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4224,11 +4224,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
 }
 
 
-struct mmid_row_mapping {
-    int32_t i1;
-    int32_t i2;
-};
-
 __dpct_inline__ static void k_copy_src1_to_contiguous(
     const char *__restrict__ src1_original, char *__restrict__ src1_contiguous,
     const mmid_row_mapping *__restrict__ row_mapping,
@@ -4399,6 +4394,8 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
 
     SYCL_CHECK(CHECK_TRY_ERROR(
         stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids))));
+
+    // also ensures ctx.mmid_row_mapping_host is drained before we use it again
     SYCL_CHECK(CHECK_TRY_ERROR(stream->wait()));
 
     ggml_tensor src0_row = *src0;
@@ -4456,7 +4453,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
         // where each expert's slice starts and the previous ends (row indices, right-exclusive)
         std::vector<int64_t> expert_row_offsets;
         // the sources (slot/token pairs) of contiguous rows to guide k_copy_src1_to_contiguous
-        std::vector<mmid_row_mapping> routed_row_src;
+        std::vector<mmid_row_mapping> & routed_row_src = ctx.mmid_row_mapping_host;
 
         mmid_counting_sort_rows(ids, ids_host.data(), n_ids, n_as, n_routed_rows,
                                 expert_row_counts, expert_row_offsets, routed_row_src);

From 58728bdbf0421f3f6d4407436ccbadf0ee90ab53 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Wed, 17 Jun 2026 13:58:03 +0800
Subject: [PATCH 007/292] sycl : Enable to support fp16 by OPs: SQR, SQRT, LOG,
 SIN, COS, CLAMP (#24692)

---
 docs/ops.md                      | 12 ++++-----
 docs/ops/SYCL.csv                | 44 ++++++++++++++++----------------
 ggml/src/ggml-sycl/ggml-sycl.cpp |  5 ----
 3 files changed, 28 insertions(+), 33 deletions(-)

diff --git a/docs/ops.md b/docs/ops.md
index fe74df805..d46f9b731 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -23,7 +23,7 @@ Legend:
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -32,7 +32,7 @@ Legend:
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
@@ -65,7 +65,7 @@ Legend:
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
@@ -99,13 +99,13 @@ Legend:
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index 9ff664a5b..19a5346f8 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -9743,29 +9743,29 @@
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL"
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL"
 "SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL"
-"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","0","no","SYCL"
-"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","0","no","SYCL"
-"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","0","no","SYCL"
-"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","SYCL"
+"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","SYCL"
+"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
 "SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
 "SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
-"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","SQR","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL"
-"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","SQRT","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL"
-"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","LOG","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL"
-"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","SIN","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL"
-"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","COS","type=f16,ne=[1024,1024,1,1]","support","0","no","SYCL"
-"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","SYCL"
-"SYCL0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","0","no","SYCL"
+"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","SQR","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","SQRT","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","LOG","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","SIN","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","COS","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL"
+"SYCL0","CLAMP","type=f16,ne=[1024,1024,1,1],min=-0.500000,max=0.500000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f16,ne_a=[1024,1024,1,1],negative_slope=0.100000","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
@@ -11046,8 +11046,8 @@
 "SYCL0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","0","no","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","0","no","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","0","no","SYCL"
@@ -11095,8 +11095,8 @@
 "SYCL0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","0","no","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","0","no","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","0","no","SYCL"
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 376d4376f..057e0dccd 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -5582,11 +5582,6 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
         case GGML_OP_LOG:
-#if defined (GGML_SYCL_F16)
-            return ((op->type == GGML_TYPE_F32 || op->type == GGML_SYCL_F16) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_SYCL_F16) && (op->type == op->src[0]->type));
-#else
-            return (op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32) && (op->type == op->src[0]->type);
-#endif
         case GGML_OP_NORM:
         case GGML_OP_L2_NORM:
         case GGML_OP_GROUP_NORM:

From 890f1a27ed4810d9655fc3f748d9b6bc483932fb Mon Sep 17 00:00:00 2001
From: Zijun Yu <zijun.yu@intel.com>
Date: Wed, 17 Jun 2026 14:11:21 +0800
Subject: [PATCH 008/292] openvino: OV 2026.2, context-shift, Q5_1 support,
 gemma4 dense/embedding, and -fa off (#24503)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add interface is_model_splitted() to check the c-graph is splited or not

* Infer and propagate dynamic-dimension indices for all tensors in the GGML graph in api compute_model_outputs()

* Only do this for fallback sub graph

* Move dynamic dims compute in graph missmatch

* ggml-openvino: fix tensor data handling for PERMUTE/VIEW ops in split models

* ggml-openvino:add comments

* ggml-openvino: override VIEW op_case to 0 for split model inputs

* openvino backend: Handle unsupported VIEW shape-mismatch in OpenVINO backend

* Enable additional mul_mat tests and add tensor data saving function (#81)

* ggml-openvino: fix CONT/TRANSPOSE mapping and improve dynamic-dimension handling

* OpenVINO: add NORM/TANH support and rework SOFT_MAX translation

* ggml-openvino: extend VIEW handling

* Enable -fa off (#118)

* Enable --context-shift

* Fix llm param compute error for normal softmax not the softmax in attention

* OpenVINO backend: fix error for attention size compute in llm param

* use tensor->extra in infer_request i/o

* OpenVINO backend: refacter the compute_llm_params() func add get_attention_pattern_case to easy extand

* OpenVINO backend: clean unused code

* 1to1 match op update (#146)

* added translate_1to1_match_1_input function and updated gelu and tanh translations

* Remove unused translation function calls

---------

Co-authored-by: Mustafa Cavus <mustafacavus@intel.com>

* initial gemma4 support

* removed hardcoded names for kv cache slicing

* OpenVINO backend: Add new attention pattern for llm parameters compute

* flash attn Q shape static conversion

* Remove slice in permute translation when n_seq is 1

* return optional in extract_layer_from_name

* OpenVINO backend: refactor VIEW related operation (#148)

* OpenVINO backend: refactor VIEW related operation

* Enable VIEW handling in following ops

* OpenVINO backend does not support GGML_OP_NORM & GGML_OP_L2_NORM with VIEW input accuracy issue from OpenVINO

* OpenVINO backend: Add ops l2_norm & pad

* OpenVINO backend does not support CPY with non-contiguous data or mismatched types

* add op SSM_CONV GATED_DELTA_NET

* OpenVINO backend: fix error for bf16 in OV gpu plugin

* reverted static Q input shape for attention layer

* OpenVINO backend: remove hardcode name inp_tokens, which ignore some leaf case

* Disable remote tensor due to bug in ov gpu

* Disable n_token > 1 GATED_DELTA_NET on gpu

* OpenVINO backend: fix the view op dynamic handling issue in gemma4 & enable view + get_row

* OpenVINO backend: clean code

* OpenVINO backend: enable view + norm/rms_norm

* OpenVINO backend: concat op

* OpenVINO backend: argsort op

* OpenVINO backend: enable unary + view & GGML_UNARY_OP_SOFTPLUS

* Fix issue for test-backend-ops in TOPK_MOE, which compare VIEW ops result, VIEW node in OpenVINO no need compare, the whole graph result is correct

* OpenVINO backend: enable sum_rows

* OpenVINO backend: enable clamp

* OpenVINO backend: enable DIV

* OpenVINO backend: enable GGML_OP_MUL_MAT_ID

* OpenVINO backend: disable MUL_MAT_ID_FUSION case with large mem needed

* OpenVINO backend: Disable GGML_OP_ARGSORT, cause test_backend-ops failed

* OpenVINO backend: fix issue in mul_mat_id

* OpenVINO backend: Disable DIV with broadcast on GPU

* OpenVINO backend: update DIV

* use ov internal op GatedDeltaNet

* OpenVINO backend: enable llama erch test qwen3next

* OpenVINO backend: enable RMS_NORM + VIEW & remove op_case 2 for rope

* OpenVINO backend: fix error

* suggested changes, need review

* suggested changes, need review

* OpenVINO backend: clean unused code & fix build warning

* OpenVINO backend: enable minicpm3 for arch test

* Disable GDN op (#177)

* disable gated_delta_net

* update stateful_kv_size correctly in mismatch case

* OpenVINO backend: enable arch test for qwen3vl

* OpenVINO backend: enable cohere2 for arch test

* OpenVINO backend: enable t5 for arch test

* OpenVINO backend: enable jamba for arch test

* OpenVINO backend: remove warning for tmp

* OpenVINO backend: enable kimi-linear for arch test

* Remove unused

* Fix gpt-oss accuracy issue

* OpenVINO backend: enable arctic for arch test

* OpenVINO backend: enable grok for arch test

* Gemma4 initial npu support (#179)

* Initiall gemma4 npu support

* temp. fix for gemma4 accuracy bug on npu

* Remove hardcoded names for npu-fold handling

* revert static n tokens for cont translation as it is not needed

* removed unused variable

* ggml-openvino: add GGML_OPENVINO_ENABLE_CACHE env var to control decoder cache. Add environment variable GGML_OPENVINO_ENABLE_CACHE (default: YES). When set to NO, the decoder_cache is bypassed and models are rebuilt from the cgraph on every inference call in both dynamic and static compute paths. This is useful for debugging and verifying correctness without caching interference.

* Revert "Gemma4 initial npu support (#179)"

This reverts commit 0d29a9c4a52dc2c8aa52990f1a3854cfb01768ad.

* OpenVINO backend: disable debug log print

* Update TBB discovery. Delegated to OpenVINOs own config.

* OpenVINO backend: GGML_OPENVINO_ENABLE_CACHE YES -> 1

* OpenVINO backend: fallback FLASH_ATTN_EXT in gemma3n to CPU backend

* Add raw ov infer profiling metric

* Add OV raw infer time metric to static compute path

Co-authored-by: virajwad <84867530+virajwad@users.noreply.github.com>

* Modify precision of static profiling

* update to OV 2026.2, add OV windows CI

* fix editorconfig-checks

* Initiall gemma4 npu support

* temp. fix for gemma4 accuracy bug on npu

* Remove hardcoded names for npu-fold handling

* revert static n tokens for cont translation as it is not needed

* removed unused variable

* test-llama-archs fix

* Fix gemma4 flash_attn fallback

* support im2col

* fix code style

* disable add_rope_sin_cos optimization

* stateless boradcast and rope optimizations

* Enable manual gqa attn by default for stateless gpu

* manual gqa: fixed static batch

* gemma4 llama-bench ctx update fix

* Update OV win CI

* stateful rope fusion temp. fix

* OpenVINO backend: Conslolidate supported ops

* Exclude unsupported GGML_OP_SUB cases

* Exclude unsupported TOPK_MOE cases

* OpenVINO Backend: MUL_MAT enhancements

* Update OV CI

* support f16 mask input for npu

* Make GGML_OPENVINO_* env vars usage uniform

Standardize all GGML_OPENVINO_* env flags:
positive integers >0 to enable. Unset, empty, =0, or non-numeric values to disable.
This fixes cases where text values or empty strings enabled features.

* OpenVINO backend: Enhance envvar handling

* more cleanup

* move ggml_openvino_env_flag to appropriate place

* OpenVINO backend: add REPEAT translator, Q5_1 weights, and GLU view-input fix

* ggml-openvino: fix -Werror=cast-qual in extract_q5_1_data

* Update openvino.Dockerfile

Use BuildKit cache mounts for faster Docker rebuilds.
Use apt instead of dpkg, remove unused .ddeb downloads, add DLLAMA_BUILD_TESTS=OFF.

* ggml-openvino: centralize env var access via *getenv_str/getenv_int helpers

Replace getenv and legacy flags with _str and _int helpers.Minor cleanup, doc updates.

* OpenVINO backend: Enable GGML_OP_ADD_ID

* Uptade openvino backend clamg-format

* clang-format

* Update OPENVINO.md (#211)

* OpenVINO backend: fix accuracy issue for op CONCAT with i64 precision

* Remove strict concurrency for gpu-openvino-low-perf

* Update openvino CI keynames; add ccache-clear

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>

* Fix formatting

---------

Co-authored-by: Xuejun Zhai <Xuejun.Zhai@intel.com>
Co-authored-by: Mustafa Cavus <mustafa.cavus@intel.com>
Co-authored-by: Mustafa Cavus <mustafacavus@intel.com>
Co-authored-by: Xuejun <XuejunZhai@intel.com>
Co-authored-by: Wang Yang <yang4.wang@intel.com>
Co-authored-by: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
Co-authored-by: virajwad <84867530+virajwad@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Mostafa Faheem <mostafaaafaheem@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
---
 .devops/openvino.Dockerfile                   | 109 ++-
 .../actions/windows-setup-openvino/action.yml |  24 +
 .github/workflows/build-cache.yml             |  32 +-
 .github/workflows/build-openvino.yml          |  89 +-
 .github/workflows/build-self-hosted.yml       |   8 +-
 .github/workflows/release.yml                 | 110 ++-
 docs/backend/OPENVINO.md                      | 667 +++++++++++---
 ggml/src/ggml-openvino/.clang-format          |   5 -
 ggml/src/ggml-openvino/CMakeLists.txt         |   6 +-
 ggml/src/ggml-openvino/ggml-decoder.cpp       | 868 ++++++++++++++++--
 ggml/src/ggml-openvino/ggml-decoder.h         |  99 +-
 .../src/ggml-openvino/ggml-openvino-extra.cpp |  60 +-
 ggml/src/ggml-openvino/ggml-openvino-extra.h  |  37 +-
 ggml/src/ggml-openvino/ggml-openvino.cpp      | 376 ++++++--
 ggml/src/ggml-openvino/ggml-quants.cpp        |  66 ++
 ggml/src/ggml-openvino/ggml-quants.h          |  14 +-
 ggml/src/ggml-openvino/openvino/decoder.h     |  72 +-
 ggml/src/ggml-openvino/openvino/frontend.h    |   2 +-
 ggml/src/ggml-openvino/openvino/input_model.h |   8 +-
 .../src/ggml-openvino/openvino/node_context.h | 133 ++-
 ggml/src/ggml-openvino/openvino/op/add_id.cpp |  62 ++
 .../src/ggml-openvino/openvino/op/argsort.cpp |  47 +
 ggml/src/ggml-openvino/openvino/op/clamp.cpp  |  33 +
 ggml/src/ggml-openvino/openvino/op/concat.cpp |  48 +
 ggml/src/ggml-openvino/openvino/op/cont.cpp   |  24 +-
 ggml/src/ggml-openvino/openvino/op/cpy.cpp    |  15 +-
 ggml/src/ggml-openvino/openvino/op/div.cpp    | 146 +++
 .../openvino/op/flash_attn_ext.cpp            | 129 ++-
 .../openvino/op/gated_delta_net.cpp           | 282 ++++++
 .../openvino/op/gated_delta_net.hpp           |  65 ++
 .../ggml-openvino/openvino/op/get_rows.cpp    |  11 +-
 .../ggml-openvino/openvino/op/glu_geglu.cpp   |  28 +-
 .../ggml-openvino/openvino/op/glu_swiglu.cpp  |  17 +-
 ggml/src/ggml-openvino/openvino/op/im2col.cpp | 120 +++
 .../src/ggml-openvino/openvino/op/l2_norm.cpp |  44 +
 .../ggml-openvino/openvino/op/mul_mat_id.cpp  | 108 +++
 ggml/src/ggml-openvino/openvino/op/mulmat.cpp |  28 +-
 ggml/src/ggml-openvino/openvino/op/norm.cpp   |  58 ++
 ggml/src/ggml-openvino/openvino/op/pad.cpp    |  95 ++
 .../src/ggml-openvino/openvino/op/permute.cpp |  71 +-
 ggml/src/ggml-openvino/openvino/op/repeat.cpp |  74 ++
 .../src/ggml-openvino/openvino/op/reshape.cpp |  19 +-
 .../ggml-openvino/openvino/op/rms_norm.cpp    |   2 +-
 ggml/src/ggml-openvino/openvino/op/rope.cpp   | 166 +++-
 .../ggml-openvino/openvino/op/set_rows.cpp    |   6 +-
 .../src/ggml-openvino/openvino/op/softmax.cpp | 121 +--
 .../ggml-openvino/openvino/op/ssm_conv.cpp    |  59 ++
 .../ggml-openvino/openvino/op/sum_rows.cpp    |  27 +
 .../ggml-openvino/openvino/op/transpose.cpp   |  33 +-
 .../ggml-openvino/openvino/op/unary_gelu.cpp  |  25 -
 .../ggml-openvino/openvino/op/unary_silu.cpp  |   2 +-
 .../openvino/op/unary_softplus.cpp            |  38 +
 ggml/src/ggml-openvino/openvino/op/view.cpp   | 117 ++-
 ggml/src/ggml-openvino/openvino/op_table.cpp  |  63 +-
 ggml/src/ggml-openvino/openvino/op_table.h    |  22 +-
 ...k_decompression_convert_constant_folding.h |   2 +-
 .../openvino/translate_session.cpp            |  83 +-
 .../openvino/translate_session.h              |   9 +-
 ggml/src/ggml-openvino/openvino/utils.cpp     | 551 ++++++++++-
 ggml/src/ggml-openvino/openvino/utils.h       |  54 +-
 ggml/src/ggml-openvino/utils.cpp              | 481 ++++++++--
 ggml/src/ggml-openvino/utils.h                |  19 +-
 62 files changed, 5280 insertions(+), 909 deletions(-)
 create mode 100644 .github/actions/windows-setup-openvino/action.yml
 create mode 100644 ggml/src/ggml-openvino/openvino/op/add_id.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/argsort.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/clamp.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/concat.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/div.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/gated_delta_net.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/gated_delta_net.hpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/im2col.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/l2_norm.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/norm.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/pad.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/repeat.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/ssm_conv.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/sum_rows.cpp
 delete mode 100644 ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp
 create mode 100644 ggml/src/ggml-openvino/openvino/op/unary_softplus.cpp

diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile
index e85c585e6..9e96244ce 100644
--- a/.devops/openvino.Dockerfile
+++ b/.devops/openvino.Dockerfile
@@ -1,17 +1,17 @@
-ARG OPENVINO_VERSION_MAJOR=2026.0
-ARG OPENVINO_VERSION_FULL=2026.0.0.20965.c6d6a13a886
+ARG OPENVINO_VERSION_MAJOR=2026.2
+ARG OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857
 ARG UBUNTU_VERSION=24.04
 
 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.30.1
-ARG IGC_VERSION_FULL=2_2.30.1+20950
-ARG COMPUTE_RUNTIME_VERSION=26.09.37435.1
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.09.37435.1-0
-ARG IGDGMM_VERSION=22.9.0
+ARG IGC_VERSION=v2.34.4
+ARG IGC_VERSION_FULL=2_2.34.4+21428
+ARG COMPUTE_RUNTIME_VERSION=26.18.38308.1
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.18.38308.1-0
+ARG IGDGMM_VERSION=22.10.0
 
 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
-ARG NPU_DRIVER_VERSION=v1.32.0
-ARG NPU_DRIVER_FULL=v1.32.0.20260402-23905121947
+ARG NPU_DRIVER_VERSION=v1.33.0
+ARG NPU_DRIVER_FULL=v1.33.0.20260529-26625960453
 ARG LIBZE1_VERSION=1.27.0-1~24.04~ppa2
 
 # Optional proxy build arguments
@@ -46,13 +46,18 @@ RUN apt-get update && \
         intel-opencl-icd && \
     rm -rf /var/lib/apt/lists/*
 
-# Install OpenVINO for Ubuntu 24.04
+# OpenVINO toolkit and GPU/NPU drivers are cached via BuildKit cache mounts to avoid re-downloading on rebuilds.
+# Install OpenVINO for Ubuntu 24.04.
 ARG OPENVINO_VERSION_MAJOR
 ARG OPENVINO_VERSION_FULL
-RUN mkdir -p /opt/intel && \
-    wget https://storage.openvinotoolkit.org/repositories/openvino/packages/${OPENVINO_VERSION_MAJOR}/linux/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \
-    tar -xf openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \
-    mv openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64 /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \
+RUN --mount=type=cache,target=/var/cache/openvino,sharing=locked \
+    mkdir -p /opt/intel && \
+    TGZ=/var/cache/openvino/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz && \
+    if [ ! -f "$TGZ" ]; then \
+        wget -O "$TGZ" https://storage.openvinotoolkit.org/repositories/openvino/packages/${OPENVINO_VERSION_MAJOR}/linux/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz; \
+    fi && \
+    tar -xf "$TGZ" -C /opt/intel/ && \
+    mv /opt/intel/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64 /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \
     cd /opt/intel/openvino_${OPENVINO_VERSION_MAJOR} && \
     echo "Y" | ./install_dependencies/install_openvino_dependencies.sh && \
     cd - && \
@@ -68,14 +73,14 @@ COPY . .
 RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \
     cmake -B build/ReleaseOV -G Ninja \
         -DCMAKE_BUILD_TYPE=Release \
+        -DLLAMA_BUILD_TESTS=OFF \
         -DGGML_OPENVINO=ON && \
-    cmake --build build/ReleaseOV -j$(nproc)"
+    cmake --build build/ReleaseOV --parallel "
 
-# Copy all necessary libraries
+# Copy all necessary libraries (build outputs + OpenVINO runtime libs)
 RUN mkdir -p /app/lib && \
-    find build/ReleaseOV -name '*.so*' -exec cp {} /app/lib \; && \
-    find ${OpenVINO_DIR}/runtime/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \; 2>/dev/null || \
-    find ${OpenVINO_DIR}/lib/intel64 -name '*.so*' -exec cp -P {} /app/lib \;
+    find build/ReleaseOV -name '*.so*' -exec cp -P {} /app/lib \; && \
+    find "${OpenVINO_DIR}/runtime/lib/intel64" -name '*.so*' -exec cp -P {} /app/lib \;
 
 # Create runtime directories and copy binaries
 RUN mkdir -p /app/full \
@@ -120,33 +125,41 @@ ARG IGC_VERSION_FULL
 ARG COMPUTE_RUNTIME_VERSION
 ARG COMPUTE_RUNTIME_VERSION_FULL
 ARG IGDGMM_VERSION
-RUN mkdir /tmp/neo/ && cd /tmp/neo/ \
-    && wget https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
-    && wget https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-ocloc-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-ocloc_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-opencl-icd-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-opencl-icd_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libigdgmm12_${IGDGMM_VERSION}_amd64.deb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libze-intel-gpu1-dbgsym_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.ddeb \
-    && wget https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libze-intel-gpu1_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
-    && dpkg --install *.deb \
-    && rm -rf /tmp/neo/
+RUN --mount=type=cache,target=/var/cache/intel-gpu,sharing=locked \
+    set -eux; \
+    cd /var/cache/intel-gpu; \
+    for url in \
+        https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-core-${IGC_VERSION_FULL}_amd64.deb \
+        https://github.com/intel/intel-graphics-compiler/releases/download/${IGC_VERSION}/intel-igc-opencl-${IGC_VERSION_FULL}_amd64.deb \
+        https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-ocloc_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+        https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/intel-opencl-icd_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb \
+        https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libigdgmm12_${IGDGMM_VERSION}_amd64.deb \
+        https://github.com/intel/compute-runtime/releases/download/${COMPUTE_RUNTIME_VERSION}/libze-intel-gpu1_${COMPUTE_RUNTIME_VERSION_FULL}_amd64.deb ; do \
+        f=$(basename "$url"); \
+        [ -f "$f" ] || wget -q -O "$f" "$url"; \
+    done; \
+    apt-get update; \
+    apt-get install -y --no-install-recommends ./*.deb; \
+    rm -rf /var/lib/apt/lists/*
 
 # Install NPU drivers
 ARG NPU_DRIVER_VERSION
 ARG NPU_DRIVER_FULL
 ARG LIBZE1_VERSION
-RUN mkdir /tmp/npu/ && cd /tmp/npu/ \
-    && wget https://github.com/intel/linux-npu-driver/releases/download/${NPU_DRIVER_VERSION}/linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz \
-    && tar -xf linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz \
-    && dpkg --install *.deb \
-    && rm -rf /tmp/npu/
-
-RUN cd /tmp \
-    && wget https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260324T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb \
-    && dpkg --install libze1_${LIBZE1_VERSION}_amd64.deb \
-    && rm libze1_${LIBZE1_VERSION}_amd64.deb
+RUN --mount=type=cache,target=/var/cache/intel-npu,sharing=locked \
+    set -eux; \
+    TGZ=/var/cache/intel-npu/linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz; \
+    if [ ! -f "$TGZ" ]; then \
+        wget -q -O "$TGZ" https://github.com/intel/linux-npu-driver/releases/download/${NPU_DRIVER_VERSION}/linux-npu-driver-${NPU_DRIVER_FULL}-ubuntu2404.tar.gz; \
+    fi; \
+    DEB=/var/cache/intel-npu/libze1_${LIBZE1_VERSION}_amd64.deb; \
+    if [ ! -f "$DEB" ]; then \
+        wget -q -O "$DEB" https://snapshot.ppa.launchpadcontent.net/kobuk-team/intel-graphics/ubuntu/20260324T100000Z/pool/main/l/level-zero-loader/libze1_${LIBZE1_VERSION}_amd64.deb; \
+    fi; \
+    mkdir /tmp/npu/ && cd /tmp/npu/ && tar -xf "$TGZ" && cp "$DEB" .; \
+    apt-get update; \
+    apt-get install -y --no-install-recommends ./*.deb; \
+    rm -rf /tmp/npu/ /var/lib/apt/lists/*
 
 COPY --from=build /app/lib/ /app/
 
@@ -166,22 +179,26 @@ RUN apt-get update && \
     python3 \
     python3-venv \
     python3-pip && \
-    python3 -m venv /ov-venv && \
-    /ov-venv/bin/pip install --no-cache-dir --upgrade pip setuptools wheel && \
-    /ov-venv/bin/pip install --no-cache-dir -r requirements.txt && \
+    python3 -m venv /openvino-venv && \
+    /openvino-venv/bin/pip install --no-cache-dir --upgrade pip setuptools wheel && \
+    /openvino-venv/bin/pip install --no-cache-dir -r requirements.txt && \
     apt-get autoremove -y && \
     apt-get clean && \
     rm -rf /tmp/* /var/tmp/* && \
     find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \
     find /var/cache -type f -delete
 
-ENTRYPOINT ["/bin/bash", "-c", "source /ov-venv/bin/activate && exec /app/tools.sh \"$@\"", "--"]
+# Activate the venv
+ENV VIRTUAL_ENV=/openvino-venv \
+    PATH=/openvino-venv/bin:$PATH
+
+ENTRYPOINT ["/app/tools.sh"]
 
 
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app/
 
 WORKDIR /app
 
diff --git a/.github/actions/windows-setup-openvino/action.yml b/.github/actions/windows-setup-openvino/action.yml
new file mode 100644
index 000000000..f983df560
--- /dev/null
+++ b/.github/actions/windows-setup-openvino/action.yml
@@ -0,0 +1,24 @@
+name: "Windows - Setup OpenVINO Toolkit"
+description: "Setup OpenVINO Toolkit for Windows"
+inputs:
+  path:
+    description: "Installation path"
+    required: true
+  version_major:
+    description: "OpenVINO major version (e.g., 2026.2)"
+    required: true
+  version_full:
+    description: "OpenVINO full version"
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Download and extract OpenVINO Runtime
+      shell: powershell
+      run: |
+        $url = "https://storage.openvinotoolkit.org/repositories/openvino/packages/${{ inputs.version_major }}/windows/openvino_toolkit_windows_${{ inputs.version_full }}_x86_64.zip"
+        $out = "openvino.zip"
+        Invoke-WebRequest -Uri $url -OutFile $out
+        Expand-Archive -Path $out -DestinationPath ${{ inputs.path }} -Force
+        Remove-Item $out
diff --git a/.github/workflows/build-cache.yml b/.github/workflows/build-cache.yml
index 53d65f376..b36c6e1ea 100644
--- a/.github/workflows/build-cache.yml
+++ b/.github/workflows/build-cache.yml
@@ -68,8 +68,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
 
     steps:
       - name: Clone
@@ -91,6 +91,34 @@ jobs:
           version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
           version_full: ${{ env.OPENVINO_VERSION_FULL }}
 
+  windows-2022-openvino-cache:
+    runs-on: windows-2022
+
+    env:
+      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Setup Cache
+        uses: actions/cache@v5
+        id: cache-openvino
+        with:
+          path: ./openvino_toolkit
+          key: cache-gha-openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
+
+      - name: Setup OpenVINO Toolkit
+        if: steps.cache-openvino.outputs.cache-hit != 'true'
+        uses: ./.github/actions/windows-setup-openvino
+        with:
+          path: ./openvino_toolkit
+          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
+          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+
   windows-2022-rocm-cache:
     runs-on: windows-2022
 
diff --git a/.github/workflows/build-openvino.yml b/.github/workflows/build-openvino.yml
index ddcbc6697..49ab13695 100644
--- a/.github/workflows/build-openvino.yml
+++ b/.github/workflows/build-openvino.yml
@@ -37,14 +37,10 @@ jobs:
   ubuntu-24-openvino:
     runs-on: [self-hosted, Linux, Intel, OpenVINO]
 
-    concurrency:
-      group: openvino-gpu-${{ github.head_ref || github.ref }}
-      cancel-in-progress: false
-
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
 
     steps:
       - name: Clone
@@ -78,7 +74,7 @@ jobs:
           cmake -B build/ReleaseOV -G Ninja \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENVINO=ON
-          time cmake --build build/ReleaseOV --config Release -j $(nproc)
+          time cmake --build build/ReleaseOV --config Release --parallel
 
       - name: Test (CPU)
         id: cmake_test_cpu
@@ -93,4 +89,81 @@ jobs:
         run: |
           cd ${{ github.workspace }}
           export GGML_OPENVINO_DEVICE=GPU
-          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 2000
+          ctest --test-dir build/ReleaseOV -L main -E "test-llama-archs" --verbose --timeout 3000
+
+  openvino-windows-2022:
+    runs-on: windows-2022
+
+    env:
+      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: openvino-windows-2022
+          variant: ccache
+          evict-old-files: 1d
+          save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
+
+      - name: Setup Cache
+        uses: actions/cache@v5
+        id: cache-openvino
+        with:
+          path: ./openvino_toolkit
+          key: cache-gha-openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
+
+      - name: Setup OpenVINO Toolkit
+        if: steps.cache-openvino.outputs.cache-hit != 'true'
+        uses: ./.github/actions/windows-setup-openvino
+        with:
+          path: ./openvino_toolkit
+          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
+          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+
+      - name: Install OpenCL using vcpkg
+        shell: powershell
+        run: |
+          git clone https://github.com/microsoft/vcpkg C:\vcpkg
+          C:\vcpkg\bootstrap-vcpkg.bat
+          C:\vcpkg\vcpkg install opencl
+
+      - name: Build
+        id: cmake_build
+        shell: cmd
+        run: |
+          REM Find extracted OpenVINO folder dynamically
+          for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
+
+          if not exist "%OPENVINO_ROOT%\runtime\cmake\OpenVINOConfig.cmake" (
+              echo ERROR: OpenVINOConfig.cmake not found
+              exit /b 1
+          )
+
+          call "%OPENVINO_ROOT%\setupvars.bat"
+
+          cmake -B build\ReleaseOV -G "Visual Studio 17 2022" ^
+            -A x64 ^
+            -DCMAKE_BUILD_TYPE=Release ^
+            -DGGML_OPENVINO=ON ^
+            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
+
+          cmake --build build\ReleaseOV --config Release -- /m
+
+      - name: Test (CPU)
+        id: cmake_test_cpu
+        shell: cmd
+        # TODO: fix and re-enable the `test-llama-archs` test below
+        run: |
+          REM Find extracted OpenVINO folder dynamically
+          for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
+          call "%OPENVINO_ROOT%\setupvars.bat"
+
+          cd build
+          ctest --test-dir ReleaseOV -L main -E "test-llama-archs" -C Release --verbose --timeout 3000
diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml
index 436100c8a..c4366ece3 100644
--- a/.github/workflows/build-self-hosted.yml
+++ b/.github/workflows/build-self-hosted.yml
@@ -264,14 +264,10 @@ jobs:
   gpu-openvino-low-perf:
     runs-on: [self-hosted, Linux, Intel, OpenVINO]
 
-    concurrency:
-      group: openvino-gpu-${{ github.head_ref || github.ref }}
-      cancel-in-progress: false
-
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
 
     steps:
       - name: Clone
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9c8fda51b..d5bcb4d4e 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -443,9 +443,9 @@ jobs:
       openvino_version: ${{ steps.openvino_version.outputs.value }}
 
     env:
-      # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.0"
-      OPENVINO_VERSION_FULL: "2026.0.0.20965.c6d6a13a886"
+      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
 
     steps:
       - name: Set OpenVINO version output
@@ -528,6 +528,108 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
           name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
 
+  windows-openvino:
+    runs-on: windows-2022
+
+    outputs:
+      openvino_version: ${{ steps.openvino_version.outputs.value }}
+
+    env:
+      # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
+      OPENVINO_VERSION_MAJOR: "2026.2"
+      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+
+    steps:
+      - name: Set OpenVINO version output
+        id: openvino_version
+        run: echo "value=${{ env.OPENVINO_VERSION_MAJOR }}" >> $GITHUB_OUTPUT
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+            fetch-depth: 0
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "24"
+          cache: "npm"
+          cache-dependency-path: "tools/ui/package-lock.json"
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.21
+        with:
+          key: release-windows-2022-openvino
+          variant: ccache
+          evict-old-files: 1d
+
+      - name: Setup Cache
+        uses: actions/cache@v5
+        id: cache-openvino
+        with:
+          path: ./openvino_toolkit
+          key: cache-gha-openvino-toolkit-v${{ env.OPENVINO_VERSION_FULL }}-${{ runner.os }}
+
+      - name: Setup OpenVINO Toolkit
+        if: steps.cache-openvino.outputs.cache-hit != 'true'
+        uses: ./.github/actions/windows-setup-openvino
+        with:
+          path: ./openvino_toolkit
+          version_major: ${{ env.OPENVINO_VERSION_MAJOR }}
+          version_full: ${{ env.OPENVINO_VERSION_FULL }}
+
+      - name: Install OpenCL using vcpkg
+        shell: powershell
+        run: |
+          git clone https://github.com/microsoft/vcpkg C:\vcpkg
+          C:\vcpkg\bootstrap-vcpkg.bat
+          C:\vcpkg\vcpkg install opencl
+
+      - name: Build
+        id: cmake_build
+        shell: cmd
+        run: |
+          REM Find extracted OpenVINO folder dynamically
+          for /d %%i in (openvino_toolkit\*) do set OPENVINO_ROOT=%%i
+
+          if not exist "%OPENVINO_ROOT%\runtime\cmake\OpenVINOConfig.cmake" (
+              echo ERROR: OpenVINOConfig.cmake not found
+              exit /b 1
+          )
+
+          call "%OPENVINO_ROOT%\setupvars.bat"
+
+          cmake -B build\ReleaseOV -G "Visual Studio 17 2022" ^
+            -A x64 ^
+            -DCMAKE_BUILD_TYPE=Release ^
+            -DGGML_OPENVINO=ON ^
+            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
+
+          cmake --build build\ReleaseOV --config Release -- /m
+
+      - name: ccache-clear
+        uses: ./.github/actions/ccache-clear
+        with:
+          key: release-windows-2022-openvino
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        shell: powershell
+        run: |
+          Copy-Item LICENSE .\build\ReleaseOV\bin\
+          7z a -snl llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip .\build\ReleaseOV\bin\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v6
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip
+          name: llama-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip
+
   windows-cpu:
     needs: [check-release]
     if: ${{ needs.check-release.outputs.should_release == 'true' }}
@@ -1403,6 +1505,7 @@ jobs:
       - windows-cuda
       #- windows-sycl
       - windows-hip
+      - windows-openvino
       - ubuntu-22-rocm
       - ubuntu-cpu
       - ubuntu-vulkan
@@ -1524,6 +1627,7 @@ jobs:
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
+            - [Windows x64 (OpenVINO)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ needs.windows-openvino.outputs.openvino_version }}-x64.zip)
             - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
             - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)
 
diff --git a/docs/backend/OPENVINO.md b/docs/backend/OPENVINO.md
index b0e19abb0..631d4bc3b 100644
--- a/docs/backend/OPENVINO.md
+++ b/docs/backend/OPENVINO.md
@@ -12,6 +12,25 @@ The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a t
 - Compiles and caches the model for the target device.
 - Binds GGML tensor memory to OpenVINO inference tensors and runs inference.
 
+## Contents
+
+- [Supported Devices](#supported-devices)
+- [Supported Model Precisions](#supported-model-precisions)
+- [Supported Llama.cpp Tools](#supported-llamacpp-tools)
+- [Validated Models](#validated-models)
+- [Build Instructions](#build-instructions)
+  - [0. Prerequisites](#0-prerequisites)
+  - [1. Install OpenVINO Runtime](#1-install-openvino-runtime)
+  - [2. Build llama.cpp with OpenVINO Backend](#2-build-llamacpp-with-openvino-backend)
+    - [Automated Ubuntu Build Script](#automated-ubuntu-build-script)
+    - [Automated Windows Build Script](#automated-windows-build-script)
+  - [3. Download Sample Model](#3-download-sample-model)
+  - [4. Run Inference with OpenVINO Backend](#4-run-inference-with-openvino-backend)
+  - [5. Docker Build](#5-docker-build)
+- [GGML OpenVINO Backend Runtime Configurations](#ggml-openvino-backend-runtime-configurations)
+- [Known Limitations](#known-limitations)
+- [Work in Progress](#work-in-progress)
+
 ## Supported Devices
 
 OpenVINO backend supports the following hardware:
@@ -31,55 +50,102 @@ Although OpenVINO supports a wide range of [Intel hardware](https://docs.openvin
 - `Q4_1`
 - `Q4_K`
 - `Q4_K_M`
-- `Q5_K` (converted to Q8_0_C at runtime)
-- `Q6_K` (converted to Q8_0_C at runtime)
+- `Q5_K` (converted to `Q8_0_C` at runtime)
+- `Q6_K` (converted to `Q8_0_C` at runtime)
 
 > [!NOTE]
 > Accuracy validation and performance optimizations for quantized models are a work in progress.
 
-## Quantization Support Details
-
-### CPU and GPU
-
-- **`Q4_0`, `Q4_1`, `Q4_K_M`, `Q6_K` models are supported**
+**CPU and GPU Quantization Details:**
 - `Q5_K` and `Q6_K` tensors are converted to `Q8_0_C`
 
-### NPU
-
-- **Primary supported quantization scheme is `Q4_0`**
+**NPU Quantization Details:**
+- Primary supported quantization scheme is `Q4_0`
 - `Q6_K` tensors are requantized to `Q4_0_128` in general. For embedding weights, `Q6_K` tensors are requantized to `Q8_0_C` except for the token embedding matrix which is dequantized to fp16
 
-### Additional Notes
-
+**Additional Notes:**
 - Both `Q4_0` and `Q4_1` models use `Q6_K` for the token embedding tensor and the final matmul weight tensor (often the same tensor)
 - `Q4_0` models may produce some `Q4_1` tensors if an imatrix is provided during quantization using `llama-quantize`
 - `Q4_K_M` models may include both `Q6_K` and `Q5_K` tensors (observed in Phi-3)
+- `Q5_1` tensors are dequantized natively (weights, scales, and zero-points extracted directly)
+
+## Supported Llama.cpp Tools
+
+The OpenVINO backend integrates with the standard llama.cpp tools listed below.
+However, all the tools coverage across all devices is not uniform and exhaustive validation is work in progress.
+
+- llama-bench
+- llama-cli
+- llama-completion
+- llama-embedding
+- llama-perplexity
+- llama-run
+- llama-server
+- llama-simple
 
 ## Validated Models
 
-The following models were validated on Intel® Core™ Ultra Series 2. While our testing was limited, the OpenVINO backend is expected to work across a broad range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html).
-- Use `GGML_OPENVINO_STATEFUL_EXECUTION=1` when using GPU device.
-- `-fa 1` is required when running llama-bench with the OpenVINO backend.
-- Additional model support, quantization formats and validations are work in progress.
+Although, the validated models below were tested with `llama-cli` using the `Q4_K_M` quantization format on Intel® Core™ Ultra Series 2 (Lunar Lake), the OpenVINO backend is expected to work across a broader range of [Intel hardware](https://docs.openvino.ai/2026/about-openvino/release-notes-openvino/system-requirements.html), [supported model precisions](#supported-model-precisions), [supported llama.cpp tools](#supported-llamacpp-tools) and additional model architectures.
 
-| Model  | Validated   | Known Issues  |
-| :------| :---------- | :-------------|
-| [Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
-| [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) | `Q8_0`, `Q4_K_M` on CPU/GPU/NPU | `Q4_0_8_8`, `Q4_0_4_8`, `Q4_0_4_4` fail |
-| [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | `FP16`, `Q4` on CPU/NPU | GPU unsupported for `FP16` and `Q4` (`llama-cli`, `llama-bench`) |
-| [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
-| [Qwen3-8B-Instruct](https://huggingface.co/Qwen/Qwen3-8B-GGUF) | `FP16`, `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/NPU; GPU works via `llama-bench` | GPU `llama-cli` unsupported for all quantizations |
-| [MiniCPM-V-2_6-GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `Q4_0` on CPU/GPU/NPU | — |
-| [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M` on CPU/GPU/NPU | — |
-| [Hunyuan-7B-Instruct](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | CPU: `Q8_0`, `Q4_0`, `Q4_1`, `Q4_K_M`; GPU: `Q8_0`, `Q4_0`, `Q4_1`; NPU (`llama-bench` only): `Q4_0`, `Q4_1`, `Q4_K_M` | GPU `Q4_K_M` unsupported; NPU `llama-cli` unsupported |
-| [Mistral-7B-Instruct-v0.3](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF/) | CPU/GPU: `Q8_0`, `Q4_K_M`; NPU: `Q8_0`, `Q4_K_M` (via `llama-bench`) | NPU `llama-cli` unsupported for `Q8_0`, `Q4_K_M` |
+> [!NOTE]
+> Extensive accuracy validation, performance optimizations, and broader architecture coverage are work in progress.
+
+**Legend & Test Configuration:**
+- **Status:** ✓ = Passed | ✗ = Failed or Unsupported
+- **Execution Modes:**
+  - **SL** = Stateless (`GGML_OPENVINO_STATEFUL_EXECUTION=0`)
+  - **SF** = Stateful (`GGML_OPENVINO_STATEFUL_EXECUTION=1`)
+  - Note: The NPU operates in stateless mode only.
+- **Validation system:** Intel® Core™ Ultra 5 238V (Lunar Lake) | 32 GB RAM | Ubuntu 24.04 | Intel OpenCL GPU Driver 26.18.38308.1 | Intel NPU Driver 1.33.0.
+- See [Known Limitations](#known-limitations) for context on observed failures.
+
+| Model | CPU (SL / SF) | GPU (SL / SF) | NPU (SL) |
+| :--- | :---: | :---: | :---: |
+| [bartowski/Llama-3.2-1B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/Llama-3.2-3B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/Meta-Llama-3.1-8B-Instruct-Q4_K_M](https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+|  |  |  |  |
+| [Qwen/qwen2.5-1.5b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [Qwen/qwen2.5-coder-7b-instruct-q4_k_m](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Qwen_Qwen3-0.6B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-0.6B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Qwen_Qwen3-1.7B-Q4_K_M](https://huggingface.co/bartowski/Qwen_Qwen3-1.7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [Qwen/Qwen3-4B-Q4_K_M](https://huggingface.co/Qwen/Qwen3-4B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [lm-kit/Qwen3-8B-Q4_K_M](https://huggingface.co/lm-kit/qwen-3-8b-instruct-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
+|  |  |  |  |
+| [unsloth/gemma-3-4b-it-Q4_K_M](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/google_gemma-4-E2B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E2B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
+| [bartowski/google_gemma-4-E4B-it-Q4_K_M](https://huggingface.co/bartowski/google_gemma-4-E4B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✓ |
+| [bartowski/gemma-4-12B-it-Q4_K_M](https://huggingface.co/bartowski/gemma-4-12B-it-GGUF) | ✓ / ✗ | ✓ / ✗ | ✗ |
+|  |  |  |  |
+| [bartowski/Phi-3-mini-4k-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/Phi-3.5-mini-instruct-Q4_K_M](https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+|  |  |  |  |
+| [bartowski/Mistral-7B-Instruct-v0.3-Q4_K_M](https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [QuantFactory/Ministral-3b-instruct.Q4_K_M](https://huggingface.co/QuantFactory/Ministral-3b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/Ministral-8B-Instruct-2410-Q4_K_M](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+|  |  |  |  |
+| [bartowski/DeepSeek-R1-Distill-Llama-8B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [bartowski/DeepSeek-R1-Distill-Qwen-7B-Q4_K_M](https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+|  |  |  |  |
+| [ibm-granite/granite-4.0-350m-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-350m-GGUF) | ✓ / ✓ | ✗ / ✗ | ✓ |
+| [ibm-granite/granite-4.0-micro-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-micro-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [ibm-granite/granite-4.0-1b-Q4_K_M](https://huggingface.co/ibm-granite/granite-4.0-1b-GGUF) | ✓ / ✓ | ✗ / ✗ | ✗ |
+| [ibm-research/granite-3.2-8b-instruct-Q4_K_M](https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+|  |  |  |  |
+| [HuggingFaceTB/smollm2-1.7b-instruct-q4_k_m](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✓ | ✓ |
+| [openbmb/MiniCPM-V-2_6-Q4_K_M](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/tencent_Hunyuan-7B-Instruct-Q4_K_M](https://huggingface.co/bartowski/tencent_Hunyuan-7B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-Q4_K_M](https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+| [bartowski/prism-ml_Bonsai-8B-unpacked-Q4_K_M](https://huggingface.co/bartowski/prism-ml_Bonsai-8B-unpacked-GGUF) | ✓ / ✓ | ✓ / ✗ | ✓ |
+|  |  |  |  |
+| [gpustack/bge-m3-Q4_K_M.gguf](https://huggingface.co/gpustack/bge-m3-GGUF) | ✓ | ✗ | ✗ |
 
 ## Build Instructions
 
-### Prerequisites
+### 0. Prerequisites
 
 - Linux or Windows system with Intel hardware (CPU, GPU, or NPU)
-- **For Intel GPU or NPU Usage**: Install the appropriate hardware drivers for your Intel GPU or NPU. For detailed instructions, see: [Additional Configurations for Hardware Acceleration](https://docs.openvino.ai/2025/get-started/install-openvino/configurations.html).
+- **For Intel GPU or NPU Usage**: Install the appropriate hardware drivers for your Intel GPU or NPU. For detailed instructions, see: [Additional Configurations for Hardware Acceleration](https://docs.openvino.ai/2026/get-started/install-openvino/configurations.html).
 
 - **Linux:**
     - Git, CMake, and Ninja software tools are needed for building.
@@ -119,28 +185,14 @@ The following models were validated on Intel® Core™ Ultra Series 2. While our
 
 - Follow the guide to install OpenVINO Runtime from an archive file: [Linux](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-linux.html) | [Windows](https://docs.openvino.ai/2026/get-started/install-openvino/install-openvino-archive-windows.html)
 
-- **Linux:**
-
-    <details>
-    <summary>📦 Click to expand OpenVINO installation from an archive file on Ubuntu</summary>
-    <br>
-
-    ```bash
-    wget https://raw.githubusercontent.com/ravi9/misc-scripts/main/openvino/ov-archive-install/install-openvino-from-archive.sh
-    chmod +x install-openvino-from-archive.sh
-    ./install-openvino-from-archive.sh
-    ```
-
-    Verify OpenVINO is initialized properly:
-    ```bash
-    echo $OpenVINO_DIR
-    ```
-    </details>
-
+- Verify OpenVINO is initialized properly:
+  ```bash
+  echo $OpenVINO_DIR
+  ```
 
 ### 2. Build llama.cpp with OpenVINO Backend
 
-Clone the OpenVINO-enabled llama.cpp fork and build it:
+Clone llama.cpp repo and build :
 
 ```bash
 git clone https://github.com/ggml-org/llama.cpp
@@ -148,39 +200,375 @@ cd llama.cpp
 ```
 
 - **Linux:**
-    ```bash
-    source /opt/intel/openvino/setupvars.sh
-    cmake -B build/ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON
-    cmake --build build/ReleaseOV --parallel
-    ```
+```bash
+source /opt/intel/openvino/setupvars.sh
+cmake -B build/ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON
+cmake --build build/ReleaseOV --parallel
+```
+
+- **Windows:** Open a **Developer Command Prompt for VS 2022** (so the MSVC toolchain is on `PATH`), then run:
+
+```cmd
+C:\Intel\openvino\setupvars.bat
+cmake -B build\ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
+cmake --build build\ReleaseOV --parallel
+```
 
-- **Windows:**
-    ```cmd
-    # x64 Native Tools Command Prompt for VS 2022
-    "C:\Program Files (x86)\Intel\openvino_2026.0\setupvars.bat"
-    cmake -B build\ReleaseOV -G Ninja -DCMAKE_BUILD_TYPE=Release -DGGML_OPENVINO=ON -DLLAMA_CURL=OFF -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
-    cmake --build build\ReleaseOV --parallel
-    ```
 > [!NOTE]
-> Use `x64 Native Tools Command Prompt` for Windows build. After building, you could use either `cmd` or `PowerShell` to run the OpenVINO backend.
+> The Windows install path is `C:\Intel\openvino` (no spaces) to avoid quoting problems some CMake/Ninja toolchains have with `C:\Program Files (x86)\...`. Adjust to wherever you installed OpenVINO Runtime. From `cmd`, run `C:\Intel\openvino\setupvars.bat`; from PowerShell, run `& "C:\Intel\openvino\setupvars.ps1"` instead. Once the build is finished you can launch the binaries from any `cmd` or `PowerShell` window after sourcing the matching `setupvars` script for that shell.
+
+#### Automated Ubuntu Build Script
+
+For Ubuntu24 users, the following shell script automates the prerequisite installs (build tools, OpenCL ICD), the OpenVINO Runtime download/extract/setup, and the Ninja-based llama.cpp build.
+Save the following as `ubuntu-llamacpp-ov-install.sh` next to where you want the `llama.cpp` folder to land, then run it:
+
+```bash
+chmod +x ubuntu-llamacpp-ov-install.sh
+./ubuntu-llamacpp-ov-install.sh
+```
+
+<details>
+<summary>Click to expand <code>ubuntu-llamacpp-ov-install.sh</code></summary>
+
+```bash
+#!/usr/bin/env bash
+# ============================================
+# llama.cpp OpenVINO Build Script (Ninja)
+# ============================================
+set -euo pipefail
+
+OPENVINO_VERSION_MAJOR="2026.2"
+OPENVINO_VERSION_FULL="2026.2.0.21903.52ddc073857"
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
+OPENVINO_LINK_DIR="/opt/intel/openvino"
+OPENVINO_TGZ="${SCRIPT_DIR}/openvino.tgz"
+OPENVINO_URL="https://storage.openvinotoolkit.org/repositories/openvino/packages/${OPENVINO_VERSION_MAJOR}/linux/openvino_toolkit_ubuntu24_${OPENVINO_VERSION_FULL}_x86_64.tgz"
+
+echo "============================================"
+echo "Installing prerequisites (apt)..."
+echo "============================================"
+sudo apt-get update
+sudo apt-get install -y \
+    build-essential libcurl4-openssl-dev libtbb12 \
+    cmake ninja-build python3-pip \
+    curl wget tar git
+
+echo "============================================"
+echo "Installing OpenCL runtime + headers..."
+echo "============================================"
+sudo apt-get install -y \
+    ocl-icd-opencl-dev opencl-headers opencl-clhpp-headers intel-opencl-icd
+
+cd "${SCRIPT_DIR}"
+
+# ============================================
+# Clone llama.cpp if missing
+# ============================================
+if [[ ! -f "llama.cpp/CMakeLists.txt" ]]; then
+    echo "Cloning llama.cpp..."
+    git clone https://github.com/ggml-org/llama.cpp
+fi
+
+# ============================================
+# Setup OpenVINO: download & extract to /opt/intel/openvino_${OPENVINO_VERSION_MAJOR},
+# then point /opt/intel/openvino at it via symlink so the active version is swappable.
+# ============================================
+if [[ -f "${OPENVINO_INSTALL_DIR}/setupvars.sh" ]]; then
+    echo "OpenVINO ${OPENVINO_VERSION_MAJOR} already installed at ${OPENVINO_INSTALL_DIR}. Skipping download."
+else
+    echo "OpenVINO not found at ${OPENVINO_INSTALL_DIR}. Starting download..."
+    curl -L -o "${OPENVINO_TGZ}" "${OPENVINO_URL}"
+
+    echo "Extracting OpenVINO to ${OPENVINO_INSTALL_DIR}..."
+    sudo mkdir -p "${OPENVINO_INSTALL_DIR}"
+    sudo tar -xzf "${OPENVINO_TGZ}" -C "${OPENVINO_INSTALL_DIR}" --strip-components=1
+    rm -f "${OPENVINO_TGZ}"
+fi
+
+# Refresh symlink: /opt/intel/openvino -> /opt/intel/openvino_${OPENVINO_VERSION_MAJOR}
+sudo ln -sfn "${OPENVINO_INSTALL_DIR}" "${OPENVINO_LINK_DIR}"
+
+OPENVINO_ROOT="${OPENVINO_LINK_DIR}"
+echo "OpenVINO Ready: ${OPENVINO_ROOT} -> ${OPENVINO_INSTALL_DIR}"
+
+# Install OpenVINO's own runtime dependencies (one-time per system).
+if [[ -x "${OPENVINO_ROOT}/install_dependencies/install_openvino_dependencies.sh" ]]; then
+    echo "============================================"
+    echo "Installing OpenVINO runtime dependencies..."
+    echo "============================================"
+    echo "Y" | sudo -E "${OPENVINO_ROOT}/install_dependencies/install_openvino_dependencies.sh"
+fi
+
+# ============================================
+# Clean old build cache
+# ============================================
+cd "${SCRIPT_DIR}/llama.cpp"
+if [[ -d "build/ReleaseOV" ]]; then
+    echo "Removing old build directory..."
+    rm -rf "build/ReleaseOV"
+fi
+
+echo "============================================"
+echo "Configuring with CMake..."
+echo "============================================"
+# shellcheck disable=SC1091
+source "${OPENVINO_ROOT}/setupvars.sh"
+
+cmake -B build/ReleaseOV -G Ninja \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DGGML_OPENVINO=ON
+
+cmake --build build/ReleaseOV --parallel
+
+echo "============================================"
+echo "Build completed successfully!"
+echo "============================================"
+echo "Binaries: $(pwd)/build/ReleaseOV/bin"
+echo
+echo "NOTE: To run, source setupvars.sh and pick a device:"
+echo "  source /opt/intel/openvino/setupvars.sh"
+echo "  export GGML_OPENVINO_DEVICE=CPU   # or GPU / NPU"
+echo "  ./build/ReleaseOV/bin/llama-cli -m model.gguf"
+```
+
+> [!NOTE]
+> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
+
+</details>
+
+#### Automated Windows Build Script
+
+For Windows users, the following `.bat` script automates the prerequisite installs (Git, Ninja, CMake, Visual Studio 2022 Build Tools, vcpkg + OpenCL), the OpenVINO Runtime download/extract, and the Ninja-based llama.cpp build.
+Save the following as `windows-llamacpp-ov-install.bat` next to where you want the `llama.cpp` to land, then run it from either **Command Prompt** or **PowerShell**:
+
+```cmd
+:: Command Prompt
+windows-llamacpp-ov-install.bat
+```
+
+```powershell
+# PowerShell
+.\windows-llamacpp-ov-install.bat
+```
+
+<details>
+<summary>Click to expand <code>windows-llamacpp-ov-install.bat</code></summary>
+
+```bat
+@echo off
+setlocal enabledelayedexpansion
+
+REM ============================================
+REM llama.cpp OpenVINO Build Script (Ninja)
+REM ============================================
+
+set "OPENVINO_VERSION_MAJOR=2026.2"
+set "OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857"
+
+set "SCRIPT_DIR=%~dp0"
+set "VCPKG_DIR=C:\vcpkg"
+set "OPENVINO_INSTALL_DIR=C:\Intel\openvino_%OPENVINO_VERSION_MAJOR%"
+set "OPENVINO_LINK_DIR=C:\Intel\openvino"
+set "OPENVINO_ZIP=%SCRIPT_DIR%openvino.zip"
+set "OPENVINO_EXTRACT_TMP=%SCRIPT_DIR%openvino_extract_tmp"
+set "OPENVINO_URL=https://storage.openvinotoolkit.org/repositories/openvino/packages/%OPENVINO_VERSION_MAJOR%/windows/openvino_toolkit_windows_%OPENVINO_VERSION_FULL%_x86_64.zip"
+
+echo ============================================
+echo Installing prerequisites...
+echo ============================================
+winget install --id Git.Git -e --accept-source-agreements --accept-package-agreements 2>nul
+winget install --id Ninja-build.Ninja -e --accept-source-agreements --accept-package-agreements 2>nul
+winget install --id Kitware.CMake -e --accept-source-agreements --accept-package-agreements 2>nul
+
+REM Ensure Visual Studio Build Tools are installed.
+echo Checking for Visual Studio Build Tools...
+set "VSWHERE=%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe"
+set "VS_INSTALLED="
+if exist "%VSWHERE%" (
+    for /f "usebackq tokens=*" %%i in (`"%VSWHERE%" -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath 2^>nul`) do (
+        set "VS_INSTALLED=%%i"
+    )
+)
+if defined VS_INSTALLED (
+    echo Visual Studio with VC++ x86/x64 tools already present at "!VS_INSTALLED!". Skipping winget install.
+) else (
+    winget install --id Microsoft.VisualStudio.2022.BuildTools -e --override "--wait --passive --add Microsoft.VisualStudio.Workload.VCTools --includeRecommended" --accept-source-agreements --accept-package-agreements
+    if errorlevel 1 (
+        echo WARNING: winget could not install Visual Studio Build Tools automatically.
+        echo Install manually from https://aka.ms/vs/17/release/vs_BuildTools.exe ^(select the "Desktop development with C++" workload^)
+        echo and re-run this script from a "Developer Command Prompt for VS 2022".
+    )
+)
+
+echo ============================================
+echo Installing OpenCL via vcpkg...
+echo ============================================
+if not exist "%VCPKG_DIR%" (
+    git clone https://github.com/microsoft/vcpkg "%VCPKG_DIR%"
+    cd /d "%VCPKG_DIR%"
+    call bootstrap-vcpkg.bat
+    call vcpkg integrate install
+)
+cd /d "%VCPKG_DIR%"
+call vcpkg install opencl
+
+cd /d "%SCRIPT_DIR%"
+
+REM ============================================
+REM Clone llama.cpp if missing
+REM ============================================
+if not exist "llama.cpp\CMakeLists.txt" (
+    echo Cloning llama.cpp...
+    git clone https://github.com/ggml-org/llama.cpp
+)
+
+cd /d "llama.cpp"
+set "SCRIPT_DIR=%CD%"
+
+REM ============================================
+REM Setup OpenVINO: download & extract to C:\Intel\openvino_%OPENVINO_VERSION_MAJOR%,
+REM then point C:\Intel\openvino at it via a directory junction (mklink /J).
+REM ============================================
+
+if exist "%OPENVINO_INSTALL_DIR%\setupvars.bat" (
+    echo OpenVINO %OPENVINO_VERSION_MAJOR% already installed at "%OPENVINO_INSTALL_DIR%". Skipping download.
+) else (
+    echo OpenVINO not found at "%OPENVINO_INSTALL_DIR%". Starting download...
+
+    curl -L -o "%OPENVINO_ZIP%" "%OPENVINO_URL%"
+    if errorlevel 1 (
+        echo ERROR: Download failed.
+        exit /b 1
+    )
+
+    echo Extracting OpenVINO...
+    if exist "%OPENVINO_EXTRACT_TMP%" rmdir /s /q "%OPENVINO_EXTRACT_TMP%"
+    mkdir "%OPENVINO_EXTRACT_TMP%"
+    tar -xf "%OPENVINO_ZIP%" -C "%OPENVINO_EXTRACT_TMP%"
+    if errorlevel 1 (
+        echo ERROR: Extraction failed.
+        exit /b 1
+    )
+
+    REM Move the single top-level folder contents into the versioned install dir.
+    REM NOTE: delayed expansion (!VAR!) is required because the surrounding else( ... )
+    REM block is parsed once up-front, so %OPENVINO_EXTRACTED% would expand to "" here
+    REM and xcopy would then treat "\*" as C:\* and fail with "Cannot perform a cyclic copy".
+    set "OPENVINO_EXTRACTED="
+    for /d %%i in ("%OPENVINO_EXTRACT_TMP%\*") do set "OPENVINO_EXTRACTED=%%i"
+    if not defined OPENVINO_EXTRACTED (
+        echo ERROR: Could not locate extracted OpenVINO folder under "%OPENVINO_EXTRACT_TMP%".
+        exit /b 1
+    )
+    if not exist "%OPENVINO_INSTALL_DIR%" mkdir "%OPENVINO_INSTALL_DIR%"
+    xcopy /e /i /y /q "!OPENVINO_EXTRACTED!\*" "%OPENVINO_INSTALL_DIR%\" >nul
+    if errorlevel 1 (
+        echo ERROR: Failed to copy OpenVINO from "!OPENVINO_EXTRACTED!" to "%OPENVINO_INSTALL_DIR%".
+        echo Re-run this script from an elevated Command Prompt ^(Run as administrator^) if access is denied.
+        exit /b 1
+    )
+
+    rmdir /s /q "%OPENVINO_EXTRACT_TMP%"
+    del "%OPENVINO_ZIP%"
+)
+
+REM Refresh junction: C:\Intel\openvino -> C:\Intel\openvino_<version>.
+REM `mklink /J` creates a directory junction (no admin / Developer Mode required).
+if exist "%OPENVINO_LINK_DIR%" rmdir "%OPENVINO_LINK_DIR%"
+mklink /J "%OPENVINO_LINK_DIR%" "%OPENVINO_INSTALL_DIR%" >nul
+if errorlevel 1 (
+    echo ERROR: Failed to create junction "%OPENVINO_LINK_DIR%" -^> "%OPENVINO_INSTALL_DIR%".
+    echo If "%OPENVINO_LINK_DIR%" already exists as a regular non-empty folder, remove it manually and re-run.
+    exit /b 1
+)
+
+set "OPENVINO_ROOT=%OPENVINO_LINK_DIR%"
+echo OpenVINO Ready: %OPENVINO_ROOT% -^> %OPENVINO_INSTALL_DIR%
+
+
+echo ============================================
+echo Setting up compiler environment...
+echo ============================================
+REM Locate Visual Studio Build Tools vcvars64.bat
+set "VSWHERE=%ProgramFiles(x86)%\Microsoft Visual Studio\Installer\vswhere.exe"
+if exist "%VSWHERE%" (
+    for /f "usebackq tokens=*" %%i in (`"%VSWHERE%" -latest -products Microsoft.VisualStudio.Product.BuildTools -property installationPath`) do (
+        set "VS_PATH=%%i"
+    )
+)
+if defined VS_PATH (
+    call "%VS_PATH%\VC\Auxiliary\Build\vcvars64.bat" >nul
+) else (
+    echo WARNING: Visual Studio Build Tools not found. Compiler may be missing.
+)
+
+REM ============================================
+REM Clean old build cache
+REM ============================================
+if exist "build\ReleaseOV" (
+    echo Removing old build directory ...
+    rmdir /s /q "build\ReleaseOV"
+)
+
+echo ============================================
+echo Configuring with CMake...
+echo ============================================
+call "%OPENVINO_ROOT%\setupvars.bat" >nul 2>nul
+
+cmake -B build\ReleaseOV -G Ninja ^
+    -DCMAKE_BUILD_TYPE=Release ^
+    -DGGML_OPENVINO=ON ^
+    -DCMAKE_TOOLCHAIN_FILE="%VCPKG_DIR%\scripts\buildsystems\vcpkg.cmake"
+
+if errorlevel 1 (
+    echo If you continue to face CMAKE errors, make sure to install:
+    echo   winget install Microsoft.VisualStudio.2022.BuildTools
+    echo   Then run the "Developer Command Prompt for VS 2022" and launch this script from there.
+    exit /b 1
+)
+
+cmake --build build\ReleaseOV --config Release
+if errorlevel 1 exit /b 1
+
+echo ============================================
+echo Build completed successfully!
+echo ============================================
+echo Binaries: %CD%\build\ReleaseOV\bin
+echo.
+echo NOTE: To run, source setupvars.bat and pick a device:
+echo   call "C:\Intel\openvino\setupvars.bat"
+echo   set GGML_OPENVINO_DEVICE=CPU   ^&^& REM or GPU / NPU
+echo   build\ReleaseOV\bin\llama-cli.exe -m model.gguf
+echo.
+
+endlocal
+```
+
+> [!NOTE]
+> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
+
+</details>
+
 
 ### 3. Download Sample Model
 
-Download models for testing:
+Download sample model for testing.
 
 ```bash
 # Linux
 mkdir -p ~/models/
-wget https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf \
-     -O ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf
+wget https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf \
+     -O ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf
 
 # Windows PowerShell
 mkdir C:\models
-Invoke-WebRequest -Uri https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -OutFile C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf
+Invoke-WebRequest -Uri https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf -OutFile C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf
 
 # Windows Command Line
 mkdir C:\models
-curl -L https://huggingface.co/unsloth/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf -o C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf
+curl -L https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf -o C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf
 ```
 
 ### 4. Run Inference with OpenVINO Backend
@@ -196,65 +584,45 @@ When using the OpenVINO backend, the first inference token may have slightly hig
 
 # Linux
 export GGML_OPENVINO_DEVICE=GPU
-# Enable stateful execution with GPU device to avoid known stateless execution failures.
+# Optional: enable stateful execution for improved GPU performance (recommended).
 export GGML_OPENVINO_STATEFUL_EXECUTION=1
 # To run llama-simple:
-./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "
+./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -n 50 "The story of AI is "
 # To run in chat mode:
-./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 1024
+./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -c 1024
 # To run llama-bench, -fa 1 is needed
-GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./build/ReleaseOV/bin/llama-bench -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -fa 1
+GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./build/ReleaseOV/bin/llama-bench -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -fa 1
 
 # NPU: keep context small to avoid failures from very large model context windows.
 export GGML_OPENVINO_DEVICE=NPU
-./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 512
+./build/ReleaseOV/bin/llama-cli -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -c 512
 
 # Windows Command Line
 set GGML_OPENVINO_DEVICE=GPU
-# Enable stateful execution with GPU device to avoid known stateless execution failures.
+# Optional: enable stateful execution for improved GPU performance (recommended).
 set GGML_OPENVINO_STATEFUL_EXECUTION=1
 # Windows PowerShell
 $env:GGML_OPENVINO_DEVICE = "GPU"
 $env:GGML_OPENVINO_STATEFUL_EXECUTION = "1"
 
 # To run llama-simple
-build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "
+build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -n 50 "The story of AI is "
 # To run in chat mode:
-build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -c 1024
+build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -c 1024
 # To run llama-bench, -fa 1 is needed
-build\ReleaseOV\bin\llama-bench.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -fa 1
+build\ReleaseOV\bin\llama-bench.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -fa 1
 
 # NPU: keep context small to avoid failures from very large model context windows.
 # Windows Command Line
 set GGML_OPENVINO_DEVICE=NPU
 # Windows PowerShell
 $env:GGML_OPENVINO_DEVICE = "NPU"
-build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -c 512
+build\ReleaseOV\bin\llama-cli.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -c 512
 ```
 > [!NOTE]
 > On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html) for more details.
 
-### Known Issues and Current Workarounds
-
-- GPU stateless execution is currently affected by a known issue.
-  - Workaround: set `GGML_OPENVINO_STATEFUL_EXECUTION=1` when using GPU device.
-- NPU failures can happen when context size is too large. Recent llama.cpp behavior may resolve context size to the model training context (for example, 131072 for Llama 3.2 1B), which is too large for current NPU usage and can also stress laptop CPU/GPU on larger models. To inspect the selected context size, run `llama-cli` or `llama-server` with `-lv 3`.
-  - Workaround: explicitly set context size, for ex. `-c 1024` for NPU runs. Performance will be better with lower context size.
-- Additional NPU limitations:
-  - Model caching is not yet supported.
-  - `llama-server -np > 1` (multiple parallel sequences) is not supported.
-  - `llama-perplexity` is only supported with `-b 512` or smaller.
-- `--context-shift` with `llama-cli` is currently not supported with OpenVINO backend across CPU, GPU, and NPU devices.
-- Encoder models (embedding, reranking) are not supported with the current OpenVINO backend implementation.
-- `-fa 1` is required when running llama-bench with the OpenVINO backend.
-  - `GGML_OPENVINO_STATEFUL_EXECUTION=1 GGML_OPENVINO_DEVICE=GPU ./llama-bench -fa 1`
-- `llama-server` with OpenVINO backend supports only one chat session/thread, when `GGML_OPENVINO_STATEFUL_EXECUTION=1` is enabled.
-
-> [!NOTE]
-> The OpenVINO backend is actively under development. Fixes are underway, and this document will continue to be updated as issues are resolved.
-
-
-### Docker Build
+### 5. Docker Build
 
 You can build and run llama.cpp with OpenVINO backend using Docker.
 
@@ -272,7 +640,7 @@ docker build --target=light -t llama-openvino:light -f .devops/openvino.Dockerfi
 docker build --target=server -t llama-openvino:server -f .devops/openvino.Dockerfile .
 
 # If you are behind a proxy:
-docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --target=light -t llama-openvino:light -f .devops/openvino.Dockerfile .
+docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --target=server -t llama-openvino:server -f .devops/openvino.Dockerfile .
 ```
 
 Run llama.cpp with OpenVINO backend Docker container.
@@ -281,19 +649,19 @@ Save sample models in `~/models` as [shown above](#3-download-sample-model). It
 
 ```bash
 #  Run Docker container
-docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+docker run --rm -it -v ~/models:/models llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf
 
 # With Intel GPU access (iGPU or dGPU)
 docker run --rm -it -v ~/models:/models \
 --device=/dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
 --env=GGML_OPENVINO_DEVICE=GPU --env=GGML_OPENVINO_STATEFUL_EXECUTION=1 \
-llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf
 
 # With Intel NPU access
 docker run --rm -it -v ~/models:/models \
 --device=/dev/accel --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
 --env=GGML_OPENVINO_DEVICE=NPU \
-llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf
+llama-openvino:light --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf
 ```
 
 Run Llama.cpp Server with OpenVINO Backend.
@@ -301,17 +669,30 @@ Run Llama.cpp Server with OpenVINO Backend.
 > `llama-server` with OpenVINO backend supports only one chat session/thread, when `GGML_OPENVINO_STATEFUL_EXECUTION=1` is enabled.
 
 ```bash
-# Run the Server Docker container
-docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_0.gguf -c 1024
-# Or Using llama-server executable
-./build/ReleaseOV/bin/llama-server -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf --port 8080 -c 1024
+# Run the llama-openvino:server Docker container (CPU)
+docker run --rm -it -p 8080:8080 -v ~/models:/models llama-openvino:server --no-warmup -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -c 1024 --host 0.0.0.0
 
-# If you are behind a proxy, make sure to set NO_PROXY to avoid proxy for localhost
-export NO_PROXY=localhost,127.0.0.1
+# Run the llama-openvino:server Docker container with Intel GPU access (iGPU or dGPU)
+docker run --rm -it -v ~/models:/models \
+--device=/dev/dri --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
+-p 8080:8080 --env=GGML_OPENVINO_DEVICE=GPU  \
+llama-openvino:server --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf --host 0.0.0.0
+
+# Run the llama-openvino:server Docker container with Intel NPU access
+docker run --rm -it -v ~/models:/models \
+--device=/dev/accel --group-add=$(stat -c "%g" /dev/dri/render* | head -n 1) -u $(id -u):$(id -g) \
+-p 8080:8080 --env=GGML_OPENVINO_DEVICE=NPU \
+llama-openvino:server --no-warmup -c 1024 -m /models/Llama-3.2-1B-Instruct-Q4_K_M.gguf --host 0.0.0.0
+
+# Or Using llama-server executable
+./build/ReleaseOV/bin/llama-server -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf --port 8080 -c 1024
 
 # Option 1: Open your browser to http://localhost:8080 to access the web UI for the llama.cpp server.
 # Option 2: In a NEW terminal, test the server with curl
 
+# If you are behind a proxy, make sure to set NO_PROXY to avoid proxy for localhost
+export NO_PROXY=localhost,127.0.0.1
+
 # Test health endpoint
 curl -f http://localhost:8080/health
 
@@ -320,24 +701,26 @@ curl -X POST "http://localhost:8080/v1/chat/completions" -H "Content-Type: appli
  -d '{"messages":[{"role":"user","content":"Write a poem about OpenVINO"}],"max_tokens":100}' | jq .
 ```
 
-## Runtime Configuration
+## GGML OpenVINO Backend Runtime Configurations
 
 The OpenVINO backend can be configured using the following environment variables at runtime to control device selection, caching, debugging, and profiling behavior.
+Boolean flags follow a uniform convention: set to a **positive integer** (e.g. `1`) to enable; unset, empty, `0`, negative, or non-numeric values are treated as disabled.
 
-### Configuration Options
-
-| Variable                          | Default    | Description                                                                                                 |
-|-----------------------------------|------------|-------------------------------------------------------------------------------------------------------------|
-| `GGML_OPENVINO_DEVICE`            | `CPU`      | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
-| `GGML_OPENVINO_CACHE_DIR`         | `not set`  | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
-| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| `256`      | Token chunk size for **NPU** prefill.                                                                       |
-| `GGML_OPENVINO_STATEFUL_EXECUTION`| `0`        | Enable stateful KV cache on for better performance. Recommended on CPU, GPU.                                |
-| `GGML_OPENVINO_PROFILING`         | `0`        | Enable execution-time profiling.                                                                            |
-| `GGML_OPENVINO_DUMP_CGRAPH`       | `0`        | Dump the GGML compute graph to `cgraph_ov.txt`.                                                             |
-| `GGML_OPENVINO_DUMP_IR`           | `0`        | Serialize OpenVINO IR files with timestamps.                                                                |
-| `GGML_OPENVINO_DEBUG_INPUT`       | `0`        | Enable input debugging and print input tensor info.                                                         |
-| `GGML_OPENVINO_DEBUG_OUTPUT`      | `0`        | Enable output debugging and print output tensor info.                                                       |
-| `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | `0` | Print tensor address map once.                                                                           |
+| Variable                          | Type      | Default    | Description                                                                                                 |
+|-----------------------------------|-----------|------------|-------------------------------------------------------------------------------------------------------------|
+| `GGML_OPENVINO_DEVICE`            | String    | `CPU`      | Specify the target device (CPU, GPU, NPU). On systems with multiple GPUs, use `GPU.0` or `GPU.1` to explicitly target specific GPU. See [OpenVINO GPU Device](https://docs.openvino.ai/2026/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html). When set to **NPU**, static compilation mode is enabled for optimal performance. |
+| `GGML_OPENVINO_CACHE_DIR`         | String    | `not set`  | Directory for OpenVINO model caching (recommended: `/tmp/ov_cache`). Enables model caching when set. **Not supported on NPU devices.** |
+| `GGML_OPENVINO_PREFILL_CHUNK_SIZE`| Integer   | `256`      | Token chunk size for **NPU** prefill (NPU-only; ignored on CPU/GPU). Must be a positive integer; otherwise the default is used. |
+| `GGML_OPENVINO_STATEFUL_EXECUTION`| Boolean   | `0`        | Enable stateful KV cache for better performance. Recommended on CPU, GPU.                                   |
+| `GGML_OPENVINO_DISABLE_CACHE`     | Boolean   | `0`        | Disable the in-process compiled-model / decoder cache (cache is on by default). Set to `1` to disable.      |
+| `GGML_OPENVINO_DISABLE_KV_SLICE`  | Boolean   | `0`        | Disable the KV-cache input-tensor slicing optimization (slicing is on by default on CPU/GPU). Set to `1` to disable. |
+| `GGML_OPENVINO_MANUAL_GQA_ATTN`   | Boolean   | device-based | Tri-state. When **unset**, manual GQA attention is enabled by default on `GPU` and disabled on other devices. Set to a positive integer to force-enable, or `0` to force-disable. |
+| `GGML_OPENVINO_PROFILING`         | Boolean   | `0`        | Enable execution-time profiling.                                                                            |
+| `GGML_OPENVINO_DUMP_CGRAPH`       | Boolean   | `0`        | Dump the GGML compute graph to `cgraph_ov.txt`.                                                             |
+| `GGML_OPENVINO_DUMP_IR`           | Boolean   | `0`        | Serialize OpenVINO IR files with timestamps.                                                                |
+| `GGML_OPENVINO_DEBUG_INPUT`       | Boolean   | `0`        | Enable input debugging and print input tensor info.                                                         |
+| `GGML_OPENVINO_DEBUG_OUTPUT`      | Boolean   | `0`        | Enable output debugging and print output tensor info.                                                       |
+| `GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS` | Boolean | `0` | Print tensor address map once.                                                                           |
 
 > [!NOTE]
 >`GGML_OPENVINO_STATEFUL_EXECUTION` is an **Experimental** feature to allow stateful execution for managing the KV cache internally inside the OpenVINO model, improving performance on CPUs and GPUs. Stateful execution is not effective on NPUs, and not all models currently support this feature. This feature is experimental and has been validated only with the llama-simple, llama-cli, llama-bench, and llama-run applications and is recommended to enable for the best performance. Other applications, such as llama-server and llama-perplexity, are not yet supported.
@@ -355,7 +738,7 @@ export GGML_OPENVINO_PROFILING=1
 export GGML_OPENVINO_DEVICE=GPU
 export GGML_OPENVINO_STATEFUL_EXECUTION=1
 
-./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_0.gguf -n 50 "The story of AI is "
+./build/ReleaseOV/bin/llama-simple -m ~/models/Llama-3.2-1B-Instruct-Q4_K_M.gguf -n 50 "The story of AI is "
 
 # Windows Command Line
 set GGML_OPENVINO_CACHE_DIR=C:\tmp\ov_cache
@@ -369,19 +752,39 @@ $env:GGML_OPENVINO_PROFILING = "1"
 $env:GGML_OPENVINO_DEVICE = "GPU"
 $env:GGML_OPENVINO_STATEFUL_EXECUTION = "1"
 
-build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_0.gguf" -n 50 "The story of AI is "
+build\ReleaseOV\bin\llama-simple.exe -m "C:\models\Llama-3.2-1B-Instruct-Q4_K_M.gguf" -n 50 "The story of AI is "
 
 ```
 
-## Llama.cpp Tools
+## Known Limitations
 
-The following tools work with the OpenVINO backend on CPU, GPU, NPU:
-- llama-bench
-- llama-cli
-- llama-completion
-- llama-perplexity
-- llama-server
-- llama-simple
+**General (all devices)**
+
+- Llama.cpp OpenVINO backend currently supports a subset of GGML ops and text-only models. Unsupported ops or unsupported op shapes/cases fail during OpenVINO translation.
+- Multimodal features (audio/image/video) are a work in progress.
+- Limited Embedding and Reranking model support.
+- Llama.cpp tool coverage across CPU/GPU/NPU is not uniform.
+
+**Tool-specific**
+
+- `llama-bench`: requires `-fa 1` (flash-attention).
+- `llama-cli --context-shift`: stateless only (`GGML_OPENVINO_STATEFUL_EXECUTION=0`). In stateful mode the KV cache is owned by the OpenVINO model and cannot be shifted externally.
+- `llama-server`: only one chat session/thread when `GGML_OPENVINO_STATEFUL_EXECUTION=1`.
+
+**GPU-specific**
+
+- `llama-server -np > 1`: concurrent requests are batched together, which may slightly reduce per-request throughput.
+
+**NPU-specific**
+
+- Default context resolves to the model's training context (e.g. 131072 for Llama 3.2 1B), which can OOM or fail or degrade performance on NPU. Inspect the resolved value with `-lv 3`.
+  - **Workaround:** Pass an explicit `-c <N>`, e.g. `-c 1024`.
+- NPU device uses a static graph with a fixed prefill chunk size (defaults to 256), configurable with `GGML_OPENVINO_PREFILL_CHUNK_SIZE`. Large prefill/batch settings may need tuning.
+- `llama-server -np > 1` (multiple parallel sequences) is not supported.
+- `llama-perplexity`: requires `-b 512` or smaller.
+
+> [!NOTE]
+> The OpenVINO backend is actively under development. Fixes and improvements are underway, and this document will continue to be updated.
 
 ## Work in Progress
 
diff --git a/ggml/src/ggml-openvino/.clang-format b/ggml/src/ggml-openvino/.clang-format
index a2a24d7d3..4a5c7c208 100644
--- a/ggml/src/ggml-openvino/.clang-format
+++ b/ggml/src/ggml-openvino/.clang-format
@@ -2,12 +2,7 @@
 # Override root .clang-format
 AlignConsecutiveAssignments: false
 AlignConsecutiveDeclarations: false
-Cpp11BracedListStyle: true
-SpacesInContainerLiterals: false
-BreakBeforeBraces: Attach
 AccessModifierOffset: -4
-IndentCaseBlocks: false
-IndentCaseLabels: false
 
 Language:        Cpp
 AlignAfterOpenBracket: Align
diff --git a/ggml/src/ggml-openvino/CMakeLists.txt b/ggml/src/ggml-openvino/CMakeLists.txt
index 175b58566..cc089b721 100644
--- a/ggml/src/ggml-openvino/CMakeLists.txt
+++ b/ggml/src/ggml-openvino/CMakeLists.txt
@@ -1,8 +1,6 @@
-find_package(OpenVINO REQUIRED)
+find_package(OpenVINO REQUIRED COMPONENTS Runtime Threading)
 find_package(OpenCL REQUIRED)
 
-include("${OpenVINO_DIR}/../3rdparty/tbb/lib/cmake/TBB/TBBConfig.cmake")
-
 file(GLOB_RECURSE GGML_HEADERS_OPENVINO "*.h" "*.hpp")
 file(GLOB_RECURSE GGML_SOURCES_OPENVINO "*.cpp")
 
@@ -11,7 +9,7 @@ ggml_add_backend_library(ggml-openvino
     ${GGML_HEADERS_OPENVINO}
 )
 
-target_link_libraries(ggml-openvino PRIVATE openvino::runtime TBB::tbb OpenCL::OpenCL)
+target_link_libraries(ggml-openvino PRIVATE openvino::runtime openvino::threading OpenCL::OpenCL)
 
 if (GGML_OPENVINO)
     if (CMAKE_SYSTEM_PROCESSOR STREQUAL "aarch64")
diff --git a/ggml/src/ggml-openvino/ggml-decoder.cpp b/ggml/src/ggml-openvino/ggml-decoder.cpp
index 5095e7998..b6df4f0fb 100644
--- a/ggml/src/ggml-openvino/ggml-decoder.cpp
+++ b/ggml/src/ggml-openvino/ggml-decoder.cpp
@@ -1,20 +1,17 @@
 #include "ggml-decoder.h"
 
-#include "ggml-backend-impl.h"
-#include "ggml-backend.h"
+#include "ggml-impl.h"
 #include "ggml-openvino-extra.h"
 #include "ggml-openvino.h"
 #include "ggml-quants.h"
-
-#include <ggml-impl.h>
-#include <ggml.h>
+#include "ggml.h"
+#include "utils.h"
 
 #include <algorithm>
 #include <cassert>
 #include <cstddef>
 #include <cstdint>
 #include <cstdlib>
-#include <execution>
 #include <fstream>
 #include <iomanip>
 #include <map>
@@ -30,12 +27,10 @@
 #include <openvino/op/convert.hpp>
 #include <openvino/op/parameter.hpp>
 #include <openvino/runtime/tensor.hpp>
-#include <optional>
 #include <ostream>
 #include <set>
 #include <stdexcept>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
 GgmlOvDecoder::GgmlOvDecoder(ggml_cgraph * cgraph,
@@ -44,6 +39,7 @@ GgmlOvDecoder::GgmlOvDecoder(ggml_cgraph * cgraph,
                              std::map<std::string, std::shared_ptr<ov::Node>> & model_weights,
                              bool is_static,
                              bool is_stateful,
+                             bool model_is_splitted,
                              bool is_prefill,
                              int prefill_chunk_size) :
     m_is_static(is_static),
@@ -51,22 +47,23 @@ GgmlOvDecoder::GgmlOvDecoder(ggml_cgraph * cgraph,
     m_is_prefill(is_prefill),
     m_naive(false),
     m_prefill_chunk_size(prefill_chunk_size),
+    m_model_is_splitted(model_is_splitted),
     m_cgraph(cgraph),
     m_model_weights(model_weights),
     m_model_params(model_params),
     m_compute_params(compute_params) {
-    if (auto * env = getenv("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS"); env && std::string(env) != "0") {
-#ifdef _WIN32
-        _putenv_s("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS", "");
-#else
-        unsetenv("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS");
-#endif
-        print_tensor_address_map(cgraph);
+    static bool printed_address_map = false;
+    if (!printed_address_map) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS")) {
+            printed_address_map = true;
+            print_tensor_address_map(cgraph);
+        }
     }
 
     validate_cgraph();
 
     set_input_output();
+    compute_node_dynamic_dims();
     compute_model_inputs();
     compute_model_outputs();
 
@@ -136,6 +133,29 @@ void GgmlOvDecoder::set_input_output() {
             }
             current_node_info.node_inputs[src_name] = src;
             current_node_info.node_inputs_names.push_back(src_name);
+
+            if (src->op == GGML_OP_VIEW) {
+                // Traverse upward through nested VIEW operations
+                std::remove_reference_t<decltype(current_node_info.node_inputs_views[src_name])> view_chain;
+                auto current = src;
+
+                while (current != nullptr) {
+                    auto current_name = std::string(current->name);
+                    if (current->flags & GGML_TENSOR_FLAG_INPUT) {
+                        current_name = get_graph_input_ov_name(current, node);
+                    }
+                    view_chain.emplace_back(current_name, current);
+                    // If current src is also a VIEW, continue traversing
+                    if (current->src[0] != nullptr && current->src[0]->op == GGML_OP_VIEW) {
+                        current = current->src[0];
+                    } else {
+                        break;
+                    }
+                }
+
+                // Assign all collected view inputs to node_inputs_views
+                current_node_info.node_inputs_views[src_name] = view_chain;
+            }
         }
 
         m_node_info_list.push_back(current_node_info);
@@ -156,20 +176,13 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
             if (src->ne[2] * src->ne[3] == node->ne[1]) {
                 op_case = 5;
             }
-        } else if (src->ne[0] * src->ne[1] == node->ne[1]) {
+        } else if (src->ne[0] * src->ne[1] * src->ne[2] == node->ne[1]) {
             op_case = 3;
         } else if (src->ne[1] * src->ne[2] == node->ne[1]) {
             op_case = 6;
         }
-        break;
-    }
-    case GGML_OP_CONT: {
-        if (node->src[0]->op == GGML_OP_PERMUTE) {
-            op_case = 1;
-        } else if (node->src[0]->op == GGML_OP_TRANSPOSE) {
-            op_case = 2;
-        } else if (node->src[0]->op == GGML_OP_VIEW) {
-            op_case = 3;
+        if (op_case == 0 && ggml_nelements(node) == ggml_nelements(src)) {
+            op_case = 6;
         }
         break;
     }
@@ -179,23 +192,41 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
         } else if (node->src[0]->src[0]->op == GGML_OP_NONE) {
             // kv cache tensor
             std::string src_name(node->view_src->name);
-            int layer = extract_layer_from_name(src_name);
-            if (!is_swa_layer(layer)) {
-                op_case = 2;
+            int layer = extract_layer_from_name(src_name).value();
+            if (ggml_is_contiguous(node->src[0])) {
+                // -  19: [    64,     8,   256,     1] VIEW            cache_k_l0 (view)             [ 2,   128,  1024, 1048576]
+                //         [   512,  1024,     1,     1]      0: NONE     cache_k_l0                    [ 2,  1024, 1048576, 1048576]
+                // -  20: [    64,   256,     8,     1] PERMUTE         cache_k_l0 (view) (permuted)  [ 2,  1024,   128, 1048576]
+                //         [    64,     8,   256,     1]      0: VIEW     cache_k_l0 (view)             [ 2,   128,  1024, 1048576]
+                if (!is_swa_layer(layer)) {
+                    op_case = 3;
+                } else {
+                    op_case = 4;
+                }
             } else {
-                op_case = 3;
+                // special case of cache v when `-fa off`
+                // -  17: [   256,     8,    64,     1] VIEW            cache_v_l0 (view)             [ 2, 131072,  2048, 1048576]
+                //         [   512,  1024,     1,     1]      0: NONE     cache_v_l0                   [ 2,  1024, 1048576, 1048576]
+                // -  18: [   256,    64,     8,     1] PERMUTE         cache_v_l0 (view) (permuted)  [ 2,  2048, 131072, 1048576]
+                //         [   256,     8,    64,     1]      0: VIEW     cache_v_l0 (view)            [ 2, 131072,  2048, 1048576]
+                if (!is_swa_layer(layer)) {
+                    op_case = 5;
+                } else {
+                    op_case = 6;
+                }
             }
         } else {
             // rope'ed query tensor
-            op_case = 4;
+            op_case = 2;
         }
         break;
     }
     case GGML_OP_MUL_MAT: {
-        if (node->src[0]->op == GGML_OP_CONT && node->src[0]->src[0]->op == GGML_OP_TRANSPOSE) {
-            op_case = 2;
-        } else if (node->src[0]->op == GGML_OP_VIEW && node->src[1]->op == GGML_OP_VIEW) {
+        if (node->src[0]->op == GGML_OP_VIEW && node->src[1]->op == GGML_OP_VIEW) {
             op_case = 3;
+        } else if (node->src[1]->op == GGML_OP_SOFT_MAX) {
+            // In the case of `-fa off`, softmax is used, v_trans=true, the dynamic dim is ne[0] for cache_v
+            op_case = 2;
         }
         break;
     }
@@ -208,43 +239,57 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
     case GGML_OP_ROPE: {
         const int mode = node->op_params[2];
         switch (mode) {
-       case GGML_ROPE_TYPE_NEOX: {
-            op_case = 0x00010000;
+        case GGML_ROPE_TYPE_NEOX: {
+            op_case = 1;
             break;
         }
-       case GGML_ROPE_TYPE_IMROPE: {
-            op_case = 0x00020000;
+        case GGML_ROPE_TYPE_IMROPE: {
+            op_case = 2;
             break;
         }
         default:
-            op_case = 0x00000000;
+            op_case = 0;
             break;
         }
-        if (node->src[0]->op == GGML_OP_VIEW) {
-            op_case = (op_case | 0x00000002);
-        }
         break;
     }
     case GGML_OP_VIEW: {
         if (node->src[0]->op == GGML_OP_VIEW) {
             auto * src = node->src[0];
             if (ggml_nelements(node) != ggml_nelements(src)) {
-                throw std::runtime_error("Unsupported VIEW case");
+                // throw std::runtime_error("Unsupported VIEW case");
+            }
+            op_case = 0;
+            if (m_model_is_splitted && m_model_inputs.find(std::string(src->name)) != m_model_inputs.end()) {
+                op_case = 0;
             }
-            op_case = 2;
         }
         {
             auto * src = node->src[0];
-            if ((ggml_nelements(node) != ggml_nelements(src)) && m_naive) {
-                // Compare each dimension of node and src, if only one dimension differs then op_case=3
+            if (ggml_nelements(node) != ggml_nelements(src)) {
+                // Case 4: select one slice on src dim1 (via view offset), keep src dim2 as output dim1.
+                // Typical pattern:
+                //   src: ne=[N, M, K, 1], nb=[b0, b1, b2, b3]
+                //   dst: ne=[N, K, 1, 1], nb=[b0, b2, b3, b3]
+                if (node->ne[0] == src->ne[0] && node->ne[1] == src->ne[2] && node->ne[2] == 1 &&
+                    node->nb[0] == src->nb[0] && node->nb[1] == src->nb[2] && src->ne[1] > 1) {
+                    op_case = 0;
+                    break;
+                }
+
+                // General case 3: shape differs from source (one or more dims) and is handled as VIEW slicing.
                 int diff_count = 0;
                 for (int i = 0; i < GGML_MAX_DIMS; i++) {
                     if (node->ne[i] != src->ne[i]) {
                         diff_count++;
                     }
+                    // if node ne[i] > src ne[i], case = 0
+                    if (node->ne[i] > src->ne[i]) {
+                        return 0;
+                    }
                 }
-                if (diff_count == 1) {
-                    op_case = 3;
+                if (diff_count >= 1) {
+                    op_case = 0;
                 }
             }
         }
@@ -256,9 +301,11 @@ int GgmlOvDecoder::compute_op_case(const ggml_tensor * node) const {
     return op_case;
 }
 
-int extract_layer_from_name(const std::string & name) {
+std::optional<int> extract_layer_from_name(const std::string & name) {
     size_t pos1 = name.find("_l");
-    assert(pos1 != std::string::npos);
+    if (pos1 == std::string::npos) {
+        return std::nullopt;
+    }
     pos1 += 2;
     size_t pos2 = name.find(' ', pos1);
     if (pos2 == std::string::npos) {
@@ -272,26 +319,101 @@ int extract_layer_from_name(const std::string & name) {
 std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgraph * cgraph, bool is_static) {
     ModelParams model_params;
     ComputeParams compute_params;
+    auto get_attention_pattern_case = [](const ggml_tensor * node) -> int {
+        if (node == nullptr) {
+            return -1;
+        }
+
+        switch (node->op) {
+        case GGML_OP_FLASH_ATTN_EXT:
+            if (node->src[0] == nullptr || node->src[1] == nullptr || node->src[3] == nullptr) {
+                return -1;
+            }
+            switch (node->src[1]->op) {
+            case GGML_OP_PERMUTE:
+                // case 0: node op is FLASH_ATTN_EXT, src 1 not null & op is PERMUTE & the permuted tensor src is the view of cache k
+                if (node->src[1]->src[0] != nullptr && node->src[1]->src[0]->op == GGML_OP_VIEW) {
+                    return 0;
+                }
+                break;
+            case GGML_OP_CPY:
+                // case 1: node op is FLASH_ATTN_EXT, src 1 not null & op is CPY & the copied tensor src is PERMUTE & the permuted tensor src is the view of cache k
+                if (node->src[1]->src[0] != nullptr && node->src[1]->src[0]->op == GGML_OP_PERMUTE &&
+                    node->src[1]->src[0]->src[0] != nullptr && node->src[1]->src[0]->src[0]->op == GGML_OP_VIEW) {
+                    return 1;
+                }
+                break;
+            default:
+                break;
+            }
+            break;
+        case GGML_OP_SOFT_MAX:
+            // case 2: node op is SOFT_MAX, src 0 not null & op is MUL_MAT & the src 0 of MUL_MAT is PERMUTE & the permuted tensor src is the view of cache k
+            if (node->src[0] != nullptr && node->src[1] != nullptr && node->src[0]->op == GGML_OP_MUL_MAT &&
+                node->src[0]->src[0] != nullptr && node->src[0]->src[1] != nullptr &&
+                node->src[0]->src[0]->op == GGML_OP_PERMUTE && node->src[0]->src[0]->src[0] != nullptr &&
+                node->src[0]->src[0]->src[0]->op == GGML_OP_VIEW) {
+                return 2;
+            }
+            // case 3: node op is SOFT_MAX, src 0 not null & op is ADD & the src 0 of ADD is MUL_MAT & the src 0 of MUL_MAT is PERMUTE
+            if (node->src[0]->op == GGML_OP_ADD && node->src[0]->src[0] != nullptr &&
+                node->src[0]->src[0]->op == GGML_OP_MUL_MAT && node->src[0]->src[0]->src[0] != nullptr &&
+                node->src[0]->src[0]->src[0]->op == GGML_OP_PERMUTE) {
+                return 3;
+            }
+            break;
+        default:
+            break;
+        }
+
+        return -1;
+    };
+
+    bool rope_seen = false;
     for (int i = 0; i < cgraph->n_nodes; i++) {
         auto * node = cgraph->nodes[i];
         std::string name = std::string(node->name);
-        if (node->op == GGML_OP_FLASH_ATTN_EXT) {
-            model_params.n_heads = node->src[0]->ne[2];
-            model_params.n_heads_kv = node->src[1]->ne[2];
-            model_params.head_size = node->src[0]->ne[0];
-            compute_params.input_len = node->src[0]->ne[1];
+        const int attention_pattern_case = get_attention_pattern_case(node);
+        if (attention_pattern_case != -1) {
+            ggml_tensor * cache_k_permute = nullptr;
+            ggml_tensor * mask = nullptr;
 
-            auto * cache_k_perm = node->src[1];
-            if (cache_k_perm->op == GGML_OP_CPY) {
-                cache_k_perm = cache_k_perm->src[0];
+            switch (attention_pattern_case) {
+            case 0:
+                cache_k_permute = node->src[1];
+                mask = node->src[3];
+                break;
+            case 1:
+                cache_k_permute = node->src[1]->src[0];
+                mask = node->src[3];
+                break;
+            case 2:
+                cache_k_permute = node->src[0]->src[0];
+                mask = node->src[1];
+                break;
+            case 3:
+                cache_k_permute = node->src[0]->src[0]->src[0];
+                mask = node->src[1];
+                break;
+            default:
+                break;
             }
-            assert(cache_k_perm->op == GGML_OP_PERMUTE);
-            auto * cache_k_view = cache_k_perm->src[0];
-            assert(cache_k_view->op == GGML_OP_VIEW);
 
-            auto * cache_k = cache_k_view->src[0];
-            int layer = extract_layer_from_name(cache_k->name);
-            auto * mask = node->src[3];
+            assert(cache_k_permute != nullptr);
+
+            model_params.head_size = cache_k_permute->ne[0];
+            model_params.n_heads_kv = cache_k_permute->ne[2];
+            compute_params.input_len = node->src[0]->ne[1];
+            compute_params.token_len_per_seq = node->src[0]->ne[1];
+
+            auto * cache_k_view = cache_k_permute->src[0];
+            if (cache_k_view->op != GGML_OP_VIEW || mask == nullptr) {
+                continue;
+            }
+
+            ggml_tensor * cache_k = cache_k_view->src[0];
+            int layer = extract_layer_from_name(cache_k->name).value();
+
             std::string mask_name(mask->name);
 
             model_params.kv_buffer_ctx_id = ggml_backend_openvino_buffer_get_ctx_id(cache_k->buffer);
@@ -308,7 +430,6 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
             size_t offset;
             memcpy(&offset, cache_k_view->op_params, sizeof(size_t));
             compute_params.seq_active_start = offset / seq_size;
-            compute_params.token_len_per_seq = node->ne[2];
 
             if (mask_name.find("swa") != std::string::npos) {
                 compute_params.attention_size_swa = mask->ne[0];
@@ -320,10 +441,40 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
                 compute_params.attention_size_swa = model_params.ctx_per_seq_swa;
                 compute_params.token_len_per_seq = 1;
             }
-            break;
+        }
+
+        if (node->op == GGML_OP_MUL_MAT && node->src[0]->op == GGML_OP_PERMUTE &&
+            node->src[0]->src[0]->op == GGML_OP_VIEW && is_kvcache(node->src[0]->view_src, node->view_src)) {
+            if (node->src[1]->op == GGML_OP_PERMUTE && node->src[1]->src[0]->op == GGML_OP_VIEW &&
+                node->src[1]->src[0]->src[0]->op == GGML_OP_ROPE) {
+                compute_params.attention_size = node->ne[0];
+            }
+        }
+
+        // if the node op is TRANSPOSE and its input is PERMUTE and the source of the PERMUTE is VIEW, then get the attention size with the TRANSPOSE node ne[0] (in case no GGML_OP_FLASH_ATTN_EXT)
+        if (node->op == GGML_OP_TRANSPOSE && node->src[0]->op == GGML_OP_PERMUTE &&
+            node->src[0]->src[0]->op == GGML_OP_VIEW) {
+            compute_params.attention_size = node->ne[0];
+            if (is_static) {
+                compute_params.attention_size = model_params.ctx_per_seq;
+            }
         }
         if (node->op == GGML_OP_ROPE) {
-            memcpy(model_params.rope_params, node->op_params, sizeof(int32_t) * 15);
+            if (compute_params.token_len_per_seq == -1 && node->src[1] != nullptr) {
+                compute_params.token_len_per_seq = ggml_nelements(node->src[1]);
+            }
+
+            // When multiple ROPE ops in the graph disagree on op_params (e.g. gemma4's
+            // mixed SWA/non-SWA layers with different n_dims or freq_base), we cannot
+            // share a single precomputed rope_sin/rope_cos. Track divergence so the
+            // translator falls back to per-op make_sin_cos in that case.
+            static_assert(sizeof(model_params.rope_params) == sizeof(int32_t) * 15, "rope_params size");
+            if (!rope_seen) {
+                memcpy(model_params.rope_params, node->op_params, sizeof(int32_t) * 15);
+                rope_seen = true;
+            } else if (memcmp(model_params.rope_params, node->op_params, sizeof(int32_t) * 15) != 0) {
+                model_params.mixed_rope_params = true;
+            }
         }
     }
     auto * output_tensor = cgraph->nodes[cgraph->n_nodes - 1];
@@ -333,7 +484,6 @@ std::pair<ModelParams, ComputeParams> GgmlOvDecoder::compute_llm_params(ggml_cgr
         compute_params.output_len = 1;
     }
     model_params.ctx = model_params.ctx_per_seq * model_params.n_seq;
-    model_params.ctx_swa = model_params.ctx_per_seq_swa * model_params.n_seq;
     return {model_params, compute_params};
 }
 
@@ -343,9 +493,11 @@ void GgmlOvDecoder::validate_cgraph() const {
     }
 }
 
-ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op, const ggml_tensor * input) const {
+ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op,
+                                                      const ggml_tensor * input,
+                                                      int dynamic_dim_index) const {
     if (m_naive) {
-        return input!= nullptr ? ov::PartialShape{get_shape(input)} : ov::PartialShape{get_shape(op)};
+        return input != nullptr ? ov::PartialShape{get_shape(input)} : ov::PartialShape{get_shape(op)};
     }
     auto name = std::string(input->name);
     ov::PartialShape input_shape;
@@ -394,6 +546,15 @@ ov::PartialShape GgmlOvDecoder::get_graph_input_shape(const ggml_tensor * op, co
     } else {
         input_shape = ov::PartialShape{get_shape(input)};
     }
+    if (dynamic_dim_index != -1 && m_model_is_splitted) {
+        input_shape[3 - dynamic_dim_index] = -1;
+    }
+    if (op->op == GGML_OP_SOFT_MAX && op->src[1] != nullptr && op->src[1]->op == GGML_OP_NONE &&
+        op->src[1]->flags & GGML_TENSOR_FLAG_INPUT && op->src[1] == input) {
+        // for softmax input mask, the shape is [1, 1, seq_active, seq_active], where seq_active is determined by the input active sequence length instead of the kv cache sequence length
+        input_shape[2] = -1;
+        input_shape[3] = -1;
+    }
     return input_shape;
 }
 
@@ -421,15 +582,19 @@ void GgmlOvDecoder::add_extra_inputs() {
         }
     };
 
-    create_1d_input("attention_size", m_compute_params.attention_size);
+    if (m_compute_params.attention_size != -1) {
+        create_1d_input("attention_size", m_compute_params.attention_size);
+    }
     if (m_compute_params.attention_size_swa != -1) {
         create_1d_input("attention_size_swa", m_compute_params.attention_size_swa);
     }
     create_1d_input("n_seq_active", m_compute_params.n_seq_active);
     create_1d_input("seq_active_start", m_compute_params.seq_active_start);
     create_1d_input("seq_active_end", m_compute_params.seq_active_start + m_compute_params.n_seq_active);
-    create_1d_input("token_len_per_seq", m_compute_params.token_len_per_seq);
-    // create_1d_input("token_len", m_token_len_per_seq * m_n_seq_active);
+    if (m_compute_params.token_len_per_seq != -1) {
+        create_1d_input("token_len_per_seq", m_compute_params.token_len_per_seq);
+    }
+    // create_1d_input("token_len", m_compute_params.token_len_per_seq * m_compute_params.n_seq_active);
 }
 
 bool GgmlOvDecoder::node_is_used_as_src(const int node_idx) {
@@ -455,8 +620,8 @@ void GgmlOvDecoder::compute_model_inputs() {
             std::string node_name(node->name);
             if (m_model_weights.find(node_name) == m_model_weights.end()) {
                 m_inputs[node_name] = node;
-                auto param_node =
-                    std::make_shared<ov::op::v0::Parameter>(get_ov_type(node), get_graph_input_shape(node, nullptr));
+                auto param_node = std::make_shared<ov::op::v0::Parameter>(
+                    get_ov_type(node), get_graph_input_shape(node, nullptr, m_node_dynamic_dims[node]));
                 param_node->set_friendly_name(node_name);
                 param_node->output(0).get_tensor().set_names({node_name});
                 m_model_inputs[node_name] = param_node;
@@ -500,7 +665,13 @@ void GgmlOvDecoder::compute_model_inputs() {
                     m_model_params.kv_names.push_back(src_name);
                 }
             }
-            ov::PartialShape param_shape = get_graph_input_shape(node, src);
+            // Resolve nested VIEW nodes by following src[0] until the first non-VIEW tensor.
+            while (src->op == GGML_OP_VIEW && src->src[0] != nullptr) {
+                src = src->src[0];
+                src_name = std::string(src->name);
+            }
+            m_inputs[src_name] = src;
+            ov::PartialShape param_shape = get_graph_input_shape(node, src, m_node_dynamic_dims[src]);
             auto param_node = std::make_shared<ov::op::v0::Parameter>(get_ov_type(src), param_shape);
             param_node->set_friendly_name(src_name);
             param_node->output(0).get_tensor().set_names({src_name});
@@ -515,7 +686,7 @@ void GgmlOvDecoder::compute_model_outputs() {
     for (int node_n = 0; node_n < m_cgraph->n_nodes; node_n++) {
         auto * cur_node = m_cgraph->nodes[node_n];
         // if the node op is NONE means this node is not used at all, we can skip it directly without adding to model outputs.
-        if (cur_node->op == GGML_OP_NONE) {
+        if (cur_node->op == GGML_OP_NONE || cur_node->op == GGML_OP_VIEW || cur_node->op == GGML_OP_RESHAPE) {
             continue;
         }
         auto cur_node_use_count = m_cgraph->use_counts[ggml_hash_find(&m_cgraph->visited_hash_set, cur_node)];
@@ -644,15 +815,26 @@ std::shared_ptr<ov::Node> GgmlOvDecoder::create_weight_node(ggml_tensor * tensor
         }
     }
 
+    // MUL_MAT_ID expert weights are 3D GGML tensors [k, m, n_expert].
+    // Keep the full reversed 4D shape when materializing non-quantized constants,
+    // otherwise the expert dimension is collapsed and later Gather/MatMul logic
+    // only sees a single expert slice.
+    if (!ggml_is_quantized(tensor->type) && (tensor->ne[2] > 1 || tensor->ne[3] > 1)) {
+        auto weight_tensor = ov::Tensor(get_ov_type(tensor), get_shape(tensor), tensor->data);
+        auto weight_node = std::make_shared<ov::op::v0::Constant>(weight_tensor);
+        weight_node->set_friendly_name(tensor->name);
+        return weight_node;
+    }
+
     // There are three cases where we need to create a new weight node:
     // 1. weights are in openvino_host_buffer. Weight loading to host buffer will not trigger backend_buffer_set_tensor
     // 2. weights are in cpu/cpu_mapped buffer. On token_embd.weight goes to case 1 or 2, depending on whether mmap or direct_io is used
     // 3. test-backend-ops. buffers in test-backend-ops does not set USAGE_WEIGHT so backend_buffer_set_tensor will not create weight node
 
     // GGML_LOG_DEBUG("%s: creating new weight node for %s\n", __func__, tensor->name);
-    static const std::set<ggml_type> weight_types = {GGML_TYPE_F32,  GGML_TYPE_F16,  GGML_TYPE_BF16,
-                                                     GGML_TYPE_Q8_0, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
-                                                     GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K};
+    static const std::set<ggml_type> weight_types = {GGML_TYPE_F32,  GGML_TYPE_F16,  GGML_TYPE_BF16, GGML_TYPE_Q8_0,
+                                                     GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1, GGML_TYPE_Q4_K,
+                                                     GGML_TYPE_Q5_K, GGML_TYPE_Q6_K};
     if (weight_types.find(tensor->type) == weight_types.end()) {
         throw std::runtime_error("Unexpected weight tensor type: " + std::string(tensor->name) + " with type " +
                                  ggml_type_name(tensor->type));
@@ -860,6 +1042,161 @@ std::vector<size_t> GgmlOvDecoder::get_input_stride(int node_idx, const std::str
     return get_stride(m_node_info_list[node_idx].node_inputs.at(name));
 }
 
+size_t GgmlOvDecoder::get_view_input_size(int node_idx, const std::string & name) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        return it->second.size();
+    }
+    return 0;
+}
+
+size_t GgmlOvDecoder::get_view_input_offset(int node_idx, const std::string & name, size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            return it->second[view_index].second->view_offs;
+        }
+    }
+    return 0;
+}
+
+size_t GgmlOvDecoder::get_view_input_src_offset(int node_idx, const std::string & name, size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * view_tensor = it->second[view_index].second;
+            if (view_tensor && view_tensor->src[0]) {
+                return view_tensor->src[0]->view_offs;
+            }
+        }
+    }
+    return 0;
+}
+
+std::vector<size_t> GgmlOvDecoder::get_view_input_stride(int node_idx,
+                                                         const std::string & name,
+                                                         size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            return get_stride(it->second[view_index].second);
+        }
+    }
+    return {};
+}
+
+std::vector<size_t> GgmlOvDecoder::get_view_input_src_stride(int node_idx,
+                                                             const std::string & name,
+                                                             size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * view_tensor = it->second[view_index].second;
+            if (view_tensor && view_tensor->src[0]) {
+                return get_stride(view_tensor->src[0]);
+            }
+        }
+    }
+    return {};
+}
+
+ov::Shape GgmlOvDecoder::get_view_input_ggml_shape(int node_idx, const std::string & name, size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            return get_shape(it->second[view_index].second);
+        }
+    }
+    return {};
+}
+
+ov::Shape GgmlOvDecoder::get_view_input_src_ggml_shape(int node_idx,
+                                                       const std::string & name,
+                                                       size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * view_tensor = it->second[view_index].second;
+            if (view_tensor && view_tensor->src[0]) {
+                return get_shape(view_tensor->src[0]);
+            }
+        }
+    }
+    return {};
+}
+
+ov::PartialShape GgmlOvDecoder::get_view_input_ov_shape(int node_idx,
+                                                        const std::string & name,
+                                                        size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * tensor = it->second[view_index].second;
+            ov::PartialShape shape = ov::PartialShape{get_shape(tensor)};
+
+            // Check if this tensor has a dynamic dimension
+            auto dynamic_it = m_node_dynamic_dims.find(tensor);
+            if (dynamic_it != m_node_dynamic_dims.end() && dynamic_it->second != -1) {
+                int dynamic_dim_index = dynamic_it->second;
+                // GGML uses reverse indexing, so convert to OpenVINO indexing
+                shape[3 - dynamic_dim_index] = m_is_static ? get_static_n_tokens() : -1;
+            }
+
+            return shape;
+        }
+    }
+    return {};
+}
+
+ov::PartialShape GgmlOvDecoder::get_view_input_src_ov_shape(int node_idx,
+                                                            const std::string & name,
+                                                            size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * view_tensor = it->second[view_index].second;
+            if (view_tensor && view_tensor->src[0]) {
+                auto * src_tensor = view_tensor->src[0];
+                ov::PartialShape shape = ov::PartialShape{get_shape(src_tensor)};
+
+                // Check if this tensor has a dynamic dimension
+                auto dynamic_it = m_node_dynamic_dims.find(src_tensor);
+                if (dynamic_it != m_node_dynamic_dims.end() && dynamic_it->second != -1) {
+                    int dynamic_dim_index = dynamic_it->second;
+                    // GGML uses reverse indexing, so convert to OpenVINO indexing
+                    shape[3 - dynamic_dim_index] = m_is_static ? get_static_n_tokens() : -1;
+                }
+
+                return shape;
+            }
+        }
+    }
+    return {};
+}
+
+std::string GgmlOvDecoder::get_view_input_name(int node_idx, const std::string & name, size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            return it->second[view_index].second->name;
+        }
+    }
+    return "";
+}
+
+std::string GgmlOvDecoder::get_view_input_src_name(int node_idx, const std::string & name, size_t view_index) const {
+    auto it = m_node_info_list[node_idx].node_inputs_views.find(name);
+    if (it != m_node_info_list[node_idx].node_inputs_views.end()) {
+        if (view_index < it->second.size()) {
+            auto * view_tensor = it->second[view_index].second;
+            if (view_tensor && view_tensor->src[0]) {
+                return view_tensor->src[0]->name;
+            }
+        }
+    }
+    return "";
+}
+
 ov::element::Type GgmlOvDecoder::get_input_type(int node_idx, const std::string & name) const {
     return get_ov_type(m_node_info_list[node_idx].node_inputs.at(name));
 }
@@ -885,6 +1222,11 @@ ov::element::Type GgmlOvDecoder::get_output_type(const int node_idx) const {
     return get_ov_type(m_node_info_list[node_idx].node);
 }
 
+std::vector<size_t> GgmlOvDecoder::get_output_stride(int node_idx) const {
+    auto * ggml_tensor = m_node_info_list[node_idx].node;
+    return get_stride(ggml_tensor);
+}
+
 std::vector<std::string> GgmlOvDecoder::get_output_names(int node_idx) const {
     return {m_node_info_list[node_idx].node_output_name};
 }
@@ -894,6 +1236,14 @@ const std::string & GgmlOvDecoder::get_op_name() const {
     return unknown_name;
 }
 
+int32_t GgmlOvDecoder::get_op_dynamic_dim(int node_idx) const {
+    auto it = m_node_dynamic_dims.find(m_node_info_list[node_idx].node);
+    if (it == m_node_dynamic_dims.end()) {
+        return -1;
+    }
+    return it->second;
+}
+
 const std::string & GgmlOvDecoder::get_op_name(int node_idx) const {
     return m_node_info_list[node_idx].node_name;
 }
@@ -906,6 +1256,10 @@ int32_t * GgmlOvDecoder::get_output_op_params(int node_idx) const {
     return m_node_info_list[node_idx].node->op_params;
 }
 
+size_t GgmlOvDecoder::get_output_op_offset(int node_idx) const {
+    return m_node_info_list[node_idx].node->view_offs;
+}
+
 void GgmlOvDecoder::visit_subgraph(std::function<void(std::shared_ptr<GgmlDecoder>, int node_idx)> node_visitor) const {
     for (int node_idx = 0; node_idx < m_cgraph->n_nodes; node_idx++) {
         if (m_cgraph->nodes[node_idx]->op == GGML_OP_NONE) {
@@ -917,28 +1271,41 @@ void GgmlOvDecoder::visit_subgraph(std::function<void(std::shared_ptr<GgmlDecode
 
 std::string GgmlOvDecoder::compute_op_type(const ggml_tensor * node) {
     static const std::map<ggml_op, std::string> ops = {
-        {GGML_OP_NONE,           "GGML_OP_NONE"          },
-        {GGML_OP_ACC,            "GGML_OP_ACC"           },
-        {GGML_OP_ADD,            "GGML_OP_ADD"           },
-        {GGML_OP_ADD1,           "GGML_OP_ADD1"          },
-        {GGML_OP_CONT,           "GGML_OP_CONT"          },
-        {GGML_OP_DIV,            "GGML_OP_DIV"           },
-        {GGML_OP_DUP,            "GGML_OP_DUP"           },
-        {GGML_OP_GET_ROWS,       "GGML_OP_GET_ROWS"      },
-        {GGML_OP_MUL,            "GGML_OP_MUL"           },
-        {GGML_OP_MUL_MAT,        "GGML_OP_MUL_MAT"       },
-        {GGML_OP_PERMUTE,        "GGML_OP_PERMUTE"       },
-        {GGML_OP_RESHAPE,        "GGML_OP_RESHAPE"       },
-        {GGML_OP_RMS_NORM,       "GGML_OP_RMS_NORM"      },
-        {GGML_OP_ROPE,           "GGML_OP_ROPE"          },
-        {GGML_OP_SCALE,          "GGML_OP_SCALE"         },
-        {GGML_OP_SOFT_MAX,       "GGML_OP_SOFT_MAX"      },
-        {GGML_OP_SUB,            "GGML_OP_SUB"           },
-        {GGML_OP_TRANSPOSE,      "GGML_OP_TRANSPOSE"     },
-        {GGML_OP_VIEW,           "GGML_OP_VIEW"          },
-        {GGML_OP_SET_ROWS,       "GGML_OP_SET_ROWS"      },
-        {GGML_OP_CPY,            "GGML_OP_CPY"           },
-        {GGML_OP_FLASH_ATTN_EXT, "GGML_OP_FLASH_ATTN_EXT"},
+        {GGML_OP_NONE,            "GGML_OP_NONE"           },
+        {GGML_OP_ACC,             "GGML_OP_ACC"            },
+        {GGML_OP_ADD,             "GGML_OP_ADD"            },
+        {GGML_OP_ADD1,            "GGML_OP_ADD1"           },
+        {GGML_OP_ADD_ID,          "GGML_OP_ADD_ID"         },
+        {GGML_OP_CONCAT,          "GGML_OP_CONCAT"         },
+        {GGML_OP_CONT,            "GGML_OP_CONT"           },
+        {GGML_OP_DIV,             "GGML_OP_DIV"            },
+        {GGML_OP_DUP,             "GGML_OP_DUP"            },
+        {GGML_OP_GET_ROWS,        "GGML_OP_GET_ROWS"       },
+        {GGML_OP_MUL,             "GGML_OP_MUL"            },
+        {GGML_OP_MUL_MAT,         "GGML_OP_MUL_MAT"        },
+        {GGML_OP_MUL_MAT_ID,      "GGML_OP_MUL_MAT_ID"     },
+        {GGML_OP_PERMUTE,         "GGML_OP_PERMUTE"        },
+        {GGML_OP_RESHAPE,         "GGML_OP_RESHAPE"        },
+        {GGML_OP_RMS_NORM,        "GGML_OP_RMS_NORM"       },
+        {GGML_OP_NORM,            "GGML_OP_NORM"           },
+        {GGML_OP_ROPE,            "GGML_OP_ROPE"           },
+        {GGML_OP_SCALE,           "GGML_OP_SCALE"          },
+        {GGML_OP_SOFT_MAX,        "GGML_OP_SOFT_MAX"       },
+        {GGML_OP_SUM_ROWS,        "GGML_OP_SUM_ROWS"       },
+        {GGML_OP_SUB,             "GGML_OP_SUB"            },
+        {GGML_OP_TRANSPOSE,       "GGML_OP_TRANSPOSE"      },
+        {GGML_OP_VIEW,            "GGML_OP_VIEW"           },
+        {GGML_OP_SET_ROWS,        "GGML_OP_SET_ROWS"       },
+        {GGML_OP_CPY,             "GGML_OP_CPY"            },
+        {GGML_OP_FLASH_ATTN_EXT,  "GGML_OP_FLASH_ATTN_EXT" },
+        {GGML_OP_L2_NORM,         "GGML_OP_L2_NORM"        },
+        {GGML_OP_CLAMP,           "GGML_OP_CLAMP"          },
+        {GGML_OP_PAD,             "GGML_OP_PAD"            },
+        {GGML_OP_SSM_CONV,        "GGML_OP_SSM_CONV"       },
+        {GGML_OP_GATED_DELTA_NET, "GGML_OP_GATED_DELTA_NET"},
+        {GGML_OP_ARGSORT,         "GGML_OP_ARGSORT"        },
+        {GGML_OP_REPEAT,          "GGML_OP_REPEAT"         },
+        {GGML_OP_IM2COL,          "GGML_OP_IM2COL"         }
     };
     static const std::map<ggml_unary_op, std::string> unary_ops = {
         {GGML_UNARY_OP_ABS,         "GGML_UNARY_OP_ABS"        },
@@ -952,6 +1319,7 @@ std::string GgmlOvDecoder::compute_op_type(const ggml_tensor * node) {
         {GGML_UNARY_OP_GELU,        "GGML_UNARY_OP_GELU"       },
         {GGML_UNARY_OP_GELU_QUICK,  "GGML_UNARY_OP_GELU_QUICK" },
         {GGML_UNARY_OP_SILU,        "GGML_UNARY_OP_SILU"       },
+        {GGML_UNARY_OP_SOFTPLUS,    "GGML_UNARY_OP_SOFTPLUS"   },
         {GGML_UNARY_OP_HARDSWISH,   "GGML_UNARY_OP_HARDSWISH"  },
         {GGML_UNARY_OP_HARDSIGMOID, "GGML_UNARY_OP_HARDSIGMOID"},
         {GGML_UNARY_OP_EXP,         "GGML_UNARY_OP_EXP"        },
@@ -983,3 +1351,301 @@ const std::string & GgmlOvDecoder::get_op_type() const {
     static const std::string unknown_op = "UNKNOWN_GGML_OP";
     return unknown_op;
 }
+
+void GgmlOvDecoder::compute_node_dynamic_dims() {
+    auto visit_node = [&](auto && self, ggml_tensor * node) -> void {
+        if (!node) {
+            return;
+        }
+
+        if (node->op == GGML_OP_CPY) {
+            m_node_dynamic_dims[node] = -1;
+        }
+
+        if (m_node_dynamic_dims.count(node)) {
+            return;
+        }
+        for (int i = 0; i < GGML_MAX_SRC; i++) {
+            ggml_tensor * src = node->src[i];
+            if (src == nullptr) {
+                continue;
+            }
+            struct ggml_tensor * root_src = nullptr;
+            // if (src->org_src) {
+            //     root_src = src->org_src;
+            // }
+            if (root_src) {
+                if (is_inp_tok(root_src, node) || is_inp_pos(root_src, node) || is_output_idx(root_src, node)) {
+                    m_node_dynamic_dims[root_src] = 0;
+                    m_node_dynamic_dims[src] = m_node_dynamic_dims[root_src];
+                    continue;
+                }
+                self(self, root_src);
+                m_node_dynamic_dims[src] = m_node_dynamic_dims[root_src];
+            } else {
+                if (is_inp_tok(src, node) || is_inp_pos(src, node) || is_output_idx(src, node)) {
+                    m_node_dynamic_dims[src] = 0;
+                    continue;
+                }
+                if (node->op == GGML_OP_VIEW && src->op == GGML_OP_NONE && !is_stateful() && !m_model_is_splitted) {
+                    m_node_dynamic_dims[src] = 1;
+                    continue;
+                }
+                self(self, src);
+            }
+        }
+        switch (node->op) {
+        case GGML_OP_NONE:
+            m_node_dynamic_dims[node] = -1;
+            break;
+        case GGML_OP_GET_ROWS:
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[1]] != -1) {
+                auto dynamic_dim_idx = m_node_dynamic_dims[node->src[1]];
+                if (dynamic_dim_idx == 0) {
+                    m_node_dynamic_dims[node] = 1;
+                } else {
+                    auto dynamic_dim_stride = node->src[1]->nb[dynamic_dim_idx] / ggml_type_size(node->src[1]->type) *
+                                              ggml_type_size(node->src[0]->type);
+                    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                        if (dynamic_dim_stride == node->src[0]->nb[i]) {
+                            m_node_dynamic_dims[node] = i;
+                            break;
+                        }
+                    }
+                }
+                // OPENVINO_ASSERT(dynamic_dim_value == node->ne[m_node_dynamic_dims[node]],
+                //                 "Dynamic dim value mismatch for node: " + std::string(node->name) +
+                //                     " and its src[1]: " + std::string(node->src[1]->name));
+            }
+            break;
+        case GGML_OP_MUL:
+        case GGML_OP_MUL_MAT:
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                m_node_dynamic_dims[node] = m_node_dynamic_dims[node->src[0]];
+            }
+            if (m_node_dynamic_dims[node->src[1]] != -1) {
+                m_node_dynamic_dims[node] = m_node_dynamic_dims[node->src[1]];
+            }
+            break;
+        case GGML_OP_PERMUTE:
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                auto dynamic_dim_idx = m_node_dynamic_dims[node->src[0]];
+                // auto dynamic_dim_value = node->src[0]->ne[dynamic_dim_idx];
+                for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                    if (node->op_params[i] == dynamic_dim_idx) {
+                        m_node_dynamic_dims[node] = i;
+                        break;
+                    }
+                }
+                // OPENVINO_ASSERT(dynamic_dim_value == node->ne[m_node_dynamic_dims[node]],
+                //                 "Dynamic dim value mismatch for node: " + std::string(node->name) +
+                //                     " and its src[0]: " + std::string(node->src[0]->name));
+            }
+            break;
+        case GGML_OP_VIEW: {
+            // Use stride-based matching: the stride of a VIEW dimension directly
+            // encodes which source dimension it indexes into, so it uniquely
+            // identifies the dynamic dim even when two dims share the same size.
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                if (node->src[0]->op == GGML_OP_NONE) {
+                    m_node_dynamic_dims[node] = m_node_dynamic_dims[node->src[0]];
+                    break;
+                }
+                auto dynamic_dim_idx = m_node_dynamic_dims[node->src[0]];
+                auto dynamic_dim_value = node->src[0]->ne[dynamic_dim_idx];
+                auto dynamic_dim_stride =
+                    node->src[0]->nb[dynamic_dim_idx] / ggml_type_size(node->src[0]->type) * ggml_type_size(node->type);
+                for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                    if (node->nb[i] == dynamic_dim_stride) {
+                        m_node_dynamic_dims[node] = i;
+                        break;
+                    }
+                }
+                if (m_node_dynamic_dims[node] != -1 && dynamic_dim_value != node->ne[m_node_dynamic_dims[node]]) {
+                    m_node_dynamic_dims[node] = -1;
+                    // std::cout << "Warning: Dynamic dim value mismatch for node: " << node->name
+                    //           << " and its src[0]: " << node->src[0]->name << std::endl;
+                }
+            }
+            break;
+        }
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_RESHAPE: {
+            // RESHAPE requires src[0] to be contiguous, so both src and result
+            // have standard compact strides: nb[i] = type_size * prod(ne[0..i-1]).
+            // Match src->nb[dynamic_dim] against result->nb[i] to find the output
+            // dimension whose flat-memory boundary aligns with the source dynamic
+            // boundary. This is unambiguous (result strides are strictly monotone)
+            // and handles merged-lower-dim cases that ne-value matching misses.
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                auto dynamic_dim_idx = m_node_dynamic_dims[node->src[0]];
+                auto dynamic_dim_stride = node->src[0]->nb[dynamic_dim_idx];
+                for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                    if (node->nb[i] == dynamic_dim_stride && node->ne[i] == node->src[0]->ne[dynamic_dim_idx]) {
+                        m_node_dynamic_dims[node] = i;
+                        break;
+                    }
+                }
+                if (m_node_dynamic_dims[node] == -1) {
+                    // std::cout << "Cannot determine dynamic dim for RESHAPE node: " << node->name << std::endl;
+                }
+            }
+            break;
+        }
+        case GGML_OP_FLASH_ATTN_EXT: {
+            // Output shape is hard-coded in ggml_flash_attn_ext as:
+            //   ne = { v->ne[0], q->ne[2], q->ne[1], q->ne[3] }
+            // i.e. output dim 0 <- v dim 0 (head_size, static)
+            //      output dim 1 <- q dim 2 (n_heads,   static)
+            //      output dim 2 <- q dim 1 (n_tokens,  potentially dynamic)
+            //      output dim 3 <- q dim 3 (batch,     static)
+            // Using the fixed q-dim -> output-dim mapping table.
+            // q is src[0]; the mapping from q's dynamic dim to the output dim is:
+            //   q dim 1 -> output dim 2
+            //   q dim 2 -> output dim 1
+            //   q dim 3 -> output dim 3
+            //   q dim 0 -> output dim 0  (head_size axis, unlikely to be dynamic)
+            constexpr int q_to_out[GGML_MAX_DIMS] = {0, 2, 1, 3};
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                auto q_dynamic_dim = m_node_dynamic_dims[node->src[0]];
+                m_node_dynamic_dims[node] = q_to_out[q_dynamic_dim];
+            }
+            break;
+        }
+        case GGML_OP_CONT:
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[0]] != -1) {
+                auto dynamic_dim_idx = m_node_dynamic_dims[node->src[0]];
+                if (ggml_are_same_shape(node, node->src[0])) {
+                    m_node_dynamic_dims[node] = dynamic_dim_idx;
+                } else {
+                    size_t src_logical_nb[GGML_MAX_DIMS];
+                    src_logical_nb[0] = ggml_type_size(node->src[0]->type);
+                    src_logical_nb[1] = src_logical_nb[0] * (node->src[0]->ne[0] / ggml_blck_size(node->src[0]->type));
+                    for (int i = 2; i < GGML_MAX_DIMS; i++) {
+                        src_logical_nb[i] = src_logical_nb[i - 1] * node->src[0]->ne[i - 1];
+                    }
+
+                    auto dynamic_dim_stride = src_logical_nb[dynamic_dim_idx] / ggml_type_size(node->src[0]->type) *
+                                              ggml_type_size(node->type);
+                    int matched_dim_count = 0;
+                    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+                        if (node->nb[i] == dynamic_dim_stride && node->ne[i] == node->src[0]->ne[dynamic_dim_idx]) {
+                            m_node_dynamic_dims[node] = i;
+                            matched_dim_count++;
+                        }
+                    }
+                    if (matched_dim_count != 1) {
+                        m_node_dynamic_dims[node] = -1;
+                        // std::cout << "Warning: Cannot determine dynamic dim for CONT node: " << node->name
+                        //           << " and its src[0]: " << node->src[0]->name << std::endl;
+                    }
+                }
+            }
+            break;
+        case GGML_OP_RMS_NORM:
+        case GGML_OP_NORM:
+        case GGML_OP_ADD:
+        case GGML_OP_GLU:
+        case GGML_OP_ROPE:
+        case GGML_OP_SCALE:
+        case GGML_OP_SOFT_MAX:
+        case GGML_OP_ARGSORT:
+        case GGML_OP_ADD_ID:
+        case GGML_OP_UNARY:
+            m_node_dynamic_dims[node] = m_node_dynamic_dims[node->src[0]];
+            break;
+        case GGML_OP_MUL_MAT_ID:
+            m_node_dynamic_dims[node] = m_node_dynamic_dims[node->src[1]];
+            break;
+        case GGML_OP_CPY:
+        case GGML_OP_SET_ROWS:
+            m_node_dynamic_dims[node] = -1;
+            break;
+        case GGML_OP_IM2COL: {
+            m_node_dynamic_dims[node] = -1;
+            if (m_node_dynamic_dims[node->src[1]] != -1) {
+                const bool is_2D = node->op_params[6] == 1;
+                const int src_dyn = m_node_dynamic_dims[node->src[1]];
+                if (is_2D) {
+                    if (src_dyn == 0) {
+                        m_node_dynamic_dims[node] = 1;  // IW -> OW
+                    } else if (src_dyn == 1) {
+                        m_node_dynamic_dims[node] = 2;  // IH -> OH
+                    } else if (src_dyn == 3) {
+                        m_node_dynamic_dims[node] = 3;  // N  -> N
+                    }
+                } else {
+                    if (src_dyn == 0) {
+                        m_node_dynamic_dims[node] = 1;  // IW -> OW
+                    } else if (src_dyn == 2) {
+                        m_node_dynamic_dims[node] = 2;  // N  -> N  (1D: b->ne[2] is the batch/channel dim)
+                    }
+                }
+                if (m_node_dynamic_dims[node] != -1) {
+                    OPENVINO_ASSERT(node->src[1]->ne[src_dyn] == node->ne[m_node_dynamic_dims[node]],
+                                    "Dynamic dim value mismatch for IM2COL node: " + std::string(node->name) +
+                                        " and its src[1]: " + std::string(node->src[1]->name));
+                }
+            }
+            break;
+        }
+        default:
+            // std::cout << "Doesn't handle node name: " << node->name << " op: " << ggml_op_name(node->op) << std::endl;
+            break;
+        }
+    };
+
+    for (int i = 0; i < m_cgraph->n_nodes; i++) {
+        ggml_tensor * node = m_cgraph->nodes[i];
+        visit_node(visit_node, node);
+    }
+
+    // print the nodes in m_cgraph name & shape with the dynamic dim (the dynamic dim is the dimension with -1 in m_node_dynamic_dims) for debugging
+    if (0) {
+        for (int i = 0; i < m_cgraph->n_nodes; i++) {
+            ggml_tensor * node = m_cgraph->nodes[i];
+            int dynamic_dim = m_node_dynamic_dims[node];
+            std::cout << "[" << i << "] " << "node_name: " << node->name << " op: " << ggml_op_name(node->op)
+                      << " shape: [";
+            for (int j = 0; j < 4; j++) {
+                if (j == dynamic_dim) {
+                    std::cout << "*";
+                } else {
+                    std::cout << node->ne[j];
+                }
+                if (j < 3) {
+                    std::cout << ", ";
+                }
+            }
+            std::cout << "]" << std::endl;
+            // print the src name & shape with the dynamic dim for debugging
+            for (int j = 0; j < GGML_MAX_SRC; j++) {
+                ggml_tensor * src = node->src[j];
+                if (src == nullptr) {
+                    continue;
+                }
+                int src_dynamic_dim = m_node_dynamic_dims[src];
+                std::cout << "    [" << j << "] src_name: " << src->name << " [";
+                for (int k = 0; k < 4; k++) {
+                    if (k == src_dynamic_dim) {
+                        std::cout << "*";
+                    } else {
+                        std::cout << src->ne[k];
+                    }
+                    if (k < 3) {
+                        std::cout << ", ";
+                    }
+                }
+                std::cout << "]" << std::endl;
+            }
+            std::cout << std::endl;
+        }
+    }
+}
diff --git a/ggml/src/ggml-openvino/ggml-decoder.h b/ggml/src/ggml-openvino/ggml-decoder.h
index 3ae25ddda..ae545f47e 100644
--- a/ggml/src/ggml-openvino/ggml-decoder.h
+++ b/ggml/src/ggml-openvino/ggml-decoder.h
@@ -1,6 +1,7 @@
 #pragma once
 
-#include "ggml-quants.h"
+#include "ggml-backend-impl.h"
+#include "ggml-backend.h"
 #include "ggml.h"
 #include "openvino/decoder.h"
 
@@ -14,21 +15,21 @@
 
 struct ModelParams {
     int ctx = -1;
-    int ctx_swa = -1;
     int ctx_per_seq = -1;
     int ctx_per_seq_swa = -1;
     int n_seq = 1;
-    int n_heads = -1;
     int n_heads_kv = -1;
     int head_size = -1;
     int32_t rope_params[15];
+    bool mixed_rope_params = false;
     std::vector<int> swa_layers;
 
     std::vector<std::string> kv_names;
     size_t kv_buffer_ctx_id = 0;
 
     bool same_rope_params(const ModelParams & other) const {
-        return memcmp(rope_params, other.rope_params, sizeof(int32_t) * 15) == 0;
+        return mixed_rope_params == other.mixed_rope_params &&
+               memcmp(rope_params, other.rope_params, sizeof(int32_t) * 15) == 0;
     }
 
     bool can_reuse_dynamically(const ModelParams & other) const { return same_rope_params(other); }
@@ -56,12 +57,14 @@ public:
         std::string node_name;
         std::string node_op_type;
         std::map<std::string, ggml_tensor *> node_inputs;
+        std::map<std::string, std::vector<std::pair<std::string, ggml_tensor *>>> node_inputs_views;
         std::vector<std::string> node_inputs_names;
         ggml_tensor * node_output;
         std::string node_output_name;
         int node_op_case = 0;
         void * data_addr;
     };
+
     // Graph decoder
     GgmlOvDecoder(ggml_cgraph * cgraph,
                   ModelParams & model_params,
@@ -69,6 +72,7 @@ public:
                   std::map<std::string, std::shared_ptr<ov::Node>> & model_weights,
                   bool is_static,
                   bool is_stateful = false,
+                  bool model_is_splitted = false,
                   bool is_prefill = false,
                   int prefill_chunk_size = 256);
 
@@ -84,6 +88,42 @@ public:
 
     virtual std::vector<size_t> get_input_stride(int node_idx, const std::string & name) const override;
 
+    virtual size_t get_view_input_size(int node_idx, const std::string & name) const override;
+
+    virtual size_t get_view_input_offset(int node_idx, const std::string & name, size_t view_index) const override;
+
+    virtual size_t get_view_input_src_offset(int node_idx, const std::string & name, size_t view_index) const override;
+
+    virtual std::vector<size_t> get_view_input_stride(int node_idx,
+                                                      const std::string & name,
+                                                      size_t view_index) const override;
+
+    virtual std::vector<size_t> get_view_input_src_stride(int node_idx,
+                                                          const std::string & name,
+                                                          size_t view_index) const override;
+
+    virtual ov::Shape get_view_input_ggml_shape(int node_idx,
+                                                const std::string & name,
+                                                size_t view_index) const override;
+
+    virtual ov::Shape get_view_input_src_ggml_shape(int node_idx,
+                                                    const std::string & name,
+                                                    size_t view_index) const override;
+
+    virtual ov::PartialShape get_view_input_ov_shape(int node_idx,
+                                                     const std::string & name,
+                                                     size_t view_index) const override;
+
+    virtual ov::PartialShape get_view_input_src_ov_shape(int node_idx,
+                                                         const std::string & name,
+                                                         size_t view_index) const override;
+
+    virtual std::string get_view_input_name(int node_idx, const std::string & name, size_t view_index) const override;
+
+    virtual std::string get_view_input_src_name(int node_idx,
+                                                const std::string & name,
+                                                size_t view_index) const override;
+
     virtual ov::element::Type get_input_type(int node_idx, const std::string & name) const override;
 
     virtual size_t get_input_size() const override;
@@ -106,10 +146,14 @@ public:
 
     virtual ov::element::Type get_output_type(int node_idx) const override;
 
+    virtual std::vector<size_t> get_output_stride(int node_idx) const override;
+
     virtual int32_t * get_input_op_params(int node_idx, const std::string & name) const override;
 
     virtual int32_t * get_output_op_params(int node_idx) const override;
 
+    virtual size_t get_output_op_offset(int node_idx) const override;
+
     virtual std::vector<std::string> get_output_names(int node_idx) const override;
 
     virtual const std::string & get_op_type() const override;
@@ -120,7 +164,10 @@ public:
 
     virtual const std::string & get_op_name(int node_idx) const override;
 
-    virtual void visit_subgraph(std::function<void(std::shared_ptr<GgmlDecoder>, int node_idx)> node_visitor) const override;
+    virtual int32_t get_op_dynamic_dim(int node_idx) const override;
+
+    virtual void visit_subgraph(
+        std::function<void(std::shared_ptr<GgmlDecoder>, int node_idx)> node_visitor) const override;
 
     ggml_tensor * get_input_ggml_tensor(const std::string & name) const { return m_inputs.at(name); }
 
@@ -142,16 +189,12 @@ public:
         return m_model_weights;
     }
 
-    virtual std::vector<std::string> get_model_output_names() const override {
-        return m_model_output_names;
-    }
+    virtual std::vector<std::string> get_model_output_names() const override { return m_model_output_names; }
 
     const std::map<std::string, ggml_tensor *> & get_model_outputs() const { return m_model_outputs; }
 
     virtual int get_ctx_size() const { return m_model_params.ctx; }
 
-    virtual int get_ctx_swa_size() const { return m_model_params.ctx_swa; }
-
     virtual int get_ctx_per_seq() const { return m_model_params.ctx_per_seq; }
 
     virtual int get_ctx_per_seq_swa() const { return m_model_params.ctx_per_seq_swa; }
@@ -169,13 +212,21 @@ public:
 
     virtual int32_t * get_rope_params() const override { return const_cast<int32_t *>(m_model_params.rope_params); }
 
+    virtual bool has_mixed_rope_params() const override { return m_model_params.mixed_rope_params; }
+
     virtual std::map<std::string, std::string> get_kv_param_res_names() const override;
 
     virtual bool is_static() const override { return m_is_static; }
 
     virtual bool is_stateful() const override { return m_is_stateful; }
 
-    ov::PartialShape get_graph_input_shape(const ggml_tensor * op, const ggml_tensor * input) const;
+    int get_static_n_tokens() const { return m_is_prefill ? m_prefill_chunk_size : 1; }
+
+    virtual bool is_splited_model() const override { return m_model_is_splitted; }
+
+    ov::PartialShape get_graph_input_shape(const ggml_tensor * op,
+                                           const ggml_tensor * input,
+                                           int dynamic_dim_index = -1) const;
 
     static void dump_cgraph(const ggml_cgraph * cgraph, std::string & filename);
 
@@ -205,6 +256,7 @@ public:
     bool m_is_prefill = false;
     bool m_naive = false;
     int m_prefill_chunk_size = 0;
+    bool m_model_is_splitted = false;  // label the cgraph is splited or not
 
     static ov::Shape get_shape(const ggml_tensor * tensor);
     static std::vector<size_t> get_stride(const ggml_tensor * tensor);
@@ -227,7 +279,8 @@ public:
     }
 
     inline static bool is_inp_mask(const ggml_tensor * tensor, const ggml_tensor * op) {
-        return op->op == GGML_OP_CPY || (op->op == GGML_OP_FLASH_ATTN_EXT && tensor == op->src[3]);
+        return op->op == GGML_OP_CPY || (op->op == GGML_OP_FLASH_ATTN_EXT && tensor == op->src[3]) ||
+               (op->op == GGML_OP_SOFT_MAX && tensor == op->src[1]);
     }
 
     inline static bool is_rope_freqs_weight(const ggml_tensor * tensor, const ggml_tensor * op) {
@@ -235,7 +288,8 @@ public:
     }
 
     inline static bool is_kvcache(const ggml_tensor * tensor, const ggml_tensor * op) {
-        return op->op == GGML_OP_SET_ROWS && op->src[2] == tensor;
+        return tensor->buffer->usage == GGML_BACKEND_BUFFER_USAGE_ANY ||
+               (op != nullptr && op->op == GGML_OP_SET_ROWS && op->src[2] == tensor);
     }
 
     inline static bool is_kv_idx(const ggml_tensor * tensor, const ggml_tensor * op) {
@@ -243,23 +297,18 @@ public:
     }
 
     inline static bool is_output_idx(const ggml_tensor * tensor, const ggml_tensor * op) {
-        return op->op == GGML_OP_GET_ROWS && tensor == op->src[1] && op->src[0]->op != GGML_OP_NONE;
+        return op->op == GGML_OP_GET_ROWS && tensor == op->src[1] && op->src[0]->op != GGML_OP_NONE &&
+               op->src[1]->op == GGML_OP_NONE;
     }
 
-    static std::string get_graph_input_ov_name(const ggml_tensor * tensor, const ggml_tensor * op) {
-        if (is_inp_tok(tensor, op)) {
-            return "inp_tokens";
-        }
+    std::string get_graph_input_ov_name(const ggml_tensor * tensor, const ggml_tensor * op) {
         if (is_inp_pos(tensor, op)) {
             return "inp_pos";
         }
         if (is_inp_emb(tensor, op)) {
             return "embd";
         }
-        if (is_output_idx(tensor, op)) {
-            return "inp_out_ids";
-        }
-        if (is_inp_mask(tensor, op)) {
+        if (is_stateful() && is_inp_mask(tensor, op)) {
             return std::string(tensor->name).find("swa") == std::string::npos ? "self_kq_mask" : "self_kq_mask_swa";
         }
         return tensor->name;
@@ -272,6 +321,9 @@ private:
     void compute_model_inputs();
     void compute_model_outputs();
 
+    // Infer and propagate dynamic-dimension indices for all tensors in the GGML graph.
+    void compute_node_dynamic_dims();
+
     void validate_cgraph() const;
 
     ggml_cgraph * m_cgraph = nullptr;
@@ -284,6 +336,7 @@ private:
     std::map<std::string, ggml_tensor *> m_model_outputs;
     std::vector<std::string> m_model_output_names;
     std::vector<NodeInfo> m_node_info_list;
+    std::map<ggml_tensor *, int> m_node_dynamic_dims;
 
     ModelParams m_model_params;
     ComputeParams m_compute_params;
@@ -291,4 +344,4 @@ private:
 
 void print_tensor_address_map(const ggml_cgraph * cgraph);
 
-int extract_layer_from_name(const std::string & name);
+std::optional<int> extract_layer_from_name(const std::string & name);
diff --git a/ggml/src/ggml-openvino/ggml-openvino-extra.cpp b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
index 4140136ac..d9ad7be73 100644
--- a/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino-extra.cpp
@@ -3,6 +3,7 @@
 #include "ggml-impl.h"
 #include "ggml.h"
 
+#include <cstdlib>
 #include <cstring>
 #include <openvino/runtime/intel_gpu/ocl/ocl.hpp>
 #include <openvino/runtime/intel_npu/level_zero/level_zero.hpp>
@@ -22,7 +23,38 @@ void ggml_openvino_device_config::init() {
     if (initialized) {
         return;
     }
-    device_name = getenv("GGML_OPENVINO_DEVICE") ? getenv("GGML_OPENVINO_DEVICE") : "CPU";
+
+    // All recognized GGML_OPENVINO_* env vars. Their values are cached here
+    // once at backend init time and read back via ggml_openvino_getenv_str()
+    // (raw string) or ggml_openvino_getenv_int() (integer / boolean toggle).
+    static constexpr const char * env_var_names[] = {
+        // String values (use ggml_openvino_getenv_str)
+        "GGML_OPENVINO_DEVICE",
+        "GGML_OPENVINO_CACHE_DIR",
+        // Integer values (use ggml_openvino_getenv_int)
+        "GGML_OPENVINO_PREFILL_CHUNK_SIZE",
+        // Boolean toggles (treated as int flags via ggml_openvino_getenv_int)
+        "GGML_OPENVINO_STATEFUL_EXECUTION",
+        "GGML_OPENVINO_PROFILING",
+        "GGML_OPENVINO_DUMP_CGRAPH",
+        "GGML_OPENVINO_DUMP_IR",
+        "GGML_OPENVINO_DEBUG_INPUT",
+        "GGML_OPENVINO_DEBUG_OUTPUT",
+        "GGML_OPENVINO_PRINT_CGRAPH_TENSOR_ADDRESS",
+        "GGML_OPENVINO_ENABLE_CACHE",
+        "GGML_OPENVINO_DISABLE_CACHE",
+        "GGML_OPENVINO_DISABLE_KV_SLICE",
+        "GGML_OPENVINO_MANUAL_GQA_ATTN",
+    };
+
+    for (const char * const & env_var : env_var_names) {
+        auto * env = getenv(env_var);
+        if (env) {
+            environment_variables[env_var] = env;
+        }
+    }
+
+    device_name = ggml_openvino_getenv_str("GGML_OPENVINO_DEVICE", "CPU");
     auto available_devices = ov_singleton_core().get_available_devices();
     if (std::find(available_devices.begin(), available_devices.end(), device_name) == available_devices.end()) {
         GGML_LOG_WARN("GGML OpenVINO Backend: device %s is not available, fallback to CPU\n", device_name.c_str());
@@ -30,7 +62,7 @@ void ggml_openvino_device_config::init() {
     }
     is_npu = (device_name == "NPU");
 
-    auto * cache_dir = getenv("GGML_OPENVINO_CACHE_DIR");
+    const char * cache_dir = ggml_openvino_getenv_str("GGML_OPENVINO_CACHE_DIR");
     if (device_name == "NPU") {
         compile_config = {
             {"NPU_COMPILER_DYNAMIC_QUANTIZATION", "YES"   },
@@ -119,6 +151,23 @@ const std::string & ggml_openvino_get_device_name() {
     return ggml_openvino_get_device_config().device_name;
 }
 
+// Get the value of a GGML_OPENVINO_* env var as a string. Returns
+// default_value when the var is unset or set to an empty string.
+const char * ggml_openvino_getenv_str(const char * var, const char * default_value) {
+    auto & env_map = ggml_openvino_get_device_config().environment_variables;
+    auto it = env_map.find(var);
+    return (it == env_map.end() || it->second.empty()) ? default_value : it->second.c_str();
+}
+
+// Get the value of a GGML_OPENVINO_* env var as an int (via std::atoi).
+// Returns default_value (0) when the var is unset or empty. Used for both
+// integer settings (e.g. GGML_OPENVINO_PREFILL_CHUNK_SIZE) and boolean
+// toggles: "0" disables, any non-zero integer enables.
+int ggml_openvino_getenv_int(const char * var, int default_value) {
+    const char * v = ggml_openvino_getenv_str(var, nullptr);
+    return v ? std::atoi(v) : default_value;
+}
+
 // Check if running on NPU
 bool ggml_openvino_is_npu() {
     return ggml_openvino_get_device_config().is_npu;
@@ -173,7 +222,8 @@ std::optional<ExtraQuantType> ggml_openvino_get_requant_type(const ggml_tensor *
         return std::nullopt;
     }
     if (strncmp(tensor->name, "token_embd.weight", 17) == 0) {
-        return ((ggml_openvino_is_npu() && tensor->type == GGML_TYPE_Q6_K) ? ExtraQuantType::F16 : ExtraQuantType::Q8_0_C);
+        return ((ggml_openvino_is_npu() && tensor->type == GGML_TYPE_Q6_K) ? ExtraQuantType::F16 :
+                                                                             ExtraQuantType::Q8_0_C);
     }
     if (strncmp(tensor->name, "output.weight", 13) == 0) {
         return ExtraQuantType::Q8_0_C;
@@ -298,6 +348,10 @@ ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_ten
         layout.is_symmetric = true;
         break;
 
+    case GGML_TYPE_Q5_1:
+        // u8 weights (5-bit values), asymmetric (scale + zero point)
+        break;
+
     case GGML_TYPE_Q6_K:
         layout.weights_per_block = 16;
         layout.is_symmetric = true;
diff --git a/ggml/src/ggml-openvino/ggml-openvino-extra.h b/ggml/src/ggml-openvino/ggml-openvino-extra.h
index cd0baf4a6..c2654fbfa 100644
--- a/ggml/src/ggml-openvino/ggml-openvino-extra.h
+++ b/ggml/src/ggml-openvino/ggml-openvino-extra.h
@@ -64,6 +64,7 @@ struct ggml_openvino_device_config {
     bool initialized = false;
     std::optional<ov::RemoteContext> remote_context;
     ov::AnyMap compile_config;
+    std::unordered_map<std::string, std::string> environment_variables;
     cl_command_queue cl_queue = nullptr;
 
     void init();
@@ -79,6 +80,22 @@ void ggml_openvino_init_device_config();
 // Get the device name
 const std::string & ggml_openvino_get_device_name();
 
+// Environment variable accessors. All GGML_OPENVINO_* env vars are read once
+// during backend init and cached on the device config; consumers must go
+// through these helpers (never call ::getenv directly) so behavior stays
+// consistent and centralized.
+//
+// Use ggml_openvino_getenv_str() for string / path values
+// (e.g. GGML_OPENVINO_DEVICE, GGML_OPENVINO_CACHE_DIR). The optional
+// default_value is returned when the var is unset or empty.
+//
+// Use ggml_openvino_getenv_int() for boolean toggles and integer settings.
+// It returns std::atoi(value) when set, otherwise default_value. For
+// boolean use, `if (ggml_openvino_getenv_int(name))` is true iff the value
+// is a non-zero integer (so "0" disables, "1" enables).
+const char * ggml_openvino_getenv_str(const char * var, const char * default_value = nullptr);
+int ggml_openvino_getenv_int(const char * var, int default_value = 0);
+
 // Check if running on NPU
 bool ggml_openvino_is_npu();
 
@@ -115,9 +132,9 @@ struct ggml_openvino_weight_extra : public ggml_openvino_extra_base {
 
 // Extra data for quantized weight tensors - stores extracted weights/scales/zp and weight node
 struct ggml_openvino_quantized_weight_extra : public ggml_openvino_extra_base {
-    ov::Tensor weights;   // U4 or U8 extracted weights
-    ov::Tensor scales;    // F16 scales
-    ov::Tensor zp;        // U4 or U8 zero points (same type as weights)
+    ov::Tensor weights;                     // U4 or U8 extracted weights
+    ov::Tensor scales;                      // F16 scales
+    ov::Tensor zp;                          // U4 or U8 zero points (same type as weights)
     std::shared_ptr<ov::Node> weight_node;  // Pre-built OpenVINO weight subgraph
 
     ggml_openvino_quantized_weight_extra(ov::Tensor w, ov::Tensor s, ov::Tensor z, std::shared_ptr<ov::Node> n) :
@@ -132,8 +149,9 @@ struct ggml_openvino_quantized_weight_extra : public ggml_openvino_extra_base {
 struct ggml_openvino_tensor_extra : public ggml_openvino_extra_base {
     std::shared_ptr<ov::Tensor> tensor;  // For direct use with infer_request
 
-    explicit ggml_openvino_tensor_extra(std::shared_ptr<ov::Tensor> t)
-        : ggml_openvino_extra_base(Type::TENSOR), tensor(std::move(t)) {}
+    explicit ggml_openvino_tensor_extra(std::shared_ptr<ov::Tensor> t) :
+        ggml_openvino_extra_base(Type::TENSOR),
+        tensor(std::move(t)) {}
 };
 
 // =====================================================
@@ -152,11 +170,11 @@ struct ggml_openvino_extracted_layout {
     size_t zp_size = 0;         // Size of zero points in bytes (U4 or U8)
     bool is_u4;                 // true for U4 weights, false for U8
     int64_t weights_per_block;  // weights per scale/zp block
-    bool is_symmetric;        // true for symmetric quantization
+    bool is_symmetric;          // true for symmetric quantization
 
     // Requantization info
-    bool is_requant = false;                      // true if this tensor needs requantization
-    std::optional<ExtraQuantType> requant_type;   // target requant type if is_requant
+    bool is_requant = false;                     // true if this tensor needs requantization
+    std::optional<ExtraQuantType> requant_type;  // target requant type if is_requant
 };
 
 // Calculate the buffer layout for extracted quantized data
@@ -164,6 +182,9 @@ ggml_openvino_extracted_layout ggml_openvino_get_extracted_layout(const ggml_ten
 
 ggml_openvino_tensor_extra * ggml_openvino_create_tensor_extra(const ggml_tensor * tensor, bool is_remote);
 
+// Check if a tensor's buffer uses remote (device) memory (e.g. GPU USM)
+bool ggml_openvino_buffer_is_remote(const ggml_tensor * tensor);
+
 // Register an extra with the tensor's OpenVINO buffer context for proper lifetime management.
 // This sets tensor->extra and tracks the extra in the buffer context for cleanup.
 void ggml_openvino_buffer_register_extra(ggml_tensor * tensor, ggml_openvino_extra_base * extra);
diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp
index 4f3ebf253..943aef864 100644
--- a/ggml/src/ggml-openvino/ggml-openvino.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino.cpp
@@ -4,13 +4,14 @@
 #include "ggml-backend.h"
 #include "ggml-impl.h"
 #include "ggml-openvino-extra.h"
+#include "ggml-openvino/openvino/op_table.h"
 #include "ggml-openvino/utils.h"
 #include "ggml-quants.h"
 #include "ggml.h"
 
 #include <atomic>
-#include <cstdlib>
 #include <cstdint>
+#include <cstdlib>
 #include <cstring>
 #include <memory>
 #include <mutex>
@@ -146,8 +147,7 @@ static void * ggml_backend_openvino_buffer_get_base(ggml_backend_buffer_t buffer
 }
 
 static bool is_stateful_enabled() {
-    static const auto * stateful = getenv("GGML_OPENVINO_STATEFUL_EXECUTION");
-    return stateful && *stateful != '\0' && strcmp(stateful, "0") != 0;
+    return ggml_openvino_getenv_int("GGML_OPENVINO_STATEFUL_EXECUTION") != 0;
 }
 
 static enum ggml_status ggml_backend_openvino_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
@@ -367,11 +367,9 @@ static bool ggml_backend_openvino_buffer_cpy_tensor(ggml_backend_buffer_t buffer
             ggml_backend_openvino_buffer_context * src_ctx =
                 (ggml_backend_openvino_buffer_context *) src->buffer->context;
             if (src_ctx->is_remote) {
-                cl_int err =
-                    mem_cpy_fn(queue, CL_TRUE, dst->data, src->data, ggml_nbytes(src), 0, nullptr, nullptr);
+                cl_int err = mem_cpy_fn(queue, CL_TRUE, dst->data, src->data, ggml_nbytes(src), 0, nullptr, nullptr);
                 if (err != CL_SUCCESS) {
-                    GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL (device-to-device) failed with error %d\n", __func__,
-                                   err);
+                    GGML_LOG_ERROR("%s: clEnqueueMemcpyINTEL (device-to-device) failed with error %d\n", __func__, err);
                     return false;
                 }
                 return true;
@@ -579,6 +577,17 @@ size_t ggml_backend_openvino_buffer_get_ctx_id(ggml_backend_buffer_t buffer) {
     return ctx->id;
 }
 
+bool ggml_openvino_buffer_is_remote(const ggml_tensor * tensor) {
+    if (tensor == nullptr || tensor->buffer == nullptr) {
+        return false;
+    }
+    if (!ggml_backend_buffer_is_openvino(tensor->buffer)) {
+        return false;
+    }
+    auto * ctx = static_cast<ggml_backend_openvino_buffer_context *>(tensor->buffer->context);
+    return ctx->is_remote;
+}
+
 void ggml_openvino_buffer_register_extra(ggml_tensor * tensor, ggml_openvino_extra_base * extra) {
     GGML_ASSERT(tensor != nullptr);
     GGML_ASSERT(tensor->buffer != nullptr);
@@ -785,6 +794,18 @@ static bool has_view_op_input(const ggml_tensor * op) {
     return false;
 }
 
+static bool has_non_contiguous_view_input(const ggml_tensor * op) {
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op->src[i] == nullptr) {
+            break;
+        }
+        if (op->src[i]->op == GGML_OP_VIEW && !ggml_is_contiguous(op->src[i])) {
+            return true;
+        }
+    }
+    return false;
+}
+
 static bool is_supported_flash_attn_pattern(const ggml_tensor * op) {
     // pattern of q,k,v should be q->op==PERMUTE, q->src[0]->op==VIEW, q->src[0]->src[0]->view_src==nullptr
     for (int i = 0; i < 3; i++) {
@@ -797,17 +818,107 @@ static bool is_supported_flash_attn_pattern(const ggml_tensor * op) {
     return true;
 }
 
+static bool is_gemma3n_flash_attn_pattern(const ggml_tensor * op) {
+    if (!is_supported_flash_attn_pattern(op)) {
+        return false;
+    }
+
+    const ggml_tensor * q_base =
+        op->src[0] != nullptr && op->src[0]->src[0] != nullptr ? op->src[0]->src[0]->src[0] : nullptr;
+    const ggml_tensor * k_base =
+        op->src[1] != nullptr && op->src[1]->src[0] != nullptr ? op->src[1]->src[0]->src[0] : nullptr;
+    const ggml_tensor * v_base =
+        op->src[2] != nullptr && op->src[2]->src[0] != nullptr ? op->src[2]->src[0]->src[0] : nullptr;
+
+    if (q_base == nullptr || q_base->op != GGML_OP_ROPE) {
+        return false;
+    }
+
+    // gemma3n direct attention path (no KV cache): q=ROPE, k=ROPE, v=RMS_NORM
+    // Only match this specific pattern to avoid falsely catching other models
+    // (e.g. Gemma4) that also use scale=1.0 with KV-cache backed attention.
+    const bool is_qkv_direct =
+        k_base != nullptr && v_base != nullptr && k_base->op == GGML_OP_ROPE && v_base->op == GGML_OP_RMS_NORM;
+
+    return is_qkv_direct;
+}
+
+static bool checked_mul_size(size_t a, size_t b, size_t & out) {
+    if (a == 0 || b == 0) {
+        out = 0;
+        return true;
+    }
+    if (a > SIZE_MAX / b) {
+        return false;
+    }
+    out = a * b;
+    return true;
+}
+
+static bool mul_mat_id_requires_large_tmp(const ggml_tensor * op) {
+    const ggml_tensor * as = op->src[0];
+    const ggml_tensor * ids = op->src[2];
+    if (as == nullptr || ids == nullptr) {
+        return true;
+    }
+
+    // The current OpenVINO translation materializes selected expert weights with
+    // shape [n_tokens, n_used, rows, k]. Skip cases that would create a very
+    // large temporary on GPU and let the scheduler fall back instead.
+    size_t tmp_elems = 1;
+    if (!checked_mul_size(tmp_elems, static_cast<size_t>(ids->ne[1]), tmp_elems) ||
+        !checked_mul_size(tmp_elems, static_cast<size_t>(ids->ne[0]), tmp_elems) ||
+        !checked_mul_size(tmp_elems, static_cast<size_t>(as->ne[1]), tmp_elems) ||
+        !checked_mul_size(tmp_elems, static_cast<size_t>(as->ne[0]), tmp_elems)) {
+        return true;
+    }
+
+    size_t tmp_bytes = 0;
+    if (!checked_mul_size(tmp_elems, sizeof(float), tmp_bytes)) {
+        return true;
+    }
+
+    static constexpr size_t mul_mat_id_tmp_limit = 1ULL << 30;  // 1 GiB
+    return tmp_bytes > mul_mat_id_tmp_limit;
+}
+
 static bool is_op_unsupported_case(const ggml_tensor * op) {
     switch (op->op) {
+    case GGML_OP_CONCAT: {
+        if (op->type == GGML_TYPE_I64) {
+            return true;
+        }
+        break;
+    }
     case GGML_OP_GET_ROWS:
     case GGML_OP_SET_ROWS: {
         if (op->ne[3] != 1) {
             return true;
         }
+        if (op->ne[0] == 256 && (op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K)) {
+            // ERR = 0.000000306 > 0.000000100   GET_ROWS(type=q4_K,n=256,m=5,r=4,be1=1,be2=1,v=0)
+            // ERR = 0.000000197 > 0.000000100   GET_ROWS(type=q5_K,n=256,m=5,r=4,be1=1,be2=1,v=0)
+            return true;
+        }
+
+        // Keep the MoE routing weights gather on CPU for GPU runs. Splitting
+        // only at the later SUM/CLAMP/DIV nodes still leaves this routing path
+        // numerically unstable for arctic-style MoE graphs.
+        if (strncmp(op->name, "ffn_moe_weights", sizeof("ffn_moe_weights") - 1) == 0) {
+            return true;
+        }
+        break;
+    }
+    case GGML_OP_RESHAPE: {
+        if (strncmp(op->name, "ffn_moe_weights", sizeof("ffn_moe_weights") - 1) == 0 ||
+            strncmp(op->name, "ffn_norm_exps", sizeof("ffn_norm_exps") - 1) == 0) {
+            return true;
+        }
         break;
     }
     case GGML_OP_ADD:
-    case GGML_OP_MUL: {
+    case GGML_OP_MUL:
+    case GGML_OP_SUB: {
         if (op->src[1]->op == GGML_OP_PERMUTE) {
             return true;
         }
@@ -818,30 +929,79 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
         }
         break;
     }
+    case GGML_OP_ADD_ID: {
+        // Keep support aligned with the CPU backend implementation, which only handles f32 inputs/output and i32 ids.
+        if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type != GGML_TYPE_F32 ||
+            op->src[2]->type != GGML_TYPE_I32) {
+            return true;
+        }
+        break;
+    }
+    case GGML_OP_DIV: {
+        bool requires_broadcast = false;
+        for (int i = 0; i < 4; i++) {
+            if (op->src[0]->ne[i] == op->src[1]->ne[i]) {
+                continue;
+            }
+
+            if (op->src[0]->ne[i] != 1 && op->src[1]->ne[i] != 1) {
+                return true;
+            }
+
+            requires_broadcast = true;
+        }
+
+        // The GPU plugin can fuse broadcast DIV into the preceding FFN GEMM path
+        // and produce infs for per-channel scale vectors. Keep those DIVs on CPU
+        // until the fused GPU kernel is reliable. (falied case llama-arch-test mpt)
+        if (requires_broadcast && ggml_openvino_get_device_name() == "GPU") {
+            return true;
+        }
+
+        // qwen3next MoE weight normalization is numerically sensitive on the GPU
+        // path. Keep the normalization divide on CPU to match the reference.
+        if (strncmp(op->name, "ffn_moe_weights_norm", sizeof("ffn_moe_weights_norm") - 1) == 0) {
+            return true;
+        }
+        break;
+    }
     case GGML_OP_SOFT_MAX: {
         if (op->src[2] != nullptr) {
             // GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with sinks\n");
             return true;
         }
-        float scale = 1.0f;
-        float max_bias = 0.0f;
-        const auto * op_params = op->op_params;
-        memcpy(&scale, (const float *) op_params + 0, sizeof(float));
-        memcpy(&max_bias, (const float *) op_params + 1, sizeof(float));
-        if (max_bias > 0) {
-            // GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with max_bias > 0\n");
+
+        if (strncmp(op->name, "ffn_moe_probs", sizeof("ffn_moe_probs") - 1) == 0) {
+            return true;
+        }
+
+        // GPU execution of the MoE routing weights softmax is numerically unstable
+        // when fused with the surrounding GET_ROWS/reshape path. Keep this softmax
+        // on CPU so the scheduler splits at the same boundary that restores parity.
+        if (op->src[0] != nullptr && op->src[0]->op == GGML_OP_RESHAPE && op->src[0]->src[0] != nullptr &&
+            strncmp(op->src[0]->src[0]->name, "ffn_moe_weights", sizeof("ffn_moe_weights") - 1) == 0) {
+            return true;
+        }
+        break;
+    }
+    case GGML_OP_SUM_ROWS: {
+        if (strncmp(op->name, "ffn_moe_weights_sum", sizeof("ffn_moe_weights_sum") - 1) == 0) {
+            return true;
+        }
+
+        // if the input is PERMUTE skip
+        if (op->src[0]->op == GGML_OP_PERMUTE) {
+            return true;
+        }
+        break;
+    }
+    case GGML_OP_CLAMP: {
+        if (strncmp(op->name, "ffn_moe_weights_sum_clamped", sizeof("ffn_moe_weights_sum_clamped") - 1) == 0) {
             return true;
         }
         break;
     }
     case GGML_OP_FLASH_ATTN_EXT: {
-        if (op->src[4] != nullptr) {
-            // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n");
-            return true;
-        }
-        if (!is_supported_flash_attn_pattern(op)) {
-            return true;
-        }
         float scale = 1.0f;
         float max_bias = 0.0f;
         float logit_softcap = 0.0f;
@@ -849,6 +1009,21 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
         memcpy(&scale, (const float *) op_params + 0, sizeof(float));
         memcpy(&max_bias, (const float *) op_params + 1, sizeof(float));
         memcpy(&logit_softcap, (const float *) op_params + 2, sizeof(float));
+
+        // Keep gemma3n flash-attn pattern on CPU for GPU runs to avoid
+        // accuracy drift in the OpenVINO path. Restrict by scale=1.0 to avoid
+        // affecting non-gemma3n models such as Llama-3.2.
+        if (fabsf(scale - 1.0f) < 1e-6f && is_gemma3n_flash_attn_pattern(op)) {
+            return true;
+        }
+
+        if (op->src[4] != nullptr) {
+            // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n");
+            return true;
+        }
+        if (!is_supported_flash_attn_pattern(op)) {
+            return true;
+        }
         if (max_bias > 0) {
             // GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with max_bias > 0\n");
             return true;
@@ -868,34 +1043,44 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
         break;
     }
     case GGML_OP_CPY: {
-        if (op->src[1] != op) {
-            // GGML_LOG_WARN("OpenVINO backend only supports CPY that is a cast\n");
+        if (op->src[0]->type == GGML_TYPE_BF16 || op->src[1]->type == GGML_TYPE_BF16) {
+            // GGML_LOG_WARN("OpenVINO backend does not support CPY with non-contiguous data or bf16 types\n");
+            return true;
+        }
+        // op test case with non-contiguous src or dst
+        if ((op->ne[0] == 3 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2) ||
+            (op->ne[0] == 1 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2) ||
+            (op->ne[0] == 2 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2)) {
             return true;
         }
         break;
     }
     case GGML_OP_MUL_MAT: {
-        if (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16) {
-            // Has accuracy issue, try enabling this and see `test-backend-ops -o "MUL_MAT"`
-            // GGML_LOG_WARN("OpenVINO backend does not support MUL_MAT with two F16 tensors\n");
+        if (ggml_openvino_get_device_name() == "GPU" && op->src[1]->op == GGML_OP_SOFT_MAX &&
+            op->src[0]->op == GGML_OP_CONT && op->src[0]->src[0] != nullptr &&
+            op->src[0]->src[0]->op == GGML_OP_TRANSPOSE && op->src[0]->src[0]->src[0] != nullptr &&
+            op->src[0]->src[0]->src[0]->op == GGML_OP_PERMUTE) {
             return true;
         }
         if (op->src[0]->ne[3] != op->src[1]->ne[3] && op->src[0]->ne[3] != 1 && op->src[1]->ne[3] != 1) {
             return true;
         }
-        if (op->src[0]->op == GGML_OP_PERMUTE || op->src[1]->op == GGML_OP_PERMUTE) {
-            return true;
-        }
-        if (ggml_is_quantized(op->src[0]->type) && op->src[0]->ne[1] == 1) {
-            // MUL_MAT(type_a=q4_0,type_b=f32,m=1,n=2048,k=8192,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1)
-            // triggers a bug in ov matmul_shape_inference.hpp
-            return true;
-        }
         if (op->src[0]->op == GGML_OP_VIEW && op->src[1]->op == GGML_OP_VIEW) {
             return true;
         }
         break;
     }
+    case GGML_OP_MUL_MAT_ID: {
+        if (strncmp(op->name, "ffn_moe_gate_up", sizeof("ffn_moe_gate_up") - 1) == 0 ||
+            strncmp(op->name, "ffn_moe_down", sizeof("ffn_moe_down") - 1) == 0) {
+            return true;
+        }
+
+        if (mul_mat_id_requires_large_tmp(op)) {
+            return true;
+        }
+        break;
+    }
     case GGML_OP_ROPE: {
         const int32_t * op_params = op->op_params;
         const int n_dims = op_params[1];
@@ -909,7 +1094,7 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
             //               op->src[0]->ne[0]);
             return true;
         }
-        if (op->type != GGML_TYPE_F32) {
+        if (op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) {
             // GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type));
             return true;
         }
@@ -930,15 +1115,54 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
         }
         break;
     }
-    default:
-        break;
-    }
-    if (op->op == GGML_OP_GET_ROWS) {
-        if (op->ne[0] == 256 && (op->src[0]->type == GGML_TYPE_Q4_K || op->src[0]->type == GGML_TYPE_Q5_K)) {
-            // ERR = 0.000000306 > 0.000000100   GET_ROWS(type=q4_K,n=256,m=5,r=4,be1=1,be2=1,v=0)
-            // ERR = 0.000000197 > 0.000000100   GET_ROWS(type=q5_K,n=256,m=5,r=4,be1=1,be2=1,v=0)
+    case GGML_OP_TRANSPOSE: {
+        // if the type is bf16, will return true
+        if (op->type == GGML_TYPE_BF16) {
+            // GGML_LOG_WARN("OpenVINO backend does not support CONT with BF16 type\n");
             return true;
         }
+        break;
+    }
+    case GGML_OP_GATED_DELTA_NET: {
+        // enable after https://github.com/openvinotoolkit/openvino/pull/35917 is included in OV release
+        return true;
+        // if (ggml_openvino_get_device_name() == "GPU" && op->src[0]->ne[2] > 1) {
+        //     // CVS-186471
+        //     return true;
+        // }
+        if (op->src[2]->op == GGML_OP_PERMUTE) {
+            return true;
+        }
+        // kda (per-key-dimension gating) not supported by fused GatedDeltaNet op
+        if (op->src[3]->ne[0] != 1) {
+            return true;
+        }
+        // v_repeat > 1 (GQA): ggml uses modulo head mapping (h_q = h_v % H_k)
+        // but the fused op uses consecutive mapping (h_q = h_v / group_size)
+        if (op->src[2]->ne[1] != op->src[0]->ne[1]) {
+            return true;
+        }
+        // K > 1 (multiple state snapshots) not supported by fused op
+        if (op->src[5]->ne[1] > 1) {
+            return true;
+        }
+        break;
+    }
+    case GGML_OP_SSM_CONV: {
+        // qwen3next is numerically unstable with OpenVINO SSM_CONV.
+        // Keep this op on CPU until the OpenVINO implementation is fixed.
+        return true;
+    }
+    case GGML_OP_VIEW: {
+        // Skip TOPK_MOE fused tests until it is fully supported
+        // the argsort_top_k VIEW wrapping ARGSORT is named "selected_experts" in test_topk_moe
+        if (strcmp(op->name, "selected_experts") == 0) {
+            return true;
+        }
+        break;
+    }
+    default:
+        break;
     }
     return false;
 }
@@ -946,24 +1170,47 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
 static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
     GGML_ASSERT(dev->reg != nullptr);
 
-    static std::set<ggml_type> supported_types{GGML_TYPE_F32,  GGML_TYPE_F16,  GGML_TYPE_BF16, GGML_TYPE_I64,
-                                               GGML_TYPE_I32,  GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q4_K,
-                                               GGML_TYPE_Q5_K, GGML_TYPE_Q8_0, GGML_TYPE_Q6_K};
+    static std::unordered_set<ggml_type> supported_types{
+        GGML_TYPE_F32,  GGML_TYPE_F16,  GGML_TYPE_BF16, GGML_TYPE_I64,  GGML_TYPE_I32,  GGML_TYPE_Q4_0,
+        GGML_TYPE_Q4_1, GGML_TYPE_Q4_K, GGML_TYPE_Q5_1, GGML_TYPE_Q5_K, GGML_TYPE_Q8_0, GGML_TYPE_Q6_K};
 
-    static const std::set<ggml_op> supported_ops{GGML_OP_NONE, GGML_OP_ADD, GGML_OP_MUL, GGML_OP_MUL_MAT, GGML_OP_VIEW,
-                                                 /*GGML_OP_CONT,*/ GGML_OP_RESHAPE, GGML_OP_PERMUTE, GGML_OP_TRANSPOSE,
-                                                 GGML_OP_GET_ROWS, GGML_OP_ROPE, GGML_OP_RMS_NORM, GGML_OP_SCALE,
-                                                 // softmax is not updated due to replaced by flash_attn_ext
-                                                 // GGML_OP_SOFT_MAX,
-                                                 GGML_OP_SET_ROWS, GGML_OP_FLASH_ATTN_EXT, GGML_OP_CPY};
-    static const std::set<ggml_unary_op> supported_unary_ops{
-        GGML_UNARY_OP_GELU,
-        GGML_UNARY_OP_SILU,
-    };
-    static const std::set<ggml_glu_op> supported_glu_ops{
-        GGML_GLU_OP_SWIGLU,
-        GGML_GLU_OP_GEGLU,
+    // derive supported op sets from the op_table map, keys in
+    // the map use the full macro name (e.g. "GGML_OP_ADD"), while
+    // the ggml_*_op_name() helpers return only the trailing part (e.g. "ADD").
+    // each set is built once and cached.
+    static const auto build_supported_sets = [] {
+        const auto & table = ov::frontend::ggml::get_supported_ops();
+        std::unordered_set<ggml_op> ops;
+        std::unordered_set<ggml_unary_op> unary_ops;
+        std::unordered_set<ggml_glu_op> glu_ops;
+
+        // GGML_OP_NONE has no translator but is always safe to add to the supported set.
+        ops.insert(GGML_OP_NONE);
+
+        for (int i = 0; i < GGML_OP_COUNT; ++i) {
+            const std::string key = std::string("GGML_OP_") + ggml_op_name(static_cast<ggml_op>(i));
+            if (table.count(key)) {
+                ops.insert(static_cast<ggml_op>(i));
+            }
+        }
+        for (int i = 0; i < GGML_UNARY_OP_COUNT; ++i) {
+            const std::string key = std::string("GGML_UNARY_OP_") + ggml_unary_op_name(static_cast<ggml_unary_op>(i));
+            if (table.count(key)) {
+                unary_ops.insert(static_cast<ggml_unary_op>(i));
+            }
+        }
+        for (int i = 0; i < GGML_GLU_OP_COUNT; ++i) {
+            const std::string key = std::string("GGML_GLU_OP_") + ggml_glu_op_name(static_cast<ggml_glu_op>(i));
+            if (table.count(key)) {
+                glu_ops.insert(static_cast<ggml_glu_op>(i));
+            }
+        }
+        return std::make_tuple(ops, unary_ops, glu_ops);
     };
+    static const auto supported_sets = build_supported_sets();
+    static const auto & supported_ops = std::get<0>(supported_sets);
+    static const auto & supported_unary_ops = std::get<1>(supported_sets);
+    static const auto & supported_glu_ops = std::get<2>(supported_sets);
 
     switch (op->op) {
     case GGML_OP_UNARY: {
@@ -972,11 +1219,6 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
             // GGML_LOG_WARN("OpenVINO backend does not support unary op %s\n", ggml_unary_op_name(ggml_get_unary_op(op)));
             return false;
         }
-        if (has_view_op_input(op)) {
-            // GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
-            //               ggml_unary_op_name(ggml_get_unary_op(op)));
-            return false;
-        }
         break;
     }
     case GGML_OP_GLU: {
@@ -1003,13 +1245,15 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
             return false;
         }
         static std::set<ggml_op> ops_not_support_view_input{
-            GGML_OP_GET_ROWS,
-            GGML_OP_RMS_NORM,
+            GGML_OP_L2_NORM,
         };
         if (ops_not_support_view_input.find(op->op) != ops_not_support_view_input.end() && has_view_op_input(op)) {
             // GGML_LOG_WARN("OpenVINO backend does not support op %s with view input\n", ggml_op_name(op->op));
             return false;
         }
+        if (op->op == GGML_OP_RMS_NORM && has_non_contiguous_view_input(op)) {
+            return false;
+        }
     }
     }
 
diff --git a/ggml/src/ggml-openvino/ggml-quants.cpp b/ggml/src/ggml-openvino/ggml-quants.cpp
index 57d66df4f..275b95428 100644
--- a/ggml/src/ggml-openvino/ggml-quants.cpp
+++ b/ggml/src/ggml-openvino/ggml-quants.cpp
@@ -126,6 +126,68 @@ void extract_q4_1_data(const ggml_tensor * tensor,
     }
 }
 
+// Extracts (weight, scales, zp) from Q5_1 tensors.
+// Data layout is: |16 bit scale|16 bit min|32 bit qh (5th bits)|32 x 4bit low nibbles|.
+// Reconstructed quant q in [0,31]: q = (low nibble) | (qh_bit << 4). Dequant: w*d + m.
+// Weights are stored as u8 (5-bit values do not fit u4), matching make_int8_weights.
+void extract_q5_1_data(const ggml_tensor * tensor,
+                       ov::Tensor & weights_arr,
+                       ov::Tensor & scales_arr,
+                       ov::Tensor & zp_arr,
+                       bool use_bias) {
+    const uint64_t bytes_per_block = 24;  // 2 scale + 2 min + 4 qh + 16 (32x0.5) weights
+    const int qk = 32;
+
+    auto * data = static_cast<uint8_t *>(tensor->data);
+    auto * weights = static_cast<uint8_t *>(weights_arr.data());  // u8 weights, one byte per weight
+    auto * scales = scales_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
+
+    // Read a 16-bit little-endian value without aliasing/const-qual violations.
+    auto read_u16 = [](const uint8_t * p) {
+        uint16_t v;
+        memcpy(&v, p, sizeof(v));
+        return v;
+    };
+
+    auto unpack_block = [&](const uint8_t * block, uint8_t * dst) {
+        uint32_t qh;
+        memcpy(&qh, block + 4, sizeof(uint32_t));
+        const uint8_t * qs = block + 8;
+        for (int j = 0; j < qk / 2; ++j) {
+            const uint8_t lo = qs[j] & 0x0F;
+            const uint8_t hi = qs[j] >> 4;
+            const uint8_t bit_lo = (qh >> j) & 1;
+            const uint8_t bit_hi = (qh >> (j + qk / 2)) & 1;
+            dst[j] = lo | (bit_lo << 4);           // first 16 weights
+            dst[j + qk / 2] = hi | (bit_hi << 4);  // last 16 weights
+        }
+    };
+
+    if (use_bias) {
+        // Store bias (min) directly as f16: dequant w*d + m
+        auto * bias = zp_arr.data<ov::element_type_traits<ov::element::f16>::value_type>();
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            const uint8_t * block = data + i * bytes_per_block;
+            float scale = static_cast<float>(ov::float16::from_bits(read_u16(block)));
+            float min = static_cast<float>(ov::float16::from_bits(read_u16(block + 2)));
+            scales[i] = ov::float16(scale);
+            bias[i] = ov::float16(min);
+            unpack_block(block, weights + i * qk);
+        });
+    } else {
+        auto * zp = static_cast<uint8_t *>(zp_arr.data());  // u8 zero points
+        ov::parallel_for(scales_arr.get_size(), [&](size_t i) {
+            const uint8_t * block = data + i * bytes_per_block;
+            float scale = static_cast<float>(ov::float16::from_bits(read_u16(block)));
+            float min = static_cast<float>(ov::float16::from_bits(read_u16(block + 2)));
+            scales[i] = ov::float16(scale);
+            // zp = -min / scale (dequant: (w - zp) * s == w*s + min)
+            zp[i] = (scale != 0.0f) ? (uint8_t) std::lround(-min / scale) : 0;
+            unpack_block(block, weights + i * qk);
+        });
+    }
+}
+
 // Extracts (weight, scales, zp) from Q8_0 tensors.
 // Data layout is: |16 bit scale|32 x 8bit weights|.
 // When zp_arr is empty (symmetric), weights are stored as signed i8 directly.
@@ -577,6 +639,7 @@ std::shared_ptr<ov::Node> extract_quantized_weights(const ggml_tensor * tensor,
         weights_per_block = 32;
         break;
     case GGML_TYPE_Q8_0:
+    case GGML_TYPE_Q5_1:
     case GGML_TYPE_Q5_K:
         is_u4 = false;
         weights_per_block = 32;
@@ -601,6 +664,9 @@ std::shared_ptr<ov::Node> extract_quantized_weights(const ggml_tensor * tensor,
     case GGML_TYPE_Q4_K:
         extract_q4_k_data(&temp_tensor, weights, scales, zp, use_bias);
         break;
+    case GGML_TYPE_Q5_1:
+        extract_q5_1_data(&temp_tensor, weights, scales, zp, use_bias);
+        break;
     case GGML_TYPE_Q8_0:
         extract_q8_0_data(&temp_tensor, weights, scales, zp);
         break;
diff --git a/ggml/src/ggml-openvino/ggml-quants.h b/ggml/src/ggml-openvino/ggml-quants.h
index e4a02297c..28b7c1213 100644
--- a/ggml/src/ggml-openvino/ggml-quants.h
+++ b/ggml/src/ggml-openvino/ggml-quants.h
@@ -6,7 +6,7 @@
 #include <openvino/op/constant.hpp>
 #include <openvino/runtime/tensor.hpp>
 
-void unpack_32_4(const uint8_t* data, uint8_t* dst);
+void unpack_32_4(const uint8_t * data, uint8_t * dst);
 
 void extract_q4_0_data(const ggml_tensor * tensor,
                        ov::Tensor & weights_arr,
@@ -19,12 +19,18 @@ void extract_q4_1_data(const ggml_tensor * tensor,
                        ov::Tensor & zp_arr,
                        bool use_bias = false);
 
+void extract_q5_1_data(const ggml_tensor * tensor,
+                       ov::Tensor & weights_arr,
+                       ov::Tensor & scales_arr,
+                       ov::Tensor & zp_arr,
+                       bool use_bias = false);
+
 void extract_q8_0_data(const ggml_tensor * tensor,
                        ov::Tensor & weights_arr,
                        ov::Tensor & scales_arr,
                        ov::Tensor & zp_arr);
 
-void unpack_256_4(const uint8_t* data, uint8_t* dst);
+void unpack_256_4(const uint8_t * data, uint8_t * dst);
 
 void extract_q4_k_data(const ggml_tensor * tensor,
                        ov::Tensor & weights_arr,
@@ -145,8 +151,8 @@ namespace ov {
 namespace op {
 namespace util {
 // From <openvino>/src/common/transformations/include/transformations/utils/utils.hpp
-bool get_single_value(const std::shared_ptr<ov::op::v0::Constant>& const_node,
-                      float& value,
+bool get_single_value(const std::shared_ptr<ov::op::v0::Constant> & const_node,
+                      float & value,
                       bool check_value_range = true);
 }  // namespace util
 }  // namespace op
diff --git a/ggml/src/ggml-openvino/openvino/decoder.h b/ggml/src/ggml-openvino/openvino/decoder.h
index 3b8da2be5..9d64fe575 100644
--- a/ggml/src/ggml-openvino/openvino/decoder.h
+++ b/ggml/src/ggml-openvino/openvino/decoder.h
@@ -3,6 +3,8 @@
 #include <cstdint>
 #include <map>
 #include <openvino/core/node.hpp>
+#include <openvino/core/partial_shape.hpp>
+#include <openvino/core/shape.hpp>
 #include <openvino/frontend/decoder.hpp>
 #include <string>
 
@@ -12,22 +14,50 @@ namespace ggml {
 
 class GgmlDecoder : public DecoderBase {
 public:
-    virtual ov::Any get_attribute(const std::string& name) const = 0;
+    virtual ov::Any get_attribute(const std::string & name) const = 0;
 
-    virtual PartialShape get_input_shape(int node_idx, const std::string& name) const = 0;
+    virtual PartialShape get_input_shape(int node_idx, const std::string & name) const = 0;
 
-    virtual std::vector<size_t> get_input_stride(int node_idx, const std::string& name) const = 0;
+    virtual std::vector<size_t> get_input_stride(int node_idx, const std::string & name) const = 0;
 
-    virtual element::Type get_input_type(int node_idx, const std::string& name) const = 0;
+    virtual size_t get_view_input_size(int node_idx, const std::string & name) const = 0;
+
+    virtual size_t get_view_input_offset(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual size_t get_view_input_src_offset(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual std::vector<size_t> get_view_input_stride(int node_idx,
+                                                      const std::string & name,
+                                                      size_t view_index) const = 0;
+
+    virtual std::vector<size_t> get_view_input_src_stride(int node_idx,
+                                                          const std::string & name,
+                                                          size_t view_index) const = 0;
+
+    virtual Shape get_view_input_ggml_shape(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual Shape get_view_input_src_ggml_shape(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual PartialShape get_view_input_ov_shape(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual PartialShape get_view_input_src_ov_shape(int node_idx,
+                                                     const std::string & name,
+                                                     size_t view_index) const = 0;
+
+    virtual std::string get_view_input_name(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual std::string get_view_input_src_name(int node_idx, const std::string & name, size_t view_index) const = 0;
+
+    virtual element::Type get_input_type(int node_idx, const std::string & name) const = 0;
 
     virtual size_t get_input_size() const = 0;
 
     virtual size_t get_input_size(int node_idx) const = 0;
 
     virtual void get_input_node(size_t input_port_idx,
-                                std::string& producer_name,
-                                std::string& producer_output_port_name,
-                                size_t& producer_output_port_index) const = 0;
+                                std::string & producer_name,
+                                std::string & producer_output_port_name,
+                                size_t & producer_output_port_index) const = 0;
 
     virtual std::vector<std::string> get_input_names(int node_idx) const = 0;
 
@@ -35,30 +65,36 @@ public:
 
     virtual element::Type get_output_type(const int node_idx) const = 0;
 
-    virtual int32_t* get_input_op_params(int node_idx, const std::string& name) const = 0;
+    virtual std::vector<size_t> get_output_stride(int node_idx) const = 0;
+
+    virtual int32_t * get_input_op_params(int node_idx, const std::string & name) const = 0;
 
     virtual int32_t * get_output_op_params(int node_idx) const = 0;
 
+    virtual size_t get_output_op_offset(int node_idx) const = 0;
+
     virtual std::vector<std::string> get_output_names(int node_idx) const = 0;
 
-    virtual const std::string& get_op_type() const = 0;
+    virtual const std::string & get_op_type() const = 0;
 
-    virtual const std::string& get_op_type(int node_idx) const = 0;
+    virtual const std::string & get_op_type(int node_idx) const = 0;
 
-    virtual const std::string& get_op_name() const = 0;
+    virtual const std::string & get_op_name() const = 0;
 
-    virtual const std::string& get_op_name(int node_idx) const = 0;
+    virtual const std::string & get_op_name(int node_idx) const = 0;
 
     virtual void visit_subgraph(std::function<void(std::shared_ptr<GgmlDecoder>, int node_idx)> node_visitor) const = 0;
 
     virtual int get_op_case(int node_idx) const = 0;
 
-    virtual const std::map<std::string, std::shared_ptr<ov::Node>>& get_model_inputs() const = 0;
-    virtual const std::map<std::string, std::shared_ptr<ov::Node>>& get_model_extra_inputs() const = 0;
-    virtual const std::map<std::string, std::shared_ptr<ov::Node>>& get_model_weights() const = 0;
+    virtual const std::map<std::string, std::shared_ptr<ov::Node>> & get_model_inputs() const = 0;
+    virtual const std::map<std::string, std::shared_ptr<ov::Node>> & get_model_extra_inputs() const = 0;
+    virtual const std::map<std::string, std::shared_ptr<ov::Node>> & get_model_weights() const = 0;
     virtual std::vector<std::string> get_model_output_names() const = 0;
 
-    virtual int32_t* get_rope_params() const = 0;
+    virtual int32_t * get_rope_params() const = 0;
+
+    virtual bool has_mixed_rope_params() const = 0;
 
     virtual std::map<std::string, std::string> get_kv_param_res_names() const = 0;
 
@@ -66,7 +102,11 @@ public:
 
     virtual bool is_stateful() const = 0;
 
+    virtual bool is_splited_model() const = 0;
+
     virtual int is_swa_layer(int layer) const = 0;
+
+    virtual int32_t get_op_dynamic_dim(int node_idx) const = 0;
 };
 
 }  // namespace ggml
diff --git a/ggml/src/ggml-openvino/openvino/frontend.h b/ggml/src/ggml-openvino/openvino/frontend.h
index f1c6f0c3e..72134a3e8 100644
--- a/ggml/src/ggml-openvino/openvino/frontend.h
+++ b/ggml/src/ggml-openvino/openvino/frontend.h
@@ -15,7 +15,7 @@ public:
     using Ptr = std::shared_ptr<FrontEnd>;
     FrontEnd();
 
-    static std::shared_ptr<Model> convert(const InputModel::Ptr& model, bool naive = false);
+    static std::shared_ptr<Model> convert(const InputModel::Ptr & model, bool naive = false);
 };
 
 }  // namespace ggml
diff --git a/ggml/src/ggml-openvino/openvino/input_model.h b/ggml/src/ggml-openvino/openvino/input_model.h
index ce8434426..6ddcea996 100644
--- a/ggml/src/ggml-openvino/openvino/input_model.h
+++ b/ggml/src/ggml-openvino/openvino/input_model.h
@@ -1,9 +1,9 @@
 #pragma once
 
-#include <openvino/frontend/input_model.hpp>
-
 #include "decoder.h"
 
+#include <openvino/frontend/input_model.hpp>
+
 namespace ov {
 namespace frontend {
 namespace ggml {
@@ -16,9 +16,9 @@ class InputModel : public ov::frontend::InputModel {
     friend class ::ov::frontend::ggml::FrontEnd;
 
 public:
-    explicit InputModel(const std::shared_ptr<GgmlDecoder>& gdecoder);
+    explicit InputModel(const std::shared_ptr<GgmlDecoder> & gdecoder);
 
-    const std::shared_ptr<GgmlDecoder>& get_model_decoder() const;
+    const std::shared_ptr<GgmlDecoder> & get_model_decoder() const;
 
 private:
     std::shared_ptr<GgmlDecoder> m_decoder;
diff --git a/ggml/src/ggml-openvino/openvino/node_context.h b/ggml/src/ggml-openvino/openvino/node_context.h
index aa484128a..9769c3009 100644
--- a/ggml/src/ggml-openvino/openvino/node_context.h
+++ b/ggml/src/ggml-openvino/openvino/node_context.h
@@ -1,11 +1,11 @@
 #pragma once
 
+#include "decoder.h"
+
 #include <cstdint>
 #include <openvino/frontend/node_context.hpp>
 #include <string>
 
-#include "decoder.h"
-
 namespace ov {
 namespace frontend {
 namespace ggml {
@@ -16,28 +16,24 @@ typedef std::map<std::string, Output<Node>> TensorMap;
 
 class NodeContext : public frontend::NodeContext {
 public:
-    NodeContext(const std::shared_ptr<GgmlDecoder>& decoder,
-                std::shared_ptr<TensorMap>& tensor_map,
+    NodeContext(const std::shared_ptr<GgmlDecoder> & decoder,
+                std::shared_ptr<TensorMap> & tensor_map,
                 int node_idx,
-                TranslateSession* translate_session = nullptr)
-        : ov::frontend::NodeContext(decoder->get_op_type(node_idx)),
-          m_decoder(decoder),
-          m_tensor_map(tensor_map),
-          m_node_idx(node_idx),
-          m_translate_session(translate_session) {
+                TranslateSession * translate_session = nullptr) :
+        ov::frontend::NodeContext(decoder->get_op_type(node_idx)),
+        m_decoder(decoder),
+        m_tensor_map(tensor_map),
+        m_node_idx(node_idx),
+        m_translate_session(translate_session) {
         m_input_names = decoder->get_input_names(m_node_idx);
         m_output_names = decoder->get_output_names(m_node_idx);
     }
 
-    TranslateSession* get_translate_session() const {
-        return m_translate_session;
-    }
+    TranslateSession * get_translate_session() const { return m_translate_session; }
 
-    const std::vector<std::string>& get_input_names() const { return m_input_names; }
+    const std::vector<std::string> & get_input_names() const { return m_input_names; }
 
-    size_t get_input_size() const override {
-        return m_decoder->get_input_size(m_node_idx);
-    }
+    size_t get_input_size() const override { return m_decoder->get_input_size(m_node_idx); }
 
     ov::element::Type get_input_type(size_t index) const {
         return m_decoder->get_input_type(m_node_idx, m_input_names[index]);
@@ -55,42 +51,103 @@ public:
 
     PartialShape get_output_shape() const { return m_decoder->get_output_shape(m_node_idx); }
 
-    int32_t* get_input_op_params(size_t index) const {
+    int32_t * get_input_op_params(size_t index) const {
         return m_decoder->get_input_op_params(m_node_idx, m_input_names[index]);
     }
 
-    int32_t * get_output_op_params() const { return m_decoder->get_output_op_params(m_node_idx); }
-
-    ov::element::Type get_output_type() const {
-        return m_decoder->get_output_type(m_node_idx);
+    size_t get_view_input_size(size_t index) const {
+        return m_decoder->get_view_input_size(m_node_idx, m_input_names[index]);
     }
 
+    size_t get_view_input_offset(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_offset(m_node_idx, m_input_names[index], view_index);
+    }
+
+    size_t get_view_input_src_offset(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_src_offset(m_node_idx, m_input_names[index], view_index);
+    }
+
+    std::vector<size_t> get_view_input_stride(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_stride(m_node_idx, m_input_names[index], view_index);
+    }
+
+    std::vector<size_t> get_view_input_src_stride(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_src_stride(m_node_idx, m_input_names[index], view_index);
+    }
+
+    ov::Shape get_view_input_ggml_shape(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_ggml_shape(m_node_idx, m_input_names[index], view_index);
+    }
+
+    ov::Shape get_view_input_src_ggml_shape(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_src_ggml_shape(m_node_idx, m_input_names[index], view_index);
+    }
+
+    ov::PartialShape get_view_input_ov_shape(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_ov_shape(m_node_idx, m_input_names[index], view_index);
+    }
+
+    ov::PartialShape get_view_input_src_ov_shape(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_src_ov_shape(m_node_idx, m_input_names[index], view_index);
+    }
+
+    std::string get_view_input_name(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_name(m_node_idx, m_input_names[index], view_index);
+    }
+
+    std::string get_view_input_src_name(size_t index, size_t view_index) const {
+        return m_decoder->get_view_input_src_name(m_node_idx, m_input_names[index], view_index);
+    }
+
+    int32_t get_op_dynamic_dim() const { return m_decoder->get_op_dynamic_dim(m_node_idx); }
+
+    int32_t * get_output_op_params() const { return m_decoder->get_output_op_params(m_node_idx); }
+
+    size_t get_output_op_offset() const { return m_decoder->get_output_op_offset(m_node_idx); }
+
+    ov::element::Type get_output_type() const { return m_decoder->get_output_type(m_node_idx); }
+
+    std::vector<size_t> get_output_stride() const { return m_decoder->get_output_stride(m_node_idx); }
+
     Output<Node> get_input(int idx) const override {
+        // Check if this input is a VIEW
+        size_t view_input_size = m_decoder->get_view_input_size(m_node_idx, m_input_names[idx]);
+        if (view_input_size > 0) {
+            // This is a VIEW input, get the base tensor name (last element in the chain)
+            std::string base_name =
+                m_decoder->get_view_input_src_name(m_node_idx, m_input_names[idx], view_input_size - 1);
+            // Check if the VIEW has been resolved (translate_view produced a Slice)
+            auto view_it = m_tensor_map->find(m_input_names[idx]);
+            if (!base_name.empty() && view_it != m_tensor_map->end()) {
+                auto base_it = m_tensor_map->find(base_name);
+                if (base_it != m_tensor_map->end() &&
+                    view_it->second.get_node_shared_ptr() != base_it->second.get_node_shared_ptr()) {
+                    return view_it->second;
+                }
+                return base_it->second;
+            }
+            if (!base_name.empty()) {
+                return m_tensor_map->at(base_name);
+            }
+        }
+        // Not a VIEW or failed to get base name, use the original logic
         return m_tensor_map->at(m_input_names[idx]);
     }
 
-    Output<Node> get_input(const std::string& name) const override {
+    Output<Node> get_input(const std::string & name) const override {
         if (m_tensor_map->find(name) == m_tensor_map->end()) {
             throw std::runtime_error("'" + name + "' not found in tensor map.");
         }
         return m_tensor_map->at(name);
     }
 
-    bool has_input(const std::string& name) const {
-        return m_tensor_map->find(name) != m_tensor_map->end();
-    }
+    bool has_input(const std::string & name) const { return m_tensor_map->find(name) != m_tensor_map->end(); }
 
-    const std::string& get_name() const override {
-        return m_decoder->get_op_name(m_node_idx);
-    }
+    const std::string & get_name() const override { return m_decoder->get_op_name(m_node_idx); }
 
-    ov::Any get_attribute_as_any(const std::string& name) const override {
-        return m_decoder->get_attribute(name);
-    }
+    ov::Any get_attribute_as_any(const std::string & name) const override { return m_decoder->get_attribute(name); }
 
-    int get_op_case() const {
-        return m_decoder->get_op_case(m_node_idx);
-    }
+    int get_op_case() const { return m_decoder->get_op_case(m_node_idx); }
 
     bool is_static() const { return m_decoder->is_static(); }
 
@@ -98,14 +155,14 @@ public:
 
 private:
     std::shared_ptr<GgmlDecoder> m_decoder;
-    std::shared_ptr<TensorMap>& m_tensor_map;
+    std::shared_ptr<TensorMap> & m_tensor_map;
     int m_node_idx;
-    TranslateSession* m_translate_session;
+    TranslateSession * m_translate_session;
     std::vector<std::string> m_input_names;
     std::vector<std::string> m_output_names;
 };
 
-using CreatorFunction = std::function<ov::OutputVector(const ov::frontend::ggml::NodeContext&)>;
+using CreatorFunction = std::function<ov::OutputVector(const ov::frontend::ggml::NodeContext &)>;
 
 }  // namespace ggml
 }  // namespace frontend
diff --git a/ggml/src/ggml-openvino/openvino/op/add_id.cpp b/ggml/src/ggml-openvino/openvino/op/add_id.cpp
new file mode 100644
index 000000000..c8bf08152
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/add_id.cpp
@@ -0,0 +1,62 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/core/node.hpp>
+#include <openvino/core/node_output.hpp>
+#include <openvino/op/add.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/convert.hpp>
+#include <openvino/op/gather.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/shape_of.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_add_id(const NodeContext & context) {
+    num_inputs_check(context, 3, 3);
+
+    auto input = process_view_input_new(context, 0);
+    auto bias = process_view_input_new(context, 1);
+    auto ids = process_view_input_new(context, 2);
+
+    // OpenVINO uses reversed GGML dimensions:
+    //   input: [1, n_token, n_used, n_embd]
+    //   bias:  [1, 1, n_expert, n_embd]
+    //   ids:   [1, 1, n_token, n_used]
+    auto bias_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(bias, ov::element::i64);
+    auto ids_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
+
+    bias = std::make_shared<ov::op::v1::Reshape>(bias, get_dimensions(bias_shape_4d, {2, 3}), false);
+    ids = std::make_shared<ov::op::v1::Reshape>(ids, get_dimensions(ids_shape_4d, {2, 3}), false);
+
+    if (ids.get_element_type() != ov::element::i32 && ids.get_element_type() != ov::element::i64) {
+        ids = std::make_shared<ov::op::v0::Convert>(ids, ov::element::i32);
+    }
+
+    auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0});
+    ov::Output<ov::Node> selected_bias = std::make_shared<ov::op::v8::Gather>(bias, ids, gather_axis);
+    selected_bias = std::make_shared<ov::op::v1::Reshape>(
+        selected_bias, std::make_shared<ov::op::v3::ShapeOf>(input, ov::element::i64), false);
+
+    if (selected_bias.get_element_type() != input.get_element_type()) {
+        selected_bias = std::make_shared<ov::op::v0::Convert>(selected_bias, input.get_element_type());
+    }
+
+    ov::Output<ov::Node> res = std::make_shared<ov::op::v1::Add>(input, selected_bias);
+    const auto output_type = context.get_output_type();
+    if (res.get_element_type() != output_type) {
+        res = std::make_shared<ov::op::v0::Convert>(res, output_type);
+    }
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/argsort.cpp b/ggml/src/ggml-openvino/openvino/op/argsort.cpp
new file mode 100644
index 000000000..bb8344af8
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/argsort.cpp
@@ -0,0 +1,47 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+#include "ggml.h"
+
+#include <openvino/frontend/exception.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/squeeze.hpp>
+#include <openvino/op/topk.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_argsort(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = process_view_input_new(context, 0);
+
+    const int32_t order = context.get_output_op_params()[0];
+
+    ov::op::v11::TopK::Mode mode;
+    switch (order) {
+    case GGML_SORT_ORDER_ASC:
+        mode = ov::op::v11::TopK::Mode::MIN;
+        break;
+    case GGML_SORT_ORDER_DESC:
+        mode = ov::op::v11::TopK::Mode::MAX;
+        break;
+    default:
+        FRONT_END_OP_CONVERSION_CHECK(false, "Unsupported GGML_OP_ARGSORT order: ", order);
+    }
+
+    auto k = std::make_shared<ov::op::v0::Squeeze>(get_dimensions(input.get_node_shared_ptr(), {3}),
+                                                   ov::op::v0::Constant::create(ov::element::i64, {1}, {0}));
+
+    auto topk = std::make_shared<ov::op::v11::TopK>(input, k, 3, mode, ov::op::v11::TopK::SortType::SORT_VALUES,
+                                                    context.get_output_type(), false);
+
+    return rename_outputs_with_suffix({topk->output(1)}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/clamp.cpp b/ggml/src/ggml-openvino/openvino/op/clamp.cpp
new file mode 100644
index 000000000..070ad33b7
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/clamp.cpp
@@ -0,0 +1,33 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <cstring>
+#include <openvino/op/clamp.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_clamp(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = process_view_input_new(context, 0);
+
+    const int32_t * op_params = context.get_output_op_params();
+    FRONT_END_CHECK_IMPLEMENTED(op_params != nullptr, "CLAMP requires output op params");
+
+    float min;
+    float max;
+    std::memcpy(&min, reinterpret_cast<const float *>(op_params) + 0, sizeof(float));
+    std::memcpy(&max, reinterpret_cast<const float *>(op_params) + 1, sizeof(float));
+
+    auto res = std::make_shared<ov::op::v0::Clamp>(input, min, max);
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/concat.cpp b/ggml/src/ggml-openvino/openvino/op/concat.cpp
new file mode 100644
index 000000000..4d36a666b
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/concat.cpp
@@ -0,0 +1,48 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/frontend/exception.hpp>
+#include <openvino/op/concat.hpp>
+#include <openvino/op/convert.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_concat(const NodeContext & context) {
+    num_inputs_check(context, 2, 2);
+
+    const int32_t * op_params = context.get_output_op_params();
+    FRONT_END_CHECK_IMPLEMENTED(op_params != nullptr, "CONCAT requires output op params");
+
+    const auto output_shape = context.get_output_shape();
+    FRONT_END_CHECK_IMPLEMENTED(output_shape.rank().is_static(), "CONCAT requires static output rank");
+
+    const auto rank = output_shape.rank().get_length();
+    const int32_t ggml_dim = op_params[0];
+    FRONT_END_CHECK_IMPLEMENTED(ggml_dim >= 0 && ggml_dim < rank, "CONCAT axis is out of range");
+
+    auto input_0 = process_view_input_new(context, 0);
+    auto input_1 = process_view_input_new(context, 1);
+    const auto output_type = context.get_output_type();
+
+    if (input_0.get_element_type() != output_type) {
+        input_0 = std::make_shared<ov::op::v0::Convert>(input_0, output_type);
+    }
+    if (input_1.get_element_type() != output_type) {
+        input_1 = std::make_shared<ov::op::v0::Convert>(input_1, output_type);
+    }
+
+    const auto axis = static_cast<int64_t>(rank - 1 - ggml_dim);
+    auto res = std::make_shared<ov::op::v0::Concat>(OutputVector{input_0, input_1}, axis);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/cont.cpp b/ggml/src/ggml-openvino/openvino/op/cont.cpp
index 6160dd744..1d6cc6721 100644
--- a/ggml/src/ggml-openvino/openvino/op/cont.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/cont.cpp
@@ -18,27 +18,19 @@ namespace op {
 OutputVector translate_cont(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
-    int op_case = context.get_op_case();
-    FRONT_END_CHECK_IMPLEMENTED(op_case == 1 || op_case == 2 || op_case == 3, "Unsupported CONT case");
-
     auto src_shape = context.get_input_shape(0).to_shape();
     auto dst_shape = context.get_output_shape().to_shape();
-    ov::Output<Node> res;
 
-    if (op_case == 1) {
-        // The input comes from a PERMUTE
-        throw std::runtime_error("Code of this case might be outdated");
-        dst_shape[1] = -1;
-        res = std::make_shared<ov::op::v1::Reshape>(
-            context.get_input(0), ov::op::v0::Constant::create(ov::element::i64, {dst_shape.size()}, dst_shape), false);
-    } else if (op_case == 2) {
-        // The input comes from a TRANSPOSE
-        return {context.get_input(0)};
-    } else {
-        // The input comes from a VIEW
-        res = process_view_input(context, 0);
+    if (context.get_op_dynamic_dim() != -1) {
+        dst_shape[3 - context.get_op_dynamic_dim()] = -1;
     }
 
+    auto input = process_view_input_new(context, 0);
+
+    ov::Output<Node> res;
+    res = std::make_shared<ov::op::v1::Reshape>(
+        input, ov::op::v0::Constant::create(ov::element::i64, {dst_shape.size()}, dst_shape), false);
+
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op/cpy.cpp b/ggml/src/ggml-openvino/openvino/op/cpy.cpp
index 831117208..3a4355021 100644
--- a/ggml/src/ggml-openvino/openvino/op/cpy.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/cpy.cpp
@@ -3,7 +3,9 @@
 #include "../utils.h"
 
 #include <memory>
+#include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
+#include <openvino/op/reshape.hpp>
 
 namespace ov {
 namespace frontend {
@@ -11,7 +13,18 @@ namespace ggml {
 namespace op {
 
 OutputVector translate_cpy(const NodeContext & context) {
-    auto res = std::make_shared<ov::op::v0::Convert>(context.get_input(0), context.get_output_type());
+    auto input = process_view_input_new(context, 0);
+    auto input_shape = context.get_input_shape(0);
+    auto output_shape = context.get_output_shape();
+
+    // Non-cast CPY may need a reshape (e.g. [3,192,1,1] -> [576,1,1,1])
+    if (input_shape != output_shape) {
+        auto new_shape = ov::op::v0::Constant::create(
+            ov::element::i64, {static_cast<size_t>(output_shape.rank().get_length())}, output_shape.to_shape());
+        input = std::make_shared<ov::op::v1::Reshape>(input, new_shape, false);
+    }
+
+    auto res = std::make_shared<ov::op::v0::Convert>(input, context.get_output_type());
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op/div.cpp b/ggml/src/ggml-openvino/openvino/op/div.cpp
new file mode 100644
index 000000000..11dd9dece
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/div.cpp
@@ -0,0 +1,146 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+#include "ggml.h"
+
+#include <memory>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/convert.hpp>
+#include <openvino/op/divide.hpp>
+#include <openvino/op/multiply.hpp>
+#include <openvino/op/shape_of.hpp>
+#include <openvino/op/sigmoid.hpp>
+#include <openvino/op/tile.hpp>
+#include <openvino/op/util/precision_sensitive_attribute.hpp>
+#include <vector>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+namespace {
+
+bool is_silu_div_pattern(const ov::Output<ov::Node> & numerator,
+                         const ov::Output<ov::Node> & denominator,
+                         const NodeContext & context) {
+    if (context.get_input_size() != 2) {
+        return false;
+    }
+
+    const auto * unary_op = reinterpret_cast<const ggml_unary_op *>(context.get_input_op_params(0));
+    if (unary_op == nullptr || *unary_op != GGML_UNARY_OP_SILU) {
+        return false;
+    }
+
+    auto mul = std::dynamic_pointer_cast<ov::op::v1::Multiply>(numerator.get_node_shared_ptr());
+    if (!mul) {
+        return false;
+    }
+
+    const auto denom_node = denominator.get_node_shared_ptr();
+    const auto mul_input_0 = mul->input_value(0).get_node_shared_ptr();
+    const auto mul_input_1 = mul->input_value(1).get_node_shared_ptr();
+
+    auto sigmoid = std::dynamic_pointer_cast<ov::op::v0::Sigmoid>(mul_input_1);
+    if (mul_input_0 == denom_node && sigmoid && sigmoid->input_value(0).get_node_shared_ptr() == denom_node) {
+        return true;
+    }
+
+    sigmoid = std::dynamic_pointer_cast<ov::op::v0::Sigmoid>(mul_input_0);
+    return mul_input_1 == denom_node && sigmoid && sigmoid->input_value(0).get_node_shared_ptr() == denom_node;
+}
+
+ov::Output<ov::Node> repeat_input_to_match(const NodeContext & context,
+                                           const ov::Output<ov::Node> & input,
+                                           const ov::Output<ov::Node> & target,
+                                           size_t input_index) {
+    const auto input_shape = context.get_input_shape(input_index);
+    const auto target_shape = context.get_input_shape(0);
+
+    if (input_shape == target_shape) {
+        return input;
+    }
+
+    if (input_shape.rank().is_static() && target_shape.rank().is_static()) {
+        const auto rank = static_cast<size_t>(input_shape.rank().get_length());
+        std::vector<int64_t> repeats(rank, 1);
+        bool needs_repeat = false;
+
+        for (size_t axis = 0; axis < rank; ++axis) {
+            FRONT_END_OP_CONVERSION_CHECK(input_shape[axis].is_static() && target_shape[axis].is_static(),
+                                          "DIV repeat requires static dimensions on both inputs");
+
+            const int64_t input_dim = input_shape[axis].get_length();
+            const int64_t target_dim = target_shape[axis].get_length();
+
+            FRONT_END_OP_CONVERSION_CHECK(input_dim > 0 && target_dim > 0 && target_dim % input_dim == 0,
+                                          "DIV input shape ", input_shape, " cannot repeat to match ", target_shape);
+
+            repeats[axis] = target_dim / input_dim;
+            needs_repeat = needs_repeat || repeats[axis] != 1;
+        }
+
+        if (!needs_repeat) {
+            return input;
+        }
+
+        auto repeats_node = ov::op::v0::Constant::create(ov::element::i64, {repeats.size()}, repeats);
+        return std::make_shared<ov::op::v0::Tile>(input, repeats_node);
+    }
+
+    auto input_shape_node = std::make_shared<ov::op::v3::ShapeOf>(input, ov::element::i64);
+    auto target_shape_node = std::make_shared<ov::op::v3::ShapeOf>(target, ov::element::i64);
+    auto repeats_node = std::make_shared<ov::op::v1::Divide>(target_shape_node, input_shape_node);
+    return std::make_shared<ov::op::v0::Tile>(input, repeats_node);
+}
+
+}  // namespace
+
+OutputVector translate_div(const NodeContext & context) {
+    num_inputs_check(context, 2, 2);
+
+    auto input_0 = process_view_input_new(context, 0);
+    auto input_1 = process_view_input_new(context, 1);
+
+    if (is_silu_div_pattern(input_0, input_1, context)) {
+        ov::Output<ov::Node> res = std::make_shared<ov::op::v0::Sigmoid>(input_1);
+        if (res.get_element_type() != context.get_output_type()) {
+            res = std::make_shared<ov::op::v0::Convert>(res, context.get_output_type());
+        }
+        return rename_outputs_with_suffix({res}, context.get_name());
+    }
+
+    input_1 = repeat_input_to_match(context, input_1, input_0, 1);
+
+    const auto output_type = context.get_output_type();
+    const bool use_f32_compute = input_0.get_element_type() != ov::element::f32 ||
+                                 input_1.get_element_type() != ov::element::f32 || output_type != ov::element::f32;
+
+    if (use_f32_compute) {
+        input_0 = std::make_shared<ov::op::v0::Convert>(input_0, ov::element::f32);
+        input_1 = std::make_shared<ov::op::v0::Convert>(input_1, ov::element::f32);
+    }
+
+    ov::Output<ov::Node> res = std::make_shared<ov::op::v1::Divide>(input_0, input_1);
+    if (use_f32_compute) {
+        // Keep the reciprocal/divide path in FP32. Without this hint, the GPU
+        // plugin can still compress the subgraph back to FP16 and overflow on
+        // small shexp gate values (e.g. silu(x) / x in qwen2moe).
+        ov::mark_as_precision_sensitive(res.get_node_shared_ptr()->input(0));
+        ov::mark_as_precision_sensitive(res.get_node_shared_ptr()->input(1));
+    }
+    if (res.get_element_type() != output_type) {
+        auto output_convert = std::make_shared<ov::op::v0::Convert>(res, output_type);
+        if (use_f32_compute) {
+            ov::mark_as_precision_sensitive(output_convert->input(0));
+        }
+        res = output_convert;
+    }
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp b/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp
index 42602a730..582df0130 100644
--- a/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/flash_attn_ext.cpp
@@ -1,15 +1,21 @@
 #include "../node_context.h"
 #include "../op_table.h"
 #include "../utils.h"
+#include "ggml-openvino/ggml-openvino-extra.h"
 
 #include <cstdint>
+#include <cstdlib>
 #include <memory>
+#include <openvino/op/add.hpp>
 #include <openvino/op/broadcast.hpp>
 #include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
+#include <openvino/op/matmul.hpp>
+#include <openvino/op/multiply.hpp>
 #include <openvino/op/reshape.hpp>
 #include <openvino/op/scaled_dot_product_attention.hpp>
+#include <openvino/op/softmax.hpp>
 #include <openvino/op/transpose.hpp>
 #include <openvino/op/unsqueeze.hpp>
 #include <string>
@@ -34,36 +40,115 @@ OutputVector translate_flash_attn_ext(const NodeContext & context) {
     auto q = std::make_shared<ov::op::v0::Convert>(q_f32, ov::element::f16);
     auto scale_node = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{}, std::vector<float>{scale});
 
-    ov::Output<ov::Node> mask_sliced, res;
+    ov::Output<ov::Node> res;
+
+    // For stateful
     std::string mask_name = "KQ_mask_sliced";
     if (context.get_input_names()[3].find("swa") != std::string::npos) {
         mask_name = "KQ_mask_swa_sliced";
     }
     if (context.has_input(mask_name)) {
-        mask_sliced = context.get_input(mask_name);
-    } else {
-        auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-        auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
-        auto token_len = get_dimensions(q, {2});
-        mask_sliced = std::make_shared<ov::op::v8::Slice>(mask, zero, token_len, one, two);
+        mask = context.get_input(mask_name);
     }
 
-    if (mask_sliced.get_element_type() != ov::element::f16) {
-        mask_sliced = std::make_shared<ov::op::v0::Convert>(mask_sliced, ov::element::f16);
+    if (mask.get_element_type() != ov::element::f16) {
+        mask = std::make_shared<ov::op::v0::Convert>(mask, ov::element::f16);
     }
 
-    auto tile_kv = [&](int64_t num_heads, int64_t num_heads_kv, int64_t head_size, ov::Output<Node> kv) {
-        int64_t factor = num_heads / num_heads_kv;
-        if (factor > 1 && num_heads_kv > 1) {
+    //auto tile_kv = [&](int64_t num_heads, int64_t num_heads_kv, int64_t head_size, ov::Output<Node> kv) {
+    //    int64_t factor = num_heads / num_heads_kv;
+    //    if (factor > 1 && num_heads_kv > 1) {
+    auto q_shape = context.get_input_shape(0).to_shape();
+    auto k_shape = context.get_input_shape(1).to_shape();
+    const int64_t num_heads = q_shape[1];
+    const int64_t num_heads_kv = k_shape[1];
+    const int64_t head_size = q_shape[3];
+    const int64_t factor = num_heads / num_heads_kv;
+
+    // Manual GQA attention: enabled by default on GPU in stateless mode.
+    // Set GGML_OPENVINO_MANUAL_GQA_ATTN to a positive value (e.g. 1) to force-enable,
+    // or to 0 to force-disable. Unset falls back to the device-based default.
+    static const bool manual_gqa_enabled = []() {
+        const char * env = ggml_openvino_getenv_str("GGML_OPENVINO_MANUAL_GQA_ATTN");
+        if (env != nullptr) {
+            return ggml_openvino_getenv_int("GGML_OPENVINO_MANUAL_GQA_ATTN") > 0;
+        }
+        const char * dev = ggml_openvino_getenv_str("GGML_OPENVINO_DEVICE");
+        return dev != nullptr && std::string(dev) == "GPU";
+    }();
+    const bool use_manual_gqa_attention =
+        manual_gqa_enabled && factor > 1 && num_heads_kv > 1 && !context.is_stateful();
+
+    if (use_manual_gqa_attention) {
+        // Q, K, V arrive as [B, n_heads(_kv), S, head_size], where B is the active
+        // batch (n_seq_active) and may be > 1 (llama-perplexity, llama-server -np > 1)
+        // or dynamic. Reshape to
+        //   K_r: [B, num_heads_kv, 1, S, head_size]
+        //   Q_r: [B, num_heads_kv, factor, S_q, head_size]
+        // and let MatMul broadcast across the factor dim without materialising
+        // an expanded K/V. The leading 0 + special_zero=true copies B at runtime,
+        // so this is correct for B == 1, B > 1, and dynamic B alike. Only the head
+        // dims and head_size are baked in as literals; the sequence dim stays -1.
+        auto k_5d_shape = ov::op::v0::Constant::create(ov::element::i64, {5},
+                                                       std::vector<int64_t>{0, num_heads_kv, 1, -1, head_size});
+        auto v_5d_shape = ov::op::v0::Constant::create(ov::element::i64, {5},
+                                                       std::vector<int64_t>{0, num_heads_kv, 1, -1, head_size});
+        auto q_5d_shape = ov::op::v0::Constant::create(ov::element::i64, {5},
+                                                       std::vector<int64_t>{0, num_heads_kv, factor, -1, head_size});
+
+        auto k_r = std::make_shared<ov::op::v1::Reshape>(k, k_5d_shape, true);
+        auto v_r = std::make_shared<ov::op::v1::Reshape>(v, v_5d_shape, true);
+        auto q_r = std::make_shared<ov::op::v1::Reshape>(q, q_5d_shape, true);
+
+        // QK^T → [B, num_heads_kv, factor, S_q, S_k]
+        auto qk = std::make_shared<ov::op::v0::MatMul>(q_r, k_r, /*tA=*/false, /*tB=*/true);
+        auto qk_scaled = std::make_shared<ov::op::v1::Multiply>(qk, scale_node);
+
+        // Mask arrives as [B, 1, S_q, S_k]. Unsqueeze a factor axis at position 2 to
+        // get [B, 1, 1, S_q, S_k], which NUMPY-broadcasts cleanly against the
+        // [B, num_heads_kv, factor, S_q, S_k] scores: B==B, then 1→num_heads_kv and
+        // 1→factor on the head dims.
+        auto mask_unsq1 =
+            std::make_shared<ov::op::v0::Unsqueeze>(mask, ov::op::v0::Constant::create(ov::element::i64, {1}, {2}));
+        // mask_unsq1: [B, 1, 1, S_q, S_k] (rank 5)
+        ov::Output<ov::Node> qk_masked = std::make_shared<ov::op::v1::Add>(qk_scaled, mask_unsq1);
+
+        auto softmax = std::make_shared<ov::op::v8::Softmax>(qk_masked, /*axis=*/-1);
+
+        // softmax @ V → [B, num_heads_kv, factor, S_q, head_size]
+        auto attn = std::make_shared<ov::op::v0::MatMul>(softmax, v_r);
+
+        // Reshape back to [B, num_heads, S_q, head_size] (combine num_heads_kv * factor).
+        // Leading 0 + special_zero=true copies B at runtime.
+        auto out_4d_shape =
+            ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{0, num_heads, -1, head_size});
+        auto out_4d = std::make_shared<ov::op::v1::Reshape>(attn, out_4d_shape, true);
+
+        // The standard SDPA path's downstream is Transpose(0,2,1,3) → Convert(f32).
+        // Replicate it here so callers see the same output layout/dtype.
+        res = std::make_shared<ov::op::v1::Transpose>(
+            out_4d, ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}));
+        res = std::make_shared<ov::op::v0::Convert>(res, ov::element::f32);
+        return rename_outputs_with_suffix({res}, context.get_name());
+    }
+
+    // Default path: explicit Broadcast → SDPA. Kept as the fallback because
+    // (a) it goes through the GPU plugin's micro-SDPA fast path (FlashAttention
+    // tiles via DPAS), and (b) the manual path above is still being validated.
+    auto tile_kv = [&](int64_t n_heads, int64_t n_heads_kv, int64_t hs, ov::Output<Node> kv) {
+        int64_t f = n_heads / n_heads_kv;
+        if (f > 1 && n_heads_kv > 1) {
             ov::Output<ov::Node> kv_broadcast_shape, kv_unsqueezed, new_kv_shape;
             auto unsqueeze_axes = ov::op::v0::Constant::create(ov::element::i64, Shape{}, {2});
             kv_unsqueezed = std::make_shared<ov::op::v0::Unsqueeze>(kv, unsqueeze_axes);
 
-            kv_broadcast_shape = ov::op::v0::Constant::create(
-                ov::element::i64, {5}, {(int64_t) 1, (int64_t) 1, factor, (int64_t) 1, (int64_t) 1});
+            kv_broadcast_shape = ov::op::v0::Constant::create(ov::element::i64, {5},
+                                                              {(int64_t) 1, (int64_t) 1, f, (int64_t) 1, (int64_t) 1});
             new_kv_shape =
-                ov::op::v0::Constant::create(ov::element::i64, {4}, {(int64_t) 0, num_heads, (int64_t) -1, head_size});
+                ov::op::v0::Constant::create(ov::element::i64, {4}, {(int64_t) 0, n_heads, (int64_t) -1, hs});
+            //    ov::element::i64, {5}, {(int64_t) 1, (int64_t) 1, factor, (int64_t) 1, (int64_t) 1});
+            //new_kv_shape =
+            //    ov::op::v0::Constant::create(ov::element::i64, {4}, {(int64_t) 0, num_heads, (int64_t) -1, head_size});
 
             kv = std::make_shared<ov::op::v3::Broadcast>(kv_unsqueezed, kv_broadcast_shape,
                                                          ov::op::BroadcastType::BIDIRECTIONAL);
@@ -72,12 +157,14 @@ OutputVector translate_flash_attn_ext(const NodeContext & context) {
         return kv;
     };
 
-    auto q_shape = context.get_input_shape(0).to_shape();
-    auto k_shape = context.get_input_shape(1).to_shape();
-    k = tile_kv(q_shape[1], k_shape[1], q_shape[3], k);
-    v = tile_kv(q_shape[1], k_shape[1], q_shape[3], v);
+    //auto q_shape = context.get_input_shape(0).to_shape();
+    //auto k_shape = context.get_input_shape(1).to_shape();
+    //k = tile_kv(q_shape[1], k_shape[1], q_shape[3], k);
+    //v = tile_kv(q_shape[1], k_shape[1], q_shape[3], v);
+    k = tile_kv(num_heads, num_heads_kv, head_size, k);
+    v = tile_kv(num_heads, num_heads_kv, head_size, v);
 
-    auto sdpa = std::make_shared<ov::op::v13::ScaledDotProductAttention>(q, k, v, mask_sliced, scale_node, false);
+    auto sdpa = std::make_shared<ov::op::v13::ScaledDotProductAttention>(q, k, v, mask, scale_node, false);
     res = std::make_shared<ov::op::v1::Transpose>(sdpa,
                                                   ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3}));
     res = std::make_shared<ov::op::v0::Convert>(res, ov::element::f32);
diff --git a/ggml/src/ggml-openvino/openvino/op/gated_delta_net.cpp b/ggml/src/ggml-openvino/openvino/op/gated_delta_net.cpp
new file mode 100644
index 000000000..26c4bbfa9
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/gated_delta_net.cpp
@@ -0,0 +1,282 @@
+#include "gated_delta_net.hpp"
+
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <cmath>
+#include <cstdint>
+#include <memory>
+#include <openvino/op/add.hpp>
+#include <openvino/op/broadcast.hpp>
+#include <openvino/op/concat.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/exp.hpp>
+#include <openvino/op/gather.hpp>
+#include <openvino/op/loop.hpp>
+#include <openvino/op/matmul.hpp>
+#include <openvino/op/multiply.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/squeeze.hpp>
+#include <openvino/op/subtract.hpp>
+#include <openvino/op/transpose.hpp>
+#include <openvino/op/unsqueeze.hpp>
+#include <vector>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+static OutputVector translate_gated_delta_net_ref(const NodeContext & context);
+
+OutputVector translate_gated_delta_net(const NodeContext & context) {
+    // auto v_shape = context.get_input_shape(2).to_shape();  // [B, T, H_v, S_v]
+    // auto q_shape = context.get_input_shape(0).to_shape();  // [B, T, H_k, S_k]
+
+    // // Fused GatedDeltaNet op only supports scalar gate (kda=0).
+    // // Fall back to reference implementation for per-key-dimension gating.
+    // // if (kda) {
+    // //     return translate_gated_delta_net_ref(context);
+    // // }
+
+    // auto q = context.get_input(0);
+    // auto k = context.get_input(1);
+    // auto v = context.get_input(2);
+    // auto g = context.get_input(3);
+    // auto beta = context.get_input(4);
+    // auto state = context.get_input(5);
+
+    // const int64_t B = v_shape[0];
+    // const int64_t T = v_shape[1];
+    // const int64_t H_v = v_shape[2];
+    // const int64_t S_v = v_shape[3];
+    // const int64_t S_k = q_shape[3];
+
+    // // ggml state layout (OV notation): [B, H_v, value_dim, key_dim]
+    // // GatedDeltaNet op expects: [B, H_v, key_dim, value_dim]
+    // auto state_reshape_shape =
+    //     ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{B, H_v, S_v, S_k});
+    // state = std::make_shared<ov::op::v1::Reshape>(state, state_reshape_shape, false);
+    // auto state_perm = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{0, 1, 3, 2});
+    // state = std::make_shared<ov::op::v1::Transpose>(state, state_perm);
+
+    // g = std::make_shared<ov::op::v0::Squeeze>(g, ov::op::v0::Constant::create(ov::element::i64, {1}, {3}));
+    // beta = std::make_shared<ov::op::v0::Squeeze>(beta, ov::op::v0::Constant::create(ov::element::i64, {1}, {3}));
+
+    // auto gdn = std::make_shared<ov::op::internal::GatedDeltaNet>(q, k, v, state, g, beta);
+
+    // auto attn_4d = gdn->output(0);
+    // auto state_4d = gdn->output(1);  // [B, H_v, key_dim, value_dim]
+    // // Transpose output state back to ggml layout [B, H_v, value_dim, key_dim]
+    // auto state_transposed = std::make_shared<ov::op::v1::Transpose>(state_4d, state_perm);
+    // auto flat_shape_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+    // auto attn = std::make_shared<ov::op::v1::Reshape>(attn_4d, flat_shape_1d, false);
+    // auto new_state = std::make_shared<ov::op::v1::Reshape>(state_transposed, flat_shape_1d, false);
+    // auto packed = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{attn, new_state}, 0);
+    // auto out_shape =
+    //     ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{1, 1, T * B + S_v * B, S_v * H_v});
+    // auto res = std::make_shared<ov::op::v1::Reshape>(packed, out_shape, false);
+
+    // return rename_outputs_with_suffix({res}, context.get_name());
+
+    // The OV version in CI does not have the GatedDeltaNet op, so use reference implementation for now.
+    return translate_gated_delta_net_ref(context);
+}
+
+static OutputVector translate_gated_delta_net_ref(const NodeContext & context) {
+    num_inputs_check(context, 6, 6);
+
+    // Inputs (OV shapes are reversed from ggml):
+    // ggml: q[S_k, H_k, T, B], k[S_k, H_k, T, B], v[S_v, H_v, T, B]
+    // OV:   q[B, T, H_k, S_k], k[B, T, H_k, S_k], v[B, T, H_v, S_v]
+    // ggml: g[1 or S_v, H_v, T, B], beta[1, H_v, T, B]
+    // OV:   g[B, T, H_v, 1 or S_v], beta[B, T, H_v, 1]
+    // ggml: state[S_v, S_v, H_v, B]
+    // OV:   state[B, H_v, S_v, S_v]
+    auto q = process_view_input_new(context, 0);
+    auto k = process_view_input_new(context, 1);
+    auto v = process_view_input_new(context, 2);
+    auto g = process_view_input_new(context, 3);
+    auto beta = process_view_input_new(context, 4);
+    auto state = process_view_input_new(context, 5);
+
+    auto v_shape = context.get_input_shape(2).to_shape();  // [B, T, H_v, S_v]
+    auto q_shape = context.get_input_shape(0).to_shape();  // [B, T, H_k, S_k]
+    auto g_shape = context.get_input_shape(3).to_shape();  // [B, T, H_v, 1 or S_v]
+
+    const int64_t B = v_shape[0];
+    const int64_t T = v_shape[1];
+    const int64_t H_v = v_shape[2];
+    const int64_t S_v = v_shape[3];
+    const int64_t H_k = q_shape[2];
+    const bool kda = (g_shape[3] == (size_t) S_v);
+
+    const int64_t rq1 = H_v / H_k;  // head repeat factor
+    const float scale = 1.0f / std::sqrt((float) S_v);
+
+    auto axis_1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+    auto axis_2 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
+
+    // Transpose inputs from [B, T, H, S] to [B, H, T, S] for easier per-head processing
+    auto perm_0213 = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{0, 2, 1, 3});
+    auto q_t = std::make_shared<ov::op::v1::Transpose>(q, perm_0213);        // [B, H_k, T, S_k]
+    auto k_t = std::make_shared<ov::op::v1::Transpose>(k, perm_0213);        // [B, H_k, T, S_k]
+    auto v_t = std::make_shared<ov::op::v1::Transpose>(v, perm_0213);        // [B, H_v, T, S_v]
+    auto g_t = std::make_shared<ov::op::v1::Transpose>(g, perm_0213);        // [B, H_v, T, 1 or S_v]
+    auto beta_t = std::make_shared<ov::op::v1::Transpose>(beta, perm_0213);  // [B, H_v, T, 1]
+
+    // Broadcast Q, K heads to match V heads if GQA is used (H_v > H_k)
+    ov::Output<ov::Node> q_bh = q_t;
+    ov::Output<ov::Node> k_bh = k_t;
+    if (rq1 > 1) {
+        auto q_unsq = std::make_shared<ov::op::v0::Unsqueeze>(q_t, axis_2);  // [B, H_k, 1, T, S]
+        auto k_unsq = std::make_shared<ov::op::v0::Unsqueeze>(k_t, axis_2);  // [B, H_k, 1, T, S]
+
+        auto bcast_shape = ov::op::v0::Constant::create(ov::element::i64, {5}, std::vector<int64_t>{1, 1, rq1, 1, 1});
+        auto q_bcast =
+            std::make_shared<ov::op::v3::Broadcast>(q_unsq, bcast_shape, ov::op::BroadcastType::BIDIRECTIONAL);
+        auto k_bcast =
+            std::make_shared<ov::op::v3::Broadcast>(k_unsq, bcast_shape, ov::op::BroadcastType::BIDIRECTIONAL);
+
+        // Transpose [B, H_k, rq1, T, S] -> [B, rq1, H_k, T, S] so that reshape merges
+        // as [rq1, H_k] giving repeat-blocks pattern matching CPU: iq1 = iv1 % H_k
+        auto perm_5d = ov::op::v0::Constant::create(ov::element::i64, {5}, std::vector<int64_t>{0, 2, 1, 3, 4});
+        auto q_transposed = std::make_shared<ov::op::v1::Transpose>(q_bcast, perm_5d);
+        auto k_transposed = std::make_shared<ov::op::v1::Transpose>(k_bcast, perm_5d);
+
+        auto new_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{B, H_v, T, S_v});
+        q_bh = std::make_shared<ov::op::v1::Reshape>(q_transposed, new_shape, false);
+        k_bh = std::make_shared<ov::op::v1::Reshape>(k_transposed, new_shape, false);
+    }
+
+    // Merge batch and head dims: [B*H_v, T, S_v]
+    auto merge_bh = [&](ov::Output<ov::Node> x, int64_t last_dim) {
+        auto shape = ov::op::v0::Constant::create(ov::element::i64, {3}, std::vector<int64_t>{B * H_v, T, last_dim});
+        return std::make_shared<ov::op::v1::Reshape>(x, shape, false);
+    };
+
+    auto q_m = merge_bh(q_bh, S_v);           // [B*H_v, T, S_v]
+    auto k_m = merge_bh(k_bh, S_v);           // [B*H_v, T, S_v]
+    auto v_m = merge_bh(v_t, S_v);            // [B*H_v, T, S_v]
+    auto g_m = merge_bh(g_t, kda ? S_v : 1);  // [B*H_v, T, 1 or S_v]
+    auto beta_m = merge_bh(beta_t, 1);        // [B*H_v, T, 1]
+
+    // State: [B, H_v, S_v, S_v] -> [B*H_v, S_v, S_v]
+    auto state_shape = ov::op::v0::Constant::create(ov::element::i64, {3}, std::vector<int64_t>{B * H_v, S_v, S_v});
+    auto state_m = std::make_shared<ov::op::v1::Reshape>(state, state_shape, false);
+
+    auto scale_const = ov::op::v0::Constant::create(ov::element::f32, {}, std::vector<float>{scale});
+
+    // --- Build Loop body ---
+    // Body parameters (no iteration counter needed, use -1 in special ports)
+    auto body_state = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_q = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_k = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_v = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_g = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_beta = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
+    auto body_iter = std::make_shared<ov::op::v0::Parameter>(ov::element::i64, ov::Shape{1});
+
+    // Condition output (always true - we rely on trip_count for termination)
+    auto body_cond_out = ov::op::v0::Constant::create(ov::element::boolean, ov::Shape{1}, std::vector<bool>{true});
+
+    // Gather current token from invariant inputs using iteration counter
+    auto q_t_cur = std::make_shared<ov::op::v8::Gather>(body_q, body_iter, axis_1);     // [B*H_v, 1, S_v]
+    auto k_t_cur = std::make_shared<ov::op::v8::Gather>(body_k, body_iter, axis_1);     // [B*H_v, 1, S_v]
+    auto v_t_cur = std::make_shared<ov::op::v8::Gather>(body_v, body_iter, axis_1);     // [B*H_v, 1, S_v]
+    auto g_t_cur = std::make_shared<ov::op::v8::Gather>(body_g, body_iter, axis_1);     // [B*H_v, 1, 1 or S_v]
+    auto b_t_cur = std::make_shared<ov::op::v8::Gather>(body_beta, body_iter, axis_1);  // [B*H_v, 1, 1]
+
+    // Squeeze token dim
+    auto q_cur = std::make_shared<ov::op::v0::Squeeze>(q_t_cur, axis_1);  // [B*H_v, S_v]
+    auto k_cur = std::make_shared<ov::op::v0::Squeeze>(k_t_cur, axis_1);  // [B*H_v, S_v]
+    auto v_cur = std::make_shared<ov::op::v0::Squeeze>(v_t_cur, axis_1);  // [B*H_v, S_v]
+    auto g_cur = std::make_shared<ov::op::v0::Squeeze>(g_t_cur, axis_1);  // [B*H_v, 1 or S_v]
+    auto b_cur = std::make_shared<ov::op::v0::Squeeze>(b_t_cur, axis_1);  // [B*H_v, 1]
+
+    // Step 1: Apply decay gate to state
+    auto exp_g = std::make_shared<ov::op::v0::Exp>(g_cur);                                // [B*H_v, 1 or S_v]
+    auto exp_g_unsq = std::make_shared<ov::op::v0::Unsqueeze>(exp_g, axis_1);             // [B*H_v, 1, 1 or S_v]
+    auto state_decayed = std::make_shared<ov::op::v1::Multiply>(body_state, exp_g_unsq);  // [B*H_v, S_v, S_v]
+
+    // Step 2: delta = (v - S @ k) * beta
+    auto k_col = std::make_shared<ov::op::v0::Unsqueeze>(k_cur, axis_2);                 // [B*H_v, S_v, 1]
+    auto sk = std::make_shared<ov::op::v0::MatMul>(state_decayed, k_col, false, false);  // [B*H_v, S_v, 1]
+    auto sk_sq = std::make_shared<ov::op::v0::Squeeze>(sk, axis_2);                      // [B*H_v, S_v]
+    auto v_minus_sk = std::make_shared<ov::op::v1::Subtract>(v_cur, sk_sq);              // [B*H_v, S_v]
+    auto delta = std::make_shared<ov::op::v1::Multiply>(v_minus_sk, b_cur);              // [B*H_v, S_v]
+
+    // Step 3: state += outer(delta, k)
+    auto delta_col = std::make_shared<ov::op::v0::Unsqueeze>(delta, axis_2);                 // [B*H_v, S_v, 1]
+    auto k_row = std::make_shared<ov::op::v0::Unsqueeze>(k_cur, axis_1);                     // [B*H_v, 1, S_v]
+    auto outer_prod = std::make_shared<ov::op::v0::MatMul>(delta_col, k_row, false, false);  // [B*H_v, S_v, S_v]
+    auto state_updated = std::make_shared<ov::op::v1::Add>(state_decayed, outer_prod);       // [B*H_v, S_v, S_v]
+
+    // Step 4: attn_out = S @ q * scale
+    auto q_col = std::make_shared<ov::op::v0::Unsqueeze>(q_cur, axis_2);                 // [B*H_v, S_v, 1]
+    auto sq = std::make_shared<ov::op::v0::MatMul>(state_updated, q_col, false, false);  // [B*H_v, S_v, 1]
+    auto sq_squeezed = std::make_shared<ov::op::v0::Squeeze>(sq, axis_2);                // [B*H_v, S_v]
+    auto attn_out = std::make_shared<ov::op::v1::Multiply>(sq_squeezed, scale_const);    // [B*H_v, S_v]
+
+    // Unsqueeze attn_out to [B*H_v, 1, S_v] for scan output concatenation
+    auto attn_out_unsq = std::make_shared<ov::op::v0::Unsqueeze>(attn_out, axis_1);  // [B*H_v, 1, S_v]
+
+    // --- Assemble Loop ---
+    // Body: results = [condition, state_updated, attn_out_unsq]
+    auto body = std::make_shared<ov::Model>(
+        ov::OutputVector{body_cond_out, state_updated, attn_out_unsq},
+        ov::ParameterVector{body_iter, body_state, body_q, body_k, body_v, body_g, body_beta});
+
+    auto trip_count = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, std::vector<int64_t>{T});
+    auto exec_cond = ov::op::v0::Constant::create(ov::element::boolean, ov::Shape{1}, std::vector<bool>{true});
+
+    auto loop = std::make_shared<ov::op::v5::Loop>(trip_count, exec_cond);
+    loop->set_function(body);
+    loop->set_special_body_ports(ov::op::v5::Loop::SpecialBodyPorts{0, 0});
+
+    // Carried state: feeds back from body output 1 to body_state param
+    loop->set_merged_input(body_state, state_m, state_updated);
+    // Invariant inputs: passed through unchanged each iteration
+    loop->set_invariant_input(body_q, q_m);
+    loop->set_invariant_input(body_k, k_m);
+    loop->set_invariant_input(body_v, v_m);
+    loop->set_invariant_input(body_g, g_m);
+    loop->set_invariant_input(body_beta, beta_m);
+
+    // Loop outputs:
+    // 1) Final state (last iteration value of state_updated)
+    auto final_state_out = loop->get_iter_value(state_updated, -1);  // [B*H_v, S_v, S_v]
+    // 2) Concatenated attention outputs across all iterations along axis 1
+    auto attn_concat_out = loop->get_concatenated_slices(attn_out_unsq, 0, 1, 1, -1, 1);  // [B*H_v, T, S_v]
+
+    // --- Pack outputs to match ggml layout ---
+    // ggml output ne = {S_v*H, T*B + S_v*B, 1, 1} -> OV [1, 1, T*B+S_v*B, S_v*H_v]
+    // attn: [B, T, H_v, S_v] row-major, state: [B, H_v, S_v, S_v] row-major
+
+    // attn: [B*H_v, T, S_v] -> [B, H_v, T, S_v] -> transpose to [B, T, H_v, S_v] -> flatten
+    auto attn_4d_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{B, H_v, T, S_v});
+    auto attn_4d = std::make_shared<ov::op::v1::Reshape>(attn_concat_out, attn_4d_shape, false);
+    auto attn_perm = std::make_shared<ov::op::v1::Transpose>(attn_4d, perm_0213);  // [B, T, H_v, S_v]
+
+    auto flat_shape_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, std::vector<int64_t>{-1});
+    auto attn_1d = std::make_shared<ov::op::v1::Reshape>(attn_perm, flat_shape_1d, false);
+
+    // state: [B*H_v, S_v, S_v] -> [B, H_v, S_v, S_v] -> flatten
+    auto state_4d_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{B, H_v, S_v, S_v});
+    auto state_4d = std::make_shared<ov::op::v1::Reshape>(final_state_out, state_4d_shape, false);
+    auto state_1d = std::make_shared<ov::op::v1::Reshape>(state_4d, flat_shape_1d, false);
+
+    // Concat [attn | state] and reshape to final output
+    auto packed = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{attn_1d, state_1d}, 0);
+    auto out_shape =
+        ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{1, 1, T * B + S_v * B, S_v * H_v});
+    auto res = std::make_shared<ov::op::v1::Reshape>(packed, out_shape, false);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/gated_delta_net.hpp b/ggml/src/ggml-openvino/openvino/op/gated_delta_net.hpp
new file mode 100644
index 000000000..20a4cfdfe
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/gated_delta_net.hpp
@@ -0,0 +1,65 @@
+#pragma once
+
+#include "openvino/op/op.hpp"
+
+namespace ov::op::internal {
+/// \note GatedDeltaNet op class is under development and subject to change
+///
+/// \brief Operator performing Gated Delta Net computation
+/// \ingroup ov_ops_cpp_api
+class OPENVINO_API GatedDeltaNet : public ov::op::Op {
+public:
+    OPENVINO_OP("GatedDeltaNet")
+
+    GatedDeltaNet() = default;
+    /// \brief Constructs a GatedDeltaNet operation.
+    ///
+    /// \param query Query tensor input.
+    /// \param key Key tensor input.
+    /// \param value Value tensor input.
+    /// \param recurrent_state Initial recurrent state tensor.
+    /// \param gate Gate tensor controlling state decay/update.
+    /// \param beta Beta tensor scaling the delta update.
+    /// \param fuse_qk_l2norm Enables fusing q/k L2-normalization into this op.
+    /// \param q_l2_norm_eps Epsilon used for query L2-normalization when fusion is enabled.
+    /// \param k_l2_norm_eps Epsilon used for key L2-normalization when fusion is enabled.
+    GatedDeltaNet(const Output<Node>& query,
+                  const Output<Node>& key,
+                  const Output<Node>& value,
+                  const Output<Node>& recurrent_state,
+                  const Output<Node>& gate,
+                  const Output<Node>& beta,
+                  const bool fuse_qk_l2norm = false,
+                  const float q_l2_norm_eps = 1e-6F,
+                  const float k_l2_norm_eps = 1e-6F);
+
+    /// \brief Constructs a GatedDeltaNet operation from input vector.
+    ///
+    /// \param args Input tensor vector in order: query, key, value, recurrent_state, gate, beta.
+    /// \param fuse_qk_l2norm Enables fusing q/k L2-normalization into this op.
+    /// \param q_l2_norm_eps Epsilon used for query L2-normalization when fusion is enabled.
+    /// \param k_l2_norm_eps Epsilon used for key L2-normalization when fusion is enabled.
+    GatedDeltaNet(const ov::OutputVector& args,
+                  const bool fuse_qk_l2norm = false,
+                  const float q_l2_norm_eps = 1e-6F,
+                  const float k_l2_norm_eps = 1e-6F);
+    void validate_and_infer_types() override;
+    bool visit_attributes(AttributeVisitor& visitor) override;
+    std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override;
+    bool get_fuse_qk_l2norm() const {
+        return m_fuse_qk_l2norm;
+    }
+    float get_q_l2_norm_eps() const {
+        return m_q_l2_norm_eps;
+    }
+    float get_k_l2_norm_eps() const {
+        return m_k_l2_norm_eps;
+    }
+
+private:
+    bool m_fuse_qk_l2norm = false;
+    float m_q_l2_norm_eps = 1e-6F;
+    float m_k_l2_norm_eps = 1e-6F;
+};
+
+}  // namespace ov::op::internal
diff --git a/ggml/src/ggml-openvino/openvino/op/get_rows.cpp b/ggml/src/ggml-openvino/openvino/op/get_rows.cpp
index 49f51b7ca..380e70a72 100644
--- a/ggml/src/ggml-openvino/openvino/op/get_rows.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/get_rows.cpp
@@ -18,16 +18,9 @@ namespace op {
 OutputVector translate_get_rows(const NodeContext & context) {
     num_inputs_check(context, 2, 2);
 
-    int op_case = context.get_op_case();
-
     Output<Node> res;
-    auto data = context.get_input(0);
-    auto indices = context.get_input(1);
-
-    if (op_case == 2) {
-        // The input comes from a VIEW
-        indices = process_view_input(context, 1);
-    }
+    auto data = process_view_input_new(context, 0);
+    auto indices = process_view_input_new(context, 1);
 
     // data[1,b,x,y] ind[1,1,b,x'] test-backend-ops case
     // data[x,y] ind[1,1,1,x'] normal case
diff --git a/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp b/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp
index d9fa4c243..a54870d9d 100644
--- a/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/glu_geglu.cpp
@@ -4,6 +4,7 @@
 
 #include <memory>
 #include <openvino/core/node_output.hpp>
+#include <openvino/op/clamp.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/gelu.hpp>
 #include <openvino/op/multiply.hpp>
@@ -21,23 +22,26 @@ OutputVector translate_glu_geglu(const NodeContext & context) {
     ov::Output<ov::Node> src0;
     ov::Output<ov::Node> src1;
     if (context.get_input_size() == 2) {
-        src0 = context.get_input(0);
-        src1 = context.get_input(1);
+        // Inputs may be VIEW slices of a combined gate_up tensor (MoE experts):
+        // resolve them so each half has its real sliced shape, not the base tensor.
+        src0 = process_view_input_new(context, 0);
+        src1 = process_view_input_new(context, 1);
     } else {
         // GGML splits along ne[0] (OV last axis) using floor division: nc = ne[0] / 2.
         // Both halves are nc elements; if the dimension is odd, the last element is dropped.
         // Use Slice instead of Split to handle odd dimensions correctly.
-        auto combined = context.get_input(0);
+        // Resolve a VIEW input (e.g. non-contiguous slice) to its real shape first.
+        auto combined = process_view_input_new(context, 0);
         auto combined_shape = combined.get_partial_shape();
         int64_t last_dim_val = combined_shape[combined_shape.rank().get_length() - 1].get_length();
         int64_t nc = last_dim_val / 2;
 
-        auto axis   = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
-        auto step   = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+        auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+        auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
         auto start0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto stop0  = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
+        auto stop0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
         auto start1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
-        auto stop1  = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc});
+        auto stop1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc});
 
         src0 = std::make_shared<ov::op::v8::Slice>(combined, start0, stop0, step, axis);
         src1 = std::make_shared<ov::op::v8::Slice>(combined, start1, stop1, step, axis);
@@ -49,6 +53,16 @@ OutputVector translate_glu_geglu(const NodeContext & context) {
         std::swap(src0, src1);
     }
 
+    if (context.is_static()) {
+        // TODO: Temporary solution for NPU accuracy issue due to fp16 overflow
+        // To be removed once permanent solution is implemented
+        // Justification:
+        // For |x| > 5, GELU(x) ≈ max(x, 0)  (behaves like ReLU)
+        // So Clamp(-10, 10) only affects values where GELU would return ≈ x anyway.
+        // The only loss: values > 10 get mapped to 10 instead of x.
+        // In practice, FFN intermediates rarely exceed 10 after GEGLU gating.
+        src0 = std::make_shared<ov::op::v0::Clamp>(src0, -10.0, 10.0);
+    }
     auto gelu = std::make_shared<ov::op::v7::Gelu>(src0);
     auto res = std::make_shared<ov::op::v1::Multiply>(gelu, src1);
 
diff --git a/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
index 00ed7951a..5c46e0713 100644
--- a/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
@@ -21,23 +21,26 @@ OutputVector translate_glu_swiglu(const NodeContext & context) {
     ov::Output<ov::Node> src0;
     ov::Output<ov::Node> src1;
     if (context.get_input_size() == 2) {
-        src0 = context.get_input(0);
-        src1 = context.get_input(1);
+        // Inputs may be VIEW slices of a combined gate_up tensor (MoE experts):
+        // resolve them so each half has its real sliced shape, not the base tensor.
+        src0 = process_view_input_new(context, 0);
+        src1 = process_view_input_new(context, 1);
     } else {
         // GGML splits along ne[0] (OV last axis) using floor division: nc = ne[0] / 2.
         // Both halves are nc elements; if the dimension is odd, the last element is dropped.
         // Use Slice instead of Split to handle odd dimensions correctly.
-        auto combined = context.get_input(0);
+        // Resolve a VIEW input (e.g. non-contiguous slice) to its real shape first.
+        auto combined = process_view_input_new(context, 0);
         auto combined_shape = combined.get_partial_shape();
         int64_t last_dim_val = combined_shape[combined_shape.rank().get_length() - 1].get_length();
         int64_t nc = last_dim_val / 2;
 
-        auto axis   = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
-        auto step   = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+        auto axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+        auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
         auto start0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto stop0  = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
+        auto stop0 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
         auto start1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {nc});
-        auto stop1  = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc});
+        auto stop1 = ov::op::v0::Constant::create(ov::element::i64, {1}, {2 * nc});
 
         src0 = std::make_shared<ov::op::v8::Slice>(combined, start0, stop0, step, axis);
         src1 = std::make_shared<ov::op::v8::Slice>(combined, start1, stop1, step, axis);
diff --git a/ggml/src/ggml-openvino/openvino/op/im2col.cpp b/ggml/src/ggml-openvino/openvino/op/im2col.cpp
new file mode 100644
index 000000000..856e97f79
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/im2col.cpp
@@ -0,0 +1,120 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+#include "ggml-impl.h"
+
+#include <cstddef>
+#include <memory>
+#include <openvino/core/shape.hpp>
+#include <openvino/core/strides.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/convert.hpp>
+#include <openvino/op/extractimagepatches.hpp>
+#include <openvino/op/pad.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/transpose.hpp>
+#include <openvino/op/util/attr_types.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_im2col(const NodeContext & context) {
+    num_inputs_check(context, 2, 2);
+    const int32_t * params = context.get_output_op_params();
+    int32_t s0 = params[0];
+    int32_t s1 = params[1];
+    int32_t p0 = params[2];
+    int32_t p1 = params[3];
+    int32_t d0 = params[4];
+    int32_t d1 = params[5];
+    bool is_2D = params[6] == 1;
+    ov::Output<Node> res;
+
+    ov::Output<Node> image = context.get_input(1);
+    const ov::Shape kernel_shape = context.get_input(0).get_shape();
+
+    const size_t IC = is_2D ? kernel_shape[1] : kernel_shape[2];
+    const size_t KH = is_2D ? kernel_shape[2] : 1;
+    const size_t KW = kernel_shape[3];
+
+    int32_t stride_w = s0;
+    int32_t stride_h = is_2D ? s1 : 1;
+    int32_t pad_w = p0;
+    int32_t pad_h = is_2D ? p1 : 0;
+    int32_t dil_w = d0;
+    int32_t dil_h = is_2D ? d1 : 1;
+
+    if (!is_2D) {
+        // GGML input shape: [IW, IC, N, 1]
+        // OpenVINO input shape: [1, N, IC, IW]
+        // Reshape image to: [N, IC, 1, IW]
+        const ov::Shape image_shape = image.get_shape();
+        const size_t N = image_shape[1];
+        const size_t IW = image_shape[3];
+        auto image_reshape_shape = ov::op::v0::Constant::create(
+            ov::element::i64, ov::Shape{4},
+            std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(IC), 1, static_cast<int64_t>(IW)});
+        image = std::make_shared<ov::op::v1::Reshape>(image, image_reshape_shape, false);
+    }
+
+    const ov::Shape patch_sizes = {KH, KW};
+    const ov::Strides strides = {static_cast<size_t>(stride_h), static_cast<size_t>(stride_w)};
+    const ov::Shape rates = {static_cast<size_t>(dil_h), static_cast<size_t>(dil_w)};
+
+    auto pads_begin =
+        ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, pad_h, pad_w});
+    auto pads_end =
+        ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 0, pad_h, pad_w});
+
+    auto pad = std::make_shared<ov::op::v1::Pad>(image, pads_begin, pads_end, ov::op::PadMode::CONSTANT);
+    auto patches =
+        std::make_shared<ov::op::v3::ExtractImagePatches>(pad, patch_sizes, strides, rates, ov::op::PadType::VALID);
+
+    // [N, KH*KW*IC, OH, OW] → [N, OH, OW, KH*KW*IC]
+    auto perm1 = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{0, 2, 3, 1});
+    auto t1 = std::make_shared<ov::op::v1::Transpose>(patches, perm1);
+
+    // [N, OH, OW, KH*KW*IC] → [N, OH, OW, KH*KW, IC]
+    const ov::Shape out_shape = t1->get_output_shape(0);
+    const size_t N = out_shape[0];
+    const size_t OH = out_shape[1];
+    const size_t OW = out_shape[2];
+    auto reshape1_shape = ov::op::v0::Constant::create(
+        ov::element::i64, ov::Shape{5},
+        std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(OH), static_cast<int64_t>(OW),
+                             static_cast<int64_t>(KH * KW), static_cast<int64_t>(IC)});
+    auto r1 = std::make_shared<ov::op::v1::Reshape>(t1, reshape1_shape, false);
+
+    // [N, OH, OW, KH*KW, IC] → [N, OH, OW, IC, KH*KW]
+    auto perm2 = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{5}, std::vector<int64_t>{0, 1, 2, 4, 3});
+    auto t2 = std::make_shared<ov::op::v1::Transpose>(r1, perm2);
+
+    // flatten back to [N, OH, OW, IC*KH*KW]
+    auto r2_shape = ov::op::v0::Constant::create(
+        ov::element::i64, ov::Shape{4},
+        std::vector<int64_t>{static_cast<int64_t>(N), static_cast<int64_t>(OH), static_cast<int64_t>(OW),
+                             static_cast<int64_t>(IC * KH * KW)});
+    res = std::make_shared<ov::op::v1::Reshape>(t2, r2_shape, false);
+
+    if (!is_2D) {
+        // [N, 1, OW, IC * KW] -> [1, N, OW, IC * KW]
+        auto final_reshape_shape = ov::op::v0::Constant::create(
+            ov::element::i64, ov::Shape{4},
+            std::vector<int64_t>{1, static_cast<int64_t>(N), static_cast<int64_t>(OW), static_cast<int64_t>(IC * KW)});
+        res = std::make_shared<ov::op::v1::Reshape>(res, final_reshape_shape, false);
+    }
+
+    auto output_type = context.get_output_type();
+    if (res.get_element_type() != output_type) {
+        res = std::make_shared<ov::op::v0::Convert>(res, output_type);
+    }
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/l2_norm.cpp b/ggml/src/ggml-openvino/openvino/op/l2_norm.cpp
new file mode 100644
index 000000000..4b8ed3b6c
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/l2_norm.cpp
@@ -0,0 +1,44 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/divide.hpp>
+#include <openvino/op/maximum.hpp>
+#include <openvino/op/multiply.hpp>
+#include <openvino/op/reduce_sum.hpp>
+#include <openvino/op/sqrt.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_l2_norm(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input_node = process_view_input_new(context, 0);
+
+    auto squared = std::make_shared<ov::op::v1::Multiply>(input_node, input_node);
+
+    auto sum_squared = std::make_shared<ov::op::v1::ReduceSum>(
+        squared, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {-1}), true);
+
+    auto l2_norm = std::make_shared<ov::op::v0::Sqrt>(sum_squared);
+
+    float eps;
+    memcpy(&eps, context.get_output_op_params(), sizeof(float));
+
+    auto eps_const = ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {eps});
+    auto clamped_norm = std::make_shared<ov::op::v1::Maximum>(l2_norm, eps_const);
+
+    auto res = std::make_shared<ov::op::v1::Divide>(input_node, clamped_norm);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
new file mode 100644
index 000000000..09e29d4cc
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
@@ -0,0 +1,108 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/op/broadcast.hpp>
+#include <openvino/op/concat.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/convert.hpp>
+#include <openvino/op/gather.hpp>
+#include <openvino/op/matmul.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/shape_of.hpp>
+#include <openvino/op/squeeze.hpp>
+#include <openvino/op/unsqueeze.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_mul_mat_id(const NodeContext & context) {
+    num_inputs_check(context, 3, 3);
+
+    auto expert_weights = process_view_input_new(context, 0);
+    auto activations = process_view_input_new(context, 1);
+    auto ids = process_view_input_new(context, 2);
+
+    // OpenVINO sees GGML tensors in reversed dimension order:
+    //   weights: [1, n_expert, m, k]
+    //   activations: [1, n_tokens, n_used_or_1, k]
+    //   ids: [1, 1, n_tokens, n_used]
+    // Rebuild the logical ranks explicitly from the 4D inputs instead of relying
+    // on fixed squeeze axes: real graphs can arrive through VIEW/RESHAPE chains
+    // where singleton axes are still represented differently at this point.
+    auto expert_weights_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(expert_weights, ov::element::i64);
+    auto activations_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
+    auto ids_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
+
+    auto expert_weights_shape_3d = get_dimensions(expert_weights_shape_4d, {1, 2, 3});
+    auto activations_shape_3d = get_dimensions(activations_shape_4d, {1, 2, 3});
+    auto ids_shape_2d = get_dimensions(ids_shape_4d, {2, 3});
+
+    expert_weights = std::make_shared<ov::op::v1::Reshape>(expert_weights, expert_weights_shape_3d, false);
+    activations = std::make_shared<ov::op::v1::Reshape>(activations, activations_shape_3d, false);
+    ids = std::make_shared<ov::op::v1::Reshape>(ids, ids_shape_2d, false);
+
+    if (ids.get_element_type() != ov::element::i32 && ids.get_element_type() != ov::element::i64) {
+        ids = std::make_shared<ov::op::v0::Convert>(ids, ov::element::i32);
+    }
+
+    auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0});
+    ov::Output<ov::Node> selected_weights = std::make_shared<ov::op::v8::Gather>(expert_weights, ids, gather_axis);
+
+    const auto output_type = context.get_output_type();
+    if (selected_weights.get_element_type() != ov::element::f32) {
+        selected_weights = std::make_shared<ov::op::v0::Convert>(selected_weights, ov::element::f32);
+    }
+    if (activations.get_element_type() != ov::element::f32) {
+        activations = std::make_shared<ov::op::v0::Convert>(activations, ov::element::f32);
+    }
+
+    auto activations_shape = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
+    auto ids_shape = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
+    ov::Output<ov::Node> acts_target_dims = std::make_shared<ov::op::v0::Concat>(
+        ov::OutputVector{
+            get_dimensions(activations_shape, {0}),
+            get_dimensions(ids_shape, {1}),
+            get_dimensions(activations_shape, {2}),
+        },
+        0);
+    ov::Output<ov::Node> acts_broadcasted =
+        std::make_shared<ov::op::v3::Broadcast>(activations, acts_target_dims, ov::op::BroadcastType::BIDIRECTIONAL);
+
+    auto unsqueeze_axes = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
+    auto activations_expanded = std::make_shared<ov::op::v0::Unsqueeze>(acts_broadcasted, unsqueeze_axes);
+
+    auto batch_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+    auto output_shape = context.get_output_shape();
+    FRONT_END_OP_CONVERSION_CHECK(output_shape.rank().is_static() && output_shape.rank().get_length() == 4,
+                                  "Unexpected MUL_MAT_ID output rank");
+    FRONT_END_OP_CONVERSION_CHECK(output_shape[3].is_static(), "Expected static row dimension for MUL_MAT_ID output");
+    const auto row_dim_value = output_shape[3].get_length();
+    auto row_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {row_dim_value});
+
+    ov::Output<ov::Node> result =
+        std::make_shared<ov::op::v0::MatMul>(activations_expanded, selected_weights, false, true);
+
+    auto result_target_dims = std::make_shared<ov::op::v0::Concat>(
+        ov::OutputVector{
+            batch_dim,
+            get_dimensions(ids_shape, {0, 1}),
+            row_dim,
+        },
+        0);
+    result = std::make_shared<ov::op::v1::Reshape>(result, result_target_dims, false);
+
+    if (result.get_element_type() != output_type) {
+        result = std::make_shared<ov::op::v0::Convert>(result, output_type);
+    }
+
+    return rename_outputs_with_suffix({result}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/mulmat.cpp b/ggml/src/ggml-openvino/openvino/op/mulmat.cpp
index 38edec85d..41d7c54ae 100644
--- a/ggml/src/ggml-openvino/openvino/op/mulmat.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/mulmat.cpp
@@ -30,17 +30,16 @@ OutputVector translate_mulmat(const NodeContext & context) {
     int op_case = context.get_op_case();
 
     ov::Output<Node> res;
-    ov::Output<ov::Node> B = context.get_input(0);
-    ov::Output<ov::Node> A = context.get_input(1);
-
-    bool transpose_b = true;
-    if (op_case == 2) {
-        B = B.get_node_shared_ptr()->input_value(0);
-        transpose_b = false;
-    } else if (op_case == 3) {
+    ov::Output<ov::Node> B;
+    ov::Output<ov::Node> A;
+    if (op_case == 3) {
         B = process_view_input(context, 0);
         A = process_view_input(context, 1);
+    } else {
+        B = process_view_input_new(context, 0);
+        A = process_view_input_new(context, 1);
     }
+
     if (A.get_element_type() != B.get_element_type()) {
         B = std::make_shared<ov::op::v0::Convert>(context.get_input(0), context.get_input_type(1));
     }
@@ -55,6 +54,7 @@ OutputVector translate_mulmat(const NodeContext & context) {
     auto batch_small = A_batch_larger ? B_batch : A_batch;
 
     Output<Node> Z = A_batch_larger ? B : A;
+    auto Z_shape = A_batch_larger ? B_shape : A_shape;
     int64_t factor = batch_large / batch_small;
     if (factor > 1 && batch_small > 1) {
         auto batch_large_node = ov::op::v0::Constant::create(ov::element::i64, {1}, std::vector<int64_t>{batch_large});
@@ -67,7 +67,11 @@ OutputVector translate_mulmat(const NodeContext & context) {
         auto broadcast_shape = ov::op::v0::Constant::create(
             ov::element::i64, {5}, {(int64_t) 1, (int64_t) 1, factor, (int64_t) 1, (int64_t) 1});
         auto new_Z_shape = ov::op::v0::Constant::create(ov::element::i64, {4},
-                                                        {(int64_t) 0, batch_large, (int64_t) -1, (int64_t) A_shape[3]});
+                                                        {(int64_t) 0, batch_large, (int64_t) -1, (int64_t) Z_shape[3]});
+        if (op_case == 2) {
+            new_Z_shape = ov::op::v0::Constant::create(ov::element::i64, {4},
+                                                       {(int64_t) 0, batch_large, (int64_t) Z_shape[2], (int64_t) -1});
+        }
 
         auto Z_broadcasted = std::make_shared<ov::op::v3::Broadcast>(Z_unsqueezed, broadcast_shape,
                                                                      ov::op::BroadcastType::BIDIRECTIONAL);
@@ -79,8 +83,14 @@ OutputVector translate_mulmat(const NodeContext & context) {
         A = Z;
     }
 
+    bool transpose_b = true;
     res = std::make_shared<ov::op::v0::MatMul>(A, B, false, transpose_b);
 
+    const auto output_type = context.get_output_type();
+    if (res.get_element_type() != output_type) {
+        res = std::make_shared<ov::op::v0::Convert>(res, output_type);
+    }
+
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op/norm.cpp b/ggml/src/ggml-openvino/openvino/op/norm.cpp
new file mode 100644
index 000000000..c8bedb6db
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/norm.cpp
@@ -0,0 +1,58 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/op/add.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/divide.hpp>
+#include <openvino/op/multiply.hpp>
+#include <openvino/op/power.hpp>
+#include <openvino/op/reduce_mean.hpp>
+#include <openvino/op/sqrt.hpp>
+#include <openvino/op/subtract.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_norm(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input_node = process_view_input_new(context, 0);
+
+    // Step 1: Calculate mean along the last dimension
+    // mean = reduce_mean(input, axis=-1, keepdims=true)
+    auto mean = std::make_shared<ov::op::v1::ReduceMean>(
+        input_node, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {-1}), true);
+
+    // Step 2: Calculate (input - mean)
+    auto centered = std::make_shared<ov::op::v1::Subtract>(input_node, mean);
+
+    // Step 3: Calculate squared differences (input - mean)^2
+    auto squared = std::make_shared<ov::op::v1::Power>(
+        centered, ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {2.0f}));
+
+    // Step 4: Calculate variance = mean((input - mean)^2)
+    auto variance = std::make_shared<ov::op::v1::ReduceMean>(
+        squared, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {-1}), true);
+
+    // Step 5: Get epsilon from op_params
+    float eps;
+    memcpy(&eps, context.get_output_op_params(), sizeof(float));
+
+    // Step 6: Calculate std = sqrt(variance + eps)
+    auto std_dev = std::make_shared<ov::op::v0::Sqrt>(std::make_shared<ov::op::v1::Add>(
+        variance, ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {eps})));
+
+    // Step 7: Normalize: output = (input - mean) / std
+    auto res = std::make_shared<ov::op::v1::Divide>(centered, std_dev);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/pad.cpp b/ggml/src/ggml-openvino/openvino/op/pad.cpp
new file mode 100644
index 000000000..492033d1b
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/pad.cpp
@@ -0,0 +1,95 @@
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <array>
+#include <cstdint>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/gather.hpp>
+#include <openvino/op/pad.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/shape_of.hpp>
+#include <vector>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+namespace {
+
+ov::Output<ov::Node> translate_circular_pad(ov::Output<ov::Node> input,
+                                            const std::array<int32_t, 8> & pads,
+                                            const ov::Shape & input_shape) {
+    ov::Output<ov::Node> result = input;
+
+    const std::array<int32_t, 4> pads_begin = {pads[6], pads[4], pads[2], pads[0]};
+    const std::array<int32_t, 4> pads_end = {pads[7], pads[5], pads[3], pads[1]};
+
+    for (size_t axis = 0; axis < input_shape.size(); ++axis) {
+        const int64_t input_dim = static_cast<int64_t>(input_shape[axis]);
+        const int64_t pad_begin = pads_begin[axis];
+        const int64_t pad_end = pads_end[axis];
+
+        if (pad_begin == 0 && pad_end == 0) {
+            continue;
+        }
+
+        FRONT_END_CHECK_IMPLEMENTED(input_dim > 0, "Circular PAD requires static non-zero input dimensions");
+
+        std::vector<int64_t> indices(static_cast<size_t>(input_dim + pad_begin + pad_end));
+        for (int64_t index = 0; index < static_cast<int64_t>(indices.size()); ++index) {
+            int64_t wrapped = (index - pad_begin) % input_dim;
+            if (wrapped < 0) {
+                wrapped += input_dim;
+            }
+            indices[static_cast<size_t>(index)] = wrapped;
+        }
+
+        auto gather_indices = ov::op::v0::Constant::create(ov::element::i64, {indices.size()}, indices);
+        auto gather_axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {axis});
+        result = std::make_shared<ov::op::v8::Gather>(result, gather_indices, gather_axis);
+    }
+
+    return result;
+}
+
+}  // namespace
+
+OutputVector translate_pad(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = process_view_input_new(context, 0);
+    if (context.get_input_shape(0) == context.get_output_shape()) {
+        auto input_shape = std::make_shared<ov::op::v3::ShapeOf>(input);
+        auto res = std::make_shared<ov::op::v1::Reshape>(input, input_shape, false);
+        return rename_outputs_with_suffix({res}, context.get_name());
+    }
+
+    const int32_t * op_params = context.get_output_op_params();
+    FRONT_END_CHECK_IMPLEMENTED(op_params != nullptr, "PAD requires output op params");
+
+    const std::array<int32_t, 8> pads = {op_params[0], op_params[1], op_params[2], op_params[3],
+                                         op_params[4], op_params[5], op_params[6], op_params[7]};
+    const bool circular = op_params[8] != 0;
+
+    if (circular) {
+        auto res = translate_circular_pad(input, pads, context.get_input_shape(0).to_shape());
+        return rename_outputs_with_suffix({res}, context.get_name());
+    }
+
+    const std::vector<int64_t> pads_begin = {pads[6], pads[4], pads[2], pads[0]};
+    const std::vector<int64_t> pads_end = {pads[7], pads[5], pads[3], pads[1]};
+
+    auto pads_begin_node = ov::op::v0::Constant::create(ov::element::i64, {pads_begin.size()}, pads_begin);
+    auto pads_end_node = ov::op::v0::Constant::create(ov::element::i64, {pads_end.size()}, pads_end);
+    auto pad_value = ov::op::v0::Constant::create(context.get_input_type(0), ov::Shape{}, {0});
+    auto res =
+        std::make_shared<ov::op::v1::Pad>(input, pads_begin_node, pads_end_node, pad_value, ov::op::PadMode::CONSTANT);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/permute.cpp b/ggml/src/ggml-openvino/openvino/op/permute.cpp
index 4c800f9ee..85550bff3 100644
--- a/ggml/src/ggml-openvino/openvino/op/permute.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/permute.cpp
@@ -12,6 +12,7 @@
 #include <openvino/op/reshape.hpp>
 #include <openvino/op/slice.hpp>
 #include <openvino/op/transpose.hpp>
+#include <vector>
 
 namespace ov {
 namespace frontend {
@@ -22,16 +23,33 @@ OutputVector translate_permute(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
     int op_case = context.get_op_case();
-    FRONT_END_CHECK_IMPLEMENTED(op_case == 1 || op_case == 2 || op_case == 3 || op_case == 4,
-                                "Unsupported PERMUTE case");
+    FRONT_END_CHECK_IMPLEMENTED(op_case != 0, "Unsupported PERMUTE case");
+    // op_case 1 is trivial permute
+    // op_case 2 is to permute Q. It has a preceding VIEW that reshapes Q to restore the sequqence dimension
+    // op_case 3 4 it to permute KV cache in the default layout
+    // op_case 5 6 is to permute V cache when `-fa off`, where v_trans=true
 
     ov::Output<Node> res;
-    auto src = context.get_input(0);
-    auto perm = ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 2, 1, 3});
+    ov::Output<Node> src;
+    if (op_case == 3 || op_case == 4 || op_case == 5 || op_case == 6) {
+        src = context.get_input(0);
+    } else {
+        src = process_view_input_new(context, 0);
+    }
+    std::vector<int64_t> perm_values{0, 2, 1, 3};
+    const int32_t * op_params = context.get_output_op_params();
+    if (op_params != nullptr) {
+        for (size_t input_axis = 0; input_axis < perm_values.size(); ++input_axis) {
+            const size_t output_axis = static_cast<size_t>(op_params[input_axis]);
+            perm_values[perm_values.size() - 1 - output_axis] =
+                static_cast<int64_t>(perm_values.size() - 1 - input_axis);
+        }
+    }
+    auto perm = ov::op::v0::Constant::create(ov::element::i64, {4}, perm_values);
 
     if (op_case == 1 || context.is_stateful()) {
         res = std::make_shared<ov::op::v1::Transpose>(src, perm);
-    } else if (op_case == 4) {
+    } else if (op_case == 2) {
         auto output_shape = context.get_output_shape().to_shape();
         auto n_heads = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[1]});
         auto head_size = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3]});
@@ -54,13 +72,17 @@ OutputVector translate_permute(const NodeContext & context) {
         auto output_shape = context.get_output_shape().to_shape();
         int64_t head_size = output_shape[3];
         int64_t n_heads = output_shape[1];
+        if (op_case == 5 || op_case == 6) {
+            head_size = output_shape[2];
+            n_heads = output_shape[1];
+        }
         int64_t ctx_per_seq = cache_shape[2].is_static() ? cache_shape[2].get_length() : -1;
         int64_t n_seq = cache_shape[1].get_length();
 
         Output<Node> attention_size;
         if (!context.has_input("attention_size")) {
             attention_size = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[2]});
-        } else if (op_case == 2) {
+        } else if (op_case == 3 || op_case == 5) {
             attention_size = context.get_input("attention_size");
         } else {
             attention_size = context.get_input("attention_size_swa");
@@ -80,18 +102,41 @@ OutputVector translate_permute(const NodeContext & context) {
             seq_active_end = ov::op::v0::Constant::create(ov::element::i64, {1}, {seq_active_end_val});
         }
 
-        // 1. reshape to [n_seq, ctx_per_seq, n_heads, head_size]
+        // 1. reshape to [n_seq, ctx_per_seq, n_heads, head_size] (for `-fa off` [n_seq, n_heads, head_size, ctx_per_seq])
         // 2. slice out the active sequences
         // 3. slice out the attention part in each sequence
-        // 4. permute
+        // 4. permute (skip for `-fa off`)
         auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
         auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
 
-        auto src_reshaped = std::make_shared<ov::op::v1::Reshape>(
-            src, ov::op::v0::Constant::create(ov::element::i64, {4}, {n_seq, ctx_per_seq, n_heads, head_size}), false);
-        auto slice1 = std::make_shared<ov::op::v8::Slice>(src_reshaped, seq_active_start, seq_active_end, one, zero);
-        auto slice2 = std::make_shared<ov::op::v8::Slice>(slice1, zero, attention_size, one, one);
-        res = std::make_shared<ov::op::v1::Transpose>(slice2, perm);
+        if (op_case == 3 || op_case == 4) {
+            auto src_reshaped = std::make_shared<ov::op::v1::Reshape>(
+                src, ov::op::v0::Constant::create(ov::element::i64, {4}, {n_seq, ctx_per_seq, n_heads, head_size}),
+                false);
+            ov::Output<ov::Node> after_seq_slice;
+            if (n_seq == 1) {
+                after_seq_slice = src_reshaped;
+            } else {
+                after_seq_slice =
+                    std::make_shared<ov::op::v8::Slice>(src_reshaped, seq_active_start, seq_active_end, one, zero);
+            }
+            auto slice2 = std::make_shared<ov::op::v8::Slice>(after_seq_slice, zero, attention_size, one, one);
+            res = std::make_shared<ov::op::v1::Transpose>(slice2, perm);
+        } else {
+            auto three = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
+            auto src_reshaped = std::make_shared<ov::op::v1::Reshape>(
+                src, ov::op::v0::Constant::create(ov::element::i64, {4}, {n_seq, n_heads, head_size, ctx_per_seq}),
+                false);
+            ov::Output<ov::Node> after_seq_slice;
+            if (n_seq == 1) {
+                after_seq_slice = src_reshaped;
+            } else {
+                after_seq_slice =
+                    std::make_shared<ov::op::v8::Slice>(src_reshaped, seq_active_start, seq_active_end, one, zero);
+            }
+            auto slice2 = std::make_shared<ov::op::v8::Slice>(after_seq_slice, zero, attention_size, one, three);
+            res = slice2;
+        }
     }
     return rename_outputs_with_suffix({res}, context.get_name());
 }
diff --git a/ggml/src/ggml-openvino/openvino/op/repeat.cpp b/ggml/src/ggml-openvino/openvino/op/repeat.cpp
new file mode 100644
index 000000000..4b742134b
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/repeat.cpp
@@ -0,0 +1,74 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+#include "ggml.h"
+
+#include <memory>
+#include <openvino/op/broadcast.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/divide.hpp>
+#include <openvino/op/shape_of.hpp>
+#include <openvino/op/tile.hpp>
+#include <vector>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+// GGML_OP_REPEAT tiles src[0] to fill the destination shape. Every destination
+// dimension is an integer multiple of the corresponding source dimension.
+OutputVector translate_repeat(const NodeContext & context) {
+    num_inputs_check(context, 1, 2);
+
+    auto input = process_view_input_new(context, 0);
+
+    const auto input_shape = context.get_input_shape(0);
+    const auto output_shape = context.get_output_shape();
+
+    if (input_shape.rank().is_static() && output_shape.rank().is_static() &&
+        input_shape.rank() == output_shape.rank()) {
+        const auto rank = static_cast<size_t>(input_shape.rank().get_length());
+        std::vector<int64_t> repeats(rank, 1);
+        bool all_static = true;
+
+        for (size_t axis = 0; axis < rank; ++axis) {
+            if (!input_shape[axis].is_static() || !output_shape[axis].is_static()) {
+                all_static = false;
+                break;
+            }
+
+            const int64_t input_dim = input_shape[axis].get_length();
+            const int64_t output_dim = output_shape[axis].get_length();
+
+            FRONT_END_OP_CONVERSION_CHECK(input_dim > 0 && output_dim > 0 && output_dim % input_dim == 0,
+                                          "REPEAT input shape ", input_shape, " cannot tile to match ", output_shape);
+
+            repeats[axis] = output_dim / input_dim;
+        }
+
+        if (all_static) {
+            auto repeats_node = ov::op::v0::Constant::create(ov::element::i64, {repeats.size()}, repeats);
+            ov::Output<ov::Node> res = std::make_shared<ov::op::v0::Tile>(input, repeats_node);
+            return rename_outputs_with_suffix({res}, context.get_name());
+        }
+    }
+
+    // Dynamic fallback: tile by the ratio of output to input shape.
+    auto input_shape_node = std::make_shared<ov::op::v3::ShapeOf>(input, ov::element::i64);
+    std::shared_ptr<ov::Node> target_shape_node;
+    if (output_shape.rank().is_static() && output_shape.is_static()) {
+        target_shape_node =
+            ov::op::v0::Constant::create(ov::element::i64, {output_shape.to_shape().size()}, output_shape.to_shape());
+    } else {
+        target_shape_node = std::make_shared<ov::op::v3::ShapeOf>(context.get_input(1), ov::element::i64);
+    }
+    auto repeats_node = std::make_shared<ov::op::v1::Divide>(target_shape_node, input_shape_node);
+    ov::Output<ov::Node> res = std::make_shared<ov::op::v0::Tile>(input, repeats_node);
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/reshape.cpp b/ggml/src/ggml-openvino/openvino/op/reshape.cpp
index efd9a5a86..602d3387c 100644
--- a/ggml/src/ggml-openvino/openvino/op/reshape.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/reshape.cpp
@@ -10,7 +10,6 @@
 #include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/reshape.hpp>
-#include <stdexcept>
 #include <vector>
 
 namespace ov {
@@ -20,7 +19,8 @@ namespace op {
 
 OutputVector translate_reshape(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
-    if (context.get_input_shape(0) == context.get_output_shape()) {
+    if (context.get_input(0).get_partial_shape().is_static() &&
+        context.get_input_shape(0) == context.get_output_shape()) {
         return {context.get_input(0)};
     }
 
@@ -34,12 +34,12 @@ OutputVector translate_reshape(const NodeContext & context) {
     if (op_case == 1) {
         if (context.is_stateful()) {
             new_shape_node = ov::op::v0::Constant::create(
-                ov::element::i64, {3},
-                std::vector<int64_t>{-1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
+                ov::element::i64, {3}, std::vector<int64_t>{-1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
         } else {
             new_shape_node = ov::op::v0::Constant::create(
                 ov::element::i64, {4},
-                std::vector<int64_t>{(int64_t) output_shape[0], -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
+                std::vector<int64_t>{(int64_t) output_shape[0], -1, (int64_t) output_shape[2],
+                                     (int64_t) output_shape[3]});
         }
     } else if (op_case == 2) {
         new_shape_node = ov::op::v0::Constant::create(
@@ -47,7 +47,14 @@ OutputVector translate_reshape(const NodeContext & context) {
             std::vector<int64_t>{(int64_t) output_shape[0], (int64_t) output_shape[1], -1, (int64_t) output_shape[3]});
 
     } else if (op_case == 3) {
-        throw std::runtime_error("might be outdated RESHAPE case");
+        //  -  14: [     1,  1024,     1,     1] RESHAPE              Vcur-0 (reshaped) (reshaped)
+        //         [   512,     2,     1,     1]            0: RESHAPE     Vcur-0 (reshaped)
+        //  -  15: [     1, 524288,     1,     1] RESHAPE              cache_v_l0 (reshaped)
+        //         [   512,  1024,     1,     1]            0: NONE        cache_v_l0
+        //  -  16: [     1, 524288,     1,     1] SET_ROWS             cache_v_l0 (reshaped) (view)
+        //         [     1,  1024,     1,     1]            0: RESHAPE     Vcur-0 (reshaped) (reshaped)
+        //         [  1024,     1,     1,     1]            1: NONE        leaf_11
+        //         [     1, 524288,     1,     1]            2: RESHAPE     cache_v_l0 (reshaped)
         new_shape_node = ov::op::v0::Constant::create(
             ov::element::i64, {4}, std::vector<int64_t>{(int64_t) output_shape[0], (int64_t) output_shape[1], -1, 1});
 
diff --git a/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp b/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp
index 72cf92283..e76ec55b8 100644
--- a/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/rms_norm.cpp
@@ -19,7 +19,7 @@ namespace op {
 OutputVector translate_rms_norm(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
-    auto input_node = context.get_input(0);
+    auto input_node = process_view_input_new(context, 0);
     auto square = std::make_shared<ov::op::v1::Power>(
         input_node, ov::op::v0::Constant::create(ov::element::f32, ov::Shape{1}, {2.0f}));
 
diff --git a/ggml/src/ggml-openvino/openvino/op/rope.cpp b/ggml/src/ggml-openvino/openvino/op/rope.cpp
index a8db9b389..9bb2d75d0 100644
--- a/ggml/src/ggml-openvino/openvino/op/rope.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/rope.cpp
@@ -7,6 +7,7 @@
 #include <openvino/core/node.hpp>
 #include <openvino/core/node_output.hpp>
 #include <openvino/op/add.hpp>
+#include <openvino/op/broadcast.hpp>
 #include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
@@ -38,8 +39,7 @@ OutputVector translate_rope(const NodeContext & context) {
     auto data_node = context.get_input(0).get_node_shared_ptr();
     auto output_shape = context.get_output_shape().to_shape();
     int32_t * op_params = context.get_output_op_params();
-    const int mode = (op_case & 0xFFFF0000) >> 16;
-    op_case = (op_case & 0x0000FFFF);
+    const int mode = op_case;
 
     constexpr int TYPE_NORMAL = 0;
     constexpr int TYPE_NEOX = 1;
@@ -56,55 +56,146 @@ OutputVector translate_rope(const NodeContext & context) {
         if (context.get_input_size() == 3) {
             rope_freqs_weight = context.get_input(2).get_node_shared_ptr();
         }
-        auto sin_cos = make_sin_cos(op_params, inp_pos, rope_freqs_weight, mode == TYPE_IMROPE);
+        auto sin_cos = make_sin_cos(op_params, inp_pos, rope_freqs_weight, mode == TYPE_IMROPE, false);
         sin_theta_node = sin_cos.first;
         cos_theta_node = sin_cos.second;
     }
 
-    if (op_case == 2) {
-        // The input comes from a VIEW
-        int slice_len = output_shape[2] * output_shape[3];
-        data_node = process_view_input(context, 0, slice_len).get_node_shared_ptr();
+    if (context.get_view_input_size(0) > 0) {
+        data_node = process_view_input_new(context, 0).get_node_shared_ptr();
         if (context.is_stateful()) {
             auto data_shape = ov::op::v0::Constant::create(
                 ov::element::i64, {3}, std::vector<int64_t>{-1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
             data_node = std::make_shared<ov::op::v1::Reshape>(data_node, data_shape, false);
         } else {
             auto data_shape = ov::op::v0::Constant::create(
-                ov::element::i64, {4}, std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
+                ov::element::i64, {4},
+                std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
             data_node = std::make_shared<ov::op::v1::Reshape>(data_node, data_shape, false);
         }
     }
 
+    auto output_type = context.get_output_type();
+    if (data_node->get_element_type() != ov::element::f32) {
+        data_node = std::make_shared<ov::op::v0::Convert>(data_node, ov::element::f32);
+    }
+
+    // TODO(openvino-gpu-rope-fusion): TEMPORARY WORKAROUND - do NOT revert until the
+    // OpenVINO GPU plugin is updated.
+    //
+    // For TYPE_NORMAL rope (both stateful and stateless) we emit the Flux-style
+    // interleaved pattern below so the GPU plugin's RoPEFusionFlux matcher folds it
+    // into ov::op::internal::RoPE. The matcher requires rank-4 inputs, which is why
+    // the original even/odd Slice translation (kept in the `else if (mode ==
+    // TYPE_NORMAL)` branch below for reference) does not get fused.
+    //
+    // Once the GPU plugin's RoPE fusion is extended to also recognize the original
+    // even/odd Slice form, this Flux rewrite should be removed and both modes should
+    // be restored to the captured even/odd translation. Until then, keep both paths:
+    // the active Flux rewrite here and the previous translation preserved below.
     if (mode == TYPE_NORMAL) {
-        auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
-        auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-        auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
-        auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3]});
-        Output<Node> even_slice;
-        Output<Node> odd_slice;
-        int32_t unsqueeze_dim = context.is_stateful() ? 3 : 4;
-        even_slice = std::make_shared<ov::op::v8::Slice>(data_node, zero, end, two, neg_one);
-        odd_slice = std::make_shared<ov::op::v8::Slice>(data_node, one, end, two, neg_one);
+        // Emit the Flux-style interleaved-RoPE pattern so the GPU plugin's
+        // RoPEFusionFlux matcher folds this subgraph into ov::op::internal::RoPE:
+        //   x_paired   = Reshape(x, [1, S, n_heads, head_size/2, 2])
+        //   x0, x1     = Split(x_paired, axis=-1, num_splits=2)
+        //   x1_neg     = x1 * -1
+        //   x_rotated  = Reshape(Concat([x1_neg, x0], axis=-1), [1, S, n_heads, head_size])
+        //   y          = x * t_cos + x_rotated * t_sin
+        // Mathematically equivalent to the even/odd Slice form below.
+        //
+        // RoPEFusionFlux requires rank_equals(4) on x, t_cos and t_sin. The cos/sin
+        // tables are already built rank-4 ([1, S, 1, head_size/2]) for both modes. In
+        // stateful mode the data arrives rank-3 ([S, n_heads, head_size]), so lift it
+        // to rank-4 ([1, S, n_heads, head_size]) here. Stateful RoPE already produced
+        // rank-4 output, so downstream attention is unaffected.
+        if (context.is_stateful()) {
+            auto r4_shape = ov::op::v0::Constant::create(
+                ov::element::i64, {4},
+                std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
+            data_node = std::make_shared<ov::op::v1::Reshape>(data_node, r4_shape, false);
+        }
+        const int64_t head_size = static_cast<int64_t>(output_shape[3]);
+        const int64_t n_heads = static_cast<int64_t>(output_shape[2]);
+        const int64_t half = head_size / 2;
 
-        Output<Node> first_half =
-            std::make_shared<ov::op::v1::Subtract>(std::make_shared<ov::op::v1::Multiply>(even_slice, cos_theta_node),
-                                                   std::make_shared<ov::op::v1::Multiply>(odd_slice, sin_theta_node));
-        Output<Node> second_half =
-            std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(even_slice, sin_theta_node),
-                                              std::make_shared<ov::op::v1::Multiply>(odd_slice, cos_theta_node));
+        auto neg_one_f = ov::op::v0::Constant::create(data_node->get_element_type(), ov::Shape{}, {-1.0f});
 
-        first_half = std::make_shared<ov::op::v0::Unsqueeze>(first_half,
-                                                             ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim}));
-        second_half = std::make_shared<ov::op::v0::Unsqueeze>(second_half,
-                                                              ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim}));
-        auto stack = std::make_shared<ov::op::v0::Concat>(OutputVector{first_half, second_half}, unsqueeze_dim);
+        auto paired_shape =
+            ov::op::v0::Constant::create(ov::element::i64, {5}, std::vector<int64_t>{1, -1, n_heads, half, 2});
+        auto x_paired = std::make_shared<ov::op::v1::Reshape>(data_node, paired_shape, false);
 
-        auto data_shape = ov::op::v0::Constant::create(
-            ov::element::i64, {4}, std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
-        res = std::make_shared<ov::op::v1::Reshape>(stack, data_shape, false);
-    } else if (mode == TYPE_NEOX) {
+        auto split_axis = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {-1});
+        auto data_split = std::make_shared<ov::op::v1::Split>(x_paired, split_axis, 2);
+        Output<Node> x0 = data_split->outputs()[0];
+        Output<Node> x1 = data_split->outputs()[1];
+
+        auto x1_neg = std::make_shared<ov::op::v1::Multiply>(x1, neg_one_f);
+        auto x_rotated_paired = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{x1_neg, x0}, -1);
+
+        auto flat_shape =
+            ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{1, -1, n_heads, head_size});
+        auto x_rotated = std::make_shared<ov::op::v1::Reshape>(x_rotated_paired, flat_shape, false);
+
+        // Expand cos/sin from [..., head_size/2] to [..., head_size] by repeating each
+        // entry twice. Use special_zero on the final Reshape so the seq dim passes
+        // through dynamically. Final rank is 4 to satisfy the matcher's predicate.
+        auto expand_cos_sin = [&](Output<Node> cs) {
+            auto cs_unsq =
+                std::make_shared<ov::op::v0::Unsqueeze>(cs, ov::op::v0::Constant::create(ov::element::i64, {1}, {-1}));
+            auto bcast_target =
+                ov::op::v0::Constant::create(ov::element::i64, {5}, std::vector<int64_t>{1, 1, 1, half, 2});
+            auto bcast =
+                std::make_shared<ov::op::v3::Broadcast>(cs_unsq, bcast_target, ov::op::BroadcastType::BIDIRECTIONAL);
+            auto flat = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{0, 0, 0, head_size});
+            return std::make_shared<ov::op::v1::Reshape>(bcast, flat, true);
+        };
+        Output<Node> cos_full = expand_cos_sin(cos_theta_node);
+        Output<Node> sin_full = expand_cos_sin(sin_theta_node);
+
+        auto y1 = std::make_shared<ov::op::v1::Multiply>(data_node, cos_full);
+        auto y2 = std::make_shared<ov::op::v1::Multiply>(x_rotated, sin_full);
+        res = std::make_shared<ov::op::v1::Add>(y1, y2);
+    }
+    // PRESERVED PREVIOUS TRANSLATION - Re-enable this branch (and remove the Flux branch above) once
+    // the GPU plugin's RoPE fusion is updated to recognize the even/odd Slice form;
+    // see the TODO(openvino-gpu-rope-fusion) note above. Do not delete.
+    //
+    // Original even/odd Slice form. In stateless mode it ran on rank-4 data
+    // ([1, S, n_heads, head_size]); in stateful mode on rank-3 data
+    // ([S, n_heads, head_size]). Either way it does not match RoPEFusionFlux
+    // (which needs rank-4 x in the interleaved layout), so the RoPE stays as
+    // discrete elementwise ops.
+    //
+    // } else if (mode == TYPE_NORMAL) {
+    //     auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+    //     auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
+    //     auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+    //     auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
+    //     auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3]});
+    //     Output<Node> even_slice;
+    //     Output<Node> odd_slice;
+    //     // stateful data is rank 3 (unsqueeze at axis 3), stateless is rank 4 (axis 4)
+    //     int32_t unsqueeze_dim = context.is_stateful() ? 3 : 4;
+    //     even_slice = std::make_shared<ov::op::v8::Slice>(data_node, zero, end, two, neg_one);
+    //     odd_slice = std::make_shared<ov::op::v8::Slice>(data_node, one, end, two, neg_one);
+    //
+    //     Output<Node> first_half =
+    //         std::make_shared<ov::op::v1::Subtract>(std::make_shared<ov::op::v1::Multiply>(even_slice, cos_theta_node),
+    //                                                std::make_shared<ov::op::v1::Multiply>(odd_slice, sin_theta_node));
+    //     Output<Node> second_half =
+    //         std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(even_slice, sin_theta_node),
+    //                                           std::make_shared<ov::op::v1::Multiply>(odd_slice, cos_theta_node));
+    //
+    //     first_half = std::make_shared<ov::op::v0::Unsqueeze>(first_half,
+    //                                                          ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim}));
+    //     second_half = std::make_shared<ov::op::v0::Unsqueeze>(second_half,
+    //                                                           ov::op::v0::Constant::create(ov::element::i64, {1}, {unsqueeze_dim}));
+    //     auto stack = std::make_shared<ov::op::v0::Concat>(OutputVector{first_half, second_half}, unsqueeze_dim);
+    //
+    //     auto data_shape = ov::op::v0::Constant::create(
+    //         ov::element::i64, {4}, std::vector<int64_t>{1, -1, (int64_t) output_shape[2], (int64_t) output_shape[3]});
+    //     res = std::make_shared<ov::op::v1::Reshape>(stack, data_shape, false);
+    else if (mode == TYPE_NEOX) {
         auto data_split = std::make_shared<ov::op::v1::Split>(
             data_node, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {-1}), 2);
         Output<Node> slice_data_node_0 = data_split->outputs()[0];
@@ -120,8 +211,9 @@ OutputVector translate_rope(const NodeContext & context) {
 
         res = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{first_half_node, second_half_node}, -1);
     } else if (mode == TYPE_IMROPE) {
-        int64_t n_dims = data_node->get_shape()[3];
-        auto cos_sin_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int64_t>{1,-1,1,(n_dims >> 1)});
+        int64_t n_dims = data_node->get_output_partial_shape(0)[3].get_length();
+        auto cos_sin_shape = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4},
+                                                                    std::vector<int64_t>{1, -1, 1, (n_dims >> 1)});
         auto cos_reshaped = std::make_shared<ov::op::v1::Reshape>(cos_theta_node, cos_sin_shape, true);
         auto sin_reshaped = std::make_shared<ov::op::v1::Reshape>(sin_theta_node, cos_sin_shape, true);
 
@@ -140,6 +232,10 @@ OutputVector translate_rope(const NodeContext & context) {
         res = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{sub, add}, 3);
     }
 
+    if (res.get_element_type() != output_type) {
+        res = std::make_shared<ov::op::v0::Convert>(res, output_type);
+    }
+
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op/set_rows.cpp b/ggml/src/ggml-openvino/openvino/op/set_rows.cpp
index 136e4265b..18643371e 100644
--- a/ggml/src/ggml-openvino/openvino/op/set_rows.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/set_rows.cpp
@@ -28,20 +28,20 @@ namespace op {
 OutputVector translate_set_rows(const NodeContext & context) {
     num_inputs_check(context, 3, 3);
 
-    auto data = context.get_input(0);
+    auto data = process_view_input_new(context, 0);
     auto indices = context.get_input(1);
     auto dst = context.get_input(2);
 
     data = std::make_shared<ov::op::v0::Convert>(data, context.get_output_type());
 
-    auto dst_shape = context.get_output_shape().to_shape();
+    auto row_size = context.get_input_shape(2)[3].get_length();
 
     auto ind_squeezed =
         std::make_shared<ov::op::v0::Squeeze>(indices, ov::op::v0::Constant::create(ov::element::i64, {3}, {0, 1, 2}));
     auto data_reshaped = std::make_shared<ov::op::v1::Reshape>(
         data,
         ov::op::v0::Constant::create(ov::element::i64, {4},
-                                     {(int64_t) 1, (int64_t) 1, (int64_t) -1, (int64_t) dst_shape[3]}),
+                                     {(int64_t) 1, (int64_t) 1, (int64_t) -1, (int64_t) row_size}),
         false);
     auto axes = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{}, {2});
 
diff --git a/ggml/src/ggml-openvino/openvino/op/softmax.cpp b/ggml/src/ggml-openvino/openvino/op/softmax.cpp
index 9f6330862..287faedbb 100644
--- a/ggml/src/ggml-openvino/openvino/op/softmax.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/softmax.cpp
@@ -2,18 +2,16 @@
 #include "../op_table.h"
 #include "../utils.h"
 
-#include <climits>
+#include <cmath>
 #include <cstdint>
+#include <cstring>
 #include <memory>
-#include <openvino/core/node.hpp>
-#include <openvino/core/node_output.hpp>
+#include <openvino/frontend/exception.hpp>
 #include <openvino/op/add.hpp>
-#include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
-#include <openvino/op/matmul.hpp>
 #include <openvino/op/multiply.hpp>
-#include <openvino/op/slice.hpp>
+#include <openvino/op/reshape.hpp>
 #include <openvino/op/softmax.hpp>
 #include <vector>
 
@@ -22,63 +20,82 @@ namespace frontend {
 namespace ggml {
 namespace op {
 
+// Reimplementation of GGML_OP_SOFT_MAX semantics for OpenVINO backend:
+// 1) logits = src0 * scale
+// 2) logits += mask (if provided)
+// 3) softmax over the last dimension
 OutputVector translate_soft_max(const NodeContext & context) {
-    // TODO code is outdated
     num_inputs_check(context, 1, 2);
 
-    auto input_node = context.get_input(0).get_node_shared_ptr();
-    ov::Output<Node> res;
-
     float scale = 1.0f;
     float max_bias = 0.0f;
-    auto * op_params = context.get_output_op_params();
-    memcpy(&scale, (float *) op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) op_params + 1, sizeof(float));
-    auto src0_shape = context.get_input_shape(0).get_shape();
-    const uint32_t h = src0_shape[2];
-    const uint32_t n_head = src0_shape[0];
-    const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
+    memcpy(&scale, (float *) context.get_output_op_params() + 0, sizeof(float));
+    memcpy(&max_bias, (float *) context.get_output_op_params() + 1, sizeof(float));
 
-    const float m0 = powf(2.0f, -(max_bias) / n_head_log2);
-    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
-    const float slope =
-        (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2 * (h - n_head_log2) + 1) : 1.0f;
+    ov::Output<ov::Node> logits = context.get_input(0);
 
-    auto scale_node = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{}, std::vector<float>{scale});
-    auto scaled_input = std::make_shared<ov::op::v1::Multiply>(input_node, scale_node);
-
-    if (context.get_input_size() < 2) {
-        res = std::make_shared<ov::op::v8::Softmax>(scaled_input, 2);
-        return rename_outputs_with_suffix({res}, context.get_name());
+    // Apply scale first: logits = src0 * scale
+    if (scale != 1.0f) {
+        auto scale_const =
+            std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{}, std::vector<float>{scale});
+        logits = std::make_shared<ov::op::v1::Multiply>(logits, scale_const);
     }
 
-    ov::Output<ov::Node> mask_node_sliced;
-    if (context.has_input("KQ_mask_sliced")) {
-        mask_node_sliced = context.get_input("KQ_mask_sliced");
-    } else {
-        auto token_len = get_dimensions(input_node, {1});
-        auto mask_node = context.get_input(1);
-        auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-        mask_node_sliced = std::make_shared<ov::op::v8::Slice>(mask_node, zero, token_len, one, one);
+    FRONT_END_CHECK_IMPLEMENTED(!(max_bias > 0.0f && context.get_input_size() < 2),
+                                "OpenVINO softmax ALiBi path requires mask input");
+
+    // Optional mask add: logits += mask
+    // For max_bias > 0 (ALiBi), apply per-head slope to mask before adding.
+    if (context.get_input_size() > 1) {
+        ov::Output<ov::Node> mask = context.get_input(1);
+
+        // For stateful
+        std::string mask_name = "KQ_mask_sliced";
+        if (context.get_input_names()[1].find("swa") != std::string::npos) {
+            mask_name = "KQ_mask_swa_sliced";
+        }
+        if (context.has_input(mask_name)) {
+            mask = context.get_input(mask_name);
+        }
+
+        if (mask.get_element_type() != logits.get_element_type()) {
+            mask = std::make_shared<ov::op::v0::Convert>(mask, logits.get_element_type());
+        }
+
+        if (max_bias > 0.0f) {
+            auto out_shape = context.get_output_shape().to_shape();
+            FRONT_END_CHECK_IMPLEMENTED(out_shape.size() == 4, "OpenVINO softmax ALiBi path expects rank-4 tensor");
+
+            const uint32_t n_head = static_cast<uint32_t>(out_shape[1]);
+            FRONT_END_CHECK_IMPLEMENTED(n_head > 0, "OpenVINO softmax ALiBi path expects n_head > 0");
+
+            const uint32_t n_head_log2 = 1u << static_cast<uint32_t>(std::floor(std::log2(static_cast<float>(n_head))));
+            const float m0 = std::pow(2.0f, -(max_bias) / static_cast<float>(n_head_log2));
+            const float m1 = std::pow(2.0f, -(max_bias / 2.0f) / static_cast<float>(n_head_log2));
+
+            std::vector<float> slopes(n_head);
+            for (uint32_t h = 0; h < n_head; ++h) {
+                slopes[h] = h < n_head_log2 ? std::pow(m0, static_cast<float>(h + 1)) :
+                                              std::pow(m1, static_cast<float>(2 * (h - n_head_log2) + 1));
+            }
+
+            ov::Output<ov::Node> slope_node =
+                std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{n_head}, slopes);
+            if (slope_node.get_element_type() != mask.get_element_type()) {
+                slope_node = std::make_shared<ov::op::v0::Convert>(slope_node, mask.get_element_type());
+            }
+
+            auto slope_shape = std::make_shared<ov::op::v0::Constant>(
+                ov::element::i64, ov::Shape{4}, std::vector<int64_t>{1, static_cast<int64_t>(n_head), 1, 1});
+            auto slope_4d = std::make_shared<ov::op::v1::Reshape>(slope_node, slope_shape, false);
+            mask = std::make_shared<ov::op::v1::Multiply>(mask, slope_4d);
+        }
+
+        logits = std::make_shared<ov::op::v1::Add>(logits, mask);
     }
 
-    if (mask_node_sliced.get_element_type() != context.get_output_type()) {
-        mask_node_sliced = std::make_shared<ov::op::v0::Convert>(mask_node_sliced, context.get_output_type());
-    }
-
-    Output<Node> slope_mask;
-    if (slope != 1.0f) {
-        auto slope_node =
-            std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{}, std::vector<float>{slope});
-        slope_mask = std::make_shared<ov::op::v1::Multiply>(mask_node_sliced, slope_node);
-        throw std::runtime_error("Slope != 1.0f in softmax has not been tested, verify it before use.");
-    }
-    slope_mask = mask_node_sliced;
-
-    auto input_slope_mask_node = std::make_shared<ov::op::v1::Add>(scaled_input, slope_mask);
-
-    res = std::make_shared<ov::op::v8::Softmax>(input_slope_mask_node, 2);
+    // Softmax along last dimension (equivalent to ggml softmax over ne[0]).
+    auto res = std::make_shared<ov::op::v8::Softmax>(logits, -1);
 
     return rename_outputs_with_suffix({res}, context.get_name());
 }
diff --git a/ggml/src/ggml-openvino/openvino/op/ssm_conv.cpp b/ggml/src/ggml-openvino/openvino/op/ssm_conv.cpp
new file mode 100644
index 000000000..522308726
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/ssm_conv.cpp
@@ -0,0 +1,59 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <openvino/op/constant.hpp>
+#include <openvino/op/group_conv.hpp>
+#include <openvino/op/reshape.hpp>
+#include <openvino/op/transpose.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_ssm_conv(const NodeContext & context) {
+    num_inputs_check(context, 2, 2);
+
+    auto sx = context.get_input(0);                         // conv state + input: OV shape [1, n_s, d_inner, ncs]
+    auto c = context.get_input(1);                          // conv1d weight:      OV shape [1, 1, d_inner, d_conv]
+
+    auto sx_shape = context.get_input_shape(0).to_shape();  // [1, n_s, d_inner, ncs]
+    auto c_shape = context.get_input_shape(1).to_shape();   // [1, 1, d_inner, d_conv]
+
+    int64_t n_s = sx_shape[1];
+    int64_t d_inner = sx_shape[2];
+    int64_t ncs = sx_shape[3];  // d_conv - 1 + n_t
+    int64_t d_conv = c_shape[3];
+    int64_t n_t = ncs - d_conv + 1;
+
+    // Reshape sx from [1, n_s, d_inner, ncs] to [n_s, d_inner, ncs] for 1D GroupConvolution
+    auto sx_new_shape = ov::op::v0::Constant::create(ov::element::i64, {3}, std::vector<int64_t>{n_s, d_inner, ncs});
+    auto sx_reshaped = std::make_shared<ov::op::v1::Reshape>(sx, sx_new_shape, false);
+
+    // Reshape c from [1, 1, d_inner, d_conv] to [d_inner, 1, 1, d_conv]
+    // GroupConvolution filter: [groups, out_channels/groups, in_channels/groups, kernel_size]
+    auto c_new_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{d_inner, 1, 1, d_conv});
+    auto c_reshaped = std::make_shared<ov::op::v1::Reshape>(c, c_new_shape, false);
+
+    // Depthwise 1D convolution: groups=d_inner, stride=1, no padding, no dilation
+    // Input: [n_s, d_inner, ncs], Filter: [d_inner, 1, 1, d_conv]
+    // Output: [n_s, d_inner, n_t]
+    auto conv = std::make_shared<ov::op::v1::GroupConvolution>(
+        sx_reshaped, c_reshaped, ov::Strides{1}, ov::CoordinateDiff{0}, ov::CoordinateDiff{0}, ov::Strides{1});
+
+    // Transpose from [n_s, d_inner, n_t] to [n_s, n_t, d_inner]
+    auto perm = ov::op::v0::Constant::create(ov::element::i64, {3}, std::vector<int64_t>{0, 2, 1});
+    auto transposed = std::make_shared<ov::op::v1::Transpose>(conv, perm);
+
+    // Reshape to output shape [1, n_s, n_t, d_inner]
+    auto out_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, std::vector<int64_t>{1, n_s, n_t, d_inner});
+    auto res = std::make_shared<ov::op::v1::Reshape>(transposed, out_shape, false);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/sum_rows.cpp b/ggml/src/ggml-openvino/openvino/op/sum_rows.cpp
new file mode 100644
index 000000000..d04e6443b
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/sum_rows.cpp
@@ -0,0 +1,27 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <memory>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/reduce_sum.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_sum_rows(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = process_view_input_new(context, 0);
+    auto res = std::make_shared<ov::op::v1::ReduceSum>(
+        input, ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {-1}), true);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/transpose.cpp b/ggml/src/ggml-openvino/openvino/op/transpose.cpp
index 8e62e83c0..8d89ca556 100644
--- a/ggml/src/ggml-openvino/openvino/op/transpose.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/transpose.cpp
@@ -12,8 +12,39 @@ namespace op {
 OutputVector translate_transpose(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
+    // Compute permute order from input/output shape and stride information
+    // so it adapts to different input and output layouts.
+    auto input_shape = context.get_input_shape(0).to_shape();
+    auto input_stride = context.get_input_stride(0);
+    auto output_shape = context.get_output_shape().to_shape();
+    auto output_stride = context.get_output_stride();
+
+    // Compute permute order by matching output and input stride rankings.
+    // Build <stride, dim_index> pairs.
+    std::vector<std::pair<size_t, int>> output_stride_dims;
+    std::vector<std::pair<size_t, int>> input_stride_dims;
+
+    for (int i = 0; i < 4; ++i) {
+        output_stride_dims.push_back({output_stride[i], i});
+        input_stride_dims.push_back({input_stride[i], i});
+    }
+
+    // Sort by stride in descending order.
+    std::sort(output_stride_dims.rbegin(), output_stride_dims.rend());
+    std::sort(input_stride_dims.rbegin(), input_stride_dims.rend());
+
+    // Build permute order.
+    std::vector<int64_t> permute_order(4);
+    for (int i = 0; i < 4; ++i) {
+        int output_dim = output_stride_dims[i].second;
+        int input_dim = input_stride_dims[i].second;
+        permute_order[output_dim] = input_dim;
+    }
+
+    auto input = process_view_input_new(context, 0);
+
     auto res = std::make_shared<ov::op::v1::Transpose>(
-        context.get_input(0), ov::op::v0::Constant::create(ov::element::i64, {4}, {0, 1, 3, 2}));
+        input, ov::op::v0::Constant::create(ov::element::i64, {4}, permute_order));
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp b/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp
deleted file mode 100644
index d1e9efc33..000000000
--- a/ggml/src/ggml-openvino/openvino/op/unary_gelu.cpp
+++ /dev/null
@@ -1,25 +0,0 @@
-#include "../node_context.h"
-#include "../op_table.h"
-#include "../utils.h"
-
-#include <openvino/core/node_output.hpp>
-#include <openvino/op/gelu.hpp>
-
-namespace ov {
-namespace frontend {
-namespace ggml {
-namespace op {
-
-OutputVector translate_unary_gelu(const NodeContext & context) {
-    num_inputs_check(context, 1, 1);
-
-    auto input = context.get_input(0);
-    auto res = std::make_shared<ov::op::v7::Gelu>(input);
-
-    return rename_outputs_with_suffix({res}, context.get_name());
-}
-
-}  // namespace op
-}  // namespace ggml
-}  // namespace frontend
-}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp b/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp
index 037e0b94d..48ee0431f 100644
--- a/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/unary_silu.cpp
@@ -14,7 +14,7 @@ namespace op {
 OutputVector translate_unary_silu(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
-    auto input = context.get_input(0);
+    auto input = process_view_input_new(context, 0);
     auto sigmoid = std::make_shared<ov::op::v0::Sigmoid>(input);
     auto res = std::make_shared<ov::op::v1::Multiply>(input, sigmoid);
 
diff --git a/ggml/src/ggml-openvino/openvino/op/unary_softplus.cpp b/ggml/src/ggml-openvino/openvino/op/unary_softplus.cpp
new file mode 100644
index 000000000..756d9c33d
--- /dev/null
+++ b/ggml/src/ggml-openvino/openvino/op/unary_softplus.cpp
@@ -0,0 +1,38 @@
+#include "../node_context.h"
+#include "../op_table.h"
+#include "../utils.h"
+
+#include <openvino/op/abs.hpp>
+#include <openvino/op/add.hpp>
+#include <openvino/op/constant.hpp>
+#include <openvino/op/exp.hpp>
+#include <openvino/op/log.hpp>
+#include <openvino/op/negative.hpp>
+#include <openvino/op/relu.hpp>
+
+namespace ov {
+namespace frontend {
+namespace ggml {
+namespace op {
+
+OutputVector translate_unary_softplus(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+
+    auto input = process_view_input_new(context, 0);
+    const auto element_type = input.get_element_type();
+    auto one = ov::op::v0::Constant::create(element_type, ov::Shape{}, {1.0f});
+
+    auto positive = std::make_shared<ov::op::v0::Relu>(input);
+    auto abs = std::make_shared<ov::op::v0::Abs>(input);
+    auto neg_abs = std::make_shared<ov::op::v0::Negative>(abs);
+    auto exp_neg_abs = std::make_shared<ov::op::v0::Exp>(neg_abs);
+    auto log_term = std::make_shared<ov::op::v0::Log>(std::make_shared<ov::op::v1::Add>(one, exp_neg_abs));
+    auto res = std::make_shared<ov::op::v1::Add>(positive, log_term);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+}  // namespace op
+}  // namespace ggml
+}  // namespace frontend
+}  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/op/view.cpp b/ggml/src/ggml-openvino/openvino/op/view.cpp
index 8528d2523..28004dcd2 100644
--- a/ggml/src/ggml-openvino/openvino/op/view.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/view.cpp
@@ -1,6 +1,11 @@
 #include "../op_table.h"
 #include "../utils.h"
+
+#include <openvino/op/constant.hpp>
 #include <openvino/op/reshape.hpp>
+#include <openvino/op/slice.hpp>
+#include <set>
+
 namespace ov {
 namespace frontend {
 namespace ggml {
@@ -9,42 +14,102 @@ namespace op {
 OutputVector translate_view(const NodeContext & context) {
     num_inputs_check(context, 1, 1);
 
-    if (context.get_op_case() == 2) {
-        auto dst_shape = context.get_output_shape().to_shape();
-        return rename_outputs_with_suffix({process_view_input(context, 0, dst_shape[2] * dst_shape[3])},
-                                          context.get_name());
+    if (!context.is_static()) {
+        return {context.get_input(0)};
     }
-    // op_case 3
-    if (context.get_op_case() == 3) {
-        auto input = context.get_input(0);
-        auto input_ov_shape = input.get_partial_shape();
 
-        auto input_llama_shape = context.get_input_shape(0).to_shape();
+    auto input = context.get_input(0);
+    auto src_shape = context.get_input_shape(0);
+    auto dst_shape = context.get_output_shape();
 
-        // if the input ov shape size is different from the input llama shape size, it means the input is already reshaped and we need to reshape it back to the original shape before slicing
-        if (input_ov_shape.size() != input_llama_shape.size()) {
-            input = std::make_shared<ov::op::v1::Reshape>(input, ov::op::v0::Constant::create(ov::element::i64, {input_llama_shape.size()}, input_llama_shape), false);
+    if (src_shape.rank().is_dynamic() || dst_shape.rank().is_dynamic()) {
+        return {input};
+    }
+
+    int64_t src_elems = 1, dst_elems = 1;
+    for (int64_t i = 0; i < src_shape.rank().get_length(); ++i) {
+        if (src_shape[i].is_dynamic()) {
+            return {input};
         }
+        src_elems *= src_shape[i].get_length();
+    }
+    for (int64_t i = 0; i < dst_shape.rank().get_length(); ++i) {
+        if (dst_shape[i].is_dynamic()) {
+            return {input};
+        }
+        dst_elems *= dst_shape[i].get_length();
+    }
 
-        auto dst_shape = context.get_output_shape().to_shape();
+    if (dst_elems >= src_elems) {
+        return {input};
+    }
 
-        // find the index of dst_shape that is different from input shape, and use that index to slice the input
-        int slice_dim = -1;
-        for (size_t i = 0; i < dst_shape.size(); ++i) {
-            if (dst_shape[i] != input_llama_shape[i]) {
-                slice_dim = i;
+    auto src_stride = context.get_input_stride(0);
+    auto dst_stride = context.get_output_stride();
+    size_t view_offset = context.get_output_op_offset();
+
+    bool same_stride = (src_stride.size() == dst_stride.size());
+    if (same_stride) {
+        for (size_t i = 0; i < src_stride.size(); ++i) {
+            if (src_stride[i] != dst_stride[i]) {
+                same_stride = false;
                 break;
             }
         }
-
-        auto begin = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-        auto end = ov::op::v0::Constant::create(ov::element::i64, {1}, {dst_shape[slice_dim]});
-        auto stride = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-        auto axes = ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_dim});
-        auto sliced = std::make_shared<ov::op::v8::Slice>(input, begin, end, stride, axes);
-        return {sliced};
     }
-    return {context.get_input(0)};
+
+    if (!same_stride) {
+        return {input};
+    }
+
+    auto src_ov_shape = src_shape.to_shape();
+    auto dst_ov_shape = dst_shape.to_shape();
+    size_t ndims = src_ov_shape.size();
+    if (dst_ov_shape.size() != ndims) {
+        return {input};
+    }
+
+    std::vector<int> diff_dims;
+    for (size_t i = 0; i < ndims; ++i) {
+        if (src_ov_shape[i] != dst_ov_shape[i]) {
+            diff_dims.push_back(static_cast<int>(i));
+        }
+    }
+
+    if (diff_dims.size() != 1) {
+        return {input};
+    }
+
+    int slice_dim = diff_dims[0];
+    int64_t dim_size = static_cast<int64_t>(src_ov_shape[slice_dim]);
+
+    size_t ov_stride_for_dim = 1;
+    for (size_t i = slice_dim + 1; i < ndims; ++i) {
+        ov_stride_for_dim *= src_ov_shape[i];
+    }
+    size_t elem_size = src_stride.back();
+    if (elem_size == 0) {
+        elem_size = 1;
+    }
+
+    int64_t begin_val = 0;
+    if (ov_stride_for_dim > 0 && elem_size > 0) {
+        begin_val = static_cast<int64_t>((view_offset / elem_size) / ov_stride_for_dim);
+    }
+    int64_t end_val = begin_val + static_cast<int64_t>(dst_ov_shape[slice_dim]);
+
+    if (begin_val < 0 || end_val > dim_size) {
+        return {input};
+    }
+
+    auto sliced =
+        std::make_shared<ov::op::v8::Slice>(input, ov::op::v0::Constant::create(ov::element::i64, {1}, {begin_val}),
+                                            ov::op::v0::Constant::create(ov::element::i64, {1}, {end_val}),
+                                            ov::op::v0::Constant::create(ov::element::i64, {1}, {1}),
+                                            ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_dim}));
+
+    sliced->set_friendly_name(context.get_output_name());
+    return {sliced->output(0)};
 }
 
 }  // namespace op
diff --git a/ggml/src/ggml-openvino/openvino/op_table.cpp b/ggml/src/ggml-openvino/openvino/op_table.cpp
index 138553927..f84a1bf93 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.cpp
+++ b/ggml/src/ggml-openvino/openvino/op_table.cpp
@@ -5,9 +5,11 @@
 #include <openvino/op/add.hpp>
 #include <openvino/op/divide.hpp>
 #include <openvino/op/gather.hpp>
+#include <openvino/op/gelu.hpp>
 #include <openvino/op/matmul.hpp>
 #include <openvino/op/multiply.hpp>
 #include <openvino/op/subtract.hpp>
+#include <openvino/op/tanh.hpp>
 
 namespace ov {
 namespace frontend {
@@ -16,29 +18,44 @@ namespace ggml {
 std::unordered_map<std::string, CreatorFunction> get_supported_ops() {
     using namespace ov::op;
     return {
-        {"GGML_OP_ADD",            op::translate_1to1_match_2_inputs<v1::Add>     },
-        {"GGML_OP_ADD1",           op::translate_1to1_match_2_inputs<v1::Add>     },
-        {"GGML_OP_CONT",           op::translate_cont                             },
-        {"GGML_OP_DIV",            op::translate_1to1_match_2_inputs<v1::Divide>  },
-        {"GGML_OP_GET_ROWS",       op::translate_get_rows                         },
-        {"GGML_OP_MUL",            op::translate_1to1_match_2_inputs<v1::Multiply>},
-        {"GGML_OP_MUL_MAT",        op::translate_mulmat                           },
-        {"GGML_OP_PERMUTE",        op::translate_permute                          },
-        {"GGML_OP_RESHAPE",        op::translate_reshape                          },
-        {"GGML_OP_RMS_NORM",       op::translate_rms_norm                         },
-        {"GGML_OP_ROPE",           op::translate_rope                             },
-        {"GGML_OP_SCALE",          op::translate_scale                            },
-        {"GGML_OP_SOFT_MAX",       op::translate_soft_max                         },
-        {"GGML_OP_SUB",            op::translate_1to1_match_2_inputs<v1::Subtract>},
-        {"GGML_OP_TRANSPOSE",      op::translate_transpose                        },
-        {"GGML_UNARY_OP_GELU",     op::translate_unary_gelu                       },
-        {"GGML_UNARY_OP_SILU",     op::translate_unary_silu                       },
-        {"GGML_OP_VIEW",           op::translate_view                             },
-        {"GGML_GLU_OP_SWIGLU",     op::translate_glu_swiglu                       },
-        {"GGML_GLU_OP_GEGLU",      op::translate_glu_geglu                        },
-        {"GGML_OP_SET_ROWS",       op::translate_set_rows                         },
-        {"GGML_OP_CPY",            op::translate_cpy                              },
-        {"GGML_OP_FLASH_ATTN_EXT", op::translate_flash_attn_ext                   },
+        {"GGML_OP_ADD",             op::translate_1to1_match_2_inputs<v1::Add>     },
+        {"GGML_OP_ADD1",            op::translate_1to1_match_2_inputs<v1::Add>     },
+        {"GGML_OP_ADD_ID",          op::translate_add_id                           },
+        {"GGML_OP_CONCAT",          op::translate_concat                           },
+        {"GGML_OP_CONT",            op::translate_cont                             },
+        {"GGML_OP_DIV",             op::translate_div                              },
+        {"GGML_OP_GET_ROWS",        op::translate_get_rows                         },
+        {"GGML_OP_IM2COL",          op::translate_im2col                           },
+        {"GGML_OP_MUL",             op::translate_1to1_match_2_inputs<v1::Multiply>},
+        {"GGML_OP_MUL_MAT",         op::translate_mulmat                           },
+        {"GGML_OP_MUL_MAT_ID",      op::translate_mul_mat_id                       },
+        {"GGML_OP_PERMUTE",         op::translate_permute                          },
+        {"GGML_OP_RESHAPE",         op::translate_reshape                          },
+        {"GGML_OP_RMS_NORM",        op::translate_rms_norm                         },
+        {"GGML_OP_NORM",            op::translate_norm                             },
+        {"GGML_OP_L2_NORM",         op::translate_l2_norm                          },
+        {"GGML_OP_SUM_ROWS",        op::translate_sum_rows                         },
+        {"GGML_OP_ROPE",            op::translate_rope                             },
+        {"GGML_OP_SCALE",           op::translate_scale                            },
+        {"GGML_OP_SOFT_MAX",        op::translate_soft_max                         },
+        {"GGML_OP_ARGSORT",         op::translate_argsort                          },
+        {"GGML_OP_SUB",             op::translate_1to1_match_2_inputs<v1::Subtract>},
+        {"GGML_OP_TRANSPOSE",       op::translate_transpose                        },
+        {"GGML_UNARY_OP_GELU",      op::translate_1to1_match_1_input<v7::Gelu>     },
+        {"GGML_UNARY_OP_SILU",      op::translate_unary_silu                       },
+        {"GGML_UNARY_OP_SOFTPLUS",  op::translate_unary_softplus                   },
+        {"GGML_UNARY_OP_TANH",      op::translate_1to1_match_1_input<v0::Tanh>     },
+        {"GGML_OP_VIEW",            op::translate_view                             },
+        {"GGML_GLU_OP_SWIGLU",      op::translate_glu_swiglu                       },
+        {"GGML_GLU_OP_GEGLU",       op::translate_glu_geglu                        },
+        {"GGML_OP_SET_ROWS",        op::translate_set_rows                         },
+        {"GGML_OP_CPY",             op::translate_cpy                              },
+        {"GGML_OP_FLASH_ATTN_EXT",  op::translate_flash_attn_ext                   },
+        {"GGML_OP_CLAMP",           op::translate_clamp                            },
+        {"GGML_OP_PAD",             op::translate_pad                              },
+        {"GGML_OP_SSM_CONV",        op::translate_ssm_conv                         },
+        {"GGML_OP_GATED_DELTA_NET", op::translate_gated_delta_net                  },
+        {"GGML_OP_REPEAT",          op::translate_repeat                           },
     };
 }
 
diff --git a/ggml/src/ggml-openvino/openvino/op_table.h b/ggml/src/ggml-openvino/openvino/op_table.h
index f546796d2..c90ff8377 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.h
+++ b/ggml/src/ggml-openvino/openvino/op_table.h
@@ -8,20 +8,26 @@ namespace ggml {
 
 namespace op {
 
-#define GGML_OP_CONVERTER(op) OutputVector op(const NodeContext& context)
+#define GGML_OP_CONVERTER(op) OutputVector op(const NodeContext & context)
 
-GGML_OP_CONVERTER(translate_add);
 GGML_OP_CONVERTER(translate_cont);
+GGML_OP_CONVERTER(translate_concat);
+GGML_OP_CONVERTER(translate_add_id);
+GGML_OP_CONVERTER(translate_div);
 GGML_OP_CONVERTER(translate_get_rows);
-GGML_OP_CONVERTER(translate_mul);
+GGML_OP_CONVERTER(translate_im2col);
 GGML_OP_CONVERTER(translate_mulmat);
+GGML_OP_CONVERTER(translate_mul_mat_id);
 GGML_OP_CONVERTER(translate_permute);
 GGML_OP_CONVERTER(translate_reshape);
 GGML_OP_CONVERTER(translate_rms_norm);
+GGML_OP_CONVERTER(translate_norm);
+GGML_OP_CONVERTER(translate_l2_norm);
+GGML_OP_CONVERTER(translate_sum_rows);
 GGML_OP_CONVERTER(translate_rope);
 GGML_OP_CONVERTER(translate_scale);
 GGML_OP_CONVERTER(translate_unary_silu);
-GGML_OP_CONVERTER(translate_unary_gelu);
+GGML_OP_CONVERTER(translate_unary_softplus);
 GGML_OP_CONVERTER(translate_soft_max);
 GGML_OP_CONVERTER(translate_transpose);
 GGML_OP_CONVERTER(translate_view);
@@ -29,9 +35,15 @@ GGML_OP_CONVERTER(translate_glu_swiglu);
 GGML_OP_CONVERTER(translate_glu_geglu);
 GGML_OP_CONVERTER(translate_set_rows);
 GGML_OP_CONVERTER(translate_cpy);
+GGML_OP_CONVERTER(translate_argsort);
 GGML_OP_CONVERTER(translate_flash_attn_ext);
+GGML_OP_CONVERTER(translate_clamp);
+GGML_OP_CONVERTER(translate_pad);
+GGML_OP_CONVERTER(translate_ssm_conv);
+GGML_OP_CONVERTER(translate_gated_delta_net);
+GGML_OP_CONVERTER(translate_repeat);
 
-} // namespace op
+}  // namespace op
 
 std::unordered_map<std::string, CreatorFunction> get_supported_ops();
 
diff --git a/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h b/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h
index b95385611..c229e25fb 100644
--- a/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h
+++ b/ggml/src/ggml-openvino/openvino/pass/mark_decompression_convert_constant_folding.h
@@ -1,8 +1,8 @@
 #pragma once
 
 #include "mark_decompression_convert_constant_folding.h"
-#include "openvino/pass/matcher_pass.hpp"
 #include "openvino/core/visibility.hpp"
+#include "openvino/pass/matcher_pass.hpp"
 
 #ifdef OPENVINO_STATIC_LIBRARY
 #    define TRANSFORMATIONS_API
diff --git a/ggml/src/ggml-openvino/openvino/translate_session.cpp b/ggml/src/ggml-openvino/openvino/translate_session.cpp
index 0f68a1f50..d00c438e2 100644
--- a/ggml/src/ggml-openvino/openvino/translate_session.cpp
+++ b/ggml/src/ggml-openvino/openvino/translate_session.cpp
@@ -13,6 +13,7 @@
 #include <memory>
 #include <openvino/core/node.hpp>
 #include <openvino/core/preprocess/pre_post_process.hpp>
+#include <openvino/core/type/element_type.hpp>
 #include <openvino/op/add.hpp>
 #include <openvino/op/broadcast.hpp>
 #include <openvino/op/concat.hpp>
@@ -77,49 +78,48 @@ ov::pass::MakeStateful::ParamResPairs get_kv_param_res_pairs(
     return pairs;
 }
 
-void add_sliced_mask(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) {
-
-    auto create_sliced_mask = [&](const std::string & mask_name, const std::string & sliced_name, bool is_static) {
+void add_sliced_mask_stateful(TensorMap & tensor_map) {
+    auto create_sliced_mask = [&](const std::string & mask_name, const std::string & sliced_name) {
         if ((tensor_map.find(mask_name) != tensor_map.end()) &&
             (tensor_map.find("token_len_per_seq") != tensor_map.end())) {
             auto token_len_per_seq = tensor_map.at("token_len_per_seq").get_node_shared_ptr();
             auto mask = tensor_map.at(mask_name).get_node_shared_ptr();
-            std::shared_ptr<ov::Node> mask_sliced;
-            if (is_static) {
-                mask_sliced = mask;
-            } else if (ggml_model_decoder.is_stateful()) {
-                auto zero_2d = ov::op::v0::Constant::create(ov::element::i64, {2}, {0,0});
-                auto one_2d = ov::op::v0::Constant::create(ov::element::i64, {2}, {1,1});
-                auto zero_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-                auto three_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
-                auto neg_one_1d = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
-                auto axes = ov::op::v0::Constant::create(ov::element::i64, {2}, {-2,-1});
-                auto inp_pos = tensor_map.at("inp_pos").get_node_shared_ptr();
-                auto gather_inp_pos = std::make_shared<ov::op::v8::Gather>(inp_pos, neg_one_1d, three_1d);
-                auto reshaped_inp_pos = std::make_shared<ov::op::v1::Reshape>(gather_inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {1}), false);
-                auto inp_pos_incremented = std::make_shared<ov::op::v1::Add>(reshaped_inp_pos, ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {1}));
-                auto stop = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{token_len_per_seq, std::make_shared<v1::ConvertLike>(inp_pos_incremented, token_len_per_seq)}, 0);
-                mask_sliced =
-                    std::make_shared<ov::op::v8::Slice>(mask, zero_2d, stop, one_2d, axes);
-                mask_sliced = std::make_shared<ov::op::v0::Convert>(mask_sliced, ov::element::f16);
-                mask_sliced->set_friendly_name(sliced_name);
-            } else {
-                auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
-                auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-                auto two = ov::op::v0::Constant::create(ov::element::i64, {1}, {2});
-                mask_sliced = std::make_shared<ov::op::v8::Slice>(mask, zero, token_len_per_seq, one, two);
-                mask_sliced = std::make_shared<ov::op::v0::Convert>(mask_sliced, ov::element::f16);
-                mask_sliced->set_friendly_name(sliced_name);
-            }
+            std::shared_ptr<ov::Node> mask_sliced = mask;
+            auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+            auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
+            auto three = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
+            auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+
+            auto step = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+            auto axes = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
+
+            auto inp_pos = tensor_map.at("inp_pos").get_node_shared_ptr();
+            auto last_inp_pos = std::make_shared<ov::op::v8::Gather>(inp_pos, neg_one, three);
+            auto last_inp_pos_1d = std::make_shared<ov::op::v1::Reshape>(
+                last_inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {1}), false);
+            auto last_inp_pos_cvt = std::make_shared<ov::op::v0::Convert>(last_inp_pos_1d, ov::element::i64);
+            auto last_inp_pos_inc = std::make_shared<ov::op::v1::Add>(last_inp_pos_cvt, one);
+
+            mask_sliced = std::make_shared<ov::op::v8::Slice>(mask, zero, last_inp_pos_inc, step, axes);
+            mask_sliced = std::make_shared<ov::op::v0::Convert>(mask_sliced, ov::element::f16);
+            mask_sliced->set_friendly_name(sliced_name);
+
             tensor_map.insert({sliced_name, mask_sliced->output(0)});
         }
     };
 
-    create_sliced_mask("self_kq_mask", "KQ_mask_sliced", ggml_model_decoder.is_static());
-    create_sliced_mask("self_kq_mask_swa", "KQ_mask_swa_sliced", ggml_model_decoder.is_static());
+    create_sliced_mask("self_kq_mask", "KQ_mask_sliced");
+    create_sliced_mask("self_kq_mask_swa", "KQ_mask_swa_sliced");
 }
 
 void add_rope_sin_cos(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) {
+    // When ROPE ops in the graph have divergent op_params (e.g. gemma4's mixed
+    // SWA/non-SWA layers with different n_dims or freq_base), a shared sin/cos
+    // precompute cannot broadcast across every ROPE use. Skip it here and let
+    // translate_rope() build sin/cos per-op from its own op_params.
+    if (ggml_model_decoder.has_mixed_rope_params()) {
+        return;
+    }
     int32_t * rope_params = ggml_model_decoder.get_rope_params();
     if (tensor_map.find("inp_pos") == tensor_map.end() || rope_params == nullptr) {
         return;
@@ -142,8 +142,11 @@ void add_rope_sin_cos(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder)
 
 // Create common patterns
 void preprocess(TensorMap & tensor_map, GgmlDecoder & ggml_model_decoder) {
-    add_sliced_mask(tensor_map, ggml_model_decoder);
-    add_rope_sin_cos(tensor_map, ggml_model_decoder);
+    if (ggml_model_decoder.is_stateful()) {
+        add_sliced_mask_stateful(tensor_map);
+    }
+    // This optimization is error-prone
+    // add_rope_sin_cos(tensor_map, ggml_model_decoder);
 }
 
 }  // namespace
@@ -288,19 +291,19 @@ std::shared_ptr<Model> TranslateSession::apply_transformations(std::shared_ptr<M
         if (ggml_model_decoder->is_stateful()) {
             auto output_names = ggml_model_decoder->get_model_output_names();
             std::map<std::string, int> model_output_indexes;
-            for (size_t i=0; i<output_names.size(); i++) {
+            for (size_t i = 0; i < output_names.size(); i++) {
                 model_output_indexes.insert(std::make_pair(output_names[i], i));
             }
             ov::preprocess::PrePostProcessor ppp(model);
-            for (size_t i=0; i<model->get_output_size(); i++) {
+            for (size_t i = 0; i < model->get_output_size(); i++) {
                 auto output_friendly_name = model->output(i).get_node_shared_ptr()->get_friendly_name();
                 auto output_id = model_output_indexes[output_friendly_name];
                 auto model_output_shape = model->output(i).get_partial_shape();
                 auto decoder_output_shape = ggml_model_decoder->get_output_shape(output_id);
-                if (model_output_shape.rank().is_static() && decoder_output_shape.rank().is_static()
-                    && model_output_shape.rank().get_length() + 1 == decoder_output_shape.rank().get_length()
-                    && decoder_output_shape[0].is_static() && decoder_output_shape[0].get_length() == 1) {
-                    ppp.output(i).postprocess().custom([](const ov::Output<ov::Node>& node) {
+                if (model_output_shape.rank().is_static() && decoder_output_shape.rank().is_static() &&
+                    model_output_shape.rank().get_length() + 1 == decoder_output_shape.rank().get_length() &&
+                    decoder_output_shape[0].is_static() && decoder_output_shape[0].get_length() == 1) {
+                    ppp.output(i).postprocess().custom([](const ov::Output<ov::Node> & node) {
                         auto axes = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {0});
                         return std::make_shared<ov::op::v0::Unsqueeze>(node, axes);
                     });
diff --git a/ggml/src/ggml-openvino/openvino/translate_session.h b/ggml/src/ggml-openvino/openvino/translate_session.h
index 56a14ae7c..675e63223 100644
--- a/ggml/src/ggml-openvino/openvino/translate_session.h
+++ b/ggml/src/ggml-openvino/openvino/translate_session.h
@@ -9,16 +9,17 @@ namespace ggml {
 
 class TranslateSession {
 public:
-    TranslateSession(const frontend::InputModel::Ptr& input_model,
-                     const std::unordered_map<std::string, CreatorFunction>& translator_map, bool naive = false);
+    TranslateSession(const frontend::InputModel::Ptr & input_model,
+                     const std::unordered_map<std::string, CreatorFunction> & translator_map,
+                     bool naive = false);
 
     std::shared_ptr<Model> get_converted_model();
-    std::shared_ptr<Model> translate_graph(const frontend::InputModel::Ptr& input_model);
+    std::shared_ptr<Model> translate_graph(const frontend::InputModel::Ptr & input_model);
 
 private:
     std::shared_ptr<Model> apply_transformations(std::shared_ptr<Model> model);
     const frontend::InputModel::Ptr m_input_model;
-    const std::unordered_map<std::string, CreatorFunction>& m_translator_map;
+    const std::unordered_map<std::string, CreatorFunction> & m_translator_map;
     std::shared_ptr<Model> m_ov_model;
     bool m_naive;
 };
diff --git a/ggml/src/ggml-openvino/openvino/utils.cpp b/ggml/src/ggml-openvino/openvino/utils.cpp
index 0baaf88e1..4e4f5dd04 100644
--- a/ggml/src/ggml-openvino/openvino/utils.cpp
+++ b/ggml/src/ggml-openvino/openvino/utils.cpp
@@ -17,6 +17,7 @@
 #include <openvino/op/reshape.hpp>
 #include <openvino/op/shape_of.hpp>
 #include <openvino/op/sin.hpp>
+#include <openvino/op/split.hpp>
 #include <openvino/op/squeeze.hpp>
 #include <openvino/op/subtract.hpp>
 #include <openvino/op/transpose.hpp>
@@ -123,7 +124,8 @@ std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params
                                                            bool imrope,
                                                            bool stateful) {
     if (stateful) {
-        inp_pos = std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {0}));
+        inp_pos =
+            std::make_shared<ov::op::v0::Squeeze>(inp_pos, ov::op::v0::Constant::create(ov::element::i64, {1}, {0}));
         inp_pos = std::make_shared<ov::op::v0::Convert>(inp_pos, ov::element::f32);
         auto pos_perm =
             std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{3}, std::vector<int64_t>{2, 1, 0});
@@ -212,8 +214,9 @@ std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params
             }
             auto one_minus_ramp = std::make_shared<ov::op::v1::Subtract>(one, ramp_mix);
 
-            theta = std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(theta_interp, one_minus_ramp),
-                                                      std::make_shared<ov::op::v1::Multiply>(theta_extrap, ramp_mix));
+            theta =
+                std::make_shared<ov::op::v1::Add>(std::make_shared<ov::op::v1::Multiply>(theta_interp, one_minus_ramp),
+                                                  std::make_shared<ov::op::v1::Multiply>(theta_extrap, ramp_mix));
             mscale *= (1.0f + 0.1f * std::log(1.0f / freq_scale));
         }
     }
@@ -252,6 +255,548 @@ ov::Output<ov::Node> process_view_input(const NodeContext & context, int input_i
     return sliced;
 }
 
+ov::Output<ov::Node> process_view_input_new(const NodeContext & context, int input_index) {
+    auto input = context.get_input(input_index);
+
+    // Check if this input has view inputs
+    size_t view_input_size = context.get_view_input_size(input_index);
+    if (view_input_size == 0) {
+        // No view inputs, return the input as is
+        return input;
+    }
+
+    // If translate_view already resolved this VIEW (produced a Slice), the input
+    // will already have the expected shape — skip re-slicing.
+    auto expected_ov_shape = context.get_view_input_ov_shape(input_index, 0);
+    auto actual_shape = input.get_partial_shape();
+    if (expected_ov_shape.rank().is_static() && actual_shape.rank().is_static() &&
+        expected_ov_shape.rank() == actual_shape.rank()) {
+        bool shapes_match = true;
+        for (int64_t i = 0; i < expected_ov_shape.rank().get_length(); ++i) {
+            if (!expected_ov_shape[i].is_static() || !actual_shape[i].is_static()) {
+                shapes_match = false;
+                break;
+            }
+            if (expected_ov_shape[i] != actual_shape[i]) {
+                shapes_match = false;
+                break;
+            }
+        }
+        if (shapes_match) {
+            return input;
+        }
+    }
+
+    // In static mode, use Split instead of Slice for single-dimension reductions.
+    // This ensures NPUW's FOLD doesn't parametrize per-layer slice indices (which
+    // would introduce dynamic shapes). A shared Split node sits outside the repeated
+    // subgraph boundary; each layer receives one of its output ports.
+    if (context.is_static() && view_input_size == 1) {
+        auto view_stride_v = context.get_view_input_stride(input_index, 0);
+        auto view_src_stride_v = context.get_view_input_src_stride(input_index, 0);
+        auto view_ggml_shape = context.get_view_input_ggml_shape(input_index, 0);
+        auto view_src_ggml_shape = context.get_view_input_src_ggml_shape(input_index, 0);
+        auto view_offset = context.get_view_input_offset(input_index, 0);
+        auto view_src_offset = context.get_view_input_src_offset(input_index, 0);
+
+        size_t ndims = view_ggml_shape.size();
+        std::vector<int> diff_dims;
+        if (view_src_ggml_shape.size() == ndims) {
+            for (size_t i = 0; i < ndims; ++i) {
+                if (view_ggml_shape[i] != view_src_ggml_shape[i]) {
+                    diff_dims.push_back(static_cast<int>(i));
+                }
+            }
+        }
+
+        if (diff_dims.size() == 1) {
+            int split_dim = diff_dims[0];
+            int64_t num_splits = static_cast<int64_t>(view_src_ggml_shape[split_dim]);
+            int64_t chunk_size = static_cast<int64_t>(view_ggml_shape[split_dim]);
+
+            // Only apply when slicing exactly 1 element from a multi-element dimension
+            if (chunk_size == 1 && num_splits > 1) {
+                // Check suffix strides match (dimensions after split_dim)
+                bool suffix_ok = view_stride_v.size() == view_src_stride_v.size();
+                if (suffix_ok) {
+                    for (size_t i = static_cast<size_t>(split_dim) + 1; i < ndims; ++i) {
+                        if (view_stride_v[i] != view_src_stride_v[i]) {
+                            suffix_ok = false;
+                            break;
+                        }
+                    }
+                }
+
+                if (suffix_ok && view_src_stride_v[split_dim] > 0) {
+                    size_t relative_offset = view_offset >= view_src_offset ? view_offset - view_src_offset : 0;
+                    int64_t split_index = static_cast<int64_t>(relative_offset / view_src_stride_v[split_dim]);
+
+                    if (split_index >= 0 && split_index < num_splits) {
+                        auto src_node = input.get_node_shared_ptr();
+                        std::string rt_key = "split_dim_" + std::to_string(split_dim);
+                        auto & rt_info = src_node->get_rt_info();
+
+                        if (rt_info.find(rt_key) == rt_info.end()) {
+                            auto axis_const =
+                                ov::op::v0::Constant::create(ov::element::i64, {}, {static_cast<int64_t>(split_dim)});
+                            auto split_node =
+                                std::make_shared<ov::op::v1::Split>(input, axis_const, static_cast<size_t>(num_splits));
+                            split_node->set_friendly_name(src_node->get_friendly_name() + "_split");
+                            rt_info[rt_key] = split_node;
+                        }
+
+                        auto split_node = rt_info[rt_key].as<std::shared_ptr<ov::op::v1::Split>>();
+                        return split_node->output(static_cast<size_t>(split_index));
+                    }
+                }
+            }
+        }
+    }
+
+    // Lambda function to process a single view operation
+    auto process_single_view =
+        [](ov::Output<ov::Node> current, size_t view_offset, const std::vector<size_t> & view_stride,
+           const ov::Shape & view_ggml_shape, const ov::PartialShape & view_ov_shape, const std::string & view_name,
+           size_t view_src_offset, const std::vector<size_t> & view_src_stride, const ov::Shape & view_src_ggml_shape,
+           const ov::PartialShape & view_src_ov_shape, const std::string & view_src_name) -> ov::Output<ov::Node> {
+        auto build_reshape_pattern = [](const ov::PartialShape & target_ov_shape,
+                                        const ov::Shape & target_ggml_shape) -> std::vector<int64_t> {
+            const size_t ndims = target_ggml_shape.size();
+            std::vector<int64_t> reshape_pattern(ndims);
+            size_t dynamic_dims = 0;
+
+            if (target_ov_shape.rank().is_static() &&
+                target_ov_shape.rank().get_length() == static_cast<int64_t>(ndims)) {
+                for (size_t i = 0; i < ndims; ++i) {
+                    if (target_ov_shape[i].is_static()) {
+                        reshape_pattern[i] = target_ov_shape[i].get_length();
+                    } else {
+                        reshape_pattern[i] = -1;
+                        ++dynamic_dims;
+                    }
+                }
+            } else {
+                dynamic_dims = 2;
+            }
+
+            if (dynamic_dims > 1) {
+                for (size_t i = 0; i < ndims; ++i) {
+                    reshape_pattern[i] = static_cast<int64_t>(target_ggml_shape[i]);
+                }
+            }
+
+            return reshape_pattern;
+        };
+
+        auto build_prefix_tail_reshape_pattern = [](const ov::PartialShape & target_ov_shape,
+                                                    const ov::Shape & target_ggml_shape, size_t prefix_dims,
+                                                    int64_t tail_dim) -> std::vector<int64_t> {
+            std::vector<int64_t> reshape_pattern(prefix_dims + 1);
+            size_t dynamic_dims = 0;
+
+            if (target_ov_shape.rank().is_static() &&
+                target_ov_shape.rank().get_length() == static_cast<int64_t>(target_ggml_shape.size())) {
+                for (size_t i = 0; i < prefix_dims; ++i) {
+                    if (target_ov_shape[i].is_static()) {
+                        reshape_pattern[i] = target_ov_shape[i].get_length();
+                    } else {
+                        reshape_pattern[i] = -1;
+                        ++dynamic_dims;
+                    }
+                }
+            } else {
+                dynamic_dims = 2;
+            }
+
+            if (dynamic_dims > 1) {
+                for (size_t i = 0; i < prefix_dims; ++i) {
+                    reshape_pattern[i] = static_cast<int64_t>(target_ggml_shape[i]);
+                }
+            }
+
+            reshape_pattern[prefix_dims] = tail_dim;
+            return reshape_pattern;
+        };
+
+        bool same_stride = view_stride.size() == view_src_stride.size();
+        if (same_stride) {
+            for (size_t i = 0; i < view_stride.size(); ++i) {
+                if (view_stride[i] != view_src_stride[i]) {
+                    same_stride = false;
+                    break;
+                }
+            }
+        }
+
+        bool same_ggml_shape = view_ggml_shape.size() == view_src_ggml_shape.size();
+        if (same_ggml_shape) {
+            for (size_t i = 0; i < view_ggml_shape.size(); ++i) {
+                if (view_ggml_shape[i] != view_src_ggml_shape[i]) {
+                    same_ggml_shape = false;
+                    break;
+                }
+            }
+        }
+
+        if (same_stride && same_ggml_shape) {
+            return current;
+        }
+
+        if (same_stride) {
+            const size_t relative_offset = view_offset >= view_src_offset ? view_offset - view_src_offset : 0;
+            const size_t ndims = view_stride.size();
+
+            std::vector<int> diff_dims;
+            if (view_ggml_shape.size() == ndims && view_src_ggml_shape.size() == ndims) {
+                for (size_t i = 0; i < ndims; ++i) {
+                    if (view_ggml_shape[i] != view_src_ggml_shape[i]) {
+                        diff_dims.push_back(static_cast<int>(i));
+                    }
+                }
+            }
+
+            if (diff_dims.size() == 1) {
+                const int slice_dim = diff_dims[0];
+                const int64_t dim_size = static_cast<int64_t>(view_src_ggml_shape[slice_dim]);
+
+                if (view_stride[slice_dim] > 0 && relative_offset % view_stride[slice_dim] == 0) {
+                    const int64_t begin_val = static_cast<int64_t>((relative_offset / view_stride[slice_dim]) %
+                                                                   static_cast<size_t>(dim_size));
+                    const int64_t end_val = begin_val + static_cast<int64_t>(view_ggml_shape[slice_dim]);
+
+                    if (begin_val >= 0 && end_val <= dim_size) {
+                        auto sliced = std::make_shared<ov::op::v8::Slice>(
+                            current, ov::op::v0::Constant::create(ov::element::i64, {1}, {begin_val}),
+                            ov::op::v0::Constant::create(ov::element::i64, {1}, {end_val}),
+                            ov::op::v0::Constant::create(ov::element::i64, {1}, {1}),
+                            ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_dim}));
+
+                        if (view_ov_shape.is_static()) {
+                            auto reshaped = std::make_shared<ov::op::v1::Reshape>(
+                                sliced,
+                                ov::op::v0::Constant::create(ov::element::i64, {ndims}, view_ov_shape.to_shape()),
+                                false);
+                            reshaped->set_friendly_name(view_name);
+                            return reshaped;
+                        }
+
+                        sliced->set_friendly_name(view_name);
+                        return sliced;
+                    }
+                }
+
+                int64_t tail_src_elems = 1;
+                int64_t tail_dst_elems = 1;
+                for (size_t i = slice_dim; i < ndims; ++i) {
+                    tail_src_elems *= static_cast<int64_t>(view_src_ggml_shape[i]);
+                    tail_dst_elems *= static_cast<int64_t>(view_ggml_shape[i]);
+                }
+
+                const size_t elem_stride = view_stride[ndims - 1];
+                int64_t tail_begin = 0;
+                if (elem_stride > 0) {
+                    tail_begin =
+                        static_cast<int64_t>((relative_offset / elem_stride) % static_cast<size_t>(tail_src_elems));
+                }
+                const int64_t tail_end = tail_begin + tail_dst_elems;
+
+                if (tail_begin >= 0 && tail_end <= tail_src_elems) {
+                    std::vector<int64_t> flat_shape;
+                    for (int i = 0; i < slice_dim; ++i) {
+                        flat_shape.push_back(static_cast<int64_t>(view_src_ggml_shape[i]));
+                    }
+                    flat_shape.push_back(tail_src_elems);
+                    const size_t flat_ndims = flat_shape.size();
+
+                    auto flat = std::make_shared<ov::op::v1::Reshape>(
+                        current, ov::op::v0::Constant::create(ov::element::i64, {flat_ndims}, flat_shape), false);
+
+                    auto sliced = std::make_shared<ov::op::v8::Slice>(
+                        flat, ov::op::v0::Constant::create(ov::element::i64, {1}, {tail_begin}),
+                        ov::op::v0::Constant::create(ov::element::i64, {1}, {tail_end}),
+                        ov::op::v0::Constant::create(ov::element::i64, {1}, {1}),
+                        ov::op::v0::Constant::create(ov::element::i64, {1}, {slice_dim}));
+
+                    if (view_ov_shape.is_static()) {
+                        auto reshaped = std::make_shared<ov::op::v1::Reshape>(
+                            sliced, ov::op::v0::Constant::create(ov::element::i64, {ndims}, view_ov_shape.to_shape()),
+                            false);
+                        reshaped->set_friendly_name(view_name);
+                        return reshaped;
+                    }
+
+                    sliced->set_friendly_name(view_name);
+                    return sliced;
+                }
+            }
+
+            std::vector<int64_t> begin(ndims, 0);
+            std::vector<int64_t> end(ndims, 0);
+            std::vector<int64_t> step(ndims, 1);
+            std::vector<int64_t> axes(ndims, 0);
+
+            size_t remaining_offset = relative_offset;
+            for (size_t i = 0; i < ndims; ++i) {
+                axes[i] = static_cast<int64_t>(i);
+                if (view_stride[i] > 0) {
+                    begin[i] = static_cast<int64_t>(remaining_offset / view_stride[i]);
+                    remaining_offset %= view_stride[i];
+                }
+                end[i] = begin[i] + static_cast<int64_t>(view_ggml_shape[i]);
+            }
+
+            bool in_bounds = view_src_ggml_shape.size() == ndims && view_ggml_shape.size() == ndims;
+            if (in_bounds) {
+                for (size_t i = 0; i < ndims; ++i) {
+                    if (end[i] > static_cast<int64_t>(view_src_ggml_shape[i])) {
+                        in_bounds = false;
+                        break;
+                    }
+                }
+            }
+
+            if (in_bounds && remaining_offset == 0) {
+                auto sliced = std::make_shared<ov::op::v8::Slice>(
+                    current, ov::op::v0::Constant::create(ov::element::i64, {ndims}, begin),
+                    ov::op::v0::Constant::create(ov::element::i64, {ndims}, end),
+                    ov::op::v0::Constant::create(ov::element::i64, {ndims}, step),
+                    ov::op::v0::Constant::create(ov::element::i64, {ndims}, axes));
+
+                sliced->set_friendly_name(view_name);
+                return sliced;
+            }
+        } else {
+            bool same_rank = view_stride.size() == view_src_stride.size() &&
+                             view_ggml_shape.size() == view_src_ggml_shape.size() &&
+                             view_stride.size() == view_ggml_shape.size();
+            const size_t relative_offset = view_offset >= view_src_offset ? view_offset - view_src_offset : 0;
+
+            if (same_rank) {
+                const size_t ndims = view_ggml_shape.size();
+                std::vector<int> diff_dims;
+                for (size_t i = 0; i < ndims; ++i) {
+                    if (view_ggml_shape[i] != view_src_ggml_shape[i]) {
+                        diff_dims.push_back(static_cast<int>(i));
+                    }
+                }
+
+                if (diff_dims.size() == 1) {
+                    const size_t slice_dim = static_cast<size_t>(diff_dims[0]);
+                    bool suffix_stride_match = true;
+                    for (size_t i = slice_dim + 1; i < ndims; ++i) {
+                        if (view_stride[i] != view_src_stride[i]) {
+                            suffix_stride_match = false;
+                            break;
+                        }
+                    }
+
+                    if (suffix_stride_match && view_src_stride[slice_dim] > 0 &&
+                        relative_offset % view_src_stride[slice_dim] == 0) {
+                        const int64_t begin_val = static_cast<int64_t>(relative_offset / view_src_stride[slice_dim]);
+                        const int64_t end_val = begin_val + static_cast<int64_t>(view_ggml_shape[slice_dim]);
+                        const int64_t dim_size = static_cast<int64_t>(view_src_ggml_shape[slice_dim]);
+
+                        if (begin_val >= 0 && end_val <= dim_size) {
+                            auto sliced = std::make_shared<ov::op::v8::Slice>(
+                                current, ov::op::v0::Constant::create(ov::element::i64, {1}, {begin_val}),
+                                ov::op::v0::Constant::create(ov::element::i64, {1}, {end_val}),
+                                ov::op::v0::Constant::create(ov::element::i64, {1}, {1}),
+                                ov::op::v0::Constant::create(ov::element::i64, {1}, {static_cast<int64_t>(slice_dim)}));
+                            sliced->set_friendly_name(view_name);
+                            return sliced;
+                        }
+                    }
+                }
+            }
+
+            size_t view_elems = 1;
+            size_t src_elems = 1;
+            if (same_rank) {
+                for (size_t i = 0; i < view_ggml_shape.size(); ++i) {
+                    view_elems *= view_ggml_shape[i];
+                    src_elems *= view_src_ggml_shape[i];
+                }
+            }
+
+            bool same_num_elements = same_rank && view_elems == src_elems;
+
+            if (same_rank && relative_offset == 0 && same_num_elements) {
+                auto reshape_pattern = build_reshape_pattern(view_ov_shape, view_ggml_shape);
+
+                auto reshaped = std::make_shared<ov::op::v1::Reshape>(
+                    current, ov::op::v0::Constant::create(ov::element::i64, {reshape_pattern.size()}, reshape_pattern),
+                    false);
+                reshaped->set_friendly_name(view_name);
+                return reshaped;
+            }
+
+            if (same_rank) {
+                const size_t ndims = view_ggml_shape.size();
+
+                // Match views that can be expressed as a regular strided slice over the
+                // already reconstructed source tensor, e.g. offset on one axis plus step > 1
+                // on another axis.
+                bool is_regular_slice = view_src_ggml_shape.size() == ndims;
+                std::vector<int64_t> begin(ndims, 0);
+                std::vector<int64_t> end(ndims, 0);
+                std::vector<int64_t> step(ndims, 1);
+                std::vector<int64_t> axes(ndims, 0);
+                size_t remaining_offset = relative_offset;
+
+                if (is_regular_slice) {
+                    for (size_t i = 0; i < ndims; ++i) {
+                        axes[i] = static_cast<int64_t>(i);
+
+                        if (view_src_stride[i] == 0 || view_stride[i] == 0 ||
+                            view_stride[i] % view_src_stride[i] != 0) {
+                            is_regular_slice = false;
+                            break;
+                        }
+
+                        step[i] = static_cast<int64_t>(view_stride[i] / view_src_stride[i]);
+                        if (step[i] <= 0) {
+                            is_regular_slice = false;
+                            break;
+                        }
+
+                        begin[i] = static_cast<int64_t>(remaining_offset / view_src_stride[i]);
+                        remaining_offset %= view_src_stride[i];
+
+                        if (view_ggml_shape[i] == 0) {
+                            end[i] = begin[i];
+                            continue;
+                        }
+
+                        end[i] = begin[i] + step[i] * static_cast<int64_t>(view_ggml_shape[i] - 1) + 1;
+
+                        if (begin[i] < 0 || end[i] > static_cast<int64_t>(view_src_ggml_shape[i])) {
+                            is_regular_slice = false;
+                            break;
+                        }
+                    }
+                }
+
+                if (is_regular_slice && remaining_offset == 0) {
+                    auto sliced = std::make_shared<ov::op::v8::Slice>(
+                        current, ov::op::v0::Constant::create(ov::element::i64, {ndims}, begin),
+                        ov::op::v0::Constant::create(ov::element::i64, {ndims}, end),
+                        ov::op::v0::Constant::create(ov::element::i64, {ndims}, step),
+                        ov::op::v0::Constant::create(ov::element::i64, {ndims}, axes));
+
+                    sliced->set_friendly_name(view_name);
+                    return sliced;
+                }
+
+                const size_t elem_stride = view_src_stride.back();
+                const bool aligned_offset = elem_stride > 0 && relative_offset % elem_stride == 0;
+
+                if (aligned_offset) {
+                    size_t suffix_start = 0;
+                    size_t expected_stride = elem_stride;
+                    for (int i = static_cast<int>(ndims) - 1; i >= 0; --i) {
+                        if (view_stride[i] != expected_stride) {
+                            suffix_start = static_cast<size_t>(i + 1);
+                            break;
+                        }
+                        expected_stride *= view_ggml_shape[i];
+                    }
+
+                    size_t prefix_elems = 1;
+                    size_t suffix_elems = 1;
+                    for (size_t i = 0; i < suffix_start; ++i) {
+                        prefix_elems *= view_ggml_shape[i];
+                    }
+                    for (size_t i = suffix_start; i < ndims; ++i) {
+                        suffix_elems *= view_ggml_shape[i];
+                    }
+
+                    if (prefix_elems > 0 && src_elems % prefix_elems == 0) {
+                        const size_t src_tail_elems = src_elems / prefix_elems;
+                        const int64_t tail_begin = static_cast<int64_t>(relative_offset / elem_stride);
+                        const int64_t tail_end = tail_begin + static_cast<int64_t>(suffix_elems);
+
+                        if (tail_begin >= 0 && tail_end <= static_cast<int64_t>(src_tail_elems)) {
+                            auto prefix_tail_pattern = build_prefix_tail_reshape_pattern(
+                                view_ov_shape, view_ggml_shape, suffix_start, static_cast<int64_t>(src_tail_elems));
+
+                            auto prefix_tail = std::make_shared<ov::op::v1::Reshape>(
+                                current,
+                                ov::op::v0::Constant::create(ov::element::i64, {prefix_tail_pattern.size()},
+                                                             prefix_tail_pattern),
+                                false);
+
+                            ov::Output<ov::Node> selected = prefix_tail;
+                            if (tail_begin != 0 || tail_end != static_cast<int64_t>(src_tail_elems)) {
+                                selected = std::make_shared<ov::op::v8::Slice>(
+                                    prefix_tail, ov::op::v0::Constant::create(ov::element::i64, {1}, {tail_begin}),
+                                    ov::op::v0::Constant::create(ov::element::i64, {1}, {tail_end}),
+                                    ov::op::v0::Constant::create(ov::element::i64, {1}, {1}),
+                                    ov::op::v0::Constant::create(ov::element::i64, {1},
+                                                                 {static_cast<int64_t>(suffix_start)}));
+                            }
+
+                            auto reshape_pattern = build_reshape_pattern(view_ov_shape, view_ggml_shape);
+                            auto reshaped = std::make_shared<ov::op::v1::Reshape>(
+                                selected,
+                                ov::op::v0::Constant::create(ov::element::i64, {reshape_pattern.size()},
+                                                             reshape_pattern),
+                                false);
+                            reshaped->set_friendly_name(view_name);
+                            return reshaped;
+                        }
+                    }
+                }
+            }
+
+            return current;
+        }
+
+        (void) view_name;
+        (void) view_src_ov_shape;
+        (void) view_src_name;
+
+        return current;
+    };
+
+    // Process views from the base tensor (last) to the current view (first)
+    // Start with the base tensor
+    ov::Output<ov::Node> current = input;
+
+    // Process each view in reverse order (from base to current)
+    for (int view_idx = view_input_size - 1; view_idx >= 0; view_idx--) {
+        auto view_offset = context.get_view_input_offset(input_index, view_idx);
+        auto view_stride = context.get_view_input_stride(input_index, view_idx);
+        auto view_ggml_shape = context.get_view_input_ggml_shape(input_index, view_idx);
+        auto view_ov_shape = context.get_view_input_ov_shape(input_index, view_idx);
+        auto view_name = context.get_view_input_name(input_index, view_idx);
+
+        // print view info
+        // std::cout << "View " << view_idx << ": name = " << view_name << ", offset = " << view_offset << ", stride = ["
+        //       << view_stride[0] << "," << view_stride[1] << "," << view_stride[2] << "," << view_stride[3]
+        //       << "], ggml shape = [" << view_ggml_shape[0] << "," << view_ggml_shape[1] << ","
+        //       << view_ggml_shape[2] << "," << view_ggml_shape[3] << "], ov shape = " << view_ov_shape << std::endl;
+
+        auto view_src_offset = context.get_view_input_src_offset(input_index, view_idx);
+        auto view_src_stride = context.get_view_input_src_stride(input_index, view_idx);
+        auto view_src_ggml_shape = context.get_view_input_src_ggml_shape(input_index, view_idx);
+        auto view_src_ov_shape = context.get_view_input_src_ov_shape(input_index, view_idx);
+        auto view_src_name = context.get_view_input_src_name(input_index, view_idx);
+        // print source view info
+        // std::cout << "View " << view_idx << ": source name = " << view_src_name
+        //           << ", source offset = " << view_src_offset << ", source stride = [" << view_src_stride[0] << ","
+        //           << view_src_stride[1] << "," << view_src_stride[2] << "," << view_src_stride[3]
+        //           << "], source ggml shape = [" << view_src_ggml_shape[0] << "," << view_src_ggml_shape[1] << ","
+        //           << view_src_ggml_shape[2] << "," << view_src_ggml_shape[3]
+        //           << "], source ov shape = " << view_src_ov_shape << std::endl;
+
+        current = process_single_view(current, view_offset, view_stride, view_ggml_shape, view_ov_shape, view_name,
+                                      view_src_offset, view_src_stride, view_src_ggml_shape, view_src_ov_shape,
+                                      view_src_name);
+    }
+
+    return current;
+}
+
 }  // namespace ggml
 }  // namespace frontend
 }  // namespace ov
diff --git a/ggml/src/ggml-openvino/openvino/utils.h b/ggml/src/ggml-openvino/openvino/utils.h
index 767dd4c53..8dc3e8765 100644
--- a/ggml/src/ggml-openvino/openvino/utils.h
+++ b/ggml/src/ggml-openvino/openvino/utils.h
@@ -1,13 +1,13 @@
 #pragma once
 
+#include "node_context.h"
+
 #include <memory>
 #include <openvino/core/node.hpp>
 #include <openvino/op/shape_of.hpp>
 #include <openvino/op/slice.hpp>
 #include <utility>
 
-#include "node_context.h"
-
 namespace ov {
 namespace frontend {
 namespace ggml {
@@ -16,30 +16,23 @@ std::string getCurrentTime();
 
 void dump_ov_model(std::shared_ptr<ov::Model> model);
 
-void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs);
+void num_inputs_check(const NodeContext & context, size_t min_inputs, size_t max_inputs);
 
 int non_cont_dim(std::vector<size_t> ne, std::vector<size_t> nb);
 
-template <typename T>
-std::vector<int> argsort_descend(const std::vector<T>& v) {
+template <typename T> std::vector<int> argsort_descend(const std::vector<T> & v) {
     std::vector<int> idx(v.size());
     std::iota(idx.begin(), idx.end(), 0);
-    std::sort(idx.begin(), idx.end(), [&v](int i1, int i2) {
-        return v[i1] > v[i2];
-    });
+    std::sort(idx.begin(), idx.end(), [&v](int i1, int i2) { return v[i1] > v[i2]; });
     return idx;
 }
 
-template <typename T>
-std::vector<T> sorted_descend(std::vector<T> v) {
-    std::sort(v.begin(), v.end(), [](T a, T b) {
-        return a > b;
-    });
+template <typename T> std::vector<T> sorted_descend(std::vector<T> v) {
+    std::sort(v.begin(), v.end(), [](T a, T b) { return a > b; });
     return v;
 }
 
-template <typename T>
-bool is_permuted(const std::vector<T>& strides) {
+template <typename T> bool is_permuted(const std::vector<T> & strides) {
     for (size_t i = 0; i < strides.size() - 1; ++i) {
         if (strides[i] < strides[i + 1]) {
             return true;
@@ -48,8 +41,7 @@ bool is_permuted(const std::vector<T>& strides) {
     return false;
 }
 
-template <typename T>
-std::vector<T> permute(const std::vector<T>& x, const std::vector<int>& perm) {
+template <typename T> std::vector<T> permute(const std::vector<T> & x, const std::vector<int> & perm) {
     std::vector<T> result;
     result.reserve(perm.size());
     for (int i : perm) {
@@ -58,25 +50,35 @@ std::vector<T> permute(const std::vector<T>& x, const std::vector<int>& perm) {
     return result;
 }
 
-std::shared_ptr<ov::Node> get_dimensions(const std::shared_ptr<ov::op::v3::ShapeOf>& shape,
-                                         const std::vector<int>& dims);
-std::shared_ptr<ov::Node> get_dimensions(const std::shared_ptr<ov::Node>& node, const std::vector<int>& dims);
+std::shared_ptr<ov::Node> get_dimensions(const std::shared_ptr<ov::op::v3::ShapeOf> & shape,
+                                         const std::vector<int> & dims);
+std::shared_ptr<ov::Node> get_dimensions(const std::shared_ptr<ov::Node> & node, const std::vector<int> & dims);
 
-OutputVector rename_outputs_with_suffix(const OutputVector& outputs, const std::string& suffix);
+OutputVector rename_outputs_with_suffix(const OutputVector & outputs, const std::string & suffix);
 
-std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t* rope_params,
+std::pair<ov::Output<Node>, ov::Output<Node>> make_sin_cos(int32_t * rope_params,
                                                            std::shared_ptr<ov::Node> inp_pos,
                                                            std::shared_ptr<ov::Node> rope_freqs_weight = nullptr,
                                                            bool imrope = false,
                                                            bool stateful = false);
 
-ov::Output<ov::Node> process_view_input(const NodeContext& context, int input_index, int slice_len = 0);
+ov::Output<ov::Node> process_view_input(const NodeContext & context, int input_index, int slice_len = 0);
+
+ov::Output<ov::Node> process_view_input_new(const NodeContext & context, int input_index);
 
 namespace op {
-template <typename T>
-OutputVector translate_1to1_match_2_inputs(const NodeContext& context) {
+template <typename T> OutputVector translate_1to1_match_2_inputs(const NodeContext & context) {
     num_inputs_check(context, 2, 2);
-    auto res = std::make_shared<T>(context.get_input(0), context.get_input(1));
+    auto input_0 = process_view_input_new(context, 0);
+    auto input_1 = process_view_input_new(context, 1);
+    auto res = std::make_shared<T>(input_0, input_1);
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
+template <typename T> OutputVector translate_1to1_match_1_input(const NodeContext & context) {
+    num_inputs_check(context, 1, 1);
+    auto input = process_view_input_new(context, 0);
+    auto res = std::make_shared<T>(input);
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 }  // namespace op
diff --git a/ggml/src/ggml-openvino/utils.cpp b/ggml/src/ggml-openvino/utils.cpp
index 998ef7c9e..70af08bdf 100644
--- a/ggml/src/ggml-openvino/utils.cpp
+++ b/ggml/src/ggml-openvino/utils.cpp
@@ -14,6 +14,7 @@
 #include <cstdint>
 #include <cstdlib>
 #include <cstring>
+#include <fstream>
 #include <iomanip>
 #include <iostream>
 #include <memory>
@@ -25,9 +26,11 @@
 #include <openvino/openvino.hpp>
 #include <openvino/runtime/compiled_model.hpp>
 #include <openvino/runtime/infer_request.hpp>
+#include <openvino/runtime/intel_gpu/ocl/ocl.hpp>
 #include <openvino/runtime/intel_npu/properties.hpp>
 #include <openvino/runtime/properties.hpp>
 #include <openvino/runtime/tensor.hpp>
+#include <optional>
 #include <string>
 #include <unordered_map>
 #include <vector>
@@ -39,7 +42,7 @@
 enum ggml_status ov_graph_compute(ggml_cgraph * cgraph, ggml_backend_t backend) {
     ggml_backend_openvino_context * ctx = (ggml_backend_openvino_context *) backend->context;
     try {
-        if (getenv("GGML_OPENVINO_DUMP_CGRAPH")) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_CGRAPH")) {
             std::string filename = "cgraph_ov.txt";
             GgmlOvDecoder::dump_cgraph(cgraph, filename);
         }
@@ -62,10 +65,92 @@ enum ggml_status ov_graph_compute(ggml_cgraph * cgraph, ggml_backend_t backend)
     }
 }
 
+// For a KV cache input, return an ov::Tensor sized to n_kv (== attention_size
+// for that layer) instead of the fully-allocated ctx_per_seq. Pre-conditions:
+//   * non-static (CPU/GPU) backend, single sequence, seq_active_start == 0
+//   * ggml KV layout is a contiguous [1, 1, ctx_per_seq, n_heads_kv*head_size]
+//     so the first n_kv rows are the live prefix and shrinking the ctx axis
+//     gives a valid tensor over the same host storage
+//   * not an SWA layer (ring cache): once the window has wrapped the first
+//     n_kv rows no longer contain the live prefix
+// On any unmet pre-condition returns std::nullopt; the caller falls back to
+// the full-size tensor.
+static std::optional<ov::Tensor> try_make_kv_sliced_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
+                                                           const std::string & name,
+                                                           const ggml_tensor * ggml_tensor) {
+    static const bool kv_slice_disabled = ggml_openvino_getenv_int("GGML_OPENVINO_DISABLE_KV_SLICE");
+    if (kv_slice_disabled) {
+        return std::nullopt;
+    }
+    if (ggml_decoder->is_static() || ggml_decoder->is_stateful()) {
+        return std::nullopt;
+    }
+    if (ggml_tensor->op != GGML_OP_NONE || ggml_tensor->view_src != nullptr) {
+        return std::nullopt;
+    }
+    const auto * op = ggml_decoder->get_tensor_used_op(ggml_tensor);
+    if (!GgmlOvDecoder::is_kvcache(ggml_tensor, op)) {
+        return std::nullopt;
+    }
+
+    const auto & compute_params = ggml_decoder->get_compute_params();
+    if (compute_params.n_seq_active != 1 || compute_params.seq_active_start != 0) {
+        return std::nullopt;
+    }
+
+    int layer;
+    if (auto layer_opt = extract_layer_from_name(name); layer_opt.has_value()) {
+        layer = layer_opt.value();
+    } else {
+        return std::nullopt;
+    }
+
+    const bool is_swa = ggml_decoder->is_swa_layer(layer);
+    if (is_swa) {
+        return std::nullopt;
+    }
+    const int ctx_per_seq = ggml_decoder->get_ctx_per_seq();
+    const int n_kv = compute_params.attention_size;
+    if (ctx_per_seq <= 0 || n_kv <= 0 || n_kv >= ctx_per_seq) {
+        return std::nullopt;
+    }
+
+    ov::Shape full_shape = ggml_decoder->get_shape(ggml_tensor);
+    if (full_shape.size() != 4 || full_shape[0] != 1 || full_shape[1] != 1 ||
+        static_cast<int>(full_shape[2]) != ctx_per_seq) {
+        return std::nullopt;
+    }
+
+    ov::Shape sliced_shape = full_shape;
+    sliced_shape[2] = static_cast<size_t>(n_kv);
+
+    // Disabling for now as gpu has bug with in-place ScatterUpdate with remote tensors, can re-enable once CVS-186519 is fixed
+    // if (ggml_openvino_buffer_is_remote(ggml_tensor)) {
+    //     auto remote_context = ggml_openvino_get_remote_context();
+    //     auto gpu_context = remote_context->as<ov::intel_gpu::ocl::ClContext>();
+    //     return gpu_context.create_tensor(ggml_decoder->get_ov_type(ggml_tensor), sliced_shape, ggml_tensor->data);
+    // }
+
+    return ov::Tensor(ggml_decoder->get_ov_type(ggml_tensor), sliced_shape, ggml_tensor->data);
+}
+
 ov::Tensor create_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
                                    std::shared_ptr<ov::InferRequest> infer_request,
                                    int output_index,
                                    const ggml_tensor * ggml_tensor) {
+    if (auto sliced = try_make_kv_sliced_tensor(ggml_decoder, std::string(ggml_tensor->name), ggml_tensor)) {
+        return *sliced;
+    }
+
+    // Disabling for now as gpu has bug with in-place ScatterUpdate with remote tensors, can re-enable once CVS-186519 is fixed
+    // if (ggml_tensor->extra != nullptr && !ggml_decoder->is_splited_model()) {
+    //     auto * extra_base = static_cast<ggml_openvino_extra_base *>(ggml_tensor->extra);
+    //     if (extra_base->type == ggml_openvino_extra_base::Type::TENSOR) {
+    //         auto * tensor_extra = static_cast<ggml_openvino_tensor_extra *>(extra_base);
+    //         return *tensor_extra->tensor;
+    //     }
+    // }
+
     auto output_type = ggml_decoder->get_ov_type(ggml_tensor);
     ov::Shape output_shape;
     if (ggml_decoder->is_static()) {
@@ -86,7 +171,9 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
     static auto is_static = false;
 
     if (is_naive(cgraph)) {
-        return naive_compute(cgraph, core, device, config);
+        if (!is_model_splitted(cgraph)) {
+            return naive_compute(cgraph, core, device, config);
+        }
     }
 
     auto start_time = ggml_time_us();
@@ -98,18 +185,20 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
     std::tie(m_params, c_params) = GgmlOvDecoder::compute_llm_params(cgraph, is_static);
 
     graph_key key(cgraph);
-    bool cache_hit;
+    static const bool cache_enabled = !ggml_openvino_getenv_int("GGML_OPENVINO_DISABLE_CACHE");
+    bool cache_hit = false;
 
     int64_t decoder_end_time;
     int64_t conversion_end_time;
     int64_t compile_end_time;
     int64_t infer_end_time;
+    int64_t ov_raw_infer_start;
 
     {
         std::shared_ptr<decoder_runtime_ctx> entry;
         ModelParams old_m_params;
 
-        {
+        if (cache_enabled) {
             std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
             auto it = r_ctx->decoder_cache.find(key);
             cache_hit = it != r_ctx->decoder_cache.end();
@@ -120,6 +209,10 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
                 entry = std::make_shared<decoder_runtime_ctx>(mutex);
                 r_ctx->decoder_cache[key] = entry;
             }
+        } else {
+            auto mutex = std::make_shared<std::mutex>();
+            entry = std::make_shared<decoder_runtime_ctx>(mutex);
+            cache_hit = false;
         }
 
         std::lock_guard<std::mutex> lock(*(entry->mutex));
@@ -127,9 +220,14 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
         if (cache_hit) {
             ggml_decoder = entry->ptr;
             old_m_params = ggml_decoder->get_model_params();
-            cache_hit = old_m_params.can_reuse_dynamically(m_params);
+            if (!ggml_decoder->is_splited_model()) {
+                cache_hit = old_m_params.can_reuse_dynamically(m_params);
+            }
         }
 
+        std::vector<std::string> ov_input_names;
+        std::vector<std::string> ov_output_names;
+
         if (cache_hit) {
             std::map<std::string, std::shared_ptr<ov::Node>> model_weights;
             ggml_decoder->set_compute_params(c_params);
@@ -141,6 +239,8 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             {
                 std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
                 infer_request = r_ctx->infer_request_cache.at(key);
+                ov_input_names = r_ctx->ov_input_names_cache.at(key);
+                ov_output_names = r_ctx->ov_output_names_cache.at(key);
             }
 
             if (stateful) {
@@ -162,14 +262,15 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
                             try {
                                 state_name = r_ctx->kv_state_input_name_map.at(state.get_name());
                             } catch (...) {
-                                GGML_LOG_ERROR("GGML OpenVINO backend stateful inference failed: no input found for the state\n");
+                                GGML_LOG_ERROR(
+                                    "GGML OpenVINO backend stateful inference failed: no input found for the state\n");
                                 return GGML_STATUS_FAILED;
                             }
                             auto kv_tensor = get_ov_input_tensor(ggml_decoder, state_name);
-                            kv_tensor.set_shape({state_tensor_shape[0], kv_tensor.get_shape()[2],
-                                                 state_tensor_shape[2], state_tensor_shape[3]});
-                           state_tensor = kv_tensor;
-                           state_tensor_shape = state_tensor.get_shape();
+                            kv_tensor.set_shape({state_tensor_shape[0], kv_tensor.get_shape()[2], state_tensor_shape[2],
+                                                 state_tensor_shape[3]});
+                            state_tensor = kv_tensor;
+                            state_tensor_shape = state_tensor.get_shape();
                         }
                         ov::Coordinate begin = {0, 0, 0, 0};
                         ov::Coordinate end = {state_tensor_shape[0], static_cast<uint32_t>(pos_data[0]),
@@ -177,7 +278,7 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
                         ov::Tensor new_state_tensor(state_tensor, begin, end);
                         state.set_state(new_state_tensor);
                     }
-                    r_ctx->stateful_kv_size = pos_data[0] + 1;
+                    r_ctx->stateful_kv_size = pos_data[0] + pos_shape[3];
                 }
             }
 
@@ -185,15 +286,17 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             conversion_end_time = decoder_end_time;
             compile_end_time = decoder_end_time;
         } else {
-            {
+            if (cache_enabled) {
                 std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
                 r_ctx->infer_request_cache.erase(key);
             }
+            bool model_is_splitted = is_model_splitted(cgraph);
 
             std::shared_ptr<ov::Model> model;
             auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
 
-            ggml_decoder = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights, is_static, stateful);
+            ggml_decoder = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights, is_static,
+                                                           stateful, model_is_splitted);
             decoder_end_time = ggml_time_us();
 
             auto input_model = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder);
@@ -201,7 +304,7 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             ggml_decoder->clear_model_weights();
             conversion_end_time = ggml_time_us();
 
-            if (getenv("GGML_OPENVINO_DUMP_IR")) {
+            if (ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_IR")) {
                 char timestamped_filename[64];
                 auto timestamp = (long long) ggml_time_us();
                 snprintf(timestamped_filename, sizeof(timestamped_filename), "model_%lld.xml", timestamp);
@@ -219,8 +322,6 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
             infer_request = std::make_shared<ov::InferRequest>(compiled_model.create_infer_request());
             entry->ptr = ggml_decoder;
 
-            std::vector<std::string> ov_input_names;
-            std::vector<std::string> ov_output_names;
             for (const auto & ov_param : model->get_parameters()) {
                 ov_input_names.push_back(ov_param->get_friendly_name());
             }
@@ -228,66 +329,64 @@ enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, std::shared_ptr<
                 ov_output_names.push_back(ov_output->get_friendly_name());
             }
 
-            {
+            if (cache_enabled) {
                 std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
                 r_ctx->infer_request_cache[key] = infer_request;
-                r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
-                r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+                r_ctx->ov_input_names_cache[key] = ov_input_names;
+                r_ctx->ov_output_names_cache[key] = ov_output_names;
             }
 
-            if (stateful) {
+            if (stateful && cache_enabled) {
                 const auto * inp_pos = get_inp_pos_tensor(cgraph);
                 auto pos_shape = ggml_decoder->get_shape(inp_pos);
                 r_ctx->stateful_kv_size = pos_shape[3];
                 const auto kv_param_res_names = ggml_decoder->get_kv_param_res_names();
-                for (const auto& pair : kv_param_res_names) {
-                    r_ctx->kv_state_input_name_map[pair.first+pair.second] = pair.first;
+                for (const auto & pair : kv_param_res_names) {
+                    r_ctx->kv_state_input_name_map[pair.first + pair.second] = pair.first;
                 }
             }
         }
 
-        std::vector<std::string> ov_input_names;
-        std::vector<std::string> ov_output_names;
-        {
-            std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
-            ov_input_names = r_ctx->ov_input_names_cache[key];
-            ov_output_names = r_ctx->ov_output_names_cache[key];
-        }
-
         for (size_t i = 0; i < ov_input_names.size(); i++) {
             auto param_name = ov_input_names[i];
             auto input_tensor = get_ov_input_tensor(ggml_decoder, param_name);
             infer_request->set_input_tensor(i, input_tensor);
 
-            if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
+            if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_INPUT")) {
                 print_input_tensor_info(param_name, input_tensor);
             }
         }
 
         for (size_t i = 0; i < ov_output_names.size(); i++) {
             auto * ggml_tensor = ggml_decoder->get_model_outputs().at(ov_output_names[i]);
+            if (ggml_nbytes(ggml_tensor) == 0) {
+                continue;
+            }
             auto output_tensor = create_ov_output_tensor(ggml_decoder, infer_request, i, ggml_tensor);
             infer_request->set_output_tensor(i, output_tensor);
         }
 
+        ov_raw_infer_start = ggml_time_us();
         infer_request->infer();
         infer_end_time = ggml_time_us();
 
-        if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_OUTPUT")) {
             for (size_t i = 0; i < ov_output_names.size(); i++) {
                 const auto output_tensor = infer_request->get_output_tensor(i);
                 print_output_tensor_info(ov_output_names[i], output_tensor, output_tensor.data());
             }
         }
 
-        if (getenv("GGML_OPENVINO_PROFILING")) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_PROFILING")) {
             GGML_LOG_INFO("\nGGML OpenVINO Backend: \n");
-            GGML_LOG_INFO("  - Graph decoder time: %ld ms \n", (decoder_end_time - start_time) / 1000);
+            GGML_LOG_INFO("  - Graph decoder time: %.3f ms \n", (decoder_end_time - start_time) / 1000.0);
             if (!cache_hit) {
-                GGML_LOG_INFO("  - Graph conversion time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000);
-                GGML_LOG_INFO("  - Graph compile time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000);
+                GGML_LOG_INFO("  - Graph conversion time: %.3f ms \n",
+                              (conversion_end_time - decoder_end_time) / 1000.0);
+                GGML_LOG_INFO("  - Graph compile time: %.3f ms \n", (compile_end_time - conversion_end_time) / 1000.0);
             }
-            GGML_LOG_INFO("  - Graph inference time: %ld ms \n", (infer_end_time - compile_end_time) / 1000);
+            GGML_LOG_INFO("  - Graph inference time: %.3f ms \n", (infer_end_time - compile_end_time) / 1000.0);
+            GGML_LOG_INFO("  - OV raw infer time: %.3f ms \n", (infer_end_time - ov_raw_infer_start) / 1000.0);
         }
     }
 
@@ -298,17 +397,18 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
     auto & core = ov_singleton_core();
 
     auto get_prefill_chunk_size = [] {
-        const char * chunk_size_str = getenv("GGML_OPENVINO_PREFILL_CHUNK_SIZE");
-        if (chunk_size_str && atoi(chunk_size_str) > 0) {
-            return atoi(chunk_size_str);
-        }
-        return 256;
+        static const int chunk_size = []() {
+            int env_prefill_chunk_size = ggml_openvino_getenv_int("GGML_OPENVINO_PREFILL_CHUNK_SIZE");
+            return env_prefill_chunk_size > 0 ? env_prefill_chunk_size : 256;
+        }();
+        return chunk_size;
     };
 
     static std::string device = "NPU";
     static auto is_static = true;
     static auto stateful = false;
-    static auto prefill_chunk_size = get_prefill_chunk_size();
+
+    auto prefill_chunk_size = get_prefill_chunk_size();
     const auto & config = ggml_openvino_get_compile_config();
 
     if (is_naive(cgraph)) {
@@ -326,17 +426,20 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
     const auto * inp_pos = get_inp_pos_tensor(cgraph);
     const auto is_prefill = get_is_prefill(inp_pos);
     graph_key key(cgraph);
-    bool cache_hit;
+    static const bool cache_enabled = !ggml_openvino_getenv_int("GGML_OPENVINO_DISABLE_CACHE");
+    bool cache_hit = false;
 
     int64_t decoder_end_time;
     int64_t conversion_end_time;
     int64_t compile_end_time;
     int64_t infer_end_time;
+    int64_t ov_raw_infer_start;
+    int64_t ov_raw_infer_total = 0;
 
     std::shared_ptr<decoder_runtime_ctx> entry;
     ModelParams old_m_params;
 
-    {
+    if (cache_enabled) {
         std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
         auto it = r_ctx->decoder_cache.find(key);
         cache_hit = it != r_ctx->decoder_cache.end();
@@ -347,6 +450,10 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             entry = std::make_shared<decoder_runtime_ctx>(mutex);
             r_ctx->decoder_cache[key] = entry;
         }
+    } else {
+        auto mutex = std::make_shared<std::mutex>();
+        entry = std::make_shared<decoder_runtime_ctx>(mutex);
+        cache_hit = false;
     }
 
     std::lock_guard<std::mutex> lock(*(entry->mutex));
@@ -357,6 +464,9 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         cache_hit = old_m_params.can_reuse_statically(m_params);
     }
 
+    std::vector<std::string> ov_input_names_local;
+    std::vector<std::string> ov_output_names_local;
+
     if (cache_hit) {
         std::map<std::string, std::shared_ptr<ov::Node>> model_weights;
         ggml_decoder->m_is_prefill = is_prefill;
@@ -370,13 +480,15 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
             infer_request =
                 is_prefill ? r_ctx->infer_request_cache_prefill.at(key) : r_ctx->infer_request_cache.at(key);
+            ov_input_names_local = r_ctx->ov_input_names_cache.at(key);
+            ov_output_names_local = r_ctx->ov_output_names_cache.at(key);
         }
 
         decoder_end_time = ggml_time_us();
         conversion_end_time = decoder_end_time;
         compile_end_time = decoder_end_time;
     } else {
-        {
+        if (cache_enabled) {
             std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
             r_ctx->infer_request_cache.erase(key);
             r_ctx->infer_request_cache_prefill.erase(key);
@@ -385,10 +497,14 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         std::shared_ptr<ov::Model> model;
         auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
 
-        auto ggml_decoder_prefill = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights,
-                                                                    is_static, stateful, true, prefill_chunk_size);
+        if (m_params.n_heads_kv == -1) {
+            // graph is not a LLM, e.g. context-shift graph
+            prefill_chunk_size = inp_pos->ne[0];
+        }
+        auto ggml_decoder_prefill = std::make_shared<GgmlOvDecoder>(
+            cgraph, m_params, c_params, model_weights, is_static, stateful, false, true, prefill_chunk_size);
         auto ggml_decoder_decode = std::make_shared<GgmlOvDecoder>(cgraph, m_params, c_params, model_weights, is_static,
-                                                                   stateful, false, prefill_chunk_size);
+                                                                   stateful, false, false, prefill_chunk_size);
         decoder_end_time = ggml_time_us();
 
         auto input_model_prefill = std::make_shared<ov::frontend::ggml::InputModel>(ggml_decoder_prefill);
@@ -400,7 +516,7 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         ggml_decoder_decode->clear_model_weights();
         conversion_end_time = ggml_time_us();
 
-        if (getenv("GGML_OPENVINO_DUMP_IR")) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_IR")) {
             char timestamped_filename[64];
             auto timestamp = (long long) ggml_time_us();
             snprintf(timestamped_filename, sizeof(timestamped_filename), "model_prefill_%lld.xml", timestamp);
@@ -429,32 +545,22 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         infer_request = is_prefill ? infer_request_prefill : infer_request_decode;
         entry->ptr = ggml_decoder;
 
-        std::vector<std::string> ov_input_names;
-        std::vector<std::string> ov_output_names;
         for (const auto & ov_param : model->get_parameters()) {
-            ov_input_names.push_back(ov_param->get_friendly_name());
+            ov_input_names_local.push_back(ov_param->get_friendly_name());
         }
         for (const auto & ov_output : model->get_results()) {
-            ov_output_names.push_back(ov_output->get_friendly_name());
+            ov_output_names_local.push_back(ov_output->get_friendly_name());
         }
 
-        {
+        if (cache_enabled) {
             std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
             r_ctx->infer_request_cache_prefill[key] = infer_request_prefill;
             r_ctx->infer_request_cache[key] = infer_request_decode;
-            r_ctx->ov_input_names_cache[key] = std::move(ov_input_names);
-            r_ctx->ov_output_names_cache[key] = std::move(ov_output_names);
+            r_ctx->ov_input_names_cache[key] = ov_input_names_local;
+            r_ctx->ov_output_names_cache[key] = ov_output_names_local;
         }
     }
 
-    std::vector<std::string> ov_input_names_local;
-    std::vector<std::string> ov_output_names_local;
-    {
-        std::lock_guard<std::mutex> map_lock(r_ctx->ctx_mutex);
-        ov_input_names_local = r_ctx->ov_input_names_cache[key];
-        ov_output_names_local = r_ctx->ov_output_names_cache[key];
-    }
-
     if (is_prefill) {
         auto inp_len = inp_pos->ne[0];
         for (int chunk_index = 0; chunk_index * prefill_chunk_size < inp_len; chunk_index++) {
@@ -463,7 +569,7 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
                 auto input_tensor = get_ov_input_tensor_static_prefill(ggml_decoder, param_name, chunk_index);
                 infer_request->set_input_tensor(i, input_tensor);
 
-                if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
+                if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_INPUT")) {
                     const auto input_tensor = infer_request->get_input_tensor(i);
                     print_input_tensor_info(param_name, input_tensor);
                 }
@@ -475,9 +581,11 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
                 infer_request->set_output_tensor(i, output_tensor);
             }
 
+            ov_raw_infer_start = ggml_time_us();
             infer_request->infer();
+            ov_raw_infer_total += ggml_time_us() - ov_raw_infer_start;
 
-            if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
+            if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_OUTPUT")) {
                 for (size_t i = 0; i < ov_output_names_local.size(); i++) {
                     const auto output_tensor = infer_request->get_output_tensor(i);
                     print_output_tensor_info(ov_output_names_local[i], output_tensor, output_tensor.data());
@@ -491,7 +599,7 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             auto input_tensor = get_ov_input_tensor_static_decode(ggml_decoder, param_name);
             infer_request->set_input_tensor(i, input_tensor);
 
-            if (getenv("GGML_OPENVINO_DEBUG_INPUT")) {
+            if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_INPUT")) {
                 const auto input_tensor = infer_request->get_input_tensor(i);
                 print_input_tensor_info(param_name, input_tensor);
             }
@@ -503,10 +611,12 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
             infer_request->set_output_tensor(i, output_tensor);
         }
 
+        ov_raw_infer_start = ggml_time_us();
         infer_request->infer();
         infer_end_time = ggml_time_us();
+        ov_raw_infer_total = infer_end_time - ov_raw_infer_start;
 
-        if (getenv("GGML_OPENVINO_DEBUG_OUTPUT")) {
+        if (ggml_openvino_getenv_int("GGML_OPENVINO_DEBUG_OUTPUT")) {
             for (size_t i = 0; i < ov_output_names_local.size(); i++) {
                 const auto output_tensor = infer_request->get_output_tensor(i);
                 print_output_tensor_info(ov_output_names_local[i], output_tensor, output_tensor.data());
@@ -514,19 +624,75 @@ enum ggml_status ov_graph_compute_static(ggml_cgraph * cgraph, std::shared_ptr<o
         }
     }
 
-    if (getenv("GGML_OPENVINO_PROFILING")) {
+    if (ggml_openvino_getenv_int("GGML_OPENVINO_PROFILING")) {
         GGML_LOG_INFO("\nGGML OpenVINO Backend: \n");
-        GGML_LOG_INFO("  - Graph decoder time: %ld ms \n", (decoder_end_time - start_time) / 1000);
+        GGML_LOG_INFO("  - Graph decoder time: %.3f ms \n", (decoder_end_time - start_time) / 1000.0);
         if (!cache_hit) {
-            GGML_LOG_INFO("  - Graph conversion time: %ld ms \n", (conversion_end_time - decoder_end_time) / 1000);
-            GGML_LOG_INFO("  - Graph compile time: %ld ms \n", (compile_end_time - conversion_end_time) / 1000);
+            GGML_LOG_INFO("  - Graph conversion time: %.3f ms \n", (conversion_end_time - decoder_end_time) / 1000.0);
+            GGML_LOG_INFO("  - Graph compile time: %.3f ms \n", (compile_end_time - conversion_end_time) / 1000.0);
         }
-        GGML_LOG_INFO("  - Graph inference time: %ld ms \n", (infer_end_time - compile_end_time) / 1000);
+        GGML_LOG_INFO("  - Graph inference time: %.3f ms \n", (infer_end_time - compile_end_time) / 1000.0);
+        GGML_LOG_INFO("  - OV raw infer time: %.3f ms \n", ov_raw_infer_total / 1000.0);
     }
 
     return GGML_STATUS_SUCCESS;
 }
 
+// Detect whether a cgraph is a split subgraph or not.
+// Step 1 compares each node's recorded use_count with actual fan-out references in node->src.
+// Step 2 verifies that node inputs come from model nodes/weights/leafs; external sources imply split.
+bool is_model_splitted(ggml_cgraph * cgraph) {
+    // check the nodes of the model are used by the following nodes, through compare the node's use count and the count of nodes that use it as input. If does not match, return true, else return false.
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+        int use_count = cgraph->use_counts[ggml_hash_find(&cgraph->visited_hash_set, node)];
+        // TODO: this is a workround for the tests case from llama.cpp, fix should from the root cause in the future.
+        if ((cgraph->n_nodes <= 1 && use_count == 0) ||
+            (cgraph->n_nodes <= 1 && node->op == GGML_OP_VIEW && use_count == 1 && node->src[0] != nullptr &&
+             node->src[0]->op == GGML_OP_NONE)) {
+            return false;
+        }
+        if (cgraph->n_nodes == 1 &&
+            (cgraph->nodes[0]->op == GGML_OP_TRANSPOSE || cgraph->nodes[0]->op == GGML_OP_PERMUTE)) {
+            return false;
+        }
+        int input_use_count = 0;
+        for (int j = 0; j < cgraph->n_nodes; j++) {
+            ggml_tensor * other_node = cgraph->nodes[j];
+            for (int k = 0; k < GGML_MAX_SRC; k++) {
+                if (other_node->src[k] == node) {
+                    input_use_count++;
+                }
+            }
+        }
+        if (use_count != input_use_count && node->op != GGML_OP_NONE) {
+            return true;
+        }
+    }
+    // if all nodes's src node's src is not come from the nodes in the model, we think the model is splitted. This is a complementary check for the above check, because for some special case like the output node is not used by any node, the use count and input use count are both 0, we can not determine whether the model is splitted or not just based on the first check.
+    auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph, true);
+    std::set<ggml_tensor *> model_nodes(cgraph->nodes, cgraph->nodes + cgraph->n_nodes);
+    // leaf nodes
+    std::set<ggml_tensor *> model_leafs(cgraph->leafs, cgraph->leafs + cgraph->n_leafs);
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor * node = cgraph->nodes[i];
+        for (int j = 0; j < GGML_MAX_SRC; j++) {
+            ggml_tensor * src = node->src[j];
+            // the src is also not the model weights, we think the model is splitted.
+            // the src is also not in model leafs, we think the model is splitted.
+            if (src != nullptr && model_nodes.find(src) == model_nodes.end() &&
+                model_weights.find(std::string(src->name)) == model_weights.end() && !model_leafs.empty() == false &&
+                model_leafs.find(src) == model_leafs.end()) {
+                if (GgmlOvDecoder::is_inp_tok(src, node)) {
+                    return false;
+                }
+                return true;
+            }
+        }
+    }
+    return false;
+}
+
 bool is_naive(ggml_cgraph * cgraph) {
     constexpr int naive_graph_size_threshold = 20;
     int count = 0;
@@ -551,7 +717,7 @@ enum ggml_status naive_compute(ggml_cgraph * cgraph,
     auto decoder = std::make_shared<GgmlOvDecoder>(cgraph, model_weights);
     auto input_model = std::make_shared<ov::frontend::ggml::InputModel>(decoder);
     auto model = ov::frontend::ggml::FrontEnd::convert(input_model, naive);
-    if (getenv("GGML_OPENVINO_DUMP_IR")) {
+    if (ggml_openvino_getenv_int("GGML_OPENVINO_DUMP_IR")) {
         ov::serialize(model, "IR_naive.xml");
     }
 
@@ -578,40 +744,92 @@ enum ggml_status naive_compute(ggml_cgraph * cgraph,
         infer_request->set_input_tensor(i, input_tensor);
     }
 
-    auto ov_results = model->get_results();
-    for (size_t i = 0; i < ov_results.size(); i++) {
-        auto * ggml_tensor = decoder->get_model_outputs().at(ov_results[i]->get_friendly_name());
-        auto output_tensor = create_ov_output_tensor(decoder, infer_request, i, ggml_tensor);
-        infer_request->set_output_tensor(i, output_tensor);
-    }
+    // Use get_output_tensor + memcpy instead of set_output_tensor to avoid memory overwritten
+    // when i/o buffer overlaps, e.g. the cgraph is a single PERMUTE
 
     infer_request->infer();
+
+    auto ov_results = model->get_results();
+    for (size_t i = 0; i < ov_results.size(); i++) {
+        auto output_tensor = infer_request->get_output_tensor(i);
+        auto * ggml_tensor = decoder->get_model_outputs().at(ov_results[i]->get_friendly_name());
+        std::memcpy(ggml_tensor->data, output_tensor.data(), output_tensor.get_byte_size());
+    }
     return GGML_STATUS_SUCCESS;
 }
 
 namespace {
+template <typename T> void set_zero_diagonal(std::vector<T> & matrix, size_t rows, size_t cols, T zero_value = T{}) {
+    for (size_t i = 0; i < rows; ++i) {
+        size_t diag_col = std::min(i, cols - 1);
+        matrix[i * cols + diag_col] = zero_value;
+    }
+}
+
+ov::Tensor make_contiguous_split_input_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
+                                              const struct ggml_tensor * ggml_tensor,
+                                              const ov::Shape & input_shape) {
+    const size_t element_size = ggml_type_size(ggml_tensor->type);
+    const size_t block_size = ggml_blck_size(ggml_tensor->type);
+
+    GGML_ASSERT(block_size == 1 && "non-contiguous split inputs must be plain element types");
+
+    const struct ggml_tensor * source_tensor = ggml_tensor->view_src != nullptr ? ggml_tensor->view_src : ggml_tensor;
+    const size_t source_offset = ggml_tensor->view_src != nullptr ? ggml_tensor->view_offs : 0;
+
+    std::vector<uint8_t> source_data(ggml_nbytes(source_tensor));
+    ggml_backend_tensor_get(source_tensor, source_data.data(), 0, source_data.size());
+
+    ov::Tensor input_tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape);
+    auto * dst = static_cast<uint8_t *>(input_tensor.data());
+    size_t dst_offset = 0;
+
+    for (size_t i3 = 0; i3 < static_cast<size_t>(ggml_tensor->ne[3]); ++i3) {
+        for (size_t i2 = 0; i2 < static_cast<size_t>(ggml_tensor->ne[2]); ++i2) {
+            for (size_t i1 = 0; i1 < static_cast<size_t>(ggml_tensor->ne[1]); ++i1) {
+                for (size_t i0 = 0; i0 < static_cast<size_t>(ggml_tensor->ne[0]); ++i0) {
+                    const size_t src_offset = source_offset + i3 * ggml_tensor->nb[3] + i2 * ggml_tensor->nb[2] +
+                                              i1 * ggml_tensor->nb[1] + i0 * ggml_tensor->nb[0];
+                    std::memcpy(dst + dst_offset, source_data.data() + src_offset, element_size);
+                    dst_offset += element_size;
+                }
+            }
+        }
+    }
+
+    return input_tensor;
+}
+
 ov::Tensor convert_ggml_input_to_ov(std::shared_ptr<GgmlOvDecoder> ggml_decoder, const std::string & name) {
     const auto * ggml_tensor = ggml_decoder->get_input_ggml_tensor(name);
 
-    if (ggml_tensor->extra != nullptr) {
-        // GGML_LOG_DEBUG("Using ggml_tensor->extra as ov::Tensor for input: %s\n", name.c_str());
+    if (auto sliced = try_make_kv_sliced_tensor(ggml_decoder, name, ggml_tensor)) {
+        return *sliced;
+    }
+
+    if (ggml_tensor->extra != nullptr && !ggml_decoder->is_splited_model()) {
         auto * extra_base = static_cast<ggml_openvino_extra_base *>(ggml_tensor->extra);
-        if (extra_base->type != ggml_openvino_extra_base::Type::TENSOR) {
-            throw std::runtime_error("ggml tensor extra is not of type TENSOR for input: " + name);
+        if (extra_base->type == ggml_openvino_extra_base::Type::TENSOR) {
+            // GGML_LOG_DEBUG("Using ggml_tensor->extra as ov::Tensor for input: %s\n", name.c_str());
+            auto * tensor_extra = static_cast<ggml_openvino_tensor_extra *>(extra_base);
+            return *tensor_extra->tensor;
         }
-        auto * tensor_extra = static_cast<ggml_openvino_tensor_extra *>(extra_base);
-        return *tensor_extra->tensor;
     }
 
     // GGML_LOG_DEBUG("Converting ggml tensor to ov::Tensor for input: %s\n", name.c_str());
     auto * input_data = ggml_tensor->data;
     ov::Shape input_shape;
-    if (ggml_tensor->op == GGML_OP_VIEW) {
+    if (ggml_tensor->op == GGML_OP_VIEW && !ggml_decoder->is_splited_model()) {
         // This case is added to make test-backend-ops work
         input_shape = ggml_decoder->get_shape(ggml_tensor->view_src);
     } else {
         input_shape = ggml_decoder->get_shape(ggml_tensor);
     }
+
+    if (ggml_decoder->is_splited_model() && !ggml_is_contiguous(ggml_tensor)) {
+        return make_contiguous_split_input_tensor(ggml_decoder, ggml_tensor, input_shape);
+    }
+
     auto input_tensor = ov::Tensor(ggml_decoder->get_ov_type(ggml_tensor), input_shape, input_data);
     return input_tensor;
 }
@@ -660,6 +878,14 @@ ov::Tensor get_ov_input_tensor_static_decode(std::shared_ptr<GgmlOvDecoder> ggml
 
     if (GgmlOvDecoder::is_inp_mask(ggml_tensor, op)) {
         size_t context_size = ggml_decoder->get_ctx_size();
+        if (ggml_tensor->type == GGML_TYPE_F16) {
+            std::vector<ggml_fp16_t> padded_data =
+                pad_input<ggml_fp16_t>(ggml_tensor, 1, context_size, GGML_FP32_TO_FP16(-INFINITY));
+            ov::Tensor input_tensor(ov::element::f16, ov::Shape{1, 1, 1, context_size});
+            std::memcpy(input_tensor.data(), padded_data.data(), padded_data.size() * sizeof(ggml_fp16_t));
+            return input_tensor;
+        }
+
         std::vector<float> padded_data = pad_input<float>(ggml_tensor, 1, context_size, -INFINITY);
         ov::Tensor input_tensor(ov::element::f32, ov::Shape{1, 1, 1, context_size});
         auto * data_ptr = input_tensor.data<float>();
@@ -728,9 +954,20 @@ ov::Tensor get_ov_input_tensor_static_prefill(std::shared_ptr<GgmlOvDecoder> ggm
     if (GgmlOvDecoder::is_inp_mask(ggml_tensor, op)) {
         size_t cols = ggml_tensor->ne[0];
         size_t rows = ggml_tensor->ne[1];
-        float * ggml_data = (float *) ggml_tensor->data + chunk_index * chunk_size * cols;
         size_t chunk_valid_rows = std::min(chunk_size, rows - chunk_index * chunk_size);
         size_t context_size = ggml_decoder->get_ctx_size();
+        if (ggml_tensor->type == GGML_TYPE_F16) {
+            const auto * ggml_data =
+                static_cast<const ggml_fp16_t *>(ggml_tensor->data) + chunk_index * chunk_size * cols;
+            std::vector<ggml_fp16_t> padded_data = pad_input<ggml_fp16_t>(ggml_data, chunk_valid_rows, cols, chunk_size,
+                                                                          context_size, GGML_FP32_TO_FP16(-INFINITY));
+            set_zero_diagonal(padded_data, chunk_size, context_size, GGML_FP32_TO_FP16(0.0f));
+            ov::Tensor input_tensor(ov::element::f16, ov::Shape{1, 1, chunk_size, context_size});
+            std::memcpy(input_tensor.data(), padded_data.data(), padded_data.size() * sizeof(ggml_fp16_t));
+            return input_tensor;
+        }
+
+        const auto * ggml_data = static_cast<const float *>(ggml_tensor->data) + chunk_index * chunk_size * cols;
         std::vector<float> padded_data =
             pad_input<float>(ggml_data, chunk_valid_rows, cols, chunk_size, context_size, -INFINITY);
         set_zero_diagonal(padded_data, chunk_size, context_size);
@@ -753,6 +990,65 @@ size_t checksum(const void * data, size_t size) {
     return sum;
 }
 
+bool save_ggml_tensor_data_to_txt(const ggml_tensor * tensor, const std::string & file_path) {
+    if (tensor == nullptr || tensor->data == nullptr) {
+        return false;
+    }
+
+    std::ofstream out(file_path);
+    if (!out.is_open()) {
+        return false;
+    }
+
+    const size_t n = ggml_nelements(tensor);
+    out << "name: " << tensor->name << ", type: " << ggml_type_name(tensor->type) << ", shape: [" << tensor->ne[0]
+        << ", " << tensor->ne[1] << ", " << tensor->ne[2] << ", " << tensor->ne[3] << "]" << ", elements: " << n
+        << ", data:" << '\n';
+
+    switch (tensor->type) {
+    case GGML_TYPE_F32: {
+        const auto * data = static_cast<const float *>(tensor->data);
+        for (size_t i = 0; i < n; ++i) {
+            out << data[i] << '\n';
+        }
+        break;
+    }
+    case GGML_TYPE_F16: {
+        const auto * data = static_cast<const ggml_fp16_t *>(tensor->data);
+        for (size_t i = 0; i < n; ++i) {
+            out << ggml_fp16_to_fp32(data[i]) << '\n';
+        }
+        break;
+    }
+    case GGML_TYPE_BF16: {
+        const auto * data = static_cast<const ggml_bf16_t *>(tensor->data);
+        for (size_t i = 0; i < n; ++i) {
+            out << ggml_bf16_to_fp32(data[i]) << '\n';
+        }
+        break;
+    }
+    case GGML_TYPE_I32: {
+        const auto * data = static_cast<const int32_t *>(tensor->data);
+        for (size_t i = 0; i < n; ++i) {
+            out << data[i] << '\n';
+        }
+        break;
+    }
+    case GGML_TYPE_I64: {
+        const auto * data = static_cast<const int64_t *>(tensor->data);
+        for (size_t i = 0; i < n; ++i) {
+            out << data[i] << '\n';
+        }
+        break;
+    }
+    default:
+        out << "unsupported tensor type for text dump" << '\n';
+        return false;
+    }
+
+    return true;
+}
+
 void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor) {
     std::cout << "Input name: " << name << ", Input shape: " << tensor.get_shape() << ", Address: " << tensor.data()
               << std::endl;
@@ -849,13 +1145,6 @@ void print_output_tensor_info(const std::string & name, const ov::Tensor & tenso
     }
 }
 
-void set_zero_diagonal(std::vector<float> & matrix, size_t rows, size_t cols) {
-    for (size_t i = 0; i < rows; ++i) {
-        size_t diag_col = std::min(i, cols - 1);
-        matrix[i * cols + diag_col] = 0.0f;
-    }
-}
-
 const ggml_tensor * get_inp_pos_tensor(ggml_cgraph * cgraph) {
     for (int i = 0; i < cgraph->n_nodes; ++i) {
         auto * op = cgraph->nodes[i];
diff --git a/ggml/src/ggml-openvino/utils.h b/ggml/src/ggml-openvino/utils.h
index 2c72e33c3..c2c7b7cda 100644
--- a/ggml/src/ggml-openvino/utils.h
+++ b/ggml/src/ggml-openvino/utils.h
@@ -1,4 +1,3 @@
-#include "ggml-backend-impl.h"
 #include "ggml-decoder.h"
 #include "ggml-impl.h"
 
@@ -45,6 +44,7 @@ struct graph_key_hash {
 
 struct decoder_runtime_ctx {
     decoder_runtime_ctx(std::shared_ptr<std::mutex> mutex) : mutex(std::move(mutex)) {}
+
     std::shared_ptr<std::mutex> mutex;
     std::shared_ptr<GgmlOvDecoder> ptr;
 };
@@ -64,11 +64,7 @@ struct ov_runtime_context {
     std::map<std::string, std::string> kv_state_input_name_map;
     std::atomic<int> backend_count;
 
-    ov_runtime_context() :
-        device("CPU"),
-        stateful(false),
-        stateful_kv_size(0),
-        backend_count(0) {}
+    ov_runtime_context() : device("CPU"), stateful(false), stateful_kv_size(0), backend_count(0) {}
 
     void clear_caches() {
         std::lock_guard<std::mutex> lock(ctx_mutex);
@@ -87,6 +83,8 @@ enum ggml_status ov_graph_compute_static(struct ggml_cgraph * cgraph, std::share
 
 size_t checksum(const void * data, size_t size);
 
+bool save_ggml_tensor_data_to_txt(const ggml_tensor * tensor, const std::string & file_path);
+
 void print_input_tensor_info(const std::string & name, const ov::Tensor & tensor);
 
 void print_output_tensor_info(const std::string & name, const ov::Tensor & tensor, const void * output_dst);
@@ -117,8 +115,6 @@ std::vector<T> pad_input(const ggml_tensor * tensor, size_t padded_rows, size_t
                         padded_rows, padded_cols, pad_value);
 }
 
-void set_zero_diagonal(std::vector<float> & matrix, size_t rows, size_t cols);
-
 const ggml_tensor * get_inp_pos_tensor(struct ggml_cgraph * cgraph);
 
 bool get_is_prefill(const ggml_tensor * inp_pos);
@@ -137,6 +133,13 @@ ov::Tensor create_ov_output_tensor(std::shared_ptr<GgmlOvDecoder> ggml_decoder,
 
 bool is_naive(struct ggml_cgraph * cgraph);
 
+/**
+ * @brief Heuristically checks whether the given computation graph is a split-model fragment.
+ * @param cgraph Pointer to the GGML computation graph to analyze.
+ * @return true if the graph is identified as split; otherwise false.
+ */
+bool is_model_splitted(struct ggml_cgraph * cgraph);
+
 enum ggml_status naive_compute(struct ggml_cgraph * cgraph,
                                ov::Core & core,
                                const std::string & device,

From cda63856b804af3aae3e35c1da3eacaeb4bfeb0a Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Tue, 16 Jun 2026 23:19:11 -0700
Subject: [PATCH 009/292] common: update logging to enforce max_capacity and
 optimize queue resizing (#24490)

* common: update logging to enforce max_capacity and optimize queue resizing logic

* common/log: remove queue expansion logic
---
 common/log.cpp | 158 +++++++++++++++++++++++++------------------------
 1 file changed, 82 insertions(+), 76 deletions(-)

diff --git a/common/log.cpp b/common/log.cpp
index bd62616d8..2d1e74ad1 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -11,8 +11,13 @@
 #include <sstream>
 #include <thread>
 #include <vector>
+#include <algorithm>
 
 #if defined(_WIN32)
+#    define WIN32_LEAN_AND_MEAN
+#    ifndef NOMINMAX
+#       define NOMINMAX
+#    endif
 #    include <io.h>
 #    include <windows.h>
 #    define isatty _isatty
@@ -62,16 +67,15 @@ static const char* g_col[] = {
 };
 
 struct common_log_entry {
-    enum ggml_log_level level;
-
-    bool prefix;
-
-    int64_t timestamp;
+    enum ggml_log_level level {GGML_LOG_LEVEL_INFO};
 
     std::vector<char> msg;
 
-    // signals the worker thread to stop
-    bool is_end;
+    int64_t timestamp { 0 };
+    bool is_end       { false }; // signals the worker thread to stop
+    bool prefix       { false };
+
+    common_log_entry(size_t size = 256) : msg(size) { }
 
     void print(FILE * file = nullptr) const {
         FILE * fcur = file;
@@ -122,22 +126,15 @@ struct common_log_entry {
 };
 
 struct common_log {
-    // default capacity - will be expanded if needed
-    common_log() : common_log(256) {}
-
-    common_log(size_t capacity) {
-        file = nullptr;
-        prefix = false;
+    // default capacity
+    common_log(size_t capacity = 512) {
+        file       = nullptr;
+        prefix     = false;
         timestamps = false;
-        running = false;
-        t_start = t_us();
-
-        // initial message size - will be expanded if longer messages arrive
-        entries.resize(capacity);
-        for (auto & entry : entries) {
-            entry.msg.resize(256);
-        }
+        running    = false;
+        t_start    = t_us();
 
+        queue.resize(capacity, common_log_entry(256));
         head = 0;
         tail = 0;
 
@@ -152,9 +149,10 @@ struct common_log {
     }
 
 private:
-    std::mutex mtx;
-    std::thread thrd;
-    std::condition_variable cv;
+    std::mutex              mtx;
+    std::thread             thrd;
+    std::condition_variable cv_new;  // new entry
+    std::condition_variable cv_full; // wait on full
 
     FILE * file;
 
@@ -164,24 +162,53 @@ private:
 
     int64_t t_start;
 
-    // ring buffer of entries
-    std::vector<common_log_entry> entries;
+    // queue of entries
+    std::vector<common_log_entry> queue;
     size_t head;
     size_t tail;
 
-    // worker thread copies into this
-    common_log_entry cur;
+    bool print_entry(const common_log_entry & e) const {
+        if (e.is_end) return true;
+
+        e.print();
+        if (file) {
+            e.print(file);
+        }
+        return false;
+    }
+
+    bool flush_queue(size_t start_head, size_t end_tail, size_t & out_head) const {
+        bool stop = false;
+        size_t h = start_head;
+        while (h != end_tail && !stop) {
+            stop = print_entry(queue[h]);
+            h = (h + 1) % queue.size();
+        }
+        out_head = h;
+        return stop;
+    }
 
 public:
+    bool is_full() const {
+        return ((tail + 1) % queue.size()) == head;
+    }
+
+    bool is_empty() const {
+        return head == tail;
+    }
+
     void add(enum ggml_log_level level, const char * fmt, va_list args) {
-        std::lock_guard<std::mutex> lock(mtx);
+        std::unique_lock<std::mutex> lock(mtx);
+
+        // block if the queue is full
+        cv_full.wait(lock, [this]() { return !running || !is_full(); });
 
         if (!running) {
             // discard messages while the worker thread is paused
             return;
         }
 
-        auto & entry = entries[tail];
+        auto & entry = queue[tail];
 
         {
             // cannot use args twice, so make a copy in case we need to expand the buffer
@@ -216,38 +243,16 @@ public:
             va_end(args_copy);
         }
 
-        entry.level = level;
-        entry.prefix = prefix;
+        entry.is_end    = false;
+        entry.level     = level;
+        entry.prefix    = prefix;
         entry.timestamp = 0;
         if (timestamps) {
             entry.timestamp = t_us() - t_start;
         }
-        entry.is_end = false;
 
-        tail = (tail + 1) % entries.size();
-        if (tail == head) {
-            // expand the buffer
-            std::vector<common_log_entry> new_entries(2*entries.size());
-
-            size_t new_tail = 0;
-
-            do {
-                new_entries[new_tail] = std::move(entries[head]);
-
-                head     = (head     + 1) % entries.size();
-                new_tail = (new_tail + 1);
-            } while (head != tail);
-
-            head = 0;
-            tail = new_tail;
-
-            for (size_t i = tail; i < new_entries.size(); i++) {
-                new_entries[i].msg.resize(256);
-            }
-
-            entries = std::move(new_entries);
-        }
-        cv.notify_one();
+        tail = (tail + 1) % queue.size();
+        cv_new.notify_one();
     }
 
     void resume() {
@@ -261,23 +266,24 @@ public:
 
         thrd = std::thread([this]() {
             while (true) {
-                {
-                    std::unique_lock<std::mutex> lock(mtx);
-                    cv.wait(lock, [this]() { return head != tail; });
-                    cur = entries[head];
+                std::unique_lock<std::mutex> lock(mtx);
+                cv_new.wait(lock, [this]() { return !is_empty(); });
 
-                    head = (head + 1) % entries.size();
-                }
+                size_t cached_head = head;
+                size_t cached_tail = tail;
 
-                if (cur.is_end) {
+                lock.unlock(); // drop the lock during flush
+
+                size_t next_head;
+                bool stop = flush_queue(cached_head, cached_tail, next_head);
+
+                lock.lock();
+                head = next_head;
+                cv_full.notify_all();
+
+                if (stop) {
                     break;
                 }
-
-                cur.print(); // stdout and stderr
-
-                if (file) {
-                    cur.print(file);
-                }
             }
         });
     }
@@ -293,13 +299,13 @@ public:
             running = false;
 
             // push an entry to signal the worker thread to stop
-            {
-                auto & entry = entries[tail];
-                entry.is_end = true;
+            auto & entry = queue[tail];
+            entry.is_end = true;
+            tail = (tail + 1) % queue.size();
 
-                tail = (tail + 1) % entries.size();
-            }
-            cv.notify_one();
+            // wakeup everyone
+            cv_new.notify_one();
+            cv_full.notify_all();
         }
 
         thrd.join();

From 51571722aa88a8b24d8a274c5fa603b6c3ee9c36 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Tue, 16 Jun 2026 23:21:26 -0700
Subject: [PATCH 010/292] opencl: optimize mul_mat_f16_f32_l4 for decode
 (#24504)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp          |  58 +++-
 .../ggml-opencl/kernels/mul_mv_f16_f32_l4.cl  | 296 ++++++++++++++++++
 2 files changed, 348 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index ca2002424..5ad8d76fa 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -564,6 +564,9 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mat_f16_f32_1row;
     cl_kernel kernel_mul_mat_f16_f32;
     cl_kernel kernel_mul_mat_f16_f32_l4;
+    cl_kernel kernel_mul_mat_f16_f32_l4_dr;
+    cl_kernel kernel_mul_mat_f16_f32_l4_dr_ls;
+    cl_kernel kernel_mul_mat_f16_f32_l4_dr_lq;
     cl_kernel kernel_mul_mat_f16_f32_tiled;
     cl_kernel kernel_adreno_xmem_pack_src_f32;
     cl_kernel kernel_adreno_xmem_prepack_weight_f16;
@@ -1787,6 +1790,11 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
 
         CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4   = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4", &err), err));
+        CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4_dr = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4_dr", &err), err));
+        if (backend_ctx->gpu_family == ADRENO) {
+            CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4_dr_ls = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4_dr_ls", &err), err));
+            CL_CHECK((backend_ctx->kernel_mul_mat_f16_f32_l4_dr_lq = clCreateKernel(backend_ctx->program_mul_mv_f16_f32_l4, "kernel_mul_mat_f16_f32_l4_dr_lq", &err), err));
+        }
         GGML_LOG_CONT(".");
     }
 
@@ -14570,11 +14578,31 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             }
 
             if (src1t == GGML_TYPE_F32) {
+                // heuristic for packing more work for Adreno
+                const bool adreno_use_lane_split =
+                    backend_ctx->gpu_family == ADRENO &&
+                    ne11 == 1 &&
+                    ne01 >= 8 &&
+                    ne00 % 4 == 0 &&
+                    r3 == 1 && r2 >= 1 && r2 <= 8 &&
+                    (ne12 % r2) == 0;
+
                 if (ne11 * ne12 < 4) {
                     kernel = backend_ctx->kernel_mul_mat_f16_f32_1row;
+                } else if (adreno_use_lane_split && ne00 >= 64 && ne00 <= 128) {
+                    kernel = backend_ctx->kernel_mul_mat_f16_f32_l4_dr_lq;
+                    nrows  = 1;
+                } else if (adreno_use_lane_split && r2 >= 2 && ne00 > 128 && ne00 <= 256) {
+                    kernel = backend_ctx->kernel_mul_mat_f16_f32_l4_dr_ls;
+                    nrows  = 1;
                 } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
-                    kernel = backend_ctx->kernel_mul_mat_f16_f32_l4;
-                    nrows = ne11;
+                    if (ne11 == 1) {
+                        kernel = backend_ctx->kernel_mul_mat_f16_f32_l4_dr;
+                        nrows  = 1; // not used by this kernel
+                    } else {
+                        kernel = backend_ctx->kernel_mul_mat_f16_f32_l4;
+                        nrows  = ne11;
+                    }
                 } else {
                     kernel = backend_ctx->kernel_mul_mat_f16_f32;
                     nrows = 4;
@@ -15353,12 +15381,30 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
         backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
     } else {
-        int64_t ny = (ne11 + nrows - 1)/nrows;
+        if (kernel == backend_ctx->kernel_mul_mat_f16_f32_l4_dr) {
+            const int NDST_DR = 4;
+            size_t global_work_size[] = {(size_t)CEIL_DIV(ne01, NDST_DR)*nth0, (size_t)nth1, (size_t)ne12*ne13};
+            size_t local_work_size[]  = {(size_t)nth0, (size_t)nth1, 1};
 
-        size_t global_work_size[] = {(size_t)ne01*nth0, (size_t)ny*nth1, (size_t)ne12*ne13};
-        size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+            backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+        } else if (kernel == backend_ctx->kernel_mul_mat_f16_f32_l4_dr_ls) {
+            size_t global_work_size[] = {(size_t)CEIL_DIV(ne01, 2)*nth0, (size_t)nth1, (size_t)ne02*ne03};
+            size_t local_work_size[]  = {(size_t)nth0, (size_t)nth1, 1};
 
-        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+            backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+        } else if (kernel == backend_ctx->kernel_mul_mat_f16_f32_l4_dr_lq) {
+            size_t global_work_size[] = {(size_t)CEIL_DIV(ne01, 4)*nth0, (size_t)nth1, (size_t)ne02*ne03};
+            size_t local_work_size[]  = {(size_t)nth0, (size_t)nth1, 1};
+
+            backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+        } else {
+            int64_t ny = (ne11 + nrows - 1)/nrows;
+
+            size_t global_work_size[] = {(size_t)ne01*nth0, (size_t)ny*nth1, (size_t)ne12*ne13};
+            size_t local_work_size[] = {(size_t)nth0, (size_t)nth1, 1};
+
+            backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+        }
     }
 }
 
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl
index cdf8197c4..a639ec664 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_f16_f32_l4.cl
@@ -82,3 +82,299 @@ kernel void kernel_mul_mat_f16_f32_l4(
         }
     }
 }
+
+// Each subgroup produces DR_NDST outputs, assumes ne11 == 1
+#define MUL_MAT_F16_F32_L4_DR_NDST 4
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mat_f16_f32_l4_dr(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global float*)((global char*)dst  + offsetd);
+
+    const int r0_base = get_group_id(0) * MUL_MAT_F16_F32_L4_DR_NDST;
+    const int im      = get_group_id(2);
+
+    const int i12 = im % ne12;
+    const int i13 = im / ne12;
+
+    // assume ne11 == 1
+    const ulong offset_src1 = i12*nb12 + i13*nb13;
+    global float4 * y4 = (global float4 *)(src1 + offset_src1);
+
+    global half4 * x4[MUL_MAT_F16_F32_L4_DR_NDST];
+    float          sumf[MUL_MAT_F16_F32_L4_DR_NDST];
+
+    const ulong   k_head_off = (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    #pragma unroll
+    for (int n = 0; n < MUL_MAT_F16_F32_L4_DR_NDST; ++n) {
+        int       r0   = r0_base + n;
+        int       r0c  = r0 < ne01 ? r0 : 0;
+        ulong     off  = (ulong)r0c*nb01 + k_head_off;
+        x4[n]   = (global half4 *)(src0 + off);
+        sumf[n] = 0.0f;
+    }
+
+    const int n_chunks = ne00 / 4;
+    const int sg_size  = get_max_sub_group_size();
+    const int lid      = get_sub_group_local_id();
+
+    for (int i = lid; i < n_chunks; i += sg_size) {
+        float4 q = y4[i];
+        #pragma unroll
+        for (int n = 0; n < MUL_MAT_F16_F32_L4_DR_NDST; ++n) {
+            float4 k = convert_float4(x4[n][i]);
+            sumf[n] = mad(k.s0, q.s0, sumf[n]);
+            sumf[n] = mad(k.s1, q.s1, sumf[n]);
+            sumf[n] = mad(k.s2, q.s2, sumf[n]);
+            sumf[n] = mad(k.s3, q.s3, sumf[n]);
+        }
+    }
+
+    #pragma unroll
+    for (int n = 0; n < MUL_MAT_F16_F32_L4_DR_NDST; ++n) {
+        float reduced = sub_group_reduce_add(sumf[n]);
+        int   r0      = r0_base + n;
+        if (lid == 0 && r0 < ne01) {
+            dst[im*ne1*ne0 + r0] = reduced;
+        }
+    }
+}
+
+// Kernels for decoding, Adreno only for now
+#define MUL_MAT_F16_F32_L4_DR_LS_R2_MAX 8
+
+#ifdef ADRENO_GPU
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+#define sub_group_shuffle_xor(val, mask) qcom_sub_group_shuffle_xor((val), (mask), CLK_SUB_GROUP_SHUFFLE_WIDTH_WAVE_SIZE_QCOM, 0.0f)
+
+REQD_SUBGROUP_SIZE_64
+kernel void kernel_mul_mat_f16_f32_l4_dr_ls(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global float*)((global char*)dst  + offsetd);
+
+    const int r0_base = get_group_id(0) * 2;
+    const int kv_grp  = get_group_id(2);   // KV head group; im = kv_grp*r2 + q
+
+    const int i12_kv = kv_grp % ne02;
+    const int i13_kv = kv_grp / ne02;
+
+    const int lid     = get_sub_group_local_id();
+    const int subhalf = lid >> 5;          // 0 or 1 (which K row in the WG)
+    const int intra   = lid & 31;          // 0..31 (lane within the half)
+
+    const int r0  = r0_base + subhalf;
+    const int r0c = r0 < ne01 ? r0 : 0;    // clamp OOB to row 0; skip write below
+
+    // K row pointer for this lane (one K row per half-wave).
+    const ulong k_off = (ulong)r0c*nb01 + (ulong)i12_kv*nb02 + (ulong)i13_kv*nb03;
+    global half4 * x4 = (global half4 *)(src0 + k_off);
+
+    global float4 * y4[MUL_MAT_F16_F32_L4_DR_LS_R2_MAX];
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        const int i12_q = i12_kv*r2 + q;
+        const ulong q_off = (ulong)i12_q*nb12 + (ulong)i13_kv*nb13;
+        y4[q] = (global float4 *)(src1 + q_off);
+    }
+
+    float partial[MUL_MAT_F16_F32_L4_DR_LS_R2_MAX];
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        partial[q] = 0.0f;
+    }
+
+    const int n_chunks = ne00 / 4;
+
+    for (int i = intra; i < n_chunks; i += 32) {
+        float4 k = convert_float4(x4[i]);
+
+        #pragma unroll
+        for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+            if (q < r2) {
+                float4 v = y4[q][i];
+                partial[q] = mad(k.s0, v.s0, partial[q]);
+                partial[q] = mad(k.s1, v.s1, partial[q]);
+                partial[q] = mad(k.s2, v.s2, partial[q]);
+                partial[q] = mad(k.s3, v.s3, partial[q]);
+            }
+        }
+    }
+
+    // half-wave reduction
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        if (q < r2) {
+            partial[q] += sub_group_shuffle_xor(partial[q],  1u);
+            partial[q] += sub_group_shuffle_xor(partial[q],  2u);
+            partial[q] += sub_group_shuffle_xor(partial[q],  4u);
+            partial[q] += sub_group_shuffle_xor(partial[q],  8u);
+            partial[q] += sub_group_shuffle_xor(partial[q], 16u);
+        }
+    }
+
+    if (intra == 0 && r0 < ne01) {
+        #pragma unroll
+        for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+            if (q < r2) {
+                const int im = i12_kv*r2 + q + i13_kv*ne12;
+                dst[im*ne1*ne0 + r0] = partial[q];
+            }
+        }
+    }
+}
+
+REQD_SUBGROUP_SIZE_64
+kernel void kernel_mul_mat_f16_f32_l4_dr_lq(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global float*)((global char*)dst  + offsetd);
+
+    const int r0_base = get_group_id(0) * 4;
+    const int kv_grp  = get_group_id(2);
+
+    const int i12_kv = kv_grp % ne02;
+    const int i13_kv = kv_grp / ne02;
+
+    const int lid   = get_sub_group_local_id();
+    const int subq  = lid >> 4;            // 0..3 (which K row)
+    const int intra = lid & 15;            // 0..15 (lane within quarter)
+
+    const int r0  = r0_base + subq;
+    const int r0c = r0 < ne01 ? r0 : 0;
+
+    const ulong k_off = (ulong)r0c*nb01 + (ulong)i12_kv*nb02 + (ulong)i13_kv*nb03;
+    global half4 * x4 = (global half4 *)(src0 + k_off);
+
+    global float4 * y4[MUL_MAT_F16_F32_L4_DR_LS_R2_MAX];
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        const int i12_q = i12_kv*r2 + q;
+        const ulong q_off = (ulong)i12_q*nb12 + (ulong)i13_kv*nb13;
+        y4[q] = (global float4 *)(src1 + q_off);
+    }
+
+    float partial[MUL_MAT_F16_F32_L4_DR_LS_R2_MAX];
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        partial[q] = 0.0f;
+    }
+
+    const int n_chunks = ne00 / 4;
+
+    for (int i = intra; i < n_chunks; i += 16) {
+        float4 k = convert_float4(x4[i]);
+
+        #pragma unroll
+        for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+            if (q < r2) {
+                float4 v = y4[q][i];
+                partial[q] = mad(k.s0, v.s0, partial[q]);
+                partial[q] = mad(k.s1, v.s1, partial[q]);
+                partial[q] = mad(k.s2, v.s2, partial[q]);
+                partial[q] = mad(k.s3, v.s3, partial[q]);
+            }
+        }
+    }
+
+    // quarter-wave reduction
+    #pragma unroll
+    for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+        if (q < r2) {
+            partial[q] += sub_group_shuffle_xor(partial[q], 1u);
+            partial[q] += sub_group_shuffle_xor(partial[q], 2u);
+            partial[q] += sub_group_shuffle_xor(partial[q], 4u);
+            partial[q] += sub_group_shuffle_xor(partial[q], 8u);
+        }
+    }
+
+    if (intra == 0 && r0 < ne01) {
+        #pragma unroll
+        for (int q = 0; q < MUL_MAT_F16_F32_L4_DR_LS_R2_MAX; ++q) {
+            if (q < r2) {
+                const int im = i12_kv*r2 + q + i13_kv*ne12;
+                dst[im*ne1*ne0 + r0] = partial[q];
+            }
+        }
+    }
+}
+#endif // ADRENO_GPU

From bae36efa30012d5dbaf842f893c5d034c0ba0157 Mon Sep 17 00:00:00 2001
From: Harapan Rachman <harapanrachman@gmail.com>
Date: Wed, 17 Jun 2026 13:26:30 +0700
Subject: [PATCH 011/292] =?UTF-8?q?UI=20:=20fix=20SSE=20transport=20detect?=
 =?UTF-8?q?ion=20and=20routing=20through=20CORS=20proxy.=20Assi=E2=80=A6?=
 =?UTF-8?q?=20(#24500)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* UI : fix SSE transport detection and routing through CORS proxy. Assisted-by: Antigravity

* ui : replace magic strings with constants in MCP transport handling
---
 tools/ui/src/lib/constants/mcp.ts        |  5 ++
 tools/ui/src/lib/services/mcp.service.ts | 59 +++++++++++++++++++++++-
 tools/ui/src/lib/utils/mcp.ts            | 23 +++++++--
 3 files changed, 82 insertions(+), 5 deletions(-)

diff --git a/tools/ui/src/lib/constants/mcp.ts b/tools/ui/src/lib/constants/mcp.ts
index 918eb9f94..5b11f989e 100644
--- a/tools/ui/src/lib/constants/mcp.ts
+++ b/tools/ui/src/lib/constants/mcp.ts
@@ -84,3 +84,8 @@ export const MCP_TRANSPORT_ICONS: Record<MCPTransportType, Component> = {
 	[MCPTransportType.STREAMABLE_HTTP]: Globe,
 	[MCPTransportType.SSE]: Radio
 };
+
+/** Standard SSE endpoint path indicators */
+export const MCP_SSE_ENDPOINT = '/sse';
+export const MCP_SSE_ENDPOINT_SLASH = '/sse/';
+export const MCP_SSE_ENDPOINT_QUERY = '/sse?';
diff --git a/tools/ui/src/lib/services/mcp.service.ts b/tools/ui/src/lib/services/mcp.service.ts
index d596381aa..0aa58dc5d 100644
--- a/tools/ui/src/lib/services/mcp.service.ts
+++ b/tools/ui/src/lib/services/mcp.service.ts
@@ -16,7 +16,8 @@ import {
 	DEFAULT_MCP_CONFIG,
 	DEFAULT_CLIENT_VERSION,
 	DEFAULT_IMAGE_MIME_TYPE,
-	MCP_PARTIAL_REDACT_HEADERS
+	MCP_PARTIAL_REDACT_HEADERS,
+	CORS_PROXY_ENDPOINT
 } from '$lib/constants';
 import {
 	MCPConnectionPhase,
@@ -236,6 +237,36 @@ export class MCPService {
 
 		return {
 			fetch: async (input, init) => {
+				if (useProxy && typeof window !== 'undefined') {
+					let requestUrlStr = '';
+					if (typeof input === 'string') {
+						requestUrlStr = input;
+					} else if (input instanceof URL) {
+						requestUrlStr = input.href;
+					}
+
+					if (requestUrlStr) {
+						const parsedRequestUrl = new URL(requestUrlStr, window.location.origin);
+						if (
+							parsedRequestUrl.origin === window.location.origin &&
+							!parsedRequestUrl.pathname.includes(CORS_PROXY_ENDPOINT)
+						) {
+							const originalConfigUrl = new URL(config.url);
+							const realTargetUrl = new URL(
+								parsedRequestUrl.pathname + parsedRequestUrl.search,
+								originalConfigUrl.origin
+							);
+							const proxiedUrl = buildProxiedUrl(realTargetUrl.href);
+
+							if (typeof input === 'string') {
+								input = proxiedUrl.href;
+							} else if (input instanceof URL) {
+								input = proxiedUrl;
+							}
+						}
+					}
+				}
+
 				const startedAt = performance.now();
 				const requestHeaders = new Headers(baseInit.headers);
 
@@ -403,6 +434,32 @@ export class MCPService {
 			};
 		}
 
+		if (config.transport === MCPTransportType.SSE) {
+			const url = useProxy ? buildProxiedUrl(config.url) : new URL(config.url);
+			const { fetch: diagnosticFetch, disable: stopPhaseLogging } = this.createDiagnosticFetch(
+				serverName,
+				config,
+				requestInit,
+				url,
+				useProxy,
+				onLog
+			);
+
+			if (import.meta.env.DEV && import.meta.env.VITE_DEBUG) {
+				console.log(`[MCPService] Creating SSE transport for ${url.href}`);
+			}
+
+			return {
+				transport: new SSEClientTransport(url, {
+					requestInit,
+					fetch: diagnosticFetch,
+					eventSourceInit: { fetch: diagnosticFetch }
+				}),
+				type: MCPTransportType.SSE,
+				stopPhaseLogging
+			};
+		}
+
 		const url = useProxy ? buildProxiedUrl(config.url) : new URL(config.url);
 		const { fetch: diagnosticFetch, disable: stopPhaseLogging } = this.createDiagnosticFetch(
 			serverName,
diff --git a/tools/ui/src/lib/utils/mcp.ts b/tools/ui/src/lib/utils/mcp.ts
index 05fe90048..7ce7f19fe 100644
--- a/tools/ui/src/lib/utils/mcp.ts
+++ b/tools/ui/src/lib/utils/mcp.ts
@@ -19,7 +19,10 @@ import {
 	DISPLAY_NAME_SEPARATOR_REGEX,
 	PATH_SEPARATOR,
 	RESOURCE_TEXT_CONTENT_SEPARATOR,
-	DEFAULT_RESOURCE_FILENAME
+	DEFAULT_RESOURCE_FILENAME,
+	MCP_SSE_ENDPOINT,
+	MCP_SSE_ENDPOINT_SLASH,
+	MCP_SSE_ENDPOINT_QUERY
 } from '$lib/constants';
 import {
 	Database,
@@ -41,10 +44,22 @@ import type { MimeTypeUnion } from '$lib/types/common';
 export function detectMcpTransportFromUrl(url: string): MCPTransportType {
 	const normalized = url.trim().toLowerCase();
 
-	return normalized.startsWith(UrlProtocol.WEBSOCKET) ||
+	if (
+		normalized.startsWith(UrlProtocol.WEBSOCKET) ||
 		normalized.startsWith(UrlProtocol.WEBSOCKET_SECURE)
-		? MCPTransportType.WEBSOCKET
-		: MCPTransportType.STREAMABLE_HTTP;
+	) {
+		return MCPTransportType.WEBSOCKET;
+	}
+
+	if (
+		normalized.endsWith(MCP_SSE_ENDPOINT) ||
+		normalized.endsWith(MCP_SSE_ENDPOINT_SLASH) ||
+		normalized.includes(MCP_SSE_ENDPOINT_QUERY)
+	) {
+		return MCPTransportType.SSE;
+	}
+
+	return MCPTransportType.STREAMABLE_HTTP;
 }
 
 /**

From d5376cf5d711b9be87232b25fea82fe62d607400 Mon Sep 17 00:00:00 2001
From: kononnable <kononnable@gmail.com>
Date: Wed, 17 Jun 2026 09:43:45 +0200
Subject: [PATCH 012/292] ci: fix vulkan docker images (#24595)

* Update vulkan-shaders-gen.cpp

* Update vulkan-shaders-gen.cpp

add comment describing code change intention

* Update vulkan-shaders-gen.cpp

fix potential UB
---
 ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 27d088e0e..ca6b44431 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -407,7 +407,7 @@ std::map<std::string, std::string> merge_maps(const std::map<std::string, std::s
     return result;
 }
 
-static std::vector<std::future<void>> compiles;
+static std::deque<std::future<void>> compiles;
 void string_to_spv(std::string name, const std::string& source, const std::map<std::string, std::string>& defines, bool fp16 = true, bool coopmat = false, bool coopmat2 = false, bool f16acc = false, const std::string& suffix = "") {
     name = name + (f16acc ? "_f16acc" : "") + (coopmat ? "_cm1" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32")) + suffix;
     std::string out_path = join_paths(output_dir, name + ".spv");
@@ -426,6 +426,11 @@ void string_to_spv(std::string name, const std::string& source, const std::map<s
         string_to_spv_func, name, input_filepath, out_path, defines, coopmat, generate_dep_file, std::move(slot)));
     // Don't write the same dep file from multiple processes
     generate_dep_file = false;
+
+    // Clean up completed futures - don't accumulate virtual memory for completed threads' stacks.
+    while (!compiles.empty() && compiles.front().wait_for(std::chrono::seconds(0)) == std::future_status::ready) {
+        compiles.pop_front();
+    }
 }
 
 void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool coopmat2, bool f16acc, bool dot2 = false) {

From 097cc91424c5278da5c4d509f62ff1a98433ef69 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Wed, 17 Jun 2026 05:31:16 -0300
Subject: [PATCH 013/292] sd: sync with master-707-5a34bc7 (#2274)

* sd: sync with master-692-9b0fceb

* sd: sync with master-694-276025e

* sd: sync with master-697-5db680c

* sd: sync to master-700-c2df4e1

* sd: sync with master-704-6e66a1a

* sd: sync with master-707-5a34bc7
---
 Makefile                                      |    2 +-
 otherarch/sdcpp/examples/cli/main.cpp         |   23 +-
 otherarch/sdcpp/examples/common/common.cpp    |  167 +-
 otherarch/sdcpp/examples/common/common.h      |   12 +-
 otherarch/sdcpp/include/stable-diffusion.h    |   28 +-
 otherarch/sdcpp/sdtype_adapter.cpp            |    7 +-
 .../sdcpp/src/conditioning/conditioner.hpp    |  430 +++---
 otherarch/sdcpp/src/core/ggml_extend.hpp      | 1009 ++++--------
 .../sdcpp/src/core/ggml_extend_backend.cpp    |   93 +-
 .../sdcpp/src/core/ggml_extend_backend.h      |    7 +-
 otherarch/sdcpp/src/core/ggml_graph_cut.cpp   |  247 ++-
 otherarch/sdcpp/src/core/ggml_graph_cut.h     |   13 +-
 otherarch/sdcpp/src/core/layer_registry.cpp   |  132 --
 otherarch/sdcpp/src/core/layer_registry.h     |   50 -
 otherarch/sdcpp/src/core/util.cpp             |   14 +-
 .../src/extensions/generation_extension.h     |   36 +-
 .../src/extensions/photomaker_extension.cpp   |   89 +-
 .../sdcpp/src/extensions/pulid_extension.cpp  |  123 ++
 otherarch/sdcpp/src/model.h                   |    1 +
 otherarch/sdcpp/src/model/adapter/lora.hpp    |  106 +-
 otherarch/sdcpp/src/model/adapter/pmid.hpp    |   56 +-
 otherarch/sdcpp/src/model/adapter/pulid.hpp   |   76 +
 otherarch/sdcpp/src/model/common/block.hpp    |   17 +-
 otherarch/sdcpp/src/model/diffusion/anima.hpp |   10 +-
 .../sdcpp/src/model/diffusion/control.hpp     |  130 +-
 .../sdcpp/src/model/diffusion/ernie_image.hpp |   10 +-
 otherarch/sdcpp/src/model/diffusion/flux.hpp  |  158 +-
 .../sdcpp/src/model/diffusion/hidream_o1.hpp  |   55 +-
 .../sdcpp/src/model/diffusion/ideogram4.hpp   |   40 +-
 otherarch/sdcpp/src/model/diffusion/lens.hpp  |   10 +-
 otherarch/sdcpp/src/model/diffusion/ltxv.hpp  |   34 +-
 otherarch/sdcpp/src/model/diffusion/mmdit.hpp |   35 +-
 otherarch/sdcpp/src/model/diffusion/model.hpp |   11 +-
 otherarch/sdcpp/src/model/diffusion/pid.hpp   |    8 +-
 .../sdcpp/src/model/diffusion/qwen_image.hpp  |   42 +-
 otherarch/sdcpp/src/model/diffusion/unet.hpp  |    8 +-
 otherarch/sdcpp/src/model/diffusion/wan.hpp   |   39 +-
 .../sdcpp/src/model/diffusion/z_image.hpp     |   38 +-
 otherarch/sdcpp/src/model/te/clip.hpp         |   19 +-
 otherarch/sdcpp/src/model/te/llm.hpp          |   67 +-
 otherarch/sdcpp/src/model/te/t5.hpp           |   57 +-
 otherarch/sdcpp/src/model/upscaler/esrgan.hpp |  314 ++--
 .../model/upscaler/ltx_latent_upscaler.hpp    |  279 ++--
 .../sdcpp/src/model/vae/auto_encoder_kl.hpp   |   32 +-
 .../sdcpp/src/model/vae/ltx_audio_vae.hpp     |   43 +-
 otherarch/sdcpp/src/model/vae/ltx_vae.hpp     |   52 +-
 otherarch/sdcpp/src/model/vae/tae.hpp         |   34 +-
 otherarch/sdcpp/src/model/vae/vae.hpp         |   21 +-
 otherarch/sdcpp/src/model/vae/wan_vae.hpp     |   45 +-
 otherarch/sdcpp/src/model_loader.cpp          |  209 ++-
 otherarch/sdcpp/src/model_loader.h            |   15 +-
 otherarch/sdcpp/src/model_manager.cpp         |  939 ++++++++++++
 otherarch/sdcpp/src/model_manager.h           |  167 ++
 otherarch/sdcpp/src/name_conversion.cpp       |   39 +
 otherarch/sdcpp/src/runtime/guidance.cpp      |    4 +-
 otherarch/sdcpp/src/stable-diffusion.cpp      | 1356 +++++++++--------
 otherarch/sdcpp/src/upscaler.cpp              |   57 +-
 otherarch/sdcpp/src/upscaler.h                |    4 +-
 otherarch/sdcpp/src/weight_manager.h          |   15 +
 59 files changed, 4125 insertions(+), 3009 deletions(-)
 delete mode 100644 otherarch/sdcpp/src/core/layer_registry.cpp
 delete mode 100644 otherarch/sdcpp/src/core/layer_registry.h
 create mode 100644 otherarch/sdcpp/src/extensions/pulid_extension.cpp
 create mode 100644 otherarch/sdcpp/src/model/adapter/pulid.hpp
 create mode 100644 otherarch/sdcpp/src/model_manager.cpp
 create mode 100644 otherarch/sdcpp/src/model_manager.h
 create mode 100644 otherarch/sdcpp/src/weight_manager.h

diff --git a/Makefile b/Makefile
index 2a80ef65f..4f2420a35 100644
--- a/Makefile
+++ b/Makefile
@@ -699,7 +699,7 @@ llama-impl.o: src/llama-impl.cpp src/llama-impl.h
 budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/layer_registry.cpp src/core/layer_registry.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h
+SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
 SDCPP_MAIN_BASENAMES := examples/cli/image_metadata.cpp examples/cli/image_metadata.h examples/cli/main.cpp examples/cli/msf_gif.h examples/common/common.cpp examples/common/common.h examples/common/log.cpp examples/common/log.h examples/common/media_io.cpp examples/common/media_io.h examples/common/resource_owners.hpp src/tokenizers/vocab/clip_merges.hpp src/tokenizers/vocab/gemma2_merges.hpp src/tokenizers/vocab/gemma2_vocab.hpp src/tokenizers/vocab/gemma_merges.hpp src/tokenizers/vocab/gemma_vocab.hpp src/tokenizers/vocab/gpt_oss_merges.hpp src/tokenizers/vocab/gpt_oss_vocab.hpp src/tokenizers/vocab/mistral_merges.hpp src/tokenizers/vocab/mistral_vocab.hpp src/tokenizers/vocab/qwen_merges.hpp src/tokenizers/vocab/t5.hpp src/tokenizers/vocab/umt5.hpp src/tokenizers/vocab/vocab.cpp src/convert.cpp src/version.cpp
 
diff --git a/otherarch/sdcpp/examples/cli/main.cpp b/otherarch/sdcpp/examples/cli/main.cpp
index decee0a94..bb5d6862c 100644
--- a/otherarch/sdcpp/examples/cli/main.cpp
+++ b/otherarch/sdcpp/examples/cli/main.cpp
@@ -623,8 +623,6 @@ int main(int argc, const char* argv[]) {
         }
     }
 
-    bool vae_decode_only = true;
-
     auto load_image_and_update_size = [&](const std::string& path,
                                           SDImageOwner& image,
                                           bool resize_image    = true,
@@ -646,21 +644,18 @@ int main(int argc, const char* argv[]) {
     };
 
     if (gen_params.init_image_path.size() > 0) {
-        vae_decode_only = false;
         if (!load_image_and_update_size(gen_params.init_image_path, gen_params.init_image)) {
             return 1;
         }
     }
 
     if (gen_params.end_image_path.size() > 0) {
-        vae_decode_only = false;
         if (!load_image_and_update_size(gen_params.end_image_path, gen_params.end_image)) {
             return 1;
         }
     }
 
     if (gen_params.ref_image_paths.size() > 0) {
-        vae_decode_only = false;
         gen_params.ref_images.clear();
         for (auto& path : gen_params.ref_image_paths) {
             SDImageOwner ref_image({0, 0, 3, nullptr});
@@ -735,18 +730,7 @@ int main(int argc, const char* argv[]) {
         }
     }
 
-    if (cli_params.mode == VID_GEN) {
-        vae_decode_only = false;
-    }
-
-    if (gen_params.hires_enabled &&
-        (gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_MODEL ||
-         gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_LANCZOS ||
-         gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_NEAREST)) {
-        vae_decode_only = false;
-    }
-
-    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(vae_decode_only, true, cli_params.taesd_preview);
+    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(cli_params.taesd_preview);
 
     SDImageVec results;
     int num_results             = 0;
@@ -798,12 +782,11 @@ int main(int argc, const char* argv[]) {
     int upscale_factor = 4;  // unused for RealESRGAN_x4plus_anime_6B.pth
     if (ctx_params.esrgan_path.size() > 0 && gen_params.upscale_repeats > 0) {
         UpscalerCtxPtr upscaler_ctx(new_upscaler_ctx(ctx_params.esrgan_path.c_str(),
-                                                     ctx_params.offload_params_to_cpu,
                                                      ctx_params.diffusion_conv_direct,
                                                      ctx_params.n_threads,
                                                      gen_params.upscale_tile_size,
-                                                     ctx_params.backend.c_str(),
-                                                     ctx_params.params_backend.c_str()));
+                                                     sd_ctx_params.backend,
+                                                     sd_ctx_params.params_backend));
 
         if (upscaler_ctx == nullptr) {
             LOG_ERROR("new_upscaler_ctx failed");
diff --git a/otherarch/sdcpp/examples/common/common.cpp b/otherarch/sdcpp/examples/common/common.cpp
index 3ae5faba7..dd5d35055 100644
--- a/otherarch/sdcpp/examples/common/common.cpp
+++ b/otherarch/sdcpp/examples/common/common.cpp
@@ -51,6 +51,10 @@ static sd_vae_format_t str_to_vae_format(const std::string& value) {
     return SD_VAE_FORMAT_COUNT;
 }
 
+static void prepend_backend_assignment(std::string& spec, const char* assignment) {
+    spec = spec.empty() ? assignment : std::string(assignment) + "," + spec;
+}
+
 #if defined(_WIN32)
 static std::string utf16_to_utf8(const std::wstring& wstr) {
     if (wstr.empty())
@@ -411,6 +415,10 @@ ArgOptions SDContextParams::get_options() {
          "--photo-maker",
          "path to PHOTOMAKER model",
          &photo_maker_path},
+        {"",
+         "--pulid-weights",
+         "path to PuLID Flux weights",
+         &pulid_weights_path},
         {"",
          "--upscale-model",
          "path to esrgan model.",
@@ -421,8 +429,16 @@ ArgOptions SDContextParams::get_options() {
          &backend},
         {"",
          "--params-backend",
-         "parameter backend assignment, e.g. cpu or diffusion=cpu,clip=cpu",
+         "parameter backend assignment, e.g. disk, cpu, or diffusion=disk,clip=cpu",
          &params_backend},
+        {"",
+         "--rpc-servers",
+         "comma-separated list of RPC servers to connect to for offloading, in the format host:port, e.g. localhost:50052,192.168.1.3:50052",
+         &rpc_servers},
+        {"",
+         "--max-vram",
+         "maximum VRAM budget in GiB for graph-cut segmented execution. Accepts a single value or assignments by backend/device, e.g. 6 or cuda0=6,vulkan0=4. 0 disables graph splitting; a negative value auto-detects free VRAM, sparing the specified value",
+         &max_vram},
     };
 
     options.int_options = {
@@ -437,13 +453,6 @@ ArgOptions SDContextParams::get_options() {
          &chroma_t5_mask_pad},
     };
 
-    options.float_options = {
-        {"",
-         "--max-vram",
-         "maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables graph splitting; a negative value auto-detects free VRAM, sparing the specified value (e.g. -0.5 will keep at least 0.5 GiB free)",
-         &max_vram},
-    };
-
     options.bool_options = {
         {"",
          "--stream-layers",
@@ -463,15 +472,15 @@ ArgOptions SDContextParams::get_options() {
          true, &enable_mmap},
         {"",
          "--control-net-cpu",
-         "keep controlnet in cpu (for low vram)",
+         "deprecated; use --backend controlnet=cpu",
          true, &control_net_cpu},
         {"",
          "--clip-on-cpu",
-         "keep clip in cpu (for low vram)",
+         "deprecated; use --backend te=cpu",
          true, &clip_on_cpu},
         {"",
          "--vae-on-cpu",
-         "keep vae in cpu (for low vram)",
+         "deprecated; use --backend vae=cpu",
          true, &vae_on_cpu},
         {"",
          "--fa",
@@ -688,6 +697,25 @@ bool SDContextParams::resolve_and_validate(SDMode mode) {
     return true;
 }
 
+void SDContextParams::prepare_backend_assignments() {
+    effective_backend        = backend;
+    effective_params_backend = params_backend;
+
+    if (offload_params_to_cpu) {
+        prepend_backend_assignment(effective_params_backend, "*=cpu");
+    }
+
+    if (clip_on_cpu) {
+        prepend_backend_assignment(effective_backend, "te=cpu");
+    }
+    if (vae_on_cpu) {
+        prepend_backend_assignment(effective_backend, "vae=cpu");
+    }
+    if (control_net_cpu) {
+        prepend_backend_assignment(effective_backend, "controlnet=cpu");
+    }
+}
+
 std::string SDContextParams::to_string() const {
     std::ostringstream emb_ss;
     emb_ss << "{\n";
@@ -731,7 +759,7 @@ std::string SDContextParams::to_string() const {
         << "  rng_type: " << sd_rng_type_name(rng_type) << ",\n"
         << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
         << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
-        << "  max_vram: " << max_vram << ",\n"
+        << "  max_vram: \"" << max_vram << "\",\n"
         << "  stream_layers: " << (stream_layers ? "true" : "false") << ",\n"
         << "  backend: \"" << backend << "\",\n"
         << "  params_backend: \"" << params_backend << "\",\n"
@@ -757,7 +785,8 @@ std::string SDContextParams::to_string() const {
     return oss.str();
 }
 
-sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool vae_decode_only, bool free_params_immediately, bool taesd_preview) {
+sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
+    prepare_backend_assignments();
     embedding_vec.clear();
     embedding_vec.reserve(embedding_map.size());
     for (const auto& kv : embedding_map) {
@@ -767,57 +796,53 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool vae_decode_only, bool f
         embedding_vec.emplace_back(item);
     }
 
-    sd_ctx_params_t sd_ctx_params = {
-        model_path.c_str(),
-        clip_l_path.c_str(),
-        clip_g_path.c_str(),
-        clip_vision_path.c_str(),
-        t5xxl_path.c_str(),
-        llm_path.c_str(),
-        llm_vision_path.c_str(),
-        diffusion_model_path.c_str(),
-        high_noise_diffusion_model_path.c_str(),
-        uncond_diffusion_model_path.c_str(),
-        embeddings_connectors_path.c_str(),
-        vae_path.c_str(),
-        audio_vae_path.c_str(),
-        taesd_path.c_str(),
-        control_net_path.c_str(),
-        embedding_vec.data(),
-        static_cast<uint32_t>(embedding_vec.size()),
-        photo_maker_path.c_str(),
-        tensor_type_rules.c_str(),
-        vae_decode_only,
-        free_params_immediately,
-        n_threads,
-        wtype,
-        rng_type,
-        sampler_rng_type,
-        prediction,
-        lora_apply_mode,
-        offload_params_to_cpu,
-        enable_mmap,
-        clip_on_cpu,
-        control_net_cpu,
-        vae_on_cpu,
-        flash_attn,
-        diffusion_flash_attn,
-        taesd_preview,
-        diffusion_conv_direct,
-        vae_conv_direct,
-        circular || circular_x,
-        circular || circular_y,
-        force_sdxl_vae_conv_scale,
-        chroma_use_dit_mask,
-        chroma_use_t5_mask,
-        chroma_t5_mask_pad,
-        qwen_image_zero_cond_t,
-        str_to_vae_format(vae_format),
-        max_vram,
-        stream_layers,
-        backend.c_str(),
-        params_backend.c_str(),
-    };
+    sd_ctx_params_t sd_ctx_params;
+    sd_ctx_params_init(&sd_ctx_params);
+    sd_ctx_params.model_path                      = model_path.c_str();
+    sd_ctx_params.clip_l_path                     = clip_l_path.c_str();
+    sd_ctx_params.clip_g_path                     = clip_g_path.c_str();
+    sd_ctx_params.clip_vision_path                = clip_vision_path.c_str();
+    sd_ctx_params.t5xxl_path                      = t5xxl_path.c_str();
+    sd_ctx_params.llm_path                        = llm_path.c_str();
+    sd_ctx_params.llm_vision_path                 = llm_vision_path.c_str();
+    sd_ctx_params.diffusion_model_path            = diffusion_model_path.c_str();
+    sd_ctx_params.high_noise_diffusion_model_path = high_noise_diffusion_model_path.c_str();
+    sd_ctx_params.uncond_diffusion_model_path     = uncond_diffusion_model_path.c_str();
+    sd_ctx_params.embeddings_connectors_path      = embeddings_connectors_path.c_str();
+    sd_ctx_params.vae_path                        = vae_path.c_str();
+    sd_ctx_params.audio_vae_path                  = audio_vae_path.c_str();
+    sd_ctx_params.taesd_path                      = taesd_path.c_str();
+    sd_ctx_params.control_net_path                = control_net_path.c_str();
+    sd_ctx_params.embeddings                      = embedding_vec.data();
+    sd_ctx_params.embedding_count                 = static_cast<uint32_t>(embedding_vec.size());
+    sd_ctx_params.photo_maker_path                = photo_maker_path.c_str();
+    sd_ctx_params.pulid_weights_path              = pulid_weights_path.c_str();
+    sd_ctx_params.tensor_type_rules               = tensor_type_rules.c_str();
+    sd_ctx_params.n_threads                       = n_threads;
+    sd_ctx_params.wtype                           = wtype;
+    sd_ctx_params.rng_type                        = rng_type;
+    sd_ctx_params.sampler_rng_type                = sampler_rng_type;
+    sd_ctx_params.prediction                      = prediction;
+    sd_ctx_params.lora_apply_mode                 = lora_apply_mode;
+    sd_ctx_params.enable_mmap                     = enable_mmap;
+    sd_ctx_params.flash_attn                      = flash_attn;
+    sd_ctx_params.diffusion_flash_attn            = diffusion_flash_attn;
+    sd_ctx_params.tae_preview_only                = taesd_preview;
+    sd_ctx_params.diffusion_conv_direct           = diffusion_conv_direct;
+    sd_ctx_params.vae_conv_direct                 = vae_conv_direct;
+    sd_ctx_params.circular_x                      = circular || circular_x;
+    sd_ctx_params.circular_y                      = circular || circular_y;
+    sd_ctx_params.force_sdxl_vae_conv_scale       = force_sdxl_vae_conv_scale;
+    sd_ctx_params.chroma_use_dit_mask             = chroma_use_dit_mask;
+    sd_ctx_params.chroma_use_t5_mask              = chroma_use_t5_mask;
+    sd_ctx_params.chroma_t5_mask_pad              = chroma_t5_mask_pad;
+    sd_ctx_params.qwen_image_zero_cond_t          = qwen_image_zero_cond_t;
+    sd_ctx_params.vae_format                      = str_to_vae_format(vae_format);
+    sd_ctx_params.max_vram                        = max_vram.c_str();
+    sd_ctx_params.stream_layers                   = stream_layers;
+    sd_ctx_params.backend                         = effective_backend.c_str();
+    sd_ctx_params.params_backend                  = effective_params_backend.c_str();
+    sd_ctx_params.rpc_servers                     = rpc_servers.c_str();
     return sd_ctx_params;
 }
 
@@ -867,6 +892,10 @@ ArgOptions SDGenerationParams::get_options() {
          "--pm-id-embed-path",
          "path to PHOTOMAKER v2 id embed",
          &pm_id_embed_path},
+        {"",
+         "--pulid-id-embedding",
+         "path to PuLID id embedding",
+         &pulid_id_embedding_path},
         {"",
          "--hires-upscaler",
          "highres fix upscaler, Lanczos, Nearest, Latent, Latent (nearest), Latent (nearest-exact), "
@@ -1017,6 +1046,10 @@ ArgOptions SDGenerationParams::get_options() {
          "--pm-style-strength",
          "",
          &pm_style_strength},
+        {"",
+         "--pulid-id-weight",
+         "strength of PuLID identity injection",
+         &pulid_id_weight},
         {"",
          "--control-strength",
          "strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image",
@@ -2249,6 +2282,11 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
         pm_style_strength,
     };
 
+    sd_pulid_params_t pulid_params = {
+        pulid_id_embedding_path.empty() ? nullptr : pulid_id_embedding_path.c_str(),
+        pulid_id_weight,
+    };
+
     params.loras                 = lora_vec.empty() ? nullptr : lora_vec.data();
     params.lora_count            = static_cast<uint32_t>(lora_vec.size());
     params.prompt                = prompt.c_str();
@@ -2269,6 +2307,7 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
     params.control_image         = control_image.get();
     params.control_strength      = control_strength;
     params.pm_params             = pm_params;
+    params.pulid_params          = pulid_params;
     params.vae_tiling_params     = vae_tiling_params;
     params.cache                 = cache_params;
 
diff --git a/otherarch/sdcpp/examples/common/common.h b/otherarch/sdcpp/examples/common/common.h
index a90a33132..fcf9840db 100644
--- a/otherarch/sdcpp/examples/common/common.h
+++ b/otherarch/sdcpp/examples/common/common.h
@@ -133,6 +133,7 @@ struct SDContextParams {
     std::string control_net_path;
     std::string embedding_dir;
     std::string photo_maker_path;
+    std::string pulid_weights_path;
     sd_type_t wtype = SD_TYPE_COUNT;
     std::string tensor_type_rules;
     std::string lora_model_dir = ".";
@@ -144,10 +145,13 @@ struct SDContextParams {
     rng_type_t rng_type         = CUDA_RNG;
     rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
     bool offload_params_to_cpu  = false;
-    float max_vram              = 0.f;
+    std::string max_vram        = "0";
     bool stream_layers          = false;
     std::string backend;
     std::string params_backend;
+    std::string rpc_servers;
+    std::string effective_backend;
+    std::string effective_params_backend;
     bool enable_mmap           = false;
     bool control_net_cpu       = false;
     bool clip_on_cpu           = false;
@@ -175,11 +179,12 @@ struct SDContextParams {
     float flow_shift = INFINITY;
     ArgOptions get_options();
     void build_embedding_map();
+    void prepare_backend_assignments();
     bool resolve(SDMode mode);
     bool validate(SDMode mode);
     bool resolve_and_validate(SDMode mode);
     std::string to_string() const;
-    sd_ctx_params_t to_sd_ctx_params_t(bool vae_decode_only, bool free_params_immediately, bool taesd_preview);
+    sd_ctx_params_t to_sd_ctx_params_t(bool taesd_preview);
 };
 
 struct SDGenerationParams {
@@ -230,6 +235,9 @@ struct SDGenerationParams {
     std::string pm_id_embed_path;
     float pm_style_strength = 20.f;
 
+    std::string pulid_id_embedding_path;
+    float pulid_id_weight = 1.0f;
+
     int upscale_repeats   = 1;
     int upscale_tile_size = 128;
 
diff --git a/otherarch/sdcpp/include/stable-diffusion.h b/otherarch/sdcpp/include/stable-diffusion.h
index 2175f895a..1c04367b1 100644
--- a/otherarch/sdcpp/include/stable-diffusion.h
+++ b/otherarch/sdcpp/include/stable-diffusion.h
@@ -195,20 +195,15 @@ typedef struct {
     const sd_embedding_t* embeddings;
     uint32_t embedding_count;
     const char* photo_maker_path;
+    const char* pulid_weights_path;
     const char* tensor_type_rules;
-    bool vae_decode_only;
-    bool free_params_immediately;
     int n_threads;
     enum sd_type_t wtype;
     enum rng_type_t rng_type;
     enum rng_type_t sampler_rng_type;
     enum prediction_t prediction;
     enum lora_apply_mode_t lora_apply_mode;
-    bool offload_params_to_cpu;
     bool enable_mmap;
-    bool keep_clip_on_cpu;
-    bool keep_control_net_on_cpu;
-    bool keep_vae_on_cpu;
     bool flash_attn;
     bool diffusion_flash_attn;
     bool tae_preview_only;
@@ -222,10 +217,11 @@ typedef struct {
     int chroma_t5_mask_pad;
     bool qwen_image_zero_cond_t;
     enum sd_vae_format_t vae_format;
-    float max_vram;  // GiB budget for graph-cut segmented param offload (0 = disabled, -1 = auto free VRAM minus 1 GiB)
+    const char* max_vram;  // GiB budget or backend assignment spec for graph-cut segmented param offload (0 = disabled, -1 = auto)
     bool stream_layers;  // Enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram)
     const char* backend;
     const char* params_backend;
+    const char* rpc_servers;
 } sd_ctx_params_t;
 
 typedef struct {
@@ -277,6 +273,11 @@ typedef struct {
     float style_strength;
 } sd_pm_params_t;  // photo maker
 
+typedef struct {
+    const char* id_embedding_path;
+    float id_weight;
+} sd_pulid_params_t;
+
 enum sd_cache_mode_t {
     SD_CACHE_DISABLED = 0,
     SD_CACHE_EASYCACHE,
@@ -369,6 +370,7 @@ typedef struct {
     sd_image_t control_image;
     float control_strength;
     sd_pm_params_t pm_params;
+    sd_pulid_params_t pulid_params;
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
     sd_hires_params_t hires;
@@ -450,6 +452,17 @@ SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
 SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);
 
+enum sd_cancel_mode_t {
+    // Stop the current generation as soon as possible.
+    SD_CANCEL_ALL,
+    // Finish the current image sample, then skip additional batch latents and return completed images.
+    SD_CANCEL_NEW_LATENTS,
+    // Clear a pending cancellation request.
+    SD_CANCEL_RESET
+};
+
+SD_API void sd_cancel_generation(sd_ctx_t* sd_ctx, enum sd_cancel_mode_t mode);
+
 SD_API void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params);
 SD_API bool generate_video(sd_ctx_t* sd_ctx,
                            const sd_vid_gen_params_t* sd_vid_gen_params,
@@ -460,7 +473,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
 typedef struct upscaler_ctx_t upscaler_ctx_t;
 
 SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
-                                        bool offload_params_to_cpu,
                                         bool direct,
                                         int n_threads,
                                         int tile_size,
diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index c37c9fcb8..839f95141 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -412,8 +412,10 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     } else if (inputs.use_mmap) {
         printf("Using mmap for I/O\n");
     }
+    std::string max_vram;
     if(inputs.max_vram != 0.f) {
         printf("Using max VRAM = %0.2f GB\n", inputs.max_vram);
+        max_vram = std::to_string(inputs.max_vram);
     }
     if(inputs.quant > 0)
     {
@@ -470,8 +472,6 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     params.taesd_path = sd_params->taesd_path.c_str();
     params.photo_maker_path = sd_params->stacked_id_embeddings_path.c_str();
 
-    params.vae_decode_only = false;
-    params.free_params_immediately = false;
     params.rng_type = CUDA_RNG;
 
     params.n_threads = sd_params->n_threads;
@@ -480,7 +480,7 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     params.diffusion_conv_direct = sd_params->diffusion_conv_direct;
     params.vae_conv_direct = sd_params->vae_conv_direct;
     params.chroma_use_dit_mask = true;
-    params.max_vram = inputs.max_vram;
+    params.max_vram = max_vram.c_str();
     params.stream_layers = inputs.stream_layers;
     params.enable_mmap = inputs.use_mmap;
     params.params_backend = inputs.offload_cpu ? "CPU" : "";
@@ -539,7 +539,6 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     if (upscaler_filename!="") {
         const int upscale_tile_size = 128;
         upscaler_ctx = new_upscaler_ctx(upscaler_filename.c_str(),
-                                        inputs.offload_cpu,
                                         params.diffusion_conv_direct,
                                         params.n_threads,
                                         upscale_tile_size,
diff --git a/otherarch/sdcpp/src/conditioning/conditioner.hpp b/otherarch/sdcpp/src/conditioning/conditioner.hpp
index 5e74af073..b5dda4c0e 100644
--- a/otherarch/sdcpp/src/conditioning/conditioner.hpp
+++ b/otherarch/sdcpp/src/conditioning/conditioner.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
+#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
 #define __SD_CONDITIONING_CONDITIONER_HPP__
 
 #include <cmath>
@@ -113,14 +113,12 @@ struct Conditioner {
 public:
     virtual SDCondition get_learned_condition(int n_threads,
                                               const ConditionerParams& conditioner_params) = 0;
-    virtual bool alloc_params_buffer()                                                     = 0;
-    virtual void free_params_buffer()                                                      = 0;
     virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors)           = 0;
-    virtual size_t get_params_buffer_size()                                                = 0;
     virtual void set_max_graph_vram_bytes(size_t max_vram_bytes) {}
     virtual void set_stream_layers_enabled(bool enabled) {}
     virtual void set_flash_attention_enabled(bool enabled) = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
+    virtual void runner_done() {}
 };
 
 // ldm.modules.encoders.modules.FrozenCLIPEmbedder
@@ -138,10 +136,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
     std::map<std::string, std::pair<int, int>> embedding_pos_map;
 
     FrozenCLIPEmbedderWithCustomWords(ggml_backend_t backend,
-                                      ggml_backend_t params_backend,
                                       const String2TensorStorage& tensor_storage_map,
                                       const std::map<std::string, std::string>& orig_embedding_map,
-                                      SDVersion version = VERSION_SD1)
+                                      SDVersion version                                   = VERSION_SD1,
+                                      std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : version(version), tokenizer(sd_version_is_sd2(version) ? 0 : 49407) {
         for (const auto& kv : orig_embedding_map) {
             std::string name = kv.first;
@@ -151,12 +149,12 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
         bool force_clip_f32 = !embedding_map.empty();
         if (sd_version_is_sd1(version)) {
-            text_model = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32);
+            text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, force_clip_f32, weight_manager);
         } else if (sd_version_is_sd2(version)) {
-            text_model = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32);
+            text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, true, force_clip_f32, weight_manager);
         } else if (sd_version_is_sdxl(version)) {
-            text_model  = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32);
-            text_model2 = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32);
+            text_model  = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, force_clip_f32, weight_manager);
+            text_model2 = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, force_clip_f32, weight_manager);
         }
     }
 
@@ -167,33 +165,6 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
     }
 
-    bool alloc_params_buffer() override {
-        if (!text_model->alloc_params_buffer()) {
-            return false;
-        }
-        if (sd_version_is_sdxl(version)) {
-            if (!text_model2->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        text_model->free_params_buffer();
-        if (sd_version_is_sdxl(version)) {
-            text_model2->free_params_buffer();
-        }
-    }
-
-    size_t get_params_buffer_size() override {
-        size_t buffer_size = text_model->get_params_buffer_size();
-        if (sd_version_is_sdxl(version)) {
-            buffer_size += text_model2->get_params_buffer_size();
-        }
-        return buffer_size;
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         text_model->set_max_graph_vram_bytes(max_vram_bytes);
         if (sd_version_is_sdxl(version)) {
@@ -222,6 +193,13 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
     }
 
+    void runner_done() override {
+        text_model->runner_done();
+        if (sd_version_is_sdxl(version)) {
+            text_model2->runner_done();
+        }
+    }
+
     bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t>& bpe_tokens) {
         ModelLoader model_loader;
         if (!model_loader.init_from_file_and_convert_name(embd_path)) {
@@ -263,7 +241,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
             }
             return true;
         };
-        model_loader.load_tensors(on_load, 1);
+        model_loader.set_n_threads(1);
+        model_loader.load_tensors(on_load);
         int pos_start = num_custom_embeddings;
         if (embd) {
             int64_t hidden_size = text_model->model.hidden_size;
@@ -432,7 +411,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                                                token_embed_custom.data(),
                                                                max_token_idx,
                                                                false,
-                                                               clip_skip);
+                                                               clip_skip,
+                                                               false,
+                                                               true,
+                                                               true);
                 GGML_ASSERT(!chunk_hidden_states.empty());
                 if (sd_version_is_sdxl(version)) {
                     auto chunk_hidden_states2 = text_model2->compute(n_threads,
@@ -441,7 +423,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                                                      token_embed_custom.data(),
                                                                      max_token_idx,
                                                                      false,
-                                                                     clip_skip);
+                                                                     clip_skip,
+                                                                     false,
+                                                                     true,
+                                                                     true);
                     GGML_ASSERT(!chunk_hidden_states2.empty());
                     chunk_hidden_states = sd::ops::concat(chunk_hidden_states, chunk_hidden_states2, 0);
 
@@ -452,7 +437,10 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                                       token_embed_custom.data(),
                                                       max_token_idx,
                                                       true,
-                                                      clip_skip);
+                                                      clip_skip,
+                                                      false,
+                                                      true,
+                                                      true);
                         GGML_ASSERT(!pooled.empty());
                     }
                 }
@@ -523,15 +511,15 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
 
 struct FrozenCLIPVisionEmbedder : public GGMLRunner {
     CLIPVisionModelProjection vision_model;
+    std::string weight_prefix = "cond_stage_model.transformer";
 
     FrozenCLIPVisionEmbedder(ggml_backend_t backend,
-                             ggml_backend_t params_backend,
-                             const String2TensorStorage& tensor_storage_map = {})
-        : GGMLRunner(backend, params_backend) {
-        std::string prefix = "cond_stage_model.transformer";
-        bool proj_in       = false;
+                             const String2TensorStorage& tensor_storage_map      = {},
+                             std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager) {
+        bool proj_in = false;
         for (const auto& [name, tensor_storage] : tensor_storage_map) {
-            if (!starts_with(name, prefix)) {
+            if (!starts_with(name, weight_prefix)) {
                 continue;
             }
             if (contains(name, "self_attn.in_proj")) {
@@ -540,7 +528,7 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
             }
         }
         vision_model = CLIPVisionModelProjection(OPEN_CLIP_VIT_H_14, false, proj_in);
-        vision_model.init(params_ctx, tensor_storage_map, prefix);
+        vision_model.init(params_ctx, tensor_storage_map, weight_prefix);
     }
 
     std::string get_desc() override {
@@ -548,7 +536,7 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
     }
 
     void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
-        vision_model.get_param_tensors(tensors, "cond_stage_model.transformer");
+        vision_model.get_param_tensors(tensors, weight_prefix);
     }
 
     ggml_cgraph* build_graph(const sd::Tensor<float>& pixel_values_tensor, bool return_pooled, int clip_skip) {
@@ -571,7 +559,7 @@ struct FrozenCLIPVisionEmbedder : public GGMLRunner {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(pixel_values, return_pooled, clip_skip);
         };
-        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
+        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true, true, true));
     }
 };
 
@@ -584,8 +572,8 @@ struct SD3CLIPEmbedder : public Conditioner {
     std::shared_ptr<T5Runner> t5;
 
     SD3CLIPEmbedder(ggml_backend_t backend,
-                    ggml_backend_t params_backend,
-                    const String2TensorStorage& tensor_storage_map = {})
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : clip_g_tokenizer(0) {
         bool use_clip_l = false;
         bool use_clip_g = false;
@@ -604,13 +592,13 @@ struct SD3CLIPEmbedder : public Conditioner {
             return;
         }
         if (use_clip_l) {
-            clip_l = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
+            clip_l = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false, false, weight_manager);
         }
         if (use_clip_g) {
-            clip_g = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
+            clip_g = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false, false, weight_manager);
         }
         if (use_t5) {
-            t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer");
+            t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", false, weight_manager);
         }
     }
 
@@ -626,51 +614,6 @@ struct SD3CLIPEmbedder : public Conditioner {
         }
     }
 
-    bool alloc_params_buffer() override {
-        if (clip_l) {
-            if (!clip_l->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        if (clip_g) {
-            if (!clip_g->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        if (t5) {
-            if (!t5->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        if (clip_l) {
-            clip_l->free_params_buffer();
-        }
-        if (clip_g) {
-            clip_g->free_params_buffer();
-        }
-        if (t5) {
-            t5->free_params_buffer();
-        }
-    }
-
-    size_t get_params_buffer_size() override {
-        size_t buffer_size = 0;
-        if (clip_l) {
-            buffer_size += clip_l->get_params_buffer_size();
-        }
-        if (clip_g) {
-            buffer_size += clip_g->get_params_buffer_size();
-        }
-        if (t5) {
-            buffer_size += t5->get_params_buffer_size();
-        }
-        return buffer_size;
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         if (clip_l) {
             clip_l->set_max_graph_vram_bytes(max_vram_bytes);
@@ -719,6 +662,18 @@ struct SD3CLIPEmbedder : public Conditioner {
         }
     }
 
+    void runner_done() override {
+        if (clip_l) {
+            clip_l->runner_done();
+        }
+        if (clip_g) {
+            clip_g->runner_done();
+        }
+        if (t5) {
+            t5->runner_done();
+        }
+    }
+
     std::vector<std::pair<std::vector<int>, std::vector<float>>> tokenize(std::string text,
                                                                           size_t min_length          = 0,
                                                                           size_t max_length          = 0,
@@ -834,7 +789,10 @@ struct SD3CLIPEmbedder : public Conditioner {
                                                         nullptr,
                                                         max_token_idx,
                                                         false,
-                                                        clip_skip);
+                                                        clip_skip,
+                                                        false,
+                                                        true,
+                                                        true);
                 GGML_ASSERT(!chunk_hidden_states_l.empty());
                 chunk_hidden_states_l = ::apply_token_weights(std::move(chunk_hidden_states_l), chunk_weights);
 
@@ -847,7 +805,10 @@ struct SD3CLIPEmbedder : public Conditioner {
                                                     nullptr,
                                                     max_token_idx,
                                                     true,
-                                                    clip_skip);
+                                                    clip_skip,
+                                                    false,
+                                                    true,
+                                                    true);
                     GGML_ASSERT(!pooled_l.empty());
                 }
             } else {
@@ -875,7 +836,10 @@ struct SD3CLIPEmbedder : public Conditioner {
                                                         nullptr,
                                                         max_token_idx,
                                                         false,
-                                                        clip_skip);
+                                                        clip_skip,
+                                                        false,
+                                                        true,
+                                                        true);
                 GGML_ASSERT(!chunk_hidden_states_g.empty());
                 chunk_hidden_states_g = ::apply_token_weights(std::move(chunk_hidden_states_g), chunk_weights);
 
@@ -888,7 +852,10 @@ struct SD3CLIPEmbedder : public Conditioner {
                                                     nullptr,
                                                     max_token_idx,
                                                     true,
-                                                    clip_skip);
+                                                    clip_skip,
+                                                    false,
+                                                    true,
+                                                    true);
                     GGML_ASSERT(!pooled_g.empty());
                 }
             } else {
@@ -910,7 +877,10 @@ struct SD3CLIPEmbedder : public Conditioner {
 
                 chunk_hidden_states_t5 = t5->compute(n_threads,
                                                      input_ids,
-                                                     sd::Tensor<float>());
+                                                     sd::Tensor<float>(),
+                                                     false,
+                                                     true,
+                                                     true);
                 GGML_ASSERT(!chunk_hidden_states_t5.empty());
                 chunk_hidden_states_t5 = ::apply_token_weights(std::move(chunk_hidden_states_t5), chunk_weights);
             } else {
@@ -971,8 +941,8 @@ struct FluxCLIPEmbedder : public Conditioner {
     size_t chunk_len = 256;
 
     FluxCLIPEmbedder(ggml_backend_t backend,
-                     ggml_backend_t params_backend,
-                     const String2TensorStorage& tensor_storage_map = {}) {
+                     const String2TensorStorage& tensor_storage_map      = {},
+                     std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
         bool use_clip_l = false;
         bool use_t5     = false;
         for (auto pair : tensor_storage_map) {
@@ -989,12 +959,12 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
 
         if (use_clip_l) {
-            clip_l = std::make_shared<CLIPTextModelRunner>(backend, params_backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
+            clip_l = std::make_shared<CLIPTextModelRunner>(backend, tensor_storage_map, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true, false, weight_manager);
         } else {
             LOG_WARN("clip_l text encoder not found! Prompt adherence might be degraded.");
         }
         if (use_t5) {
-            t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer");
+            t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", false, weight_manager);
         } else {
             LOG_WARN("t5xxl text encoder not found! Prompt adherence might be degraded.");
         }
@@ -1009,40 +979,6 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
     }
 
-    bool alloc_params_buffer() override {
-        if (clip_l) {
-            if (!clip_l->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        if (t5) {
-            if (!t5->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        if (clip_l) {
-            clip_l->free_params_buffer();
-        }
-        if (t5) {
-            t5->free_params_buffer();
-        }
-    }
-
-    size_t get_params_buffer_size() override {
-        size_t buffer_size = 0;
-        if (clip_l) {
-            buffer_size += clip_l->get_params_buffer_size();
-        }
-        if (t5) {
-            buffer_size += t5->get_params_buffer_size();
-        }
-        return buffer_size;
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         if (clip_l) {
             clip_l->set_max_graph_vram_bytes(max_vram_bytes);
@@ -1070,7 +1006,7 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
     }
 
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         if (clip_l) {
             clip_l->set_weight_adapter(adapter);
         }
@@ -1079,6 +1015,15 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
     }
 
+    void runner_done() override {
+        if (clip_l) {
+            clip_l->runner_done();
+        }
+        if (t5) {
+            t5->runner_done();
+        }
+    }
+
     std::vector<std::pair<std::vector<int>, std::vector<float>>> tokenize(std::string text,
                                                                           size_t min_length = 0,
                                                                           size_t max_length = 0) {
@@ -1177,7 +1122,10 @@ struct FluxCLIPEmbedder : public Conditioner {
                                              nullptr,
                                              max_token_idx,
                                              true,
-                                             clip_skip);
+                                             clip_skip,
+                                             false,
+                                             true,
+                                             true);
                     GGML_ASSERT(!pooled.empty());
                 } else {
                     pooled = sd::Tensor<float>::zeros({768});
@@ -1195,7 +1143,10 @@ struct FluxCLIPEmbedder : public Conditioner {
                 sd::Tensor<int32_t> input_ids({static_cast<int64_t>(chunk_tokens.size())}, chunk_tokens);
                 chunk_hidden_states = t5->compute(n_threads,
                                                   input_ids,
-                                                  sd::Tensor<float>());
+                                                  sd::Tensor<float>(),
+                                                  false,
+                                                  true,
+                                                  true);
                 GGML_ASSERT(!chunk_hidden_states.empty());
                 chunk_hidden_states = ::apply_token_weights(std::move(chunk_hidden_states), chunk_weights);
                 if (zero_out_masked) {
@@ -1239,11 +1190,11 @@ struct T5CLIPEmbedder : public Conditioner {
     bool is_umt5     = false;
 
     T5CLIPEmbedder(ggml_backend_t backend,
-                   ggml_backend_t params_backend,
-                   const String2TensorStorage& tensor_storage_map = {},
-                   bool use_mask                                  = false,
-                   int mask_pad                                   = 0,
-                   bool is_umt5                                   = false)
+                   const String2TensorStorage& tensor_storage_map      = {},
+                   bool use_mask                                       = false,
+                   int mask_pad                                        = 0,
+                   bool is_umt5                                        = false,
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
         bool use_t5 = false;
         for (auto pair : tensor_storage_map) {
@@ -1256,7 +1207,7 @@ struct T5CLIPEmbedder : public Conditioner {
             LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
             return;
         } else {
-            t5 = std::make_shared<T5Runner>(backend, params_backend, tensor_storage_map, "text_encoders.t5xxl.transformer", is_umt5);
+            t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", is_umt5, weight_manager);
         }
     }
 
@@ -1266,29 +1217,6 @@ struct T5CLIPEmbedder : public Conditioner {
         }
     }
 
-    bool alloc_params_buffer() override {
-        if (t5) {
-            if (!t5->alloc_params_buffer()) {
-                return false;
-            }
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        if (t5) {
-            t5->free_params_buffer();
-        }
-    }
-
-    size_t get_params_buffer_size() override {
-        size_t buffer_size = 0;
-        if (t5) {
-            buffer_size += t5->get_params_buffer_size();
-        }
-        return buffer_size;
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         if (t5) {
             t5->set_max_graph_vram_bytes(max_vram_bytes);
@@ -1313,6 +1241,12 @@ struct T5CLIPEmbedder : public Conditioner {
         }
     }
 
+    void runner_done() override {
+        if (t5) {
+            t5->runner_done();
+        }
+    }
+
     std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
                                                                                   size_t min_length = 0,
                                                                                   size_t max_length = 0) {
@@ -1406,7 +1340,10 @@ struct T5CLIPEmbedder : public Conditioner {
 
             auto chunk_hidden_states = t5->compute(n_threads,
                                                    input_ids,
-                                                   t5_attn_mask_chunk);
+                                                   t5_attn_mask_chunk,
+                                                   false,
+                                                   true,
+                                                   true);
             GGML_ASSERT(!chunk_hidden_states.empty());
             chunk_hidden_states = apply_token_weights(std::move(chunk_hidden_states), chunk_weights);
 
@@ -1450,36 +1387,21 @@ struct AnimaConditioner : public Conditioner {
     std::shared_ptr<LLM::LLMRunner> llm;
 
     AnimaConditioner(ggml_backend_t backend,
-                     ggml_backend_t params_backend,
-                     const String2TensorStorage& tensor_storage_map = {}) {
+                     const String2TensorStorage& tensor_storage_map      = {},
+                     std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
         qwen_tokenizer = std::make_shared<Qwen2Tokenizer>();
         llm            = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::QWEN3,
                                                backend,
-                                               params_backend,
                                                tensor_storage_map,
                                                "text_encoders.llm",
-                                               false);
+                                               false,
+                                               weight_manager);
     }
 
     void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         llm->get_param_tensors(tensors, "text_encoders.llm");
     }
 
-    bool alloc_params_buffer() override {
-        if (!llm->alloc_params_buffer()) {
-            return false;
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        llm->free_params_buffer();
-    }
-
-    size_t get_params_buffer_size() override {
-        return llm->get_params_buffer_size();
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         llm->set_max_graph_vram_bytes(max_vram_bytes);
     }
@@ -1496,6 +1418,10 @@ struct AnimaConditioner : public Conditioner {
         llm->set_weight_adapter(adapter);
     }
 
+    void runner_done() override {
+        llm->runner_done();
+    }
+
     std::tuple<std::vector<int>, std::vector<float>, std::vector<int>, std::vector<float>> tokenize(std::string text) {
         auto parsed_attention = parse_prompt_attention(text);
 
@@ -1553,7 +1479,11 @@ struct AnimaConditioner : public Conditioner {
                                           input_ids,
                                           sd::Tensor<float>(),
                                           {},
-                                          {});
+                                          {},
+                                          false,
+                                          false,
+                                          true,
+                                          true);
         GGML_ASSERT(!hidden_states.empty());
         hidden_states         = apply_token_weights(std::move(hidden_states), qwen_weights);
         auto t5_ids_tensor    = sd::Tensor<int32_t>::from_vector(t5_tokens);
@@ -1576,11 +1506,11 @@ struct LLMEmbedder : public Conditioner {
     std::shared_ptr<LLM::LLMRunner> llm;
 
     LLMEmbedder(ggml_backend_t backend,
-                ggml_backend_t params_backend,
-                const String2TensorStorage& tensor_storage_map = {},
-                SDVersion version                              = VERSION_QWEN_IMAGE,
-                const std::string prefix                       = "",
-                bool enable_vision                             = false)
+                const String2TensorStorage& tensor_storage_map      = {},
+                SDVersion version                                   = VERSION_QWEN_IMAGE,
+                const std::string prefix                            = "",
+                bool enable_vision                                  = false,
+                std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : version(version) {
         LLM::LLMArch arch = LLM::LLMArch::QWEN2_5_VL;
         if (version == VERSION_FLUX2) {
@@ -1607,33 +1537,16 @@ struct LLMEmbedder : public Conditioner {
         }
         llm = std::make_shared<LLM::LLMRunner>(arch,
                                                backend,
-                                               params_backend,
                                                tensor_storage_map,
                                                "text_encoders.llm",
-                                               enable_vision);
+                                               enable_vision,
+                                               weight_manager);
     }
 
     void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         llm->get_param_tensors(tensors, "text_encoders.llm");
     }
 
-    bool alloc_params_buffer() override {
-        if (!llm->alloc_params_buffer()) {
-            return false;
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        llm->free_params_buffer();
-    }
-
-    size_t get_params_buffer_size() override {
-        size_t buffer_size = 0;
-        buffer_size += llm->get_params_buffer_size();
-        return buffer_size;
-    }
-
     void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
         llm->set_max_graph_vram_bytes(max_vram_bytes);
     }
@@ -1652,6 +1565,12 @@ struct LLMEmbedder : public Conditioner {
         }
     }
 
+    void runner_done() override {
+        if (llm) {
+            llm->runner_done();
+        }
+    }
+
     std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
                                                                                   const std::pair<int, int>& attn_range,
                                                                                   size_t min_length = 0,
@@ -1747,7 +1666,11 @@ struct LLMEmbedder : public Conditioner {
                                           input_ids,
                                           attention_mask,
                                           image_embeds,
-                                          out_layers);
+                                          out_layers,
+                                          false,
+                                          false,
+                                          true,
+                                          true);
         GGML_ASSERT(!hidden_states.empty());
         hidden_states = apply_token_weights(std::move(hidden_states), weights);
         GGML_ASSERT(hidden_states.shape()[1] > prompt_template_encode_start_idx);
@@ -1825,7 +1748,7 @@ struct LLMEmbedder : public Conditioner {
 
                     auto resized_image = clip_preprocess(image, w_bar, h_bar);
 
-                    auto image_embed = llm->encode_image(n_threads, resized_image);
+                    auto image_embed = llm->encode_image(n_threads, resized_image, false, true, true);
                     GGML_ASSERT(!image_embed.empty());
                     image_embeds.emplace_back(image_embed_idx, image_embed);
                     image_embed_idx += 1 + static_cast<int>(image_embed.shape()[1]) + 6;
@@ -1895,7 +1818,7 @@ struct LLMEmbedder : public Conditioner {
                     LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, height, width, h_bar, w_bar);
 
                     auto resized_image = clip_preprocess(image, w_bar, h_bar);
-                    auto image_embed   = llm->encode_image(n_threads, resized_image);
+                    auto image_embed   = llm->encode_image(n_threads, resized_image, false, true, true);
                     GGML_ASSERT(!image_embed.empty());
                     image_embeds.emplace_back(image_embed_idx, image_embed);
                     image_embed_idx += 1 + static_cast<int>(image_embed.shape()[1]) + 6;
@@ -2138,10 +2061,10 @@ struct LTXAVTextProjectionRunner : public GGMLRunner {
     LTXAVTextProjection model;
 
     LTXAVTextProjectionRunner(ggml_backend_t backend,
-                              ggml_backend_t params_backend,
-                              const String2TensorStorage& tensor_storage_map = {},
-                              const std::string& prefix                      = "")
-        : GGMLRunner(backend, params_backend),
+                              const String2TensorStorage& tensor_storage_map      = {},
+                              const std::string& prefix                           = "",
+                              std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager),
           model(tensor_storage_map.find(prefix + ".video_aggregate_embed.weight") != tensor_storage_map.end()) {
         model.init(params_ctx, tensor_storage_map, prefix);
     }
@@ -2163,11 +2086,15 @@ struct LTXAVTextProjectionRunner : public GGMLRunner {
         return gf;
     }
 
-    sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& x) {
+    sd::Tensor<float> compute(int n_threads,
+                              const sd::Tensor<float>& x,
+                              bool auto_free           = true,
+                              bool free_compute_buffer = true,
+                              bool free_compute_params = true) {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(x);
         };
-        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
+        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params));
     }
 };
 
@@ -2182,22 +2109,22 @@ struct LTXAVEmbedder : public Conditioner {
     bool dual_projection = false;
 
     LTXAVEmbedder(ggml_backend_t backend,
-                  ggml_backend_t params_backend,
-                  const String2TensorStorage& tensor_storage_map = {},
-                  const std::string& llm_prefix                  = "text_encoders.llm",
-                  const std::string& projector_prefix            = "text_embedding_projection") {
+                  const String2TensorStorage& tensor_storage_map      = {},
+                  const std::string& llm_prefix                       = "text_encoders.llm",
+                  const std::string& projector_prefix                 = "text_embedding_projection",
+                  std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
         tokenizer       = std::make_shared<GemmaTokenizer>();
         llm             = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::GEMMA3_12B,
                                                backend,
-                                               params_backend,
                                                tensor_storage_map,
                                                llm_prefix,
-                                               false);
+                                               false,
+                                               weight_manager);
         dual_projection = tensor_storage_map.find(projector_prefix + ".video_aggregate_embed.weight") != tensor_storage_map.end();
         projector       = std::make_shared<LTXAVTextProjectionRunner>(backend,
-                                                                params_backend,
                                                                 tensor_storage_map,
-                                                                projector_prefix);
+                                                                projector_prefix,
+                                                                weight_manager);
     }
 
     void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
@@ -2205,25 +2132,6 @@ struct LTXAVEmbedder : public Conditioner {
         projector->get_param_tensors(tensors, "text_embedding_projection");
     }
 
-    bool alloc_params_buffer() override {
-        if (!llm->alloc_params_buffer()) {
-            return false;
-        }
-        if (!projector->alloc_params_buffer()) {
-            return false;
-        }
-        return true;
-    }
-
-    void free_params_buffer() override {
-        llm->free_params_buffer();
-        projector->free_params_buffer();
-    }
-
-    size_t get_params_buffer_size() override {
-        return llm->get_params_buffer_size() + projector->get_params_buffer_size();
-    }
-
     void set_flash_attention_enabled(bool enabled) override {
         llm->set_flash_attention_enabled(enabled);
         projector->set_flash_attention_enabled(enabled);
@@ -2239,6 +2147,11 @@ struct LTXAVEmbedder : public Conditioner {
         projector->set_weight_adapter(adapter);
     }
 
+    void runner_done() override {
+        llm->runner_done();
+        projector->runner_done();
+    }
+
     std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
                                                                                   const std::pair<int, int>& attn_range) {
         std::vector<std::pair<std::string, float>> parsed_attention;
@@ -2302,6 +2215,9 @@ struct LTXAVEmbedder : public Conditioner {
                                           attention_mask,
                                           {},
                                           {},
+                                          true,
+                                          false,
+                                          true,
                                           true);
         GGML_ASSERT(!hidden_states.empty());
         hidden_states = apply_token_weights(std::move(hidden_states), weights);
@@ -2361,7 +2277,7 @@ struct LTXAVEmbedder : public Conditioner {
         }
 
         hidden_states.reshape_({kNumStates * kHiddenSize, valid_tokens});
-        return projector->compute(n_threads, hidden_states);
+        return projector->compute(n_threads, hidden_states, false, true, true);
     }
 
     SDCondition get_learned_condition(int n_threads,
diff --git a/otherarch/sdcpp/src/core/ggml_extend.hpp b/otherarch/sdcpp/src/core/ggml_extend.hpp
index c85728529..f10a84ffd 100644
--- a/otherarch/sdcpp/src/core/ggml_extend.hpp
+++ b/otherarch/sdcpp/src/core/ggml_extend.hpp
@@ -25,7 +25,6 @@
 
 #include "core/ggml_extend_backend.h"
 #include "core/ggml_graph_cut.h"
-#include "core/layer_registry.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml.h"
@@ -36,6 +35,7 @@
 #include "core/rng.hpp"
 #include "core/tensor_ggml.hpp"
 #include "core/util.h"
+#include "weight_manager.h"
 
 #define EPS 1e-05f
 
@@ -1655,6 +1655,7 @@ struct GGMLRunnerContext {
     std::vector<std::pair<ggml_tensor*, std::string>>* debug_tensors = nullptr;
     std::function<ggml_tensor*(const std::string&)> get_cache_tensor;
     std::function<void(const std::string&, ggml_tensor*)> cache_tensor;
+    std::function<void(ggml_tensor*, const void*)> set_backend_tensor_data;
 
     void capture_tensor(const std::string& name, ggml_tensor* tensor) {
         if (debug_tensors == nullptr || tensor == nullptr) {
@@ -1680,6 +1681,13 @@ struct GGMLRunnerContext {
         }
         cache_tensor(name, tensor);
     }
+
+    void bind_backend_tensor_data(ggml_tensor* tensor, const void* data) const {
+        if (!set_backend_tensor_data || tensor == nullptr || data == nullptr) {
+            return;
+        }
+        set_backend_tensor_data(tensor, data);
+    }
 };
 
 struct GGMLRunner {
@@ -1688,14 +1696,9 @@ protected:
     using GraphCutSegment = sd::ggml_graph_cut::Segment;
     using GraphCutPlan    = sd::ggml_graph_cut::Plan;
 
-    ggml_backend_t params_backend  = nullptr;
     ggml_backend_t runtime_backend = nullptr;
 
-    ggml_context* params_ctx                    = nullptr;
-    ggml_backend_buffer_t params_buffer         = nullptr;
-    ggml_context* offload_ctx                   = nullptr;
-    ggml_backend_buffer_t runtime_params_buffer = nullptr;
-    bool params_on_runtime_backend              = false;
+    ggml_context* params_ctx = nullptr;
 
     ggml_context* cache_ctx            = nullptr;
     ggml_backend_buffer_t cache_buffer = nullptr;
@@ -1703,24 +1706,16 @@ protected:
     ggml_context* compute_ctx    = nullptr;
     ggml_gallocr* compute_allocr = nullptr;
 
-    ggml_context* partial_offload_ctx                   = nullptr;
-    ggml_backend_buffer_t partial_runtime_params_buffer = nullptr;
-    std::vector<std::pair<ggml_tensor*, ggml_tensor*>> partial_offload_pairs;
-
-    // Params kept on the runtime backend across streaming segments.
-    ggml_context* resident_offload_ctx = nullptr;
-    std::vector<std::pair<ggml_tensor*, ggml_tensor*>> resident_offload_pairs;
-    ggml_backend_buffer_t resident_runtime_params_buffer = nullptr;
-    std::unordered_set<ggml_tensor*> resident_param_set;
-    uint64_t resident_state_token = 0;
-
     size_t max_graph_vram_bytes           = 0;
     bool stream_layers_enabled            = false;
     size_t observed_max_effective_budget_ = 0;
 
-    sd::layer_registry::LayerRegistry layer_registry_;
-
     std::shared_ptr<WeightAdapter> weight_adapter = nullptr;
+    std::weak_ptr<RunnerWeightManager> weight_manager;
+    std::unordered_set<const ggml_tensor*> kept_compute_param_tensor_set;
+    std::vector<ggml_tensor*> runner_param_tensors;
+    std::unordered_set<const ggml_tensor*> runner_param_tensor_set;
+    bool params_tensor_set_dirty_ = true;
 
     std::vector<float> one_vec = {1.f};
     ggml_tensor* one_tensor    = nullptr;
@@ -1776,10 +1771,7 @@ protected:
         params_ctx = ggml_init(params);
         GGML_ASSERT(params_ctx != nullptr);
         params_tensor_set_.clear();
-        if (params_backend != runtime_backend) {
-            offload_ctx = ggml_init(params);
-            GGML_ASSERT(offload_ctx != nullptr);
-        }
+        params_tensor_set_dirty_ = true;
     }
 
     void free_params_ctx() {
@@ -1788,14 +1780,7 @@ protected:
             params_ctx = nullptr;
         }
         params_tensor_set_.clear();
-        if (offload_ctx != nullptr) {
-            ggml_free(offload_ctx);
-            offload_ctx = nullptr;
-        }
-        if (partial_offload_ctx != nullptr) {
-            ggml_free(partial_offload_ctx);
-            partial_offload_ctx = nullptr;
-        }
+        params_tensor_set_dirty_ = true;
     }
 
     void alloc_cache_ctx() {
@@ -1835,6 +1820,9 @@ protected:
     }
 
     void rebuild_params_tensor_set() {
+        if (!params_tensor_set_dirty_) {
+            return;
+        }
         params_tensor_set_.clear();
         if (params_ctx == nullptr) {
             return;
@@ -1842,6 +1830,93 @@ protected:
         for (ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != nullptr; t = ggml_get_next_tensor(params_ctx, t)) {
             params_tensor_set_.insert(t);
         }
+        params_tensor_set_dirty_ = false;
+    }
+
+    std::vector<ggml_tensor*> collect_used_param_tensors(ggml_cgraph* gf) {
+        std::vector<ggml_tensor*> used_params;
+        rebuild_params_tensor_set();
+        if (gf == nullptr || params_tensor_set_.empty()) {
+            return used_params;
+        }
+
+        std::unordered_set<const ggml_tensor*> seen_params;
+        const int n_leafs = sd::ggml_graph_cut::leaf_count(gf);
+        seen_params.reserve(static_cast<size_t>(n_leafs));
+        for (int i = 0; i < n_leafs; ++i) {
+            ggml_tensor* leaf       = sd::ggml_graph_cut::leaf_tensor(gf, i);
+            ggml_tensor* param_leaf = leaf;
+            if (param_leaf != nullptr && params_tensor_set_.find(param_leaf) == params_tensor_set_.end()) {
+                param_leaf = param_leaf->view_src;
+            }
+            if (param_leaf != nullptr &&
+                params_tensor_set_.find(param_leaf) != params_tensor_set_.end() &&
+                seen_params.insert(param_leaf).second) {
+                used_params.push_back(param_leaf);
+            }
+        }
+        return used_params;
+    }
+
+    bool prepare_execute_graph_weights(ggml_cgraph* gf,
+                                       std::vector<ggml_tensor*>& graph_param_tensors,
+                                       std::vector<ggml_tensor*>& params_to_prepare,
+                                       bool keep_compute_params) {
+        graph_param_tensors = collect_used_param_tensors(gf);
+        params_to_prepare.clear();
+        params_to_prepare.reserve(graph_param_tensors.size());
+        for (ggml_tensor* param : graph_param_tensors) {
+            if (param == nullptr) {
+                continue;
+            }
+            if (keep_compute_params &&
+                kept_compute_param_tensor_set.find(param) != kept_compute_param_tensor_set.end()) {
+                continue;
+            }
+            params_to_prepare.push_back(param);
+        }
+        auto manager = weight_manager.lock();
+        if (manager == nullptr) {
+            if (!params_to_prepare.empty()) {
+                LOG_ERROR("%s weight manager is not set for graph params", get_desc().c_str());
+                return false;
+            }
+            return true;
+        }
+
+        if (!manager->prepare_params(params_to_prepare)) {
+            LOG_ERROR("%s prepare graph weights failed", get_desc().c_str());
+            return false;
+        }
+        for (ggml_tensor* param : params_to_prepare) {
+            if (param == nullptr) {
+                continue;
+            }
+            if (runner_param_tensor_set.insert(param).second) {
+                runner_param_tensors.push_back(param);
+            }
+        }
+        return true;
+    }
+
+    void free_compute_backend_param_tensors(const std::vector<ggml_tensor*>& tensors) {
+        if (tensors.empty()) {
+            return;
+        }
+        auto manager = weight_manager.lock();
+        if (manager != nullptr) {
+            manager->release_compute_backend_params(tensors);
+        }
+    }
+
+    void free_params_backend_param_tensors(const std::vector<ggml_tensor*>& tensors) {
+        if (tensors.empty()) {
+            return;
+        }
+        auto manager = weight_manager.lock();
+        if (manager != nullptr) {
+            manager->release_params_backend_params(tensors);
+        }
     }
 
     void prepare_build_in_tensor_before() {
@@ -1932,6 +2007,10 @@ protected:
     }
 
     bool copy_cache_tensors_to_cache_buffer(const std::unordered_set<std::string>* cache_keep_names = nullptr) {
+        if (cache_tensor_map.empty() && cache_keep_names == nullptr) {
+            return true;
+        }
+
         ggml_context* old_cache_ctx            = cache_ctx;
         ggml_backend_buffer_t old_cache_buffer = cache_buffer;
         cache_ctx                              = nullptr;
@@ -2109,328 +2188,16 @@ protected:
         }
     }
 
-    bool offload_all_params() {
-        restore_partial_params();
-        if (params_backend == runtime_backend) {
-            return true;
-        }
-        if (params_on_runtime_backend) {
-            return true;
-        }
-        GGML_ASSERT(runtime_params_buffer == nullptr);
-        int64_t t0         = ggml_time_ms();
-        size_t num_tensors = ggml_tensor_num(offload_ctx);
-        if (num_tensors == 0) {
-            for (ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != nullptr; t = ggml_get_next_tensor(params_ctx, t)) {
-                GGML_ASSERT(t->view_src == nullptr);
-                ggml_dup_tensor(offload_ctx, t);
-            }
-        }
-        num_tensors = ggml_tensor_num(offload_ctx);
-        GGML_ASSERT(num_tensors == ggml_tensor_num(params_ctx));
-
-        runtime_params_buffer = ggml_backend_alloc_ctx_tensors(offload_ctx, runtime_backend);
-
-        if (runtime_params_buffer == nullptr) {
-            LOG_ERROR("%s alloc runtime params backend buffer failed, num_tensors = %i",
-                      get_desc().c_str(),
-                      num_tensors);
-            return false;
-        }
-        ggml_backend_buffer_set_usage(runtime_params_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-
-        ggml_tensor* t         = ggml_get_first_tensor(params_ctx);
-        ggml_tensor* offload_t = ggml_get_first_tensor(offload_ctx);
-
-        while (t != nullptr && offload_t != nullptr) {
-            ggml_backend_tensor_copy(t, offload_t);
-            std::swap(t->buffer, offload_t->buffer);
-            std::swap(t->data, offload_t->data);
-            std::swap(t->extra, offload_t->extra);
-
-            t         = ggml_get_next_tensor(params_ctx, t);
-            offload_t = ggml_get_next_tensor(offload_ctx, offload_t);
-        }
-
-        int64_t t1 = ggml_time_ms();
-
-        size_t params_buffer_size = ggml_backend_buffer_get_size(runtime_params_buffer);
-        LOG_INFO("%s offload params (%6.2f MB, %i tensors) to runtime backend (%s), taking %.2fs",
-                 get_desc().c_str(),
-                 params_buffer_size / (1024.f * 1024.f),
-                 num_tensors,
-                 ggml_backend_name(runtime_backend),
-                 (t1 - t0) * 1.0f / 1000);
-
-        params_on_runtime_backend = true;
-
-        return true;
-    }
-
-    bool offload_partial_params(const std::vector<ggml_tensor*>& tensors) {
-        restore_partial_params();
-        if (params_backend == runtime_backend) {
-            return true;
-        }
-        if (tensors.empty()) {
-            return true;
-        }
-        GGML_ASSERT(!params_on_runtime_backend);
-        GGML_ASSERT(partial_runtime_params_buffer == nullptr);
-
-        std::vector<ggml_tensor*> unique_tensors;
-        std::unordered_set<ggml_tensor*> seen_tensors;
-        unique_tensors.reserve(tensors.size());
-        seen_tensors.reserve(tensors.size());
-        for (ggml_tensor* tensor : tensors) {
-            if (tensor == nullptr) {
-                continue;
-            }
-            if (resident_param_set.find(tensor) != resident_param_set.end()) {
-                continue;
-            }
-            if (seen_tensors.insert(tensor).second) {
-                unique_tensors.push_back(tensor);
-            }
-        }
-        if (unique_tensors.empty()) {
-            return true;
-        }
-
-        ggml_init_params params;
-        params.mem_size   = std::max<size_t>(1, unique_tensors.size()) * ggml_tensor_overhead();
-        params.mem_buffer = nullptr;
-        params.no_alloc   = true;
-
-        partial_offload_ctx = ggml_init(params);
-        GGML_ASSERT(partial_offload_ctx != nullptr);
-
-        partial_offload_pairs.clear();
-        partial_offload_pairs.reserve(unique_tensors.size());
-
-        for (ggml_tensor* tensor : unique_tensors) {
-            GGML_ASSERT(tensor->view_src == nullptr);
-            ggml_tensor* offload_tensor = ggml_dup_tensor(partial_offload_ctx, tensor);
-            ggml_set_name(offload_tensor, tensor->name);
-            partial_offload_pairs.push_back({tensor, offload_tensor});
-        }
-
-        partial_runtime_params_buffer = ggml_backend_alloc_ctx_tensors(partial_offload_ctx, runtime_backend);
-        if (partial_runtime_params_buffer == nullptr) {
-            LOG_ERROR("%s alloc partial runtime params backend buffer failed, num_tensors = %zu",
-                      get_desc().c_str(),
-                      partial_offload_pairs.size());
-            ggml_free(partial_offload_ctx);
-            partial_offload_ctx = nullptr;
-            partial_offload_pairs.clear();
-            return false;
-        }
-        ggml_backend_buffer_set_usage(partial_runtime_params_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-
-        for (auto& pair : partial_offload_pairs) {
-            ggml_tensor* tensor         = pair.first;
-            ggml_tensor* offload_tensor = pair.second;
-
-            ggml_backend_tensor_copy(tensor, offload_tensor);
-            std::swap(tensor->buffer, offload_tensor->buffer);
-            std::swap(tensor->data, offload_tensor->data);
-            std::swap(tensor->extra, offload_tensor->extra);
-        }
-
-        size_t params_buffer_size = ggml_backend_buffer_get_size(partial_runtime_params_buffer);
-        LOG_DEBUG("%s offload partial params (%6.2f MB, %zu tensors) to runtime backend (%s)",
-                  get_desc().c_str(),
-                  params_buffer_size / (1024.f * 1024.f),
-                  partial_offload_pairs.size(),
-                  ggml_backend_name(runtime_backend));
-
-        return true;
-    }
-
-    void restore_all_params() {
-        restore_partial_params();
-        if (!params_on_runtime_backend) {
-            return;
-        }
-        ggml_tensor* t         = ggml_get_first_tensor(params_ctx);
-        ggml_tensor* offload_t = ggml_get_first_tensor(offload_ctx);
-
-        while (t != nullptr && offload_t != nullptr) {
-            t->buffer         = offload_t->buffer;
-            t->data           = offload_t->data;
-            t->extra          = offload_t->extra;
-            offload_t->buffer = nullptr;
-            offload_t->data   = nullptr;
-            offload_t->extra  = nullptr;
-
-            t         = ggml_get_next_tensor(params_ctx, t);
-            offload_t = ggml_get_next_tensor(offload_ctx, offload_t);
-        }
-
-        if (runtime_params_buffer != nullptr) {
-            ggml_backend_buffer_free(runtime_params_buffer);
-            runtime_params_buffer = nullptr;
-        }
-        params_on_runtime_backend = false;
-    }
-
-    void restore_partial_params() {
-        if (partial_offload_pairs.empty()) {
-            if (partial_runtime_params_buffer != nullptr) {
-                ggml_backend_buffer_free(partial_runtime_params_buffer);
-                partial_runtime_params_buffer = nullptr;
-            }
-            if (partial_offload_ctx != nullptr) {
-                ggml_free(partial_offload_ctx);
-                partial_offload_ctx = nullptr;
-            }
-            return;
-        }
-
-        for (auto& pair : partial_offload_pairs) {
-            ggml_tensor* tensor         = pair.first;
-            ggml_tensor* offload_tensor = pair.second;
-
-            tensor->buffer         = offload_tensor->buffer;
-            tensor->data           = offload_tensor->data;
-            tensor->extra          = offload_tensor->extra;
-            offload_tensor->buffer = nullptr;
-            offload_tensor->data   = nullptr;
-            offload_tensor->extra  = nullptr;
-        }
-
-        if (partial_runtime_params_buffer != nullptr) {
-            ggml_backend_buffer_free(partial_runtime_params_buffer);
-            partial_runtime_params_buffer = nullptr;
-        }
-        partial_offload_pairs.clear();
-
-        if (partial_offload_ctx != nullptr) {
-            ggml_free(partial_offload_ctx);
-            partial_offload_ctx = nullptr;
-        }
-    }
-
-    bool offload_resident_params(const std::vector<ggml_tensor*>& tensors) {
-        if (params_backend == runtime_backend) {
-            return true;
-        }
-        if (tensors.empty()) {
-            return true;
-        }
-        GGML_ASSERT(resident_runtime_params_buffer == nullptr);
-        GGML_ASSERT(resident_offload_ctx == nullptr);
-        GGML_ASSERT(resident_offload_pairs.empty());
-        GGML_ASSERT(resident_param_set.empty());
-
-        std::vector<ggml_tensor*> unique_tensors;
-        std::unordered_set<ggml_tensor*> seen;
-        unique_tensors.reserve(tensors.size());
-        seen.reserve(tensors.size());
-        for (ggml_tensor* t : tensors) {
-            if (t == nullptr)
-                continue;
-            if (seen.insert(t).second)
-                unique_tensors.push_back(t);
-        }
-        if (unique_tensors.empty())
-            return true;
-
-        ggml_init_params init = {};
-        init.mem_size         = std::max<size_t>(1, unique_tensors.size()) * ggml_tensor_overhead();
-        init.mem_buffer       = nullptr;
-        init.no_alloc         = true;
-        resident_offload_ctx  = ggml_init(init);
-        GGML_ASSERT(resident_offload_ctx != nullptr);
-
-        resident_offload_pairs.reserve(unique_tensors.size());
-        for (ggml_tensor* t : unique_tensors) {
-            GGML_ASSERT(t->view_src == nullptr);
-            ggml_tensor* twin = ggml_dup_tensor(resident_offload_ctx, t);
-            ggml_set_name(twin, t->name);
-            resident_offload_pairs.push_back({t, twin});
-        }
-
-        resident_runtime_params_buffer = ggml_backend_alloc_ctx_tensors(resident_offload_ctx, runtime_backend);
-        if (resident_runtime_params_buffer == nullptr) {
-            LOG_ERROR("%s alloc resident runtime params backend buffer failed, num_tensors = %zu",
-                      get_desc().c_str(), resident_offload_pairs.size());
-            ggml_free(resident_offload_ctx);
-            resident_offload_ctx = nullptr;
-            resident_offload_pairs.clear();
-            return false;
-        }
-        ggml_backend_buffer_set_usage(resident_runtime_params_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-
-        for (auto& pair : resident_offload_pairs) {
-            ggml_tensor* t    = pair.first;
-            ggml_tensor* twin = pair.second;
-            ggml_backend_tensor_copy(t, twin);
-            std::swap(t->buffer, twin->buffer);
-            std::swap(t->data, twin->data);
-            std::swap(t->extra, twin->extra);
-            resident_param_set.insert(t);
-        }
-        ggml_backend_synchronize(runtime_backend);
-
-        size_t sz = ggml_backend_buffer_get_size(resident_runtime_params_buffer);
-        LOG_INFO("%s offload resident params (%6.2f MB, %zu tensors) to runtime backend (%s)",
-                 get_desc().c_str(),
-                 sz / (1024.f * 1024.f),
-                 resident_offload_pairs.size(),
-                 ggml_backend_name(runtime_backend));
-        return true;
-    }
-
-    void restore_resident_params() {
-        if (resident_offload_pairs.empty()) {
-            if (resident_runtime_params_buffer != nullptr) {
-                ggml_backend_buffer_free(resident_runtime_params_buffer);
-                resident_runtime_params_buffer = nullptr;
-            }
-            if (resident_offload_ctx != nullptr) {
-                ggml_free(resident_offload_ctx);
-                resident_offload_ctx = nullptr;
-            }
-            resident_param_set.clear();
-            resident_state_token = 0;
-            return;
-        }
-        for (auto& pair : resident_offload_pairs) {
-            ggml_tensor* t    = pair.first;
-            ggml_tensor* twin = pair.second;
-            t->buffer         = twin->buffer;
-            t->data           = twin->data;
-            t->extra          = twin->extra;
-            twin->buffer      = nullptr;
-            twin->data        = nullptr;
-            twin->extra       = nullptr;
-        }
-        if (resident_runtime_params_buffer != nullptr) {
-            ggml_backend_buffer_free(resident_runtime_params_buffer);
-            resident_runtime_params_buffer = nullptr;
-        }
-        resident_offload_pairs.clear();
-        if (resident_offload_ctx != nullptr) {
-            ggml_free(resident_offload_ctx);
-            resident_offload_ctx = nullptr;
-        }
-        resident_param_set.clear();
-        resident_state_token = 0;
-    }
-
     bool should_use_graph_cut_segmented_compute(const GraphCutPlan& plan) {
         return plan.has_cuts &&
                plan.valid &&
                max_graph_vram_bytes > 0 &&
                plan.segments.size() > 1 &&
-               params_backend != runtime_backend &&
                !sd_backend_is_cpu(runtime_backend);
     }
 
     bool can_attempt_graph_cut_segmented_compute() const {
         return max_graph_vram_bytes > 0 &&
-               params_backend != runtime_backend &&
                !sd_backend_is_cpu(runtime_backend);
     }
 
@@ -2440,18 +2207,12 @@ protected:
         GGML_ASSERT(plan_out != nullptr);
         GGML_ASSERT(gf != nullptr);
 
-        // Keep the plan and resident params under the same live-VRAM cap.
-        // Add back our own resident buffer so we don't see chunk-K's
-        // allocation as "taken" VRAM and shrink the budget on every step.
         size_t effective_budget = max_graph_vram_bytes;
         if (stream_layers_enabled && max_graph_vram_bytes > 0 && runtime_backend != nullptr) {
             ggml_backend_dev_t dev = ggml_backend_get_device(runtime_backend);
             if (dev != nullptr && ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
                 size_t free_vram = 0, total_vram = 0;
                 ggml_backend_dev_memory(dev, &free_vram, &total_vram);
-                if (resident_runtime_params_buffer != nullptr) {
-                    free_vram += ggml_backend_buffer_get_size(resident_runtime_params_buffer);
-                }
                 constexpr size_t safety_margin = 512ull * 1024 * 1024;
                 size_t free_clamp              = (free_vram > safety_margin) ? (free_vram - safety_margin) : 0;
                 if (free_clamp < effective_budget) {
@@ -2500,6 +2261,9 @@ protected:
                                                      planner_budget,
                                                      params_tensor_set_,
                                                      get_desc().c_str());
+        if (stream_layers_enabled) {
+            sd::ggml_graph_cut::annotate_residency(*plan_out, effective_budget);
+        }
         if (stream_layers_enabled) {
             if (budget_increased) {
                 LOG_INFO("%s streaming budget = %.2f MB",
@@ -2635,310 +2399,173 @@ protected:
     template <typename T>
     std::optional<sd::Tensor<T>> execute_graph(ggml_cgraph* gf,
                                                int n_threads,
-                                               bool free_compute_buffer_immediately,
-                                               const std::vector<ggml_tensor*>& runtime_param_tensors,
+                                               bool free_compute_buffer,
+                                               bool free_compute_params,
                                                bool preserve_backend_tensor_data_map,
                                                bool no_return                                          = false,
                                                const std::unordered_set<std::string>* cache_keep_names = nullptr) {
-        int64_t t_execute_begin              = ggml_time_ms();
-        const bool use_partial_param_offload = !runtime_param_tensors.empty();
-        int64_t t_offload_begin              = ggml_time_ms();
-        if (use_partial_param_offload) {
-            if (!offload_partial_params(runtime_param_tensors)) {
-                LOG_ERROR("%s offload partial params to runtime backend failed", get_desc().c_str());
-                return std::nullopt;
-            }
-        } else {
-            if (!offload_all_params()) {
-                LOG_ERROR("%s offload params to runtime backend failed", get_desc().c_str());
-                return std::nullopt;
-            }
-        }
-        int64_t t_offload_end = ggml_time_ms();
-
-        int64_t t_alloc_begin = ggml_time_ms();
-        if (!alloc_compute_buffer(gf)) {
-            LOG_ERROR("%s alloc compute buffer failed", get_desc().c_str());
-            if (use_partial_param_offload) {
-                restore_partial_params();
-            }
+        std::vector<ggml_tensor*> graph_param_tensors;
+        std::vector<ggml_tensor*> params_to_prepare;
+        if (!prepare_execute_graph_weights(gf, graph_param_tensors, params_to_prepare, !free_compute_params)) {
             return std::nullopt;
         }
+        struct GraphWeightDoneGuard {
+            GraphWeightDoneGuard(GGMLRunner* runner, const std::vector<ggml_tensor*>* tensors)
+                : runner(runner),
+                  tensors(tensors) {}
+
+            GGMLRunner* runner                       = nullptr;
+            const std::vector<ggml_tensor*>* tensors = nullptr;
+            bool enabled                             = true;
+
+            ~GraphWeightDoneGuard() {
+                if (enabled && runner != nullptr && tensors != nullptr) {
+                    runner->free_compute_backend_param_tensors(*tensors);
+                }
+            }
+
+            void dismiss() { enabled = false; }
+
+            GraphWeightDoneGuard(const GraphWeightDoneGuard&)            = delete;
+            GraphWeightDoneGuard& operator=(const GraphWeightDoneGuard&) = delete;
+        };
+        GraphWeightDoneGuard graph_weight_done_guard(this, &params_to_prepare);
+
+        if (!alloc_compute_buffer(gf)) {
+            LOG_ERROR("%s alloc compute buffer failed", get_desc().c_str());
+            return std::nullopt;
+        }
+        struct ComputeBufferGuard {
+            ComputeBufferGuard(GGMLRunner* runner, bool enabled)
+                : runner(runner),
+                  enabled(enabled) {}
+
+            GGMLRunner* runner = nullptr;
+            bool enabled       = false;
+
+            ~ComputeBufferGuard() {
+                if (enabled && runner != nullptr) {
+                    runner->free_compute_buffer();
+                }
+            }
+
+            ComputeBufferGuard(const ComputeBufferGuard&)            = delete;
+            ComputeBufferGuard& operator=(const ComputeBufferGuard&) = delete;
+        };
+        ComputeBufferGuard compute_buffer_guard(this, free_compute_buffer);
 
         if (!ggml_gallocr_alloc_graph(compute_allocr, gf)) {
             LOG_ERROR("%s alloc compute graph failed", get_desc().c_str());
-            if (free_compute_buffer_immediately) {
-                free_compute_buffer();
-            } else if (use_partial_param_offload) {
-                restore_partial_params();
-            }
             return std::nullopt;
         }
-        int64_t t_alloc_end = ggml_time_ms();
 
-        int64_t t_copy_begin = ggml_time_ms();
         copy_data_to_backend_tensor(gf, !preserve_backend_tensor_data_map);
-        int64_t t_copy_end = ggml_time_ms();
         if (sd_backend_is_cpu(runtime_backend)) {
             sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
         }
 
-        int64_t t_compute_begin = ggml_time_ms();
-        ggml_status status      = ggml_backend_graph_compute(runtime_backend, gf);
-        int64_t t_compute_end   = ggml_time_ms();
+        ggml_status status = ggml_backend_graph_compute(runtime_backend, gf);
         if (status != GGML_STATUS_SUCCESS) {
             LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
-            if (free_compute_buffer_immediately) {
-                free_compute_buffer();
-            } else if (use_partial_param_offload) {
-                restore_partial_params();
-            }
             return std::nullopt;
         }
 
-        std::unordered_set<const ggml_tensor*> debug_graph_tensor_set;
-        const int n_debug_leafs = sd::ggml_graph_cut::leaf_count(gf);
-        const int n_debug_nodes = ggml_graph_n_nodes(gf);
-        debug_graph_tensor_set.reserve(static_cast<size_t>(n_debug_leafs + n_debug_nodes));
-        for (int i = 0; i < n_debug_leafs; ++i) {
-            debug_graph_tensor_set.insert(sd::ggml_graph_cut::leaf_tensor(gf, i));
-        }
-        for (int i = 0; i < n_debug_nodes; ++i) {
-            debug_graph_tensor_set.insert(ggml_graph_node(gf, i));
+        if (!debug_tensors.empty()) {
+            std::unordered_set<const ggml_tensor*> debug_graph_tensor_set;
+            const int n_debug_leafs = sd::ggml_graph_cut::leaf_count(gf);
+            const int n_debug_nodes = ggml_graph_n_nodes(gf);
+            debug_graph_tensor_set.reserve(static_cast<size_t>(n_debug_leafs + n_debug_nodes));
+            for (int i = 0; i < n_debug_leafs; ++i) {
+                debug_graph_tensor_set.insert(sd::ggml_graph_cut::leaf_tensor(gf, i));
+            }
+            for (int i = 0; i < n_debug_nodes; ++i) {
+                debug_graph_tensor_set.insert(ggml_graph_node(gf, i));
+            }
+
+            for (const auto& entry : debug_tensors) {
+                auto tensor = entry.first;
+                if (tensor == nullptr) {
+                    continue;
+                }
+                if (debug_graph_tensor_set.find(tensor) == debug_graph_tensor_set.end()) {
+                    continue;
+                }
+                ggml_backend_buffer_t tensor_buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+                if (tensor_buf == nullptr) {
+                    LOG_WARN("%s skip debug tensor '%s': tensor buffer not set",
+                             get_desc().c_str(),
+                             entry.second.c_str());
+                    continue;
+                }
+                if (tensor->type != GGML_TYPE_F32) {
+                    LOG_WARN("%s skip debug tensor '%s': only GGML_TYPE_F32 is supported, got %s",
+                             get_desc().c_str(),
+                             entry.second.c_str(),
+                             ggml_type_name(tensor->type));
+                    continue;
+                }
+                auto debug_tensor = sd::make_sd_tensor_from_ggml<float>(tensor);
+                print_sd_tensor(debug_tensor, false, entry.second.c_str());
+            }
         }
 
-        for (const auto& entry : debug_tensors) {
-            auto tensor = entry.first;
-            if (tensor == nullptr) {
-                continue;
-            }
-            if (debug_graph_tensor_set.find(tensor) == debug_graph_tensor_set.end()) {
-                continue;
-            }
-            ggml_backend_buffer_t tensor_buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
-            if (tensor_buf == nullptr) {
-                LOG_WARN("%s skip debug tensor '%s': tensor buffer not set",
-                         get_desc().c_str(),
-                         entry.second.c_str());
-                continue;
-            }
-            if (tensor->type != GGML_TYPE_F32) {
-                LOG_WARN("%s skip debug tensor '%s': only GGML_TYPE_F32 is supported, got %s",
-                         get_desc().c_str(),
-                         entry.second.c_str(),
-                         ggml_type_name(tensor->type));
-                continue;
-            }
-            auto debug_tensor = sd::make_sd_tensor_from_ggml<float>(tensor);
-            print_sd_tensor(debug_tensor, false, entry.second.c_str());
-        }
-
-        int64_t t_cache_begin = ggml_time_ms();
         if (!copy_cache_tensors_to_cache_buffer(cache_keep_names)) {
-            if (free_compute_buffer_immediately) {
-                free_compute_buffer();
-            } else if (use_partial_param_offload) {
-                restore_partial_params();
-            }
             return std::nullopt;
         }
-        int64_t t_cache_end = ggml_time_ms();
-        auto result         = ggml_get_tensor(compute_ctx, final_result_name.c_str());
+        auto result = ggml_get_tensor(compute_ctx, final_result_name.c_str());
         std::optional<sd::Tensor<T>> output;
         if (!no_return) {
             output = read_graph_tensor<T>(result, "output");
             if (!output.has_value()) {
-                if (free_compute_buffer_immediately) {
-                    free_compute_buffer();
-                } else if (use_partial_param_offload) {
-                    restore_partial_params();
-                }
                 return std::nullopt;
             }
         } else {
             output = sd::Tensor<T>();
         }
 
-        if (free_compute_buffer_immediately) {
-            free_compute_buffer();
-        } else if (use_partial_param_offload) {
-            restore_partial_params();
-        }
-        if (use_partial_param_offload) {
-            LOG_DEBUG("%s execute_graph timing: offload=%lld ms alloc=%lld ms copy_in=%lld ms compute=%lld ms cache=%lld ms total=%lld ms",
-                      get_desc().c_str(),
-                      t_offload_end - t_offload_begin,
-                      t_alloc_end - t_alloc_begin,
-                      t_copy_end - t_copy_begin,
-                      t_compute_end - t_compute_begin,
-                      t_cache_end - t_cache_begin,
-                      ggml_time_ms() - t_execute_begin);
-        }
-        return output;
-    }
-
-    template <typename T>
-    std::optional<sd::Tensor<T>> compute_with_graph_cuts(ggml_cgraph* gf,
-                                                         const GraphCutPlan& plan,
-                                                         int n_threads,
-                                                         bool free_compute_buffer_immediately,
-                                                         bool no_return = false) {
-        GGML_ASSERT(gf != nullptr);
-
-        free_compute_buffer();
-        free_cache_ctx_and_buffer();
-
-        std::unordered_map<ggml_tensor*, PersistentExternalBinding> persistent_externals;
-        snapshot_persistent_externals(plan, gf, persistent_externals);
-
-        std::optional<sd::Tensor<T>> output = sd::Tensor<T>();
-        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); ++seg_idx) {
-            int64_t t_segment_begin = ggml_time_ms();
-            const auto& segment     = plan.segments[seg_idx];
-            auto future_cut_names   = sd::ggml_graph_cut::collect_future_input_names(gf, plan, seg_idx);
-            LOG_DEBUG("%s graph cut executing segment %zu/%zu: %s",
-                      get_desc().c_str(),
-                      seg_idx + 1,
-                      plan.segments.size(),
-                      segment.group_name.c_str());
-
-            reset_segment_runtime_tensors(segment, gf, &persistent_externals);
-            if (!bind_segment_cached_inputs(gf, segment)) {
-                free_cache_ctx_and_buffer();
-                free_compute_buffer();
-                free_compute_ctx();
-                return std::nullopt;
-            }
-
-            const bool is_last_segment = seg_idx + 1 == plan.segments.size();
-            if (!is_last_segment) {
-                for (size_t output_idx = 0; output_idx < segment.output_node_indices.size(); ++output_idx) {
-                    ggml_tensor* output_tensor = sd::ggml_graph_cut::output_tensor(gf, segment, output_idx);
-                    if (output_tensor != nullptr &&
-                        sd::ggml_graph_cut::is_graph_cut_tensor(output_tensor) &&
-                        future_cut_names.find(output_tensor->name) != future_cut_names.end()) {
-                        cache(output_tensor->name, output_tensor);
-                    }
+        if (!free_compute_params) {
+            for (ggml_tensor* param : params_to_prepare) {
+                if (param == nullptr) {
+                    continue;
                 }
+                kept_compute_param_tensor_set.insert(param);
             }
-
-            ggml_context* segment_graph_ctx = nullptr;
-            ggml_cgraph* segment_graph      = sd::ggml_graph_cut::build_segment_graph(gf, segment, &segment_graph_ctx);
-            auto segment_output             = execute_graph<T>(segment_graph,
-                                                   n_threads,
-                                                   true,
-                                                   sd::ggml_graph_cut::runtime_param_tensors(gf, segment, get_desc().c_str()),
-                                                   true,
-                                                   !is_last_segment || no_return,
-                                                   &future_cut_names);
-            ggml_free(segment_graph_ctx);
-            if (!segment_output.has_value()) {
-                free_cache_ctx_and_buffer();
-                free_compute_buffer();
-                free_compute_ctx();
-                return std::nullopt;
-            }
-            output = std::move(segment_output);
+            graph_weight_done_guard.dismiss();
         }
-
-        backend_tensor_data_map.clear();
-        free_cache_ctx_and_buffer();
-        free_compute_ctx();
         return output;
     }
 
-public:
-    void release_streaming_residency() {
-        restore_resident_params();
-    }
-
     template <typename T>
-    std::optional<sd::Tensor<T>> compute_streaming_segments(ggml_cgraph* gf,
+    std::optional<sd::Tensor<T>> compute_graph_cut_segments(ggml_cgraph* gf,
                                                             const GraphCutPlan& plan,
-                                                            size_t residency_budget_bytes,
                                                             int n_threads,
-                                                            bool free_compute_buffer_immediately,
+                                                            bool log_residency,
                                                             bool no_return = false) {
         GGML_ASSERT(gf != nullptr);
 
-        // Runtime LoRA composes `weight + diff` in the compute graph via
-        // ggml_add; the resident weight tensor's data is never mutated, so
-        // chunk-K residency stays valid across sampling steps.
-        // Reserve room for the worst merged segment so chunk-K can't grow
-        // large enough to starve later partial-param allocations.
-        size_t worst_merged_segment_footprint = 0;
-        for (const auto& seg : plan.segments) {
-            const size_t fp = seg.input_param_bytes +
-                              seg.compute_buffer_size +
-                              seg.output_bytes +
-                              seg.input_previous_cut_bytes +
-                              seg.input_external_bytes;
-            if (fp > worst_merged_segment_footprint) {
-                worst_merged_segment_footprint = fp;
-            }
-        }
-        const size_t residency_budget_for_annotate =
-            residency_budget_bytes > worst_merged_segment_footprint
-                ? residency_budget_bytes - worst_merged_segment_footprint
-                : 0;
-
-        sd::ggml_graph_cut::Plan& base_plan = graph_cut_plan_cache_.graph_cut_plan;
-        if (base_plan.available) {
-            sd::ggml_graph_cut::annotate_residency(base_plan, residency_budget_for_annotate);
-
-            std::vector<ggml_tensor*> resident_params;
-            uint64_t token = 0;
-            for (const auto& segment : base_plan.segments) {
-                if (segment.residency != sd::ggml_graph_cut::SegmentResidency::RESIDENT) {
-                    continue;
-                }
-                auto seg_params = sd::ggml_graph_cut::param_tensors(gf, segment);
-                for (ggml_tensor* t : seg_params) {
-                    if (t == nullptr)
-                        continue;
-                    resident_params.push_back(t);
-                    token ^= reinterpret_cast<uintptr_t>(t) * 0x9E3779B97F4A7C15ull;
-                }
-            }
-            if (token != resident_state_token) {
-                restore_resident_params();
-                if (!resident_params.empty()) {
-                    if (offload_resident_params(resident_params)) {
-                        resident_state_token = token;
-                    } else {
-                        LOG_ERROR("%s chunk-K: resident offload failed; continuing with per-segment streaming",
-                                  get_desc().c_str());
-                        restore_resident_params();
-                    }
-                }
-            }
-        }
-
         free_compute_buffer();
         free_cache_ctx_and_buffer();
 
-        layer_registry_.move_layer_to_gpu("_global");
-
         std::unordered_map<ggml_tensor*, PersistentExternalBinding> persistent_externals;
         snapshot_persistent_externals(plan, gf, persistent_externals);
 
         std::optional<sd::Tensor<T>> output = sd::Tensor<T>();
         for (size_t seg_idx = 0; seg_idx < plan.segments.size(); ++seg_idx) {
-            int64_t t_segment_begin = ggml_time_ms();
-            const auto& segment     = plan.segments[seg_idx];
-            const bool is_last      = seg_idx + 1 == plan.segments.size();
-            auto future_cut_names   = sd::ggml_graph_cut::collect_future_input_names(gf, plan, seg_idx);
-
-            LOG_DEBUG("%s streaming-cut executing segment %zu/%zu: %s (residency=%s)",
-                      get_desc().c_str(),
-                      seg_idx + 1,
-                      plan.segments.size(),
-                      segment.group_name.c_str(),
-                      segment.residency == sd::ggml_graph_cut::SegmentResidency::RESIDENT ? "RESIDENT" : "STREAMED");
-
-            if (!layer_registry_.move_layer_to_gpu(segment.group_name)) {
-                LOG_DEBUG("%s streaming: no registry entry for group '%s' (using upstream offload path)",
+            const auto& segment   = plan.segments[seg_idx];
+            const bool is_last    = seg_idx + 1 == plan.segments.size();
+            auto future_cut_names = sd::ggml_graph_cut::collect_future_input_names(gf, plan, seg_idx);
+            if (log_residency) {
+                LOG_DEBUG("%s graph cut executing segment %zu/%zu: %s (residency=%s)",
                           get_desc().c_str(),
+                          seg_idx + 1,
+                          plan.segments.size(),
+                          segment.group_name.c_str(),
+                          segment.residency == sd::ggml_graph_cut::SegmentResidency::RESIDENT ? "RESIDENT" : "STREAMED");
+            } else {
+                LOG_DEBUG("%s graph cut executing segment %zu/%zu: %s",
+                          get_desc().c_str(),
+                          seg_idx + 1,
+                          plan.segments.size(),
                           segment.group_name.c_str());
             }
 
@@ -2952,23 +2579,24 @@ public:
 
             if (!is_last) {
                 for (size_t output_idx = 0; output_idx < segment.output_node_indices.size(); ++output_idx) {
-                    ggml_tensor* out_tensor = sd::ggml_graph_cut::output_tensor(gf, segment, output_idx);
-                    if (out_tensor != nullptr &&
-                        sd::ggml_graph_cut::is_graph_cut_tensor(out_tensor) &&
-                        future_cut_names.find(out_tensor->name) != future_cut_names.end()) {
-                        cache(out_tensor->name, out_tensor);
+                    ggml_tensor* output_tensor = sd::ggml_graph_cut::output_tensor(gf, segment, output_idx);
+                    if (output_tensor != nullptr &&
+                        sd::ggml_graph_cut::is_graph_cut_tensor(output_tensor) &&
+                        future_cut_names.find(output_tensor->name) != future_cut_names.end()) {
+                        cache(output_tensor->name, output_tensor);
                     }
                 }
             }
 
             ggml_context* segment_graph_ctx = nullptr;
             ggml_cgraph* segment_graph      = sd::ggml_graph_cut::build_segment_graph(gf, segment, &segment_graph_ctx);
+            const bool keep_segment_params  = segment.residency == sd::ggml_graph_cut::SegmentResidency::RESIDENT;
             auto segment_output             = execute_graph<T>(segment_graph,
                                                    n_threads,
-                                                   /*free_compute_buffer_immediately=*/true,
-                                                   sd::ggml_graph_cut::runtime_param_tensors(gf, segment, get_desc().c_str()),
-                                                   /*preserve_backend_tensor_data_map=*/true,
-                                                   /*no_return=*/!is_last || no_return,
+                                                   true,
+                                                   !keep_segment_params,
+                                                   true,
+                                                   !is_last || no_return,
                                                    &future_cut_names);
             ggml_free(segment_graph_ctx);
             if (!segment_output.has_value()) {
@@ -2978,11 +2606,6 @@ public:
                 return std::nullopt;
             }
             output = std::move(segment_output);
-
-            if (segment.residency == sd::ggml_graph_cut::SegmentResidency::STREAMED) {
-                layer_registry_.move_layer_to_cpu(segment.group_name);
-            }
-            (void)t_segment_begin;
         }
 
         backend_tensor_data_map.clear();
@@ -2991,21 +2614,29 @@ public:
         return output;
     }
 
+public:
+    void runner_done() {
+        free_compute_buffer();
+        std::vector<ggml_tensor*> tensors_to_release = std::move(this->runner_param_tensors);
+        this->runner_param_tensors.clear();
+        runner_param_tensor_set.clear();
+        kept_compute_param_tensor_set.clear();
+        free_compute_backend_param_tensors(tensors_to_release);
+        free_params_backend_param_tensors(tensors_to_release);
+    }
+
 public:
     virtual std::string get_desc() = 0;
 
-    GGMLRunner(ggml_backend_t backend, ggml_backend_t params_backend)
-        : params_backend(params_backend),
-          runtime_backend(backend) {
+    GGMLRunner(ggml_backend_t backend,
+               std::shared_ptr<RunnerWeightManager> manager = nullptr)
+        : runtime_backend(backend),
+          weight_manager(manager) {
         GGML_ASSERT(runtime_backend != nullptr);
-        GGML_ASSERT(params_backend != nullptr);
         alloc_params_ctx();
-        layer_registry_.set_backends(runtime_backend, params_backend);
     }
 
     virtual ~GGMLRunner() {
-        restore_resident_params();
-        free_params_buffer();
         free_compute_buffer();
         free_params_ctx();
         free_compute_ctx();
@@ -3028,6 +2659,9 @@ public:
         runner_ctx.cache_tensor = [this](const std::string& name, ggml_tensor* tensor) {
             this->cache(name, tensor);
         };
+        runner_ctx.set_backend_tensor_data = [this](ggml_tensor* tensor, const void* data) {
+            this->set_backend_tensor_data(tensor, data);
+        };
         return runner_ctx;
     }
 
@@ -3036,74 +2670,7 @@ public:
         alloc_compute_ctx();
     }
 
-    bool alloc_params_buffer() {
-        size_t num_tensors = ggml_tensor_num(params_ctx);
-        if (num_tensors > 0) {
-            // ggml_backend_alloc_ctx_tensors fails when all tensors are already allocated
-            // (typical for memory-mapped weights). See ggml-alloc.c n_buffers==0 branch.
-            bool all_have_data = true;
-            for (ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != nullptr; t = ggml_get_next_tensor(params_ctx, t)) {
-                if (t->data == nullptr) {
-                    all_have_data = false;
-                    break;
-                }
-            }
-            if (all_have_data) {
-                LOG_DEBUG("%s all params already mmap-allocated (no separate buffer needed)", get_desc().c_str());
-                params_buffer = nullptr;
-                rebuild_params_tensor_set();
-                return true;
-            }
-        } else {
-            LOG_DEBUG("%s skipping params allocation (no tensors)", get_desc().c_str());
-            return true;
-        }
-        // Pinned host buffer when CPU-offloaded for DMA-direct H2D.
-        ggml_backend_buffer_type_t params_buft = nullptr;
-        if (params_backend != runtime_backend) {
-            ggml_backend_dev_t runtime_dev = ggml_backend_get_device(runtime_backend);
-            if (runtime_dev != nullptr) {
-                params_buft = ggml_backend_dev_host_buffer_type(runtime_dev);
-            }
-        }
-        if (params_buft == nullptr) {
-            params_buft = ggml_backend_get_default_buffer_type(params_backend);
-        }
-        params_buffer = ggml_backend_alloc_ctx_tensors_from_buft(params_ctx, params_buft);
-        if (params_buffer == nullptr) {
-            LOG_ERROR("%s alloc params backend buffer failed, num_tensors = %i",
-                      get_desc().c_str(),
-                      num_tensors);
-            return false;
-        }
-        rebuild_params_tensor_set();
-        ggml_backend_buffer_set_usage(params_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-        size_t params_buffer_size = ggml_backend_buffer_get_size(params_buffer);
-        LOG_DEBUG("%s params backend buffer size = % 6.2f MB(%s) (%i tensors)",
-                  get_desc().c_str(),
-                  params_buffer_size / (1024.f * 1024.f),
-                  sd_backend_is_cpu(params_backend) ? "RAM" : "VRAM",
-                  num_tensors);
-        return true;
-    }
-
-    void free_params_buffer() {
-        // Restore swapped resident params before freeing their backing buffer.
-        restore_resident_params();
-        if (params_buffer != nullptr) {
-            ggml_backend_buffer_free(params_buffer);
-            params_buffer = nullptr;
-        }
-        observed_max_effective_budget_ = 0;
-    }
-
-    size_t get_params_buffer_size() {
-        if (params_buffer != nullptr) {
-            return ggml_backend_buffer_get_size(params_buffer);
-        }
-        return 0;
-    }
-
+public:
     void free_cache_ctx_and_buffer() {
         free_cache_buffer();
         free_cache_ctx();
@@ -3114,8 +2681,6 @@ public:
             ggml_gallocr_free(compute_allocr);
             compute_allocr = nullptr;
         }
-        restore_partial_params();
-        restore_all_params();
     }
 
     // do copy after alloc graph
@@ -3180,47 +2745,57 @@ public:
     template <typename T>
     std::optional<sd::Tensor<T>> compute(get_graph_cb_t get_graph,
                                          int n_threads,
-                                         bool free_compute_buffer_immediately,
-                                         bool no_return = false) {
+                                         bool auto_free           = true,
+                                         bool free_compute_buffer = true,
+                                         bool free_compute_params = true,
+                                         bool no_return           = false) {
+        struct RunnerDoneGuard {
+            RunnerDoneGuard(GGMLRunner* runner, bool enabled)
+                : runner(runner),
+                  enabled(enabled) {}
+
+            ~RunnerDoneGuard() {
+                if (enabled && runner != nullptr) {
+                    runner->runner_done();
+                }
+            }
+
+            RunnerDoneGuard(const RunnerDoneGuard&)            = delete;
+            RunnerDoneGuard& operator=(const RunnerDoneGuard&) = delete;
+
+            GGMLRunner* runner = nullptr;
+            bool enabled       = false;
+        };
+        RunnerDoneGuard runner_done_guard(this, auto_free);
+
         ggml_cgraph* gf = nullptr;
         if (!prepare_compute_graph(get_graph, &gf)) {
             return std::nullopt;
         }
         GGML_ASSERT(gf != nullptr);
+        rebuild_params_tensor_set();
 
         if (can_attempt_graph_cut_segmented_compute()) {
             GraphCutPlan plan;
-            size_t effective_graph_vram_bytes = 0;
-            if (!resolve_graph_cut_plan(gf, &plan, &effective_graph_vram_bytes)) {
+            if (!resolve_graph_cut_plan(gf, &plan)) {
                 free_compute_ctx();
                 return std::nullopt;
             }
             if (should_use_graph_cut_segmented_compute(plan)) {
-                if (stream_layers_enabled) {
-                    return compute_streaming_segments<T>(gf,
-                                                         plan,
-                                                         effective_graph_vram_bytes,
-                                                         n_threads,
-                                                         free_compute_buffer_immediately,
-                                                         no_return);
-                }
-                return compute_with_graph_cuts<T>(gf,
-                                                  plan,
-                                                  n_threads,
-                                                  free_compute_buffer_immediately,
-                                                  no_return);
+                return compute_graph_cut_segments<T>(gf,
+                                                     plan,
+                                                     n_threads,
+                                                     stream_layers_enabled,
+                                                     no_return);
             }
         }
-        if (!alloc_compute_buffer(gf)) {
-            LOG_ERROR("%s alloc compute buffer failed", get_desc().c_str());
-            return std::nullopt;
-        }
         return execute_graph<T>(gf,
                                 n_threads,
-                                free_compute_buffer_immediately,
-                                {},
+                                free_compute_buffer,
+                                free_compute_params,
                                 false,
-                                no_return);
+                                no_return,
+                                nullptr);
     }
 
     void set_flash_attention_enabled(bool enabled) {
@@ -3247,16 +2822,6 @@ public:
     void set_stream_layers_enabled(bool enabled) {
         stream_layers_enabled = enabled;
     }
-
-    sd::layer_registry::LayerRegistry& get_layer_registry() { return layer_registry_; }
-
-    ggml_backend_t get_runtime_backend() {
-        return runtime_backend;
-    }
-
-    ggml_backend_t get_params_backend() {
-        return params_backend;
-    }
 };
 
 class GGMLBlock {
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
index d085129db..f3e2cceba 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
@@ -45,6 +45,10 @@ static bool is_default_backend_token(const std::string& name) {
     return lower.empty() || lower == "default" || lower == "auto";
 }
 
+static bool is_disk_backend_token(const std::string& name) {
+    return lower_copy(trim_copy(name)) == "disk";
+}
+
 static bool parse_backend_module(const std::string& raw_name, SDBackendModule* module) {
     std::string name = lower_copy(trim_copy(raw_name));
     name.erase(std::remove(name.begin(), name.end(), '-'), name.end());
@@ -200,6 +204,36 @@ void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value
     }
 }
 
+bool add_rpc_devices(const std::string& servers) {
+    const std::string in = trim_copy(servers);
+    if (in.empty()) {
+        return true;
+    }
+    auto rpc_servers = split_copy(in, ',');
+    if (rpc_servers.empty()) {
+        LOG_ERROR("invalid RPC servers specification: '%s'", servers.c_str());
+        return false;
+    }
+    ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
+    if (!rpc_reg) {
+        LOG_ERROR("RPC backend not found, cannot add RPC servers");
+        return false;
+    }
+    typedef ggml_backend_reg_t (*ggml_backend_rpc_add_server_t)(const char* endpoint);
+    ggml_backend_rpc_add_server_t ggml_backend_rpc_add_server_fn = (ggml_backend_rpc_add_server_t)ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_server");
+    if (!ggml_backend_rpc_add_server_fn) {
+        LOG_ERROR("RPC backend does not have ggml_backend_rpc_add_server function, cannot add RPC servers");
+        return false;
+    }
+    for (const auto& server : rpc_servers) {
+        LOG_INFO("Adding RPC server: %s", server.c_str());
+        auto reg = ggml_backend_rpc_add_server_fn(server.c_str());
+        // no return value to check for success but should print errors from the RPC backend if it fails to add the server
+        ggml_backend_register(reg);
+    }
+    return true;
+}
+
 static void ggml_backend_load_all_once() {
     // If the registry already has devices and the CPU backend is present,
     // assume either static registration or explicit host-side preloading has
@@ -246,7 +280,7 @@ static std::string get_default_backend_name() {
     return resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
 }
 
-static std::string sd_resolve_backend_name(const std::string& name) {
+std::string sd_backend_resolve_name(const std::string& name) {
     ggml_backend_load_all_once();
     std::string requested = trim_copy(name);
     std::string lower     = lower_copy(requested);
@@ -284,7 +318,7 @@ static std::string sd_resolve_backend_name(const std::string& name) {
 }
 
 static bool backend_name_exists(const std::string& name) {
-    return !sd_resolve_backend_name(name).empty();
+    return !sd_backend_resolve_name(name).empty();
 }
 
 static ggml_backend_t init_named_backend(const std::string& name) {
@@ -294,7 +328,7 @@ static ggml_backend_t init_named_backend(const std::string& name) {
         return ggml_backend_init_best();
     }
 
-    std::string resolved = sd_resolve_backend_name(name);
+    std::string resolved = sd_backend_resolve_name(name);
     if (resolved.empty()) {
         return nullptr;
     }
@@ -504,6 +538,9 @@ ggml_backend_t SDBackendManager::params_backend(SDBackendModule module) {
     if (name.empty()) {
         return runtime_backend(module);
     }
+    if (is_disk_backend_token(name)) {
+        return runtime_backend(module);
+    }
     return init_cached_backend(name);
 }
 
@@ -515,6 +552,10 @@ bool SDBackendManager::params_backend_is_cpu(SDBackendModule module) {
     return sd_backend_is_cpu(params_backend(module));
 }
 
+bool SDBackendManager::params_backend_is_disk(SDBackendModule module) const {
+    return is_disk_backend_token(params_assignment_.get(module));
+}
+
 bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule module) {
     ggml_backend_t backend = runtime_backend(module);
     if (backend == nullptr) {
@@ -534,10 +575,6 @@ bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule modu
 
 bool SDBackendManager::init(const char* backend_spec,
                             const char* params_backend_spec,
-                            bool offload_params_to_cpu,
-                            bool keep_clip_on_cpu,
-                            bool keep_vae_on_cpu,
-                            bool keep_control_net_on_cpu,
                             std::string* error) {
     reset();
 
@@ -548,31 +585,21 @@ bool SDBackendManager::init(const char* backend_spec,
         return false;
     }
 
-    if (runtime_assignment_.empty()) {
-        if (keep_clip_on_cpu) {
-            runtime_assignment_.set_module(SDBackendModule::TE, "cpu");
-        }
-        if (keep_vae_on_cpu) {
-            runtime_assignment_.set_module(SDBackendModule::VAE, "cpu");
-        }
-        if (keep_control_net_on_cpu) {
-            runtime_assignment_.set_module(SDBackendModule::CONTROL_NET, "cpu");
-        }
-    }
-
-    if (params_assignment_.empty() && offload_params_to_cpu) {
-        params_assignment_.set_default("cpu");
-    }
-
     return validate(error);
 }
 
 bool SDBackendManager::validate(std::string* error) const {
-    auto validate_name = [&](const std::string& name) -> bool {
+    auto validate_runtime_name = [&](const std::string& name) -> bool {
         if (is_default_backend_token(name)) {
             return true;
         }
-        if (!sd_resolve_backend_name(name).empty()) {
+        if (is_disk_backend_token(name)) {
+            if (error != nullptr) {
+                *error = "backend 'disk' is only supported by params_backend";
+            }
+            return false;
+        }
+        if (!sd_backend_resolve_name(name).empty()) {
             return true;
         }
         if (error != nullptr) {
@@ -580,18 +607,24 @@ bool SDBackendManager::validate(std::string* error) const {
         }
         return false;
     };
+    auto validate_params_name = [&](const std::string& name) -> bool {
+        if (is_disk_backend_token(name)) {
+            return true;
+        }
+        return validate_runtime_name(name);
+    };
 
-    if (!validate_name(runtime_assignment_.default_name) ||
-        !validate_name(params_assignment_.default_name)) {
+    if (!validate_runtime_name(runtime_assignment_.default_name) ||
+        !validate_params_name(params_assignment_.default_name)) {
         return false;
     }
     for (const auto& kv : runtime_assignment_.module_names) {
-        if (!validate_name(kv.second)) {
+        if (!validate_runtime_name(kv.second)) {
             return false;
         }
     }
     for (const auto& kv : params_assignment_.module_names) {
-        if (!validate_name(kv.second)) {
+        if (!validate_params_name(kv.second)) {
             return false;
         }
     }
@@ -599,7 +632,7 @@ bool SDBackendManager::validate(std::string* error) const {
 }
 
 ggml_backend_t SDBackendManager::init_cached_backend(const std::string& name) {
-    std::string resolved   = sd_resolve_backend_name(name);
+    std::string resolved   = sd_backend_resolve_name(name);
     std::string key        = lower_copy(resolved);
     ggml_backend_t backend = nullptr;
 
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.h b/otherarch/sdcpp/src/core/ggml_extend_backend.h
index fc071ffda..9aecf97c0 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.h
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.h
@@ -51,10 +51,6 @@ public:
 
     bool init(const char* backend_spec,
               const char* params_backend_spec,
-              bool offload_params_to_cpu,
-              bool keep_clip_on_cpu,
-              bool keep_vae_on_cpu,
-              bool keep_control_net_on_cpu,
               std::string* error);
     void reset();
 
@@ -63,6 +59,7 @@ public:
 
     bool runtime_backend_is_cpu(SDBackendModule module);
     bool params_backend_is_cpu(SDBackendModule module);
+    bool params_backend_is_disk(SDBackendModule module) const;
     bool runtime_backend_supports_host_buffer(SDBackendModule module);
 
 private:
@@ -74,6 +71,8 @@ bool sd_backend_is(ggml_backend_t backend, const std::string& name);
 bool sd_backend_is_cpu(ggml_backend_t backend);
 ggml_backend_t sd_backend_cpu_init();
 bool sd_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+std::string sd_backend_resolve_name(const std::string& name);
 const char* sd_backend_module_name(SDBackendModule module);
 void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
+bool add_rpc_devices(const std::string& servers);
 #endif  // __SD_CORE_GGML_EXTEND_BACKEND_H__
diff --git a/otherarch/sdcpp/src/core/ggml_graph_cut.cpp b/otherarch/sdcpp/src/core/ggml_graph_cut.cpp
index c77a45448..d4874b054 100644
--- a/otherarch/sdcpp/src/core/ggml_graph_cut.cpp
+++ b/otherarch/sdcpp/src/core/ggml_graph_cut.cpp
@@ -1,6 +1,8 @@
 #include "core/ggml_graph_cut.h"
 
 #include <algorithm>
+#include <cctype>
+#include <cmath>
 #include <cstring>
 #include <map>
 #include <set>
@@ -8,6 +10,7 @@
 #include <stack>
 #include <unordered_map>
 
+#include "core/ggml_extend_backend.h"
 #include "core/util.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -44,7 +47,9 @@ namespace sd::ggml_graph_cut {
         if (tensor == nullptr) {
             return false;
         }
-        return params_tensor_set.find(tensor) != params_tensor_set.end();
+        return params_tensor_set.find(tensor) != params_tensor_set.end() ||
+               (tensor->view_src != nullptr &&
+                params_tensor_set.find(tensor->view_src) != params_tensor_set.end());
     }
 
     static int graph_node_index_by_name(ggml_cgraph* gf, const char* name) {
@@ -81,6 +86,157 @@ namespace sd::ggml_graph_cut {
                segment.output_bytes;
     }
 
+    static std::string lower_ascii_copy(std::string value) {
+        std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+            return static_cast<char>(std::tolower(c));
+        });
+        return value;
+    }
+
+    static std::string normalize_backend_budget_key(const std::string& value) {
+        return lower_ascii_copy(trim(value));
+    }
+
+    static bool is_default_max_vram_key(const std::string& key) {
+        std::string normalized = normalize_backend_budget_key(key);
+        return normalized == "all" || normalized == "default" || normalized == "*";
+    }
+
+    static bool parse_max_vram_budget_value(const std::string& text, float* value, std::string* error) {
+        float parsed = 0.f;
+        if (!parse_strict_float(text, parsed) || !std::isfinite(parsed)) {
+            if (error != nullptr) {
+                *error = "invalid --max-vram value '" + text + "'";
+            }
+            return false;
+        }
+        *value = parsed;
+        return true;
+    }
+
+    static std::vector<std::string> backend_budget_keys(ggml_backend_t backend) {
+        std::vector<std::string> keys;
+        if (backend == nullptr) {
+            return keys;
+        }
+
+        ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+        if (dev != nullptr) {
+            keys.push_back(normalize_backend_budget_key(ggml_backend_dev_name(dev)));
+        }
+        const char* backend_name = ggml_backend_name(backend);
+        if (backend_name != nullptr) {
+            keys.push_back(normalize_backend_budget_key(backend_name));
+        }
+        return keys;
+    }
+
+    void MaxVramAssignment::reset(float fallback_gib) {
+        default_gib = fallback_gib;
+        backend_gib.clear();
+        resolved_backend_bytes.clear();
+    }
+
+    bool MaxVramAssignment::parse(const std::string& raw_spec, std::string* error) {
+        const std::string in = trim(raw_spec);
+        if (in.empty()) {
+            return true;
+        }
+
+        for (const std::string& raw_part : split_string(in, ',')) {
+            const std::string part = trim(raw_part);
+            if (part.empty()) {
+                continue;
+            }
+
+            const size_t eq = part.find('=');
+            if (eq == std::string::npos) {
+                float value = 0.f;
+                if (!parse_max_vram_budget_value(part, &value, error)) {
+                    return false;
+                }
+                default_gib = value;
+                continue;
+            }
+
+            const std::string key        = trim(part.substr(0, eq));
+            const std::string value_text = trim(part.substr(eq + 1));
+            if (key.empty() || value_text.empty()) {
+                if (error != nullptr) {
+                    *error = "invalid --max-vram assignment '" + part + "'";
+                }
+                return false;
+            }
+
+            float value = 0.f;
+            if (!parse_max_vram_budget_value(value_text, &value, error)) {
+                return false;
+            }
+
+            if (is_default_max_vram_key(key)) {
+                default_gib = value;
+                continue;
+            }
+
+            const std::string backend_key = trim(key);
+            if (backend_key.empty()) {
+                if (error != nullptr) {
+                    *error = "invalid --max-vram backend key in '" + part + "'";
+                }
+                return false;
+            }
+            backend_gib[backend_key] = value;
+        }
+        resolved_backend_bytes.clear();
+        return true;
+    }
+
+    bool MaxVramAssignment::canonicalize_backend_keys(std::string* error) {
+        if (backend_gib.empty()) {
+            return true;
+        }
+
+        std::unordered_map<std::string, float> normalized;
+        for (const auto& kv : backend_gib) {
+            std::string resolved = sd_backend_resolve_name(kv.first);
+            if (resolved.empty()) {
+                if (error != nullptr) {
+                    *error = "unknown --max-vram backend '" + kv.first + "'";
+                }
+                return false;
+            }
+            normalized[normalize_backend_budget_key(resolved)] = kv.second;
+        }
+        backend_gib = std::move(normalized);
+        resolved_backend_bytes.clear();
+        return true;
+    }
+
+    size_t MaxVramAssignment::bytes_for_backend(ggml_backend_t backend) {
+        std::vector<std::string> keys = backend_budget_keys(backend);
+        const std::string cache_key   = keys.empty() ? std::string("<none>") : keys.front();
+        auto cached                   = resolved_backend_bytes.find(cache_key);
+        if (cached != resolved_backend_bytes.end()) {
+            return cached->second;
+        }
+
+        float budget_gib = default_gib;
+        if (!backend_gib.empty()) {
+            for (const std::string& key : keys) {
+                auto backend_it = backend_gib.find(key);
+                if (backend_it != backend_gib.end()) {
+                    budget_gib = backend_it->second;
+                    break;
+                }
+            }
+        }
+
+        const float resolved_gib          = resolve_max_vram_gib(budget_gib, backend);
+        const size_t bytes                = max_vram_gib_to_bytes(resolved_gib);
+        resolved_backend_bytes[cache_key] = bytes;
+        return bytes;
+    }
+
     size_t max_vram_gib_to_bytes(float max_vram) {
         if (max_vram <= 0.f) {
             return 0;
@@ -135,6 +291,24 @@ namespace sd::ggml_graph_cut {
         return max_vram_bytes_to_gib(resolve_auto_max_vram_bytes(-max_vram, backend));
     }
 
+    static bool is_segment_output_needed_after(const Plan& plan,
+                                               size_t end_segment_index,
+                                               int output_node_index) {
+        if (end_segment_index + 1 >= plan.segments.size()) {
+            return false;
+        }
+        for (size_t seg_idx = end_segment_index + 1; seg_idx < plan.segments.size(); ++seg_idx) {
+            const auto& segment = plan.segments[seg_idx];
+            for (const auto& input_ref : segment.input_refs) {
+                if (input_ref.type == Segment::INPUT_PREVIOUS_CUT &&
+                    input_ref.node_index == output_node_index) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
     static Segment make_segment_seed(const Plan& plan,
                                      size_t start_segment_index,
                                      size_t end_segment_index) {
@@ -147,8 +321,11 @@ namespace sd::ggml_graph_cut {
         const auto& target_segment = plan.segments[end_segment_index];
         std::unordered_set<int> seen_output_node_indices;
         for (size_t seg_idx = start_segment_index; seg_idx <= end_segment_index; ++seg_idx) {
+            const bool is_boundary_segment = seg_idx == end_segment_index;
             for (int output_node_index : plan.segments[seg_idx].output_node_indices) {
-                if (seen_output_node_indices.insert(output_node_index).second) {
+                if ((is_boundary_segment ||
+                     is_segment_output_needed_after(plan, end_segment_index, output_node_index)) &&
+                    seen_output_node_indices.insert(output_node_index).second) {
                     seed.output_node_indices.push_back(output_node_index);
                 }
             }
@@ -400,23 +577,6 @@ namespace sd::ggml_graph_cut {
         return tensors;
     }
 
-    std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc) {
-        std::vector<ggml_tensor*> tensors = param_tensors(gf, segment);
-        std::vector<ggml_tensor*> filtered_tensors;
-        filtered_tensors.reserve(tensors.size());
-        for (ggml_tensor* tensor : tensors) {
-            if (tensor_buffer(tensor) == nullptr) {
-                LOG_WARN("%s graph cut skipping param input without buffer: segment=%s tensor=%s",
-                         log_desc == nullptr ? "unknown" : log_desc,
-                         segment.group_name.c_str(),
-                         tensor->name);
-                continue;
-            }
-            filtered_tensors.push_back(tensor);
-        }
-        return filtered_tensors;
-    }
-
     std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
                                                                const Plan& plan,
                                                                size_t current_segment_index) {
@@ -487,6 +647,44 @@ namespace sd::ggml_graph_cut {
             return 0;
         }
 
+        struct TensorRuntimeBinding {
+            ggml_backend_buffer_t buffer = nullptr;
+            void* data                   = nullptr;
+            void* extra                  = nullptr;
+        };
+        std::unordered_map<ggml_tensor*, TensorRuntimeBinding> saved_bindings;
+        auto mark_measurement_external = [&](ggml_tensor* tensor) {
+            if (tensor == nullptr) {
+                return;
+            }
+            auto save_tensor = [&](ggml_tensor* t) {
+                if (t == nullptr || saved_bindings.find(t) != saved_bindings.end()) {
+                    return;
+                }
+                saved_bindings[t] = {t->buffer, t->data, t->extra};
+                // During real execution params and previous-cut inputs already
+                // have backend/cache buffers, so gallocr must not reserve them.
+                t->data = reinterpret_cast<void*>(static_cast<uintptr_t>(1));
+            };
+            save_tensor(tensor);
+            save_tensor(tensor->view_src);
+        };
+        for (const auto& input : segment.input_refs) {
+            if (input.type != Segment::INPUT_PARAM &&
+                input.type != Segment::INPUT_PREVIOUS_CUT) {
+                continue;
+            }
+            mark_measurement_external(input_tensor(gf, input));
+        }
+
+        std::unordered_map<ggml_tensor*, int32_t> saved_output_flags;
+        for (int output_node_index : segment.output_node_indices) {
+            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
+            if (output != nullptr && saved_output_flags.find(output) == saved_output_flags.end()) {
+                saved_output_flags[output] = output->flags;
+            }
+        }
+
         ggml_context* graph_ctx    = nullptr;
         ggml_cgraph* segment_graph = build_segment_graph(gf, segment, &graph_ctx);
         ggml_gallocr_t allocr      = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
@@ -502,6 +700,14 @@ namespace sd::ggml_graph_cut {
 
         ggml_gallocr_free(allocr);
         ggml_free(graph_ctx);
+        for (const auto& kv : saved_output_flags) {
+            kv.first->flags = kv.second;
+        }
+        for (const auto& kv : saved_bindings) {
+            kv.first->buffer = kv.second.buffer;
+            kv.first->data   = kv.second.data;
+            kv.first->extra  = kv.second.extra;
+        }
         return buffer_size;
     }
 
@@ -669,7 +875,8 @@ namespace sd::ggml_graph_cut {
                 GGML_ASSERT(!candidate_plan.segments.empty());
 
                 const auto& candidate_segment = candidate_plan.segments.back();
-                if (graph_cut_segment_vram_bytes(candidate_segment) > max_graph_vram_bytes) {
+                const size_t candidate_bytes  = graph_cut_segment_vram_bytes(candidate_segment);
+                if (candidate_bytes > max_graph_vram_bytes) {
                     break;
                 }
 
diff --git a/otherarch/sdcpp/src/core/ggml_graph_cut.h b/otherarch/sdcpp/src/core/ggml_graph_cut.h
index 7919acadd..17f2f1d7d 100644
--- a/otherarch/sdcpp/src/core/ggml_graph_cut.h
+++ b/otherarch/sdcpp/src/core/ggml_graph_cut.h
@@ -4,6 +4,7 @@
 #include <array>
 #include <cstdint>
 #include <string>
+#include <unordered_map>
 #include <unordered_set>
 #include <vector>
 
@@ -68,6 +69,17 @@ namespace sd::ggml_graph_cut {
 
     static constexpr const char* GGML_RUNNER_CUT_PREFIX = "ggml_runner_cut:";
 
+    struct MaxVramAssignment {
+        float default_gib = 0.f;
+        std::unordered_map<std::string, float> backend_gib;
+        std::unordered_map<std::string, size_t> resolved_backend_bytes;
+
+        void reset(float fallback_gib);
+        bool parse(const std::string& raw_spec, std::string* error);
+        bool canonicalize_backend_keys(std::string* error);
+        size_t bytes_for_backend(ggml_backend_t backend);
+    };
+
     bool is_graph_cut_tensor(const ggml_tensor* tensor);
     std::string make_graph_cut_name(const std::string& group, const std::string& output);
     void mark_graph_cut(ggml_tensor* tensor, const std::string& group, const std::string& output);
@@ -80,7 +92,6 @@ namespace sd::ggml_graph_cut {
     ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index);
     ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref);
     std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment);
-    std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc);
     std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
                                                                const Plan& plan,
                                                                size_t current_segment_index);
diff --git a/otherarch/sdcpp/src/core/layer_registry.cpp b/otherarch/sdcpp/src/core/layer_registry.cpp
deleted file mode 100644
index 65771ee9e..000000000
--- a/otherarch/sdcpp/src/core/layer_registry.cpp
+++ /dev/null
@@ -1,132 +0,0 @@
-#include "core/layer_registry.h"
-
-#include <utility>
-
-#include "core/util.h"
-
-namespace sd::layer_registry {
-
-    void LayerRegistry::register_layer(const std::string& name, ggml_tensor* tensor) {
-        auto& info = layers_[name];
-        info.tensors.push_back(tensor);
-        info.bytes += ggml_nbytes(tensor);
-    }
-
-    bool LayerRegistry::move_layer_to_gpu(const std::string& name) {
-        auto it = layers_.find(name);
-        if (it == layers_.end())
-            return false;
-
-        LayerInfo& info = it->second;
-        if (info.on_gpu)
-            return true;
-        if (gpu_backend_ == nullptr || cpu_backend_ == nullptr) {
-            LOG_ERROR("layer_registry: backends not set; cannot move '%s' to GPU",
-                      name.c_str());
-            return false;
-        }
-        if (info.tensors.empty()) {
-            info.on_gpu = true;
-            return true;
-        }
-
-        // 1. Build a no_alloc context big enough to hold one twin tensor per CPU
-        //    tensor, plus a little overhead.
-        const size_t ctx_size = info.tensors.size() * ggml_tensor_overhead() + 1024;
-        ggml_init_params ctx_params{ctx_size, /*mem_buffer=*/nullptr, /*no_alloc=*/true};
-        ggml_context* twin_ctx = ggml_init(ctx_params);
-        if (twin_ctx == nullptr) {
-            LOG_ERROR("layer_registry: failed to allocate twin context for '%s'",
-                      name.c_str());
-            return false;
-        }
-
-        // 2. Create one GPU twin per CPU tensor. The twin shares the original
-        //    name so any name-based lookup keeps working.
-        std::vector<ggml_tensor*> gpu_twins;
-        gpu_twins.reserve(info.tensors.size());
-        for (ggml_tensor* cpu_t : info.tensors) {
-            ggml_tensor* twin = ggml_dup_tensor(twin_ctx, cpu_t);
-            if (cpu_t->name[0] != '\0') {
-                ggml_set_name(twin, cpu_t->name);
-            }
-            gpu_twins.push_back(twin);
-        }
-
-        // 3. Back the twins with a GPU buffer in one alloc call.
-        ggml_backend_buffer_t gpu_buffer = ggml_backend_alloc_ctx_tensors(twin_ctx, gpu_backend_);
-        if (gpu_buffer == nullptr) {
-            LOG_ERROR("layer_registry: failed to allocate GPU buffer for '%s'",
-                      name.c_str());
-            ggml_free(twin_ctx);
-            return false;
-        }
-
-        // 4. H2D copy + sync.
-        for (size_t i = 0; i < info.tensors.size(); ++i) {
-            ggml_backend_tensor_copy(info.tensors[i], gpu_twins[i]);
-        }
-        ggml_backend_synchronize(gpu_backend_);
-
-        // 5. Swap buffer/data/extra so the originals now point at GPU memory.
-        for (size_t i = 0; i < info.tensors.size(); ++i) {
-            std::swap(info.tensors[i]->buffer, gpu_twins[i]->buffer);
-            std::swap(info.tensors[i]->data, gpu_twins[i]->data);
-            std::swap(info.tensors[i]->extra, gpu_twins[i]->extra);
-        }
-
-        info.gpu_twins  = std::move(gpu_twins);
-        info.twin_ctx   = twin_ctx;
-        info.gpu_buffer = gpu_buffer;
-        info.on_gpu     = true;
-        return true;
-    }
-
-    bool LayerRegistry::move_layer_to_cpu(const std::string& name) {
-        auto it = layers_.find(name);
-        if (it == layers_.end())
-            return false;
-
-        LayerInfo& info = it->second;
-        if (!info.on_gpu)
-            return true;
-        if (info.tensors.size() != info.gpu_twins.size()) {
-            LOG_ERROR("layer_registry: twin/tensor count mismatch for '%s'",
-                      name.c_str());
-            return false;
-        }
-
-        // 1. Swap back: originals point at CPU memory again.
-        for (size_t i = 0; i < info.tensors.size(); ++i) {
-            if (info.gpu_twins[i] == nullptr)
-                continue;
-            std::swap(info.tensors[i]->buffer, info.gpu_twins[i]->buffer);
-            std::swap(info.tensors[i]->data, info.gpu_twins[i]->data);
-            std::swap(info.tensors[i]->extra, info.gpu_twins[i]->extra);
-        }
-
-        // 2. Free the GPU buffer + twin context.
-        if (info.gpu_buffer != nullptr) {
-            ggml_backend_buffer_free(info.gpu_buffer);
-            info.gpu_buffer = nullptr;
-        }
-        if (info.twin_ctx != nullptr) {
-            ggml_free(info.twin_ctx);
-            info.twin_ctx = nullptr;
-        }
-        info.gpu_twins.clear();
-        info.on_gpu = false;
-        return true;
-    }
-
-    bool LayerRegistry::is_layer_on_gpu(const std::string& name) const {
-        auto it = layers_.find(name);
-        return it != layers_.end() && it->second.on_gpu;
-    }
-
-    size_t LayerRegistry::get_layer_size(const std::string& name) const {
-        auto it = layers_.find(name);
-        return it != layers_.end() ? it->second.bytes : 0;
-    }
-
-}  // namespace sd::layer_registry
diff --git a/otherarch/sdcpp/src/core/layer_registry.h b/otherarch/sdcpp/src/core/layer_registry.h
deleted file mode 100644
index c0b980d0b..000000000
--- a/otherarch/sdcpp/src/core/layer_registry.h
+++ /dev/null
@@ -1,50 +0,0 @@
-#ifndef __SD_CORE_LAYER_REGISTRY_H__
-#define __SD_CORE_LAYER_REGISTRY_H__
-
-#include <map>
-#include <set>
-#include <string>
-#include <vector>
-
-#include "ggml-backend.h"
-#include "ggml.h"
-
-namespace sd::layer_registry {
-
-    struct LayerInfo {
-        std::vector<ggml_tensor*> tensors;
-        std::vector<ggml_tensor*> gpu_twins;
-        ggml_context* twin_ctx           = nullptr;
-        ggml_backend_buffer_t gpu_buffer = nullptr;
-        bool on_gpu                      = false;
-        size_t bytes                     = 0;
-    };
-
-    class LayerRegistry {
-    public:
-        LayerRegistry() = default;
-        LayerRegistry(ggml_backend_t gpu_backend, ggml_backend_t cpu_backend)
-            : gpu_backend_(gpu_backend), cpu_backend_(cpu_backend) {}
-
-        void set_backends(ggml_backend_t gpu_backend, ggml_backend_t cpu_backend) {
-            gpu_backend_ = gpu_backend;
-            cpu_backend_ = cpu_backend;
-        }
-        void register_layer(const std::string& name, ggml_tensor* tensor);
-        bool move_layer_to_gpu(const std::string& name);
-        bool move_layer_to_cpu(const std::string& name);
-        bool is_layer_on_gpu(const std::string& name) const;
-        size_t get_layer_size(const std::string& name) const;
-        size_t get_layer_count() const { return layers_.size(); }
-
-        const std::map<std::string, LayerInfo>& layers() const { return layers_; }
-
-    private:
-        ggml_backend_t gpu_backend_ = nullptr;
-        ggml_backend_t cpu_backend_ = nullptr;
-        std::map<std::string, LayerInfo> layers_;
-    };
-
-}  // namespace sd::layer_registry
-
-#endif  // __SD_CORE_LAYER_REGISTRY_H__
diff --git a/otherarch/sdcpp/src/core/util.cpp b/otherarch/sdcpp/src/core/util.cpp
index 950254431..a70722af2 100644
--- a/otherarch/sdcpp/src/core/util.cpp
+++ b/otherarch/sdcpp/src/core/util.cpp
@@ -507,7 +507,7 @@ static int sdloglevel = 0; //-1 = hide all, 0 = normal, 1 = showall
 static bool sdquiet = false;
 // } kcpp
 
-static std::string build_progress_bar(int step, int steps) {
+static std::string build_progress_bar(int step, int steps, char progress_char = '=', bool show_head = true) {
     std::string progress = "  |";
     int max_progress     = 50;
     int32_t current      = 0;
@@ -517,21 +517,21 @@ static std::string build_progress_bar(int step, int steps) {
     for (int i = 0; i < 50; i++) {
         if (i > current) {
             progress += " ";
-        } else if (i == current && i != max_progress - 1) {
+        } else if (show_head && i == current && i != max_progress - 1) {
             progress += ">";
         } else {
-            progress += "=";
+            progress += progress_char;
         }
     }
     progress += "|";
     return progress;
 }
 
-static void print_progress_line(int step, int steps, const std::string& speed_text) {
+static void print_progress_line(int step, int steps, const std::string& speed_text, char progress_char = '=', bool show_head = true) {
     if (step == 0) {
         return;
     }
-    std::string progress = build_progress_bar(step, steps);
+    std::string progress = build_progress_bar(step, steps, progress_char, show_head);
     const char* lf       = (step == steps ? "\n" : "");
     printf("\r%s %i/%i - %s\033[K%s", progress.c_str(), step, steps, speed_text.c_str(), lf);
     fflush(stdout);  // for linux
@@ -574,9 +574,9 @@ void pretty_bytes_progress(int step, int steps, uint64_t bytes_processed, float
 
     double speed_mb = bytes_per_second / (1024.0 * 1024.0);
     if (speed_mb >= 1024.0) {
-        print_progress_line(step, steps, sd_format("%.2fGB/s", speed_mb / 1024.0));
+        print_progress_line(step, steps, sd_format("%.2fGB/s", speed_mb / 1024.0), '#', false);
     } else {
-        print_progress_line(step, steps, sd_format("%.2fMB/s", speed_mb));
+        print_progress_line(step, steps, sd_format("%.2fMB/s", speed_mb), '#', false);
     }
 }
 
diff --git a/otherarch/sdcpp/src/extensions/generation_extension.h b/otherarch/sdcpp/src/extensions/generation_extension.h
index 1e6d1341e..67085c157 100644
--- a/otherarch/sdcpp/src/extensions/generation_extension.h
+++ b/otherarch/sdcpp/src/extensions/generation_extension.h
@@ -6,10 +6,13 @@
 #include <memory>
 #include <set>
 #include <string>
+#include <vector>
 
 #include "conditioning/conditioner.hpp"
 #include "core/ggml_extend_backend.h"
+#include "model/diffusion/model.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 #include "stable-diffusion.h"
 
 struct GenerationExtensionInitContext {
@@ -17,27 +20,20 @@ struct GenerationExtensionInitContext {
     SDVersion version;
     const String2TensorStorage& tensor_storage_map;
     ModelLoader& model_loader;
+    std::shared_ptr<ModelManager> model_manager;
     int n_threads;
     std::function<bool(SDBackendModule)> ensure_backend_pair;
     std::function<ggml_backend_t(SDBackendModule)> backend_for;
     std::function<ggml_backend_t(SDBackendModule)> params_backend_for;
 };
 
-struct GenerationExtensionTensorContext {
-    std::map<std::string, ggml_tensor*>& tensors;
-    std::map<std::string, ggml_tensor*>& mmap_able_tensors;
-    std::function<bool(SDBackendModule)> module_can_mmap;
-};
-
 struct GenerationExtensionConditionContext {
     Conditioner* conditioner;
     ConditionerParams& condition_params;
     const sd_pm_params_t& pm_params;
-    std::map<std::string, ggml_tensor*>& tensors;
-    SDVersion version;
+    const sd_pulid_params_t& pulid_params;
     int n_threads;
     int total_steps;
-    bool free_params_immediately;
 };
 
 struct GenerationExtension {
@@ -50,14 +46,10 @@ struct GenerationExtension {
     virtual bool init(const GenerationExtensionInitContext&) {
         return true;
     }
-    virtual void collect_param_tensors(GenerationExtensionTensorContext&) {}
+    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>&) {}
+    virtual void collect_loras(std::vector<ModelManager::LoraSpec>&) {}
     virtual void add_ignore_tensors(std::set<std::string>&) const {}
-    virtual bool alloc_params_buffer() {
-        return true;
-    }
-    virtual size_t get_params_buffer_size() const {
-        return 0;
-    }
+    virtual void runner_done() {}
     virtual void reset_runtime_condition() {}
     virtual bool prepare_condition(GenerationExtensionConditionContext&) {
         return false;
@@ -66,8 +58,20 @@ struct GenerationExtension {
                                                 const SDCondition& condition) const {
         return condition;
     }
+
+    // Called in the denoise loop for each enabled extension, after the per-step
+    // DiffusionParams (including its version-specific `extra`) has been built,
+    // but before diffusion_model->compute(). Lets an extension feed data into
+    // the diffusion forward that the conditioning-side hooks can't reach -- it
+    // can set/override fields on `params` (typically the architecture-specific
+    // `params.extra`, e.g. a guidance tensor, control payload, or an identity
+    // embedding for an adapter that injects inside the model's blocks). The
+    // extension targets whichever `extra` variant matches the active model.
+    // Mutates `params` only, never the extension. Default no-op.
+    virtual void before_diffusion(DiffusionParams& /*params*/, int /*step*/) const {}
 };
 
 std::shared_ptr<GenerationExtension> create_photomaker_extension();
+std::shared_ptr<GenerationExtension> create_pulid_extension();
 
 #endif
diff --git a/otherarch/sdcpp/src/extensions/photomaker_extension.cpp b/otherarch/sdcpp/src/extensions/photomaker_extension.cpp
index ac3949a11..78c5cdb9b 100644
--- a/otherarch/sdcpp/src/extensions/photomaker_extension.cpp
+++ b/otherarch/sdcpp/src/extensions/photomaker_extension.cpp
@@ -7,7 +7,6 @@
 
 #include "core/tensor_ggml.hpp"
 #include "core/util.h"
-#include "model/adapter/lora.hpp"
 #include "model/adapter/pmid.hpp"
 
 static std::tuple<std::vector<int>, std::vector<float>, std::vector<bool>>
@@ -103,7 +102,6 @@ static std::string remove_photomaker_trigger_from_prompt(FrozenCLIPEmbedderWithC
 
 struct PhotoMakerExtension : public GenerationExtension {
     std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
-    std::shared_ptr<LoraModel> pmid_lora;
     bool enabled = false;
     std::string model_path;
     std::string trigger_word = "img";
@@ -129,54 +127,45 @@ struct PhotoMakerExtension : public GenerationExtension {
         }
 
         PMVersion pm_version = std::strstr(model_path.c_str(), "v2") != nullptr ? PM_VERSION_2 : PM_VERSION_1;
-        pmid_model           = std::make_shared<PhotoMakerIDEncoder>(ctx.backend_for(SDBackendModule::PHOTOMAKER),
-                                                           ctx.params_backend_for(SDBackendModule::PHOTOMAKER),
-                                                           ctx.tensor_storage_map,
-                                                           "pmid",
-                                                           ctx.version,
-                                                           pm_version);
-        if (pm_version == PM_VERSION_2) {
-            LOG_INFO("using PhotoMaker Version 2");
-        }
-
-        pmid_lora               = std::make_shared<LoraModel>("pmid",
-                                                ctx.backend_for(SDBackendModule::PHOTOMAKER),
-                                                ctx.params_backend_for(SDBackendModule::PHOTOMAKER),
-                                                model_path,
-                                                "",
-                                                ctx.version);
-        auto lora_tensor_filter = [&](const std::string& tensor_name) {
-            return starts_with(tensor_name, "lora.model");
-        };
-        if (!pmid_lora->load_from_file(ctx.n_threads, lora_tensor_filter)) {
-            LOG_WARN("load photomaker lora tensors from %s failed", model_path.c_str());
-            return false;
-        }
-
         LOG_INFO("loading stacked ID embedding (PHOTOMAKER) model file from '%s'", model_path.c_str());
         if (!ctx.model_loader.init_from_file_and_convert_name(model_path, "pmid.")) {
             LOG_WARN("loading stacked ID embedding from '%s' failed", model_path.c_str());
             return true;
         }
 
+        pmid_model = std::make_shared<PhotoMakerIDEncoder>(ctx.backend_for(SDBackendModule::PHOTOMAKER),
+                                                           ctx.tensor_storage_map,
+                                                           "pmid",
+                                                           ctx.version,
+                                                           pm_version,
+                                                           20.f,
+                                                           ctx.model_manager);
+        if (pm_version == PM_VERSION_2) {
+            LOG_INFO("using PhotoMaker Version 2");
+        }
+
         enabled = true;
         return true;
     }
 
-    void collect_param_tensors(GenerationExtensionTensorContext& ctx) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
         if (!enabled || pmid_model == nullptr) {
             return;
         }
 
-        std::map<std::string, ggml_tensor*> temp;
-        pmid_model->get_param_tensors(temp, "pmid");
-        bool do_mmap = ctx.module_can_mmap(SDBackendModule::PHOTOMAKER);
-        for (const auto& [key, tensor] : temp) {
-            ctx.tensors[key] = tensor;
-            if (do_mmap) {
-                ctx.mmap_able_tensors[key] = tensor;
-            }
+        pmid_model->get_param_tensors(tensors, "pmid");
+    }
+
+    void collect_loras(std::vector<ModelManager::LoraSpec>& loras) override {
+        if (!enabled || model_path.empty()) {
+            return;
         }
+        ModelManager::LoraSpec lora;
+        lora.path                      = model_path;
+        lora.multiplier                = 1.0f;
+        lora.tensor_name_prefix_filter = "lora.model";
+        lora.required                  = true;
+        loras.push_back(std::move(lora));
     }
 
     void add_ignore_tensors(std::set<std::string>& ignore_tensors) const override {
@@ -186,18 +175,10 @@ struct PhotoMakerExtension : public GenerationExtension {
         ignore_tensors.insert("pmid.unet.");
     }
 
-    bool alloc_params_buffer() override {
-        if (!enabled || pmid_model == nullptr) {
-            return true;
+    void runner_done() override {
+        if (pmid_model != nullptr) {
+            pmid_model->runner_done();
         }
-        return pmid_model->alloc_params_buffer();
-    }
-
-    size_t get_params_buffer_size() const override {
-        if (!enabled || pmid_model == nullptr) {
-            return 0;
-        }
-        return pmid_model->get_params_buffer_size();
     }
 
     void reset_runtime_condition() override {
@@ -207,21 +188,10 @@ struct PhotoMakerExtension : public GenerationExtension {
 
     bool prepare_condition(GenerationExtensionConditionContext& ctx) override {
         reset_runtime_condition();
-        if (!enabled || pmid_model == nullptr || pmid_lora == nullptr) {
+        if (!enabled || pmid_model == nullptr) {
             return false;
         }
 
-        if (!pmid_lora->applied) {
-            int64_t t0 = ggml_time_ms();
-            pmid_lora->apply(ctx.tensors, ctx.version, ctx.n_threads);
-            int64_t t1         = ggml_time_ms();
-            pmid_lora->applied = true;
-            LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
-            if (ctx.free_params_immediately) {
-                pmid_lora->free_params_buffer();
-            }
-        }
-
         bool pmv2 = pmid_model->get_version() == PM_VERSION_2;
         if (ctx.pm_params.id_images_count <= 0 || ctx.pm_params.id_images == nullptr) {
             LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
@@ -305,9 +275,6 @@ struct PhotoMakerExtension : public GenerationExtension {
         LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
         LOG_INFO("PHOTOMAKER: start_merge_step: %d", start_merge_step);
 
-        if (ctx.free_params_immediately) {
-            pmid_model->free_params_buffer();
-        }
         return true;
     }
 
diff --git a/otherarch/sdcpp/src/extensions/pulid_extension.cpp b/otherarch/sdcpp/src/extensions/pulid_extension.cpp
new file mode 100644
index 000000000..d529e5710
--- /dev/null
+++ b/otherarch/sdcpp/src/extensions/pulid_extension.cpp
@@ -0,0 +1,123 @@
+#include "extensions/generation_extension.h"
+
+#include <cstring>
+#include <variant>
+
+#include "core/tensor_ggml.hpp"
+#include "core/util.h"
+#include "gguf.h"
+
+static sd::Tensor<float> load_pulid_id_embedding(const char* path) {
+    sd::Tensor<float> empty;
+    if (path == nullptr || strlen(path) == 0) {
+        return empty;
+    }
+
+    struct ggml_context* ctx_data = nullptr;
+    struct gguf_init_params gp    = {/*.no_alloc =*/false, /*.ctx =*/&ctx_data};
+    struct gguf_context* gguf_ctx = gguf_init_from_file(path, gp);
+    if (gguf_ctx == nullptr || ctx_data == nullptr) {
+        LOG_WARN("PuLID id-embedding: cannot read gguf '%s'", path);
+        if (gguf_ctx != nullptr)
+            gguf_free(gguf_ctx);
+        if (ctx_data != nullptr)
+            ggml_free(ctx_data);
+        return empty;
+    }
+
+    struct ggml_tensor* t = ggml_get_tensor(ctx_data, "pulid_id");
+    if (t == nullptr) {
+        LOG_WARN("PuLID id-embedding: no 'pulid_id' tensor in '%s'", path);
+        gguf_free(gguf_ctx);
+        ggml_free(ctx_data);
+        return empty;
+    }
+
+    const int64_t token_dim  = t->ne[0];
+    const int64_t num_tokens = t->ne[1];
+    if (token_dim <= 0 || num_tokens <= 0 || token_dim > 65536 || num_tokens > 1024 ||
+        t->ne[2] != 1 || t->ne[3] != 1) {
+        LOG_WARN("PuLID id-embedding: implausible shape [%lld, %lld] in '%s'",
+                 (long long)token_dim, (long long)num_tokens, path);
+        gguf_free(gguf_ctx);
+        ggml_free(ctx_data);
+        return empty;
+    }
+
+    const size_t n_elem = (size_t)token_dim * (size_t)num_tokens;
+    sd::Tensor<float> out({token_dim, num_tokens, 1});
+    float* dst = out.data();
+    if (t->type == GGML_TYPE_F32) {
+        memcpy(dst, t->data, n_elem * sizeof(float));
+    } else if (t->type == GGML_TYPE_F16) {
+        const ggml_fp16_t* src = reinterpret_cast<const ggml_fp16_t*>(t->data);
+        for (size_t i = 0; i < n_elem; i++) {
+            dst[i] = ggml_fp16_to_fp32(src[i]);
+        }
+    } else if (t->type == GGML_TYPE_BF16) {
+        const ggml_bf16_t* src = reinterpret_cast<const ggml_bf16_t*>(t->data);
+        for (size_t i = 0; i < n_elem; i++) {
+            dst[i] = ggml_bf16_to_fp32(src[i]);
+        }
+    } else {
+        LOG_WARN("PuLID id-embedding: unsupported tensor type %s in '%s'",
+                 ggml_type_name(t->type), path);
+        gguf_free(gguf_ctx);
+        ggml_free(ctx_data);
+        return empty;
+    }
+
+    LOG_INFO("PuLID id-embedding: loaded [%lld, %lld] type=%s from '%s'",
+             (long long)token_dim, (long long)num_tokens, ggml_type_name(t->type), path);
+    gguf_free(gguf_ctx);
+    ggml_free(ctx_data);
+    return out;
+}
+
+struct PuLIDExtension : public GenerationExtension {
+    bool enabled = false;
+    sd::Tensor<float> id_embedding;
+    float id_weight = 1.0f;
+
+    const char* name() const override {
+        return "pulid";
+    }
+
+    bool is_enabled() const override {
+        return enabled;
+    }
+
+    bool init(const GenerationExtensionInitContext& ctx) override {
+        enabled = strlen(SAFE_STR(ctx.params->pulid_weights_path)) > 0;
+        return true;
+    }
+
+    void reset_runtime_condition() override {
+        id_embedding = {};
+        id_weight    = 1.0f;
+    }
+
+    bool prepare_condition(GenerationExtensionConditionContext& ctx) override {
+        reset_runtime_condition();
+        if (!enabled) {
+            return false;
+        }
+        id_embedding = load_pulid_id_embedding(ctx.pulid_params.id_embedding_path);
+        id_weight    = ctx.pulid_params.id_weight;
+        return false;  // PuLID does not modify the conditioning
+    }
+
+    void before_diffusion(DiffusionParams& params, int /*step*/) const override {
+        if (!enabled || id_embedding.empty()) {
+            return;
+        }
+        if (auto* flux_extra = std::get_if<FluxDiffusionExtra>(&params.extra)) {
+            flux_extra->pulid_id        = &id_embedding;
+            flux_extra->pulid_id_weight = id_weight;
+        }
+    }
+};
+
+std::shared_ptr<GenerationExtension> create_pulid_extension() {
+    return std::make_shared<PuLIDExtension>();
+}
diff --git a/otherarch/sdcpp/src/model.h b/otherarch/sdcpp/src/model.h
index d037705e7..a62c4d1bf 100644
--- a/otherarch/sdcpp/src/model.h
+++ b/otherarch/sdcpp/src/model.h
@@ -48,6 +48,7 @@ enum SDVersion {
     VERSION_LONGCAT,
     VERSION_PID,
     VERSION_IDEOGRAM4,
+    VERSION_ESRGAN,
     VERSION_COUNT,
 };
 
diff --git a/otherarch/sdcpp/src/model/adapter/lora.hpp b/otherarch/sdcpp/src/model/adapter/lora.hpp
index 2945b32af..77b26ef99 100644
--- a/otherarch/sdcpp/src/model/adapter/lora.hpp
+++ b/otherarch/sdcpp/src/model/adapter/lora.hpp
@@ -4,6 +4,7 @@
 #include <mutex>
 #include "core/ggml_extend.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 
 #define LORA_GRAPH_BASE_SIZE 10240
 
@@ -14,22 +15,24 @@ struct LoraModel : public GGMLRunner {
     std::map<ggml_tensor*, ggml_tensor*> original_tensor_to_final_tensor;
     std::set<std::string> applied_lora_tensors;
     std::string file_path;
-    ModelLoader model_loader;
-    bool load_failed         = false;
-    bool applied             = false;
-    bool tensor_preprocessed = false;
+    std::shared_ptr<ModelManager> model_manager;
+    ggml_backend_t params_backend = nullptr;
+    bool load_failed              = false;
+    bool applied                  = false;
+    bool tensor_preprocessed      = false;
 
     typedef std::function<bool(const std::string&)> filter_t;
 
     LoraModel(const std::string& lora_id,
               ggml_backend_t backend,
-              ggml_backend_t params_backend,
-              const std::string& file_path = "",
-              std::string prefix           = "",
-              SDVersion version            = VERSION_COUNT)
-        : lora_id(lora_id), file_path(file_path), GGMLRunner(backend, params_backend) {
+              ggml_backend_t params_backend_,
+              const std::string& file_path          = "",
+              std::string prefix                    = "",
+              SDVersion version                     = VERSION_COUNT,
+              std::shared_ptr<ModelManager> manager = std::make_shared<ModelManager>())
+        : GGMLRunner(backend, manager), lora_id(lora_id), file_path(file_path), model_manager(std::move(manager)), params_backend(params_backend_) {
         prefix = "lora." + prefix;
-        if (!model_loader.init_from_file_and_convert_name(file_path, prefix, version)) {
+        if (model_manager == nullptr || !model_manager->loader().init_from_file_and_convert_name(file_path, prefix, version)) {
             load_failed = true;
         }
     }
@@ -71,7 +74,11 @@ struct LoraModel : public GGMLRunner {
             return true;
         };
 
-        model_loader.load_tensors(on_new_tensor_cb, n_threads);
+        if (model_manager != nullptr) {
+            model_manager->set_n_threads(n_threads);
+        }
+        ModelLoader& model_loader = model_manager->loader();
+        model_loader.load_tensors(on_new_tensor_cb);
 
         if (tensors_to_create.empty()) {
             return true;
@@ -87,25 +94,64 @@ struct LoraModel : public GGMLRunner {
             lora_tensors[name] = real;
         }
 
-        if (!alloc_params_buffer()) {
-            LOG_ERROR("lora model buffer allocation failed");
+        std::map<std::string, ggml_tensor*> tensors;
+        for (const auto& pair : lora_tensors) {
+            tensors[pair.first] = pair.second;
+        }
+        if (model_manager == nullptr ||
+            !model_manager->register_param_tensors("LoRA",
+                                                   std::move(tensors),
+                                                   ModelManager::ResidencyMode::ParamBackend,
+                                                   runtime_backend,
+                                                   params_backend) ||
+            !model_manager->validate_registered_tensors()) {
+            LOG_ERROR("lora model manager registration failed");
+            return false;
+        }
+        std::vector<ggml_tensor*> lora_params;
+        lora_params.reserve(lora_tensors.size());
+        for (const auto& pair : lora_tensors) {
+            lora_params.push_back(pair.second);
+        }
+        if (!model_manager->prepare_params(lora_params)) {
+            LOG_ERROR("lora model manager prepare params failed");
             return false;
         }
-
-        dry_run = false;
-        model_loader.load_tensors(on_new_tensor_cb, n_threads);
 
         LOG_DEBUG("finished loaded lora");
         return true;
     }
 
-    void preprocess_lora_tensors(const std::map<std::string, ggml_tensor*>& model_tensors) {
+    void release_loaded_tensors() {
+        runner_done();
+        free_compute_buffer();
+        model_manager.reset();
+        free_params_ctx();
+        alloc_params_ctx();
+        model_manager  = std::make_shared<ModelManager>();
+        weight_manager = model_manager;
+        lora_tensors.clear();
+        original_tensor_to_final_tensor.clear();
+        applied_lora_tensors.clear();
+        applied             = false;
+        tensor_preprocessed = false;
+    }
+
+    static std::set<std::string> tensor_names(const std::map<std::string, ggml_tensor*>& model_tensors) {
+        std::set<std::string> names;
+        for (const auto& item : model_tensors) {
+            names.insert(item.first);
+        }
+        return names;
+    }
+
+    void preprocess_lora_tensors(const std::set<std::string>& model_tensor_names) {
         if (tensor_preprocessed) {
             return;
         }
         tensor_preprocessed = true;
         // I really hate these hardcoded processes.
-        if (model_tensors.find("cond_stage_model.1.transformer.text_model.encoder.layers.0.self_attn.in_proj.weight") != model_tensors.end()) {
+        if (model_tensor_names.find("cond_stage_model.1.transformer.text_model.encoder.layers.0.self_attn.in_proj.weight") != model_tensor_names.end()) {
             std::unordered_map<std::string, ggml_tensor*> new_lora_tensors;
             for (auto& [old_name, tensor] : lora_tensors) {
                 std::string new_name = old_name;
@@ -612,7 +658,7 @@ struct LoraModel : public GGMLRunner {
                 if (lokr_w2)
                     applied_lora_tensors.insert(lokr_w2_name);
                 if (lokr_w2_a)
-                    applied_lora_tensors.insert(lokr_w2_name);
+                    applied_lora_tensors.insert(lokr_w2_a_name);
                 if (lokr_w2_b)
                     applied_lora_tensors.insert(lokr_w2_b_name);
                 applied_lora_tensors.insert(alpha_name);
@@ -753,11 +799,13 @@ struct LoraModel : public GGMLRunner {
         return out_diff;
     }
 
-    ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors, SDVersion version) {
+    ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors,
+                                  const std::set<std::string>& model_tensor_names,
+                                  SDVersion version) {
         size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
         ggml_cgraph* gf        = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
 
-        preprocess_lora_tensors(model_tensors);
+        preprocess_lora_tensors(model_tensor_names);
 
         original_tensor_to_final_tensor.clear();
         applied_lora_tensors.clear();
@@ -794,12 +842,16 @@ struct LoraModel : public GGMLRunner {
         return gf;
     }
 
-    void apply(std::map<std::string, ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
+    void apply(std::map<std::string, ggml_tensor*> model_tensors,
+               const std::set<std::string>& model_tensor_names,
+               SDVersion version,
+               int n_threads,
+               bool warn_unused = true) {
         auto get_graph = [&]() -> ggml_cgraph* {
-            return build_lora_graph(model_tensors, version);
+            return build_lora_graph(model_tensors, model_tensor_names, version);
         };
-        GGMLRunner::compute<float>(get_graph, n_threads, false, true);
-        stat();
+        GGMLRunner::compute<float>(get_graph, n_threads, false, false, false, true);
+        stat(!warn_unused);
         for (auto item : original_tensor_to_final_tensor) {
             ggml_tensor* original_tensor = item.first;
             ggml_tensor* final_tensor    = item.second;
@@ -810,6 +862,10 @@ struct LoraModel : public GGMLRunner {
         GGMLRunner::free_compute_buffer();
     }
 
+    void apply(std::map<std::string, ggml_tensor*> model_tensors, SDVersion version, int n_threads, bool warn_unused = true) {
+        apply(model_tensors, tensor_names(model_tensors), version, n_threads, warn_unused);
+    }
+
     void stat(bool at_runntime = false) {
         size_t total_lora_tensors_count   = 0;
         size_t applied_lora_tensors_count = 0;
diff --git a/otherarch/sdcpp/src/model/adapter/pmid.hpp b/otherarch/sdcpp/src/model/adapter/pmid.hpp
index 3cf59a470..8f7d4dbde 100644
--- a/otherarch/sdcpp/src/model/adapter/pmid.hpp
+++ b/otherarch/sdcpp/src/model/adapter/pmid.hpp
@@ -413,13 +413,13 @@ public:
 
 public:
     PhotoMakerIDEncoder(ggml_backend_t backend,
-                        ggml_backend_t params_backend,
                         const String2TensorStorage& tensor_storage_map,
                         const std::string prefix,
-                        SDVersion version = VERSION_SDXL,
-                        PMVersion pm_v    = PM_VERSION_1,
-                        float sty         = 20.f)
-        : GGMLRunner(backend, params_backend),
+                        SDVersion version                                   = VERSION_SDXL,
+                        PMVersion pm_v                                      = PM_VERSION_1,
+                        float sty                                           = 20.f,
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager),
           version(version),
           pm_version(pm_v),
           style_strength(sty) {
@@ -558,24 +558,25 @@ public:
             return build_graph(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds);
         };
 
-        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
+        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true, true, true));
     }
 };
 
 struct PhotoMakerIDEmbed : public GGMLRunner {
     std::map<std::string, ggml_tensor*> tensors;
     std::string file_path;
-    ModelLoader* model_loader;
-    bool load_failed = false;
-    bool applied     = false;
+    std::shared_ptr<ModelManager> model_manager;
+    ggml_backend_t params_backend = nullptr;
+    bool load_failed              = false;
+    bool applied                  = false;
 
     PhotoMakerIDEmbed(ggml_backend_t backend,
-                      ggml_backend_t params_backend,
-                      ModelLoader* ml,
-                      const std::string& file_path = "",
-                      const std::string& prefix    = "")
-        : file_path(file_path), GGMLRunner(backend, params_backend), model_loader(ml) {
-        if (!model_loader->init_from_file_and_convert_name(file_path, prefix)) {
+                      ggml_backend_t params_backend_,
+                      std::shared_ptr<ModelManager> manager = std::make_shared<ModelManager>(),
+                      const std::string& file_path          = "",
+                      const std::string& prefix             = "")
+        : GGMLRunner(backend, manager), file_path(file_path), model_manager(std::move(manager)), params_backend(params_backend_) {
+        if (model_manager == nullptr || !model_manager->loader().init_from_file_and_convert_name(file_path, prefix)) {
             load_failed = true;
         }
     }
@@ -616,14 +617,27 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
             return true;
         };
 
-        model_loader->load_tensors(on_new_tensor_cb, n_threads);
-        if (!alloc_params_buffer()) {
-            LOG_ERROR("PhotoMaker ID embeds buffer allocation failed");
+        model_manager->set_n_threads(n_threads);
+        ModelLoader& model_loader = model_manager->loader();
+        model_loader.load_tensors(on_new_tensor_cb);
+        if (!model_manager->register_param_tensors("PhotoMaker ID embeds",
+                                                   tensors,
+                                                   ModelManager::ResidencyMode::ParamBackend,
+                                                   runtime_backend,
+                                                   params_backend) ||
+            !model_manager->validate_registered_tensors()) {
+            LOG_ERROR("PhotoMaker ID embeds model manager registration failed");
+            return false;
+        }
+        std::vector<ggml_tensor*> id_embed_params;
+        id_embed_params.reserve(tensors.size());
+        for (const auto& pair : tensors) {
+            id_embed_params.push_back(pair.second);
+        }
+        if (!model_manager->prepare_params(id_embed_params)) {
+            LOG_ERROR("PhotoMaker ID embeds model manager prepare params failed");
             return false;
         }
-
-        dry_run = false;
-        model_loader->load_tensors(on_new_tensor_cb, n_threads);
 
         LOG_DEBUG("finished loading PhotoMaker ID Embeds ");
         return true;
diff --git a/otherarch/sdcpp/src/model/adapter/pulid.hpp b/otherarch/sdcpp/src/model/adapter/pulid.hpp
new file mode 100644
index 000000000..442c5b8b2
--- /dev/null
+++ b/otherarch/sdcpp/src/model/adapter/pulid.hpp
@@ -0,0 +1,76 @@
+#ifndef __PULID_HPP__
+#define __PULID_HPP__
+
+#include "core/ggml_extend.hpp"
+#include "model/common/block.hpp"
+
+class PuLIDPerceiverAttentionCA : public GGMLBlock {
+public:
+    static constexpr int64_t DEFAULT_DIM      = 3072;  // Flux hidden size
+    static constexpr int64_t DEFAULT_DIM_HEAD = 128;
+    static constexpr int64_t DEFAULT_HEADS    = 16;
+    static constexpr int64_t DEFAULT_KV_DIM   = 2048;  // PuLID ID-embedding dim
+
+protected:
+    int64_t dim;
+    int64_t dim_head;
+    int64_t heads;
+    int64_t kv_dim;
+    int64_t inner_dim;
+
+public:
+    PuLIDPerceiverAttentionCA(int64_t dim      = DEFAULT_DIM,
+                              int64_t dim_head = DEFAULT_DIM_HEAD,
+                              int64_t heads    = DEFAULT_HEADS,
+                              int64_t kv_dim   = DEFAULT_KV_DIM)
+        : dim(dim),
+          dim_head(dim_head),
+          heads(heads),
+          kv_dim(kv_dim),
+          inner_dim(dim_head * heads) {
+        blocks["norm1"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(kv_dim));
+        blocks["norm2"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
+        blocks["to_q"]   = std::shared_ptr<GGMLBlock>(new Linear(dim, inner_dim, /*bias=*/false));
+        blocks["to_kv"]  = std::shared_ptr<GGMLBlock>(new Linear(kv_dim, inner_dim * 2, /*bias=*/false));
+        blocks["to_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim, /*bias=*/false));
+    }
+
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* id_embedding,
+                         ggml_tensor* image_tokens) {
+        auto norm1  = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
+        auto norm2  = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
+        auto to_q   = std::dynamic_pointer_cast<Linear>(blocks["to_q"]);
+        auto to_kv  = std::dynamic_pointer_cast<Linear>(blocks["to_kv"]);
+        auto to_out = std::dynamic_pointer_cast<Linear>(blocks["to_out"]);
+
+        ggml_tensor* x_normed   = norm1->forward(ctx, id_embedding);
+        ggml_tensor* lat_normed = norm2->forward(ctx, image_tokens);
+
+        ggml_tensor* q  = to_q->forward(ctx, lat_normed);  // [N, T_img, 2048]
+        ggml_tensor* kv = to_kv->forward(ctx, x_normed);   // [N, T_img, 3072]
+
+        ggml_tensor* k = ggml_view_3d(ctx->ggml_ctx, kv,
+                                      inner_dim, kv->ne[1], kv->ne[2],
+                                      kv->nb[1], kv->nb[2],
+                                      /*offset=*/0);
+        ggml_tensor* v = ggml_view_3d(ctx->ggml_ctx, kv,
+                                      inner_dim, kv->ne[1], kv->ne[2],
+                                      kv->nb[1], kv->nb[2],
+                                      /*offset=*/inner_dim * ggml_element_size(kv));
+        k              = ggml_cont(ctx->ggml_ctx, k);
+        v              = ggml_cont(ctx->ggml_ctx, v);
+
+        ggml_tensor* attn_out = ggml_ext_attention_ext(
+            ctx->ggml_ctx, ctx->backend,
+            q, k, v,
+            heads,
+            /*mask=*/nullptr,
+            /*diag_mask_inf=*/false);
+
+        ggml_tensor* out = to_out->forward(ctx, attn_out);
+        return out;
+    }
+};
+
+#endif  // __PULID_HPP__
diff --git a/otherarch/sdcpp/src/model/common/block.hpp b/otherarch/sdcpp/src/model/common/block.hpp
index 69db0a906..15bfa3767 100644
--- a/otherarch/sdcpp/src/model/common/block.hpp
+++ b/otherarch/sdcpp/src/model/common/block.hpp
@@ -560,11 +560,11 @@ protected:
         params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
-    float get_alpha() {
+    ggml_tensor* get_alpha(GGMLRunnerContext* ctx) {
         // image_only_indicator is always tensor([0.]) and since mix_factor.shape is [1,]
         // so learned_with_images is same as learned
-        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
-        return sigmoid(alpha);
+        auto mix_factor = ggml_ext_cast_f32(ctx->ggml_ctx, ctx->backend, params["mix_factor"]);
+        return ggml_sigmoid(ctx->ggml_ctx, mix_factor);
     }
 
 public:
@@ -578,11 +578,12 @@ public:
                          ggml_tensor* x_spatial,
                          ggml_tensor* x_temporal) {
         // image_only_indicator is always tensor([0.])
-        float alpha = get_alpha();
-        auto x      = ggml_add(ctx->ggml_ctx,
-                               ggml_ext_scale(ctx->ggml_ctx, x_spatial, alpha),
-                               ggml_ext_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
-        return x;
+        auto alpha = get_alpha(ctx);
+        return ggml_add(ctx->ggml_ctx,
+                        x_temporal,
+                        ggml_mul(ctx->ggml_ctx,
+                                 ggml_sub(ctx->ggml_ctx, x_spatial, x_temporal),
+                                 alpha));
     }
 };
 
diff --git a/otherarch/sdcpp/src/model/diffusion/anima.hpp b/otherarch/sdcpp/src/model/diffusion/anima.hpp
index 49c2e45ae..6042516a9 100644
--- a/otherarch/sdcpp/src/model/diffusion/anima.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/anima.hpp
@@ -561,10 +561,10 @@ namespace Anima {
         AnimaNet net;
 
         AnimaRunner(ggml_backend_t backend,
-                    ggml_backend_t params_backend,
-                    const String2TensorStorage& tensor_storage_map = {},
-                    const std::string prefix                       = "model.diffusion_model")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    const std::string prefix                            = "model.diffusion_model",
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(AnimaConfig::detect_from_weights(tensor_storage_map, prefix + ".net")) {
             net = AnimaNet(config);
             net.init(params_ctx, tensor_storage_map, prefix + ".net");
@@ -697,7 +697,7 @@ namespace Anima {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, t5_ids, t5_weights);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/control.hpp b/otherarch/sdcpp/src/model/diffusion/control.hpp
index 2f5eb5742..57e3616f2 100644
--- a/otherarch/sdcpp/src/model/diffusion/control.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/control.hpp
@@ -1,8 +1,9 @@
-#ifndef __SD_MODEL_DIFFUSION_CONTROL_HPP__
+#ifndef __SD_MODEL_DIFFUSION_CONTROL_HPP__
 #define __SD_MODEL_DIFFUSION_CONTROL_HPP__
 
 #include "model/common/block.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 
 #define CONTROL_NET_GRAPH_SIZE 1536
 
@@ -309,73 +310,47 @@ public:
 struct ControlNet : public GGMLRunner {
     SDVersion version = VERSION_SD1;
     ControlNetBlock control_net;
+    std::string weight_prefix;
 
-    ggml_backend_buffer_t control_buffer = nullptr;
-    ggml_context* control_ctx            = nullptr;
     std::vector<ggml_tensor*> control_outputs_ggml;
     ggml_tensor* guided_hint_output_ggml = nullptr;
     std::vector<sd::Tensor<float>> controls;
-    sd::Tensor<float> guided_hint;
     bool guided_hint_cached = false;
+    std::shared_ptr<ModelManager> owned_model_manager;
+    ggml_backend_t params_backend = nullptr;
+
+    static const char* guided_hint_cache_name() {
+        return "controlnet.guided_hint";
+    }
 
     ControlNet(ggml_backend_t backend,
-               ggml_backend_t params_backend,
-               const String2TensorStorage& tensor_storage_map = {},
-               SDVersion version                              = VERSION_SD1)
-        : GGMLRunner(backend, params_backend), control_net(version) {
-        control_net.init(params_ctx, tensor_storage_map, "");
+               ggml_backend_t params_backend_,
+               const String2TensorStorage& tensor_storage_map      = {},
+               SDVersion version                                   = VERSION_SD1,
+               const std::string& prefix                           = "",
+               std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager), version(version), control_net(version), weight_prefix(prefix), params_backend(params_backend_) {
+        control_net.init(params_ctx, tensor_storage_map, prefix);
     }
 
     ~ControlNet() override {
         free_control_ctx();
     }
 
-    void alloc_control_ctx(std::vector<ggml_tensor*> outs) {
-        ggml_init_params params;
-        params.mem_size   = static_cast<size_t>(outs.size() * ggml_tensor_overhead()) + 1024 * 1024;
-        params.mem_buffer = nullptr;
-        params.no_alloc   = true;
-        control_ctx       = ggml_init(params);
-
-        control_outputs_ggml.resize(outs.size() - 1);
-
-        size_t control_buffer_size = 0;
-
-        guided_hint_output_ggml = ggml_dup_tensor(control_ctx, outs[0]);
-        control_buffer_size += ggml_nbytes(guided_hint_output_ggml);
-
-        for (int i = 0; i < outs.size() - 1; i++) {
-            control_outputs_ggml[i] = ggml_dup_tensor(control_ctx, outs[i + 1]);
-            control_buffer_size += ggml_nbytes(control_outputs_ggml[i]);
-        }
-
-        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, runtime_backend);
-
-        LOG_DEBUG("control buffer size %.2fMB", control_buffer_size * 1.f / 1024.f / 1024.f);
-    }
-
     void free_control_ctx() {
-        if (control_buffer != nullptr) {
-            ggml_backend_buffer_free(control_buffer);
-            control_buffer = nullptr;
-        }
-        if (control_ctx != nullptr) {
-            ggml_free(control_ctx);
-            control_ctx = nullptr;
-        }
         guided_hint_output_ggml = nullptr;
         guided_hint_cached      = false;
-        guided_hint             = {};
         control_outputs_ggml.clear();
         controls.clear();
+        free_cache_ctx_and_buffer();
     }
 
     std::string get_desc() override {
         return "control_net";
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
-        control_net.get_param_tensors(tensors, prefix);
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
+        control_net.get_param_tensors(tensors, weight_prefix);
     }
 
     ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
@@ -391,11 +366,17 @@ struct ControlNet : public GGMLRunner {
         ggml_tensor* context   = make_optional_input(context_tensor);
         ggml_tensor* y         = make_optional_input(y_tensor);
 
+        guided_hint_output_ggml = nullptr;
+        control_outputs_ggml.clear();
+
         ggml_tensor* guided_hint_input = nullptr;
-        if (guided_hint_cached && !guided_hint.empty()) {
-            guided_hint_input = make_input(guided_hint);
-            hint              = nullptr;
-        } else {
+        if (guided_hint_cached) {
+            guided_hint_input = get_cache_tensor_by_name(guided_hint_cache_name());
+            if (guided_hint_input == nullptr) {
+                guided_hint_cached = false;
+            }
+        }
+        if (guided_hint_input == nullptr) {
             hint = make_input(hint_tensor);
         }
 
@@ -409,13 +390,19 @@ struct ControlNet : public GGMLRunner {
                                         context,
                                         y);
 
-        if (control_ctx == nullptr) {
-            alloc_control_ctx(outs);
+        if (guided_hint_input == nullptr && !outs.empty()) {
+            guided_hint_output_ggml = outs[0];
+            ggml_set_output(guided_hint_output_ggml);
+            cache(guided_hint_cache_name(), guided_hint_output_ggml);
+            ggml_build_forward_expand(gf, guided_hint_output_ggml);
         }
 
-        ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[0], guided_hint_output_ggml));
-        for (int i = 0; i < outs.size() - 1; i++) {
-            ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[i + 1], control_outputs_ggml[i]));
+        control_outputs_ggml.reserve(outs.size() > 0 ? outs.size() - 1 : 0);
+        for (size_t i = 1; i < outs.size(); i++) {
+            ggml_tensor* control_output = outs[i];
+            ggml_set_output(control_output);
+            ggml_build_forward_expand(gf, control_output);
+            control_outputs_ggml.push_back(control_output);
         }
 
         return gf;
@@ -435,15 +422,12 @@ struct ControlNet : public GGMLRunner {
             return build_graph(x, hint, timesteps, context, y);
         };
 
-        auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false);
+        auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false, false, false, true);
         if (!compute_result.has_value()) {
             return std::nullopt;
         }
 
-        if (guided_hint_output_ggml != nullptr) {
-            guided_hint = restore_trailing_singleton_dims(sd::make_sd_tensor_from_ggml<float>(guided_hint_output_ggml),
-                                                          4);
-        }
+        guided_hint_cached = get_cache_tensor_by_name(guided_hint_cache_name()) != nullptr;
         controls.clear();
         controls.reserve(control_outputs_ggml.size());
         for (ggml_tensor* control : control_outputs_ggml) {
@@ -451,36 +435,40 @@ struct ControlNet : public GGMLRunner {
             GGML_ASSERT(!control_host.empty());
             controls.push_back(std::move(control_host));
         }
-        guided_hint_cached = true;
         return controls;
     }
 
     bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading control net from '%s'", file_path.c_str());
-        if (!alloc_params_buffer()) {
-            LOG_ERROR("control net model buffer allocation failed");
-            return false;
-        }
-
         std::map<std::string, ggml_tensor*> tensors;
         control_net.get_param_tensors(tensors);
-        std::set<std::string> ignore_tensors;
 
-        ModelLoader model_loader;
+        auto manager = std::dynamic_pointer_cast<ModelManager>(weight_manager.lock());
+        if (manager == nullptr) {
+            owned_model_manager = std::make_shared<ModelManager>();
+            weight_manager      = owned_model_manager;
+            manager             = owned_model_manager;
+        }
+
+        ModelLoader& model_loader = manager->loader();
         if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init control net model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
 
-        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load control net tensors from model loader failed");
+        manager->set_n_threads(n_threads);
+        if (!manager->register_param_tensors("ControlNet",
+                                             std::move(tensors),
+                                             ModelManager::ResidencyMode::ParamBackend,
+                                             runtime_backend,
+                                             params_backend) ||
+            !manager->validate_registered_tensors()) {
+            LOG_ERROR("register control net tensors with model manager failed");
             return false;
         }
 
         LOG_INFO("control net model loaded");
-        return success;
+        return true;
     }
 };
 
diff --git a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
index 09bcba3b1..12fcada59 100644
--- a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
@@ -387,10 +387,10 @@ namespace ErnieImage {
         std::vector<float> pe_vec;
 
         ErnieImageRunner(ggml_backend_t backend,
-                         ggml_backend_t params_backend,
-                         const String2TensorStorage& tensor_storage_map = {},
-                         const std::string prefix                       = "")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                         const String2TensorStorage& tensor_storage_map      = {},
+                         const std::string prefix                            = "",
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(ErnieImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
             ernie_image = ErnieImageModel(config);
             ernie_image.init(params_ctx, tensor_storage_map, prefix);
@@ -440,7 +440,7 @@ namespace ErnieImage {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/flux.hpp b/otherarch/sdcpp/src/model/diffusion/flux.hpp
index 1d01041b7..b5e6c63bf 100644
--- a/otherarch/sdcpp/src/model/diffusion/flux.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/flux.hpp
@@ -4,6 +4,7 @@
 #include <memory>
 #include <vector>
 
+#include "model/adapter/pulid.hpp"
 #include "model/common/rope.hpp"
 #include "model/diffusion/dit.hpp"
 #include "model/diffusion/model.hpp"
@@ -49,6 +50,10 @@ namespace Flux {
         float ref_index_scale     = 1.f;
         ChromaRadianceConfig chroma_radiance_params;
 
+        bool pulid_enabled        = false;
+        int pulid_double_interval = 2;
+        int pulid_single_interval = 4;
+
         static FluxConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
                                               const std::string& prefix,
                                               SDVersion version = VERSION_FLUX) {
@@ -138,6 +143,9 @@ namespace Flux {
                 if (ends_with(name, "double_blocks.0.txt_attn.norm.key_norm.scale")) {
                     head_dim = tensor_storage.ne[0];
                 }
+                if (name.find("pulid_ca.") != std::string::npos) {
+                    config.pulid_enabled = true;
+                }
             }
             if (actual_radiance_patch_size > 0 && actual_radiance_patch_size != config.patch_size) {
                 GGML_ASSERT(config.patch_size == 2 * actual_radiance_patch_size);
@@ -957,6 +965,20 @@ namespace Flux {
                 blocks["double_stream_modulation_txt"] = std::make_shared<Modulation>(config.hidden_size, true, !config.disable_bias);
                 blocks["single_stream_modulation"]     = std::make_shared<Modulation>(config.hidden_size, false, !config.disable_bias);
             }
+
+            if (config.pulid_enabled) {
+                int num_double_ca = (config.depth + config.pulid_double_interval - 1) / config.pulid_double_interval;
+                int num_single_ca = (config.depth_single_blocks + config.pulid_single_interval - 1) / config.pulid_single_interval;
+                int num_ca        = num_double_ca + num_single_ca;
+                for (int i = 0; i < num_ca; i++) {
+                    blocks["pulid_ca." + std::to_string(i)] =
+                        std::shared_ptr<GGMLBlock>(new PuLIDPerceiverAttentionCA(
+                            /*dim=*/config.hidden_size,
+                            /*dim_head=*/PuLIDPerceiverAttentionCA::DEFAULT_DIM_HEAD,
+                            /*heads=*/PuLIDPerceiverAttentionCA::DEFAULT_HEADS,
+                            /*kv_dim=*/PuLIDPerceiverAttentionCA::DEFAULT_KV_DIM));
+                }
+            }
         }
 
         ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
@@ -967,7 +989,9 @@ namespace Flux {
                                   ggml_tensor* guidance,
                                   ggml_tensor* pe,
                                   ggml_tensor* mod_index_arange = nullptr,
-                                  std::vector<int> skip_layers  = {}) {
+                                  std::vector<int> skip_layers  = {},
+                                  ggml_tensor* pulid_id         = nullptr,
+                                  float pulid_id_weight         = 1.0f) {
             auto img_in      = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
             auto txt_in      = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
             auto final_layer = std::dynamic_pointer_cast<LastLayer>(blocks["final_layer"]);
@@ -1044,6 +1068,13 @@ namespace Flux {
             sd::ggml_graph_cut::mark_graph_cut(txt, "flux.prelude", "txt");
             sd::ggml_graph_cut::mark_graph_cut(vec, "flux.prelude", "vec");
 
+            const bool pulid_active = config.pulid_enabled && pulid_id != nullptr;
+            if (pulid_active && !skip_layers.empty()) {
+                LOG_WARN("PuLID + skip_layers is not supported; disabling PuLID for this generation.");
+            }
+            const bool pulid_run = pulid_active && skip_layers.empty();
+            int ca_idx           = 0;
+
             for (int i = 0; i < config.depth; i++) {
                 if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
                     continue;
@@ -1056,9 +1087,19 @@ namespace Flux {
                 txt          = img_txt.second;  // [N, n_txt_token, hidden_size]
                 sd::ggml_graph_cut::mark_graph_cut(img, "flux.double_blocks." + std::to_string(i), "img");
                 sd::ggml_graph_cut::mark_graph_cut(txt, "flux.double_blocks." + std::to_string(i), "txt");
+
+                if (pulid_run && (i % config.pulid_double_interval == 0)) {
+                    auto pulid_ca = std::dynamic_pointer_cast<PuLIDPerceiverAttentionCA>(
+                        blocks["pulid_ca." + std::to_string(ca_idx)]);
+                    ggml_tensor* ca_out = pulid_ca->forward(ctx, pulid_id, img);  // [N, n_img_token, hidden_size]
+                    img                 = ggml_add(ctx->ggml_ctx, img, ggml_scale(ctx->ggml_ctx, ca_out, pulid_id_weight));
+                    sd::ggml_graph_cut::mark_graph_cut(img, "flux.pulid_ca." + std::to_string(ca_idx), "img");
+                    ca_idx++;
+                }
             }
 
-            auto txt_img = ggml_concat(ctx->ggml_ctx, txt, img, 1);  // [N, n_txt_token + n_img_token, hidden_size]
+            auto txt_img            = ggml_concat(ctx->ggml_ctx, txt, img, 1);  // [N, n_txt_token + n_img_token, hidden_size]
+            const int64_t n_txt_tok = txt->ne[1];
             for (int i = 0; i < config.depth_single_blocks; i++) {
                 if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i + config.depth) != skip_layers.end()) {
                     continue;
@@ -1067,6 +1108,29 @@ namespace Flux {
 
                 txt_img = block->forward(ctx, txt_img, vec, pe, txt_img_mask, ss_mods);
                 sd::ggml_graph_cut::mark_graph_cut(txt_img, "flux.single_blocks." + std::to_string(i), "txt_img");
+
+                if (pulid_run && (i % config.pulid_single_interval == 0)) {
+                    auto pulid_ca = std::dynamic_pointer_cast<PuLIDPerceiverAttentionCA>(
+                        blocks["pulid_ca." + std::to_string(ca_idx)]);
+                    ggml_tensor* txt_part = ggml_view_3d(ctx->ggml_ctx, txt_img,
+                                                         txt_img->ne[0], n_txt_tok, txt_img->ne[2],
+                                                         txt_img->nb[1], txt_img->nb[2],
+                                                         0);
+                    ggml_tensor* img_part = ggml_view_3d(ctx->ggml_ctx, txt_img,
+                                                         txt_img->ne[0],
+                                                         txt_img->ne[1] - n_txt_tok,
+                                                         txt_img->ne[2],
+                                                         txt_img->nb[1],
+                                                         txt_img->nb[2],
+                                                         n_txt_tok * txt_img->nb[1]);
+                    txt_part              = ggml_cont(ctx->ggml_ctx, txt_part);
+                    img_part              = ggml_cont(ctx->ggml_ctx, img_part);
+                    ggml_tensor* ca_out   = pulid_ca->forward(ctx, pulid_id, img_part);
+                    img_part              = ggml_add(ctx->ggml_ctx, img_part, ggml_scale(ctx->ggml_ctx, ca_out, pulid_id_weight));
+                    txt_img               = ggml_concat(ctx->ggml_ctx, txt_part, img_part, 1);
+                    sd::ggml_graph_cut::mark_graph_cut(txt_img, "flux.pulid_ca." + std::to_string(ca_idx), "txt_img");
+                    ca_idx++;
+                }
             }
 
             img = ggml_view_3d(ctx->ggml_ctx,
@@ -1105,7 +1169,9 @@ namespace Flux {
                                              ggml_tensor* mod_index_arange         = nullptr,
                                              ggml_tensor* dct                      = nullptr,
                                              std::vector<ggml_tensor*> ref_latents = {},
-                                             std::vector<int> skip_layers          = {}) {
+                                             std::vector<int> skip_layers          = {},
+                                             ggml_tensor* pulid_id                 = nullptr,
+                                             float pulid_id_weight                 = 1.0f) {
             GGML_ASSERT(x->ne[3] == 1);
 
             int64_t W      = x->ne[0];
@@ -1131,7 +1197,8 @@ namespace Flux {
             img = ggml_reshape_3d(ctx->ggml_ctx, img, img->ne[0] * img->ne[1], img->ne[2], img->ne[3]);  // [N, hidden_size, H/patch_size*W/patch_size]
             img = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img, 1, 0, 2, 3));      // [N, H/patch_size*W/patch_size, hidden_size]
 
-            auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers);  // [N, n_img_token, hidden_size]
+            auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers,
+                                    pulid_id, pulid_id_weight);  // [N, n_img_token, hidden_size]
 
             // nerf decode
             auto nerf_image_embedder   = std::dynamic_pointer_cast<NerfEmbedder>(blocks["nerf_image_embedder"]);
@@ -1179,7 +1246,9 @@ namespace Flux {
                                          ggml_tensor* mod_index_arange         = nullptr,
                                          ggml_tensor* dct                      = nullptr,
                                          std::vector<ggml_tensor*> ref_latents = {},
-                                         std::vector<int> skip_layers          = {}) {
+                                         std::vector<int> skip_layers          = {},
+                                         ggml_tensor* pulid_id                 = nullptr,
+                                         float pulid_id_weight                 = 1.0f) {
             GGML_ASSERT(x->ne[3] == 1);
 
             int64_t W      = x->ne[0];
@@ -1226,7 +1295,8 @@ namespace Flux {
                 }
             }
 
-            auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers);  // [N, num_tokens, C * patch_size * patch_size]
+            auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers,
+                                    pulid_id, pulid_id_weight);  // [N, num_tokens, C * patch_size * patch_size]
 
             if (out->ne[1] > img_tokens) {
                 out = ggml_view_3d(ctx->ggml_ctx, out, out->ne[0], img_tokens, out->ne[2], out->nb[1], out->nb[2], 0);
@@ -1248,7 +1318,9 @@ namespace Flux {
                              ggml_tensor* mod_index_arange         = nullptr,
                              ggml_tensor* dct                      = nullptr,
                              std::vector<ggml_tensor*> ref_latents = {},
-                             std::vector<int> skip_layers          = {}) {
+                             std::vector<int> skip_layers          = {},
+                             ggml_tensor* pulid_id                 = nullptr,
+                             float pulid_id_weight                 = 1.0f) {
             // Forward pass of DiT.
             // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
             // timestep: (N,) tensor of diffusion timesteps
@@ -1271,7 +1343,9 @@ namespace Flux {
                                                mod_index_arange,
                                                dct,
                                                ref_latents,
-                                               skip_layers);
+                                               skip_layers,
+                                               pulid_id,
+                                               pulid_id_weight);
             } else {
                 return forward_flux_chroma(ctx,
                                            x,
@@ -1284,7 +1358,9 @@ namespace Flux {
                                            mod_index_arange,
                                            dct,
                                            ref_latents,
-                                           skip_layers);
+                                           skip_layers,
+                                           pulid_id,
+                                           pulid_id_weight);
             }
         }
     };
@@ -1301,12 +1377,12 @@ namespace Flux {
         bool use_mask = false;
 
         FluxRunner(ggml_backend_t backend,
-                   ggml_backend_t params_backend,
-                   const String2TensorStorage& tensor_storage_map = {},
-                   const std::string prefix                       = "",
-                   SDVersion version                              = VERSION_FLUX,
-                   bool use_mask                                  = false)
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                   const String2TensorStorage& tensor_storage_map      = {},
+                   const std::string prefix                            = "",
+                   SDVersion version                                   = VERSION_FLUX,
+                   bool use_mask                                       = false,
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(FluxConfig::detect_from_weights(tensor_storage_map, prefix, version)),
               version(version),
               use_mask(use_mask) {
@@ -1384,7 +1460,9 @@ namespace Flux {
                                  const sd::Tensor<float>& guidance_tensor                 = {},
                                  const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
                                  bool increase_ref_index                                  = false,
-                                 std::vector<int> skip_layers                             = {}) {
+                                 std::vector<int> skip_layers                             = {},
+                                 const sd::Tensor<float>& pulid_id_tensor                 = {},
+                                 float pulid_id_weight                                    = 1.0f) {
             ggml_tensor* x         = make_input(x_tensor);
             ggml_tensor* timesteps = make_input(timesteps_tensor);
             ggml_tensor* context   = make_optional_input(context_tensor);
@@ -1461,6 +1539,10 @@ namespace Flux {
                 set_backend_tensor_data(dct, dct_vec.data());
             }
 
+            ggml_tensor* pulid_id = pulid_id_tensor.empty()
+                                        ? nullptr
+                                        : make_input(pulid_id_tensor);
+
             auto runner_ctx = get_context();
 
             ggml_tensor* out = flux.forward(&runner_ctx,
@@ -1474,7 +1556,9 @@ namespace Flux {
                                             mod_index_arange,
                                             dct,
                                             ref_latents,
-                                            skip_layers);
+                                            skip_layers,
+                                            pulid_id,
+                                            pulid_id_weight);
 
             ggml_build_forward_expand(gf, out);
 
@@ -1490,17 +1574,20 @@ namespace Flux {
                                   const sd::Tensor<float>& guidance                 = {},
                                   const std::vector<sd::Tensor<float>>& ref_latents = {},
                                   bool increase_ref_index                           = false,
-                                  std::vector<int> skip_layers                      = std::vector<int>()) {
+                                  std::vector<int> skip_layers                      = std::vector<int>(),
+                                  const sd::Tensor<float>& pulid_id                 = {},
+                                  float pulid_id_weight                             = 1.0f) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
             // y: [N, adm_in_channels] or [1, adm_in_channels]
             // guidance: [N, ]
+            // pulid_id: empty (no injection) or [N, num_id_tokens=32, kv_dim=2048]
             auto get_graph = [&]() -> ggml_cgraph* {
-                return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers);
+                return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers, pulid_id, pulid_id_weight);
             };
 
-            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
             return result;
         }
 
@@ -1520,7 +1607,9 @@ namespace Flux {
                            tensor_or_empty(extra->guidance),
                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
                            diffusion_params.increase_ref_index,
-                           extra->skip_layers ? *extra->skip_layers : empty_skip_layers);
+                           extra->skip_layers ? *extra->skip_layers : empty_skip_layers,
+                           tensor_or_empty(extra->pulid_id),
+                           extra->pulid_id_weight);
         }
 
         void test() {
@@ -1583,7 +1672,8 @@ namespace Flux {
             ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_COUNT;
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
@@ -1599,24 +1689,20 @@ namespace Flux {
             }
 
             std::shared_ptr<FluxRunner> flux = std::make_shared<FluxRunner>(backend,
-                                                                            backend,
                                                                             tensor_storage_map,
                                                                             "model.diffusion_model",
                                                                             VERSION_FLUX2,
-                                                                            false);
+                                                                            false,
+                                                                            model_manager);
 
-            if (!flux->alloc_params_buffer()) {
-                LOG_ERROR("flux model allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            flux->get_param_tensors(tensors, "model.diffusion_model");
-
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("Flux test",
+                                                       *flux,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register flux tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp b/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
index 3d384def2..559f61bcf 100644
--- a/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
+#ifndef __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
 #define __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
 
 #include <algorithm>
@@ -282,10 +282,10 @@ namespace HiDreamO1 {
         std::array<std::vector<float>, 4> pos_embed_weight_data_;
 
         HiDreamO1VisionRunner(ggml_backend_t backend,
-                              ggml_backend_t params_backend,
-                              const String2TensorStorage& tensor_storage_map = {},
-                              const std::string& prefix                      = "model.visual")
-            : GGMLRunner(backend, params_backend),
+                              const String2TensorStorage& tensor_storage_map      = {},
+                              const std::string& prefix                           = "model.visual",
+                              std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager),
               config(HiDreamO1Config::detect_from_weights(tensor_storage_map, prefix)),
               model(std::make_shared<LLM::VisionModel>(false, config.llm.vision)) {
             model->init(params_ctx, tensor_storage_map, prefix);
@@ -323,11 +323,15 @@ namespace HiDreamO1 {
             return gf;
         }
 
-        sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& image) {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& image,
+                                  bool auto_free           = true,
+                                  bool free_compute_buffer = true,
+                                  bool free_compute_params = true) {
             auto get_graph = [&]() {
                 return build_graph(image);
             };
-            auto output = GGMLRunner::compute<float>(get_graph, n_threads, false);
+            auto output = GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params);
             return output.has_value() ? std::move(output.value()) : sd::Tensor<float>();
         }
     };
@@ -339,10 +343,10 @@ namespace HiDreamO1 {
         std::vector<float> attention_mask_vec;
 
         HiDreamO1Runner(ggml_backend_t backend,
-                        ggml_backend_t params_backend,
-                        const String2TensorStorage& tensor_storage_map = {},
-                        const std::string& prefix                      = "model")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                        const String2TensorStorage& tensor_storage_map      = {},
+                        const std::string& prefix                           = "model",
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(HiDreamO1Config::detect_from_weights(tensor_storage_map, prefix)) {
             model = HiDreamO1Model(config);
             model.init(params_ctx, tensor_storage_map, prefix);
@@ -455,7 +459,7 @@ namespace HiDreamO1 {
             auto get_graph = [&]() {
                 return build_graph(x, timestep, input_ids, input_pos, token_types, vinput_mask, image_embeds, ref_images);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
@@ -486,29 +490,14 @@ namespace HiDreamO1 {
         std::shared_ptr<HiDreamO1VisionRunner> vision_runner;
 
         HiDreamO1Conditioner(ggml_backend_t backend,
-                             ggml_backend_t params_backend,
-                             const String2TensorStorage& tensor_storage_map = {})
-            : vision_runner(std::make_shared<HiDreamO1VisionRunner>(backend, params_backend, tensor_storage_map)) {}
+                             const String2TensorStorage& tensor_storage_map      = {},
+                             std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : vision_runner(std::make_shared<HiDreamO1VisionRunner>(backend, tensor_storage_map, "model.visual", weight_manager)) {}
 
         void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
             vision_runner->get_param_tensors(tensors);
         }
 
-        bool alloc_params_buffer() override {
-            if (!vision_runner->alloc_params_buffer()) {
-                return false;
-            }
-            return true;
-        }
-
-        void free_params_buffer() override {
-            vision_runner->free_params_buffer();
-        }
-
-        size_t get_params_buffer_size() override {
-            return vision_runner->get_params_buffer_size();
-        }
-
         void set_max_graph_vram_bytes(size_t max_graph_vram_bytes) override {
             vision_runner->set_max_graph_vram_bytes(max_graph_vram_bytes);
         }
@@ -521,6 +510,10 @@ namespace HiDreamO1 {
             vision_runner->set_weight_adapter(adapter);
         }
 
+        void runner_done() override {
+            vision_runner->runner_done();
+        }
+
         SDCondition get_learned_condition(int n_threads,
                                           const ConditionerParams& conditioner_params) override {
             SDCondition result;
@@ -666,7 +659,7 @@ namespace HiDreamO1 {
             result.c_vinput_mask  = sd::Tensor<int32_t>(vinput_mask_shape, std::move(vinput_mask));
             result.c_image_embeds.reserve(vlm_images.size());
             for (const auto& vlm_image : vlm_images) {
-                auto image_embed = vision_runner->compute(n_threads, vlm_image.second);
+                auto image_embed = vision_runner->compute(n_threads, vlm_image.second, false, true, true);
                 if (image_embed.empty()) {
                     LOG_ERROR("hidream_o1 conditioner: encode VLM image failed");
                     return SDCondition();
diff --git a/otherarch/sdcpp/src/model/diffusion/ideogram4.hpp b/otherarch/sdcpp/src/model/diffusion/ideogram4.hpp
index 2f53c787c..bfa2f86a4 100644
--- a/otherarch/sdcpp/src/model/diffusion/ideogram4.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/ideogram4.hpp
@@ -189,11 +189,11 @@ namespace Ideogram4 {
         }
 
         return Rope::embed_interleaved_mrope(ids,
-                                            bs,
-                                            static_cast<float>(rope_theta),
-                                            head_dim,
-                                            mrope_section,
-                                            axis_wrap_dims);
+                                             bs,
+                                             static_cast<float>(rope_theta),
+                                             head_dim,
+                                             mrope_section,
+                                             axis_wrap_dims);
     }
 
     class Ideogram4Attention : public GGMLBlock {
@@ -449,10 +449,10 @@ namespace Ideogram4 {
         std::vector<int32_t> image_indicator_vec;
 
         Ideogram4Runner(ggml_backend_t backend,
-                        ggml_backend_t params_backend,
-                        const String2TensorStorage& tensor_storage_map = {},
-                        const std::string prefix                       = "")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                        const String2TensorStorage& tensor_storage_map      = {},
+                        const std::string prefix                            = "",
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(Ideogram4Config::detect_from_weights(tensor_storage_map, prefix)),
               uncond_prefix(prefix + ".uncond") {
             model = Ideogram4Transformer(config);
@@ -505,16 +505,16 @@ namespace Ideogram4 {
             int64_t head_dim = config.emb_dim / config.num_heads;
 
             auto runner_ctx = get_context();
-            pe_vec  = gen_ideogram4_pe(static_cast<int>(grid_h),
-                                       static_cast<int>(grid_w),
-                                       static_cast<int>(x->ne[3]),
-                                       static_cast<int>(context_len),
-                                       static_cast<int>(head_dim),
-                                       static_cast<int>(config.rope_theta),
-                                       config.mrope_section,
-                                       runner_ctx.circular_x_enabled,
-                                       runner_ctx.circular_y_enabled);
-            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, head_dim / 2, pos_len);
+            pe_vec          = gen_ideogram4_pe(static_cast<int>(grid_h),
+                                               static_cast<int>(grid_w),
+                                               static_cast<int>(x->ne[3]),
+                                               static_cast<int>(context_len),
+                                               static_cast<int>(head_dim),
+                                               static_cast<int>(config.rope_theta),
+                                               config.mrope_section,
+                                               runner_ctx.circular_x_enabled,
+                                               runner_ctx.circular_y_enabled);
+            auto pe         = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, head_dim / 2, pos_len);
             set_backend_tensor_data(pe, pe_vec.data());
 
             image_indicator_vec.assign(static_cast<size_t>(pos_len), 1);
@@ -537,7 +537,7 @@ namespace Ideogram4 {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, use_uncond_model);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/lens.hpp b/otherarch/sdcpp/src/model/diffusion/lens.hpp
index 740ff8249..931a8527c 100644
--- a/otherarch/sdcpp/src/model/diffusion/lens.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/lens.hpp
@@ -356,10 +356,10 @@ namespace Lens {
         std::vector<float> pe_vec;
 
         LensRunner(ggml_backend_t backend,
-                   ggml_backend_t params_backend,
-                   const String2TensorStorage& tensor_storage_map = {},
-                   const std::string prefix                       = "")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                   const String2TensorStorage& tensor_storage_map      = {},
+                   const std::string prefix                            = "",
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(LensConfig::detect_from_weights(tensor_storage_map, prefix)) {
             lens = LensModel(config);
             lens.init(params_ctx, tensor_storage_map, prefix);
@@ -408,7 +408,7 @@ namespace Lens {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/ltxv.hpp b/otherarch/sdcpp/src/model/diffusion/ltxv.hpp
index a86b4cf50..b89ff32c6 100644
--- a/otherarch/sdcpp/src/model/diffusion/ltxv.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/ltxv.hpp
@@ -1686,10 +1686,10 @@ namespace LTXV {
         sd::Tensor<float> ax_input_cache;
 
         LTXAVRunner(ggml_backend_t backend,
-                    ggml_backend_t params_backend,
-                    const String2TensorStorage& tensor_storage_map = {},
-                    const std::string& prefix                      = "model.diffusion_model")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    const std::string& prefix                           = "model.diffusion_model",
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(LTXAVConfig::detect_from_weights(tensor_storage_map, prefix)),
               model(config) {
             model.init(params_ctx, tensor_storage_map, prefix);
@@ -1939,7 +1939,7 @@ namespace LTXV {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, audio_x, audio_timesteps, audio_length, frame_rate, video_positions);
             };
-            auto out = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            auto out = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
             return out;
         }
 
@@ -2025,7 +2025,8 @@ namespace LTXV {
             ggml_backend_t backend = sd_backend_cpu_init();
             LOG_INFO("loading ltxav from '%s'", model_path.c_str());
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(model_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", model_path.c_str());
                 return;
@@ -2040,19 +2041,18 @@ namespace LTXV {
 
             auto& tensor_storage_map           = model_loader.get_tensor_storage_map();
             std::shared_ptr<LTXAVRunner> ltxav = std::make_shared<LTXAVRunner>(backend,
-                                                                               backend,
                                                                                tensor_storage_map,
-                                                                               "model.diffusion_model");
+                                                                               "model.diffusion_model",
+                                                                               model_manager);
 
-            if (!ltxav->alloc_params_buffer()) {
-                LOG_ERROR("ltxav buffer allocation failed");
-                return;
-            }
-            std::map<std::string, ggml_tensor*> tensors;
-            ltxav->get_param_tensors(tensors, "model.diffusion_model");
-
-            if (!model_loader.load_tensors(tensors)) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("LTXAV test",
+                                                       *ltxav,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register ltxav tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/diffusion/mmdit.hpp b/otherarch/sdcpp/src/model/diffusion/mmdit.hpp
index 844339452..d8e76dfb9 100644
--- a/otherarch/sdcpp/src/model/diffusion/mmdit.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/mmdit.hpp
@@ -879,10 +879,10 @@ struct MMDiTRunner : public DiffusionModelRunner {
     MMDiT mmdit;
 
     MMDiTRunner(ggml_backend_t backend,
-                ggml_backend_t params_backend,
-                const String2TensorStorage& tensor_storage_map = {},
-                const std::string prefix                       = "")
-        : DiffusionModelRunner(backend, params_backend, prefix),
+                const String2TensorStorage& tensor_storage_map      = {},
+                const std::string prefix                            = "",
+                std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : DiffusionModelRunner(backend, prefix, weight_manager),
           config(MMDiTConfig::detect_from_weights(tensor_storage_map, prefix)),
           mmdit(config) {
         mmdit.init(params_ctx, tensor_storage_map, prefix);
@@ -935,7 +935,7 @@ struct MMDiTRunner : public DiffusionModelRunner {
             return build_graph(x, timesteps, context, y, skip_layers);
         };
 
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
     }
 
     sd::Tensor<float> compute(int n_threads,
@@ -1001,28 +1001,25 @@ struct MMDiTRunner : public DiffusionModelRunner {
         // ggml_backend_t backend    = ggml_backend_cuda_init(0);
         ggml_backend_t backend             = sd_backend_cpu_init();
         ggml_type model_data_type          = GGML_TYPE_F16;
-        std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, backend);
+        auto model_manager                 = std::make_shared<ModelManager>();
+        std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, String2TensorStorage{}, "", model_manager);
         {
             LOG_INFO("loading from '%s'", file_path.c_str());
 
-            if (!mmdit->alloc_params_buffer()) {
-                LOG_ERROR("mmdit embeds buffer allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            mmdit->get_param_tensors(tensors, "model.diffusion_model");
-
-            ModelLoader model_loader;
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path)) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }
 
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("MMDiT test",
+                                                       *mmdit,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register mmdit tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/diffusion/model.hpp b/otherarch/sdcpp/src/model/diffusion/model.hpp
index b386711b7..67f0fee02 100644
--- a/otherarch/sdcpp/src/model/diffusion/model.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/model.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_DIFFUSION_MODEL_HPP__
+#ifndef __SD_MODEL_DIFFUSION_MODEL_HPP__
 #define __SD_MODEL_DIFFUSION_MODEL_HPP__
 
 #include <string>
@@ -7,6 +7,7 @@
 
 #include "core/ggml_extend.hpp"
 #include "core/tensor_ggml.hpp"
+#include "model_manager.h"
 
 struct UNetDiffusionExtra {
     int num_video_frames                           = -1;
@@ -21,6 +22,8 @@ struct SkipLayerDiffusionExtra {
 struct FluxDiffusionExtra {
     const sd::Tensor<float>* guidance   = nullptr;
     const std::vector<int>* skip_layers = nullptr;
+    const sd::Tensor<float>* pulid_id   = nullptr;
+    float pulid_id_weight               = 1.0f;
 };
 
 struct AnimaDiffusionExtra {
@@ -88,9 +91,9 @@ protected:
 
 public:
     DiffusionModelRunner(ggml_backend_t backend,
-                         ggml_backend_t params_backend,
-                         const std::string& prefix)
-        : GGMLRunner(backend, params_backend),
+                         const std::string& prefix,
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager),
           prefix(prefix) {}
 
     virtual sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/pid.hpp b/otherarch/sdcpp/src/model/diffusion/pid.hpp
index ac5851516..68dca00f8 100644
--- a/otherarch/sdcpp/src/model/diffusion/pid.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/pid.hpp
@@ -710,10 +710,10 @@ namespace Pid {
         std::vector<float> pixel_pos_comp_vec;
 
         PiDRunner(ggml_backend_t backend,
-                  ggml_backend_t params_backend,
                   const String2TensorStorage& tensor_storage_map,
-                  const std::string prefix = "model.diffusion_model")
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                  const std::string prefix                            = "model.diffusion_model",
+                  std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(PixelDiTConfig::detect_from_weights(tensor_storage_map, prefix)) {
             model = PixelDiT(config);
             model.init(params_ctx, tensor_storage_map, prefix);
@@ -823,7 +823,7 @@ namespace Pid {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(x, timesteps, context, lq_latent, degrade_sigma);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
index 678c34675..aecd8bcef 100644
--- a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
@@ -518,12 +518,12 @@ namespace Qwen {
         SDVersion version;
 
         QwenImageRunner(ggml_backend_t backend,
-                        ggml_backend_t params_backend,
-                        const String2TensorStorage& tensor_storage_map = {},
-                        const std::string prefix                       = "",
-                        SDVersion version                              = VERSION_QWEN_IMAGE,
-                        bool zero_cond_t                               = false)
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                        const String2TensorStorage& tensor_storage_map      = {},
+                        const std::string prefix                            = "",
+                        SDVersion version                                   = VERSION_QWEN_IMAGE,
+                        bool zero_cond_t                                    = false,
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(QwenImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
             config.zero_cond_t = config.zero_cond_t || zero_cond_t;
             qwen_image         = QwenImageModel(config);
@@ -627,7 +627,7 @@ namespace Qwen {
                 return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
             };
 
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
@@ -691,7 +691,8 @@ namespace Qwen {
             ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
@@ -705,23 +706,20 @@ namespace Qwen {
             }
 
             std::shared_ptr<QwenImageRunner> qwen_image = std::make_shared<QwenImageRunner>(backend,
-                                                                                            backend,
                                                                                             tensor_storage_map,
                                                                                             "model.diffusion_model",
-                                                                                            VERSION_QWEN_IMAGE);
+                                                                                            VERSION_QWEN_IMAGE,
+                                                                                            false,
+                                                                                            model_manager);
 
-            if (!qwen_image->alloc_params_buffer()) {
-                LOG_ERROR("qwen_image buffer allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            qwen_image->get_param_tensors(tensors, "model.diffusion_model");
-
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("Qwen image test",
+                                                       *qwen_image,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register qwen_image tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/diffusion/unet.hpp b/otherarch/sdcpp/src/model/diffusion/unet.hpp
index f1a96f9d3..253b3b4b2 100644
--- a/otherarch/sdcpp/src/model/diffusion/unet.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/unet.hpp
@@ -694,11 +694,11 @@ struct UNetModelRunner : public DiffusionModelRunner {
     UnetModelBlock unet;
 
     UNetModelRunner(ggml_backend_t backend,
-                    ggml_backend_t params_backend,
                     const String2TensorStorage& tensor_storage_map,
                     const std::string prefix,
-                    SDVersion version = VERSION_SD1)
-        : DiffusionModelRunner(backend, params_backend, prefix),
+                    SDVersion version                                   = VERSION_SD1,
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : DiffusionModelRunner(backend, prefix, weight_manager),
           config(UNetConfig::detect_from_weights(tensor_storage_map, prefix, version)),
           unet(config) {
         unet.init(params_ctx, tensor_storage_map, prefix);
@@ -772,7 +772,7 @@ struct UNetModelRunner : public DiffusionModelRunner {
             return build_graph(x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength);
         };
 
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
     }
 
     sd::Tensor<float> compute(int n_threads,
diff --git a/otherarch/sdcpp/src/model/diffusion/wan.hpp b/otherarch/sdcpp/src/model/diffusion/wan.hpp
index 92f49dc21..9a907dcfa 100644
--- a/otherarch/sdcpp/src/model/diffusion/wan.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/wan.hpp
@@ -799,11 +799,11 @@ namespace WAN {
         SDVersion version;
 
         WanRunner(ggml_backend_t backend,
-                  ggml_backend_t params_backend,
-                  const String2TensorStorage& tensor_storage_map = {},
-                  const std::string prefix                       = "",
-                  SDVersion version                              = VERSION_WAN2)
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                  const String2TensorStorage& tensor_storage_map      = {},
+                  const std::string prefix                            = "",
+                  SDVersion version                                   = VERSION_WAN2,
+                  std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(WanConfig::detect_from_weights(tensor_storage_map, prefix)) {
             if (config.num_layers == 30) {
                 if (version == VERSION_WAN2_2_TI2V) {
@@ -950,7 +950,7 @@ namespace WAN {
                 return build_graph(x, timesteps, context, clip_fea, c_concat, time_dim_concat, vace_context, vace_strength);
             };
 
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
@@ -1017,7 +1017,8 @@ namespace WAN {
             ggml_type model_data_type = GGML_TYPE_F16;
             LOG_INFO("loading from '%s'", file_path.c_str());
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
@@ -1031,23 +1032,19 @@ namespace WAN {
             }
 
             std::shared_ptr<WanRunner> wan = std::make_shared<WanRunner>(backend,
-                                                                         backend,
                                                                          tensor_storage_map,
                                                                          "model.diffusion_model",
-                                                                         VERSION_WAN2_2_TI2V);
+                                                                         VERSION_WAN2_2_TI2V,
+                                                                         model_manager);
 
-            if (!wan->alloc_params_buffer()) {
-                LOG_ERROR("wan buffer allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            wan->get_param_tensors(tensors, "model.diffusion_model");
-
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("Wan test",
+                                                       *wan,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register wan tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/diffusion/z_image.hpp b/otherarch/sdcpp/src/model/diffusion/z_image.hpp
index c35c4495d..d23c28563 100644
--- a/otherarch/sdcpp/src/model/diffusion/z_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/z_image.hpp
@@ -553,11 +553,11 @@ namespace ZImage {
         SDVersion version;
 
         ZImageRunner(ggml_backend_t backend,
-                     ggml_backend_t params_backend,
-                     const String2TensorStorage& tensor_storage_map = {},
-                     const std::string prefix                       = "",
-                     SDVersion version                              = VERSION_Z_IMAGE)
-            : DiffusionModelRunner(backend, params_backend, prefix),
+                     const String2TensorStorage& tensor_storage_map      = {},
+                     const std::string prefix                            = "",
+                     SDVersion version                                   = VERSION_Z_IMAGE,
+                     std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
               config(ZImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
             z_image = ZImageModel(config);
             z_image.init(params_ctx, tensor_storage_map, prefix);
@@ -634,7 +634,7 @@ namespace ZImage {
                 return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
             };
 
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         }
 
         sd::Tensor<float> compute(int n_threads,
@@ -698,7 +698,8 @@ namespace ZImage {
             ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_Q8_0;
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path, "model.diffusion_model.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
@@ -714,22 +715,19 @@ namespace ZImage {
             }
 
             std::shared_ptr<ZImageRunner> z_image = std::make_shared<ZImageRunner>(backend,
-                                                                                   backend,
                                                                                    tensor_storage_map,
                                                                                    "model.diffusion_model",
-                                                                                   VERSION_QWEN_IMAGE);
+                                                                                   VERSION_QWEN_IMAGE,
+                                                                                   model_manager);
 
-            if (!z_image->alloc_params_buffer()) {
-                LOG_ERROR("z_image buffer allocation failed");
-                return;
-            }
-            std::map<std::string, ggml_tensor*> tensors;
-            z_image->get_param_tensors(tensors, "model.diffusion_model");
-
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("ZImage test",
+                                                       *z_image,
+                                                       "model.diffusion_model",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register z_image tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/te/clip.hpp b/otherarch/sdcpp/src/model/te/clip.hpp
index 7b7c883e0..6dc8a947b 100644
--- a/otherarch/sdcpp/src/model/te/clip.hpp
+++ b/otherarch/sdcpp/src/model/te/clip.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_CLIP_HPP__
+#ifndef __SD_MODEL_TE_CLIP_HPP__
 #define __SD_MODEL_TE_CLIP_HPP__
 
 #include "core/ggml_extend.hpp"
@@ -469,13 +469,13 @@ struct CLIPTextModelRunner : public GGMLRunner {
     std::vector<float> attention_mask_vec;
 
     CLIPTextModelRunner(ggml_backend_t backend,
-                        ggml_backend_t params_backend,
                         const String2TensorStorage& tensor_storage_map,
                         const std::string prefix,
-                        CLIPVersion version = OPENAI_CLIP_VIT_L_14,
-                        bool with_final_ln  = true,
-                        bool force_clip_f32 = false)
-        : GGMLRunner(backend, params_backend) {
+                        CLIPVersion version                                 = OPENAI_CLIP_VIT_L_14,
+                        bool with_final_ln                                  = true,
+                        bool force_clip_f32                                 = false,
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager) {
         bool proj_in = false;
         for (const auto& [name, tensor_storage] : tensor_storage_map) {
             if (!starts_with(name, prefix)) {
@@ -567,11 +567,14 @@ struct CLIPTextModelRunner : public GGMLRunner {
                               void* custom_embeddings_data,
                               size_t max_token_idx,
                               bool return_pooled,
-                              int clip_skip) {
+                              int clip_skip,
+                              bool auto_free           = true,
+                              bool free_compute_buffer = true,
+                              bool free_compute_params = true) {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };
-        auto result = GGMLRunner::compute<float>(get_graph, n_threads, true);
+        auto result = GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params);
         if (return_pooled) {
             return take_or_empty(std::move(result));
         }
diff --git a/otherarch/sdcpp/src/model/te/llm.hpp b/otherarch/sdcpp/src/model/te/llm.hpp
index 3a22e881a..74dc232e5 100644
--- a/otherarch/sdcpp/src/model/te/llm.hpp
+++ b/otherarch/sdcpp/src/model/te/llm.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_LLM_HPP__
+#ifndef __SD_MODEL_TE_LLM_HPP__
 #define __SD_MODEL_TE_LLM_HPP__
 
 #include <algorithm>
@@ -22,6 +22,7 @@
 #include "json.hpp"
 #include "model/common/rope.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 #include "tokenizers/bpe_tokenizer.h"
 #include "tokenizers/gemma_tokenizer.h"
 #include "tokenizers/gpt_oss_tokenizer.h"
@@ -1571,11 +1572,11 @@ namespace LLM {
     public:
         LLMRunner(LLMArch arch,
                   ggml_backend_t backend,
-                  ggml_backend_t params_backend,
                   const String2TensorStorage& tensor_storage_map,
                   const std::string prefix,
-                  bool enable_vision_ = false)
-            : GGMLRunner(backend, params_backend),
+                  bool enable_vision_                                 = false,
+                  std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager),
               config(LLMConfig::detect_from_weights(tensor_storage_map, prefix, arch)),
               enable_vision(enable_vision_) {
             if (enable_vision && !config.have_vision_weight) {
@@ -1733,7 +1734,10 @@ namespace LLM {
                                   const sd::Tensor<float>& attention_mask,
                                   const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds,
                                   std::set<int> out_layers,
-                                  bool return_all_hidden_states = false) {
+                                  bool return_all_hidden_states = false,
+                                  bool auto_free                = true,
+                                  bool free_compute_buffer      = true,
+                                  bool free_compute_params      = true) {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_graph(input_ids,
                                    attention_mask,
@@ -1741,7 +1745,7 @@ namespace LLM {
                                    out_layers,
                                    return_all_hidden_states);
             };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true),
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params),
                                                    input_ids.dim() + 1);
         }
 
@@ -1802,11 +1806,14 @@ namespace LLM {
         }
 
         sd::Tensor<float> encode_image(const int n_threads,
-                                       const sd::Tensor<float>& image) {
+                                       const sd::Tensor<float>& image,
+                                       bool auto_free           = false,
+                                       bool free_compute_buffer = false,
+                                       bool free_compute_params = false) {
             auto get_graph = [&]() -> ggml_cgraph* {
                 return build_encode_image_graph(image);
             };
-            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, false));
+            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params));
         }
     };
 
@@ -1816,11 +1823,11 @@ namespace LLM {
 
         LLMEmbedder(LLMArch arch,
                     ggml_backend_t backend,
-                    ggml_backend_t params_backend,
-                    const String2TensorStorage& tensor_storage_map = {},
-                    const std::string prefix                       = "",
-                    bool enable_vision                             = false)
-            : model(arch, backend, params_backend, tensor_storage_map, prefix, enable_vision) {
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    const std::string prefix                            = "",
+                    bool enable_vision                                  = false,
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : model(arch, backend, tensor_storage_map, prefix, enable_vision, weight_manager) {
             if (arch == LLMArch::MISTRAL_SMALL_3_2 || arch == LLMArch::MINISTRAL_3_3B) {
                 tokenizer = std::make_shared<MistralTokenizer>();
             } else if (arch == LLMArch::GPT_OSS_20B) {
@@ -1834,13 +1841,6 @@ namespace LLM {
             model.get_param_tensors(tensors, prefix);
         }
 
-        bool alloc_params_buffer() {
-            if (!model.alloc_params_buffer()) {
-                return false;
-            }
-            return true;
-        }
-
         std::tuple<std::vector<int>, std::vector<float>> tokenize(std::string text,
                                                                   std::pair<int, int> attn_range,
                                                                   size_t max_length = 0,
@@ -2056,7 +2056,8 @@ namespace LLM {
             ggml_backend_t backend    = sd_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_COUNT;
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file_and_convert_name(file_path, "text_encoders.llm.")) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
@@ -2074,24 +2075,20 @@ namespace LLM {
             LLMArch arch = LLMArch::QWEN3;
 
             std::shared_ptr<LLMEmbedder> llm = std::make_shared<LLMEmbedder>(arch,
-                                                                             backend,
                                                                              backend,
                                                                              tensor_storage_map,
                                                                              "text_encoders.llm",
-                                                                             true);
+                                                                             true,
+                                                                             model_manager);
 
-            if (!llm->alloc_params_buffer()) {
-                LOG_ERROR("llm model allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            llm->get_param_tensors(tensors, "text_encoders.llm");
-
-            bool success = model_loader.load_tensors(tensors);
-
-            if (!success) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("LLM test",
+                                                       *llm,
+                                                       "text_encoders.llm",
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register llm tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/te/t5.hpp b/otherarch/sdcpp/src/model/te/t5.hpp
index 41d9978e5..23da08222 100644
--- a/otherarch/sdcpp/src/model/te/t5.hpp
+++ b/otherarch/sdcpp/src/model/te/t5.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_T5_HPP__
+#ifndef __SD_MODEL_TE_T5_HPP__
 #define __SD_MODEL_TE_T5_HPP__
 
 #include <cfloat>
@@ -12,6 +12,7 @@
 
 #include "core/ggml_extend.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 #include "tokenizers/t5_unigram_tokenizer.h"
 
 struct T5Config {
@@ -334,11 +335,11 @@ struct T5Runner : public GGMLRunner {
     std::vector<int> relative_position_bucket_vec;
 
     T5Runner(ggml_backend_t backend,
-             ggml_backend_t params_backend,
              const String2TensorStorage& tensor_storage_map,
              const std::string prefix,
-             bool is_umt5 = false)
-        : GGMLRunner(backend, params_backend),
+             bool is_umt5                                        = false,
+             std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager),
           config(T5Config::detect_from_weights(tensor_storage_map, prefix, is_umt5)) {
         model = T5(config);
         model.init(params_ctx, tensor_storage_map, prefix);
@@ -394,11 +395,14 @@ struct T5Runner : public GGMLRunner {
 
     sd::Tensor<float> compute(const int n_threads,
                               const sd::Tensor<int32_t>& input_ids,
-                              const sd::Tensor<float>& attention_mask) {
+                              const sd::Tensor<float>& attention_mask,
+                              bool auto_free           = true,
+                              bool free_compute_buffer = true,
+                              bool free_compute_params = true) {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input_ids, attention_mask);
         };
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true), 3);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, auto_free, free_compute_buffer, free_compute_params), 3);
     }
 
     static std::vector<int> _relative_position_bucket(const std::vector<int>& relative_position,
@@ -474,24 +478,17 @@ struct T5Embedder {
     T5Runner model;
 
     T5Embedder(ggml_backend_t backend,
-               ggml_backend_t params_backend,
-               const String2TensorStorage& tensor_storage_map = {},
-               const std::string prefix                       = "",
-               bool is_umt5                                   = false)
-        : model(backend, params_backend, tensor_storage_map, prefix, is_umt5), tokenizer(is_umt5) {
+               const String2TensorStorage& tensor_storage_map      = {},
+               const std::string prefix                            = "",
+               bool is_umt5                                        = false,
+               std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : model(backend, tensor_storage_map, prefix, is_umt5, weight_manager), tokenizer(is_umt5) {
     }
 
     void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
-    bool alloc_params_buffer() {
-        if (!model.alloc_params_buffer()) {
-            return false;
-        }
-        return true;
-    }
-
     std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> tokenize(std::string text,
                                                                                   size_t max_length = 0,
                                                                                   bool padding      = false) {
@@ -576,7 +573,8 @@ struct T5Embedder {
         ggml_backend_t backend    = sd_backend_cpu_init();
         ggml_type model_data_type = GGML_TYPE_F16;
 
-        ModelLoader model_loader;
+        auto model_manager        = std::make_shared<ModelManager>();
+        ModelLoader& model_loader = model_manager->loader();
         if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
             return;
@@ -589,19 +587,16 @@ struct T5Embedder {
             }
         }
 
-        std::shared_ptr<T5Embedder> t5 = std::make_shared<T5Embedder>(backend, backend, tensor_storage_map, "", true);
+        std::shared_ptr<T5Embedder> t5 = std::make_shared<T5Embedder>(backend, tensor_storage_map, "", true, model_manager);
 
-        if (!t5->alloc_params_buffer()) {
-            LOG_ERROR("t5 params buffer allocation failed");
-            return;
-        }
-        std::map<std::string, ggml_tensor*> tensors;
-        t5->get_param_tensors(tensors, "");
-
-        bool success = model_loader.load_tensors(tensors);
-
-        if (!success) {
-            LOG_ERROR("load tensors from model loader failed");
+        if (!model_manager->register_runner_params("T5 test",
+                                                   *t5,
+                                                   "",
+                                                   ModelManager::ResidencyMode::ParamBackend,
+                                                   backend,
+                                                   backend) ||
+            !model_manager->validate_registered_tensors()) {
+            LOG_ERROR("register t5 tensors with model manager failed");
             return;
         }
 
diff --git a/otherarch/sdcpp/src/model/upscaler/esrgan.hpp b/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
index a56ebfe57..4afbab07d 100644
--- a/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
+++ b/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
@@ -1,8 +1,14 @@
-#ifndef __SD_MODEL_UPSCALER_ESRGAN_HPP__
+#ifndef __SD_MODEL_UPSCALER_ESRGAN_HPP__
 #define __SD_MODEL_UPSCALER_ESRGAN_HPP__
 
+#include <algorithm>
+#include <map>
+#include <string>
+#include <utility>
+#include <vector>
+
 #include "core/ggml_extend.hpp"
-#include "model_loader.h"
+#include "core/util.h"
 
 /*
     ===================================    ESRGAN  ===================================
@@ -12,6 +18,74 @@
 
 */
 
+struct ESRGANConfig {
+    int scale       = 4;
+    int num_block   = 23;
+    int num_in_ch   = 3;
+    int num_out_ch  = 3;
+    int num_feat    = 64;
+    int num_grow_ch = 32;
+
+    static ESRGANConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                            const std::string& prefix = "") {
+        ESRGANConfig config;
+        auto find_weight = [&](const std::string& suffix) -> const TensorStorage* {
+            std::string name = prefix.empty() ? suffix : prefix + "." + suffix;
+            auto iter        = tensor_storage_map.find(name);
+            if (iter == tensor_storage_map.end()) {
+                return nullptr;
+            }
+            return &iter->second;
+        };
+
+        int detected_num_block        = 0;
+        const std::string body_prefix = prefix.empty() ? "body." : prefix + ".body.";
+        for (const auto& [name, _] : tensor_storage_map) {
+            if (!starts_with(name, body_prefix)) {
+                continue;
+            }
+            size_t pos = name.find('.', body_prefix.size());
+            if (pos == std::string::npos) {
+                continue;
+            }
+            try {
+                int idx            = std::stoi(name.substr(body_prefix.size(), pos - body_prefix.size()));
+                detected_num_block = std::max(detected_num_block, idx + 1);
+            } catch (...) {
+            }
+        }
+        if (detected_num_block > 0) {
+            config.num_block = detected_num_block;
+        }
+
+        bool has_conv_up2 = find_weight("conv_up2.weight") != nullptr;
+        bool has_conv_up1 = find_weight("conv_up1.weight") != nullptr;
+        bool has_model_tensor =
+            detected_num_block > 0 ||
+            find_weight("conv_first.weight") != nullptr ||
+            find_weight("conv_hr.weight") != nullptr ||
+            find_weight("conv_last.weight") != nullptr;
+        if (has_conv_up2) {
+            config.scale = 4;
+        } else if (has_conv_up1) {
+            config.scale = 2;
+        } else if (has_model_tensor) {
+            config.scale = 1;
+        }
+
+        if (has_model_tensor || has_conv_up1 || has_conv_up2) {
+            LOG_DEBUG("esrgan: scale = %d, num_block = %d, num_in_ch = %d, num_out_ch = %d, num_feat = %d, num_grow_ch = %d",
+                      config.scale,
+                      config.num_block,
+                      config.num_in_ch,
+                      config.num_out_ch,
+                      config.num_feat,
+                      config.num_grow_ch);
+        }
+        return config;
+    }
+};
+
 class ResidualDenseBlock : public GGMLBlock {
 protected:
     int num_feat;
@@ -83,34 +157,29 @@ public:
 
 class RRDBNet : public GGMLBlock {
 protected:
-    int scale       = 4;
-    int num_block   = 23;
-    int num_in_ch   = 3;
-    int num_out_ch  = 3;
-    int num_feat    = 64;
-    int num_grow_ch = 32;
+    ESRGANConfig config;
 
 public:
-    RRDBNet(int scale, int num_block, int num_in_ch, int num_out_ch, int num_feat, int num_grow_ch)
-        : scale(scale), num_block(num_block), num_in_ch(num_in_ch), num_out_ch(num_out_ch), num_feat(num_feat), num_grow_ch(num_grow_ch) {
-        blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_in_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        for (int i = 0; i < num_block; i++) {
+    explicit RRDBNet(ESRGANConfig config)
+        : config(std::move(config)) {
+        blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_in_ch, this->config.num_feat, {3, 3}, {1, 1}, {1, 1}));
+        for (int i = 0; i < this->config.num_block; i++) {
             std::string name = "body." + std::to_string(i);
-            blocks[name]     = std::shared_ptr<GGMLBlock>(new RRDB(num_feat, num_grow_ch));
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new RRDB(this->config.num_feat, this->config.num_grow_ch));
         }
-        blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        if (scale >= 2) {
-            blocks["conv_up1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_feat, this->config.num_feat, {3, 3}, {1, 1}, {1, 1}));
+        if (this->config.scale >= 2) {
+            blocks["conv_up1"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_feat, this->config.num_feat, {3, 3}, {1, 1}, {1, 1}));
         }
-        if (scale == 4) {
-            blocks["conv_up2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        if (this->config.scale == 4) {
+            blocks["conv_up2"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_feat, this->config.num_feat, {3, 3}, {1, 1}, {1, 1}));
         }
-        blocks["conv_hr"]   = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_out_ch, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_hr"]   = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_feat, this->config.num_feat, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.num_feat, this->config.num_out_ch, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    int get_scale() { return scale; }
-    int get_num_block() { return num_block; }
+    int get_scale() { return config.scale; }
+    int get_num_block() { return config.num_block; }
 
     ggml_tensor* lrelu(GGMLRunnerContext* ctx, ggml_tensor* x) {
         return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
@@ -127,7 +196,7 @@ public:
         auto feat = conv_first->forward(ctx, x);
         sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.prelude", "feat");
         auto body_feat = feat;
-        for (int i = 0; i < num_block; i++) {
+        for (int i = 0; i < config.num_block; i++) {
             std::string name = "body." + std::to_string(i);
             auto block       = std::dynamic_pointer_cast<RRDB>(blocks[name]);
 
@@ -138,11 +207,11 @@ public:
         feat      = ggml_add(ctx->ggml_ctx, feat, body_feat);
         sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.body.out", "feat");
         // upsample
-        if (scale >= 2) {
+        if (config.scale >= 2) {
             auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
             feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
             sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.up1", "feat");
-            if (scale == 4) {
+            if (config.scale == 4) {
                 auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
                 feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
                 sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.up2", "feat");
@@ -156,199 +225,28 @@ public:
 };
 
 struct ESRGAN : public GGMLRunner {
+    ESRGANConfig config;
     std::unique_ptr<RRDBNet> rrdb_net;
-    int scale     = 4;
-    int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
     ESRGAN(ggml_backend_t backend,
-           ggml_backend_t params_backend,
-           int tile_size                                  = 128,
-           const String2TensorStorage& tensor_storage_map = {})
-        : GGMLRunner(backend, params_backend) {
-        this->tile_size = tile_size;
+           const String2TensorStorage& tensor_storage_map      = {},
+           std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : GGMLRunner(backend, weight_manager),
+          config(ESRGANConfig::detect_from_weights(tensor_storage_map)),
+          rrdb_net(std::make_unique<RRDBNet>(config)) {
+        rrdb_net->init(params_ctx, tensor_storage_map, "");
     }
 
     std::string get_desc() override {
         return "esrgan";
     }
 
-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading esrgan from '%s'", file_path.c_str());
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file_and_convert_name(file_path)) {
-            LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
-            return false;
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
+        if (!rrdb_net) {
+            return;
         }
 
-        // Get tensor names
-        auto tensor_names = model_loader.get_tensor_names();
-
-        // Detect if it's ESRGAN format
-        bool is_ESRGAN = std::find(tensor_names.begin(), tensor_names.end(), "model.0.weight") != tensor_names.end();
-
-        // Detect parameters from tensor names
-        int detected_num_block = 0;
-        if (is_ESRGAN) {
-            for (const auto& name : tensor_names) {
-                if (name.find("model.1.sub.") == 0) {
-                    size_t first_dot = name.find('.', 12);
-                    if (first_dot != std::string::npos) {
-                        size_t second_dot = name.find('.', first_dot + 1);
-                        if (second_dot != std::string::npos && name.substr(first_dot + 1, 3) == "RDB") {
-                            try {
-                                int idx            = std::stoi(name.substr(12, first_dot - 12));
-                                detected_num_block = std::max(detected_num_block, idx + 1);
-                            } catch (...) {
-                            }
-                        }
-                    }
-                }
-            }
-        } else {
-            // Original format
-            for (const auto& name : tensor_names) {
-                if (name.find("body.") == 0) {
-                    size_t pos = name.find('.', 5);
-                    if (pos != std::string::npos) {
-                        try {
-                            int idx            = std::stoi(name.substr(5, pos - 5));
-                            detected_num_block = std::max(detected_num_block, idx + 1);
-                        } catch (...) {
-                        }
-                    }
-                }
-            }
-        }
-
-        int detected_scale = 4;  // default
-        if (is_ESRGAN) {
-            // For ESRGAN format, detect scale by highest model number
-            int max_model_num = 0;
-            for (const auto& name : tensor_names) {
-                if (name.find("model.") == 0) {
-                    size_t dot_pos = name.find('.', 6);
-                    if (dot_pos != std::string::npos) {
-                        try {
-                            int num       = std::stoi(name.substr(6, dot_pos - 6));
-                            max_model_num = std::max(max_model_num, num);
-                        } catch (...) {
-                        }
-                    }
-                }
-            }
-            if (max_model_num <= 4) {
-                detected_scale = 1;
-            } else if (max_model_num <= 7) {
-                detected_scale = 2;
-            } else {
-                detected_scale = 4;
-            }
-        } else {
-            // Original format
-            bool has_conv_up2 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
-                return name == "conv_up2.weight";
-            });
-            bool has_conv_up1 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
-                return name == "conv_up1.weight";
-            });
-            if (has_conv_up2) {
-                detected_scale = 4;
-            } else if (has_conv_up1) {
-                detected_scale = 2;
-            } else {
-                detected_scale = 1;
-            }
-        }
-
-        int detected_num_in_ch   = 3;
-        int detected_num_out_ch  = 3;
-        int detected_num_feat    = 64;
-        int detected_num_grow_ch = 32;
-
-        // Create RRDBNet with detected parameters
-        rrdb_net = std::make_unique<RRDBNet>(detected_scale, detected_num_block, detected_num_in_ch, detected_num_out_ch, detected_num_feat, detected_num_grow_ch);
-        rrdb_net->init(params_ctx, {}, "");
-
-        if (!alloc_params_buffer()) {
-            LOG_ERROR("esrgan model buffer allocation failed");
-            return false;
-        }
-
-        std::map<std::string, ggml_tensor*> esrgan_tensors;
-        rrdb_net->get_param_tensors(esrgan_tensors);
-
-        bool success;
-        if (is_ESRGAN) {
-            // Build name mapping for ESRGAN format
-            std::map<std::string, std::string> expected_to_model;
-            expected_to_model["conv_first.weight"] = "model.0.weight";
-            expected_to_model["conv_first.bias"]   = "model.0.bias";
-
-            for (int i = 0; i < detected_num_block; i++) {
-                for (int j = 1; j <= 3; j++) {
-                    for (int k = 1; k <= 5; k++) {
-                        std::string expected_weight        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".weight";
-                        std::string model_weight           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.weight";
-                        expected_to_model[expected_weight] = model_weight;
-
-                        std::string expected_bias        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".bias";
-                        std::string model_bias           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.bias";
-                        expected_to_model[expected_bias] = model_bias;
-                    }
-                }
-            }
-
-            if (detected_scale == 1) {
-                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
-                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
-                expected_to_model["conv_hr.weight"]   = "model.2.weight";
-                expected_to_model["conv_hr.bias"]     = "model.2.bias";
-                expected_to_model["conv_last.weight"] = "model.4.weight";
-                expected_to_model["conv_last.bias"]   = "model.4.bias";
-            } else {
-                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
-                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
-                if (detected_scale >= 2) {
-                    expected_to_model["conv_up1.weight"] = "model.3.weight";
-                    expected_to_model["conv_up1.bias"]   = "model.3.bias";
-                }
-                if (detected_scale == 4) {
-                    expected_to_model["conv_up2.weight"]  = "model.6.weight";
-                    expected_to_model["conv_up2.bias"]    = "model.6.bias";
-                    expected_to_model["conv_hr.weight"]   = "model.8.weight";
-                    expected_to_model["conv_hr.bias"]     = "model.8.bias";
-                    expected_to_model["conv_last.weight"] = "model.10.weight";
-                    expected_to_model["conv_last.bias"]   = "model.10.bias";
-                } else if (detected_scale == 2) {
-                    expected_to_model["conv_hr.weight"]   = "model.5.weight";
-                    expected_to_model["conv_hr.bias"]     = "model.5.bias";
-                    expected_to_model["conv_last.weight"] = "model.7.weight";
-                    expected_to_model["conv_last.bias"]   = "model.7.bias";
-                }
-            }
-
-            std::map<std::string, ggml_tensor*> model_tensors;
-            for (auto& p : esrgan_tensors) {
-                auto it = expected_to_model.find(p.first);
-                if (it != expected_to_model.end()) {
-                    model_tensors[it->second] = p.second;
-                }
-            }
-
-            success = model_loader.load_tensors(model_tensors, {}, n_threads);
-        } else {
-            success = model_loader.load_tensors(esrgan_tensors, {}, n_threads);
-        }
-
-        if (!success) {
-            LOG_ERROR("load esrgan tensors from model loader failed");
-            return false;
-        }
-
-        scale = rrdb_net->get_scale();
-        LOG_INFO("esrgan model loaded with scale=%d, num_block=%d", scale, detected_num_block);
-        return success;
+        rrdb_net->get_param_tensors(tensors);
     }
 
     ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor) {
@@ -367,7 +265,7 @@ struct ESRGAN : public GGMLRunner {
     sd::Tensor<float> compute(const int n_threads,
                               const sd::Tensor<float>& x) {
         auto get_graph = [&]() -> ggml_cgraph* { return build_graph(x); };
-        auto result    = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        auto result    = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
         return result;
     }
 };
diff --git a/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp b/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
index b411e8aaf..5343ad036 100644
--- a/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
+++ b/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
@@ -1,9 +1,9 @@
-#ifndef __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
+#ifndef __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
 #define __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
 
+#include <algorithm>
 #include <cinttypes>
 #include <cmath>
-#include <cstdlib>
 #include <map>
 #include <memory>
 #include <set>
@@ -32,90 +32,100 @@ namespace LTXVUpsampler {
         int spatial_up_num       = 2;
         int spatial_down_den     = 1;
         int temporal_up_factor   = 1;
-    };
 
-    static inline bool has_tensor(const String2TensorStorage& tensor_storage_map,
-                                  const std::string& name) {
-        return tensor_storage_map.find(name) != tensor_storage_map.end();
-    }
+        static LatentUpsamplerConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                                         const std::string& prefix = "") {
+            LatentUpsamplerConfig config;
+            auto find_weight = [&](const std::string& suffix) -> const TensorStorage* {
+                std::string name = prefix.empty() ? suffix : prefix + "." + suffix;
+                auto iter        = tensor_storage_map.find(name);
+                if (iter == tensor_storage_map.end()) {
+                    return nullptr;
+                }
+                return &iter->second;
+            };
 
-    static inline int64_t get_tensor_ne(const String2TensorStorage& tensor_storage_map,
-                                        const std::string& name,
-                                        int axis,
-                                        int64_t fallback) {
-        auto it = tensor_storage_map.find(name);
-        if (it == tensor_storage_map.end() || axis < 0 || axis >= GGML_MAX_DIMS) {
-            return fallback;
-        }
-        return it->second.ne[axis];
-    }
+            bool inferred = false;
 
-    static inline int64_t get_tensor_ne0(const String2TensorStorage& tensor_storage_map,
-                                         const std::string& name,
-                                         int64_t fallback) {
-        return get_tensor_ne(tensor_storage_map, name, 0, fallback);
-    }
-
-    static inline int count_module_blocks(const String2TensorStorage& tensor_storage_map,
-                                          const std::string& module_name) {
-        int max_block            = -1;
-        const std::string prefix = module_name + ".";
-        for (const auto& pair : tensor_storage_map) {
-            const std::string& name = pair.first;
-            if (name.find(prefix) != 0) {
-                continue;
+            const TensorStorage* initial_norm = find_weight("initial_norm.weight");
+            if (initial_norm != nullptr) {
+                config.mid_channels = initial_norm->ne[0];
+                inferred            = true;
             }
-            size_t begin = prefix.size();
-            size_t end   = name.find('.', begin);
-            if (end == std::string::npos) {
-                continue;
-            }
-            int index = atoi(name.substr(begin, end - begin).c_str());
-            max_block = std::max(max_block, index);
-        }
-        return max_block + 1;
-    }
 
-    static inline LatentUpsamplerConfig detect_config_from_weights(const String2TensorStorage& tensor_storage_map) {
-        LatentUpsamplerConfig config;
-        config.mid_channels = get_tensor_ne0(tensor_storage_map, "initial_norm.weight", config.mid_channels);
-        config.in_channels  = get_tensor_ne0(tensor_storage_map, "final_conv.bias", config.in_channels);
-        int detected_blocks = count_module_blocks(tensor_storage_map, "res_blocks");
-        if (detected_blocks > 0) {
-            config.num_blocks_per_stage = detected_blocks;
-        }
-        config.rational_resampler      = has_tensor(tensor_storage_map, "upsampler.conv.weight");
-        int64_t upsampler_out_channels = get_tensor_ne0(tensor_storage_map, "upsampler.0.bias", 0);
-        config.spatial_upsample        = config.rational_resampler || upsampler_out_channels == 4 * config.mid_channels;
-        config.temporal_upsample       = upsampler_out_channels == 2 * config.mid_channels;
-        if (config.temporal_upsample) {
-            config.temporal_up_factor = 2;
-        }
-        if (config.rational_resampler) {
-            int64_t out_channels = get_tensor_ne(tensor_storage_map,
-                                                 "upsampler.conv.weight",
-                                                 3,
-                                                 config.mid_channels * 9);
-            if (config.mid_channels > 0 && out_channels % config.mid_channels == 0) {
-                int64_t ratio = out_channels / config.mid_channels;
-                int num       = static_cast<int>(std::round(std::sqrt(static_cast<double>(ratio))));
-                if (num > 0 && static_cast<int64_t>(num) * num == ratio) {
-                    config.spatial_up_num = num;
+            const TensorStorage* final_conv = find_weight("final_conv.bias");
+            if (final_conv != nullptr) {
+                config.in_channels = final_conv->ne[0];
+                inferred           = true;
+            }
+
+            int detected_blocks                 = 0;
+            const std::string res_blocks_prefix = prefix.empty() ? "res_blocks." : prefix + ".res_blocks.";
+            for (const auto& [name, _] : tensor_storage_map) {
+                if (!starts_with(name, res_blocks_prefix)) {
+                    continue;
+                }
+                size_t begin = res_blocks_prefix.size();
+                size_t end   = name.find('.', begin);
+                if (end == std::string::npos) {
+                    continue;
+                }
+                try {
+                    int idx         = std::stoi(name.substr(begin, end - begin));
+                    detected_blocks = std::max(detected_blocks, idx + 1);
+                } catch (...) {
                 }
             }
-            if (config.spatial_up_num == 3) {
-                config.spatial_down_den = 2;
-                config.spatial_scale    = 1.5f;
-            } else if (config.spatial_up_num == 4) {
-                config.spatial_down_den = 1;
-                config.spatial_scale    = 4.f;
-            } else {
-                config.spatial_down_den = 1;
-                config.spatial_scale    = static_cast<float>(config.spatial_up_num);
+            if (detected_blocks > 0) {
+                config.num_blocks_per_stage = detected_blocks;
+                inferred                    = true;
             }
+
+            const TensorStorage* rational_upsampler_weight = find_weight("upsampler.conv.weight");
+            const TensorStorage* upsampler_bias            = find_weight("upsampler.0.bias");
+            config.rational_resampler                      = rational_upsampler_weight != nullptr;
+            int64_t upsampler_out_channels                 = upsampler_bias == nullptr ? 0 : upsampler_bias->ne[0];
+            config.spatial_upsample                        = config.rational_resampler || upsampler_out_channels == 4 * config.mid_channels;
+            config.temporal_upsample                       = upsampler_out_channels == 2 * config.mid_channels;
+            if (config.rational_resampler || upsampler_out_channels > 0) {
+                inferred = true;
+            }
+            if (config.temporal_upsample) {
+                config.temporal_up_factor = 2;
+            }
+            if (rational_upsampler_weight != nullptr) {
+                int64_t out_channels = rational_upsampler_weight->ne[3];
+                if (config.mid_channels > 0 && out_channels % config.mid_channels == 0) {
+                    int64_t ratio = out_channels / config.mid_channels;
+                    int num       = static_cast<int>(std::round(std::sqrt(static_cast<double>(ratio))));
+                    if (num > 0 && static_cast<int64_t>(num) * num == ratio) {
+                        config.spatial_up_num = num;
+                    }
+                }
+                if (config.spatial_up_num == 3) {
+                    config.spatial_down_den = 2;
+                    config.spatial_scale    = 1.5f;
+                } else if (config.spatial_up_num == 4) {
+                    config.spatial_down_den = 1;
+                    config.spatial_scale    = 4.f;
+                } else {
+                    config.spatial_down_den = 1;
+                    config.spatial_scale    = static_cast<float>(config.spatial_up_num);
+                }
+            }
+
+            if (inferred) {
+                LOG_DEBUG("ltx latent upsampler: in_channels = %" PRId64 ", mid_channels = %" PRId64 ", num_blocks_per_stage = %d, spatial_scale = %.3f, temporal_up_factor = %d, rational_resampler = %d",
+                          config.in_channels,
+                          config.mid_channels,
+                          config.num_blocks_per_stage,
+                          config.spatial_scale,
+                          config.temporal_up_factor,
+                          config.rational_resampler);
+            }
+            return config;
         }
-        return config;
-    }
+    };
 
     class VideoGroupNorm : public GGMLBlock {
     protected:
@@ -240,20 +250,25 @@ namespace LTXVUpsampler {
     protected:
         int64_t channels;
         int stride;
-        ggml_tensor* kernel = nullptr;
         std::vector<float> kernel_data;
+        std::string kernel_name;
 
         void init_params(ggml_context* ctx,
                          const String2TensorStorage& tensor_storage_map = {},
                          const std::string prefix                       = "") override {
+            SD_UNUSED(ctx);
             SD_UNUSED(tensor_storage_map);
             if (stride == 1) {
                 return;
             }
-            kernel           = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 5, 5, 1, channels);
-            std::string name = prefix + "kernel";
-            ggml_set_name(kernel, name.c_str());
+            kernel_name = prefix + "kernel";
+        }
 
+    public:
+        BlurDownsample(int64_t channels, int stride)
+            : channels(channels),
+              stride(stride) {
+            GGML_ASSERT(stride >= 1);
             static const float binomial[5] = {1.f, 4.f, 6.f, 4.f, 1.f};
             kernel_data.resize(static_cast<size_t>(5 * 5 * channels));
             for (int64_t c = 0; c < channels; ++c) {
@@ -266,26 +281,16 @@ namespace LTXVUpsampler {
             }
         }
 
-    public:
-        BlurDownsample(int64_t channels, int stride)
-            : channels(channels),
-              stride(stride) {
-            GGML_ASSERT(stride >= 1);
-        }
-
-        void load_fixed_tensors() {
-            if (kernel == nullptr || kernel_data.empty()) {
-                return;
-            }
-            ggml_backend_tensor_set(kernel, kernel_data.data(), 0, kernel_data.size() * sizeof(float));
-        }
-
         ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             if (stride == 1) {
                 return x;
             }
-            GGML_ASSERT(kernel != nullptr);
+            GGML_ASSERT(ctx != nullptr);
+            GGML_ASSERT(!kernel_data.empty());
             GGML_ASSERT(x->ne[2] == channels);
+            ggml_tensor* kernel = ggml_new_tensor_4d(ctx->ggml_ctx, GGML_TYPE_F32, 5, 5, 1, channels);
+            ggml_set_name(kernel, kernel_name.empty() ? "blur_down.kernel" : kernel_name.c_str());
+            ctx->bind_backend_tensor_data(kernel, kernel_data.data());
             if (ctx->conv2d_direct_enabled) {
                 return ggml_conv_2d_dw_direct(ctx->ggml_ctx, kernel, x, stride, stride, 2, 2, 1, 1);
             }
@@ -311,11 +316,6 @@ namespace LTXVUpsampler {
             blocks["blur_down"]     = std::shared_ptr<GGMLBlock>(new BlurDownsample(mid_channels, den));
         }
 
-        void load_fixed_tensors() {
-            auto blur_down = std::dynamic_pointer_cast<BlurDownsample>(blocks["blur_down"]);
-            blur_down->load_fixed_tensors();
-        }
-
         ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto conv          = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
             auto pixel_shuffle = std::dynamic_pointer_cast<PixelShuffleND>(blocks["pixel_shuffle"]);
@@ -426,45 +426,17 @@ namespace LTXVUpsampler {
             sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.final", "x");
             return x;
         }
-
-        void load_fixed_tensors() {
-            if (!config.rational_resampler) {
-                return;
-            }
-            auto upsampler = std::dynamic_pointer_cast<SpatialRationalResampler>(blocks["upsampler"]);
-            upsampler->load_fixed_tensors();
-        }
     };
 
     struct LatentUpsamplerRunner : public GGMLRunner {
+        LatentUpsamplerConfig config;
         std::unique_ptr<LatentUpsampler> model;
 
         LatentUpsamplerRunner(ggml_backend_t backend,
-                              ggml_backend_t params_backend)
-            : GGMLRunner(backend, params_backend) {}
-
-        std::string get_desc() override {
-            return "ltx_latent_upsampler";
-        }
-
-        bool load_from_file(const std::string& file_path, int n_threads) {
-            LOG_INFO("loading LTX latent upsampler from '%s'", file_path.c_str());
-            ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path)) {
-                LOG_ERROR("init LTX latent upsampler model loader from file failed: '%s'", file_path.c_str());
-                return false;
-            }
-
-            const auto& tensor_storage_map = model_loader.get_tensor_storage_map();
-            bool has_regular_upsampler     = has_tensor(tensor_storage_map, "upsampler.0.weight");
-            bool has_rational_spatial      = has_tensor(tensor_storage_map, "upsampler.conv.weight");
-            if (!has_tensor(tensor_storage_map, "post_upsample_res_blocks.0.conv2.bias") ||
-                (!has_regular_upsampler && !has_rational_spatial)) {
-                LOG_ERROR("unsupported LTX latent upsampler weights: expected upsampler tensors");
-                return false;
-            }
-
-            LatentUpsamplerConfig config = detect_config_from_weights(tensor_storage_map);
+                              const String2TensorStorage& tensor_storage_map,
+                              std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager),
+              config(LatentUpsamplerConfig::detect_from_weights(tensor_storage_map)) {
             if (config.dims != 3 || (!config.spatial_upsample && !config.temporal_upsample) ||
                 config.spatial_up_num < 1 || config.spatial_down_den < 1 || config.temporal_up_factor < 1) {
                 LOG_ERROR("unsupported LTX latent upsampler config: dims=%d spatial=%d temporal=%d rational=%d scale=%.3f temporal_factor=%d",
@@ -474,36 +446,21 @@ namespace LTXVUpsampler {
                           config.rational_resampler,
                           config.spatial_scale,
                           config.temporal_up_factor);
-                return false;
+                return;
             }
 
             model = std::make_unique<LatentUpsampler>(config);
             model->init(params_ctx, tensor_storage_map, "");
-            if (!alloc_params_buffer()) {
-                LOG_ERROR("LTX latent upsampler params buffer allocation failed");
-                return false;
-            }
+        }
 
-            std::map<std::string, ggml_tensor*> tensors;
-            model->get_param_tensors(tensors);
-            std::set<std::string> ignore_tensors;
-            if (config.rational_resampler) {
-                ignore_tensors.insert("upsampler.blur_down.kernel");
-            }
-            if (!model_loader.load_tensors(tensors, ignore_tensors, n_threads)) {
-                LOG_ERROR("load LTX latent upsampler tensors failed");
-                return false;
-            }
-            model->load_fixed_tensors();
+        std::string get_desc() override {
+            return "ltx_latent_upsampler";
+        }
 
-            LOG_INFO("LTX latent upsampler loaded: in_channels=%" PRId64 ", mid_channels=%" PRId64 ", blocks=%d, scale=%.3f, temporal_factor=%d, rational=%d",
-                     config.in_channels,
-                     config.mid_channels,
-                     config.num_blocks_per_stage,
-                     config.spatial_scale,
-                     config.temporal_up_factor,
-                     config.rational_resampler);
-            return true;
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
+            if (model) {
+                model->get_param_tensors(tensors);
+            }
         }
 
         ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor) {
@@ -534,15 +491,15 @@ namespace LTXVUpsampler {
                           (long long)x.shape()[4]);
                 return {};
             }
-            if (x.shape()[3] != model->config.in_channels) {
+            if (x.shape()[3] != config.in_channels) {
                 LOG_ERROR("LTX latent upsampler expected %" PRId64 " channels, got %lld",
-                          model->config.in_channels,
+                          config.in_channels,
                           (long long)x.shape()[3]);
                 return {};
             }
             size_t expected_dim = static_cast<size_t>(x.dim());
             auto get_graph      = [&]() -> ggml_cgraph* { return build_graph(x); };
-            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), expected_dim);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), expected_dim);
         }
     };
 
diff --git a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
index 49472ff47..478b18edb 100644
--- a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
+++ b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
@@ -213,9 +213,9 @@ protected:
         params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
-    float get_alpha() {
-        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
-        return sigmoid(alpha);
+    ggml_tensor* get_alpha(GGMLRunnerContext* ctx) {
+        auto mix_factor = ggml_ext_cast_f32(ctx->ggml_ctx, ctx->backend, params["mix_factor"]);
+        return ggml_sigmoid(ctx->ggml_ctx, mix_factor);
     }
 
 public:
@@ -250,10 +250,12 @@ public:
 
         x = time_stack->forward(ctx, x);  // b t c (h w)
 
-        float alpha = get_alpha();
-        x           = ggml_add(ctx->ggml_ctx,
-                               ggml_ext_scale(ctx->ggml_ctx, x, alpha),
-                               ggml_ext_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
+        auto alpha = get_alpha(ctx);
+        x          = ggml_add(ctx->ggml_ctx,
+                              x_mix,
+                              ggml_mul(ctx->ggml_ctx,
+                                       ggml_sub(ctx->ggml_ctx, x, x_mix),
+                                       alpha));
 
         x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
         x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
@@ -664,13 +666,13 @@ struct AutoEncoderKL : public VAE {
     AutoEncoderKLModel ae;
 
     AutoEncoderKL(ggml_backend_t backend,
-                  ggml_backend_t params_backend,
                   const String2TensorStorage& tensor_storage_map,
                   const std::string prefix,
-                  bool decode_only       = false,
-                  bool use_video_decoder = false,
-                  SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), VAE(version, backend, params_backend) {
+                  bool decode_only                                    = false,
+                  bool use_video_decoder                              = false,
+                  SDVersion version                                   = VERSION_SD1,
+                  std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : VAE(version, backend, prefix, weight_manager), decode_only(decode_only) {
         if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
             scale_factor = 0.18215f;
             shift_factor = 0.f;
@@ -718,8 +720,8 @@ struct AutoEncoderKL : public VAE {
         return "vae";
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
-        ae.get_param_tensors(tensors, prefix);
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        ae.get_param_tensors(tensors, weight_prefix);
     }
 
     ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
@@ -742,7 +744,7 @@ struct AutoEncoderKL : public VAE {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(z, decode_graph);
         };
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z.dim());
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), z.dim());
     }
 
     sd::Tensor<float> gaussian_latent_sample(const sd::Tensor<float>& moments, std::shared_ptr<RNG> rng) {
diff --git a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
index d41a79a43..997c57a5b 100644
--- a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
+#ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 #define __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 
 #include <cmath>
@@ -9,6 +9,7 @@
 
 #include "core/ggml_extend.hpp"
 #include "model_loader.h"
+#include "model_manager.h"
 
 namespace LTXV {
 
@@ -997,13 +998,15 @@ namespace LTXV {
     struct LTXAudioVAERunner : public GGMLRunner {
         LTXAudioVAEConfig config;
         LTXAudioVAE model;
+        std::string weight_prefix;
         sd::Tensor<float> bwe_skip_filter_tensor;
 
         LTXAudioVAERunner(ggml_backend_t backend,
-                          ggml_backend_t params_backend,
                           const String2TensorStorage& tensor_storage_map,
-                          const std::string& prefix = "")
-            : GGMLRunner(backend, params_backend),
+                          const std::string& prefix                           = "",
+                          std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : GGMLRunner(backend, weight_manager),
+              weight_prefix(prefix),
               config(LTXAudioVAEConfig::detect_from_weights(tensor_storage_map)),
               model(config) {
             model.init(params_ctx, tensor_storage_map, prefix);
@@ -1013,11 +1016,11 @@ namespace LTXV {
             }
         }
 
-        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
-            model.get_param_tensors(tensors, prefix);
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
+            model.get_param_tensors(tensors, weight_prefix);
         }
 
-        size_t get_params_buffer_size() {
+        size_t get_params_mem_size() {
             return model.get_params_mem_size();
         }
 
@@ -1037,7 +1040,7 @@ namespace LTXV {
                 ggml_build_forward_expand(gf, waveform);
                 return gf;
             };
-            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), 4);
+            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), 4);
             int64_t t1  = ggml_time_ms();
             LOG_INFO("ltx audio vae decode completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
             return result;
@@ -1064,7 +1067,8 @@ namespace LTXV {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
             LOG_INFO("loading ltx audio vae from '%s'", model_path.c_str());
 
-            ModelLoader model_loader;
+            auto model_manager        = std::make_shared<ModelManager>();
+            ModelLoader& model_loader = model_manager->loader();
             if (!model_loader.init_from_file(model_path)) {
                 LOG_ERROR("init model loader from file failed: '%s'", model_path.c_str());
                 return;
@@ -1072,20 +1076,17 @@ namespace LTXV {
 
             auto& tensor_storage_map = model_loader.get_tensor_storage_map();
             auto ltx_audio_vae       = std::make_shared<LTXAudioVAERunner>(backend,
-                                                                     backend,
                                                                      tensor_storage_map,
-                                                                     prefix);
+                                                                     prefix,
+                                                                     model_manager);
 
-            if (!ltx_audio_vae->alloc_params_buffer()) {
-                LOG_ERROR("ltx audio vae buffer allocation failed");
-                return;
-            }
-
-            std::map<std::string, ggml_tensor*> tensors;
-            ltx_audio_vae->get_param_tensors(tensors, "");
-
-            if (!model_loader.load_tensors(tensors)) {
-                LOG_ERROR("load tensors from model loader failed");
+            if (!model_manager->register_runner_params("LTX audio VAE test",
+                                                       *ltx_audio_vae,
+                                                       ModelManager::ResidencyMode::ParamBackend,
+                                                       backend,
+                                                       backend) ||
+                !model_manager->validate_registered_tensors()) {
+                LOG_ERROR("register ltx audio vae tensors with model manager failed");
                 return;
             }
 
diff --git a/otherarch/sdcpp/src/model/vae/ltx_vae.hpp b/otherarch/sdcpp/src/model/vae/ltx_vae.hpp
index 86fcdcb09..a19ce820d 100644
--- a/otherarch/sdcpp/src/model/vae/ltx_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/ltx_vae.hpp
@@ -957,8 +957,8 @@ namespace LTXVAE {
 
             ggml_tensor* scaled_timestep = timestep;
             if (timestep_conditioning) {
-                auto multiplier = ggml_ext_backend_tensor_get_f32(params["timestep_scale_multiplier"]);
-                scaled_timestep = ggml_ext_scale(ctx->ggml_ctx, timestep, multiplier);
+                auto multiplier = ggml_ext_cast_f32(ctx->ggml_ctx, ctx->backend, params["timestep_scale_multiplier"]);
+                scaled_timestep = ggml_mul(ctx->ggml_ctx, timestep, multiplier);
             }
 
             x = conv_in->forward(ctx, x, causal_decoder);
@@ -1008,8 +1008,8 @@ namespace LTXVAE {
 
             ggml_tensor* scaled_timestep = timestep;
             if (timestep_conditioning && timestep != nullptr) {
-                auto multiplier = ggml_ext_backend_tensor_get_f32(params["timestep_scale_multiplier"]);
-                scaled_timestep = ggml_ext_scale(ctx->ggml_ctx, timestep, multiplier);
+                auto multiplier = ggml_ext_cast_f32(ctx->ggml_ctx, ctx->backend, params["timestep_scale_multiplier"]);
+                scaled_timestep = ggml_mul(ctx->ggml_ctx, timestep, multiplier);
             }
 
             // conv_in with feat_map for left temporal context
@@ -1223,11 +1223,11 @@ struct LTXVideoVAE : public VAE {
     LTXVAE::VideoVAE vae;
 
     LTXVideoVAE(ggml_backend_t backend,
-                ggml_backend_t params_backend,
                 const String2TensorStorage& tensor_storage_map,
                 const std::string& prefix,
-                bool decode_only  = true,
-                SDVersion version = VERSION_LTXAV)
+                bool decode_only                                    = true,
+                SDVersion version                                   = VERSION_LTXAV,
+                std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : decode_only(decode_only),
           ltx_vae_version(LTXVAE::detect_ltx_vae_version(tensor_storage_map, prefix)),
           timestep_conditioning(LTXVAE::detect_ltx_vae_timestep_conditioning(tensor_storage_map, prefix)),
@@ -1239,7 +1239,7 @@ struct LTXVideoVAE : public VAE {
               patch_size,
               tensor_storage_map,
               prefix),
-          VAE(version, backend, params_backend) {
+          VAE(version, backend, prefix, weight_manager) {
         vae.init(params_ctx, tensor_storage_map, prefix);
         decode_timestep_tensor.values()[0] = vae.decode_timestep;
     }
@@ -1271,8 +1271,8 @@ struct LTXVideoVAE : public VAE {
         }
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
-        vae.get_param_tensors(tensors, prefix);
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        vae.get_param_tensors(tensors, weight_prefix);
     }
 
     struct TemporalTilePlan {
@@ -1396,7 +1396,7 @@ struct LTXVideoVAE : public VAE {
                                                  static_cast<int>(start),
                                                  chunk_overlap);
             };
-            auto chunk = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true),
+            auto chunk = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true, true, true),
                                                          expected_dim);
             if (chunk.empty()) {
                 free_cache_ctx_and_buffer();
@@ -1426,7 +1426,7 @@ struct LTXVideoVAE : public VAE {
                                const sd::Tensor<float>& z,
                                bool decode_graph) override {
         if (!decode_graph && decode_only) {
-            LOG_ERROR("LTX video VAE encode requires encoder weights; create the context with vae_decode_only=false");
+            LOG_ERROR("LTX video VAE encode requires encoder weights");
             return {};
         }
         sd::Tensor<float> input = z;
@@ -1452,7 +1452,7 @@ struct LTXVideoVAE : public VAE {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_graph(input, decode_graph);
         };
-        auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), expected_dim);
+        auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), expected_dim);
         if (result.empty()) {
             return {};
         }
@@ -1465,7 +1465,7 @@ struct LTXVideoVAE : public VAE {
         auto get_graph = [&]() -> ggml_cgraph* {
             return build_latent_statistics_graph(z, normalize);
         };
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false),
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false),
                                                static_cast<size_t>(z.dim()));
     }
 
@@ -1521,7 +1521,8 @@ struct LTXVideoVAE : public VAE {
         ggml_backend_t backend = sd_backend_cpu_init();
         LOG_INFO("loading ltx vae from '%s'", model_path.c_str());
 
-        ModelLoader model_loader;
+        auto model_manager        = std::make_shared<ModelManager>();
+        ModelLoader& model_loader = model_manager->loader();
         if (!model_loader.init_from_file_and_convert_name(model_path, "vae.")) {
             LOG_ERROR("init model loader from file failed: '%s'", model_path.c_str());
             return;
@@ -1529,22 +1530,19 @@ struct LTXVideoVAE : public VAE {
 
         auto& tensor_storage_map         = model_loader.get_tensor_storage_map();
         std::shared_ptr<LTXVideoVAE> vae = std::make_shared<LTXVideoVAE>(backend,
-                                                                         backend,
                                                                          tensor_storage_map,
                                                                          "first_stage_model",
                                                                          true,
-                                                                         VERSION_LTXAV);
+                                                                         VERSION_LTXAV,
+                                                                         model_manager);
 
-        if (!vae->alloc_params_buffer()) {
-            LOG_ERROR("vae buffer allocation failed");
-            return;
-        }
-
-        std::map<std::string, ggml_tensor*> tensors;
-        vae->get_param_tensors(tensors, "first_stage_model");
-
-        if (!model_loader.load_tensors(tensors)) {
-            LOG_ERROR("load tensors from model loader failed");
+        if (!model_manager->register_runner_params("LTX VAE test",
+                                                   *vae,
+                                                   ModelManager::ResidencyMode::ParamBackend,
+                                                   backend,
+                                                   backend) ||
+            !model_manager->validate_registered_tensors()) {
+            LOG_ERROR("register ltx vae tensors with model manager failed");
             return;
         }
 
diff --git a/otherarch/sdcpp/src/model/vae/tae.hpp b/otherarch/sdcpp/src/model/vae/tae.hpp
index fcb62c2ea..7c6e1d35c 100644
--- a/otherarch/sdcpp/src/model/vae/tae.hpp
+++ b/otherarch/sdcpp/src/model/vae/tae.hpp
@@ -623,14 +623,14 @@ struct TinyImageAutoEncoder : public VAE {
     bool decode_only = false;
 
     TinyImageAutoEncoder(ggml_backend_t backend,
-                         ggml_backend_t params_backend,
                          const String2TensorStorage& tensor_storage_map,
                          const std::string prefix,
-                         bool decoder_only = true,
-                         SDVersion version = VERSION_SD1)
-        : decode_only(decoder_only),
-          taesd(decoder_only, version),
-          VAE(version, backend, params_backend) {
+                         bool decoder_only                                   = true,
+                         SDVersion version                                   = VERSION_SD1,
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : VAE(version, backend, "tae", weight_manager),
+          decode_only(decoder_only),
+          taesd(decoder_only, version) {
         scale_input = false;
         taesd.init(params_ctx, tensor_storage_map, prefix);
     }
@@ -639,8 +639,8 @@ struct TinyImageAutoEncoder : public VAE {
         return "taesd";
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
-        taesd.get_param_tensors(tensors, prefix);
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        taesd.get_param_tensors(tensors, weight_prefix);
     }
 
     sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
@@ -676,7 +676,7 @@ struct TinyImageAutoEncoder : public VAE {
             return build_graph(z_tensor, decode_graph);
         };
 
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), z_tensor.dim());
     }
 };
 
@@ -686,13 +686,13 @@ struct TinyVideoAutoEncoder : public VAE {
     bool is_wide     = false;
 
     TinyVideoAutoEncoder(ggml_backend_t backend,
-                         ggml_backend_t params_backend,
                          const String2TensorStorage& tensor_storage_map,
                          const std::string prefix,
-                         bool decoder_only = true,
-                         SDVersion version = VERSION_WAN2)
-        : decode_only(decoder_only),
-          VAE(version, backend, params_backend) {
+                         bool decoder_only                                   = true,
+                         SDVersion version                                   = VERSION_WAN2,
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : VAE(version, backend, "tae", weight_manager),
+          decode_only(decoder_only) {
         for (auto tensor_storage : tensor_storage_map) {
             if (tensor_storage.first.find(prefix + ".3.conv.6.weight") != std::string::npos) {
                 is_wide = true;
@@ -708,8 +708,8 @@ struct TinyVideoAutoEncoder : public VAE {
         return "taehv";
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
-        taehv.get_param_tensors(tensors, prefix);
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        taehv.get_param_tensors(tensors, weight_prefix);
     }
 
     sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
@@ -746,7 +746,7 @@ struct TinyVideoAutoEncoder : public VAE {
             return build_graph(z_tensor, decode_graph);
         };
 
-        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), z_tensor.dim());
     }
 };
 
diff --git a/otherarch/sdcpp/src/model/vae/vae.hpp b/otherarch/sdcpp/src/model/vae/vae.hpp
index bd0ce6c4e..af091bb57 100644
--- a/otherarch/sdcpp/src/model/vae/vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/vae.hpp
@@ -1,12 +1,14 @@
-#ifndef __SD_MODEL_VAE_VAE_HPP__
+#ifndef __SD_MODEL_VAE_VAE_HPP__
 #define __SD_MODEL_VAE_VAE_HPP__
 
 #include "core/tensor_ggml.hpp"
 #include "model/common/block.hpp"
+#include "model_manager.h"
 
 struct VAE : public GGMLRunner {
 protected:
     SDVersion version;
+    std::string weight_prefix;
     bool scale_input                                      = true;
     virtual sd::Tensor<float> _compute(const int n_threads,
                                        const sd::Tensor<float>& z,
@@ -62,8 +64,11 @@ protected:
     }
 
 public:
-    VAE(SDVersion version, ggml_backend_t backend, ggml_backend_t params_backend)
-        : version(version), GGMLRunner(backend, params_backend) {}
+    VAE(SDVersion version,
+        ggml_backend_t backend,
+        const std::string& weight_prefix                    = "",
+        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : version(version), weight_prefix(weight_prefix), GGMLRunner(backend, weight_manager) {}
 
     int get_scale_factor() {
         int scale_factor = 8;
@@ -214,7 +219,7 @@ public:
     virtual sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) = 0;
     virtual sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents)                           = 0;
     virtual sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents)                           = 0;
-    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix)         = 0;
+    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors)                                   = 0;
     virtual void set_conv2d_scale(float scale) { SD_UNUSED(scale); };
     virtual void set_temporal_tiling_enabled(bool enabled) { SD_UNUSED(enabled); };
     virtual void set_tiling_params(const sd_tiling_params_t& params) {
@@ -223,8 +228,10 @@ public:
 };
 
 struct FakeVAE : public VAE {
-    FakeVAE(SDVersion version, ggml_backend_t backend, ggml_backend_t params_backend)
-        : VAE(version, backend, params_backend) {}
+    FakeVAE(SDVersion version,
+            ggml_backend_t backend,
+            std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+        : VAE(version, backend, "", weight_manager) {}
 
     int get_encoder_output_channels(int input_channels) {
         return input_channels;
@@ -251,7 +258,7 @@ struct FakeVAE : public VAE {
         return latents;
     }
 
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {}
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {}
 
     std::string get_desc() override {
         return "fake_vae";
diff --git a/otherarch/sdcpp/src/model/vae/wan_vae.hpp b/otherarch/sdcpp/src/model/vae/wan_vae.hpp
index c20764cde..8a845c7ca 100644
--- a/otherarch/sdcpp/src/model/vae/wan_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/wan_vae.hpp
@@ -1124,12 +1124,12 @@ namespace WAN {
         WanVAE ae;
 
         WanVAERunner(ggml_backend_t backend,
-                     ggml_backend_t params_backend,
-                     const String2TensorStorage& tensor_storage_map = {},
-                     const std::string prefix                       = "",
-                     bool decode_only                               = false,
-                     SDVersion version                              = VERSION_WAN2)
-            : decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V), VAE(version, backend, params_backend) {
+                     const String2TensorStorage& tensor_storage_map      = {},
+                     const std::string prefix                            = "",
+                     bool decode_only                                    = false,
+                     SDVersion version                                   = VERSION_WAN2,
+                     std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : VAE(version, backend, prefix, weight_manager), decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V) {
             ae.init(params_ctx, tensor_storage_map, prefix);
         }
 
@@ -1137,8 +1137,8 @@ namespace WAN {
             return "wan_vae";
         }
 
-        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
-            ae.get_param_tensors(tensors, prefix);
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+            ae.get_param_tensors(tensors, weight_prefix);
         }
 
         sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
@@ -1255,7 +1255,7 @@ namespace WAN {
                         return build_graph(input, decode_graph);
                     }
                 };
-                auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true),
+                auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, true, true, true),
                                                               input.empty() ? z.dim() : input.dim());
                 if (!result.empty() && z.dim() == 4) {
                     result.squeeze_(2);
@@ -1268,7 +1268,7 @@ namespace WAN {
                 auto get_graph = [&]() -> ggml_cgraph* {
                     return build_graph_partial(z, decode_graph, i);
                 };
-                auto out_opt = GGMLRunner::compute<float>(get_graph, n_threads, true);
+                auto out_opt = GGMLRunner::compute<float>(get_graph, n_threads, true, true, true);
                 if (!out_opt.has_value()) {
                     return {};
                 }
@@ -1281,7 +1281,7 @@ namespace WAN {
                 sd::Tensor<float> output = std::move(out);
 
                 for (i = 1; i < t; i++) {
-                    auto chunk_opt = GGMLRunner::compute<float>(get_graph, n_threads, true);
+                    auto chunk_opt = GGMLRunner::compute<float>(get_graph, n_threads, true, true, true);
                     if (!chunk_opt.has_value()) {
                         return {};
                     }
@@ -1327,27 +1327,24 @@ namespace WAN {
             // ggml_backend_t backend = ggml_backend_cuda_init(0);
             ggml_backend_t backend            = sd_backend_cpu_init();
             ggml_type model_data_type         = GGML_TYPE_F16;
-            std::shared_ptr<WanVAERunner> vae = std::make_shared<WanVAERunner>(backend, backend, String2TensorStorage{}, "", false, VERSION_WAN2_2_TI2V);
+            auto model_manager                = std::make_shared<ModelManager>();
+            std::shared_ptr<WanVAERunner> vae = std::make_shared<WanVAERunner>(backend, String2TensorStorage{}, "first_stage_model", false, VERSION_WAN2_2_TI2V, model_manager);
             {
                 LOG_INFO("loading from '%s'", file_path.c_str());
 
-                if (!vae->alloc_params_buffer()) {
-                    LOG_ERROR("vae buffer allocation failed");
-                    return;
-                }
-                std::map<std::string, ggml_tensor*> tensors;
-                vae->get_param_tensors(tensors, "first_stage_model");
-
-                ModelLoader model_loader;
+                ModelLoader& model_loader = model_manager->loader();
                 if (!model_loader.init_from_file_and_convert_name(file_path, "vae.")) {
                     LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                     return;
                 }
 
-                bool success = model_loader.load_tensors(tensors);
-
-                if (!success) {
-                    LOG_ERROR("load tensors from model loader failed");
+                if (!model_manager->register_runner_params("Wan VAE test",
+                                                           *vae,
+                                                           ModelManager::ResidencyMode::ParamBackend,
+                                                           backend,
+                                                           backend) ||
+                    !model_manager->validate_registered_tensors()) {
+                    LOG_ERROR("register wan vae tensors with model manager failed");
                     return;
                 }
 
diff --git a/otherarch/sdcpp/src/model_loader.cpp b/otherarch/sdcpp/src/model_loader.cpp
index da53772f8..a1788bfbd 100644
--- a/otherarch/sdcpp/src/model_loader.cpp
+++ b/otherarch/sdcpp/src/model_loader.cpp
@@ -1,6 +1,7 @@
 #include <algorithm>
 #include <atomic>
 #include <chrono>
+#include <cinttypes>
 #include <cstdarg>
 #include <cstdlib>
 #include <fstream>
@@ -218,10 +219,28 @@ void convert_tensor(void* src,
 
 /*================================================= ModelLoader ==================================================*/
 
+ModelLoader::ModelLoader()
+    : n_threads_(sd_get_num_physical_cores()) {
+}
+
+size_t ModelLoader::add_file_path(const std::string& file_path) {
+    if (model_files_processed) {
+        file_data.clear();
+        model_files_processed = false;
+    }
+    file_paths_.push_back(file_path);
+    return file_paths_.size() - 1;
+}
+
 void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
     tensor_storage_map[tensor_storage.name] = tensor_storage;
 }
 
+void ModelLoader::set_n_threads(int n_threads) {
+    n_threads_ = n_threads > 0 ? n_threads : sd_get_num_physical_cores();
+    LOG_DEBUG("using %d threads for model loading", n_threads_);
+}
+
 bool ModelLoader::init_from_file(const std::string& file_path, const std::string& prefix) {
     return [&](const std::string& file_path) { // kcpp u8 file path
     if (is_directory(file_path)) {
@@ -287,8 +306,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
         return false;
     }
 
-    file_paths_.push_back(file_path);
-    size_t file_index = file_paths_.size() - 1;
+    size_t file_index = add_file_path(file_path);
 
     for (auto& tensor_storage : tensor_storages) {
         // LOG_DEBUG("%s", tensor_storage.name.c_str());
@@ -316,8 +334,7 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
         return false;
     }
 
-    file_paths_.push_back(file_path);
-    size_t file_index = file_paths_.size() - 1;
+    size_t file_index = add_file_path(file_path);
 
     for (auto& tensor_storage : tensor_storages) {
         if (is_unused_tensor(tensor_storage.name)) {
@@ -353,8 +370,7 @@ bool ModelLoader::init_from_torch_legacy_file(const std::string& file_path, cons
         return false;
     }
 
-    file_paths_.push_back(file_path);
-    size_t file_index = file_paths_.size() - 1;
+    size_t file_index = add_file_path(file_path);
 
     for (auto& tensor_storage : tensor_storages) {
         if (is_unused_tensor(tensor_storage.name)) {
@@ -384,8 +400,7 @@ bool ModelLoader::init_from_torch_zip_file(const std::string& file_path, const s
         return false;
     }
 
-    file_paths_.push_back(file_path);
-    size_t file_index = file_paths_.size() - 1;
+    size_t file_index = add_file_path(file_path);
 
     for (auto& tensor_storage : tensor_storages) {
         if (!starts_with(tensor_storage.name, prefix)) {
@@ -788,8 +803,6 @@ void ModelLoader::process_model_files(bool enable_mmap, bool writable_mmap) {
         return;
     }
 
-    int64_t start_time = ggml_time_ms();
-
     std::vector<TensorStorage> processed_tensor_storages;
     for (const auto& [name, tensor_storage] : tensor_storage_map) {
         if (is_unused_tensor(tensor_storage.name)) {
@@ -840,20 +853,12 @@ void ModelLoader::process_model_files(bool enable_mmap, bool writable_mmap) {
             } else {
                 LOG_WARN("failed to memory-map '%s' (falling back to read())", file_path.c_str());
             }
-        } else if (!is_zip) {
-            LOG_INFO("NOT using mmap for '%s' (mmap disabled by caller)",
-                     file_path.c_str());
         }
 
         file_data.push_back(std::move(fdata));
     }
 
     model_files_processed = true;
-
-    int64_t end_time        = ggml_time_ms();
-    int64_t process_time_ms = end_time - start_time;
-
-    LOG_INFO("model files processing completed in %.2fs", process_time_ms / 1000.f);
 }
 
 std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
@@ -947,7 +952,9 @@ std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggm
     return result;
 }
 
-bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p, bool enable_mmap) {
+bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
+                               bool enable_mmap,
+                               const std::set<std::string>* target_tensor_names) {
     process_model_files(enable_mmap, false);
 
     std::atomic<int64_t> read_time_ms(0);
@@ -956,14 +963,26 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     std::atomic<int64_t> convert_time_ms(0);
     std::atomic<uint64_t> bytes_processed(0);
 
-    int num_threads_to_use = n_threads_p > 0 ? n_threads_p : sd_get_num_physical_cores();
-    LOG_DEBUG("using %d threads for model loading", num_threads_to_use);
+    int num_threads_to_use = n_threads_;
 
     int64_t start_time = ggml_time_ms();
 
     size_t total_tensors_to_process = 0;
+    std::vector<size_t> file_tensors_to_process_counts;
+    file_tensors_to_process_counts.reserve(file_data.size());
     for (const auto& fdata : file_data) {
-        total_tensors_to_process += fdata.tensors.size();
+        size_t file_tensors_to_process = 0;
+        if (target_tensor_names == nullptr) {
+            file_tensors_to_process = fdata.tensors.size();
+        } else {
+            for (const TensorStorage& tensor_storage : fdata.tensors) {
+                if (target_tensor_names->find(tensor_storage.name) != target_tensor_names->end()) {
+                    file_tensors_to_process++;
+                }
+            }
+        }
+        file_tensors_to_process_counts.push_back(file_tensors_to_process);
+        total_tensors_to_process += file_tensors_to_process;
     }
 
     bool success                   = true;
@@ -971,17 +990,38 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     const int64_t t_start          = start_time;
     int last_n_threads             = 1;
 
-    for (auto& fdata : file_data) {
+    for (size_t file_index = 0; file_index < file_data.size(); ++file_index) {
+        auto& fdata                  = file_data[file_index];
         const std::string& file_path = fdata.path;
-        LOG_DEBUG("loading tensors from %s", file_path.c_str());
 
         const std::vector<TensorStorage>& file_tensors = fdata.tensors;
+        std::vector<const TensorStorage*> tensors_to_process;
+        size_t file_tensors_to_process = file_tensors_to_process_counts[file_index];
+        tensors_to_process.reserve(file_tensors_to_process);
+        if (target_tensor_names == nullptr) {
+            for (const TensorStorage& tensor_storage : file_tensors) {
+                tensors_to_process.push_back(&tensor_storage);
+            }
+        } else {
+            for (const TensorStorage& tensor_storage : file_tensors) {
+                if (target_tensor_names->find(tensor_storage.name) != target_tensor_names->end()) {
+                    tensors_to_process.push_back(&tensor_storage);
+                }
+            }
+        }
+        if (tensors_to_process.empty()) {
+            continue;
+        }
+        LOG_DEBUG("loading %zu/%zu tensors from %s",
+                  tensors_to_process.size(),
+                  file_tensors.size(),
+                  file_path.c_str());
 
         bool is_zip = fdata.is_zip;
 
         std::shared_ptr<MmapWrapper> mmapped = fdata.mmapped;
 
-        int n_threads = is_zip ? 1 : std::min(num_threads_to_use, (int)file_tensors.size());
+        int n_threads = is_zip ? 1 : std::min(num_threads_to_use, (int)tensors_to_process.size());
         if (n_threads < 1) {
             n_threads = 1;
         }
@@ -990,6 +1030,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
         std::atomic<size_t> tensor_idx(0);
         std::atomic<bool> failed(false);
         std::vector<std::thread> workers;
+        std::mutex rpc_backend_mutex;
 
         for (int i = 0; i < n_threads; ++i) {
             workers.emplace_back([&, file_path, is_zip]() {
@@ -1017,11 +1058,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                 while (true) {
                     int64_t t0, t1;
                     size_t idx = tensor_idx.fetch_add(1);
-                    if (idx >= file_tensors.size() || failed) {
+                    if (idx >= tensors_to_process.size() || failed) {
                         break;
                     }
 
-                    const TensorStorage& tensor_storage = file_tensors[idx];
+                    const TensorStorage& tensor_storage = *tensors_to_process[idx];
                     ggml_tensor* dst_tensor             = nullptr;
 
                     t0 = ggml_time_ms();
@@ -1146,7 +1187,19 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
 
                     if (dst_tensor->buffer != nullptr && !ggml_backend_buffer_is_host(dst_tensor->buffer)) {
                         t0 = ggml_time_ms();
-                        ggml_backend_tensor_set(dst_tensor, convert_buf, 0, ggml_nbytes(dst_tensor));
+
+                        // RPC backends require serialized access to prevent concurrency issues
+                        const char* buffer_type_name = ggml_backend_buft_name(ggml_backend_buffer_get_type(dst_tensor->buffer));
+                        bool is_rpc_buffer           = buffer_type_name != nullptr &&
+                                             std::string(buffer_type_name).find("RPC") != std::string::npos;
+
+                        if (is_rpc_buffer) {
+                            std::lock_guard<std::mutex> lock(rpc_backend_mutex);
+                            ggml_backend_tensor_set(dst_tensor, convert_buf, 0, ggml_nbytes(dst_tensor));
+                        } else {
+                            ggml_backend_tensor_set(dst_tensor, convert_buf, 0, ggml_nbytes(dst_tensor));
+                        }
+
                         t1 = ggml_time_ms();
                         copy_to_backend_time_ms.fetch_add(t1 - t0);
                     }
@@ -1161,16 +1214,18 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
 
         while (true) {
             size_t current_idx = tensor_idx.load();
-            if (current_idx >= file_tensors.size() || failed) {
+            if (current_idx >= tensors_to_process.size() || failed) {
                 break;
             }
             size_t curr_num       = total_tensors_processed + current_idx;
             float elapsed_seconds = (ggml_time_ms() - t_start) / 1000.0f;
-            pretty_bytes_progress(static_cast<int>(curr_num),
-                                  static_cast<int>(total_tensors_to_process),
-                                  bytes_processed.load(),
-                                  elapsed_seconds);
-            std::this_thread::sleep_for(std::chrono::milliseconds(200));
+            if (total_tensors_to_process > 0) {
+                pretty_bytes_progress(static_cast<int>(curr_num),
+                                      static_cast<int>(total_tensors_to_process),
+                                      bytes_processed.load(),
+                                      elapsed_seconds);
+            }
+            std::this_thread::sleep_for(std::chrono::milliseconds(total_tensors_to_process <= 4 ? 10 : 200));
         }
 
         for (auto& w : workers) {
@@ -1181,12 +1236,14 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
             success = false;
             break;
         }
-        total_tensors_processed += file_tensors.size();
-        pretty_bytes_progress(static_cast<int>(total_tensors_processed),
-                              static_cast<int>(total_tensors_to_process),
-                              bytes_processed.load(),
-                              (ggml_time_ms() - t_start) / 1000.0f);
-        if (total_tensors_processed < total_tensors_to_process) {
+        total_tensors_processed += tensors_to_process.size();
+        if (total_tensors_to_process > 0) {
+            pretty_bytes_progress(static_cast<int>(total_tensors_processed),
+                                  static_cast<int>(total_tensors_to_process),
+                                  bytes_processed.load(),
+                                  (ggml_time_ms() - t_start) / 1000.0f);
+        }
+        if (total_tensors_processed < total_tensors_to_process && total_tensors_to_process > 0) {
             printf("\n");
         }
     }
@@ -1201,9 +1258,77 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     return success;
 }
 
+bool ModelLoader::load_float_tensor(const std::string& name,
+                                    std::vector<float>& data,
+                                    int n_threads,
+                                    bool use_mmap) {
+    data.clear();
+
+    auto tensor_storage_it = tensor_storage_map.find(name);
+    if (tensor_storage_it == tensor_storage_map.end()) {
+        return false;
+    }
+
+    const TensorStorage& tensor_storage = tensor_storage_it->second;
+    int64_t n_elements                  = tensor_storage.nelements();
+    if (n_elements <= 0) {
+        LOG_ERROR("tensor '%s' has invalid element count: %" PRId64, name.c_str(), n_elements);
+        return false;
+    }
+    if (tensor_storage.n_dims <= 0 || tensor_storage.n_dims > GGML_MAX_DIMS) {
+        LOG_ERROR("tensor '%s' has unsupported dims: %d", name.c_str(), tensor_storage.n_dims);
+        return false;
+    }
+
+    std::vector<float> loaded_data(static_cast<size_t>(n_elements));
+    ggml_init_params params;
+    params.mem_size   = ggml_tensor_overhead();
+    params.mem_buffer = nullptr;
+    params.no_alloc   = true;
+
+    ggml_context* ctx = ggml_init(params);
+    if (ctx == nullptr) {
+        LOG_ERROR("failed to create context for tensor '%s'", name.c_str());
+        return false;
+    }
+
+    ggml_tensor* tensor = ggml_new_tensor(ctx, GGML_TYPE_F32, tensor_storage.n_dims, tensor_storage.ne);
+    ggml_set_name(tensor, name.c_str());
+    tensor->data = loaded_data.data();
+
+    bool loaded           = false;
+    auto on_new_tensor_cb = [&](const TensorStorage& current_tensor_storage, ggml_tensor** dst_tensor) -> bool {
+        *dst_tensor = nullptr;
+        if (current_tensor_storage.name != name) {
+            return true;
+        }
+        if (current_tensor_storage.nelements() != n_elements) {
+            LOG_ERROR("tensor '%s' element count changed during load", name.c_str());
+            return false;
+        }
+        *dst_tensor = tensor;
+        loaded      = true;
+        return true;
+    };
+
+    std::set<std::string> target_tensor_names{name};
+    if (n_threads > 0) {
+        set_n_threads(n_threads);
+    }
+    bool success = load_tensors(on_new_tensor_cb, use_mmap, &target_tensor_names);
+    ggml_free(ctx);
+
+    if (!success || !loaded) {
+        data.clear();
+        return false;
+    }
+
+    data = std::move(loaded_data);
+    return true;
+}
+
 bool ModelLoader::load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                                std::set<std::string> ignore_tensors,
-                               int n_threads,
                                bool enable_mmap) {
     std::set<std::string> tensor_names_in_file;
     std::mutex tensor_names_mutex;
@@ -1247,7 +1372,7 @@ bool ModelLoader::load_tensors(std::map<std::string, ggml_tensor*>& tensors,
         return true;
     };
 
-    bool success = load_tensors(on_new_tensor_cb, n_threads, enable_mmap);
+    bool success = load_tensors(on_new_tensor_cb, enable_mmap);
     if (!success) {
         LOG_ERROR("load tensors from file failed");
         return false;
diff --git a/otherarch/sdcpp/src/model_loader.h b/otherarch/sdcpp/src/model_loader.h
index 3863116f3..0d894a1a2 100644
--- a/otherarch/sdcpp/src/model_loader.h
+++ b/otherarch/sdcpp/src/model_loader.h
@@ -34,7 +34,9 @@ protected:
     std::vector<ModelFileData> file_data;
     bool model_files_processed = false;
     String2TensorStorage tensor_storage_map;
+    int n_threads_;
 
+    size_t add_file_path(const std::string& file_path);
     void add_tensor_storage(const TensorStorage& tensor_storage);
 
     bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
@@ -44,6 +46,8 @@ protected:
     bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
 
 public:
+    ModelLoader();
+
     bool init_from_file(const std::string& file_path, const std::string& prefix = "");
     void convert_tensors_name();
     bool init_from_file_and_convert_name(const std::string& file_path,
@@ -55,16 +59,23 @@ public:
     std::map<ggml_type, uint32_t> get_diffusion_model_wtype_stat();
     std::map<ggml_type, uint32_t> get_vae_wtype_stat();
     String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
+    const String2TensorStorage& get_tensor_storage_map() const { return tensor_storage_map; }
+    void set_n_threads(int n_threads);
     void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
     void process_model_files(bool enable_mmap = false, bool writable_mmap = true);
     std::vector<MmapTensorStore> mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
                                               std::set<std::string> ignore_tensors = {},
                                               bool writable                        = true);
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
+                      bool use_mmap                                    = false,
+                      const std::set<std::string>* target_tensor_names = nullptr);
     bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
-                      int n_threads                        = 0,
                       bool use_mmap                        = false);
+    bool load_float_tensor(const std::string& name,
+                           std::vector<float>& data,
+                           int n_threads = 0,
+                           bool use_mmap = false);
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;
diff --git a/otherarch/sdcpp/src/model_manager.cpp b/otherarch/sdcpp/src/model_manager.cpp
new file mode 100644
index 000000000..5287e1069
--- /dev/null
+++ b/otherarch/sdcpp/src/model_manager.cpp
@@ -0,0 +1,939 @@
+#include "model_manager.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <iterator>
+#include <mutex>
+#include <unordered_set>
+
+#include "core/ggml_extend_backend.h"
+#include "core/util.h"
+#include "model/adapter/lora.hpp"
+
+static size_t aligned_offset(const void* buffer, size_t offset, size_t alignment) {
+    GGML_ASSERT(alignment != 0 && (alignment & (alignment - 1)) == 0);
+    size_t align = (alignment - ((reinterpret_cast<uintptr_t>(buffer) + offset) % alignment)) % alignment;
+    return offset + align;
+}
+
+static bool lora_specs_equal(const std::vector<ModelManager::LoraSpec>& lhs,
+                             const std::vector<ModelManager::LoraSpec>& rhs) {
+    if (lhs.size() != rhs.size()) {
+        return false;
+    }
+    for (size_t i = 0; i < lhs.size(); ++i) {
+        if (lhs[i].path != rhs[i].path ||
+            lhs[i].multiplier != rhs[i].multiplier ||
+            lhs[i].is_high_noise != rhs[i].is_high_noise ||
+            lhs[i].tensor_name_prefix_filter != rhs[i].tensor_name_prefix_filter ||
+            lhs[i].required != rhs[i].required) {
+            return false;
+        }
+    }
+    return true;
+}
+
+static std::string lora_id(const ModelManager::LoraSpec& lora) {
+    return lora.is_high_noise ? "|high_noise|" + lora.path : lora.path;
+}
+
+static bool backend_supports_host_buffer(ggml_backend_t backend) {
+    if (backend == nullptr) {
+        return false;
+    }
+    if (sd_backend_is_cpu(backend)) {
+        return true;
+    }
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    if (dev == nullptr) {
+        return false;
+    }
+    ggml_backend_dev_props props;
+    ggml_backend_dev_get_props(dev, &props);
+    return props.caps.buffer_from_host_ptr;
+}
+
+ModelManager::~ModelManager() {
+    release_all();
+}
+
+void ModelManager::set_common_ignore_tensors(std::set<std::string> ignore_tensors) {
+    common_ignore_tensors_ = std::move(ignore_tensors);
+}
+
+void ModelManager::set_loras(std::vector<LoraSpec> loras, SDVersion version) {
+    if (loras.empty() && loras_.empty()) {
+        lora_version_ = version;
+        return;
+    }
+    if (lora_version_ == version && lora_specs_equal(loras_, loras)) {
+        return;
+    }
+
+    loras_        = std::move(loras);
+    lora_version_ = version;
+    current_lora_epoch_++;
+    reset_lora_applied_params();
+}
+
+std::set<std::string> ModelManager::tensor_names() const {
+    std::set<std::string> names;
+    for (const auto& state : tensor_states_) {
+        if (state != nullptr) {
+            names.insert(state->name);
+        }
+    }
+    return names;
+}
+
+size_t estimate_tensors_size(const std::map<std::string, ggml_tensor*>& tensors) {
+    size_t size = 0;
+    std::unordered_set<ggml_tensor*> seen;
+    for (const auto& pair : tensors) {
+        ggml_tensor* tensor = pair.second;
+        if (tensor == nullptr || seen.find(tensor) != seen.end()) {
+            continue;
+        }
+        seen.insert(tensor);
+        size += ggml_nbytes(tensor);
+    }
+    return size;
+}
+
+bool ModelManager::register_param_tensors(const std::string& desc,
+                                          std::map<std::string, ggml_tensor*> tensors,
+                                          ResidencyMode residency_mode,
+                                          ggml_backend_t compute_backend,
+                                          ggml_backend_t params_backend,
+                                          size_t* registered_tensor_size) {
+    if (desc.empty()) {
+        LOG_ERROR("model manager tensor desc is empty");
+        return false;
+    }
+    if (registered_tensor_size != nullptr) {
+        *registered_tensor_size += estimate_tensors_size(tensors);
+    }
+
+    std::vector<std::unique_ptr<TensorState>> new_states;
+    new_states.reserve(tensors.size());
+
+    for (const auto& pair : tensors) {
+        const std::string& name = pair.first;
+        ggml_tensor* tensor     = pair.second;
+        if (tensor == nullptr) {
+            continue;
+        }
+        if (tensor_states_by_name_.find(name) != tensor_states_by_name_.end()) {
+            LOG_ERROR("model manager tensor name '%s' is already registered", name.c_str());
+            return false;
+        }
+        ggml_set_name(tensor, name.c_str());
+
+        auto state             = std::make_unique<TensorState>();
+        state->name            = name;
+        state->tensor          = tensor;
+        state->desc            = desc;
+        state->residency_mode  = residency_mode;
+        state->compute_backend = compute_backend;
+        state->params_backend  = params_backend;
+        new_states.push_back(std::move(state));
+    }
+
+    for (auto& state : new_states) {
+        TensorState* registered_state                  = state.get();
+        tensor_states_by_name_[registered_state->name] = registered_state;
+        tensor_states_.push_back(std::move(state));
+    }
+    return true;
+}
+
+bool ModelManager::validate_registered_tensors() {
+    bool ok = true;
+    for (const auto& state : tensor_states_) {
+        if (state == nullptr) {
+            ok = false;
+            continue;
+        }
+        bool state_ok = validate_tensor(*state);
+        if (state_ok) {
+            state->metadata_validated = true;
+        }
+        ok = state_ok && ok;
+    }
+    return ok;
+}
+
+bool ModelManager::load_tensors_to_params_backend(const std::vector<TensorState*>& states) {
+    std::vector<TensorState*> need_load;
+    need_load.reserve(states.size());
+    for (TensorState* state : states) {
+        if (state == nullptr || should_ignore(*state) || is_optional_missing_tensor(state->name)) {
+            continue;
+        }
+        if (!state->metadata_validated) {
+            if (!validate_tensor(*state)) {
+                return false;
+            }
+            state->metadata_validated = true;
+        }
+        if (!state->loaded_to_params_backend) {
+            need_load.push_back(state);
+        }
+    }
+    if (need_load.empty()) {
+        return true;
+    }
+
+    std::vector<ParamsStorageBlock*> created_storage_blocks;
+    if (!mmap_params(need_load, created_storage_blocks)) {
+        for (ParamsStorageBlock* block : created_storage_blocks) {
+            if (block != nullptr) {
+                free_params_storage_block(*block);
+                erase_params_storage_block(block);
+            }
+        }
+        return false;
+    }
+
+    std::vector<TensorState*> need_alloc;
+    need_alloc.reserve(need_load.size());
+    for (TensorState* state : need_load) {
+        if (state->tensor != nullptr && state->tensor->data == nullptr && state->tensor->view_src == nullptr) {
+            need_alloc.push_back(state);
+        }
+    }
+
+    if (!alloc_params_buffers(need_alloc, created_storage_blocks) ||
+        !load_tensors(need_load)) {
+        for (ParamsStorageBlock* block : created_storage_blocks) {
+            if (block != nullptr) {
+                free_params_storage_block(*block);
+                erase_params_storage_block(block);
+            }
+        }
+        return false;
+    }
+    for (ParamsStorageBlock* block : created_storage_blocks) {
+        if (block != nullptr && block->buffer != nullptr) {
+            LOG_DEBUG("model manager prepared params backend buffer (%6.2f MB, %zu tensors, %s)",
+                      ggml_backend_buffer_get_size(block->buffer) / (1024.f * 1024.f),
+                      block->states.size(),
+                      ggml_backend_buffer_is_host(block->buffer) ? "RAM" : "VRAM");
+        }
+    }
+
+    return true;
+}
+
+bool ModelManager::stage_tensors_to_compute_backend(const std::vector<TensorState*>& states) {
+    std::map<ggml_backend_t, std::vector<TensorState*>> states_by_compute_backend;
+    for (TensorState* state : states) {
+        if (state == nullptr || should_ignore(*state) || is_optional_missing_tensor(state->name)) {
+            continue;
+        }
+        if (state->compute_backend == nullptr) {
+            LOG_ERROR("model manager compute backend is null for tensor '%s'", state->name.c_str());
+            return false;
+        }
+        if (state->params_backend == nullptr) {
+            LOG_ERROR("model manager params backend is null for tensor '%s'", state->name.c_str());
+            return false;
+        }
+        if (state->compute_backend == state->params_backend || state->staged_to_compute_backend) {
+            continue;
+        }
+        if (!state->loaded_to_params_backend || state->tensor == nullptr || state->tensor->data == nullptr) {
+            LOG_ERROR("model manager tensor '%s' is not loaded to params backend", state->name.c_str());
+            return false;
+        }
+        states_by_compute_backend[state->compute_backend].push_back(state);
+    }
+
+    for (const auto& pair : states_by_compute_backend) {
+        ggml_backend_t compute_backend          = pair.first;
+        const std::vector<TensorState*>& states = pair.second;
+        if (states.empty()) {
+            continue;
+        }
+
+        int64_t t0 = ggml_time_ms();
+
+        ggml_init_params init_params;
+        init_params.mem_size   = std::max<size_t>(1, states.size()) * ggml_tensor_overhead();
+        init_params.mem_buffer = nullptr;
+        init_params.no_alloc   = true;
+
+        ggml_context* staging_ctx = ggml_init(init_params);
+        GGML_ASSERT(staging_ctx != nullptr);
+
+        std::vector<std::pair<TensorState*, ggml_tensor*>> staged_tensors;
+        staged_tensors.reserve(states.size());
+        for (TensorState* state : states) {
+            ggml_tensor* staging_tensor = ggml_dup_tensor(staging_ctx, state->tensor);
+            ggml_set_name(staging_tensor, state->tensor->name);
+            staged_tensors.push_back({state, staging_tensor});
+        }
+
+        ggml_backend_buffer_t compute_buffer = ggml_backend_alloc_ctx_tensors(staging_ctx, compute_backend);
+        if (compute_buffer == nullptr) {
+            LOG_ERROR("model manager alloc compute params backend buffer failed, num_tensors = %zu",
+                      staged_tensors.size());
+            ggml_free(staging_ctx);
+            return false;
+        }
+        ggml_backend_buffer_set_usage(compute_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+
+        for (auto& staged_tensor : staged_tensors) {
+            TensorState* state          = staged_tensor.first;
+            ggml_tensor* managed_tensor = state->tensor;
+            ggml_tensor* staging_tensor = staged_tensor.second;
+            ggml_backend_tensor_copy(managed_tensor, staging_tensor);
+            std::swap(managed_tensor->buffer, staging_tensor->buffer);
+            std::swap(managed_tensor->data, staging_tensor->data);
+            std::swap(managed_tensor->extra, staging_tensor->extra);
+        }
+        ggml_backend_synchronize(compute_backend);
+
+        auto block             = std::make_unique<ComputeStagingBlock>();
+        block->compute_backend = compute_backend;
+        block->buffer          = compute_buffer;
+        block->staging_ctx     = staging_ctx;
+        block->staged_tensors  = std::move(staged_tensors);
+        for (auto& staged_tensor : block->staged_tensors) {
+            TensorState* state               = staged_tensor.first;
+            state->staged_to_compute_backend = true;
+        }
+        compute_staging_blocks_.push_back(std::move(block));
+
+        int64_t t1 = ggml_time_ms();
+        LOG_DEBUG("model manager staged compute params (%6.2f MB, %zu tensors) to %s, taking %.2fs",
+                  ggml_backend_buffer_get_size(compute_buffer) / (1024.f * 1024.f),
+                  states.size(),
+                  ggml_backend_name(compute_backend),
+                  (t1 - t0) * 1.0f / 1000);
+    }
+
+    return true;
+}
+
+bool ModelManager::apply_loras_to_params(const std::vector<TensorState*>& states) {
+    if (loras_.empty()) {
+        return true;
+    }
+
+    struct LoraApplyGroup {
+        std::map<std::string, ggml_tensor*> model_tensors;
+        std::vector<TensorState*> states;
+    };
+
+    std::map<ggml_backend_t, LoraApplyGroup> groups;
+    for (TensorState* state : states) {
+        if (state == nullptr || state->tensor == nullptr ||
+            should_ignore(*state) || is_optional_missing_tensor(state->name)) {
+            continue;
+        }
+        if (state->applied_lora_epoch == current_lora_epoch_) {
+            continue;
+        }
+        if (state->compute_backend == nullptr) {
+            LOG_ERROR("model manager compute backend is null for lora target tensor '%s'", state->name.c_str());
+            return false;
+        }
+        if (state->tensor->data == nullptr) {
+            LOG_ERROR("model manager lora target tensor '%s' is not prepared", state->name.c_str());
+            return false;
+        }
+        LoraApplyGroup& group            = groups[state->compute_backend];
+        group.model_tensors[state->name] = state->tensor;
+        group.states.push_back(state);
+    }
+
+    if (groups.empty()) {
+        return true;
+    }
+
+    std::set<std::string> all_tensor_names = tensor_names();
+    for (auto& group_pair : groups) {
+        ggml_backend_t compute_backend = group_pair.first;
+        LoraApplyGroup& group          = group_pair.second;
+        for (const LoraSpec& lora_spec : loras_) {
+            if (group.model_tensors.empty()) {
+                continue;
+            }
+
+            std::string id = lora_id(lora_spec);
+            auto lora      = std::make_shared<LoraModel>(id,
+                                                    compute_backend,
+                                                    compute_backend,
+                                                    lora_spec.path,
+                                                    lora_spec.is_high_noise ? "model.high_noise_" : "",
+                                                    lora_version_);
+
+            LoraModel::filter_t lora_tensor_filter = nullptr;
+            if (!lora_spec.tensor_name_prefix_filter.empty()) {
+                lora_tensor_filter = [&](const std::string& tensor_name) {
+                    return starts_with(tensor_name, lora_spec.tensor_name_prefix_filter);
+                };
+            }
+            if (!lora->load_from_file(n_threads_, lora_tensor_filter)) {
+                LOG_WARN("load lora tensors from %s failed", lora_spec.path.c_str());
+                if (lora_spec.required) {
+                    return false;
+                }
+                continue;
+            }
+            if (lora->lora_tensors.empty()) {
+                if (lora_spec.required) {
+                    LOG_ERROR("required lora has no tensors: %s", lora_spec.path.c_str());
+                    return false;
+                }
+                continue;
+            }
+            lora->multiplier = lora_spec.multiplier;
+            lora->apply(group.model_tensors, all_tensor_names, lora_version_, n_threads_, false);
+            lora->release_loaded_tensors();
+        }
+
+        for (TensorState* state : group.states) {
+            if (state != nullptr) {
+                state->applied_lora_epoch = current_lora_epoch_;
+            }
+        }
+    }
+    return true;
+}
+
+void ModelManager::reset_lora_applied_params() {
+    release_compute_staging_blocks(true);
+    release_params_storage_blocks(true);
+    for (auto& state : tensor_states_) {
+        state->applied_lora_epoch = UINT64_MAX;
+    }
+}
+
+bool ModelManager::should_ignore(const TensorState& state) const {
+    for (const auto& ignore_prefix : common_ignore_tensors_) {
+        if (starts_with(state.name, ignore_prefix)) {
+            return true;
+        }
+    }
+    return false;
+}
+
+bool ModelManager::is_optional_missing_tensor(const std::string& name) const {
+    return name.find("cond_stage_model.transformer.text_model.encoder.layers.23") != std::string::npos ||
+           name.find("alphas_cumprod") != std::string::npos;
+}
+
+bool ModelManager::validate_tensor(const TensorState& state) const {
+    if (state.tensor == nullptr || should_ignore(state) || is_optional_missing_tensor(state.name)) {
+        return true;
+    }
+
+    const auto& tensor_storage_map = model_loader_.get_tensor_storage_map();
+    auto ts_it                     = tensor_storage_map.find(state.name);
+    if (ts_it == tensor_storage_map.end()) {
+        LOG_ERROR("%s tensor '%s' not in model metadata", state.desc.c_str(), state.name.c_str());
+        return false;
+    }
+
+    const TensorStorage& tensor_storage = ts_it->second;
+    if (state.tensor->ne[0] != tensor_storage.ne[0] ||
+        state.tensor->ne[1] != tensor_storage.ne[1] ||
+        state.tensor->ne[2] != tensor_storage.ne[2] ||
+        state.tensor->ne[3] != tensor_storage.ne[3]) {
+        LOG_ERROR(
+            "%s tensor '%s' has wrong shape in model metadata: got [%d, %d, %d, %d], expected [%d, %d, %d, %d]",
+            state.desc.c_str(),
+            state.name.c_str(),
+            (int)tensor_storage.ne[0], (int)tensor_storage.ne[1], (int)tensor_storage.ne[2], (int)tensor_storage.ne[3],
+            (int)state.tensor->ne[0], (int)state.tensor->ne[1], (int)state.tensor->ne[2], (int)state.tensor->ne[3]);
+        return false;
+    }
+    return true;
+}
+
+bool ModelManager::mmap_params(const std::vector<TensorState*>& states,
+                               std::vector<ParamsStorageBlock*>& created_storage_blocks) {
+    std::map<std::string, ggml_tensor*> mmap_candidates;
+    std::map<std::string, TensorState*> mmap_states;
+    for (TensorState* state : states) {
+        if (state == nullptr || !can_mmap_storage(*state) || state->tensor == nullptr ||
+            state->tensor->data != nullptr || state->tensor->view_src != nullptr) {
+            continue;
+        }
+        mmap_candidates[state->name] = state->tensor;
+        mmap_states[state->name]     = state;
+    }
+    if (mmap_candidates.empty()) {
+        return true;
+    }
+
+    auto mmap_store = model_loader_.mmap_tensors(mmap_candidates, {}, true);
+    if (mmap_store.empty()) {
+        return true;
+    }
+
+    auto block                = std::make_unique<ParamsStorageBlock>();
+    block->mmap_tensor_stores = std::move(mmap_store);
+    ParamsStorageBlock* raw   = block.get();
+    for (const auto& pair : mmap_states) {
+        TensorState* state = pair.second;
+        if (state != nullptr && state->tensor != nullptr && state->tensor->data != nullptr) {
+            block->states.push_back(state);
+        }
+    }
+
+    if (!block->states.empty()) {
+        params_storage_blocks_.push_back(std::move(block));
+        created_storage_blocks.push_back(raw);
+    }
+    return true;
+}
+
+bool ModelManager::can_mmap_storage(const TensorState& state) const {
+    if (!enable_mmap_ || state.residency_mode != ResidencyMode::ParamBackend) {
+        return false;
+    }
+    if (state.compute_backend == nullptr || state.params_backend == nullptr) {
+        return false;
+    }
+    return sd_backend_is_cpu(state.compute_backend) ||
+           sd_backend_is_cpu(state.params_backend) ||
+           backend_supports_host_buffer(state.compute_backend);
+}
+
+bool ModelManager::alloc_params_buffers(const std::vector<TensorState*>& states,
+                                        std::vector<ParamsStorageBlock*>& created_storage_blocks) {
+    std::map<std::pair<ggml_backend_buffer_type_t, int>, std::vector<TensorState*>> states_by_buffer_type;
+    for (TensorState* state : states) {
+        if (state == nullptr || state->tensor == nullptr) {
+            continue;
+        }
+        ggml_backend_buffer_type_t params_buft = params_buffer_type_for(*state);
+        if (params_buft == nullptr) {
+            return false;
+        }
+        states_by_buffer_type[{params_buft, static_cast<int>(state->residency_mode)}].push_back(state);
+    }
+
+    for (const auto& pair : states_by_buffer_type) {
+        ggml_backend_buffer_type_t params_buft  = pair.first.first;
+        const std::vector<TensorState*>& states = pair.second;
+        size_t alignment                        = ggml_backend_buft_get_alignment(params_buft);
+        size_t max_size                         = ggml_backend_buft_get_max_size(params_buft);
+
+        auto alloc_chunk = [&](const std::vector<TensorState*>& chunk, size_t chunk_size) -> bool {
+            if (chunk.empty() || chunk_size == 0) {
+                return true;
+            }
+
+            ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(params_buft, chunk_size);
+            if (buffer == nullptr) {
+                LOG_ERROR("model manager alloc params backend buffer failed, size = %.2fMB",
+                          chunk_size / (1024.0 * 1024.0));
+                return false;
+            }
+            ggml_backend_buffer_set_usage(buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+
+            std::vector<ggml_tensor*> initialized_tensors;
+            void* base    = ggml_backend_buffer_get_base(buffer);
+            size_t offset = aligned_offset(base, 0, ggml_backend_buffer_get_alignment(buffer));
+            for (TensorState* state : chunk) {
+                ggml_tensor* tensor     = state->tensor;
+                size_t tensor_size      = GGML_PAD(ggml_backend_buffer_get_alloc_size(buffer, tensor),
+                                                   ggml_backend_buffer_get_alignment(buffer));
+                enum ggml_status status = ggml_backend_tensor_alloc(buffer, tensor, static_cast<char*>(base) + offset);
+                if (status != GGML_STATUS_SUCCESS) {
+                    LOG_ERROR("model manager failed to initialize params tensor '%s'", ggml_get_name(tensor));
+                    for (ggml_tensor* initialized : initialized_tensors) {
+                        initialized->buffer = nullptr;
+                        initialized->data   = nullptr;
+                        initialized->extra  = nullptr;
+                    }
+                    LOG_DEBUG("model manager releasing params backend buffer (%6.2f MB, %zu tensors, %s)",
+                              ggml_backend_buffer_get_size(buffer) / (1024.f * 1024.f),
+                              initialized_tensors.size(),
+                              ggml_backend_buffer_is_host(buffer) ? "RAM" : "VRAM");
+                    ggml_backend_buffer_free(buffer);
+                    return false;
+                }
+                initialized_tensors.push_back(tensor);
+                offset += tensor_size;
+            }
+
+            auto block              = std::make_unique<ParamsStorageBlock>();
+            block->buffer           = buffer;
+            block->states           = chunk;
+            ParamsStorageBlock* raw = block.get();
+            params_storage_blocks_.push_back(std::move(block));
+            created_storage_blocks.push_back(raw);
+
+            return true;
+        };
+
+        std::vector<TensorState*> chunk;
+        size_t chunk_size = 0;
+        for (TensorState* state : states) {
+            ggml_tensor* tensor = state->tensor;
+            size_t tensor_size  = GGML_PAD(ggml_backend_buft_get_alloc_size(params_buft, tensor), alignment);
+            // Some backends, e.g. Vulkan, report a preferred chunk size here rather than a
+            // hard per-tensor allocation limit. Oversized tensors are allocated alone.
+            if (!chunk.empty() && max_size > 0 && chunk_size + tensor_size > max_size) {
+                if (!alloc_chunk(chunk, chunk_size)) {
+                    return false;
+                }
+                chunk.clear();
+                chunk_size = 0;
+            }
+            chunk.push_back(state);
+            chunk_size += tensor_size;
+        }
+
+        if (!alloc_chunk(chunk, chunk_size)) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+bool ModelManager::load_tensors(const std::vector<TensorState*>& states) {
+    std::map<std::string, TensorState*> states_by_name;
+    std::set<std::string> target_tensor_names;
+    for (TensorState* state : states) {
+        if (state == nullptr) {
+            continue;
+        }
+        states_by_name[state->name] = state;
+        target_tensor_names.insert(state->name);
+    }
+    if (states_by_name.empty()) {
+        return true;
+    }
+
+    std::set<std::string> loaded_names;
+    std::mutex loaded_names_mutex;
+    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
+        const std::string& name = tensor_storage.name;
+        *dst_tensor             = nullptr;
+
+        auto state_it = states_by_name.find(name);
+        if (state_it == states_by_name.end()) {
+            return true;
+        }
+
+        TensorState* state = state_it->second;
+        if (state == nullptr || state->tensor == nullptr) {
+            LOG_ERROR("model manager tensor '%s' is null", name.c_str());
+            return false;
+        }
+
+        if (state->tensor->ne[0] != tensor_storage.ne[0] ||
+            state->tensor->ne[1] != tensor_storage.ne[1] ||
+            state->tensor->ne[2] != tensor_storage.ne[2] ||
+            state->tensor->ne[3] != tensor_storage.ne[3]) {
+            LOG_ERROR(
+                "model manager tensor '%s' has wrong shape in model file: got [%d, %d, %d, %d], expected [%d, %d, %d, %d]",
+                name.c_str(),
+                (int)tensor_storage.ne[0], (int)tensor_storage.ne[1], (int)tensor_storage.ne[2], (int)tensor_storage.ne[3],
+                (int)state->tensor->ne[0], (int)state->tensor->ne[1], (int)state->tensor->ne[2], (int)state->tensor->ne[3]);
+            return false;
+        }
+
+        {
+            std::lock_guard<std::mutex> lock(loaded_names_mutex);
+            loaded_names.insert(name);
+        }
+        *dst_tensor = state->tensor;
+        return true;
+    };
+
+    if (!model_loader_.load_tensors(on_new_tensor_cb, enable_mmap_, &target_tensor_names)) {
+        LOG_ERROR("model manager load tensors failed");
+        return false;
+    }
+
+    bool missing = false;
+    for (const auto& pair : states_by_name) {
+        const std::string& name = pair.first;
+        if (loaded_names.find(name) == loaded_names.end()) {
+            LOG_ERROR("model manager tensor '%s' was not loaded", name.c_str());
+            missing = true;
+        }
+    }
+    if (missing) {
+        return false;
+    }
+
+    for (const auto& pair : states_by_name) {
+        pair.second->loaded_to_params_backend = true;
+    }
+    return true;
+}
+
+ggml_backend_buffer_type_t ModelManager::params_buffer_type_for(const TensorState& state) const {
+    if (state.params_backend == nullptr) {
+        LOG_ERROR("model manager params backend is null for tensor '%s'", state.name.c_str());
+        return nullptr;
+    }
+    ggml_backend_buffer_type_t params_buft = nullptr;
+    if (state.compute_backend != nullptr && state.params_backend != state.compute_backend) {
+        ggml_backend_dev_t compute_dev = ggml_backend_get_device(state.compute_backend);
+        if (compute_dev != nullptr) {
+            params_buft = ggml_backend_dev_host_buffer_type(compute_dev);
+        }
+    }
+    if (params_buft == nullptr) {
+        params_buft = ggml_backend_get_default_buffer_type(state.params_backend);
+    }
+    return params_buft;
+}
+
+void ModelManager::free_compute_staging_block(ComputeStagingBlock& block) {
+    for (auto& staged_tensor : block.staged_tensors) {
+        TensorState* state          = staged_tensor.first;
+        ggml_tensor* staging_tensor = staged_tensor.second;
+        if (state == nullptr || state->tensor == nullptr || staging_tensor == nullptr) {
+            continue;
+        }
+        ggml_tensor* managed_tensor = state->tensor;
+        managed_tensor->buffer      = staging_tensor->buffer;
+        managed_tensor->data        = staging_tensor->data;
+        managed_tensor->extra       = staging_tensor->extra;
+        staging_tensor->buffer      = nullptr;
+        staging_tensor->data        = nullptr;
+        staging_tensor->extra       = nullptr;
+
+        state->staged_to_compute_backend = false;
+        state->applied_lora_epoch        = UINT64_MAX;
+    }
+
+    if (block.buffer != nullptr) {
+        LOG_DEBUG("model manager releasing compute params (%6.2f MB, %zu tensors) from %s",
+                  ggml_backend_buffer_get_size(block.buffer) / (1024.f * 1024.f),
+                  block.staged_tensors.size(),
+                  block.compute_backend != nullptr ? ggml_backend_name(block.compute_backend) : "unknown");
+        ggml_backend_buffer_free(block.buffer);
+        block.buffer = nullptr;
+    }
+    if (block.staging_ctx != nullptr) {
+        ggml_free(block.staging_ctx);
+        block.staging_ctx = nullptr;
+    }
+    block.staged_tensors.clear();
+}
+
+void ModelManager::release_compute_staging_blocks(bool force,
+                                                  const std::unordered_set<TensorState*>* target_states) {
+    for (auto it = compute_staging_blocks_.begin(); it != compute_staging_blocks_.end();) {
+        ComputeStagingBlock* block = it->get();
+        bool can_release           = force;
+        if (!can_release) {
+            can_release = std::all_of(block->staged_tensors.begin(),
+                                      block->staged_tensors.end(),
+                                      [target_states](const std::pair<TensorState*, ggml_tensor*>& pair) {
+                                          TensorState* state = pair.first;
+                                          if (state == nullptr) {
+                                              return true;
+                                          }
+                                          if (target_states != nullptr &&
+                                              target_states->find(state) == target_states->end()) {
+                                              return false;
+                                          }
+                                          return state->active_prepare_count == 0;
+                                      });
+        }
+
+        if (can_release) {
+            free_compute_staging_block(*block);
+            it = compute_staging_blocks_.erase(it);
+        } else {
+            ++it;
+        }
+    }
+}
+
+void ModelManager::free_params_storage_block(ParamsStorageBlock& block) {
+    if (block.buffer != nullptr) {
+        LOG_DEBUG("model manager releasing params backend buffer (%6.2f MB, %zu tensors, %s)",
+                  ggml_backend_buffer_get_size(block.buffer) / (1024.f * 1024.f),
+                  block.states.size(),
+                  ggml_backend_buffer_is_host(block.buffer) ? "RAM" : "VRAM");
+        ggml_backend_buffer_free(block.buffer);
+        block.buffer = nullptr;
+    }
+    block.mmap_tensor_stores.clear();
+
+    for (TensorState* state : block.states) {
+        if (state == nullptr || state->tensor == nullptr) {
+            continue;
+        }
+        state->tensor->buffer = nullptr;
+        state->tensor->data   = nullptr;
+        state->tensor->extra  = nullptr;
+
+        state->loaded_to_params_backend = false;
+        state->applied_lora_epoch       = UINT64_MAX;
+    }
+    block.states.clear();
+}
+
+void ModelManager::release_params_storage_blocks(bool force,
+                                                 const std::unordered_set<TensorState*>* target_states) {
+    for (auto it = params_storage_blocks_.begin(); it != params_storage_blocks_.end();) {
+        ParamsStorageBlock* block = it->get();
+        bool can_release          = force;
+        if (!can_release) {
+            can_release = std::all_of(block->states.begin(),
+                                      block->states.end(),
+                                      [target_states](TensorState* state) {
+                                          if (state == nullptr) {
+                                              return true;
+                                          }
+                                          if (target_states != nullptr &&
+                                              target_states->find(state) == target_states->end()) {
+                                              return false;
+                                          }
+                                          return state->active_prepare_count == 0 &&
+                                                 !state->staged_to_compute_backend &&
+                                                 state->residency_mode == ResidencyMode::Disk;
+                                      });
+        }
+
+        if (can_release) {
+            free_params_storage_block(*block);
+            it = params_storage_blocks_.erase(it);
+        } else {
+            ++it;
+        }
+    }
+}
+
+void ModelManager::erase_params_storage_block(ParamsStorageBlock* block) {
+    auto it = std::find_if(params_storage_blocks_.begin(),
+                           params_storage_blocks_.end(),
+                           [block](const std::unique_ptr<ParamsStorageBlock>& item) {
+                               return item.get() == block;
+                           });
+    if (it != params_storage_blocks_.end()) {
+        params_storage_blocks_.erase(it);
+    }
+}
+
+void ModelManager::release_all() {
+    for (auto& state : tensor_states_) {
+        state->active_prepare_count = 0;
+        state->applied_lora_epoch   = UINT64_MAX;
+    }
+    release_compute_staging_blocks(true);
+    release_params_storage_blocks(true);
+}
+
+bool ModelManager::resolve_required_tensor_states(const std::vector<ggml_tensor*>& tensors,
+                                                  std::vector<TensorState*>& required_states) const {
+    required_states.clear();
+    std::unordered_set<TensorState*> seen;
+    for (ggml_tensor* tensor : tensors) {
+        if (tensor == nullptr) {
+            continue;
+        }
+        const char* raw_name = ggml_get_name(tensor);
+        if (raw_name == nullptr || raw_name[0] == '\0') {
+            LOG_ERROR("model manager unnamed tensor is not registered");
+            return false;
+        }
+        auto state_it = tensor_states_by_name_.find(raw_name);
+        if (state_it == tensor_states_by_name_.end()) {
+            LOG_ERROR("model manager tensor '%s' is not registered", raw_name);
+            return false;
+        }
+        TensorState* state = state_it->second;
+        if (state == nullptr) {
+            LOG_ERROR("model manager tensor '%s' has no tensor state", raw_name);
+            return false;
+        }
+        if (seen.insert(state).second) {
+            required_states.push_back(state);
+        }
+    }
+    return true;
+}
+
+bool ModelManager::prepare_params(const std::vector<ggml_tensor*>& tensors) {
+    if (tensors.empty()) {
+        return true;
+    }
+
+    std::vector<TensorState*> required_states;
+    if (!resolve_required_tensor_states(tensors, required_states)) {
+        return false;
+    }
+
+    if (!load_tensors_to_params_backend(required_states)) {
+        return false;
+    }
+
+    if (!stage_tensors_to_compute_backend(required_states)) {
+        release_compute_staging_blocks(false);
+        release_params_storage_blocks(false);
+        return false;
+    }
+
+    if (!apply_loras_to_params(required_states)) {
+        release_compute_staging_blocks(false);
+        release_params_storage_blocks(false);
+        return false;
+    }
+
+    for (TensorState* state : required_states) {
+        if (state == nullptr) {
+            continue;
+        }
+        state->active_prepare_count++;
+    }
+    return true;
+}
+
+void ModelManager::finish_compute_backend_usage(const std::vector<TensorState*>& states) {
+    if (states.empty()) {
+        return;
+    }
+
+    std::unordered_set<TensorState*> target_states;
+    for (TensorState* state : states) {
+        if (state == nullptr || !target_states.insert(state).second) {
+            continue;
+        }
+        if (state->active_prepare_count > 0) {
+            state->active_prepare_count--;
+        }
+    }
+    release_compute_staging_blocks(false, &target_states);
+}
+
+void ModelManager::release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) {
+    if (tensors.empty()) {
+        return;
+    }
+    std::vector<TensorState*> required_states;
+    if (!resolve_required_tensor_states(tensors, required_states)) {
+        return;
+    }
+    finish_compute_backend_usage(required_states);
+}
+
+void ModelManager::release_params_backend_params(const std::vector<ggml_tensor*>& tensors) {
+    if (tensors.empty()) {
+        return;
+    }
+    std::vector<TensorState*> required_states;
+    if (!resolve_required_tensor_states(tensors, required_states)) {
+        return;
+    }
+    if (required_states.empty()) {
+        return;
+    }
+    std::unordered_set<TensorState*> target_states(required_states.begin(), required_states.end());
+    release_params_storage_blocks(false, &target_states);
+}
diff --git a/otherarch/sdcpp/src/model_manager.h b/otherarch/sdcpp/src/model_manager.h
new file mode 100644
index 000000000..1a414c15c
--- /dev/null
+++ b/otherarch/sdcpp/src/model_manager.h
@@ -0,0 +1,167 @@
+#ifndef __MODEL_MANAGER_H__
+#define __MODEL_MANAGER_H__
+
+#include <cstdint>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "model_loader.h"
+#include "weight_manager.h"
+
+class ModelManager : public RunnerWeightManager {
+public:
+    enum class ResidencyMode {
+        Disk,
+        ParamBackend,
+    };
+
+    struct LoraSpec {
+        std::string path;
+        float multiplier   = 1.0f;
+        bool is_high_noise = false;
+        std::string tensor_name_prefix_filter;
+        bool required = false;
+    };
+
+private:
+    struct TensorState {
+        std::string name;
+        ggml_tensor* tensor = nullptr;
+        std::string desc;
+
+        ResidencyMode residency_mode   = ResidencyMode::ParamBackend;
+        ggml_backend_t compute_backend = nullptr;
+        ggml_backend_t params_backend  = nullptr;
+        bool metadata_validated        = false;
+
+        int active_prepare_count = 0;
+
+        bool loaded_to_params_backend  = false;
+        bool staged_to_compute_backend = false;
+        uint64_t applied_lora_epoch    = UINT64_MAX;
+    };
+
+    struct ParamsStorageBlock {
+        ggml_backend_buffer_t buffer = nullptr;
+        std::vector<MmapTensorStore> mmap_tensor_stores;
+        std::vector<TensorState*> states;
+    };
+
+    struct ComputeStagingBlock {
+        ggml_backend_t compute_backend = nullptr;
+        ggml_backend_buffer_t buffer   = nullptr;
+        ggml_context* staging_ctx      = nullptr;
+        std::vector<std::pair<TensorState*, ggml_tensor*>> staged_tensors;
+    };
+
+    ModelLoader model_loader_;
+    std::vector<std::unique_ptr<TensorState>> tensor_states_;
+    std::map<std::string, TensorState*> tensor_states_by_name_;
+    std::vector<std::unique_ptr<ParamsStorageBlock>> params_storage_blocks_;
+    std::vector<std::unique_ptr<ComputeStagingBlock>> compute_staging_blocks_;
+    std::set<std::string> common_ignore_tensors_;
+    std::vector<LoraSpec> loras_;
+    SDVersion lora_version_      = VERSION_COUNT;
+    uint64_t current_lora_epoch_ = 0;
+    int n_threads_               = 0;
+    bool enable_mmap_            = false;
+
+    void finish_compute_backend_usage(const std::vector<TensorState*>& states);
+    void release_all();
+
+    bool resolve_required_tensor_states(const std::vector<ggml_tensor*>& tensors,
+                                        std::vector<TensorState*>& required_states) const;
+    bool should_ignore(const TensorState& state) const;
+    bool is_optional_missing_tensor(const std::string& name) const;
+    bool validate_tensor(const TensorState& state) const;
+
+    bool load_tensors_to_params_backend(const std::vector<TensorState*>& states);
+    bool apply_loras_to_params(const std::vector<TensorState*>& states);
+    bool mmap_params(const std::vector<TensorState*>& states,
+                     std::vector<ParamsStorageBlock*>& created_storage_blocks);
+    bool can_mmap_storage(const TensorState& state) const;
+    bool alloc_params_buffers(const std::vector<TensorState*>& states,
+                              std::vector<ParamsStorageBlock*>& created_storage_blocks);
+    bool load_tensors(const std::vector<TensorState*>& states);
+    bool stage_tensors_to_compute_backend(const std::vector<TensorState*>& states);
+
+    ggml_backend_buffer_type_t params_buffer_type_for(const TensorState& state) const;
+    void release_compute_staging_blocks(bool force                                            = false,
+                                        const std::unordered_set<TensorState*>* target_states = nullptr);
+    void release_params_storage_blocks(bool force                                            = false,
+                                       const std::unordered_set<TensorState*>* target_states = nullptr);
+    void free_compute_staging_block(ComputeStagingBlock& block);
+    void free_params_storage_block(ParamsStorageBlock& block);
+    void erase_params_storage_block(ParamsStorageBlock* block);
+    void reset_lora_applied_params();
+
+public:
+    ~ModelManager() override;
+
+    ModelLoader& loader() { return model_loader_; }
+    const ModelLoader& loader() const { return model_loader_; }
+
+    void set_n_threads(int n_threads) {
+        n_threads_ = n_threads;
+        model_loader_.set_n_threads(n_threads);
+    }
+    void set_enable_mmap(bool enable_mmap) { enable_mmap_ = enable_mmap; }
+    void set_common_ignore_tensors(std::set<std::string> ignore_tensors);
+    void set_loras(std::vector<LoraSpec> loras, SDVersion version);
+
+    std::set<std::string> tensor_names() const;
+
+    bool register_param_tensors(const std::string& desc,
+                                std::map<std::string, ggml_tensor*> tensors,
+                                ResidencyMode residency_mode,
+                                ggml_backend_t compute_backend,
+                                ggml_backend_t params_backend,
+                                size_t* registered_tensor_size = nullptr);
+
+    template <typename Runner>
+    bool register_runner_params(const std::string& desc,
+                                Runner& runner,
+                                ResidencyMode residency_mode,
+                                ggml_backend_t compute_backend,
+                                ggml_backend_t params_backend,
+                                size_t* registered_tensor_size = nullptr) {
+        std::map<std::string, ggml_tensor*> tensors;
+        runner.get_param_tensors(tensors);
+        return register_param_tensors(desc,
+                                      std::move(tensors),
+                                      residency_mode,
+                                      compute_backend,
+                                      params_backend,
+                                      registered_tensor_size);
+    }
+
+    template <typename Runner>
+    bool register_runner_params(const std::string& desc,
+                                Runner& runner,
+                                const std::string& prefix,
+                                ResidencyMode residency_mode,
+                                ggml_backend_t compute_backend,
+                                ggml_backend_t params_backend,
+                                size_t* registered_tensor_size = nullptr) {
+        std::map<std::string, ggml_tensor*> tensors;
+        runner.get_param_tensors(tensors, prefix);
+        return register_param_tensors(desc,
+                                      std::move(tensors),
+                                      residency_mode,
+                                      compute_backend,
+                                      params_backend,
+                                      registered_tensor_size);
+    }
+
+    bool validate_registered_tensors();
+
+    bool prepare_params(const std::vector<ggml_tensor*>& tensors) override;
+    void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) override;
+    void release_params_backend_params(const std::vector<ggml_tensor*>& tensors) override;
+};
+
+#endif  // __MODEL_MANAGER_H__
diff --git a/otherarch/sdcpp/src/name_conversion.cpp b/otherarch/sdcpp/src/name_conversion.cpp
index e316f8c4b..4b7b4008d 100644
--- a/otherarch/sdcpp/src/name_conversion.cpp
+++ b/otherarch/sdcpp/src/name_conversion.cpp
@@ -990,7 +990,46 @@ bool is_first_stage_model_name(const std::string& name) {
     return false;
 }
 
+static std::string convert_esrgan_tensor_name(std::string name) {
+    static std::unordered_map<std::string, std::string> esrgan_name_map;
+
+    if (esrgan_name_map.empty()) {
+        esrgan_name_map["model.0."] = "conv_first.";
+
+        constexpr int max_num_blocks = 64;
+        for (int i = 0; i < max_num_blocks; i++) {
+            std::string block_prefix = "model.1.sub." + std::to_string(i) + ".";
+            for (int rdb = 1; rdb <= 3; rdb++) {
+                for (int conv = 1; conv <= 5; conv++) {
+                    esrgan_name_map[block_prefix + "RDB" + std::to_string(rdb) + ".conv" + std::to_string(conv) + ".0."] =
+                        "body." + std::to_string(i) + ".rdb" + std::to_string(rdb) + ".conv" + std::to_string(conv) + ".";
+                }
+            }
+            esrgan_name_map[block_prefix + "weight"] = "conv_body.weight";
+            esrgan_name_map[block_prefix + "bias"]   = "conv_body.bias";
+        }
+
+        // RealESRGAN stores only the learned layers in a Sequential. These indices
+        // cover the common x1, x2 and x4 layouts.
+        esrgan_name_map["model.2."]  = "conv_hr.";
+        esrgan_name_map["model.3."]  = "conv_up1.";
+        esrgan_name_map["model.4."]  = "conv_last.";
+        esrgan_name_map["model.5."]  = "conv_hr.";
+        esrgan_name_map["model.6."]  = "conv_up2.";
+        esrgan_name_map["model.7."]  = "conv_last.";
+        esrgan_name_map["model.8."]  = "conv_hr.";
+        esrgan_name_map["model.10."] = "conv_last.";
+    }
+
+    replace_with_prefix_map(name, esrgan_name_map);
+    return name;
+}
+
 std::string convert_tensor_name(std::string name, SDVersion version) {
+    if (version == VERSION_ESRGAN) {
+        return convert_esrgan_tensor_name(std::move(name));
+    }
+
     bool is_lora                             = false;
     bool is_lycoris_underline                = false;
     bool is_underline                        = false;
diff --git a/otherarch/sdcpp/src/runtime/guidance.cpp b/otherarch/sdcpp/src/runtime/guidance.cpp
index a83680cd5..f925b4b8c 100644
--- a/otherarch/sdcpp/src/runtime/guidance.cpp
+++ b/otherarch/sdcpp/src/runtime/guidance.cpp
@@ -172,8 +172,8 @@ namespace sd::guidance {
             momentum_buffer_ = deltas;
         }
 
-        float diff_norm = 0.0f;
-        const int standard_res = 2 * 1024 / 8; // Use SDXL as the standard resolution (1024x1024, 8x8 patches, 4=2x2 channels)
+        float diff_norm        = 0.0f;
+        const int standard_res = 2 * 1024 / 8;  // Use SDXL as the standard resolution (1024x1024, 8x8 patches, 4=2x2 channels)
         if (params_.norm_threshold > 0.0f) {
             diff_norm = std::sqrt((deltas * deltas).sum()) * standard_res / std::sqrt(static_cast<float>(deltas.numel()));
         }
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index 678787c08..68a4d6aae 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -3,6 +3,8 @@
 #include <cstdlib>
 #include <filesystem>
 #include <set>
+#include <unordered_set>
+#include <vector>
 
 #include "core/ggml_extend.hpp"
 #include "core/ggml_graph_cut.h"
@@ -12,6 +14,7 @@
 #include "core/rng_philox.hpp"
 #include "core/util.h"
 #include "model_loader.h"
+#include "model_manager.h"
 #include "stable-diffusion.h"
 
 #include "conditioning/conditioner.hpp"
@@ -51,6 +54,8 @@
 const char* sd_vae_format_name(enum sd_vae_format_t format);
 static SDVersion sd_vae_format_to_version(enum sd_vae_format_t format, SDVersion fallback);
 
+#include <atomic>
+
 const char* model_version_to_str[] = {
     "SD 1.x",
     "SD 1.x Inpaint",
@@ -89,6 +94,7 @@ const char* model_version_to_str[] = {
     "Longcat-Image",
     "PiD",
     "Ideogram 4",
+    "ESRGAN",
 };
 
 const char* sampling_methods_str[] = {
@@ -156,15 +162,15 @@ static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
 
 /*=============================================== StableDiffusionGGML ================================================*/
 
+static_assert(std::atomic<sd_cancel_mode_t>::is_always_lock_free,
+              "sd_cancel_mode_t must be lock-free");
+
 class StableDiffusionGGML {
 public:
-    std::vector<MmapTensorStore> mmap_tensor_store;
     SDBackendManager backend_manager;
 
     SDVersion version;
-    bool vae_decode_only         = false;
     bool external_vae_is_invalid = false;
-    bool free_params_immediately = false;
 
     bool circular_x = false;
     bool circular_y = false;
@@ -183,30 +189,26 @@ public:
     std::shared_ptr<LTXV::LTXAudioVAERunner> audio_vae_model;
     std::shared_ptr<ControlNet> control_net;
     std::vector<std::shared_ptr<GenerationExtension>> generation_extensions;
-    std::vector<std::shared_ptr<LoraModel>> cond_stage_lora_models;
-    std::vector<std::shared_ptr<LoraModel>> diffusion_lora_models;
-    std::vector<std::shared_ptr<LoraModel>> first_stage_lora_models;
+    std::vector<std::shared_ptr<LoraModel>> runtime_lora_models;
     bool apply_lora_immediately = false;
     std::map<std::string, std::shared_ptr<LoraModel>> kcpp_lora_cache;
     bool kcpp_lora_cache_populate = false;
 
     std::string taesd_path;
     sd_tiling_params_t vae_tiling_params = {false, false, 0, 0, 0.5f, 0, 0, nullptr};
-    bool offload_params_to_cpu           = false;
-    float max_vram                       = 0.f;
-    bool stream_layers                   = false;
+    bool enable_mmap                     = false;
+    sd::ggml_graph_cut::MaxVramAssignment max_vram_assignment;
+    bool stream_layers = false;
     std::string backend_spec;
     std::string params_backend_spec;
 
     bool is_using_v_parameterization     = false;
     bool is_using_edm_v_parameterization = false;
 
-    std::map<std::string, ggml_tensor*> tensors;
-
-    // lora_name => multiplier
-    std::unordered_map<std::string, float> curr_lora_state;
+    std::shared_ptr<ModelManager> model_manager;
 
     std::shared_ptr<Denoiser> denoiser = std::make_shared<CompVisDenoiser>();
+    std::vector<float> file_alphas_cumprod;
 
     StableDiffusionGGML() = default;
 
@@ -228,15 +230,22 @@ public:
         return module_backend;
     }
 
-    std::string toLowerCase(const std::string& str) {
-        std::string result;
-        std::locale loc;
+    std::atomic<sd_cancel_mode_t> cancellation_flag = SD_CANCEL_RESET;
 
-        for (char ch : str) {
-            result += std::tolower(ch, loc); // Use locale-aware tolower
-        }
+    void set_cancel_flag(enum sd_cancel_mode_t flag) {
+        cancellation_flag.store(flag, std::memory_order_release);
+    }
 
-        return result;
+    void reset_cancel_flag() {
+        set_cancel_flag(SD_CANCEL_RESET);
+    }
+
+    enum sd_cancel_mode_t get_cancel_flag() {
+        return cancellation_flag.load(std::memory_order_acquire);
+    }
+
+    size_t max_graph_vram_bytes_for_module(SDBackendModule module) {
+        return max_vram_assignment.bytes_for_backend(backend_for(module));
     }
 
     bool ensure_backend_pair(SDBackendModule module) {
@@ -246,14 +255,31 @@ public:
         return params_backend_for(module) != nullptr;
     }
 
-    bool init_backend(const sd_ctx_params_t* sd_ctx_params) {
+    template <typename T>
+    bool register_runner_params(const std::string& desc,
+                                const std::shared_ptr<T>& model,
+                                SDBackendModule module,
+                                size_t* params_mem_size = nullptr) {
+        if (model == nullptr) {
+            return true;
+        }
+        std::map<std::string, ggml_tensor*> group_tensors;
+        model->get_param_tensors(group_tensors);
+        if (model_manager == nullptr) {
+            return true;
+        }
+        return model_manager->register_param_tensors(desc,
+                                                     std::move(group_tensors),
+                                                     backend_manager.params_backend_is_disk(module) ? ModelManager::ResidencyMode::Disk : ModelManager::ResidencyMode::ParamBackend,
+                                                     backend_for(module),
+                                                     params_backend_for(module),
+                                                     params_mem_size);
+    }
+
+    bool init_backend() {
         std::string error;
-        if (!backend_manager.init(sd_ctx_params->backend,
-                                  sd_ctx_params->params_backend,
-                                  offload_params_to_cpu,
-                                  sd_ctx_params->keep_clip_on_cpu,
-                                  sd_ctx_params->keep_vae_on_cpu,
-                                  sd_ctx_params->keep_control_net_on_cpu,
+        if (!backend_manager.init(backend_spec.c_str(),
+                                  params_backend_spec.c_str(),
                                   &error)) {
             LOG_ERROR("backend config failed: %s", error.c_str());
             return false;
@@ -271,30 +297,71 @@ public:
         }
     }
 
+    void refresh_compvis_denoiser_sigmas() {
+        auto comp_vis_denoiser = std::dynamic_pointer_cast<CompVisDenoiser>(denoiser);
+        if (!comp_vis_denoiser) {
+            return;
+        }
+        std::vector<float> alphas_cumprod(TIMESTEPS);
+        if (file_alphas_cumprod.size() == TIMESTEPS) {
+            alphas_cumprod = file_alphas_cumprod;
+        } else {
+            calculate_alphas_cumprod(alphas_cumprod.data());
+        }
+        for (int i = 0; i < TIMESTEPS; i++) {
+            comp_vis_denoiser->sigmas[i]     = std::sqrt((1 - alphas_cumprod[i]) / alphas_cumprod[i]);
+            comp_vis_denoiser->log_sigmas[i] = std::log(comp_vis_denoiser->sigmas[i]);
+        }
+    }
+
+    void load_alphas_cumprod(ModelLoader& model_loader) {
+        file_alphas_cumprod.clear();
+
+        std::vector<float> loaded_alphas;
+        if (!model_loader.load_float_tensor("alphas_cumprod", loaded_alphas, n_threads, enable_mmap)) {
+            return;
+        }
+        if (loaded_alphas.size() != TIMESTEPS) {
+            LOG_WARN("ignore alphas_cumprod from model file: expected %d values, got %zu",
+                     TIMESTEPS,
+                     loaded_alphas.size());
+            return;
+        }
+        for (float alpha : loaded_alphas) {
+            if (!std::isfinite(alpha) || alpha <= 0.0f || alpha > 1.0f) {
+                LOG_WARN("ignore invalid alphas_cumprod from model file");
+                return;
+            }
+        }
+
+        file_alphas_cumprod = std::move(loaded_alphas);
+        LOG_DEBUG("loaded alphas_cumprod from model file");
+    }
+
     bool init(const sd_ctx_params_t* sd_ctx_params_kcpp) {
         // kcpp make sd_ctx_params mutable
         sd_ctx_params_t sd_ctx_params_local = *sd_ctx_params_kcpp;
         sd_ctx_params_t *sd_ctx_params = &sd_ctx_params_local;
-        n_threads               = sd_ctx_params->n_threads;
-        vae_decode_only         = sd_ctx_params->vae_decode_only;
-        free_params_immediately = sd_ctx_params->free_params_immediately;
-        offload_params_to_cpu   = sd_ctx_params->offload_params_to_cpu;
-        max_vram                = sd_ctx_params->max_vram;
-        stream_layers           = sd_ctx_params->stream_layers;
-        backend_spec            = SAFE_STR(sd_ctx_params->backend);
-        params_backend_spec     = SAFE_STR(sd_ctx_params->params_backend);
-        if (stream_layers && max_vram == 0.f) {
-            LOG_WARN("--stream-layers has no effect without --max-vram set; ignoring");
-            stream_layers = false;
-        }
-        if (stream_layers && !offload_params_to_cpu && params_backend_spec.empty()) {
-            // Streaming needs CPU-resident params.
-            LOG_WARN("--stream-layers has no effect without --offload-to-cpu (or --params-backend); ignoring");
-            stream_layers = false;
+        n_threads           = sd_ctx_params->n_threads;
+        enable_mmap         = sd_ctx_params->enable_mmap;
+        stream_layers       = sd_ctx_params->stream_layers;
+        backend_spec        = SAFE_STR(sd_ctx_params->backend);
+        params_backend_spec = SAFE_STR(sd_ctx_params->params_backend);
+        max_vram_assignment.reset(0.f);
+        {
+            std::string error;
+            if (!max_vram_assignment.parse(SAFE_STR(sd_ctx_params->max_vram), &error)) {
+                LOG_ERROR("%s", error.c_str());
+                return false;
+            }
         }
 
-        bool use_tae       = false;
-        bool use_audio_vae = false;
+        std::string rpc_servers_spec = SAFE_STR(sd_ctx_params->rpc_servers);
+        add_rpc_devices(rpc_servers_spec);
+
+        bool use_tae         = false;
+        bool use_audio_vae   = false;
+        bool use_control_net = false;
 
         rng = get_rng(sd_ctx_params->rng_type);
         if (sd_ctx_params->sampler_rng_type != RNG_TYPE_COUNT && sd_ctx_params->sampler_rng_type != sd_ctx_params->rng_type) {
@@ -305,10 +372,20 @@ public:
 
         ggml_log_set(ggml_log_callback_default, nullptr);
 
-        if (!init_backend(sd_ctx_params)) {
+        if (!init_backend()) {
             return false;
         }
-        max_vram = sd::ggml_graph_cut::resolve_max_vram_gib(max_vram, backend_for(SDBackendModule::DIFFUSION));
+        {
+            std::string error;
+            if (!max_vram_assignment.canonicalize_backend_keys(&error)) {
+                LOG_ERROR("%s", error.c_str());
+                return false;
+            }
+        }
+        if (stream_layers && !backend_manager.params_backend_is_cpu(SDBackendModule::DIFFUSION)) {
+            LOG_WARN("--stream-layers has no effect unless diffusion params backend is cpu; ignoring");
+            stream_layers = false;
+        }
 
         std::string clip_vision_fixed     = SAFE_STR(sd_ctx_params->clip_vision_path);
         std::string clipg_path_fixed      = SAFE_STR(sd_ctx_params->clip_g_path);
@@ -320,7 +397,10 @@ public:
         std::string embed_connector_fixed = SAFE_STR(sd_ctx_params->embeddings_connectors_path);
         std::string vae_path_fixed        = SAFE_STR(sd_ctx_params->vae_path);
 
-        ModelLoader model_loader;
+        model_manager = std::make_shared<ModelManager>();
+        model_manager->set_n_threads(n_threads);
+        model_manager->set_enable_mmap(enable_mmap);
+        ModelLoader& model_loader = model_manager->loader();
 
         if (strlen(SAFE_STR(sd_ctx_params->model_path)) > 0) {
             LOG_INFO("loading model from '%s'", sd_ctx_params->model_path);
@@ -385,6 +465,15 @@ public:
             tempver = model_loader.get_sd_version();
         }
 
+        auto toLowerCase = [](const std::string& str) -> std::string {
+            std::string result;
+            std::locale loc;
+            for (char ch : str) {
+                result += std::tolower(ch, loc); // Use locale-aware tolower
+            }
+            return result;
+        };
+
         bool iswan = sd_version_is_wan(tempver);
         bool is_wan21 = sd_version_is_wan(tempver) && tempver != VERSION_WAN2_2_TI2V;
         bool is_qwenimg = sd_version_is_qwen_image(tempver);
@@ -589,6 +678,14 @@ public:
             }
         }
 
+        if (strlen(SAFE_STR(sd_ctx_params->pulid_weights_path)) > 0) {
+            LOG_INFO("loading PuLID weights from '%s'", sd_ctx_params->pulid_weights_path);
+            if (!model_loader.init_from_file(sd_ctx_params->pulid_weights_path,
+                                             "model.diffusion_model.")) {
+                LOG_WARN("loading PuLID weights from '%s' failed", sd_ctx_params->pulid_weights_path);
+            }
+        }
+
         if (strlen(SAFE_STR(sd_ctx_params->llm_path)) > 0) {
             LOG_INFO("loading llm from '%s'", sd_ctx_params->llm_path);
             if (!model_loader.init_from_file(sd_ctx_params->llm_path, "text_encoders.llm.")) {
@@ -636,6 +733,15 @@ public:
             }
         }
 
+        if (strlen(SAFE_STR(sd_ctx_params->control_net_path)) > 0) {
+            if (!model_loader.init_from_file(sd_ctx_params->control_net_path)) {
+                LOG_ERROR("init control net model loader from file failed: '%s'", sd_ctx_params->control_net_path);
+                return false;
+            } else {
+                use_control_net = true;
+            }
+        }
+
         model_loader.convert_tensors_name();
 
         version = model_loader.get_sd_version();
@@ -659,6 +765,7 @@ public:
         if (wtype != GGML_TYPE_COUNT || tensor_type_rules.size() > 0) {
             model_loader.set_wtype_override(wtype, tensor_type_rules);
         }
+        model_loader.process_model_files(enable_mmap, true);
 
         std::map<ggml_type, uint32_t> wtype_stat                 = model_loader.get_wtype_stat();
         std::map<ggml_type, uint32_t> conditioner_wtype_stat     = model_loader.get_conditioner_wtype_stat();
@@ -699,8 +806,8 @@ public:
                 }
             }
             // Avoid full-model LoRA merge buffers on constrained setups.
-            const bool streaming_constrained = stream_layers ||
-                                               sd_ctx_params->offload_params_to_cpu;
+            const bool params_offloaded      = params_backend_for(SDBackendModule::DIFFUSION) != backend_for(SDBackendModule::DIFFUSION);
+            const bool streaming_constrained = stream_layers || params_offloaded;
             if (have_quantized_weight || streaming_constrained) {
                 apply_lora_immediately = false;
             } else {
@@ -712,51 +819,17 @@ public:
             apply_lora_immediately = false;
         }
 
-        std::map<std::string, ggml_tensor*> mmap_able_tensors;
-        bool enable_mmap_tensors = false;
-        bool needs_writable_mmap = false;
-        if (sd_ctx_params->enable_mmap) {
-            if (apply_lora_immediately) {
-                needs_writable_mmap = true;
-                LOG_WARN("in mode 'immediately', LoRAs will cause extra memory usage with mmap");
-            }
-            enable_mmap_tensors = true;
+        if (enable_mmap && apply_lora_immediately) {
+            LOG_WARN("in mode 'immediately', LoRAs will cause extra memory usage with mmap");
         }
+        load_alphas_cumprod(model_loader);
 
-        // split definition to avoid msvc choking on the extra parameter handling
-        auto module_can_mmap = [&](SDBackendModule module) {
-            return enable_mmap_tensors &&
-                   (backend_manager.runtime_backend_is_cpu(module) ||
-                    backend_manager.params_backend_is_cpu(module) ||
-                    backend_manager.runtime_backend_supports_host_buffer(module));
-        };
+        size_t text_encoder_params_mem_size = 0;
+        size_t unet_params_mem_size         = 0;
+        size_t vae_params_mem_size          = 0;
+        size_t control_net_params_mem_size  = 0;
+        size_t extension_params_mem_size    = 0;
 
-        auto get_param_tensors_p = [&](auto&& model, bool do_mmap, const char* prefix) {
-            std::map<std::string, ggml_tensor*> temp;
-            model->get_param_tensors(temp, prefix);
-            for (const auto& [key, tensor] : temp) {
-                tensors[key] = tensor;
-                if (do_mmap) {
-                    mmap_able_tensors[key] = tensor;
-                }
-            }
-        };
-
-        auto get_param_tensors = [&](auto&& model, bool do_mmap) {
-            std::map<std::string, ggml_tensor*> temp;
-            model->get_param_tensors(temp);
-            for (const auto& [key, tensor] : temp) {
-                tensors[key] = tensor;
-                if (do_mmap) {
-                    mmap_able_tensors[key] = tensor;
-                }
-            }
-        };
-
-        if (sd_version_is_control(version)) {
-            // Might need vae encode for control cond
-            vae_decode_only = false;
-        }
         bool tae_preview_only = sd_ctx_params->tae_preview_only;
         if (version == VERSION_SDXS_512_DS || version == VERSION_SDXS_09) {
             tae_preview_only = false;
@@ -767,8 +840,6 @@ public:
             LOG_INFO("Using circular padding for convolutions");
         }
 
-        const size_t max_graph_vram_bytes = sd::ggml_graph_cut::max_vram_gib_to_bytes(max_vram);
-
         {
             if (!ensure_backend_pair(SDBackendModule::TE) ||
                 !ensure_backend_pair(SDBackendModule::DIFFUSION)) {
@@ -777,33 +848,34 @@ public:
 
             if (sd_version_is_sd3(version)) {
                 cond_stage_model = std::make_shared<SD3CLIPEmbedder>(backend_for(SDBackendModule::TE),
-                                                                     params_backend_for(SDBackendModule::TE),
-                                                                     tensor_storage_map);
+                                                                     tensor_storage_map,
+                                                                     model_manager);
                 diffusion_model  = std::make_shared<MMDiTRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                params_backend_for(SDBackendModule::DIFFUSION),
                                                                 tensor_storage_map,
-                                                                "model.diffusion_model");
+                                                                "model.diffusion_model",
+                                                                model_manager);
             } else if (sd_version_is_pid(version)) {
-                vae_decode_only  = false;
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
-                                                                 tensor_storage_map,
-                                                                 version);
-                diffusion_model  = std::make_shared<Pid::PiDRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                   params_backend_for(SDBackendModule::DIFFUSION),
-                                                                   tensor_storage_map,
-                                                                   "model.diffusion_model.net");
-            } else if (sd_version_is_ideogram4(version)) {
-                cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
                                                                  version,
                                                                  "",
-                                                                 false);
+                                                                 false,
+                                                                 model_manager);
+                diffusion_model  = std::make_shared<Pid::PiDRunner>(backend_for(SDBackendModule::DIFFUSION),
+                                                                   tensor_storage_map,
+                                                                   "model.diffusion_model.net",
+                                                                   model_manager);
+            } else if (sd_version_is_ideogram4(version)) {
+                cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
+                                                                 tensor_storage_map,
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<Ideogram4::Ideogram4Runner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                               params_backend_for(SDBackendModule::DIFFUSION),
                                                                                tensor_storage_map,
-                                                                               "model.diffusion_model");
+                                                                               "model.diffusion_model",
+                                                                               model_manager);
             } else if (sd_version_is_flux(version)) {
                 bool is_chroma = false;
                 for (auto pair : tensor_storage_map) {
@@ -814,66 +886,71 @@ public:
                 }
                 if (is_chroma) {
                     cond_stage_model = std::make_shared<T5CLIPEmbedder>(backend_for(SDBackendModule::TE),
-                                                                        params_backend_for(SDBackendModule::TE),
                                                                         tensor_storage_map,
                                                                         sd_ctx_params->chroma_use_t5_mask,
-                                                                        sd_ctx_params->chroma_t5_mask_pad);
+                                                                        sd_ctx_params->chroma_t5_mask_pad,
+                                                                        false,
+                                                                        model_manager);
                 } else if (version == VERSION_OVIS_IMAGE) {
                     cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                     params_backend_for(SDBackendModule::TE),
                                                                      tensor_storage_map,
                                                                      version,
                                                                      "",
-                                                                     false);
+                                                                     false,
+                                                                     model_manager);
                 } else {
                     cond_stage_model = std::make_shared<FluxCLIPEmbedder>(backend_for(SDBackendModule::TE),
-                                                                          params_backend_for(SDBackendModule::TE),
-                                                                          tensor_storage_map);
+                                                                          tensor_storage_map,
+                                                                          model_manager);
                 }
                 diffusion_model = std::make_shared<Flux::FluxRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                     params_backend_for(SDBackendModule::DIFFUSION),
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask);
+                                                                     sd_ctx_params->chroma_use_dit_mask,
+                                                                     model_manager);
             } else if (sd_version_is_flux2(version)) {
                 bool is_chroma   = false;
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
-                                                                 version);
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<Flux::FluxRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                     params_backend_for(SDBackendModule::DIFFUSION),
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask);
+                                                                     sd_ctx_params->chroma_use_dit_mask,
+                                                                     model_manager);
             } else if (sd_version_is_ltxav(version)) {
                 cond_stage_model = std::make_shared<LTXAVEmbedder>(backend_for(SDBackendModule::TE),
-                                                                   params_backend_for(SDBackendModule::TE),
-                                                                   tensor_storage_map);
+                                                                   tensor_storage_map,
+                                                                   "text_encoders.llm",
+                                                                   "text_embedding_projection",
+                                                                   model_manager);
                 diffusion_model  = std::make_shared<LTXV::LTXAVRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                      params_backend_for(SDBackendModule::DIFFUSION),
                                                                       tensor_storage_map,
-                                                                      "model.diffusion_model");
+                                                                      "model.diffusion_model",
+                                                                      model_manager);
             } else if (sd_version_is_wan(version)) {
                 cond_stage_model = std::make_shared<T5CLIPEmbedder>(backend_for(SDBackendModule::TE),
-                                                                    params_backend_for(SDBackendModule::TE),
                                                                     tensor_storage_map,
                                                                     true,
                                                                     0,
-                                                                    true);
+                                                                    true,
+                                                                    model_manager);
                 diffusion_model  = std::make_shared<WAN::WanRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                   params_backend_for(SDBackendModule::DIFFUSION),
                                                                    tensor_storage_map,
                                                                    "model.diffusion_model",
-                                                                   version);
+                                                                   version,
+                                                                   model_manager);
                 if (strlen(SAFE_STR(sd_ctx_params->high_noise_diffusion_model_path)) > 0) {
                     high_noise_diffusion_model = std::make_shared<WAN::WanRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                                  params_backend_for(SDBackendModule::DIFFUSION),
                                                                                   tensor_storage_map,
                                                                                   "model.high_noise_diffusion_model",
-                                                                                  version);
+                                                                                  version,
+                                                                                  model_manager);
                 }
                 if (diffusion_model->get_desc() == "Wan2.1-I2V-14B" ||
                     diffusion_model->get_desc() == "Wan2.1-FLF2V-14B" ||
@@ -882,150 +959,163 @@ public:
                         return false;
                     }
                     clip_vision = std::make_shared<FrozenCLIPVisionEmbedder>(backend_for(SDBackendModule::CLIP_VISION),
-                                                                             params_backend_for(SDBackendModule::CLIP_VISION),
-                                                                             tensor_storage_map);
-                    clip_vision->set_max_graph_vram_bytes(max_graph_vram_bytes);
-                    get_param_tensors(clip_vision, module_can_mmap(SDBackendModule::CLIP_VISION));
+                                                                             tensor_storage_map,
+                                                                             model_manager);
+                    clip_vision->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::CLIP_VISION));
+                    if (!register_runner_params("CLIP vision",
+                                                clip_vision,
+                                                SDBackendModule::CLIP_VISION)) {
+                        return false;
+                    }
                 }
             } else if (sd_version_is_qwen_image(version)) {
-                bool enable_vision = false;
-                if (!vae_decode_only) {
-                    enable_vision = true;
-                }
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
                                                                  version,
                                                                  "",
-                                                                 enable_vision);
+                                                                 true,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<Qwen::QwenImageRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                          params_backend_for(SDBackendModule::DIFFUSION),
                                                                           tensor_storage_map,
                                                                           "model.diffusion_model",
                                                                           version,
-                                                                          sd_ctx_params->qwen_image_zero_cond_t);
+                                                                          sd_ctx_params->qwen_image_zero_cond_t,
+                                                                          model_manager);
             } else if (sd_version_is_longcat(version)) {
-                bool enable_vision = false;
-                if (!vae_decode_only) {
-                    enable_vision = true;
-                }
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
                                                                  version,
                                                                  "",
-                                                                 enable_vision);
+                                                                 true,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<Flux::FluxRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                     params_backend_for(SDBackendModule::DIFFUSION),
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask);
+                                                                     sd_ctx_params->chroma_use_dit_mask,
+                                                                     model_manager);
             } else if (version == VERSION_HIDREAM_O1) {
                 cond_stage_model = std::make_shared<HiDreamO1::HiDreamO1Conditioner>(backend_for(SDBackendModule::TE),
-                                                                                     params_backend_for(SDBackendModule::TE),
-                                                                                     tensor_storage_map);
+                                                                                     tensor_storage_map,
+                                                                                     model_manager);
                 diffusion_model  = std::make_shared<HiDreamO1::HiDreamO1Runner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                               params_backend_for(SDBackendModule::DIFFUSION),
                                                                                tensor_storage_map,
-                                                                               "model");
+                                                                               "model",
+                                                                               model_manager);
             } else if (sd_version_is_anima(version)) {
                 cond_stage_model = std::make_shared<AnimaConditioner>(backend_for(SDBackendModule::TE),
-                                                                      params_backend_for(SDBackendModule::TE),
-                                                                      tensor_storage_map);
+                                                                      tensor_storage_map,
+                                                                      model_manager);
                 diffusion_model  = std::make_shared<Anima::AnimaRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                       params_backend_for(SDBackendModule::DIFFUSION),
                                                                        tensor_storage_map,
-                                                                       "model.diffusion_model");
+                                                                       "model.diffusion_model",
+                                                                       model_manager);
             } else if (sd_version_is_z_image(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
-                                                                 version);
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<ZImage::ZImageRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                         params_backend_for(SDBackendModule::DIFFUSION),
                                                                          tensor_storage_map,
                                                                          "model.diffusion_model",
-                                                                         version);
+                                                                         version,
+                                                                         model_manager);
             } else if (sd_version_is_ernie_image(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
-                                                                 version);
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<ErnieImage::ErnieImageRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                                 params_backend_for(SDBackendModule::DIFFUSION),
                                                                                  tensor_storage_map,
-                                                                                 "model.diffusion_model");
+                                                                                 "model.diffusion_model",
+                                                                                 model_manager);
             } else if (sd_version_is_lens(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
-                                                                 params_backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
-                                                                 version);
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
                 diffusion_model  = std::make_shared<Lens::LensRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                     params_backend_for(SDBackendModule::DIFFUSION),
                                                                      tensor_storage_map,
-                                                                     "model.diffusion_model");
+                                                                     "model.diffusion_model",
+                                                                     model_manager);
             } else {  // SD1.x SD2.x SDXL
                 std::map<std::string, std::string> embbeding_map;
                 for (uint32_t i = 0; i < sd_ctx_params->embedding_count; i++) {
                     embbeding_map.emplace(SAFE_STR(sd_ctx_params->embeddings[i].name), SAFE_STR(sd_ctx_params->embeddings[i].path));
                 }
                 cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(backend_for(SDBackendModule::TE),
-                                                                                       params_backend_for(SDBackendModule::TE),
                                                                                        tensor_storage_map,
                                                                                        embbeding_map,
-                                                                                       version);
+                                                                                       version,
+                                                                                       model_manager);
                 diffusion_model  = std::make_shared<UNetModelRunner>(backend_for(SDBackendModule::DIFFUSION),
-                                                                    params_backend_for(SDBackendModule::DIFFUSION),
                                                                     tensor_storage_map,
                                                                     "model.diffusion_model",
-                                                                    version);
+                                                                    version,
+                                                                    model_manager);
                 if (sd_ctx_params->diffusion_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the diffusion model");
                     diffusion_model->set_conv2d_direct_enabled(true);
                 }
             }
 
-            cond_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);
-            get_param_tensors(cond_stage_model, module_can_mmap(SDBackendModule::TE));
+            cond_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::TE));
+            if (!register_runner_params("Conditioner model",
+                                        cond_stage_model,
+                                        SDBackendModule::TE,
+                                        &text_encoder_params_mem_size)) {
+                return false;
+            }
 
-            diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes);
+            diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::DIFFUSION));
             diffusion_model->set_stream_layers_enabled(stream_layers);
-            get_param_tensors(diffusion_model, module_can_mmap(SDBackendModule::DIFFUSION));
-
-            if (sd_version_is_unet_edit(version)) {
-                vae_decode_only = false;
+            if (!register_runner_params("Diffusion model",
+                                        diffusion_model,
+                                        SDBackendModule::DIFFUSION,
+                                        &unet_params_mem_size)) {
+                return false;
             }
 
             if (high_noise_diffusion_model) {
-                high_noise_diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes);
+                high_noise_diffusion_model->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::DIFFUSION));
                 high_noise_diffusion_model->set_stream_layers_enabled(stream_layers);
-                get_param_tensors(high_noise_diffusion_model, module_can_mmap(SDBackendModule::DIFFUSION));
+                if (!register_runner_params("High noise diffusion model",
+                                            high_noise_diffusion_model,
+                                            SDBackendModule::DIFFUSION,
+                                            &unet_params_mem_size)) {
+                    return false;
+                }
             }
 
             if (!ensure_backend_pair(SDBackendModule::VAE)) {
                 return false;
             }
 
-            auto create_tae = [&]() -> std::shared_ptr<VAE> {
+            auto create_tae = [&](bool decode_only) -> std::shared_ptr<VAE> {
                 if (sd_version_is_wan(version) ||
                     sd_version_is_qwen_image(version) ||
                     sd_version_is_anima(version) ||
                     sd_version_is_ltxav(version)) {
                     return std::make_shared<TinyVideoAutoEncoder>(backend_for(SDBackendModule::VAE),
-                                                                  params_backend_for(SDBackendModule::VAE),
                                                                   tensor_storage_map,
                                                                   "decoder",
-                                                                  vae_decode_only,
-                                                                  version);
+                                                                  decode_only,
+                                                                  version,
+                                                                  model_manager);
 
                 } else {
                     auto model = std::make_shared<TinyImageAutoEncoder>(backend_for(SDBackendModule::VAE),
-                                                                        params_backend_for(SDBackendModule::VAE),
                                                                         tensor_storage_map,
                                                                         "decoder.layers",
-                                                                        vae_decode_only,
-                                                                        version);
+                                                                        decode_only,
+                                                                        version,
+                                                                        model_manager);
                     return model;
                 }
             };
@@ -1043,28 +1133,28 @@ public:
             auto create_vae = [&]() -> std::shared_ptr<VAE> {
                 if (sd_version_is_ltxav(version)) {
                     return std::make_shared<LTXVideoVAE>(backend_for(SDBackendModule::VAE),
-                                                         params_backend_for(SDBackendModule::VAE),
                                                          tensor_storage_map,
                                                          "first_stage_model",
-                                                         vae_decode_only,
-                                                         version);
+                                                         false,
+                                                         version,
+                                                         model_manager);
                 } else if (sd_version_is_wan(version) ||
                            sd_version_is_qwen_image(version) ||
                            sd_version_is_anima(version)) {
                     return std::make_shared<WAN::WanVAERunner>(backend_for(SDBackendModule::VAE),
-                                                               params_backend_for(SDBackendModule::VAE),
                                                                tensor_storage_map,
                                                                "first_stage_model",
-                                                               vae_decode_only,
-                                                               version);
+                                                               false,
+                                                               version,
+                                                               model_manager);
                 } else {
                     auto model = std::make_shared<AutoEncoderKL>(backend_for(SDBackendModule::VAE),
-                                                                 params_backend_for(SDBackendModule::VAE),
                                                                  tensor_storage_map,
                                                                  "first_stage_model",
-                                                                 vae_decode_only,
                                                                  false,
-                                                                 vae_version);
+                                                                 false,
+                                                                 vae_version,
+                                                                 model_manager);
                     if (sd_version_is_sdxl(version) &&
                         (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale || external_vae_is_invalid)) {
                         float vae_conv_2d_scale = 1.f / 32.f;
@@ -1078,36 +1168,61 @@ public:
                 }
             };
 
-            bool vae_mmap = module_can_mmap(SDBackendModule::VAE);
-
             if (version == VERSION_CHROMA_RADIANCE || version == VERSION_HIDREAM_O1) {
                 LOG_INFO("using FakeVAE");
                 first_stage_model = std::make_shared<FakeVAE>(version,
                                                               backend_for(SDBackendModule::VAE),
-                                                              params_backend_for(SDBackendModule::VAE));
+                                                              model_manager);
+                if (!register_runner_params("VAE",
+                                            first_stage_model,
+                                            SDBackendModule::VAE,
+                                            &vae_params_mem_size)) {
+                    return false;
+                }
             } else if (use_tae && !tae_preview_only) {
                 LOG_INFO("using TAE for encoding / decoding");
-                first_stage_model = create_tae();
-                first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);
-                get_param_tensors_p(first_stage_model, vae_mmap, "tae");
+                first_stage_model = create_tae(false);
+                first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::VAE));
+                if (!register_runner_params("VAE",
+                                            first_stage_model,
+                                            SDBackendModule::VAE,
+                                            &vae_params_mem_size)) {
+                    return false;
+                }
             } else {
                 LOG_INFO("using VAE for encoding / decoding");
                 first_stage_model = create_vae();
-                first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes);
-                get_param_tensors_p(first_stage_model, vae_mmap, "first_stage_model");
+                first_stage_model->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::VAE));
+                if (!register_runner_params("VAE",
+                                            first_stage_model,
+                                            SDBackendModule::VAE,
+                                            &vae_params_mem_size)) {
+                    return false;
+                }
                 if (use_tae && tae_preview_only) {
                     LOG_INFO("using TAE for preview");
-                    preview_vae = create_tae();
-                    preview_vae->set_max_graph_vram_bytes(max_graph_vram_bytes);
-                    get_param_tensors_p(preview_vae, vae_mmap, "tae");
+                    preview_vae = create_tae(true);
+                    preview_vae->set_max_graph_vram_bytes(max_graph_vram_bytes_for_module(SDBackendModule::VAE));
+                    if (!register_runner_params("preview VAE",
+                                                preview_vae,
+                                                SDBackendModule::VAE,
+                                                &vae_params_mem_size)) {
+                        return false;
+                    }
                 }
             }
 
             if (use_audio_vae) {
                 audio_vae_model = std::make_shared<LTXV::LTXAudioVAERunner>(backend_for(SDBackendModule::VAE),
-                                                                            params_backend_for(SDBackendModule::VAE),
-                                                                            tensor_storage_map);
-                get_param_tensors_p(audio_vae_model, vae_mmap, "");
+                                                                            tensor_storage_map,
+                                                                            "",
+                                                                            model_manager);
+                if (!register_runner_params("LTX audio VAE",
+                                            audio_vae_model,
+                                            SDBackendModule::VAE,
+                                            &vae_params_mem_size)) {
+                    return false;
+                }
             }
 
             if (sd_ctx_params->vae_conv_direct) {
@@ -1118,18 +1233,26 @@ public:
                 }
             }
 
-            if (strlen(SAFE_STR(sd_ctx_params->control_net_path)) > 0) {
+            if (use_control_net) {
                 if (!ensure_backend_pair(SDBackendModule::CONTROL_NET)) {
                     return false;
                 }
                 control_net = std::make_shared<ControlNet>(backend_for(SDBackendModule::CONTROL_NET),
                                                            params_backend_for(SDBackendModule::CONTROL_NET),
-                                                           tensor_storage_map,
-                                                           version);
+                                                           model_loader.get_tensor_storage_map(),
+                                                           version,
+                                                           "",
+                                                           model_manager);
                 if (sd_ctx_params->diffusion_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the control net");
                     control_net->set_conv2d_direct_enabled(true);
                 }
+                if (!register_runner_params("ControlNet",
+                                            control_net,
+                                            SDBackendModule::CONTROL_NET,
+                                            &control_net_params_mem_size)) {
+                    return false;
+                }
             }
 
             {
@@ -1140,6 +1263,7 @@ public:
                     version,
                     tensor_storage_map,
                     model_loader,
+                    model_manager,
                     n_threads,
                     [this](SDBackendModule module) { return ensure_backend_pair(module); },
                     [this](SDBackendModule module) { return backend_for(module); },
@@ -1151,15 +1275,21 @@ public:
                 if (photomaker_extension->is_enabled()) {
                     generation_extensions.push_back(photomaker_extension);
                 }
+
+                auto pulid_extension = create_pulid_extension();
+                if (!pulid_extension->init(extension_ctx)) {
+                    return false;
+                }
+                if (pulid_extension->is_enabled()) {
+                    generation_extensions.push_back(pulid_extension);
+                }
             }
-            {
-                GenerationExtensionTensorContext extension_tensor_ctx{
-                    tensors,
-                    mmap_able_tensors,
-                    module_can_mmap,
-                };
-                for (auto& extension : generation_extensions) {
-                    extension->collect_param_tensors(extension_tensor_ctx);
+            for (auto& extension : generation_extensions) {
+                if (!register_runner_params(extension->name(),
+                                            extension,
+                                            SDBackendModule::PHOTOMAKER,
+                                            &extension_params_mem_size)) {
+                    return false;
                 }
             }
 
@@ -1199,21 +1329,9 @@ public:
             circular_y = sd_ctx_params->circular_y;
         }
 
-        ggml_init_params params;
-        params.mem_size   = static_cast<size_t>(10 * 1024) * 1024;  // 10M
-        params.mem_buffer = nullptr;
-        params.no_alloc   = false;
-        // LOG_DEBUG("mem_size %u ", params.mem_size);
-        ggml_context* ctx = ggml_init(params);  // for  alphas_cumprod and is_using_v_parameterization check
-        GGML_ASSERT(ctx != nullptr);
-        ggml_tensor* alphas_cumprod_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, TIMESTEPS);
-        calculate_alphas_cumprod((float*)alphas_cumprod_tensor->data);
-
-        // load weights
-        LOG_DEBUG("loading weights");
+        LOG_DEBUG("validating model metadata");
 
         std::set<std::string> ignore_tensors;
-        tensors["alphas_cumprod"] = alphas_cumprod_tensor;
         if (use_tae && !tae_preview_only) {
             ignore_tensors.insert("first_stage_model.");
         }
@@ -1224,13 +1342,6 @@ public:
         ignore_tensors.insert("model.diffusion_model.__32x32__");
         ignore_tensors.insert("model.diffusion_model.__index_timestep_zero__");
 
-        if (vae_decode_only) {
-            ignore_tensors.insert("first_stage_model.encoder");
-            ignore_tensors.insert("first_stage_model.conv1");
-            ignore_tensors.insert("first_stage_model.quant");
-            ignore_tensors.insert("tae.encoder");
-            ignore_tensors.insert("text_encoders.llm.visual.");
-        }
         if (audio_vae_model) {
             ignore_tensors.insert("audio_vae.encoder");
         }
@@ -1262,93 +1373,15 @@ public:
             ignore_tensors.insert("model.visual.deepstack_merger_list.");
         }
 
-        if (enable_mmap_tensors) {
-            if (mmap_able_tensors.empty()) {
-                LOG_DEBUG("no tensors could be memory-mapped");
-            } else {
-                mmap_tensor_store = model_loader.mmap_tensors(mmap_able_tensors, ignore_tensors, needs_writable_mmap);
-            }
-        }
-
-        if (clip_vision && !clip_vision->alloc_params_buffer()) {
-            LOG_ERROR("CLIP vision params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (cond_stage_model && !cond_stage_model->alloc_params_buffer()) {
-            LOG_ERROR("Conditioner model params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (diffusion_model && !diffusion_model->alloc_params_buffer()) {
-            LOG_ERROR("Diffusion model params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (high_noise_diffusion_model && !high_noise_diffusion_model->alloc_params_buffer()) {
-            LOG_ERROR("High noise diffusion model params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (first_stage_model && !first_stage_model->alloc_params_buffer()) {
-            LOG_ERROR("VAE params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (preview_vae && !preview_vae->alloc_params_buffer()) {
-            LOG_ERROR("preview VAE params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        if (audio_vae_model && !audio_vae_model->alloc_params_buffer()) {
-            LOG_ERROR("LTX audio VAE params buffer allocation failed");
-            ggml_free(ctx);
-            return false;
-        }
-        for (auto& extension : generation_extensions) {
-            if (!extension->alloc_params_buffer()) {
-                LOG_ERROR("%s params buffer allocation failed", extension->name());
-                ggml_free(ctx);
-                return false;
-            }
-        }
-
-        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads, sd_ctx_params->enable_mmap);
-        if (!success) {
-            LOG_ERROR("load tensors from model loader failed");
-            ggml_free(ctx);
+        model_manager->set_common_ignore_tensors(ignore_tensors);
+        if (!model_manager->validate_registered_tensors()) {
+            LOG_ERROR("model metadata validation failed");
             return false;
         }
 
-        LOG_DEBUG("finished loaded file");
+        LOG_DEBUG("model metadata validated; weights will be prepared lazily");
 
         {
-            size_t clip_params_mem_size = cond_stage_model->get_params_buffer_size();
-            size_t unet_params_mem_size = diffusion_model->get_params_buffer_size();
-            if (high_noise_diffusion_model) {
-                unet_params_mem_size += high_noise_diffusion_model->get_params_buffer_size();
-            }
-            size_t vae_params_mem_size = 0;
-            vae_params_mem_size        = first_stage_model->get_params_buffer_size();
-            if (preview_vae) {
-                vae_params_mem_size += preview_vae->get_params_buffer_size();
-            }
-            if (audio_vae_model) {
-                vae_params_mem_size += audio_vae_model->get_params_buffer_size();
-            }
-            size_t control_net_params_mem_size = 0;
-            if (control_net) {
-                if (!control_net->load_from_file(SAFE_STR(sd_ctx_params->control_net_path), n_threads)) {
-                    ggml_free(ctx);
-                    return false;
-                }
-                control_net_params_mem_size = control_net->get_params_buffer_size();
-            }
-            size_t extension_params_mem_size = 0;
-            for (auto& extension : generation_extensions) {
-                extension_params_mem_size += extension->get_params_buffer_size();
-            }
-
             size_t total_params_ram_size  = 0;
             size_t total_params_vram_size = 0;
             auto add_params_memory        = [&](size_t size, SDBackendModule module) {
@@ -1377,12 +1410,11 @@ public:
                 return sd_backend_is_cpu(module_backend) ? "RAM" : "VRAM";
             };
 
-            if (!add_params_memory(clip_params_mem_size, SDBackendModule::TE) ||
+            if (!add_params_memory(text_encoder_params_mem_size, SDBackendModule::TE) ||
                 !add_params_memory(extension_params_mem_size, SDBackendModule::PHOTOMAKER) ||
                 !add_params_memory(unet_params_mem_size, SDBackendModule::DIFFUSION) ||
                 !add_params_memory(vae_params_mem_size, SDBackendModule::VAE) ||
                 !add_params_memory(control_net_params_mem_size, SDBackendModule::CONTROL_NET)) {
-                ggml_free(ctx);
                 return false;
             }
 
@@ -1393,8 +1425,8 @@ public:
                 total_params_size / 1024.0 / 1024.0,
                 total_params_vram_size / 1024.0 / 1024.0,
                 total_params_ram_size / 1024.0 / 1024.0,
-                clip_params_mem_size / 1024.0 / 1024.0,
-                params_memory_location(clip_params_mem_size, SDBackendModule::TE),
+                text_encoder_params_mem_size / 1024.0 / 1024.0,
+                params_memory_location(text_encoder_params_mem_size, SDBackendModule::TE),
                 unet_params_mem_size / 1024.0 / 1024.0,
                 params_memory_location(unet_params_mem_size, SDBackendModule::DIFFUSION),
                 vae_params_mem_size / 1024.0 / 1024.0,
@@ -1411,12 +1443,7 @@ public:
 
             if (pred_type == PREDICTION_COUNT) {
                 if (sd_version_is_sd2(version)) {
-                    // check is_using_v_parameterization_for_sd2
-                    if (is_using_v_parameterization_for_sd2(sd_version_is_inpaint(version))) {
-                        pred_type = V_PRED;
-                    } else {
-                        pred_type = EPS_PRED;
-                    }
+                    pred_type = is_using_v_parameterization_for_sd2(sd_version_is_inpaint(version)) ? V_PRED : EPS_PRED;
                 } else if (sd_version_is_sdxl(version)) {
                     if (tensor_storage_map.find("edm_vpred.sigma_max") != tensor_storage_map.end()) {
                         // CosXL models
@@ -1509,25 +1536,27 @@ public:
                 }
                 default: {
                     LOG_ERROR("Unknown predition type %i", pred_type);
-                    ggml_free(ctx);
                     return false;
                 }
             }
 
-            auto comp_vis_denoiser = std::dynamic_pointer_cast<CompVisDenoiser>(denoiser);
-            if (comp_vis_denoiser) {
-                for (int i = 0; i < TIMESTEPS; i++) {
-                    comp_vis_denoiser->sigmas[i]     = std::sqrt((1 - ((float*)alphas_cumprod_tensor->data)[i]) / ((float*)alphas_cumprod_tensor->data)[i]);
-                    comp_vis_denoiser->log_sigmas[i] = std::log(comp_vis_denoiser->sigmas[i]);
-                }
-            }
+            refresh_compvis_denoiser_sigmas();
         }
 
-        ggml_free(ctx);
         return true;
     }
 
     bool is_using_v_parameterization_for_sd2(bool is_inpaint = false) {
+        struct RunnerDoneOnExit {
+            GGMLRunner* runner = nullptr;
+            ~RunnerDoneOnExit() {
+                if (runner != nullptr) {
+                    runner->runner_done();
+                }
+            }
+        };
+        RunnerDoneOnExit diffusion_runner_done{diffusion_model.get()};
+
         sd::Tensor<float> x_t   = sd::full<float>({8, 8, 4, 1}, 0.5f);
         sd::Tensor<float> c     = sd::full<float>({1024, 2, 1, 1}, 0.5f);
         sd::Tensor<float> steps = sd::full<float>({1}, 999.0f);
@@ -1549,7 +1578,6 @@ public:
         auto out_opt = diffusion_model->compute(n_threads, diffusion_params);
         GGML_ASSERT(!out_opt.empty());
         out = std::move(out_opt);
-        diffusion_model->free_compute_buffer();
 
         double result = static_cast<double>((out - x_t).mean());
         int64_t t1    = ggml_time_ms();
@@ -1557,42 +1585,46 @@ public:
         return result < -1;
     }
 
-    std::shared_ptr<LoraModel> load_lora_model_from_file(const std::string& lora_id,
-                                                         float multiplier,
-                                                         SDBackendModule module,
-                                                         LoraModel::filter_t lora_tensor_filter = nullptr) {
+    static std::string lora_log_id(const ModelManager::LoraSpec& lora) {
+        return lora.is_high_noise ? "|high_noise|" + lora.path : lora.path;
+    }
+
+    std::shared_ptr<LoraModel> load_lora_model(const ModelManager::LoraSpec& lora_spec,
+                                               SDBackendModule module,
+                                               LoraModel::filter_t module_filter = nullptr) {
+        if (!ensure_backend_pair(module)) {
+            return nullptr;
+        }
+        if (lora_spec.is_high_noise) {
+            LOG_DEBUG("high noise lora: %s", lora_spec.path.c_str());
+        }
         // kcpp
         // first check the cache
-        std::string lora_key = "|" + std::to_string(static_cast<int>(module)) + "|" + lora_id;
+        std::string lora_key = "|" + std::to_string(static_cast<int>(module)) + "|" + lora_log_id(lora_spec);
         if (!apply_lora_immediately) {
             auto it = kcpp_lora_cache.find(lora_key);
             if (it != kcpp_lora_cache.end()) {
                 if (it->second) {
-                    it->second->multiplier = multiplier;
+                    it->second->multiplier = lora_spec.multiplier;
                 }
                 return it->second;
             }
         }
 
-        std::string lora_path             = lora_id;
-        static std::string high_noise_tag = "|high_noise|";
-        bool is_high_noise                = false;
-        if (starts_with(lora_path, high_noise_tag)) {
-            lora_path     = lora_path.substr(high_noise_tag.size());
-            is_high_noise = true;
-            LOG_DEBUG("high noise lora: %s", lora_path.c_str());
-        }
-        if (!ensure_backend_pair(module)) {
-            return nullptr;
-        }
-        auto lora = std::make_shared<LoraModel>(lora_id,
+        auto lora                              = std::make_shared<LoraModel>(lora_log_id(lora_spec),
                                                 backend_for(module),
                                                 backend_for(module),
-                                                lora_path,
-                                                is_high_noise ? "model.high_noise_" : "",
+                                                lora_spec.path,
+                                                lora_spec.is_high_noise ? "model.high_noise_" : "",
                                                 version);
+        LoraModel::filter_t lora_tensor_filter = module_filter;
+        if (!lora_spec.tensor_name_prefix_filter.empty()) {
+            lora_tensor_filter = [module_filter, prefix = lora_spec.tensor_name_prefix_filter](const std::string& tensor_name) {
+                return starts_with(tensor_name, prefix) && (!module_filter || module_filter(tensor_name));
+            };
+        }
         if (!lora->load_from_file(n_threads, lora_tensor_filter)) {
-            LOG_WARN("load lora tensors from %s failed", lora_path.c_str());
+            LOG_WARN("load lora tensors from %s failed", lora_spec.path.c_str());
             // also cache negatives to avoid I/O at runtime
             lora = nullptr;
             if (!apply_lora_immediately && kcpp_lora_cache_populate)
@@ -1600,60 +1632,13 @@ public:
             return lora;
         }
 
-        lora->multiplier = multiplier;
+        lora->multiplier = lora_spec.multiplier;
         if (!apply_lora_immediately && kcpp_lora_cache_populate)
             kcpp_lora_cache[lora_key] = lora;
         return lora;
     }
 
-    void apply_loras_immediately(const std::unordered_map<std::string, float>& lora_state) {
-        std::unordered_map<std::string, float> lora_state_diff;
-        for (auto& kv : lora_state) {
-            const std::string& lora_name = kv.first;
-            float multiplier             = kv.second;
-            lora_state_diff[lora_name] += multiplier;
-        }
-        for (auto& kv : curr_lora_state) {
-            const std::string& lora_name = kv.first;
-            float curr_multiplier        = kv.second;
-            lora_state_diff[lora_name] -= curr_multiplier;
-        }
-
-        if (lora_state_diff.empty()) {
-            return;
-        }
-
-        LOG_INFO("apply lora immediately");
-
-        size_t rm = lora_state_diff.size() - lora_state.size();
-        if (rm != 0) {
-            LOG_INFO("attempting to apply %lu LoRAs (removing %lu applied LoRAs)", lora_state.size(), rm);
-        } else {
-            LOG_INFO("attempting to apply %lu LoRAs", lora_state.size());
-        }
-
-        for (auto& kv : lora_state_diff) {
-            int64_t t0 = ggml_time_ms();
-
-            auto lora = load_lora_model_from_file(kv.first, kv.second, SDBackendModule::DIFFUSION);
-            if (!lora || lora->lora_tensors.empty()) {
-                continue;
-            }
-            lora->apply(tensors, version, n_threads);
-            lora->free_params_buffer();
-
-            int64_t t1 = ggml_time_ms();
-
-            LOG_INFO("lora '%s' applied, taking %.2fs", kv.first.c_str(), (t1 - t0) * 1.0f / 1000);
-        }
-
-        curr_lora_state = lora_state;
-    }
-
-    void apply_loras_at_runtime(const std::unordered_map<std::string, float>& lora_state) {
-        cond_stage_lora_models.clear();
-        diffusion_lora_models.clear();
-        first_stage_lora_models.clear();
+    void clear_lora_adapters() {
         if (cond_stage_model) {
             cond_stage_model->set_weight_adapter(nullptr);
         }
@@ -1666,39 +1651,74 @@ public:
         if (first_stage_model) {
             first_stage_model->set_weight_adapter(nullptr);
         }
-        if (lora_state.empty()) {
+    }
+
+    std::vector<std::shared_ptr<LoraModel>> load_runtime_loras_for_module(const std::vector<ModelManager::LoraSpec>& loras,
+                                                                          const std::set<std::string>& model_tensor_names,
+                                                                          SDBackendModule module,
+                                                                          LoraModel::filter_t module_filter = nullptr) {
+        std::vector<std::shared_ptr<LoraModel>> module_lora_models;
+        for (const auto& lora_spec : loras) {
+            auto lora = load_lora_model(lora_spec, module, module_filter);
+            if (lora == nullptr) {
+                if (lora_spec.required) {
+                    LOG_ERROR("required lora load failed: %s", lora_spec.path.c_str());
+                }
+                continue;
+            }
+            if (lora->lora_tensors.empty()) {
+                continue;
+            }
+
+            lora->preprocess_lora_tensors(model_tensor_names);
+            runtime_lora_models.push_back(lora);
+            module_lora_models.push_back(std::move(lora));
+        }
+        return module_lora_models;
+    }
+
+    void apply_loras_immediately(const std::vector<ModelManager::LoraSpec>& loras) {
+        if (model_manager == nullptr) {
+            if (!loras.empty()) {
+                LOG_WARN("model manager is not available for immediate lora");
+            }
             return;
         }
+
+        clear_lora_adapters();
+        runtime_lora_models.clear();
+
+        model_manager->set_loras(loras, version);
+    }
+
+    void apply_loras_at_runtime(const std::vector<ModelManager::LoraSpec>& loras) {
+        if (model_manager != nullptr) {
+            model_manager->set_loras({}, version);
+        }
+        runtime_lora_models.clear();
+        clear_lora_adapters();
+        if (loras.empty()) {
+            return;
+        }
+
+        std::set<std::string> model_tensor_names;
+        if (model_manager != nullptr) {
+            model_tensor_names = model_manager->tensor_names();
+        }
+
         LOG_INFO("apply lora at runtime");
         if (cond_stage_model) {
-            std::vector<std::shared_ptr<LoraModel>> lora_models;
-            auto lora_state_diff = lora_state;
-            for (auto& lora_model : cond_stage_lora_models) {
-                auto iter = lora_state_diff.find(lora_model->lora_id);
-
-                if (iter != lora_state_diff.end()) {
-                    lora_model->multiplier = iter->second;
-                    lora_models.push_back(lora_model);
-                    lora_state_diff.erase(iter);
-                }
-            }
-            cond_stage_lora_models  = lora_models;
             auto lora_tensor_filter = [&](const std::string& tensor_name) {
                 if (is_cond_stage_model_name(tensor_name)) {
                     return true;
                 }
                 return false;
             };
-            for (auto& kv : lora_state_diff) {
-                const std::string& lora_id = kv.first;
-                float multiplier           = kv.second;
-
-                auto lora = load_lora_model_from_file(lora_id, multiplier, SDBackendModule::TE, lora_tensor_filter);
-                if (lora && !lora->lora_tensors.empty()) {
-                    lora->preprocess_lora_tensors(tensors);
-                    cond_stage_lora_models.push_back(lora);
-                }
-            }
+            auto cond_stage_lora_models =
+                load_runtime_loras_for_module(loras,
+                                              model_tensor_names,
+                                              SDBackendModule::TE,
+                                              lora_tensor_filter);
             // Only attach the adapter when there are LoRAs targeting the cond_stage model.
             // An empty MultiLoraAdapter still routes every linear/conv through
             // forward_with_lora() instead of the direct kernel path — slower for no benefit.
@@ -1708,34 +1728,17 @@ public:
             }
         }
         if (diffusion_model) {
-            std::vector<std::shared_ptr<LoraModel>> lora_models;
-            auto lora_state_diff = lora_state;
-            for (auto& lora_model : diffusion_lora_models) {
-                auto iter = lora_state_diff.find(lora_model->lora_id);
-
-                if (iter != lora_state_diff.end()) {
-                    lora_model->multiplier = iter->second;
-                    lora_models.push_back(lora_model);
-                    lora_state_diff.erase(iter);
-                }
-            }
-            diffusion_lora_models   = lora_models;
             auto lora_tensor_filter = [&](const std::string& tensor_name) {
                 if (is_diffusion_model_name(tensor_name)) {
                     return true;
                 }
                 return false;
             };
-            for (auto& kv : lora_state_diff) {
-                const std::string& lora_name = kv.first;
-                float multiplier             = kv.second;
-
-                auto lora = load_lora_model_from_file(lora_name, multiplier, SDBackendModule::DIFFUSION, lora_tensor_filter);
-                if (lora && !lora->lora_tensors.empty()) {
-                    lora->preprocess_lora_tensors(tensors);
-                    diffusion_lora_models.push_back(lora);
-                }
-            }
+            auto diffusion_lora_models =
+                load_runtime_loras_for_module(loras,
+                                              model_tensor_names,
+                                              SDBackendModule::DIFFUSION,
+                                              lora_tensor_filter);
             if (!diffusion_lora_models.empty()) {
                 auto multi_lora_adapter = std::make_shared<MultiLoraAdapter>(diffusion_lora_models);
                 diffusion_model->set_weight_adapter(multi_lora_adapter);
@@ -1746,34 +1749,17 @@ public:
         }
 
         if (first_stage_model) {
-            std::vector<std::shared_ptr<LoraModel>> lora_models;
-            auto lora_state_diff = lora_state;
-            for (auto& lora_model : first_stage_lora_models) {
-                auto iter = lora_state_diff.find(lora_model->lora_id);
-
-                if (iter != lora_state_diff.end()) {
-                    lora_model->multiplier = iter->second;
-                    lora_models.push_back(lora_model);
-                    lora_state_diff.erase(iter);
-                }
-            }
-            first_stage_lora_models = lora_models;
             auto lora_tensor_filter = [&](const std::string& tensor_name) {
                 if (is_first_stage_model_name(tensor_name)) {
                     return true;
                 }
                 return false;
             };
-            for (auto& kv : lora_state_diff) {
-                const std::string& lora_name = kv.first;
-                float multiplier             = kv.second;
-
-                auto lora = load_lora_model_from_file(lora_name, multiplier, SDBackendModule::VAE, lora_tensor_filter);
-                if (lora && !lora->lora_tensors.empty()) {
-                    lora->preprocess_lora_tensors(tensors);
-                    first_stage_lora_models.push_back(lora);
-                }
-            }
+            auto first_stage_lora_models =
+                load_runtime_loras_for_module(loras,
+                                              model_tensor_names,
+                                              SDBackendModule::VAE,
+                                              lora_tensor_filter);
             if (!first_stage_lora_models.empty()) {
                 auto multi_lora_adapter = std::make_shared<MultiLoraAdapter>(first_stage_lora_models);
                 first_stage_model->set_weight_adapter(multi_lora_adapter);
@@ -1782,46 +1768,42 @@ public:
     }
 
     void lora_stat() {
-        if (!cond_stage_lora_models.empty()) {
-            LOG_INFO("cond_stage_lora_models:");
-            for (auto& lora_model : cond_stage_lora_models) {
-                lora_model->stat();
-            }
-        }
-
-        if (!diffusion_lora_models.empty()) {
-            LOG_INFO("diffusion_lora_models:");
-            for (auto& lora_model : diffusion_lora_models) {
-                lora_model->stat();
-            }
-        }
-
-        if (!first_stage_lora_models.empty()) {
-            LOG_INFO("first_stage_lora_models:");
-            for (auto& lora_model : first_stage_lora_models) {
+        if (!runtime_lora_models.empty()) {
+            LOG_INFO("runtime_lora_models:");
+            for (auto& lora_model : runtime_lora_models) {
                 lora_model->stat();
             }
         }
     }
 
     void apply_loras(const sd_lora_t* loras, uint32_t lora_count) {
-        std::unordered_map<std::string, float> lora_f2m;
+        std::vector<ModelManager::LoraSpec> all_loras;
+        all_loras.reserve(lora_count);
         for (uint32_t i = 0; i < lora_count; i++) {
             std::string lora_id = SAFE_STR(loras[i].path);
+            ModelManager::LoraSpec lora_spec;
+            lora_spec.path          = lora_id;
+            lora_spec.multiplier    = loras[i].multiplier;
+            lora_spec.is_high_noise = loras[i].is_high_noise;
+            all_loras.push_back(std::move(lora_spec));
             if (loras[i].is_high_noise) {
                 lora_id = "|high_noise|" + lora_id;
             }
-            lora_f2m[lora_id] = loras[i].multiplier;
             LOG_DEBUG("lora %s:%.2f", lora_id.c_str(), loras[i].multiplier);
         }
+
+        for (auto& extension : generation_extensions) {
+            extension->collect_loras(all_loras);
+        }
+
         int64_t t0 = ggml_time_ms();
         if (apply_lora_immediately) {
-            apply_loras_immediately(lora_f2m);
+            apply_loras_immediately(all_loras);
         } else {
-            apply_loras_at_runtime(lora_f2m);
+            apply_loras_at_runtime(all_loras);
         }
         int64_t t1 = ggml_time_ms();
-        if (!lora_f2m.empty()) {
+        if (!all_loras.empty()) {
             LOG_INFO("apply_loras completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
         }
     }
@@ -1833,6 +1815,7 @@ public:
     }
 
     void prepare_generation_extensions(const sd_pm_params_t& pm_params,
+                                       const sd_pulid_params_t& pulid_params,
                                        ConditionerParams& condition_params,
                                        int total_steps) {
         reset_generation_extensions();
@@ -1840,11 +1823,9 @@ public:
             cond_stage_model.get(),
             condition_params,
             pm_params,
-            tensors,
-            version,
+            pulid_params,
             n_threads,
             total_steps,
-            free_params_immediately,
         };
 
         for (auto& extension : generation_extensions) {
@@ -2108,11 +2089,17 @@ public:
                                     sd_get_preview_mode()};
     }
 
-    void report_sample_progress(int step, size_t total_steps, int64_t t0) {
-        int64_t t1 = ggml_time_us();
+    void report_sample_progress(int step, size_t total_steps, int64_t* last_progress_us) {
         if (step > 0 || step == -(int)total_steps) {
-            int showstep = std::abs(step);
-            pretty_progress(showstep, (int)total_steps, (t1 - t0) / 1000000.f / showstep);
+            int64_t now        = ggml_time_us();
+            int showstep       = std::abs(step);
+            float step_seconds = last_progress_us != nullptr && *last_progress_us > 0
+                                     ? (now - *last_progress_us) / 1000000.f
+                                     : 0.f;
+            pretty_progress(showstep, (int)total_steps, step_seconds);
+            if (last_progress_us != nullptr) {
+                *last_progress_us = now;
+            }
         }
     }
 
@@ -2166,6 +2153,18 @@ public:
                              float frame_rate,
                              const sd_cache_params_t* cache_params,
                              const sd::Tensor<float>& video_positions = {}) {
+        struct RunnerDoneOnExit {
+            GGMLRunner* runner = nullptr;
+            ~RunnerDoneOnExit() {
+                if (runner != nullptr) {
+                    runner->runner_done();
+                }
+            }
+        };
+        RunnerDoneOnExit sample_diffusion_runner_done{work_diffusion_model.get()};
+
+        RunnerDoneOnExit sample_control_runner_done{!control_image.empty() && control_net != nullptr ? control_net.get() : nullptr};
+
         std::vector<int> skip_layers(guidance.slg.layers, guidance.slg.layers + guidance.slg.layer_count);
         float cfg_scale     = guidance.txt_cfg;
         float img_cfg_scale = guidance.img_cfg;
@@ -2211,7 +2210,7 @@ public:
             noise *= eta;
         }
 
-        int64_t t0                   = ggml_time_us();
+        int64_t last_progress_us     = ggml_time_us();
         sd::Tensor<float> x_t        = !noise.empty()
                                            ? denoiser->noise_scaling(sigmas[0], noise, init_latent)
                                            : init_latent;
@@ -2219,8 +2218,14 @@ public:
         SamplePreviewContext preview = prepare_sample_preview_context();
 
         auto denoise = [&](const sd::Tensor<float>& x, float sigma, int step) -> sd::guidance::GuiderOutput {
+            if (get_cancel_flag() == SD_CANCEL_ALL) {
+                LOG_DEBUG("cancelling generation");
+                return {};
+            }
+
             if (step == 1 || step == -1) {
                 pretty_progress(0, (int)steps, 0);
+                last_progress_us = ggml_time_us();
             }
 
             std::vector<float> scaling = denoiser->get_scalings(sigma);
@@ -2258,7 +2263,7 @@ public:
                 if (sd_should_preview_denoised() && preview.callback != nullptr) {
                     preview_image(step, denoised, version, preview.mode, preview.callback, preview.data, false);
                 }
-                report_sample_progress(step, steps, t0);
+                report_sample_progress(step, steps, &last_progress_us);
                 sd::guidance::GuiderOutput output;
                 output.pred = denoised;
                 return output;
@@ -2338,6 +2343,10 @@ public:
                     return std::move(cached_output);
                 }
 
+                for (const auto& extension : generation_extensions) {
+                    extension->before_diffusion(diffusion_params, step);
+                }
+
                 auto output_opt = work_diffusion_model->compute(n_threads, diffusion_params);
                 if (output_opt.empty()) {
                     LOG_ERROR("diffusion model compute failed");
@@ -2441,7 +2450,7 @@ public:
             if (sd_should_preview_denoised() && preview.callback != nullptr) {
                 preview_image(step, denoised, version, preview.mode, preview.callback, preview.data, false);
             }
-            report_sample_progress(step, steps, t0);
+            report_sample_progress(step, steps, &last_progress_us);
             output.pred = denoised;
             return output;
         };
@@ -2587,15 +2596,6 @@ public:
         if (sd_version_is_pid(version)) {
             return sd::ops::clamp((x + 1.f) * 0.5f, 0.0f, 1.0f);
         }
-        // Free resident diffusion params before VAE allocates its compute buffer.
-        if (stream_layers) {
-            if (diffusion_model) {
-                diffusion_model->release_streaming_residency();
-            }
-            if (high_noise_diffusion_model) {
-                high_noise_diffusion_model->release_streaming_residency();
-            }
-        }
         auto latents = first_stage_model->diffusion_to_vae_latents(x);
         first_stage_model->set_temporal_tiling_enabled(vae_tiling_params.temporal_tiling);
         return first_stage_model->decode(n_threads, latents, vae_tiling_params, decode_video, circular_x, circular_y);
@@ -2624,9 +2624,6 @@ public:
             return {};
         }
         auto waveform = audio_vae_model->decode(n_threads, audio_latent);
-        if (free_params_immediately) {
-            audio_vae_model->free_params_buffer();
-        }
         return waveform;
     }
 
@@ -2946,31 +2943,27 @@ void sd_hires_params_init(sd_hires_params_t* hires_params) {
 }
 
 void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
-    *sd_ctx_params                         = {};
-    sd_ctx_params->vae_decode_only         = true;
-    sd_ctx_params->free_params_immediately = true;
-    sd_ctx_params->n_threads               = sd_get_num_physical_cores();
-    sd_ctx_params->wtype                   = SD_TYPE_COUNT;
-    sd_ctx_params->rng_type                = CUDA_RNG;
-    sd_ctx_params->sampler_rng_type        = RNG_TYPE_COUNT;
-    sd_ctx_params->prediction              = PREDICTION_COUNT;
-    sd_ctx_params->lora_apply_mode         = LORA_APPLY_AUTO;
-    sd_ctx_params->offload_params_to_cpu   = false;
-    sd_ctx_params->max_vram                = 0.f;
-    sd_ctx_params->stream_layers           = false;
-    sd_ctx_params->enable_mmap             = false;
-    sd_ctx_params->keep_clip_on_cpu        = false;
-    sd_ctx_params->keep_control_net_on_cpu = false;
-    sd_ctx_params->keep_vae_on_cpu         = false;
-    sd_ctx_params->diffusion_flash_attn    = false;
-    sd_ctx_params->circular_x              = false;
-    sd_ctx_params->circular_y              = false;
-    sd_ctx_params->chroma_use_dit_mask     = true;
-    sd_ctx_params->chroma_use_t5_mask      = false;
-    sd_ctx_params->chroma_t5_mask_pad      = 1;
-    sd_ctx_params->vae_format              = SD_VAE_FORMAT_AUTO;
-    sd_ctx_params->backend                 = nullptr;
-    sd_ctx_params->params_backend          = nullptr;
+    *sd_ctx_params                      = {};
+    sd_ctx_params->n_threads            = sd_get_num_physical_cores();
+    sd_ctx_params->wtype                = SD_TYPE_COUNT;
+    sd_ctx_params->rng_type             = CUDA_RNG;
+    sd_ctx_params->sampler_rng_type     = RNG_TYPE_COUNT;
+    sd_ctx_params->prediction           = PREDICTION_COUNT;
+    sd_ctx_params->lora_apply_mode      = LORA_APPLY_AUTO;
+    sd_ctx_params->max_vram             = nullptr;
+    sd_ctx_params->stream_layers        = false;
+    sd_ctx_params->enable_mmap          = false;
+    sd_ctx_params->diffusion_flash_attn = false;
+    sd_ctx_params->circular_x           = false;
+    sd_ctx_params->circular_y           = false;
+    sd_ctx_params->chroma_use_dit_mask  = true;
+    sd_ctx_params->chroma_use_t5_mask   = false;
+    sd_ctx_params->chroma_t5_mask_pad   = 1;
+    sd_ctx_params->vae_format           = SD_VAE_FORMAT_AUTO;
+    sd_ctx_params->backend              = nullptr;
+    sd_ctx_params->params_backend       = nullptr;
+    sd_ctx_params->rpc_servers          = nullptr;
+    sd_ctx_params->pulid_weights_path   = nullptr;
 }
 
 char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
@@ -2996,22 +2989,17 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "taesd_path: %s\n"
              "control_net_path: %s\n"
              "photo_maker_path: %s\n"
+             "pulid_weights_path: %s\n"
              "tensor_type_rules: %s\n"
-             "vae_decode_only: %s\n"
-             "free_params_immediately: %s\n"
              "n_threads: %d\n"
              "wtype: %s\n"
              "rng_type: %s\n"
              "sampler_rng_type: %s\n"
              "prediction: %s\n"
-             "offload_params_to_cpu: %s\n"
-             "max_vram: %.3f\n"
+             "max_vram: %s\n"
              "stream_layers: %s\n"
              "backend: %s\n"
              "params_backend: %s\n"
-             "keep_clip_on_cpu: %s\n"
-             "keep_control_net_on_cpu: %s\n"
-             "keep_vae_on_cpu: %s\n"
              "flash_attn: %s\n"
              "diffusion_flash_attn: %s\n"
              "circular_x: %s\n"
@@ -3036,22 +3024,17 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              SAFE_STR(sd_ctx_params->taesd_path),
              SAFE_STR(sd_ctx_params->control_net_path),
              SAFE_STR(sd_ctx_params->photo_maker_path),
+             SAFE_STR(sd_ctx_params->pulid_weights_path),
              SAFE_STR(sd_ctx_params->tensor_type_rules),
-             BOOL_STR(sd_ctx_params->vae_decode_only),
-             BOOL_STR(sd_ctx_params->free_params_immediately),
              sd_ctx_params->n_threads,
              sd_type_name(sd_ctx_params->wtype),
              sd_rng_type_name(sd_ctx_params->rng_type),
              sd_rng_type_name(sd_ctx_params->sampler_rng_type),
              sd_prediction_name(sd_ctx_params->prediction),
-             BOOL_STR(sd_ctx_params->offload_params_to_cpu),
-             sd_ctx_params->max_vram,
+             SAFE_STR(sd_ctx_params->max_vram),
              BOOL_STR(sd_ctx_params->stream_layers),
              SAFE_STR(sd_ctx_params->backend),
              SAFE_STR(sd_ctx_params->params_backend),
-             BOOL_STR(sd_ctx_params->keep_clip_on_cpu),
-             BOOL_STR(sd_ctx_params->keep_control_net_on_cpu),
-             BOOL_STR(sd_ctx_params->keep_vae_on_cpu),
              BOOL_STR(sd_ctx_params->flash_attn),
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
              BOOL_STR(sd_ctx_params->circular_x),
@@ -3136,6 +3119,7 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->batch_count       = 1;
     sd_img_gen_params->control_strength  = 0.9f;
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
+    sd_img_gen_params->pulid_params      = {nullptr, 1.0f};
     sd_img_gen_params->vae_tiling_params = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
     sd_cache_params_init(&sd_img_gen_params->cache);
     sd_hires_params_init(&sd_img_gen_params->hires);
@@ -3278,6 +3262,15 @@ void free_sd_ctx(sd_ctx_t* sd_ctx) {
     free(sd_ctx);
 }
 
+SD_API void sd_cancel_generation(sd_ctx_t* sd_ctx, enum sd_cancel_mode_t mode) {
+    if (sd_ctx && sd_ctx->sd) {
+        if (mode < SD_CANCEL_ALL || mode > SD_CANCEL_RESET) {
+            mode = SD_CANCEL_ALL;
+        }
+        sd_ctx->sd->set_cancel_flag(mode);
+    }
+}
+
 static sd_audio_t* waveform_to_sd_audio(const StableDiffusionGGML* sd,
                                         const sd::Tensor<float>& waveform) {
     if (sd == nullptr || waveform.empty()) {
@@ -3437,6 +3430,7 @@ struct GenerationRequest {
     sd_guidance_params_t guidance            = {};
     sd_guidance_params_t high_noise_guidance = {};
     sd_pm_params_t pm_params                 = {};
+    sd_pulid_params_t pulid_params           = {};
     sd_hires_params_t hires                  = {};
     int frames                               = -1;
     int requested_frames                     = -1;
@@ -3462,6 +3456,7 @@ struct GenerationRequest {
         has_ref_images              = sd_img_gen_params->ref_images_count > 0;
         guidance                    = sd_img_gen_params->sample_params.guidance;
         pm_params                   = sd_img_gen_params->pm_params;
+        pulid_params                = sd_img_gen_params->pulid_params;
         hires                       = sd_img_gen_params->hires;
         cache_params                = &sd_img_gen_params->cache;
         resolve(sd_ctx);
@@ -4074,6 +4069,15 @@ struct ImageGenerationEmbeds {
     SDCondition img_uncond;
 };
 
+struct ConditionerRunnerDoneOnExit {
+    Conditioner* conditioner = nullptr;
+    ~ConditionerRunnerDoneOnExit() {
+        if (conditioner != nullptr) {
+            conditioner->runner_done();
+        }
+    }
+};
+
 struct CircularAxesState {
     bool circular_x = false;
     bool circular_y = false;
@@ -4212,7 +4216,7 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
         }
     }
 
-    if (!control_image_tensor.empty() && !sd_ctx->sd->vae_decode_only) {
+    if (!control_image_tensor.empty()) {
         control_latent = sd_ctx->sd->encode_first_stage(control_image_tensor);
         if (control_latent.empty()) {
             LOG_ERROR("failed to encode control image");
@@ -4369,6 +4373,8 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
                                                                             GenerationRequest* request,
                                                                             SamplePlan* plan,
                                                                             ImageGenerationLatents* latents) {
+    ConditionerRunnerDoneOnExit conditioner_runner_done{sd_ctx->sd->cond_stage_model.get()};
+
     ConditionerParams condition_params;
     condition_params.text       = request->prompt;
     condition_params.clip_skip  = request->clip_skip;
@@ -4377,6 +4383,7 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     condition_params.ref_images = &latents->ref_images;
 
     sd_ctx->sd->prepare_generation_extensions(request->pm_params,
+                                              request->pulid_params,
                                               condition_params,
                                               plan->total_steps);
     int64_t prepare_start_ms         = ggml_time_ms();
@@ -4440,10 +4447,6 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     int64_t t1 = ggml_time_ms();
     LOG_INFO("get_learned_condition completed, taking %.2fs", (t1 - prepare_start_ms) * 1.0f / 1000);
 
-    if (sd_ctx->sd->free_params_immediately) {
-        sd_ctx->sd->cond_stage_model->free_params_buffer();
-    }
-
     ImageGenerationEmbeds embeds;
     embeds.img_uncond = std::move(img_uncond);
     embeds.cond       = std::move(cond);
@@ -4455,22 +4458,33 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
 static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
                                         const GenerationRequest& request,
                                         const std::vector<sd::Tensor<float>>& final_latents) {
-    if (final_latents.size() != static_cast<size_t>(request.batch_count)) {
-        LOG_ERROR("expected %d latents, got %zu", request.batch_count, final_latents.size());
+    if (final_latents.empty()) {
+        LOG_ERROR("no latent images to decode");
         return nullptr;
     }
-    LOG_INFO("decoding %zu latents", final_latents.size());
+    if (final_latents.size() > static_cast<size_t>(request.batch_count)) {
+        LOG_ERROR("expected at most %d latents, got %zu", request.batch_count, final_latents.size());
+        return nullptr;
+    }
+    if (final_latents.size() < static_cast<size_t>(request.batch_count)) {
+        LOG_INFO("decoding %zu/%d latents", final_latents.size(), request.batch_count);
+    } else {
+        LOG_INFO("decoding %zu latents", final_latents.size());
+    }
     std::vector<sd::Tensor<float>> decoded_images;
-    int64_t t0 = ggml_time_ms();
+    int64_t t0     = ggml_time_ms();
+    bool cancelled = false;
 
     for (size_t i = 0; i < final_latents.size(); i++) {
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling latent decodings");
+            cancelled = true;
+            break;
+        }
         int64_t t1              = ggml_time_ms();
         sd::Tensor<float> image = sd_ctx->sd->decode_first_stage(final_latents[i]);
         if (image.empty()) {
             LOG_ERROR("decode_first_stage failed for latent %" PRId64, i + 1);
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->first_stage_model->free_params_buffer();
-            }
             return nullptr;
         }
         decoded_images.push_back(std::move(image));
@@ -4480,8 +4494,9 @@ static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
 
     int64_t t4 = ggml_time_ms();
     LOG_INFO("decode_first_stage completed, taking %.2fs", (t4 - t0) * 1.0f / 1000);
-    if (sd_ctx->sd->free_params_immediately) {
-        sd_ctx->sd->first_stage_model->free_params_buffer();
+    if (decoded_images.empty()) {
+        LOG_ERROR(cancelled ? "cancelled before any latent images were decoded" : "no decoded images");
+        return nullptr;
     }
 
     sd_image_t* result_images = (sd_image_t*)calloc(request.batch_count, sizeof(sd_image_t));
@@ -4501,6 +4516,11 @@ static sd::Tensor<float> upscale_hires_latent(sd_ctx_t* sd_ctx,
                                               const sd::Tensor<float>& latent,
                                               const GenerationRequest& request,
                                               UpscalerGGML* upscaler) {
+    if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+        LOG_ERROR("cancelling hires latent upscale");
+        return {};
+    }
+
     auto get_hires_latent_target_shape = [&]() {
         std::vector<int64_t> target_shape = latent.shape();
         if (target_shape.size() < 2) {
@@ -4562,11 +4582,6 @@ static sd::Tensor<float> upscale_hires_latent(sd_ctx_t* sd_ctx,
     } else if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL ||
                request.hires.upscaler == SD_HIRES_UPSCALER_LANCZOS ||
                request.hires.upscaler == SD_HIRES_UPSCALER_NEAREST) {
-        if (sd_ctx->sd->vae_decode_only) {
-            LOG_ERROR("hires %s upscaler requires VAE encoder weights; create the context with vae_decode_only=false",
-                      sd_hires_upscaler_name(request.hires.upscaler));
-            return {};
-        }
         if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL && upscaler == nullptr) {
             LOG_ERROR("hires model upscaler context is null");
             return {};
@@ -4578,6 +4593,10 @@ static sd::Tensor<float> upscale_hires_latent(sd_ctx_t* sd_ctx,
                       sd_hires_upscaler_name(request.hires.upscaler));
             return {};
         }
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling hires image upscale");
+            return {};
+        }
 
         sd::Tensor<float> upscaled_tensor;
         if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL) {
@@ -4614,6 +4633,10 @@ static sd::Tensor<float> upscale_hires_latent(sd_ctx_t* sd_ctx,
             upscaled_tensor = sd::ops::clamp(upscaled_tensor, 0.0f, 1.0f);
         }
 
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling hires latent encode");
+            return {};
+        }
         sd::Tensor<float> upscaled_latent = sd_ctx->sd->encode_first_stage(upscaled_tensor);
         if (upscaled_latent.empty()) {
             LOG_ERROR("encode_first_stage failed after hires %s upscale",
@@ -4678,6 +4701,8 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         return nullptr;
     }
 
+    sd_ctx->sd->reset_cancel_flag();
+
     int64_t t0                    = ggml_time_ms();
     sd_ctx->sd->vae_tiling_params = sd_img_gen_params->vae_tiling_params;
     GenerationRequest request(sd_ctx, sd_img_gen_params);
@@ -4713,6 +4738,18 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
     std::vector<sd::Tensor<float>> final_latents;
     int64_t denoise_start = ggml_time_ms();
     for (int b = 0; b < request.batch_count; b++) {
+        sd_cancel_mode_t cancel = sd_ctx->sd->get_cancel_flag();
+        if (cancel == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation");
+            return nullptr;
+        }
+        if (cancel == SD_CANCEL_NEW_LATENTS) {
+            LOG_INFO("cancelling new latent generation, returning %zu/%d completed latents",
+                     final_latents.size(),
+                     request.batch_count);
+            break;
+        }
+
         int64_t sampling_start = ggml_time_ms();
         int64_t cur_seed       = request.seed + b;
         LOG_INFO("generating image: %i/%i - seed %" PRId64, b + 1, request.batch_count, cur_seed);
@@ -4756,39 +4793,41 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                   b + 1,
                   request.batch_count,
                   (sampling_end - sampling_start) * 1.0f / 1000);
-        if (sd_ctx->sd->free_params_immediately) {
-            sd_ctx->sd->diffusion_model->free_params_buffer();
-        }
         return nullptr;
     }
-    if (sd_ctx->sd->free_params_immediately && !request.hires.enabled) {
-        sd_ctx->sd->diffusion_model->free_params_buffer();
-    }
     int64_t denoise_end = ggml_time_ms();
     LOG_INFO("generating %zu latent images completed, taking %.2fs",
              final_latents.size(),
              (denoise_end - denoise_start) * 1.0f / 1000);
+    if (final_latents.empty()) {
+        LOG_ERROR("no latent images generated");
+        return nullptr;
+    }
 
     if (request.hires.enabled && request.hires.target_width > 0) {
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation before hires fix");
+            return nullptr;
+        }
         LOG_INFO("hires fix: upscaling to %dx%d", request.hires.target_width, request.hires.target_height);
 
         std::unique_ptr<UpscalerGGML> hires_upscaler;
         if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL) {
+            if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+                LOG_ERROR("cancelling generation before hires model load");
+                return nullptr;
+            }
             LOG_INFO("hires fix: loading model upscaler from '%s'", request.hires.model_path);
             hires_upscaler                    = std::make_unique<UpscalerGGML>(sd_ctx->sd->n_threads,
                                                             false,
                                                             request.hires.upscale_tile_size,
                                                             sd_ctx->sd->backend_spec,
                                                             sd_ctx->sd->params_backend_spec);
-            const size_t max_graph_vram_bytes = sd::ggml_graph_cut::max_vram_gib_to_bytes(sd_ctx->sd->max_vram);
+            const size_t max_graph_vram_bytes = sd_ctx->sd->max_graph_vram_bytes_for_module(SDBackendModule::UPSCALER);
             hires_upscaler->set_max_graph_vram_bytes(max_graph_vram_bytes);
             if (!hires_upscaler->load_from_file(request.hires.model_path,
-                                                sd_ctx->sd->offload_params_to_cpu,
                                                 sd_ctx->sd->n_threads)) {
                 LOG_ERROR("load hires model upscaler failed");
-                if (sd_ctx->sd->free_params_immediately) {
-                    sd_ctx->sd->diffusion_model->free_params_buffer();
-                }
                 return nullptr;
             }
         }
@@ -4811,6 +4850,10 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         std::vector<sd::Tensor<float>> hires_final_latents;
         int64_t hires_denoise_start = ggml_time_ms();
         for (int b = 0; b < (int)final_latents.size(); b++) {
+            if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+                LOG_ERROR("cancelling generation during hires fix");
+                return nullptr;
+            }
             int64_t cur_seed = request.seed + b;
             sd_ctx->sd->rng->manual_seed(cur_seed);
             sd_ctx->sd->sampler_rng->manual_seed(cur_seed);
@@ -4820,9 +4863,6 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                               request,
                                                               hires_upscaler.get());
             if (upscaled.empty()) {
-                if (sd_ctx->sd->free_params_immediately) {
-                    sd_ctx->sd->diffusion_model->free_params_buffer();
-                }
                 return nullptr;
             }
 
@@ -4877,14 +4917,8 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                       b + 1,
                       (int)final_latents.size(),
                       (hires_sample_end - hires_sample_start) * 1.0f / 1000);
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->diffusion_model->free_params_buffer();
-            }
             return nullptr;
         }
-        if (sd_ctx->sd->free_params_immediately) {
-            sd_ctx->sd->diffusion_model->free_params_buffer();
-        }
         int64_t hires_denoise_end = ggml_time_ms();
         LOG_INFO("hires fix completed, taking %.2fs", (hires_denoise_end - hires_denoise_start) * 1.0f / 1000);
 
@@ -4932,11 +4966,6 @@ static std::optional<ImageGenerationLatents> prepare_video_generation_latents(sd
         }
 
         if (!start_image.empty() || !end_image.empty()) {
-            if (sd_ctx->sd->vae_decode_only) {
-                LOG_ERROR("LTXAV image conditioning requires VAE encoder weights; create the context with vae_decode_only=false");
-                return std::nullopt;
-            }
-
             if (!start_image.empty() && !end_image.empty()) {
                 LOG_INFO("FLF2V");
             } else if (!start_image.empty()) {
@@ -5220,6 +5249,8 @@ static ImageGenerationEmbeds prepare_video_generation_embeds(sd_ctx_t* sd_ctx,
                                                              const sd_vid_gen_params_t* sd_vid_gen_params,
                                                              const GenerationRequest& request,
                                                              const ImageGenerationLatents& latents) {
+    ConditionerRunnerDoneOnExit conditioner_runner_done{sd_ctx->sd->cond_stage_model.get()};
+
     ImageGenerationEmbeds embeds;
     ConditionerParams condition_params;
     condition_params.clip_skip       = request.clip_skip;
@@ -5242,9 +5273,6 @@ static ImageGenerationEmbeds prepare_video_generation_embeds(sd_ctx_t* sd_ctx,
     int64_t t1 = ggml_time_ms();
     LOG_INFO("get_learned_condition completed, taking %.2fs", (t1 - prepare_start_ms) * 1.0f / 1000);
 
-    if (sd_ctx->sd->free_params_immediately) {
-        sd_ctx->sd->cond_stage_model->free_params_buffer();
-    }
     return embeds;
 }
 
@@ -5256,6 +5284,10 @@ static sd_image_t* decode_video_outputs(sd_ctx_t* sd_ctx,
         LOG_ERROR("no latent video to decode");
         return nullptr;
     }
+    if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+        LOG_ERROR("cancelling video decode");
+        return nullptr;
+    }
     sd::Tensor<float> video_latent = final_latent;
     if (sd_version_is_ltxav(sd_ctx->sd->version) &&
         video_latent.shape()[3] > sd_ctx->sd->get_latent_channel()) {
@@ -5271,9 +5303,6 @@ static sd_image_t* decode_video_outputs(sd_ctx_t* sd_ctx,
     sd::Tensor<float> vid = sd_ctx->sd->decode_first_stage(video_latent, true);
     int64_t t5            = ggml_time_ms();
     LOG_INFO("decode_first_stage completed, taking %.2fs", (t5 - t4) * 1.0f / 1000);
-    if (sd_ctx->sd->free_params_immediately) {
-        sd_ctx->sd->first_stage_model->free_params_buffer();
-    }
     if (vid.empty()) {
         LOG_ERROR("decode_first_stage failed for video");
         return nullptr;
@@ -5338,17 +5367,40 @@ static sd::Tensor<float> upscale_ltx_spatial_video_latent(sd_ctx_t* sd_ctx,
         return {};
     }
 
+    auto upsampler_manager = std::make_shared<ModelManager>();
+    upsampler_manager->set_n_threads(sd_ctx->sd->n_threads);
+    upsampler_manager->set_enable_mmap(sd_ctx->sd->enable_mmap);
+    ModelLoader& model_loader = upsampler_manager->loader();
+    if (!model_loader.init_from_file(model_path)) {
+        LOG_ERROR("init LTX latent upsampler model loader from file failed: '%s'", model_path);
+        return {};
+    }
+
     std::unique_ptr<LTXVUpsampler::LatentUpsamplerRunner> upsampler =
         std::make_unique<LTXVUpsampler::LatentUpsamplerRunner>(sd_ctx->sd->backend_for(SDBackendModule::UPSCALER),
-                                                               sd_ctx->sd->params_backend_for(SDBackendModule::UPSCALER));
-    const size_t max_graph_vram_bytes = sd::ggml_graph_cut::max_vram_gib_to_bytes(sd_ctx->sd->max_vram);
+                                                               model_loader.get_tensor_storage_map(),
+                                                               upsampler_manager);
+    const size_t max_graph_vram_bytes = sd_ctx->sd->max_graph_vram_bytes_for_module(SDBackendModule::UPSCALER);
     upsampler->set_max_graph_vram_bytes(max_graph_vram_bytes);
-    if (!upsampler->load_from_file(model_path, sd_ctx->sd->n_threads)) {
-        LOG_ERROR("load LTX latent upsampler failed");
+    if (upsampler->model == nullptr) {
+        LOG_ERROR("init LTX latent upsampler from metadata failed");
+        return {};
+    }
+
+    std::map<std::string, ggml_tensor*> tensors;
+    upsampler->get_param_tensors(tensors);
+    if (!upsampler_manager->register_param_tensors("LTX latent upsampler",
+                                                   std::move(tensors),
+                                                   ModelManager::ResidencyMode::ParamBackend,
+                                                   sd_ctx->sd->backend_for(SDBackendModule::UPSCALER),
+                                                   sd_ctx->sd->params_backend_for(SDBackendModule::UPSCALER)) ||
+        !upsampler_manager->validate_registered_tensors()) {
+        LOG_ERROR("register LTX latent upsampler tensors with model manager failed");
         return {};
     }
 
     sd::Tensor<float> upscaled = upsampler->compute(sd_ctx->sd->n_threads, unnormalized);
+    upsampler_manager.reset();
     upsampler.reset();
     if (upscaled.empty()) {
         LOG_ERROR("LTX latent spatial upscale failed");
@@ -5382,11 +5434,6 @@ static bool apply_ltxv_refine_image_conditioning(sd_ctx_t* sd_ctx,
         sd_vid_gen_params->end_image.data == nullptr) {
         return true;
     }
-    if (sd_ctx->sd->vae_decode_only) {
-        LOG_ERROR("LTXV refine image conditioning requires VAE encoder weights; create the context with vae_decode_only=false");
-        return false;
-    }
-
     constexpr float conditioning_strength = 1.f;
     int latent_channels                   = sd_ctx->sd->get_latent_channel();
     sd::Tensor<float> video_latent        = *latent;
@@ -5486,6 +5533,9 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     if (audio_out != nullptr) {
         *audio_out = nullptr;
     }
+
+    sd_ctx->sd->reset_cancel_flag();
+
     if (num_frames_out != nullptr) {
         *num_frames_out = 0;
     }
@@ -5547,6 +5597,10 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     sd::Tensor<float> noise = sd::Tensor<float>::randn_like(x_t, sd_ctx->sd->rng);
 
     if (plan.high_noise_sample_steps > 0) {
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation before high-noise sampling");
+            return false;
+        }
         LOG_DEBUG("sample(high noise) %dx%dx%d", W, H, T);
 
         int64_t sampling_start = ggml_time_ms();
@@ -5581,20 +5635,18 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
         int64_t sampling_end          = ggml_time_ms();
         if (x_t_sampled.empty()) {
             LOG_ERROR("sampling(high noise) failed after %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->high_noise_diffusion_model->free_params_buffer();
-            }
             return false;
         }
 
         x_t   = std::move(x_t_sampled);
         noise = {};
         LOG_INFO("sampling(high noise) completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
-        if (sd_ctx->sd->free_params_immediately) {
-            sd_ctx->sd->high_noise_diffusion_model->free_params_buffer();
-        }
     }
 
+    if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+        LOG_ERROR("cancelling generation before sampling");
+        return false;
+    }
     LOG_DEBUG("sample %dx%dx%d", W, H, T);
     int64_t sampling_start         = ggml_time_ms();
     sd::Tensor<float> final_latent = sd_ctx->sd->sample(sd_ctx->sd->diffusion_model,
@@ -5625,15 +5677,16 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
 
     int64_t sampling_end = ggml_time_ms();
     if (final_latent.empty()) {
-        if (sd_ctx->sd->free_params_immediately) {
-            sd_ctx->sd->diffusion_model->free_params_buffer();
-        }
         LOG_ERROR("sampling failed after %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
         return false;
     }
     LOG_INFO("sampling completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
 
     if (latent_upscale_enabled) {
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation before latent upscale");
+            return false;
+        }
         int64_t upscale_start             = ggml_time_ms();
         sd::Tensor<float> upscaled_latent = upscale_ltx_spatial_video_latent(sd_ctx,
                                                                              request.hires.model_path,
@@ -5641,9 +5694,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                                              latents.audio_length);
         int64_t upscale_end               = ggml_time_ms();
         if (upscaled_latent.empty()) {
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->diffusion_model->free_params_buffer();
-            }
             return false;
         }
         LOG_INFO("LTX latent spatial upscale completed, taking %.2fs",
@@ -5676,9 +5726,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                     LOG_ERROR("failed to resize LTX audio latent for latent upscale: %d -> %d",
                               latents.audio_length,
                               target_audio_length);
-                    if (sd_ctx->sd->free_params_immediately) {
-                        sd_ctx->sd->diffusion_model->free_params_buffer();
-                    }
                     return false;
                 }
                 x_t                  = pack_ltxav_audio_and_video_latents(video_latent, audio_latent);
@@ -5699,6 +5746,10 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
         }
         sd::Tensor<float> hires_denoise_mask;
         sd::Tensor<float> hires_video_positions;
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation before latent upscale refine");
+            return false;
+        }
         if (!apply_ltxv_refine_image_conditioning(sd_ctx,
                                                   sd_vid_gen_params,
                                                   hires_request,
@@ -5706,9 +5757,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                   &x_t,
                                                   &hires_denoise_mask,
                                                   &hires_video_positions)) {
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->diffusion_model->free_params_buffer();
-            }
             return false;
         }
         noise = sd::Tensor<float>::randn_like(x_t, sd_ctx->sd->rng);
@@ -5765,9 +5813,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                             hires_request.cache_params,
                                             hires_video_positions);
         sampling_end   = ggml_time_ms();
-        if (sd_ctx->sd->free_params_immediately) {
-            sd_ctx->sd->diffusion_model->free_params_buffer();
-        }
         if (final_latent.empty()) {
             LOG_ERROR("sampling(latent upscale) failed after %.2fs",
                       (sampling_end - sampling_start) * 1.0f / 1000);
@@ -5775,8 +5820,6 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
         }
         LOG_INFO("sampling(latent upscale) completed, taking %.2fs",
                  (sampling_end - sampling_start) * 1.0f / 1000);
-    } else if (sd_ctx->sd->free_params_immediately) {
-        sd_ctx->sd->diffusion_model->free_params_buffer();
     }
 
     int64_t latent_end = ggml_time_ms();
@@ -5786,6 +5829,10 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     if (sd_version_is_ltxav(sd_ctx->sd->version) &&
         latents.audio_length > 0 &&
         sd_ctx->sd->audio_vae_model != nullptr) {
+        if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+            LOG_ERROR("cancelling generation before audio decode");
+            return false;
+        }
         int64_t audio_latent_decode_start = ggml_time_ms();
 
         auto audio_latent = unpack_ltxav_audio_latent(final_latent,
@@ -5818,6 +5865,11 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
         final_latent = sd::ops::slice(final_latent, 2, latents.ref_image_num, final_latent.shape()[2]);
     }
 
+    if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+        LOG_ERROR("cancelling generation before video decode");
+        free_sd_audio(generated_audio);
+        return false;
+    }
     auto result = decode_video_outputs(sd_ctx, latent_upscale_enabled ? hires_request : request, final_latent, num_frames_out);
     if (result == nullptr) {
         free_sd_audio(generated_audio);
diff --git a/otherarch/sdcpp/src/upscaler.cpp b/otherarch/sdcpp/src/upscaler.cpp
index 8635f6778..d02366ecb 100644
--- a/otherarch/sdcpp/src/upscaler.cpp
+++ b/otherarch/sdcpp/src/upscaler.cpp
@@ -18,6 +18,12 @@ UpscalerGGML::UpscalerGGML(int n_threads,
       params_backend_spec(std::move(params_backend_spec)) {
 }
 
+UpscalerGGML::~UpscalerGGML() {
+    // ModelManager holds raw ggml tensor pointers owned by the runner context.
+    model_manager.reset();
+    esrgan_upscaler.reset();
+}
+
 void UpscalerGGML::set_max_graph_vram_bytes(size_t max_vram_bytes) {
     max_graph_vram_bytes = max_vram_bytes;
     if (esrgan_upscaler) {
@@ -33,17 +39,12 @@ void UpscalerGGML::set_stream_layers_enabled(bool enabled) {
 }
 
 bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
-                                  bool offload_params_to_cpu,
                                   int n_threads) {
     ggml_log_set(ggml_log_callback_default, nullptr);
 
     std::string error;
     if (!backend_manager.init(backend_spec.c_str(),
                               params_backend_spec.c_str(),
-                              offload_params_to_cpu,
-                              false,
-                              false,
-                              false,
                               &error)) {
         LOG_ERROR("upscaler backend config failed: %s", error.c_str());
         return false;
@@ -72,22 +73,39 @@ bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
         return false;
     }
 
-    ModelLoader model_loader;
-    if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
+    model_manager = std::make_shared<ModelManager>();
+    model_manager->set_n_threads(n_threads);
+    model_manager->set_enable_mmap(false);
+
+    ModelLoader& model_loader = model_manager->loader();
+    if (!model_loader.init_from_file_and_convert_name(esrgan_path, "", VERSION_ESRGAN)) {
         LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
+        return false;
     }
     model_loader.set_wtype_override(model_data_type);
     LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
     esrgan_upscaler = std::make_shared<ESRGAN>(backend_for(SDBackendModule::UPSCALER),
-                                               params_backend_for(SDBackendModule::UPSCALER),
-                                               tile_size,
-                                               model_loader.get_tensor_storage_map());
+                                               model_loader.get_tensor_storage_map(),
+                                               model_manager);
+    if (esrgan_upscaler == nullptr || esrgan_upscaler->rrdb_net == nullptr) {
+        LOG_ERROR("init esrgan model from metadata failed: '%s'", esrgan_path.c_str());
+        return false;
+    }
     esrgan_upscaler->set_max_graph_vram_bytes(max_graph_vram_bytes);
     esrgan_upscaler->set_stream_layers_enabled(stream_layers_enabled);
     if (direct) {
         esrgan_upscaler->set_conv2d_direct_enabled(true);
     }
-    if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
+
+    std::map<std::string, ggml_tensor*> tensors;
+    esrgan_upscaler->get_param_tensors(tensors);
+    if (!model_manager->register_param_tensors("ESRGAN",
+                                               std::move(tensors),
+                                               backend_manager.params_backend_is_disk(SDBackendModule::UPSCALER) ? ModelManager::ResidencyMode::Disk : ModelManager::ResidencyMode::ParamBackend,
+                                               backend_for(SDBackendModule::UPSCALER),
+                                               params_backend_for(SDBackendModule::UPSCALER)) ||
+        !model_manager->validate_registered_tensors()) {
+        LOG_ERROR("register esrgan tensors with model manager failed");
         return false;
     }
     return true;
@@ -95,6 +113,7 @@ bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
 
 sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_tensor) {
     sd::Tensor<float> upscaled;
+    const int scale = esrgan_upscaler->config.scale;
     if (tile_size <= 0 || (input_tensor.shape()[0] <= tile_size && input_tensor.shape()[1] <= tile_size)) {
         upscaled = esrgan_upscaler->compute(n_threads, input_tensor);
     } else {
@@ -108,9 +127,9 @@ sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_te
         };
 
         upscaled = process_tiles_2d(input_tensor,
-                                    static_cast<int>(input_tensor.shape()[0] * esrgan_upscaler->scale),
-                                    static_cast<int>(input_tensor.shape()[1] * esrgan_upscaler->scale),
-                                    esrgan_upscaler->scale,
+                                    static_cast<int>(input_tensor.shape()[0] * scale),
+                                    static_cast<int>(input_tensor.shape()[1] * scale),
+                                    scale,
                                     tile_size,
                                     tile_size,
                                     0.25f,
@@ -129,8 +148,9 @@ sd::Tensor<float> UpscalerGGML::upscale_tensor(const sd::Tensor<float>& input_te
 sd_image_t UpscalerGGML::upscale(sd_image_t input_image, uint32_t upscale_factor) {
     // upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
     sd_image_t upscaled_image = {0, 0, 0, nullptr};
-    int output_width          = (int)input_image.width * esrgan_upscaler->scale;
-    int output_height         = (int)input_image.height * esrgan_upscaler->scale;
+    const int scale           = esrgan_upscaler->config.scale;
+    int output_width          = (int)input_image.width * scale;
+    int output_height         = (int)input_image.height * scale;
     LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
              input_image.width, input_image.height, output_width, output_height);
 
@@ -153,7 +173,6 @@ struct upscaler_ctx_t {
 };
 
 upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
-                                 bool offload_params_to_cpu,
                                  bool direct,
                                  int n_threads,
                                  int tile_size,
@@ -170,7 +189,7 @@ upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
         return nullptr;
     }
 
-    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu, n_threads)) {
+    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, n_threads)) {
         delete upscaler_ctx->upscaler;
         upscaler_ctx->upscaler = nullptr;
         free(upscaler_ctx);
@@ -187,7 +206,7 @@ int get_upscale_factor(upscaler_ctx_t* upscaler_ctx) {
     if (upscaler_ctx == nullptr || upscaler_ctx->upscaler == nullptr || upscaler_ctx->upscaler->esrgan_upscaler == nullptr) {
         return 1;
     }
-    return upscaler_ctx->upscaler->esrgan_upscaler->scale;
+    return upscaler_ctx->upscaler->esrgan_upscaler->config.scale;
 }
 
 void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx) {
diff --git a/otherarch/sdcpp/src/upscaler.h b/otherarch/sdcpp/src/upscaler.h
index c9fce1844..38150f59f 100644
--- a/otherarch/sdcpp/src/upscaler.h
+++ b/otherarch/sdcpp/src/upscaler.h
@@ -4,6 +4,7 @@
 #include "core/ggml_extend_backend.h"
 #include "core/tensor.hpp"
 #include "model/upscaler/esrgan.hpp"
+#include "model_manager.h"
 #include "stable-diffusion.h"
 
 #include <memory>
@@ -11,6 +12,7 @@
 
 struct UpscalerGGML {
     SDBackendManager backend_manager;
+    std::shared_ptr<ModelManager> model_manager;
     ggml_type model_data_type = GGML_TYPE_F16;
     std::shared_ptr<ESRGAN> esrgan_upscaler;
     std::string esrgan_path;
@@ -27,9 +29,9 @@ struct UpscalerGGML {
                  int tile_size                   = 128,
                  std::string backend_spec        = "",
                  std::string params_backend_spec = "");
+    ~UpscalerGGML();
 
     bool load_from_file(const std::string& esrgan_path,
-                        bool offload_params_to_cpu,
                         int n_threads);
     void set_max_graph_vram_bytes(size_t max_vram_bytes);
     void set_stream_layers_enabled(bool enabled);
diff --git a/otherarch/sdcpp/src/weight_manager.h b/otherarch/sdcpp/src/weight_manager.h
new file mode 100644
index 000000000..28d6cf5c4
--- /dev/null
+++ b/otherarch/sdcpp/src/weight_manager.h
@@ -0,0 +1,15 @@
+#ifndef __WEIGHT_MANAGER_H__
+#define __WEIGHT_MANAGER_H__
+
+#include <vector>
+
+struct ggml_tensor;
+
+struct RunnerWeightManager {
+    virtual ~RunnerWeightManager()                                                        = default;
+    virtual bool prepare_params(const std::vector<ggml_tensor*>& tensors)                 = 0;
+    virtual void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) = 0;
+    virtual void release_params_backend_params(const std::vector<ggml_tensor*>& tensors)  = 0;
+};
+
+#endif  // __WEIGHT_MANAGER_H__

From 1e81db242685390d4c0b2612f8ab9efa01a616b0 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 17 Jun 2026 16:35:58 +0800
Subject: [PATCH 014/292] updated cmake

---
 CMakeLists.txt | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c44618e7e..40816ed33 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -495,8 +495,9 @@ add_library(sdtype_adapter
             otherarch/sdcpp/src/core/ggml_graph_cut.cpp
             otherarch/sdcpp/src/core/ggml_graph_cut.h
             otherarch/sdcpp/examples/cli/image_metadata.cpp
-            otherarch/sdcpp/src/core/layer_registry.cpp
-            otherarch/sdcpp/src/core/layer_registry.h
+            otherarch/sdcpp/src/model_manager.cpp
+            otherarch/sdcpp/src/model_manager.h
+            otherarch/sdcpp/src/extensions/pulid_extension.cpp
             otherarch/sdcpp/src/model_loader.cpp
             otherarch/sdcpp/src/extensions/photomaker_extension.cpp
             otherarch/sdcpp/src/runtime/sample-cache.cpp

From ea21e03955e116423890af168b03360bd6bbfb2a Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <rortlam@redhat.com>
Date: Wed, 17 Jun 2026 10:59:35 +0200
Subject: [PATCH 015/292] Revert "cuda: reset cuda context after reading memory
 size (#23935)" (#24715)

This reverts commit 0f7fada56bea32c9e0db3874b801d887ff6c4529.
---
 ggml/src/ggml-cuda/ggml-cuda.cu | 75 ++++-----------------------------
 1 file changed, 9 insertions(+), 66 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 01c29a8c6..34cdbc81c 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -622,18 +622,6 @@ ggml_backend_cuda_context::~ggml_backend_cuda_context() {
 
 // cuda buffer
 
-struct ggml_backend_cuda_device_context {
-    int device;
-    std::string name;
-    std::string description;
-    std::string pci_bus_id;
-    int op_offload_min_batch_size;
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    std::mutex device_mutex;
-    int active_count = 0;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-};
-
 struct ggml_backend_cuda_buffer_context {
     int device;
     void * dev_ptr = nullptr;
@@ -651,13 +639,6 @@ struct ggml_backend_cuda_buffer_context {
 
 static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
-
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) buffer->buft->device->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count--;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     delete ctx;
 }
 
@@ -810,12 +791,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_bac
 
     ggml_backend_cuda_buffer_context * ctx = new ggml_backend_cuda_buffer_context(buft_ctx->device, dev_ptr);
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) buft->device->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count++;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     return ggml_backend_buffer_init(buft, ggml_backend_cuda_buffer_interface, ctx, size);
 }
 
@@ -1515,12 +1490,6 @@ static bool ggml_backend_buft_is_cuda_host(ggml_backend_buffer_type_t buft) {
 }
 
 static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) buffer->buft->device->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count--;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     CUDA_CHECK(cudaFreeHost(buffer->context));
 }
 
@@ -1529,8 +1498,6 @@ static void * ggml_cuda_host_malloc(size_t size) {
         return nullptr;
     }
 
-    ggml_cuda_set_device(0); // cudaMallocHost can create the implicit CUDA device context, make sure that this is consistently done on device 0.
-
     void * ptr = nullptr;
     cudaError_t err = cudaMallocHost((void **) &ptr, size);
     if (err != cudaSuccess) {
@@ -1556,12 +1523,6 @@ static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggm
     buffer->buft = buft;
     buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) buft->device->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count++;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     return buffer;
 }
 
@@ -3179,12 +3140,6 @@ static const char * ggml_backend_cuda_get_name(ggml_backend_t backend) {
 static void ggml_backend_cuda_free(ggml_backend_t backend) {
     ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) backend->device->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count--;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     delete cuda_ctx;
     delete backend;
 }
@@ -4916,6 +4871,14 @@ void ggml_backend_cuda_unregister_host_buffer(void * buffer) {
 
 // backend device
 
+struct ggml_backend_cuda_device_context {
+    int device;
+    std::string name;
+    std::string description;
+    std::string pci_bus_id;
+    int op_offload_min_batch_size;
+};
+
 static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
     ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
     return ctx->name.c_str();
@@ -5004,11 +4967,6 @@ static bool ggml_backend_cuda_get_available_uma_memory(long * available_memory_k
 
 static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
     ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
-
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    std::lock_guard<std::mutex> lock(ctx->device_mutex);
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     ggml_cuda_set_device(ctx->device);
     CUDA_CHECK(cudaMemGetInfo(free, total));
 
@@ -5035,13 +4993,6 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t *
     }
 #endif // defined(__linux__)
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    // If no backends or buffers are active, the cudaMemGetInfo call above lazily created a CUDA
-    // context that permanently consumes VRAM. Reset the device to free it.
-    if (ctx->active_count == 0) {
-        CUDA_CHECK(cudaDeviceReset());
-    }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 }
 
 static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend_dev_t dev) {
@@ -5745,21 +5696,13 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
         return nullptr;
     }
 
-    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), device);
-
     ggml_backend_t cuda_backend = new ggml_backend {
         /* .guid    = */ ggml_backend_cuda_guid(),
         /* .iface   = */ ggml_backend_cuda_interface,
-        /* .device  = */ dev,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_cuda_reg(), device),
         /* .context = */ ctx,
     };
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *) dev->context;
-    std::lock_guard<std::mutex> lock(dev_ctx->device_mutex);
-    dev_ctx->active_count++;
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-
     return cuda_backend;
 }
 

From 558e221b70c63d4bb1d54b73f9a0bdd3d9475a8f Mon Sep 17 00:00:00 2001
From: Winston Ma <winstonma@ymail.com>
Date: Wed, 17 Jun 2026 17:14:48 +0800
Subject: [PATCH 016/292] vulkan: record actual memory properties during buffer
 creation (#24326)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 243ab76ee..9a36b45de 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -3008,13 +3008,13 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std
             if (memory_type_indices.empty()) {
                 continue;
             }
-            buf->memory_property_flags = req_flags;
 
             bool done = false;
 
             for (auto mtype_it = memory_type_indices.begin(); mtype_it != memory_type_indices.end(); mtype_it++) {
                 try {
                     buf->device_memory = device->device.allocateMemory({ mem_req.size, *mtype_it, &mem_flags_info });
+                    buf->memory_property_flags = mem_props.memoryTypes[*mtype_it].propertyFlags;
                     done = true;
                     break;
                 } catch (const vk::SystemError& e) {

From 8086439a4cea94c71a5dfb8fe4ad1546aebd640f Mon Sep 17 00:00:00 2001
From: Julien Chaumond <julien@huggingface.co>
Date: Wed, 17 Jun 2026 13:25:47 +0200
Subject: [PATCH 017/292] webui: export conversations as jsonl (#24688)

* webui: export conversations as jsonl

each session is one jsonl file, a session header line followed by one line per message
exporting multiple conversations bundles them into a zip, one jsonl file each

* webui: import jsonl and zip conversation exports

parse the new jsonl session format and zip archives on import
keep supporting the legacy json format
---
 tools/ui/package-lock.json                    |   8 +
 tools/ui/package.json                         |   1 +
 .../SettingsChatImportExportTab.svelte        |  38 ++--
 tools/ui/src/lib/enums/files.enums.ts         |   6 +-
 .../ui/src/lib/stores/conversations.svelte.ts | 180 ++++++++++++++++--
 5 files changed, 189 insertions(+), 44 deletions(-)

diff --git a/tools/ui/package-lock.json b/tools/ui/package-lock.json
index 61fa529d2..9d0cdfea6 100644
--- a/tools/ui/package-lock.json
+++ b/tools/ui/package-lock.json
@@ -40,6 +40,7 @@
 				"eslint-config-prettier": "10.1.8",
 				"eslint-plugin-storybook": "10.4.2",
 				"eslint-plugin-svelte": "3.19.0",
+				"fflate": "0.8.3",
 				"globals": "16.5.0",
 				"highlight.js": "11.11.1",
 				"http-server": "14.1.1",
@@ -9454,6 +9455,13 @@
 				}
 			}
 		},
+		"node_modules/fflate": {
+			"version": "0.8.3",
+			"resolved": "https://registry.npmjs.org/fflate/-/fflate-0.8.3.tgz",
+			"integrity": "sha512-tbZNuJrLwGUp3zshBtdy4W+ORxZuIh8a5ilyIEQDC5rY1f3U20JMry0Ll3WBzU58EZKsEuJFXhb5gwv8CsPvgA==",
+			"dev": true,
+			"license": "MIT"
+		},
 		"node_modules/file-entry-cache": {
 			"version": "8.0.0",
 			"resolved": "https://registry.npmjs.org/file-entry-cache/-/file-entry-cache-8.0.0.tgz",
diff --git a/tools/ui/package.json b/tools/ui/package.json
index 2f47992e1..480392288 100644
--- a/tools/ui/package.json
+++ b/tools/ui/package.json
@@ -59,6 +59,7 @@
 		"eslint-config-prettier": "10.1.8",
 		"eslint-plugin-storybook": "10.4.2",
 		"eslint-plugin-svelte": "3.19.0",
+		"fflate": "0.8.3",
 		"globals": "16.5.0",
 		"highlight.js": "11.11.1",
 		"http-server": "14.1.1",
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
index b7b91d65b..a86d68584 100644
--- a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChatImportExportTab.svelte
@@ -132,14 +132,18 @@
 
 	async function handleExportConfirm(selectedConversations: DatabaseConversation[]) {
 		try {
-			const allData: ExportedConversations = await Promise.all(
+			const allData: ExportedConversation[] = await Promise.all(
 				selectedConversations.map(async (conv) => {
 					const messages = await conversationsStore.getConversationMessages(conv.id);
 					return { conv: $state.snapshot(conv), messages: $state.snapshot(messages) };
 				})
 			);
 
-			conversationsStore.downloadConversationFile(allData);
+			if (allData.length === 1) {
+				conversationsStore.downloadConversationFile(allData[0]);
+			} else {
+				conversationsStore.downloadConversationsArchive(allData);
+			}
 
 			exportedConversations = selectedConversations;
 			showExportSummary = true;
@@ -156,37 +160,21 @@
 			const input = document.createElement('input');
 
 			input.type = HtmlInputType.FILE;
-			input.accept = FileExtensionText.JSON;
+			input.accept = `${FileExtensionText.JSON},${FileExtensionText.JSONL},${FileExtensionText.ZIP}`;
 
 			input.onchange = async (e) => {
 				const file = (e.target as HTMLInputElement)?.files?.[0];
 				if (!file) return;
 
 				try {
-					const text = await file.text();
-					const parsedData = JSON.parse(text);
-					let importedData: ExportedConversations;
+					const importedData = await conversationsStore.parseImportFile(file);
 
-					if (Array.isArray(parsedData)) {
-						importedData = parsedData;
-					} else if (
-						parsedData &&
-						typeof parsedData === 'object' &&
-						'conv' in parsedData &&
-						'messages' in parsedData
-					) {
-						// Single conversation object
-						importedData = [parsedData];
-					} else {
-						throw new Error(
-							'Invalid file format: expected array of conversations or single conversation object'
-						);
+					if (importedData.length === 0) {
+						throw new Error('No conversations found in file');
 					}
 
 					fullImportData = importedData;
-					availableConversations = importedData.map(
-						(item: { conv: DatabaseConversation; messages: DatabaseMessage[] }) => item.conv
-					);
+					availableConversations = importedData.map((item) => item.conv);
 					messageCountMap = createMessageCountMap(importedData);
 					showImportDialog = true;
 				} catch (err: unknown) {
@@ -258,7 +246,7 @@
 	<SettingsGroup title="Conversations">
 		<SettingsChatImportExportSection
 			title="Export"
-			description="Download your conversations as a JSON file. This includes all messages, attachments, and conversation history."
+			description="Download your conversations as a ZIP of JSONL files. This includes all messages, attachments, and conversation history."
 			IconComponent={Download}
 			buttonText="Export conversations"
 			onclick={handleExportClick}
@@ -267,7 +255,7 @@
 
 		<SettingsChatImportExportSection
 			title="Import"
-			description="Import one or more conversations from a previously exported JSON file. This will merge with your existing conversations."
+			description="Import one or more conversations from a previously exported ZIP or JSONL file. This will merge with your existing conversations."
 			IconComponent={Upload}
 			buttonText="Import conversations"
 			onclick={handleImportClick}
diff --git a/tools/ui/src/lib/enums/files.enums.ts b/tools/ui/src/lib/enums/files.enums.ts
index dbe07ea58..a908b8996 100644
--- a/tools/ui/src/lib/enums/files.enums.ts
+++ b/tools/ui/src/lib/enums/files.enums.ts
@@ -123,6 +123,8 @@ export enum FileExtensionText {
 	HTML = '.html',
 	HTM = '.htm',
 	JSON = '.json',
+	JSONL = '.jsonl',
+	ZIP = '.zip',
 	XML = '.xml',
 	YAML = '.yaml',
 	YML = '.yml',
@@ -179,7 +181,8 @@ export enum UriPattern {
 // MIME type enums
 export enum MimeTypeApplication {
 	PDF = 'application/pdf',
-	OCTET_STREAM = 'application/octet-stream'
+	OCTET_STREAM = 'application/octet-stream',
+	ZIP = 'application/zip'
 }
 
 export enum MimeTypeAudio {
@@ -226,6 +229,7 @@ export enum MimeTypeText {
 	CSS = 'text/css',
 	HTML = 'text/html',
 	JSON = 'application/json',
+	JSONL = 'application/jsonl',
 	XML_TEXT = 'text/xml',
 	XML_APP = 'application/xml',
 	YAML_TEXT = 'text/yaml',
diff --git a/tools/ui/src/lib/stores/conversations.svelte.ts b/tools/ui/src/lib/stores/conversations.svelte.ts
index bba5191ed..235015213 100644
--- a/tools/ui/src/lib/stores/conversations.svelte.ts
+++ b/tools/ui/src/lib/stores/conversations.svelte.ts
@@ -26,7 +26,15 @@ import { MigrationService } from '$lib/services/migration.service';
 import { config } from '$lib/stores/settings.svelte';
 import { filterByLeafNodeId, findLeafNode, generateConversationTitle } from '$lib/utils';
 import type { McpServerOverride } from '$lib/types/database';
-import { MessageRole, HtmlInputType, FileExtensionText, ReasoningEffort } from '$lib/enums';
+import { zipSync, unzipSync, strToU8, strFromU8 } from 'fflate';
+import {
+	MessageRole,
+	HtmlInputType,
+	FileExtensionText,
+	MimeTypeText,
+	MimeTypeApplication,
+	ReasoningEffort
+} from '$lib/enums';
 import {
 	ISO_DATE_TIME_SEPARATOR,
 	ISO_DATE_TIME_SEPARATOR_REPLACEMENT,
@@ -934,41 +942,177 @@ class ConversationsStore {
 			.replace(ISO_DATE_TIME_SEPARATOR, ISO_DATE_TIME_SEPARATOR_REPLACEMENT)
 			.replaceAll(ISO_TIME_SEPARATOR, ISO_TIME_SEPARATOR_REPLACEMENT);
 		const trimmedConvId = conversation.id?.slice(0, EXPORT_CONV_ID_TRIM_LENGTH) ?? '';
-		return `${formattedDate}_conv_${trimmedConvId}_${sanitizedName}.json`;
+		return `${formattedDate}_conv_${trimmedConvId}_${sanitizedName}${FileExtensionText.JSONL}`;
+	}
+
+	/**
+	 * Serializes a session (a conversation with its messages) as JSONL.
+	 * The first line is the session header (a `type: 'session'` record carrying the
+	 * conversation properties); each subsequent line is a single message.
+	 * @param data - The exported conversation payload
+	 * @returns The JSONL string (one record per line)
+	 */
+	serializeSessionToJsonl(data: ExportedConversation): string {
+		const { conv, messages } = data;
+
+		const sessionLine = JSON.stringify({ type: 'session', harness: 'llama.app', ...conv });
+		const messageLines = messages.map((message: DatabaseMessage) => {
+			// `toolCalls` is stored as a JSON string; drop it when empty, otherwise parse it.
+			const { toolCalls, ...rest } = message;
+			const normalized = toolCalls ? { ...rest, toolCalls: JSON.parse(toolCalls) } : rest;
+
+			return JSON.stringify({ type: 'message', message: normalized });
+		});
+
+		return [sessionLine, ...messageLines].join('\n');
+	}
+
+	/**
+	 * Parses the JSONL session format produced by {@link serializeSessionToJsonl}.
+	 * A `type: 'session'` line starts a new session; following `type: 'message'`
+	 * lines are appended to it. Supports multiple sessions in a single file.
+	 * @param text - The JSONL file contents
+	 * @returns The parsed conversations with their messages
+	 */
+	parseSessionsJsonl(text: string): ExportedConversation[] {
+		const sessions: ExportedConversation[] = [];
+		let current: ExportedConversation | null = null;
+
+		for (const line of text.split('\n')) {
+			const trimmed = line.trim();
+			if (!trimmed) continue;
+
+			const record = JSON.parse(trimmed);
+
+			if (record.type === 'session') {
+				// Drop the discriminator and harness marker; the rest is the conversation.
+				const conv = { ...record };
+				delete conv.type;
+				delete conv.harness;
+				current = { conv: conv as DatabaseConversation, messages: [] };
+				sessions.push(current);
+			} else if (record.type === 'message') {
+				if (!current) {
+					throw new Error('Invalid JSONL: message record before any session record');
+				}
+
+				const message = record.message as DatabaseMessage;
+				// `toolCalls` is parsed to an array on export; the DB stores it as a string.
+				if (message.toolCalls !== undefined && typeof message.toolCalls !== 'string') {
+					message.toolCalls = JSON.stringify(message.toolCalls);
+				}
+				current.messages.push(message);
+			}
+			// Ignore unknown record types for forward compatibility.
+		}
+
+		return sessions;
+	}
+
+	/**
+	 * Parses an import file into conversations, accepting the current `.jsonl` and
+	 * `.zip` formats as well as the legacy `.json` format.
+	 * @param file - The user-selected file
+	 * @returns The parsed conversations with their messages
+	 */
+	async parseImportFile(file: File): Promise<ExportedConversation[]> {
+		const name = file.name.toLowerCase();
+
+		if (name.endsWith(FileExtensionText.ZIP)) {
+			const entries = unzipSync(new Uint8Array(await file.arrayBuffer()));
+			const sessions: ExportedConversation[] = [];
+			for (const [entryName, bytes] of Object.entries(entries)) {
+				if (!entryName.toLowerCase().endsWith(FileExtensionText.JSONL)) continue;
+				sessions.push(...this.parseSessionsJsonl(strFromU8(bytes)));
+			}
+			return sessions;
+		}
+
+		const text = await file.text();
+
+		if (name.endsWith(FileExtensionText.JSONL)) {
+			return this.parseSessionsJsonl(text);
+		}
+
+		// Legacy JSON format: an array of conversations or a single conversation object.
+		const parsed = JSON.parse(text);
+		if (Array.isArray(parsed)) {
+			return parsed;
+		}
+		if (parsed && typeof parsed === 'object' && 'conv' in parsed && 'messages' in parsed) {
+			return [parsed];
+		}
+		throw new Error(
+			'Invalid file format: expected array of conversations or single conversation object'
+		);
 	}
 
 	/**
 	 * Triggers a browser download of the provided exported conversation data
-	 * @param data - The exported conversation payload (either a single conversation or array of them)
+	 * @param data - The exported conversation payload (a single conversation with its messages)
 	 * @param filename - Filename; if omitted, a deterministic name is generated
 	 */
-	downloadConversationFile(data: ExportedConversations, filename?: string): void {
-		// Choose the first conversation or message
-		const conversation =
-			'conv' in data ? data.conv : Array.isArray(data) ? data[0]?.conv : undefined;
-		const msgs =
-			'messages' in data ? data.messages : Array.isArray(data) ? data[0]?.messages : undefined;
+	downloadConversationFile(data: ExportedConversation, filename?: string): void {
+		const { conv: conversation, messages: msgs } = data;
 
 		if (!conversation) {
 			console.error('Invalid data: missing conversation');
 			return;
 		}
 
-		let downloadFilename: string;
+		const downloadFilename = filename ?? this.generateConversationFilename(conversation, msgs);
 
-		if (filename) {
-			downloadFilename = filename;
-		} else if (Array.isArray(data) && data.length > 1) {
-			downloadFilename = `${new Date().toISOString().split(ISO_DATE_TIME_SEPARATOR)[0]}_conversations.json`;
-		} else {
-			downloadFilename = this.generateConversationFilename(conversation, msgs);
+		const jsonl = this.serializeSessionToJsonl(data);
+		const blob = new Blob([jsonl], { type: MimeTypeText.JSONL });
+		this.triggerDownload(blob, downloadFilename);
+	}
+
+	/**
+	 * Triggers a browser download of multiple conversations as a `.zip`, one
+	 * `.jsonl` file per conversation.
+	 * @param data - The conversations to export
+	 */
+	downloadConversationsArchive(data: ExportedConversation[]): void {
+		if (data.length === 0) {
+			console.error('Invalid data: no conversations to export');
+			return;
 		}
 
-		const blob = new Blob([JSON.stringify(data, null, 2)], { type: 'application/json' });
+		const usedNames = new SvelteSet<string>();
+		const files: Record<string, Uint8Array> = {};
+
+		for (const session of data) {
+			const baseName = this.generateConversationFilename(session.conv, session.messages);
+
+			// Disambiguate any duplicate filenames within the archive.
+			let entryName = baseName;
+			let suffix = 1;
+			while (usedNames.has(entryName)) {
+				entryName = baseName.replace(
+					new RegExp(`${FileExtensionText.JSONL}$`),
+					`_${suffix++}${FileExtensionText.JSONL}`
+				);
+			}
+			usedNames.add(entryName);
+
+			files[entryName] = strToU8(this.serializeSessionToJsonl(session));
+		}
+
+		const archiveName = `${new Date().toISOString().split(ISO_DATE_TIME_SEPARATOR)[0]}_conversations${FileExtensionText.ZIP}`;
+
+		const zipped = zipSync(files);
+		const blob = new Blob([zipped], { type: MimeTypeApplication.ZIP });
+		this.triggerDownload(blob, archiveName);
+	}
+
+	/**
+	 * Triggers a browser download of a blob under the given filename.
+	 */
+	private triggerDownload(blob: Blob, filename: string): void {
 		const url = URL.createObjectURL(blob);
 		const a = document.createElement('a');
 		a.href = url;
-		a.download = downloadFilename;
+		a.download = filename;
 		document.body.appendChild(a);
 		a.click();
 		document.body.removeChild(a);

From d1759e4156cece09a6ee5231ae1219f461b16d23 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Wed, 17 Jun 2026 22:20:01 +0800
Subject: [PATCH 018/292] [SYCL] Add conv_3d (#24691)

* add conv_3d

* optimize

* update ops.md

* restore test script

* rm unused code

* rm copyright notes
---
 docs/ops.md                      |   2 +-
 docs/ops/SYCL.csv                | 516 +++++++++++++++----------------
 ggml/src/ggml-sycl/backend.hpp   |   1 +
 ggml/src/ggml-sycl/conv3d.cpp    | 218 +++++++++++++
 ggml/src/ggml-sycl/conv3d.hpp    |   8 +
 ggml/src/ggml-sycl/ggml-sycl.cpp |  14 +
 6 files changed, 500 insertions(+), 259 deletions(-)
 create mode 100644 ggml/src/ggml-sycl/conv3d.cpp
 create mode 100644 ggml/src/ggml-sycl/conv3d.hpp

diff --git a/docs/ops.md b/docs/ops.md
index d46f9b731..9e9442024 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -29,7 +29,7 @@ Legend:
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index 19a5346f8..116197d2f 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -4676,264 +4676,264 @@
 "SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","SYCL"
 "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","SYCL"
 "SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","SYCL"
-"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","1","yes","SYCL"
+"SYCL0","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","1","yes","SYCL"
diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp
index a526d8e58..1f5a91272 100644
--- a/ggml/src/ggml-sycl/backend.hpp
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -17,6 +17,7 @@
 #include "common.hpp"
 #include "concat.hpp"
 #include "conv.hpp"
+#include "conv3d.hpp"
 #include "convert.hpp"
 #include "count-equal.hpp"
 #include "cpy.hpp"
diff --git a/ggml/src/ggml-sycl/conv3d.cpp b/ggml/src/ggml-sycl/conv3d.cpp
new file mode 100644
index 000000000..2fa29f930
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv3d.cpp
@@ -0,0 +1,218 @@
+#include "conv3d.hpp"
+
+static inline int64_t ggml_sycl_conv3d_calc_patch_total(const ggml_tensor * dst, int32_t n) {
+    return (int64_t) n * dst->ne[0] * dst->ne[1] * dst->ne[2];
+}
+
+static inline int64_t ggml_sycl_conv3d_calc_knl_n_total(const ggml_tensor * src0, int32_t c) {
+    return (int64_t) src0->ne[0] * src0->ne[1] * src0->ne[2] * c;
+}
+
+static inline void ggml_sycl_conv3d_write_output(
+        const ggml_tensor * dst,
+        const float * src, float * dst_data,
+        int64_t patch_total, int64_t oc,
+        int64_t dst_w, int64_t dst_h, int64_t dst_d,
+        dpct::queue_ptr stream) {
+    const int64_t dst_nb0 = dst->nb[0];
+    const int64_t dst_nb1 = dst->nb[1];
+    const int64_t dst_nb2 = dst->nb[2];
+    const int64_t dst_nb3 = dst->nb[3];
+    const int64_t total = patch_total * oc;
+    const int64_t block_size = 256;
+    const int64_t num_work_items = ((total + block_size - 1) / block_size) * block_size;
+
+    stream->parallel_for(sycl::range<1>(num_work_items), [=](sycl::id<1> id) {
+        const int64_t i = id[0];
+        if (i >= total) {
+            return;
+        }
+
+        const int64_t patch_idx = i / oc;
+        const int64_t out_ch = i % oc;
+        const int64_t p_in_batch = patch_idx % (dst_w * dst_h * dst_d);
+        const int64_t batch_idx = patch_idx / (dst_w * dst_h * dst_d);
+        const int64_t dst_z = p_in_batch / (dst_w * dst_h);
+        const int64_t dst_y = (p_in_batch % (dst_w * dst_h)) / dst_w;
+        const int64_t dst_x = p_in_batch % dst_w;
+        const int64_t ocn_idx = batch_idx * oc + out_ch;
+
+        const int64_t dst_offset = dst_x * dst_nb0 + dst_y * dst_nb1 + dst_z * dst_nb2 + ocn_idx * dst_nb3;
+        // `src` is a column-major (m x n) GEMM output where m == patch_total, n == oc.
+        // GEMM stores element (row, col) at index `row + col*m`, so compute index accordingly.
+        const int64_t src_index = patch_idx + out_ch * patch_total;
+        const float value = src[src_index];
+        *(float *)((char *)dst_data + dst_offset) = value;
+    });
+}
+
+void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+
+    const int32_t * opts = (const int32_t *) dst->op_params;
+    const int32_t s0 = opts[0];
+    const int32_t s1 = opts[1];
+    const int32_t s2 = opts[2];
+    const int32_t p0 = opts[3];
+    const int32_t p1 = opts[4];
+    const int32_t p2 = opts[5];
+    const int32_t d0 = opts[6];
+    const int32_t d1 = opts[7];
+    const int32_t d2 = opts[8];
+    const int32_t c  = opts[9];
+    const int32_t n  = opts[10];
+    const int32_t oc = opts[11];
+
+    const int64_t knl_w = src0->ne[0];
+    const int64_t knl_h = src0->ne[1];
+    const int64_t knl_d = src0->ne[2];
+
+    const int64_t patch_total = ggml_sycl_conv3d_calc_patch_total(dst, n);
+    const int64_t knl_n_total = ggml_sycl_conv3d_calc_knl_n_total(src0, c);
+
+    const size_t kernel_type_size = ggml_element_size(src0);
+
+    ggml_sycl_pool_alloc<float> gemm_output(ctx.pool());
+    gemm_output.alloc((size_t) patch_total * oc);
+
+    ggml_tensor dst_mat = {};
+    dst_mat.type = GGML_TYPE_F32;
+    dst_mat.ne[0] = patch_total;
+    dst_mat.ne[1] = oc;
+    dst_mat.ne[2] = 1;
+    dst_mat.ne[3] = 1;
+    dst_mat.nb[0] = sizeof(float);
+    dst_mat.nb[1] = dst_mat.nb[0] * dst_mat.ne[0];
+    dst_mat.nb[2] = dst_mat.nb[1];
+    dst_mat.nb[3] = dst_mat.nb[2];
+    dst_mat.data = gemm_output.get();
+    dst_mat.buffer = dst->buffer;
+    dst_mat.extra = dst->extra;
+
+    dpct::queue_ptr stream = ctx.stream();
+
+    // allocate packed arrays: A_packed (k x m), B_packed (k x n)
+    ggml_sycl_pool_alloc<float> A_packed_alloc(ctx.pool());
+    ggml_sycl_pool_alloc<float> B_packed_alloc(ctx.pool());
+    A_packed_alloc.alloc((size_t) knl_n_total * patch_total * sizeof(float));
+    B_packed_alloc.alloc((size_t) knl_n_total * oc * sizeof(float));
+
+    float * A_packed = A_packed_alloc.get();
+    float * B_packed = B_packed_alloc.get();
+
+    const int m = (int) patch_total;
+    const int n_gemm = (int) oc;
+    const int k = (int) knl_n_total;
+
+    // Combined kernel: im2col -> pack A, and pack B simultaneously
+    const char * src1_base = (const char *) src1->data;
+    const int64_t src1_nb0 = src1->nb[0];
+    const int64_t src1_nb1 = src1->nb[1];
+    const int64_t src1_nb2 = src1->nb[2];
+    const int64_t src1_nb3 = src1->nb[3];
+
+    // Compute correct strides for src0 as (knl_n_total, oc) matrix
+    const int64_t src0_packed_nb0 = kernel_type_size;
+    const int64_t src0_packed_nb1 = kernel_type_size * knl_n_total;
+
+    const int64_t KW = knl_w;
+    const int64_t KH = knl_h;
+    const int64_t KD = knl_d;
+    const int64_t PW = dst->ne[0];
+    const int64_t PH = dst->ne[1];
+    const int64_t PD = dst->ne[2];
+
+    // Pack A (with inline im2col): for each (row, col) in k x m matrix
+    const int64_t A_total = (int64_t)k * m;
+    const int64_t A_block_size = 256;
+    const int64_t A_num_work = ((A_total + A_block_size - 1) / A_block_size) * A_block_size;
+
+    stream->parallel_for(sycl::range<1>(A_num_work), [=](sycl::id<1> id) {
+        const int64_t t = id[0];
+        if (t >= A_total) return;
+
+        const int64_t row = t % k;
+        const int64_t col = t / k;
+
+        // Inline im2col for this element
+        const int64_t k_index = row;
+        const int64_t patch_idx = col;
+
+        const int64_t ic = k_index / (KD * KH * KW);
+        const int64_t rem = k_index - ic * (KD * KH * KW);
+        const int64_t kz = rem / (KH * KW);
+        const int64_t rem2 = rem - kz * (KH * KW);
+        const int64_t ky = rem2 / KW;
+        const int64_t kx = rem2 % KW;
+
+        const int64_t p_in_batch = patch_idx % (PW * PH * PD);
+        const int64_t batch_idx = patch_idx / (PW * PH * PD);
+        const int64_t dst_z = p_in_batch / (PW * PH);
+        const int64_t dst_y = (p_in_batch % (PW * PH)) / PW;
+        const int64_t dst_x = p_in_batch % PW;
+
+        const int64_t sx = dst_x * s0 + kx * d0 - p0;
+        const int64_t sy = dst_y * s1 + ky * d1 - p1;
+        const int64_t sz = dst_z * s2 + kz * d2 - p2;
+
+        float val = 0.0f;
+        if (sx >= 0 && sx < src1->ne[0] && sy >= 0 && sy < src1->ne[1] && sz >= 0 && sz < src1->ne[2]) {
+            const int64_t channel_idx = batch_idx * c + ic;
+            const char * ptr = src1_base + sx * src1_nb0 + sy * src1_nb1 + sz * src1_nb2 + channel_idx * src1_nb3;
+            val = *(const float *) ptr;
+        }
+        A_packed[row + col * (int64_t)k] = val;
+    });
+
+    // Pack B: for each (row, col) in k x n_gemm matrix
+    const int64_t B_total = (int64_t)k * n_gemm;
+    const int64_t B_block_size = 256;
+    const int64_t B_num_work = ((B_total + B_block_size - 1) / B_block_size) * B_block_size;
+
+    stream->parallel_for(sycl::range<1>(B_num_work), [=](sycl::id<1> id) {
+        const int64_t t = id[0];
+        if (t >= B_total) return;
+
+        const int64_t row = t % k;
+        const int64_t col = t / k;
+        const char * src_ptr = (const char *) src0->data + row * src0_packed_nb0 + col * src0_packed_nb1;
+        float v;
+        if (src0->type == GGML_TYPE_F32) {
+            v = *(const float *) src_ptr;
+        } else {
+            v = sycl::vec<sycl::half, 1>(*(const sycl::half *) src_ptr).convert<float, sycl::rounding_mode::automatic>()[0];
+        }
+        B_packed[row + col * (int64_t)k] = v;
+    });
+
+    // GEMM: C = A^T * B where A is (k x m), B is (k x n), C is (m x n)
+    const float alpha = 1.0f;
+    const float beta  = 0.0f;
+    const int lda = k;
+    const int ldb = k;
+    const int ldc = m;
+
+    SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
+        *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans,
+        m, n_gemm, k,
+        dpct::get_value(&alpha, *stream),
+        (const float *) A_packed, lda,
+        (const float *) B_packed, ldb,
+        dpct::get_value(&beta, *stream),
+        (float *) dst_mat.data, ldc)));
+
+    const float * gemm_data = (const float *) dst_mat.data;
+    float * dst_data = (float *) dst->data;
+
+    ggml_sycl_conv3d_write_output(dst, gemm_data, dst_data, patch_total, oc,
+                                  dst->ne[0], dst->ne[1], dst->ne[2], stream);
+}
diff --git a/ggml/src/ggml-sycl/conv3d.hpp b/ggml/src/ggml-sycl/conv3d.hpp
new file mode 100644
index 000000000..5852f393f
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv3d.hpp
@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_CONV3D_HPP
+#define GGML_SYCL_CONV3D_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_CONV3D_HPP
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 057e0dccd..43c7e0a93 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4572,6 +4572,11 @@ static void ggml_sycl_im2col_3d(ggml_backend_sycl_context & ctx, ggml_tensor * d
     ggml_sycl_op_im2col_3d(ctx, dst);
 }
 
+static void ggml_sycl_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    ggml_sycl_op_conv_3d(ctx, dst);
+}
+
 static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
     GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
@@ -4638,6 +4643,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_CONV_TRANSPOSE_1D:
             ggml_sycl_op_conv_transpose_1d(ctx, dst);
             break;
+        case GGML_OP_CONV_3D:
+            ggml_sycl_conv_3d(ctx, dst);
+            break;
         case GGML_OP_REPEAT:
             ggml_sycl_repeat(ctx, dst);
             break;
@@ -5615,6 +5623,12 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_IM2COL_3D:
         case GGML_OP_UPSCALE:
             return true;
+        case GGML_OP_CONV_3D:
+            return op->type == GGML_TYPE_F32 &&
+                   (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                   op->src[1]->type == GGML_TYPE_F32 &&
+                   ggml_is_contiguous(op->src[0]) &&
+                   ggml_is_contiguous(op->src[1]);
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_MEAN:

From 74a80dd9c052bd2d3e3e8134436ad05cc6396d1a Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Wed, 17 Jun 2026 22:21:34 +0800
Subject: [PATCH 019/292] [SYCL] add dev2dev memcpy by SYCL API (#24476)

* add dev2dev memcpy by SYCL API

* mv GGML_SYCL_DEV2DEV_MEMCPY to runntime table

* update the detect method for p2p comm

* fix the erro created during fix confilct

---------

Co-authored-by: Neo Zhang <NA>
---
 docs/backend/SYCL.md               |  2 ++
 ggml/src/ggml-sycl/common.hpp      |  6 ++++
 ggml/src/ggml-sycl/dpct/helper.hpp | 22 +++++++++----
 ggml/src/ggml-sycl/ggml-sycl.cpp   | 52 ++++++++++++++++++++++--------
 4 files changed, 61 insertions(+), 21 deletions(-)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 97d4b5216..6617aa2a5 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -712,6 +712,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
+| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default) or 1 | Choose the SYCL or L0 API in dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default)<br>* 1: L0 API -- L0 API is found to lead to abnormal crash in some case. This debug flag is used to check the issue.|
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for Intel devices older than Gen 10) |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
@@ -731,6 +732,7 @@ Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spo
 | DEBUG_SYCL_POOL | Enable device memory pool logging on teardown. Useful for profiling allocations. |
 | DEBUG_SYCL_MALLOC | Enable verbose per-call logging of device pool alloc/free operations. |
 
+
 ## Design Rule
 
 - Open to all contributors.
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 96586ea46..c87a4636e 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -62,6 +62,7 @@ extern int g_ggml_sycl_debug;
 extern int g_ggml_sycl_disable_optimize;
 extern int g_ggml_sycl_prioritize_dmmv;
 extern int g_ggml_sycl_enable_flash_attention;
+extern int g_ggml_sycl_dev2dev_memcpy;
 
 
 #if defined(__clang__) && __has_builtin(__builtin_expect)
@@ -126,6 +127,11 @@ enum ggml_sycl_backend_gpu_mode {
   SYCL_MUL_GPU_MODE
 };
 
+enum ggml_sycl_dev2dev_memcpy_mode {
+  DEV2DEV_MEMCPY_SYCL = 0,
+  DEV2DEV_MEMCPY_L0 = 1,
+};
+
 static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
 
 static void crash() {
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index 791d3cac5..664b8e969 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -13,14 +13,14 @@
 #ifndef GGML_SYCL_DPCT_HELPER_HPP
 #define GGML_SYCL_DPCT_HELPER_HPP
 
+#include <cstdlib>
+#include <iostream>
+#include <map>
+
 #include <sycl/sycl.hpp>
 #include <sycl/half_type.hpp>
 #include <oneapi/mkl.hpp>
 
-#include <map>
-
-#include "ggml.h"
-
 #if defined(__linux__)
 #include <sys/mman.h>
 #elif defined(_WIN64)
@@ -43,6 +43,7 @@
 #include <windows.h>
 #endif
 
+
 #define DPCT_COMPATIBILITY_TEMP (900)
 
 #if defined(_MSC_VER)
@@ -59,6 +60,13 @@
 #define __dpct_noinline__ __attribute__((noinline))
 #endif
 
+#define DPCT_UNUSED(x) (void)(x)
+
+inline void _abort(const char * str) {
+    std::cerr << str << std::endl;
+    std::abort();
+}
+
 inline std::string get_device_type_name(const sycl::device &Device) {
     auto DeviceType = Device.get_info<sycl::info::device::device_type>();
     switch (DeviceType) {
@@ -1017,7 +1025,7 @@ namespace dpct
             if (backend == "opencl:cpu") return 4;
             if (backend == "opencl:acc") return 5;
             printf("convert_backend_index: can't handle backend=%s\n", backend.c_str());
-            GGML_ABORT("fatal error");
+            _abort("fatal error");
         }
         static bool compare_backend(std::string &backend1, std::string &backend2) {
             return convert_backend_index(backend1) < convert_backend_index(backend2);
@@ -1426,7 +1434,7 @@ namespace dpct
             if (!size)
                 return sycl::event{};
             return q.memcpy(to_ptr, from_ptr, size, dep_events);
-            GGML_UNUSED(direction);
+            DPCT_UNUSED(direction);
         }
 
         // Get actual copy range and make sure it will not exceed range.
@@ -2092,7 +2100,7 @@ namespace dpct
         if (!size)
             return sycl::event{};
         return q.memcpy(to_ptr, from_ptr, size, dep_events);
-        GGML_UNUSED(direction);
+        DPCT_UNUSED(direction);
     }
 
     // Get actual copy range and make sure it will not exceed range.
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 43c7e0a93..4c0567669 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -86,6 +86,7 @@ int g_ggml_sycl_use_async_mem_op = 0;
 int g_ggml_sycl_use_async_mem_op_requested = 1;
 int g_ggml_sycl_enable_level_zero = 0;
 int g_ggml_sycl_enable_flash_attention = 1;
+int g_ggml_sycl_dev2dev_memcpy = DEV2DEV_MEMCPY_SYCL;
 int g_ggml_sycl_usm_system = 0;
 
 static ggml_sycl_device_info ggml_sycl_init() {
@@ -272,6 +273,11 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_enable_vmm = ggml_sycl_get_env("GGML_SYCL_ENABLE_VMM", 1);
         g_ggml_sycl_prioritize_dmmv = ggml_sycl_get_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
 
+        g_ggml_sycl_dev2dev_memcpy = ggml_sycl_get_env("GGML_SYCL_DEV2DEV_MEMCPY", DEV2DEV_MEMCPY_SYCL);
+        if (g_ggml_sycl_enable_level_zero == 0) {
+            g_ggml_sycl_dev2dev_memcpy = DEV2DEV_MEMCPY_SYCL;
+        }
+
 #ifdef SYCL_FLASH_ATTN
         g_ggml_sycl_enable_flash_attention = ggml_sycl_get_env("GGML_SYCL_ENABLE_FLASH_ATTN", 1);
 #else
@@ -324,8 +330,11 @@ static void ggml_check_sycl() try {
 #endif
 #ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
         GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: %d\n", g_ggml_sycl_enable_level_zero);
+        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d\n", g_ggml_sycl_dev2dev_memcpy);
 #else
         GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: Level Zero disabled by compile flag\n");
+        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d, enable to SYCL API since missing GGML_SYCL_SUPPORT_LEVEL_ZERO\n",
+                      g_ggml_sycl_dev2dev_memcpy);
 #endif
 #if GGML_SYCL_DNNL
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_DNN: %d\n", g_ggml_sycl_disable_dnn);
@@ -598,27 +607,42 @@ static bool ggml_sycl_is_l0_discrete_gpu(int device) {
 
 static void dev2dev_memcpy(int device_dst, sycl::queue &q_dst, int device_src, sycl::queue &q_src, void *ptr_dst,
                     const void *ptr_src, size_t size) {
+
 #ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
-    // Use Level Zero direct copy for dGPU-to-dGPU transfers.
-    const bool l0_copy_supported = g_ggml_sycl_enable_level_zero &&
-        ggml_sycl_is_l0_discrete_gpu(device_dst) && ggml_sycl_is_l0_discrete_gpu(device_src);
-    if (l0_copy_supported) {
-        auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_context());
-        auto ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_device());
-        ze_command_queue_desc_t cq_desc = {ZE_STRUCTURE_TYPE_COMMAND_QUEUE_DESC, nullptr, 0, 0,
-                                           0, ZE_COMMAND_QUEUE_MODE_SYNCHRONOUS, ZE_COMMAND_QUEUE_PRIORITY_NORMAL};
-        ze_command_list_handle_t cl;
-        ze_result_t r = zeCommandListCreateImmediate(ze_ctx, ze_dev, &cq_desc, &cl);
-        if (r == ZE_RESULT_SUCCESS) {
-            r = zeCommandListAppendMemoryCopy(cl, ptr_dst, ptr_src, size, nullptr, 0, nullptr);
-            zeCommandListDestroy(cl);
+    if (g_ggml_sycl_dev2dev_memcpy == DEV2DEV_MEMCPY_L0) {
+        // Use Level Zero direct copy for dGPU-to-dGPU transfers.
+        const bool l0_copy_supported =
+            ggml_sycl_is_l0_discrete_gpu(device_dst) && ggml_sycl_is_l0_discrete_gpu(device_src);
+        if (g_ggml_sycl_enable_level_zero && l0_copy_supported) {
+            auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_context());
+            auto ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_device());
+            ze_command_queue_desc_t cq_desc = {ZE_STRUCTURE_TYPE_COMMAND_QUEUE_DESC, nullptr, 0, 0,
+                                            0, ZE_COMMAND_QUEUE_MODE_SYNCHRONOUS, ZE_COMMAND_QUEUE_PRIORITY_NORMAL};
+            ze_command_list_handle_t cl;
+            ze_result_t r = zeCommandListCreateImmediate(ze_ctx, ze_dev, &cq_desc, &cl);
             if (r == ZE_RESULT_SUCCESS) {
-                return;
+                GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by L0\n");
+                r = zeCommandListAppendMemoryCopy(cl, ptr_dst, ptr_src, size, nullptr, 0, nullptr);
+                zeCommandListDestroy(cl);
+                if (r == ZE_RESULT_SUCCESS) {
+                    return;
+                }
             }
         }
     }
 #endif
+
+    if (g_ggml_sycl_dev2dev_memcpy == DEV2DEV_MEMCPY_SYCL) {
+        if (q_dst.get_device().ext_oneapi_can_access_peer(q_src.get_device(),
+                                                          sycl::ext::oneapi::peer_access::access_supported)) {
+            GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by SYCL\n");
+            SYCL_CHECK(CHECK_TRY_ERROR(q_dst.memcpy(ptr_dst, ptr_src, size).wait()));
+            return;
+        }
+    }
+
     // Host-staged copy
+    GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward\n");
     char *host_buf = (char *)malloc(size);
     q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
     q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();

From 1a2dea29b9cf95416db358c5ffb02018a7cb04fb Mon Sep 17 00:00:00 2001
From: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Wed, 17 Jun 2026 16:29:49 +0200
Subject: [PATCH 020/292] spec: fix segfault error on long prompts for eagle3
 (#24707)

---
 src/llama-context.cpp | 2 +-
 src/llama-hparams.cpp | 4 ++++
 src/llama-hparams.h   | 7 +++++++
 src/models/eagle3.cpp | 8 ++++----
 4 files changed, 16 insertions(+), 5 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 168dbabd7..529bc4a5e 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1382,7 +1382,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
     const auto & hparams = model.hparams;
 
     // eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
-    const int64_t n_embd = hparams.n_embd_inp();
+    const int64_t n_embd = hparams.n_embd_inp_enc();
     const int64_t n_vocab = model.vocab.n_tokens();
 
     // note: during encode, we always pass the full sequence starting from pos = 0
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 2bf576873..9d0683d2f 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -104,6 +104,10 @@ uint32_t llama_hparams::n_embd_inp() const {
     return n_embd_inp;
 }
 
+uint32_t llama_hparams::n_embd_inp_enc() const {
+    return n_embd_inp_enc_impl > 0 ? n_embd_inp_enc_impl : n_embd_inp();
+}
+
 uint32_t llama_hparams::n_embd_out() const {
     return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd;
 }
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index d045059a6..2eadeb214 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -189,6 +189,10 @@ struct llama_hparams {
     // input embedding dimension (0 = use n_embd)
     uint32_t n_embd_inp_impl = 0;
 
+    // encoder input embedding dimension (0 = use n_embd_inp())
+    // e.g. the eagle3 encoder fuses target_layers * target_hidden features
+    uint32_t n_embd_inp_enc_impl = 0;
+
     // output embedding dimension (0 = use n_embd)
     uint32_t n_embd_out_impl = 0;
 
@@ -305,6 +309,9 @@ struct llama_hparams {
     // dimension of main + auxiliary input embeddings
     uint32_t n_embd_inp() const;
 
+    // dimension of the encoder input embeddings
+    uint32_t n_embd_inp_enc() const;
+
     // dimension of output embeddings
     uint32_t n_embd_out() const;
 
diff --git a/src/models/eagle3.cpp b/src/models/eagle3.cpp
index 3321b3905..9d96fae59 100644
--- a/src/models/eagle3.cpp
+++ b/src/models/eagle3.cpp
@@ -19,7 +19,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
     LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
 
-    hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
+    hparams.n_embd_inp_enc_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
 
     // eagle3 norm_before_residual (optional, default false)
     // compatible with Readhat eagle3 speculator model
@@ -34,7 +34,7 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
 void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
     LLAMA_LOAD_LOCALS;
 
-    const int64_t n_embd_inp = hparams.n_embd_inp();
+    const int64_t n_embd_inp = hparams.n_embd_inp_enc();
     const int64_t n_embd_attn_input = 2 * n_embd;
 
     // Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
@@ -109,8 +109,8 @@ ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
 
     // Input: Target model features (3 layers concatenated: low, mid, high)
     // Data will be provided via ubatch->embd in encode_eagle3_features()
-    auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
-    inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
+    auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp_enc());
+    inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp_enc(), n_tokens);
     ggml_set_input(inp_target->embd);
 
     cur = inp_target->embd;

From b4024af6c244e355cac1a0c3368c3bca623acf1b Mon Sep 17 00:00:00 2001
From: Dev-iL <6509619+Dev-iL@users.noreply.github.com>
Date: Wed, 17 Jun 2026 17:30:26 +0300
Subject: [PATCH 021/292] llama : skip main_gpu validation when no devices are
 available (#23405)

---
 src/llama.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama.cpp b/src/llama.cpp
index a67fa8039..0de6048f2 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -249,7 +249,7 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama
     }
 
     // if using single GPU mode, remove all except the main GPU
-    if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
+    if (params.split_mode == LLAMA_SPLIT_MODE_NONE && !model->devices.empty()) {
         if (params.main_gpu < 0) {
             model->devices.clear();
         } else {

From eb05dd5fabac47205c8438f8979b5aeba3f51f34 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 17 Jun 2026 23:50:08 +0800
Subject: [PATCH 022/292] support audio to video for ltx 2.3

---
 expose.h                                      |   1 +
 koboldcpp.py                                  |   9 +-
 otherarch/sdcpp/include/stable-diffusion.h    |   1 +
 otherarch/sdcpp/sdtype_adapter.cpp            |  49 ++++
 .../sdcpp/src/model/vae/ltx_audio_vae.hpp     | 264 +++++++++++++++++-
 otherarch/sdcpp/src/stable-diffusion.cpp      | 153 +++++++++-
 otherarch/utils.cpp                           |  49 ++++
 otherarch/utils.h                             |   3 +-
 8 files changed, 519 insertions(+), 10 deletions(-)

diff --git a/expose.h b/expose.h
index c5f8dcf78..b6e0045cd 100644
--- a/expose.h
+++ b/expose.h
@@ -223,6 +223,7 @@ struct sd_generation_inputs
     const char * negative_prompt = nullptr;
     const char * init_images = "";
     const char * mask = "";
+    const char * audio_data = "";
     const int extra_images_len = 0;
     const char ** extra_images = nullptr;
     const bool reverse_refimg = false;
diff --git a/koboldcpp.py b/koboldcpp.py
index 25ead4dfa..0459b5192 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -412,6 +412,7 @@ class sd_generation_inputs(ctypes.Structure):
                 ("negative_prompt", ctypes.c_char_p),
                 ("init_images", ctypes.c_char_p),
                 ("mask", ctypes.c_char_p),
+                ("audio_data", ctypes.c_char_p),
                 ("extra_images_len", ctypes.c_int),
                 ("extra_images", ctypes.POINTER(ctypes.c_char_p)),
                 ("reverse_refimg", ctypes.c_bool),
@@ -1111,7 +1112,7 @@ def is_incomplete_utf8_sequence(byte_seq): #note, this will only flag INCOMPLETE
 def strip_base64_prefix(encoded_data):
     if not encoded_data:
         return ""
-    if encoded_data.startswith("data:image"):
+    if encoded_data.startswith("data:image") or encoded_data.startswith("data:audio"):
         encoded_data = encoded_data.split(',', 1)[-1]
     return encoded_data
 
@@ -2814,6 +2815,11 @@ def sd_generate(genparams):
     extra_images_arr = genparams.get("extra_images", [])
     extra_images_arr = ([] if not extra_images_arr else extra_images_arr)
     extra_images_arr = [img for img in extra_images_arr if img not in (None, "")]
+
+    audio_data = next((img for img in extra_images_arr if img.startswith("data:audio")), None)
+    extra_images_arr = [img for img in extra_images_arr if not img.startswith("data:audio")]
+    audio_data = strip_base64_prefix(audio_data)
+
     extra_images_arr = extra_images_arr[:extra_images_max]
     lora_filenames, lora_multipliers = prepare_lora_multipliers(genparams.get("lora", []))
 
@@ -2840,6 +2846,7 @@ def sd_generate(genparams):
     inputs.negative_prompt = negative_prompt.encode("UTF-8")
     inputs.init_images = init_images.encode("UTF-8")
     inputs.mask = "".encode("UTF-8") if not mask else mask.encode("UTF-8")
+    inputs.audio_data = "".encode("UTF-8") if not audio_data else audio_data.encode("UTF-8")
     inputs.extra_images_len = len(extra_images_arr)
     inputs.extra_images = (ctypes.c_char_p * inputs.extra_images_len)()
     for n, estr in enumerate(extra_images_arr):
diff --git a/otherarch/sdcpp/include/stable-diffusion.h b/otherarch/sdcpp/include/stable-diffusion.h
index 1c04367b1..bfcd909cc 100644
--- a/otherarch/sdcpp/include/stable-diffusion.h
+++ b/otherarch/sdcpp/include/stable-diffusion.h
@@ -395,6 +395,7 @@ typedef struct {
     int64_t seed;
     int video_frames;
     int fps;
+    const sd_audio_t* input_audio;
     float vace_strength;
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 839f95141..4880c3ba8 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -939,6 +939,37 @@ static std::string raw_image_to_png_base64(const sd_image_t& img, std::string pa
     return result;
 }
 
+static sd_audio_t load_audio_from_b64(const std::string& b64audio) {
+    sd_audio_t audio = {0, 0, 0, nullptr};
+    if (b64audio.empty()) {
+        return audio;
+    }
+
+    std::vector<uint8_t> audio_data = kcpp_base64_decode(b64audio);
+    std::vector<float> decoded_samples;
+    int sample_rate = 0;
+    int channels = 0;
+    if (!kcpp_decode_audio_file_from_buf(audio_data.data(), audio_data.size(), sample_rate, channels, decoded_samples)) {
+        printf("KCPP SD: failed to decode input audio\n");
+        return audio;
+    }
+
+    uint64_t sample_count = static_cast<uint64_t>(decoded_samples.size() / static_cast<size_t>(channels));
+    size_t float_count = decoded_samples.size();
+    float* samples = (float*)malloc(float_count * sizeof(float));
+    if (samples == nullptr || sample_count == 0) {
+        free(samples);
+        return audio;
+    }
+
+    std::memcpy(samples, decoded_samples.data(), float_count * sizeof(float));
+    audio.sample_rate = static_cast<uint32_t>(sample_rate);
+    audio.channels = static_cast<uint32_t>(channels);
+    audio.sample_count = sample_count;
+    audio.data = samples;
+    return audio;
+}
+
 bool supports_reference_images(kcpp_sd::model_info info)
 {
     bool supported = (info.is_wan || info.is_ltx || info.is_qwenimg || info.is_flux2 || info.is_kontext || photomaker_enabled);
@@ -955,6 +986,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
 
     std::string img2img_data = std::string(inputs.init_images);
     std::string img2img_mask = std::string(inputs.mask);
+    std::string input_audio_data = std::string(inputs.audio_data ? inputs.audio_data : "");
     std::vector<std::string> extra_image_data;
     for(int i=0;i<inputs.extra_images_len;++i)
     {
@@ -1283,6 +1315,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
      //special case, is img2img and denoise strength is 0 and steps is 1, do a passthru
     bool is_passthrough = (sd_params->sample_steps<=1 && sd_params->strength<=0 && is_img2img && vid_req_frames<=1 && extra_image_data.size()==0);
     sd_audio_t* generated_audio = nullptr;
+    sd_audio_t input_audio = {0, 0, 0, nullptr};
 
     if(is_vid_model)
     {
@@ -1302,6 +1335,13 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         vid_gen_params.video_frames = vid_req_frames;
         vid_gen_params.fps = vid_fps;
         vid_gen_params.vae_tiling_params = params.vae_tiling_params;
+        if (!input_audio_data.empty()) {
+            input_audio = load_audio_from_b64(input_audio_data);
+            if (input_audio.data == nullptr) {
+                return sd_generation.error("KCPP SD: load audio from base64 failed!");
+            }
+            vid_gen_params.input_audio = &input_audio;
+        }
         if (wan_imgs.size() > 0) {
             if (wan_imgs.size() >= 2) {
                 vid_gen_params.init_image = wan_imgs[0];
@@ -1327,6 +1367,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
             << "\nFRAMES:"   << vid_gen_params.video_frames
             << "\nCTRL_FRM:" << vid_gen_params.control_frames_size
             << "\nINIT_IMGS:" << wan_imgs.size()
+            << "\nINPUT_AUDIO:" << (vid_gen_params.input_audio ? "true" : "false")
             << "\n\n";
             printf("%s", ss.str().c_str());
         }
@@ -1429,6 +1470,10 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
     }
 
     if (!is_passthrough && results == NULL) {
+        if (input_audio.data) {
+            free(input_audio.data);
+            input_audio.data = nullptr;
+        }
         return sd_generation.error("KCPP SD generate failed!");
     }
 
@@ -1576,6 +1621,10 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         free_sd_audio(generated_audio);
         generated_audio = nullptr;
     }
+    if (input_audio.data) {
+        free(input_audio.data);
+        input_audio.data = nullptr;
+    }
 
     free(results);
 
diff --git a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
index 997c57a5b..7a3fb25a2 100644
--- a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
@@ -1,6 +1,7 @@
 #ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 #define __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 
+#include <algorithm>
 #include <cmath>
 #include <limits>
 #include <numeric>
@@ -21,6 +22,10 @@ namespace LTXV {
         int latent_channels                                        = 8;
         int latent_frequency_bins                                  = 16;
         int audio_channels                                         = 2;
+        bool has_encoder                                           = false;
+        int encoder_channels                                       = 128;
+        std::vector<int> encoder_channel_multipliers               = {1, 2, 4};
+        int encoder_num_res_blocks                                 = 2;
         int decoder_channels                                       = 128;
         std::vector<int> decoder_channel_multipliers               = {1, 2, 4};
         int decoder_num_res_blocks                                 = 2;
@@ -74,6 +79,7 @@ namespace LTXV {
 
             const TensorStorage* decoder_conv_in   = require("audio_vae.decoder.conv_in.conv.weight");
             const TensorStorage* decoder_conv_out  = require("audio_vae.decoder.conv_out.conv.weight");
+            const TensorStorage* encoder_conv_in   = require("audio_vae.encoder.conv_in.conv.weight");
             const TensorStorage* latent_std        = require("audio_vae.per_channel_statistics.std-of-means");
             const TensorStorage* vocoder_conv_pre  = require("vocoder.vocoder.conv_pre.weight");
             const TensorStorage* vocoder_conv_post = require("vocoder.vocoder.conv_post.weight");
@@ -98,6 +104,40 @@ namespace LTXV {
                 return config;
             }
 
+            if (encoder_conv_in != nullptr) {
+                config.has_encoder      = true;
+                config.audio_channels   = static_cast<int>(encoder_conv_in->ne[2]);
+                config.encoder_channels = static_cast<int>(encoder_conv_in->ne[3]);
+
+                std::vector<std::pair<int, int>> encoder_level_channels;
+                for (const auto& pair : tensor_storage_map) {
+                    const std::string& name  = pair.first;
+                    const std::string prefix = "audio_vae.encoder.down.";
+                    const std::string suffix = ".block.0.conv1.conv.weight";
+                    if (!starts_with(name, prefix) || !ends_with(name, suffix)) {
+                        continue;
+                    }
+                    std::string level_str = name.substr(prefix.size(), name.size() - prefix.size() - suffix.size());
+                    int level             = std::stoi(level_str);
+                    encoder_level_channels.push_back({level, static_cast<int>(pair.second.ne[3])});
+                }
+                std::sort(encoder_level_channels.begin(), encoder_level_channels.end());
+                if (!encoder_level_channels.empty()) {
+                    config.encoder_channel_multipliers.clear();
+                    for (const auto& level_channel : encoder_level_channels) {
+                        config.encoder_channel_multipliers.push_back(level_channel.second / std::max(1, config.encoder_channels));
+                    }
+                }
+
+                int encoder_block_count = 0;
+                while (tensor_storage_map.find("audio_vae.encoder.down.0.block." + std::to_string(encoder_block_count) + ".conv1.conv.weight") != tensor_storage_map.end()) {
+                    ++encoder_block_count;
+                }
+                if (encoder_block_count > 0) {
+                    config.encoder_num_res_blocks = encoder_block_count;
+                }
+            }
+
             std::vector<std::pair<int, int>> level_channels;
             for (const auto& pair : tensor_storage_map) {
                 const std::string& name  = pair.first;
@@ -171,16 +211,89 @@ namespace LTXV {
             if (config.audio_channels != 2 || config.latent_channels != 8 || config.mel_bins != 64) {
                 return config;
             }
-            LOG_DEBUG("ltx_audio_vae: sample_rate = %d, mel_bins = %d, latent_channels = %d, latent_frequency_bins = %d, has_bwe = %s",
+            LOG_DEBUG("ltx_audio_vae: sample_rate = %d, mel_bins = %d, latent_channels = %d, latent_frequency_bins = %d, has_encoder = %s, has_bwe = %s",
                       config.sample_rate,
                       config.mel_bins,
                       config.latent_channels,
                       config.latent_frequency_bins,
+                      config.has_encoder ? "true" : "false",
                       config.has_bwe ? "true" : "false");
             return config;
         }
     };
 
+    static double ltx_audio_hz_to_mel(double freq) {
+        constexpr double min_log_hz  = 1000.0;
+        constexpr double min_log_mel = 15.0;
+        constexpr double logstep     = 0.06875177742094912;  // log(6.4) / 27
+        constexpr double f_sp        = 200.0 / 3.0;
+        if (freq < min_log_hz) {
+            return freq / f_sp;
+        }
+        return min_log_mel + std::log(freq / min_log_hz) / logstep;
+    }
+
+    static double ltx_audio_mel_to_hz(double mel) {
+        constexpr double min_log_hz  = 1000.0;
+        constexpr double min_log_mel = 15.0;
+        constexpr double logstep     = 0.06875177742094912;  // log(6.4) / 27
+        constexpr double f_sp        = 200.0 / 3.0;
+        if (mel < min_log_mel) {
+            return mel * f_sp;
+        }
+        return min_log_hz * std::exp(logstep * (mel - min_log_mel));
+    }
+
+    static sd::Tensor<float> build_encoder_stft_basis(int n_fft) {
+        constexpr double kPi = 3.14159265358979323846;
+        const int n_freqs    = n_fft / 2 + 1;
+        sd::Tensor<float> basis({n_fft, 1, n_freqs * 2});
+        for (int k = 0; k < n_freqs; ++k) {
+            for (int n = 0; n < n_fft; ++n) {
+                double window          = 0.5 - 0.5 * std::cos(2.0 * kPi * n / static_cast<double>(n_fft));
+                double phase           = 2.0 * kPi * k * n / static_cast<double>(n_fft);
+                basis.index(n, 0, k)   = static_cast<float>(std::cos(phase) * window);
+                basis.index(n, 0, k + n_freqs) = static_cast<float>(-std::sin(phase) * window);
+            }
+        }
+        return basis;
+    }
+
+    static sd::Tensor<float> build_encoder_mel_basis(int sample_rate, int n_fft, int n_mels) {
+        const int n_freqs = n_fft / 2 + 1;
+        sd::Tensor<float> basis({n_freqs, n_mels});
+        std::vector<double> fft_freqs(n_freqs);
+        for (int i = 0; i < n_freqs; ++i) {
+            fft_freqs[i] = (static_cast<double>(sample_rate) * 0.5) * static_cast<double>(i) / static_cast<double>(n_freqs - 1);
+        }
+
+        std::vector<double> mel_f(n_mels + 2);
+        const double min_mel = ltx_audio_hz_to_mel(0.0);
+        const double max_mel = ltx_audio_hz_to_mel(static_cast<double>(sample_rate) * 0.5);
+        for (int i = 0; i < n_mels + 2; ++i) {
+            double mel = min_mel + (max_mel - min_mel) * static_cast<double>(i) / static_cast<double>(n_mels + 1);
+            mel_f[i]   = ltx_audio_mel_to_hz(mel);
+        }
+
+        for (int m = 0; m < n_mels; ++m) {
+            double lower = mel_f[m];
+            double center = mel_f[m + 1];
+            double upper = mel_f[m + 2];
+            double enorm = 2.0 / std::max(upper - lower, 1e-12);
+            for (int f = 0; f < n_freqs; ++f) {
+                double freq = fft_freqs[f];
+                double value = 0.0;
+                if (freq > lower && freq <= center) {
+                    value = (freq - lower) / std::max(center - lower, 1e-12);
+                } else if (freq > center && freq < upper) {
+                    value = (upper - freq) / std::max(upper - center, 1e-12);
+                }
+                basis.index(f, m) = static_cast<float>(value * enorm);
+            }
+        }
+        return basis;
+    }
+
     static ggml_tensor* compute_log_mel_spectrogram(GGMLRunnerContext* runner_ctx,
                                                     ggml_tensor* waveform,
                                                     ggml_tensor* forward_basis,
@@ -478,6 +591,31 @@ namespace LTXV {
         }
     };
 
+    struct AudioDownsample2D : public GGMLBlock {
+        AudioDownsample2D(int64_t channels) {
+            blocks["conv"] = std::make_shared<Conv2d>(channels,
+                                                      channels,
+                                                      std::pair<int, int>{3, 3},
+                                                      std::pair<int, int>{2, 2},
+                                                      std::pair<int, int>{0, 0});
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
+            x         = ggml_ext_pad_ext(ctx->ggml_ctx,
+                                         x,
+                                         0,
+                                         1,
+                                         2,
+                                         0,
+                                         0,
+                                         0,
+                                         0,
+                                         0);
+            return conv->forward(ctx, x);
+        }
+    };
+
     struct AudioResnetBlock2D : public GGMLBlock {
         int64_t in_channels;
         int64_t out_channels;
@@ -514,6 +652,86 @@ namespace LTXV {
         }
     };
 
+    struct AudioEncoder : public GGMLBlock {
+        LTXAudioVAEConfig config;
+
+        explicit AudioEncoder(const LTXAudioVAEConfig& config)
+            : config(config) {
+            int block_in      = config.encoder_channels;
+            blocks["conv_in"] = std::make_shared<HeightCausalConv2D>(config.audio_channels, block_in, std::pair<int, int>{3, 3});
+
+            for (int level = 0; level < static_cast<int>(config.encoder_channel_multipliers.size()); ++level) {
+                int block_out = config.encoder_channels * config.encoder_channel_multipliers[level];
+                for (int block_idx = 0; block_idx < config.encoder_num_res_blocks; ++block_idx) {
+                    blocks["down." + std::to_string(level) + ".block." + std::to_string(block_idx)] =
+                        std::make_shared<AudioResnetBlock2D>(block_in, block_out);
+                    block_in = block_out;
+                }
+                if (level != static_cast<int>(config.encoder_channel_multipliers.size()) - 1) {
+                    blocks["down." + std::to_string(level) + ".downsample"] = std::make_shared<AudioDownsample2D>(block_in);
+                }
+            }
+
+            blocks["mid.block_1"] = std::make_shared<AudioResnetBlock2D>(block_in, block_in);
+            blocks["mid.block_2"] = std::make_shared<AudioResnetBlock2D>(block_in, block_in);
+            blocks["norm_out"]    = std::make_shared<PixelNorm2D>();
+            blocks["conv_out"]    = std::make_shared<HeightCausalConv2D>(block_in, config.latent_channels * 2, std::pair<int, int>{3, 3});
+        }
+
+        ggml_tensor* normalize_latent(GGMLRunnerContext* ctx,
+                                      ggml_tensor* latent,
+                                      ggml_tensor* mean,
+                                      ggml_tensor* stddev) {
+            latent = ggml_ext_slice(ctx->ggml_ctx, latent, 2, 0, config.latent_channels);
+            latent = ggml_permute(ctx->ggml_ctx, latent, 0, 2, 1, 3);
+            latent = ggml_cont(ctx->ggml_ctx, latent);
+            latent = ggml_reshape_4d(ctx->ggml_ctx, latent, config.latent_frequency_bins * config.latent_channels, latent->ne[2], 1, latent->ne[3]);
+
+            mean   = ggml_reshape_4d(ctx->ggml_ctx, mean, mean->ne[0], 1, 1, 1);
+            stddev = ggml_reshape_4d(ctx->ggml_ctx, stddev, stddev->ne[0], 1, 1, 1);
+            latent = ggml_div(ctx->ggml_ctx, ggml_sub(ctx->ggml_ctx, latent, mean), stddev);
+
+            latent = ggml_reshape_4d(ctx->ggml_ctx,
+                                     latent,
+                                     config.latent_frequency_bins,
+                                     config.latent_channels,
+                                     latent->ne[1],
+                                     latent->ne[3]);
+            latent = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, latent, 0, 2, 1, 3));
+            return latent;
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* spectrogram,
+                             ggml_tensor* mean,
+                             ggml_tensor* stddev) {
+            auto conv_in     = std::dynamic_pointer_cast<HeightCausalConv2D>(blocks["conv_in"]);
+            auto mid_block_1 = std::dynamic_pointer_cast<AudioResnetBlock2D>(blocks["mid.block_1"]);
+            auto mid_block_2 = std::dynamic_pointer_cast<AudioResnetBlock2D>(blocks["mid.block_2"]);
+            auto norm_out    = std::dynamic_pointer_cast<PixelNorm2D>(blocks["norm_out"]);
+            auto conv_out    = std::dynamic_pointer_cast<HeightCausalConv2D>(blocks["conv_out"]);
+
+            auto x = conv_in->forward(ctx, spectrogram);
+            for (int level = 0; level < static_cast<int>(config.encoder_channel_multipliers.size()); ++level) {
+                for (int block_idx = 0; block_idx < config.encoder_num_res_blocks; ++block_idx) {
+                    auto block = std::dynamic_pointer_cast<AudioResnetBlock2D>(blocks["down." + std::to_string(level) + ".block." + std::to_string(block_idx)]);
+                    x          = block->forward(ctx, x);
+                }
+                if (level != static_cast<int>(config.encoder_channel_multipliers.size()) - 1) {
+                    auto downsample = std::dynamic_pointer_cast<AudioDownsample2D>(blocks["down." + std::to_string(level) + ".downsample"]);
+                    x               = downsample->forward(ctx, x);
+                }
+            }
+
+            x = mid_block_1->forward(ctx, x);
+            x = mid_block_2->forward(ctx, x);
+            x = norm_out->forward(ctx, x);
+            x = ggml_silu_inplace(ctx->ggml_ctx, x);
+            x = conv_out->forward(ctx, x);
+            return normalize_latent(ctx, x, mean, stddev);
+        }
+    };
+
     struct Conv1D : public UnaryBlock {
         int64_t in_channels;
         int64_t out_channels;
@@ -914,6 +1132,9 @@ namespace LTXV {
 
         explicit LTXAudioVAE(const LTXAudioVAEConfig& config)
             : config(config) {
+            if (config.has_encoder) {
+                blocks["audio_vae.encoder"] = std::make_shared<AudioEncoder>(config);
+            }
             blocks["audio_vae.decoder"] = std::make_shared<AudioDecoder>(config);
             blocks["vocoder.vocoder"]   = std::make_shared<Vocoder>(config);
             if (config.has_bwe) {
@@ -993,6 +1214,18 @@ namespace LTXV {
 
             return waveform;
         }
+
+        ggml_tensor* encode(GGMLRunnerContext* ctx,
+                            ggml_tensor* waveform,
+                            ggml_tensor* stft_basis,
+                            ggml_tensor* mel_basis) {
+            GGML_ASSERT(config.has_encoder);
+            auto encoder = std::dynamic_pointer_cast<AudioEncoder>(blocks["audio_vae.encoder"]);
+            auto mean    = params["audio_vae.per_channel_statistics.mean-of-means"];
+            auto stddev  = params["audio_vae.per_channel_statistics.std-of-means"];
+            auto mel     = compute_log_mel_spectrogram(ctx, waveform, stft_basis, mel_basis, config.mel_hop_length);
+            return encoder->forward(ctx, mel, mean, stddev);
+        }
     };
 
     struct LTXAudioVAERunner : public GGMLRunner {
@@ -1000,6 +1233,8 @@ namespace LTXV {
         LTXAudioVAE model;
         std::string weight_prefix;
         sd::Tensor<float> bwe_skip_filter_tensor;
+        sd::Tensor<float> encoder_stft_basis_tensor;
+        sd::Tensor<float> encoder_mel_basis_tensor;
 
         LTXAudioVAERunner(ggml_backend_t backend,
                           const String2TensorStorage& tensor_storage_map,
@@ -1014,6 +1249,10 @@ namespace LTXV {
                 const int bwe_ratio    = config.bwe_output_sample_rate / config.bwe_input_sample_rate;
                 bwe_skip_filter_tensor = sd::Tensor<float>::from_vector(build_hann_resample_filter(bwe_ratio));
             }
+            if (config.has_encoder) {
+                encoder_stft_basis_tensor = build_encoder_stft_basis(config.n_fft);
+                encoder_mel_basis_tensor  = build_encoder_mel_basis(config.sample_rate, config.n_fft, config.mel_bins);
+            }
         }
 
         void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
@@ -1046,6 +1285,29 @@ namespace LTXV {
             return result;
         }
 
+        sd::Tensor<float> encode(int n_threads,
+                                 const sd::Tensor<float>& waveform_tensor) {
+            if (!config.has_encoder || waveform_tensor.empty() ||
+                encoder_stft_basis_tensor.empty() || encoder_mel_basis_tensor.empty()) {
+                return {};
+            }
+            int64_t t0     = ggml_time_ms();
+            auto get_graph = [&]() -> ggml_cgraph* {
+                auto waveform   = make_input(waveform_tensor);
+                auto stft_basis = make_input(encoder_stft_basis_tensor);
+                auto mel_basis  = make_input(encoder_mel_basis_tensor);
+                ggml_cgraph* gf = new_graph_custom(655360);
+                auto runner_ctx = GGMLRunner::get_context();
+                auto latent     = model.encode(&runner_ctx, waveform, stft_basis, mel_basis);
+                ggml_build_forward_expand(gf, latent);
+                return gf;
+            };
+            auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), 4);
+            int64_t t1  = ggml_time_ms();
+            LOG_INFO("ltx audio vae encode completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
+            return result;
+        }
+
         void test(const std::string& input_path) {
             auto z = sd::load_tensor_from_file_as_tensor<float>(input_path);
             GGML_ASSERT(!z.empty());
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index 68a4d6aae..765d59fd3 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -2,6 +2,7 @@
 #include <cmath>
 #include <cstdlib>
 #include <filesystem>
+#include <limits>
 #include <set>
 #include <unordered_set>
 #include <vector>
@@ -1342,9 +1343,6 @@ public:
         ignore_tensors.insert("model.diffusion_model.__32x32__");
         ignore_tensors.insert("model.diffusion_model.__index_timestep_zero__");
 
-        if (audio_vae_model) {
-            ignore_tensors.insert("audio_vae.encoder");
-        }
         if (version == VERSION_OVIS_IMAGE) {
             ignore_tensors.insert("text_encoders.llm.vision_model.");
             ignore_tensors.insert("text_encoders.llm.visual_tokenizer.");
@@ -3205,6 +3203,7 @@ void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
     sd_vid_gen_params->seed                                  = -1;
     sd_vid_gen_params->video_frames                          = 6;
     sd_vid_gen_params->fps                                   = 16;
+    sd_vid_gen_params->input_audio                           = nullptr;
     sd_vid_gen_params->moe_boundary                          = 0.875f;
     sd_vid_gen_params->vace_strength                         = 1.f;
     sd_vid_gen_params->vae_tiling_params                     = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
@@ -3304,6 +3303,77 @@ static sd_audio_t* waveform_to_sd_audio(const StableDiffusionGGML* sd,
     return audio;
 }
 
+static sd_audio_t* clone_sd_audio(const sd_audio_t* src) {
+    if (src == nullptr || src->data == nullptr || src->sample_rate == 0 || src->channels == 0 || src->sample_count == 0) {
+        return nullptr;
+    }
+
+    sd_audio_t* audio = (sd_audio_t*)malloc(sizeof(sd_audio_t));
+    if (audio == nullptr) {
+        return nullptr;
+    }
+
+    audio->sample_rate  = src->sample_rate;
+    audio->channels     = src->channels;
+    audio->sample_count = src->sample_count;
+    size_t sample_bytes = static_cast<size_t>(src->sample_count) * static_cast<size_t>(src->channels) * sizeof(float);
+    audio->data         = (float*)malloc(sample_bytes);
+    if (audio->data == nullptr) {
+        free(audio);
+        return nullptr;
+    }
+
+    std::memcpy(audio->data, src->data, sample_bytes);
+    return audio;
+}
+
+static sd::Tensor<float> sd_audio_to_ltx_waveform_tensor(const sd_audio_t* audio,
+                                                         int target_sample_rate,
+                                                         int target_channels) {
+    if (audio == nullptr || audio->data == nullptr || audio->sample_rate == 0 ||
+        audio->channels == 0 || audio->sample_count == 0 || target_sample_rate <= 0 ||
+        target_channels <= 0) {
+        return {};
+    }
+
+    uint64_t out_samples_u64 = (audio->sample_count * static_cast<uint64_t>(target_sample_rate) +
+                                static_cast<uint64_t>(audio->sample_rate) - 1) /
+                               static_cast<uint64_t>(audio->sample_rate);
+    if (out_samples_u64 == 0 || out_samples_u64 > static_cast<uint64_t>(std::numeric_limits<int64_t>::max())) {
+        return {};
+    }
+
+    int64_t out_samples = static_cast<int64_t>(out_samples_u64);
+    sd::Tensor<float> waveform({out_samples, target_channels, 1, 1});
+    const double src_rate = static_cast<double>(audio->sample_rate);
+    const double dst_rate = static_cast<double>(target_sample_rate);
+    const int src_channels = static_cast<int>(audio->channels);
+
+    auto src_value = [&](uint64_t sample, int channel) -> float {
+        int src_channel = channel;
+        if (src_channels == 1) {
+            src_channel = 0;
+        } else if (channel >= src_channels) {
+            src_channel = src_channels - 1;
+        }
+        return audio->data[static_cast<size_t>(sample) * static_cast<size_t>(src_channels) + static_cast<size_t>(src_channel)];
+    };
+
+    for (int64_t t = 0; t < out_samples; ++t) {
+        double src_pos = static_cast<double>(t) * src_rate / dst_rate;
+        uint64_t i0    = static_cast<uint64_t>(std::floor(src_pos));
+        uint64_t i1    = std::min<uint64_t>(i0 + 1, audio->sample_count - 1);
+        float frac     = static_cast<float>(src_pos - static_cast<double>(i0));
+        for (int ch = 0; ch < target_channels; ++ch) {
+            float v0 = src_value(i0, ch);
+            float v1 = src_value(i1, ch);
+            waveform.index(t, ch, 0, 0) = v0 + (v1 - v0) * frac;
+        }
+    }
+
+    return waveform;
+}
+
 void free_sd_audio(sd_audio_t* audio) {
     if (audio == nullptr) {
         return;
@@ -3872,7 +3942,8 @@ static sd::Tensor<float> pack_ltxav_audio_and_video_latents(const sd::Tensor<flo
 
 static sd::Tensor<float> pack_ltxav_audio_and_video_denoise_mask(const sd::Tensor<float>& video_mask,
                                                                  const sd::Tensor<float>& video_latent,
-                                                                 const sd::Tensor<float>& audio_latent) {
+                                                                 const sd::Tensor<float>& audio_latent,
+                                                                 float audio_mask_value = 1.f) {
     if (video_mask.empty() || audio_latent.empty()) {
         return video_mask;
     }
@@ -3915,7 +3986,7 @@ static sd::Tensor<float> pack_ltxav_audio_and_video_denoise_mask(const sd::Tenso
 
     std::vector<int64_t> audio_mask_shape = video_latent.shape();
     audio_mask_shape[3]                   = extra_ch;
-    auto audio_mask                       = sd::Tensor<float>::ones(audio_mask_shape);
+    auto audio_mask                       = sd::full<float>(audio_mask_shape, audio_mask_value);
     return sd::ops::concat(video_mask_full, audio_mask, 3);
 }
 
@@ -4957,6 +5028,44 @@ static std::optional<ImageGenerationLatents> prepare_video_generation_latents(sd
     if (sd_version_is_ltxav(sd_ctx->sd->version)) {
         latents.audio_length = get_ltxav_num_audio_latents(request->frames, request->fps);
         latents.audio_latent = make_ltxav_empty_audio_latent(latents.audio_length);
+        if (sd_vid_gen_params->input_audio != nullptr &&
+            sd_vid_gen_params->input_audio->data != nullptr &&
+            sd_vid_gen_params->input_audio->sample_count > 0) {
+            if (sd_ctx->sd->audio_vae_model == nullptr || !sd_ctx->sd->audio_vae_model->config.has_encoder) {
+                LOG_ERROR("LTX A2V requires an audio VAE with encoder weights");
+                return std::nullopt;
+            }
+
+            int64_t audio_encode_start = ggml_time_ms();
+            auto waveform = sd_audio_to_ltx_waveform_tensor(sd_vid_gen_params->input_audio,
+                                                            sd_ctx->sd->audio_vae_model->config.sample_rate,
+                                                            sd_ctx->sd->audio_vae_model->config.audio_channels);
+            if (waveform.empty()) {
+                LOG_ERROR("failed to convert source audio for LTX A2V encoding");
+                return std::nullopt;
+            }
+
+            auto encoded_audio_latent = sd_ctx->sd->audio_vae_model->encode(sd_ctx->sd->n_threads, waveform);
+            if (encoded_audio_latent.empty()) {
+                LOG_ERROR("LTX A2V audio latent encoding failed");
+                return std::nullopt;
+            }
+
+            latents.audio_latent = resize_ltxav_audio_latent(encoded_audio_latent, latents.audio_length);
+            if (latents.audio_latent.empty()) {
+                LOG_ERROR("failed to resize encoded LTX A2V audio latent");
+                return std::nullopt;
+            }
+
+            int64_t audio_encode_end = ggml_time_ms();
+            LOG_INFO("encoded LTX A2V source audio latent %dx%dx%dx%d -> length %d, taking %.2fs",
+                     (int)encoded_audio_latent.shape()[0],
+                     (int)encoded_audio_latent.shape()[1],
+                     (int)encoded_audio_latent.shape()[2],
+                     (int)encoded_audio_latent.shape()[3],
+                     latents.audio_length,
+                     (audio_encode_end - audio_encode_start) * 1.0f / 1000);
+        }
     }
 
     if (sd_version_is_ltxav(sd_ctx->sd->version)) {
@@ -5234,10 +5343,17 @@ static std::optional<ImageGenerationLatents> prepare_video_generation_latents(sd
     }
 
     if (sd_version_is_ltxav(sd_ctx->sd->version) && !latents.audio_latent.empty()) {
+        bool has_input_audio = sd_vid_gen_params->input_audio != nullptr &&
+                               sd_vid_gen_params->input_audio->data != nullptr &&
+                               sd_vid_gen_params->input_audio->sample_count > 0;
+        if (has_input_audio && latents.denoise_mask.empty()) {
+            latents.denoise_mask = make_ltxav_video_denoise_mask(latents.init_latent, 1.f);
+        }
         if (!latents.denoise_mask.empty()) {
             latents.denoise_mask = pack_ltxav_audio_and_video_denoise_mask(latents.denoise_mask,
                                                                            latents.init_latent,
-                                                                           latents.audio_latent);
+                                                                           latents.audio_latent,
+                                                                           has_input_audio ? 0.f : 1.f);
         }
         latents.init_latent = pack_ltxav_audio_and_video_latents(latents.init_latent, latents.audio_latent);
     }
@@ -5509,8 +5625,14 @@ static bool apply_ltxv_refine_image_conditioning(sd_ctx_t* sd_ctx,
     }
 
     if (!audio_latent.empty()) {
+        bool has_input_audio = sd_vid_gen_params->input_audio != nullptr &&
+                               sd_vid_gen_params->input_audio->data != nullptr &&
+                               sd_vid_gen_params->input_audio->sample_count > 0;
         *latent       = pack_ltxav_audio_and_video_latents(video_latent, audio_latent);
-        *denoise_mask = pack_ltxav_audio_and_video_denoise_mask(video_mask, video_latent, audio_latent);
+        *denoise_mask = pack_ltxav_audio_and_video_denoise_mask(video_mask,
+                                                                video_latent,
+                                                                audio_latent,
+                                                                has_input_audio ? 0.f : 1.f);
     } else {
         *latent       = std::move(video_latent);
         *denoise_mask = std::move(video_mask);
@@ -5542,6 +5664,9 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     int64_t t0                    = ggml_time_ms();
     sd_ctx->sd->vae_tiling_params = sd_vid_gen_params->vae_tiling_params;
     GenerationRequest request(sd_ctx, sd_vid_gen_params);
+    bool has_input_audio = sd_vid_gen_params->input_audio != nullptr &&
+                           sd_vid_gen_params->input_audio->data != nullptr &&
+                           sd_vid_gen_params->input_audio->sample_count > 0;
     bool latent_upscale_enabled     = request.hires.enabled;
     GenerationRequest hires_request = request;
     if (latent_upscale_enabled) {
@@ -5759,6 +5884,17 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
                                                   &hires_video_positions)) {
             return false;
         }
+        if (has_input_audio && hires_denoise_mask.empty() && x_t.shape()[3] > sd_ctx->sd->get_latent_channel()) {
+            int latent_channels = sd_ctx->sd->get_latent_channel();
+            auto video_latent   = sd::ops::slice(x_t, 3, 0, latent_channels);
+            auto audio_latent   = unpack_ltxav_audio_latent(x_t, latents.audio_length, latent_channels);
+            if (!audio_latent.empty()) {
+                hires_denoise_mask = pack_ltxav_audio_and_video_denoise_mask(make_ltxav_video_denoise_mask(video_latent, 1.f),
+                                                                              video_latent,
+                                                                              audio_latent,
+                                                                              0.f);
+            }
+        }
         noise = sd::Tensor<float>::randn_like(x_t, sd_ctx->sd->rng);
 
         W                                   = hires_request.width / hires_request.vae_scale_factor;
@@ -5827,6 +5963,9 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
 
     sd_audio_t* generated_audio = nullptr;
     if (sd_version_is_ltxav(sd_ctx->sd->version) &&
+        has_input_audio) {
+        generated_audio = clone_sd_audio(sd_vid_gen_params->input_audio);
+    } else if (sd_version_is_ltxav(sd_ctx->sd->version) &&
         latents.audio_length > 0 &&
         sd_ctx->sd->audio_vae_model != nullptr) {
         if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
diff --git a/otherarch/utils.cpp b/otherarch/utils.cpp
index 108c31747..2e2170ac1 100644
--- a/otherarch/utils.cpp
+++ b/otherarch/utils.cpp
@@ -949,6 +949,55 @@ bool kcpp_decode_audio_from_buf(const unsigned char * buf_in, size_t len, int ta
     return true;
 }
 
+bool kcpp_decode_audio_file_from_buf(const unsigned char * buf_in, size_t len, int & sample_rate, int & channels, std::vector<float> & pcmf32_interleaved) {
+    sample_rate = 0;
+    channels = 0;
+    pcmf32_interleaved.clear();
+
+    if (!buf_is_audio_file((const char *)buf_in, len))
+    {
+        return false;
+    }
+
+    ma_result result;
+    ma_decoder_config decoder_config = ma_decoder_config_init(ma_format_f32, 0, 0);
+    ma_decoder decoder;
+
+    result = ma_decoder_init_memory(buf_in, len, &decoder_config, &decoder);
+    if (result != MA_SUCCESS) {
+        return false;
+    }
+
+    ma_uint32 decoded_channels = 0;
+    ma_uint32 decoded_sample_rate = 0;
+    result = ma_decoder_get_data_format(&decoder, nullptr, &decoded_channels, &decoded_sample_rate, nullptr, 0);
+    if (result != MA_SUCCESS || decoded_channels == 0 || decoded_sample_rate == 0) {
+        ma_decoder_uninit(&decoder);
+        return false;
+    }
+
+    ma_uint64 frame_count;
+    ma_uint64 frames_read;
+    result = ma_decoder_get_length_in_pcm_frames(&decoder, &frame_count);
+    if (result != MA_SUCCESS) {
+        ma_decoder_uninit(&decoder);
+        return false;
+    }
+
+    pcmf32_interleaved.resize(static_cast<size_t>(frame_count) * static_cast<size_t>(decoded_channels));
+    result = ma_decoder_read_pcm_frames(&decoder, pcmf32_interleaved.data(), frame_count, &frames_read);
+    ma_decoder_uninit(&decoder);
+    if (result != MA_SUCCESS) {
+        pcmf32_interleaved.clear();
+        return false;
+    }
+
+    pcmf32_interleaved.resize(static_cast<size_t>(frames_read) * static_cast<size_t>(decoded_channels));
+    sample_rate = static_cast<int>(decoded_sample_rate);
+    channels = static_cast<int>(decoded_channels);
+    return !pcmf32_interleaved.empty();
+}
+
 //this version is specifically required for ace-step
 bool kcpp_decode_audio_to_f32_stereo_48k(const uint8_t * data, size_t data_size, std::vector<float> & pcm, int & T_audio) {
     ma_result result;
diff --git a/otherarch/utils.h b/otherarch/utils.h
index 1c292db3e..ea489449a 100644
--- a/otherarch/utils.h
+++ b/otherarch/utils.h
@@ -61,6 +61,7 @@ int32_t kcpp_quick_sample(float * logits, const int n_logits, const std::vector<
 
 std::vector<std::string> split_string(const std::string& input, const std::string& separator);
 bool kcpp_decode_audio_from_buf(const unsigned char * buf_in, size_t len, int target_sampler_rate, std::vector<float> & pcmf32_mono);
+bool kcpp_decode_audio_file_from_buf(const unsigned char * buf_in, size_t len, int & sample_rate, int & channels, std::vector<float> & pcmf32_interleaved);
 bool kcpp_decode_audio_to_f32_stereo_48k(const uint8_t * data, size_t data_size, std::vector<float> & pcm, int & T_audio);
 
 typedef struct ggml_backend_device * ggml_backend_dev_t;
@@ -112,4 +113,4 @@ struct wav_ulaw_header {
 std::string save_ulaw_wav8_base64(const std::vector<float> &data, int sample_rate);
 std::string save_wav16_base64(const std::vector<float> &data, int sample_rate);
 std::string save_stereo_wav16_base64(const std::vector<float> & raw_audio, int T_audio, int sample_rate);
-std::string save_stereo_mp3_base64(const std::vector<float> & raw_audio,int T_audio,int sample_rate);
\ No newline at end of file
+std::string save_stereo_mp3_base64(const std::vector<float> & raw_audio,int T_audio,int sample_rate);

From 11cd91658b51777533da930be1d962f7eff00190 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 17 Jun 2026 23:53:06 +0800
Subject: [PATCH 023/292] minor linting

---
 otherarch/sdcpp/examples/common/media_io.cpp  | 115 ++++++++++++++++++
 otherarch/sdcpp/examples/common/media_io.h    |   2 +
 .../sdcpp/src/model/vae/ltx_audio_vae.hpp     |  14 +--
 otherarch/sdcpp/src/stable-diffusion.cpp      |  14 +--
 4 files changed, 131 insertions(+), 14 deletions(-)

diff --git a/otherarch/sdcpp/examples/common/media_io.cpp b/otherarch/sdcpp/examples/common/media_io.cpp
index 506c67f4d..f0fdf374e 100644
--- a/otherarch/sdcpp/examples/common/media_io.cpp
+++ b/otherarch/sdcpp/examples/common/media_io.cpp
@@ -191,6 +191,20 @@ uint32_t read_u32_le_bytes(const uint8_t* data) {
            (static_cast<uint32_t>(data[3]) << 24);
 }
 
+uint16_t read_u16_le_bytes(const uint8_t* p) {
+    return static_cast<uint16_t>(p[0]) | (static_cast<uint16_t>(p[1]) << 8);
+}
+
+int32_t read_s24_le_bytes(const uint8_t* p) {
+    int32_t value = static_cast<int32_t>(p[0]) |
+                    (static_cast<int32_t>(p[1]) << 8) |
+                    (static_cast<int32_t>(p[2]) << 16);
+    if (value & 0x00800000) {
+        value |= 0xff000000;
+    }
+    return value;
+}
+
 int stbi_ext_write_png_to_func(stbi_write_func* func,
                                void* context,
                                int x,
@@ -1374,3 +1388,104 @@ bool write_wav_to_file(const std::string& path,
     file.write(reinterpret_cast<const char*>(pcm.data()), static_cast<std::streamsize>(pcm.size() * sizeof(int16_t)));
     return file.good();
 }
+
+sd_audio_t load_pcm_wav_from_file(const std::string& path) {
+    sd_audio_t audio = {0, 0, 0, nullptr};
+    if (path.empty()) {
+        return audio;
+    }
+
+    std::vector<uint8_t> wav;
+    if (!read_binary_file_bytes(path.c_str(), wav)) {
+        LOG_ERROR("load WAV from '%s' failed", path.c_str());
+        return audio;
+    }
+    if (wav.size() < 44 || std::memcmp(wav.data(), "RIFF", 4) != 0 || std::memcmp(wav.data() + 8, "WAVE", 4) != 0) {
+        LOG_ERROR("input audio file '%s' is not a RIFF/WAVE file", path.c_str());
+        return audio;
+    }
+
+    uint16_t format          = 0;
+    uint16_t channels        = 0;
+    uint32_t sample_rate     = 0;
+    uint16_t bits_per_sample = 0;
+    const uint8_t* data      = nullptr;
+    uint32_t data_size       = 0;
+
+    size_t pos = 12;
+    while (pos + 8 <= wav.size()) {
+        const uint8_t* chunk = wav.data() + pos;
+        uint32_t chunk_size  = read_u32_le_bytes(chunk + 4);
+        size_t chunk_data    = pos + 8;
+        if (chunk_data + chunk_size > wav.size()) {
+            break;
+        }
+
+        if (std::memcmp(chunk, "fmt ", 4) == 0 && chunk_size >= 16) {
+            format          = read_u16_le_bytes(wav.data() + chunk_data);
+            channels        = read_u16_le_bytes(wav.data() + chunk_data + 2);
+            sample_rate     = read_u32_le_bytes(wav.data() + chunk_data + 4);
+            bits_per_sample = read_u16_le_bytes(wav.data() + chunk_data + 14);
+        } else if (std::memcmp(chunk, "data", 4) == 0) {
+            data      = wav.data() + chunk_data;
+            data_size = chunk_size;
+        }
+        pos = chunk_data + chunk_size + (chunk_size & 1);
+    }
+
+    if (data == nullptr || data_size == 0 || channels == 0 || sample_rate == 0) {
+        LOG_ERROR("input WAV '%s' is missing fmt/data chunks", path.c_str());
+        return audio;
+    }
+    if (format != 1 && format != 3) {
+        LOG_ERROR("unsupported WAV format %u in '%s', only PCM and float WAV are supported",
+                  static_cast<unsigned>(format),
+                  path.c_str());
+        return audio;
+    }
+
+    uint16_t bytes_per_sample = static_cast<uint16_t>((bits_per_sample + 7) / 8);
+    uint32_t frame_bytes      = static_cast<uint32_t>(bytes_per_sample) * channels;
+    if (bytes_per_sample == 0 || frame_bytes == 0 || data_size < frame_bytes) {
+        LOG_ERROR("invalid WAV sample format in '%s'", path.c_str());
+        return audio;
+    }
+
+    uint64_t sample_count = data_size / frame_bytes;
+    size_t float_count    = static_cast<size_t>(sample_count) * channels;
+    float* samples        = (float*)malloc(float_count * sizeof(float));
+    if (samples == nullptr) {
+        return audio;
+    }
+
+    for (uint64_t i = 0; i < sample_count; ++i) {
+        for (uint16_t ch = 0; ch < channels; ++ch) {
+            const uint8_t* src = data + i * frame_bytes + ch * bytes_per_sample;
+            float sample       = 0.f;
+            if (format == 3 && bits_per_sample == 32) {
+                std::memcpy(&sample, src, sizeof(float));
+            } else if (format == 1 && bits_per_sample == 8) {
+                sample = (static_cast<int>(src[0]) - 128) / 128.f;
+            } else if (format == 1 && bits_per_sample == 16) {
+                sample = static_cast<int16_t>(read_u16_le_bytes(src)) / 32768.f;
+            } else if (format == 1 && bits_per_sample == 24) {
+                sample = read_s24_le_bytes(src) / 8388608.f;
+            } else if (format == 1 && bits_per_sample == 32) {
+                sample = static_cast<int32_t>(read_u32_le_bytes(src)) / 2147483648.f;
+            } else {
+                LOG_ERROR("unsupported WAV bit depth %u in '%s'",
+                          static_cast<unsigned>(bits_per_sample),
+                          path.c_str());
+                free(samples);
+                return audio;
+            }
+            samples[i * channels + ch] = std::clamp(sample, -1.0f, 1.0f);
+        }
+    }
+
+    audio.sample_rate  = sample_rate;
+    audio.channels     = channels;
+    audio.sample_count = sample_count;
+    audio.data         = samples;
+    return audio;
+}
diff --git a/otherarch/sdcpp/examples/common/media_io.h b/otherarch/sdcpp/examples/common/media_io.h
index 0f7679d7f..df2fd019b 100644
--- a/otherarch/sdcpp/examples/common/media_io.h
+++ b/otherarch/sdcpp/examples/common/media_io.h
@@ -110,4 +110,6 @@ bool write_wav_to_file(const std::string& path,
                        uint32_t channels,
                        uint32_t sample_rate);
 
+sd_audio_t load_pcm_wav_from_file(const std::string& path);
+
 #endif  // __MEDIA_IO_H__
diff --git a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
index 7a3fb25a2..49847445d 100644
--- a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
@@ -250,9 +250,9 @@ namespace LTXV {
         sd::Tensor<float> basis({n_fft, 1, n_freqs * 2});
         for (int k = 0; k < n_freqs; ++k) {
             for (int n = 0; n < n_fft; ++n) {
-                double window          = 0.5 - 0.5 * std::cos(2.0 * kPi * n / static_cast<double>(n_fft));
-                double phase           = 2.0 * kPi * k * n / static_cast<double>(n_fft);
-                basis.index(n, 0, k)   = static_cast<float>(std::cos(phase) * window);
+                double window                  = 0.5 - 0.5 * std::cos(2.0 * kPi * n / static_cast<double>(n_fft));
+                double phase                   = 2.0 * kPi * k * n / static_cast<double>(n_fft);
+                basis.index(n, 0, k)           = static_cast<float>(std::cos(phase) * window);
                 basis.index(n, 0, k + n_freqs) = static_cast<float>(-std::sin(phase) * window);
             }
         }
@@ -276,12 +276,12 @@ namespace LTXV {
         }
 
         for (int m = 0; m < n_mels; ++m) {
-            double lower = mel_f[m];
+            double lower  = mel_f[m];
             double center = mel_f[m + 1];
-            double upper = mel_f[m + 2];
-            double enorm = 2.0 / std::max(upper - lower, 1e-12);
+            double upper  = mel_f[m + 2];
+            double enorm  = 2.0 / std::max(upper - lower, 1e-12);
             for (int f = 0; f < n_freqs; ++f) {
-                double freq = fft_freqs[f];
+                double freq  = fft_freqs[f];
                 double value = 0.0;
                 if (freq > lower && freq <= center) {
                     value = (freq - lower) / std::max(center - lower, 1e-12);
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index 765d59fd3..e2a80a035 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -3345,8 +3345,8 @@ static sd::Tensor<float> sd_audio_to_ltx_waveform_tensor(const sd_audio_t* audio
 
     int64_t out_samples = static_cast<int64_t>(out_samples_u64);
     sd::Tensor<float> waveform({out_samples, target_channels, 1, 1});
-    const double src_rate = static_cast<double>(audio->sample_rate);
-    const double dst_rate = static_cast<double>(target_sample_rate);
+    const double src_rate  = static_cast<double>(audio->sample_rate);
+    const double dst_rate  = static_cast<double>(target_sample_rate);
     const int src_channels = static_cast<int>(audio->channels);
 
     auto src_value = [&](uint64_t sample, int channel) -> float {
@@ -3365,8 +3365,8 @@ static sd::Tensor<float> sd_audio_to_ltx_waveform_tensor(const sd_audio_t* audio
         uint64_t i1    = std::min<uint64_t>(i0 + 1, audio->sample_count - 1);
         float frac     = static_cast<float>(src_pos - static_cast<double>(i0));
         for (int ch = 0; ch < target_channels; ++ch) {
-            float v0 = src_value(i0, ch);
-            float v1 = src_value(i1, ch);
+            float v0                    = src_value(i0, ch);
+            float v1                    = src_value(i1, ch);
             waveform.index(t, ch, 0, 0) = v0 + (v1 - v0) * frac;
         }
     }
@@ -5037,9 +5037,9 @@ static std::optional<ImageGenerationLatents> prepare_video_generation_latents(sd
             }
 
             int64_t audio_encode_start = ggml_time_ms();
-            auto waveform = sd_audio_to_ltx_waveform_tensor(sd_vid_gen_params->input_audio,
-                                                            sd_ctx->sd->audio_vae_model->config.sample_rate,
-                                                            sd_ctx->sd->audio_vae_model->config.audio_channels);
+            auto waveform              = sd_audio_to_ltx_waveform_tensor(sd_vid_gen_params->input_audio,
+                                                                         sd_ctx->sd->audio_vae_model->config.sample_rate,
+                                                                         sd_ctx->sd->audio_vae_model->config.audio_channels);
             if (waveform.empty()) {
                 LOG_ERROR("failed to convert source audio for LTX A2V encoding");
                 return std::nullopt;

From 4b4d13ae721e5eb79b749ca2c6feefd157f90ed7 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 17 Jun 2026 18:04:58 +0200
Subject: [PATCH 024/292] server: (router) add model management API (#23976)

* wip

* server: (router) add SSE realtime updates API

* nits

* wip

* add download API

* add download api

* update docs

* add delete endpoint

* fix std::terminate

* fix crash

* fix 2

* add tests

* nits
---
 common/download.cpp                    |  84 +++++
 common/download.h                      |   7 +
 common/hf-cache.cpp                    |  15 +
 common/hf-cache.h                      |   3 +
 tools/server/README-dev.md             |  18 +
 tools/server/README.md                 | 115 +++++++
 tools/server/server-http.cpp           |  17 +
 tools/server/server-http.h             |   1 +
 tools/server/server-models.cpp         | 433 +++++++++++++++++++++++--
 tools/server/server-models.h           |  89 +++--
 tools/server/server-queue.cpp          |  11 +
 tools/server/server-queue.h            |   6 +-
 tools/server/server-task.h             |  12 +
 tools/server/server.cpp                |   8 +
 tools/server/tests/unit/test_router.py |  96 ++++++
 tools/server/tests/utils.py            |   3 +
 16 files changed, 855 insertions(+), 63 deletions(-)

diff --git a/common/download.cpp b/common/download.cpp
index 40f6eb780..c3c8ff49b 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -997,3 +997,87 @@ std::vector<common_cached_model_info> common_list_cached_models() {
 
     return result;
 }
+
+bool common_download_remove(const std::string & hf_repo_with_tag) {
+    namespace fs = std::filesystem;
+
+    auto [repo_id, tag] = common_download_split_repo_tag(hf_repo_with_tag);
+
+    if (tag.empty()) {
+        return hf_cache::remove_cached_repo(repo_id);
+    }
+
+    std::string tag_upper = tag;
+    for (char & c : tag_upper) {
+        c = (char) std::toupper((unsigned char) c);
+    }
+
+    auto files = hf_cache::get_cached_files(repo_id);
+    if (files.empty()) {
+        return false;
+    }
+
+    // collect snapshot entries whose tag matches
+    std::vector<fs::path> to_remove;
+    for (const auto & f : files) {
+        auto split = get_gguf_split_info(f.path);
+        if (split.tag == tag_upper) {
+            to_remove.emplace_back(f.local_path);
+        }
+    }
+
+    if (to_remove.empty()) {
+        return false;
+    }
+
+    // resolve blob paths from symlinks before deleting snapshot entries
+    std::vector<fs::path> blobs_to_check;
+    for (const auto & p : to_remove) {
+        std::error_code ec;
+        if (fs::is_symlink(p, ec)) {
+            auto target = fs::read_symlink(p, ec);
+            if (!ec) {
+                blobs_to_check.push_back((p.parent_path() / target).lexically_normal());
+            }
+        }
+    }
+
+    // remove snapshot entries
+    for (const auto & p : to_remove) {
+        std::error_code ec;
+        fs::remove(p, ec);
+        if (ec) {
+            LOG_WRN("%s: failed to remove %s: %s\n", __func__, p.string().c_str(), ec.message().c_str());
+        }
+    }
+
+    if (blobs_to_check.empty()) {
+        return true;
+    }
+
+    // collect blobs still referenced by remaining snapshot entries
+    std::unordered_set<std::string> still_referenced;
+    for (const auto & f : hf_cache::get_cached_files(repo_id)) {
+        fs::path p(f.local_path);
+        std::error_code ec;
+        if (fs::is_symlink(p, ec)) {
+            auto target = fs::read_symlink(p, ec);
+            if (!ec) {
+                still_referenced.insert((p.parent_path() / target).lexically_normal().string());
+            }
+        }
+    }
+
+    // remove orphaned blobs
+    for (const auto & blob : blobs_to_check) {
+        if (still_referenced.find(blob.string()) == still_referenced.end()) {
+            std::error_code ec;
+            fs::remove(blob, ec);
+            if (ec) {
+                LOG_WRN("%s: failed to remove blob %s: %s\n", __func__, blob.string().c_str(), ec.message().c_str());
+            }
+        }
+    }
+
+    return true;
+}
diff --git a/common/download.h b/common/download.h
index ebeedd605..237179764 100644
--- a/common/download.h
+++ b/common/download.h
@@ -115,3 +115,10 @@ int common_download_file_single(const std::string & url,
 // resolve and download model from Docker registry
 // return local path to downloaded model file
 std::string common_docker_resolve_model(const std::string & docker);
+
+// Remove a cached model from disk
+// input format: "user/model" or "user/model:tag"
+// - if tag is omitted, removes the entire repo cache directory
+// - if tag is present, removes only files matching that tag (and orphaned blobs)
+// returns true if anything was removed
+bool common_download_remove(const std::string & hf_repo_with_tag);
diff --git a/common/hf-cache.cpp b/common/hf-cache.cpp
index ba7417a12..f1dacaa47 100644
--- a/common/hf-cache.cpp
+++ b/common/hf-cache.cpp
@@ -495,4 +495,19 @@ std::string finalize_file(const hf_file & file) {
     return file.final_path;
 }
 
+bool remove_cached_repo(const std::string & repo_id) {
+    if (!is_valid_repo_id(repo_id)) {
+        LOG_WRN("%s: invalid repository: %s\n", __func__, repo_id.c_str());
+        return false;
+    }
+    fs::path repo_path = get_repo_path(repo_id);
+    std::error_code ec;
+    auto removed = fs::remove_all(repo_path, ec);
+    if (ec) {
+        LOG_ERR("%s: failed to remove repo cache %s: %s\n", __func__, repo_path.string().c_str(), ec.message().c_str());
+        return false;
+    }
+    return removed > 0;
+}
+
 } // namespace hf_cache
diff --git a/common/hf-cache.h b/common/hf-cache.h
index 23fa0adb7..42c9c6ce3 100644
--- a/common/hf-cache.h
+++ b/common/hf-cache.h
@@ -29,4 +29,7 @@ hf_files get_cached_files(const std::string & repo_id = {});
 // Create snapshot path (link or move/copy) and return it
 std::string finalize_file(const hf_file & file);
 
+// Remove the entire cached directory for a repo, returns true if removed
+bool remove_cached_repo(const std::string & repo_id);
+
 } // namespace hf_cache
diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index 0ff334724..4c4103123 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -180,6 +180,24 @@ That requires `JSON.stringify` when formatted to message content:
 }
 ```
 
+### Model management API (router mode)
+
+Model management API was added via PR [#23976](https://github.com/ggml-org/llama.cpp/pull/23976)
+
+The main goal of this API is to allow downloading models and/or removing models from the web UI. It relies on the model cache infrastructure under the hood to manage the list of models dynamically.
+
+Instead of building everything from the ground up (like what most AI agents will do when you ask them to implement a similar feature), we built on top of existing, already well-engineered components inside the codebase:
+- Model cache infrastructure as mentioned above (`common/download.h`)
+- Server response queue (`server-queue.h`). We use this feature to broadcast events to SSE clients.
+- Server router thread management (`server-models.h`). We re-use the same thread model that is used for managing subprocess life cycle, except that we don't create a new subprocess, but launch the download right inside the thread.
+
+The flow for downloading a new model:
+- POST request comes in --> `post_router_models` --> validation
+- `server_models::download()` is called
+    - Sets up a new thread `inst.th` and runs the download inside
+- If a stop request comes in, set `stop_download` to `true`
+- Otherwise, upon completion, we call `load_models()` to refresh the list of models
+
 ### Notable Related PRs
 
 - Initial server implementation: https://github.com/ggml-org/llama.cpp/pull/1443
diff --git a/tools/server/README.md b/tools/server/README.md
index b41491059..88a507e2c 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1778,6 +1778,20 @@ The `status` object can be:
 }
 ```
 
+Note: for "downloading" state, there can be multiple files be downloading in parallel
+
+```json
+"status": {
+  "value": "downloading",
+  "progress": {
+    "https://...model.gguf": {
+      "done": 195963406,
+      "total": 219307424
+    }
+  }
+}
+```
+
 ### POST `/models/load`: Load a model
 
 Load a model
@@ -1820,6 +1834,107 @@ Response:
 }
 ```
 
+### GET `/models/sse`: Real-time events
+
+Example events:
+
+```js
+{
+  "model": "...",
+  "event": "model_status",
+  "data": {
+    "status": "loading"
+  }
+}
+
+{
+  "model": "...",
+  "event": "download_progress",
+  "data": {
+    // note: there can be multiple files being downloaded in parallel
+    "https://...model.gguf": {
+      "done": 195963406,
+      "total": 219307424
+    }
+  }
+}
+
+{
+  "model": "...",
+  "event": "download_finished",
+  "data": {
+    "status": "loading"
+  }
+}
+
+{
+  "model": "...",
+  "event": "model_remove"
+}
+
+// special event: reload of the list of all models
+{
+  "model": "*",
+  "event": "models_reload"
+}
+```
+
+### POST `/models`: Download new model
+
+Trigger a new download (non-blocking), the progress can be tracked via SSE endpoint `/models/sse`
+
+To cancel model downloading, send an event to `/models/unload`
+
+Download procedure:
+- Send POST request to `/models`
+- Subscribe to `/models/sse` for updates
+- On downloading completed, you will receive either `download_finished` or `download_failed` event
+- Call GET `/models` to trigger model list update. If the download success, you should see the new model in the list
+
+Payload:
+
+```json
+{
+  "model": "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M",
+}
+```
+
+Response (download is started in the background):
+
+```json
+{
+  "success": true
+}
+```
+
+Response (error, cannot start the download):
+
+```json
+{
+  "error": {
+    "code": 400,
+    "message": "model validation failed, unable to download",
+    "type": "invalid_request_error"
+  }
+}
+```
+
+### DELETE `/models`: Delete a model from cache
+
+IMPORTANT: only model stored in cache can be deleted. You cannot delete models in a preset.
+
+Model name must be passed via query param: `?model={name}`
+
+If delete success, it will send an SSE event of type `model_remove`
+
+Response:
+
+```json
+{
+  "success": true
+}
+```
+
 ## API errors
 
 `llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index 3c58fcfec..5defee1f5 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -588,6 +588,23 @@ void server_http_context::post(const std::string & path, const server_http_conte
     });
 }
 
+void server_http_context::del(const std::string & path, const server_http_context::handler_t & handler) const {
+    handlers.emplace(path, handler);
+    pimpl->srv->Delete(path_prefix + path, [handler](const httplib::Request & req, httplib::Response & res) {
+        server_http_req_ptr request = std::make_unique<server_http_req>(server_http_req{
+            get_params(req),
+            get_headers(req),
+            req.path,
+            build_query_string(req),
+            req.body,
+            {},
+            req.is_connection_closed
+        });
+        server_http_res_ptr response = handler(*request);
+        process_handler_response(std::move(request), response, res);
+    });
+}
+
 //
 // Vertex AI Prediction protocol (AIP_PREDICT_ROUTE)
 // https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements
diff --git a/tools/server/server-http.h b/tools/server/server-http.h
index 25c7f1062..6b4a4b87a 100644
--- a/tools/server/server-http.h
+++ b/tools/server/server-http.h
@@ -86,6 +86,7 @@ struct server_http_context {
 
     void get(const std::string & path, const handler_t & handler) const;
     void post(const std::string & path, const handler_t & handler) const;
+    void del(const std::string & path, const handler_t & handler) const;
 
     // Register the Google Cloud Platform (Vertex AI) compat (AIP_PREDICT_ROUTE env var, or /predict)
     // Must be called AFTER all other API routes are registered
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 49b0e423f..ff9a0df12 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -9,6 +9,7 @@
 #include <sheredom/subprocess.h>
 
 #include <functional>
+#include <optional>
 #include <algorithm>
 #include <thread>
 #include <mutex>
@@ -51,6 +52,21 @@ extern char **environ;
 // ref: https://github.com/ggml-org/llama.cpp/issues/17862
 #define CHILD_ADDR "127.0.0.1"
 
+struct server_subproc {
+    std::optional<subprocess_s> sproc; // empty while in DOWNLOADING state
+    std::atomic<bool> stop_download{false}; // flag to signal download cancellation
+
+    subprocess_s & get() {
+        GGML_ASSERT(sproc.has_value() && "subprocess not initialized");
+        return sproc.value();
+    }
+
+    bool is_alive() {
+        return sproc.has_value() && subprocess_alive(&sproc.value());
+    }
+};
+
+
 static std::filesystem::path get_server_exec_path() {
 #if defined(_WIN32)
     wchar_t buf[32768] = { 0 };  // Large buffer to handle long paths
@@ -272,12 +288,25 @@ void server_models::add_model(server_model_meta && meta) {
     meta.update_caps();
     std::string name = meta.name;
     mapping[name] = instance_t{
-        /* subproc */ std::make_shared<subprocess_s>(),
+        /* subproc */ std::make_shared<server_subproc>(),
         /* th      */ std::thread(),
         /* meta    */ std::move(meta)
     };
 }
 
+void server_models::notify_sse(const std::string & event, const std::string & model_id, const json & data) {
+    std::unique_ptr<server_task_result_router> result = std::make_unique<server_task_result_router>();
+    result->data = {
+        {"model", model_id},
+        {"event", event},
+    };
+    if (!data.is_null()) {
+        result->data["data"] = data;
+    }
+    SRV_DBG("notifying SSE clients about event '%s' for model '%s': %s\n", event.c_str(), model_id.c_str(), safe_json_to_str(result->data).c_str());
+    sse.broadcast(std::move(result));
+}
+
 void server_models::load_models() {
     // Phase 1: load presets from all sources — pure I/O, no lock needed
     // 1. cached models
@@ -304,19 +333,27 @@ void server_models::load_models() {
 
     // note: if a model exists in both cached and local, local takes precedence
     common_presets final_presets;
-    for (const auto & [name, preset] : cached_models) final_presets[name] = preset;
-    for (const auto & [name, preset] : local_models)  final_presets[name] = preset;
+    std::unordered_map<std::string, server_model_source> source_map;
+    for (const auto & [name, preset] : cached_models) {
+        final_presets[name] = preset;
+        source_map[name] = SERVER_MODEL_SOURCE_CACHE;
+    }
+    for (const auto & [name, preset] : local_models)  {
+        final_presets[name] = preset;
+        source_map[name] = SERVER_MODEL_SOURCE_MODELS_DIR;
+    }
     for (const auto & [name, custom] : custom_presets) {
         if (final_presets.find(name) != final_presets.end()) {
             final_presets[name].merge(custom);
         } else {
             final_presets[name] = custom;
         }
+        source_map[name] = SERVER_MODEL_SOURCE_PRESET;
     }
-    // server base preset from CLI args takes highest precedence
-    for (auto & [name, preset] : final_presets) {
-        preset.merge(base_preset);
-    }
+
+    auto get_source = [&](const std::string & name) {
+        return source_map.count(name) ? source_map.at(name) : SERVER_MODEL_SOURCE_PRESET;
+    };
 
     // Helpers that read `mapping` — must be called while holding the lock.
     std::unordered_set<std::string> custom_names;
@@ -366,12 +403,15 @@ void server_models::load_models() {
     // (unload, load) or when joining threads (the monitoring thread calls update_status
     // which locks the mutex, so joining while holding it would deadlock).
     std::unique_lock<std::mutex> lk(mutex);
+
+    need_reload = false;
     bool is_first_load = mapping.empty();
 
     if (is_first_load) {
         // FIRST LOAD: add all models, then unlock for autoloading
         for (const auto & [name, preset] : final_presets) {
             server_model_meta meta{
+                /* source        */ get_source(name),
                 /* preset        */ preset,
                 /* name          */ name,
                 /* aliases       */ {},
@@ -384,7 +424,7 @@ void server_models::load_models() {
                 /* exit_code     */ 0,
                 /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                 /* multimodal    */ mtmd_caps{false, false},
-                /* need_download */ false,
+                // /* need_download */ false,
             };
             add_model(std::move(meta));
         }
@@ -453,6 +493,9 @@ void server_models::load_models() {
             }
         }
         for (auto & [name, inst] : mapping) {
+            if (inst.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
+                continue; // downloading models are not from config sources, leave them alone
+            }
             if (final_presets.find(name) == final_presets.end() && !inst.meta.is_running() && inst.th.joinable()) {
                 threads_to_join.push_back(std::move(inst.th));
             }
@@ -465,7 +508,15 @@ void server_models::load_models() {
 
         // erase models no longer in any source
         for (auto it = mapping.begin(); it != mapping.end(); ) {
-            if (final_presets.find(it->first) == final_presets.end()) {
+            if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
+                ++it; // download thread is still busy, skip
+            } else if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADED) {
+                // download finished, safe to erase
+                if (it->second.th.joinable()) {
+                    it->second.th.join();
+                }
+                it = mapping.erase(it);
+            } else if (final_presets.find(it->first) == final_presets.end()) {
                 SRV_INF("(reload) removing model name=%s (no longer in source)\n", it->first.c_str());
                 GGML_ASSERT(!it->second.th.joinable()); // must have been joined above
                 it = mapping.erase(it);
@@ -526,6 +577,7 @@ void server_models::load_models() {
         for (const auto & [name, preset] : final_presets) {
             if (mapping.find(name) == mapping.end()) {
                 server_model_meta meta{
+                    /* source        */ get_source(name),
                     /* preset        */ preset,
                     /* name          */ name,
                     /* aliases       */ {},
@@ -538,7 +590,7 @@ void server_models::load_models() {
                     /* exit_code     */ 0,
                     /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                     /* multimodal    */ mtmd_caps{false, false},
-                    /* need_download */ false,
+                    // /* need_download */ false,
                 };
                 add_model(std::move(meta));
                 newly_added.push_back(name);
@@ -571,6 +623,8 @@ void server_models::load_models() {
             SRV_INF("(reload) loading new model %s\n", name.c_str());
             load(name);
         }
+
+        notify_sse("models_reload", "*");
     }
 }
 
@@ -597,7 +651,13 @@ bool server_models::has_model(const std::string & name) {
 }
 
 std::optional<server_model_meta> server_models::get_meta(const std::string & name) {
-    std::lock_guard<std::mutex> lk(mutex);
+    std::unique_lock<std::mutex> lk(mutex);
+    if (need_reload) {
+        lk.unlock();
+        load_models();
+        lk.lock();
+    }
+
     auto it = mapping.find(name);
     if (it != mapping.end()) {
         return it->second.meta;
@@ -683,7 +743,13 @@ static std::vector<char *> to_char_ptr_array(const std::vector<std::string> & ve
 }
 
 std::vector<server_model_meta> server_models::get_all_meta() {
-    std::lock_guard<std::mutex> lk(mutex);
+    std::unique_lock<std::mutex> lk(mutex);
+    if (need_reload) {
+        lk.unlock();
+        load_models();
+        lk.lock();
+    }
+
     std::vector<server_model_meta> result;
     result.reserve(mapping.size());
     for (const auto & [name, inst] : mapping) {
@@ -770,7 +836,7 @@ void server_models::load(const std::string & name) {
         throw std::runtime_error("failed to get a port number");
     }
 
-    inst.subproc = std::make_shared<subprocess_s>();
+    inst.subproc = std::make_shared<server_subproc>();
     {
         SRV_INF("spawning server instance with name=%s on port %d\n", inst.meta.name.c_str(), inst.meta.port);
 
@@ -792,19 +858,20 @@ void server_models::load(const std::string & name) {
         // TODO @ngxson : maybe separate stdout and stderr in the future
         //                so that we can use stdout for commands and stderr for logging
         int options = subprocess_option_no_window | subprocess_option_combined_stdout_stderr;
-        int result = subprocess_create_ex(argv.data(), options, envp.data(), inst.subproc.get());
+        inst.subproc->sproc.emplace();
+        int result = subprocess_create_ex(argv.data(), options, envp.data(), &inst.subproc->get());
         if (result != 0) {
             throw std::runtime_error("failed to spawn server instance");
         }
 
-        inst.stdin_file = subprocess_stdin(inst.subproc.get());
+        inst.stdin_file = subprocess_stdin(&inst.subproc->get());
     }
 
     // start a thread to manage the child process
     // captured variables are guaranteed to be destroyed only after the thread is joined
     inst.th = std::thread([this, name, child_proc = inst.subproc, port = inst.meta.port, stop_timeout = inst.meta.stop_timeout]() {
-        FILE * stdin_file = subprocess_stdin(child_proc.get());
-        FILE * stdout_file = subprocess_stdout(child_proc.get()); // combined stdout/stderr
+        FILE * stdin_file = subprocess_stdin(&child_proc->get());
+        FILE * stdout_file = subprocess_stdout(&child_proc->get()); // combined stdout/stderr
 
         std::thread log_thread([&]() {
             // read stdout/stderr and forward to main server log
@@ -834,14 +901,14 @@ void server_models::load(const std::string & name) {
                 return this->stopping_models.find(name) != this->stopping_models.end();
             };
             auto should_wake = [&]() {
-                return is_stopping() || !subprocess_alive(child_proc.get());
+                return is_stopping() || !child_proc->is_alive();
             };
             {
                 std::unique_lock<std::mutex> lk(this->mutex);
                 this->cv_stop.wait(lk, should_wake);
             }
             // child may have already exited (e.g. crashed) — skip shutdown sequence
-            if (!subprocess_alive(child_proc.get())) {
+            if (!child_proc->is_alive()) {
                 return;
             }
             SRV_INF("stopping model instance name=%s\n", name.c_str());
@@ -859,7 +926,7 @@ void server_models::load(const std::string & name) {
                 if (elapsed >= stop_timeout * 1000) {
                     // timeout, force kill
                     SRV_WRN("force-killing model instance name=%s after %d seconds timeout\n", name.c_str(), stop_timeout);
-                    subprocess_terminate(child_proc.get());
+                    subprocess_terminate(&child_proc->get());
                     return;
                 }
                 this->cv_stop.wait_for(lk, std::chrono::seconds(1));
@@ -884,8 +951,8 @@ void server_models::load(const std::string & name) {
 
         // get the exit code
         int exit_code = 0;
-        subprocess_join(child_proc.get(), &exit_code);
-        subprocess_destroy(child_proc.get());
+        subprocess_join(&child_proc->get(), &exit_code);
+        subprocess_destroy(&child_proc->get());
 
         // update status and exit code
         this->update_status(name, SERVER_MODEL_STATUS_UNLOADED, exit_code);
@@ -896,30 +963,118 @@ void server_models::load(const std::string & name) {
     {
         auto & old_instance = mapping[name];
         // old process should have exited already, but just in case, we clean it up here
-        if (subprocess_alive(old_instance.subproc.get())) {
+        if (old_instance.subproc->is_alive()) {
             SRV_WRN("old process for model name=%s is still alive, this is unexpected\n", name.c_str());
-            subprocess_terminate(old_instance.subproc.get()); // force kill
+            subprocess_terminate(&old_instance.subproc->get()); // force kill
         }
         if (old_instance.th.joinable()) {
             old_instance.th.join();
         }
     }
 
+    notify_sse("model_status", name, {
+        {"status", server_model_status_to_string(inst.meta.status)},
+    });
+
     mapping[name] = std::move(inst);
     cv.notify_all();
 }
 
+// callback for model downloading functionality
+struct server_models_download_res : public common_download_callback {
+    common_params_model model;
+    common_download_opts opts;
+
+    std::function<bool()> should_stop;
+    std::function<void(const common_download_progress & p)> on_progress;
+
+    bool is_ok = false;
+
+    bool run() {
+        try {
+            common_download_model(model, opts);
+            is_ok = true;
+        } catch (const std::exception & e) {
+            SRV_ERR("download failed for model name=%s: %s\n", model.name.c_str(), e.what());
+            is_ok = false;
+        }
+        return is_ok;
+    }
+    void on_start(const common_download_progress & p) override {
+        on_progress(p);
+    }
+    void on_update(const common_download_progress & p) override {
+        on_progress(p);
+    }
+    void on_done(const common_download_progress &, bool ok) override {
+        is_ok = ok;
+    }
+    bool is_cancelled() const override {
+        return should_stop();
+    }
+};
+
+void server_models::download(common_params_model && model, common_download_opts && opts) {
+    std::string name = model.name;
+    GGML_ASSERT(name == model.hf_repo);
+
+    std::unique_lock<std::mutex> lk(mutex);
+    if (mapping.find(name) != mapping.end()) {
+        throw std::runtime_error("model name=" + name + " already exists");
+    }
+
+    instance_t inst;
+    inst.meta.name   = name;
+    inst.meta.status = SERVER_MODEL_STATUS_DOWNLOADING;
+    inst.subproc     = std::make_shared<server_subproc>();
+
+    auto dl = std::make_unique<server_models_download_res>();
+    dl->model = model; // copy
+    dl->opts  = opts;  // copy
+
+    dl->should_stop = [sp = inst.subproc]() {
+        return sp->stop_download.load(std::memory_order_relaxed);
+    };
+
+    dl->on_progress = [this, name](const common_download_progress & p) {
+        update_download_progress(name, p, false);
+    };
+
+    inst.th = std::thread([this, dl = std::move(dl)]() {
+        dl->opts.callback = dl.get();
+        bool ok = dl->run();
+        SRV_INF("download finished for model name=%s with status=%s\n",
+                    dl->model.name.c_str(), ok ? "success" : "failure");
+        update_download_progress(dl->model.name, {}, true, ok);
+        // need_reload is set inside update_download_progress under the mutex;
+        // the next load_models() call will clean up this instance
+    });
+
+    mapping[name] = std::move(inst);
+    notify_sse("status_update", name, {
+        {"status", server_model_status_to_string(SERVER_MODEL_STATUS_DOWNLOADING)},
+    });
+    cv.notify_all();
+}
+
 void server_models::unload(const std::string & name) {
-    std::lock_guard<std::mutex> lk(mutex);
+    std::unique_lock<std::mutex> lk(mutex);
     auto it = mapping.find(name);
     if (it != mapping.end()) {
-        if (it->second.meta.is_running()) {
+        if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
+            SRV_INF("cancelling download for model name=%s\n", name.c_str());
+            it->second.subproc->stop_download.store(true, std::memory_order_relaxed);
+            // for convenience, we wait the status change here
+            wait(lk, name, [](const server_model_meta & new_meta) {
+                return new_meta.status != SERVER_MODEL_STATUS_DOWNLOADING;
+            });
+        } else if (it->second.meta.is_running()) {
             SRV_INF("stopping model instance name=%s\n", name.c_str());
             stopping_models.insert(name);
             if (it->second.meta.status == SERVER_MODEL_STATUS_LOADING) {
                 // special case: if model is in loading state, unloading means force-killing it
                 SRV_WRN("model name=%s is still loading, force-killing\n", name.c_str());
-                subprocess_terminate(it->second.subproc.get());
+                subprocess_terminate(&it->second.subproc->get());
             }
             cv_stop.notify_all();
             // status change will be handled by the managing thread
@@ -934,7 +1089,10 @@ void server_models::unload_all() {
     {
         std::lock_guard<std::mutex> lk(mutex);
         for (auto & [name, inst] : mapping) {
-            if (inst.meta.is_running()) {
+            if (inst.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
+                SRV_INF("cancelling download for model name=%s\n", name.c_str());
+                inst.subproc->stop_download.store(true, std::memory_order_relaxed);
+            } else if (inst.meta.is_running()) {
                 SRV_INF("stopping model instance name=%s\n", name.c_str());
                 stopping_models.insert(name);
                 cv_stop.notify_all();
@@ -959,6 +1117,17 @@ void server_models::update_status(const std::string & name, server_model_status
         meta.status    = status;
         meta.exit_code = exit_code;
     }
+    // broadcast status change to SSE
+    {
+        json data = {
+            {"status", server_model_status_to_string(status)},
+        };
+        if (status == SERVER_MODEL_STATUS_UNLOADED) {
+            data["exit_code"] = exit_code;
+        }
+        // note: notify_sse doesn't acquire the lock, so no deadlock here
+        notify_sse("status_change", name, data);
+    }
     cv.notify_all();
 }
 
@@ -985,12 +1154,82 @@ void server_models::update_loaded_info(const std::string & name, std::string & r
     cv.notify_all();
 }
 
-void server_models::wait_until_loading_finished(const std::string & name) {
-    std::unique_lock<std::mutex> lk(mutex);
-    cv.wait(lk, [this, &name]() {
+void server_models::update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok) {
+    json curr;
+    {
+        std::lock_guard<std::mutex> lk(mutex);
         auto it = mapping.find(name);
         if (it != mapping.end()) {
-            return it->second.meta.status != SERVER_MODEL_STATUS_LOADING;
+            if (done) {
+                // mark the instance to be erased on next load_models() call
+                it->second.meta.status = SERVER_MODEL_STATUS_DOWNLOADED;
+                need_reload = true;
+            } else {
+                json & info = it->second.meta.loaded_info;
+                if (!info.contains("progress")) {
+                    info["progress"] = json{};
+                }
+                info["progress"][progress.url] = {
+                    {"done",  progress.downloaded},
+                    {"total", progress.total},
+                };
+                curr = it->second.meta.loaded_info; // copy
+            }
+        }
+    }
+    if (done) {
+        cv.notify_all(); // notify in case unload() is waiting for download to be cancelled
+        notify_sse(ok ? "download_finished" : "download_failed", name, {});
+    } else {
+        notify_sse("download_progress", name, curr);
+    }
+}
+
+bool server_models::remove(const std::string & name) {
+    auto meta = get_meta(name);
+
+    if (!meta.has_value()) {
+        throw std::runtime_error("model name=" + name + " is not found");
+    }
+    if (meta->source != SERVER_MODEL_SOURCE_CACHE) {
+        throw std::runtime_error("model name=" + name + " is not removable (not from cache)");
+    }
+
+    unload(name); // cancel download or stop running instance
+    {
+        std::unique_lock<std::mutex> lk(mutex);
+        // a cancelled download lands on DOWNLOADED; a stopped instance lands on UNLOADED
+        wait(lk, name, [](const server_model_meta & new_meta) {
+            return new_meta.status == SERVER_MODEL_STATUS_UNLOADED
+                || new_meta.status == SERVER_MODEL_STATUS_DOWNLOADED;
+        });
+        // join before erasing - after status reaches UNLOADED/DOWNLOADED the thread no
+        // longer acquires this mutex, so joining while holding it is safe
+        if (mapping[name].th.joinable()) {
+            mapping[name].th.join();
+        }
+        // remove the model from disk (hold lock to prevent concurrent load)
+        bool ok = common_download_remove(name);
+        if (ok) {
+            mapping.erase(name);
+        }
+        SRV_INF("removing model name=%s from cache (%s)\n", name.c_str(), ok ? "succeeded" : "failed");
+        notify_sse("model_remove", name, {});
+        return ok;
+    }
+}
+
+void server_models::wait(const std::string & name, std::function<bool(const server_model_meta &)> predicate) {
+    std::unique_lock<std::mutex> lk(mutex);
+    wait(lk, name, predicate);
+}
+
+void server_models::wait(std::unique_lock<std::mutex> & lk, const std::string & name, std::function<bool(const server_model_meta &)> predicate) {
+    cv.wait(lk, [this, &name, &predicate]() {
+        auto it = mapping.find(name);
+        if (it != mapping.end()) {
+            return predicate(it->second.meta);
+
         }
         return false;
     });
@@ -1014,10 +1253,15 @@ bool server_models::ensure_model_ready(const std::string & name) {
 
     // wait for loading to complete
     SRV_INF("waiting until model name=%s is fully loaded...\n", name.c_str());
-    wait_until_loading_finished(name);
+    wait(name, [&meta](const server_model_meta & new_meta) {
+        if (new_meta.status != SERVER_MODEL_STATUS_LOADING) {
+            meta = new_meta; // update meta for final check after wait
+            return true;
+        }
+        return false;
+    });
 
     // check final status
-    meta = get_meta(name);
     if (!meta.has_value() || meta->is_failed()) {
         throw std::runtime_error("model name=" + name + " failed to load");
     }
@@ -1111,6 +1355,42 @@ void server_models::notify_router_sleeping_state(bool is_sleeping) {
 // server_models_routes
 //
 
+// RAII wrapper similar to server_response_reader, but doesn't use server_queue
+static std::atomic<int> sse_client_id_counter = 0;
+struct server_models_sse_client {
+    server_response & queue_results;
+    int client_id;
+    server_models_sse_client(server_response & q)
+            : queue_results(q), client_id(sse_client_id_counter.fetch_add(1, std::memory_order_relaxed)) {
+        SRV_DBG("new SSE client connected, assigned client_id=%d\n", client_id);
+        queue_results.add_waiting_task_id(client_id);
+    }
+    ~server_models_sse_client() {
+        SRV_DBG("SSE client disconnected, removing client_id=%d\n", client_id);
+        queue_results.remove_waiting_task_id(client_id);
+    }
+
+    // return nullptr if should_stop() is true before receiving a result
+    // note: if one error is received, it will stop further processing and return error result
+    server_task_result_ptr next(const std::function<bool()> & should_stop) {
+        while (true) {
+            static const int http_polling_seconds = 1; // check should_stop every 1 second
+            server_task_result_ptr result = queue_results.recv_with_timeout({client_id}, http_polling_seconds);
+            if (result == nullptr) {
+                // timeout, check stop condition
+                if (should_stop()) {
+                    return nullptr;
+                }
+                // continue waiting otherwise
+            } else {
+                SRV_DBG("recv result for client_id=%d: %s\n", client_id, safe_json_to_str(result->to_json()).c_str());
+                return result;
+            }
+        }
+        // should not reach here
+    }
+};
+
 static void res_ok(std::unique_ptr<server_http_res> & res, const json & response_data) {
     res->status = 200;
     res->data = safe_json_to_str(response_data);
@@ -1274,7 +1554,9 @@ void server_models_routes::init_routes() {
                 {"created",       t},          // for OAI-compat
                 {"status",        status},
                 {"architecture",  architecture},
-                {"need_download", meta.need_download},
+                {"source",        server_model_source_to_string(meta.source)},
+                {"can_remove",    meta.source == SERVER_MODEL_SOURCE_CACHE},
+                // {"need_download", meta.need_download},
                 // TODO: add other fields, may require reading GGUF metadata
             };
 
@@ -1312,6 +1594,87 @@ void server_models_routes::init_routes() {
         res_ok(res, {{"success", true}});
         return res;
     };
+
+    this->get_router_models_sse = [this](const server_http_req & req) {
+        auto res = std::make_unique<server_http_res>();
+        res->status = 200;
+        res->content_type = "text/event-stream";
+        auto sse_client = std::make_shared<server_models_sse_client>(models.sse);
+        res->next = [this, sse_client, &req](std::string & output) -> bool {
+            auto result = sse_client->next([&]() {
+                return stopping.load(std::memory_order_relaxed) || req.should_stop();
+            });
+            if (result == nullptr) {
+                return false; // client disconnected or should_stop
+            }
+            output = "data: " + safe_json_to_str(result->to_json()) + "\n\n";
+            return true; // listen for the next event
+        };
+        return res;
+    };
+
+    this->post_router_models = [this](const server_http_req & req) {
+        auto res = std::make_unique<server_http_res>();
+
+        json body = json::parse(req.body);
+        std::string name = json_value(body, "model", std::string());
+        if (name.empty()) {
+            throw std::invalid_argument("model must be a non-empty string");
+        }
+
+        common_params_model model;
+        common_download_opts opts;
+
+        model.name           = name;
+        model.hf_repo        = name;
+        opts.bearer_token    = params.hf_token;
+        opts.download_mmproj = true;
+        opts.download_mtp    = true;
+
+        // first, only check if the model is valid and can be downloaded
+        opts.skip_download = true;
+        bool ok = false;
+        try {
+            auto validation = common_download_model(model, opts);
+            ok = !validation.model_path.empty();
+        } catch (const common_skip_download_exception &) {
+            // model is valid and will be downloaded
+            ok = true;
+        } catch (...) {
+            SRV_ERR("unknown error while validating model '%s'\n", name.c_str());
+            // other exceptions will be handled by the outer ex_wrapper()
+            throw;
+        }
+
+        if (!ok) {
+            throw std::invalid_argument("model validation failed, unable to download");
+        }
+
+        // then, proceed with the actual download
+        opts.skip_download = false;
+        SRV_INF("starting download for model '%s'\n", name.c_str());
+        models.download(std::move(model), std::move(opts));
+
+        res_ok(res, {{"success", true}});
+        return res;
+    };
+
+    this->del_router_models = [this](const server_http_req & req) {
+        auto res = std::make_unique<server_http_res>();
+
+        std::string name = req.get_param("model");
+        if (name.empty()) {
+            throw std::invalid_argument("model must be a non-empty string");
+        }
+
+        bool ok = models.remove(name);
+        if (!ok) {
+            throw std::runtime_error("failed to remove model '" + name + "'");
+        }
+
+        res_ok(res, {{"success", true}});
+        return res;
+    };
 }
 
 
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 2198589a7..319c4352e 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -1,9 +1,11 @@
 #pragma once
 
 #include "common.h"
+#include "download.h"
 #include "preset.h"
 #include "server-common.h"
 #include "server-http.h"
+#include "server-queue.h"
 
 #include <mutex>
 #include <condition_variable>
@@ -14,6 +16,8 @@
 /**
  * state diagram:
  *
+ * DOWNLOADING ──► DOWNLOADED ──► (replaced by new instance)
+ *
  * UNLOADED ──► LOADING ──► LOADED ◄──── SLEEPING
  *  ▲            │            │               ▲
  *  └───failed───┘            │               │
@@ -22,39 +26,43 @@
  */
 enum server_model_status {
     // TODO: also add downloading state when the logic is added
+    SERVER_MODEL_STATUS_DOWNLOADING,
+    SERVER_MODEL_STATUS_DOWNLOADED,
     SERVER_MODEL_STATUS_UNLOADED,
     SERVER_MODEL_STATUS_LOADING,
     SERVER_MODEL_STATUS_LOADED,
     SERVER_MODEL_STATUS_SLEEPING
 };
 
-static server_model_status server_model_status_from_string(const std::string & status_str) {
-    if (status_str == "unloaded") {
-        return SERVER_MODEL_STATUS_UNLOADED;
-    }
-    if (status_str == "loading") {
-        return SERVER_MODEL_STATUS_LOADING;
-    }
-    if (status_str == "loaded") {
-        return SERVER_MODEL_STATUS_LOADED;
-    }
-    if (status_str == "sleeping") {
-        return SERVER_MODEL_STATUS_SLEEPING;
-    }
-    throw std::runtime_error("invalid server model status");
-}
+enum server_model_source {
+    SERVER_MODEL_SOURCE_PRESET,
+    SERVER_MODEL_SOURCE_MODELS_DIR,
+    SERVER_MODEL_SOURCE_CACHE,
+};
 
 static std::string server_model_status_to_string(server_model_status status) {
     switch (status) {
-        case SERVER_MODEL_STATUS_UNLOADED: return "unloaded";
-        case SERVER_MODEL_STATUS_LOADING:  return "loading";
-        case SERVER_MODEL_STATUS_LOADED:   return "loaded";
-        case SERVER_MODEL_STATUS_SLEEPING: return "sleeping";
-        default:                           return "unknown";
+        case SERVER_MODEL_STATUS_DOWNLOADING: return "downloading";
+        case SERVER_MODEL_STATUS_DOWNLOADED:  return "downloaded";
+        case SERVER_MODEL_STATUS_UNLOADED:    return "unloaded";
+        case SERVER_MODEL_STATUS_LOADING:     return "loading";
+        case SERVER_MODEL_STATUS_LOADED:      return "loaded";
+        case SERVER_MODEL_STATUS_SLEEPING:    return "sleeping";
+        default:                              return "unknown";
+    }
+}
+
+static std::string server_model_source_to_string(server_model_source source) {
+    switch (source) {
+        case SERVER_MODEL_SOURCE_PRESET:     return "preset";
+        case SERVER_MODEL_SOURCE_MODELS_DIR: return "models_dir";
+        case SERVER_MODEL_SOURCE_CACHE:      return "cache";
+        default:                             return "unknown";
     }
 }
 
 struct server_model_meta {
+    server_model_source source = SERVER_MODEL_SOURCE_CACHE;
     common_preset preset;
     std::string name;
     std::set<std::string> aliases; // additional names that resolve to this model
@@ -63,11 +71,11 @@ struct server_model_meta {
     server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
     int64_t last_used = 0; // for LRU unloading
     std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
-    json loaded_info; // info to be reflected via /v1/models endpoint
+    json loaded_info; // info to be reflected via /v1/models endpoint ; if in DOWNLOADING state, it should contain download progress info
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
     int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
     mtmd_caps multimodal; // multimodal capabilities
-    bool need_download = false; // whether the model needs to be downloaded before loading
+    // bool need_download = false; // whether the model needs to be downloaded before loading // TODO @ngxson: implement this
 
     bool is_ready() const {
         return status == SERVER_MODEL_STATUS_LOADED;
@@ -85,12 +93,15 @@ struct server_model_meta {
     void update_caps();
 };
 
-struct subprocess_s;
+struct server_models_routes;
+struct server_subproc; // defined in server-models.cpp
 
 struct server_models {
+    friend struct server_models_routes;
+
 private:
     struct instance_t {
-        std::shared_ptr<subprocess_s> subproc; // shared between main thread and monitoring thread
+        std::shared_ptr<server_subproc> subproc; // shared between main thread and monitoring thread
         std::thread th;
         server_model_meta meta;
         FILE * stdin_file = nullptr;
@@ -107,6 +118,9 @@ private:
     // set to true while load_models() is executing a reload; load() will wait until clear
     bool is_reloading = false;
 
+    // if true, the next get_meta() will trigger a reload of model list
+    bool need_reload = false;
+
     common_preset_context ctx_preset;
 
     common_params base_params;
@@ -122,9 +136,14 @@ private:
     // not thread-safe, caller must hold mutex
     void add_model(server_model_meta && meta);
 
+    // notify SSE clients
+    void notify_sse(const std::string & event, const std::string & model_id, const json & data = nullptr);
+
 public:
     server_models(const common_params & params, int argc, char ** argv);
 
+    server_response sse; // for real-time updates via SSE endpoint
+
     // (re-)load the list of models from various sources and prepare the metadata mapping
     // - if this is called the first time, simply populate the metadata
     // - if this is called subsequently (e.g. when refreshing from disk):
@@ -147,13 +166,24 @@ public:
     void unload(const std::string & name);
     void unload_all();
 
+    // download a new model, progress is reported via SSE
+    // to stop the download, call unload()
+    void download(common_params_model && model, common_download_opts && opts);
+
     // update the status of a model instance (thread-safe)
     void update_status(const std::string & name, server_model_status status, int exit_code);
     void update_loaded_info(const std::string & name, std::string & raw_info);
+    void update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok = true);
+
+    // remove a cache model from disk and update the list (thread-safe)
+    // note: only cache models can be removed; returns false if the model doesn't exist or is not a cache model
+    bool remove(const std::string & name);
 
     // wait until the model instance is fully loaded (thread-safe)
+    // note: predicate is called while holding the lock
     // return when the model no longer in "loading" state
-    void wait_until_loading_finished(const std::string & name);
+    void wait(const std::string & name, std::function<bool(const server_model_meta &)> predicate);
+    void wait(std::unique_lock<std::mutex> & lk, const std::string & name, std::function<bool(const server_model_meta &)> predicate);
 
     // ensure the model is in ready state (thread-safe)
     // return false if model is ready
@@ -176,8 +206,9 @@ public:
 
 struct server_models_routes {
     common_params params;
-    json ui_settings = json::object();          // Primary: new name
-    json webui_settings = json::object();        // Deprecated: use ui_settings (kept for compat)
+    json ui_settings = json::object();     // Primary: new name
+    json webui_settings = json::object();  // Deprecated: use ui_settings (kept for compat)
+    std::atomic<bool> stopping = false;    // for graceful disconnecting SSE clients during shutdown
     server_models models;
     server_models_routes(const common_params & params, int argc, char ** argv)
             : params(params), models(params, argc, argv) {
@@ -206,6 +237,10 @@ struct server_models_routes {
     server_http_context::handler_t get_router_models;
     server_http_context::handler_t post_router_models_load;
     server_http_context::handler_t post_router_models_unload;
+    // management API
+    server_http_context::handler_t get_router_models_sse;
+    server_http_context::handler_t post_router_models;
+    server_http_context::handler_t del_router_models;
 };
 
 /**
diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp
index 32cfe7830..5d37c3453 100644
--- a/tools/server/server-queue.cpp
+++ b/tools/server/server-queue.cpp
@@ -331,6 +331,17 @@ void server_response::send(server_task_result_ptr && result) {
     }
 }
 
+void server_response::broadcast(server_task_result_ptr && result) {
+    std::unique_lock<std::mutex> lock(mutex_results);
+    for (const auto & id_task : waiting_task_ids) {
+        RES_DBG("task id = %d pushed to result queue\n", id_task);
+        server_task_result_ptr res_copy(result->clone());
+        res_copy->id = id_task; // override id with target task id
+        queue_results.emplace_back(std::move(res_copy));
+    }
+    condition_results.notify_all();
+}
+
 void server_response::terminate() {
     running = false;
     condition_results.notify_all();
diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h
index 35f010401..0b674d6ff 100644
--- a/tools/server/server-queue.h
+++ b/tools/server/server-queue.h
@@ -154,11 +154,15 @@ public:
     // Send a new result to a waiting id_task
     void send(server_task_result_ptr && result);
 
+    // broadcast a new result to all waiting tasks
+    // (used by router mode)
+    void broadcast(server_task_result_ptr && result);
+
     // terminate the waiting loop
     void terminate();
 };
 
-// utility class to make working with server_queue and server_response easier
+// RAII wrapper to make working with server_queue and server_response easier
 // it provides a generator-like API for server responses
 // support pooling connection state and aggregating multiple results
 struct server_response_reader {
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index bdadcff76..1a03d5f26 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -312,6 +312,9 @@ struct server_task_result {
     }
     virtual json to_json() = 0;
     virtual ~server_task_result() = default;
+    virtual server_task_result * clone() const {
+        GGML_ABORT("not implemented for this task type");
+    }
 };
 
 // using shared_ptr for polymorphism of server_task_result
@@ -649,3 +652,12 @@ struct server_prompt_cache {
 
     void update();
 };
+
+// used exclusively by router mode
+struct server_task_result_router : server_task_result {
+    json data;
+    virtual json to_json() override { return data; }
+    virtual server_task_result * clone() const override {
+        return new server_task_result_router(*this);
+    }
+};
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index da635c625..0364d7d3b 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -174,8 +174,11 @@ int llama_server(int argc, char ** argv) {
         routes.get_props                   = models_routes->get_router_props;
         routes.get_models                  = models_routes->get_router_models;
 
+        ctx_http.post("/models",               ex_wrapper(models_routes->post_router_models));
         ctx_http.post("/models/load",          ex_wrapper(models_routes->post_router_models_load));
         ctx_http.post("/models/unload",        ex_wrapper(models_routes->post_router_models_unload));
+        ctx_http.get ("/models/sse",           ex_wrapper(models_routes->get_router_models_sse));
+        ctx_http.del ("/models",               ex_wrapper(models_routes->del_router_models));
     }
 
     ctx_http.get ("/health",                   ex_wrapper(routes.get_health)); // public endpoint (no API key check)
@@ -261,6 +264,7 @@ int llama_server(int argc, char ** argv) {
         clean_up = [&models_routes]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
             if (models_routes.has_value()) {
+                models_routes->stopping.store(true); // maybe redundant, but just to be safe
                 models_routes->models.unload_all();
             }
             llama_backend_free();
@@ -274,6 +278,10 @@ int llama_server(int argc, char ** argv) {
         ctx_http.is_ready.store(true);
 
         shutdown_handler = [&](int) {
+            if (models_routes.has_value()) {
+                // important to disconnect any SSE clients
+                models_routes->stopping.store(true);
+            }
             ctx_http.stop();
         };
 
diff --git a/tools/server/tests/unit/test_router.py b/tools/server/tests/unit/test_router.py
index c93b92b0b..11c77ca7a 100644
--- a/tools/server/tests/unit/test_router.py
+++ b/tools/server/tests/unit/test_router.py
@@ -1,3 +1,4 @@
+import threading
 import pytest
 from utils import *
 
@@ -253,3 +254,98 @@ def test_router_reload_models():
         assert "model-reload-c" in ids, "newly added model should appear"
     finally:
         os.remove(preset_path)
+
+
+MODEL_DOWNLOAD_ID = "ggml-org/test-model-router-download:F16"
+MODEL_DOWNLOAD_TIMEOUT = 300
+
+
+def _listen_sse(server: ServerProcess, collected: list, stop: threading.Event):
+    """Collect /models/sse events into `collected` until `stop` is set."""
+    url = f"http://{server.server_host}:{server.server_port}/models/sse"
+    try:
+        with requests.get(url, stream=True, timeout=MODEL_DOWNLOAD_TIMEOUT) as resp:
+            for line_bytes in resp.iter_lines():
+                if stop.is_set():
+                    break
+                line = line_bytes.decode("utf-8")
+                if line.startswith("data: "):
+                    collected.append(json.loads(line[6:]))
+    except Exception:
+        pass
+
+
+def _wait_for_sse_event(collected: list, event_type: str, model: str, timeout: int) -> bool:
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        if any(e.get("event") == event_type and e.get("model") == model for e in collected):
+            return True
+        time.sleep(0.5)
+    return False
+
+
+def test_router_download_model():
+    """Case 1: download a model, verify SSE events and GET /models."""
+    global server
+    server.start()
+
+    # Ensure the model is not present before we start
+    server.make_request("DELETE", f"/models?model={MODEL_DOWNLOAD_ID}")
+
+    sse_events: list = []
+    stop = threading.Event()
+    sse_thread = threading.Thread(
+        target=_listen_sse, args=(server, sse_events, stop), daemon=True
+    )
+    sse_thread.start()
+
+    # Trigger the download
+    res = server.make_request("POST", "/models", data={"model": MODEL_DOWNLOAD_ID})
+    assert res.status_code == 200
+    assert res.body.get("success") is True
+
+    # Wait for download_finished SSE event
+    finished = _wait_for_sse_event(
+        sse_events, "download_finished", MODEL_DOWNLOAD_ID, MODEL_DOWNLOAD_TIMEOUT
+    )
+    stop.set()
+
+    assert finished, "Never received download_finished SSE event"
+    assert any(
+        e.get("event") == "download_progress" and e.get("model") == MODEL_DOWNLOAD_ID
+        for e in sse_events
+    ), "No download_progress events received"
+
+    # Model should now appear in GET /models
+    ids = _get_model_ids(is_reload=False)
+    assert MODEL_DOWNLOAD_ID in ids, f"{MODEL_DOWNLOAD_ID} not found in /models after download"
+
+
+def test_router_delete_model():
+    """Case 2: delete the downloaded model, verify it disappears from GET /models."""
+    global server
+    server.start()
+
+    # Ensure the model exists (download it if needed)
+    if MODEL_DOWNLOAD_ID not in _get_model_ids(is_reload=False):
+        res = server.make_request("POST", "/models", data={"model": MODEL_DOWNLOAD_ID})
+        assert res.status_code == 200
+        sse_events: list = []
+        stop = threading.Event()
+        threading.Thread(
+            target=_listen_sse, args=(server, sse_events, stop), daemon=True
+        ).start()
+        finished = _wait_for_sse_event(
+            sse_events, "download_finished", MODEL_DOWNLOAD_ID, MODEL_DOWNLOAD_TIMEOUT
+        )
+        stop.set()
+        assert finished, "Model did not finish downloading before delete test"
+
+    # Delete the model
+    del_res = server.make_request("DELETE", f"/models?model={MODEL_DOWNLOAD_ID}")
+    assert del_res.status_code == 200
+    assert del_res.body.get("success") is True
+
+    # Model should no longer appear in GET /models
+    ids = _get_model_ids(is_reload=False)
+    assert MODEL_DOWNLOAD_ID not in ids, f"{MODEL_DOWNLOAD_ID} still present after deletion"
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index c5dba1c13..c50c9a0f5 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -340,6 +340,9 @@ class ServerProcess:
         elif method == "POST":
             response = requests.post(url, headers=headers, json=data, timeout=timeout)
             parse_body = True
+        elif method == "DELETE":
+            response = requests.delete(url, headers=headers, timeout=timeout)
+            parse_body = True
         elif method == "OPTIONS":
             response = requests.options(url, headers=headers, timeout=timeout)
         else:

From e3c9601d37199077bb4ebc92f7beefbcd9faf794 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 00:15:39 +0800
Subject: [PATCH 025/292] updated sdui

---
 embd_res/kcpp_sdui.embd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/embd_res/kcpp_sdui.embd b/embd_res/kcpp_sdui.embd
index 1fe8dcb96..50807c23b 100644
--- a/embd_res/kcpp_sdui.embd
+++ b/embd_res/kcpp_sdui.embd
@@ -506,9 +506,9 @@ gl_FragColor.rgb *= color.a;
 `?(s++,f[s]=0):f[s]++;f[0]>0&&(this.insertCharStyleObject(c.lineIndex,c.charIndex,f[0],d),d=d&&d.slice(f[0]+1)),s&&this.insertNewlineStyleObject(c.lineIndex,c.charIndex+f[0],s);for(var h=1;h<s;h++)f[h]>0?this.insertCharStyleObject(c.lineIndex+h,0,f[h],d):d&&this.styles[c.lineIndex+h]&&d[0]&&(this.styles[c.lineIndex+h][0]=d[0]),d=d&&d.slice(f[h]+1);f[h]>0&&this.insertCharStyleObject(c.lineIndex+h,0,f[h],d)},setSelectionStartEndWithShift:function(a,u,d){d<=a?(u===a?this._selectionDirection="left":this._selectionDirection==="right"&&(this._selectionDirection="left",this.selectionEnd=a),this.selectionStart=d):d>a&&d<u?this._selectionDirection==="right"?this.selectionEnd=d:this.selectionStart=d:(u===a?this._selectionDirection="right":this._selectionDirection==="left"&&(this._selectionDirection="right",this.selectionStart=u),this.selectionEnd=d)},setSelectionInBoundaries:function(){var a=this.text.length;this.selectionStart>a?this.selectionStart=a:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>a?this.selectionEnd=a:this.selectionEnd<0&&(this.selectionEnd=0)}})}(),t.util.object.extend(t.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(o){if(!!this.canvas){this.__newClickTime=+new Date;var a=o.pointer;this.isTripleClick(a)&&(this.fire("tripleclick",o),this._stopEvent(o.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=a,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(o){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===o.x&&this.__lastPointer.y===o.y},_stopEvent:function(o){o.preventDefault&&o.preventDefault(),o.stopPropagation&&o.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(o){!this.isEditing||this.selectWord(this.getSelectionStartFromPointer(o.e))},tripleClickHandler:function(o){!this.isEditing||this.selectLine(this.getSelectionStartFromPointer(o.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(o.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(o){if(this.__isMousedown=!1,!(!this.editable||this.group||o.transform&&o.transform.actionPerformed||o.e.button&&o.e.button!==1)){if(this.canvas){var a=this.canvas._activeObject;if(a&&a!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(o.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(o){var a=this.getSelectionStartFromPointer(o),u=this.selectionStart,d=this.selectionEnd;o.shiftKey?this.setSelectionStartEndWithShift(u,d,a):(this.selectionStart=a,this.selectionEnd=a),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(o){for(var a=this.getLocalPointer(o),u=0,d=0,c=0,f=0,s=0,h,v,m=0,y=this._textLines.length;m<y&&c<=a.y;m++)c+=this.getHeightOfLine(m)*this.scaleY,s=m,m>0&&(f+=this._textLines[m-1].length+this.missingNewlineOffset(m-1));h=this._getLineLeftOffset(s),d=h*this.scaleX,v=this._textLines[s],this.direction==="rtl"&&(a.x=this.width*this.scaleX-a.x+d);for(var b=0,_=v.length;b<_&&(u=d,d+=this.__charBounds[s][b].kernedWidth*this.scaleX,d<=a.x);b++)f++;return this._getNewSelectionStartFromOffset(a,u,d,f,_)},_getNewSelectionStartFromOffset:function(o,a,u,d,c){var f=o.x-a,s=u-o.x,h=s>f||s<0?0:1,v=d+h;return this.flipX&&(v=c-v),v>this._text.length&&(v=this._text.length),v}}),t.util.object.extend(t.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=t.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var o=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+o.top+"; left: "+o.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; padding\uFF70top: "+o.fontSize+";",this.hiddenTextareaContainer?this.hiddenTextareaContainer.appendChild(this.hiddenTextarea):t.document.body.appendChild(this.hiddenTextarea),t.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),t.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),t.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),t.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(t.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},keysMapRtl:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorLeft",36:"moveCursorRight",37:"moveCursorRight",38:"moveCursorUp",39:"moveCursorLeft",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(o){if(!!this.isEditing){var a=this.direction==="rtl"?this.keysMapRtl:this.keysMap;if(o.keyCode in a)this[a[o.keyCode]](o);else if(o.keyCode in this.ctrlKeysMapDown&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapDown[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),o.keyCode>=33&&o.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(o){if(!this.isEditing||this._copyDone||this.inCompositionMode){this._copyDone=!1;return}if(o.keyCode in this.ctrlKeysMapUp&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapUp[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),this.canvas&&this.canvas.requestRenderAll()},onInput:function(o){var a=this.fromPaste;if(this.fromPaste=!1,o&&o.stopPropagation(),!!this.isEditing){var u=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,d=this._text.length,c=u.length,f,s,h=c-d,v=this.selectionStart,m=this.selectionEnd,y=v!==m,b,_,w;if(this.hiddenTextarea.value===""){this.styles={},this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll());return}var S=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),x=v>S.selectionStart;y?(f=this._text.slice(v,m),h+=m-v):c<d&&(x?f=this._text.slice(m+h,m):f=this._text.slice(v,v-h)),s=u.slice(S.selectionEnd-h,S.selectionEnd),f&&f.length&&(s.length&&(b=this.getSelectionStyles(v,v+1,!1),b=s.map(function(){return b[0]})),y?(_=v,w=m):x?(_=m-f.length,w=m):(_=m,w=m+f.length),this.removeStyleFromTo(_,w)),s.length&&(a&&s.join("")===t.copiedText&&!t.disableStyleCopyPaste&&(b=t.copiedTextStyle),this.insertNewStyleBlock(s,v,b)),this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll())}},onCompositionStart:function(){this.inCompositionMode=!0},onCompositionEnd:function(){this.inCompositionMode=!1},onCompositionUpdate:function(o){this.compositionStart=o.target.selectionStart,this.compositionEnd=o.target.selectionEnd,this.updateTextareaPosition()},copy:function(){this.selectionStart!==this.selectionEnd&&(t.copiedText=this.getSelectedText(),t.disableStyleCopyPaste?t.copiedTextStyle=null:t.copiedTextStyle=this.getSelectionStyles(this.selectionStart,this.selectionEnd,!0),this._copyDone=!0)},paste:function(){this.fromPaste=!0},_getClipboardData:function(o){return o&&o.clipboardData||t.window.clipboardData},_getWidthBeforeCursor:function(o,a){var u=this._getLineLeftOffset(o),d;return a>0&&(d=this.__charBounds[o][a-1],u+=d.left+d.width),u},getDownCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===this._textLines.length-1||o.metaKey||o.keyCode===34)return this._text.length-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c+1,s),v=this._textLines[c].slice(f);return v.length+h+1+this.missingNewlineOffset(c)},_getSelectionForOffset:function(o,a){return o.shiftKey&&this.selectionStart!==this.selectionEnd&&a?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===0||o.metaKey||o.keyCode===33)return-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c-1,s),v=this._textLines[c].slice(0,f),m=this.missingNewlineOffset(c-1);return-this._textLines[c-1].length+h-v.length+(1-m)},_getIndexOnLine:function(o,a){for(var u=this._textLines[o],d=this._getLineLeftOffset(o),c=d,f=0,s,h,v=0,m=u.length;v<m;v++)if(s=this.__charBounds[o][v].width,c+=s,c>a){h=!0;var y=c-s,b=c,_=Math.abs(y-a),w=Math.abs(b-a);f=w<_?v:v-1;break}return h||(f=u.length-1),f},moveCursorDown:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorUpOrDown("Down",o)},moveCursorUp:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorUpOrDown("Up",o)},_moveCursorUpOrDown:function(o,a){var u="get"+o+"CursorOffset",d=this[u](a,this._selectionDirection==="right");a.shiftKey?this.moveCursorWithShift(d):this.moveCursorWithoutShift(d),d!==0&&(this.setSelectionInBoundaries(),this.abortCursorAnimation(),this._currentCursorOpacity=1,this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorWithShift:function(o){var a=this._selectionDirection==="left"?this.selectionStart+o:this.selectionEnd+o;return this.setSelectionStartEndWithShift(this.selectionStart,this.selectionEnd,a),o!==0},moveCursorWithoutShift:function(o){return o<0?(this.selectionStart+=o,this.selectionEnd=this.selectionStart):(this.selectionEnd+=o,this.selectionStart=this.selectionEnd),o!==0},moveCursorLeft:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorLeftOrRight("Left",o)},_move:function(o,a,u){var d;if(o.altKey)d=this["findWordBoundary"+u](this[a]);else if(o.metaKey||o.keyCode===35||o.keyCode===36)d=this["findLineBoundary"+u](this[a]);else return this[a]+=u==="Left"?-1:1,!0;if(typeof d<"u"&&this[a]!==d)return this[a]=d,!0},_moveLeft:function(o,a){return this._move(o,a,"Left")},_moveRight:function(o,a){return this._move(o,a,"Right")},moveCursorLeftWithoutShift:function(o){var a=!0;return this._selectionDirection="left",this.selectionEnd===this.selectionStart&&this.selectionStart!==0&&(a=this._moveLeft(o,"selectionStart")),this.selectionEnd=this.selectionStart,a},moveCursorLeftWithShift:function(o){if(this._selectionDirection==="right"&&this.selectionStart!==this.selectionEnd)return this._moveLeft(o,"selectionEnd");if(this.selectionStart!==0)return this._selectionDirection="left",this._moveLeft(o,"selectionStart")},moveCursorRight:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorLeftOrRight("Right",o)},_moveCursorLeftOrRight:function(o,a){var u="moveCursor"+o+"With";this._currentCursorOpacity=1,a.shiftKey?u+="Shift":u+="outShift",this[u](a)&&(this.abortCursorAnimation(),this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorRightWithShift:function(o){if(this._selectionDirection==="left"&&this.selectionStart!==this.selectionEnd)return this._moveRight(o,"selectionStart");if(this.selectionEnd!==this._text.length)return this._selectionDirection="right",this._moveRight(o,"selectionEnd")},moveCursorRightWithoutShift:function(o){var a=!0;return this._selectionDirection="right",this.selectionStart===this.selectionEnd?(a=this._moveRight(o,"selectionStart"),this.selectionEnd=this.selectionStart):this.selectionStart=this.selectionEnd,a},removeChars:function(o,a){typeof a>"u"&&(a=o+1),this.removeStyleFromTo(o,a),this._text.splice(o,a-o),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()},insertChars:function(o,a,u,d){typeof d>"u"&&(d=u),d>u&&this.removeStyleFromTo(u,d);var c=t.util.string.graphemeSplit(o);this.insertNewStyleBlock(c,u,a),this._text=[].concat(this._text.slice(0,u),c,this._text.slice(d)),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()}}),function(){var o=t.util.toFixed,a=/  +/g;t.util.object.extend(t.Text.prototype,{_toSVG:function(){var u=this._getSVGLeftTopOffsets(),d=this._getSVGTextAndBg(u.textTop,u.textLeft);return this._wrapSVGTextAndBg(d)},toSVG:function(u){return this._createBaseSVGMarkup(this._toSVG(),{reviver:u,noStyle:!0,withShadow:!0})},_getSVGLeftTopOffsets:function(){return{textLeft:-this.width/2,textTop:-this.height/2,lineTop:this.getHeightOfLine(0)}},_wrapSVGTextAndBg:function(u){var d=!0,c=this.getSvgTextDecoration(this);return[u.textBgRects.join(""),'		<text xml:space="preserve" ',this.fontFamily?'font-family="'+this.fontFamily.replace(/"/g,"'")+'" ':"",this.fontSize?'font-size="'+this.fontSize+'" ':"",this.fontStyle?'font-style="'+this.fontStyle+'" ':"",this.fontWeight?'font-weight="'+this.fontWeight+'" ':"",c?'text-decoration="'+c+'" ':"",'style="',this.getSvgStyles(d),'"',this.addPaintOrder()," >",u.textSpans.join(""),`</text>
 `]},_getSVGTextAndBg:function(u,d){var c=[],f=[],s=u,h;this._setSVGBg(f);for(var v=0,m=this._textLines.length;v<m;v++)h=this._getLineLeftOffset(v),(this.textBackgroundColor||this.styleHas("textBackgroundColor",v))&&this._setSVGTextLineBg(f,v,d+h,s),this._setSVGTextLineText(c,v,d+h,s),s+=this.getHeightOfLine(v);return{textSpans:c,textBgRects:f}},_createTextCharSpan:function(u,d,c,f){var s=u!==u.trim()||u.match(a),h=this.getSvgSpanStyles(d,s),v=h?'style="'+h+'"':"",m=d.deltaY,y="",b=t.Object.NUM_FRACTION_DIGITS;return m&&(y=' dy="'+o(m,b)+'" '),['<tspan x="',o(c,b),'" y="',o(f,b),'" ',y,v,">",t.util.string.escapeXml(u),"</tspan>"].join("")},_setSVGTextLineText:function(u,d,c,f){var s=this.getHeightOfLine(d),h=this.textAlign.indexOf("justify")!==-1,v,m,y="",b,_,w=0,S=this._textLines[d],x;f+=s*(1-this._fontSizeFraction)/this.lineHeight;for(var T=0,E=S.length-1;T<=E;T++)x=T===E||this.charSpacing,y+=S[T],b=this.__charBounds[d][T],w===0?(c+=b.kernedWidth-b.width,w+=b.width):w+=b.kernedWidth,h&&!x&&this._reSpaceAndTab.test(S[T])&&(x=!0),x||(v=v||this.getCompleteStyleDeclaration(d,T),m=this.getCompleteStyleDeclaration(d,T+1),x=t.util.hasStyleChanged(v,m,!0)),x&&(_=this._getStyleDeclaration(d,T)||{},u.push(this._createTextCharSpan(y,_,c,f)),y="",v=m,c+=w,w=0)},_pushTextBgRect:function(u,d,c,f,s,h){var v=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(d),' x="',o(c,v),'" y="',o(f,v),'" width="',o(s,v),'" height="',o(h,v),`"></rect>
 `)},_setSVGTextLineBg:function(u,d,c,f){for(var s=this._textLines[d],h=this.getHeightOfLine(d)/this.lineHeight,v=0,m=0,y,b,_=this.getValueOfPropertyAt(d,0,"textBackgroundColor"),w=0,S=s.length;w<S;w++)y=this.__charBounds[d][w],b=this.getValueOfPropertyAt(d,w,"textBackgroundColor"),b!==_?(_&&this._pushTextBgRect(u,_,c+m,f,v,h),m=y.left,v=y.width,_=b):v+=y.kernedWidth;b&&this._pushTextBgRect(u,b,c+m,f,v,h)},_getFillAttributes:function(u){var d=u&&typeof u=="string"?new t.Color(u):"";return!d||!d.getSource()||d.getAlpha()===1?'fill="'+u+'"':'opacity="'+d.getAlpha()+'" fill="'+d.setAlpha(1).toRgb()+'"'},_getSVGLineTopOffset:function(u){for(var d=0,c=0,f=0;f<u;f++)d+=this.getHeightOfLine(f);return c=this.getHeightOfLine(f),{lineTop:d,offset:(this._fontSizeMult-this._fontSizeFraction)*c/(this.lineHeight*this._fontSizeMult)}},getSvgStyles:function(u){var d=t.Object.prototype.getSvgStyles.call(this,u);return d+" white-space: pre;"}})}(),function(o){var a=o.fabric||(o.fabric={});a.Textbox=a.util.createClass(a.IText,a.Observable,{type:"textbox",minWidth:20,dynamicMinWidth:2,__cachedLines:null,lockScalingFlip:!0,noScaleCache:!1,_dimensionAffectingProps:a.Text.prototype._dimensionAffectingProps.concat("width"),_wordJoiners:/[ \t\r]/,splitByGrapheme:!1,initDimensions:function(){this.__skipDimension||(this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this._clearCache(),this.dynamicMinWidth=0,this._styleMap=this._generateStyleMap(this._splitText()),this.dynamicMinWidth>this.width&&this._set("width",this.dynamicMinWidth),this.textAlign.indexOf("justify")!==-1&&this.enlargeSpaces(),this.height=this.calcTextHeight(),this.saveState({propertySet:"_dimensionAffectingProps"}))},_generateStyleMap:function(u){for(var d=0,c=0,f=0,s={},h=0;h<u.graphemeLines.length;h++)u.graphemeText[f]===`
-`&&h>0?(c=0,f++,d++):!this.splitByGrapheme&&this._reSpaceAndTab.test(u.graphemeText[f])&&h>0&&(c++,f++),s[h]={line:d,offset:c},f+=u.graphemeLines[h].length,c+=u.graphemeLines[h].length;return s},styleHas:function(u,d){if(this._styleMap&&!this.isWrapping){var c=this._styleMap[d];c&&(d=c.line)}return a.Text.prototype.styleHas.call(this,u,d)},isEmptyStyles:function(u){if(!this.styles)return!0;var d=0,c=u+1,f,s,h=!1,v=this._styleMap[u],m=this._styleMap[u+1];v&&(u=v.line,d=v.offset),m&&(c=m.line,h=c===u,f=m.offset),s=typeof u>"u"?this.styles:{line:this.styles[u]};for(var y in s)for(var b in s[y])if(b>=d&&(!h||b<f))for(var _ in s[y][b])return!1;return!0},_getStyleDeclaration:function(u,d){if(this._styleMap&&!this.isWrapping){var c=this._styleMap[u];if(!c)return null;u=c.line,d=c.offset+d}return this.callSuper("_getStyleDeclaration",u,d)},_setStyleDeclaration:function(u,d,c){var f=this._styleMap[u];u=f.line,d=f.offset+d,this.styles[u][d]=c},_deleteStyleDeclaration:function(u,d){var c=this._styleMap[u];u=c.line,d=c.offset+d,delete this.styles[u][d]},_getLineStyle:function(u){var d=this._styleMap[u];return!!this.styles[d.line]},_setLineStyle:function(u){var d=this._styleMap[u];this.styles[d.line]={}},_wrapText:function(u,d){var c=[],f;for(this.isWrapping=!0,f=0;f<u.length;f++)c=c.concat(this._wrapLine(u[f],f,d));return this.isWrapping=!1,c},_measureWord:function(u,d,c){var f=0,s,h=!0;c=c||0;for(var v=0,m=u.length;v<m;v++){var y=this._getGraphemeBox(u[v],d,v+c,s,h);f+=y.kernedWidth,s=u[v]}return f},_wrapLine:function(u,d,c,D){var s=0,h=this.splitByGrapheme,v=[],m=[],y=h?a.util.string.graphemeSplit(u):u.split(this._wordJoiners),b="",_=0,w=h?"":" ",S=0,x=0,T=0,E=!0,I=this._getWidthOfCharSpacing(),D=D||0;y.length===0&&y.push([]),c-=D;for(var N=0;N<y.length;N++)b=h?y[N]:a.util.string.graphemeSplit(y[N]),S=this._measureWord(b,d,_),_+=b.length,s+=x+S-I,s>c&&!E?(v.push(m),m=[],s=S,E=!0):s+=I,!E&&!h&&m.push(w),m=m.concat(b),x=h?0:this._measureWord([w],d,_),_++,E=!1,S>T&&(T=S);return N&&v.push(m),T+D>this.dynamicMinWidth&&(this.dynamicMinWidth=T-I+D),v},isEndOfWrapping:function(u){return!this._styleMap[u+1]||this._styleMap[u+1].line!==this._styleMap[u].line},missingNewlineOffset:function(u){return this.splitByGrapheme?this.isEndOfWrapping(u)?1:0:1},_splitTextIntoLines:function(u){for(var d=a.Text.prototype._splitTextIntoLines.call(this,u),c=this._wrapText(d.lines,this.width),f=new Array(c.length),s=0;s<c.length;s++)f[s]=c[s].join("");return d.lines=f,d.graphemeLines=c,d},getMinWidth:function(){return Math.max(this.minWidth,this.dynamicMinWidth)},_removeExtraneousStyles:function(){var u={};for(var d in this._styleMap)this._textLines[d]&&(u[this._styleMap[d].line]=1);for(var d in this.styles)u[d]||delete 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Uint8Array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,7]),I2=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),CW=512,Gr=new Array((pl+2)*2);Go(Gr);const Rs=new Array(Co*2);Go(Rs);const ml=new Array(CW);Go(ml);const gl=new Array(bW-yW+1);Go(gl);const _m=new Array(vm);Go(_m);const kc=new Array(Co);Go(kc);function Qf(e,t,n,r,i){this.static_tree=e,this.extra_bits=t,this.extra_base=n,this.elems=r,this.max_length=i,this.has_stree=e&&e.length}let M2,L2,R2;function ed(e,t){this.dyn_tree=e,this.max_code=0,this.stat_desc=t}const D2=e=>e<256?ml[e]:ml[256+(e>>>7)],vl=(e,t)=>{e.pending_buf[e.pending++]=t&255,e.pending_buf[e.pending++]=t>>>8&255},En=(e,t,n)=>{e.bi_valid>Jf-n?(e.bi_buf|=t<<e.bi_valid&65535,vl(e,e.bi_buf),e.bi_buf=t>>Jf-e.bi_valid,e.bi_valid+=n-Jf):(e.bi_buf|=t<<e.bi_valid&65535,e.bi_valid+=n)},Tr=(e,t,n)=>{En(e,n[t*2],n[t*2+1])},$2=(e,t)=>{let n=0;do n|=e&1,e>>>=1,n<<=1;while(--t>0);return n>>>1},SW=e=>{e.bi_valid===16?(vl(e,e.bi_buf),e.bi_buf=0,e.bi_valid=0):e.bi_valid>=8&&(e.pending_buf[e.pending++]=e.bi_buf&255,e.bi_buf>>=8,e.bi_valid-=8)},xW=(e,t)=>{const n=t.dyn_tree,r=t.max_code,i=t.stat_desc.static_tree,l=t.stat_desc.has_stree,g=t.stat_desc.extra_bits,o=t.stat_desc.extra_base,a=t.stat_desc.max_length;let u,d,c,f,s,h,v=0;for(f=0;f<=_a;f++)e.bl_count[f]=0;for(n[e.heap[e.heap_max]*2+1]=0,u=e.heap_max+1;u<E2;u++)d=e.heap[u],f=n[n[d*2+1]*2+1]+1,f>a&&(f=a,v++),n[d*2+1]=f,!(d>r)&&(e.bl_count[f]++,s=0,d>=o&&(s=g[d-o]),h=n[d*2],e.opt_len+=h*(f+s),l&&(e.static_len+=h*(i[d*2+1]+s)));if(v!==0){do{for(f=a-1;e.bl_count[f]===0;)f--;e.bl_count[f]--,e.bl_count[f+1]+=2,e.bl_count[a]--,v-=2}while(v>0);for(f=a;f!==0;f--)for(d=e.bl_count[f];d!==0;)c=e.heap[--u],!(c>r)&&(n[c*2+1]!==f&&(e.opt_len+=(f-n[c*2+1])*n[c*2],n[c*2+1]=f),d--)}},B2=(e,t,n)=>{const r=new Array(_a+1);let i=0,l,g;for(l=1;l<=_a;l++)i=i+n[l-1]<<1,r[l]=i;for(g=0;g<=t;g++){let o=e[g*2+1];o!==0&&(e[g*2]=$2(r[o]++,o))}},TW=()=>{let e,t,n,r,i;const l=new Array(_a+1);for(n=0,r=0;r<vm-1;r++)for(_m[r]=n,e=0;e<1<<Eh[r];e++)gl[n++]=r;for(gl[n-1]=r,i=0,r=0;r<16;r++)for(kc[r]=i,e=0;e<1<<Xu[r];e++)ml[i++]=r;for(i>>=7;r<Co;r++)for(kc[r]=i<<7,e=0;e<1<<Xu[r]-7;e++)ml[256+i++]=r;for(t=0;t<=_a;t++)l[t]=0;for(e=0;e<=143;)Gr[e*2+1]=8,e++,l[8]++;for(;e<=255;)Gr[e*2+1]=9,e++,l[9]++;for(;e<=279;)Gr[e*2+1]=7,e++,l[7]++;for(;e<=287;)Gr[e*2+1]=8,e++,l[8]++;for(B2(Gr,pl+1,l),e=0;e<Co;e++)Rs[e*2+1]=5,Rs[e*2]=$2(e,5);M2=new Qf(Gr,Eh,Vl+1,pl,_a),L2=new Qf(Rs,Xu,0,Co,_a),R2=new Qf(new Array(0),wW,0,ym,_W)},F2=e=>{let t;for(t=0;t<pl;t++)e.dyn_ltree[t*2]=0;for(t=0;t<Co;t++)e.dyn_dtree[t*2]=0;for(t=0;t<ym;t++)e.bl_tree[t*2]=0;e.dyn_ltree[bm*2]=1,e.opt_len=e.static_len=0,e.sym_next=e.matches=0},z2=e=>{e.bi_valid>8?vl(e,e.bi_buf):e.bi_valid>0&&(e.pending_buf[e.pending++]=e.bi_buf),e.bi_buf=0,e.bi_valid=0},Z0=(e,t,n,r)=>{const i=t*2,l=n*2;return e[i]<e[l]||e[i]===e[l]&&r[t]<=r[n]},td=(e,t,n)=>{const r=e.heap[n];let i=n<<1;for(;i<=e.heap_len&&(i<e.heap_len&&Z0(t,e.heap[i+1],e.heap[i],e.depth)&&i++,!Z0(t,r,e.heap[i],e.depth));)e.heap[n]=e.heap[i],n=i,i<<=1;e.heap[n]=r},J0=(e,t,n)=>{let r,i,l=0,g,o;if(e.sym_next!==0)do r=e.pending_buf[e.sym_buf+l++]&255,r+=(e.pending_buf[e.sym_buf+l++]&255)<<8,i=e.pending_buf[e.sym_buf+l++],r===0?Tr(e,i,t):(g=gl[i],Tr(e,g+Vl+1,t),o=Eh[g],o!==0&&(i-=_m[g],En(e,i,o)),r--,g=D2(r),Tr(e,g,n),o=Xu[g],o!==0&&(r-=kc[g],En(e,r,o)));while(l<e.sym_next);Tr(e,bm,t)},Oh=(e,t)=>{const n=t.dyn_tree,r=t.stat_desc.static_tree,i=t.stat_desc.has_stree,l=t.stat_desc.elems;let g,o,a=-1,u;for(e.heap_len=0,e.heap_max=E2,g=0;g<l;g++)n[g*2]!==0?(e.heap[++e.heap_len]=a=g,e.depth[g]=0):n[g*2+1]=0;for(;e.heap_len<2;)u=e.heap[++e.heap_len]=a<2?++a:0,n[u*2]=1,e.depth[u]=0,e.opt_len--,i&&(e.static_len-=r[u*2+1]);for(t.max_code=a,g=e.heap_len>>1;g>=1;g--)td(e,n,g);u=l;do g=e.heap[1],e.heap[1]=e.heap[e.heap_len--],td(e,n,1),o=e.heap[1],e.heap[--e.heap_max]=g,e.heap[--e.heap_max]=o,n[u*2]=n[g*2]+n[o*2],e.depth[u]=(e.depth[g]>=e.depth[o]?e.depth[g]:e.depth[o])+1,n[g*2+1]=n[o*2+1]=u,e.heap[1]=u++,td(e,n,1);while(e.heap_len>=2);e.heap[--e.heap_max]=e.heap[1],xW(e,t),B2(n,a,e.bl_count)},Q0=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),t[(n+1)*2+1]=65535,r=0;r<=n;r++)l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)&&(o<u?e.bl_tree[l*2]+=o:l!==0?(l!==i&&e.bl_tree[l*2]++,e.bl_tree[O2*2]++):o<=10?e.bl_tree[A2*2]++:e.bl_tree[P2*2]++,o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4))},ey=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),r=0;r<=n;r++)if(l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)){if(o<u)do Tr(e,l,e.bl_tree);while(--o!==0);else l!==0?(l!==i&&(Tr(e,l,e.bl_tree),o--),Tr(e,O2,e.bl_tree),En(e,o-3,2)):o<=10?(Tr(e,A2,e.bl_tree),En(e,o-3,3)):(Tr(e,P2,e.bl_tree),En(e,o-11,7));o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4)}},kW=e=>{let t;for(Q0(e,e.dyn_ltree,e.l_desc.max_code),Q0(e,e.dyn_dtree,e.d_desc.max_code),Oh(e,e.bl_desc),t=ym-1;t>=3&&e.bl_tree[I2[t]*2+1]===0;t--);return e.opt_len+=3*(t+1)+5+5+4,t},EW=(e,t,n,r)=>{let i;for(En(e,t-257,5),En(e,n-1,5),En(e,r-4,4),i=0;i<r;i++)En(e,e.bl_tree[I2[i]*2+1],3);ey(e,e.dyn_ltree,t-1),ey(e,e.dyn_dtree,n-1)},OW=e=>{let t=4093624447,n;for(n=0;n<=31;n++,t>>>=1)if(t&1&&e.dyn_ltree[n*2]!==0)return G0;if(e.dyn_ltree[9*2]!==0||e.dyn_ltree[10*2]!==0||e.dyn_ltree[13*2]!==0)return q0;for(n=32;n<Vl;n++)if(e.dyn_ltree[n*2]!==0)return q0;return G0};let ty=!1;const AW=e=>{ty||(TW(),ty=!0),e.l_desc=new ed(e.dyn_ltree,M2),e.d_desc=new ed(e.dyn_dtree,L2),e.bl_desc=new ed(e.bl_tree,R2),e.bi_buf=0,e.bi_valid=0,F2(e)},N2=(e,t,n,r)=>{En(e,(gW<<1)+(r?1:0),3),z2(e),vl(e,n),vl(e,~n),n&&e.pending_buf.set(e.window.subarray(t,t+n),e.pending),e.pending+=n},PW=e=>{En(e,k2<<1,3),Tr(e,bm,Gr),SW(e)},IW=(e,t,n,r)=>{let i,l,g=0;e.level>0?(e.strm.data_type===mW&&(e.strm.data_type=OW(e)),Oh(e,e.l_desc),Oh(e,e.d_desc),g=kW(e),i=e.opt_len+3+7>>>3,l=e.static_len+3+7>>>3,l<=i&&(i=l)):i=l=n+5,n+4<=i&&t!==-1?N2(e,t,n,r):e.strategy===pW||l===i?(En(e,(k2<<1)+(r?1:0),3),J0(e,Gr,Rs)):(En(e,(vW<<1)+(r?1:0),3),EW(e,e.l_desc.max_code+1,e.d_desc.max_code+1,g+1),J0(e,e.dyn_ltree,e.dyn_dtree)),F2(e),r&&z2(e)},MW=(e,t,n)=>(e.pending_buf[e.sym_buf+e.sym_next++]=t,e.pending_buf[e.sym_buf+e.sym_next++]=t>>8,e.pending_buf[e.sym_buf+e.sym_next++]=n,t===0?e.dyn_ltree[n*2]++:(e.matches++,t--,e.dyn_ltree[(gl[n]+Vl+1)*2]++,e.dyn_dtree[D2(t)*2]++),e.sym_next===e.sym_end);var LW=AW,RW=N2,DW=IW,$W=MW,BW=PW,FW={_tr_init:LW,_tr_stored_block:RW,_tr_flush_block:DW,_tr_tally:$W,_tr_align:BW};const zW=(e,t,n,r)=>{let i=e&65535|0,l=e>>>16&65535|0,g=0;for(;n!==0;){g=n>2e3?2e3:n,n-=g;do i=i+t[r++]|0,l=l+i|0;while(--g);i%=65521,l%=65521}return i|l<<16|0};var yl=zW;const NW=()=>{let e,t=[];for(var n=0;n<256;n++){e=n;for(var r=0;r<8;r++)e=e&1?3988292384^e>>>1:e>>>1;t[n]=e}return t},jW=new Uint32Array(NW()),VW=(e,t,n,r)=>{const i=jW,l=r+n;e^=-1;for(let g=r;g<l;g++)e=e>>>8^i[(e^t[g])&255];return e^-1};var Zt=VW,$a={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"},qo={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:UW,_tr_stored_block:Ah,_tr_flush_block:HW,_tr_tally:$i,_tr_align:WW}=FW,{Z_NO_FLUSH:Bi,Z_PARTIAL_FLUSH:YW,Z_FULL_FLUSH:XW,Z_FINISH:Gn,Z_BLOCK:ny,Z_OK:rn,Z_STREAM_END:ry,Z_STREAM_ERROR:Pr,Z_DATA_ERROR:KW,Z_BUF_ERROR:nd,Z_DEFAULT_COMPRESSION:GW,Z_FILTERED:qW,Z_HUFFMAN_ONLY:_u,Z_RLE:ZW,Z_FIXED:JW,Z_DEFAULT_STRATEGY:QW,Z_UNKNOWN:eY,Z_DEFLATED:lf}=qo,tY=9,nY=15,rY=8,iY=29,aY=256,Ph=aY+1+iY,oY=30,sY=19,lY=2*Ph+1,uY=15,lt=3,Mi=258,Ir=Mi+lt+1,cY=32,$o=42,wm=57,Ih=69,Mh=73,Lh=91,Rh=103,wa=113,ms=666,wn=1,Zo=2,Ba=3,Jo=4,fY=3,Ca=(e,t)=>(e.msg=$a[t],t),iy=e=>e*2-(e>4?9:0),Ai=e=>{let t=e.length;for(;--t>=0;)e[t]=0},dY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let hY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,Fi=hY;const $n=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},jn=(e,t)=>{HW(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,$n(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},us=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Dh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=yl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Zt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},j2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Ir?e.strstart-(e.w_size-Ir):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Mi;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Mi-(f-r),r=f-Mi,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Bo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Ir)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),dY(e),r+=t),e.strm.avail_in===0)break;if(n=Dh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=Fi(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=Fi(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Ir&&e.strm.avail_in!==0)},V2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==Gn||t===Bi||r!==i+e.strm.avail_in)))break;g=t===Gn&&r===i+e.strm.avail_in?1:0,Ah(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,$n(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Dh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?Jo:t!==Bi&&t!==Gn&&e.strm.avail_in===0&&e.strstart===e.block_start?Zo:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Dh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===Gn)&&t!==Bi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===Gn&&e.strm.avail_in===0&&r===i?1:0,Ah(e,e.block_start,r,g),e.block_start+=r,$n(e.strm)),g?Ba:wn)},rd=(e,t)=>{let n,r;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n)),e.match_length>=lt)if(r=$i(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+1]);else r=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},ao=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n),e.match_length<=5&&(e.strategy===qW||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=$i(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(jn(e,!1),e.strm.avail_out===0))return wn}else if(e.match_available){if(r=$i(e,0,e.window[e.strstart-1]),r&&jn(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return wn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=$i(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},pY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Mi){if(Bo(e),e.lookahead<=Mi&&t===Bi)return wn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Mi;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Mi-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=$i(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},mY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Bo(e),e.lookahead===0)){if(t===Bi)return wn;break}if(e.match_length=0,n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo};function wr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const gs=[new wr(0,0,0,0,V2),new wr(4,4,8,4,rd),new wr(4,5,16,8,rd),new wr(4,6,32,32,rd),new wr(4,4,16,16,ao),new wr(8,16,32,32,ao),new wr(8,16,128,128,ao),new wr(8,32,128,256,ao),new wr(32,128,258,1024,ao),new wr(32,258,258,4096,ao)],gY=e=>{e.window_size=2*e.w_size,Ai(e.head),e.max_lazy_match=gs[e.level].max_lazy,e.good_match=gs[e.level].good_length,e.nice_match=gs[e.level].nice_length,e.max_chain_length=gs[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function vY(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=lf,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(lY*2),this.dyn_dtree=new Uint16Array((2*oY+1)*2),this.bl_tree=new Uint16Array((2*sY+1)*2),Ai(this.dyn_ltree),Ai(this.dyn_dtree),Ai(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(uY+1),this.heap=new Uint16Array(2*Ph+1),Ai(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(2*Ph+1),Ai(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ul=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.status!==$o&&t.status!==wm&&t.status!==Ih&&t.status!==Mh&&t.status!==Lh&&t.status!==Rh&&t.status!==wa&&t.status!==ms?1:0},U2=e=>{if(Ul(e))return Ca(e,Pr);e.total_in=e.total_out=0,e.data_type=eY;const t=e.state;return t.pending=0,t.pending_out=0,t.wrap<0&&(t.wrap=-t.wrap),t.status=t.wrap===2?wm:t.wrap?$o:wa,e.adler=t.wrap===2?0:1,t.last_flush=-2,UW(t),rn},H2=e=>{const t=U2(e);return t===rn&&gY(e.state),t},yY=(e,t)=>Ul(e)||e.state.wrap!==2?Pr:(e.state.gzhead=t,rn),W2=(e,t,n,r,i,l)=>{if(!e)return Pr;let g=1;if(t===GW&&(t=6),r<0?(g=0,r=-r):r>15&&(g=2,r-=16),i<1||i>tY||n!==lf||r<8||r>15||t<0||t>9||l<0||l>JW||r===8&&g!==1)return Ca(e,Pr);r===8&&(r=9);const o=new vY;return e.state=o,o.strm=e,o.status=$o,o.wrap=g,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=i+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+lt-1)/lt),o.window=new Uint8Array(o.w_size*2),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<i+6,o.pending_buf_size=o.lit_bufsize*4,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=(o.lit_bufsize-1)*3,o.level=t,o.strategy=l,o.method=n,H2(e)},bY=(e,t)=>W2(e,t,lf,nY,rY,QW),_Y=(e,t)=>{if(Ul(e)||t>ny||t<0)return e?Ca(e,Pr):Pr;const n=e.state;if(!e.output||e.avail_in!==0&&!e.input||n.status===ms&&t!==Gn)return Ca(e,e.avail_out===0?nd:Pr);const r=n.last_flush;if(n.last_flush=t,n.pending!==0){if($n(e),e.avail_out===0)return n.last_flush=-1,rn}else if(e.avail_in===0&&iy(t)<=iy(r)&&t!==Gn)return Ca(e,nd);if(n.status===ms&&e.avail_in!==0)return Ca(e,nd);if(n.status===$o&&n.wrap===0&&(n.status=wa),n.status===$o){let i=lf+(n.w_bits-8<<4)<<8,l=-1;if(n.strategy>=_u||n.level<2?l=0:n.level<6?l=1:n.level===6?l=2:l=3,i|=l<<6,n.strstart!==0&&(i|=cY),i+=31-i%31,us(n,i),n.strstart!==0&&(us(n,e.adler>>>16),us(n,e.adler&65535)),e.adler=1,n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(n.status===wm){if(e.adler=0,mt(n,31),mt(n,139),mt(n,8),n.gzhead)mt(n,(n.gzhead.text?1:0)+(n.gzhead.hcrc?2:0)+(n.gzhead.extra?4:0)+(n.gzhead.name?8:0)+(n.gzhead.comment?16:0)),mt(n,n.gzhead.time&255),mt(n,n.gzhead.time>>8&255),mt(n,n.gzhead.time>>16&255),mt(n,n.gzhead.time>>24&255),mt(n,n.level===9?2:n.strategy>=_u||n.level<2?4:0),mt(n,n.gzhead.os&255),n.gzhead.extra&&n.gzhead.extra.length&&(mt(n,n.gzhead.extra.length&255),mt(n,n.gzhead.extra.length>>8&255)),n.gzhead.hcrc&&(e.adler=Zt(e.adler,n.pending_buf,n.pending,0)),n.gzindex=0,n.status=Ih;else if(mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,n.level===9?2:n.strategy>=_u||n.level<2?4:0),mt(n,fY),n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(n.status===Ih){if(n.gzhead.extra){let i=n.pending,l=(n.gzhead.extra.length&65535)-n.gzindex;for(;n.pending+l>n.pending_buf_size;){let o=n.pending_buf_size-n.pending;if(n.pending_buf.set(n.gzhead.extra.subarray(n.gzindex,n.gzindex+o),n.pending),n.pending=n.pending_buf_size,n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex+=o,$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0,l-=o}let g=new Uint8Array(n.gzhead.extra);n.pending_buf.set(g.subarray(n.gzindex,n.gzindex+l),n.pending),n.pending+=l,n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Mh}if(n.status===Mh){if(n.gzhead.name){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0}n.gzindex<n.gzhead.name.length?l=n.gzhead.name.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Lh}if(n.status===Lh){if(n.gzhead.comment){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0}n.gzindex<n.gzhead.comment.length?l=n.gzhead.comment.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i))}n.status=Rh}if(n.status===Rh){if(n.gzhead.hcrc){if(n.pending+2>n.pending_buf_size&&($n(e),n.pending!==0))return n.last_flush=-1,rn;mt(n,e.adler&255),mt(n,e.adler>>8&255),e.adler=0}if(n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(e.avail_in!==0||n.lookahead!==0||t!==Bi&&n.status!==ms){let 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Pr;if(i===1&&(e.adler=yl(e.adler,t,n,0)),r.wrap=0,n>=r.w_size){i===0&&(Ai(r.head),r.strstart=0,r.block_start=0,r.insert=0);let a=new Uint8Array(r.w_size);a.set(t.subarray(n-r.w_size,n),0),t=a,n=r.w_size}const l=e.avail_in,g=e.next_in,o=e.input;for(e.avail_in=n,e.next_in=0,e.input=t,Bo(r);r.lookahead>=lt;){let a=r.strstart,u=r.lookahead-(lt-1);do r.ins_h=Fi(r,r.ins_h,r.window[a+lt-1]),r.prev[a&r.w_mask]=r.head[r.ins_h],r.head[r.ins_h]=a,a++;while(--u);r.strstart=a,r.lookahead=lt-1,Bo(r)}return r.strstart+=r.lookahead,r.block_start=r.strstart,r.insert=r.lookahead,r.lookahead=0,r.match_length=r.prev_length=lt-1,r.match_available=0,e.next_in=g,e.input=o,e.avail_in=l,r.wrap=i,rn};var SY=bY,xY=W2,TY=H2,kY=U2,EY=yY,OY=_Y,AY=wY,PY=CY,IY="pako deflate (from Nodeca project)",Ds={deflateInit:SY,deflateInit2:xY,deflateReset:TY,deflateResetKeep:kY,deflateSetHeader:EY,deflate:OY,deflateEnd:AY,deflateSetDictionary:PY,deflateInfo:IY};const MY=(e,t)=>Object.prototype.hasOwnProperty.call(e,t);var 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tr;n=e.state,n.mode===Ur&&(n.mode=ad),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,c=o,f=a,E=Fa;e:for(;;)switch(n.mode){case cf:if(n.wrap===0){n.mode=ad;break}for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&2&&u===35615){n.wbits===0&&(n.wbits=15),n.check=0,I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0),u=0,d=0,n.mode=fy;break}if(n.head&&(n.head.done=!1),!(n.wrap&1)||(((u&255)<<8)+(u>>8))%31){e.msg="incorrect header check",n.mode=Bt;break}if((u&15)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(u>>>=4,d-=4,T=(u&15)+8,n.wbits===0&&(n.wbits=T),T>15||T>n.wbits){e.msg="invalid window size",n.mode=Bt;break}n.dmax=1<<n.wbits,n.flags=0,e.adler=n.check=1,n.mode=u&512?by:Ur,u=0,d=0;break;case fy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.flags=u,(n.flags&255)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(n.flags&57344){e.msg="unknown header flags set",n.mode=Bt;break}n.head&&(n.head.text=u>>8&1),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=dy;case dy:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.time=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,I[2]=u>>>16&255,I[3]=u>>>24&255,n.check=Zt(n.check,I,4,0)),u=0,d=0,n.mode=hy;case hy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.xflags=u&255,n.head.os=u>>8),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=py;case py:if(n.flags&1024){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length=u,n.head&&(n.head.extra_len=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0}else n.head&&(n.head.extra=null);n.mode=my;case my:if(n.flags&1024&&(s=n.length,s>o&&(s=o),s&&(n.head&&(T=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new 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e;o--,u+=r[l++]<<d,d+=8}e.adler=n.check=My(u),u=0,d=0,n.mode=Oc;case Oc:if(n.havedict===0)return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,dX;e.adler=n.check=1,n.mode=Ur;case Ur:if(t===cX||t===Cu)break e;case ad:if(n.last){u>>>=d&7,d-=d&7,n.mode=sd;break}for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}switch(n.last=u&1,u>>>=1,d-=1,u&3){case 0:n.mode=_y;break;case 1:if(_X(n),n.mode=Su,t===Cu){u>>>=2,d-=2;break e}break;case 2:n.mode=Cy;break;case 3:e.msg="invalid block type",n.mode=Bt}u>>>=2,d-=2;break;case _y:for(u>>>=d&7,d-=d&7;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((u&65535)!==(u>>>16^65535)){e.msg="invalid stored block lengths",n.mode=Bt;break}if(n.length=u&65535,u=0,d=0,n.mode=od,t===Cu)break e;case od:n.mode=wy;case wy:if(s=n.length,s){if(s>o&&(s=o),s>a&&(s=a),s===0)break e;i.set(r.subarray(l,l+s),g),o-=s,l+=s,a-=s,g+=s,n.length-=s;break}n.mode=Ur;break;case Cy:for(;d<14;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.nlen=(u&31)+257,u>>>=5,d-=5,n.ndist=(u&31)+1,u>>>=5,d-=5,n.ncode=(u&15)+4,u>>>=4,d-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=Bt;break}n.have=0,n.mode=Sy;case Sy:for(;n.have<n.ncode;){for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.lens[L[n.have++]]=u&7,u>>>=3,d-=3}for(;n.have<19;)n.lens[L[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,D={bits:n.lenbits},E=$s(uX,n.lens,0,19,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid code lengths set",n.mode=Bt;break}n.have=0,n.mode=xy;case xy:for(;n.have<n.nlen+n.ndist;){for(;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(_<16)u>>>=y,d-=y,n.lens[n.have++]=_;else{if(_===16){for(N=y+2;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(u>>>=y,d-=y,n.have===0){e.msg="invalid bit length repeat",n.mode=Bt;break}T=n.lens[n.have-1],s=3+(u&3),u>>>=2,d-=2}else if(_===17){for(N=y+3;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=3+(u&7),u>>>=3,d-=3}else{for(N=y+7;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=11+(u&127),u>>>=7,d-=7}if(n.have+s>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=Bt;break}for(;s--;)n.lens[n.have++]=T}}if(n.mode===Bt)break;if(n.lens[256]===0){e.msg="invalid code -- missing end-of-block",n.mode=Bt;break}if(n.lenbits=9,D={bits:n.lenbits},E=$s(G2,n.lens,0,n.nlen,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid literal/lengths set",n.mode=Bt;break}if(n.distbits=6,n.distcode=n.distdyn,D={bits:n.distbits},E=$s(q2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,D),n.distbits=D.bits,E){e.msg="invalid distances set",n.mode=Bt;break}if(n.mode=Su,t===Cu)break e;case Su:n.mode=xu;case xu:if(o>=6&&a>=258){e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,rX(e,f),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,n.mode===Ur&&(n.back=-1);break}for(n.back=0;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(b&&(b&240)===0){for(w=y,S=b,x=_;m=n.lencode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,n.length=_,b===0){n.mode=Ay;break}if(b&32){n.back=-1,n.mode=Ur;break}if(b&64){e.msg="invalid literal/length code",n.mode=Bt;break}n.extra=b&15,n.mode=Ty;case Ty:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=ky;case ky:for(;m=n.distcode[u&(1<<n.distbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((b&240)===0){for(w=y,S=b,x=_;m=n.distcode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,b&64){e.msg="invalid distance code",n.mode=Bt;break}n.offset=_,n.extra=b&15,n.mode=Ey;case Ey:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.offset+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=Bt;break}n.mode=Oy;case Oy:if(a===0)break e;if(s=f-a,n.offset>s){if(s=n.offset-s,s>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=Bt;break}s>n.wnext?(s-=n.wnext,h=n.wsize-s):h=n.wnext-s,s>n.length&&(s=n.length),v=n.window}else v=i,h=g-n.offset,s=n.length;s>a&&(s=a),a-=s,n.length-=s;do i[g++]=v[h++];while(--s);n.length===0&&(n.mode=xu);break;case Ay:if(a===0)break e;i[g++]=n.length,a--,n.mode=xu;break;case sd:if(n.wrap){for(;d<32;){if(o===0)break e;o--,u|=r[l++]<<d,d+=8}if(f-=a,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Zt(n.check,i,f,g-f):yl(n.check,i,f,g-f)),f=a,n.wrap&4&&(n.flags?u:My(u))!==n.check){e.msg="incorrect data check",n.mode=Bt;break}u=0,d=0}n.mode=Py;case Py:if(n.wrap&&n.flags){for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.total&4294967295)){e.msg="incorrect length check",n.mode=Bt;break}u=0,d=0}n.mode=Iy;case Iy:E=fX;break e;case Bt:E=Z2;break e;case Q2:return J2;case eC:default:return tr}return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,(n.wsize||f!==e.avail_out&&n.mode<Bt&&(n.mode<sd||t!==uy))&&aC(e,e.output,e.next_out,f-e.avail_out),c-=e.avail_in,f-=e.avail_out,e.total_in+=c,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Zt(n.check,i,f,e.next_out-f):yl(n.check,i,f,e.next_out-f)),e.data_type=n.bits+(n.last?64:0)+(n.mode===Ur?128:0)+(n.mode===Su||n.mode===od?256:0),(c===0&&f===0||t===uy)&&E===Fa&&(E=hX),E},CX=e=>{if(Ga(e))return tr;let t=e.state;return t.window&&(t.window=null),e.state=null,Fa},SX=(e,t)=>{if(Ga(e))return tr;const n=e.state;return(n.wrap&2)===0?tr:(n.head=t,t.done=!1,Fa)},xX=(e,t)=>{const n=t.length;let r,i,l;return Ga(e)||(r=e.state,r.wrap!==0&&r.mode!==Oc)?tr:r.mode===Oc&&(i=1,i=yl(i,t,n,0),i!==r.check)?Z2:(l=aC(e,t,n,n),l?(r.mode=Q2,J2):(r.havedict=1,Fa))};var TX=nC,kX=rC,EX=tC,OX=bX,AX=iC,PX=wX,IX=CX,MX=SX,LX=xX,RX="pako inflate (from Nodeca 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n=qr.inflateInit2(this.strm,t.windowBits);if(n!==wl)throw new Error($a[n]);if(this.header=new $X,qr.inflateGetHeader(this.strm,this.header),t.dictionary&&(typeof t.dictionary=="string"?t.dictionary=_l.string2buf(t.dictionary):oC.call(t.dictionary)==="[object ArrayBuffer]"&&(t.dictionary=new Uint8Array(t.dictionary)),t.raw&&(n=qr.inflateSetDictionary(this.strm,t.dictionary),n!==wl)))throw new Error($a[n])}Wl.prototype.push=function(e,t){const n=this.strm,r=this.options.chunkSize,i=this.options.dictionary;let l,g,o;if(this.ended)return!1;for(t===~~t?g=t:g=t===!0?FX:BX,oC.call(e)==="[object ArrayBuffer]"?n.input=new Uint8Array(e):n.input=e,n.next_in=0,n.avail_in=n.input.length;;){for(n.avail_out===0&&(n.output=new Uint8Array(r),n.next_out=0,n.avail_out=r),l=qr.inflate(n,g),l===fd&&i&&(l=qr.inflateSetDictionary(n,i),l===wl?l=qr.inflate(n,g):l===Ry&&(l=fd));n.avail_in>0&&l===cd&&n.state.wrap>0&&e[n.next_in]!==0;)qr.inflateReset(n),l=qr.inflate(n,g);switch(l){case zX:case Ry:case fd:case NX:return this.onEnd(l),this.ended=!0,!1}if(o=n.avail_out,n.next_out&&(n.avail_out===0||l===cd))if(this.options.to==="string"){let a=_l.utf8border(n.output,n.next_out),u=n.next_out-a,d=_l.buf2string(n.output,a);n.next_out=u,n.avail_out=r-u,u&&n.output.set(n.output.subarray(a,a+u),0),this.onData(d)}else this.onData(n.output.length===n.next_out?n.output:n.output.subarray(0,n.next_out));if(!(l===wl&&o===0)){if(l===cd)return l=qr.inflateEnd(this.strm),this.onEnd(l),this.ended=!0,!0;if(n.avail_in===0)break}}return!0};Wl.prototype.onData=function(e){this.chunks.push(e)};Wl.prototype.onEnd=function(e){e===wl&&(this.options.to==="string"?this.result=this.chunks.join(""):this.result=uf.flattenChunks(this.chunks)),this.chunks=[],this.err=e,this.msg=this.strm.msg};function Sm(e,t){const n=new Wl(t);if(n.push(e),n.err)throw n.msg||$a[n.err];return n.result}function jX(e,t){return t=t||{},t.raw=!0,Sm(e,t)}var 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l!==0?(l!==i&&(Tr(e,l,e.bl_tree),o--),Tr(e,O2,e.bl_tree),En(e,o-3,2)):o<=10?(Tr(e,A2,e.bl_tree),En(e,o-3,3)):(Tr(e,P2,e.bl_tree),En(e,o-11,7));o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4)}},kW=e=>{let t;for(Q0(e,e.dyn_ltree,e.l_desc.max_code),Q0(e,e.dyn_dtree,e.d_desc.max_code),Oh(e,e.bl_desc),t=ym-1;t>=3&&e.bl_tree[I2[t]*2+1]===0;t--);return e.opt_len+=3*(t+1)+5+5+4,t},EW=(e,t,n,r)=>{let i;for(En(e,t-257,5),En(e,n-1,5),En(e,r-4,4),i=0;i<r;i++)En(e,e.bl_tree[I2[i]*2+1],3);ey(e,e.dyn_ltree,t-1),ey(e,e.dyn_dtree,n-1)},OW=e=>{let t=4093624447,n;for(n=0;n<=31;n++,t>>>=1)if(t&1&&e.dyn_ltree[n*2]!==0)return G0;if(e.dyn_ltree[9*2]!==0||e.dyn_ltree[10*2]!==0||e.dyn_ltree[13*2]!==0)return q0;for(n=32;n<Vl;n++)if(e.dyn_ltree[n*2]!==0)return q0;return G0};let ty=!1;const AW=e=>{ty||(TW(),ty=!0),e.l_desc=new ed(e.dyn_ltree,M2),e.d_desc=new ed(e.dyn_dtree,L2),e.bl_desc=new ed(e.bl_tree,R2),e.bi_buf=0,e.bi_valid=0,F2(e)},N2=(e,t,n,r)=>{En(e,(gW<<1)+(r?1:0),3),z2(e),vl(e,n),vl(e,~n),n&&e.pending_buf.set(e.window.subarray(t,t+n),e.pending),e.pending+=n},PW=e=>{En(e,k2<<1,3),Tr(e,bm,Gr),SW(e)},IW=(e,t,n,r)=>{let i,l,g=0;e.level>0?(e.strm.data_type===mW&&(e.strm.data_type=OW(e)),Oh(e,e.l_desc),Oh(e,e.d_desc),g=kW(e),i=e.opt_len+3+7>>>3,l=e.static_len+3+7>>>3,l<=i&&(i=l)):i=l=n+5,n+4<=i&&t!==-1?N2(e,t,n,r):e.strategy===pW||l===i?(En(e,(k2<<1)+(r?1:0),3),J0(e,Gr,Rs)):(En(e,(vW<<1)+(r?1:0),3),EW(e,e.l_desc.max_code+1,e.d_desc.max_code+1,g+1),J0(e,e.dyn_ltree,e.dyn_dtree)),F2(e),r&&z2(e)},MW=(e,t,n)=>(e.pending_buf[e.sym_buf+e.sym_next++]=t,e.pending_buf[e.sym_buf+e.sym_next++]=t>>8,e.pending_buf[e.sym_buf+e.sym_next++]=n,t===0?e.dyn_ltree[n*2]++:(e.matches++,t--,e.dyn_ltree[(gl[n]+Vl+1)*2]++,e.dyn_dtree[D2(t)*2]++),e.sym_next===e.sym_end);var LW=AW,RW=N2,DW=IW,$W=MW,BW=PW,FW={_tr_init:LW,_tr_stored_block:RW,_tr_flush_block:DW,_tr_tally:$W,_tr_align:BW};const zW=(e,t,n,r)=>{let i=e&65535|0,l=e>>>16&65535|0,g=0;for(;n!==0;){g=n>2e3?2e3:n,n-=g;do i=i+t[r++]|0,l=l+i|0;while(--g);i%=65521,l%=65521}return i|l<<16|0};var yl=zW;const NW=()=>{let e,t=[];for(var n=0;n<256;n++){e=n;for(var r=0;r<8;r++)e=e&1?3988292384^e>>>1:e>>>1;t[n]=e}return t},jW=new Uint32Array(NW()),VW=(e,t,n,r)=>{const i=jW,l=r+n;e^=-1;for(let g=r;g<l;g++)e=e>>>8^i[(e^t[g])&255];return e^-1};var Zt=VW,$a={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"},qo={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:UW,_tr_stored_block:Ah,_tr_flush_block:HW,_tr_tally:$i,_tr_align:WW}=FW,{Z_NO_FLUSH:Bi,Z_PARTIAL_FLUSH:YW,Z_FULL_FLUSH:XW,Z_FINISH:Gn,Z_BLOCK:ny,Z_OK:rn,Z_STREAM_END:ry,Z_STREAM_ERROR:Pr,Z_DATA_ERROR:KW,Z_BUF_ERROR:nd,Z_DEFAULT_COMPRESSION:GW,Z_FILTERED:qW,Z_HUFFMAN_ONLY:_u,Z_RLE:ZW,Z_FIXED:JW,Z_DEFAULT_STRATEGY:QW,Z_UNKNOWN:eY,Z_DEFLATED:lf}=qo,tY=9,nY=15,rY=8,iY=29,aY=256,Ph=aY+1+iY,oY=30,sY=19,lY=2*Ph+1,uY=15,lt=3,Mi=258,Ir=Mi+lt+1,cY=32,$o=42,wm=57,Ih=69,Mh=73,Lh=91,Rh=103,wa=113,ms=666,wn=1,Zo=2,Ba=3,Jo=4,fY=3,Ca=(e,t)=>(e.msg=$a[t],t),iy=e=>e*2-(e>4?9:0),Ai=e=>{let t=e.length;for(;--t>=0;)e[t]=0},dY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let hY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,Fi=hY;const $n=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},jn=(e,t)=>{HW(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,$n(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},us=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Dh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=yl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Zt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},j2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Ir?e.strstart-(e.w_size-Ir):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Mi;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Mi-(f-r),r=f-Mi,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Bo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Ir)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),dY(e),r+=t),e.strm.avail_in===0)break;if(n=Dh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=Fi(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=Fi(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Ir&&e.strm.avail_in!==0)},V2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==Gn||t===Bi||r!==i+e.strm.avail_in)))break;g=t===Gn&&r===i+e.strm.avail_in?1:0,Ah(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,$n(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Dh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?Jo:t!==Bi&&t!==Gn&&e.strm.avail_in===0&&e.strstart===e.block_start?Zo:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Dh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===Gn)&&t!==Bi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===Gn&&e.strm.avail_in===0&&r===i?1:0,Ah(e,e.block_start,r,g),e.block_start+=r,$n(e.strm)),g?Ba:wn)},rd=(e,t)=>{let n,r;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n)),e.match_length>=lt)if(r=$i(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+1]);else r=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},ao=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n),e.match_length<=5&&(e.strategy===qW||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=$i(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(jn(e,!1),e.strm.avail_out===0))return wn}else if(e.match_available){if(r=$i(e,0,e.window[e.strstart-1]),r&&jn(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return wn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=$i(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},pY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Mi){if(Bo(e),e.lookahead<=Mi&&t===Bi)return wn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Mi;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Mi-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=$i(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},mY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Bo(e),e.lookahead===0)){if(t===Bi)return wn;break}if(e.match_length=0,n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo};function wr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const gs=[new wr(0,0,0,0,V2),new wr(4,4,8,4,rd),new wr(4,5,16,8,rd),new wr(4,6,32,32,rd),new wr(4,4,16,16,ao),new wr(8,16,32,32,ao),new wr(8,16,128,128,ao),new wr(8,32,128,256,ao),new wr(32,128,258,1024,ao),new wr(32,258,258,4096,ao)],gY=e=>{e.window_size=2*e.w_size,Ai(e.head),e.max_lazy_match=gs[e.level].max_lazy,e.good_match=gs[e.level].good_length,e.nice_match=gs[e.level].nice_length,e.max_chain_length=gs[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function 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n.next_out>0&&this.onData(n.output.subarray(0,n.next_out)),i=Ds.deflateEnd(this.strm),this.onEnd(i),this.ended=!0,i===Ec;if(n.avail_out===0){this.onData(n.output);continue}if(l>0&&n.next_out>0){this.onData(n.output.subarray(0,n.next_out)),n.avail_out=0;continue}if(n.avail_in===0)break}return!0};Hl.prototype.onData=function(e){this.chunks.push(e)};Hl.prototype.onEnd=function(e){e===Ec&&(this.result=uf.flattenChunks(this.chunks)),this.chunks=[],this.err=e,this.msg=this.strm.msg};function Cm(e,t){const n=new Hl(t);if(n.push(e,!0),n.err)throw n.msg||$a[n.err];return n.result}function KY(e,t){return t=t||{},t.raw=!0,Cm(e,t)}function GY(e,t){return t=t||{},t.gzip=!0,Cm(e,t)}var qY=Hl,ZY=Cm,JY=KY,QY=GY,eX=qo,tX={Deflate:qY,deflate:ZY,deflateRaw:JY,gzip:QY,constants:eX};const wu=16209,nX=16191;var rX=function(t,n){let r,i,l,g,o,a,u,d,c,f,s,h,v,m,y,b,_,w,S,x,T,E,I,D;const N=t.state;r=t.next_in,I=t.input,i=r+(t.avail_in-5),l=t.next_out,D=t.output,g=l-(n-t.avail_out),o=l+(t.avail_out-257),a=N.dmax,u=N.wsize,d=N.whave,c=N.wnext,f=N.window,s=N.hold,h=N.bits,v=N.lencode,m=N.distcode,y=(1<<N.lenbits)-1,b=(1<<N.distbits)-1;e:do{h<15&&(s+=I[r++]<<h,h+=8,s+=I[r++]<<h,h+=8),_=v[s&y];t:for(;;){if(w=_>>>24,s>>>=w,h-=w,w=_>>>16&255,w===0)D[l++]=_&65535;else if(w&16){S=_&65535,w&=15,w&&(h<w&&(s+=I[r++]<<h,h+=8),S+=s&(1<<w)-1,s>>>=w,h-=w),h<15&&(s+=I[r++]<<h,h+=8,s+=I[r++]<<h,h+=8),_=m[s&b];n:for(;;){if(w=_>>>24,s>>>=w,h-=w,w=_>>>16&255,w&16){if(x=_&65535,w&=15,h<w&&(s+=I[r++]<<h,h+=8,h<w&&(s+=I[r++]<<h,h+=8)),x+=s&(1<<w)-1,x>a){t.msg="invalid distance too far back",N.mode=wu;break e}if(s>>>=w,h-=w,w=l-g,x>w){if(w=x-w,w>d&&N.sane){t.msg="invalid distance too far back",N.mode=wu;break e}if(T=0,E=f,c===0){if(T+=u-w,w<S){S-=w;do D[l++]=f[T++];while(--w);T=l-x,E=D}}else if(c<w){if(T+=u+c-w,w-=c,w<S){S-=w;do D[l++]=f[T++];while(--w);if(T=0,c<S){w=c,S-=w;do D[l++]=f[T++];while(--w);T=l-x,E=D}}}else if(T+=c-w,w<S){S-=w;do D[l++]=f[T++];while(--w);T=l-x,E=D}for(;S>2;)D[l++]=E[T++],D[l++]=E[T++],D[l++]=E[T++],S-=3;S&&(D[l++]=E[T++],S>1&&(D[l++]=E[T++]))}else{T=l-x;do D[l++]=D[T++],D[l++]=D[T++],D[l++]=D[T++],S-=3;while(S>2);S&&(D[l++]=D[T++],S>1&&(D[l++]=D[T++]))}}else if((w&64)===0){_=m[(_&65535)+(s&(1<<w)-1)];continue n}else{t.msg="invalid distance code",N.mode=wu;break e}break}}else if((w&64)===0){_=v[(_&65535)+(s&(1<<w)-1)];continue t}else if(w&32){N.mode=nX;break e}else{t.msg="invalid literal/length code",N.mode=wu;break e}break}}while(r<i&&l<o);S=h>>3,r-=S,h-=S<<3,s&=(1<<h)-1,t.next_in=r,t.next_out=l,t.avail_in=r<i?5+(i-r):5-(r-i),t.avail_out=l<o?257+(o-l):257-(l-o),N.hold=s,N.bits=h};const oo=15,ay=852,oy=592,sy=0,id=1,ly=2,iX=new Uint16Array([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0]),aX=new Uint8Array([16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78]),oX=new Uint16Array([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0]),sX=new Uint8Array([16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64]),lX=(e,t,n,r,i,l,g,o)=>{const a=o.bits;let u=0,d=0,c=0,f=0,s=0,h=0,v=0,m=0,y=0,b=0,_,w,S,x,T,E=null,I;const D=new Uint16Array(oo+1),N=new Uint16Array(oo+1);let L=null,F,O,V;for(u=0;u<=oo;u++)D[u]=0;for(d=0;d<r;d++)D[t[n+d]]++;for(s=a,f=oo;f>=1&&D[f]===0;f--);if(s>f&&(s=f),f===0)return i[l++]=1<<24|64<<16|0,i[l++]=1<<24|64<<16|0,o.bits=1,0;for(c=1;c<f&&D[c]===0;c++);for(s<c&&(s=c),m=1,u=1;u<=oo;u++)if(m<<=1,m-=D[u],m<0)return-1;if(m>0&&(e===sy||f!==1))return-1;for(N[1]=0,u=1;u<oo;u++)N[u+1]=N[u]+D[u];for(d=0;d<r;d++)t[n+d]!==0&&(g[N[t[n+d]]++]=d);if(e===sy?(E=L=g,I=20):e===id?(E=iX,L=aX,I=257):(E=oX,L=sX,I=0),b=0,d=0,u=c,T=l,h=s,v=0,S=-1,y=1<<s,x=y-1,e===id&&y>ay||e===ly&&y>oy)return 1;for(;;){F=u-v,g[d]+1<I?(O=0,V=g[d]):g[d]>=I?(O=L[g[d]-I],V=E[g[d]-I]):(O=32+64,V=0),_=1<<u-v,w=1<<h,c=w;do w-=_,i[T+(b>>v)+w]=F<<24|O<<16|V|0;while(w!==0);for(_=1<<u-1;b&_;)_>>=1;if(_!==0?(b&=_-1,b+=_):b=0,d++,--D[u]===0){if(u===f)break;u=t[n+g[d]]}if(u>s&&(b&x)!==S){for(v===0&&(v=s),T+=c,h=u-v,m=1<<h;h+v<f&&(m-=D[h+v],!(m<=0));)h++,m<<=1;if(y+=1<<h,e===id&&y>ay||e===ly&&y>oy)return 1;S=b&x,i[S]=s<<24|h<<16|T-l|0}}return b!==0&&(i[T+b]=u-v<<24|64<<16|0),o.bits=s,0};var $s=lX;const uX=0,G2=1,q2=2,{Z_FINISH:uy,Z_BLOCK:cX,Z_TREES:Cu,Z_OK:Fa,Z_STREAM_END:fX,Z_NEED_DICT:dX,Z_STREAM_ERROR:tr,Z_DATA_ERROR:Z2,Z_MEM_ERROR:J2,Z_BUF_ERROR:hX,Z_DEFLATED:cy}=qo,cf=16180,fy=16181,dy=16182,hy=16183,py=16184,my=16185,gy=16186,vy=16187,yy=16188,by=16189,Oc=16190,Ur=16191,ad=16192,_y=16193,od=16194,wy=16195,Cy=16196,Sy=16197,xy=16198,Su=16199,xu=16200,Ty=16201,ky=16202,Ey=16203,Oy=16204,Ay=16205,sd=16206,Py=16207,Iy=16208,Bt=16209,Q2=16210,eC=16211,pX=852,mX=592,gX=15,vX=gX,My=e=>(e>>>24&255)+(e>>>8&65280)+((e&65280)<<8)+((e&255)<<24);function yX(){this.strm=null,this.mode=0,this.last=!1,this.wrap=0,this.havedict=!1,this.flags=0,this.dmax=0,this.check=0,this.total=0,this.head=null,this.wbits=0,this.wsize=0,this.whave=0,this.wnext=0,this.window=null,this.hold=0,this.bits=0,this.length=0,this.offset=0,this.extra=0,this.lencode=null,this.distcode=null,this.lenbits=0,this.distbits=0,this.ncode=0,this.nlen=0,this.ndist=0,this.have=0,this.next=null,this.lens=new Uint16Array(320),this.work=new Uint16Array(288),this.lendyn=null,this.distdyn=null,this.sane=0,this.back=0,this.was=0}const Ga=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.mode<cf||t.mode>eC?1:0},tC=e=>{if(Ga(e))return tr;const t=e.state;return e.total_in=e.total_out=t.total=0,e.msg="",t.wrap&&(e.adler=t.wrap&1),t.mode=cf,t.last=0,t.havedict=0,t.flags=-1,t.dmax=32768,t.head=null,t.hold=0,t.bits=0,t.lencode=t.lendyn=new Int32Array(pX),t.distcode=t.distdyn=new Int32Array(mX),t.sane=1,t.back=-1,Fa},nC=e=>{if(Ga(e))return tr;const t=e.state;return t.wsize=0,t.whave=0,t.wnext=0,tC(e)},rC=(e,t)=>{let n;if(Ga(e))return tr;const r=e.state;return t<0?(n=0,t=-t):(n=(t>>4)+5,t<48&&(t&=15)),t&&(t<8||t>15)?tr:(r.window!==null&&r.wbits!==t&&(r.window=null),r.wrap=n,r.wbits=t,nC(e))},iC=(e,t)=>{if(!e)return tr;const n=new yX;e.state=n,n.strm=e,n.window=null,n.mode=cf;const r=rC(e,t);return r!==Fa&&(e.state=null),r},bX=e=>iC(e,vX);let Ly=!0,ld,ud;const _X=e=>{if(Ly){ld=new Int32Array(512),ud=new Int32Array(32);let t=0;for(;t<144;)e.lens[t++]=8;for(;t<256;)e.lens[t++]=9;for(;t<280;)e.lens[t++]=7;for(;t<288;)e.lens[t++]=8;for($s(G2,e.lens,0,288,ld,0,e.work,{bits:9}),t=0;t<32;)e.lens[t++]=5;$s(q2,e.lens,0,32,ud,0,e.work,{bits:5}),Ly=!1}e.lencode=ld,e.lenbits=9,e.distcode=ud,e.distbits=5},aC=(e,t,n,r)=>{let i;const l=e.state;return l.window===null&&(l.wsize=1<<l.wbits,l.wnext=0,l.whave=0,l.window=new Uint8Array(l.wsize)),r>=l.wsize?(l.window.set(t.subarray(n-l.wsize,n),0),l.wnext=0,l.whave=l.wsize):(i=l.wsize-l.wnext,i>r&&(i=r),l.window.set(t.subarray(n-r,n-r+i),l.wnext),r-=i,r?(l.window.set(t.subarray(n-r,n),0),l.wnext=r,l.whave=l.wsize):(l.wnext+=i,l.wnext===l.wsize&&(l.wnext=0),l.whave<l.wsize&&(l.whave+=i))),0},wX=(e,t)=>{let n,r,i,l,g,o,a,u,d,c,f,s,h,v,m=0,y,b,_,w,S,x,T,E;const I=new Uint8Array(4);let D,N;const L=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);if(Ga(e)||!e.output||!e.input&&e.avail_in!==0)return tr;n=e.state,n.mode===Ur&&(n.mode=ad),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,c=o,f=a,E=Fa;e:for(;;)switch(n.mode){case cf:if(n.wrap===0){n.mode=ad;break}for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&2&&u===35615){n.wbits===0&&(n.wbits=15),n.check=0,I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0),u=0,d=0,n.mode=fy;break}if(n.head&&(n.head.done=!1),!(n.wrap&1)||(((u&255)<<8)+(u>>8))%31){e.msg="incorrect header check",n.mode=Bt;break}if((u&15)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(u>>>=4,d-=4,T=(u&15)+8,n.wbits===0&&(n.wbits=T),T>15||T>n.wbits){e.msg="invalid window size",n.mode=Bt;break}n.dmax=1<<n.wbits,n.flags=0,e.adler=n.check=1,n.mode=u&512?by:Ur,u=0,d=0;break;case fy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.flags=u,(n.flags&255)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(n.flags&57344){e.msg="unknown header flags set",n.mode=Bt;break}n.head&&(n.head.text=u>>8&1),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=dy;case dy:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.time=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,I[2]=u>>>16&255,I[3]=u>>>24&255,n.check=Zt(n.check,I,4,0)),u=0,d=0,n.mode=hy;case hy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.xflags=u&255,n.head.os=u>>8),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=py;case py:if(n.flags&1024){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length=u,n.head&&(n.head.extra_len=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0}else n.head&&(n.head.extra=null);n.mode=my;case my:if(n.flags&1024&&(s=n.length,s>o&&(s=o),s&&(n.head&&(T=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new Uint8Array(n.head.extra_len)),n.head.extra.set(r.subarray(l,l+s),T)),n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,n.length-=s),n.length))break e;n.length=0,n.mode=gy;case gy:if(n.flags&2048){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.name+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.name=null);n.length=0,n.mode=vy;case vy:if(n.flags&4096){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.comment+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.comment=null);n.mode=yy;case yy:if(n.flags&512){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.check&65535)){e.msg="header crc mismatch",n.mode=Bt;break}u=0,d=0}n.head&&(n.head.hcrc=n.flags>>9&1,n.head.done=!0),e.adler=n.check=0,n.mode=Ur;break;case by:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}e.adler=n.check=My(u),u=0,d=0,n.mode=Oc;case Oc:if(n.havedict===0)return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,dX;e.adler=n.check=1,n.mode=Ur;case Ur:if(t===cX||t===Cu)break e;case ad:if(n.last){u>>>=d&7,d-=d&7,n.mode=sd;break}for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}switch(n.last=u&1,u>>>=1,d-=1,u&3){case 0:n.mode=_y;break;case 1:if(_X(n),n.mode=Su,t===Cu){u>>>=2,d-=2;break e}break;case 2:n.mode=Cy;break;case 3:e.msg="invalid block type",n.mode=Bt}u>>>=2,d-=2;break;case _y:for(u>>>=d&7,d-=d&7;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((u&65535)!==(u>>>16^65535)){e.msg="invalid stored block lengths",n.mode=Bt;break}if(n.length=u&65535,u=0,d=0,n.mode=od,t===Cu)break e;case od:n.mode=wy;case wy:if(s=n.length,s){if(s>o&&(s=o),s>a&&(s=a),s===0)break e;i.set(r.subarray(l,l+s),g),o-=s,l+=s,a-=s,g+=s,n.length-=s;break}n.mode=Ur;break;case Cy:for(;d<14;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.nlen=(u&31)+257,u>>>=5,d-=5,n.ndist=(u&31)+1,u>>>=5,d-=5,n.ncode=(u&15)+4,u>>>=4,d-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=Bt;break}n.have=0,n.mode=Sy;case Sy:for(;n.have<n.ncode;){for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.lens[L[n.have++]]=u&7,u>>>=3,d-=3}for(;n.have<19;)n.lens[L[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,D={bits:n.lenbits},E=$s(uX,n.lens,0,19,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid code lengths set",n.mode=Bt;break}n.have=0,n.mode=xy;case xy:for(;n.have<n.nlen+n.ndist;){for(;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(_<16)u>>>=y,d-=y,n.lens[n.have++]=_;else{if(_===16){for(N=y+2;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(u>>>=y,d-=y,n.have===0){e.msg="invalid bit length repeat",n.mode=Bt;break}T=n.lens[n.have-1],s=3+(u&3),u>>>=2,d-=2}else if(_===17){for(N=y+3;d<N;){if(o===0)break 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From 8d2e5806322d5207c164717eda617de58a1729c1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 17 Jun 2026 19:38:55 +0300
Subject: [PATCH 026/292] metal : add f16 and bf16 support for concat operator
 (#24724)

* metal : add f16 and bf16 support for concat operator

Extend the Metal backend concat operator to support f16 and bf16 tensor
types in addition to the existing f32 and i32 support.

- Template kernel_concat on type T with specializations for float, half,
  bfloat, and int
- Add type-specific pipeline getter ggml_metal_library_get_pipeline_concat()
- Update device support check to allow f16 unconditionally and bf16 when
  device supports bfloat16
- Update dispatch to select the correct kernel specialization by type

Assisted-by: pi:llama.cpp/Qwen3.6-27B

* metal : extend concat operator to support f16, bf16, i8, i16 and i64

Assisted-by: pi:llama.cpp/Qwen3.6-27B
---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 16 ++++++++++-
 ggml/src/ggml-metal/ggml-metal-device.h   |  1 +
 ggml/src/ggml-metal/ggml-metal-device.m   | 21 ++++++++++----
 ggml/src/ggml-metal/ggml-metal-ops.cpp    |  2 +-
 ggml/src/ggml-metal/ggml-metal.metal      | 35 +++++++++++++++--------
 5 files changed, 56 insertions(+), 19 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 4f4f073cb..cbc9459c9 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -66,7 +66,6 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_base(ggml
     const char * op_str = "undefined";
     switch (op) {
         case GGML_OP_ADD_ID: op_str = "add_id"; break;
-        case GGML_OP_CONCAT: op_str = "concat"; break;
         default: GGML_ABORT("fatal error");
     };
 
@@ -211,6 +210,21 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat(ggml_meta
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat(ggml_metal_library_t lib, ggml_type tsrc) {
+    char base[256];
+    char name[256];
+
+    snprintf(base, 256, "kernel_concat_%s", ggml_type_name(tsrc));
+    snprintf(name, 256, "%s", base);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+    }
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal_library_t lib, const ggml_tensor * op) {
     char base[256];
     char name[256];
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index 4a3ebb556..d465f31c0 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -115,6 +115,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_get_rows
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_set_rows          (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag              (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat            (ggml_metal_library_t lib, enum ggml_type tsrc);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat            (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary             (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_glu               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum               (ggml_metal_library_t lib, const struct ggml_tensor * op);
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index d583bd6ef..26b0e77f2 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1123,13 +1123,24 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return true;
         case GGML_OP_CONCAT:
             {
-                // kernel_concat copies one float-sized value per element.
-                // Other scalar types need a type-generic copy kernel first.
                 const enum ggml_type src0_type = op->src[0]->type;
                 const enum ggml_type src1_type = op->src[1]->type;
-                return src0_type == src1_type &&
-                       src0_type == op->type &&
-                       (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_I32);
+                if (src0_type != src1_type || src0_type != op->type) {
+                    return false;
+                }
+                switch (src0_type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_I8:
+                    case GGML_TYPE_I16:
+                    case GGML_TYPE_I32:
+                    case GGML_TYPE_I64:
+                        return true;
+                    case GGML_TYPE_BF16:
+                        return has_bfloat;
+                    default:
+                        return false;
+                }
             }
         case GGML_OP_ADD:
         case GGML_OP_SUB:
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index e2ce56e9e..b13bf2711 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -556,7 +556,7 @@ int ggml_metal_op_concat(ggml_metal_op_t ctx, int idx) {
         /*.dim  =*/ dim,
     };
 
-    auto pipeline = ggml_metal_library_get_pipeline_base(lib, GGML_OP_CONCAT);
+    auto pipeline = ggml_metal_library_get_pipeline_concat(lib, op->type);
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 072f14e68..97abecb47 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -7513,14 +7513,15 @@ template [[host_name("kernel_cpy_q5_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<
 template [[host_name("kernel_cpy_q5_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_cpy_q8_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q8_0, 2, dequantize_q8_0>;
 
+template<typename T>
 kernel void kernel_concat(
-    constant ggml_metal_kargs_concat & args,
-    device  const char * src0,
-    device  const char * src1,
-    device        char * dst,
-    uint3   tgpig[[threadgroup_position_in_grid]],
-    ushort3 tpitg[[thread_position_in_threadgroup]],
-    ushort3   ntg[[threads_per_threadgroup]]) {
+        constant ggml_metal_kargs_concat & args,
+        device  const char * src0,
+        device  const char * src1,
+        device        char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
 
     const int i3 = tgpig.z;
     const int i2 = tgpig.y;
@@ -7533,21 +7534,31 @@ kernel void kernel_concat(
     int o[4] = {0, 0, 0, 0};
     o[args.dim] = args.dim == 0 ? args.ne00 : (args.dim == 1 ? args.ne01 : (args.dim == 2 ? args.ne02 : args.ne03));
 
-    device const float * x;
-
     for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+        device const T * x;
+
         if (i0 < args.ne00 && i1 < args.ne01 && i2 < args.ne02 && i3 < args.ne03) {
-            x = (device const float *)(src0 + (i3       )*args.nb03 + (i2       )*args.nb02 + (i1       )*args.nb01 + (i0       )*args.nb00);
+            x = (device const T *)(src0 + (i3       )*args.nb03 + (i2       )*args.nb02 + (i1       )*args.nb01 + (i0       )*args.nb00);
         } else {
-            x = (device const float *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10);
+            x = (device const T *)(src1 + (i3 - o[3])*args.nb13 + (i2 - o[2])*args.nb12 + (i1 - o[1])*args.nb11 + (i0 - o[0])*args.nb10);
         }
 
-        device float * y = (device float *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
+        device T * y = (device T *)(dst + i3*args.nb3 + i2*args.nb2 + i1*args.nb1 + i0*args.nb0);
 
         *y = *x;
     }
 }
 
+typedef decltype(kernel_concat<float>) kernel_concat_t;
+
+template [[host_name("kernel_concat_f32")]]  kernel kernel_concat_t kernel_concat<float>;
+template [[host_name("kernel_concat_f16")]]  kernel kernel_concat_t kernel_concat<half>;
+template [[host_name("kernel_concat_bf16")]] kernel kernel_concat_t kernel_concat<bfloat>;
+template [[host_name("kernel_concat_i8")]]   kernel kernel_concat_t kernel_concat<char>;
+template [[host_name("kernel_concat_i16")]]  kernel kernel_concat_t kernel_concat<short>;
+template [[host_name("kernel_concat_i32")]]  kernel kernel_concat_t kernel_concat<int>;
+template [[host_name("kernel_concat_i64")]]  kernel kernel_concat_t kernel_concat<long>;
+
 template<int nr0, typename args_t>
 void kernel_mul_mv_q2_K_f32_impl(
         args_t args,

From 0843245cb1ed0129c81c0ae58c633329c496a3d7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 17 Jun 2026 20:36:05 +0300
Subject: [PATCH 027/292] metal : implement rope_back operator (#24725)

Reuse existing rope kernels with a function constant to toggle forward/backward
rotation, avoiding duplicate kernel code.

Assisted-by: pi:llama.cpp/Qwen3.6-27B
---
 ggml/src/ggml-metal/ggml-metal-device.cpp | 7 +++++--
 ggml/src/ggml-metal/ggml-metal-device.m   | 1 +
 ggml/src/ggml-metal/ggml-metal-ops.cpp    | 1 +
 ggml/src/ggml-metal/ggml-metal.metal      | 4 ++++
 4 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index cbc9459c9..0e1f1de45 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -1703,7 +1703,9 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm(ggml_metal_
 }
 
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rope(ggml_metal_library_t lib, const ggml_tensor * op) {
-    assert(op->op == GGML_OP_ROPE);
+    assert(op->op == GGML_OP_ROPE || op->op == GGML_OP_ROPE_BACK);
+
+    const bool is_back = op->op == GGML_OP_ROPE_BACK;
 
     char base[256];
     char name[256];
@@ -1727,13 +1729,14 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rope(ggml_metal_
         snprintf(base, 256, "kernel_rope_norm_%s", ggml_type_name(op->src[0]->type));
     }
 
-    snprintf(name, 256, "%s_imrope=%d", base, is_imrope ? 1 : 0);
+    snprintf(name, 256, "%s_imrope=%d_is_back=%d", base, is_imrope ? 1 : 0, is_back ? 1 : 0);
 
     ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
     if (!res.pipeline) {
         ggml_metal_cv_t cv = ggml_metal_cv_init();
 
         ggml_metal_cv_set_bool(cv, is_imrope, FC_ROPE + 0);
+        ggml_metal_cv_set_bool(cv, is_back,   FC_ROPE + 1);
 
         res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
 
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 26b0e77f2..a7cbc60eb 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1184,6 +1184,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_RMS_NORM:
             return has_simdgroup_reduction && (ggml_is_contiguous_rows(op->src[0]));
         case GGML_OP_ROPE:
+        case GGML_OP_ROPE_BACK:
             return true;
         case GGML_OP_IM2COL:
             return ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32 && (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index b13bf2711..18656b346 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -375,6 +375,7 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
                 n_fuse = ggml_metal_op_norm(ctx, idx);
             } break;
         case GGML_OP_ROPE:
+        case GGML_OP_ROPE_BACK:
             {
                 n_fuse = ggml_metal_op_rope(ctx, idx);
             } break;
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 97abecb47..31b5326ec 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -4358,6 +4358,7 @@ template [[host_name("kernel_mul_mv_bf16_bf16_short")]] kernel mul_mv_t_t_short_
 #endif
 
 constant bool FC_rope_is_imrope [[function_constant(FC_ROPE + 0)]];
+constant bool FC_rope_is_back   [[function_constant(FC_ROPE + 1)]];
 
 static float rope_yarn_ramp(const float low, const float high, const int i0) {
     const float y = (i0 / 2 - low) / max(0.001f, high - low);
@@ -4381,6 +4382,9 @@ static void rope_yarn(
     }
     *cos_theta = cos(theta) * mscale;
     *sin_theta = sin(theta) * mscale;
+    if (FC_rope_is_back) {
+        *sin_theta *= -1.0f;
+    }
 }
 
 // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get

From 2e88c49c90f0add8796f633fea8c3d65b975f295 Mon Sep 17 00:00:00 2001
From: shalinib-ibm <Shalini.Salomi.Bodapati@ibm.com>
Date: Thu, 18 Jun 2026 00:15:19 +0530
Subject: [PATCH 028/292] ggml-cpu: Conditionally enable power11 backend based
 on compiler support (#24687)

* ggml: Conditionally enable power11 backend based on compiler support

Guard POWER11 backend creation behind a compiler flag check for -mcpu=power11. This avoids build failures on current GCC/Clang toolchains while preserving forward compatibility once POWER11 support becomes available.

* Update CMakeLists.txt

ggml-cpu: Use -mcpu=power10 for P10 and P11
---
 ggml/src/CMakeLists.txt          | 9 ++++++++-
 ggml/src/ggml-cpu/CMakeLists.txt | 2 +-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index c26c3f147..89e5180d9 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -438,7 +438,14 @@ if (GGML_CPU_ALL_VARIANTS)
             ggml_add_cpu_backend_variant(power8_2       POWER8  VSX)
             ggml_add_cpu_backend_variant(power9         POWER9  VSX)
             ggml_add_cpu_backend_variant(power10        POWER10 VSX)
-            ggml_add_cpu_backend_variant(power11        POWER11 VSX)
+            # POWER11 backend: only if compiler supports -mcpu=power11
+            check_cxx_compiler_flag("-mcpu=power11" GGML_CXX_SUPPORTS_POWER11)
+            if (GGML_CXX_SUPPORTS_POWER11)
+                message(STATUS "Compiler supports -mcpu=power11, enabling POWER11 backend")
+                ggml_add_cpu_backend_variant(power11 POWER11 VSX)
+            else()
+                message(STATUS "Skipping POWER11 backend: compiler does not support -mcpu=power11")
+            endif()
         else()
             message(FATAL_ERROR "Unsupported PowerPC target OS: ${CMAKE_SYSTEM_NAME}")
         endif()
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 8c735a045..f7c557af4 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -389,7 +389,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             string(REGEX MATCHALL "POWER *([0-9]+)" MATCHED_STRING "${POWER10_M_UPPER}")
             string(REGEX REPLACE "POWER *([0-9]+)" "\\1" EXTRACTED_NUMBER "${MATCHED_STRING}")
 
-            if (EXTRACTED_NUMBER GREATER_EQUAL 10)
+            if (EXTRACTED_NUMBER EQUAL 10 OR EXTRACTED_NUMBER EQUAL 11)
                 list(APPEND ARCH_FLAGS -mcpu=power10)
             elseif (EXTRACTED_NUMBER EQUAL 9)
                 list(APPEND ARCH_FLAGS -mcpu=power9)

From f3e182816421c648188b5eab269853bf1531d950 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 17 Jun 2026 22:40:50 +0200
Subject: [PATCH 029/292] mtmd: llava_uhd should no longer use batch dim
 (#24732)

---
 tools/mtmd/mtmd-image.cpp | 21 +++++++++++++++++++++
 tools/mtmd/mtmd-image.h   |  6 ++++++
 tools/mtmd/mtmd.cpp       | 31 ++++++-------------------------
 3 files changed, 33 insertions(+), 25 deletions(-)

diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index bedf44e07..656d75d80 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -1105,6 +1105,8 @@ bool mtmd_image_preprocessor_internvl::preprocess(const clip_image_u8 & img, cli
         img_u8_to_f32(*imgs[i], *res, hparams.image_mean, hparams.image_std);
         output.entries.push_back(std::move(res));
     }
+    output.grid_x = inst.grid_size.width;
+    output.grid_y = inst.grid_size.height;
     return true;
 }
 
@@ -1558,3 +1560,22 @@ bool mtmd_image_preprocessor_youtuvl::preprocess(const clip_image_u8 & img, clip
     output.entries.push_back(std::move(img_f32));
     return true;
 }
+
+bool mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+    // call super class preprocessor
+    bool ok = mtmd_image_preprocessor_llava_uhd::preprocess(img, output);
+    if (!ok) {
+        return false;
+    }
+    if (output.entries.size() == 1) {
+        // Single-tile (overview only): append one newline row.
+        output.entries[0]->add_newline = true;
+    } else {
+        // Multi-tile: overview gets no newline, grid tiles get one.
+        output.entries[0]->add_newline = false;
+        for (size_t i = 1; i < output.entries.size(); ++i) {
+            output.entries[i]->add_newline = true;
+        }
+    }
+    return true;
+}
diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h
index 91a5bc253..3ddbec2e4 100644
--- a/tools/mtmd/mtmd-image.h
+++ b/tools/mtmd/mtmd-image.h
@@ -197,3 +197,9 @@ struct mtmd_image_preprocessor_youtuvl : mtmd_image_preprocessor {
     mtmd_image_preprocessor_youtuvl(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
     bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
 };
+
+// similar to llava_uhd, but has add_newline
+struct mtmd_image_preprocessor_granite : mtmd_image_preprocessor_llava_uhd {
+    mtmd_image_preprocessor_granite(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
+    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+};
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index ad709227f..0834e4938 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -639,7 +639,7 @@ struct mtmd_context {
                 {
                     img_beg = "<image>";
                     img_end = "";
-                    image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
+                    image_preproc = std::make_unique<mtmd_image_preprocessor_granite>(ctx_v);
                 } break;
             default:
                 throw std::runtime_error(string_format("%s: unexpected vision projector type %d\n", __func__, proj));
@@ -1033,7 +1033,10 @@ struct mtmd_tokenizer {
     int32_t add_media(std::vector<const mtmd_bitmap *> & bitmaps) {
         GGML_ASSERT(!bitmaps.empty());
 
-        if (!bitmaps[0]->is_audio) {
+        // note: only one type of media is supported per call, caller should enforce this
+        const bool is_vision = !bitmaps[0]->is_audio;
+
+        if (is_vision) {
             // handle image
 
             if (!ctx->ctx_v) {
@@ -1085,31 +1088,9 @@ struct mtmd_tokenizer {
                 batch_f32.grid_y = tmp_batch.grid_y;
             }
 
-            // Annotate llava-next style tiles so clip_n_output_tokens accounts
-            // for per-tile newline injection.
-            if (ctx->proj_type_v() == PROJECTOR_TYPE_GRANITE4_VISION) {
-                if (batch_f32.entries.size() == 1) {
-                    // Single-tile (overview only): append one newline row.
-                    batch_f32.entries[0]->add_newline = true;
-                } else {
-                    // Multi-tile: overview gets no newline, grid tiles get one.
-                    batch_f32.entries[0]->add_newline = false;
-                    for (size_t i = 1; i < batch_f32.entries.size(); ++i) {
-                        batch_f32.entries[i]->add_newline = true;
-                    }
-                }
-            }
-
             // handle llava-uhd style preprocessing
             const bool has_tiling_grid = batch_f32.grid_x > 0 && batch_f32.grid_y > 0;
-            if (
-                ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_IDEFICS3
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_STEP3VL
-                || (ctx->slice_tmpl == MTMD_SLICE_TMPL_LFM2 && has_tiling_grid)
-            ) {
+            if (has_tiling_grid) {
                 // [QWEN_VIDEO] we do not support "frame merging" for llama-uhd style, so no batching for now
                 GGML_ASSERT(bitmaps.size() == 1);
 

From cae0a3b0b074cbb111202c3b7e7884e8de56b23a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 18 Jun 2026 09:16:06 +0300
Subject: [PATCH 030/292] metal : check for BF16 support in concat kernel
 (#24747)

---
 ggml/src/ggml-metal/ggml-metal.metal | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 31b5326ec..25e78e100 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -7557,7 +7557,9 @@ typedef decltype(kernel_concat<float>) kernel_concat_t;
 
 template [[host_name("kernel_concat_f32")]]  kernel kernel_concat_t kernel_concat<float>;
 template [[host_name("kernel_concat_f16")]]  kernel kernel_concat_t kernel_concat<half>;
+#if defined(GGML_METAL_HAS_BF16)
 template [[host_name("kernel_concat_bf16")]] kernel kernel_concat_t kernel_concat<bfloat>;
+#endif
 template [[host_name("kernel_concat_i8")]]   kernel kernel_concat_t kernel_concat<char>;
 template [[host_name("kernel_concat_i16")]]  kernel kernel_concat_t kernel_concat<short>;
 template [[host_name("kernel_concat_i32")]]  kernel kernel_concat_t kernel_concat<int>;

From 4a79037b8bd8e6adc7f6379d3054934d4b5df0d0 Mon Sep 17 00:00:00 2001
From: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
Date: Wed, 17 Jun 2026 23:30:08 -0700
Subject: [PATCH 031/292] ci : fix Windows x64 (OpenVINO) release link (#24731)

---
 .github/workflows/release.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index d5bcb4d4e..d452e3787 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -542,6 +542,7 @@ jobs:
     steps:
       - name: Set OpenVINO version output
         id: openvino_version
+        shell: bash
         run: echo "value=${{ env.OPENVINO_VERSION_MAJOR }}" >> $GITHUB_OUTPUT
 
       - name: Clone

From 0b73fc79fe7c8968b040e9c96d91610d7d2a8206 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Thu, 18 Jun 2026 08:33:50 +0200
Subject: [PATCH 032/292] ui: Update code formatting command in pre-commit hook
 (#24685)

---
 tools/ui/scripts/git-hooks/pre-commit.sh | 2 +-
 tools/ui/scripts/git-hooks/pre-push.sh   | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/ui/scripts/git-hooks/pre-commit.sh b/tools/ui/scripts/git-hooks/pre-commit.sh
index 1fa83efde..208ef56fc 100755
--- a/tools/ui/scripts/git-hooks/pre-commit.sh
+++ b/tools/ui/scripts/git-hooks/pre-commit.sh
@@ -27,7 +27,7 @@ echo "Running pre-commit checks for llama-ui..."
 # Format only staged files
 staged_ui=$(git diff --cached --name-only -- tools/ui/)
 if [ -n "$staged_ui" ]; then
-    echo "$staged_ui" | xargs npx --no-install prettier --write
+    echo "$staged_ui" | xargs npm run format
     format_ok=$?
     # Re-stage formatted files
     git add tools/ui/
diff --git a/tools/ui/scripts/git-hooks/pre-push.sh b/tools/ui/scripts/git-hooks/pre-push.sh
index 953d3a224..66d79b950 100755
--- a/tools/ui/scripts/git-hooks/pre-push.sh
+++ b/tools/ui/scripts/git-hooks/pre-push.sh
@@ -57,6 +57,7 @@ if [ $lint_ok -ne 0 ]; then
     echo "❌ Lint failed"
     exit 1
 fi
+
 if [ $test_ok -ne 0 ]; then
     echo "❌ Tests failed"
     exit 1

From 6f1034b32af66d3efa2a572a386cc9bccb01ba55 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Thu, 18 Jun 2026 14:40:03 +0800
Subject: [PATCH 033/292] [SYCL] support OPs: conv_2d, conv_2d_dw,
 conv2d_transpose (#24600)

* fix conflict

* fix format issue, rename

* rm debug code

* correct the file name
---
 docs/backend/SYCL.md                    |   58 +
 docs/ops.md                             |    6 +-
 docs/ops/SYCL.csv                       | 3164 +++++++++++------------
 examples/sycl/update-ops-doc.sh         |    9 +
 examples/sycl/win-update-ops-doc.bat    |    8 +
 ggml/src/ggml-sycl/conv2d-dw.cpp        |  158 ++
 ggml/src/ggml-sycl/conv2d-dw.hpp        |   10 +
 ggml/src/ggml-sycl/conv2d-transpose.cpp |  125 +
 ggml/src/ggml-sycl/conv2d-transpose.hpp |   10 +
 ggml/src/ggml-sycl/conv2d.cpp           |  150 ++
 ggml/src/ggml-sycl/conv2d.hpp           |   10 +
 ggml/src/ggml-sycl/ggml-sycl.cpp        |   20 +-
 12 files changed, 2141 insertions(+), 1587 deletions(-)
 create mode 100755 examples/sycl/update-ops-doc.sh
 create mode 100644 examples/sycl/win-update-ops-doc.bat
 create mode 100644 ggml/src/ggml-sycl/conv2d-dw.cpp
 create mode 100644 ggml/src/ggml-sycl/conv2d-dw.hpp
 create mode 100644 ggml/src/ggml-sycl/conv2d-transpose.cpp
 create mode 100644 ggml/src/ggml-sycl/conv2d-transpose.hpp
 create mode 100644 ggml/src/ggml-sycl/conv2d.cpp
 create mode 100644 ggml/src/ggml-sycl/conv2d.hpp

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 6617aa2a5..68f6e60e6 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -161,6 +161,64 @@ You could update your test result in it directly.
 
 Please refer to [Docker with SYCL](../docker.md#docker-with-sycl) for details.
 
+## Quick Development WOW
+
+This chapter is for quick development & try with SYCL backend on Intel GPU.
+
+You need to install following sofeware before development:
+   - Intel GPU driver
+   - oneAPI package
+   - other development tools.
+
+Please refer to [Linux](#linux) or [Windows](#windows-1) for above installation and resolve the trouble in usage. There are the detailed guide.
+
+- Linux
+
+```
+## build from source code
+./examples/sycl/build.sh
+
+## run CONV_2D_DW unit test cases
+./build/bin/test-backend-ops -b SYCL0 -o CONV_2D_DW
+
+## run all unit test cases
+./build/bin/test-backend-ops -b SYCL0
+
+## run with LLM on the first GPU
+./examples/sycl/test.sh -mg 0 -m xxxx.gguf
+
+## run service with LLM on the first GPU
+export ONEAPI_DEVICE_SELECTOR="level_zero:0"
+./examples/sycl/start-svr.sh -m xxxx.gguf
+
+## update the docs/ops.md for new/update OPs
+./examples/sycl/update-ops-doc.sh
+```
+
+- Windows
+
+```
+## build from source code
+examples\sycl\win-build-sycl.bat
+
+## run CONV_2D_DW unit test cases
+build\bin\test-backend-ops.exe -b SYCL0 -o CONV_2D_DW
+
+## run all unit test cases
+build\bin\test-backend-ops.exe -b SYCL0
+
+## run LLM on the first GPU
+examples\sycl\win-test.bat -mg 0 -m xxxx.gguf
+
+## run service with LLM on the first GPU
+set ONEAPI_DEVICE_SELECTOR="level_zero:0"
+examples\sycl\win-start-svr.bat -m xxxx.gguf
+
+## update the docs/ops.md for new/update OPs
+examples\sycl\win-update-ops-doc.bat
+```
+
+
 ## Linux
 
 ### I. Setup Environment
diff --git a/docs/ops.md b/docs/ops.md
index 9e9442024..6fc8454c8 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -27,11 +27,11 @@ Legend:
 |                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index 116197d2f..80a1c29c1 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -3104,1578 +3104,1578 @@
 "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=1,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
 "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=0","support","1","yes","SYCL"
 "SYCL0","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=3,d1=3,d2=3,v=1","support","1","yes","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=1,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=2,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,1,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,2,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,3,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[1,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[2,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[3,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,1,2],ne_kernel=[11,11,1,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,1],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,1,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,2,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[1,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
-"SYCL0","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","SYCL"
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+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","1","yes","SYCL"
+"SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","1","yes","SYCL"
+"SYCL0","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","1","yes","SYCL"
 "SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","1","yes","SYCL"
 "SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","1","yes","SYCL"
 "SYCL0","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","1","yes","SYCL"
@@ -5083,12 +5083,12 @@
 "SYCL0","COL2IM_1D","type=bf16,K=16,OC=1,T_in=197,s0=8,p0=0","support","0","no","SYCL"
 "SYCL0","COL2IM_1D","type=bf16,K=1,OC=5,T_in=13,s0=3,p0=0","support","0","no","SYCL"
 "SYCL0","COL2IM_1D","type=bf16,K=8,OC=2,T_in=3,s0=2,p0=5","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
-"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f32,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","kernel_type=f16,ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL"
 "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL"
@@ -11046,8 +11046,8 @@
 "SYCL0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","0","no","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","0","no","SYCL"
@@ -11095,8 +11095,8 @@
 "SYCL0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","SYCL"
-"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","0","no","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","0","no","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","0","no","SYCL"
diff --git a/examples/sycl/update-ops-doc.sh b/examples/sycl/update-ops-doc.sh
new file mode 100755
index 000000000..6f26fc457
--- /dev/null
+++ b/examples/sycl/update-ops-doc.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2026 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+./build/bin/test-backend-ops support --output csv > docs/ops/SYCL.csv
+./scripts/create_ops_docs.py
+
diff --git a/examples/sycl/win-update-ops-doc.bat b/examples/sycl/win-update-ops-doc.bat
new file mode 100644
index 000000000..b032bcfe1
--- /dev/null
+++ b/examples/sycl/win-update-ops-doc.bat
@@ -0,0 +1,8 @@
+@echo off
+
+rem MIT license
+rem Copyright (C) 2026 Intel Corporation
+rem SPDX-License-Identifier: MIT
+
+build\bin\test-backend-ops support --output csv > docs\ops\SYCL.csv
+python scripts\create_ops_docs.py
diff --git a/ggml/src/ggml-sycl/conv2d-dw.cpp b/ggml/src/ggml-sycl/conv2d-dw.cpp
new file mode 100644
index 000000000..0a52b7917
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d-dw.cpp
@@ -0,0 +1,158 @@
+#include "conv2d-dw.hpp"
+
+struct conv2d_dw_params {
+    int in_w, in_h;
+    int out_w, out_h;
+    int kernel_w, kernel_h;
+    int stride_x, stride_y;
+    int padding_x, padding_y;
+    int dilation_x, dilation_y;
+    int channels, batches;
+};
+
+struct conv2d_dw_kernel_bounds {
+    int y_min, y_max;
+    int x_min, x_max;
+};
+
+static inline conv2d_dw_kernel_bounds dw_calculate_kernel_bounds(int out_x, int out_y,
+                                                                  const conv2d_dw_params & p) {
+    conv2d_dw_kernel_bounds bounds;
+    bounds.y_min = sycl::max(0, (p.padding_y - out_y * p.stride_y + p.dilation_y - 1) / p.dilation_y);
+    bounds.y_max = sycl::min(p.kernel_h,
+                             (p.in_h + p.padding_y - out_y * p.stride_y + p.dilation_y - 1) / p.dilation_y);
+    bounds.x_min = sycl::max(0, (p.padding_x - out_x * p.stride_x + p.dilation_x - 1) / p.dilation_x);
+    bounds.x_max = sycl::min(p.kernel_w,
+                             (p.in_w + p.padding_x - out_x * p.stride_x + p.dilation_x - 1) / p.dilation_x);
+    return bounds;
+}
+
+static inline int dw_calculate_input_coord(int out_coord, int kern_coord, int stride, int dilation, int padding) {
+    return out_coord * stride + kern_coord * dilation - padding;
+}
+
+// whcn layout: input/output stored as [N, C, H, W]
+struct dw_whcn_layout {
+    static int input_index(int n, int c, int y, int x, const conv2d_dw_params & p) {
+        return n * (p.channels * p.in_w * p.in_h) + c * p.in_w * p.in_h + y * p.in_w + x;
+    }
+    static int kernel_index(int c, int ky, int kx, const conv2d_dw_params & p) {
+        return c * p.kernel_h * p.kernel_w + ky * p.kernel_w + kx;
+    }
+    static int output_index(int n, int c, int y, int x, const conv2d_dw_params & p) {
+        return n * (p.channels * p.out_w * p.out_h) + c * p.out_w * p.out_h + y * p.out_w + x;
+    }
+    static void unpack_indices(int global_idx, const conv2d_dw_params & p,
+                               int & n, int & c, int & out_y, int & out_x) {
+        out_x  = global_idx % p.out_w;
+        out_y  = (global_idx / p.out_w) % p.out_h;
+        c      = (global_idx / (p.out_w * p.out_h)) % p.channels;
+        n      = global_idx / (p.out_w * p.out_h * p.channels);
+    }
+};
+
+// cwhn layout: input/output stored as [N, H, W, C]
+struct dw_cwhn_layout {
+    static int input_index(int n, int c, int y, int x, const conv2d_dw_params & p) {
+        return n * (p.channels * p.in_w * p.in_h) + (y * p.in_w + x) * p.channels + c;
+    }
+    static int kernel_index(int c, int ky, int kx, const conv2d_dw_params & p) {
+        return (ky * p.kernel_w + kx) * p.channels + c;
+    }
+    static int output_index(int n, int c, int y, int x, const conv2d_dw_params & p) {
+        return n * (p.channels * p.out_w * p.out_h) + y * (p.out_w * p.channels) + x * p.channels + c;
+    }
+    static void unpack_indices(int global_idx, const conv2d_dw_params & p,
+                               int & n, int & c, int & out_y, int & out_x) {
+        c      = global_idx % p.channels;
+        out_x  = (global_idx / p.channels) % p.out_w;
+        out_y  = (global_idx / (p.channels * p.out_w)) % p.out_h;
+        n      = global_idx / (p.channels * p.out_w * p.out_h);
+    }
+};
+
+template <typename Layout>
+static void conv2d_dw_kernel(const float * input, const float * kernel, float * output,
+                             const conv2d_dw_params p, const sycl::nd_item<3> & item_ct1) {
+    const int global_idx     = item_ct1.get_local_id(2) +
+                               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    const int total_elements = p.batches * p.channels * p.out_h * p.out_w;
+
+    if (global_idx >= total_elements) {
+        return;
+    }
+
+    int n, c, out_y, out_x;
+    Layout::unpack_indices(global_idx, p, n, c, out_y, out_x);
+
+    float acc = 0.0f;
+    const conv2d_dw_kernel_bounds bounds = dw_calculate_kernel_bounds(out_x, out_y, p);
+
+    for (int ky = bounds.y_min; ky < bounds.y_max; ++ky) {
+        const int in_y = dw_calculate_input_coord(out_y, ky, p.stride_y, p.dilation_y, p.padding_y);
+        for (int kx = bounds.x_min; kx < bounds.x_max; ++kx) {
+            const int in_x = dw_calculate_input_coord(out_x, kx, p.stride_x, p.dilation_x, p.padding_x);
+            acc += input[Layout::input_index(n, c, in_y, in_x, p)] *
+                   kernel[Layout::kernel_index(c, ky, kx, p)];
+        }
+    }
+
+    output[Layout::output_index(n, c, out_y, out_x, p)] = acc;
+}
+
+template <typename Layout>
+static void conv2d_dw_sycl(const float * x_d, const float * w_d, float * y_d,
+                            const conv2d_dw_params p, const queue_ptr & stream) {
+    const int total      = p.batches * p.channels * p.out_h * p.out_w;
+    const int num_blocks = (total + SYCL_CONV2D_DW_BLOCK_SIZE - 1) / SYCL_CONV2D_DW_BLOCK_SIZE;
+    const sycl::range<3> block_dims(1, 1, SYCL_CONV2D_DW_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, 1, num_blocks);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            conv2d_dw_kernel<Layout>(x_d, w_d, y_d, p, item_ct1);
+        });
+}
+
+void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+
+    GGML_ASSERT(kernel->type == GGML_TYPE_F32 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+
+    const float * w_d = (const float *) kernel->data;
+    const float * x_d = (const float *) input->data;
+    float *       y_d = (float *) dst->data;
+
+    const int32_t * p          = (const int32_t *) dst->op_params;
+    const int       stride_x   = p[0];
+    const int       stride_y   = p[1];
+    const int       padding_x  = p[2];
+    const int       padding_y  = p[3];
+    const int       dilation_x = p[4];
+    const int       dilation_y = p[5];
+
+    const int in_w     = input->ne[0];
+    const int in_h     = input->ne[1];
+    const int kernel_w = kernel->ne[0];
+    const int kernel_h = kernel->ne[1];
+    const int out_w    = dst->ne[0];
+    const int out_h    = dst->ne[1];
+    const int channels = dst->ne[2];
+    const int batches  = dst->ne[3];
+
+    const conv2d_dw_params params = { in_w, in_h, out_w, out_h, kernel_w, kernel_h,
+                                      stride_x, stride_y, padding_x, padding_y,
+                                      dilation_x, dilation_y, channels, batches };
+
+    const queue_ptr stream = ctx.stream();
+
+    if (ggml_is_contiguous(input)) {
+        conv2d_dw_sycl<dw_whcn_layout>(x_d, w_d, y_d, params, stream);
+    } else if (ggml_is_contiguous_channels(input)) {
+        conv2d_dw_sycl<dw_cwhn_layout>(x_d, w_d, y_d, params, stream);
+    } else {
+        GGML_ABORT("Unsupported memory layout for conv2d_dw");
+    }
+}
diff --git a/ggml/src/ggml-sycl/conv2d-dw.hpp b/ggml/src/ggml-sycl/conv2d-dw.hpp
new file mode 100644
index 000000000..532892221
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d-dw.hpp
@@ -0,0 +1,10 @@
+#ifndef GGML_SYCL_CONV2D_DW_HPP
+#define GGML_SYCL_CONV2D_DW_HPP
+
+#include "common.hpp"
+
+#define SYCL_CONV2D_DW_BLOCK_SIZE 256
+
+void ggml_sycl_op_conv2d_dw(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_CONV2D_DW_HPP
diff --git a/ggml/src/ggml-sycl/conv2d-transpose.cpp b/ggml/src/ggml-sycl/conv2d-transpose.cpp
new file mode 100644
index 000000000..07c325cc6
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d-transpose.cpp
@@ -0,0 +1,125 @@
+#include "conv2d-transpose.hpp"
+#include "convert.hpp"
+
+template <typename kernel_t>
+static void conv2d_transpose_kernel(const float * input, const kernel_t * kernel, float * output,
+                                    const int in_w, const int in_h,
+                                    const int out_w, const int out_h,
+                                    const int kernel_w, const int kernel_h,
+                                    const int stride,
+                                    const int c_in, const int c_out, const int batches,
+                                    const sycl::nd_item<3> & item_ct1) {
+    const int global_idx     = item_ct1.get_local_id(2) +
+                               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+    const int total_elements = out_w * out_h * c_out * batches;
+
+    if (global_idx >= total_elements) {
+        return;
+    }
+
+    const int out_x = global_idx % out_w;
+    const int out_y = (global_idx / out_w) % out_h;
+    const int c_idx = (global_idx / (out_w * out_h)) % c_out;
+    const int n_idx = global_idx / (out_w * out_h * c_out);
+
+    float acc = 0.0f;
+
+    for (int c_in_idx = 0; c_in_idx < c_in; ++c_in_idx) {
+        for (int kh = 0; kh < kernel_h; ++kh) {
+            int in_y = out_y - kh;
+            if (in_y < 0 || in_y % stride) {
+                continue;
+            }
+            in_y /= stride;
+            if (in_y >= in_h) {
+                continue;
+            }
+
+            for (int kw = 0; kw < kernel_w; ++kw) {
+                int in_x = out_x - kw;
+                if (in_x < 0 || in_x % stride) {
+                    continue;
+                }
+                in_x /= stride;
+                if (in_x >= in_w) {
+                    continue;
+                }
+
+                const int input_idx  = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + in_w * in_y + in_x;
+                const int kernel_idx = (kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx +
+                                       kernel_w * kh + kw;
+
+                acc += input[input_idx] * ggml_sycl_cast<float>(kernel[kernel_idx]);
+            }
+        }
+    }
+
+    output[(out_w * out_h * c_out) * n_idx + (out_w * out_h) * c_idx + out_w * out_y + out_x] = acc;
+}
+
+template <typename kernel_t>
+static void conv2d_transpose_sycl(const float * input_d, const kernel_t * kernel_d, float * output_d,
+                                   const int in_w, const int in_h,
+                                   const int out_w, const int out_h,
+                                   const int kernel_w, const int kernel_h,
+                                   const int stride,
+                                   const int c_in, const int c_out, const int batches,
+                                   const queue_ptr & stream) {
+    const int total      = out_w * out_h * c_out * batches;
+    const int num_blocks = (total + SYCL_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / SYCL_CONV2D_TRANSPOSE_BLOCK_SIZE;
+    const sycl::range<3> block_dims(1, 1, SYCL_CONV2D_TRANSPOSE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, 1, num_blocks);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            conv2d_transpose_kernel<kernel_t>(input_d, kernel_d, output_d,
+                                             in_w, in_h, out_w, out_h, kernel_w, kernel_h,
+                                             stride, c_in, c_out, batches, item_ct1);
+        });
+}
+
+// input:  (W, H, C_in, N)
+// kernel: (W, H, C_out, C_in)
+// output: (W, H, C_out, N)
+void ggml_sycl_op_conv2d_transpose(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+    GGML_ASSERT(input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(input));
+    GGML_ASSERT(ggml_is_contiguous(kernel));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    const float * input_d  = (const float *) input->data;
+    float *       output_d = (float *) dst->data;
+    const void *  kernel_d = kernel->data;
+
+    const int input_w      = input->ne[0];
+    const int input_h      = input->ne[1];
+    const int channels_in  = input->ne[2];
+    const int batches      = input->ne[3];
+    const int output_w     = dst->ne[0];
+    const int output_h     = dst->ne[1];
+    const int channels_out = kernel->ne[2];
+    const int kernel_w     = kernel->ne[0];
+    const int kernel_h     = kernel->ne[1];
+    const int stride       = dst->op_params[0];
+
+    GGML_ASSERT(channels_in == kernel->ne[3]);
+    GGML_ASSERT(stride > 0);
+
+    const queue_ptr stream = ctx.stream();
+
+    if (kernel->type == GGML_TYPE_F16) {
+        conv2d_transpose_sycl<sycl::half>(input_d, (const sycl::half *) kernel_d, output_d,
+                                          input_w, input_h, output_w, output_h, kernel_w, kernel_h,
+                                          stride, channels_in, channels_out, batches, stream);
+    } else {
+        conv2d_transpose_sycl<float>(input_d, (const float *) kernel_d, output_d,
+                                     input_w, input_h, output_w, output_h, kernel_w, kernel_h,
+                                     stride, channels_in, channels_out, batches, stream);
+    }
+}
diff --git a/ggml/src/ggml-sycl/conv2d-transpose.hpp b/ggml/src/ggml-sycl/conv2d-transpose.hpp
new file mode 100644
index 000000000..ca067318d
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d-transpose.hpp
@@ -0,0 +1,10 @@
+#ifndef GGML_SYCL_CONV2D_TRANSPOSE_HPP
+#define GGML_SYCL_CONV2D_TRANSPOSE_HPP
+
+#include "common.hpp"
+
+#define SYCL_CONV2D_TRANSPOSE_BLOCK_SIZE 256
+
+void ggml_sycl_op_conv2d_transpose(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_CONV2D_TRANSPOSE_HPP
diff --git a/ggml/src/ggml-sycl/conv2d.cpp b/ggml/src/ggml-sycl/conv2d.cpp
new file mode 100644
index 000000000..3b3b49d05
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d.cpp
@@ -0,0 +1,150 @@
+#include "conv2d.hpp"
+#include "convert.hpp"
+
+struct conv2d_params {
+    const int64_t IW, IH;
+    const int64_t OW, OH;
+    const int64_t KW, KH;
+    const int64_t ST_X, ST_Y;
+    const int64_t PD_X, PD_Y;
+    const int64_t DL_X, DL_Y;
+    const int64_t IC, OC;
+    const int64_t B;
+    const int64_t TOTAL;
+};
+
+struct conv2d_kernel_bounds {
+    int64_t y_min, y_max;
+    int64_t x_min, x_max;
+};
+
+static inline int64_t conv2d_max64(int64_t a, int64_t b) {
+    return (a > b) ? a : b;
+}
+
+static inline int64_t conv2d_min64(int64_t a, int64_t b) {
+    return (a < b) ? a : b;
+}
+
+static inline conv2d_kernel_bounds calculate_kernel_bounds(int64_t out_x, int64_t out_y, const conv2d_params & P) {
+    conv2d_kernel_bounds bounds;
+    bounds.y_min = conv2d_max64(0, (P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.y_max = conv2d_min64(P.KH, (P.IH + P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
+    bounds.x_min = conv2d_max64(0, (P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    bounds.x_max = conv2d_min64(P.KW, (P.IW + P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
+    return bounds;
+}
+
+static inline int calculate_input_coord(int64_t out_coord, int64_t kern_coord, int64_t stride,
+                                        int64_t dilation, int64_t padding) {
+    return out_coord * stride + kern_coord * dilation - padding;
+}
+
+// whcn layout helpers (matching ggml tensor memory order)
+static inline int64_t whcn_input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv2d_params & P) {
+    return n * (P.IC * P.IW * P.IH) + c * P.IW * P.IH + y * P.IW + x;
+}
+
+static inline int64_t whcn_kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv2d_params & P) {
+    return c_out * (P.IC * P.KH * P.KW) + c_in * (P.KH * P.KW) + ky * P.KW + kx;
+}
+
+static inline int64_t whcn_output_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv2d_params & P) {
+    return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x;
+}
+
+template <typename T>
+static void conv2d_kernel(const float * input, const T * kernel, float * output,
+                          const conv2d_params P, const sycl::nd_item<3> & item_ct1) {
+    const int64_t global_idx = item_ct1.get_local_id(2) +
+                               item_ct1.get_group(2) * item_ct1.get_local_range(2);
+
+    if (global_idx >= P.TOTAL) {
+        return;
+    }
+
+    const int64_t out_x  = global_idx % P.OW;
+    const int64_t out_y  = (global_idx / P.OW) % P.OH;
+    const int64_t c_out  = (global_idx / (P.OW * P.OH)) % P.OC;
+    const int64_t n      = global_idx / (P.OW * P.OH * P.OC);
+
+    float acc = 0.0f;
+
+    const conv2d_kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);
+
+    for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
+        for (int64_t ky = bounds.y_min; ky < bounds.y_max; ++ky) {
+            const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y);
+            for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
+                const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
+                const float input_val  = input[whcn_input_index(n, c_in, in_y, in_x, P)];
+                const T     kernel_val = kernel[whcn_kernel_index(c_out, c_in, ky, kx, P)];
+                acc += input_val * ggml_sycl_cast<float>(kernel_val);
+            }
+        }
+    }
+
+    output[whcn_output_index(n, c_out, out_y, out_x, P)] = acc;
+}
+
+template <typename T>
+static void conv2d_sycl(const float * X_D, const T * K_D, float * Y_D,
+                        const conv2d_params P, const queue_ptr & stream) {
+    const int num_blocks = (P.TOTAL + SYCL_CONV2D_BLOCK_SIZE - 1) / SYCL_CONV2D_BLOCK_SIZE;
+    const sycl::range<3> block_dims(1, 1, SYCL_CONV2D_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, 1, num_blocks);
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            conv2d_kernel<T>(X_D, K_D, Y_D, P, item_ct1);
+        });
+}
+
+void ggml_sycl_op_conv2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+
+    const ggml_tensor * kernel = dst->src[0];
+    const ggml_tensor * input  = dst->src[1];
+    const float *       K_D    = (const float *) kernel->data;
+    const float *       X_D    = (const float *) input->data;
+    float *             Y_D    = (float *) dst->data;
+
+    GGML_ASSERT(ggml_is_contiguous(kernel));
+    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+    GGML_ASSERT(input->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    // same number of input channels
+    GGML_ASSERT(input->ne[2] == kernel->ne[2]);
+
+    const queue_ptr stream = ctx.stream();
+
+    const int32_t * p    = (const int32_t *) dst->op_params;
+    const int       ST_X = p[0];
+    const int       ST_Y = p[1];
+    const int       PD_X = p[2];
+    const int       PD_Y = p[3];
+    const int       DL_X = p[4];
+    const int       DL_Y = p[5];
+
+    // no cwhn layout support
+    GGML_ASSERT(p[6] == 0);
+
+    const int IW = input->ne[0];
+    const int IH = input->ne[1];
+    const int OW = dst->ne[0];
+    const int OH = dst->ne[1];
+    const int KW = kernel->ne[0];
+    const int KH = kernel->ne[1];
+    const int IC = input->ne[2];
+    const int OC = kernel->ne[3];
+    const int B  = input->ne[3];
+
+    const int64_t     total  = (int64_t) B * OC * OH * OW;
+    const conv2d_params params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total };
+
+    if (kernel->type == GGML_TYPE_F16) {
+        conv2d_sycl<sycl::half>(X_D, (const sycl::half *) K_D, Y_D, params, stream);
+    } else {
+        conv2d_sycl<float>(X_D, K_D, Y_D, params, stream);
+    }
+}
diff --git a/ggml/src/ggml-sycl/conv2d.hpp b/ggml/src/ggml-sycl/conv2d.hpp
new file mode 100644
index 000000000..efd25ab42
--- /dev/null
+++ b/ggml/src/ggml-sycl/conv2d.hpp
@@ -0,0 +1,10 @@
+#ifndef GGML_SYCL_CONV2D_HPP
+#define GGML_SYCL_CONV2D_HPP
+
+#include "common.hpp"
+
+#define SYCL_CONV2D_BLOCK_SIZE 256
+
+void ggml_sycl_op_conv2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_CONV2D_HPP
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 4c0567669..77d145890 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -62,6 +62,9 @@
 #include "ggml-sycl/repeat_back.hpp"
 #include "ggml-sycl/set_rows.hpp"
 #include "ggml-sycl/set.hpp"
+#include "ggml-sycl/conv2d.hpp"
+#include "ggml-sycl/conv2d-dw.hpp"
+#include "ggml-sycl/conv2d-transpose.hpp"
 #include "ggml-sycl/ssm_conv.hpp"
 #include "ggml-sycl/sycl_hw.hpp"
 #include "ggml-sycl/ssm_scan.hpp"
@@ -4664,12 +4667,21 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_ARGMAX:
             ggml_sycl_argmax(ctx, dst);
             break;
-        case GGML_OP_CONV_TRANSPOSE_1D:
-            ggml_sycl_op_conv_transpose_1d(ctx, dst);
+        case GGML_OP_CONV_2D:
+            ggml_sycl_op_conv2d(ctx, dst);
+            break;
+        case GGML_OP_CONV_2D_DW:
+            ggml_sycl_op_conv2d_dw(ctx, dst);
             break;
         case GGML_OP_CONV_3D:
             ggml_sycl_conv_3d(ctx, dst);
             break;
+        case GGML_OP_CONV_TRANSPOSE_1D:
+            ggml_sycl_op_conv_transpose_1d(ctx, dst);
+            break;
+        case GGML_OP_CONV_TRANSPOSE_2D:
+            ggml_sycl_op_conv2d_transpose(ctx, dst);
+            break;
         case GGML_OP_REPEAT:
             ggml_sycl_repeat(ctx, dst);
             break;
@@ -5387,6 +5399,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 }
                 return false;
             }
+        case GGML_OP_CONV_2D:
+        case GGML_OP_CONV_2D_DW:
+        case GGML_OP_CONV_TRANSPOSE_2D:
+            return true;
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
                 case GGML_UNARY_OP_SGN:

From 32e806b9c16ab298ea8ea791c4fc0df390015a09 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Thu, 18 Jun 2026 09:32:56 +0200
Subject: [PATCH 034/292] ci : fix check-release message parsing (#24751)

---
 .github/workflows/release.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index d452e3787..9789215f2 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -46,11 +46,13 @@ jobs:
 
     steps:
       - id: check
+        env:
+          COMMIT_MESSAGE: ${{ github.event.head_commit.message }}
         run: |
           if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
             echo "should_release=true" >> $GITHUB_OUTPUT
           elif [[ "${{ github.event_name }}" == "push" && "${{ github.ref }}" == "refs/heads/master" ]]; then
-            if echo "${{ github.event.head_commit.message }}" | grep -q '\[no release\]'; then
+            if echo "$COMMIT_MESSAGE" | grep -q '\[no release\]'; then
               echo "should_release=false" >> $GITHUB_OUTPUT
             else
               echo "should_release=true" >> $GITHUB_OUTPUT

From 6ec59ddaeab5eb099bcd7c9189f4b9266123bc07 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Thu, 18 Jun 2026 09:57:59 +0200
Subject: [PATCH 035/292] app : enable self-update only when built with
 llama-install.sh (#24754)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 app/llama.cpp | 39 ++++++++++++++++++++++++++-------------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/app/llama.cpp b/app/llama.cpp
index 30b09f9ef..c4578ea53 100644
--- a/app/llama.cpp
+++ b/app/llama.cpp
@@ -20,16 +20,21 @@ int llama_fit_params(int argc, char ** argv);
 int llama_quantize(int argc, char ** argv);
 int llama_perplexity(int argc, char ** argv);
 
-// hands the update over to the install script, which downloads and swaps the binary
+// Self-update is only supported for binaries built with llama-install.sh
 static int llama_update(int argc, char ** argv) {
     (void) argc;
     (void) argv;
 
+#ifdef LLAMA_INSTALL_BUILD
 #if defined(_WIN32)
     return system("powershell -NoProfile -ExecutionPolicy Bypass -Command \"irm https://llama.app/install.ps1 | iex\"");
 #else
     return system("curl -fsSL https://llama.app/install.sh | sh");
 #endif
+#else
+    printf("Updates are available only when installed from https://llama.app\n");
+    return 1;
+#endif
 }
 
 static const char * progname;
@@ -46,21 +51,29 @@ struct command {
     int (*func)(int, char **);
 };
 
+#ifdef LLAMA_INSTALL_BUILD
+#define UPDATE_HIDDEN false
+#else
+#define UPDATE_HIDDEN true
+#endif
+
 static const command cmds[] = {
-    {"serve",         "HTTP API server",                                    {"server"},   false, llama_server       },
-    {"cli",           "Command-line interactive interface",                 {"client"},   false, llama_cli          },
-    {"update",        "Update llama to the latest release",                 {},           false, llama_update       },
-    {"completion",    "Text completion",                                    {"complete"}, true,  llama_completion   },
-    {"bench",         "Benchmark prompt processing and text generation",    {},           true,  llama_bench        },
-    {"batched-bench", "Benchmark batched decoding performance",             {},           true,  llama_batched_bench},
-    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,  llama_fit_params   },
-    {"quantize",      "Quantize a model",                                   {},           true,  llama_quantize     },
-    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,  llama_perplexity   },
-    {"version",       "Show version",                                       {},           false, version            },
-    {"licenses",      "Show third-party licenses",                          {"credits"},  false, licenses           },
-    {"help",          "Show available commands",                            {},           false, help               },
+    {"serve",         "HTTP API server",                                    {"server"},   false,         llama_server       },
+    {"cli",           "Command-line interactive interface",                 {"client"},   false,         llama_cli          },
+    {"update",        "Update llama to the latest release",                 {},           UPDATE_HIDDEN, llama_update       },
+    {"completion",    "Text completion",                                    {"complete"}, true,          llama_completion   },
+    {"bench",         "Benchmark prompt processing and text generation",    {},           true,          llama_bench        },
+    {"batched-bench", "Benchmark batched decoding performance",             {},           true,          llama_batched_bench},
+    {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,          llama_fit_params   },
+    {"quantize",      "Quantize a model",                                   {},           true,          llama_quantize     },
+    {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,          llama_perplexity   },
+    {"version",       "Show version",                                       {},           false,         version            },
+    {"licenses",      "Show third-party licenses",                          {"credits"},  false,         licenses           },
+    {"help",          "Show available commands",                            {},           false,         help               },
 };
 
+#undef UPDATE_HIDDEN
+
 static int version(int argc, char ** argv) {
     printf("%s\n", llama_build_info());
     return 0;

From dd69db292465ef698def69499ccb920a67bd613e Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Thu, 18 Jun 2026 16:17:37 +0800
Subject: [PATCH 036/292] sycl : support MUL_MAT and OUT_PROD with Q1_0
 (#24721)

---
 ggml/src/ggml-sycl/convert.cpp    |  6 +++
 ggml/src/ggml-sycl/dequantize.hpp | 15 +++++++
 ggml/src/ggml-sycl/dmmv.cpp       | 53 ++++++++++++++++++++++
 ggml/src/ggml-sycl/ggml-sycl.cpp  | 28 ++++++++----
 ggml/src/ggml-sycl/mmvq.cpp       | 74 +++++++++++++++++++++++++++++++
 ggml/src/ggml-sycl/outprod.cpp    | 54 ++++++++++++++++++----
 ggml/src/ggml-sycl/vecdotq.hpp    | 35 +++++++++++++++
 7 files changed, 247 insertions(+), 18 deletions(-)

diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index 65593402e..060d0aca2 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -642,6 +642,8 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct
 
 to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_sycl<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             if (dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
@@ -724,6 +726,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
 
 to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_sycl<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             if (dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
@@ -830,6 +834,8 @@ to_fp16_nc_sycl_t ggml_get_to_fp16_nc_sycl(ggml_type type) {
         case GGML_TYPE_BF16:
             return convert_unary_nc_sycl<sycl::ext::oneapi::bfloat16>;
 #endif
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_nc_sycl<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_block_nc_sycl<QK4_0, QR4_0, dequantize_q4_0>;
         case GGML_TYPE_Q4_1:
diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp
index ca8cd96c0..7b66c73b0 100644
--- a/ggml/src/ggml-sycl/dequantize.hpp
+++ b/ggml/src/ggml-sycl/dequantize.hpp
@@ -70,6 +70,21 @@ static __dpct_inline__ void dequantize_q4_0_reorder(const void *d_ptr, const int
 #endif // GGML_SYCL_F16
 }
 
+static __dpct_inline__ void dequantize_q1_0_reorder(const void *d_ptr, const int64_t ib, const void *qs,
+                                            const int iqs, dfloat2 &v) {
+    // Q1_0 reorder layout: scale values followed by quantized bits
+    const dfloat d = (const dfloat)*((const sycl::half*)d_ptr+ib);
+
+    const int bit_index_0 = iqs + 0;
+    const int bit_index_1 = iqs + 1;
+
+    const int bit_0 = (*((const uint8_t *)qs + bit_index_0 / 8) >> (bit_index_0 % 8)) & 1;
+    const int bit_1 = (*((const uint8_t *)qs + bit_index_1 / 8) >> (bit_index_1 % 8)) & 1;
+
+    v.x() = (2 * bit_0 - 1) * d;
+    v.y() = (2 * bit_1 - 1) * d;
+}
+
 static __dpct_inline__ void dequantize_q4_1(const void *vx, const int64_t ib,
                                             const int iqs, dfloat2 &v) {
     const block_q4_1 * x = (const block_q4_1 *) vx;
diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp
index e091e5235..fb8a1757f 100644
--- a/ggml/src/ggml-sycl/dmmv.cpp
+++ b/ggml/src/ggml-sycl/dmmv.cpp
@@ -1423,6 +1423,50 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
     }
 }
 
+static void dequantize_mul_mat_vec_q1_0_sycl_reorder(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec_reorder<QK1_0, QR1_0, dequantize_q1_0_reorder>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
+static void dequantize_mul_mat_vec_q1_0_sycl(const void *vx, const dfloat *y,
+                                             float *dst, const int ncols,
+                                             const int nrows,
+                                             dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+    {
+        dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK1_0, QR1_0, dequantize_q1_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
+    }
+}
+
 static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
                                              float *dst, const int ncols,
                                              const int nrows,
@@ -1759,6 +1803,7 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
     sycl::half *src1_dfloat = nullptr; // dfloat == half
 
     bool src1_convert_f16 =
+        src0->type == GGML_TYPE_Q1_0 ||
         src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
         src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
         src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16 ||
@@ -1777,6 +1822,14 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
 #endif // GGML_SYCL_F16
 
     switch (src0->type) {
+        case GGML_TYPE_Q1_0:
+            if ((ggml_tensor_extra_gpu*)dst->src[0]->extra &&
+                ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
+                dequantize_mul_mat_vec_q1_0_sycl_reorder(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            } else {
+                dequantize_mul_mat_vec_q1_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
+            }
+            break;
         case GGML_TYPE_Q4_0:
             if ((ggml_tensor_extra_gpu*)dst->src[0]->extra &&
                 ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 77d145890..0aebdc441 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -976,6 +976,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYC
     }
 
     switch(type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
             return max_compute_capability >= VER_GEN9 ? 128 : 64;
@@ -3507,6 +3508,7 @@ inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
 
 inline bool ggml_sycl_supports_reorder_mul_mat_sycl(enum ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q8_0:
             return true;
@@ -3522,6 +3524,7 @@ inline bool ggml_sycl_supports_reorder_mul_mat_sycl(enum ggml_type type) {
 
 inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q8_0:
             return true;
@@ -3532,6 +3535,7 @@ inline bool ggml_sycl_supports_reorder_dmmv(enum ggml_type type) {
 
 inline bool ggml_sycl_supports_reorder_mmvq(enum ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q3_K:
@@ -3546,6 +3550,7 @@ inline bool ggml_sycl_supports_reorder_mmvq(enum ggml_type type) {
 
 static bool ggml_sycl_supports_dmmv(enum ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -5385,7 +5390,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_device_buffer_from_host_ptr(ggml_
     return nullptr;
 }
 
-static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
     ggml_backend_sycl_device_context *sycl_ctx =
         (ggml_backend_sycl_device_context *)dev->context;
     int device = sycl_ctx->device;
@@ -5450,19 +5455,12 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 struct ggml_tensor * a = op->src[0];
                 struct ggml_tensor * b = op->src[1];
 
-                // disable Q1_0 until implementation
-                if (a->type == GGML_TYPE_Q1_0 || b->type == GGML_TYPE_Q1_0) {
-                    return false;
-                }
-
                 if (a->ne[3] != b->ne[3]) {
                     return false;
                 }
 
                 ggml_type src0_type = op->src[0]->type;
 
-
-
                 // TODO: The configuration below needs more work to be supported with oneDNN
                 if (ggml_is_permuted(a) && !ggml_is_contiguous(a) &&
                     a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) {
@@ -5472,12 +5470,17 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 // TODO: This specific configuration can fail with oneDNN and needs more debugging
                 if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
                     a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
+                        printf("zjy 2\n");
                     return false;
                 }
                 return true;
             }
         case GGML_OP_OUT_PROD:
-            return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1;
+            return op->type == GGML_TYPE_F32 &&
+                   (op->src[0]->type == GGML_TYPE_F32 ||
+                    (op->src[0]->type == GGML_TYPE_Q1_0 && op->src[0]->ne[2] == op->src[1]->ne[2] &&
+                     op->src[0]->ne[3] == op->src[1]->ne[3])) &&
+                   op->src[1]->type == GGML_TYPE_F32;
         case GGML_OP_GET_ROWS:
             {
                 switch (op->src[0]->type) {
@@ -5734,6 +5737,13 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
     GGML_UNUSED(dev);
 }
 
+static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    bool res = do_ggml_backend_sycl_device_supports_op(dev, op);
+    GGML_SYCL_DEBUG("[SYCL] call %s op->op=%s op->type=%s -> %s\n", __func__, ggml_op_name(op->op),
+                    ggml_type_name(op->type), res ? "true" : "false");
+    return res;
+}
+
 static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
     if (buft->iface.get_name != ggml_backend_sycl_buffer_type_get_name) {
         return false;
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
index 909c7aeb2..7b1b3d467 100644
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -1194,6 +1194,66 @@ static void mul_mat_vec_q8_0_q8_1_sycl_switch_ncols(
     }
 }
 
+static void mul_mat_vec_q1_0_q8_1_sycl(const void * vx, const void * vy,
+                                       float * dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK1_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_q<QK1_0, QI1_0, block_q1_0,
+                              VDR_Q1_0_Q8_1_MMVQ, vec_dot_q1_0_q8_1>(
+                    vx, vy, dst, ncols, nrows, item_ct1);
+            });
+    });
+}
+
+template <int ncols_dst>
+static void mul_mat_vec_q1_0_q8_1_sycl_ncols(
+        const void * vx, const void * vy, float * dst,
+        const int ncols, const int nrows,
+        const int stride_col_y, const int stride_col_dst,
+        dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK1_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_q_ncols<QK1_0, QI1_0, block_q1_0,
+                                    VDR_Q1_0_Q8_1_MMVQ, vec_dot_q1_0_q8_1, ncols_dst>(
+                    vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, item_ct1);
+            });
+    });
+}
+
+static void mul_mat_vec_q1_0_q8_1_sycl_switch_ncols(
+        const void * vx, const void * vy, float * dst,
+        const int ncols, const int nrows, const int ncols_dst,
+        const int stride_col_y, const int stride_col_dst,
+        dpct::queue_ptr stream) {
+    switch (ncols_dst) {
+        case 1: mul_mat_vec_q1_0_q8_1_sycl(vx, vy, dst, ncols, nrows, stream); break;
+        case 2: mul_mat_vec_q1_0_q8_1_sycl_ncols<2>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 3: mul_mat_vec_q1_0_q8_1_sycl_ncols<3>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 4: mul_mat_vec_q1_0_q8_1_sycl_ncols<4>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 5: mul_mat_vec_q1_0_q8_1_sycl_ncols<5>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 6: mul_mat_vec_q1_0_q8_1_sycl_ncols<6>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 7: mul_mat_vec_q1_0_q8_1_sycl_ncols<7>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 8: mul_mat_vec_q1_0_q8_1_sycl_ncols<8>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        default: GGML_ABORT("unsupported ncols_dst=%d for Q1_0 multi-col MMVQ", ncols_dst);
+    }
+}
+
 static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
                                        float *dst, const int ncols,
                                        const int nrows,
@@ -2120,6 +2180,20 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
                     mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                 }
                 break;
+            case GGML_TYPE_Q1_0:
+                if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) {
+                    const int stride_col_y   = src1_padded_col_size / QK8_1;
+                    const int stride_col_dst = dst->ne[0];
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q1_0_q8_1_sycl_switch_ncols ncols=%d\n", (int)src1_ncols);
+                    mul_mat_vec_q1_0_q8_1_sycl_switch_ncols(
+                        src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff,
+                        src1_ncols, stride_col_y, stride_col_dst, stream);
+                    return;
+                } else if (i == 0 || src1_ncols == 1) {
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q1_0_q8_1_sycl\n");
+                    mul_mat_vec_q1_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                }
+                break;
             case GGML_TYPE_Q2_K:
                 if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) {
                     const int stride_col_y   = src1_padded_col_size / QK8_1;
diff --git a/ggml/src/ggml-sycl/outprod.cpp b/ggml/src/ggml-sycl/outprod.cpp
index f52b11f0d..8d10dad8c 100644
--- a/ggml/src/ggml-sycl/outprod.cpp
+++ b/ggml/src/ggml-sycl/outprod.cpp
@@ -1,11 +1,12 @@
 #include "outprod.hpp"
+#include "convert.hpp"
 
 void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
     const ggml_tensor *src0 = dst->src[0];
     const ggml_tensor *src1 = dst->src[1];
 
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_Q1_0);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
     GGML_ASSERT(ggml_is_contiguous(src0));
@@ -20,11 +21,31 @@ void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     GGML_ASSERT(ne01 == ne11);  // Inner dimensions must match
     GGML_ASSERT(ne0 == ne00);   // Output rows match src0 rows
     GGML_ASSERT(ne1 == ne10);   // Output cols match src1 cols
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+    GGML_ASSERT(ne2 % ne02 == 0);
+    GGML_ASSERT(ne3 % ne03 == 0);
 
     // Get data pointers
-    const float* src0_d = (const float*)src0->data;
-    const float* src1_d = (const float*)src1->data;
-    float* dst_d = (float*)dst->data;
+    const float * src0_d = (const float *) src0->data;
+    const float * src1_d = (const float *) src1->data;
+    float * dst_d = (float *) dst->data;
+
+    ggml_sycl_pool_alloc<float> src0_as_f32(ctx.pool());
+    int64_t src0_nb02 = nb02;
+    int64_t src0_nb03 = nb03;
+    if (src0->type == GGML_TYPE_Q1_0) {
+        scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                             " : converting src0 Q1_0 to fp32");
+        src0_d = src0_as_f32.alloc(ne00 * ne01 * ne02 * ne03);
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type, dst);
+        GGML_ASSERT(to_fp32_sycl != nullptr);
+        to_fp32_sycl(src0->data, const_cast<float *>(src0_d), ne00 * ne01 * ne02 * ne03, stream);
+
+        // Dequantized src0 buffer is contiguous fp32 [ne00, ne01, ne02, ne03].
+        src0_nb02 = ne00 * ne01 * (int64_t) sizeof(float);
+        src0_nb03 = ne00 * ne01 * ne02 * (int64_t) sizeof(float);
+    }
 
     // GEMM parameters
     const float alpha = 1.0f;
@@ -35,12 +56,27 @@ void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     const oneapi::mkl::transpose src1_op = src1_T ? oneapi::mkl::transpose::nontrans : oneapi::mkl::transpose::trans;
     const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float);
 
+    const int64_t r2 = ne2 / ne02;
+    const int64_t r3 = ne3 / ne03;
+
     try {
-        // Perform matrix multiplication using oneMKL GEMM
-        oneapi::mkl::blas::column_major::gemm(*stream, oneapi::mkl::transpose::nontrans, src1_op,
-                                               ne0, ne1, ne01, alpha, src0_d, ne00, src1_d, ldb, beta, dst_d, ne0);
-    }
-    catch (sycl::exception const& exc) {
+        // OUT_PROD applies independently to each (i2, i3) destination plane.
+        for (int64_t i3 = 0; i3 < ne3; ++i3) {
+            for (int64_t i2 = 0; i2 < ne2; ++i2) {
+                const int64_t i03 = i3 / r3;
+                const int64_t i02 = i2 / r2;
+
+                const float * src0_plane = (const float *) ((const char *) src0_d + i02 * src0_nb02 + i03 * src0_nb03);
+                const float * src1_plane = (const float *) ((const char *) src1_d + i2 * nb12 + i3 * nb13);
+                float * dst_plane = (float *) ((char *) dst_d + i2 * nb2 + i3 * nb3);
+
+                // Perform matrix multiplication using oneMKL GEMM
+                oneapi::mkl::blas::column_major::gemm(*stream, oneapi::mkl::transpose::nontrans, src1_op,
+                                                      ne0, ne1, ne01, alpha, src0_plane, ne00,
+                                                      src1_plane, ldb, beta, dst_plane, ne0);
+            }
+        }
+    } catch (sycl::exception const& exc) {
         std::cerr << exc.what() << std::endl;
         GGML_ASSERT(false);
     }
diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp
index 4b58b09ab..765fb7f15 100644
--- a/ggml/src/ggml-sycl/vecdotq.hpp
+++ b/ggml/src/ggml-sycl/vecdotq.hpp
@@ -309,6 +309,41 @@ vec_dot_q6_K_q8_1_impl_mmvq(const int &vl, const int &vh,
         vl, vh, u[0], u[1], scales[0], scales[4], d, d8[0], d8[1]);
 }
 
+#define VDR_Q1_0_Q8_1_MMVQ 1
+#define VDR_Q1_0_Q8_1_MMQ  4
+
+static __dpct_inline__ float
+vec_dot_q1_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q1_0 * bq1_0 = (const block_q1_0 *) vbq;
+
+    const block_q8_1 * bq8_1_chunk = bq8_1 + iqs;
+    const float        d1          = bq1_0->d;
+    const int          v           = get_int_from_uint8_aligned(bq1_0->qs, iqs);
+
+    int vi_bytes[8];
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        const int shift = j * 4;
+        const int bits4 = (v >> shift) & 0x0F;
+        const int b0    = (bits4 & 0x01) ? 1 : -1;
+        const int b1    = (bits4 & 0x02) ? 1 : -1;
+        const int b2    = (bits4 & 0x04) ? 1 : -1;
+        const int b3    = (bits4 & 0x08) ? 1 : -1;
+        vi_bytes[j]     = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24);
+    }
+
+    int sumi = 0;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        const int u = get_int_from_int8_aligned(bq8_1_chunk->qs, j);
+        sumi        = ggml_sycl_dp4a(vi_bytes[j], u, sumi);
+    }
+
+    return d1 * bq8_1_chunk->ds[0] * sumi;
+}
+
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 

From 9724f664e803e70eb8d046a3fac411122ad42ff7 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Thu, 18 Jun 2026 16:18:26 +0800
Subject: [PATCH 037/292] [SYCL] rename GGML_SYCL_SUPPORT_LEVEL_ZERO (#24719)

* rename GGML_SYCL_SUPPORT_LEVEL_ZERO to GGML_SYCL_SUPPORT_LEVEL_ZERO_API, and GGML_SYCL_ENABLE_LEVEL_ZERO to  GGML_SYCL_USE_LEVEL_ZERO_API

* fix code format

* fix error when rebase
---
 docs/backend/SYCL.md              |  4 ++--
 ggml/CMakeLists.txt               |  2 +-
 ggml/src/ggml-sycl/CMakeLists.txt | 10 ++++-----
 ggml/src/ggml-sycl/common.cpp     | 10 ++++-----
 ggml/src/ggml-sycl/common.hpp     |  2 +-
 ggml/src/ggml-sycl/ggml-sycl.cpp  | 36 +++++++++++++++----------------
 6 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 68f6e60e6..d482d8840 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -759,7 +759,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | GGML_SYCL_HOST_MEM_FALLBACK | ON *(default)* \|OFF *(Optional)* | Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+. |
-| GGML_SYCL_SUPPORT_LEVEL_ZERO | ON *(default)* \|OFF *(Optional)* | Enable Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. |
+| GGML_SYCL_SUPPORT_LEVEL_ZERO_API | ON *(default)* \|OFF *(Optional)* | Support to use Level Zero API for device memory allocation. Requires Level Zero headers/library at build time and Intel GPU driver (Level Zero runtime) at run time. Reduces system RAM usage during multi-GPU inference. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
@@ -774,7 +774,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for Intel devices older than Gen 10) |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
-| GGML_SYCL_ENABLE_LEVEL_ZERO | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO=ON at build time. |
+| GGML_SYCL_USE_LEVEL_ZERO_API | 1 (default) or 0 | Use Level Zero API for device memory allocation instead of SYCL. Reduces system RAM usage on Intel dGPUs by avoiding DMA-buf/TTM host memory staging. Requires GGML_SYCL_SUPPORT_LEVEL_ZERO_API=ON at build time. SYCL backend always runs on Level Zero running time even if it's set as OFF (The SYCL api will be usage for memory allocation).|
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | GGML_SYCL_ENABLE_VMM | 0 or 1 (default) | Enable the virtual-memory device pool. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 249ed3da2..0507e0c5a 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -249,7 +249,7 @@ option(GGML_SYCL                            "ggml: use SYCL"
 option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
 option(GGML_SYCL_GRAPH                      "ggml: enable graphs in the SYCL backend"         ON)
 option(GGML_SYCL_HOST_MEM_FALLBACK          "ggml: allow host memory fallback in SYCL reorder (requires kernel 6.8+)" ON)
-option(GGML_SYCL_SUPPORT_LEVEL_ZERO         "ggml: use Level Zero API in SYCL backend"  ON)
+option(GGML_SYCL_SUPPORT_LEVEL_ZERO_API     "ggml: use Level Zero API in SYCL backend"  ON)
 option(GGML_SYCL_DNN                        "ggml: enable oneDNN in the SYCL backend"         ON)
 set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
                                             "ggml: sycl target device")
diff --git a/ggml/src/ggml-sycl/CMakeLists.txt b/ggml/src/ggml-sycl/CMakeLists.txt
index 180de9220..1c17d20df 100644
--- a/ggml/src/ggml-sycl/CMakeLists.txt
+++ b/ggml/src/ggml-sycl/CMakeLists.txt
@@ -39,8 +39,8 @@ if (WIN32)
         set(CMAKE_CXX_COMPILER "icx")
         set(CMAKE_CXX_COMPILER_ID "IntelLLVM")
     endif()
-    # Level Zero SDK path for Windows (only when GGML_SYCL_SUPPORT_LEVEL_ZERO is enabled)
-    if(GGML_SYCL_SUPPORT_LEVEL_ZERO)
+    # Level Zero SDK path for Windows (only when GGML_SYCL_SUPPORT_LEVEL_ZERO_API is enabled)
+    if(GGML_SYCL_SUPPORT_LEVEL_ZERO_API)
         if(DEFINED ENV{LEVEL_ZERO_V1_SDK_PATH})
             set(LEVEL_ZERO_V1_SDK_PATH $ENV{LEVEL_ZERO_V1_SDK_PATH})
             if(EXISTS "${LEVEL_ZERO_V1_SDK_PATH}")
@@ -105,8 +105,8 @@ endif()
 
 target_compile_options(ggml-sycl PRIVATE "-Wno-narrowing")
 
-message(STATUS "GGML_SYCL_SUPPORT_LEVEL_ZERO ${GGML_SYCL_SUPPORT_LEVEL_ZERO}")
-if (GGML_SYCL_SUPPORT_LEVEL_ZERO)
+message(STATUS "GGML_SYCL_SUPPORT_LEVEL_ZERO_API ${GGML_SYCL_SUPPORT_LEVEL_ZERO_API}")
+if (GGML_SYCL_SUPPORT_LEVEL_ZERO_API)
     # Link against Level Zero loader for direct device memory allocation.
     # Avoids sycl::malloc_device triggering DMA-buf/TTM system RAM staging
     # in the xe kernel driver during multi-GPU inference.
@@ -114,7 +114,7 @@ if (GGML_SYCL_SUPPORT_LEVEL_ZERO)
     find_library(ZE_LOADER_LIB ze_loader HINTS ${ONEAPI_ROOT}/lib ${LEVEL_ZERO_V1_SDK_LIB_PATH} ENV LD_LIBRARY_PATH)
     if(ZE_LOADER_LIB AND LEVEL_ZERO_INCLUDE_DIR)
         target_link_libraries(ggml-sycl PRIVATE ${ZE_LOADER_LIB})
-        target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_SUPPORT_LEVEL_ZERO)
+        target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_SUPPORT_LEVEL_ZERO_API)
         message(STATUS "Level Zero loader found: ${ZE_LOADER_LIB}")
         message(STATUS "Level Zero headers found: ${LEVEL_ZERO_INCLUDE_DIR}")
     else()
diff --git a/ggml/src/ggml-sycl/common.cpp b/ggml/src/ggml-sycl/common.cpp
index 38ace8bf5..e1b6db13e 100644
--- a/ggml/src/ggml-sycl/common.cpp
+++ b/ggml/src/ggml-sycl/common.cpp
@@ -12,7 +12,7 @@
 
 #include "common.hpp"
 #include <sycl/backend.hpp>
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
 #include <level_zero/ze_api.h>
 #endif
 
@@ -84,9 +84,9 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
   return sycl_down_blk_size;
 }
 
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
 static bool ggml_sycl_use_level_zero_device_alloc(sycl::queue &q) {
-    return g_ggml_sycl_enable_level_zero &&
+    return g_ggml_sycl_use_level_zero_api &&
         q.get_device().is_gpu() &&
         q.get_backend() == sycl::backend::ext_oneapi_level_zero;
 }
@@ -95,7 +95,7 @@ static bool ggml_sycl_use_level_zero_device_alloc(sycl::queue &q) {
 // Use Level Zero zeMemAllocDevice to avoid sycl::malloc_device triggering
 // DMA-buf/TTM system RAM staging in the xe kernel driver during multi-GPU inference.
 void * ggml_sycl_malloc_device(size_t size, sycl::queue &q) {
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
     if (ggml_sycl_use_level_zero_device_alloc(q)) {
         void *ptr = nullptr;
         auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_context());
@@ -127,7 +127,7 @@ void * ggml_sycl_malloc_device(size_t size, sycl::queue &q) {
 
 void ggml_sycl_free_device(void *ptr, sycl::queue &q) {
     if (!ptr) return;
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
     if (ggml_sycl_use_level_zero_device_alloc(q)) {
         auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q.get_context());
         zeMemFree(ze_ctx, ptr);
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index c87a4636e..8534bd358 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -324,7 +324,7 @@ struct ggml_tensor_extra_gpu {
   optimize_feature optimized_feature;
 };
 
-extern int g_ggml_sycl_enable_level_zero;
+extern int g_ggml_sycl_use_level_zero_api;
 void * ggml_sycl_malloc_device(size_t size, sycl::queue &q);
 void ggml_sycl_free_device(void *ptr, sycl::queue &q);
 
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 0aebdc441..d8b83d0e2 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -32,7 +32,7 @@
 
 #include <sycl/sycl.hpp>
 #include <sycl/backend.hpp>
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
 #include <level_zero/ze_api.h>
 #endif
 #if defined(GGML_SYCL_GRAPH) && SYCL_EXT_ONEAPI_ASYNC_MEMORY_ALLOC
@@ -87,7 +87,7 @@ int g_ggml_sycl_enable_vmm = 1;
 int g_ggml_sycl_prioritize_dmmv = 0;
 int g_ggml_sycl_use_async_mem_op = 0;
 int g_ggml_sycl_use_async_mem_op_requested = 1;
-int g_ggml_sycl_enable_level_zero = 0;
+int g_ggml_sycl_use_level_zero_api = 0;
 int g_ggml_sycl_enable_flash_attention = 1;
 int g_ggml_sycl_dev2dev_memcpy = DEV2DEV_MEMCPY_SYCL;
 int g_ggml_sycl_usm_system = 0;
@@ -157,7 +157,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
             info.ext_oneapi_level_zero = false;
         }
 
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
         if (info.ext_oneapi_level_zero && device.is_gpu() && device.default_queue().get_backend() == sycl::backend::ext_oneapi_level_zero) {
             ze_device_handle_t ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(device.default_queue().get_device());
             ze_device_properties_t props = {};
@@ -172,13 +172,13 @@ static ggml_sycl_device_info ggml_sycl_init() {
         info.default_tensor_split[id] /= total_vram;
     }
 
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
     // Large buffers can be allocated before ggml_check_sycl() initializes other
     // g_ggml_sycl_enable_* globals, so initialize this one as early as we can.
-    g_ggml_sycl_enable_level_zero =
-        info.ext_oneapi_level_zero && ggml_sycl_get_env("GGML_SYCL_ENABLE_LEVEL_ZERO", 1);
+    g_ggml_sycl_use_level_zero_api =
+        info.ext_oneapi_level_zero && ggml_sycl_get_env("GGML_SYCL_USE_LEVEL_ZERO_API", 1);
 #else
-    g_ggml_sycl_enable_level_zero = 0;
+    g_ggml_sycl_use_level_zero_api = 0;
 #endif
 
     return info;
@@ -277,7 +277,7 @@ static void ggml_check_sycl() try {
         g_ggml_sycl_prioritize_dmmv = ggml_sycl_get_env("GGML_SYCL_PRIORITIZE_DMMV", 0);
 
         g_ggml_sycl_dev2dev_memcpy = ggml_sycl_get_env("GGML_SYCL_DEV2DEV_MEMCPY", DEV2DEV_MEMCPY_SYCL);
-        if (g_ggml_sycl_enable_level_zero == 0) {
+        if (g_ggml_sycl_use_level_zero_api == 0) {
             g_ggml_sycl_dev2dev_memcpy = DEV2DEV_MEMCPY_SYCL;
         }
 
@@ -312,10 +312,10 @@ static void ggml_check_sycl() try {
 #else
         GGML_LOG_INFO("  GGML_SYCL_DNNL: no\n");
 #endif
-#if defined(GGML_SYCL_SUPPORT_LEVEL_ZERO)
-        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO: yes\n");
+#if defined(GGML_SYCL_SUPPORT_LEVEL_ZERO_API)
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO_API: yes\n");
 #else
-        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO: no\n");
+        GGML_LOG_INFO("  GGML_SYCL_SUPPORT_LEVEL_ZERO_API: no\n");
 #endif
 #if defined(GGML_SYCL_USE_VMM)
         GGML_LOG_INFO("  GGML_SYCL_USE_VMM: yes\n");
@@ -331,12 +331,12 @@ static void ggml_check_sycl() try {
 #else
         GGML_LOG_INFO("  GGML_SYCL_DISABLE_GRAPH: graph disabled by compile flag\n");
 #endif
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
-        GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: %d\n", g_ggml_sycl_enable_level_zero);
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
+        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: %d\n", g_ggml_sycl_use_level_zero_api);
         GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d\n", g_ggml_sycl_dev2dev_memcpy);
 #else
-        GGML_LOG_INFO("  GGML_SYCL_ENABLE_LEVEL_ZERO: Level Zero disabled by compile flag\n");
-        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d, enable to SYCL API since missing GGML_SYCL_SUPPORT_LEVEL_ZERO\n",
+        GGML_LOG_INFO("  GGML_SYCL_USE_LEVEL_ZERO_API: Disable Level Zero API usage by compile flag\n");
+        GGML_LOG_INFO("  GGML_SYCL_DEV2DEV_MEMCPY: %d, enable to SYCL API since missing GGML_SYCL_SUPPORT_LEVEL_ZERO_API\n",
                       g_ggml_sycl_dev2dev_memcpy);
 #endif
 #if GGML_SYCL_DNNL
@@ -602,7 +602,7 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
 static bool ggml_sycl_is_l0_discrete_gpu(int device) {
     return ggml_sycl_info().devices[device].l0_discrete_gpu;
 }
@@ -611,12 +611,12 @@ static bool ggml_sycl_is_l0_discrete_gpu(int device) {
 static void dev2dev_memcpy(int device_dst, sycl::queue &q_dst, int device_src, sycl::queue &q_src, void *ptr_dst,
                     const void *ptr_src, size_t size) {
 
-#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO
+#ifdef GGML_SYCL_SUPPORT_LEVEL_ZERO_API
     if (g_ggml_sycl_dev2dev_memcpy == DEV2DEV_MEMCPY_L0) {
         // Use Level Zero direct copy for dGPU-to-dGPU transfers.
         const bool l0_copy_supported =
             ggml_sycl_is_l0_discrete_gpu(device_dst) && ggml_sycl_is_l0_discrete_gpu(device_src);
-        if (g_ggml_sycl_enable_level_zero && l0_copy_supported) {
+        if (g_ggml_sycl_use_level_zero_api && l0_copy_supported) {
             auto ze_ctx = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_context());
             auto ze_dev = sycl::get_native<sycl::backend::ext_oneapi_level_zero>(q_dst.get_device());
             ze_command_queue_desc_t cq_desc = {ZE_STRUCTURE_TYPE_COMMAND_QUEUE_DESC, nullptr, 0, 0,

From 24bba7b98ea1544cc89352c7a573baedcb831a64 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 12:04:39 +0200
Subject: [PATCH 038/292] mtmd: refactor preprocessor, add
 mtmd_image_preproc_out (#24736)

* add mtmd_image_preproc_out

* add dev docs

* remove unused clip API

* rm unused clip_image_f32_batch::grid

* change preprocess() call signature
---
 docs/multimodal.md        |   5 +-
 tools/mtmd/README-dev.md  |  35 +++++++
 tools/mtmd/clip-impl.h    | 133 ++++++++++----------------
 tools/mtmd/clip.cpp       | 119 +++++------------------
 tools/mtmd/clip.h         |  31 +-----
 tools/mtmd/mtmd-image.cpp | 195 +++++++++++++++++---------------------
 tools/mtmd/mtmd-image.h   |  39 ++++----
 tools/mtmd/mtmd.cpp       |  92 +++++++++---------
 8 files changed, 279 insertions(+), 370 deletions(-)
 create mode 100644 tools/mtmd/README-dev.md

diff --git a/docs/multimodal.md b/docs/multimodal.md
index 33d1df33c..76065aac4 100644
--- a/docs/multimodal.md
+++ b/docs/multimodal.md
@@ -1,10 +1,11 @@
 # Multimodal
 
 llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools support this feature:
-- [llama-mtmd-cli](../tools/mtmd/README.md)
+- [llama-cli](../tools/cli/README.md)
 - [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API
+- [llama-mtmd-cli](../tools/mtmd/README.md), for testing and development
 
-Currently, we support **image** and **audio** input. Audio is highly experimental and may have reduced quality.
+Currently, we support **image**, **audio** and **video** input.
 
 To enable it, you can use one of the 2 methods below:
 
diff --git a/tools/mtmd/README-dev.md b/tools/mtmd/README-dev.md
new file mode 100644
index 000000000..3a0891587
--- /dev/null
+++ b/tools/mtmd/README-dev.md
@@ -0,0 +1,35 @@
+# libmtmd dev guide
+
+## History
+
+Please refer to [multimodal.md](../../docs/multimodal.md) for a broader context.
+
+In short:
+- `libmtmd` started as a wrapper around `libllava` / `clip.cpp`
+- Various components that used to be in `clip.cpp` are moved progressively to mtmd. For example, preprocessor is now part of mtmd
+
+## Terminologies
+
+- mtmd: **M**ul**T**i**M**o**D**al
+- bitmap: representing a raw input data, for example: RGB image, PCM audio
+- tiles / slices: for llava-uhd-style models, the preprocessor breaks a large input into smaller square images called tiles or slices
+- chunk: a mtmd_input_chunk represents a preprocessed input that can then be passed through `mtmd_encode()`
+
+## Pipeline
+
+A typical pipeline of the core libmtmd is as follows:
+- A bitmap (RGB image or PCM audio) is created
+- Bitmap and the text prompt is provided to `mtmd_tokenize()` that breaks the input into chunks
+    - The tokenizer function first expands a "lazy" bitmap if it finds one. Typically, this is used by video, so that one media token corresponds to one input bitmap
+    - For models that support "fused" temporal frames like Qwen-VL, the tokenizer tries to merge pair of consecutive frames into one batch
+    - The preprocessor will then be called, which produces a list of chunks
+    - Depending on the model itself, special tokens will be injected to separate image chunks (i.e. llava-uhd-style models)
+- Multiple bitmaps may be batched together to form a larger `mtmd_batch()`
+- Single image or batch is encoded, via `mtmd_encode()` or `mtmd_batch_encode()`
+- Get the output embeddings
+
+## Helper
+
+We provide a set of helper functions via `mtmd_helper` to make using libmtmd easier. The helper provides:
+- Image, audio and video file decoding (for example, decode raw JPEG into RGB bitmap)
+- Manage `llama_batch` and calls to `llama_decode`
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index b104f3736..f232b68e5 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -367,56 +367,56 @@ enum projector_type {
 };
 
 static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
-    { PROJECTOR_TYPE_MLP,       "mlp" },
-    { PROJECTOR_TYPE_LDP,       "ldp" },
-    { PROJECTOR_TYPE_LDPV2,     "ldpv2"},
-    { PROJECTOR_TYPE_MINICPMV,  "resampler"},
-    { PROJECTOR_TYPE_GLM_EDGE,  "adapter"},
-    { PROJECTOR_TYPE_QWEN2VL,   "qwen2vl_merger"},
-    { PROJECTOR_TYPE_QWEN25VL,  "qwen2.5vl_merger"},
-    { PROJECTOR_TYPE_QWEN3VL,   "qwen3vl_merger"},
-    { PROJECTOR_TYPE_STEP3VL,   "step3vl"},
-    { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
-    { PROJECTOR_TYPE_GEMMA3NV,  "gemma3nv"},
-    { PROJECTOR_TYPE_GEMMA3NA,  "gemma3na"},
-    { PROJECTOR_TYPE_GEMMA4V,   "gemma4v"},
-    { PROJECTOR_TYPE_GEMMA4A,   "gemma4a"},
-    { PROJECTOR_TYPE_GEMMA4UV,  "gemma4uv"},
-    { PROJECTOR_TYPE_GEMMA4UA,  "gemma4ua"},
-    { PROJECTOR_TYPE_PHI4,      "phi4"},
-    { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
-    { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},
-    { PROJECTOR_TYPE_ULTRAVOX,  "ultravox"},
-    { PROJECTOR_TYPE_INTERNVL,  "internvl"},
-    { PROJECTOR_TYPE_LLAMA4,    "llama4"},
-    { PROJECTOR_TYPE_QWEN2A,    "qwen2a"},
-    { PROJECTOR_TYPE_QWEN3A,    "qwen3a"},
-    { PROJECTOR_TYPE_GLMA,      "glma"},
-    { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
-    { PROJECTOR_TYPE_VOXTRAL,   "voxtral"},
-    { PROJECTOR_TYPE_MERALION,  "meralion"},
-    { PROJECTOR_TYPE_MUSIC_FLAMINGO, "musicflamingo"},
-    { PROJECTOR_TYPE_LFM2,      "lfm2"},
-    { PROJECTOR_TYPE_KIMIVL,    "kimivl"},
-    { PROJECTOR_TYPE_PADDLEOCR, "paddleocr"},
-    { PROJECTOR_TYPE_LIGHTONOCR,"lightonocr"},
-    { PROJECTOR_TYPE_COGVLM,    "cogvlm"},
-    { PROJECTOR_TYPE_JANUS_PRO, "janus_pro"},
-    { PROJECTOR_TYPE_DOTS_OCR,  "dots_ocr"},
-    { PROJECTOR_TYPE_DEEPSEEKOCR,"deepseekocr"},
-    { PROJECTOR_TYPE_DEEPSEEKOCR2,"deepseekocr2"},
-    { PROJECTOR_TYPE_LFM2A,     "lfm2a"},
-    { PROJECTOR_TYPE_GLM4V,     "glm4v"},
-    { PROJECTOR_TYPE_YOUTUVL,   "youtuvl"},
-    { PROJECTOR_TYPE_YASA2,     "yasa2"},
-    { PROJECTOR_TYPE_KIMIK25,   "kimik25"},
-    { PROJECTOR_TYPE_NEMOTRON_V2_VL, "nemotron_v2_vl"},
-    { PROJECTOR_TYPE_EXAONE4_5, "exaone4_5"},
-    { PROJECTOR_TYPE_HUNYUANVL,  "hunyuanvl"},
-    { PROJECTOR_TYPE_MINICPMV4_6, "minicpmv4_6"},
-    { PROJECTOR_TYPE_GRANITE_SPEECH, "granite_speech"},
-    { PROJECTOR_TYPE_MIMOVL,     "mimovl"},
-    { PROJECTOR_TYPE_GRANITE4_VISION, "granite4_vision"},
+    { PROJECTOR_TYPE_MLP,               "mlp" },
+    { PROJECTOR_TYPE_LDP,               "ldp" },
+    { PROJECTOR_TYPE_LDPV2,             "ldpv2"},
+    { PROJECTOR_TYPE_MINICPMV,          "resampler"},
+    { PROJECTOR_TYPE_GLM_EDGE,          "adapter"},
+    { PROJECTOR_TYPE_QWEN2VL,           "qwen2vl_merger"},
+    { PROJECTOR_TYPE_QWEN25VL,          "qwen2.5vl_merger"},
+    { PROJECTOR_TYPE_QWEN3VL,           "qwen3vl_merger"},
+    { PROJECTOR_TYPE_STEP3VL,           "step3vl"},
+    { PROJECTOR_TYPE_GEMMA3,            "gemma3"},
+    { PROJECTOR_TYPE_GEMMA3NV,          "gemma3nv"},
+    { PROJECTOR_TYPE_GEMMA3NA,          "gemma3na"},
+    { PROJECTOR_TYPE_GEMMA4V,           "gemma4v"},
+    { PROJECTOR_TYPE_GEMMA4A,           "gemma4a"},
+    { PROJECTOR_TYPE_GEMMA4UV,          "gemma4uv"},
+    { PROJECTOR_TYPE_GEMMA4UA,          "gemma4ua"},
+    { PROJECTOR_TYPE_PHI4,              "phi4"},
+    { PROJECTOR_TYPE_IDEFICS3,          "idefics3"},
+    { PROJECTOR_TYPE_PIXTRAL,           "pixtral"},
+    { PROJECTOR_TYPE_ULTRAVOX,          "ultravox"},
+    { PROJECTOR_TYPE_INTERNVL,          "internvl"},
+    { PROJECTOR_TYPE_LLAMA4,            "llama4"},
+    { PROJECTOR_TYPE_QWEN2A,            "qwen2a"},
+    { PROJECTOR_TYPE_QWEN3A,            "qwen3a"},
+    { PROJECTOR_TYPE_GLMA,              "glma"},
+    { PROJECTOR_TYPE_QWEN25O,           "qwen2.5o"},
+    { PROJECTOR_TYPE_VOXTRAL,           "voxtral"},
+    { PROJECTOR_TYPE_MERALION,          "meralion"},
+    { PROJECTOR_TYPE_MUSIC_FLAMINGO,    "musicflamingo"},
+    { PROJECTOR_TYPE_LFM2,              "lfm2"},
+    { PROJECTOR_TYPE_KIMIVL,            "kimivl"},
+    { PROJECTOR_TYPE_PADDLEOCR,         "paddleocr"},
+    { PROJECTOR_TYPE_LIGHTONOCR,        "lightonocr"},
+    { PROJECTOR_TYPE_COGVLM,            "cogvlm"},
+    { PROJECTOR_TYPE_JANUS_PRO,         "janus_pro"},
+    { PROJECTOR_TYPE_DOTS_OCR,          "dots_ocr"},
+    { PROJECTOR_TYPE_DEEPSEEKOCR,       "deepseekocr"},
+    { PROJECTOR_TYPE_DEEPSEEKOCR2,      "deepseekocr2"},
+    { PROJECTOR_TYPE_LFM2A,             "lfm2a"},
+    { PROJECTOR_TYPE_GLM4V,             "glm4v"},
+    { PROJECTOR_TYPE_YOUTUVL,           "youtuvl"},
+    { PROJECTOR_TYPE_YASA2,             "yasa2"},
+    { PROJECTOR_TYPE_KIMIK25,           "kimik25"},
+    { PROJECTOR_TYPE_NEMOTRON_V2_VL,    "nemotron_v2_vl"},
+    { PROJECTOR_TYPE_EXAONE4_5,         "exaone4_5"},
+    { PROJECTOR_TYPE_HUNYUANVL,         "hunyuanvl"},
+    { PROJECTOR_TYPE_MINICPMV4_6,       "minicpmv4_6"},
+    { PROJECTOR_TYPE_GRANITE_SPEECH,    "granite_speech"},
+    { PROJECTOR_TYPE_MIMOVL,            "mimovl"},
+    { PROJECTOR_TYPE_GRANITE4_VISION,   "granite4_vision"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -640,47 +640,18 @@ static void clip_log_internal(enum ggml_log_level level, const char * format, ..
 // cpp wrappers
 //
 
-// wrapper for clip_image_size
-struct clip_image_size_deleter {
-    void operator()(clip_image_size * val) { clip_image_size_free(val); }
-};
-typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
-
-// wrapper for clip_image_u8
-struct clip_image_u8_deleter {
-    void operator()(clip_image_u8 * val) { clip_image_u8_free(val); }
-};
-typedef std::unique_ptr<clip_image_u8, clip_image_u8_deleter> clip_image_u8_ptr;
-
-// wrapper for clip_image_f32
-struct clip_image_f32_deleter {
-    void operator()(clip_image_f32 * val) { clip_image_f32_free(val); }
-};
-typedef std::unique_ptr<clip_image_f32, clip_image_f32_deleter> clip_image_f32_ptr;
-
-struct clip_image_u8_batch {
-    std::vector<clip_image_u8_ptr> entries;
-};
-
 struct clip_image_f32_batch {
-    std::vector<clip_image_f32_ptr> entries;
+    std::vector<clip_image_f32> entries;
     bool is_audio = false;
 
-    // for llava-uhd style models, we need to know the grid size
-    // note: entries.size() == grid_x * grid_y + 1 (one overview image)
-    int grid_x = 0;
-    int grid_y = 0;
-
     clip_image_f32_batch clone() const {
         clip_image_f32_batch new_batch{
             /* entries  */ {},
             /* is_audio */ is_audio,
-            /* grid_x   */ grid_x,
-            /* grid_y   */ grid_y,
         };
         new_batch.entries.reserve(entries.size());
         for (const auto & entry : entries) {
-            new_batch.entries.emplace_back(new clip_image_f32(*entry));
+            new_batch.entries.emplace_back(entry); // copy
         }
         return new_batch;
     }
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 208486fd1..dc6223295 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -865,7 +865,7 @@ ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale
 }
 
 static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
-    const clip_image_f32 & img = *imgs.entries[0];
+    const clip_image_f32 & img = imgs.entries[0];
     std::unique_ptr<clip_graph> builder;
 
     switch (ctx->proj_type()) {
@@ -2825,16 +2825,16 @@ struct clip_model_loader {
         // create a fake batch
         const auto & hparams = ctx_clip.model.hparams;
         clip_image_f32_batch batch;
-        clip_image_f32_ptr img(clip_image_f32_init());
+        clip_image_f32 img;
         if (ctx_clip.model.modality == CLIP_MODALITY_VISION) {
             const int sz = hparams.warmup_image_size;
-            img->set_size({sz, sz}, false, false);
+            img.set_size({sz, sz}, false, false);
             LOG_INF("%s: warmup with image size = %d x %d\n", __func__, sz, sz);
         } else {
-            img->set_size({hparams.warmup_audio_size, hparams.n_mel_bins}, false, false);
+            img.set_size({hparams.warmup_audio_size, hparams.n_mel_bins}, false, false);
             LOG_INF("%s: warmup with audio size = %d\n", __func__, hparams.warmup_audio_size);
         }
-        batch.entries.push_back(std::move(img));
+        batch.entries.push_back(img);
         return batch;
     }
 
@@ -3124,64 +3124,6 @@ struct clip_cap clip_get_cap(const char * fname) {
     return res;
 }
 
-struct clip_image_size * clip_image_size_init() {
-    struct clip_image_size * load_image_size = new struct clip_image_size();
-    load_image_size->width = 448;
-    load_image_size->height = 448;
-    return load_image_size;
-}
-
-struct clip_image_u8 * clip_image_u8_init() {
-    return new clip_image_u8();
-}
-
-struct clip_image_f32 * clip_image_f32_init() {
-    return new clip_image_f32();
-}
-
-struct clip_image_f32_batch * clip_image_f32_batch_init() {
-    return new clip_image_f32_batch();
-}
-
-void clip_image_size_free(struct clip_image_size * load_image_size) {
-    if (load_image_size == nullptr) {
-        return;
-    }
-    delete load_image_size;
-}
-void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
-void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
-void clip_image_u8_batch_free(struct clip_image_u8_batch * batch) { delete batch; }
-void clip_image_f32_batch_free(struct clip_image_f32_batch * batch) { delete batch; }
-
-size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch) {
-    return batch->entries.size();
-}
-
-size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx) {
-    if (idx < 0 || idx >= (int)batch->entries.size()) {
-        LOG_ERR("%s: invalid index %d\n", __func__, idx);
-        return 0;
-    }
-    return batch->entries[idx]->nx();
-}
-
-size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx) {
-    if (idx < 0 || idx >= (int)batch->entries.size()) {
-        LOG_ERR("%s: invalid index %d\n", __func__, idx);
-        return 0;
-    }
-    return batch->entries[idx]->ny();
-}
-
-clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx) {
-    if (idx < 0 || idx >= (int)batch->entries.size()) {
-        LOG_ERR("%s: invalid index %d\n", __func__, idx);
-        return nullptr;
-    }
-    return batch->entries[idx].get();
-}
-
 void clip_free(clip_ctx * ctx) {
     if (ctx == nullptr) {
         return;
@@ -3189,23 +3131,11 @@ void clip_free(clip_ctx * ctx) {
     delete ctx;
 }
 
-int32_t clip_get_image_size(const struct clip_ctx * ctx) {
-    return ctx->model.hparams.image_size;
-}
-
-int32_t clip_get_patch_size(const struct clip_ctx * ctx) {
-    return ctx->model.hparams.patch_size;
-}
-
-int32_t clip_get_hidden_size(const struct clip_ctx * ctx) {
-    return ctx->model.hparams.n_embd;
-}
-
 const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
     return ctx->model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat";
 }
 
-int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
+int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
     const int n_total = clip_n_output_tokens(ctx, img);
     const auto & proj = ctx->proj_type();
@@ -3228,7 +3158,7 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 *
     return n_total;
 }
 
-int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
+int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
     const auto & proj = ctx->proj_type();
     switch (proj) {
@@ -3250,7 +3180,7 @@ int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 *
     return 1;
 }
 
-int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
+int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
 
     // for models with fixed size image, the input image is already pre-processed and resized to square
@@ -3500,16 +3430,15 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
     return n_patches;
 }
 
-bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, std::vector<float> & out_vec) {
+bool clip_image_encode(struct clip_ctx * ctx, int n_threads, const clip_image_f32 * img, std::vector<float> & out_vec) {
     clip_image_f32_batch imgs;
-    clip_image_f32_ptr img_copy(clip_image_f32_init());
-    *img_copy = *img;
+    clip_image_f32 img_copy = *img;
     imgs.entries.push_back(std::move(img_copy));
 
     return clip_image_batch_encode(ctx, n_threads, &imgs, out_vec);
 }
 
-bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs_c_ptr, std::vector<float> & out_batch_embd) {
+bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32_batch * imgs_c_ptr, std::vector<float> & out_batch_embd) {
     const clip_image_f32_batch & imgs = *imgs_c_ptr;
     int n_batch_cur = imgs.entries.size();
 
@@ -3533,8 +3462,8 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     const auto & model   = ctx->model;
     const auto & hparams = model.hparams;
 
-    const int image_size_width  = imgs.entries[0]->nx();
-    const int image_size_height = imgs.entries[0]->ny();
+    const int image_size_width  = imgs.entries[0].nx();
+    const int image_size_height = imgs.entries[0].ny();
 
     const int patch_size    = hparams.patch_size;
     const int num_patches   = ((image_size_width / patch_size) * (image_size_height / patch_size));
@@ -3572,7 +3501,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     if (!imgs.is_audio) {
         size_t nelem = 0;
         for (const auto & img : imgs.entries) {
-            nelem += img->nx() * img->ny() * 3;
+            nelem += img.nx() * img.ny() * 3;
         }
         std::vector<float> inp_raw(nelem);
 
@@ -3590,13 +3519,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         // IMPORTANT: [QWEN_VIDEO] the batch dim is currently used for temporal dim in Qwen-VL models
         // All entries must have the same spatial size (enforced by can_batch_with() during merging)
         {
-            const int nx = imgs.entries[0]->nx();
-            const int ny = imgs.entries[0]->ny();
+            const int nx = imgs.entries[0].nx();
+            const int ny = imgs.entries[0].ny();
             const int n  = nx * ny;
 
             for (int b = 0; b < n_batch_cur; b++) {
                 LOG_DBG("%s: copying image %d/%d to input buffer (nx=%d, ny=%d)\n", __func__, b+1, n_batch_cur, nx, ny);
-                const auto & buf = imgs.entries[b]->get_ro_buf();
+                const auto & buf = imgs.entries[b].get_ro_buf();
                 float * batch_entry = inp_raw.data() + b * (3*n);
                 for (int y = 0; y < ny; y++) {
                     for (int x = 0; x < nx; x++) {
@@ -3616,9 +3545,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         GGML_ASSERT(imgs.entries.size() == 1);
 
         const auto & mel_inp = imgs.entries[0];
-        const auto & buf = mel_inp->get_ro_buf();
-        const int n_step = mel_inp->nx();
-        const int n_mel  = mel_inp->ny();
+        const auto & buf = mel_inp.get_ro_buf();
+        const int n_step = mel_inp.nx();
+        const int n_mel  = mel_inp.ny();
         GGML_ASSERT((size_t)n_step * n_mel == buf.size());
 
         set_input_f32("inp_raw", buf);
@@ -4232,7 +4161,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 GGML_ASSERT(imgs.entries.size() == 1);
                 const auto & img0 = imgs.entries.front();
                 // Compute n_pos matching SSCP output: two stride-2 convs
-                int n_pos = img0->nx();
+                int n_pos = img0.nx();
                 for (int i = 0; i < 2; i++) { n_pos = (n_pos - 1) / 2 + 1; }
 
                 // Chunked local attention: blocked causal mask and RPE
@@ -4280,7 +4209,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         case PROJECTOR_TYPE_LFM2A:
             {
                 GGML_ASSERT(imgs.entries.size() == 1);
-                const auto n_frames = clip_n_output_tokens(ctx, imgs.entries.front().get());
+                const auto n_frames = clip_n_output_tokens(ctx, &imgs.entries.front());
 
                 auto d_model = 512;
                 auto seq_len = n_frames * 2 - 1;
@@ -4338,7 +4267,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
                 // reshapes as ggml_get_rows gathers. The names are set
                 // by g4v_gather() in models/granite4-vision.cpp.
                 const int patch_size  = model.hparams.patch_size;
-                const int image_side  = imgs.entries.front()->nx() / patch_size;
+                const int image_side  = imgs.entries.front().nx() / patch_size;
                 const int window_side = hparams.downsample_window_side;
                 const int query_side  = hparams.downsample_query_side;
                 const int n           = image_side / window_side;
@@ -4432,7 +4361,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
 
     // sanity check (assuming that all images in batch have the same number of tokens, so we only check the first one)
     const int n_tokens_out = embeddings->ne[1];
-    const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get());
+    const int expected_n_tokens_out = clip_n_output_tokens(ctx, &imgs.entries[0]);
     if (n_tokens_out != expected_n_tokens_out) {
         LOG_ERR("%s: expected output %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out);
         GGML_ABORT("Invalid number of output tokens");
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index 7197af856..f66f4bc3b 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -29,7 +29,6 @@ struct clip_image_size {
 };
 
 struct clip_image_f32;
-struct clip_image_u8_batch;
 struct clip_image_f32_batch;
 
 enum clip_modality {
@@ -63,41 +62,21 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
 
 void clip_free(struct clip_ctx * ctx);
 
-int32_t clip_get_image_size (const struct clip_ctx * ctx);
-int32_t clip_get_patch_size (const struct clip_ctx * ctx);
-int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
-
 // TODO: should be enum, not string
 const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 
-int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
+int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img);
 
 // for M-RoPE, this will be the number of token positions in X and Y directions
 // for other models, X will be the total number of tokens and Y will be 1
-int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
-int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
+int clip_n_output_tokens_x(const clip_ctx * ctx, const clip_image_f32 * img);
+int clip_n_output_tokens_y(const clip_ctx * ctx, const clip_image_f32 * img);
 
 // this should be equal to the embedding dimension of the text model
 int clip_n_mmproj_embd(const struct clip_ctx * ctx);
 
-struct clip_image_size      * clip_image_size_init(void);
-struct clip_image_u8        * clip_image_u8_init (void);
-struct clip_image_f32       * clip_image_f32_init(void);
-struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava
-
-void clip_image_size_free (struct clip_image_size * img_size);
-void clip_image_u8_free (struct clip_image_u8  * img);
-void clip_image_f32_free(struct clip_image_f32 * img);
-void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
-void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
-
-// use for accessing underlay data of clip_image_f32_batch
-size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
-size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
-size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
-struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
-
-bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, std::vector<float> & out_vec);
+// TODO: remove clip_image_encode() and always use batched version
+bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, const clip_image_f32 * img, std::vector<float> & out_vec);
 bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, std::vector<float> & out_batch_embd);
 
 bool clip_is_llava(const struct clip_ctx * ctx);
diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index 656d75d80..a807fefda 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -4,17 +4,26 @@
 #include <cmath>
 #include <vector>
 
-//
-// base implementation
-//
-
-void mtmd_image_preprocessor::img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst, const float mean[3], const float std[3]) {
-    dst.from_u8(src);
-    dst.normalize(mean, std);
+void mtmd_image_preproc_out::append(const clip_hparams & hparams, const clip_image_u8 & img, bool normalized) {
+    clip_image_f32 dst;
+    dst.from_u8(img);
+    if (normalized) {
+        dst.normalize(hparams.image_mean, hparams.image_std);
+    }
+    entries.push_back(std::move(dst));
 }
 
-void mtmd_image_preprocessor::img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst) {
-    dst.from_u8(src);
+void mtmd_image_preproc_out::append(const clip_hparams & hparams, const std::vector<clip_image_u8> & imgs, bool normalized) {
+    for (const auto & img : imgs) {
+        append(hparams, img, normalized);
+    }
+}
+
+void mtmd_image_preproc_out::append(const clip_hparams & hparams, clip_image_f32 & img, bool normalized) {
+    if (normalized) {
+        img.normalize(hparams.image_mean, hparams.image_std);
+    }
+    entries.push_back(std::move(img));
 }
 
 // set of tools to manipulate images
@@ -595,21 +604,17 @@ private:
 // mtmd_image_preprocessor_llava_uhd
 //
 
-bool mtmd_image_preprocessor_llava_uhd::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_llava_uhd::preprocess(const clip_image_u8 & img) {
     const clip_image_size original_size = img.get_size();
     auto const inst = get_slice_instructions(original_size);
-    std::vector<clip_image_u8_ptr> imgs = slice_image(img, inst);
-
-    for (size_t i = 0; i < imgs.size(); ++i) {
-        // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
-        clip_image_f32_ptr res(clip_image_f32_init());
-        img_u8_to_f32(*imgs[i], *res, hparams.image_mean, hparams.image_std);
-        output.entries.push_back(std::move(res));
-    }
+    std::vector<clip_image_u8> imgs = slice_image(img, inst);
 
+    mtmd_image_preproc_out output;
+    output.append(hparams, imgs, true);
     output.grid_x = inst.grid_size.width;
     output.grid_y = inst.grid_size.height;
-    return true;
+
+    return output;
 }
 
 mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_llava_uhd::get_slice_instructions(const clip_image_size & original_size) {
@@ -717,28 +722,28 @@ mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_ll
     return res;
 }
 
-std::vector<clip_image_u8_ptr> mtmd_image_preprocessor_llava_uhd::slice_image(const clip_image_u8 & img, const mtmd_image_preprocessor_llava_uhd::slice_instructions & inst, bool overview_first) {
-    std::vector<clip_image_u8_ptr> output;
+std::vector<clip_image_u8> mtmd_image_preprocessor_llava_uhd::slice_image(const clip_image_u8 & img, const mtmd_image_preprocessor_llava_uhd::slice_instructions & inst, bool overview_first) {
+    std::vector<clip_image_u8> output;
 
     // resize to overview size
-    clip_image_u8_ptr resized_img(clip_image_u8_init());
-    img_tool::resize(img, *resized_img, inst.overview_size, hparams.image_resize_algo_ov,
+    clip_image_u8 resized_img;
+    img_tool::resize(img, resized_img, inst.overview_size, hparams.image_resize_algo_ov,
                         hparams.image_pad_ov, hparams.image_pad_color_ov);
     if (overview_first) {
-        output.push_back(std::move(resized_img));
+        output.push_back(resized_img);
     }
 
     if (inst.slices.empty()) {
         // no slices, just return the resized image
         if (!overview_first) {
-            output.push_back(std::move(resized_img));
+            output.push_back(resized_img);
         }
         return output;
     }
 
     // resize to refined size
-    clip_image_u8_ptr refined_img(clip_image_u8_init());
-    img_tool::resize(img, *refined_img, inst.refined_size, hparams.image_resize_algo_rf,
+    clip_image_u8 refined_img;
+    img_tool::resize(img, refined_img, inst.refined_size, hparams.image_resize_algo_rf,
                         hparams.image_pad_rf, hparams.image_pad_color_rf);
 
     // create slices
@@ -748,13 +753,13 @@ std::vector<clip_image_u8_ptr> mtmd_image_preprocessor_llava_uhd::slice_image(co
         int w = slice.size.width;
         int h = slice.size.height;
 
-        clip_image_u8_ptr img_slice(clip_image_u8_init());
-        img_tool::crop(*refined_img, *img_slice, x, y, w, h);
+        clip_image_u8 img_slice;
+        img_tool::crop(refined_img, img_slice, x, y, w, h);
         output.push_back(std::move(img_slice));
     }
 
     if (!overview_first) {
-        output.push_back(std::move(resized_img));
+        output.push_back(resized_img);
     }
 
     return output;
@@ -871,24 +876,23 @@ clip_image_size mtmd_image_preprocessor_llava_uhd::get_best_grid(const int max_s
 // mtmd_image_preprocessor_fixed_size
 //
 
-bool mtmd_image_preprocessor_fixed_size::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_fixed_size::preprocess(const clip_image_u8 & img) {
     clip_image_u8 resized_image;
     int sz = hparams.image_size;
     img_tool::resize(img, resized_image, {sz, sz},
                         hparams.image_resize_algo,
                         hparams.image_resize_pad,
                         hparams.image_pad_color);
-    clip_image_f32_ptr img_f32(clip_image_f32_init());
-    img_u8_to_f32(resized_image, *img_f32, hparams.image_mean, hparams.image_std);
-    output.entries.push_back(std::move(img_f32));
-    return true;
+    mtmd_image_preproc_out output;
+    output.append(hparams, resized_image, true);
+    return output;
 }
 
 //
 // mtmd_image_preprocessor_dyn_size
 //
 
-bool mtmd_image_preprocessor_dyn_size::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_dyn_size::preprocess(const clip_image_u8 & img) {
     GGML_ASSERT(hparams.image_min_pixels > 0 && hparams.image_max_pixels > 0);
     clip_image_u8 resized_image;
     const clip_image_size original_size = img.get_size();
@@ -903,17 +907,16 @@ bool mtmd_image_preprocessor_dyn_size::preprocess(const clip_image_u8 & img, cli
                         hparams.image_resize_algo,
                         hparams.image_resize_pad,
                         hparams.image_pad_color);
-    clip_image_f32_ptr img_f32(clip_image_f32_init());
-    img_u8_to_f32(resized_image, *img_f32, hparams.image_mean, hparams.image_std);
-    output.entries.push_back(std::move(img_f32));
-    return true;
+    mtmd_image_preproc_out output;
+    output.append(hparams, resized_image, true);
+    return output;
 }
 
 //
 // mtmd_image_preprocessor_longest_edge
 //
 
-bool mtmd_image_preprocessor_longest_edge::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_longest_edge::preprocess(const clip_image_u8 & img) {
     GGML_ASSERT(hparams.image_longest_edge > 0);
     clip_image_u8 resized_image;
     const clip_image_size original_size = img.get_size();
@@ -927,10 +930,9 @@ bool mtmd_image_preprocessor_longest_edge::preprocess(const clip_image_u8 & img,
                         hparams.image_resize_algo,
                         hparams.image_resize_pad,
                         hparams.image_pad_color);
-    clip_image_f32_ptr img_f32(clip_image_f32_init());
-    img_u8_to_f32(resized_image, *img_f32, hparams.image_mean, hparams.image_std);
-    output.entries.push_back(std::move(img_f32));
-    return true;
+    mtmd_image_preproc_out output;
+    output.append(hparams, resized_image, true);
+    return output;
 }
 
 //
@@ -1040,7 +1042,7 @@ clip_image_size mtmd_image_preprocessor_lfm2::get_grid_layout(int height, int wi
 // mtmd_image_preprocessor_idefics3
 //
 
-bool mtmd_image_preprocessor_idefics3::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_idefics3::preprocess(const clip_image_u8 & img) {
     // The refined size has two steps:
     // 1. Resize w/ aspect-ratio preserving such that the longer side is
     //      the preprocessor longest size
@@ -1077,44 +1079,35 @@ bool mtmd_image_preprocessor_idefics3::preprocess(const clip_image_u8 & img, cli
     }
     auto imgs = slice_image(img, instructions);
 
-    // cast and normalize to f32
-    for (size_t i = 0; i < imgs.size(); ++i) {
-        // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
-        clip_image_f32_ptr res(clip_image_f32_init());
-        img_u8_to_f32(*imgs[i], *res, hparams.image_mean, hparams.image_std);
-        output.entries.push_back(std::move(res));
-    }
-
+    mtmd_image_preproc_out output;
+    output.append(hparams, imgs, true);
     output.grid_x = instructions.grid_size.width;
     output.grid_y = instructions.grid_size.height;
-    return true;
+    return output;
 }
 
 //
 // mtmd_image_preprocessor_internvl
 //
 
-bool mtmd_image_preprocessor_internvl::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_internvl::preprocess(const clip_image_u8 & img) {
     GGML_ASSERT(!hparams.image_res_candidates.empty());
     const clip_image_size original_size = img.get_size();
     auto const inst = get_slice_instructions(original_size);
-    std::vector<clip_image_u8_ptr> imgs = slice_image(img, inst, false);
+    std::vector<clip_image_u8> imgs = slice_image(img, inst, false);
 
-    for (size_t i = 0; i < imgs.size(); ++i) {
-        clip_image_f32_ptr res(clip_image_f32_init());
-        img_u8_to_f32(*imgs[i], *res, hparams.image_mean, hparams.image_std);
-        output.entries.push_back(std::move(res));
-    }
+    mtmd_image_preproc_out output;
+    output.append(hparams, imgs, true);
     output.grid_x = inst.grid_size.width;
     output.grid_y = inst.grid_size.height;
-    return true;
+    return output;
 }
 
 //
 // mtmd_image_preprocessor_deepseekocr
 //
 
-bool mtmd_image_preprocessor_deepseekocr::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr::preprocess(const clip_image_u8 & img) {
     static constexpr int native_resolutions[] = { 1024 /* base */, 1280 /* large */ };
     // TODO: support 512 (tiny) and 640 (small) once we have eval data for them
 
@@ -1137,14 +1130,11 @@ bool mtmd_image_preprocessor_deepseekocr::preprocess(const clip_image_u8 & img,
     clip_image_u8 padded;
     img_tool::resize(img, padded, {image_size, image_size}, RESIZE_ALGO_BICUBIC_PILLOW,
                      PAD_NEAREST, hparams.image_pad_color);
-
-    clip_image_f32_ptr res(clip_image_f32_init());
-    img_u8_to_f32(padded, *res, hparams.image_mean, hparams.image_std);
-    output.entries.push_back(std::move(res));
-
+    mtmd_image_preproc_out output;
+    output.append(hparams, padded, true);
     output.grid_x = 1;
     output.grid_y = 1;
-    return true;
+    return output;
 }
 
 //
@@ -1207,10 +1197,11 @@ clip_image_size mtmd_image_preprocessor_deepseekocr2::find_closest_aspect_ratio(
     return best_ratio;
 }
 
-bool mtmd_image_preprocessor_deepseekocr2::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr2::preprocess(const clip_image_u8 & img) {
     // emit 768x768 local tiles when the image is larger than a tile in either
     // dimension, then always a 1024x1024 global view. order: [tiles..., global].
 
+    mtmd_image_preproc_out output;
     const auto img_size = img.get_size();
     if (img_size.width > tile_size || img_size.height > tile_size) {
         const float           aspect_ratio  = static_cast<float>(img_size.width) / img_size.height;
@@ -1226,9 +1217,7 @@ bool mtmd_image_preprocessor_deepseekocr2::preprocess(const clip_image_u8 & img,
             for (int col = 0; col < grid.width; col++) {
                 clip_image_u8 tile;
                 img_tool::crop(refined, tile, col * tile_size, row * tile_size, tile_size, tile_size);
-                clip_image_f32_ptr res(clip_image_f32_init());
-                img_u8_to_f32(tile, *res, hparams.image_mean, hparams.image_std);
-                output.entries.push_back(std::move(res));
+                output.append(hparams, tile, true);
             }
         }
     }
@@ -1237,14 +1226,11 @@ bool mtmd_image_preprocessor_deepseekocr2::preprocess(const clip_image_u8 & img,
     clip_image_u8 padded;
     img_tool::resize(img, padded, { base_size, base_size }, RESIZE_ALGO_BICUBIC_PILLOW,
                      PAD_NEAREST, hparams.image_pad_color);
-    clip_image_f32_ptr global(clip_image_f32_init());
-    img_u8_to_f32(padded, *global, hparams.image_mean, hparams.image_std);
-    global->add_viewsep = true;
-    output.entries.push_back(std::move(global));
-
+    output.append(hparams, padded, true);
+    output.entries.back().add_viewsep = true;
     output.grid_x = 1;
     output.grid_y = 1;
-    return true;
+    return output;
 }
 
 //
@@ -1260,7 +1246,8 @@ void mtmd_image_preprocessor_step3vl::img_u8_resize_bilinear_to_f32(
         const float std[3]) {
     const auto src_size = src.get_size();
     if (src_size.width == target_width && src_size.height == target_height) {
-        img_u8_to_f32(src, dst, mean, std);
+        dst.from_u8(src);
+        dst.normalize(mean, std);
         return;
     }
 
@@ -1455,24 +1442,25 @@ mtmd_image_preprocessor_step3vl::slice_instructions mtmd_image_preprocessor_step
     return instructions;
 }
 
-bool mtmd_image_preprocessor_step3vl::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_step3vl::preprocess(const clip_image_u8 & img) {
     clip_image_u8 prepared = prepare_image(img, hparams);
     const auto instructions = build_slice_instructions(hparams, prepared.get_size());
 
-    clip_image_f32_ptr overview_f32(clip_image_f32_init());
+    mtmd_image_preproc_out output;
+    clip_image_f32 overview_f32;
     img_u8_resize_bilinear_to_f32(
         prepared,
-        *overview_f32,
+        overview_f32,
         hparams.image_size,
         hparams.image_size,
         hparams.image_mean,
         hparams.image_std);
-    output.entries.push_back(std::move(overview_f32));
+    output.append(hparams, overview_f32, false);
 
     if (instructions.slices.empty()) {
         output.grid_x = 0;
         output.grid_y = 0;
-        return true;
+        return output;
     }
 
     clip_image_u8 img_for_crop = prepared;
@@ -1488,28 +1476,28 @@ bool mtmd_image_preprocessor_step3vl::preprocess(const clip_image_u8 & img, clip
         // If the requested patch extends past the source image, pad the out-of-bounds area with black.
         clip_image_u8 patch = crop_with_black_padding(img_for_crop, slice.x, slice.y, slice.size.width, slice.size.height);
 
-        clip_image_f32_ptr patch_f32(clip_image_f32_init());
+        clip_image_f32 patch_f32;
         img_u8_resize_bilinear_to_f32(
             patch,
-            *patch_f32,
+            patch_f32,
             crop_size,
             crop_size,
             hparams.image_mean,
             hparams.image_std);
-        output.entries.push_back(std::move(patch_f32));
+        output.append(hparams, patch_f32, false);
     }
 
     output.grid_x = instructions.grid_size.width;
     output.grid_y = instructions.grid_size.height;
 
-    return true;
+    return output;
 }
 
 //
 // mtmd_image_preprocessor_youtuvl
 //
 
-bool mtmd_image_preprocessor_youtuvl::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
+mtmd_image_preproc_out mtmd_image_preprocessor_youtuvl::preprocess(const clip_image_u8 & img) {
     const int patch_size = hparams.patch_size;   // typically 16
     const int merge_size = hparams.n_merge;      // typically 2
     const int align_size = patch_size * merge_size;  // 32
@@ -1553,29 +1541,22 @@ bool mtmd_image_preprocessor_youtuvl::preprocess(const clip_image_u8 & img, clip
     clip_image_u8 resized;
     img_tool::resize(img, resized, new_size, hparams.image_resize_algo, hparams.image_resize_pad);
 
-    // Normalize to float32
-    clip_image_f32_ptr img_f32(clip_image_f32_init());
-    img_u8_to_f32(resized, *img_f32, hparams.image_mean, hparams.image_std);
-    // Add to results
-    output.entries.push_back(std::move(img_f32));
-    return true;
+    mtmd_image_preproc_out output;
+    output.append(hparams, resized, true);
+    return output;
 }
 
-bool mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) {
-    // call super class preprocessor
-    bool ok = mtmd_image_preprocessor_llava_uhd::preprocess(img, output);
-    if (!ok) {
-        return false;
-    }
+mtmd_image_preproc_out mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img) {
+    auto output = mtmd_image_preprocessor_llava_uhd::preprocess(img);
     if (output.entries.size() == 1) {
         // Single-tile (overview only): append one newline row.
-        output.entries[0]->add_newline = true;
+        output.entries[0].add_newline = true;
     } else {
         // Multi-tile: overview gets no newline, grid tiles get one.
-        output.entries[0]->add_newline = false;
+        output.entries[0].add_newline = false;
         for (size_t i = 1; i < output.entries.size(); ++i) {
-            output.entries[i]->add_newline = true;
+            output.entries[i].add_newline = true;
         }
     }
-    return true;
+    return output;
 }
diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h
index 3ddbec2e4..8819a135a 100644
--- a/tools/mtmd/mtmd-image.h
+++ b/tools/mtmd/mtmd-image.h
@@ -8,6 +8,16 @@
 
 #define MTMD_INTERNAL_HEADER
 
+struct mtmd_image_preproc_out {
+    std::vector<clip_image_f32> entries;
+    // grid size is required for llava-uhd style models
+    int grid_x = 0;
+    int grid_y = 0;
+    void append(const clip_hparams & hparams, const clip_image_u8 & img, bool normalized = true);
+    void append(const clip_hparams & hparams, const std::vector<clip_image_u8> & imgs, bool normalized = true);
+    void append(const clip_hparams & hparams, clip_image_f32 & img, bool normalized = true);
+};
+
 // base class, models must inherit from this class
 struct mtmd_image_preprocessor {
     const clip_hparams & hparams;
@@ -15,10 +25,7 @@ struct mtmd_image_preprocessor {
     mtmd_image_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {}
 
     virtual ~mtmd_image_preprocessor() = default;
-    virtual bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) = 0;
-
-    void img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst, const float mean[3], const float std[3]);
-    void img_u8_to_f32(const clip_image_u8 & src, clip_image_f32 & dst);
+    virtual mtmd_image_preproc_out preprocess(const clip_image_u8 & img) = 0;
 };
 
 /**
@@ -42,7 +49,7 @@ struct mtmd_image_preprocessor {
  */
 struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
     mtmd_image_preprocessor_llava_uhd(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 
     struct slice_coordinates {
         int x;
@@ -60,7 +67,7 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
     // LFM2 override this function to implement its custom slicing logic
     virtual slice_instructions get_slice_instructions(const clip_image_size & original_size);
 
-    std::vector<clip_image_u8_ptr> slice_image(const clip_image_u8 & img, const slice_instructions & inst, bool overview_first = true);
+    std::vector<clip_image_u8> slice_image(const clip_image_u8 & img, const slice_instructions & inst, bool overview_first = true);
 
 private:
     clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
@@ -91,7 +98,7 @@ private:
 // downscale or upscale the input image to fixed size
 struct mtmd_image_preprocessor_fixed_size : mtmd_image_preprocessor {
     mtmd_image_preprocessor_fixed_size(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 // resize image to multiple of patch_size*n_merge, while preserving aspect ratio
@@ -99,13 +106,13 @@ struct mtmd_image_preprocessor_fixed_size : mtmd_image_preprocessor {
 // this is used by models with native support for dynamic image size, for example: Qwen-VL, Pixtral, Kimi-VL, etc
 struct mtmd_image_preprocessor_dyn_size : mtmd_image_preprocessor {
     mtmd_image_preprocessor_dyn_size(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 // similar to mtmd_image_preprocessor_dyn_size, but resize the image to have longest edge equal to hparams.image_longest_edge, while preserving aspect ratio
 struct mtmd_image_preprocessor_longest_edge : mtmd_image_preprocessor {
     mtmd_image_preprocessor_longest_edge(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 // custom llava-uhd slicing logic for LFM2
@@ -131,17 +138,17 @@ private:
 
 struct mtmd_image_preprocessor_idefics3 : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_idefics3(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 struct mtmd_image_preprocessor_internvl : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_internvl(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 struct mtmd_image_preprocessor_deepseekocr : mtmd_image_preprocessor {
     mtmd_image_preprocessor_deepseekocr(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 // DeepSeek-OCR-2: a 1024x1024 global view, plus InternVL-style 768x768 local
@@ -153,7 +160,7 @@ struct mtmd_image_preprocessor_deepseekocr2 : mtmd_image_preprocessor {
     static constexpr int max_tiles = 6;
 
     mtmd_image_preprocessor_deepseekocr2(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 
 private:
     static std::vector<clip_image_size> get_target_ratios();
@@ -168,7 +175,7 @@ private:
 // ref: https://huggingface.co/stepfun-ai/Step3-VL-10B/blob/main/processing_step3.py
 struct mtmd_image_preprocessor_step3vl : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_step3vl(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
     static slice_instructions build_slice_instructions(const clip_hparams & params, const clip_image_size & prepared_size);
 
 private:
@@ -195,11 +202,11 @@ private:
 
 struct mtmd_image_preprocessor_youtuvl : mtmd_image_preprocessor {
     mtmd_image_preprocessor_youtuvl(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
 // similar to llava_uhd, but has add_newline
 struct mtmd_image_preprocessor_granite : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_granite(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
-    bool preprocess(const clip_image_u8 & img, clip_image_f32_batch & output) override;
+    mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index 0834e4938..f9ee021dd 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -114,7 +114,7 @@ struct mtmd_image_tokens {
     // true if one of entries in batch_f32 is a placeholder
     bool is_placeholder() const {
         for (const auto & entry : batch_f32.entries) {
-            if (entry->is_placeholder()) {
+            if (entry.is_placeholder()) {
                 return true;
             }
         }
@@ -147,7 +147,7 @@ struct mtmd_audio_tokens {
     // true if one of entries in batch_f32 is a placeholder
     bool is_placeholder() const {
         for (const auto & entry : batch_f32.entries) {
-            if (entry->is_placeholder()) {
+            if (entry.is_placeholder()) {
                 return true;
             }
         }
@@ -1050,7 +1050,7 @@ struct mtmd_tokenizer {
 
             // TODO @ngxson : this is quite hacky because preprocessor only support batch with one single element, that need to be fixed in the future (e.g. by changing the preprocessor interface always take single input)
 
-            clip_image_f32_batch batch_f32;
+            mtmd_image_preproc_out preproc_out;
 
             for (const auto * bmp : bitmaps) {
                 // sanity check
@@ -1063,42 +1063,40 @@ struct mtmd_tokenizer {
                 }
 
                 // convert mtmd_bitmap to clip_image_u8
-                clip_image_u8_ptr img_u8(clip_image_u8_init());
-                img_u8->set_size(
+                clip_image_u8 img_u8;
+                img_u8.set_size(
                     {(int)bmp->nx, (int)bmp->ny},
                     bmp->is_placeholder());
-                img_u8->cpy_buf(bmp->get_ro_buf());
+                img_u8.cpy_buf(bmp->get_ro_buf());
 
                 // preprocess image
-                clip_image_f32_batch tmp_batch;
-                bool ok = ctx->image_preproc->preprocess(*img_u8, tmp_batch);
-                if (!ok) {
-                    LOG_ERR("Unable to preprocess image\n");
-                    return 2;
-                }
+                mtmd_image_preproc_out tmp_preproc_out = ctx->image_preproc->preprocess(img_u8);
 
-                // move entries and grid dimensions to the "global" batch_f32
-                for (auto & entry : tmp_batch.entries) {
-                    batch_f32.entries.emplace_back(std::move(entry));
+                // move entries and grid dimensions to the "global" preproc_out
+                for (auto & entry : tmp_preproc_out.entries) {
+                    preproc_out.entries.emplace_back(std::move(entry));
                 }
 
                 // for llava-uhd style, we need to handle grid too
-                // we don't care about overwriting these values for now because llama-uhd doesn't support batching anyway
-                batch_f32.grid_x = tmp_batch.grid_x;
-                batch_f32.grid_y = tmp_batch.grid_y;
+                // we don't care about overwriting these values for now because the case where bitmaps.size() > 1 is only for frame merging (qwen-vl), not supported by llava-uhd
+                if (tmp_preproc_out.grid_x > 0 && tmp_preproc_out.grid_y > 0) {
+                    GGML_ASSERT(bitmaps.size() == 1);
+                    preproc_out.grid_x = tmp_preproc_out.grid_x;
+                    preproc_out.grid_y = tmp_preproc_out.grid_y;
+                }
             }
 
             // handle llava-uhd style preprocessing
-            const bool has_tiling_grid = batch_f32.grid_x > 0 && batch_f32.grid_y > 0;
+            const bool has_tiling_grid = preproc_out.grid_x > 0 && preproc_out.grid_y > 0;
             if (has_tiling_grid) {
                 // [QWEN_VIDEO] we do not support "frame merging" for llama-uhd style, so no batching for now
                 GGML_ASSERT(bitmaps.size() == 1);
 
-                const int n_col = batch_f32.grid_x;
-                const int n_row = batch_f32.grid_y;
+                const int n_col = preproc_out.grid_x;
+                const int n_row = preproc_out.grid_y;
                 // split batch into chunks of single images
-                // NOTE: batch_f32 will be invalidated after this call
-                auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmaps[0]->id);
+                // NOTE: preproc_out will be invalidated after this call
+                auto chunks = split_batch_to_chunk(std::move(preproc_out), bitmaps[0]->id);
                 GGML_ASSERT(chunks.size() > 0);
 
                 auto ov_chunk = std::move(chunks.front());
@@ -1150,8 +1148,8 @@ struct mtmd_tokenizer {
             } else {
 
                 size_t n_tokens = 0;
-                for (const auto & e : batch_f32.entries) {
-                    n_tokens += clip_n_output_tokens(ctx->ctx_v, e.get());
+                for (auto & e : preproc_out.entries) {
+                    n_tokens += clip_n_output_tokens(ctx->ctx_v, &e);
                     if (clip_model_n_temporal_merge(ctx->ctx_v) == 2) {
                         // [QWEN_VIDEO] pair input is merged to the same embd, so only count as one image
                         break;
@@ -1165,8 +1163,8 @@ struct mtmd_tokenizer {
 
                 if (mtmd_decode_use_mrope(ctx)) {
                     // for Qwen2VL, we need this information for M-RoPE decoding positions
-                    image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_v, batch_f32.entries[0].get());
-                    image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_v, batch_f32.entries[0].get());
+                    image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_v, &preproc_out.entries[0]);
+                    image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_v, &preproc_out.entries[0]);
                 } else {
                     // other models, we only need the total number of tokens
                     image_tokens->nx = n_tokens;
@@ -1181,6 +1179,12 @@ struct mtmd_tokenizer {
                     image_tokens->image_idx = n_images_added;
                     GGML_ASSERT(n_tokens == (size_t)image_tokens->n_tokens());
                 }
+
+                clip_image_f32_batch batch_f32;
+                batch_f32.is_audio = false;
+                batch_f32.entries = std::move(preproc_out.entries);
+                // do NOT use preproc_out from this point on, it's moved
+
                 image_tokens->batch_f32 = std::move(batch_f32);
                 image_tokens->id = bitmaps[0]->id; // optional
 
@@ -1260,13 +1264,13 @@ struct mtmd_tokenizer {
             for (auto & mel_spec : mel_spec_chunks) {
                 const bool is_placeholder = mel_spec.data.empty();
 
-                clip_image_f32_ptr mel_f32(clip_image_f32_init());
-                mel_f32->set_size(
+                clip_image_f32 mel_f32;
+                mel_f32.set_size(
                     {mel_spec.n_len, mel_spec.n_mel},
                     is_placeholder, /* is_audio */ true);
-                mel_f32->cpy_buf(mel_spec.data);
+                mel_f32.cpy_buf(mel_spec.data);
 
-                size_t n_tokens = clip_n_output_tokens(ctx->ctx_a, mel_f32.get());
+                size_t n_tokens = clip_n_output_tokens(ctx->ctx_a, &mel_f32);
 
                 clip_image_f32_batch batch_f32;
                 batch_f32.is_audio = true;
@@ -1296,12 +1300,12 @@ struct mtmd_tokenizer {
         return 0;
     }
 
-    std::vector<mtmd_input_chunk> split_batch_to_chunk(clip_image_f32_batch && batch_f32, const std::string & id) {
+    std::vector<mtmd_input_chunk> split_batch_to_chunk(mtmd_image_preproc_out && preproc_out, const std::string & id) {
         std::vector<mtmd_input_chunk> chunks;
 
-        for (auto & entry : batch_f32.entries) {
+        for (auto & entry : preproc_out.entries) {
             mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
-            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, entry.get());
+            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, &entry);
             image_tokens->ny = 1;
             image_tokens->batch_f32.entries.push_back(std::move(entry));
             image_tokens->id = id;
@@ -1406,16 +1410,16 @@ static int32_t mtmd_encode_impl(mtmd_context * ctx, const mtmd_image_tokens * im
         // e.g., DeepSeek-OCR-2: 144 per tile views, 257 for the global view
         size_t offset = 0;
         for (size_t i = 0; i < entries.size(); i++) {
-            if (entries[i]->is_placeholder()) {
+            if (entries[i].is_placeholder()) {
                 LOG_ERR("%s: image tokens batch entry %zu is placeholder\n", __func__, i);
                 return 1;
             }
-            int n_tokens_per_image = clip_n_output_tokens(ctx_clip, entries[i].get());
+            int n_tokens_per_image = clip_n_output_tokens(ctx_clip, &entries[i]);
             std::vector<float> tmp_embd((size_t)n_tokens_per_image * n_embd_out);
             bool ok_i = clip_image_encode(
                 ctx_clip,
                 ctx->n_threads,
-                entries[i].get(),
+                &entries[i],
                 tmp_embd);
             if (!ok_i) {
                 LOG_ERR("%s: failed to encode image %zu\n", __func__, i);
@@ -2063,16 +2067,18 @@ void mtmd_debug_preprocess_image(mtmd_context * ctx, const std::vector<uint8_t>
     clip_image_u8 img_u8;
     img_u8.set_size({nx, ny}, false);
     img_u8.cpy_buf(rgb_values);
-    clip_image_f32_batch batch_f32;
     GGML_ASSERT(ctx->image_preproc != nullptr);
-    bool ok = ctx->image_preproc->preprocess(img_u8, batch_f32);
-    if (!ok) {
-        LOG_ERR("%s: failed to preprocess image\n", __func__);
-        return;
+    mtmd_image_preproc_out preproc_out = ctx->image_preproc->preprocess(img_u8);
+
+    clip_image_f32_batch batch_f32;
+    batch_f32.is_audio = false;
+    for (auto & entry : preproc_out.entries) {
+        batch_f32.entries.push_back(std::move(entry));
     }
+
     LOG_INF("%s: preprocessed image to batch_f32 with %d entries\n", __func__, (int)batch_f32.entries.size());
     for (size_t i = 0; i < batch_f32.entries.size(); i++) {
-        LOG_INF("%s: entry %zu has nx=%d, ny=%d\n", __func__, i, batch_f32.entries[i]->nx(), batch_f32.entries[i]->ny());
+        LOG_INF("%s: entry %zu has nx=%d, ny=%d\n", __func__, i, batch_f32.entries[i].nx(), batch_f32.entries[i].ny());
         // TODO: better way to dump entry content?
     }
 }

From 968c43891abb1def4db76f25713b558629fb92b1 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 12:15:46 +0200
Subject: [PATCH 039/292] server: fix router args not being forwarded to child
 instances (#24760)

---
 tools/server/server-models.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index ff9a0df12..cc7184165 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -351,6 +351,12 @@ void server_models::load_models() {
         source_map[name] = SERVER_MODEL_SOURCE_PRESET;
     }
 
+    // overlay router's own CLI args on top of every model preset so that
+    // e.g. `llama-server --temp 0` is honoured by all child processes
+    for (auto & [name, preset] : final_presets) {
+        preset.merge(base_preset);
+    }
+
     auto get_source = [&](const std::string & name) {
         return source_map.count(name) ? source_map.at(name) : SERVER_MODEL_SOURCE_PRESET;
     };

From 552258c5350dcf86c6a1e9a2dcd45f06076a4667 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 12:45:23 +0200
Subject: [PATCH 040/292] server: (router) rework -hf preset repo (#24739)

* server: temporary remove HF remote preset

* rework remove preset.ini support

* rm unused get_remote_preset_whitelist()

* print warning

* add docs

* rm stray file
---
 common/arg.cpp          | 112 ++++++++++++----------------------------
 common/common.h         |   9 ++--
 common/download.cpp     |  53 +++++++++++++------
 common/download.h       |   1 +
 common/preset.cpp       |  50 +-----------------
 common/preset.h         |   2 +-
 docs/preset.md          |  74 +++++++++++++-------------
 tools/server/server.cpp |   6 +++
 8 files changed, 120 insertions(+), 187 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 8382c1d85..bd4b113d1 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -285,58 +285,15 @@ static std::string clean_file_name(const std::string & fname) {
     return clean_fname;
 }
 
-static bool common_params_handle_remote_preset(common_params & params, llama_example ex) {
-    GGML_ASSERT(!params.model.hf_repo.empty());
-
-    // the returned hf_repo is without tag
-    auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo);
-
-    // "latest" tag (default if not specified) is translated to "default" preset
-    if (hf_tag == "latest") {
-        hf_tag = "default";
-    }
-
-    std::string model_endpoint = common_get_model_endpoint();
-    auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
-
-    // prepare local path for caching
-    auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
-    auto preset_path = fs_get_cache_file(preset_fname);
-    common_download_opts opts;
-    opts.bearer_token = params.hf_token;
-    opts.offline = params.offline;
-
-    LOG_TRC("%s: looking for remote preset at %s\n", __func__, preset_url.c_str());
-    const int status = common_download_file_single(preset_url, preset_path, opts);
-    const bool has_preset = status >= 200 && status < 400;
-
-    // remote preset is optional, so we don't error out if not found
-    if (has_preset) {
-        LOG_TRC("%s: applying remote preset from %s\n", __func__, preset_url.c_str());
-        common_preset_context ctx(ex, /* only_remote_allowed */ true);
-        common_preset global;
-        auto remote_presets = ctx.load_from_ini(preset_path, global);
-        remote_presets = ctx.cascade(global, remote_presets);
-        if (remote_presets.find(hf_tag) != remote_presets.end()) {
-            common_preset preset = remote_presets.at(hf_tag);
-            LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline
-            preset.apply_to_params(params);
-        } else {
-            throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section");
-        }
-    } else {
-        LOG_TRC("%s: no remote preset found, skipping\n", __func__);
-    }
-
-    return has_preset;
-}
-
 struct handle_model_result {
     bool found_mmproj = false;
     common_params_model mmproj;
 
     bool found_mtp = false;
     common_params_model mtp;
+
+    bool found_preset = false;
+    std::string preset_path;
 };
 
 static handle_model_result common_params_handle_model(struct common_params_model & model,
@@ -355,6 +312,12 @@ static handle_model_result common_params_handle_model(struct common_params_model
         common_download_opts hf_opts = opts;
         auto download_result = common_download_model(model, hf_opts);
 
+        if (!download_result.preset_path.empty()) {
+            result.found_preset = true;
+            result.preset_path = download_result.preset_path;
+            return result; // skip everything else if preset.ini is used
+        }
+
         if (download_result.model_path.empty()) {
             throw std::runtime_error("failed to download model from Hugging Face");
         }
@@ -454,6 +417,17 @@ bool common_params_handle_models(common_params & params, llama_example curr_ex)
 
     try {
         auto res = common_params_handle_model(params.model, opts);
+        if (res.found_preset) {
+            if (!params.models_preset.empty()) {
+                throw std::invalid_argument("cannot use both --models-preset and -hf with a preset.ini file");
+            }
+            // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file
+            params.models_preset_hf = params.model.hf_repo; // only for showing a warning
+            params.models_preset    = res.preset_path;
+            params.model = common_params_model{}; // make sure to clear model, so server starts in router mode
+            return true;
+        }
+
         if (params.no_mmproj) {
             params.mmproj = {};
         } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
@@ -601,30 +575,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     // parse the first time to get -hf option (used for remote preset)
     parse_cli_args();
 
-    // export_graph_ops loads only metadata
-    const bool skip_model_download = ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
-
-    // maybe handle remote preset
-    if (!params.model.hf_repo.empty() && !skip_model_download) {
-        std::string cli_hf_repo = params.model.hf_repo;
-        bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex);
-
-        // special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value)
-        // this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs)
-        std::string preset_hf_repo = params.model.hf_repo;
-        bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo;
-
-        if (has_preset) {
-            // re-parse CLI args to override preset values
-            parse_cli_args();
-        }
-
-        // preserve hf_repo from preset if needed
-        if (preset_has_hf_repo) {
-            params.model.hf_repo = preset_hf_repo;
-        }
-    }
-
     postprocess_cpu_params(params.cpuparams,       nullptr);
     postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
 
@@ -635,15 +585,21 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    // handle model and download
-    if (!skip_model_download) {
-        common_params_handle_models(params, ctx_arg.ex);
-    }
+    // export_graph_ops loads only metadata
+    const bool skip_model_download = ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
 
-    // model is required (except for server)
-    // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !skip_model_download && !params.usage && !params.completion) {
-        throw std::invalid_argument("error: --model is required\n");
+    if (!skip_model_download) {
+        // handle model and download
+        common_params_handle_models(params, ctx_arg.ex);
+
+        // model is required (except for server)
+        // TODO @ngxson : maybe show a list of available models in CLI in this case
+        if (params.model.path.empty()
+                && ctx_arg.ex != LLAMA_EXAMPLE_SERVER
+                && !params.usage
+                && !params.completion) {
+            throw std::invalid_argument("error: --model is required\n");
+        }
     }
 
     if (params.escape) {
diff --git a/common/common.h b/common/common.h
index 0b284cbb3..040b9cf23 100644
--- a/common/common.h
+++ b/common/common.h
@@ -642,10 +642,11 @@ struct common_params {
     std::vector<std::string> server_tools;
 
     // router server configs
-    std::string models_dir    = ""; // directory containing models for the router server
-    std::string models_preset = ""; // directory containing model presets for the router server
-    int models_max = 4;             // maximum number of models to load simultaneously
-    bool models_autoload = true;    // automatically load models when requested via the router server
+    std::string models_dir    = "";     // directory containing models for the router server
+    std::string models_preset = "";     // directory containing model presets for the router server
+    int models_max = 4;                 // maximum number of models to load simultaneously
+    bool models_autoload = true;        // automatically load models when requested via the router server
+    std::string models_preset_hf = "";  // show a warning about remote presets on router loaded (if not empty)
 
     bool log_json = false;
 
diff --git a/common/download.cpp b/common/download.cpp
index c3c8ff49b..f32046275 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -696,6 +696,7 @@ struct hf_plan {
     hf_cache::hf_files model_files;
     hf_cache::hf_file mmproj;
     hf_cache::hf_file mtp;
+    hf_cache::hf_file preset; // if set, only this file is downloaded
 };
 
 static hf_plan get_hf_plan(const common_params_model  & model,
@@ -717,6 +718,14 @@ static hf_plan get_hf_plan(const common_params_model  & model,
         return plan;
     }
 
+    // if preset.ini exists in the repo root, download only that file
+    for (const auto & f : all) {
+        if (f.path == "preset.ini") {
+            plan.preset = f;
+            return plan;
+        }
+    }
+
     hf_cache::hf_file primary;
 
     if (!model.hf_file.empty()) {
@@ -794,14 +803,19 @@ common_download_model_result common_download_model(const common_params_model  &
 
     if (is_hf) {
         hf = get_hf_plan(model, opts, download_mmproj, download_mtp);
-        for (const auto & f : hf.model_files) {
-            tasks.push_back({f.url, f.local_path});
-        }
-        if (!hf.mmproj.path.empty()) {
-            tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
-        }
-        if (!hf.mtp.path.empty()) {
-            tasks.push_back({hf.mtp.url, hf.mtp.local_path});
+        if (!hf.preset.path.empty()) {
+            // if preset.ini exists, only download that file alone
+            tasks.push_back({hf.preset.url, hf.preset.local_path});
+        } else {
+            for (const auto & f : hf.model_files) {
+                tasks.push_back({f.url, f.local_path});
+            }
+            if (!hf.mmproj.path.empty()) {
+                tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
+            }
+            if (!hf.mtp.path.empty()) {
+                tasks.push_back({hf.mtp.url, hf.mtp.local_path});
+            }
         }
     } else if (!model.url.empty()) {
         tasks = get_url_tasks(model);
@@ -835,17 +849,22 @@ common_download_model_result common_download_model(const common_params_model  &
     }
 
     if (is_hf) {
-        for (const auto & f : hf.model_files) {
-            hf_cache::finalize_file(f);
-        }
-        result.model_path = hf.primary.final_path;
+        if (!hf.preset.path.empty()) {
+            // if preset.ini is used, do not set other paths
+            result.preset_path = hf_cache::finalize_file(hf.preset);
+        } else {
+            for (const auto & f : hf.model_files) {
+                hf_cache::finalize_file(f);
+            }
+            result.model_path = hf.primary.final_path;
 
-        if (!hf.mmproj.path.empty()) {
-            result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
-        }
+            if (!hf.mmproj.path.empty()) {
+                result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
+            }
 
-        if (!hf.mtp.path.empty()) {
-            result.mtp_path = hf_cache::finalize_file(hf.mtp);
+            if (!hf.mtp.path.empty()) {
+                result.mtp_path = hf_cache::finalize_file(hf.mtp);
+            }
         }
     } else {
         result.model_path = model.path;
diff --git a/common/download.h b/common/download.h
index 237179764..8dbf07836 100644
--- a/common/download.h
+++ b/common/download.h
@@ -63,6 +63,7 @@ struct common_download_model_result {
     std::string model_path;
     std::string mmproj_path;
     std::string mtp_path;
+    std::string preset_path;
 };
 
 // throw if the file is missing or invalid (e.g. ETag check failed)
diff --git a/common/preset.cpp b/common/preset.cpp
index 51ea984d8..f0cc1fa1a 100644
--- a/common/preset.cpp
+++ b/common/preset.cpp
@@ -16,48 +16,6 @@ static std::string rm_leading_dashes(const std::string & str) {
     return str.substr(pos);
 }
 
-// only allow a subset of args for remote presets for security reasons
-// do not add more args unless absolutely necessary
-// args that output to files are strictly prohibited
-static std::set<std::string> get_remote_preset_whitelist(const std::map<std::string, common_arg> & key_to_opt) {
-    static const std::set<std::string> allowed_options = {
-        "model-url",
-        "hf-repo",
-        "hf-repo-draft",
-        "hf-repo-v", // vocoder
-        "hf-file-v", // vocoder
-        "mmproj-url",
-        "pooling",
-        "jinja",
-        "batch-size",
-        "ubatch-size",
-        "cache-reuse",
-        "chat-template-kwargs",
-        "mmap",
-        // note: sampling params are automatically allowed by default
-        // negated args will be added automatically if the positive arg is specified above
-    };
-
-    std::set<std::string> allowed_keys;
-
-    for (const auto & it : key_to_opt) {
-        const std::string & key = it.first;
-        const common_arg & opt = it.second;
-        if (allowed_options.find(key) != allowed_options.end() || opt.is_sampling) {
-            allowed_keys.insert(key);
-            // also add variant keys (args without leading dashes and env vars)
-            for (const auto & arg : opt.get_args()) {
-                allowed_keys.insert(rm_leading_dashes(arg));
-            }
-            for (const auto & env : opt.get_env()) {
-                allowed_keys.insert(env);
-            }
-        }
-    }
-
-    return allowed_keys;
-}
-
 std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
     std::vector<std::string> args;
 
@@ -300,16 +258,10 @@ static std::string parse_bool_arg(const common_arg & arg, const std::string & ke
     return value;
 }
 
-common_preset_context::common_preset_context(llama_example ex, bool only_remote_allowed)
+common_preset_context::common_preset_context(llama_example ex)
         : ctx_params(common_params_parser_init(default_params, ex)) {
     common_params_add_preset_options(ctx_params.options);
     key_to_opt = get_map_key_opt(ctx_params);
-
-    // setup allowed keys if only_remote_allowed is true
-    if (only_remote_allowed) {
-        filter_allowed_keys = true;
-        allowed_keys = get_remote_preset_whitelist(key_to_opt);
-    }
 }
 
 common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {
diff --git a/common/preset.h b/common/preset.h
index 06f829c3e..52935ebde 100644
--- a/common/preset.h
+++ b/common/preset.h
@@ -60,7 +60,7 @@ struct common_preset_context {
     std::set<std::string> allowed_keys;
 
     // if only_remote_allowed is true, only accept whitelisted keys
-    common_preset_context(llama_example ex, bool only_remote_allowed = false);
+    common_preset_context(llama_example ex);
 
     // load presets from INI file
     common_presets load_from_ini(const std::string & path, common_preset & global) const;
diff --git a/docs/preset.md b/docs/preset.md
index d49fb0a1a..85762a420 100644
--- a/docs/preset.md
+++ b/docs/preset.md
@@ -8,55 +8,53 @@ The INI preset feature, introduced in [PR#17859](https://github.com/ggml-org/lla
 
 When running multiple models on the server (router mode), INI preset files can be used to configure model-specific parameters. Please refer to the [server documentation](../tools/server/README.md) for more details.
 
-### Using a Remote Preset
+### Using a Hugging Face Preset
 
-> [!NOTE]
+> [!IMPORTANT]
 >
-> This feature is currently only supported via the `-hf` option.
+> Please only use presets that you can trust! Unknown presets may be unsafe
 
-For GGUF models hosted on Hugging Face, you can include a `preset.ini` file in the root directory of the repository to define specific configurations for that model.
+You can push your preset to Hugging Face Hub and share with other users by:
+1. Creating an empty model repository on Hugging Face
+2. Creating a `preset.ini` file in the root directory of the repository
 
-Example:
+Example of a `preset.ini`:
 
 ```ini
-hf-repo-draft = username/my-draft-model-GGUF
-temp = 0.5
-top-k = 20
-top-p = 0.95
+[*]
+ctx-size             = 0
+mmap                 = 1
+kv-unified           = 1
+parallel             = 4
+spec-default         = 1
+
+[Qwen3.5-4B]
+hf                   = unsloth/Qwen3.5-4B-GGUF:Q4_K_M
+ctx-size             = 262144
+batch-size           = 2048
+ubatch-size          = 2048
+top-p                = 1.0
+top-k                = 0
+min-p                = 0.01
+temp                 = 1.0
+
+[gpt-oss-120b-hf]
+hf                   = ggml-org/gpt-oss-120b-GGUF
+ctx-size             = 262144
+batch-size           = 2048
+ubatch-size          = 2048
+top-p                = 1.0
+top-k                = 0
+min-p                = 0.01
+temp                 = 1.0
+chat-template-kwargs = {"reasoning_effort": "high"}
 ```
 
-For security reasons, only certain options are allowed. Please refer to [preset.cpp](../common/preset.cpp) for the complete list of permitted options.
-
-Example usage:
-
-Assuming your repository `username/my-model-with-preset` contains a `preset.ini` with the configuration above:
-
-```sh
-llama-cli -hf username/my-model-with-preset
-
-# This is equivalent to:
-llama-cli -hf username/my-model-with-preset \
-  --hf-repo-draft username/my-draft-model-GGUF \
-  --temp 0.5 \
-  --top-k 20 \
-  --top-p 0.95
-```
-
-You can also override preset arguments by specifying them on the command line:
+The preset will be loaded similarly to the `--models-preset` option. Therefore, you can also override certain params via CLI arguments:
 
 ```sh
 # Force temp = 0.1, overriding the preset value
-llama-cli -hf username/my-model-with-preset --temp 0.1
-```
-
-If you want to define multiple preset configurations for one or more GGUF models, you can create a blank HF repo for each preset. Each HF repo should contain a `preset.ini` file that references the actual model(s):
-
-```ini
-hf-repo = user/my-model-main
-hf-repo-draft = user/my-model-draft
-temp = 0.8
-ctx-size = 1024
-; (and other configurations)
+llama-cli -hf username/my-preset --temp 0.1
 ```
 
 ### Named presets
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 0364d7d3b..78ab0318c 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -349,6 +349,12 @@ int llama_server(int argc, char ** argv) {
         SRV_INF("router server is listening on %s\n", ctx_http.listening_address.c_str());
         SRV_WRN("%s", "NOTE: router mode is experimental\n");
         SRV_WRN("%s", "      it is not recommended to use this mode in untrusted environments\n");
+
+        if (!params.models_preset_hf.empty()) {
+            SRV_WRN(      "NOTE: using preset.ini from HF repo '%s'\n", params.models_preset_hf.c_str());
+            SRV_WRN("%s", "      please only use presets that you can trust! Unknown presets may be unsafe\n");
+        }
+
         if (ctx_http.thread.joinable()) {
             ctx_http.thread.join(); // keep the main thread alive
         }

From 10786217e9d40c848ac0133cbe9c5f22a52421bb Mon Sep 17 00:00:00 2001
From: Anuj Attri <anujattri01@gmail.com>
Date: Thu, 18 Jun 2026 06:49:14 -0400
Subject: [PATCH 041/292] server : return HTTP 400 on invalid grammar (#24144)
 (#24154)

Throw on grammar parse failure so the server returns HTTP 400
instead of silently dropping the constraint.
Add a regression test for the invalid-grammar response.

Fixes #24144
---
 common/sampling.cpp                             |  3 +++
 tools/server/tests/unit/test_chat_completion.py | 14 ++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/common/sampling.cpp b/common/sampling.cpp
index c537f3350..75a299e23 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -259,6 +259,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
              }
         }
     }
+    if (!grmr && !grammar_str.empty()) {
+        throw std::runtime_error("failed to parse grammar");
+    }
 
     // Compute prefill tokens from the generation prompt
     std::vector<llama_token> prefill_tokens;
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index fe55dc5ab..b00aac649 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -307,6 +307,20 @@ def test_completion_with_grammar(jinja: bool, grammar: str, n_predicted: int, re
     assert match_regex(re_content, choice["message"]["content"]), choice["message"]["content"]
 
 
+def test_completion_with_invalid_grammar():
+    global server
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "messages": [
+            {"role": "user", "content": "Does not matter what I say, does it?"},
+        ],
+        "grammar": "root ::= this is (not valid GBNF",
+    })
+    assert res.status_code == 400, res.body
+    assert "error" in res.body
+
+
 @pytest.mark.parametrize("messages", [
     None,
     "string",

From 20832179e20c3d29ea79cd106dd16578cefe93ec Mon Sep 17 00:00:00 2001
From: Amos Wong <8733840+amoshydra@users.noreply.github.com>
Date: Thu, 18 Jun 2026 19:14:20 +0800
Subject: [PATCH 042/292] ui: provide touch accessible model selection UI
 (#24604)

* ui : add model selector storybook stories

Covers list, favorites, single-model, all status states
(loading/loaded/sleeping/failed/idle), and selection states.

* ui : improve model selector mobile UX with hover media queries

Use @media (hover:none) to show action buttons directly on touch
devices and color-code them by model status (amber=sleeping,
green=loaded, muted=idle). Status dots hidden on touch. Desktop
hover behavior unchanged.
---
 .../app/models/ModelsSelectorOption.svelte    |  44 +--
 .../app/models/ModelsSelectorSheet.svelte     |   2 +-
 .../stories/ModelsSelector.stories.svelte     | 269 ++++++++++++++++++
 3 files changed, 297 insertions(+), 18 deletions(-)
 create mode 100644 tools/ui/tests/stories/ModelsSelector.stories.svelte

diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
index d103d4b67..fef1490f3 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
@@ -79,7 +79,7 @@
 		<!-- svelte-ignore a11y_no_static_element_interactions -->
 		<!-- svelte-ignore a11y_click_events_have_key_events -->
 		<div
-			class="pointer-events-none flex items-center justify-center gap-0.75 pl-2 opacity-0 group-hover:pointer-events-auto group-hover:opacity-100"
+			class="pointer-events-none flex items-center justify-center gap-0.75 pl-2 opacity-0 group-hover:pointer-events-auto group-hover:opacity-100 [@media(pointer:coarse)]:pointer-events-auto [@media(pointer:coarse)]:opacity-100"
 			onclick={(e) => e.stopPropagation()}
 		>
 			{#if isFav}
@@ -113,12 +113,16 @@
 		</div>
 
 		{#if isLoading}
-			<Loader2 class="h-4 w-4 animate-spin text-muted-foreground" />
+			<div class="flex w-4 [@media(pointer:coarse)]:w-5 items-center justify-center">
+				<Loader2 class="h-4 w-4 animate-spin text-muted-foreground" />
+			</div>
 		{:else if isFailed}
-			<div class="flex w-4 items-center justify-center">
-				<CircleAlert class="h-3.5 w-3.5 text-red-500 group-hover:hidden" />
+			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+				<CircleAlert
+					class="h-3.5 w-3.5 text-red-500 group-hover:hidden [@media(pointer:coarse)]:hidden"
+				/>
 
-				<div class="hidden group-hover:flex">
+				<div class="hidden group-hover:flex [@media(pointer:coarse)]:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={RotateCw}
@@ -130,15 +134,17 @@
 				</div>
 			</div>
 		{:else if isSleeping}
-			<div class="flex w-4 items-center justify-center">
-				<span class="h-2 w-2 rounded-full bg-orange-400 group-hover:hidden"></span>
+			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+				<span
+					class="h-2 w-2 rounded-full bg-orange-400 group-hover:hidden [@media(pointer:coarse)]:hidden"
+				></span>
 
-				<div class="hidden group-hover:flex">
+				<div class="hidden group-hover:flex [@media(pointer:coarse)]:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={PowerOff}
 						tooltip="Unload model"
-						class="h-3 w-3 text-red-500 hover:text-red-600"
+						class="h-3 w-3 text-red-500 hover:text-red-600 [@media(pointer:coarse)]:text-amber-500 [@media(pointer:coarse)]:hover:text-amber-600"
 						onclick={(e) => {
 							e?.stopPropagation();
 							modelsStore.unloadModel(option.model);
@@ -147,30 +153,34 @@
 				</div>
 			</div>
 		{:else if isLoaded}
-			<div class="flex w-4 items-center justify-center">
-				<span class="h-2 w-2 rounded-full bg-green-500 group-hover:hidden"></span>
+			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+				<span
+					class="h-2 w-2 rounded-full bg-green-500 group-hover:hidden [@media(pointer:coarse)]:hidden"
+				></span>
 
-				<div class="hidden group-hover:flex">
+				<div class="hidden group-hover:flex [@media(pointer:coarse)]:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={PowerOff}
 						tooltip="Unload model"
-						class="h-3 w-3 text-red-500 hover:text-red-600"
+						class="h-3 w-3 text-red-500 hover:text-red-600 [@media(pointer:coarse)]:text-green-500 [@media(pointer:coarse)]:hover:text-green-600"
 						onclick={() => modelsStore.unloadModel(option.model)}
 						stopPropagationOnClick
 					/>
 				</div>
 			</div>
 		{:else}
-			<div class="flex w-4 items-center justify-center">
-				<span class="h-2 w-2 rounded-full bg-muted-foreground/50 group-hover:hidden"></span>
+			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+				<span
+					class="h-2 w-2 rounded-full bg-muted-foreground/50 group-hover:hidden [@media(pointer:coarse)]:hidden"
+				></span>
 
-				<div class="hidden group-hover:flex">
+				<div class="hidden group-hover:flex [@media(pointer:coarse)]:flex">
 					<ActionIcon
 						iconSize="h-2.5 w-2.5"
 						icon={Power}
 						tooltip="Load model"
-						class="h-3 w-3"
+						class="h-3 w-3 [@media(pointer:coarse)]:text-muted-foreground"
 						onclick={() => modelsStore.loadModel(option.model)}
 						stopPropagationOnClick
 					/>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
index 2ddbf2405..e5920a2a0 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
@@ -66,7 +66,7 @@
 			<button
 				type="button"
 				class={[
-					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 max-sm:px-3 max-sm:py-2 text-xs max-sm:text-sm shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
 					!ms.isCurrentModelInCache
 						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
 						: forceForegroundText
diff --git a/tools/ui/tests/stories/ModelsSelector.stories.svelte b/tools/ui/tests/stories/ModelsSelector.stories.svelte
new file mode 100644
index 000000000..3aba0dc78
--- /dev/null
+++ b/tools/ui/tests/stories/ModelsSelector.stories.svelte
@@ -0,0 +1,269 @@
+<script module lang="ts">
+	import { defineMeta } from '@storybook/addon-svelte-csf';
+	import ModelsSelectorList from '$lib/components/app/models/ModelsSelectorList.svelte';
+	import ModelsSelectorOption from '$lib/components/app/models/ModelsSelectorOption.svelte';
+	import type { GroupedModelOptions, ModelItem } from '$lib/components/app/models/utils';
+	import { modelsStore } from '$lib/stores/models.svelte';
+	import { ServerModelStatus } from '$lib/enums';
+
+	const { Story } = defineMeta({
+		title: 'Components/ModelsSelector',
+		parameters: {
+			layout: 'centered'
+		}
+	});
+
+	const mockModel = (id: string, name: string, orgName?: string, tags?: string[]): ModelOption => ({
+		id,
+		name,
+		model: orgName ? `${orgName}/${name}` : name,
+		capabilities: [],
+		parsedId: {
+			raw: orgName ? `${orgName}/${name}` : name,
+			orgName: orgName ?? null,
+			modelName: name,
+			params: null,
+			activatedParams: null,
+			quantization: null,
+			tags: tags ?? []
+		},
+		tags
+	});
+
+	const mockRouterEntry = (modelName: string, status: ServerModelStatus): ApiModelDataEntry => ({
+		id: modelName,
+		object: 'model',
+		owned_by: 'llamacpp',
+		created: Date.now(),
+		in_cache: true,
+		path: `/models/${modelName}`,
+		status: { value: status }
+	});
+</script>
+
+<script lang="ts">
+	let selectedModel = $state<string | null>(null);
+	let activeId = $state<string | null>(null);
+
+	function mockModelsStore() {
+		modelsStore.favoriteModelIds = new Set(['qwen2.5-7b', 'llama3.2-3b']);
+
+		// Mock router models with various statuses for ModelLoadedStates story
+		modelsStore.routerModels = [
+			mockRouterEntry('meta/Model (loading)', ServerModelStatus.LOADING),
+			mockRouterEntry('meta/Model (loaded)', ServerModelStatus.LOADED),
+			mockRouterEntry('meta/Model (sleeping)', ServerModelStatus.SLEEPING),
+			mockRouterEntry('meta/Model (failed)', ServerModelStatus.FAILED)
+		];
+	}
+
+	mockModelsStore();
+
+	const loadedModels: ModelItem[] = [
+		{ option: mockModel('llama3.1-8b', 'Llama-3.1-8B-Instruct', 'meta'), flatIndex: 0 },
+		{ option: mockModel('mistral-7b', 'Mistral-7B-v0.3', 'mistralai'), flatIndex: 1 }
+	];
+
+	const favoriteModels: ModelItem[] = [
+		{ option: mockModel('qwen2.5-7b', 'Qwen2.5-7B-Instruct', 'Qwen'), flatIndex: 2 },
+		{ option: mockModel('llama3.2-3b', 'Llama-3.2-3B-Instruct', 'meta'), flatIndex: 3 }
+	];
+
+	const availableModels: ModelItem[] = [
+		{
+			option: mockModel('deepseek-coder-6.7b', 'DeepSeek-Coder-6.7B', 'deepseek', ['coding']),
+			flatIndex: 4
+		},
+		{ option: mockModel('gemma-2-9b', 'Gemma-2-9B-IT', 'google'), flatIndex: 5 },
+		{ option: mockModel('phi-3-mini', 'Phi-3-mini-4k', 'microsoft'), flatIndex: 6 },
+		{ option: mockModel('codellama-7b', 'CodeLlama-7B', 'codellama', ['coding']), flatIndex: 7 },
+		{ option: mockModel('neural-chat-7b', 'Neural-Chat-7B-v3-3', 'intel'), flatIndex: 8 }
+	];
+
+	const groupedOptions: GroupedModelOptions = {
+		loaded: loadedModels,
+		favorites: favoriteModels,
+		available: [
+			{
+				orgName: 'deepseek',
+				items: [availableModels[0]]
+			},
+			{
+				orgName: 'google',
+				items: [availableModels[1]]
+			},
+			{
+				orgName: 'microsoft',
+				items: [availableModels[2]]
+			},
+			{
+				orgName: 'codellama',
+				items: [availableModels[3]]
+			},
+			{
+				orgName: 'intel',
+				items: [availableModels[4]]
+			}
+		]
+	};
+
+	function handleSelect(modelId: string) {
+		const opt = [...loadedModels, ...favoriteModels, ...availableModels].find(
+			(m) => m.option.id === modelId
+		);
+		if (opt) {
+			selectedModel = opt.option.model;
+			activeId = modelId;
+		}
+	}
+</script>
+
+<Story name="List">
+	<div class="w-80 rounded-lg border border-border bg-popover p-2 shadow-md">
+		<ModelsSelectorList
+			groups={groupedOptions}
+			currentModel={selectedModel}
+			{activeId}
+			onSelect={handleSelect}
+			onInfoClick={(modelName) => console.log('Info clicked:', modelName)}
+		/>
+	</div>
+</Story>
+
+<Story name="SingleLoaded">
+	<div class="w-80 rounded-lg border border-border bg-popover p-2 shadow-md">
+		<ModelsSelectorList
+			groups={{
+				loaded: [loadedModels[0]],
+				favorites: [],
+				available: []
+			}}
+			currentModel={null}
+			activeId={null}
+			onSelect={handleSelect}
+			onInfoClick={(modelName) => console.log('Info clicked:', modelName)}
+		/>
+	</div>
+</Story>
+
+<Story name="WithFavoritesOnly">
+	<div class="w-80 rounded-lg border border-border bg-popover p-2 shadow-md">
+		<ModelsSelectorList
+			groups={{
+				loaded: [],
+				favorites: favoriteModels,
+				available: []
+			}}
+			currentModel={null}
+			activeId={null}
+			onSelect={handleSelect}
+			onInfoClick={(modelName) => console.log('Info clicked:', modelName)}
+		/>
+	</div>
+</Story>
+
+<Story name="ModelLoadedStates">
+	<div class="w-80 rounded-lg border border-border bg-popover p-2 shadow-md">
+		<div class="px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none">
+			Server model states
+		</div>
+		<ModelsSelectorOption
+			option={mockModel('model-idle', 'Model (idle)', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('model-loading', 'Model (loading)', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('model-loaded', 'Model (loaded)', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('model-sleeping', 'Model (sleeping)', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('model-failed', 'Model (failed)', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+	</div>
+</Story>
+
+<Story name="ModelSelectedStates">
+	<div class="w-80 rounded-lg border border-border bg-popover p-2 shadow-md">
+		<div class="px-2 py-2 text-[13px] font-semibold text-muted-foreground/70 select-none">
+			Selection states
+		</div>
+		<ModelsSelectorOption
+			option={mockModel('normal-model', 'Normal Model', 'meta')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('selected-model', 'Selected Model', 'meta')}
+			isSelected={true}
+			isHighlighted={false}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('highlighted-model', 'Highlighted Model', 'meta')}
+			isSelected={false}
+			isHighlighted={true}
+			isFav={false}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+		<ModelsSelectorOption
+			option={mockModel('fav-model', 'Favorite Model', 'Qwen')}
+			isSelected={false}
+			isHighlighted={false}
+			isFav={true}
+			hideOrgName={true}
+			onSelect={() => {}}
+			onMouseEnter={() => {}}
+			onKeyDown={() => {}}
+		/>
+	</div>
+</Story>

From 08023072ef63e7da749870b771db156f36d5c935 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Thu, 18 Jun 2026 15:02:20 +0300
Subject: [PATCH 043/292] server : add last-5-seconds generation speed display
 (#24291)

* server : add last-5-seconds generation speed display

* cont : clean-up

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tools/server/server-context.cpp | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index da6a47586..31280d63c 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -189,9 +189,10 @@ struct server_slot {
     // stats
     size_t n_sent_text = 0; // number of sent text character
 
-    int64_t t_print_last = 0;
     int64_t t_start_process_prompt;
     int64_t t_start_generation;
+    int64_t t_print_last = 0;
+    int32_t n_decoded_last = 0;
 
     double t_prompt_processing = 0.0; // ms
     double t_token_generation = 0.0;  // ms
@@ -470,11 +471,13 @@ struct server_slot {
             return;
         }
 
+        const double n_gen_second     = 1e3 / (t_token_generation)   * (n_decoded);
+        const double n_gen_second_win = 1e6 / (t_now - t_print_last) * (n_decoded - n_decoded_last);
+
         t_print_last = t_now;
+        n_decoded_last = n_decoded;
 
-        const double n_gen_second = 1e3 / t_token_generation * n_decoded;
-
-        SLT_INF(*this, "n_decoded = %6d, tg = %6.2f t/s\n", n_decoded, n_gen_second);
+        SLT_INF(*this, "n_decoded = %6d, tg = %6.2f t/s, tg_3s = %6.2f t/s\n", n_decoded, n_gen_second, n_gen_second_win);
     }
 
     void print_timings_pp() const {
@@ -3038,8 +3041,8 @@ private:
                         }
                     }
 
-                    const int64_t t_current = ggml_time_us();
-                    slot.t_prompt_processing = (t_current - slot.t_start_process_prompt) / 1e3;
+                    const int64_t t_now = ggml_time_us();
+                    slot.t_prompt_processing = (t_now - slot.t_start_process_prompt) / 1e3;
                     slot.print_timings_pp();
 
                     // truncate any tokens that are beyond n_past for this slot
@@ -3447,17 +3450,19 @@ private:
                 common_sampler_accept(slot.smpl.get(), id, true);
 
                 // here we have synchronized the llama_context (due to the sampling above), so we can do time measurement
-                const int64_t t_current = ggml_time_us();
+                const int64_t t_now = ggml_time_us();
 
                 slot.n_decoded += 1;
 
                 if (slot.n_decoded == 1) {
-                    slot.t_start_generation = t_current;
+                    slot.t_start_generation = t_now;
+                    slot.t_print_last = t_now;
+                    slot.n_decoded_last = 0;
                     slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
                     metrics.on_prompt_eval(slot);
                 }
 
-                slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
+                slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
 
                 completion_token_output result;
                 result.tok          = id;
@@ -3551,11 +3556,11 @@ private:
                     slot.spec_draft = std::move(accepted);
                 }
 
-                const int64_t t_current = ggml_time_us();
+                const int64_t t_now = ggml_time_us();
 
                 const auto ids = std::move(slot.spec_draft);
 
-                slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
+                slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
 
                 // update how many tokens out of those tested were accepted
                 slot.n_draft_accepted += ids.size() - 1;

From 635c45e1a0b2dd642112e48380b5bd0bbcffab4e Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 21:09:35 +0800
Subject: [PATCH 044/292] fix incorrect mtp layers setting (+1 squashed
 commits)

Squashed commits:

[8dad1a5c0] fix incorrect mtp layers setting
---
 gpttype_adapter.cpp | 2 +-
 koboldcpp.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 97dab95e0..0c0dc7436 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -893,7 +893,7 @@ static void mtp_decoding_setup(llama_model * main_model, llama_context * main_ct
     }
 
     draft_is_mtp = true;
-    speculative_state_setup(main_ctx, mtp_ctx_params, 0, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+    speculative_state_setup(main_ctx, mtp_ctx_params, static_cast<int32_t>(main_model->n_gpu_layers()), COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
 }
 
 //loads a model for speculative decoding.
diff --git a/koboldcpp.py b/koboldcpp.py
index 0459b5192..f6efb5741 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -78,7 +78,7 @@ dry_seq_break_max = 128
 extra_images_max = 4 # for kontext/qwen img
 
 # global vars
-KcppVersion = "1.115.2"
+KcppVersion = "1.116"
 showdebug = True
 kcpp_instance = None #global running instance
 global_memory = {"tunnel_url": "", "restart_target":"", "input_to_exit":False, "load_complete":False, "restart_override_base_config":"", "last_active_timestamp":datetime.now(), "triggered_sleeping":False, "current_model":"initial_model", "base_config":"", "swapReqType": None, "autoswapmode": False}

From e1efd0991d852b02dce3533ec8fac8bf850e1431 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 15:40:58 +0200
Subject: [PATCH 045/292] server: add "schema" and validation (#24150)

* wip

* working

* correct some limits

* add field name to error message
---
 tools/server/CMakeLists.txt     |   2 +
 tools/server/server-context.cpp |   3 +-
 tools/server/server-schema.cpp  | 635 ++++++++++++++++++++++++++++++++
 tools/server/server-schema.h    | 105 ++++++
 tools/server/server-task.cpp    | 388 -------------------
 tools/server/server-task.h      |   7 -
 6 files changed, 744 insertions(+), 396 deletions(-)
 create mode 100644 tools/server/server-schema.cpp
 create mode 100644 tools/server/server-schema.h

diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index 7d427431d..47bb582c3 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -17,6 +17,8 @@ add_library(${TARGET} STATIC
     server-context.h
     server-tools.cpp
     server-tools.h
+    server-schema.cpp
+    server-schema.h
 )
 
 if (BUILD_SHARED_LIBS)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 31280d63c..aebca306a 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -4,6 +4,7 @@
 #include "server-http.h"
 #include "server-task.h"
 #include "server-queue.h"
+#include "server-schema.h"
 
 #include "build-info.h"
 #include "common.h"
@@ -3825,7 +3826,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
             task.id = rd.get_new_id();
 
             task.tokens = std::move(inputs[i]);
-            task.params = server_task::params_from_json_cmpl(
+            task.params = server_schema::eval_llama_cmpl_schema(
                     ctx_server.vocab,
                     params,
                     meta->slot_n_ctx,
diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp
new file mode 100644
index 000000000..d5d747a65
--- /dev/null
+++ b/tools/server/server-schema.cpp
@@ -0,0 +1,635 @@
+#include "server-schema.h"
+
+#include "json-schema-to-grammar.h"
+
+namespace server_schema {
+
+//
+// llama.cpp-specific completion schema
+//
+
+std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params & params_base, task_params & params) {
+    std::vector<std::unique_ptr<field>> fields;
+    auto add = [&](field * f) {
+        fields.emplace_back(f);
+    };
+
+    add((new field_bool("timings_per_token", params.timings_per_token))
+        ->set_desc("Include prompt processing and text generation speed information in each response"));
+
+    add((new field_bool("stream", params.stream))
+        ->set_desc("Allows receiving each predicted token in real-time instead of waiting for the completion to finish"));
+
+    add((new field_nested("stream_options"))
+        ->add_subfield((new field_bool("include_usage", params.include_usage))
+            ->set_desc("Whether to include usage information in the stream"))
+        ->set_desc("Additional options for streaming responses"));
+
+    add((new field_bool("cache_prompt", params.cache_prompt))
+        ->set_desc("Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests"));
+
+    add((new field_bool("return_tokens", params.return_tokens))
+        ->set_desc("Return the raw generated token ids in the `tokens` field"));
+
+    add((new field_bool("return_progress", params.return_progress))
+        ->set_desc("Include prompt processing progress events in stream mode"));
+
+    add((new field_num("n_predict", params.n_predict))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->add_alias("max_completion_tokens")
+        ->add_alias("max_tokens")
+        ->set_desc("Set the maximum number of tokens to predict. When 0, no tokens will be generated but the prompt is evaluated into the cache"));
+
+    add((new field_num("n_indent", params.n_indent))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks"));
+
+    add((new field_num("n_keep", params.n_keep))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Specify the number of tokens from the initial prompt to retain when context size is exceeded. Use -1 to retain all tokens from the prompt"));
+
+    add((new field_num("n_discard", params.n_discard))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Number of tokens after n_keep that may be discarded when shifting context (0 = half context)"));
+
+    add((new field_num("n_cmpl", params.n_cmpl))
+        ->set_hard_limits(1, params_base.n_parallel)
+        ->add_alias("n") // alias "n" as fallback (OpenAI completions API)
+        ->set_desc("Number of completions to generate. If the input has multiple prompts, total outputs will be N prompts times n_cmpl"));
+
+    add((new field_num("n_cache_reuse", params.n_cache_reuse))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Min chunk size to attempt reusing from the cache via KV shifting. See --cache-reuse arg"));
+
+    // TODO: implement t_max_prompt_ms
+    // add((new field_num("t_max_prompt_ms", params.t_max_prompt_ms))
+
+    add((new field_num("t_max_predict_ms", params.t_max_predict_ms))
+        ->set_hard_limits(-1, std::numeric_limits<int64_t>::max())
+        ->set_desc("Set a time limit in milliseconds for the prediction phase. The timeout triggers if generation exceeds this time (measured since the first token) and a newline has been generated. Useful for FIM applications"));
+
+    add((new field_json("response_fields"))
+        ->set_desc("A list of response fields to return. Missing fields are omitted without error. Fields with a slash are unnested (e.g. generation_settings/n_predict moves n_predict to the root)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.response_fields = json_value(data, "response_fields", std::vector<std::string>());
+        }));
+
+
+    //
+    // Sampling params
+    //
+
+    add((new field_num("top_k", params.sampling.top_k))
+        ->set_limits(0, INT32_MAX)
+        ->set_desc("Limit the next token selection to the K most probable tokens (0 = disabled)"));
+
+    add((new field_num("top_p", params.sampling.top_p))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Limit the next token selection to a subset of tokens with cumulative probability above threshold P (1.0 = disabled)"));
+
+    add((new field_num("min_p", params.sampling.min_p))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("The minimum probability for a token to be considered, relative to the probability of the most likely token (0 = disabled)"));
+
+    add((new field_num("top_n_sigma", params.sampling.top_n_sigma))
+        ->set_desc("Keep tokens within n standard deviations of the top token logit (< 0 = disabled)"));
+
+    add((new field_num("xtc_probability", params.sampling.xtc_probability))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Set the chance for token removal via XTC sampler (0 = disabled)"));
+
+    add((new field_num("xtc_threshold", params.sampling.xtc_threshold))
+        ->set_limits(0.0f, 1.0f)
+        ->set_desc("Set a minimum probability threshold for tokens to be removed via XTC sampler (> 0.5 disables XTC)"));
+
+    add((new field_num("typical_p", params.sampling.typ_p))
+        // ->set_limits(0.0f, 1.0f) // what's the valid range?
+        ->set_desc("Enable locally typical sampling with parameter p (1.0 = disabled)"));
+
+    add((new field_num("temperature", params.sampling.temp))
+        ->set_limits(0.0f, std::numeric_limits<float>::infinity())
+        ->set_desc("Adjust the randomness of the generated text (0 = greedy)"));
+
+    add((new field_num("dynatemp_range", params.sampling.dynatemp_range))
+        ->set_desc("Dynamic temperature range. The final temperature will be in [temperature - range, temperature + range] (0 = disabled)"));
+
+    add((new field_num("dynatemp_exponent", params.sampling.dynatemp_exponent))
+        ->set_desc("Dynamic temperature exponent, controls how entropy maps to temperature"));
+
+    add((new field_num("repeat_last_n", params.sampling.penalty_last_n))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Last n tokens to consider for penalizing repetition (0 = disabled, -1 = ctx-size)"));
+
+    add((new field_num("repeat_penalty", params.sampling.penalty_repeat))
+        ->set_desc("Control the repetition of token sequences in the generated text (1.0 = disabled)"));
+
+    add((new field_num("frequency_penalty", params.sampling.penalty_freq))
+        ->set_desc("Repeat alpha frequency penalty (0 = disabled)"));
+
+    add((new field_num("presence_penalty", params.sampling.penalty_present))
+        ->set_desc("Repeat alpha presence penalty (0 = disabled)"));
+
+    add((new field_num("dry_multiplier", params.sampling.dry_multiplier))
+        ->set_desc("Set the DRY (Don't Repeat Yourself) repetition penalty multiplier (0 = disabled)"));
+
+    add((new field_num("dry_base", params.sampling.dry_base))
+        ->set_desc("Set the DRY repetition penalty base value (must be >= 1.0, any values < 1.0 will be replaced with the default value)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            float v = data.at("dry_base").get<float>();
+            ctx.params.sampling.dry_base = (v < 1.0f) ? params_base.sampling.dry_base : v;
+        }));
+
+    add((new field_num("dry_allowed_length", params.sampling.dry_allowed_length))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Tokens that extend repetition beyond this length receive exponentially increasing penalty: multiplier * base ^ (sequence_length - allowed_length)"));
+
+    add((new field_num("dry_penalty_last_n", params.sampling.dry_penalty_last_n))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("How many tokens to scan for repetitions (0 = disabled, -1 = context size)"));
+
+    add((new field_num("mirostat", params.sampling.mirostat))
+        ->set_limits(0, 2)
+        ->set_desc("Enable Mirostat sampling, controlling perplexity during text generation (0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"));
+
+    add((new field_num("mirostat_tau", params.sampling.mirostat_tau))
+        ->set_desc("Set the Mirostat target entropy, parameter tau"));
+
+    add((new field_num("mirostat_eta", params.sampling.mirostat_eta))
+        ->set_desc("Set the Mirostat learning rate, parameter eta"));
+
+    add((new field_num("adaptive_target", params.sampling.adaptive_target))
+        ->set_limits(-std::numeric_limits<float>::max(), 1.0f)
+        ->set_desc("Adaptive sampling target entropy (valid range 0.0 to 1.0; negative = disabled)"));
+
+    add((new field_num("adaptive_decay", params.sampling.adaptive_decay))
+        ->set_hard_limits(0.0f, 0.99f)
+        ->set_desc("EMA decay for adaptive sampling; history approximates 1/(1-decay) tokens"));
+
+    // seed is uint32_t; field_num uses int32_t so use a handler
+    add((new field_num("seed", params.sampling.seed))
+        ->set_desc("Set the random number generator (RNG) seed (-1 = random)"));
+
+    add((new field_num("n_probs", params.sampling.n_probs))
+        ->add_alias("logprobs") // use "logprobs" if "n_probs" wasn't provided
+        ->set_desc("If greater than 0, output the probabilities of top N tokens for each generated token"));
+
+    add((new field_num("min_keep", params.sampling.min_keep))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("If greater than 0, force samplers to return at least N possible tokens"));
+
+    add((new field_bool("backend_sampling", params.sampling.backend_sampling))
+        ->set_desc("Use backend sampling instead of llama.cpp sampling"));
+
+    add((new field_bool("post_sampling_probs", params.post_sampling_probs))
+        ->set_desc("Return probabilities of top n_probs tokens after applying the sampling chain"));
+
+    //
+    // Speculative decoding params
+    //
+
+    // TODO: to keep things simple, we disable speculative parameter adjustments for now
+#if 0
+    // TODO: for now, be able to adjust only the draft-model based speculative parameters
+    add((new field_num("speculative.n_max", params.speculative.draft.n_max))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Maximum number of tokens to draft during speculative decoding"));
+
+    add((new field_num("speculative.n_min", params.speculative.draft.n_min))
+        ->set_hard_limits(0, INT32_MAX)
+        ->set_desc("Minimum number of draft tokens to use for speculative decoding");
+
+    add((new field_num("speculative.p_min", params.speculative.draft.p_min))
+        ->set_hard_limits(0.0f, 1.0f)
+        ->set_desc("Minimum speculative decoding probability for draft tokens (0 = greedy)"));
+
+    add((new field_str("speculative.type"))
+        ->set_desc("Speculative decoding method (for debugging and research purposes)")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.speculative.types = { common_speculative_type_from_name(data.at("speculative.type").get<std::string>()) };
+        }));
+
+    add((new field_num("speculative.ngram_size_n", params.speculative.ngram_simple.size_n))
+        ->set_desc("Ngram size for lookup in ngram-based speculative decoding"));
+
+    add((new field_num("speculative.ngram_size_m", params.speculative.ngram_simple.size_m))
+        ->set_desc("Mgram size for speculative tokens in ngram-based speculative decoding"));
+
+    add((new field_num("speculative.ngram_min_hits", params.speculative.ngram_simple.min_hits))
+        ->set_desc("Minimum hits at ngram lookup for mgram to be proposed"));
+#endif
+
+    add((new field_json("lora"))
+        ->set_desc("A list of LoRA adapters to apply to this request. Each entry must have `id` and `scale` fields. Adapters not listed default to scale 0.0")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            const auto & lora = data.at("lora");
+            if (!lora.is_array()) {
+                throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
+            }
+            ctx.params.lora = parse_lora_request(lora);
+        }));
+
+    // sequence breakers for DRY
+    // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
+    // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
+    add((new field_json("dry_sequence_breakers"))
+        ->set_desc("Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
+            if (ctx.params.sampling.dry_sequence_breakers.empty()) {
+                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
+            }
+        }));
+
+    // handle both "json_schema" and "grammar"
+    add((new field_json("json_schema"))
+        ->add_alias("grammar")
+        ->set_desc("Set a JSON schema (json_schema) or GBNF grammar string (grammar) for constrained generation. json_schema takes precedence if both are provided")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto & params = ctx.params;
+            if (data.contains("json_schema") && !data.contains("grammar")) {
+                try {
+                    auto schema                  = json_value(data, "json_schema", json::object());
+                    SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
+                    std::string grammar_str      = json_schema_to_grammar(schema);
+                    SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
+                    params.sampling.grammar      = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
+                } catch (const std::exception & e) {
+                    throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
+                }
+            } else {
+                std::string grammar_str = json_value(data, "grammar", std::string());
+                if (!grammar_str.empty()) {
+                    // grammar_type key is set by the server when converting chat template grammars
+                    std::string grammar_type = json_value(data, "grammar_type", std::string());
+                    if (grammar_type == "tool_calls") {
+                        params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
+                    } else {
+                        // explicit grammar from the user (API field "grammar")
+                        params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
+                    }
+                    SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(params.sampling.grammar).c_str());
+                }
+            }
+        }));
+
+    add((new field_bool("grammar_lazy", params.sampling.grammar_lazy))
+        ->set_desc("Whether to apply grammar constraints lazily, only when triggered (instead of at every step)"));
+
+    //
+    // Chat parser params
+    //
+
+    // TODO: change this to string field instead
+    add((new field_json("chat_format"))
+        ->set_desc("Chat format used internally by the server")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.chat_parser_params.format = static_cast<common_chat_format>(data.at("chat_format").get<int>());
+            SRV_INF("Chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
+        }));
+
+    add((new field_str("reasoning_format"))
+        ->set_desc("Reasoning format for chain-of-thought models")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
+            ctx.params.chat_parser_params.reasoning_format = reasoning_format;
+            ctx.params.chat_parser_params.reasoning_in_content = ctx.params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+        }));
+
+    add((new field_str("generation_prompt"))
+        ->set_desc("Generation prompt appended to the chat template output")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            std::string s = data.at("generation_prompt").get<std::string>();
+            ctx.params.chat_parser_params.generation_prompt = s;
+            ctx.params.sampling.generation_prompt = s;
+        }));
+
+    add((new field_bool("parse_tool_calls", params.chat_parser_params.parse_tool_calls))
+        ->set_desc("Whether to parse tool calls from the generated output"));
+
+    add((new field_str("chat_parser"))
+        ->set_desc("Chat parser configuration string")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
+        }));
+
+    add((new field_json("continue_final_message"))
+        ->set_desc("Whether to continue the final message of the chat template")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
+            ctx.params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
+        }));
+
+    add((new field_bool("echo", params.chat_parser_params.echo))
+        ->set_desc("Whether to echo the input tokens in the output"));
+
+    //
+    // Token-level fields (require vocab)
+    //
+
+    add((new field_json("preserved_tokens"))
+        ->set_desc("List of token strings that must not be split during tokenization")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            for (const auto & t : data.at("preserved_tokens")) {
+                auto ids = common_tokenize(ctx.vocab, t.get<std::string>(), false, true);
+                if (ids.size() == 1) {
+                    ctx.params.sampling.preserved_tokens.insert(ids[0]);
+                }
+            }
+        }));
+
+    add((new field_json("grammar_triggers"))
+        ->set_desc("List of strings or patterns that trigger grammar-constrained generation")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            for (const auto & t : data.at("grammar_triggers")) {
+                server_grammar_trigger ct(t);
+                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
+                    const auto & word = ct.value.value;
+                    auto ids = common_tokenize(ctx.vocab, word, false, true);
+                    if (ids.size() == 1) {
+                        auto token = ids[0];
+                        if (std::find(ctx.params.sampling.preserved_tokens.begin(), ctx.params.sampling.preserved_tokens.end(), (llama_token) token) == ctx.params.sampling.preserved_tokens.end()) {
+                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
+                        }
+                        common_grammar_trigger trigger;
+                        trigger.type  = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
+                        trigger.value = word;
+                        trigger.token = token;
+                        ctx.params.sampling.grammar_triggers.push_back(std::move(trigger));
+                    } else {
+                        ctx.params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
+                    }
+                } else {
+                    ctx.params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
+                }
+            }
+            if (ctx.params.sampling.grammar_lazy && ctx.params.sampling.grammar_triggers.empty()) {
+                throw std::runtime_error("Error: no triggers set for lazy grammar!");
+            }
+        }));
+
+    add((new field_bool("reasoning_control", params.sampling.reasoning_control))
+        ->set_desc("Create the budget sampler on demand so reasoning can be ended at runtime"));
+
+    add((new field_num("reasoning_budget_tokens", params.sampling.reasoning_budget_tokens))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Number of tokens in the reasoning budget (-1 = disabled)"));
+
+    add((new field_str("reasoning_budget_start_tag"))
+        ->set_desc("Token string marking the start of the reasoning budget section")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            ctx.params.sampling.reasoning_budget_start = common_tokenize(ctx.vocab, data.at("reasoning_budget_start_tag").get<std::string>(), false, true);
+        }));
+
+    add((new field_str("reasoning_budget_end_tag"))
+        ->set_desc("Token string marking the end of the reasoning budget section")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            std::string end_tag = data.at("reasoning_budget_end_tag").get<std::string>();
+            ctx.params.sampling.reasoning_budget_end = common_tokenize(ctx.vocab, end_tag, false, true);
+        }));
+
+    add((new field_str("reasoning_budget_message"))
+        ->set_desc("Message to prepend to the reasoning budget end tag when forcing it")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            std::string end_tag = json_value(data, "reasoning_budget_end_tag", std::string());
+            std::string message = data.at("reasoning_budget_message").get<std::string>();
+            ctx.params.sampling.reasoning_budget_forced = common_tokenize(ctx.vocab, message + end_tag, false, true);
+        }));
+
+    add((new field_json("logit_bias"))
+        ->set_desc("Modify the likelihood of specific tokens. Accepts an array of [token, bias] pairs or an object mapping token to bias. Use false as bias to ban a token")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.vocab != nullptr);
+            ctx.params.sampling.logit_bias.clear();
+            const auto & logit_bias = data.at("logit_bias");
+            const int n_vocab = llama_vocab_n_tokens(ctx.vocab);
+            auto parse_bias = [](const json & v, float & bias) -> bool {
+                if (v.is_number())                        { bias = v.get<float>(); return true; }
+                if (v.is_boolean() && !v.get<bool>())     { bias = -INFINITY;      return true; }
+                return false;
+            };
+            if (logit_bias.is_array()) {
+                for (const auto & el : logit_bias) {
+                    if (!el.is_array() || el.size() != 2) continue;
+                    float bias;
+                    if (!parse_bias(el[1], bias)) continue;
+                    if (el[0].is_number_integer()) {
+                        llama_token tok = el[0].get<llama_token>();
+                        if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    } else if (el[0].is_string()) {
+                        for (auto tok : common_tokenize(ctx.vocab, el[0].get<std::string>(), false))
+                            ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    }
+                }
+            } else if (logit_bias.is_object()) {
+                for (const auto & el : logit_bias.items()) {
+                    float bias;
+                    if (!parse_bias(el.value(), bias)) continue;
+                    char * end;
+                    llama_token tok = strtol(el.key().c_str(), &end, 10);
+                    if (*end == 0) {
+                        if (tok >= 0 && tok < n_vocab) ctx.params.sampling.logit_bias.push_back({tok, bias});
+                    } else {
+                        for (auto t : common_tokenize(ctx.vocab, el.key(), false))
+                            ctx.params.sampling.logit_bias.push_back({t, bias});
+                    }
+                }
+            }
+        }));
+
+    add((new field_bool("ignore_eos", params.sampling.ignore_eos))
+        ->set_desc("Ignore the end-of-sequence token and continue generating")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            GGML_ASSERT(ctx.logit_bias_eog != nullptr);
+            ctx.params.sampling.ignore_eos = data.at("ignore_eos").get<bool>();
+            if (ctx.params.sampling.ignore_eos && ctx.logit_bias_eog) {
+                ctx.params.sampling.logit_bias.insert(
+                    ctx.params.sampling.logit_bias.end(),
+                    ctx.logit_bias_eog->begin(), ctx.logit_bias_eog->end());
+            }
+        }));
+
+    add((new field_json("stop"))
+        ->set_desc("Specify stopping strings. Generation stops when one is produced, and the string is not included in the output")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            ctx.params.antiprompt.clear();
+            const auto & stop = data.at("stop");
+            if (stop.is_array()) {
+                for (const auto & word : stop) {
+                    if (!word.empty()) ctx.params.antiprompt.push_back(word);
+                }
+            } else if (stop.is_string()) {
+                ctx.params.antiprompt.push_back(stop.get<std::string>());
+            }
+            // fall back to CLI defaults if the request provided no effective stop strings
+            if (ctx.params.antiprompt.empty()) {
+                ctx.params.antiprompt = params_base.antiprompt;
+            }
+        }));
+
+    add((new field_json("samplers"))
+        ->set_desc("The order in which samplers are applied. An array of sampler type names, or a single string of sampler chars")
+        ->set_handler([&](field_eval_context & ctx, const json & data) {
+            const auto & samplers = data.at("samplers");
+            if (samplers.is_array()) {
+                ctx.params.sampling.samplers = common_sampler_types_from_names(samplers);
+            } else if (samplers.is_string()) {
+                ctx.params.sampling.samplers = common_sampler_types_from_chars(samplers.get<std::string>());
+            }
+        }));
+
+    return fields;
+}
+
+task_params eval_llama_cmpl_schema(
+                const llama_vocab * vocab,
+                const common_params & params_base,
+                const int n_ctx_slot,
+                const std::vector<llama_logit_bias> & logit_bias_eog,
+                const json & data) {
+    task_params params;
+
+    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
+    params.sampling      = params_base.sampling;
+    params.speculative   = params_base.speculative;
+    params.n_keep        = params_base.n_keep;
+    params.n_predict     = params_base.n_predict;
+    params.n_cache_reuse = params_base.n_cache_reuse;
+    params.cache_prompt  = params_base.cache_prompt;
+    params.antiprompt    = params_base.antiprompt;
+
+    // enabling this will output extra debug information in the HTTP responses from the server
+    params.verbose       = params_base.verbosity > 9;
+
+    params.chat_parser_params.reasoning_format = params_base.reasoning_format;
+
+    // create context and schema
+    field_eval_context ctx(params);
+    ctx.vocab          = vocab;
+    ctx.logit_bias_eog = &logit_bias_eog;
+
+    auto schema = make_llama_cmpl_schema(params_base, params);
+
+    // eval all fields in the schema
+    for (const auto & f : schema) {
+        f->eval(ctx, data);
+    }
+
+    // post-processing
+    {
+        if (params.sampling.penalty_last_n == -1) {
+            // note: should be the slot's context and not the full context, but it's ok
+            params.sampling.penalty_last_n = n_ctx_slot;
+        }
+
+        if (params.sampling.dry_penalty_last_n == -1) {
+            params.sampling.dry_penalty_last_n = n_ctx_slot;
+        }
+
+        // if "reasoning_format" is not provided, its handler will not be called, we will need to handle it here
+        auto reasoning_format = params.chat_parser_params.reasoning_format;
+        params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
+    }
+
+    // debugging
+    {
+        auto budget = params.sampling.reasoning_budget_tokens;
+        SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
+                budget, params.sampling.generation_prompt.c_str(),
+                params.sampling.reasoning_budget_start.size(),
+                params.sampling.reasoning_budget_end.size(),
+                params.sampling.reasoning_budget_forced.size());
+    }
+
+    return params;
+}
+
+//
+// eval() implementations
+//
+
+static void handle_with_catch(const char * name, std::function<void()> func) {
+    try {
+        func();
+    } catch (const std::exception & e) {
+        throw std::invalid_argument(string_format("Field '%s': %s", name, e.what()));
+    }
+}
+
+template <typename T>
+void field_num<T>::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                if (custom_handler) {
+                custom_handler(ctx, data);
+                } else if (!is_hard_limit) {
+                    val = std::max(min, std::min(max, data.at(n).template get<T>()));
+                } else {
+                    T tmp = data.at(n).template get<T>();
+                    if (tmp < min || tmp > max) {
+                        throw std::invalid_argument(std::string("Value must be between ") + std::to_string(min) + " <= value <= " + std::to_string(max) + ", but got " + std::to_string(tmp));
+                    }
+                    val = tmp;
+                }
+            });
+            return;
+        }
+    }
+}
+
+void field_str::eval(field_eval_context & ctx, const json & data) {
+    GGML_ASSERT(custom_handler);
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                custom_handler(ctx, data);
+            });
+            return;
+        }
+    }
+}
+
+void field_bool::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                if (custom_handler) {
+                    custom_handler(ctx, data);
+                } else {
+                    val = data.at(n).get<bool>();
+                }
+            });
+            return;
+        }
+    }
+}
+
+void field_json::eval(field_eval_context & ctx, const json & data) {
+    GGML_ASSERT(custom_handler);
+    for (const auto & n : name) {
+        if (data.contains(n)) {
+            handle_with_catch(n, [&]() {
+                custom_handler(ctx, data);
+            });
+            return;
+        }
+    }
+}
+
+void field_nested::eval(field_eval_context & ctx, const json & data) {
+    for (const auto & n : name) {
+        if (data.contains(n) && data.at(n).is_object()) {
+            for (auto & f : subfields) {
+                f->eval(ctx, data.at(n));
+            }
+            return;
+        }
+    }
+}
+
+} // namespace server_schema
diff --git a/tools/server/server-schema.h b/tools/server/server-schema.h
new file mode 100644
index 000000000..08cf427dc
--- /dev/null
+++ b/tools/server/server-schema.h
@@ -0,0 +1,105 @@
+#pragma once
+
+#include "server-common.h"
+#include "server-task.h"
+
+#include "sampling.h"
+#include "speculative.h"
+
+#include <climits>
+#include <functional>
+#include <limits>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace server_schema {
+
+struct field_eval_context {
+    task_params & params;
+    const llama_vocab * vocab = nullptr;
+    const std::vector<llama_logit_bias> * logit_bias_eog = nullptr;
+    field_eval_context(task_params & params) : params(params) {}
+};
+
+using field_handler = std::function<void(field_eval_context &, const json &)>;
+
+struct field {
+    std::vector<const char *> name;
+    const char * desc = "";
+    field_handler custom_handler;
+    field() = default;
+    field(const char * n) : name({n}) {}
+    virtual ~field() = default;
+    field * set_desc(const char * s) {
+        desc = s;
+        return this;
+    }
+    // if 'name' is present, use it, otherwise look for aliases following the order they were added
+    field * add_alias(const char * n) {
+        name.push_back(n);
+        return this;
+    }
+    field * set_handler(field_handler h) { this->custom_handler = h; return this; }
+    virtual void eval(field_eval_context & ctx, const json & data) = 0;
+};
+
+template <typename T = int32_t>
+struct field_num : public field {
+    T & val;
+    T min = std::numeric_limits<T>::lowest();
+    T max = std::numeric_limits<T>::max();
+    bool is_hard_limit = false; // if true, throw error if the value is invalid
+    field_num(const char * n, T & val) : field(n), val(val) {}
+    // limits are inclusive, min <= value <= max
+    field_num * set_limits(T min, T max) {
+        this->min = min;
+        this->max = max;
+        return this;
+    }
+    field_num * set_hard_limits(T min, T max) {
+        set_limits(min, max);
+        is_hard_limit = true;
+        return this;
+    }
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_str : public field {
+    field_str(const char * n) : field(n) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_bool : public field {
+    bool & val;
+    field_bool(const char * n, bool & val) : field(n), val(val) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_json : public field {
+    field_json(const char * n) : field(n) {}
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+struct field_nested : public field {
+    std::vector<std::unique_ptr<field>> subfields;
+    field_nested(const char * n) : field(n) {}
+    field_nested * add_subfield(field * f) {
+        subfields.emplace_back(std::unique_ptr<field>(f));
+        return this;
+    }
+    virtual void eval(field_eval_context & ctx, const json & data) override;
+};
+
+std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(
+                    const common_params & params_base,
+                    task_params & params);
+
+task_params eval_llama_cmpl_schema(
+                    const llama_vocab * vocab,
+                    const common_params & params_base,
+                    const int n_ctx_slot,
+                    const std::vector<llama_logit_bias> & logit_bias_eog,
+                    const json & data);
+
+} // namespace server_schema
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 72a4bd076..9ba039c8b 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -232,396 +232,8 @@ common_chat_msg task_result_state::update_chat_msg(
     return chat_msg;
 }
 
-//
-// server_task
 //
 
-task_params server_task::params_from_json_cmpl(
-        const llama_vocab * vocab,
-        const common_params & params_base,
-        const int n_ctx_slot,
-        const std::vector<llama_logit_bias> & logit_bias_eog,
-        const json & data) {
-    task_params params;
-
-    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
-    task_params defaults;
-    defaults.sampling      = params_base.sampling;
-    defaults.speculative   = params_base.speculative;
-    defaults.n_keep        = params_base.n_keep;
-    defaults.n_predict     = params_base.n_predict;
-    defaults.n_cache_reuse = params_base.n_cache_reuse;
-    defaults.cache_prompt  = params_base.cache_prompt;
-    defaults.antiprompt    = params_base.antiprompt;
-
-    // enabling this will output extra debug information in the HTTP responses from the server
-    params.verbose           = params_base.verbosity > 9;
-    params.timings_per_token = json_value(data, "timings_per_token", false);
-
-    params.stream           = json_value(data,       "stream",             false);
-    auto stream_opt         = json_value(data,       "stream_options",     json::object());
-    params.include_usage    = json_value(stream_opt, "include_usage",      false);
-    params.cache_prompt     = json_value(data,       "cache_prompt",       defaults.cache_prompt);
-    params.return_tokens    = json_value(data,       "return_tokens",      false);
-    params.return_progress  = json_value(data,       "return_progress",    false);
-    auto max_tokens         = json_value(data,       "max_tokens",         defaults.n_predict);
-    params.n_predict        = json_value(data,       "n_predict",          json_value(data, "max_completion_tokens", max_tokens));
-    params.n_indent         = json_value(data,       "n_indent",           defaults.n_indent);
-    params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
-    params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
-    params.n_discard        = std::max(0, params.n_discard);
-    params.n_cmpl           = json_value(data,       "n_cmpl",             json_value(data, "n", 1));
-    params.n_cache_reuse    = json_value(data,       "n_cache_reuse",      defaults.n_cache_reuse);
-    //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
-    params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
-    params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());
-
-    params.sampling.top_k              = json_value(data, "top_k",               defaults.sampling.top_k);
-    params.sampling.top_p              = json_value(data, "top_p",               defaults.sampling.top_p);
-    params.sampling.min_p              = json_value(data, "min_p",               defaults.sampling.min_p);
-    params.sampling.top_n_sigma        = json_value(data, "top_n_sigma",         defaults.sampling.top_n_sigma);
-    params.sampling.xtc_probability    = json_value(data, "xtc_probability",     defaults.sampling.xtc_probability);
-    params.sampling.xtc_threshold      = json_value(data, "xtc_threshold",       defaults.sampling.xtc_threshold);
-    params.sampling.typ_p              = json_value(data, "typical_p",           defaults.sampling.typ_p);
-    params.sampling.temp               = json_value(data, "temperature",         defaults.sampling.temp);
-    params.sampling.dynatemp_range     = json_value(data, "dynatemp_range",      defaults.sampling.dynatemp_range);
-    params.sampling.dynatemp_exponent  = json_value(data, "dynatemp_exponent",   defaults.sampling.dynatemp_exponent);
-    params.sampling.penalty_last_n     = json_value(data, "repeat_last_n",       defaults.sampling.penalty_last_n);
-    params.sampling.penalty_repeat     = json_value(data, "repeat_penalty",      defaults.sampling.penalty_repeat);
-    params.sampling.penalty_freq       = json_value(data, "frequency_penalty",   defaults.sampling.penalty_freq);
-    params.sampling.penalty_present    = json_value(data, "presence_penalty",    defaults.sampling.penalty_present);
-    params.sampling.dry_multiplier     = json_value(data, "dry_multiplier",      defaults.sampling.dry_multiplier);
-    params.sampling.dry_base           = json_value(data, "dry_base",            defaults.sampling.dry_base);
-    params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length",  defaults.sampling.dry_allowed_length);
-    params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n",  defaults.sampling.dry_penalty_last_n);
-    params.sampling.mirostat           = json_value(data, "mirostat",            defaults.sampling.mirostat);
-    params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",        defaults.sampling.mirostat_tau);
-    params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",        defaults.sampling.mirostat_eta);
-    params.sampling.adaptive_target    = json_value(data, "adaptive_target",     defaults.sampling.adaptive_target);
-    params.sampling.adaptive_decay     = json_value(data, "adaptive_decay",      defaults.sampling.adaptive_decay);
-    params.sampling.seed               = json_value(data, "seed",                defaults.sampling.seed);
-    params.sampling.n_probs            = json_value(data, "n_probs",             defaults.sampling.n_probs);
-    params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
-    params.sampling.backend_sampling   = json_value(data, "backend_sampling",    defaults.sampling.backend_sampling);
-    params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);
-
-    params.speculative = defaults.speculative;
-
-    // TODO: to keep things simple, we disable speculative parameter adjustments for now
-#if 0
-    // TODO: for now, be able to adjust only the draft-model based speculative parameters
-    params.speculative.draft.n_min = json_value(data, "speculative.n_min", defaults.speculative.draft.n_min);
-    params.speculative.draft.n_max = json_value(data, "speculative.n_max", defaults.speculative.draft.n_max);
-    params.speculative.draft.p_min = json_value(data, "speculative.p_min", defaults.speculative.draft.p_min);
-
-    params.speculative.draft.n_min = std::min(params.speculative.draft.n_max, params.speculative.draft.n_min);
-    params.speculative.draft.n_min = std::max(params.speculative.draft.n_min, 0);
-    params.speculative.draft.n_max = std::max(params.speculative.draft.n_max, 0);
-
-    // for debugging and research purposes
-    params.speculative.type = common_speculative_type_from_name(json_value(data, "speculative.type", common_speculative_type_to_str(defaults.speculative.type)));
-
-    params.speculative.ngram_size_n     = json_value(data, "speculative.ngram_size_n", defaults.speculative.ngram_size_n);
-    params.speculative.ngram_size_m     = json_value(data, "speculative.ngram_size_m", defaults.speculative.ngram_size_m);
-    params.speculative.ngram_min_hits   = json_value(data, "speculative.ngram_m_hits", defaults.speculative.ngram_min_hits);
-
-    params.speculative.ngram_size_n     = std::max(std::min(1, (int) params.speculative.ngram_size_n),     1024);
-    params.speculative.ngram_size_m     = std::max(std::min(1, (int) params.speculative.ngram_size_m),     1024);
-    params.speculative.ngram_min_hits   = std::max(std::min(1, (int) params.speculative.ngram_min_hits),   1024);
-#endif
-
-    // Use OpenAI API logprobs only if n_probs wasn't provided
-    if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
-        params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
-    }
-
-    if (data.contains("lora")) {
-        if (data.at("lora").is_array()) {
-            params.lora = parse_lora_request(data.at("lora"));
-        } else {
-            throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
-        }
-    } else {
-        params.lora = {};
-    }
-
-    // TODO: add more sanity checks for the input parameters
-
-    if (params.sampling.penalty_last_n < -1) {
-        throw std::runtime_error("Error: repeat_last_n must be >= -1");
-    }
-
-    if (params.sampling.dry_penalty_last_n < -1) {
-        throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        // note: should be the slot's context and not the full context, but it's ok
-        params.sampling.penalty_last_n = n_ctx_slot;
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        params.sampling.dry_penalty_last_n = n_ctx_slot;
-    }
-
-    if (params.sampling.dry_base < 1.0f) {
-        params.sampling.dry_base = defaults.sampling.dry_base;
-    }
-
-    // sequence breakers for DRY
-    {
-        // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
-        // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39
-
-        if (data.contains("dry_sequence_breakers")) {
-            params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
-            if (params.sampling.dry_sequence_breakers.empty()) {
-                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
-            }
-        }
-    }
-
-    // process "json_schema" and "grammar"
-    if (data.contains("json_schema") && !data.contains("grammar")) {
-        try {
-            auto schema                  = json_value(data, "json_schema", json::object());
-            SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
-            std::string grammar_str      = json_schema_to_grammar(schema);
-            SRV_DBG("Converted grammar: %s\n", grammar_str.c_str());
-            params.sampling.grammar      = {COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT, std::move(grammar_str)};
-        } catch (const std::exception & e) {
-            throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
-        }
-    } else {
-        params.sampling.grammar = defaults.sampling.grammar;
-
-        std::string grammar_str = json_value(data, "grammar", std::string());
-        if (!grammar_str.empty()) {
-            // grammar_type key is set by the server when converting chat template grammars
-            std::string grammar_type = json_value(data, "grammar_type", std::string());
-            if (grammar_type == "tool_calls") {
-                params.sampling.grammar = {COMMON_GRAMMAR_TYPE_TOOL_CALLS, std::move(grammar_str)};
-            } else {
-                // explicit grammar from the user (API field "grammar")
-                params.sampling.grammar = {COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
-            }
-            SRV_DBG("Grammar (%s): %s\n", grammar_type.c_str(), common_grammar_value(params.sampling.grammar).c_str());
-        }
-        params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
-        SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
-    }
-
-    {
-        auto it = data.find("chat_format");
-        if (it != data.end()) {
-            params.chat_parser_params.format = static_cast<common_chat_format>(it->get<int>());
-            SRV_INF("Chat format: %s\n", common_chat_format_name(params.chat_parser_params.format));
-        } else {
-            params.chat_parser_params.format = defaults.chat_parser_params.format;
-        }
-        common_reasoning_format reasoning_format = params_base.reasoning_format;
-        if (data.contains("reasoning_format")) {
-            reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
-        }
-        params.chat_parser_params.reasoning_format = reasoning_format;
-        params.chat_parser_params.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
-        params.chat_parser_params.generation_prompt = json_value(data, "generation_prompt", std::string());
-        params.sampling.generation_prompt = params.chat_parser_params.generation_prompt;
-        SRV_DBG("Generation prompt: '%s'\n", params.chat_parser_params.generation_prompt.c_str());
-        params.chat_parser_params.parse_tool_calls = json_value(data, "parse_tool_calls", false);
-        if (data.contains("chat_parser")) {
-            params.chat_parser_params.parser.load(data.at("chat_parser").get<std::string>());
-        }
-        if (data.contains("continue_final_message")) {
-            auto continuation = common_chat_continuation_parse(data.at("continue_final_message"));
-            params.chat_parser_params.is_continuation = continuation != COMMON_CHAT_CONTINUATION_NONE;
-        }
-        params.chat_parser_params.echo = json_value(data, "echo", false);
-    }
-
-    {
-        const auto preserved_tokens = data.find("preserved_tokens");
-        if (preserved_tokens != data.end()) {
-            for (const auto & t : *preserved_tokens) {
-                auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
-                if (ids.size() == 1) {
-                    SRV_DBG("Preserved token: %d\n", ids[0]);
-                    params.sampling.preserved_tokens.insert(ids[0]);
-                } else {
-                    // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
-                    SRV_DBG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
-                }
-            }
-        }
-        const auto grammar_triggers = data.find("grammar_triggers");
-        if (grammar_triggers != data.end()) {
-            for (const auto & t : *grammar_triggers) {
-                server_grammar_trigger ct(t);
-                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
-                    const auto & word = ct.value.value;
-                    auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true);
-                    if (ids.size() == 1) {
-                        auto token = ids[0];
-                        if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
-                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
-                        }
-                        SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
-                        common_grammar_trigger trigger;
-                        trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
-                        trigger.value = word;
-                        trigger.token = token;
-                        params.sampling.grammar_triggers.push_back(std::move(trigger));
-                    } else {
-                        SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
-                        params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
-                    }
-                } else {
-                    if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
-                        SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
-                    } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
-                        SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
-                    } else {
-                        throw std::runtime_error("Unknown grammar trigger type");
-                    }
-                    params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
-                }
-            }
-        }
-        if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
-            throw std::runtime_error("Error: no triggers set for lazy grammar!");
-        }
-    }
-
-    // Parse reasoning budget sampler parameters
-    {
-        const int32_t budget = json_value(data, "reasoning_budget_tokens", (int32_t) -1);
-        const auto start_tag = json_value(data, "reasoning_budget_start_tag", std::string());
-        const auto end_tag   = json_value(data, "reasoning_budget_end_tag", std::string());
-        const auto message   = json_value(data, "reasoning_budget_message", std::string());
-        params.sampling.reasoning_budget_tokens = budget;
-        params.sampling.reasoning_control = json_value(data, "reasoning_control", false);
-
-        if (!start_tag.empty()) {
-            params.sampling.reasoning_budget_start = common_tokenize(vocab, start_tag, false, true);
-        }
-        if (!end_tag.empty()) {
-            params.sampling.reasoning_budget_end = common_tokenize(vocab, end_tag, false, true);
-            params.sampling.reasoning_budget_forced = common_tokenize(vocab, message + end_tag, false, true);
-
-            SRV_DBG("reasoning budget: tokens=%d, generation_prompt='%s', start=%zu toks, end=%zu toks, forced=%zu toks\n",
-                budget, params.sampling.generation_prompt.c_str(),
-                params.sampling.reasoning_budget_start.size(),
-                params.sampling.reasoning_budget_end.size(),
-                params.sampling.reasoning_budget_forced.size());
-        }
-    }
-
-    {
-        params.sampling.logit_bias.clear();
-
-        const auto & logit_bias = data.find("logit_bias");
-        if (logit_bias != data.end() && logit_bias->is_array()) {
-            const int n_vocab = llama_vocab_n_tokens(vocab);
-            for (const auto & el : *logit_bias) {
-                // TODO: we may want to throw errors here, in case "el" is incorrect
-                if (el.is_array() && el.size() == 2) {
-                    float bias;
-                    if (el[1].is_number()) {
-                        bias = el[1].get<float>();
-                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
-                        bias = -INFINITY;
-                    } else {
-                        continue;
-                    }
-
-                    if (el[0].is_number_integer()) {
-                        llama_token tok = el[0].get<llama_token>();
-                        if (tok >= 0 && tok < n_vocab) {
-                            params.sampling.logit_bias.push_back({tok, bias});
-                        }
-                    } else if (el[0].is_string()) {
-                        auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
-                        for (auto tok : toks) {
-                            params.sampling.logit_bias.push_back({tok, bias});
-                        }
-                    }
-                }
-            }
-        } else if (logit_bias != data.end() && logit_bias->is_object()) {
-            const int n_vocab = llama_vocab_n_tokens(vocab);
-            for (const auto & el : logit_bias->items()) {
-                float bias;
-                const auto & key = el.key();
-                const auto & value = el.value();
-                if (value.is_number()) {
-                    bias = value.get<float>();
-                } else if (value.is_boolean() && !value.get<bool>()) {
-                    bias = -INFINITY;
-                } else {
-                    continue;
-                }
-
-                char *end;
-                llama_token tok = strtol(key.c_str(), &end, 10);
-                if (*end == 0) {
-                    if (tok >= 0 && tok < n_vocab) {
-                        params.sampling.logit_bias.push_back({tok, bias});
-                    }
-                } else {
-                    auto toks = common_tokenize(vocab, key, false);
-                    for (auto tok : toks) {
-                        params.sampling.logit_bias.push_back({tok, bias});
-                    }
-                }
-            }
-        }
-
-        params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos);
-        if (params.sampling.ignore_eos) {
-            params.sampling.logit_bias.insert(
-                    params.sampling.logit_bias.end(),
-                    logit_bias_eog.begin(), logit_bias_eog.end());
-        }
-    }
-
-    {
-        params.antiprompt.clear();
-
-        const auto & stop = data.find("stop");
-        if (stop != data.end() && stop->is_array()) {
-            for (const auto & word : *stop) {
-                if (!word.empty()) {
-                    params.antiprompt.push_back(word);
-                }
-            }
-        }
-        // set reverse prompt from cli args if not set in the request
-        if (params.antiprompt.empty()) {
-            params.antiprompt = defaults.antiprompt;
-        }
-    }
-
-    {
-        const auto samplers = data.find("samplers");
-        if (samplers != data.end()) {
-            if (samplers->is_array()) {
-                params.sampling.samplers = common_sampler_types_from_names(*samplers);
-            } else if (samplers->is_string()){
-                params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
-            }
-        } else {
-            params.sampling.samplers = defaults.sampling.samplers;
-        }
-    }
-
-    if (params.n_cmpl > params_base.n_parallel) {
-        throw std::runtime_error("n_cmpl cannot be greater than the number of slots, please increase -np");
-    }
-
-    return params;
-}
-
-//
 // result_timings
 //
 
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 1a03d5f26..299c279d7 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -210,13 +210,6 @@ struct server_task {
         }
     }
 
-    static task_params params_from_json_cmpl(
-        const llama_vocab * vocab,
-        const common_params & params_base,
-        const int n_ctx_slot,
-        const std::vector<llama_logit_bias> & logit_bias_eog,
-        const json & data);
-
     // utility function
     static std::unordered_set<int> get_list_id(const std::vector<server_task> & tasks) {
         std::unordered_set<int> ids(tasks.size());

From fe7c8b2414bb39e3dbc192fb494a1eb054b90890 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 15:41:09 +0200
Subject: [PATCH 046/292] server: (router) fix stopping_thread potentially hang
 (#24728)

* server: (router) fix stopping_thread potentially hang

* fix windows build
---
 tools/server/server-models.cpp | 59 +++++++++++++++++++++-------------
 1 file changed, 37 insertions(+), 22 deletions(-)

diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index cc7184165..7aaad6926 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -54,7 +54,7 @@ extern char **environ;
 
 struct server_subproc {
     std::optional<subprocess_s> sproc; // empty while in DOWNLOADING state
-    std::atomic<bool> stop_download{false}; // flag to signal download cancellation
+    std::atomic<bool> stopped{false}; // set to cancel a download or signal child process exit
 
     subprocess_s & get() {
         GGML_ASSERT(sproc.has_value() && "subprocess not initialized");
@@ -64,6 +64,22 @@ struct server_subproc {
     bool is_alive() {
         return sproc.has_value() && subprocess_alive(&sproc.value());
     }
+
+    void terminate() {
+        if (!sproc.has_value()) {
+            return;
+        }
+#if defined(_WIN32)
+        if (sproc->hProcess == NULL) {
+            return;
+        }
+#else
+        if (sproc->child <= 0) {
+            return;
+        }
+#endif
+        subprocess_terminate(&sproc.value());
+    }
 };
 
 
@@ -902,50 +918,49 @@ void server_models::load(const std::string & name) {
         });
 
         std::thread stopping_thread([&]() {
-            // thread to monitor stopping signal OR child crash
+            // thread to monitor explicit stop requests; child crash is signalled via child_proc->stopped
             auto is_stopping = [this, &name]() {
                 return this->stopping_models.find(name) != this->stopping_models.end();
             };
-            auto should_wake = [&]() {
-                return is_stopping() || !child_proc->is_alive();
-            };
             {
                 std::unique_lock<std::mutex> lk(this->mutex);
-                this->cv_stop.wait(lk, should_wake);
+                this->cv_stop.wait(lk, [&]() {
+                    return is_stopping() || child_proc->stopped.load(std::memory_order_acquire);
+                });
             }
-            // child may have already exited (e.g. crashed) — skip shutdown sequence
-            if (!child_proc->is_alive()) {
+            // child crashed or finished on its own — skip graceful shutdown sequence
+            if (child_proc->stopped.load(std::memory_order_acquire)) {
                 return;
             }
             SRV_INF("stopping model instance name=%s\n", name.c_str());
-            // send interrupt to child process
             fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT);
             fflush(stdin_file);
-            // wait to stop gracefully or timeout
             int64_t start_time = ggml_time_ms();
             while (true) {
                 std::unique_lock<std::mutex> lk(this->mutex);
-                if (!is_stopping()) {
-                    return; // already stopped
+                if (!is_stopping() || child_proc->stopped.load(std::memory_order_acquire)) {
+                    return;
                 }
                 int64_t elapsed = ggml_time_ms() - start_time;
                 if (elapsed >= stop_timeout * 1000) {
-                    // timeout, force kill
+                    lk.unlock();
                     SRV_WRN("force-killing model instance name=%s after %d seconds timeout\n", name.c_str(), stop_timeout);
-                    subprocess_terminate(&child_proc->get());
+                    child_proc->terminate();
                     return;
                 }
-                this->cv_stop.wait_for(lk, std::chrono::seconds(1));
+                this->cv_stop.wait_for(lk, std::chrono::seconds(1), [&]() {
+                    return !is_stopping() || child_proc->stopped.load(std::memory_order_acquire);
+                });
             }
         });
 
-        // we reach here when the child process exits
+        // we reach here when the child process exits (stdout EOF)
         // note: we cannot join() prior to this point because it will close stdin_file
         if (log_thread.joinable()) {
             log_thread.join();
         }
 
-        // stop the timeout monitoring thread
+        child_proc->stopped.store(true, std::memory_order_release);
         {
             std::lock_guard<std::mutex> lk(this->mutex);
             stopping_models.erase(name);
@@ -971,7 +986,7 @@ void server_models::load(const std::string & name) {
         // old process should have exited already, but just in case, we clean it up here
         if (old_instance.subproc->is_alive()) {
             SRV_WRN("old process for model name=%s is still alive, this is unexpected\n", name.c_str());
-            subprocess_terminate(&old_instance.subproc->get()); // force kill
+            old_instance.subproc->terminate(); // force kill
         }
         if (old_instance.th.joinable()) {
             old_instance.th.join();
@@ -1039,7 +1054,7 @@ void server_models::download(common_params_model && model, common_download_opts
     dl->opts  = opts;  // copy
 
     dl->should_stop = [sp = inst.subproc]() {
-        return sp->stop_download.load(std::memory_order_relaxed);
+        return sp->stopped.load(std::memory_order_relaxed);
     };
 
     dl->on_progress = [this, name](const common_download_progress & p) {
@@ -1069,7 +1084,7 @@ void server_models::unload(const std::string & name) {
     if (it != mapping.end()) {
         if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
             SRV_INF("cancelling download for model name=%s\n", name.c_str());
-            it->second.subproc->stop_download.store(true, std::memory_order_relaxed);
+            it->second.subproc->stopped.store(true, std::memory_order_relaxed);
             // for convenience, we wait the status change here
             wait(lk, name, [](const server_model_meta & new_meta) {
                 return new_meta.status != SERVER_MODEL_STATUS_DOWNLOADING;
@@ -1080,7 +1095,7 @@ void server_models::unload(const std::string & name) {
             if (it->second.meta.status == SERVER_MODEL_STATUS_LOADING) {
                 // special case: if model is in loading state, unloading means force-killing it
                 SRV_WRN("model name=%s is still loading, force-killing\n", name.c_str());
-                subprocess_terminate(&it->second.subproc->get());
+                it->second.subproc->terminate();
             }
             cv_stop.notify_all();
             // status change will be handled by the managing thread
@@ -1097,7 +1112,7 @@ void server_models::unload_all() {
         for (auto & [name, inst] : mapping) {
             if (inst.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
                 SRV_INF("cancelling download for model name=%s\n", name.c_str());
-                inst.subproc->stop_download.store(true, std::memory_order_relaxed);
+                inst.subproc->stopped.store(true, std::memory_order_relaxed);
             } else if (inst.meta.is_running()) {
                 SRV_INF("stopping model instance name=%s\n", name.c_str());
                 stopping_models.insert(name);

From 2c64520ba602dfb739b7662a7785781ea2fcf365 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 21:46:54 +0800
Subject: [PATCH 047/292] failsafe target for macos

---
 .github/workflows/kcpp-build-release-macos.yaml | 10 ++++++++--
 Makefile                                        |  5 ++++-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/kcpp-build-release-macos.yaml b/.github/workflows/kcpp-build-release-macos.yaml
index 61e7ea90d..fa20518e6 100644
--- a/.github/workflows/kcpp-build-release-macos.yaml
+++ b/.github/workflows/kcpp-build-release-macos.yaml
@@ -34,10 +34,16 @@ jobs:
       - name: Build
         id: make_build
         run: |
-          make LLAMA_METAL=1 LLAMA_PORTABLE=1
+          make LLAMA_METAL=1 koboldcpp_default
+          mkdir -p build_artifacts
+          mv koboldcpp_default.so build_artifacts/
+          make clean
+          make koboldcpp_macos_failsafe
+          mv koboldcpp_macos_failsafe.so koboldcpp_failsafe.so
+          mv build_artifacts/koboldcpp_default.so .
           chmod +x './create_ver_file.sh'
           . create_ver_file.sh
-          pyinstaller --noconfirm --onefile --collect-all customtkinter --collect-all jinja2 --collect-all psutil --add-data './koboldcpp_default.so:.' --add-data './ggml-metal-merged.metal:.' --add-data './kcpp_adapters:./kcpp_adapters' --add-data './koboldcpp.py:.' --add-data './json_to_gbnf.py:.' --add-data './LICENSE.md:.' --add-data './MIT_LICENSE_GGML_SDCPP_LLAMACPP_ONLY.md:.' --add-data './embd_res:./embd_res' --version-file './version.txt' --clean --console koboldcpp.py -n "koboldcpp-mac-arm64"
+          pyinstaller --noconfirm --onefile --collect-all customtkinter --collect-all jinja2 --collect-all psutil --add-data './koboldcpp_default.so:.' --add-data './koboldcpp_failsafe.so:.' --add-data './ggml-metal-merged.metal:.' --add-data './kcpp_adapters:./kcpp_adapters' --add-data './koboldcpp.py:.' --add-data './json_to_gbnf.py:.' --add-data './LICENSE.md:.' --add-data './MIT_LICENSE_GGML_SDCPP_LLAMACPP_ONLY.md:.' --add-data './embd_res:./embd_res' --version-file './version.txt' --clean --console koboldcpp.py -n "koboldcpp-mac-arm64"
 
       - name: Test
         id: test
diff --git a/Makefile b/Makefile
index 4f2420a35..8233fc2ab 100644
--- a/Makefile
+++ b/Makefile
@@ -767,7 +767,7 @@ gpttype_adapter_vulkan_noavx2.o: $(GPTTYPE_ADAPTER)
 	$(CXX) $(CXXFLAGS) $(FAILSAFE_FLAGS) $(VULKAN_FLAGS) -c $< -o $@
 
 clean:
-	rm -vf *.o main ttsmain sdmain whispermain quantize_gguf quantize_gpt2 quantize_gptj quantize_neox quantize_mpt vulkan-shaders-gen vulkan-shaders-gen-noext gguf-split mtmd-cli mainvk fitparams embedding embeddingvk qwen3tts rpcserver llamaserver llamaservervk rpcserver.exe llamaserver.exe llamaservervk.exe qwen3tts.exe embeddingvk.exe embedding.exe fitparams.exe mainvk.exe mtmd-cli.exe gguf-split.exe vulkan-shaders-gen.exe vulkan-shaders-gen-noext.exe main.exe ttsmain.exe sdmain.exe whispermain.exe quantize_gguf.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp_default.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_vulkan_failsafe.dll koboldcpp_cublas.dll koboldcpp_hipblas.dll koboldcpp_vulkan.dll koboldcpp_vulkan_noavx2.dll koboldcpp_default.so koboldcpp_failsafe.so koboldcpp_noavx2.so koboldcpp_vulkan_failsafe.so koboldcpp_cublas.so koboldcpp_hipblas.so koboldcpp_vulkan.so koboldcpp_vulkan_noavx2.so ggml/src/ggml-vulkan-shaders.cpp ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders-noext.cpp ggml/src/ggml-vulkan-shaders-noext.hpp
+	rm -vf *.o main ttsmain sdmain whispermain quantize_gguf quantize_gpt2 quantize_gptj quantize_neox quantize_mpt vulkan-shaders-gen vulkan-shaders-gen-noext gguf-split mtmd-cli mainvk fitparams embedding embeddingvk qwen3tts rpcserver llamaserver llamaservervk rpcserver.exe llamaserver.exe llamaservervk.exe qwen3tts.exe embeddingvk.exe embedding.exe fitparams.exe mainvk.exe mtmd-cli.exe gguf-split.exe vulkan-shaders-gen.exe vulkan-shaders-gen-noext.exe main.exe ttsmain.exe sdmain.exe whispermain.exe quantize_gguf.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe quantize_mpt.exe koboldcpp_default.dll koboldcpp_failsafe.dll koboldcpp_noavx2.dll koboldcpp_vulkan_failsafe.dll koboldcpp_cublas.dll koboldcpp_hipblas.dll koboldcpp_vulkan.dll koboldcpp_vulkan_noavx2.dll koboldcpp_default.so koboldcpp_failsafe.so koboldcpp_macos_failsafe.so koboldcpp_noavx2.so koboldcpp_vulkan_failsafe.so koboldcpp_cublas.so koboldcpp_hipblas.so koboldcpp_vulkan.so koboldcpp_vulkan_noavx2.so ggml/src/ggml-vulkan-shaders.cpp ggml/src/ggml-vulkan-shaders.hpp ggml/src/ggml-vulkan-shaders-noext.cpp ggml/src/ggml-vulkan-shaders-noext.hpp
 	rm -vrf ggml/src/ggml-cuda/*.o
 	rm -vrf ggml/src/ggml-cuda/template-instances/*.o
 	rm -vrf llguidance
@@ -906,6 +906,9 @@ endif
 koboldcpp_default: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(DEFAULT_BUILD)
 
+koboldcpp_macos_failsafe: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+	$(DEFAULT_BUILD)
+
 ifdef FAILSAFE_BUILD
 koboldcpp_failsafe: ggml_v4_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FAILSAFE) $(OBJS)
 	$(FAILSAFE_BUILD)

From 1b36e7f606e1da3bdd7029895a79419da27bf653 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 22:16:29 +0800
Subject: [PATCH 048/292] option to save tts as mp3, currently slightly bugged

---
 embd_res/kcpp_musicui.embd | 11 +++++++++--
 expose.h                   |  1 +
 koboldcpp.py               |  4 +++-
 otherarch/tts_adapter.cpp  | 19 ++++++++++++++++---
 4 files changed, 29 insertions(+), 6 deletions(-)

diff --git a/embd_res/kcpp_musicui.embd b/embd_res/kcpp_musicui.embd
index 85738e1b6..3c5d44990 100644
--- a/embd_res/kcpp_musicui.embd
+++ b/embd_res/kcpp_musicui.embd
@@ -307,6 +307,11 @@ select{
     <input title="TTS Instruction" id="tts_instruction" placeholder="e.g. angry shouting loud male">
   </div>
 
+  <div style="margin-top:10px">
+    <label>Save as MP3</label>
+    <input title="Save as MP3" id="tts_use_mp3" type="checkbox" style="max-width:30px">
+  </div>
+
   <div style="margin-top:14px">
     <label>API Base URL (optional)</label>
     <input id="tts_baseUrl" placeholder="http://localhost:5001">
@@ -445,7 +450,8 @@ async function generateTTS(){
 
     const payload = {
       input: document.getElementById("tts_input").value,
-      voice: document.getElementById("tts_voice").value
+      voice: document.getElementById("tts_voice").value,
+      use_mp3: document.getElementById("tts_use_mp3").checked
     };
 
     const instruction = document.getElementById("tts_instruction").value;
@@ -495,6 +501,7 @@ async function generateTTS(){
 function clearTTS(){
   document.getElementById("tts_input").value="";
   document.getElementById("tts_instruction").value="";
+  document.getElementById("tts_use_mp3").checked=false;
 }
 
 //end of tts part
@@ -935,4 +942,4 @@ fetchStats();
 </script>
 
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/expose.h b/expose.h
index b6e0045cd..f753a2ec2 100644
--- a/expose.h
+++ b/expose.h
@@ -320,6 +320,7 @@ struct tts_generation_inputs
     const char * custom_speaker_data = "";
     const char * reference_audio = "";
     const char * speaker_instruction = "";
+    const bool use_mp3 = false;
 };
 struct tts_generation_outputs
 {
diff --git a/koboldcpp.py b/koboldcpp.py
index f6efb5741..cf2b6f51a 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -498,7 +498,8 @@ class tts_generation_inputs(ctypes.Structure):
                 ("custom_speaker_text", ctypes.c_char_p),
                 ("custom_speaker_data", ctypes.c_char_p),
                 ("reference_audio", ctypes.c_char_p),
-                ("speaker_instruction", ctypes.c_char_p)]
+                ("speaker_instruction", ctypes.c_char_p),
+                ("use_mp3", ctypes.c_bool)]
 
 class tts_generation_outputs(ctypes.Structure):
     _fields_ = [("status", ctypes.c_int),
@@ -2999,6 +3000,7 @@ def tts_generate(genparams):
     if not genparams.get("instruction", ""):
         prompt, ttsinstruction = tts_extract_instruction(prompt)
     inputs.speaker_instruction = ttsinstruction.encode("UTF-8")
+    inputs.use_mp3 = genparams.get("use_mp3", False)
     inputs.prompt = prompt.encode("UTF-8")
     inputs.speaker_seed = voice
     aseed = -1
diff --git a/otherarch/tts_adapter.cpp b/otherarch/tts_adapter.cpp
index 5284ad716..b84a1359e 100644
--- a/otherarch/tts_adapter.cpp
+++ b/otherarch/tts_adapter.cpp
@@ -492,6 +492,19 @@ static int code_terminate_id = 151670;
 static int nthreads = 4;
 static int tts_max_len = 4096;
 
+static std::string save_tts_audio_base64(const std::vector<float> & audio, int sample_rate, bool use_mp3)
+{
+    if (!use_mp3) {
+        return save_wav16_base64(audio, sample_rate);
+    }
+
+    std::vector<float> stereo_audio;
+    stereo_audio.reserve(audio.size() * 2);
+    stereo_audio.insert(stereo_audio.end(), audio.begin(), audio.end());
+    stereo_audio.insert(stereo_audio.end(), audio.begin(), audio.end());
+    return save_stereo_mp3_base64(stereo_audio, (int) audio.size(), sample_rate);
+}
+
 //ttscpp specific
 static bool is_ttscpp_file = false;
 static generation_configuration * ttscpp_config = nullptr;
@@ -718,7 +731,7 @@ static tts_generation_outputs ttstype_generate_ttscpp(const tts_generation_input
         printf("\nTTS Generated audio in %.2fs.\n",ttstime);
         std::vector<float> wavdat = std::vector(response_data.data, response_data.data + response_data.n_outputs);
         //audio_post_clean(wavdat);
-        last_generated_audio = save_wav16_base64(wavdat, ttscpp_runner->sampling_rate);
+        last_generated_audio = save_tts_audio_base64(wavdat, ttscpp_runner->sampling_rate, inputs.use_mp3);
         output.data = last_generated_audio.c_str();
         output.status = 1;
         last_generation_settings_audio_seed = 0;
@@ -1131,7 +1144,7 @@ static tts_generation_outputs ttstype_generate_outetts(const tts_generation_inpu
             return output;
         }
 
-        last_generated_audio = save_wav16_base64(audio, t_sr);
+        last_generated_audio = save_tts_audio_base64(audio, t_sr, inputs.use_mp3);
         ttstime = timer_check();
 
         printf("\nTTS Generated %d audio tokens in %.2fs.\n",(int) codes.size(),ttstime);
@@ -1239,7 +1252,7 @@ static tts_generation_outputs ttstype_generate_qwen3tts(const tts_generation_inp
 
         ttstime = timer_check();
         printf("\nTTS Generated audio in %.2fs.\n",ttstime);
-        last_generated_audio = save_wav16_base64(result.audio, result.sample_rate);
+        last_generated_audio = save_tts_audio_base64(result.audio, result.sample_rate, inputs.use_mp3);
         output.data = last_generated_audio.c_str();
         output.status = 1;
         last_generation_settings_audio_seed = inputs.audio_seed;

From 45f49f9bd9e12c2e024a0d37102271bab96b1724 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 22:33:30 +0800
Subject: [PATCH 049/292] fixed tts mp3 saving

---
 otherarch/tts_adapter.cpp |  6 +---
 otherarch/utils.cpp       | 60 +++++++++++++++++++++++++++++++++++++++
 otherarch/utils.h         |  1 +
 3 files changed, 62 insertions(+), 5 deletions(-)

diff --git a/otherarch/tts_adapter.cpp b/otherarch/tts_adapter.cpp
index b84a1359e..0da9673d2 100644
--- a/otherarch/tts_adapter.cpp
+++ b/otherarch/tts_adapter.cpp
@@ -498,11 +498,7 @@ static std::string save_tts_audio_base64(const std::vector<float> & audio, int s
         return save_wav16_base64(audio, sample_rate);
     }
 
-    std::vector<float> stereo_audio;
-    stereo_audio.reserve(audio.size() * 2);
-    stereo_audio.insert(stereo_audio.end(), audio.begin(), audio.end());
-    stereo_audio.insert(stereo_audio.end(), audio.begin(), audio.end());
-    return save_stereo_mp3_base64(stereo_audio, (int) audio.size(), sample_rate);
+    return save_mono_mp3_base64(audio, sample_rate);
 }
 
 //ttscpp specific
diff --git a/otherarch/utils.cpp b/otherarch/utils.cpp
index 2e2170ac1..c81694003 100644
--- a/otherarch/utils.cpp
+++ b/otherarch/utils.cpp
@@ -627,6 +627,66 @@ std::string save_stereo_wav16_base64(const std::vector<float> & raw_audio, int T
     return kcpp_base64_encode(wav_data);
 }
 
+std::string save_mono_mp3_base64(const std::vector<float> & raw_audio, int sample_rate) {
+    std::vector<float> limited_audio = raw_audio;
+    float peak = 0.0f;
+    for (float s : limited_audio) {
+        peak = std::max(peak, std::fabs(s));
+    }
+    if (peak > 1e-6f) {
+        const float target_amp = 0.89125094f; // -1.0 dBFS
+        const float gain = peak > target_amp ? target_amp / peak : 1.0f;
+        for (float & s : limited_audio) {
+            s = std::max(-target_amp, std::min(target_amp, s * gain));
+        }
+    }
+
+    const float * enc_audio = limited_audio.data();
+    int enc_T  = (int) limited_audio.size();
+    int enc_sr = sample_rate;
+    std::vector<float> resampled;
+
+    if (sample_rate != 32000 && sample_rate != 44100 && sample_rate != 48000) {
+        resampled = resample_wav(1, limited_audio, sample_rate, 44100);
+        enc_audio = resampled.data();
+        enc_sr    = 44100;
+        enc_T     = (int) resampled.size();
+    }
+
+    if (enc_T <= 0) {
+        return "";
+    }
+
+    mp3enc_t * enc = mp3enc_init(enc_sr, 1, 128);
+    if (!enc) {
+        fprintf(stderr, "[Audio] mp3enc_init failed\n");
+        return "";
+    }
+
+    std::string mp3_data;
+    mp3_data.reserve(enc_T / 4);
+    int chunk_size = enc_sr;
+    std::vector<float> buf((size_t) chunk_size);
+
+    for (int pos = 0; pos < enc_T; pos += chunk_size) {
+        int n = (pos + chunk_size <= enc_T) ? chunk_size : (enc_T - pos);
+        memcpy(buf.data(), enc_audio + pos, (size_t) n * sizeof(float));
+        int out_size = 0;
+        const uint8_t * mp3 = mp3enc_encode(enc, buf.data(), n, &out_size);
+        if (out_size > 0) {
+            mp3_data.append((const char *) mp3, out_size);
+        }
+    }
+
+    int flush_size = 0;
+    const uint8_t * flush_data = mp3enc_flush(enc, &flush_size);
+    if (flush_size > 0) {
+        mp3_data.append((const char *) flush_data, flush_size);
+    }
+    mp3enc_free(enc);
+    return kcpp_base64_encode(mp3_data);
+}
+
 std::string save_stereo_mp3_base64(const std::vector<float> & raw_audio,int T_audio,int sample_rate) {
     const float * enc_audio = raw_audio.data();
     int enc_T  = T_audio;
diff --git a/otherarch/utils.h b/otherarch/utils.h
index ea489449a..923055915 100644
--- a/otherarch/utils.h
+++ b/otherarch/utils.h
@@ -114,3 +114,4 @@ std::string save_ulaw_wav8_base64(const std::vector<float> &data, int sample_rat
 std::string save_wav16_base64(const std::vector<float> &data, int sample_rate);
 std::string save_stereo_wav16_base64(const std::vector<float> & raw_audio, int T_audio, int sample_rate);
 std::string save_stereo_mp3_base64(const std::vector<float> & raw_audio,int T_audio,int sample_rate);
+std::string save_mono_mp3_base64(const std::vector<float> & raw_audio, int sample_rate);

From 7b6c5a2aeda694dc3ea0479380ab27d0e63ce3d7 Mon Sep 17 00:00:00 2001
From: Kangjia Gao <145212963+kkkzbh@users.noreply.github.com>
Date: Thu, 18 Jun 2026 22:46:17 +0800
Subject: [PATCH 050/292] docs: fix export-lora --lora-scaled syntax [no
 release] (#24703)

Assisted-by: Codex
---
 tools/export-lora/README.md | 14 ++++++--------
 1 file changed, 6 insertions(+), 8 deletions(-)

diff --git a/tools/export-lora/README.md b/tools/export-lora/README.md
index 7dce99c9a..f0729341f 100644
--- a/tools/export-lora/README.md
+++ b/tools/export-lora/README.md
@@ -6,11 +6,10 @@ Apply LORA adapters to base model and export the resulting model.
 usage: llama-export-lora [options]
 
 options:
-  -m,    --model                  model path from which to load base model (default '')
-         --lora FNAME             path to LoRA adapter  (can be repeated to use multiple adapters)
-         --lora-scaled FNAME S    path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)
-  -t,    --threads N              number of threads to use during computation (default: 4)
-  -o,    --output FNAME           output file (default: 'ggml-lora-merged-f16.gguf')
+  -m,    --model FNAME                  model path from which to load base model
+         --lora FNAME                   path to LoRA adapter (use comma-separated values to load multiple adapters)
+         --lora-scaled FNAME:SCALE,...  path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)
+  -o,    --output, --output-file FNAME  output file (default: 'ggml-lora-merged-f16.gguf')
 ```
 
 For example:
@@ -22,12 +21,11 @@ For example:
     --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf
 ```
 
-Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters:
+Multiple LORA adapters can be applied by passing comma-separated values to `--lora FNAME` or `--lora-scaled FNAME:SCALE,...`:
 
 ```bash
 ./bin/llama-export-lora \
     -m your_base_model.gguf \
     -o your_merged_model.gguf \
-    --lora-scaled lora_task_A.gguf 0.5 \
-    --lora-scaled lora_task_B.gguf 0.5
+    --lora-scaled lora_task_A.gguf:0.5,lora_task_B.gguf:0.5
 ```

From d2c67959b32cc49e43de2256b7381feb9130a17a Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Thu, 18 Jun 2026 08:35:02 -0700
Subject: [PATCH 051/292] hexagon: support for  op-trace (fine-grain tracing of
 HVX/HMX/DMA events) (#24592)

* hex-optrace: add support for optrace and instrument matmul and flash-atten code

* hex-trace: improve trace event and prefetto generator

* hex-trace: add new script dedicated to handling traces, specifically perfetto traces

* hex-trace: add --head/--tail options to profile and trace tools

* hex-trace: fix whitespaces

* hex-trace: fix flake8 warnings

* hex-trace: fix flake8 warnings

* hmx-fa: restore q_tiles clearing

* hex-profile: remove circular dep in includes

* hex-trace: simplify trace sizing check

* hex-profile: sort events in the summary by name
---
 ggml/src/ggml-hexagon/ggml-hexagon.cpp        | 116 ++++-
 ggml/src/ggml-hexagon/htp/CMakeLists.txt      |   2 +-
 ggml/src/ggml-hexagon/htp/flash-attn-ops.c    |   4 +
 ggml/src/ggml-hexagon/htp/hex-dma.h           |  13 +-
 ggml/src/ggml-hexagon/htp/hex-profile.h       |  64 +++
 ggml/src/ggml-hexagon/htp/hex-utils.h         |  27 -
 .../src/ggml-hexagon/htp/hmx-flash-attn-ops.c |  92 +---
 ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c    |  96 ++--
 ggml/src/ggml-hexagon/htp/hmx-profile.h       |  34 --
 ggml/src/ggml-hexagon/htp/hmx-queue.c         |   2 +
 ggml/src/ggml-hexagon/htp/hmx-queue.h         |   2 +
 ggml/src/ggml-hexagon/htp/htp-ctx.h           |   2 +
 ggml/src/ggml-hexagon/htp/htp-ops.h           |  35 +-
 ggml/src/ggml-hexagon/htp/main.c              |  50 +-
 ggml/src/ggml-hexagon/htp/matmul-ops.c        |  46 ++
 scripts/snapdragon/ggml-hexagon-profile.py    | 297 ++++++++++-
 scripts/snapdragon/ggml-hexagon-trace.py      | 463 ++++++++++++++++++
 17 files changed, 1121 insertions(+), 224 deletions(-)
 create mode 100644 ggml/src/ggml-hexagon/htp/hex-profile.h
 delete mode 100644 ggml/src/ggml-hexagon/htp/hmx-profile.h
 create mode 100755 scripts/snapdragon/ggml-hexagon-trace.py

diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 49bd7e433..e612ec392 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -69,6 +69,7 @@ static int opt_opstage  = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
 static int opt_opbatch  = 1024; // max number of ops in a batch
 static int opt_opqueue  = 16;   // max number of pending batches
 static int opt_oppoll   = 0;    // polling for batch completions
+static int opt_optrace  = 0;    // trace buffer size per thread (0 means default)
 
 static std::regex* opt_opfilter = NULL; // regex of ops to not claim
 
@@ -118,20 +119,39 @@ static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct
                 ggml_op_desc(op), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, supp ? "yes" : "no");
 }
 
+static const char * htp_event_name(uint16_t id) {
+    switch (id) {
+        case HTP_TRACE_EVT_DMA:            return "DMA";
+        case HTP_TRACE_EVT_HVX_COMP:       return "HVX_COMP";
+        case HTP_TRACE_EVT_HVX_A_QUANT:    return "HVX_A_QUANT";
+        case HTP_TRACE_EVT_HVX_A_PREP:     return "HVX_A_PREP";
+        case HTP_TRACE_EVT_HVX_W_DEQUANT:  return "HVX_W_DEQUANT";
+        case HTP_TRACE_EVT_HVX_W_PREP:     return "HVX_W_PREP";
+        case HTP_TRACE_EVT_HVX_O_PROC:     return "HVX_O_PROC";
+        case HTP_TRACE_EVT_HMX_COMP:       return "HMX_COMP";
+        default:                           return "UNKNOWN";
+    }
+}
+
 static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_opnode & node,
-                                      uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
+                                      const htp_prof_desc & pd) {
     if (!opt_profile) return;
 
+    uint32_t op_usec = pd.usecs;
+    uint32_t op_cycles = pd.cycles_stop - pd.cycles_start;
+    const uint32_t * pmu = pd.pmu;
+
     char pmu_str[256] = "";
-    if (opt_profile > 1) {
+    if (opt_profile == 2) {
         static_assert(HTP_PROF_PMU_NCNT == 8, "current implementation assumes 8 PMU counters");
         sprintf(pmu_str, " pmu [%u,%u,%u,%u,%u,%u,%u,%u]",
                 pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
     }
 
     htp_opformat fmt(node);
-    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
-            node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, op_usec, op_cycles, pmu_str);
+    float mhz = op_usec > 0 ? (float) op_cycles / op_usec : 0.0f;
+    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u start %u mhz %.1f%s\n", sess_name.c_str(),
+            node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, op_usec, op_cycles, pd.cycles_start, mhz, pmu_str);
 }
 
 // ** backend sessions
@@ -1995,10 +2015,16 @@ struct ggml_hexagon_opqueue {
         size_t n_ops     = batch_size;
         size_t n_tensors = n_ops + n_ops * HTP_OP_MAX_INPUTS;
 
+        size_t tr_size = 0;
+        if (opt_profile == 3) {
+            tr_size = (HTP_MAX_NTHREADS + 1) * opt_optrace * sizeof(htp_trace_desc);
+        }
+
         shm_blk_size = sizeof(htp_buf_desc)  * n_bufs    +
                        sizeof(htp_tensor)    * n_tensors +
                        sizeof(htp_op_desc)   * n_ops     +
-                       sizeof(htp_prof_desc) * n_ops;
+                       sizeof(htp_prof_desc) * n_ops     +
+                       tr_size;
 
         shm_buf = new ggml_hexagon_shared_buffer(sess, shm_blk_size * depth, true /* pinned */);
 
@@ -2042,11 +2068,19 @@ struct ggml_hexagon_opqueue {
         const size_t o_size = sizeof(htp_op_desc)   * req.n_ops;
         const size_t p_size = sizeof(htp_prof_desc) * req.n_ops;
 
+        size_t tr_size = 0;
+        if (opt_profile == 3) {
+            req.n_traces = opt_optrace;
+            tr_size = (HTP_MAX_NTHREADS + 1) * req.n_traces * sizeof(htp_trace_desc);
+        } else {
+            req.n_traces = 0;
+        }
+
         dbuf.ptr      = shm_buf->base + (req.id * shm_blk_size);
         dbuf.fd       = shm_buf->fd;
         dbuf.flags    = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
         dbuf.offset   = (uint8_t*) dbuf.ptr - (uint8_t*) shm_buf->base;
-        dbuf.size     = b_size + t_size + o_size + p_size;
+        dbuf.size     = b_size + t_size + o_size + p_size + tr_size;
 
         GGML_ASSERT(dbuf.size <= shm_blk_size);
 
@@ -2092,7 +2126,14 @@ struct ggml_hexagon_opqueue {
         const size_t o_size = sizeof(htp_op_desc)   * rsp.n_ops;
         const size_t p_size = sizeof(htp_prof_desc) * rsp.n_ops;
 
-        const size_t m_size = b_size + t_size + o_size + p_size;
+        size_t tr_size = 0;
+        uint32_t n_traces = 0;
+        if (opt_profile == 3) {
+            n_traces = opt_optrace;
+            tr_size = (HTP_MAX_NTHREADS + 1) * n_traces * sizeof(htp_trace_desc);
+        }
+
+        const size_t m_size = b_size + t_size + o_size + p_size + tr_size;
         GGML_ASSERT(m_size <= shm_blk_size);
 
         HEX_VERBOSE("ggml-hex: %s op-queue pop batch #%u : n-bufs %u n-tensors %u n-ops %u : m-size %zu b-size %zu t-size %zu o-size %zu\n",
@@ -2111,13 +2152,62 @@ struct ggml_hexagon_opqueue {
             GGML_ASSERT(rsp.n_ops <= ops.size());
 
             const htp_prof_desc * pd = (const htp_prof_desc *) p_ptr;
-            for (uint32_t i = 0; i < rsp.n_ops; i++) {
-                htp_usec += pd[i].usecs;
-                ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i].usecs, pd[i].cycles, pd[i].pmu);
+
+            const htp_trace_desc * trace_events = nullptr;
+
+            if (opt_profile == 3) {
+                trace_events = (const htp_trace_desc *) (p_ptr + p_size);
             }
 
-            GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u\n",
-                           shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec);
+            uint32_t trace_idx[HTP_MAX_NTHREADS + 1] = {0};
+            uint32_t valid_cnt[HTP_MAX_NTHREADS + 1] = {0};
+
+            if (opt_profile == 3) {
+                for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) {
+                    uint32_t count = rsp.n_traces[t];
+                    valid_cnt[t] = count > n_traces ? n_traces : count;
+                }
+            }
+
+            for (uint32_t i = 0; i < rsp.n_ops; i++) {
+                htp_usec += pd[i].usecs;
+
+                ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i]);
+
+                if (opt_profile == 3) {
+                    uint32_t op_duration = pd[i].cycles_stop - pd[i].cycles_start;
+
+                    for (uint32_t t = 0; t <= HTP_MAX_NTHREADS; t++) {
+                        while (trace_idx[t] < valid_cnt[t]) {
+                            const auto & e = trace_events[t * n_traces + trace_idx[t]];
+                            uint32_t offset = e.cycles - pd[i].cycles_start;
+                            if (offset >= 0x80000000) {
+                                trace_idx[t]++;
+                                continue;
+                            }
+                            if (offset > op_duration) {
+                                break;
+                            }
+                            bool is_stop = (e.info & 0x8000) != 0;
+                            uint16_t info = e.info & 0x7FFF;
+                            GGML_LOG_DEBUG("ggml-hex: %s trace-op %s: thread %u event %s info %u %s %u\n",
+                                           shm_buf->sess->c_name(), ops[i].op_name().c_str(), t, htp_event_name(e.id), info, is_stop ? "stop" : "start", e.cycles);
+                            trace_idx[t]++;
+                        }
+                    }
+                }
+            }
+
+            char evt_str[256] = "";
+            if (opt_profile == 3) {
+                sprintf(evt_str, " evt [%u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u]",
+                        rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3],
+                        rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7],
+                        rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]);
+            }
+
+            GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u%s\n",
+                           shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec, evt_str);
         }
     }
 };
@@ -3901,6 +3991,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
     const char * str_opqueue  = getenv("GGML_HEXAGON_OPQUEUE");
     const char * str_oppoll   = getenv("GGML_HEXAGON_OPPOLL");
+    const char * str_optrace  = getenv("GGML_HEXAGON_OPTRACE");
     const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
     const char * str_profile  = getenv("GGML_HEXAGON_PROFILE");
     const char * str_etm      = getenv("GGML_HEXAGON_ETM");
@@ -3939,6 +4030,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     opt_opbatch   = str_opbatch  ? strtoul(str_opbatch, NULL, 0)          : opt_opbatch;
     opt_opqueue   = str_opqueue  ? strtoul(str_opqueue, NULL, 0)          : opt_opqueue;
     opt_oppoll    = str_oppoll   ? strtoul(str_oppoll,  NULL, 0)          : opt_oppoll;
+    opt_optrace   = str_optrace  ? strtoul(str_optrace, NULL, 0)          : (opt_opbatch * 128);
     opt_profile   = str_profile  ? atoi(str_profile)                      : 0;
     opt_etm       = str_etm      ? atoi(str_etm)                          : 0;
     opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
index f4b44fe1a..31ba52762 100644
--- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
@@ -37,8 +37,8 @@ list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
 
 if (_hmx_idx GREATER_EQUAL 0)
     target_sources(${HTP_LIB} PRIVATE
-        hmx-matmul-ops.c
         hmx-flash-attn-ops.c
+        hmx-matmul-ops.c
         hmx-queue.c
     )
 
diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
index e99621469..b7511cd64 100644
--- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
@@ -339,6 +339,9 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
 
     if (ir0 >= ir1) return;
 
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+
     dma_queue * dma = octx->ctx->dma[ith];
 
     const uint32_t DK = nek0;
@@ -615,6 +618,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
             hvx_copy_f16_f32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
         }
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 }
 
 int op_flash_attn_ext(struct htp_ops_context * octx) {
diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h
index 7685473f4..93c21ebe5 100644
--- a/ggml/src/ggml-hexagon/htp/hex-dma.h
+++ b/ggml/src/ggml-hexagon/htp/hex-dma.h
@@ -6,6 +6,8 @@
 #include <stdbool.h>
 #include <stdint.h>
 
+#include "hex-profile.h"
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -88,6 +90,7 @@ typedef struct {
     uint32_t            pop_idx;
     uint32_t            capacity;
     uint32_t            idx_mask;
+    struct htp_thread_trace * trace;
 } dma_queue;
 
 dma_queue * dma_queue_create(size_t capacity);
@@ -152,6 +155,7 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t
     q->dptr[q->push_idx] = dptr;
 
     if (size) {
+        htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx);
         dmlink(q->tail, desc);
         q->tail = (dma_descriptor_2d *) desc;
     } else {
@@ -202,6 +206,7 @@ static inline bool dma_queue_push_single_2d(dma_queue * q, dma_ptr dptr, size_t
     q->dptr[q->push_idx] = dptr;
 
     if (nrows) {
+        htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx);
         dmlink(q->tail, desc);
         q->tail = desc;
     } else {
@@ -223,10 +228,12 @@ static inline dma_ptr dma_queue_pop(dma_queue * q) {
     dma_descriptor_2d * desc = &q->desc[q->pop_idx];
 
     // Wait for desc to complete
-    while (!desc->done) {
-        // FARF(ERROR, "dma-pop: waiting for DMA : %u\n", q->pop_idx);
-        dmpoll();
+    if (!desc->done) {
+        while (!desc->done) {
+            dmpoll();
+        }
     }
+    htp_trace_event_stop(q->trace, HTP_TRACE_EVT_DMA, q->pop_idx);
 
     dptr = q->dptr[q->pop_idx];
 
diff --git a/ggml/src/ggml-hexagon/htp/hex-profile.h b/ggml/src/ggml-hexagon/htp/hex-profile.h
new file mode 100644
index 000000000..8a37a4a06
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hex-profile.h
@@ -0,0 +1,64 @@
+#ifndef HEX_PROFILE_H
+#define HEX_PROFILE_H
+
+#include <stdbool.h>
+#include <stdint.h>
+#include <qurt.h>
+
+#include "hex-utils.h"
+#include "htp-ops.h"
+
+#define HTP_TRACE_EVT_START 0
+#define HTP_TRACE_EVT_STOP  1
+
+#ifndef HEX_NUM_PMU_COUNTERS
+#define HEX_NUM_PMU_COUNTERS 8
+#endif
+
+static inline void hex_get_pmu(uint32_t counters[]) {
+#if __HVX_ARCH__ >= 79
+    asm volatile("%0 = upmucnt0" : "=r"(counters[0]));
+    asm volatile("%0 = upmucnt1" : "=r"(counters[1]));
+    asm volatile("%0 = upmucnt2" : "=r"(counters[2]));
+    asm volatile("%0 = upmucnt3" : "=r"(counters[3]));
+    asm volatile("%0 = upmucnt4" : "=r"(counters[4]));
+    asm volatile("%0 = upmucnt5" : "=r"(counters[5]));
+    asm volatile("%0 = upmucnt6" : "=r"(counters[6]));
+    asm volatile("%0 = upmucnt7" : "=r"(counters[7]));
+#else
+    counters[0] = qurt_pmu_get(QURT_PMUCNT0);
+    counters[1] = qurt_pmu_get(QURT_PMUCNT1);
+    counters[2] = qurt_pmu_get(QURT_PMUCNT2);
+    counters[3] = qurt_pmu_get(QURT_PMUCNT3);
+    counters[4] = qurt_pmu_get(QURT_PMUCNT4);
+    counters[5] = qurt_pmu_get(QURT_PMUCNT5);
+    counters[6] = qurt_pmu_get(QURT_PMUCNT6);
+    counters[7] = qurt_pmu_get(QURT_PMUCNT7);
+#endif
+}
+
+struct htp_thread_trace {
+    uint32_t count;
+    uint32_t max_events;
+    struct htp_trace_desc * events;
+};
+
+static inline void htp_trace_event(struct htp_thread_trace * tr, uint16_t id, uint16_t info, uint32_t type) {
+    if (tr && tr->events && tr->count < tr->max_events) {
+        uint32_t idx = tr->count;
+        tr->events[idx].id = id;
+        tr->events[idx].info = info | (type == HTP_TRACE_EVT_STOP ? 0x8000 : 0);
+        tr->events[idx].cycles = (uint32_t) hex_get_cycles();
+        tr->count++;
+    }
+}
+
+static inline void htp_trace_event_start(struct htp_thread_trace * tr, uint16_t id, uint16_t info) {
+    htp_trace_event(tr, id, info, HTP_TRACE_EVT_START);
+}
+
+static inline void htp_trace_event_stop(struct htp_thread_trace * tr, uint16_t id, uint16_t info) {
+    htp_trace_event(tr, id, info, HTP_TRACE_EVT_STOP);
+}
+
+#endif /* HEX_PROFILE_H */
diff --git a/ggml/src/ggml-hexagon/htp/hex-utils.h b/ggml/src/ggml-hexagon/htp/hex-utils.h
index 6239ceff4..8e6e3ea75 100644
--- a/ggml/src/ggml-hexagon/htp/hex-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hex-utils.h
@@ -107,31 +107,4 @@ static inline void hex_pause() {
     asm volatile(" pause(#255)\n");
 }
 
-#ifndef HEX_NUM_PMU_COUNTERS
-#define HEX_NUM_PMU_COUNTERS 8
-#endif
-
-static inline void hex_get_pmu(uint32_t counters[]) {
-#if __HVX_ARCH__ >= 79
-    asm volatile("%0 = upmucnt0" : "=r"(counters[0]));
-    asm volatile("%0 = upmucnt1" : "=r"(counters[1]));
-    asm volatile("%0 = upmucnt2" : "=r"(counters[2]));
-    asm volatile("%0 = upmucnt3" : "=r"(counters[3]));
-    asm volatile("%0 = upmucnt4" : "=r"(counters[4]));
-    asm volatile("%0 = upmucnt5" : "=r"(counters[5]));
-    asm volatile("%0 = upmucnt6" : "=r"(counters[6]));
-    asm volatile("%0 = upmucnt7" : "=r"(counters[7]));
-#else
-    counters[0] = qurt_pmu_get(QURT_PMUCNT0);
-    counters[1] = qurt_pmu_get(QURT_PMUCNT1);
-    counters[2] = qurt_pmu_get(QURT_PMUCNT2);
-    counters[3] = qurt_pmu_get(QURT_PMUCNT3);
-    counters[4] = qurt_pmu_get(QURT_PMUCNT4);
-    counters[5] = qurt_pmu_get(QURT_PMUCNT5);
-    counters[6] = qurt_pmu_get(QURT_PMUCNT6);
-    counters[7] = qurt_pmu_get(QURT_PMUCNT7);
-    // qurt_pmu_get_pmucnt(counters);
-#endif
-}
-
 #endif /* HEX_UTILS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
index 2796564fb..986dde148 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
@@ -18,7 +18,7 @@
 #include "ggml-common.h"
 #include "hex-dma.h"
 #include "hex-fastdiv.h"
-#include "hmx-profile.h"
+#include "hex-profile.h"
 #include "hmx-queue.h"
 #include "hmx-utils.h"
 #include "htp-ctx.h"
@@ -367,8 +367,11 @@ static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data)
         return;
     }
 
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
     hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, factx->vtcm_k_fp16[args->buf_idx], total_rows, (int) factx->DK,
                              (int) args->src_stride, start, end);
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
 }
 
 static void fa_phase_k_interleave(struct hmx_fa_context * factx, int kv_rows, size_t src_stride, size_t buf_idx) {
@@ -408,8 +411,11 @@ static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data)
 
     __fp16 * v_tiles_dest = factx->use_pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
 
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
     hmx_interleave_cols_to_tiles(v_tiles_dest, factx->vtcm_v_fp16[args->buf_idx], total_rows, (int) factx->DV,
                              (int) args->src_stride, (int) args->n_col_tiles, start, end);
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
 }
 
 static void fa_phase_v_interleave(struct hmx_fa_context * factx,
@@ -462,6 +468,9 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
         return;
     }
 
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
+
     const struct htp_tensor * q       = args->q;
     const uint32_t            q_start = args->q_start;
     const uint32_t            kv_head = args->kv_head;
@@ -515,6 +524,7 @@ static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
             }
         }
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
 }
 
 static void fa_phase_q_load(struct hmx_fa_context *   factx,
@@ -566,6 +576,9 @@ static void fa_o_store_thread(unsigned int n, unsigned int i, void * data) {
         return;
     }
 
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
+
     const struct htp_tensor * dst        = args->dst;
     const __fp16 *            o_tile_src = args->o_tile_src;
     const uint32_t            q_start    = args->q_start;
@@ -611,6 +624,7 @@ static void fa_o_store_thread(unsigned int n, unsigned int i, void * data) {
             }
         }
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
 }
 
 static void fa_phase_o_store(struct hmx_fa_context *   factx,
@@ -680,6 +694,9 @@ static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
         return;
     }
 
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, vec_start);
+
     // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride
     const size_t row_buf_stride = factx->row_buf_stride;
     HVX_Vector * my_row_buf0    = factx->vtcm_row_bufs + i * 2 * row_buf_stride;
@@ -950,6 +967,7 @@ static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
         factx->vtcm_s_rowmax[r_vec_idx] = rowmax_acc_v;
         factx->vtcm_p_rowsum[r_vec_idx] = rowsum_acc_v;
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, vec_start);
 }
 
 // Serial m/l update + build_D.  Must run after softmax barrier (s_rowmax written by all threads).
@@ -1245,6 +1263,7 @@ static __attribute__((noinline)) void fa_compute_slopes(
 // ============================================================================
 
 int hmx_flash_attn_ext(struct htp_ops_context * octx) {
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[HTP_MAX_NTHREADS] : NULL;
     const struct htp_tensor * q    = octx->src[0];
     const struct htp_tensor * k    = octx->src[1];
     const struct htp_tensor * v    = octx->src[2];
@@ -1422,19 +1441,6 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_OK;
     }
 
-    // Profiling timers
-    TIMER_DEFINE(total);
-    TIMER_DEFINE(q_load);
-    TIMER_DEFINE(kv_dma);
-    TIMER_DEFINE(k_interleave);
-    TIMER_DEFINE(v_interleave);
-    TIMER_DEFINE(qk_dot);
-    TIMER_DEFINE(softmax);
-    TIMER_DEFINE(o_update);
-    TIMER_DEFINE(o_norm);
-    TIMER_DEFINE(o_store);
-
-    TIMER_START(total);
 
     // ======== DMA setup ========
     dma_queue * const dma = ctx->dma[0];
@@ -1474,12 +1480,10 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                 const size_t   n_row_tiles = g_br_actual / HMX_FP16_TILE_N_ROWS;
 
                 // ---- Load Q block [g_br, D] -> tiles, interleaving G heads ----
-                TIMER_START(q_load);
                 if (n_rows_g < g_br) {
                     hvx_splat_u8_a(factx.vtcm_q_tiles, 0, q_tile_bytes);
                 }
                 fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g);
-                TIMER_STOP(q_load);
 
                 // ---- Initialize per-block state ----
                 hvx_splat_u8_a(factx.vtcm_l_vec,   0,      col_vec_bytes);
@@ -1558,10 +1562,8 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
 
                         // Wait for current KV DMA
-                        TIMER_START(kv_dma);
                         dma_queue_pop(dma);  // K
                         dma_queue_pop(dma);  // V
-                        TIMER_STOP(kv_dma);
 
                         // Push mask DMA for this block (single 2D DMA when broadcast)
                         bool has_mask_dma = false;
@@ -1583,10 +1585,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                             ou_job.DV               = DV;
                             hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
                         }
-
-                        TIMER_START(k_interleave);
                         fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
-                        TIMER_STOP(k_interleave);
 
                         // ---- Phase 2: qk_dot(blk) on HMX ‖ V_int(blk) + DMA prefetch on HVX ----
                         qk_job.q_tiles        = factx.vtcm_q_tiles;
@@ -1597,15 +1596,11 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         qk_job.n_dot_tiles    = DK / 32;
                         qk_job.n_tiles_per_bc = n_tiles_per_bc;
                         qk_job.hmx_scales     = factx.vtcm_hmx_scales_qk;
-                        TIMER_START(qk_dot);
                         hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job));
 
                         // DMA push next block (non-blocking, before worker_pool)
                         DMA_PREFETCH_KV(kv_blk + 1);
-
-                        TIMER_START(v_interleave);
                         fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
-                        TIMER_STOP(v_interleave);
 
                         // Pop and swap previous block's output update (deferred HMX pop)
                         if (kv_blk > 0) {
@@ -1615,7 +1610,6 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
 
                         // Pop current block's dot product job
                         hmx_queue_pop(hmx_q);
-                        TIMER_STOP(qk_dot);
 
                         // ---- Phase 3: softmax(blk) + build_D(blk) | HMX idle ----
                         // Pop mask DMA before softmax (ensures VTCM buffer is ready)
@@ -1641,10 +1635,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
                         sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
                         sargs.slopes               = factx.vtcm_slopes;
-
-                        TIMER_START(softmax);
                         fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
-                        TIMER_STOP(softmax);
 
                         buf_idx = 1 - buf_idx;
                     }  // end KV block loop (pipeline)
@@ -1664,11 +1655,8 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
                         ou_job.n_tiles_per_bc   = n_tiles_per_bc;
                         ou_job.DV               = DV;
-
-                        TIMER_START(o_update);
                         hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
                         hmx_queue_pop(hmx_q);
-                        TIMER_STOP(o_update);
 
                         hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
                     }
@@ -1683,23 +1671,14 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         const uint32_t kv_start    = kv_blk * Bc;
                         const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
                         const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
-
-                        TIMER_START(kv_dma);
                         dma_queue_pop(dma);  // K
                         dma_queue_pop(dma);  // V
-                        TIMER_STOP(kv_dma);
 
                         bool has_mask_dma = false;
                         MASK_DMA_PUSH(kv_start, kv_rows, has_mask_dma);
                         DMA_PREFETCH_KV(kv_blk + 1);
-
-                        // K interleave (multi-thread HVX)
-                        TIMER_START(k_interleave);
                         fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
-                        TIMER_STOP(k_interleave);
 
-                        // QK dot (inline HMX on main thread)
-                        TIMER_START(qk_dot);
                         {
                             const size_t n_dot_tiles       = (size_t) (DK / 32);
                             const __fp16 * restrict q_base = factx.vtcm_q_tiles;
@@ -1709,6 +1688,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                             __builtin_assume(n_col_tiles > 0);
                             __builtin_assume(n_dot_tiles > 0);
 
+                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                             Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_qk);
                             for (size_t r = 0; r < n_row_tiles; ++r) {
                                 for (size_t c = 0; c < n_col_tiles; ++c) {
@@ -1724,8 +1704,8 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                                     Q6_mxmem_AR_after_hf(out_tile, 0);
                                 }
                             }
+                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                         }
-                        TIMER_STOP(qk_dot);
 
                         // Pop mask DMA
                         MASK_DMA_POP(has_mask_dma);
@@ -1751,21 +1731,9 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
                         sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
                         sargs.slopes               = factx.vtcm_slopes;
-
-                        TIMER_START(softmax);
                         fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
-                        TIMER_STOP(softmax);
-
-                        // V interleave (multi-thread HVX)
-                        TIMER_START(v_interleave);
-                        // FIX(v-stride): use n_tiles_per_bc (block-invariant) as V tile layout
-                        // stride to match o_update's v_tile access.  Using per-block n_col_tiles
-                        // misplaces DV_tile 1..3 in the last partial KV block.
                         fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
-                        TIMER_STOP(v_interleave);
 
-                        // O update (inline HMX on main thread)
-                        TIMER_START(o_update);
                         {
                             const size_t DV_tiles           = (size_t) (DV / 32);
                             const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
@@ -1777,6 +1745,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                             __builtin_assume(n_col_tiles > 0);
                             __builtin_assume(DV_tiles > 0);
 
+                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                             Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
                             for (size_t r = 0; r < n_row_tiles; ++r) {
                                 for (size_t c = 0; c < DV_tiles; ++c) {
@@ -1798,16 +1767,15 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                                     Q6_mxmem_AR_after_hf(o_tile_out, 0);
                                 }
                             }
+                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                             hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
                         }
-                        TIMER_STOP(o_update);
 
                         buf_idx = 1 - buf_idx;
                     }  // end KV block loop (fallback)
                 }
 
                 // ---- Final normalization: O = diag(1/l) @ O ----
-                TIMER_START(o_norm);
                 {
                     fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br);
 
@@ -1830,6 +1798,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                         __builtin_assume(n_row_tiles > 0);
                         __builtin_assume(DV_tiles > 0);
 
+                        htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                         Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
                         for (size_t r = 0; r < n_row_tiles; ++r) {
                             for (size_t c = 0; c < DV_tiles; ++c) {
@@ -1842,14 +1811,12 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                                 Q6_mxmem_AR_after_hf(o_out, 0);
                             }
                         }
+                        htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                     }
                 }
-                TIMER_STOP(o_norm);
 
                 // ---- Store O block ----
-                TIMER_START(o_store);
                 fa_phase_o_store(&factx, dst, o_tile_curr, q_start, kv_head, ib3, n_rows_g);
-                TIMER_STOP(o_store);
 
 #undef MASK_DMA_PUSH
 #undef MASK_DMA_POP
@@ -1865,14 +1832,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
         HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
     }
 
-    TIMER_STOP(total);
 
-#if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "hmx-fa: %lld us, q_load=%lld kv_dma=%lld k_interleave=%lld v_interleave=%lld", TIMER_US(total),
-         TIMER_US(q_load), TIMER_US(kv_dma), TIMER_US(k_interleave), TIMER_US(v_interleave));
-    FARF(HIGH, "  qk_dot=%lld softmax=%lld o_update=%lld o_norm=%lld o_store=%lld", TIMER_US(qk_dot), TIMER_US(softmax),
-         TIMER_US(o_update), TIMER_US(o_norm), TIMER_US(o_store));
-#endif
 
     return HTP_STATUS_OK;
 }
diff --git a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c b/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
index dab605210..5c37f24ff 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
@@ -27,7 +27,7 @@
 #include "hmx-ops.h"
 #include "hmx-utils.h"
 #include "hmx-queue.h"
-#include "hmx-profile.h"
+#include "hex-profile.h"
 
 #include "vtcm-utils.h"
 
@@ -430,6 +430,7 @@ typedef struct {
     int                      n_tasks;
     int                      n_k_tiles;
     struct fastdiv_values    n_k_tiles_div;
+    struct htp_thread_trace * traces;
 } x4x2_dequantize_state_t;
 
 // Dequantize a tile range from x4x2 weight data (already in VTCM) to tile-major FP16.
@@ -533,11 +534,14 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task_##suffix(
                                                                                                                \
 static void dequantize_x4x2_worker_loop_##suffix(unsigned int n, unsigned int i, void *data) {                 \
     x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;                                          \
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;                                   \
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                                 \
     for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {                     \
         int start = task_id * state->n_tiles_per_task;                                                         \
         int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);                             \
         dequantize_x4x2_weight_to_fp16_tiles_task_##suffix(state, start, end);                                 \
     }                                                                                                          \
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                                 \
 }
 
 DEFINE_DEQUANTIZE_Q4_TASK(q4_0,   q4_0_to_fp16_lut,   q4_0, HMX_X4X2_DBLK_SIZE, (int)sizeof(__fp16))
@@ -657,11 +661,14 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task_mxfp4(
 
 static void dequantize_x4x2_worker_loop_mxfp4(unsigned int n, unsigned int i, void *data) {
     x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
     for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
         int start = task_id * state->n_tiles_per_task;
         int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
         dequantize_x4x2_weight_to_fp16_tiles_task_mxfp4(state, start, end);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
 }
 
 static void dequantize_x4x2_weight_to_fp16_tiles_task_q8_0(
@@ -717,11 +724,14 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task_q8_0(
 
 static void dequantize_x4x2_worker_loop_q8_0(unsigned int n, unsigned int i, void *data) {
     x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
     for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
         int start = task_id * state->n_tiles_per_task;
         int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
         dequantize_x4x2_weight_to_fp16_tiles_task_q8_0(state, start, end);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
 }
 
 static void convert_f16_weight_to_fp16_tiles_task(
@@ -773,11 +783,14 @@ static void convert_f16_weight_to_fp16_tiles_task(
 
 static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) {
     x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
     for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
         int start = task_id * state->n_tiles_per_task;
         int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
         convert_f16_weight_to_fp16_tiles_task(state, start, end);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
 }
 
 static void quantize_f32_weight_to_fp16_tiles_task(
@@ -833,11 +846,14 @@ static void quantize_f32_weight_to_fp16_tiles_task(
 
 static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) {
     x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
     for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
         int start = task_id * state->n_tiles_per_task;
         int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
         quantize_f32_weight_to_fp16_tiles_task(state, start, end);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
 }
 
 
@@ -868,6 +884,7 @@ static void dequantize_x4x2_weight_chunk_to_fp16_tiles(
     state.weight_type      = weight_type;
     state.n_k_tiles        = n_k_tiles;
     state.n_k_tiles_div    = n_k_tiles_div;
+    state.traces           = ctx ? ctx->trace : NULL;
 
     if (state.n_tasks == 1 || n_threads == 1) {
         dequant_worker_fn(1, 0, &state);
@@ -985,10 +1002,13 @@ typedef struct {
     int            n_chunks_per_task;
     int            n_cols;
     int            n;  // DDR row stride (total output columns)
+    struct htp_thread_trace * traces;
 } output_transfer_task_state_t;
 
 static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
     output_transfer_task_state_t *st = (output_transfer_task_state_t *) data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
 
     for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
         int    chunk_idx  = task_id * st->n_chunks_per_task;
@@ -998,6 +1018,7 @@ static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void
         const __fp16 *vtcm_src = st->vtcm_src + chunk_idx * st->n_cols;
         transfer_output_chunk_fp16_to_fp32(dst, vtcm_src, chunk_size, st->n_cols, st->n);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
 }
 
 static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const __fp16 *vtcm_src,
@@ -1015,6 +1036,7 @@ static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst,
     state.vtcm_src          = vtcm_src;
     state.n_cols            = n_cols;
     state.n                 = n;
+    state.traces            = ctx ? ctx->trace : NULL;
 
     if (state.n_tasks == 1 || n_threads == 1) {
         transfer_output_chunk_worker_fn(1, 0, &state);
@@ -1086,10 +1108,13 @@ typedef struct {
     int          n_chunks_per_task;
     int          k_block;
     int          k_stride;
+    struct htp_thread_trace * traces;
 } activation_transfer_task_state_t;
 
 static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
     activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
 
     for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
         // one chunk: one row
@@ -1100,6 +1125,7 @@ static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i,
         const float *src = st->src + chunk_idx * st->k_stride;
         transfer_activation_chunk_fp32_to_fp16(dst, src, chunk_size, st->k_block, st->k_stride);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
 }
 
 static void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, const float *src, int n_rows, int k_block, int k_stride, int n_threads) {
@@ -1117,6 +1143,7 @@ static void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *
     state.src               = src;
     state.k_block           = k_block;
     state.k_stride          = k_stride;
+    state.traces            = ctx ? ctx->trace : NULL;
 
     if (state.n_tasks == 1 || n_threads == 1) {
         transfer_activation_chunk_worker_fn(1, 0, &state);
@@ -1245,13 +1272,7 @@ int hmx_matmul_2d_f32(struct htp_context *ctx, float *restrict dst, const float
     FARF(HIGH, "hmx-mm-2d: standard : m %d k %d n %d wtype %d mc %zu nc %zu vtcm %zu/%zu",
          m, k, n, weight_type, m_chunk_n_rows, n_chunk_n_cols, vtcm_used, vtcm_budget);
 
-    TIMER_DEFINE(activation_load);
-    TIMER_DEFINE(weight_load);
-    TIMER_DEFINE(hmx_core);
-    TIMER_DEFINE(output_store);
 
-    TIMER_DEFINE(total);
-    TIMER_START(total);
 
     int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols);
 
@@ -1370,7 +1391,12 @@ int hmx_matmul_2d_f32(struct htp_context *ctx, float *restrict dst, const float
                 dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_scratch0, vtcm_weight, n_cols, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
 
                 // C: HMX Compute (Synchronous)
-                core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
+                {
+                    struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
+                    htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
+                    core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
+                    htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
+                }
 
                 // D: Output Store
                 float *output_chunk = dst + (mr * n + nc);
@@ -1380,18 +1406,7 @@ int hmx_matmul_2d_f32(struct htp_context *ctx, float *restrict dst, const float
         HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
     }
 
-    TIMER_STOP(total);
 
-#if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "hex-mm-2d: %lld us : m %d k %d n %d", TIMER_US(total), m, k, n);
-    if (!use_pipeline) {
-        FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
-             TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
-        size_t weight_size = (size_t)n * row_stride;
-        float  bandwidth   = 1e-3f * weight_size / (float)TIMER_US(weight_load);
-        FARF(HIGH, "  weight load bandwidth: %.2f GB/s", bandwidth);
-    }
-#endif
 
     return 0;
 }
@@ -1523,13 +1538,7 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
             m_chunk_n_rows, n_chunk_n_cols,
             (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
 
-    TIMER_DEFINE(activation_load);
-    TIMER_DEFINE(weight_load);
-    TIMER_DEFINE(hmx_core);
-    TIMER_DEFINE(output_store);
-    TIMER_DEFINE(total);
 
-    TIMER_START(total);
 
     const size_t fp16_row_bytes   = (size_t) params->k * sizeof(__fp16);
     const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16);
@@ -1549,7 +1558,6 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
                 // contiguous rows into a VTCM scratch buffer first, then HVX
                 // converts from the contiguous VTCM buffer.  This avoids L2 cache
                 // thrashing from HVX loads at large strides.
-                TIMER_START(activation_load);
                 for (int g = 0; g < group_size; ++g) {
                     const float *activation_chunk = hmx_matmul_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
                     __fp16 *vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
@@ -1569,7 +1577,6 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
                                                               params->k, params->act_stride, ctx->n_threads);
                     }
                 }
-                TIMER_STOP(activation_load);
 
                 void *buf_curr = vtcm_scratch0;
                 void *buf_next = vtcm_scratch1;
@@ -1584,7 +1591,6 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
                     const size_t n_cols = hex_smin((size_t) params->n - nc, n_chunk_n_cols);
                     const size_t n_col_tiles = hmx_ceil_div((int) n_cols, HMX_FP16_TILE_N_COLS);
 
-                    TIMER_START(weight_load);
                     {
                         dma_queue_pop(ctx->dma[0]);
 
@@ -1601,24 +1607,22 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
                                                  0, n_cols);
                         hex_swap_ptr(&buf_curr, &buf_next);
                     }
-                    TIMER_STOP(weight_load);
 
                     // Reuse the interleaved weight for every q_head in this GQA group
                     for (int g = 0; g < group_size; ++g) {
-                        TIMER_START(hmx_core);
                         {
                             const __fp16 * vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
+                            struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
+                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                             core_dot_chunk_fp16(vtcm_output, vtcm_act_g, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles,
                                                 params->k / 32);
+                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
                         }
-                        TIMER_STOP(hmx_core);
 
-                        TIMER_START(output_store);
                         {
                             float *output = hmx_matmul_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
                             transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride, ctx->n_threads);
                         }
-                        TIMER_STOP(output_store);
                     }
                 }
             }
@@ -1627,14 +1631,7 @@ int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32
 
     HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
 
-    TIMER_STOP(total);
 
-#if defined(ENABLE_PROFILE_TIMERS)
-    FARF(HIGH, "%s: %lld us, m=%d k=%d n=%d group=%d", __func__, TIMER_US(total),
-         params->m, params->k, params->n, group_size);
-    FARF(HIGH, "  activation_load: %lld us, weight_load: %lld us, hmx_core: %lld us, output_store: %lld us",
-         TIMER_US(activation_load), TIMER_US(weight_load), TIMER_US(hmx_core), TIMER_US(output_store));
-#endif
 
     return 0;
 }
@@ -1668,6 +1665,7 @@ typedef struct {
     size_t                          nb12;
     int                             start_row;
     int                             cne1;
+    struct htp_thread_trace        *traces;
 } activation_transfer_gathered_task_state_t;
 
 typedef struct {
@@ -1684,6 +1682,7 @@ typedef struct {
     size_t                          dst_nb2;
     int                             start_row;
     int                             cne1;
+    struct htp_thread_trace        *traces;
 } output_transfer_scattered_task_state_t;
 
 static void transfer_activation_chunk_fp32_to_fp16_gathered(
@@ -1780,6 +1779,9 @@ static void transfer_activation_chunk_fp32_to_fp16_gathered(
 
 static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigned int i, void *data) {
     activation_transfer_gathered_task_state_t *st = data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
+
     int chunk_idx = i;
     int chunk_size = st->n_chunks_per_task;
     int start_row = st->start_row + chunk_idx * chunk_size;
@@ -1791,6 +1793,7 @@ static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigne
             st->matrix_rows, st->cur_a, st->mapping_stride,
             st->ne11, &st->ne11_div, st->nb11, st->nb12, st->cne1);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
 }
 
 static void transfer_activation_chunk_gathered_threaded(
@@ -1830,6 +1833,7 @@ static void transfer_activation_chunk_gathered_threaded(
         .nb12              = nb12,
         .start_row         = start_row,
         .cne1              = cne1,
+        .traces            = ctx ? ctx->trace : NULL,
     };
 
     if (actual_threads <= 1) {
@@ -1895,6 +1899,9 @@ static void transfer_output_chunk_fp16_to_fp32_scattered(
 
 static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned int i, void *data) {
     output_transfer_scattered_task_state_t *st = data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
+
     int chunk_idx = i;
     int chunk_size = st->n_chunks_per_task;
     int start_row = st->start_row + chunk_idx * chunk_size;
@@ -1906,6 +1913,7 @@ static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned i
             st->matrix_rows, st->cur_a, st->mapping_stride,
             st->dst_nb1, st->dst_nb2, st->cne1);
     }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
 }
 
 static void transfer_output_chunk_scattered_threaded(
@@ -1942,6 +1950,7 @@ static void transfer_output_chunk_scattered_threaded(
         .dst_nb2           = dst_nb2,
         .start_row         = start_row,
         .cne1              = cne1,
+        .traces            = ctx ? ctx->trace : NULL,
     };
 
     if (actual_threads <= 1) {
@@ -2053,7 +2062,12 @@ int hmx_matmul_id_2d_f32(struct htp_context *ctx,
 
             dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_scratch0, vtcm_weight, n_cols, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
 
-            core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
+            {
+                struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
+                htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
+                core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
+                htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
+            }
 
             transfer_output_chunk_scattered_threaded(
                 ctx, dst, vtcm_output, (int) mr, (int) n_rows, (int) n_cols,
diff --git a/ggml/src/ggml-hexagon/htp/hmx-profile.h b/ggml/src/ggml-hexagon/htp/hmx-profile.h
deleted file mode 100644
index 01eece720..000000000
--- a/ggml/src/ggml-hexagon/htp/hmx-profile.h
+++ /dev/null
@@ -1,34 +0,0 @@
-// Conditional fine-grained profiling macros for HMX operations.
-//
-// Define ENABLE_PROFILE_TIMERS (via compiler flag or before including this
-// header) to instrument sub-operation latencies with HAP qtimer.  When the
-// macro is not defined the TIMER_* helpers expand to nothing so there is zero
-// overhead.
-//
-// Usage:
-//   TIMER_DEFINE(my_phase);          // declare accumulator variable
-//   TIMER_START(my_phase);           // snapshot start time
-//   ... work ...
-//   TIMER_STOP(my_phase);            // accumulate elapsed ticks
-//   FARF(ALWAYS, "my_phase: %lld us", TIMER_US(my_phase));
-
-#ifndef HMX_PROFILE_H
-#define HMX_PROFILE_H
-
-#include <HAP_perf.h>
-
-// #define ENABLE_PROFILE_TIMERS
-
-#if defined(ENABLE_PROFILE_TIMERS)
-#  define TIMER_DEFINE(name) int64_t name##_ticks = 0
-#  define TIMER_START(name)  int64_t name##_t0 = HAP_perf_get_qtimer_count()
-#  define TIMER_STOP(name)   name##_ticks += HAP_perf_get_qtimer_count() - name##_t0
-#  define TIMER_US(name)     HAP_perf_qtimer_count_to_us(name##_ticks)
-#else
-#  define TIMER_DEFINE(name)
-#  define TIMER_START(name)
-#  define TIMER_STOP(name)
-#  define TIMER_US(name)     0LL
-#endif
-
-#endif // HMX_PROFILE_H
diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.c b/ggml/src/ggml-hexagon/htp/hmx-queue.c
index 5b1d83a0c..a0007539c 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-queue.c
+++ b/ggml/src/ggml-hexagon/htp/hmx-queue.c
@@ -44,7 +44,9 @@ static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) {
                 case HMX_QUEUE_SUSPEND: hmx_unlock(q);  break;
                 default:
                     hmx_lock(q);
+                    htp_trace_event_start(q->trace, HTP_TRACE_EVT_HMX_COMP, ir);
                     d->func(d->data);
+                    htp_trace_event_stop(q->trace, HTP_TRACE_EVT_HMX_COMP, ir);
                     break;
             }
 
diff --git a/ggml/src/ggml-hexagon/htp/hmx-queue.h b/ggml/src/ggml-hexagon/htp/hmx-queue.h
index 0d48c280f..83135cd91 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-queue.h
+++ b/ggml/src/ggml-hexagon/htp/hmx-queue.h
@@ -11,6 +11,7 @@
 #include <HAP_farf.h>
 
 #include "hex-utils.h"
+#include "hex-profile.h"
 
 #ifdef __cplusplus
 extern "C" {
@@ -47,6 +48,7 @@ struct hmx_queue {
     void *           stack;
     uint32_t         hap_rctx;
     bool             hmx_locked;
+    struct htp_thread_trace * trace;
 };
 
 struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx);
diff --git a/ggml/src/ggml-hexagon/htp/htp-ctx.h b/ggml/src/ggml-hexagon/htp/htp-ctx.h
index 0f1676f07..cbb5d0878 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ctx.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ctx.h
@@ -4,6 +4,7 @@
 #include "hex-dma.h"
 #include "hmx-queue.h"
 #include "htp-ops.h"
+#include "hex-profile.h"
 #include "worker-pool.h"
 
 #include <assert.h>
@@ -70,6 +71,7 @@ struct htp_context {
     bool                   hmx_enabled;
     bool                   etm;
     uint32_t               profiler;
+    struct htp_thread_trace trace[HTP_MAX_NTHREADS + 1];
 
     uint8_t *              vtcm_base;
     size_t                 vtcm_size;
diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index fa85bf4ca..0f4b74a93 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -146,10 +146,36 @@ struct htp_op_desc {
     uint16_t dst;                       // Output tensor index
 };
 
+#ifndef HTP_MAX_NTHREADS
+#define HTP_MAX_NTHREADS 10
+#endif
+
+#define HTP_TRACE_MAX_EVENTS 256
+
 enum htp_profiler_mode {
     HTP_PROF_DISABLED = 0,
     HTP_PROF_BASIC    = 1,
     HTP_PROF_PMU      = 2,
+    HTP_PROF_TRACE    = 3,
+};
+
+enum htp_trace_event_id {
+    HTP_TRACE_EVT_DMA                 = 0,
+
+    HTP_TRACE_EVT_HVX_COMP            = 20,
+    HTP_TRACE_EVT_HVX_A_QUANT         = 21,
+    HTP_TRACE_EVT_HVX_A_PREP          = 22,
+    HTP_TRACE_EVT_HVX_W_DEQUANT       = 23,
+    HTP_TRACE_EVT_HVX_W_PREP          = 24,
+    HTP_TRACE_EVT_HVX_O_PROC          = 25,
+
+    HTP_TRACE_EVT_HMX_COMP            = 40,
+};
+
+struct htp_trace_desc {
+    uint32_t cycles;  // lower 32-bits of cycle counter
+    uint16_t id;      // Event ID
+    uint16_t info;    // bit 15: is_stop. bits 14-0: tile/chunk index or other metadata.
 };
 
 #define HTP_PROF_PMU_NCNT 8
@@ -158,8 +184,8 @@ enum htp_profiler_mode {
 struct htp_prof_desc {
     uint32_t opcode;                 // GGML/HTP Op
     uint32_t usecs;                  // Number of usec
-    uint32_t cycles;                 // Number of cycles
-    uint32_t pad;                    // Unused
+    uint32_t cycles_start;           // Start cycle counter
+    uint32_t cycles_stop;            // Stop cycle counter
     uint32_t pmu[HTP_PROF_PMU_NCNT]; // PMU counters
 };
 
@@ -168,7 +194,7 @@ struct htp_opbatch_req {
     uint32_t n_bufs;      // Number of buffers
     uint32_t n_tensors;   // Number of tensors
     uint32_t n_ops;       // Number of ops
-    uint32_t flags;       // unused
+    uint32_t n_traces;    // Number of trace descriptors per thread
     uint32_t pad;         // unused
     // struct htp_buf_desc  bufs[];    -- dspqueue buf 0
     // struct htp_tensor    tensors[]; -- dspqueue buf 0
@@ -181,7 +207,8 @@ struct htp_opbatch_rsp {
     uint32_t n_bufs;     // Number of buffers
     uint32_t n_tensors;  // Number of tensors
     uint32_t n_ops;      // Number of op profile descriptors
-    uint32_t pad;        // unused
+    uint32_t n_traces[HTP_MAX_NTHREADS + 1];
+    uint8_t  pad[8];     // align to 8 bytes
     // struct htp_prof_desc profs[];  -- dspqueue buf 0
 };
 
diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index 3715227d2..53ab33c07 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -400,7 +400,9 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
     ctx->hmx_queue   = NULL;
     if (use_hmx) {
         ctx->hmx_queue = hmx_queue_create(16, ctx->vtcm_rctx);
-        if (!ctx->hmx_queue) {
+        if (ctx->hmx_queue) {
+            ctx->hmx_queue->trace = &ctx->trace[HTP_MAX_NTHREADS];
+        } else {
             FARF(ERROR, "hmx-queue-create failed");
             ctx->hmx_enabled = false;
         }
@@ -425,6 +427,9 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
     ctx->n_threads = n_hvx;
     for (int i = 0; i < ctx->n_threads; i++) {
         ctx->dma[i] = dma_queue_create(256); // queue depth
+        if (ctx->dma[i]) {
+            ctx->dma[i]->trace = &ctx->trace[i];
+        }
     }
 
     ctx->ddr_spad_size = 512 * 1024; // 512 KB
@@ -502,7 +507,8 @@ static void htp_error_callback(dspqueue_t queue, int error, void * context) {
 
 struct profile_data {
     uint64_t usecs;
-    uint64_t cycles;
+    uint64_t cycles_start;
+    uint64_t cycles_stop;
     uint32_t pmu_counters[HEX_NUM_PMU_COUNTERS];
 };
 
@@ -512,8 +518,9 @@ static inline void profile_start(uint32_t mode, struct profile_data * d) {
             hex_get_pmu(d->pmu_counters);
             // fallthrough
         case HTP_PROF_BASIC:
+        case HTP_PROF_TRACE:
             d->usecs  = HAP_perf_get_qtimer_count();
-            d->cycles = hex_get_cycles();
+            d->cycles_start = hex_get_cycles();
             break;
         default:
             break;
@@ -530,8 +537,9 @@ static inline void profile_stop(uint32_t mode, struct profile_data * d) {
             }
             // fallthrough
         case HTP_PROF_BASIC:
+        case HTP_PROF_TRACE:
             d->usecs  = HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - d->usecs);
-            d->cycles = hex_get_cycles() - d->cycles;
+            d->cycles_stop = hex_get_cycles();
             break;
         default:
             break;
@@ -845,14 +853,15 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
         const uint32_t t_size = sizeof(struct htp_tensor)    * n_tens;
         const uint32_t o_size = sizeof(struct htp_op_desc)   * n_ops;
         const uint32_t p_size = sizeof(struct htp_prof_desc) * n_ops;
+        const uint32_t tr_size = (HTP_MAX_NTHREADS + 1) * req.n_traces * sizeof(struct htp_trace_desc);
 
-        if (dbuf.size < b_size + t_size + o_size + p_size) {
-            FARF(ERROR, "invalid opbatch memory block size %u", dbuf.size);
+        if (dbuf.size < b_size + t_size + o_size + p_size + tr_size) {
+            FARF(ERROR, "invalid opbatch memory block size %u (req %u)", dbuf.size, b_size + t_size + o_size + p_size + tr_size);
             break;
         }
 
-        FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u : m-size %u b-size %u t-size %u o-size %u", req.id,
-                n_bufs, n_tens, n_ops, dbuf.size, b_size, t_size, o_size);
+        FARF(HIGH, "processing opbatch #%u: n-bufs %u n-tensors %u n-ops %u n-traces %u : m-size %u b-size %u t-size %u o-size %u", req.id,
+                n_bufs, n_tens, n_ops, req.n_traces, dbuf.size, b_size, t_size, o_size);
 
         // Setup descriptor pointers
         uint8_t * m_ptr = dbuf.ptr;
@@ -869,6 +878,20 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
         octx->n_threads = ctx->n_threads;
         octx->ctx       = ctx;
 
+        if (ctx->profiler == HTP_PROF_TRACE) {
+            memset(ctx->trace, 0, sizeof(ctx->trace));
+            struct htp_trace_desc * trace_events = (struct htp_trace_desc *) (m_ptr + p_size);
+            for (int t = 0; t <= HTP_MAX_NTHREADS; t++) {
+                ctx->trace[t].events = &trace_events[t * req.n_traces];
+                ctx->trace[t].max_events = req.n_traces;
+            }
+        } else {
+            for (int t = 0; t <= HTP_MAX_NTHREADS; t++) {
+                ctx->trace[t].events = NULL;
+                ctx->trace[t].max_events = 0;
+            }
+        }
+
         for (uint32_t i=0; i < n_ops; i++) {
             struct profile_data prof;
 
@@ -886,7 +909,8 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             if (ctx->profiler) {
                 pds[i].opcode = ops[i].opcode;
                 pds[i].usecs  = prof.usecs;
-                pds[i].cycles = prof.cycles;
+                pds[i].cycles_start = prof.cycles_start;
+                pds[i].cycles_stop = prof.cycles_stop;
                 for (int j = 0; j < HEX_NUM_PMU_COUNTERS; j++) {
                     pds[i].pmu[j] = prof.pmu_counters[j];
                 }
@@ -899,6 +923,14 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
         rsp.n_bufs    = n_bufs;
         rsp.n_tensors = n_tens;
         rsp.n_ops     = n_ops;
+        memset(rsp.pad, 0, sizeof(rsp.pad));
+        if (ctx->profiler == HTP_PROF_TRACE) {
+            for (int t = 0; t <= HTP_MAX_NTHREADS; t++) {
+                rsp.n_traces[t] = ctx->trace[t].count;
+            }
+        } else {
+            memset(rsp.n_traces, 0, sizeof(rsp.n_traces));
+        }
 
         dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
 
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index 5121c6f9b..8e016c1be 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -3350,6 +3350,7 @@ static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void *
 
 static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -3411,10 +3412,12 @@ static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
                 float * dst_col = (float *) ((uint8_t * restrict) dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
 
                 const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end);
+                htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, iir0);
                 for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) {
                     const uint8_t * restrict src0_row = src0_base + ir0 * nb01;
                     mmctx->vec_dot_1x1(ne00, &dst_col[ir0], src0_row, src1_col);
                 }
+                htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, iir0);
             }
         }
     }
@@ -3430,6 +3433,7 @@ static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
 // src1 tensor is already in VTCM spad
 static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
     const uint32_t src1_nrows = ne11 * ne12 * ne13;  // src1 rows
@@ -3477,6 +3481,8 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
     for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+
         // Process src1 columns in pairs (2×2 tiling)
         uint32_t ir1 = 0;
         for (; ir1 + 1 < src1_nrows; ir1 += 2) {
@@ -3494,6 +3500,8 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
             mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_stride, src1_col);
         }
 
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+
         // Prefetch next (n + spad_nrows) row
         const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
         const int is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
@@ -3511,12 +3519,14 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
                        src0_stride, src0_row_size, 1);
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
         #pragma unroll(2)
         for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) {
             const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
             float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));
             mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
         }
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
     }
 
     t2 = HAP_perf_get_qtimer_count();
@@ -3530,6 +3540,7 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
 // q8x4x2 src1 tensor is already in VTCM spad
 static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const uint32_t src0_nrows = ne01;
 
@@ -3581,7 +3592,9 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
         // Process src0 rows
         for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x4; ir0 += 4) {
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_4x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, ss0 + 2 * src0_stride, ss0 + 3 * src0_stride, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
             // Prefetch next (n + spad_nrows) row
             const uint32_t pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -3599,7 +3612,9 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
             dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
                            src0_stride, src0_row_size, 2);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             ir0 += 2;
         }
         if (ir0 < src0_end_row) {
@@ -3607,7 +3622,9 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
             dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
                            src0_stride, src0_row_size, 1);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             ir0 += 1;
         }
     } else {
@@ -3627,7 +3644,9 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
         // Process src0 rows
         for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
             // Prefetch next (n + spad_nrows) row
             const uint32_t pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -3645,7 +3664,9 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
             dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
                            src0_stride, src0_row_size, 1);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
         }
     }
 
@@ -3669,6 +3690,7 @@ struct mmid_row_mapping {
 // src1 tensor is already in VTCM spad
 static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * restrict ids = octx->src[2];
     struct htp_spad * restrict   src2_spad = &octx->src2_spad;
@@ -3735,6 +3757,7 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
         for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             for (uint32_t cid = 0; cid < cne1; ++cid) {
                 struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid);
                 const int               rm1         = row_mapping.i1;  // expert idx
@@ -3746,6 +3769,7 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
 
                 mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
             }
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
             // Prefetch next (n + spad_nrows) row
             const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -3764,6 +3788,7 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
                            src0_row_size_padded, src0_row_size, 1);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             for (uint32_t cid = 0; cid < cne1; ++cid) {
                 struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid);
                 const int               rm1         = row_mapping.i1;  // expert idx
@@ -3775,6 +3800,7 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
 
                 mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
             }
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
         }
     }
 
@@ -3789,6 +3815,7 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
 // src1 tensor is already in VTCM spad
 static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * restrict ids = octx->src[2];
     struct htp_spad * restrict   src2_spad = &octx->src2_spad;
@@ -3847,7 +3874,9 @@ static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
         // Process src0 rows
         for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
             // Prefetch next (n + spad_nrows) row
             const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
@@ -3865,7 +3894,9 @@ static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
             dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
                            src0_row_size_padded, src0_row_size, 1);
             const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
             mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
         }
     }
 
@@ -4147,6 +4178,7 @@ static void quantize_row_f32_q8x4x2(float * restrict x, uint8_t * restrict y, ui
 static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
@@ -4163,6 +4195,7 @@ static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data)
     const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
 
     const uint32_t ir_first = nrows_per_thread * ith;                   // first row
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
     const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
 
     const size_t src_row_size = src->nb[1];
@@ -4189,6 +4222,7 @@ static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data)
 
     FARF(HIGH, "quantize-f32-q8x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
          ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
 }
 
 static void quantize_row_f32_q8_1x4x2(float * restrict x, uint8_t * restrict y, uint32_t k) {
@@ -4219,6 +4253,7 @@ static void quantize_row_f32_q8_1x4x2(float * restrict x, uint8_t * restrict y,
 static void quantize_f32_q8_1x4x2(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
@@ -4235,6 +4270,7 @@ static void quantize_f32_q8_1x4x2(unsigned int nth, unsigned int ith, void * dat
     const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
 
     const uint32_t ir_first = nrows_per_thread * ith;                   // first row
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
     const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
 
     const size_t src_row_size = src->nb[1];
@@ -4260,11 +4296,13 @@ static void quantize_f32_q8_1x4x2(unsigned int nth, unsigned int ith, void * dat
 
     FARF(HIGH, "quantize-f32-q8_1x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
          ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
 }
 
 static void quantize_f32_f32(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
@@ -4281,6 +4319,7 @@ static void quantize_f32_f32(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
 
     const uint32_t ir_first = nrows_per_thread * ith;                   // first row
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
     const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
 
     const size_t src_row_size = ne0 * sizeof(float);
@@ -4301,11 +4340,13 @@ static void quantize_f32_f32(unsigned int nth, unsigned int ith, void * data) {
 
     FARF(HIGH, "quantize-f32-f32: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
 }
 
 static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
@@ -4322,6 +4363,7 @@ static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
 
     const uint32_t ir_first = nrows_per_thread * ith;                   // first row
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
     const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
 
     const size_t src_row_size = ne0 * sizeof(float);
@@ -4342,12 +4384,14 @@ static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
 
     FARF(HIGH, "quantize-f32-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
 }
 
 // TODO just a plain copy that should be done via the DMA during the Op setup
 static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
@@ -4364,6 +4408,7 @@ static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
 
     const uint32_t ir_first = nrows_per_thread * ith;                   // first row
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
     const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
 
     const size_t src_row_size = ne0 * sizeof(float);
@@ -4384,6 +4429,7 @@ static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
 
     FARF(HIGH, "quantize-f16-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
         ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
 }
 
 
diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py
index fe94eb6c1..05045262f 100755
--- a/scripts/snapdragon/ggml-hexagon-profile.py
+++ b/scripts/snapdragon/ggml-hexagon-profile.py
@@ -6,6 +6,7 @@ import re
 import argparse
 import statistics
 import logging
+from typing import Any, Dict, List, Optional
 
 from collections import defaultdict
 
@@ -25,12 +26,47 @@ COL_MAP = {
 }
 
 op_pattern = re.compile(
-    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?"
+    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
+)
+
+trace_pattern = re.compile(
+    r"trace-op\s+(?P<op_name>[A-Z_0-9+]+):\s+thread\s+(?P<thread>\d+)\s+event\s+(?P<event>[A-Z_0-9\-]+)\s+info\s+(?P<info>\d+)\s+(?P<state>start|stop)\s+(?P<cycles>\d+)"
 )
 
 logger = logging.getLogger("ggml-hexagon-profile")
 
 
+def normalize_event_name(evt_type):
+    if evt_type == "HVX_COMP":
+        return "V-COMP"
+    if evt_type == "HMX_COMP":
+        return "M-COMP"
+
+    # Strip HVX_ or HMX_ prefixes
+    name = evt_type
+    if name.startswith("HVX_") or name.startswith("HMX_"):
+        name = name[4:]
+    return name.replace("_", "-")
+
+
+class CycleUnwrapper:
+    def __init__(self):
+        self.last_raw = None
+        self.high_part = 0
+
+    def unwrap(self, raw):
+        if self.last_raw is None:
+            self.last_raw = raw
+            return raw
+        diff = raw - self.last_raw
+        if diff < -0x80000000:
+            self.high_part += 0x100000000
+        elif diff > 0x80000000:
+            self.high_part -= 0x100000000
+        self.last_raw = raw
+        return raw + self.high_part
+
+
 def parse_log(file_path, pmu_index=None):
     try:
         if file_path != "-":
@@ -41,35 +77,211 @@ def parse_log(file_path, pmu_index=None):
         logger.error(f"file '{file_path}' not found.")
         sys.exit(1)
 
-    all_ops = []
+    all_ops: List[Dict[str, Any]] = []
+    current_op: Optional[Dict[str, Any]] = None
+
+    timestamp_pattern = re.compile(r"^(?P<min>\d+)\.(?P<sec>\d+)\.(?P<ms>\d+)\.(?P<us>\d+)\s+[A-Z]\s+")
+    unwrapper = CycleUnwrapper()
+
     for line in f:
-        match = op_pattern.search(line)
-        if not match: continue
+        ts_match = timestamp_pattern.match(line)
+        abs_usec = 0
+        if ts_match:
+            abs_usec = (
+                (int(ts_match.group('min')) * 60 + int(ts_match.group('sec'))) * 1000000
+                + int(ts_match.group('ms')) * 1000
+                + int(ts_match.group('us'))
+            )
 
-        pmu_raw = match.group('pmu')
-        pmu_val = None
-        if pmu_raw and pmu_index is not None:
-            try:
-                pmu_list = [int(x.strip()) for x in pmu_raw.split(',')]
-                if len(pmu_list) > pmu_index:
-                    pmu_val = pmu_list[pmu_index]
-            except (ValueError, IndexError):
-                pmu_val = None
+        op_match = op_pattern.search(line)
+        if op_match:
+            pmu_raw = op_match.group('pmu')
+            pmu_val = None
+            if pmu_raw and pmu_index is not None:
+                try:
+                    pmu_list = [int(x.strip()) for x in pmu_raw.split(',')]
+                    if len(pmu_list) > pmu_index:
+                        pmu_val = pmu_list[pmu_index]
+                except (ValueError, IndexError):
+                    pmu_val = None
 
-        all_ops.append({
-            'name':    match.group('op_name'),
-            'dims':    match.group('dims').strip(),
-            'types':   match.group('types').strip(),
-            'usec':    int(match.group('usec')),
-            'cycles':  int(match.group('cycles')),
-            'pmu_val': pmu_val
-        })
+            evt_raw = op_match.group('evt')
+            evt_val = None
+            if evt_raw:
+                try:
+                    evt_val = [int(x.strip()) for x in evt_raw.split(',')]
+                except ValueError:
+                    evt_val = None
+
+            cycles_start_raw = op_match.group('start')
+            unwrapped_cycles_start = None
+            if cycles_start_raw:
+                unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw))
+
+            idx = line.find("profile-op ")
+            op_text = line[idx + 11:].strip() if idx != -1 else line.strip()
+
+            current_op = {
+                'name':         op_match.group('op_name'),
+                'dims':         op_match.group('dims').strip(),
+                'types':        op_match.group('types').strip(),
+                'op_text':      op_text,
+                'usec':         int(op_match.group('usec')),
+                'cycles':       int(op_match.group('cycles')),
+                'cycles_start': int(cycles_start_raw) if cycles_start_raw else None,
+                'unwrapped_cycles_start': unwrapped_cycles_start,
+                'pmu_val':      pmu_val,
+                'evt_val':      evt_val,
+                'abs_usec':     abs_usec,
+                'trace_events': []
+            }
+            all_ops.append(current_op)
+            continue
+
+        trace_match = trace_pattern.search(line)
+        if trace_match and current_op:
+            if trace_match.group('op_name') == current_op['name']:
+                raw_cyc = int(trace_match.group('cycles'))
+                current_op['trace_events'].append({
+                    'thread': int(trace_match.group('thread')),
+                    'event':  trace_match.group('event'),
+                    'info':   int(trace_match.group('info')),
+                    'cycles': raw_cyc,
+                    'unwrapped_cycles': unwrapper.unwrap(raw_cyc),
+                    'state':  trace_match.group('state')
+                })
 
     f.close()
-
     return all_ops
 
 
+def print_ascii_timeline(op_name, dims, types, usec, cycles, events, evt_val=None):
+    evt_str = ""
+    if evt_val:
+        evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]"
+    logger.info("=" * 100)
+    logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}")
+    logger.info("=" * 100)
+
+    events = sorted(events, key=lambda e: e['cycles'])
+    if not events:
+        logger.info("  No trace events recorded.")
+        return
+
+    min_cycles = events[0]['cycles']
+
+    logger.info("Cycles      %-30s" % "EventDetails" + " ".join(f"T{i:<2}" for i in range(10)) + " HMX")
+    logger.info("-" * 100)
+
+    thread_stacks = [[] for _ in range(11)]
+
+    for e in events:
+        t = e['thread']
+        if t < 0 or t > 10:
+            continue
+
+        if e['cycles'] >= min_cycles:
+            rel_cycles = e['cycles'] - min_cycles
+        else:
+            rel_cycles = (e['cycles'] + 0x100000000) - min_cycles
+
+        state = e['state']
+        evt_type = e['event']
+
+        # Determine char representing the event
+        norm_evt = normalize_event_name(evt_type)
+        char = '?'
+        if norm_evt == 'V-COMP':
+            char = 'V'
+        elif norm_evt == 'M-COMP':
+            char = 'H'
+        elif norm_evt == 'A-QUANT':
+            char = 'Q'
+        elif norm_evt == 'A-PREP':
+            char = 'A'
+        elif norm_evt == 'W-DEQUANT':
+            char = 'D'
+        elif norm_evt == 'O-PROC':
+            char = 'O'
+        elif norm_evt == 'W-PREP':
+            char = 'P'
+        elif norm_evt == 'DMA':
+            char = 'M'
+
+        if state == 'start':
+            thread_stacks[t].append(char)
+        elif state == 'stop':
+            if thread_stacks[t]:
+                if thread_stacks[t][-1] == char:
+                    thread_stacks[t].pop()
+                elif char in thread_stacks[t]:
+                    thread_stacks[t].remove(char)
+                else:
+                    thread_stacks[t].pop()
+
+        cols = []
+        for i in range(11):
+            if thread_stacks[i]:
+                cols.append(f"[{thread_stacks[i][-1]}]")
+            else:
+                cols.append(" | ")
+
+        evt_desc = f"T{t}: {evt_type} {state} ({e['info']})"
+        logger.info(f"{rel_cycles:10d}  %-30s" % evt_desc + " ".join(cols[:10]) + "  " + cols[10])
+    logger.info("-" * 100)
+
+
+def print_ascii_summary(op_name, dims, types, usec, cycles, events, evt_val=None):
+    evt_str = ""
+    if evt_val:
+        evt_str = " - evt [" + ",".join(str(x) for x in evt_val) + "]"
+    logger.info("=" * 100)
+    logger.info(f"{op_name} ({dims} : {types}) - {usec} usec {cycles} cycles{evt_str}")
+    logger.info("=" * 100)
+
+    events = sorted(events, key=lambda e: e['cycles'])
+    if not events:
+        logger.info("  No trace events recorded.")
+        return
+
+    active_starts = {}
+    thread_totals = defaultdict(lambda: defaultdict(int))
+
+    for e in events:
+        t = e['thread']
+        evt = e['event']
+        info = e['info']
+        cyc = e['cycles']
+        state = e['state']
+
+        key = (t, evt, info)
+        if state == 'start':
+            active_starts[key] = cyc
+        elif state == 'stop':
+            if key in active_starts:
+                start_cyc = active_starts[key]
+                del active_starts[key]
+
+                if cyc >= start_cyc:
+                    dur = cyc - start_cyc
+                else:
+                    dur = (cyc + 0x100000000) - start_cyc
+
+                norm_evt = normalize_event_name(evt)
+                thread_totals[t][norm_evt] += dur
+
+    for t in sorted(thread_totals.keys()):
+        thread_name = f"Thread {t} (HVX)" if t != 10 else "Thread 10 (HMX)"
+        sorted_evts = sorted(thread_totals[t].items(), key=lambda item: item[0])
+
+        evt_strs = []
+        for evt, dur in sorted_evts:
+            pct = (dur / cycles * 100) if cycles > 0 else 0
+            evt_strs.append(f"{evt} {dur} ({pct:.1f}%)")
+
+        logger.info(f"  {thread_name:<16}: " + " | ".join(evt_strs))
+
+
 def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
     if not ops:
         logger.info("No valid records found.")
@@ -115,7 +327,6 @@ def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
 
     # Sorting logic
     actual_sort_key = COL_MAP[sort_col][2]
-    # We sort numeric fields descending, strings (op/dims) ascending
     is_numeric    = actual_sort_key.startswith("_") or actual_sort_key == "count"
     sorted_groups = sorted(group_stats, key=lambda x: x[actual_sort_key], reverse=is_numeric)[:top_n]
 
@@ -132,7 +343,7 @@ def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
         if "pmu" in col_name and pmu_name:
             header_text = header_text.replace("PMU", pmu_name)
 
-        natural_width = max([len(row[data_key]) for row in sorted_groups] + [len(header_text)])
+        natural_width = max([len(str(row[data_key])) for row in sorted_groups] + [len(header_text)])
         target_width  = width_overrides.get(col_name, natural_width)
 
         if target_width == 0:
@@ -152,7 +363,7 @@ def generate_report(ops, top_n, width_overrides, sort_col, pmu_name=None):
     for group in sorted_groups:
         row_vals = []
         for i, key in enumerate(final_keys):
-            val = group[key]
+            val = str(group[key])
             if len(val) > final_widths[i]:
                 val = val[:final_widths[i] - 3] + "..."
             row_vals.append(f"{val:<{final_widths[i]}}")
@@ -167,12 +378,18 @@ def main():
     parser.add_argument("--pmu-index", type=int)
     parser.add_argument("--pmu-name", type=str)
     parser.add_argument("--width", action='append', default=['dims:40'], help="Override column width, e.g. --width dims:50")
+    parser.add_argument("--timeline", type=str, nargs='?', const='summary', choices=["summary", "diagram"],
+                        help="Output ASCII art event summary or timing diagram (default: summary)")
+    parser.add_argument("--filter", type=str, help="Regex filter matching against the original profile-op line")
+
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("--head", type=int, help="Limit to first N ops")
+    group.add_argument("--tail", type=int, help="Limit to last N ops")
 
     args = parser.parse_args()
 
     logging.basicConfig(level=logging.INFO, format='%(message)s')
 
-    # Sort validation: can't sort by PMU if index isn't provided
     if "pmu" in args.sort and args.pmu_index is None:
         logger.error(f"Cannot sort by '{args.sort}' without --pmu-index.")
         sys.exit(1)
@@ -188,7 +405,33 @@ def main():
 
     final_pmu_name = (args.pmu_name or f"#{args.pmu_index}") if args.pmu_index is not None else None
     ops = parse_log(args.logfile, pmu_index=args.pmu_index)
-    generate_report(ops, args.top, overrides, args.sort, pmu_name=final_pmu_name)
+
+    if args.filter:
+        try:
+            filter_re = re.compile(args.filter)
+        except re.error as e:
+            logger.error(f"Invalid regex filter: {e}")
+            sys.exit(1)
+        ops = [op for op in ops if filter_re.search(op['op_text'])]
+
+    if args.head is not None:
+        ops = ops[:args.head]
+    elif args.tail is not None:
+        ops = ops[-args.tail:]
+
+    if args.timeline:
+        logger.info(f"\n# ASCII Timing {args.timeline.capitalize()}\n")
+        printed_cnt = 0
+        for op in ops:
+            if args.timeline == "summary":
+                print_ascii_summary(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val'))
+            elif args.timeline == "diagram":
+                print_ascii_timeline(op['name'], op['dims'], op['types'], op['usec'], op['cycles'], op['trace_events'], op.get('evt_val'))
+            printed_cnt += 1
+            if printed_cnt >= args.top:
+                break
+    else:
+        generate_report(ops, args.top, overrides, args.sort, pmu_name=final_pmu_name)
 
 
 if __name__ == "__main__":
diff --git a/scripts/snapdragon/ggml-hexagon-trace.py b/scripts/snapdragon/ggml-hexagon-trace.py
new file mode 100755
index 000000000..18ec440a9
--- /dev/null
+++ b/scripts/snapdragon/ggml-hexagon-trace.py
@@ -0,0 +1,463 @@
+#!/usr/bin/env python3
+
+import sys
+import os
+import re
+import argparse
+import statistics
+import logging
+from typing import Any, Dict, List, Optional
+from collections import defaultdict
+
+logger = logging.getLogger("ggml-hexagon-trace")
+
+op_pattern = re.compile(
+    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+(?P<strides>[\d:x\s\->!]+)\s+:\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
+)
+
+trace_pattern = re.compile(
+    r"trace-op\s+(?P<op_name>[A-Z_0-9+]+):\s+thread\s+(?P<thread>\d+)\s+event\s+(?P<event>[A-Z_0-9\-]+)\s+info\s+(?P<info>\d+)\s+(?P<state>start|stop)\s+(?P<cycles>\d+)"
+)
+
+
+def normalize_event_name(evt_type):
+    if evt_type == "HVX_COMP":
+        return "V-COMP"
+    if evt_type == "HMX_COMP":
+        return "M-COMP"
+    name = evt_type
+    if name.startswith("HVX_") or name.startswith("HMX_"):
+        name = name[4:]
+    return name.replace("_", "-")
+
+
+class CycleUnwrapper:
+    def __init__(self):
+        self.last_raw = None
+        self.high_part = 0
+
+    def unwrap(self, raw):
+        if self.last_raw is None:
+            self.last_raw = raw
+            return raw
+        diff = raw - self.last_raw
+        if diff < -0x80000000:
+            self.high_part += 0x100000000
+        elif diff > 0x80000000:
+            self.high_part -= 0x100000000
+        self.last_raw = raw
+        return raw + self.high_part
+
+
+def parse_log(file_path):
+    try:
+        if file_path != "-":
+            f = open(file_path, 'r', encoding='utf-8', errors='ignore')
+        else:
+            f = os.fdopen(0, 'r', encoding='utf-8', errors='ignore')
+    except FileNotFoundError:
+        logger.error(f"file '{file_path}' not found.")
+        sys.exit(1)
+
+    all_ops: List[Dict[str, Any]] = []
+    current_op: Optional[Dict[str, Any]] = None
+    unwrapper = CycleUnwrapper()
+    line_idx = 0
+
+    for line in f:
+        line_idx += 1
+        op_match = op_pattern.search(line)
+        if op_match:
+            cycles_start_raw = op_match.group('start')
+            unwrapped_cycles_start = None
+            if cycles_start_raw:
+                unwrapped_cycles_start = unwrapper.unwrap(int(cycles_start_raw))
+
+            idx = line.find("profile-op ")
+            op_text = line[idx + 11:].strip() if idx != -1 else line.strip()
+
+            current_op = {
+                'name':         op_match.group('op_name'),
+                'dims':         op_match.group('dims').strip() if op_match.group('dims') else '',
+                'types':        op_match.group('types').strip() if op_match.group('types') else '',
+                'strides':      op_match.group('strides').strip() if op_match.group('strides') else '',
+                'op_text':      op_text,
+                'usec':         int(op_match.group('usec')),
+                'cycles':       int(op_match.group('cycles')),
+                'cycles_start': int(cycles_start_raw) if cycles_start_raw else None,
+                'unwrapped_cycles_start': unwrapped_cycles_start,
+                'trace_events': [],
+                'line_num':     line_idx
+            }
+            all_ops.append(current_op)
+            continue
+
+        trace_match = trace_pattern.search(line)
+        if trace_match and current_op:
+            if trace_match.group('op_name') == current_op['name']:
+                raw_cyc = int(trace_match.group('cycles'))
+                current_op['trace_events'].append({
+                    'thread': int(trace_match.group('thread')),
+                    'event':  trace_match.group('event'),
+                    'info':   int(trace_match.group('info')),
+                    'cycles': raw_cyc,
+                    'unwrapped_cycles': unwrapper.unwrap(raw_cyc),
+                    'state':  trace_match.group('state')
+                })
+
+    f.close()
+    return all_ops
+
+# --- Simple protobuf encoder ---
+
+
+def write_varint(val):
+    if val < 0:
+        val = (1 << 64) + val
+    res = bytearray()
+    while True:
+        towrite = val & 0x7f
+        val >>= 7
+        if val > 0:
+            res.append(towrite | 0x80)
+        else:
+            res.append(towrite)
+            break
+    return bytes(res)
+
+
+def pb_field(num, wire, data):
+    return write_varint((num << 3) | wire) + data
+
+
+def pb_varint(num, val):
+    return pb_field(num, 0, write_varint(val))
+
+
+def pb_length_delimited(num, data):
+    return pb_field(num, 2, write_varint(len(data)) + data)
+
+
+def pb_string(num, text):
+    return pb_length_delimited(num, text.encode('utf-8'))
+
+
+# Message Encoders
+def make_process_descriptor(pid, name):
+    return pb_varint(1, pid) + pb_string(6, name)
+
+
+def make_thread_descriptor(pid, tid, name, sort_index=None):
+    payload = pb_varint(1, pid) + pb_varint(2, tid) + pb_string(5, name)
+    if sort_index is not None:
+        payload += pb_varint(3, sort_index)
+    return payload
+
+
+def make_track_descriptor(uuid, name=None, parent_uuid=None, thread=None, process=None, sibling_merge_behavior=None, child_ordering=None, sibling_order_rank=None):
+    payload = pb_varint(1, uuid)
+    if name is not None:
+        payload += pb_string(2, name)
+    if parent_uuid is not None:
+        payload += pb_varint(5, parent_uuid)
+    if process is not None:
+        payload += pb_length_delimited(3, process)
+    if thread is not None:
+        payload += pb_length_delimited(4, thread)
+    if sibling_merge_behavior is not None:
+        payload += pb_varint(15, sibling_merge_behavior)
+    if child_ordering is not None:
+        payload += pb_varint(11, child_ordering)
+    if sibling_order_rank is not None:
+        payload += pb_varint(12, sibling_order_rank)
+    return payload
+
+
+def make_debug_annotation(name, string_val=None, int_val=None):
+    payload = pb_string(10, name)
+    if string_val is not None:
+        payload += pb_string(6, string_val)
+    elif int_val is not None:
+        payload += pb_varint(4, int_val)
+    return payload
+
+
+def make_track_event(event_type, track_uuid, name=None, category=None, debug_annotations=None):
+    payload = pb_varint(9, event_type)
+    payload += pb_varint(11, track_uuid)
+    if name is not None:
+        payload += pb_string(23, name)
+    if category is not None:
+        payload += pb_string(22, category)
+    if debug_annotations is not None:
+        for da in debug_annotations:
+            payload += pb_length_delimited(4, da)
+    return payload
+
+
+def make_trace_packet(timestamp, track_event=None, track_descriptor=None, seq_id=1):
+    payload = pb_varint(8, timestamp)
+    payload += pb_varint(10, seq_id)
+    if track_event is not None:
+        payload += pb_length_delimited(11, track_event)
+    if track_descriptor is not None:
+        payload += pb_length_delimited(60, track_descriptor)
+    return payload
+
+
+def write_trace_packet_to_file(f, packet_bytes):
+    # Write as field 1 of top-level Trace message
+    f.write(pb_length_delimited(1, packet_bytes))
+
+# --- End Protobuf Encoder ---
+
+
+def generate_perfetto_trace(filtered_ops, output_path):
+    if not filtered_ops:
+        logger.warning("No operators found after filtering.")
+        return
+
+    # Compute average frequency
+    frequencies = []
+    for op in filtered_ops:
+        if op['usec'] > 0 and op['cycles'] > 0:
+            frequencies.append(op['cycles'] / op['usec'])
+    avg_freq_mhz = statistics.mean(frequencies) if frequencies else 1000.0
+    if avg_freq_mhz <= 0:
+        avg_freq_mhz = 1000.0
+
+    # Assign start and end cycles to each operator
+    for op in filtered_ops:
+        op['start_cycles'] = op['unwrapped_cycles_start']
+        op['end_cycles'] = op['start_cycles'] + op['cycles']
+
+    global_min_cyc = min(op['start_cycles'] for op in filtered_ops if op['start_cycles'] is not None)
+
+    # Process events
+    completed_events = []
+    for op in filtered_ops:
+        events = op['trace_events']
+        if not events:
+            continue
+        events = sorted(events, key=lambda e: e['unwrapped_cycles'])
+
+        active_starts = {}
+        for e in events:
+            t = e['thread']
+            evt = e['event']
+            info = e['info']
+            state = e['state']
+            cyc = e['unwrapped_cycles']
+
+            key = (t, evt, info)
+            if state == 'start':
+                active_starts[key] = cyc
+            elif state == 'stop':
+                if key in active_starts:
+                    start_cyc = active_starts[key]
+                    del active_starts[key]
+                    completed_events.append({
+                        'thread': t,
+                        'event': evt,
+                        'info': info,
+                        'start_cyc': start_cyc,
+                        'end_cyc': cyc,
+                        'op_name': op['name']
+                    })
+
+    completed_events.sort(key=lambda e: e['start_cyc'])
+
+    # Convert event times to microseconds and apply clamp rounded to 1ns resolution (3 decimals)
+    for e in completed_events:
+        start_us = (e['start_cyc'] - global_min_cyc) / avg_freq_mhz
+        dur_us = (e['end_cyc'] - e['start_cyc']) / avg_freq_mhz
+        e['ts_ns'] = int(round(start_us * 1000))
+        e['dur_ns'] = int(round(max(dur_us, 0.1) * 1000))
+
+    # Allocate slots (sub-tracks) to prevent overlaps on same virtual track
+    active_slots = defaultdict(list)
+    for e in completed_events:
+        t = e['thread']
+        evt = e['event']
+        ts = e['ts_ns']
+        dur = e['dur_ns']
+
+        norm_evt = normalize_event_name(evt)
+        if norm_evt == "DMA":
+            track_key = (t, "DMA")
+        elif t == 10:
+            track_key = (t, "HMX")
+        else:
+            track_key = (t, "HVX")
+
+        slots = active_slots[track_key]
+        allocated_slot = -1
+        for idx, slot_end_ns in enumerate(slots):
+            if ts >= slot_end_ns:
+                slots[idx] = ts + dur
+                allocated_slot = idx
+                break
+        if allocated_slot == -1:
+            slots.append(ts + dur)
+            allocated_slot = len(slots) - 1
+        e['slot'] = allocated_slot
+
+    # Generate Track IDs and track definitions
+    used_tracks = {}
+    for e in completed_events:
+        t = e['thread']
+        evt = e['event']
+        slot = e['slot']
+
+        norm_evt = normalize_event_name(evt)
+        if norm_evt == "DMA":
+            track_evt = "DMA"
+            evt_id = 1
+        elif t == 10:
+            track_evt = "HMX"
+            evt_id = 3
+        else:
+            track_evt = "HVX"
+            evt_id = 2
+
+        t_sort = 1 if t == 10 else t + 2
+        # Unique UUID for each sub-track
+        if t == 10:
+            uuid = 20  # HMX thread track UUID
+        else:
+            uuid = int(t_sort * 1000000 + evt_id * 1000 + slot)
+        e['uuid'] = uuid
+        used_tracks[uuid] = (t, track_evt, slot)
+
+    with open(output_path, "wb") as f:
+        # Define Process with EXPLICIT child sorting
+        proc_desc = make_process_descriptor(1, "HTP NPU")
+        proc_packet = make_trace_packet(0, track_descriptor=make_track_descriptor(1, process=proc_desc, child_ordering=3))
+        write_trace_packet_to_file(f, proc_packet)
+
+        # Define Operators Track (UUID = 2) as a thread track at rank 1, tid 8
+        op_thread_desc = make_thread_descriptor(1, 8, "Ops", sort_index=1)
+        op_packet = make_trace_packet(0, track_descriptor=make_track_descriptor(2, parent_uuid=1, thread=op_thread_desc))
+        write_trace_packet_to_file(f, op_packet)
+
+        # Define HMX Thread Track (UUID = 20) at rank 2, tid 9
+        hmx_thread_desc = make_thread_descriptor(1, 9, "HMX", sort_index=2)
+        hmx_packet = make_trace_packet(0, track_descriptor=make_track_descriptor(20, parent_uuid=1, thread=hmx_thread_desc))
+        write_trace_packet_to_file(f, hmx_packet)
+
+        # Define Thread Tracks (T0, T1, ..., T9)
+        unique_threads = sorted(list(set(t for (t, _, _) in used_tracks.values() if t != 10)))
+        for t in unique_threads:
+            thread_uuid = 10 + t
+            thread_name = f"T{t}"
+            # Sort order starts from index 3 (T0 -> 3, T1 -> 4, etc.)
+            sort_index = 3 + t
+            tid = 10 + t
+            thread_desc = make_thread_descriptor(1, tid, thread_name, sort_index=sort_index)
+            thread_packet = make_trace_packet(0, track_descriptor=make_track_descriptor(
+                thread_uuid,
+                parent_uuid=1,
+                thread=thread_desc,
+                sibling_order_rank=sort_index,
+                child_ordering=3  # Explicit child sorting for sub-tracks
+            ))
+            write_trace_packet_to_file(f, thread_packet)
+
+        # Define Track descriptors for sub-tracks parented to thread tracks
+        for uuid in sorted(used_tracks.keys()):
+            if uuid == 20:
+                continue
+            t, evt, slot = used_tracks[uuid]
+            name = f"T{t} {evt}"
+            rank = 0 if evt == "HVX" else 1
+            parent_thread_uuid = 10 + t
+            # Sibling merge behavior: 1 (SIBLING_MERGE_BEHAVIOR_BY_TRACK_NAME)
+            track_desc = make_track_descriptor(
+                uuid=uuid,
+                name=name,
+                parent_uuid=parent_thread_uuid,
+                sibling_merge_behavior=1,
+                sibling_order_rank=rank
+            )
+            track_packet = make_trace_packet(0, track_descriptor=track_desc)
+            write_trace_packet_to_file(f, track_packet)
+
+        # Emit Operators
+        last_op_end_ns = 0
+        for op in filtered_ops:
+            op_start_ns = int(round(((op['start_cycles'] - global_min_cyc) / avg_freq_mhz) * 1000))
+            op_dur_ns = int(round((op['cycles'] / avg_freq_mhz) * 1000))
+            if op_start_ns < last_op_end_ns:
+                op_start_ns = last_op_end_ns
+            clamped_dur = max(op_dur_ns, 100) # Clamp to 100ns (0.1us)
+
+            # Debug annotations for Ops
+            debug_annots = []
+            if 'line_num' in op:
+                debug_annots.append(make_debug_annotation("line", int_val=op['line_num']))
+            if 'strides' in op and op['strides']:
+                debug_annots.append(make_debug_annotation("strides", string_val=op['strides']))
+
+            # Slice Begin
+            evt_begin = make_track_event(1, 2, name=f"{op['name']} ({op['dims']})", category="operator", debug_annotations=debug_annots)
+            packet_begin = make_trace_packet(op_start_ns, track_event=evt_begin)
+            write_trace_packet_to_file(f, packet_begin)
+
+            # Slice End
+            evt_end = make_track_event(2, 2)
+            packet_end = make_trace_packet(op_start_ns + clamped_dur, track_event=evt_end)
+            write_trace_packet_to_file(f, packet_end)
+
+            last_op_end_ns = op_start_ns + clamped_dur
+
+        # Emit Thread Trace Events
+        for e in completed_events:
+            norm_name = normalize_event_name(e['event'])
+            name = f"DMA {e['info']}" if norm_name == "DMA" else norm_name
+
+            # Slice Begin
+            evt_begin = make_track_event(1, e['uuid'], name=name, category="trace")
+            packet_begin = make_trace_packet(e['ts_ns'], track_event=evt_begin)
+            write_trace_packet_to_file(f, packet_begin)
+
+            # Slice End
+            evt_end = make_track_event(2, e['uuid'])
+            packet_end = make_trace_packet(e['ts_ns'] + e['dur_ns'], track_event=evt_end)
+            write_trace_packet_to_file(f, packet_end)
+
+    logger.info(f"Successfully generated Perfetto trace at {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert Hexagon Op profile logs to native Perfetto Protobuf traces.")
+    parser.add_argument("logfile", help="Path to hex-log profile file")
+    parser.add_argument("-o", "--output", default="optrace.perfetto-trace", help="Output trace file path (default: optrace.perfetto-trace)")
+    parser.add_argument("--filter", type=str, help="Regex filter matching against the original profile-op line")
+
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("--head", type=int, help="Limit to first N ops")
+    group.add_argument("--tail", type=int, help="Limit to last N ops")
+
+    args = parser.parse_args()
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+
+    ops = parse_log(args.logfile)
+
+    if args.filter:
+        try:
+            filter_re = re.compile(args.filter)
+        except re.error as e:
+            logger.error(f"Invalid regex filter: {e}")
+            sys.exit(1)
+        ops = [op for op in ops if filter_re.search(op['op_text'])]
+
+    if args.head is not None:
+        ops = ops[:args.head]
+    elif args.tail is not None:
+        ops = ops[-args.tail:]
+
+    generate_perfetto_trace(ops, args.output)
+
+
+if __name__ == "__main__":
+    main()

From 2bc18617bad5ea03c7e91309433458d29d2f0e9b Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 18 Jun 2026 23:52:17 +0800
Subject: [PATCH 052/292] move jinja to the quick tab

---
 koboldcpp.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index cf2b6f51a..6e1a5f697 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -8652,7 +8652,8 @@ def show_gui():
         "Remote Tunnel": [remotetunnel_var,  "Creates a trycloudflare tunnel.\nAllows you to access koboldcpp from other devices over an internet URL."],
         "Use FlashAttention": [flashattention_var, "Enable flash attention for GGUF models."],
         "Force AutoFit": [autofit_var, "Automatically attempt to fit the model in the best possible way. Overrides everything else.\nNot recommended for multi model setups. Experimental."],
-        "Quiet Mode": [quietmode, "Prevents all generation related terminal output from being displayed."]
+        "Quiet Mode": [quietmode, "Prevents all generation related terminal output from being displayed."],
+        "Use Jinja": [jinja_var, "Enables using jinja chat template formatting for chat completions endpoint. Other endpoints are unaffected."]
     }
 
     for idx, (name, properties) in enumerate(quick_boxes.items()):

From 060ce1bf723b237b7db4c5d398aedc8a47f8dab7 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 18:53:49 +0200
Subject: [PATCH 053/292] mtmd: refactor llava-uhd overview image handling
 (always use ov_img_first) (#24769)

* add dedicated "overview" for mtmd_image_preproc_out

* corrections

* correct (again)

* nits

* nits (2)
---
 tools/mtmd/mtmd-image.cpp |  70 +++++++++++------------
 tools/mtmd/mtmd-image.h   |  16 +++++-
 tools/mtmd/mtmd.cpp       | 115 +++++++++++++++++++++-----------------
 3 files changed, 113 insertions(+), 88 deletions(-)

diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index a807fefda..57afb542d 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -26,6 +26,13 @@ void mtmd_image_preproc_out::append(const clip_hparams & hparams, clip_image_f32
     entries.push_back(std::move(img));
 }
 
+void mtmd_image_preproc_out::append_overview(const clip_hparams & hparams, const clip_image_u8 & img, bool normalized) {
+    overview.from_u8(img);
+    if (normalized) {
+        overview.normalize(hparams.image_mean, hparams.image_std);
+    }
+}
+
 // set of tools to manipulate images
 // in the future, we can have HW acceleration by allowing this struct to access 3rd party lib like imagick or opencv
 struct img_tool {
@@ -607,10 +614,11 @@ private:
 mtmd_image_preproc_out mtmd_image_preprocessor_llava_uhd::preprocess(const clip_image_u8 & img) {
     const clip_image_size original_size = img.get_size();
     auto const inst = get_slice_instructions(original_size);
-    std::vector<clip_image_u8> imgs = slice_image(img, inst);
+    auto sliced = slice_image(img, inst);
 
     mtmd_image_preproc_out output;
-    output.append(hparams, imgs, true);
+    output.append_overview(hparams, sliced.overview, true);
+    output.append(hparams, sliced.slices, true);
     output.grid_x = inst.grid_size.width;
     output.grid_y = inst.grid_size.height;
 
@@ -722,22 +730,15 @@ mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_ll
     return res;
 }
 
-std::vector<clip_image_u8> mtmd_image_preprocessor_llava_uhd::slice_image(const clip_image_u8 & img, const mtmd_image_preprocessor_llava_uhd::slice_instructions & inst, bool overview_first) {
-    std::vector<clip_image_u8> output;
+mtmd_image_preprocessor_llava_uhd::slice_output mtmd_image_preprocessor_llava_uhd::slice_image(const clip_image_u8 & img, const mtmd_image_preprocessor_llava_uhd::slice_instructions & inst) {
+    slice_output output;
 
     // resize to overview size
-    clip_image_u8 resized_img;
-    img_tool::resize(img, resized_img, inst.overview_size, hparams.image_resize_algo_ov,
+    img_tool::resize(img, output.overview, inst.overview_size, hparams.image_resize_algo_ov,
                         hparams.image_pad_ov, hparams.image_pad_color_ov);
-    if (overview_first) {
-        output.push_back(resized_img);
-    }
 
     if (inst.slices.empty()) {
-        // no slices, just return the resized image
-        if (!overview_first) {
-            output.push_back(resized_img);
-        }
+        // no slices, just return the overview image
         return output;
     }
 
@@ -755,11 +756,7 @@ std::vector<clip_image_u8> mtmd_image_preprocessor_llava_uhd::slice_image(const
 
         clip_image_u8 img_slice;
         img_tool::crop(refined_img, img_slice, x, y, w, h);
-        output.push_back(std::move(img_slice));
-    }
-
-    if (!overview_first) {
-        output.push_back(resized_img);
+        output.slices.push_back(std::move(img_slice));
     }
 
     return output;
@@ -1077,10 +1074,11 @@ mtmd_image_preproc_out mtmd_image_preprocessor_idefics3::preprocess(const clip_i
             });
         }
     }
-    auto imgs = slice_image(img, instructions);
+    auto sliced = slice_image(img, instructions);
 
     mtmd_image_preproc_out output;
-    output.append(hparams, imgs, true);
+    output.append_overview(hparams, sliced.overview, true);
+    output.append(hparams, sliced.slices, true);
     output.grid_x = instructions.grid_size.width;
     output.grid_y = instructions.grid_size.height;
     return output;
@@ -1094,10 +1092,12 @@ mtmd_image_preproc_out mtmd_image_preprocessor_internvl::preprocess(const clip_i
     GGML_ASSERT(!hparams.image_res_candidates.empty());
     const clip_image_size original_size = img.get_size();
     auto const inst = get_slice_instructions(original_size);
-    std::vector<clip_image_u8> imgs = slice_image(img, inst, false);
+    auto sliced = slice_image(img, inst);
 
     mtmd_image_preproc_out output;
-    output.append(hparams, imgs, true);
+    // InternVL: slices first, then overview
+    output.append(hparams, sliced.slices, true);
+    output.append_overview(hparams, sliced.overview, true);
     output.grid_x = inst.grid_size.width;
     output.grid_y = inst.grid_size.height;
     return output;
@@ -1131,9 +1131,10 @@ mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr::preprocess(const cli
     img_tool::resize(img, padded, {image_size, image_size}, RESIZE_ALGO_BICUBIC_PILLOW,
                      PAD_NEAREST, hparams.image_pad_color);
     mtmd_image_preproc_out output;
-    output.append(hparams, padded, true);
-    output.grid_x = 1;
-    output.grid_y = 1;
+    output.append_overview(hparams, padded, true);
+    output.grid_x = 0;
+    output.grid_y = 0;
+    // TODO @ngxson : support slicing for DeepSeek-OCR, to do in another PR
     return output;
 }
 
@@ -1226,10 +1227,8 @@ mtmd_image_preproc_out mtmd_image_preprocessor_deepseekocr2::preprocess(const cl
     clip_image_u8 padded;
     img_tool::resize(img, padded, { base_size, base_size }, RESIZE_ALGO_BICUBIC_PILLOW,
                      PAD_NEAREST, hparams.image_pad_color);
-    output.append(hparams, padded, true);
-    output.entries.back().add_viewsep = true;
-    output.grid_x = 1;
-    output.grid_y = 1;
+    output.append_overview(hparams, padded, true);
+    output.overview.add_viewsep = true;
     return output;
 }
 
@@ -1447,15 +1446,14 @@ mtmd_image_preproc_out mtmd_image_preprocessor_step3vl::preprocess(const clip_im
     const auto instructions = build_slice_instructions(hparams, prepared.get_size());
 
     mtmd_image_preproc_out output;
-    clip_image_f32 overview_f32;
+    // overview (normalized f32, already includes mean/std)
     img_u8_resize_bilinear_to_f32(
         prepared,
-        overview_f32,
+        output.overview,
         hparams.image_size,
         hparams.image_size,
         hparams.image_mean,
         hparams.image_std);
-    output.append(hparams, overview_f32, false);
 
     if (instructions.slices.empty()) {
         output.grid_x = 0;
@@ -1548,13 +1546,13 @@ mtmd_image_preproc_out mtmd_image_preprocessor_youtuvl::preprocess(const clip_im
 
 mtmd_image_preproc_out mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img) {
     auto output = mtmd_image_preprocessor_llava_uhd::preprocess(img);
-    if (output.entries.size() == 1) {
+    if (output.entries.size() == 0) {
         // Single-tile (overview only): append one newline row.
-        output.entries[0].add_newline = true;
+        output.overview.add_newline = true;
     } else {
         // Multi-tile: overview gets no newline, grid tiles get one.
-        output.entries[0].add_newline = false;
-        for (size_t i = 1; i < output.entries.size(); ++i) {
+        output.overview.add_newline = false;
+        for (size_t i = 0; i < output.entries.size(); ++i) {
             output.entries[i].add_newline = true;
         }
     }
diff --git a/tools/mtmd/mtmd-image.h b/tools/mtmd/mtmd-image.h
index 8819a135a..f458e39e7 100644
--- a/tools/mtmd/mtmd-image.h
+++ b/tools/mtmd/mtmd-image.h
@@ -11,11 +11,19 @@
 struct mtmd_image_preproc_out {
     std::vector<clip_image_f32> entries;
     // grid size is required for llava-uhd style models
+
+    clip_image_f32 overview; // overview image (downscaled image)
     int grid_x = 0;
     int grid_y = 0;
+
     void append(const clip_hparams & hparams, const clip_image_u8 & img, bool normalized = true);
     void append(const clip_hparams & hparams, const std::vector<clip_image_u8> & imgs, bool normalized = true);
     void append(const clip_hparams & hparams, clip_image_f32 & img, bool normalized = true);
+
+    void append_overview(const clip_hparams & hparams, const clip_image_u8 & img, bool normalized = true);
+    bool has_overview() const {
+        return overview.nx() > 0 || overview.ny() > 0;
+    }
 };
 
 // base class, models must inherit from this class
@@ -46,6 +54,8 @@ struct mtmd_image_preprocessor {
  * [overview] --> [slice 1] --> [slice 2]
  *           |                |
  *           +--> [slice 3] --> [slice 4]
+ *
+ * NOTE: for the ordering of overview, set "ov_img_first" on the mtmd_context
  */
 struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
     mtmd_image_preprocessor_llava_uhd(const clip_ctx * ctx) : mtmd_image_preprocessor(ctx) {}
@@ -67,7 +77,11 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
     // LFM2 override this function to implement its custom slicing logic
     virtual slice_instructions get_slice_instructions(const clip_image_size & original_size);
 
-    std::vector<clip_image_u8> slice_image(const clip_image_u8 & img, const slice_instructions & inst, bool overview_first = true);
+    struct slice_output {
+        clip_image_u8 overview;
+        std::vector<clip_image_u8> slices;
+    };
+    slice_output slice_image(const clip_image_u8 & img, const slice_instructions & inst);
 
 private:
     clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index f9ee021dd..abba2ebf2 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -516,6 +516,7 @@ struct mtmd_context {
                     LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
                             "    https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);
                     image_preproc = std::make_unique<mtmd_image_preprocessor_llava_uhd>(ctx_v);
+                    ov_img_first = false;
                 } break;
             case PROJECTOR_TYPE_STEP3VL:
                 {
@@ -539,6 +540,7 @@ struct mtmd_context {
                     img_beg = "<img>";
                     img_end = "</img>";
                     image_preproc = std::make_unique<mtmd_image_preprocessor_internvl>(ctx_v);
+                    ov_img_first = false;
                 } break;
             case PROJECTOR_TYPE_KIMIVL:
                 {
@@ -615,11 +617,13 @@ struct mtmd_context {
                 {
                     img_end = "\n"; // prevent empty batch on llama-server
                     image_preproc = std::make_unique<mtmd_image_preprocessor_deepseekocr>(ctx_v);
+                    ov_img_first = false;
                 } break;
             case PROJECTOR_TYPE_DEEPSEEKOCR2:
                 {
                     img_end = "\n"; // prevent empty batch on llama-server
                     image_preproc = std::make_unique<mtmd_image_preprocessor_deepseekocr2>(ctx_v);
+                    ov_img_first = false;
                 } break;
             case PROJECTOR_TYPE_HUNYUANVL:
                 {
@@ -640,6 +644,7 @@ struct mtmd_context {
                     img_beg = "<image>";
                     img_end = "";
                     image_preproc = std::make_unique<mtmd_image_preprocessor_granite>(ctx_v);
+                    ov_img_first = true;
                 } break;
             default:
                 throw std::runtime_error(string_format("%s: unexpected vision projector type %d\n", __func__, proj));
@@ -1079,26 +1084,38 @@ struct mtmd_tokenizer {
 
                 // for llava-uhd style, we need to handle grid too
                 // we don't care about overwriting these values for now because the case where bitmaps.size() > 1 is only for frame merging (qwen-vl), not supported by llava-uhd
-                if (tmp_preproc_out.grid_x > 0 && tmp_preproc_out.grid_y > 0) {
+                if ((tmp_preproc_out.grid_x > 0 && tmp_preproc_out.grid_y > 0)
+                        || tmp_preproc_out.has_overview()) {
                     GGML_ASSERT(bitmaps.size() == 1);
                     preproc_out.grid_x = tmp_preproc_out.grid_x;
                     preproc_out.grid_y = tmp_preproc_out.grid_y;
+                    preproc_out.overview = std::move(tmp_preproc_out.overview);
                 }
             }
 
+            LOG_DBG("%s: preproc_out has %zu entries, grid_x = %d, grid_y = %d, has_overview = %d\n",
+                    __func__, preproc_out.entries.size(), preproc_out.grid_x, preproc_out.grid_y,
+                    preproc_out.has_overview() ? 1 : 0);
+
             // handle llava-uhd style preprocessing
-            const bool has_tiling_grid = preproc_out.grid_x > 0 && preproc_out.grid_y > 0;
+            // (output either a grid, or overview-only)
+            const bool has_tiling_grid = (preproc_out.grid_x > 0 && preproc_out.grid_y > 0)
+                || preproc_out.has_overview();
+
             if (has_tiling_grid) {
                 // [QWEN_VIDEO] we do not support "frame merging" for llama-uhd style, so no batching for now
                 GGML_ASSERT(bitmaps.size() == 1);
 
                 const int n_col = preproc_out.grid_x;
                 const int n_row = preproc_out.grid_y;
+
                 // split batch into chunks of single images
-                // NOTE: preproc_out will be invalidated after this call
                 auto chunks = split_batch_to_chunk(std::move(preproc_out), bitmaps[0]->id);
                 GGML_ASSERT(chunks.size() > 0);
 
+                // NOTE: preproc_out is invalidated after this point, do not use it anymore
+
+                // split_batch_to_chunk must always put the overview image first
                 auto ov_chunk = std::move(chunks.front());
                 chunks.erase(chunks.begin());
 
@@ -1125,7 +1142,16 @@ struct mtmd_tokenizer {
                                 std::snprintf(buf.get(), sz, ctx->sli_img_start_tmpl.c_str(), y+1, x+1);
                                 add_text(std::string(buf.get(), buf.get() + sz - 1), true);
                             }
-                            cur.entries.emplace_back(std::move(chunks[y * n_col + x]));
+
+                            auto & curr_chunk = chunks[y * n_col + x];
+                            auto & curr_batch = curr_chunk.tokens_image->batch_f32;
+                            if (curr_batch.entries.size() != 1) {
+                                throw std::runtime_error(string_format("%s: expect 1 image in batch_f32", __func__));
+                            }
+
+                            LOG_DBG("%s: adding slice image at row %d col %d\n", __func__, y, x);
+                            cur.entries.emplace_back(std::move(curr_chunk));
+
                             add_text(ctx->tok_sli_img_end);
                             if (!is_last_in_row) {
                                 add_text(ctx->tok_sli_img_mid);
@@ -1147,6 +1173,11 @@ struct mtmd_tokenizer {
 
             } else {
 
+                if (preproc_out.entries.size() == 0) {
+                    LOG_ERR("%s: no image tokens produced by preprocessor (ref: https://github.com/ggml-org/llama.cpp/pull/24769)\n", __func__);
+                    return 2;
+                }
+
                 size_t n_tokens = 0;
                 for (auto & e : preproc_out.entries) {
                     n_tokens += clip_n_output_tokens(ctx->ctx_v, &e);
@@ -1303,13 +1334,15 @@ struct mtmd_tokenizer {
     std::vector<mtmd_input_chunk> split_batch_to_chunk(mtmd_image_preproc_out && preproc_out, const std::string & id) {
         std::vector<mtmd_input_chunk> chunks;
 
-        for (auto & entry : preproc_out.entries) {
+        auto process_chunk = [&](clip_image_f32 && img) {
             mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
-            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, &entry);
+            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, &img);
             image_tokens->ny = 1;
-            image_tokens->batch_f32.entries.push_back(std::move(entry));
+            image_tokens->batch_f32.entries.push_back(std::move(img));
             image_tokens->id = id;
 
+            GGML_ASSERT(image_tokens->nx > 0);
+
             mtmd_input_chunk chunk{
                 MTMD_INPUT_CHUNK_TYPE_IMAGE,
                 {}, // text tokens
@@ -1317,6 +1350,21 @@ struct mtmd_tokenizer {
                 nullptr, // audio tokens
             };
             chunks.emplace_back(std::move(chunk));
+        };
+
+        // overview image first
+        auto & overview = preproc_out.overview;
+        if (overview.nx() == 0 || overview.ny() == 0) {
+            throw std::runtime_error(string_format("%s: invalid overview image for llava-uhd style preprocessing\n", __func__));
+        }
+        process_chunk(std::move(preproc_out.overview));
+
+        // then, process slices
+        for (auto & entry : preproc_out.entries) {
+            if (entry.nx() == 0 || entry.ny() == 0) {
+                throw std::runtime_error(string_format("%s: invalid image slice for llava-uhd style preprocessing\n", __func__));
+            }
+            process_chunk(std::move(entry));
         }
 
         return chunks;
@@ -1390,57 +1438,22 @@ static int32_t mtmd_encode_impl(mtmd_context * ctx, const mtmd_image_tokens * im
         LOG_ERR("%s: this API does not support non-vision input, please use mtmd_encode_chunk instead\n", __func__);
         return 1;
     }
-    auto proj_type = clip_get_projector_type(ctx_clip);
 
     int n_embd_out = ctx->n_embd_out();
     auto n_tokens_out = image_tokens->n_tokens();
     out_embd.resize((size_t)n_embd_out * n_tokens_out);
 
-    bool ok = false;
-
-    if (clip_is_llava(ctx_clip)
-        || proj_type == PROJECTOR_TYPE_MINICPMV
-        || proj_type == PROJECTOR_TYPE_GLM_EDGE
-        || proj_type == PROJECTOR_TYPE_INTERNVL
-        || proj_type == PROJECTOR_TYPE_DEEPSEEKOCR2
-        || proj_type == PROJECTOR_TYPE_GRANITE4_VISION) {
-        // TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode()
-        const auto & entries = image_tokens->batch_f32.entries;
-        // entries may have different token counts
-        // e.g., DeepSeek-OCR-2: 144 per tile views, 257 for the global view
-        size_t offset = 0;
-        for (size_t i = 0; i < entries.size(); i++) {
-            if (entries[i].is_placeholder()) {
-                LOG_ERR("%s: image tokens batch entry %zu is placeholder\n", __func__, i);
-                return 1;
-            }
-            int n_tokens_per_image = clip_n_output_tokens(ctx_clip, &entries[i]);
-            std::vector<float> tmp_embd((size_t)n_tokens_per_image * n_embd_out);
-            bool ok_i = clip_image_encode(
-                ctx_clip,
-                ctx->n_threads,
-                &entries[i],
-                tmp_embd);
-            if (!ok_i) {
-                LOG_ERR("%s: failed to encode image %zu\n", __func__, i);
-                return 1;
-            }
-            ok = true;
-            std::copy(tmp_embd.begin(), tmp_embd.end(), out_embd.begin() + offset);
-            offset += static_cast<size_t>(n_embd_out) * n_tokens_per_image;
-        }
-    } else {
-        if (image_tokens->is_placeholder()) {
-            LOG_ERR("%s: image tokens batch is placeholder\n", __func__);
-            return 1;
-        }
-        ok = clip_image_batch_encode(
-            ctx_clip,
-            ctx->n_threads,
-            &image_tokens->batch_f32,
-            out_embd);
+    if (image_tokens->is_placeholder()) {
+        LOG_ERR("%s: image tokens batch is placeholder\n", __func__);
+        return 1;
     }
 
+    bool ok = clip_image_batch_encode(
+        ctx_clip,
+        ctx->n_threads,
+        &image_tokens->batch_f32,
+        out_embd);
+
     return ok ? 0 : 1;
 }
 

From 32eddaf2ea8dd5d499dee9655592a89b91bfde9d Mon Sep 17 00:00:00 2001
From: o7si <32285332+o7si@users.noreply.github.com>
Date: Fri, 19 Jun 2026 00:59:18 +0800
Subject: [PATCH 054/292] cmake : fix ui build with read-only source (#24752)

---
 scripts/ui-assets.cmake | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

diff --git a/scripts/ui-assets.cmake b/scripts/ui-assets.cmake
index 78a0f4c84..349fa9bf8 100644
--- a/scripts/ui-assets.cmake
+++ b/scripts/ui-assets.cmake
@@ -20,6 +20,7 @@ set(LLAMA_UI_GZIP     "" CACHE STRING "Apply gzip compress to assets to save ban
 
 set(DIST_DIR     "${UI_BINARY_DIR}/dist")
 set(SRC_DIST_DIR "${UI_SOURCE_DIR}/dist")
+set(WORK_DIR     "${UI_BINARY_DIR}/ui-src")
 set(STAMP_FILE   "${UI_BINARY_DIR}/.ui-stamp")
 set(UI_CPP       "${UI_BINARY_DIR}/ui.cpp")
 set(UI_H         "${UI_BINARY_DIR}/ui.h")
@@ -64,6 +65,22 @@ function(npm_build_should_skip out_var)
     set(${out_var} TRUE PARENT_SCOPE)
 endfunction()
 
+function(stage_sources)
+    if(EXISTS "${WORK_DIR}")
+        file(GLOB staged RELATIVE "${WORK_DIR}" "${WORK_DIR}/*")
+        list(REMOVE_ITEM staged "node_modules")
+        foreach(entry ${staged})
+            file(REMOVE_RECURSE "${WORK_DIR}/${entry}")
+        endforeach()
+    endif()
+
+    file(COPY "${UI_SOURCE_DIR}/"
+        DESTINATION "${WORK_DIR}"
+        NO_SOURCE_PERMISSIONS
+        PATTERN "node_modules" EXCLUDE
+    )
+endfunction()
+
 function(npm_build out_var)
     set(${out_var} FALSE PARENT_SCOPE)
 
@@ -89,14 +106,16 @@ function(npm_build out_var)
         return()
     endif()
 
+    stage_sources()
+
     # npm writes node_modules/.package-lock.json on every successful install,
     # so a package-lock.json newer than this marker means node_modules is stale
-    set(NPM_MARKER "${UI_SOURCE_DIR}/node_modules/.package-lock.json")
+    set(NPM_MARKER "${WORK_DIR}/node_modules/.package-lock.json")
     set(need_install FALSE)
     if(NOT EXISTS "${NPM_MARKER}")
         set(need_install TRUE)
     else()
-        file(TIMESTAMP "${UI_SOURCE_DIR}/package-lock.json" lock_ts)
+        file(TIMESTAMP "${WORK_DIR}/package-lock.json" lock_ts)
         file(TIMESTAMP "${NPM_MARKER}" marker_ts)
         if(lock_ts STRGREATER marker_ts)
             set(need_install TRUE)
@@ -107,7 +126,7 @@ function(npm_build out_var)
         message(STATUS "UI: running npm install")
         execute_process(
             COMMAND ${NPM_EXECUTABLE} install
-            WORKING_DIRECTORY "${UI_SOURCE_DIR}"
+            WORKING_DIRECTORY "${WORK_DIR}"
             RESULT_VARIABLE rc
             ERROR_VARIABLE  err
         )
@@ -124,7 +143,7 @@ function(npm_build out_var)
     execute_process(
         COMMAND ${CMAKE_COMMAND} -E env "LLAMA_UI_OUT_DIR=${DIST_DIR}" "LLAMA_UI_VERSION=${HF_VERSION}" "LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}"
                 ${NPM_EXECUTABLE} run build
-        WORKING_DIRECTORY "${UI_SOURCE_DIR}"
+        WORKING_DIRECTORY "${WORK_DIR}"
         RESULT_VARIABLE rc
         ERROR_VARIABLE  err
     )

From a6b3260a4268d3c9931e0c47859fe4ff8f108bd9 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 18 Jun 2026 21:55:04 +0200
Subject: [PATCH 055/292] mtmd: add batching for mtmd-cli, add video tests
 (#24778)

---
 tools/mtmd/mtmd-cli.cpp | 117 ++++++++++++++++++++++++++++++++++------
 tools/mtmd/tests.sh     |  52 ++++++++++++++----
 2 files changed, 145 insertions(+), 24 deletions(-)

diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index a3cad7cd0..0ad000ef0 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -32,9 +32,9 @@ static volatile bool g_is_generating = false;
 static volatile bool g_is_interrupted = false;
 
 /**
- * Please note that this is NOT a production-ready stuff.
+ * Please note that this is NOT a production-ready binary.
  * It is a playground for trying multimodal support in llama.cpp.
- * For contributors: please keep this code simple and easy to understand.
+ * For contributors: please keep this code simple and easy to understand. Do not add unnecessary complexity. The goal is to have a simple CLI for testing multimodal support.
  */
 
 static void show_additional_info(int /*argc*/, char ** argv) {
@@ -65,6 +65,14 @@ static void sigint_handler(int signo) {
 }
 #endif
 
+// this is only used by tests.sh to capture the response ; it's not meant to be used in production
+static void inject_test_response_marker() {
+    const char * env = std::getenv("MTMD_TEST_RESPONSE_MARKER");
+    if (env) {
+        LOG("%s\n", env);
+    }
+}
+
 struct mtmd_cli_context {
     mtmd::context_ptr ctx_vision;
     common_init_result_ptr llama_init;
@@ -79,6 +87,8 @@ struct mtmd_cli_context {
     mtmd::bitmaps bitmaps;
     std::vector<mtmd_helper::video_ptr> videos;
 
+    mtmd::batch_ptr mbatch;
+
     // chat template
     common_chat_templates_ptr tmpls;
     std::vector<common_chat_msg> chat_history;
@@ -233,6 +243,8 @@ static std::string chat_add_and_format(mtmd_cli_context & ctx, common_chat_msg &
 }
 
 static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg) {
+    inject_test_response_marker();
+
     bool add_bos = ctx.chat_history.empty();
     auto formatted_chat = chat_add_and_format(ctx, msg);
     LOG_DBG("formatted_chat.prompt: %s\n", formatted_chat.c_str());
@@ -259,20 +271,95 @@ static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg) {
     ctx.bitmaps.entries.clear();
     ctx.videos.clear();
 
-    llama_pos new_n_past;
-    if (mtmd_helper_eval_chunks(ctx.ctx_vision.get(),
-                ctx.lctx, // lctx
-                chunks.ptr.get(), // chunks
-                ctx.n_past, // n_past
-                0, // seq_id
-                ctx.n_batch, // n_batch
-                true, // logits_last
-                &new_n_past)) {
-        LOG_ERR("Unable to eval prompt\n");
-        return 1;
-    }
+    // batch encode all media chunks, then decode each
+    size_t n_chunks = mtmd_input_chunks_size(chunks.ptr.get());
+    for (size_t i = 0; i < n_chunks; i++) {
+        auto chunk = mtmd_input_chunks_get(chunks.ptr.get(), i);
+        auto chunk_type = mtmd_input_chunk_get_type(chunk);
 
-    ctx.n_past = new_n_past;
+        if (chunk_type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+            // decode text chunk
+            llama_pos new_n_past = ctx.n_past;
+            res = mtmd_helper_eval_chunk_single(ctx.ctx_vision.get(),
+                        ctx.lctx,
+                        chunk,
+                        ctx.n_past,
+                        0, // seq_id
+                        ctx.n_batch,
+                        i == n_chunks - 1, // logits_last
+                        &new_n_past);
+            if (res != 0) {
+                LOG_ERR("Unable to eval text chunk %zu\n", i);
+                return 1;
+            }
+            ctx.n_past = new_n_past;
+        } else {
+            // media chunk: try to get embd from existing batch, or create a new batch
+            float * embd = nullptr;
+            if (ctx.mbatch) {
+                embd = mtmd_batch_get_output_embd(ctx.mbatch.get(), chunk);
+
+                if (embd) {
+                    LOG_DBG("found embd for media chunk %zu in existing batch\n", i);
+                } else {
+                    LOG_DBG("media chunk %zu not found in existing batch, creating new batch\n", i);
+                }
+            }
+
+            if (!embd) {
+                // create and encode a new batch with as many media chunks as possible
+                ctx.mbatch.reset(mtmd_batch_init(ctx.ctx_vision.get()));
+                res = mtmd_batch_add_chunk(ctx.mbatch.get(), chunk);
+                GGML_ASSERT(res == 0); // first chunk must always succeed
+
+                int n_added = 1;
+                // add as many subsequent media chunks as possible
+                for (size_t j = i + 1; j < n_chunks; j++) {
+                    auto next_chunk = mtmd_input_chunks_get(chunks.ptr.get(), j);
+                    auto next_type = mtmd_input_chunk_get_type(next_chunk);
+                    if (next_type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+                        break; // text chunk splits the batch
+                    }
+                    res = mtmd_batch_add_chunk(ctx.mbatch.get(), next_chunk);
+                    if (res != 0) {
+                        break; // batch full or incompatible
+                    }
+                    n_added++;
+                }
+
+                int64_t time_start = ggml_time_ms();
+                LOG_INF("encoding mtmd batch, n_chunks = %d (done = %zu, total = %zu)\n", n_added, i, n_chunks);
+                res = mtmd_batch_encode(ctx.mbatch.get());
+                if (res != 0) {
+                    LOG_ERR("Failed to encode mtmd batch, res = %d\n", res);
+                    return 1;
+                }
+                LOG_INF("mtmd batch encoding done in %d ms\n", (int)(ggml_time_ms() - time_start));
+
+                embd = mtmd_batch_get_output_embd(ctx.mbatch.get(), chunk);
+            }
+
+            GGML_ASSERT(embd != nullptr);
+
+            llama_pos new_n_past = ctx.n_past;
+            res = mtmd_helper_decode_image_chunk(ctx.ctx_vision.get(),
+                        ctx.lctx,
+                        chunk,
+                        embd,
+                        ctx.n_past,
+                        0, // seq_id
+                        ctx.n_batch,
+                        &new_n_past,
+                        nullptr, // callback
+                        nullptr  // user_data
+                    );
+            if (res != 0) {
+                LOG_ERR("Unable to decode media chunk %zu\n", i);
+                return 1;
+            }
+            ctx.n_past = new_n_past;
+        }
+    }
 
     LOG("\n");
 
diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh
index 5da48d61b..6fe26478a 100755
--- a/tools/mtmd/tests.sh
+++ b/tools/mtmd/tests.sh
@@ -13,6 +13,8 @@ mkdir -p $SCRIPT_DIR/output
 PROJ_ROOT="$SCRIPT_DIR/../.."
 cd $PROJ_ROOT
 
+export MTMD_TEST_RESPONSE_MARKER="<MTMD_TEST_RESPONSE_MARKER>"
+
 # Check if the first argument is "big", then run test with big models
 # This is useful if we're running the script on a larger machine, so we can test the big models
 RUN_BIG_TESTS=false
@@ -28,6 +30,15 @@ if [ "${1:-}" = "huge" ]; then
     echo "Include BIG and HUGE models..."
 fi
 
+USE_VIDEO=false
+if [ "${1:-}" = "video" ]; then
+    USE_VIDEO=true
+    echo "Using video as input..."
+    # behavior of USE_VIDEO:
+    # do NOT check if the output contains "new york", only verify if the exit code is 0
+    # when printing the result, print the OK/FAIL line then print the generated text
+fi
+
 # Check if the second argument is "flash", then enable flash attention
 # This is useful to test if flash attention off works correctly
 FLASH_ATTN="on"
@@ -50,13 +61,20 @@ add_test_vision() {
     if [ $# -gt 0 ]; then
         extra_args=$(printf " %q" "$@")
     fi
+    if [ "$USE_VIDEO" = true ]; then
+        arr_file+=("test-3.mp4")
+    else
+        arr_file+=("test-1.jpeg")
+    fi
     arr_prefix+=("[vision]")
     arr_hf+=("$hf")
     arr_extra_args+=("$extra_args")
-    arr_file+=("test-1.jpeg")
 }
 
 add_test_audio() {
+    if [ "$USE_VIDEO" = true ]; then
+        return 0
+    fi
     local hf=$1
     shift
     local extra_args=""
@@ -166,19 +184,35 @@ for i in "${!arr_hf[@]}"; do
         cmd+=" -p \"what is the publisher name of the newspaper?\""
     fi
 
-    output=$(eval "$cmd" 2>&1 | tee /dev/tty)
+    exit_code=0
+    output=$(eval "$cmd" 2>&1 | tee /dev/tty) || exit_code=$?
 
     echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
 
-    # either contains "new york" or both "men" and "walk"
-    if echo "$output" | grep -iq "new york" \
-            || (echo "$output" | grep -iq "men" && echo "$output" | grep -iq "walk")
-    then
-        result="$prefix \033[32mOK\033[0m:   $hf"
+    if [ "$USE_VIDEO" = true ]; then
+        # for video, only check exit code; do not grep for "new york"
+        if [ $exit_code -eq 0 ]; then
+            result="$prefix \033[32mOK\033[0m:   $hf"
+        else
+            result="$prefix \033[31mFAIL\033[0m: $hf"
+        fi
+        # append generated text (after the response marker)
+        generated_text=$(echo "$output" | sed "1,/${MTMD_TEST_RESPONSE_MARKER}/d" | tail -10)
+        if [ -n "$generated_text" ]; then
+            result+="\n$generated_text"
+        fi
+        echo -e "$result"
     else
-        result="$prefix \033[31mFAIL\033[0m: $hf"
+        # either contains "new york" or both "men" and "walk"
+        if echo "$output" | grep -iq "new york" \
+                || (echo "$output" | grep -iq "men" && echo "$output" | grep -iq "walk")
+        then
+            result="$prefix \033[32mOK\033[0m:   $hf"
+        else
+            result="$prefix \033[31mFAIL\033[0m: $hf"
+        fi
+        echo -e "$result"
     fi
-    echo -e "$result"
     arr_res+=("$result")
 
     echo ""

From 40f3aafc45990c1646397c67d2bd3e8eff3f543e Mon Sep 17 00:00:00 2001
From: Reguna <contact@ericleung.dev>
Date: Fri, 19 Jun 2026 04:01:24 +0800
Subject: [PATCH 056/292] server: add "X-Accel-Buffering": "no" header to
 streaming endpoints (#24774)

* server: add "X-Accel-Buffering": "no" header to streaming endpoints

This header tells Nginx (as a reverse proxy) to NOT buffer responses. (only affects streaming endpoints)
Without it, Nginx will break streaming with certain applications (notably the Pi coding harness).
---
 tools/server/server-http.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index 5defee1f5..4f2abab00 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -492,6 +492,8 @@ using server_http_req_ptr = std::unique_ptr<server_http_req>;
 static void process_handler_response(server_http_req_ptr && request, server_http_res_ptr & response, httplib::Response & res) {
     if (response->is_stream()) {
         res.status = response->status;
+        // Tell Nginx to not buffer any streamed response
+        response->headers["X-Accel-Buffering"] = "no";
         set_headers(res, response->headers);
         const std::string content_type = response->content_type;
         // convert to shared_ptr as both chunked_content_provider() and on_complete() need to use it

From 3a3edc9ac65cca79584ca497be41d70c75a58ba8 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Thu, 18 Jun 2026 22:23:01 +0200
Subject: [PATCH 057/292] Ggml/cuda col2im 1d (#24417)

* cuda: add GGML_OP_COL2IM_1D, follow-up to the CPU op

* cuda: col2im_1d use fast_div_modulo for the index decomposition

* cuda: col2im_1d tighten supports_op, type match and contiguous dst
---
 ggml/src/ggml-cuda/col2im-1d.cu  | 81 ++++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/col2im-1d.cuh |  3 ++
 ggml/src/ggml-cuda/ggml-cuda.cu  | 12 +++++
 3 files changed, 96 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/col2im-1d.cu
 create mode 100644 ggml/src/ggml-cuda/col2im-1d.cuh

diff --git a/ggml/src/ggml-cuda/col2im-1d.cu b/ggml/src/ggml-cuda/col2im-1d.cu
new file mode 100644
index 000000000..fecd4c6a9
--- /dev/null
+++ b/ggml/src/ggml-cuda/col2im-1d.cu
@@ -0,0 +1,81 @@
+#include "col2im-1d.cuh"
+#include "convert.cuh"
+
+// col2im_1d: scatter-add GEMM columns to 1D signal (gather approach)
+// columns: [K*OC, T_in]  ->  output: [T_out, OC]
+// Supports F32, F16, BF16 data with F32 accumulator.
+
+template <typename T>
+static __global__ void col2im_1d_kernel(
+        const T * __restrict__ col,
+        T       * __restrict__ dst,
+        const int T_in, const uint3 T_out_fd,
+        const int OC, const int K, const int K_OC,
+        const int s0, const int p0, const int total) {
+
+    const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+    if (idx >= total) return;
+
+    // dst layout: [T_out, OC], ne[0]=T_out fastest
+    const uint2 qr  = fast_div_modulo((uint32_t)idx, T_out_fd);  // qr.x = idx / T_out, qr.y = idx % T_out
+    const int oc    = (int)qr.x;
+    const int t_out = (int)qr.y;
+    const int t_abs = t_out + p0;  // absolute position in uncropped signal
+
+    // Gather: find all (t_in, k) where t_in*s + k == t_abs, 0 <= k < K
+    int t_in_min = (t_abs - K + s0) / s0;  // ceil((t_abs - K + 1) / s)
+    if (t_in_min < 0) t_in_min = 0;
+    int t_in_max = t_abs / s0;
+    if (t_in_max >= T_in) t_in_max = T_in - 1;
+
+    float sum = 0.0f;
+    for (int t_in = t_in_min; t_in <= t_in_max; t_in++) {
+        const int k = t_abs - t_in * s0;
+        // col layout: [K*OC, T_in], column index = oc * K + k
+        sum += ggml_cuda_cast<float>(col[(oc * K + k) + t_in * K_OC]);
+    }
+
+    dst[idx] = ggml_cuda_cast<T>(sum);
+}
+
+void ggml_cuda_op_col2im_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
+    const int32_t OC = ((const int32_t *)(dst->op_params))[1];
+    const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
+
+    const int K_OC = (int) src0->ne[0];
+    const int T_in = (int) src0->ne[1];
+    const int K    = K_OC / OC;
+    const int T_out = (int) dst->ne[0];
+
+    const uint3 T_out_fd = init_fastdiv_values((uint32_t)T_out);
+
+    const int total = T_out * OC;
+    const int block_size = 256;
+    const int num_blocks = (total + block_size - 1) / block_size;
+
+    switch (src0->type) {
+        case GGML_TYPE_F32: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const float *)src0->data, (float *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        case GGML_TYPE_F16: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const half *)src0->data, (half *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        case GGML_TYPE_BF16: {
+            col2im_1d_kernel<<<num_blocks, block_size, 0, stream>>>(
+                (const nv_bfloat16 *)src0->data, (nv_bfloat16 *)dst->data,
+                T_in, T_out_fd, OC, K, K_OC, s0, p0, total);
+        } break;
+        default:
+            GGML_ABORT("col2im_1d: unsupported type");
+    }
+}
diff --git a/ggml/src/ggml-cuda/col2im-1d.cuh b/ggml/src/ggml-cuda/col2im-1d.cuh
new file mode 100644
index 000000000..efc3313c4
--- /dev/null
+++ b/ggml/src/ggml-cuda/col2im-1d.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_col2im_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 34cdbc81c..3d4b5f605 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -11,6 +11,7 @@
 #include "ggml-cuda/argsort.cuh"
 #include "ggml-cuda/binbcast.cuh"
 #include "ggml-cuda/clamp.cuh"
+#include "ggml-cuda/col2im-1d.cuh"
 #include "ggml-cuda/concat.cuh"
 #include "ggml-cuda/conv-transpose-1d.cuh"
 #include "ggml-cuda/conv2d.cuh"
@@ -3051,6 +3052,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_CONV_TRANSPOSE_1D:
             ggml_cuda_op_conv_transpose_1d(ctx,dst);
             break;
+        case GGML_OP_COL2IM_1D:
+            ggml_cuda_op_col2im_1d(ctx, dst);
+            break;
         case GGML_OP_POOL_2D:
             ggml_cuda_op_pool2d(ctx, dst);
             break;
@@ -5316,6 +5320,14 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 }
                 return false;
             } break;
+        case GGML_OP_COL2IM_1D:
+            {
+                ggml_type src0_type = op->src[0]->type;
+                return (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_F16 || src0_type == GGML_TYPE_BF16) &&
+                    op->type == src0_type &&
+                    ggml_is_contiguous(op->src[0]) &&
+                    ggml_is_contiguous(op);
+            } break;
         case GGML_OP_SILU_BACK:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
             break;

From db52540f730de39efcf7172d4ab1f79bb50556e2 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 01:16:16 +0200
Subject: [PATCH 058/292] mtmd: add batching support for internvl (#24775)

---
 tools/mtmd/clip.cpp            |  2 +-
 tools/mtmd/models/internvl.cpp | 18 +++++++++++-------
 tools/mtmd/models/models.h     |  1 +
 3 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index dc6223295..17079815d 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -534,7 +534,7 @@ ggml_tensor * clip_graph::build_vit(
 ggml_tensor * clip_graph::build_inp() {
     ggml_tensor * inp_raw = build_inp_raw();
     ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-    inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
+    inp = ggml_reshape_3d(ctx0, inp, n_patches, n_embd, n_batch);
     inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
     if (model.patch_bias) {
         inp = ggml_add(ctx0, inp, model.patch_bias);
diff --git a/tools/mtmd/models/internvl.cpp b/tools/mtmd/models/internvl.cpp
index 9aded3b97..65d7d5a6b 100644
--- a/tools/mtmd/models/internvl.cpp
+++ b/tools/mtmd/models/internvl.cpp
@@ -8,7 +8,9 @@ ggml_cgraph * clip_graph_internvl::build() {
     ggml_tensor * inp = build_inp();
 
     // add CLS token
-    inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
+    ggml_tensor * cls_repeated = ggml_repeat_4d(ctx0, model.class_embedding,
+            model.class_embedding->ne[0], 1, n_batch, 1);
+    inp = ggml_concat(ctx0, inp, cls_repeated, 1);
 
     // The larger models use a different ViT, which uses RMS norm instead of layer norm
     // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188
@@ -24,14 +26,15 @@ ggml_cgraph * clip_graph_internvl::build() {
                             nullptr);
 
     // remove CLS token
-    cur = ggml_view_2d(ctx0, cur,
-        n_embd, n_patches,
-        ggml_row_size(cur->type, n_embd), 0);
+    cur = ggml_view_3d(ctx0, cur,
+        n_embd, n_patches, n_batch,
+        cur->nb[1], cur->nb[2], 0);
+    cur = ggml_cont(ctx0, cur);
 
     // pixel shuffle
     {
         const int scale_factor = model.hparams.n_merge;
-        const int bsz    = 1; // batch size, always 1 for now since we don't support batching
+        const int bsz    = n_batch;
         const int height = n_patches_y;
         const int width  = n_patches_x;
         GGML_ASSERT(scale_factor > 0);
@@ -44,9 +47,10 @@ ggml_cgraph * clip_graph_internvl::build() {
             bsz);
         cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
         // flatten to 2D
-        cur = ggml_cont_2d(ctx0, cur,
+        cur = ggml_cont_3d(ctx0, cur,
             n_embd * scale_factor * scale_factor,
-            cur->ne[1] * cur->ne[2]);
+            cur->ne[1] * cur->ne[2],
+            cur->ne[3]);
     }
 
     // projector (always using GELU activation)
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
index 3a15f7682..12d5e6949 100644
--- a/tools/mtmd/models/models.h
+++ b/tools/mtmd/models/models.h
@@ -80,6 +80,7 @@ struct clip_graph_minicpmv4_6 : clip_graph {
 struct clip_graph_internvl : clip_graph {
     clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
+    bool support_batch() const override { return true; }
 };
 
 struct clip_graph_nemotron_v2_vl : clip_graph {

From 6f4325ac87e0c64a0ae17b7b733029bc771cd242 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 19 Jun 2026 10:26:34 +0800
Subject: [PATCH 059/292] support mp3 from api

---
 gpttype_adapter.cpp | 2 +-
 koboldcpp.py        | 9 ++++++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 0c0dc7436..202334038 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -893,7 +893,7 @@ static void mtp_decoding_setup(llama_model * main_model, llama_context * main_ct
     }
 
     draft_is_mtp = true;
-    speculative_state_setup(main_ctx, mtp_ctx_params, static_cast<int32_t>(main_model->n_gpu_layers()), COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+    speculative_state_setup(main_ctx, mtp_ctx_params, -1, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
 }
 
 //loads a model for speculative decoding.
diff --git a/koboldcpp.py b/koboldcpp.py
index 6e1a5f697..8e4e3f85d 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -3000,7 +3000,8 @@ def tts_generate(genparams):
     if not genparams.get("instruction", ""):
         prompt, ttsinstruction = tts_extract_instruction(prompt)
     inputs.speaker_instruction = ttsinstruction.encode("UTF-8")
-    inputs.use_mp3 = genparams.get("use_mp3", False)
+    response_format_mp3 = True if (genparams.get("response_format")=="mp3") else False
+    inputs.use_mp3 = genparams.get("use_mp3", response_format_mp3)
     inputs.prompt = prompt.encode("UTF-8")
     inputs.speaker_seed = voice
     aseed = -1
@@ -3089,7 +3090,8 @@ def music_generate_codes(genparams):
     inputs = music_generation_inputs()
     inputs.is_planner_mode = True
     inputs.stereo = genparams.get('stereo', True)
-    inputs.use_mp3 = genparams.get('use_mp3', False)
+    response_format_mp3 = True if (genparams.get("response_format")=="mp3") else False
+    inputs.use_mp3 = genparams.get("use_mp3", response_format_mp3)
     inputs.gen_codes =  genparams.get('gen_codes', False)
     inputs.rewrite_caption =  genparams.get('rewrite_caption', True)
     inputs.input_json = input_json.encode("UTF-8")
@@ -3107,7 +3109,8 @@ def music_generate_audio(genparams):
     inputs = music_generation_inputs()
     inputs.is_planner_mode = False
     inputs.stereo = genparams.get('stereo', True)
-    inputs.use_mp3 = genparams.get('use_mp3', False)
+    response_format_mp3 = True if (genparams.get("response_format")=="mp3") else False
+    inputs.use_mp3 = genparams.get("use_mp3", response_format_mp3)
     inputs.gen_codes =  genparams.get('gen_codes', False)
     inputs.rewrite_caption =  genparams.get('rewrite_caption', True)
     inputs.input_json = input_json.encode("UTF-8")

From 7780cf728879abc9342a2ae45607f47aae67786e Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 19 Jun 2026 10:30:55 +0800
Subject: [PATCH 060/292] suppress some warnings

---
 CMakeLists.txt | 3 +++
 Makefile       | 4 ++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 40816ed33..b1d98fc9d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -72,6 +72,9 @@ if (MSVC)
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
     add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
+else()
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:-Wno-unused-value>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:-Wno-unused-value>")
 endif()
 
 file(GLOB GGML_SOURCES_CUDA "ggml/src/ggml-cuda/*.cu")
diff --git a/Makefile b/Makefile
index 8233fc2ab..31694bd91 100644
--- a/Makefile
+++ b/Makefile
@@ -71,8 +71,8 @@ CXXFLAGS += -DGGML_USE_LLAMAFILE
 endif
 
 #lets try enabling everything
-CFLAGS   += -pthread -Wno-deprecated -Wno-deprecated-declarations -Wno-unused-variable
-CXXFLAGS += -pthread -Wno-multichar -Wno-write-strings -Wno-deprecated -Wno-deprecated-declarations -Wno-unused-variable
+CFLAGS   += -pthread -Wno-deprecated -Wno-deprecated-declarations -Wno-unused-variable -Wno-unused-value
+CXXFLAGS += -pthread -Wno-multichar -Wno-write-strings -Wno-deprecated -Wno-deprecated-declarations -Wno-unused-variable -Wno-unused-value
 
 LDFLAGS  =
 

From 8141e730f1598780c19b153e0e212ed70a672c53 Mon Sep 17 00:00:00 2001
From: shalinib-ibm <Shalini.Salomi.Bodapati@ibm.com>
Date: Fri, 19 Jun 2026 11:25:38 +0530
Subject: [PATCH 061/292] ggml-cpu: support K tails in power10 Q8/Q4 MMA matmul
 (#24753)

* ggml-cpu: support K tails in Power10 MMA Q8/Q4 matmul

This patch removes the requirement that K be divisible by kc in the tinyBlas_Q0_PPC tiled matmul path. Process the final K panel using its actual depth and pass the reduced panel size through packing and kernel execution.  This allows more workloads to use the MMA kernel and reduces fallback to mnpack.

* Apply suggestion from @taronaeo

Co-authored-by: Aaron Teo <taronaeo@gmail.com>

---------

Co-authored-by: Aaron Teo <taronaeo@gmail.com>
---
 ggml/src/ggml-cpu/llamafile/sgemm.cpp | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index e13828e3b..0b8323e60 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -2345,7 +2345,7 @@ class tinyBLAS_Q0_PPC {
             else if (n_aligned % 16 == 0) nc = 16;
             else                          nc = 8;
         }
-        bool can_use_tiled = n_aligned > 0 && (m % mc == 0) && (k % kc == 0);
+        bool can_use_tiled = n_aligned > 0 && (m % mc == 0);
         if (can_use_tiled) {
             matmul_tiled(m, n_aligned, mc, nc, kc);
             if (n > n_aligned) {
@@ -3063,13 +3063,14 @@ class tinyBLAS_Q0_PPC {
             int64_t ii = (job / xtiles) * mc;
             int64_t jj = (job % xtiles) * nc;
             for (int64_t kk = 0; kk < k; kk += kc) {
+                int64_t k_cur = MIN(kc, k - kk);
                 if constexpr(is_Ablock_q4) {
-                    packNormal_q4_fp16(A + ii * lda + kk, lda, mc, kc, (uint8_t *)A_pack);
+                    packNormal_q4_fp16(A + ii * lda + kk, lda, mc, k_cur, (uint8_t *)A_pack);
                 } else {
-                    packNormal_q8_fp16(A + ii * lda + kk, lda, mc, kc, (uint8_t *)A_pack);
+                    packNormal_q8_fp16(A + ii * lda + kk, lda, mc, k_cur, (uint8_t *)A_pack);
                 }
-                packNormal_q8_fp16(B + jj * ldb + kk, ldb, nc, kc, (uint8_t *)B_pack);
-                KERNEL_Q0(ii, jj, mc, nc, kc, kk, A_pack, B_pack);
+                packNormal_q8_fp16(B + jj * ldb + kk, ldb, nc, k_cur, (uint8_t *)B_pack);
+                KERNEL_Q0(ii, jj, mc, nc, k_cur, kk, A_pack, B_pack);
             }
         }
     }

From 80452d65b9b1d44b496ed729f1fb0b6c4c39d7bf Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jun 2026 09:22:34 +0300
Subject: [PATCH 062/292] server : consolidate slot selection into
 get_available_slot (#24755)

Absorb get_slot_by_id logic into get_available_slot so slot selection
is handled by a single function call. When a specific slot id is
requested, the LCP similarity check still runs to enable proper
prompt cache updates.

Assisted-by: pi:llama.cpp/Qwen3.6-27B
---
 tools/server/server-context.cpp | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index aebca306a..ded622cfd 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1395,11 +1395,23 @@ private:
 
         bool update_cache = false;
 
+        // if a specific slot is requested, use it (still goes through cache update logic below)
+        if (task.id_slot != -1) {
+            ret = get_slot_by_id(task.id_slot);
+            if (ret) {
+                SLT_INF(*ret, "selected slot by id (%d)\n", task.id_slot);
+            }
+        }
+
         // find the slot that has at least n% prompt similarity
-        if (ret == nullptr && slot_prompt_similarity != 0.0f) {
+        if (slot_prompt_similarity != 0.0f) {
             float sim_best = 0;
 
             for (server_slot & slot : slots) {
+                if (task.id_slot != -1 && slot.id != task.id_slot) {
+                    continue;
+                }
+
                 // skip the slot if it is not available
                 if (slot.is_processing()) {
                     continue;
@@ -1426,8 +1438,10 @@ private:
             if (ret != nullptr) {
                 const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size();
 
-                SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
-                        sim_best, slot_prompt_similarity, f_keep);
+                if (task.id_slot == -1) {
+                    SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
+                            sim_best, slot_prompt_similarity, f_keep);
+                }
 
                 // if we are about to lose a large portion of the existing context - save it in the prompt cache
                 if (f_keep < 0.5f) {
@@ -2180,10 +2194,9 @@ private:
                         }
                     }
 
-                    const int id_slot = task.id_slot;
                     const int id_task = task.id;
 
-                    server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task);
+                    server_slot * slot = get_available_slot(task);
 
                     //
                     // slot scheduling logic

From 5bd21b8555edf203aee78aacedf2d2744b8702a3 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jun 2026 09:34:00 +0300
Subject: [PATCH 063/292] pi : remove docs from system prompt (#24791)

---
 .pi/gg/SYSTEM.md | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/.pi/gg/SYSTEM.md b/.pi/gg/SYSTEM.md
index 197173fae..17ce71cc1 100644
--- a/.pi/gg/SYSTEM.md
+++ b/.pi/gg/SYSTEM.md
@@ -25,13 +25,3 @@ Commits:
 - Do not explicitly set the git author in commits - rely on the default git config
 - Always use `--no-gpg-sign` when committing
 - Never `git push` without explicit confirmation from the user
-
-Resources (read on demand):
-- [CONTRIBUTING.md](CONTRIBUTING.md)
-- [Build documentation](docs/build.md)
-- [Server usage documentation](tools/server/README.md)
-- [Server development documentation](tools/server/README-dev.md)
-- [PEG parser](docs/development/parsing.md)
-- [Auto parser](docs/autoparser.md)
-- [Jinja engine](common/jinja/README.md)
-- [PR template](.github/pull_request_template.md)

From 98236505e5ef02682370037978b7f2904e8facfa Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 19 Jun 2026 15:10:34 +0800
Subject: [PATCH 064/292] fix build

---
 common/download.cpp | 145 +++++++++++++++++++++++---------------------
 1 file changed, 76 insertions(+), 69 deletions(-)

diff --git a/common/download.cpp b/common/download.cpp
index e5b00e380..3740a0660 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -1002,75 +1002,6 @@ std::vector<common_cached_model_info> common_list_cached_models() {
 }
 
 
-#else
-
-// common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
-//     throw std::runtime_error("download functionality is not enabled in this build");
-// }
-
-common_download_model_result common_download_model(const common_params_model  & model,
-                                                   const common_download_opts & opts) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-std::string common_docker_resolve_model(const std::string &) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-int common_download_file_single(const std::string & url,
-                                const std::string & path,
-                                const common_download_opts & opts,
-                                bool skip_etag) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
-                                                             const common_remote_params & params) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-struct gguf_split_info {
-    std::string prefix; // tag included
-    std::string tag;
-    int index;
-    int count;
-};
-static gguf_split_info get_gguf_split_info(const std::string & path) {
-    static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
-    static const std::regex re_tag("[-.]([A-Z0-9_]+)$", std::regex::icase);
-    std::smatch m;
-
-    std::string prefix = path;
-    string_remove_suffix(prefix, ".gguf");
-
-    int index = 1;
-    int count = 1;
-
-    if (std::regex_match(prefix, m, re_split)) {
-        index = std::stoi(m[2].str());
-        count = std::stoi(m[3].str());
-        prefix = m[1].str();
-    }
-
-    std::string tag;
-    if (std::regex_search(prefix, m, re_tag)) {
-        tag = m[1].str();
-        for (char & c : tag) {
-            c = std::toupper((unsigned char)c);
-        }
-    }
-
-    return {std::move(prefix), std::move(tag), index, count};
-}
-
-std::vector<common_cached_model_info> common_list_cached_models() {
-    std::vector<common_cached_model_info> result;
-    return result;
-}
-
-
-#endif // defined(LLAMA_USE_HTTPLIB)
-
 bool common_download_remove(const std::string & hf_repo_with_tag) {
     namespace fs = std::filesystem;
 
@@ -1156,3 +1087,79 @@ bool common_download_remove(const std::string & hf_repo_with_tag) {
 }
 
 
+#else
+
+// common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
+//     throw std::runtime_error("download functionality is not enabled in this build");
+// }
+
+common_download_model_result common_download_model(const common_params_model  & model,
+                                                   const common_download_opts & opts) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+std::string common_docker_resolve_model(const std::string &) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+int common_download_file_single(const std::string & url,
+                                const std::string & path,
+                                const common_download_opts & opts,
+                                bool skip_etag) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
+                                                             const common_remote_params & params) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+struct gguf_split_info {
+    std::string prefix; // tag included
+    std::string tag;
+    int index;
+    int count;
+};
+static gguf_split_info get_gguf_split_info(const std::string & path) {
+    static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
+    static const std::regex re_tag("[-.]([A-Z0-9_]+)$", std::regex::icase);
+    std::smatch m;
+
+    std::string prefix = path;
+    string_remove_suffix(prefix, ".gguf");
+
+    int index = 1;
+    int count = 1;
+
+    if (std::regex_match(prefix, m, re_split)) {
+        index = std::stoi(m[2].str());
+        count = std::stoi(m[3].str());
+        prefix = m[1].str();
+    }
+
+    std::string tag;
+    if (std::regex_search(prefix, m, re_tag)) {
+        tag = m[1].str();
+        for (char & c : tag) {
+            c = std::toupper((unsigned char)c);
+        }
+    }
+
+    return {std::move(prefix), std::move(tag), index, count};
+}
+
+std::vector<common_cached_model_info> common_list_cached_models() {
+    std::vector<common_cached_model_info> result;
+    return result;
+}
+
+
+bool common_download_remove(const std::string & hf_repo_with_tag) {
+    return true;
+}
+
+
+#endif // defined(LLAMA_USE_HTTPLIB)
+
+
+

From df08e951d01664ad1c673029f520e1dd4966e597 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 19 Jun 2026 15:18:53 +0800
Subject: [PATCH 065/292] response format returns actual mp3 filename if
 requested

---
 koboldcpp.py | 38 ++++++++++++++++++++++++++------------
 1 file changed, 26 insertions(+), 12 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 8e4e3f85d..33defd482 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -1117,6 +1117,18 @@ def strip_base64_prefix(encoded_data):
         encoded_data = encoded_data.split(',', 1)[-1]
     return encoded_data
 
+def get_audio_response_format(genparams, audio_data):
+    if audio_data.startswith(b"ID3") or audio_data[:2] == b"\xff\xfb" or audio_data[:2] == b"\xff\xf3" or audio_data[:2] == b"\xff\xf2":
+        return "mp3", "audio/mpeg"
+    if audio_data.startswith(b"RIFF") and audio_data[8:12] == b"WAVE":
+        return "wav", "audio/wav"
+    use_mp3 = genparams.get("use_mp3", False)
+    if isinstance(use_mp3, str):
+        use_mp3 = use_mp3.lower() in ("1", "true", "yes", "on")
+    if genparams.get("response_format") == "mp3" or use_mp3:
+        return "mp3", "audio/mpeg"
+    return "wav", "audio/wav"
+
 def fix_unquoted_keys(s: str) -> str:
     """
     Fix JSON with unquoted keys by only quoting identifiers that appear
@@ -7379,14 +7391,15 @@ Change Mode<br>
                 elif is_tts:
                     try:
                         gendat = tts_generate(genparams)
-                        wav_data = b''
+                        audio_data = b''
                         if gendat:
-                            wav_data = base64.b64decode(gendat) # Decode the Base64 string into binary data
+                            audio_data = base64.b64decode(gendat) # Decode the Base64 string into binary data
+                        audio_ext, audio_content_type = get_audio_response_format(genparams, audio_data)
                         self.send_response(200)
-                        self.send_header('content-length', str(len(wav_data)))  # Set content length
-                        self.send_header('Content-Disposition', 'attachment; filename="output.wav"')
-                        self.end_headers(content_type='audio/wav')
-                        self.wfile.write(wav_data) # Write the binary WAV data to the response
+                        self.send_header('content-length', str(len(audio_data)))  # Set content length
+                        self.send_header('Content-Disposition', f'attachment; filename="output.{audio_ext}"')
+                        self.end_headers(content_type=audio_content_type)
+                        self.wfile.write(audio_data) # Write the binary audio data to the response
                     except Exception as ex:
                         utfprint(ex,1)
                         print("TTS: The response could not be sent, maybe connection was terminated?")
@@ -7436,14 +7449,15 @@ Change Mode<br>
                 elif is_music_audio:
                     try:
                         gendat = music_generate_audio(genparams)
-                        wav_data = b''
+                        audio_data = b''
                         if gendat:
-                            wav_data = base64.b64decode(gendat) # Decode the Base64 string into binary data
+                            audio_data = base64.b64decode(gendat) # Decode the Base64 string into binary data
+                        audio_ext, audio_content_type = get_audio_response_format(genparams, audio_data)
                         self.send_response(200)
-                        self.send_header('content-length', str(len(wav_data)))  # Set content length
-                        self.send_header('Content-Disposition', 'attachment; filename="output.wav"')
-                        self.end_headers(content_type='audio/wav')
-                        self.wfile.write(wav_data) # Write the binary WAV data to the response
+                        self.send_header('content-length', str(len(audio_data)))  # Set content length
+                        self.send_header('Content-Disposition', f'attachment; filename="output.{audio_ext}"')
+                        self.end_headers(content_type=audio_content_type)
+                        self.wfile.write(audio_data) # Write the binary audio data to the response
                     except Exception as ex:
                         utfprint(ex,1)
                         print("Music Gen Audio: The response could not be sent, maybe connection was terminated?")

From 1868af13ac1c2c6dd5b0dd1a4ac43f6ccc80fd80 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jun 2026 10:14:26 +0300
Subject: [PATCH 066/292] ggml : bump version to 0.15.2 (ggml/1548)

---
 ggml/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 0507e0c5a..04069784f 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -5,7 +5,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 15)
-set(GGML_VERSION_PATCH 1)
+set(GGML_VERSION_PATCH 2)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

From 5fd2dc2c41c342a75c26f9756ca6b1814ed05fb4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 19 Jun 2026 10:18:14 +0300
Subject: [PATCH 067/292] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 87d353ef4..499be5a58 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-3af5f5760e19a96427f5f7a93b79cbdf3d4b265b
+707321c4cf6d21cb4bc831aa8b687dbf01a521ce

From 159d093a43e87b977e9749b18a980c15b1a66a90 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 10:53:44 +0200
Subject: [PATCH 068/292] server: fix non-bound n_discard value (ctx shifting)
 (#24786)

* server: fix non-bound n_discard value

* Update tools/server/server-context.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tools/server/server-context.cpp | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index ded622cfd..a23b0405c 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -2565,7 +2565,10 @@ private:
                 n_keep = std::min(slot.n_ctx - 4, n_keep);
 
                 const int n_left    = slot.prompt.n_tokens() - n_keep;
-                const int n_discard = slot.task->params.n_discard ? slot.task->params.n_discard : (n_left / 2);
+                int       n_discard = slot.task->params.n_discard ? slot.task->params.n_discard : (n_left / 2);
+
+                // ref: https://github.com/ggml-org/llama.cpp/pull/24786
+                n_discard = std::clamp(n_discard, 0, std::max(0, n_left - 1));
 
                 SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
 

From b14e3fb90ca8c760f4254ddc9aa7845ebbdb2edf Mon Sep 17 00:00:00 2001
From: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Fri, 19 Jun 2026 12:08:50 +0200
Subject: [PATCH 069/292] spec: support eagle3 for qwen3.5 & 3.6 (#24593)

* spec: support qwen3.5 & 3.6 eagle3 draft

* eagle3: Add deferred boundary checkpoints restore support for hybrid models

* apply suggestions

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* spec: adapt to API change

* spec: fix naming

* cont : add TODO

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 common/common.cpp               |  4 +-
 common/common.h                 |  6 ++-
 common/speculative.cpp          | 72 +++++++++++++++++++++++++++++++++
 common/speculative.h            |  4 ++
 src/models/qwen35.cpp           |  2 +
 src/models/qwen35moe.cpp        |  2 +
 tools/server/server-context.cpp |  4 ++
 7 files changed, 92 insertions(+), 2 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index b01772e1c..f3f114f68 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -2034,7 +2034,7 @@ bool common_prompt_batch_decode(
 }
 
 size_t common_prompt_checkpoint::size() const {
-    return data_tgt.size() + data_dft.size();
+    return data_tgt.size() + data_dft.size() + data_spec.size();
 }
 
 bool common_prompt_checkpoint::empty() const {
@@ -2049,6 +2049,7 @@ void common_prompt_checkpoint::clear() {
 
     data_tgt.clear();
     data_dft.clear();
+    data_spec.clear();
 }
 
 void common_prompt_checkpoint::update_pos(
@@ -2138,4 +2139,5 @@ void common_prompt_checkpoint::clear_tgt() {
 
 void common_prompt_checkpoint::clear_dft() {
     data_dft.clear();
+    data_spec.clear();
 }
diff --git a/common/common.h b/common/common.h
index 040b9cf23..535a4ed33 100644
--- a/common/common.h
+++ b/common/common.h
@@ -363,7 +363,7 @@ struct common_params_speculative {
 
     uint32_t need_n_rs_seq() const {
         bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
-            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP;
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3;
         });
 
         return needs_rs_seq ? draft.n_max : 0u;
@@ -1065,6 +1065,10 @@ struct common_prompt_checkpoint {
     std::vector<uint8_t> data_tgt;
     std::vector<uint8_t> data_dft;
 
+    // (optional) speculative-decoding implementation state stashed with the checkpoint
+    // (e.g. eagle3's deferred-boundary g_embd row)
+    std::vector<uint8_t> data_spec;
+
     size_t size() const;
 
     bool empty() const;
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 6f387f2cf..9c20585dc 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -161,6 +161,10 @@ struct common_speculative_impl {
 
     virtual void accept(llama_seq_id seq_id, uint16_t n_accepted, bool is_other) = 0;
 
+    // (optional) serialize/restore per-seq internal state (e.g. eagle3's deferred boundary).
+    virtual bool get_state(llama_seq_id /*seq_id*/, std::vector<uint8_t> & /*data*/) const { return false; }
+    virtual void set_state(llama_seq_id /*seq_id*/, const std::vector<uint8_t> & /*data*/) {}
+
     // true if this implementation requires the target context to extract post-norm embeddings
     virtual bool need_embd() const = 0;
 
@@ -841,6 +845,49 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
                     (size_t) n_embd_dec * sizeof(float));
     }
 
+    // we only need to stash the deferred boundary's g_embd row for recurrent/hybrid targets:
+    // their single-position checkpoints drop it on restore
+    bool need_boundary_stash() const {
+        const llama_model * model_tgt = llama_get_model(params.ctx_tgt);
+        return llama_model_is_recurrent(model_tgt) || llama_model_is_hybrid(model_tgt);
+    }
+
+    bool get_state(llama_seq_id seq_id, std::vector<uint8_t> & data) const override {
+        if (!need_boundary_stash()) {
+            return false;
+        }
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq || pending_pos_last[seq_id] < 0) {
+            return false;
+        }
+
+        const llama_pos          pos = pending_pos_last[seq_id];
+        const std::vector<float> & g = pending_g_last[seq_id];
+
+        data.resize(sizeof(llama_pos) + g.size() * sizeof(float));
+        std::memcpy(data.data(),                     &pos,     sizeof(llama_pos));
+        std::memcpy(data.data() + sizeof(llama_pos), g.data(), g.size() * sizeof(float));
+        return true;
+    }
+
+    void set_state(llama_seq_id seq_id, const std::vector<uint8_t> & data) override {
+        if (!need_boundary_stash()) {
+            return;
+        }
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+        if (data.size() != sizeof(llama_pos) + (size_t) n_embd_dec * sizeof(float)) {
+            return;
+        }
+
+        llama_pos pos = -1;
+        std::memcpy(&pos, data.data(), sizeof(llama_pos));
+
+        pending_pos_last[seq_id] = pos;
+        pending_g_last[seq_id].resize(n_embd_dec);
+        std::memcpy(pending_g_last[seq_id].data(), data.data() + sizeof(llama_pos), (size_t) n_embd_dec * sizeof(float));
+    }
+
     bool need_embd() const override {
         return false;
     }
@@ -2118,6 +2165,31 @@ void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, u
     }
 }
 
+// TODO: support the case of more than one speculative implementations having a state
+bool common_speculative_get_state(common_speculative * spec, llama_seq_id seq_id, std::vector<uint8_t> & data) {
+    if (spec == nullptr) {
+        return false;
+    }
+
+    for (auto & impl : spec->impls) {
+        if (impl->get_state(seq_id, data)) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+void common_speculative_set_state(common_speculative * spec, llama_seq_id seq_id, const std::vector<uint8_t> & data) {
+    if (spec == nullptr) {
+        return;
+    }
+
+    for (auto & impl : spec->impls) {
+        impl->set_state(seq_id, data);
+    }
+}
+
 void common_speculative_print_stats(const common_speculative * spec) {
     if (spec == nullptr) {
         return;
diff --git a/common/speculative.h b/common/speculative.h
index bf76ad709..c58fac3cc 100644
--- a/common/speculative.h
+++ b/common/speculative.h
@@ -68,6 +68,10 @@ void common_speculative_draft(common_speculative * spec);
 // informs the speculative context that n_accepted tokens were accepted by the target model
 void common_speculative_accept(common_speculative * spec, llama_seq_id, uint16_t n_accepted);
 
+// (optional) get/set internal state
+bool common_speculative_get_state(common_speculative * spec, llama_seq_id seq_id, std::vector<uint8_t> & data);
+void common_speculative_set_state(common_speculative * spec, llama_seq_id seq_id, const std::vector<uint8_t> & data);
+
 // print statistics about the speculative decoding
 void common_speculative_print_stats(const common_speculative * spec);
 
diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
index 6783d98ec..d8ffe43ae 100644
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -156,6 +156,8 @@ llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_para
 
     // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
index eb5e9a406..7b0876cbb 100644
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -179,6 +179,8 @@ llama_model_qwen35moe::graph::graph(const llama_model & model, const llm_graph_p
 
     // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass.
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index a23b0405c..c887beb0a 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -2172,6 +2172,8 @@ private:
 
         cur.update_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
         cur.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+        // stash the draft's speculative state with the checkpoint
+        common_speculative_get_state(spec.get(), slot.id, cur.data_spec);
 
         SLT_INF(slot,
                 "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n",
@@ -2998,6 +3000,8 @@ private:
                                         // restore the context checkpoint
                                         it->load_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                                         it->load_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+                                        // restore the draft's speculative state
+                                        common_speculative_set_state(spec.get(), slot.id, it->data_spec);
 
                                         pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
                                         n_past   = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);

From e2e7a9b2d04e0404131e551243d4bd399ccfe606 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 12:18:36 +0200
Subject: [PATCH 070/292] mtmd: several bug fixes (#24784)

* mtmd: several bug fixes

* fix build

* fix gemma4ua

* add sanity check in get_u32()

* fix build (2)

* area() avoid overflow
---
 tools/mtmd/clip.cpp       |  30 +++++++-
 tools/mtmd/clip.h         |   3 +
 tools/mtmd/mtmd-audio.cpp | 139 +++++++++++++++++++++-----------------
 tools/mtmd/mtmd-audio.h   |  14 ++--
 tools/mtmd/mtmd.cpp       |   5 +-
 5 files changed, 119 insertions(+), 72 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 17079815d..10840a851 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1675,6 +1675,9 @@ struct clip_model_loader {
                     // note: some models having hparams.image_size == 0, which means the image size is dynamic
                     throw std::runtime_error(string_format("%s: image_size (%d) cannot be negative\n", __func__, hparams.image_size));
                 }
+                if (hparams.image_size > 65536) {
+                    throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 65536)\n", __func__, hparams.image_size));
+                }
                 if (hparams.patch_size <= 0) {
                     throw std::runtime_error(string_format("%s: patch_size (%d) must be greater than 0\n", __func__, hparams.patch_size));
                 }
@@ -1723,6 +1726,19 @@ struct clip_model_loader {
                 LOG_INF("%s: audio_n_fft:        %d\n", __func__, hparams.audio_n_fft);
                 LOG_INF("%s: audio_window_len:   %d\n", __func__, hparams.audio_window_len);
                 LOG_INF("%s: audio_hop_len:      %d\n", __func__, hparams.audio_hop_len);
+
+                // GEMMA4UA is encoder-free: it uses n_mel_bins as a raw-waveform frame size (640) and has no FFT/filterbank, so the mel-range and FFT
+                // checks below do not apply to it.
+                const bool fft_based = model.proj_type != PROJECTOR_TYPE_GEMMA4UA;
+
+                // Validate audio hparams loaded from GGUF metadata
+                if (hparams.n_mel_bins <= 0 || (fft_based && hparams.n_mel_bins > 256)) {
+                    throw std::runtime_error(string_format("%s: n_mel_bins (%d) must be in range [1, 256]\n", __func__, hparams.n_mel_bins));
+                }
+                if (fft_based && (hparams.audio_sample_rate <= 0 || hparams.audio_n_fft <= 0 || hparams.audio_hop_len <= 0 || hparams.audio_window_len <= 0)) {
+                    throw std::runtime_error(string_format("%s: audio hparams invalid: sample_rate=%d n_fft=%d window_len=%d hop_len=%d\n",
+                        __func__, hparams.audio_sample_rate, hparams.audio_n_fft, hparams.audio_window_len, hparams.audio_hop_len));
+                }
             }
             LOG_INF("\n");
             LOG_INF("%s: model size:         %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0);
@@ -2831,6 +2847,12 @@ struct clip_model_loader {
             img.set_size({sz, sz}, false, false);
             LOG_INF("%s: warmup with image size = %d x %d\n", __func__, sz, sz);
         } else {
+            // GEMMA4UA uses n_mel_bins as a raw-waveform frame size (640), not a mel-bin count,
+            // so the [1, 256] bound only applies to FFT-based models.
+            const bool fft_based = ctx_clip.model.proj_type != PROJECTOR_TYPE_GEMMA4UA;
+            if (hparams.n_mel_bins <= 0 || (fft_based && hparams.n_mel_bins > 256)) {
+                throw std::runtime_error(string_format("%s: invalid n_mel_bins (%d), must be in [1, 256]\n", __func__, hparams.n_mel_bins));
+            }
             img.set_size({hparams.warmup_audio_size, hparams.n_mel_bins}, false, false);
             LOG_INF("%s: warmup with audio size = %d\n", __func__, hparams.warmup_audio_size);
         }
@@ -2994,7 +3016,13 @@ struct clip_model_loader {
             }
             return;
         }
-        output = gguf_get_val_u32(ctx_gguf.get(), i);
+        const uint32_t val = gguf_get_val_u32(ctx_gguf.get(), i);
+        // sanity check
+        if (val > (uint32_t) INT32_MAX) {
+            throw std::runtime_error(string_format("%s: value %u for key '%s' exceeds INT32_MAX\n",
+                __func__, val, key.c_str()));
+        }
+        output = (int) val;
     }
 
     void get_f32(const std::string & key, float & output, bool required = true) const {
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index f66f4bc3b..e0f1d298c 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -24,6 +24,9 @@ struct clip_image_size {
         return !(*this == other);
     }
     int area() const {
+        // avoid overflow when computing area
+        GGML_ASSERT(width  >= 0 && width  <= 46000);
+        GGML_ASSERT(height >= 0 && height <= 46000);
         return width * height;
     }
 };
diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp
index 13f211fd9..b72fd067a 100644
--- a/tools/mtmd/mtmd-audio.cpp
+++ b/tools/mtmd/mtmd-audio.cpp
@@ -32,8 +32,8 @@ void mtmd_audio_cache::fill_hann_window(uint32_t length, bool periodic) {
     }
 }
 
-void mtmd_audio_cache::fill_mel_filterbank_matrix(int   n_mel,
-                                                  int   n_fft,
+void mtmd_audio_cache::fill_mel_filterbank_matrix(int64_t n_mel,
+                                                  int64_t n_fft,
                                                   int   sample_rate,
                                                   float fmin,
                                                   float fmax,
@@ -86,11 +86,16 @@ void mtmd_audio_cache::fill_mel_filterbank_matrix(int   n_mel,
         hz_pts[i] = mel_to_hz(mel_pts[i]);
     }
 
-    const int n_fft_bins = n_fft / 2 + 1;
+    const int64_t n_fft_bins = n_fft / 2 + 1;
+
+    // Validate allocation size
+    if ((size_t)n_mel * (size_t)n_fft_bins > SIZE_MAX) {
+        GGML_ASSERT(false && "mel filterbank allocation too large");
+    }
 
     // filterbank
-    std::vector<float> out(n_mel * n_fft_bins, 0);
-    for (int m = 0; m < n_mel; ++m) {
+    std::vector<float> out((size_t)n_mel * (size_t)n_fft_bins, 0);
+    for (int64_t m = 0; m < n_mel; ++m) {
         const double f_left   = hz_pts[m];
         const double f_center = hz_pts[m + 1];
         const double f_right  = hz_pts[m + 2];
@@ -266,8 +271,8 @@ static void ifft(const mtmd_audio_cache & cache, float * in, int N, float * out)
 }
 
 struct filter_params {
-    int32_t n_mel;
-    int32_t n_fft_bins;
+    int64_t n_mel;
+    int64_t n_fft_bins;
     int32_t hann_window_size;
     int32_t hop_length;
     int32_t sample_rate;
@@ -293,8 +298,8 @@ static void log_mel_spectrogram_worker_thread(int                        ith,
     std::vector<float> fft_in(frame_size * 2, 0.0);
     std::vector<float> fft_out(frame_size * 2 * 2 * 2);
 
-    int n_fft_bins = params.n_fft_bins;
-    int i = ith;
+    int64_t n_fft_bins = params.n_fft_bins;
+    int64_t i = ith;
 
     const auto & filters = cache.filters;
 
@@ -302,17 +307,18 @@ static void log_mel_spectrogram_worker_thread(int                        ith,
     GGML_ASSERT(n_fft_bins == 1 + (frame_size / 2));
     GGML_ASSERT(cache.sin_vals.size() == cache.cos_vals.size());
     // calculate FFT only when fft_in are not all zero
-    for (; i < std::min(n_samples / frame_step + 1, out.n_len); i += n_threads) {
-        const int offset = i * frame_step;
+    for (; i < std::min((int64_t)(n_samples / frame_step + 1), out.n_len); i += n_threads) {
+        const int64_t offset = i * frame_step;
 
         // apply Hann window (~10% faster)
-        for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
+        const int valid_len = std::min(frame_size, std::max(0, n_samples - (int)offset));
+        for (int j = 0; j < valid_len; j++) {
             fft_in[j] = hann[j] * samples[offset + j];
         }
 
         // fill the rest with zeros
-        if (n_samples - offset < frame_size) {
-            std::fill(fft_in.begin() + (n_samples - offset), fft_in.end(), 0.0);
+        if (valid_len < frame_size) {
+            std::fill(fft_in.begin() + valid_len, fft_in.end(), 0.0);
         }
 
         // FFT
@@ -325,7 +331,7 @@ static void log_mel_spectrogram_worker_thread(int                        ith,
         }
 
         // mel spectrogram
-        for (int j = 0; j < out.n_mel; j++) {
+        for (int64_t j = 0; j < out.n_mel; j++) {
             double sum = 0.0;
             // unroll loop (suggested by GH user @lunixbochs)
             int k = 0;
@@ -339,21 +345,21 @@ static void log_mel_spectrogram_worker_thread(int                        ith,
             }
             // handle n_fft remainder
             for (; k < n_fft_bins; k++) {
-                sum += fft_out[k] * filters.data[j * n_fft_bins + k];
+                sum += fft_out[k] * filters.data[(size_t)j * n_fft_bins + k];
             }
             sum = std::max(sum, (double)params.mel_floor);
             sum = params.use_natural_log
                 ? log(sum)
                 : log10(sum);
-            out.data[j * out.n_len + i] = sum;
+            out.data[(size_t)j * out.n_len + i] = sum;
         }
     }
 
     // Otherwise fft_out are all zero
     double sum = params.use_natural_log ? log(1e-10) : log10(1e-10);
     for (; i < out.n_len; i += n_threads) {
-        for (int j = 0; j < out.n_mel; j++) {
-            out.data[j * out.n_len + i] = sum;
+        for (int64_t j = 0; j < out.n_mel; j++) {
+            out.data[(size_t)j * out.n_len + i] = sum;
         }
     }
 }
@@ -437,16 +443,21 @@ static bool log_mel_spectrogram(
     GGML_ASSERT(params.hop_length > 0);
     out.n_mel = params.n_mel;
     out.n_len = (n_samples - frame_size) / frame_step + 1;
-    // TODO: handle these checks better
-    if (out.n_mel > 0 && (unsigned long)out.n_len > SIZE_MAX / out.n_mel) {
-        LOG_ERR("%s: size overflow\n", __func__);
+    // Validate dimensions before allocation to prevent integer overflow
+    if (out.n_mel <= 0 || out.n_len <= 0) {
+        LOG_ERR("%s: invalid mel dimensions n_mel=%lld n_len=%lld\n", __func__, (long long)out.n_mel, (long long)out.n_len);
+        return false;
+    }
+    const size_t total_size = (size_t)out.n_mel * (size_t)out.n_len;
+    if (total_size > SIZE_MAX / sizeof(float)) {
+        LOG_ERR("%s: size overflow: n_mel=%lld n_len=%lld\n", __func__, (long long)out.n_mel, (long long)out.n_len);
         return false;
     }
     if (n_samples < frame_size) {
         LOG_ERR("%s: not enough samples after padding\n", __func__);
         return false;
     }
-    out.data.resize(out.n_mel * out.n_len);
+    out.data.resize(total_size);
 
     {
         std::vector<std::thread> workers(n_threads - 1);
@@ -464,38 +475,39 @@ static bool log_mel_spectrogram(
         }
     }
 
-    const int effective_n_len = n_samples_in / frame_step;
+    const int64_t effective_n_len = n_samples_in / frame_step;
     if (params.norm_per_feature) {
         GGML_ASSERT(effective_n_len > 1);
-        for (int i = 0; i < out.n_mel; i++) {
+        for (int64_t i = 0; i < out.n_mel; i++) {
             double mean = 0;
-            for (int j = 0; j < effective_n_len; ++j) {
-                mean += out.data[i * out.n_len + j];
+            for (int64_t j = 0; j < effective_n_len; ++j) {
+                mean += out.data[(size_t)i * out.n_len + j];
             }
             mean /= effective_n_len;
 
             double var = 0.0;
-            for (int j = 0; j < effective_n_len; ++j) {
-                const double value = out.data[i * out.n_len + j] - mean;
+            for (int64_t j = 0; j < effective_n_len; ++j) {
+                const double value = out.data[(size_t)i * out.n_len + j] - mean;
                 var += value * value;
             }
             var /= effective_n_len - 1;  // unbiased
             const double mstd = std::sqrt(var + 1e-5);
 
-            for (int j = 0; j < effective_n_len; ++j) {
-                auto &value = out.data[i * out.n_len + j];
+            for (int64_t j = 0; j < effective_n_len; ++j) {
+                auto &value = out.data[(size_t)i * out.n_len + j];
                 value        = (value - mean) / mstd;
             }
 
             // pad the rest with zeros
-            for (int j = effective_n_len; j < out.n_len; ++j) {
-                out.data[i * out.n_len + j] = 0.0;
+            for (int64_t j = effective_n_len; j < out.n_len; ++j) {
+                out.data[(size_t)i * out.n_len + j] = 0.0;
             }
         }
     } else if (!params.no_padding) {
         // Whisper-style clamping and normalization (NOT used by Gemma4)
         double mmax = -1e20;
-        for (int i = 0; i < out.n_mel*out.n_len; i++) {
+        const size_t mel_size = (size_t)out.n_mel * (size_t)out.n_len;
+        for (size_t i = 0; i < mel_size; i++) {
             if (out.data[i] > mmax) {
                 mmax = out.data[i];
             }
@@ -503,7 +515,7 @@ static bool log_mel_spectrogram(
 
         mmax -= 8.0;
 
-        for (int i = 0; i < out.n_mel*out.n_len; i++) {
+        for (size_t i = 0; i < mel_size; i++) {
             if (out.data[i] < mmax) {
                 out.data[i] = mmax;
             }
@@ -582,13 +594,13 @@ bool mtmd_audio_preprocessor_whisper::preprocess(const float *                 s
     // because the cgraph in clip.cpp only accepts 3000 frames each, we need to split the mel
     // we always expect the mel to have 3000 silent frames at the end
     if (DEBUG) {
-        printf("output: n_mel = %d, n_len = %d\n", out_full.n_mel, out_full.n_len);
+        printf("output: n_mel = %d, n_len = %d\n", (int) out_full.n_mel, (int) out_full.n_len);
     }
     const size_t frames_per_chunk = 3000;
     GGML_ASSERT((size_t) out_full.n_len > frames_per_chunk);
     for (size_t off = 0; off < (size_t) out_full.n_len; off += frames_per_chunk) {
-        int n_len = std::min(frames_per_chunk, (size_t) out_full.n_len - off);
-        if ((size_t) n_len < frames_per_chunk) {
+        int64_t n_len = std::min((int64_t)frames_per_chunk, out_full.n_len - (int64_t)off);
+        if (n_len < (int64_t)frames_per_chunk) {
             break;  // last incomplete chunk will always be a padded chunk, safe to ignore
         }
 
@@ -596,10 +608,10 @@ bool mtmd_audio_preprocessor_whisper::preprocess(const float *                 s
         out_chunk.n_len     = n_len;
         out_chunk.n_mel     = out_full.n_mel;
         out_chunk.n_len_org = out_full.n_mel;  // unused
-        out_chunk.data.reserve(out_chunk.n_mel * out_chunk.n_len);
+        out_chunk.data.reserve((size_t)out_chunk.n_mel * (size_t)out_chunk.n_len);
 
-        for (int i = 0; i < out_full.n_mel; i++) {
-            auto src = out_full.data.begin() + i * out_full.n_len + off;
+        for (int64_t i = 0; i < out_full.n_mel; i++) {
+            auto src = out_full.data.begin() + (size_t)i * out_full.n_len + off;
             out_chunk.data.insert(out_chunk.data.end(), src, src + frames_per_chunk);
         }
 
@@ -681,8 +693,8 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float *                 sa
 
     // The effective frame count: center-padded STFT gives ~n_samples/hop_length frames.
     // We take min(mel_full.n_len, n_samples/hop + 1) to avoid including excess frames.
-    const int n_eff = std::min(mel_full.n_len,
-                               (int)(n_samples / hparams.audio_hop_len) + 1);
+    const int64_t n_eff = std::min(mel_full.n_len,
+                               (int64_t)(n_samples / hparams.audio_hop_len) + 1);
 
     // Split into inference windows matching n_window_infer=800 from model config.
     // Each window is padded to the next multiple of chunk_size for the cgraph.
@@ -690,18 +702,18 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float *                 sa
     const int chunk_size  = 100; // conv sub-chunk size (n_window * 2, n_window=50)
     const int window_size = 800; // mel frames per forward pass (n_window_infer=800)
 
-    for (int off = 0; off < n_eff; off += window_size) {
-        const int win_eff    = std::min(window_size, n_eff - off);
-        const int n_chunks   = (win_eff + chunk_size - 1) / chunk_size;
-        const int n_padded   = n_chunks * chunk_size;
+    for (int64_t off = 0; off < n_eff; off += window_size) {
+        const int64_t win_eff  = std::min((int64_t)window_size, n_eff - off);
+        const int64_t n_chunks  = (win_eff + chunk_size - 1) / chunk_size;
+        const int64_t n_padded  = n_chunks * chunk_size;
 
         mtmd_audio_mel out;
         out.n_mel     = mel_full.n_mel;
         out.n_len     = n_padded;
         out.n_len_org = win_eff;
-        out.data.assign(out.n_mel * out.n_len, 0.0f);
-        for (int m = 0; m < out.n_mel; m++) {
-            const int copy_len = std::min(win_eff, mel_full.n_len - off);
+        out.data.assign((size_t)out.n_mel * (size_t)out.n_len, 0.0f);
+        for (int64_t m = 0; m < out.n_mel; m++) {
+            const int64_t copy_len = std::min((int64_t)win_eff, mel_full.n_len - off);
             if (copy_len > 0) {
                 std::copy(mel_full.data.begin() + (size_t)m * mel_full.n_len + off,
                           mel_full.data.begin() + (size_t)m * mel_full.n_len + off + copy_len,
@@ -823,37 +835,38 @@ bool mtmd_audio_preprocessor_granite_speech::preprocess(const float *
     }
 
     double mmax = -1e20;
-    for (int i = 0; i < mel.n_mel * mel.n_len; i++) {
+    const size_t mel_size = (size_t)mel.n_mel * (size_t)mel.n_len;
+    for (size_t i = 0; i < mel_size; i++) {
         if (mel.data[i] > mmax) {
             mmax = mel.data[i];
         }
     }
     mmax -= 8.0;
 
-    for (int i = 0; i < mel.n_mel * mel.n_len; i++) {
+    for (size_t i = 0; i < mel_size; i++) {
         if (mel.data[i] < mmax) {
             mel.data[i] = mmax;
         }
         mel.data[i] = (mel.data[i] + 4.0) / 4.0;
     }
 
-    int n_frames = mel.n_len;
+    int64_t n_frames = mel.n_len;
     if (n_frames % 2 == 1) {
         n_frames--;
     }
-    const int n_mel     = mel.n_mel;
-    const int n_stacked = n_frames / 2;
+    const int64_t n_mel     = mel.n_mel;
+    const int64_t n_stacked = n_frames / 2;
 
     mtmd_audio_mel stacked;
     stacked.n_mel     = 2 * n_mel;
     stacked.n_len     = n_stacked;
-    stacked.n_len_org = (int)n_samples;
-    stacked.data.resize(2 * n_mel * n_stacked);
+    stacked.n_len_org = (int64_t)n_samples;
+    stacked.data.resize((size_t)2 * (size_t)n_mel * (size_t)n_stacked);
 
-    for (int t = 0; t < n_stacked; t++) {
-        for (int m = 0; m < n_mel; m++) {
-            stacked.data[m * n_stacked + t] = mel.data[m * mel.n_len + 2 * t];
-            stacked.data[(m + n_mel) * n_stacked + t] = mel.data[m * mel.n_len + 2 * t + 1];
+    for (int64_t t = 0; t < n_stacked; t++) {
+        for (int64_t m = 0; m < n_mel; m++) {
+            stacked.data[(size_t)m * n_stacked + t] = mel.data[(size_t)m * mel.n_len + 2 * t];
+            stacked.data[(size_t)(m + n_mel) * n_stacked + t] = mel.data[(size_t)m * mel.n_len + 2 * t + 1];
         }
     }
 
@@ -921,8 +934,8 @@ bool mtmd_audio_preprocessor_gemma4a::preprocess(const float *                 s
         const int hop = hparams.audio_hop_len;
         const int n_with_left = (int)chunk_len + pad_left;
         // PyTorch: unfold(size=frame_length+1, step=hop) on semicausal-padded waveform
-        const int pt_frames = (n_with_left - (hparams.audio_window_len + 1)) / hop + 1;
-        const int n_padded_needed = (pt_frames - 1) * hop + fft_size;
+        const int64_t pt_frames = (n_with_left - (hparams.audio_window_len + 1)) / hop + 1;
+        const int64_t n_padded_needed = (pt_frames - 1) * hop + fft_size;
         const int total_pad = std::max((int)(n_padded_needed - (int)chunk_len), pad_left);
         std::vector<float> padded_samples(total_pad + chunk_len, 0.0f);
         std::copy(chunk_ptr, chunk_ptr + chunk_len, padded_samples.data() + pad_left);
diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h
index 9656e3940..ad96bd847 100644
--- a/tools/mtmd/mtmd-audio.h
+++ b/tools/mtmd/mtmd-audio.h
@@ -10,16 +10,16 @@
 #define MTMD_INTERNAL_HEADER
 
 struct mtmd_audio_mel {
-    int n_len;
-    int n_len_org;
-    int n_mel;
+    int64_t n_len;
+    int64_t n_len_org;
+    int64_t n_mel;
 
     std::vector<float> data;
 };
 
 struct mtmd_audio_mel_filters {
-    int32_t n_mel;
-    int32_t n_fft;
+    int64_t n_mel;
+    int64_t n_fft;
 
     std::vector<float> data;
 };
@@ -39,8 +39,8 @@ struct mtmd_audio_cache {
 
     // Build mel filterbank matrix [n_mel × n_fft_bins] at runtime.
     // n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257.
-    void fill_mel_filterbank_matrix(int   n_mel,
-                                    int   n_fft,
+    void fill_mel_filterbank_matrix(int64_t n_mel,
+                                    int64_t n_fft,
                                     int   sample_rate,               // e.g. 16000
                                     float fmin             = 0.0f,   // e.g. 0.0
                                     float fmax             = -1.0f,  // e.g. sr/2; pass -1 for auto
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index abba2ebf2..cbaac1d37 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -1295,9 +1295,12 @@ struct mtmd_tokenizer {
             for (auto & mel_spec : mel_spec_chunks) {
                 const bool is_placeholder = mel_spec.data.empty();
 
+                // Validate dimensions fit in clip_image_size (int)
+                GGML_ASSERT(mel_spec.n_len <= INT32_MAX && mel_spec.n_len >= 0);
+                GGML_ASSERT(mel_spec.n_mel <= INT32_MAX && mel_spec.n_mel >= 0);
                 clip_image_f32 mel_f32;
                 mel_f32.set_size(
-                    {mel_spec.n_len, mel_spec.n_mel},
+                    {(int)mel_spec.n_len, (int)mel_spec.n_mel},
                     is_placeholder, /* is_audio */ true);
                 mel_f32.cpy_buf(mel_spec.data);
 

From 38724ab5937f6440993d4e9814563385569d3b5a Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Fri, 19 Jun 2026 08:32:31 -0500
Subject: [PATCH 071/292] docker : build the UI (#24794)

* docker : build the UI

* cont : use existing APP_VERSION
---
 .devops/cann.Dockerfile     | 16 ++++++++++++++++
 .devops/cpu.Dockerfile      | 16 ++++++++++++++++
 .devops/cuda.Dockerfile     | 16 ++++++++++++++++
 .devops/intel.Dockerfile    | 16 ++++++++++++++++
 .devops/musa.Dockerfile     | 16 ++++++++++++++++
 .devops/openvino.Dockerfile | 16 ++++++++++++++++
 .devops/rocm.Dockerfile     | 16 ++++++++++++++++
 .devops/s390x.Dockerfile    | 16 ++++++++++++++++
 .devops/vulkan.Dockerfile   | 16 ++++++++++++++++
 .devops/zendnn.Dockerfile   | 16 ++++++++++++++++
 .dockerignore               |  3 +++
 11 files changed, 163 insertions(+)

diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile
index 9df86d048..dc95e3f38 100644
--- a/.devops/cann.Dockerfile
+++ b/.devops/cann.Dockerfile
@@ -13,6 +13,20 @@ ARG APP_REVISION=N/A
 # BUILD STAGE
 # Compile all binary files and libraries
 # ==============================================================================
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${CANN_BASE_IMAGE} AS build
 
 # -- Install build dependencies --
@@ -26,6 +40,8 @@ WORKDIR /app
 # -- Copy project files --
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 # -- Set CANN environment variables (required for compilation) --
 # Using ENV instead of `source` allows environment variables to persist across the entire image layer
 ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index 9dbfdd11d..caf727bcd 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 ARG TARGETARCH
@@ -16,6 +30,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "$TARGETARCH" = "amd64" ] || [ "$TARGETARCH" = "arm64" ]; then \
         cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
     else \
diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
index 276f82c34..b16b9a8f1 100644
--- a/.devops/cuda.Dockerfile
+++ b/.devops/cuda.Dockerfile
@@ -11,6 +11,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${BASE_CUDA_DEV_CONTAINER} AS build
 
 ARG GCC_VERSION
@@ -26,6 +40,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
     export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
     fi && \
diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile
index 4d0c0a8fd..3c059eb30 100644
--- a/.devops/intel.Dockerfile
+++ b/.devops/intel.Dockerfile
@@ -5,6 +5,20 @@ ARG APP_REVISION=N/A
 
 ## Build Image
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/intel/deep-learning-essentials:$ONEAPI_VERSION AS build
 
 ARG GGML_SYCL_F16=ON
@@ -22,6 +36,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
         echo "GGML_SYCL_F16 is set" \
         && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON" \
diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile
index c98c44c95..0c23cc554 100644
--- a/.devops/musa.Dockerfile
+++ b/.devops/musa.Dockerfile
@@ -10,6 +10,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 
 # MUSA architecture to build for (defaults to all supported archs)
@@ -29,6 +43,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
         export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
     fi && \
diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile
index 9e96244ce..fec72b1c7 100644
--- a/.devops/openvino.Dockerfile
+++ b/.devops/openvino.Dockerfile
@@ -22,6 +22,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 ## Build Image
 FROM docker.io/ubuntu:${UBUNTU_VERSION} AS build
 
@@ -69,6 +83,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 # Build Stage
 RUN bash -c "source ${OpenVINO_DIR}/setupvars.sh && \
     cmake -B build/ReleaseOV -G Ninja \
diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile
index 2ab10f411..7fad0c22e 100644
--- a/.devops/rocm.Dockerfile
+++ b/.devops/rocm.Dockerfile
@@ -11,6 +11,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 ### Build image
 FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 
@@ -38,6 +52,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
     cmake -S . -B build \
         -DGGML_HIP=ON \
diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile
index d88dd2d92..149d79a61 100644
--- a/.devops/s390x.Dockerfile
+++ b/.devops/s390x.Dockerfile
@@ -4,6 +4,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 ### Build Llama.cpp stage
 FROM docker.io/gcc:${GCC_VERSION} AS build
 
@@ -20,6 +34,8 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
 WORKDIR /app
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN --mount=type=cache,target=/root/.ccache \
     --mount=type=cache,target=/app/build \
     cmake -S . -B build -G Ninja \
diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index 05df94ec4..26c1902b1 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 # Install build tools
@@ -17,6 +31,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
     cmake --build build --config Release -j$(nproc)
 
diff --git a/.devops/zendnn.Dockerfile b/.devops/zendnn.Dockerfile
index 9f811ab27..80daf5671 100644
--- a/.devops/zendnn.Dockerfile
+++ b/.devops/zendnn.Dockerfile
@@ -3,6 +3,20 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
+ARG NODE_VERSION=24
+
+FROM docker.io/node:$NODE_VERSION AS web
+
+ARG APP_VERSION
+
+WORKDIR /app/tools/ui
+
+COPY tools/ui/package.json tools/ui/package-lock.json ./
+RUN npm ci
+
+COPY tools/ui/ ./
+RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
+
 FROM docker.io/ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
@@ -14,6 +28,8 @@ WORKDIR /app
 
 COPY . .
 
+COPY --from=web /app/tools/ui/dist tools/ui/dist
+
 RUN cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_ZENDNN=ON && \
     cmake --build build -j $(nproc)
 
diff --git a/.dockerignore b/.dockerignore
index 064b7c7be..a223b7e89 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -10,6 +10,9 @@
 
 build*/
 
+tools/ui/node_modules/
+tools/ui/dist/
+
 models/*
 
 /llama-cli

From 9b1e2fa8b83fa979fa45d3c4f77f2df35ba0731e Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 19 Jun 2026 21:44:37 +0800
Subject: [PATCH 072/292] advanced onready bypass

---
 koboldcpp.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 33defd482..da43ed29e 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -7725,7 +7725,7 @@ def save_config_dict(filename, savdict, template):
         filenamestr += ".kcpps"
     if not filenamestr.endswith(".kcppt") and template:
         filenamestr += ".kcppt"
-    do_not_save = {'analyze', 'config', 'exportconfig', 'exporttemplate', 'testmemory', 'unpack', 'version'}
+    do_not_save = {'allow_config_onready', 'analyze', 'config', 'exportconfig', 'exporttemplate', 'testmemory', 'unpack', 'version'}
     filtered = {k: v for k, v in savdict.items() if k not in do_not_save}
     if 'gendefaults' in filtered:
         gendefaults = parse_json_object(filtered['gendefaults'], 'gendefaults')
@@ -10313,8 +10313,11 @@ def reload_from_new_args(newargs):
     try:
         args.istemplate = False
         newargs = convert_invalid_args(newargs)
+        protected_args = ["remotetunnel","showgui","port","host","port_param","admin","adminpassword","password","adminunloadtimeout","routermode","admindir","ssl","nocertify","benchmark","prompt","config","baseconfig","downloaddir","rpcmode","rpchost","rpcport","rpcdevice","allow_config_onready"]
         for key, value in newargs.items(): #do not overwrite certain values
-            if key not in ["remotetunnel","showgui","port","host","port_param","admin","adminpassword","password","adminunloadtimeout","routermode","admindir","ssl","nocertify","benchmark","prompt","config","baseconfig","downloaddir","onready","rpcmode","rpchost","rpcport","rpcdevice"]:
+            if key == "onready" and getattr(args, "allow_config_onready", False):
+                setattr(args, key, value)
+            elif key not in protected_args and key != "onready":
                 setattr(args, key, value)
         setattr(args,"showgui",False)
         setattr(args,"benchmark",False)
@@ -10344,8 +10347,10 @@ def load_config_cli(filename):
     with open(filename, 'r', encoding='utf-8', errors='ignore') as f:
         config = json.load(f)
         config = convert_invalid_args(config)
-        if "onready" in config:
+        if "onready" in config and not getattr(args, "allow_config_onready", False):
             config["onready"] = "" #do not allow onready commands from config
+        if "allow_config_onready" in config:
+            del config["allow_config_onready"] #do not allow configs to opt into onready commands
         args.istemplate = False
         raw_args = (sys.argv[1:]) #a lousy hack to allow for overriding kcpps
         # special: overriding model applies to model_param too
@@ -12197,6 +12202,7 @@ if __name__ == '__main__':
     advparser.add_argument("--noshift","--no-context-shift", help="If set, do not attempt to Trim and Shift the GGUF context.", action='store_true')
     advparser.add_argument("--noswa","--swa-full", help="If set, uses full-size SWA KV Cache. Otherwise, SWA will be enabled automatically on models that support it. SWA saves memory but cannot be used with context shifting.", action='store_true')
     advparser.add_argument("--onready", help="An optional shell command to execute after the model has been loaded.", metavar=('[shell command]'), type=str, default="")
+    advparser.add_argument("--allow-config-onready", help="Allow .kcpps config files loaded after launch to set --onready commands. Only use with trusted configs.", action='store_true')
     advparser.add_argument("--overridekv","--override-kv", metavar=('[name=type:value]'), help="Override metadata value by key. Separate multiple values with commas. Format is name=type:value. Types: int, float, bool, str", default="")
     advparser.add_argument("--overridenativecontext", help="Overrides the native trained context of the loaded model with a custom value to be used for Rope scaling.",metavar=('[trained context]'), type=int, default=0)
     advparser.add_argument("--overridetensors","--override-tensor","-ot", metavar=('[tensor name pattern=buffer type]'), help="Override selected backend for specific tensors matching tensor_name_regex_pattern=buffer_type, same as in llama.cpp.", default="")

From 8c2d6f6475f2586e079fa6677dec91088de85604 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 16:06:13 +0200
Subject: [PATCH 073/292] server: add --agent arg, remove redundant webui
 naming compat (#24801)

* server: add --agent arg, remove redundant webui naming compat

* corrent env

* fix the test

* llama-gen-docs

* nits: wordings
---
 common/arg.cpp                  | 70 +++++++++------------------------
 common/common.h                 |  6 ---
 tools/cli/README.md             |  3 +-
 tools/server/README.md          | 18 ++++-----
 tools/server/server-context.cpp | 15 +++----
 tools/server/server-models.cpp  | 15 ++++---
 tools/server/server-models.h    |  5 +--
 tools/server/server.cpp         |  3 +-
 8 files changed, 43 insertions(+), 92 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index bd4b113d1..52425f25e 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2830,62 +2830,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.api_prefix = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
-    // Deprecated: use --ui-config instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-config"}, "JSON",
-        "[DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)",
-        [](common_params & params, const std::string & value) {
-            params.ui_config_json = value;
-            params.webui_config_json = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
-
-    add_opt(common_arg(
-        {"--ui-config"}, "JSON",
+        {"--ui-config", "--webui-config"}, "JSON",
         "JSON that provides default UI settings (overrides UI defaults)",
         [](common_params & params, const std::string & value) {
             params.ui_config_json = value;
-            params.webui_config_json = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG"));
-
-    // Deprecated: use --ui-config-file instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-config-file"}, "PATH",
-        "[DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)",
-        [](common_params & params, const std::string & value) {
-            params.ui_config_json = read_file(value);
-            params.webui_config_json = params.ui_config_json;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
-
-    add_opt(common_arg(
-        {"--ui-config-file"}, "PATH",
+        {"--ui-config-file", "--webui-config-file"}, "PATH",
         "JSON file that provides default UI settings (overrides UI defaults)",
         [](common_params & params, const std::string & value) {
             params.ui_config_json = read_file(value);
-            params.webui_config_json = params.ui_config_json;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_CONFIG_FILE"));
-
-    // Deprecated: use --ui-mcp-proxy instead (kept for backward compat)
     add_opt(common_arg(
-        {"--webui-mcp-proxy"},
-        {"--no-webui-mcp-proxy"},
-        "[DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy",
-        [](common_params & params, bool value) {
-            params.ui_mcp_proxy = value;
-            params.webui_mcp_proxy = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_MCP_PROXY"));
-
-    add_opt(common_arg(
-        {"--ui-mcp-proxy"},
-        {"--no-ui-mcp-proxy"},
+        {"--ui-mcp-proxy", "--webui-mcp-proxy"},
+        {"--no-ui-mcp-proxy", "--no-webui-mcp-proxy"},
         "experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)",
         [](common_params & params, bool value) {
             params.ui_mcp_proxy = value;
-            params.webui_mcp_proxy = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI_MCP_PROXY"));
     add_opt(common_arg(
@@ -2897,24 +2861,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
-    // Deprecated: use --ui/--no-ui instead (kept for backward compat)
-    add_opt(common_arg(
-        {"--webui"},
-        {"--no-webui"},
-        "[DEPRECATED: use --ui/--no-ui] whether to enable the Web UI",
+        add_opt(common_arg(
+        {"-ag", "--agent"},
+        {"-no-ag", "--no-agent"},
+        "whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)",
         [](common_params & params, bool value) {
-            params.ui = value;
-            params.webui = value;
+            if (value) {
+                params.server_tools = {"all"};
+                params.ui_mcp_proxy = true;
+            } else {
+                params.server_tools.clear();
+                params.ui_mcp_proxy = false;
+            }
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
-
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_AGENT"));
     add_opt(common_arg(
-        {"--ui"},
-        {"--no-ui"},
+        {"--ui", "--webui"},
+        {"--no-ui", "--no-webui"},
         string_format("whether to enable the Web UI (default: %s)", params.ui ? "enabled" : "disabled"),
         [](common_params & params, bool value) {
             params.ui = value;
-            params.webui = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_UI"));
     add_opt(common_arg(
diff --git a/common/common.h b/common/common.h
index 535a4ed33..44c605189 100644
--- a/common/common.h
+++ b/common/common.h
@@ -624,12 +624,6 @@ struct common_params {
 
     // UI configs
     bool ui = true;
-
-    // Deprecated: use ui, ui_mcp_proxy, ui_config_json instead
-    bool webui = ui;
-    bool webui_mcp_proxy = false;
-    std::string webui_config_json;
-
     bool ui_mcp_proxy = false;
     std::string ui_config_json;
 
diff --git a/tools/cli/README.md b/tools/cli/README.md
index b11aa45ce..f93ae914c 100644
--- a/tools/cli/README.md
+++ b/tools/cli/README.md
@@ -161,7 +161,7 @@
 | `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
 | `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_AUTO) |
 | `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_OFFLOAD) |
-| `--image, --audio FILE` | path to an image or audio file. use with multimodal models, use comma-separated values for multiple files |
+| `--image, --audio, --video FILE` | path to an image, audio, or video file. use with multimodal models, use comma-separated values for multiple files |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
 | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) |
@@ -174,6 +174,7 @@
 | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek-ocr, deepseek2, deepseek3, exaone-moe, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, granite-4.0, granite-4.1, grok-2, hunyuan-dense, hunyuan-moe, hunyuan-vl, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, solar-open, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--skip-chat-parsing, --no-skip-chat-parsing` | force a pure content parser, even if a Jinja template is specified; model will output everything in the content section, including any reasoning and/or tool calls (default: disabled)<br/>(env: LLAMA_ARG_SKIP_CHAT_PARSING) |
 | `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles |
+| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) |
 | `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
 | `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
 | `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
diff --git a/tools/server/README.md b/tools/server/README.md
index 88a507e2c..1f74ba52a 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -175,13 +175,12 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-np, --parallel N` | number of server slots (default: -1, -1 = auto)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
-| `-tk, --talker-model FILE` | path to the qwen3-omni talker gguf, enables the /v1/audio/speech endpoint<br/>(env: LLAMA_ARG_TALKER_MODEL) |
-| `-c2w, --code2wav-model FILE` | path to the qwen3-omni code2wav gguf, the talker code detokenizer<br/>(env: LLAMA_ARG_CODE2WAV_MODEL) |
 | `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
 | `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_AUTO) |
 | `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_OFFLOAD) |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
+| `--mtmd-batch-max-tokens N` | maximum number of image tokens per batch when encoding images (default: 1024)<br/>(env: LLAMA_ARG_MTMD_BATCH_MAX_TOKENS) |
 | `-a, --alias STRING` | set model name aliases, comma-separated (to be used by API)<br/>(env: LLAMA_ARG_ALIAS) |
 | `--tags STRING` | set model tags, comma-separated (informational, not used for routing)<br/>(env: LLAMA_ARG_TAGS) |
 | `--embd-normalize N` | normalisation for embeddings (default: 2) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) |
@@ -190,15 +189,12 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--reuse-port` | allow multiple sockets to bind to the same port (default: disabled)<br/>(env: LLAMA_ARG_REUSE_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
 | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
-| `--webui-config JSON` | [DEPRECATED: use --ui-config] JSON that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG) |
-| `--ui-config JSON` | JSON that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG) |
-| `--webui-config-file PATH` | [DEPRECATED: use --ui-config-file] JSON file that provides default WebUI settings (overrides WebUI defaults)<br/>(env: LLAMA_ARG_WEBUI_CONFIG_FILE) |
-| `--ui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG_FILE) |
-| `--webui-mcp-proxy, --no-webui-mcp-proxy` | [DEPRECATED: use --ui-mcp-proxy/--no-ui-mcp-proxy] experimental: whether to enable MCP CORS proxy<br/>(env: LLAMA_ARG_WEBUI_MCP_PROXY) |
-| `--ui-mcp-proxy, --no-ui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_UI_MCP_PROXY) |
+| `--ui-config, --webui-config JSON` | JSON that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG) |
+| `--ui-config-file, --webui-config-file PATH` | JSON file that provides default UI settings (overrides UI defaults)<br/>(env: LLAMA_ARG_UI_CONFIG_FILE) |
+| `--ui-mcp-proxy, --webui-mcp-proxy, --no-ui-mcp-proxy, --no-webui-mcp-proxy` | experimental: whether to enable MCP CORS proxy - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_UI_MCP_PROXY) |
 | `--tools TOOL1,TOOL2,...` | experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)<br/>specify "all" to enable all tools<br/>available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, apply_diff, get_datetime<br/>(env: LLAMA_ARG_TOOLS) |
-| `--webui, --no-webui` | [DEPRECATED: use --ui/--no-ui] whether to enable the Web UI<br/>(env: LLAMA_ARG_WEBUI) |
-| `--ui, --no-ui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_UI) |
+| `-ag, --agent, -no-ag, --no-agent` | whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)<br/>(env: LLAMA_ARG_AGENT) |
+| `--ui, --webui, --no-ui, --no-webui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_UI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -207,6 +203,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
 | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) |
 | `-to, --timeout N` | server read/write timeout in seconds (default: 3600)<br/>(env: LLAMA_ARG_TIMEOUT) |
+| `--sse-ping-interval N` | server SSE ping interval in seconds (-1 = disabled, default: 30)<br/>(env: LLAMA_ARG_SSE_PING_INTERVAL) |
 | `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
 | `--cache-prompt, --no-cache-prompt` | whether to enable prompt caching (default: enabled)<br/>(env: LLAMA_ARG_CACHE_PROMPT) |
 | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: 0)<br/>[(card)](https://ggml.ai/f0.png)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
@@ -231,6 +228,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
 | `--sleep-idle-seconds SECONDS` | number of seconds of idleness after which the server will sleep (default: -1; -1 = disabled) |
+| `--log-prompts-dir PATH` | Log prompts to directory (only used for debugging, default: disabled) |
 | `--spec-draft-hf, -hfd, -hfrd, --hf-repo-draft <user>/<model>[:quant]` | Same as --hf-repo, but for the draft model (default: unused)<br/>(env: LLAMA_ARG_SPEC_DRAFT_HF_REPO) |
 | `--spec-draft-threads, -td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
 | `--spec-draft-threads-batch, -tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) |
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index c887beb0a..00ab31340 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1302,11 +1302,8 @@ private:
             }
         }
 
-        // populate UI settings (from either new ui_config_json or deprecated webui_config_json)
         {
-            const std::string & cfg = !params_base.ui_config_json.empty()
-                ? params_base.ui_config_json
-                : params_base.webui_config_json;
+            const std::string & cfg = params_base.ui_config_json;
             if (!cfg.empty()) {
                 try {
                     json json_settings = json::parse(cfg);
@@ -4304,18 +4301,18 @@ void server_routes::init_routes() {
             { "endpoint_props",              params.endpoint_props },
             { "endpoint_metrics",            params.endpoint_metrics },
             // New keys
-            { "ui",                           params.ui },
-            { "ui_settings",                  meta->json_ui_settings },
+            { "ui",                          params.ui },
+            { "ui_settings",                 meta->json_ui_settings },
             // Deprecated: use ui/ui_settings instead (kept for backward compat)
-            { "webui",                        params.webui },
-            { "webui_settings",               meta->json_webui_settings },
+            { "webui",                       params.ui },
+            { "webui_settings",              meta->json_ui_settings },
             { "chat_template",               tmpl_default },
             { "chat_template_caps",          meta->chat_template_caps },
             { "bos_token",                   meta->bos_token_str },
             { "eos_token",                   meta->eos_token_str },
             { "build_info",                  meta->build_info },
             { "is_sleeping",                 queue_tasks.is_sleeping() },
-            { "cors_proxy_enabled",          params.ui_mcp_proxy || params.webui_mcp_proxy },
+            { "cors_proxy_enabled",          params.ui_mcp_proxy },
         };
         if (params.use_jinja) {
             if (!tmpl_tools.empty()) {
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 7aaad6926..23c1f1668 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1462,9 +1462,9 @@ void server_models_routes::init_routes() {
             auto res = std::make_unique<server_http_res>();
             res_ok(res, {
                 // TODO: add support for this on web UI
-                {"role",          "router"},
-                {"max_instances", params.models_max},
-                {"models_autoload", params.models_autoload},
+                {"role",                 "router"},
+                {"max_instances",        params.models_max},
+                {"models_autoload",      params.models_autoload},
                 // this is a dummy response to make sure the UI doesn't break
                 {"model_alias", "llama-server"},
                 {"model_path",  "none"},
@@ -1473,11 +1473,10 @@ void server_models_routes::init_routes() {
                     {"n_ctx",  0},
                 }},
                 // New key
-                {"ui_settings",     ui_settings},
-                // Deprecated: use ui_settings instead (kept for backward compat)
-                {"webui_settings",  webui_settings},
-                {"build_info",     std::string(llama_build_info())},
-                {"cors_proxy_enabled", params.ui_mcp_proxy || params.webui_mcp_proxy},
+                {"ui_settings",          ui_settings},
+                {"webui_settings",       webui_settings},
+                {"build_info",           std::string(llama_build_info())},
+                {"cors_proxy_enabled",   params.ui_mcp_proxy},
             });
             return res;
         }
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 319c4352e..aeb0e874d 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -212,10 +212,7 @@ struct server_models_routes {
     server_models models;
     server_models_routes(const common_params & params, int argc, char ** argv)
             : params(params), models(params, argc, argv) {
-        // Support both new ui_config_json and deprecated webui_config_json
-        const std::string & cfg = !this->params.ui_config_json.empty()
-            ? this->params.ui_config_json
-            : this->params.webui_config_json;
+        const std::string & cfg = this->params.ui_config_json;
         if (!cfg.empty()) {
             try {
                 json json_settings = json::parse(cfg);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 78ab0318c..2a67bfcfe 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -227,8 +227,7 @@ int llama_server(int argc, char ** argv) {
     ctx_http.register_gcp_compat();
 
     // CORS proxy (EXPERIMENTAL, only used by the Web UI for MCP)
-    // Supports both new ui_mcp_proxy and deprecated webui_mcp_proxy fields
-    if (params.ui_mcp_proxy || params.webui_mcp_proxy) {
+    if (params.ui_mcp_proxy) {
         SRV_WRN("%s", "-----------------\n");
         SRV_WRN("%s", "CORS proxy is enabled, do not expose server to untrusted environments\n");
         SRV_WRN("%s", "This feature is EXPERIMENTAL and may be removed or changed in future versions\n");

From 0d2d9ccbf6aae92de310712297fd52becc134092 Mon Sep 17 00:00:00 2001
From: "Alessandro de Oliveira Faria (A.K.A.CABELO)" <cabelo@opensuse.org>
Date: Fri, 19 Jun 2026 11:16:35 -0300
Subject: [PATCH 074/292] vendor : update cpp-httplib to 0.48.0 (#24787)

---
 scripts/sync_vendor.py         |   2 +-
 vendor/cpp-httplib/httplib.cpp | 241 +++++++++++++--------------------
 vendor/cpp-httplib/httplib.h   |  81 ++++++++---
 3 files changed, 160 insertions(+), 164 deletions(-)

diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py
index 402d7bbad..f913b0c7d 100755
--- a/scripts/sync_vendor.py
+++ b/scripts/sync_vendor.py
@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.47.0"
+HTTPLIB_VERSION = "refs/tags/v0.48.0"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
diff --git a/vendor/cpp-httplib/httplib.cpp b/vendor/cpp-httplib/httplib.cpp
index 370c0e798..1ac4fa4ba 100644
--- a/vendor/cpp-httplib/httplib.cpp
+++ b/vendor/cpp-httplib/httplib.cpp
@@ -5809,11 +5809,9 @@ std::string decode_query_component(const std::string &component,
 
   for (size_t i = 0; i < component.size(); i++) {
     if (component[i] == '%' && i + 2 < component.size()) {
-      std::string hex = component.substr(i + 1, 2);
-      char *end;
-      unsigned long value = std::strtoul(hex.c_str(), &end, 16);
-      if (end == hex.c_str() + 2) {
-        result += static_cast<char>(value);
+      auto val = 0;
+      if (detail::from_hex_to_i(component, i + 1, 2, val)) {
+        result += static_cast<char>(val);
         i += 2;
       } else {
         result += component[i];
@@ -12551,6 +12549,21 @@ bool parse_ipv4(const std::string &str, unsigned char *out) {
   return *p == '\0';
 }
 
+// Parse an IP literal (IPv4 or IPv6) into raw network-order bytes.
+// `out` must have room for at least 16 bytes. Returns the address length
+// (4 for IPv4, 16 for IPv6) on success, or 0 if the string is not an IP
+// literal. Used to match a host against iPAddress SANs the same way the
+// OpenSSL backend does via X509_check_ip.
+size_t parse_ip_address(const std::string &str, unsigned char *out) {
+  if (is_ipv4_address(str)) { return parse_ipv4(str, out) ? 4 : 0; }
+  struct in6_addr addr6 = {};
+  if (inet_pton(AF_INET6, str.c_str(), &addr6) == 1) {
+    memcpy(out, &addr6, 16);
+    return 16;
+  }
+  return 0;
+}
+
 #ifdef _WIN32
 // Enumerate Windows system certificates and call callback with DER data
 template <typename Callback>
@@ -12852,6 +12865,30 @@ int openssl_verify_callback(int preverify_ok, X509_STORE_CTX *ctx) {
   return callback(verify_ctx) ? 1 : 0;
 }
 
+// X509_STORE_get0_objects is deprecated since OpenSSL 4.0 because it is not
+// thread-safe; X509_STORE_get1_objects (OpenSSL 3.3+) returns a snapshot
+// that must be released with release_store_objects
+#if !defined(OPENSSL_IS_BORINGSSL) && !defined(LIBRESSL_VERSION_NUMBER) &&     \
+    OPENSSL_VERSION_NUMBER >= 0x30300000L
+#define CPPHTTPLIB_HAS_X509_STORE_GET1_OBJECTS
+#endif
+
+STACK_OF(X509_OBJECT) * get_store_objects(X509_STORE *store) {
+#ifdef CPPHTTPLIB_HAS_X509_STORE_GET1_OBJECTS
+  return X509_STORE_get1_objects(store);
+#else
+  return X509_STORE_get0_objects(store);
+#endif
+}
+
+void release_store_objects(STACK_OF(X509_OBJECT) * objs) {
+#ifdef CPPHTTPLIB_HAS_X509_STORE_GET1_OBJECTS
+  sk_X509_OBJECT_pop_free(objs, X509_OBJECT_free);
+#else
+  (void)objs; // get0 variant returns an internal pointer; nothing to free
+#endif
+}
+
 } // namespace impl
 
 ctx_t create_client_context() {
@@ -13373,11 +13410,19 @@ std::string get_cert_subject_cn(cert_t cert) {
   auto subject_name = X509_get_subject_name(x509);
   if (!subject_name) return "";
 
-  char buf[256];
-  auto len =
-      X509_NAME_get_text_by_NID(subject_name, NID_commonName, buf, sizeof(buf));
-  if (len < 0) return "";
-  return std::string(buf, static_cast<size_t>(len));
+  // X509_NAME_get_text_by_NID is deprecated since OpenSSL 4.0
+  auto idx = X509_NAME_get_index_by_NID(subject_name, NID_commonName, -1);
+  if (idx < 0) return "";
+
+  auto entry = X509_NAME_get_entry(subject_name, idx);
+  if (!entry) return "";
+
+  auto data = X509_NAME_ENTRY_get_data(entry);
+  if (!data) return "";
+
+  return std::string(
+      reinterpret_cast<const char *>(ASN1_STRING_get0_data(data)),
+      static_cast<size_t>(ASN1_STRING_length(data)));
 }
 
 std::string get_cert_issuer_name(cert_t cert) {
@@ -13582,8 +13627,9 @@ size_t get_ca_certs(ctx_t ctx, std::vector<cert_t> &certs) {
   auto store = SSL_CTX_get_cert_store(ssl_ctx);
   if (!store) { return 0; }
 
-  auto objs = X509_STORE_get0_objects(store);
+  auto objs = impl::get_store_objects(store);
   if (!objs) { return 0; }
+  auto se = detail::scope_exit([&] { impl::release_store_objects(objs); });
 
   auto count = sk_X509_OBJECT_num(objs);
   for (decltype(count) i = 0; i < count; i++) {
@@ -13609,8 +13655,9 @@ std::vector<std::string> get_ca_names(ctx_t ctx) {
   auto store = SSL_CTX_get_cert_store(ssl_ctx);
   if (!store) { return names; }
 
-  auto objs = X509_STORE_get0_objects(store);
+  auto objs = impl::get_store_objects(store);
   if (!objs) { return names; }
+  auto se = detail::scope_exit([&] { impl::release_store_objects(objs); });
 
   auto count = sk_X509_OBJECT_num(objs);
   for (decltype(count) i = 0; i < count; i++) {
@@ -13716,110 +13763,6 @@ std::string verify_error_string(long error_code) {
 
 } // namespace tls
 
-bool SSLClient::verify_host(X509 *server_cert) const {
-  /* Quote from RFC2818 section 3.1 "Server Identity"
-
-     If a subjectAltName extension of type dNSName is present, that MUST
-     be used as the identity. Otherwise, the (most specific) Common Name
-     field in the Subject field of the certificate MUST be used. Although
-     the use of the Common Name is existing practice, it is deprecated and
-     Certification Authorities are encouraged to use the dNSName instead.
-
-     Matching is performed using the matching rules specified by
-     [RFC2459].  If more than one identity of a given type is present in
-     the certificate (e.g., more than one dNSName name, a match in any one
-     of the set is considered acceptable.) Names may contain the wildcard
-     character * which is considered to match any single domain name
-     component or component fragment. E.g., *.a.com matches foo.a.com but
-     not bar.foo.a.com. f*.com matches foo.com but not bar.com.
-
-     In some cases, the URI is specified as an IP address rather than a
-     hostname. In this case, the iPAddress subjectAltName must be present
-     in the certificate and must exactly match the IP in the URI.
-
-  */
-  return verify_host_with_subject_alt_name(server_cert) ||
-         verify_host_with_common_name(server_cert);
-}
-
-bool
-SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
-  auto ret = false;
-
-  auto type = GEN_DNS;
-
-  struct in6_addr addr6 = {};
-  struct in_addr addr = {};
-  size_t addr_len = 0;
-
-#ifndef __MINGW32__
-  if (inet_pton(AF_INET6, host_.c_str(), &addr6)) {
-    type = GEN_IPADD;
-    addr_len = sizeof(struct in6_addr);
-  } else if (inet_pton(AF_INET, host_.c_str(), &addr)) {
-    type = GEN_IPADD;
-    addr_len = sizeof(struct in_addr);
-  }
-#endif
-
-  auto alt_names = static_cast<const struct stack_st_GENERAL_NAME *>(
-      X509_get_ext_d2i(server_cert, NID_subject_alt_name, nullptr, nullptr));
-
-  if (alt_names) {
-    auto dsn_matched = false;
-    auto ip_matched = false;
-
-    auto count = sk_GENERAL_NAME_num(alt_names);
-
-    for (decltype(count) i = 0; i < count && !dsn_matched; i++) {
-      auto val = sk_GENERAL_NAME_value(alt_names, i);
-      if (!val || val->type != type) { continue; }
-
-      auto name =
-          reinterpret_cast<const char *>(ASN1_STRING_get0_data(val->d.ia5));
-      if (name == nullptr) { continue; }
-
-      auto name_len = static_cast<size_t>(ASN1_STRING_length(val->d.ia5));
-
-      switch (type) {
-      case GEN_DNS:
-        dsn_matched =
-            detail::match_hostname(std::string(name, name_len), host_);
-        break;
-
-      case GEN_IPADD:
-        if (!memcmp(&addr6, name, addr_len) || !memcmp(&addr, name, addr_len)) {
-          ip_matched = true;
-        }
-        break;
-      }
-    }
-
-    if (dsn_matched || ip_matched) { ret = true; }
-  }
-
-  GENERAL_NAMES_free(const_cast<STACK_OF(GENERAL_NAME) *>(
-      reinterpret_cast<const STACK_OF(GENERAL_NAME) *>(alt_names)));
-  return ret;
-}
-
-bool SSLClient::verify_host_with_common_name(X509 *server_cert) const {
-  const auto subject_name = X509_get_subject_name(server_cert);
-
-  if (subject_name != nullptr) {
-    char name[BUFSIZ];
-    auto name_len = X509_NAME_get_text_by_NID(subject_name, NID_commonName,
-                                              name, sizeof(name));
-
-    if (name_len != -1) {
-      return detail::match_hostname(
-          std::string(name, static_cast<size_t>(name_len)), host_);
-    }
-  }
-
-  return false;
-}
-
 #endif // CPPHTTPLIB_OPENSSL_SUPPORT
 
 /*
@@ -14622,10 +14565,10 @@ bool verify_hostname(cert_t cert, const char *hostname) {
   auto mcert = static_cast<const mbedtls_x509_crt *>(cert);
   std::string host_str(hostname);
 
-  // Check if hostname is an IP address
-  bool is_ip = impl::is_ipv4_address(host_str);
-  unsigned char ip_bytes[4];
-  if (is_ip) { impl::parse_ipv4(host_str, ip_bytes); }
+  // Check if hostname is an IP address (IPv4 or IPv6)
+  unsigned char ip_bytes[16];
+  auto ip_len = impl::parse_ip_address(host_str, ip_bytes);
+  auto is_ip = ip_len > 0;
 
   // Check Subject Alternative Names (SAN)
   // In Mbed TLS 3.x, subject_alt_names contains raw values without ASN.1 tags
@@ -14637,9 +14580,9 @@ bool verify_hostname(cert_t cert, const char *hostname) {
     size_t len = san->buf.len;
 
     if (is_ip) {
-      // Check if this SAN is an IPv4 address (4 bytes)
-      if (len == 4 && memcmp(p, ip_bytes, 4) == 0) { return true; }
-      // Check if this SAN is an IPv6 address (16 bytes) - skip for now
+      // For an IP host, only a matching iPAddress SAN of the same family
+      // (4 bytes for IPv4, 16 bytes for IPv6) may authenticate it.
+      if (len == ip_len && memcmp(p, ip_bytes, ip_len) == 0) { return true; }
     } else {
       // Check if this SAN is a DNS name (printable ASCII string)
       bool is_dns = len > 0;
@@ -14654,21 +14597,25 @@ bool verify_hostname(cert_t cert, const char *hostname) {
     san = san->next;
   }
 
-  // Fallback: Check Common Name (CN) in subject
-  char cn[256];
-  int ret = mbedtls_x509_dn_gets(cn, sizeof(cn), &mcert->subject);
-  if (ret > 0) {
-    std::string cn_str(cn);
+  // Fallback: Check Common Name (CN) in subject. Skipped for IP-literal hosts:
+  // an IP identity is only valid via an iPAddress SAN, never the CN (RFC 9110;
+  // the OpenSSL backend's X509_check_ip behaves the same way).
+  if (!is_ip) {
+    char cn[256];
+    int ret = mbedtls_x509_dn_gets(cn, sizeof(cn), &mcert->subject);
+    if (ret > 0) {
+      std::string cn_str(cn);
 
-    // Look for "CN=" in the DN string
-    size_t cn_pos = cn_str.find("CN=");
-    if (cn_pos != std::string::npos) {
-      size_t start = cn_pos + 3;
-      size_t end = cn_str.find(',', start);
-      std::string cn_value =
-          cn_str.substr(start, end == std::string::npos ? end : end - start);
+      // Look for "CN=" in the DN string
+      size_t cn_pos = cn_str.find("CN=");
+      if (cn_pos != std::string::npos) {
+        size_t start = cn_pos + 3;
+        size_t end = cn_str.find(',', start);
+        std::string cn_value =
+            cn_str.substr(start, end == std::string::npos ? end : end - start);
 
-      if (detail::match_hostname(cn_value, host_str)) { return true; }
+        if (detail::match_hostname(cn_value, host_str)) { return true; }
+      }
     }
   }
 
@@ -15774,10 +15721,10 @@ bool verify_hostname(cert_t cert, const char *hostname) {
   auto x509 = static_cast<WOLFSSL_X509 *>(cert);
   std::string host_str(hostname);
 
-  // Check if hostname is an IP address
-  bool is_ip = impl::is_ipv4_address(host_str);
-  unsigned char ip_bytes[4];
-  if (is_ip) { impl::parse_ipv4(host_str, ip_bytes); }
+  // Check if hostname is an IP address (IPv4 or IPv6)
+  unsigned char ip_bytes[16];
+  auto ip_len = impl::parse_ip_address(host_str, ip_bytes);
+  auto is_ip = ip_len > 0;
 
   // Check Subject Alternative Names
   auto *san_names = static_cast<WOLF_STACK_OF(WOLFSSL_GENERAL_NAME) *>(
@@ -15804,10 +15751,12 @@ bool verify_hostname(cert_t cert, const char *hostname) {
           }
         }
       } else if (is_ip && names->type == WOLFSSL_GEN_IPADD) {
-        // IP address
+        // IP address: only an iPAddress SAN of the same family (4 bytes for
+        // IPv4, 16 bytes for IPv6) may authenticate the host.
         unsigned char *ip_data = wolfSSL_ASN1_STRING_data(names->d.iPAddress);
-        int ip_len = wolfSSL_ASN1_STRING_length(names->d.iPAddress);
-        if (ip_data && ip_len == 4 && memcmp(ip_data, ip_bytes, 4) == 0) {
+        auto san_ip_len = wolfSSL_ASN1_STRING_length(names->d.iPAddress);
+        if (ip_data && san_ip_len == static_cast<int>(ip_len) &&
+            memcmp(ip_data, ip_bytes, ip_len) == 0) {
           wolfSSL_sk_free(san_names);
           return true;
         }
@@ -15816,8 +15765,10 @@ bool verify_hostname(cert_t cert, const char *hostname) {
     wolfSSL_sk_free(san_names);
   }
 
-  // Fallback: Check Common Name (CN) in subject
-  WOLFSSL_X509_NAME *subject = wolfSSL_X509_get_subject_name(x509);
+  // Fallback: Check Common Name (CN) in subject. Skipped for IP-literal hosts:
+  // an IP identity is only valid via an iPAddress SAN, never the CN (RFC 9110;
+  // the OpenSSL backend's X509_check_ip behaves the same way).
+  auto subject = is_ip ? nullptr : wolfSSL_X509_get_subject_name(x509);
   if (subject) {
     char cn[256] = {};
     int cn_len = wolfSSL_X509_NAME_get_text_by_NID(subject, NID_commonName, cn,
diff --git a/vendor/cpp-httplib/httplib.h b/vendor/cpp-httplib/httplib.h
index 94d93e88a..bfdbfc1da 100644
--- a/vendor/cpp-httplib/httplib.h
+++ b/vendor/cpp-httplib/httplib.h
@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.47.0"
-#define CPPHTTPLIB_VERSION_NUM "0x002f00"
+#define CPPHTTPLIB_VERSION "0.48.0"
+#define CPPHTTPLIB_VERSION_NUM "0x003000"
 
 #ifdef _WIN32
 #if defined(_WIN32_WINNT) && _WIN32_WINNT < 0x0A00
@@ -686,18 +686,70 @@ inline from_chars_result<T> from_chars(const char *first, const char *last,
   return {p, std::errc{}};
 }
 
-// from_chars for double (simple wrapper for strtod)
+// from_chars for double (hand-written, locale-independent)
+//
+// The only double consumed by this library is the HTTP quality value, whose
+// grammar is (RFC 9110 12.4.2):
+//   qvalue = ( "0" [ "." 0*3DIGIT ] ) / ( "1" [ "." 0*3("0") ] )
+// i.e. a non-negative decimal with no sign, exponent, "inf"/"nan", or wide
+// magnitude. So this parser recognizes exactly  1*DIGIT [ "." *DIGIT ]  with
+// '.' always the decimal separator (std::strtod would instead read it from the
+// global C locale, mis-parsing q-values once an embedder calls
+// setlocale(LC_ALL, "") into a comma-decimal locale). The caller range-checks
+// the result to [0, 1], so inputs outside that range need not be distinguished
+// here. Allocation-free, single pass, and free of the overflow/rounding edge
+// cases that exponent and wide-range handling would introduce.
 inline from_chars_result<double> from_chars(const char *first, const char *last,
                                             double &value) {
-  std::string s(first, last);
-  char *endptr = nullptr;
-  errno = 0;
-  value = std::strtod(s.c_str(), &endptr);
-  if (endptr == s.c_str()) { return {first, std::errc::invalid_argument}; }
-  if (errno == ERANGE) {
-    return {first + (endptr - s.c_str()), std::errc::result_out_of_range};
+  value = 0.0;
+  const char *p = first;
+
+  // Each 1eN is exactly representable, so a single final division by the
+  // matching entry yields a correctly-rounded result.
+  static const double powers_of_ten[] = {
+      1e0,  1e1,  1e2,  1e3,  1e4,  1e5,  1e6,  1e7,  1e8, 1e9,
+      1e10, 1e11, 1e12, 1e13, 1e14, 1e15, 1e16, 1e17, 1e18};
+  const int max_frac_digits =
+      static_cast<int>(sizeof(powers_of_ten) / sizeof(powers_of_ten[0])) - 1;
+
+  // Accumulate digits into a 64-bit integer and remember how many were
+  // fractional. Two independent caps keep this bounded and safe:
+  //   * accumulation saturates before mantissa could overflow uint64_t, and
+  //   * frac_digits is capped at max_frac_digits so it is always a valid index
+  //     into powers_of_ten (without this an input like "0.000...0" would never
+  //     grow mantissa, so the saturation cap alone would not bound it).
+  // Both caps only drop digits far beyond the precision a q-value needs; any
+  // value they would change is well outside [0, 1] and rejected by the caller.
+  uint64_t mantissa = 0;
+  int frac_digits = 0;
+  bool seen_digit = false;
+
+  const uint64_t limit = ((std::numeric_limits<uint64_t>::max)() - 9) / 10;
+  auto accumulate = [&](char c) {
+    if (mantissa <= limit) {
+      mantissa = mantissa * 10 + static_cast<uint64_t>(c - '0');
+      return true;
+    }
+    return false;
+  };
+
+  for (; p != last && '0' <= *p && *p <= '9'; ++p) {
+    seen_digit = true;
+    accumulate(*p);
   }
-  return {first + (endptr - s.c_str()), std::errc{}};
+
+  if (p != last && *p == '.') {
+    ++p;
+    for (; p != last && '0' <= *p && *p <= '9'; ++p) {
+      seen_digit = true;
+      if (frac_digits < max_frac_digits && accumulate(*p)) { ++frac_digits; }
+    }
+  }
+
+  if (!seen_digit) { return {first, std::errc::invalid_argument}; }
+
+  value = static_cast<double>(mantissa) / powers_of_ten[frac_digits];
+  return {p, std::errc{}};
 }
 
 inline bool parse_port(const char *s, size_t len, int &port) {
@@ -2826,13 +2878,6 @@ private:
 #endif
 
   friend class ClientImpl;
-
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
-private:
-  bool verify_host(X509 *server_cert) const;
-  bool verify_host_with_subject_alt_name(X509 *server_cert) const;
-  bool verify_host_with_common_name(X509 *server_cert) const;
-#endif
 };
 #endif // CPPHTTPLIB_SSL_ENABLED
 

From fabde3bf5136940eb03821aa2490e2360093965b Mon Sep 17 00:00:00 2001
From: Mikolaj Kucharski <mikolaj@kucharski.name>
Date: Fri, 19 Jun 2026 15:33:54 +0000
Subject: [PATCH 075/292] arg: Add comment line support to --api-key-file
 (#23168)

---
 common/arg.cpp         | 4 ++--
 tools/server/README.md | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 52425f25e..6fd366d33 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2911,7 +2911,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
     add_opt(common_arg(
         {"--api-key-file"}, "FNAME",
-        "path to file containing API keys (default: none)",
+        "path to file containing API keys, one per line; lines starting with a hash are treated as comments (default: none)",
         [](common_params & params, const std::string & value) {
             std::ifstream key_file(value);
             if (!key_file) {
@@ -2919,7 +2919,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
             std::string key;
             while (std::getline(key_file, key)) {
-                if (!key.empty()) {
+                if (!key.empty() && key[0] != '#') {
                     params.api_keys.push_back(key);
                 }
             }
diff --git a/tools/server/README.md b/tools/server/README.md
index 1f74ba52a..eb730e713 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -198,7 +198,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--rerank, --reranking` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)<br/>(env: LLAMA_API_KEY) |
-| `--api-key-file FNAME` | path to file containing API keys (default: none)<br/>(env: LLAMA_ARG_API_KEY_FILE) |
+| `--api-key-file FNAME` | path to file containing API keys, one per line; lines starting with a hash are treated as comments (default: none)<br/>(env: LLAMA_ARG_API_KEY_FILE) |
 | `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key<br/>(env: LLAMA_ARG_SSL_KEY_FILE) |
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate<br/>(env: LLAMA_ARG_SSL_CERT_FILE) |
 | `--chat-template-kwargs STRING` | sets additional params for the json template parser, must be a valid json object string, e.g. '{"key1":"value1","key2":"value2"}'<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_KWARGS) |

From 2fb3406be77f4ed8bbce2af6f84969451967fbb8 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 20 Jun 2026 00:29:48 +0800
Subject: [PATCH 076/292] added ideogram 4 support

---
 otherarch/sdcpp/src/stable-diffusion.cpp | 28 +++++++++++++++---------
 1 file changed, 18 insertions(+), 10 deletions(-)

diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index e2a80a035..99e070909 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -397,6 +397,7 @@ public:
         std::string taesd_path_fixed      = SAFE_STR(sd_ctx_params->taesd_path);
         std::string embed_connector_fixed = SAFE_STR(sd_ctx_params->embeddings_connectors_path);
         std::string vae_path_fixed        = SAFE_STR(sd_ctx_params->vae_path);
+        std::string uncond_fixed          = SAFE_STR(sd_ctx_params->uncond_diffusion_model_path);
 
         model_manager = std::make_shared<ModelManager>();
         model_manager->set_n_threads(n_threads);
@@ -486,7 +487,8 @@ public:
         bool is_longcat = sd_version_is_longcat(tempver);
         bool is_lens = sd_version_is_lens(tempver);
         bool is_ltx = sd_version_is_ltxav(tempver);
-        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx);
+        bool is_ideogram = sd_version_is_ideogram4(tempver);
+        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx || is_ideogram);
 
         //kcpp qol fallback: if a llm was loaded as t5 by mistake
         if(conditioner_is_llm && t5_path_fixed!="")
@@ -547,6 +549,11 @@ public:
                 embed_connector_fixed = clipg_path_fixed;
                 clipg_path_fixed = "";
             }
+            else if(is_ideogram)
+            {
+                uncond_fixed = clipg_path_fixed;
+                clipg_path_fixed = "";
+            }
         }
 
         //settle possible inversions for mmproj
@@ -632,15 +639,16 @@ public:
             }
         }
 
-        sd_ctx_params->clip_g_path                = clipg_path_fixed.c_str();
-        sd_ctx_params->clip_l_path                = clipl_path_fixed.c_str();
-        sd_ctx_params->clip_vision_path           = clip_vision_fixed.c_str();
-        sd_ctx_params->llm_path                   = llm_path_fixed.c_str();
-        sd_ctx_params->llm_vision_path            = llm_vision_path_fixed.c_str();
-        sd_ctx_params->t5xxl_path                 = t5_path_fixed.c_str();
-        sd_ctx_params->taesd_path                 = taesd_path_fixed.c_str();
-        sd_ctx_params->embeddings_connectors_path = embed_connector_fixed.c_str();
-        sd_ctx_params->vae_path                   = vae_path_fixed.c_str();
+        sd_ctx_params->clip_g_path                 = clipg_path_fixed.c_str();
+        sd_ctx_params->clip_l_path                 = clipl_path_fixed.c_str();
+        sd_ctx_params->clip_vision_path            = clip_vision_fixed.c_str();
+        sd_ctx_params->llm_path                    = llm_path_fixed.c_str();
+        sd_ctx_params->llm_vision_path             = llm_vision_path_fixed.c_str();
+        sd_ctx_params->t5xxl_path                  = t5_path_fixed.c_str();
+        sd_ctx_params->taesd_path                  = taesd_path_fixed.c_str();
+        sd_ctx_params->embeddings_connectors_path  = embed_connector_fixed.c_str();
+        sd_ctx_params->vae_path                    = vae_path_fixed.c_str();
+        sd_ctx_params->uncond_diffusion_model_path = uncond_fixed.c_str();
         //debug print
         // printf("\n\nclip_g: %s\nclip_l: %s\nclip_vision: %s\nllm: %s\nllm_vision: %s\nt5xxl: %s\ntaesd: %s\n",
         // sd_ctx_params->clip_g_path, sd_ctx_params->clip_l_path, sd_ctx_params->clip_vision_path,

From 175147e8f612671b7906e21fb1cdea62e4da0e21 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 22:12:46 +0200
Subject: [PATCH 077/292] server: remove all internal mentions about "webui"
 (#24817)

---
 tools/server/server-context.cpp       |  9 +--------
 tools/server/server-context.h         |  3 +--
 tools/server/server-models.cpp        |  1 -
 tools/server/server-models.h          |  2 --
 tools/server/tests/unit/test_basic.py |  8 ++++----
 tools/server/tests/unit/test_proxy.py |  6 +++---
 tools/server/tests/utils.py           | 12 ++++++------
 7 files changed, 15 insertions(+), 26 deletions(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 00ab31340..791188b1e 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -825,8 +825,7 @@ private:
 
     server_metrics metrics;
 
-    json json_ui_settings = json::object();    // Primary: new name
-    json json_webui_settings = json::object();    // Deprecated: use json_ui_settings instead (kept for compat)
+    json json_ui_settings = json::object();
 
     // Necessary similarity of prompt for slot selection
     float slot_prompt_similarity = 0.0f;
@@ -1308,7 +1307,6 @@ private:
                 try {
                     json json_settings = json::parse(cfg);
                     json_ui_settings = json_settings;
-                    json_webui_settings = json_settings; // deprecated: keep in sync
                 } catch (const std::exception & e) {
                     SRV_ERR("%s: failed to parse UI config: %s\n", __func__, e.what());
                     return false;
@@ -3687,7 +3685,6 @@ server_context_meta server_context::get_meta() const {
         /* has_inp_audio          */ impl->chat_params.allow_audio,
         /* has_inp_video          */ impl->chat_params.allow_video,
         /* json_ui_settings       */ impl->json_ui_settings,
-        /* json_webui_settings    */ impl->json_webui_settings,  // Deprecated
         /* slot_n_ctx             */ impl->get_slot_n_ctx(),
         /* pooling_type           */ llama_pooling_type(impl->ctx_tgt),
 
@@ -4300,12 +4297,8 @@ void server_routes::init_routes() {
             { "endpoint_slots",              params.endpoint_slots },
             { "endpoint_props",              params.endpoint_props },
             { "endpoint_metrics",            params.endpoint_metrics },
-            // New keys
             { "ui",                          params.ui },
             { "ui_settings",                 meta->json_ui_settings },
-            // Deprecated: use ui/ui_settings instead (kept for backward compat)
-            { "webui",                       params.ui },
-            { "webui_settings",              meta->json_ui_settings },
             { "chat_template",               tmpl_default },
             { "chat_template_caps",          meta->chat_template_caps },
             { "bos_token",                   meta->bos_token_str },
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index 0e84785af..07afabb92 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -22,8 +22,7 @@ struct server_context_meta {
     bool has_inp_image;
     bool has_inp_audio;
     bool has_inp_video;
-    json json_ui_settings;            // Primary: new name
-    json json_webui_settings;            // Deprecated: use json_ui_settings instead (kept for backward compat)
+    json json_ui_settings;
     int slot_n_ctx;
     enum llama_pooling_type pooling_type;
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 23c1f1668..1fffa6b6e 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1474,7 +1474,6 @@ void server_models_routes::init_routes() {
                 }},
                 // New key
                 {"ui_settings",          ui_settings},
-                {"webui_settings",       webui_settings},
                 {"build_info",           std::string(llama_build_info())},
                 {"cors_proxy_enabled",   params.ui_mcp_proxy},
             });
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index aeb0e874d..98872b046 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -207,7 +207,6 @@ public:
 struct server_models_routes {
     common_params params;
     json ui_settings = json::object();     // Primary: new name
-    json webui_settings = json::object();  // Deprecated: use ui_settings (kept for compat)
     std::atomic<bool> stopping = false;    // for graceful disconnecting SSE clients during shutdown
     server_models models;
     server_models_routes(const common_params & params, int argc, char ** argv)
@@ -217,7 +216,6 @@ struct server_models_routes {
             try {
                 json json_settings = json::parse(cfg);
                 ui_settings = json_settings;
-                webui_settings = json_settings;  // Deprecated: keep in sync
             } catch (const std::exception & e) {
                 LOG_ERR("%s: failed to parse UI config: %s\n", __func__, e.what());
                 throw;
diff --git a/tools/server/tests/unit/test_basic.py b/tools/server/tests/unit/test_basic.py
index d1b89cf1a..285726abf 100644
--- a/tools/server/tests/unit/test_basic.py
+++ b/tools/server/tests/unit/test_basic.py
@@ -79,9 +79,9 @@ def test_load_split_model():
     assert match_regex("(little|girl)+", res.body["content"])
 
 
-def test_no_webui():
+def test_no_ui():
     global server
-    # default: webui enabled
+    # default: UI enabled
     server.start()
     url = f"http://{server.server_host}:{server.server_port}"
     res = requests.get(url)
@@ -89,8 +89,8 @@ def test_no_webui():
     assert "<!doctype html>" in res.text
     server.stop()
 
-    # with --no-webui
-    server.no_webui = True
+    # with --no-ui, the UI should be disabled
+    server.no_ui = True
     server.start()
     res = requests.get(url)
     assert res.status_code == 404
diff --git a/tools/server/tests/unit/test_proxy.py b/tools/server/tests/unit/test_proxy.py
index b7c332618..3b86d8047 100644
--- a/tools/server/tests/unit/test_proxy.py
+++ b/tools/server/tests/unit/test_proxy.py
@@ -12,7 +12,7 @@ def create_server():
 
 def test_mcp_no_proxy():
     global server
-    server.webui_mcp_proxy = False
+    server.ui_mcp_proxy = False
     server.start()
 
     res = server.make_request("GET", "/cors-proxy")
@@ -21,7 +21,7 @@ def test_mcp_no_proxy():
 
 def test_mcp_proxy():
     global server
-    server.webui_mcp_proxy = True
+    server.ui_mcp_proxy = True
     server.start()
 
     url = f"http://{server.server_host}:{server.server_port}/cors-proxy?url=http://example.com"
@@ -32,7 +32,7 @@ def test_mcp_proxy():
 
 def test_mcp_proxy_custom_port():
     global server
-    server.webui_mcp_proxy = True
+    server.ui_mcp_proxy = True
     server.start()
 
     # try getting the server's models API via the proxy
diff --git a/tools/server/tests/utils.py b/tools/server/tests/utils.py
index c50c9a0f5..63a959449 100644
--- a/tools/server/tests/utils.py
+++ b/tools/server/tests/utils.py
@@ -94,7 +94,7 @@ class ServerProcess:
     enable_ctx_shift: int | None = False
     spec_draft_n_min: int | None = None
     spec_draft_n_max: int | None = None
-    no_webui: bool | None = None
+    no_ui: bool | None = None
     jinja: bool | None = None
     reasoning_format: Literal['deepseek', 'none', 'nothink'] | None = None
     reasoning: Literal['on', 'off', 'auto'] | None = None
@@ -107,7 +107,7 @@ class ServerProcess:
     cache_ram: int | None = None
     no_cache_idle_slots: bool = False
     log_path: str | None = None
-    webui_mcp_proxy: bool = False
+    ui_mcp_proxy: bool = False
     backend_sampling: bool = False
     gcp_compat: bool = False
 
@@ -225,8 +225,8 @@ class ServerProcess:
             server_args.extend(["--spec-draft-n-max", self.spec_draft_n_max])
         if self.spec_draft_n_min:
             server_args.extend(["--spec-draft-n-min", self.spec_draft_n_min])
-        if self.no_webui:
-            server_args.append("--no-webui")
+        if self.no_ui:
+            server_args.append("--no-ui")
         if self.no_models_autoload:
             server_args.append("--no-models-autoload")
         if self.jinja:
@@ -251,8 +251,8 @@ class ServerProcess:
             server_args.extend(["--cache-ram", self.cache_ram])
         if self.no_cache_idle_slots:
             server_args.append("--no-cache-idle-slots")
-        if self.webui_mcp_proxy:
-            server_args.append("--webui-mcp-proxy")
+        if self.ui_mcp_proxy:
+            server_args.append("--ui-mcp-proxy")
         if self.backend_sampling:
             server_args.append("--backend_sampling")
         if self.gcp_compat:

From e475fa2b5f9fb50c3d6fc3e7c6fdf1e004465b62 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 19 Jun 2026 22:28:38 +0200
Subject: [PATCH 078/292] mtmd, arg: fix utf8 handling on windows (#24779)

* mtmd, arg: fix utf8 handling on windows

* also fix ggml_fopen

* fix build fail

* also fix CLI
---
 common/arg.cpp             | 38 ++++++++++++++++++++++++++++++++++++++
 common/common.cpp          | 12 ++++++++++++
 common/common.h            |  3 +++
 ggml/src/ggml.c            | 17 +++++++----------
 tools/cli/cli.cpp          |  2 +-
 tools/mtmd/clip-impl.h     | 24 ++++++++++++++++++++++++
 tools/mtmd/clip.cpp        |  2 +-
 tools/mtmd/mtmd-cli.cpp    |  3 +++
 tools/mtmd/mtmd-helper.cpp | 18 +++++++++++++++++-
 9 files changed, 106 insertions(+), 13 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 6fd366d33..8f4f7d076 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -17,6 +17,7 @@
 #   define NOMINMAX
 #endif
 #include <windows.h>
+#include <shellapi.h>
 #endif
 
 #define JSON_ASSERT GGML_ASSERT
@@ -893,7 +894,44 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
     return true;
 }
 
+#ifdef _WIN32
+struct utf8_argv {
+    std::vector<std::string> buf;
+    std::vector<char*> ptrs;
+};
+
+static utf8_argv make_utf8_argv() {
+    utf8_argv out;
+    int wargc = 0;
+    LPWSTR* wargv = CommandLineToArgvW(GetCommandLineW(), &wargc);
+    if (!wargv) return out;
+
+    out.buf.reserve(wargc);
+    for (int i = 0; i < wargc; ++i) {
+        int n = WideCharToMultiByte(CP_UTF8, WC_ERR_INVALID_CHARS, wargv[i], -1, nullptr, 0, nullptr, nullptr);
+        if (n <= 0) { out.buf.emplace_back(); continue; }
+        auto& s = out.buf.emplace_back();
+        s.resize(static_cast<size_t>(n - 1));
+        (void)WideCharToMultiByte(CP_UTF8, 0, wargv[i], -1, s.data(), n, nullptr, nullptr);
+    }
+    LocalFree(wargv);
+
+    out.ptrs.reserve(out.buf.size() + 1);
+    for (auto& s : out.buf) out.ptrs.push_back(s.data());
+    out.ptrs.push_back(nullptr);
+    return out;
+}
+#endif
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+#ifdef _WIN32
+    auto utf8 = make_utf8_argv();
+    if (!utf8.ptrs.empty()) {
+        argc = static_cast<int>(utf8.buf.size());
+        argv = utf8.ptrs.data();
+    }
+#endif
+
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
 
diff --git a/common/common.cpp b/common/common.cpp
index f3f114f68..a14e7bbed 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1074,6 +1074,18 @@ std::vector<common_file_info> fs_list(const std::string & path, bool include_dir
     return files;
 }
 
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode) {
+#ifdef _WIN32
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, NULL, 0);
+    if (!wlen) { return std::ifstream(); }
+    std::vector<wchar_t> wfname(wlen);
+    (void)MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, wfname.data(), wlen);
+    return std::ifstream(wfname.data(), mode);
+#else
+    return std::ifstream(fname, mode);
+#endif
+}
+
 //
 // TTY utils
 //
diff --git a/common/common.h b/common/common.h
index 44c605189..254454dcb 100644
--- a/common/common.h
+++ b/common/common.h
@@ -842,6 +842,9 @@ struct common_file_info {
 };
 std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
 
+// fs open, also handle UTF8 on Windows
+std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode);
+
 //
 // TTY utils
 //
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index b43016c87..0f682fd18 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -600,18 +600,15 @@ FILE * ggml_fopen(const char * fname, const char * mode) {
     // convert fname (UTF-8)
     wchar_t * wfname = ggml_mbstowcs(fname);
     if (wfname) {
-        // convert mode (ANSI)
-        wchar_t * wmode = GGML_MALLOC((strlen(mode) + 1) * sizeof(wchar_t));
-        wchar_t * wmode_p = wmode;
-        do {
-            *wmode_p++ = (wchar_t)*mode;
-        } while (*mode++);
-
-        // open file
-        file = _wfopen(wfname, wmode);
+        // convert mode (UTF-8)
+        wchar_t * wmode = ggml_mbstowcs(mode);
+        if (wmode) {
+            // open file
+            file = _wfopen(wfname, wmode);
+            GGML_FREE(wmode);
+        }
 
         GGML_FREE(wfname);
-        GGML_FREE(wmode);
     }
 
     return file;
diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp
index c03894b4b..8b7b58693 100644
--- a/tools/cli/cli.cpp
+++ b/tools/cli/cli.cpp
@@ -202,7 +202,7 @@ struct cli_context {
 
     // TODO: support remote files in the future (http, https, etc)
     std::string load_input_file(const std::string & fname, bool is_media) {
-        std::ifstream file(fname, std::ios::binary);
+        std::ifstream file = fs_open_ifstream(fname, std::ios::binary);
         if (!file) {
             return "";
         }
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index f232b68e5..e7b530144 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -13,6 +13,14 @@
 #include <sstream>
 #include <vector>
 #include <memory>
+#include <fstream>
+
+#ifdef _WIN32
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#endif
 
 // Internal header for clip.cpp
 
@@ -661,6 +669,22 @@ struct clip_image_f32_batch {
 // common utils
 //
 
+#ifdef _WIN32
+static std::ifstream open_ifstream_binary(const std::string & fname) {
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, NULL, 0);
+    if (!wlen) {
+        throw std::runtime_error("failed to convert filename to UTF-16: " + fname);
+    }
+    std::vector<wchar_t> wfname(wlen);
+    (void)MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, wfname.data(), wlen);
+    return std::ifstream(wfname.data(), std::ios::binary);
+}
+#else
+static std::ifstream open_ifstream_binary(const std::string & fname) {
+    return std::ifstream(fname, std::ios::binary);
+}
+#endif
+
 static std::string string_format(const char * fmt, ...) {
     va_list ap;
     va_list ap2;
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 10840a851..c713703e0 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1752,7 +1752,7 @@ struct clip_model_loader {
         std::map<std::string, size_t> tensor_offset;
         std::vector<ggml_tensor *> tensors_to_load;
 
-        auto fin = std::ifstream(fname, std::ios::binary);
+        auto fin = open_ifstream_binary(fname);
         if (!fin) {
             throw std::runtime_error(string_format("%s: failed to open %s\n", __func__, fname.c_str()));
         }
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index 0ad000ef0..8704ea79d 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -396,6 +396,9 @@ int main(int argc, char ** argv) {
 
     int n_predict = params.n_predict < 0 ? INT_MAX : params.n_predict;
 
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
     // Ctrl+C handling
     {
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp
index b5c408923..3c73db443 100644
--- a/tools/mtmd/mtmd-helper.cpp
+++ b/tools/mtmd/mtmd-helper.cpp
@@ -582,13 +582,29 @@ mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx,
 }
 
 mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder) {
-    std::vector<unsigned char> buf;
+#ifdef _WIN32
+    int wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, NULL, 0);
+    if (!wlen) {
+        LOG_ERR("Unable to convert filename to UTF-16: %s\n", fname);
+        return {nullptr, nullptr};
+    }
+    std::vector<wchar_t> wfname(wlen);
+    wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, wfname.data(), wlen);
+    if (!wlen) {
+        LOG_ERR("Unable to convert filename to UTF-16: %s\n", fname);
+        return {nullptr, nullptr};
+    }
+    FILE * f = _wfopen(wfname.data(), L"rb");
+#else
     FILE * f = fopen(fname, "rb");
+#endif
     if (!f) {
         LOG_ERR("Unable to open file %s: %s\n", fname, strerror(errno));
         return {nullptr, nullptr};
     }
 
+    std::vector<unsigned char> buf;
+
     fseek(f, 0, SEEK_END);
     long file_size = ftell(f);
     fseek(f, 0, SEEK_SET);

From 4b48a53b6cc60e051f35f2acbd06264a909bb255 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Fri, 19 Jun 2026 23:26:54 +0200
Subject: [PATCH 079/292] server : optimize get_token_probabilities (#24796)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Use std::partial_sort to order only the requested top-n tokens instead
of the full vocabulary

    logprobs sort: vocab=128000 n_top=0 iters=100
    full    sort:   8555.6 us/op
    partial sort:    704.3 us/op

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 tools/server/server-common.cpp  | 36 +++++++++++++++++++++++----------
 tools/server/server-common.h    |  2 +-
 tools/server/server-context.cpp |  3 +--
 3 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index 75729e62d..3dc686bb4 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -12,6 +12,7 @@
 #include <random>
 #include <sstream>
 #include <fstream>
+#include <limits>
 
 json format_error_response(const std::string & message, const enum error_type type) {
     std::string type_str;
@@ -1238,7 +1239,7 @@ json format_response_rerank(
 // other utils
 //
 
-std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx) {
+std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx, size_t n_top) {
     std::vector<llama_token_data> cur;
 
     const auto * logits = llama_get_logits_ith(ctx, idx);
@@ -1257,21 +1258,34 @@ std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int i
         }
     }
 
-    // sort tokens by logits
-    std::sort(cur.begin(), cur.end(), [](const llama_token_data & a, const llama_token_data & b) {
-        return a.logit > b.logit;
-    });
+    // sort tokens by logits (partial: only the leading `n_top` need ordering)
+    if (n_top > cur.size()) {
+        n_top = cur.size();
+    }
+    if (n_top > 0) {
+        std::partial_sort(cur.begin(), cur.begin() + n_top, cur.end(),
+            [](const llama_token_data & a, const llama_token_data & b) {
+                return a.logit > b.logit;
+            });
+    }
 
     // apply softmax
-    float max_l = cur[0].logit;
+    float max_l = -std::numeric_limits<float>::infinity();
+    if (n_top > 0) {
+        max_l = cur[0].logit; // partial_sort guarantees the absolute maximum is at index 0
+    } else {
+        for (const auto & t : cur) {
+            max_l = std::max(max_l, t.logit);
+        }
+    }
     float cum_sum = 0.0f;
-    for (size_t i = 0; i < cur.size(); ++i) {
-        float p = expf(cur[i].logit - max_l);
-        cur[i].p = p;
+    for (auto & t : cur) {
+        float p = expf(t.logit - max_l);
+        t.p = p;
         cum_sum += p;
     }
-    for (size_t i = 0; i < cur.size(); ++i) {
-        cur[i].p /= cum_sum;
+    for (auto & t : cur) {
+        t.p /= cum_sum;
     }
 
     return cur;
diff --git a/tools/server/server-common.h b/tools/server/server-common.h
index f286b3d15..efd31733b 100644
--- a/tools/server/server-common.h
+++ b/tools/server/server-common.h
@@ -326,7 +326,7 @@ json format_response_rerank(
 // other utils
 //
 
-std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx);
+std::vector<llama_token_data> get_token_probabilities(llama_context * ctx, int idx, size_t n_top);
 
 std::string safe_json_to_str(const json & data);
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 791188b1e..1f0e1bfd4 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1824,8 +1824,7 @@ private:
                 });
             }
         } else {
-            // TODO: optimize this with min-p optimization
-            std::vector<llama_token_data> cur = get_token_probabilities(ctx_tgt, idx);
+            std::vector<llama_token_data> cur = get_token_probabilities(ctx_tgt, idx, n_probs_request);
             const size_t max_probs = cur.size();
             const size_t n_probs = std::min(max_probs, n_probs_request);
 

From 2b686a9120e2f1f9eabb65a597a4eef03eea9d87 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 20 Jun 2026 01:02:26 +0200
Subject: [PATCH 080/292] server: refactor child --> router communication
 (#24821)

* server: refactor child --> router communication

* fix wakeup case

* add docs

* improve update_status()

* nits
---
 common/arg.cpp                  |   2 -
 common/common.h                 |  11 ++-
 tools/server/README-dev.md      |  11 +++
 tools/server/server-context.cpp |  18 ++---
 tools/server/server-context.h   |  30 ++++++-
 tools/server/server-models.cpp  | 133 ++++++++++++++++++--------------
 tools/server/server-models.h    |  30 +++++--
 tools/server/server.cpp         |  29 ++++---
 8 files changed, 173 insertions(+), 91 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 8f4f7d076..a9b1a25b2 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -303,7 +303,6 @@ static handle_model_result common_params_handle_model(struct common_params_model
 
     if (!model.docker_repo.empty()) {
         model.path = common_docker_resolve_model(model.docker_repo);
-        model.name = model.docker_repo;
     } else if (!model.hf_repo.empty()) {
         // If -m was used with -hf, treat the model "path" as the hf_file to download
         if (model.hf_file.empty() && !model.path.empty()) {
@@ -323,7 +322,6 @@ static handle_model_result common_params_handle_model(struct common_params_model
             throw std::runtime_error("failed to download model from Hugging Face");
         }
 
-        model.name = model.hf_repo;
         model.path = download_result.model_path;
 
         if (!download_result.mmproj_path.empty()) {
diff --git a/common/common.h b/common/common.h
index 254454dcb..f2f2202ec 100644
--- a/common/common.h
+++ b/common/common.h
@@ -295,7 +295,16 @@ struct common_params_model {
     std::string hf_repo     = ""; // HF repo                                                // NOLINT
     std::string hf_file     = ""; // HF file                                                // NOLINT
     std::string docker_repo = ""; // Docker repo                                            // NOLINT
-    std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
+
+    std::string get_name() {
+        if (!hf_repo.empty()) {
+            return hf_repo;
+        }
+        if (!docker_repo.empty()) {
+            return docker_repo;
+        }
+        return path;
+    }
 };
 
 // draft-model-based speculative decoding parameters
diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index 4c4103123..2796d2835 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -180,6 +180,17 @@ That requires `JSON.stringify` when formatted to message content:
 }
 ```
 
+### Router mode: how child <--> router communicates
+
+Upon spawning a new child process using `subprocess`, both child and router listen to the stdout/stderr (combined)
+
+For the direction from child to router:
+- Generic messages are logs, it will be forwarded to router's stdout
+- Special state update messages are prefixed by `cmd_child_to_router:state:`, followed by a JSON. See `server_models::handle_child_state` for more
+
+For the direction from router to child:
+- When server sends `cmd_router_to_child:exit`, the child should exit gracefully --> if after `DEFAULT_STOP_TIMEOUT` and the child is still running, force-kill it
+
 ### Model management API (router mode)
 
 Model management API was added via PR [#23976](https://github.com/ggml-org/llama.cpp/pull/23976)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 1f0e1bfd4..3de1335ec 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -63,11 +63,6 @@ enum slot_state {
     SLOT_STATE_GENERATING,
 };
 
-enum server_state {
-    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
-    SERVER_STATE_READY,          // Server is ready and model is loaded
-};
-
 struct server_slot {
     int id;
 
@@ -773,6 +768,8 @@ public:
     // note: chat_params must not be refreshed upon existing sleeping state
     server_chat_params chat_params;
 
+    server_state_callback_t callback_state = [](server_state, json) -> void {};
+
     server_context_impl() {
         mtmd_helper_log_set(common_log_default_callback, nullptr);
     }
@@ -1244,8 +1241,8 @@ private:
         if (!params_base.model_alias.empty()) {
             // backward compat: use first alias as model name
             model_name = *params_base.model_alias.begin();
-        } else if (!params_base.model.name.empty()) {
-            model_name = params_base.model.name;
+        } else if (!params_base.model.get_name().empty()) {
+            model_name = params_base.model.get_name();
         } else {
             // fallback: derive model name from file name
             auto model_path = std::filesystem::path(params_base.model.path);
@@ -3734,8 +3731,11 @@ struct server_res_generator : server_http_res {
     }
 };
 
-void server_context::on_sleeping_changed(std::function<void(bool)> callback) {
-    impl->queue_tasks.on_sleeping_state(std::move(callback));
+void server_context::set_state_callback(server_state_callback_t callback) {
+    impl->callback_state = std::move(callback);
+    impl->queue_tasks.on_sleeping_state([this](bool sleeping) {
+        impl->callback_state(sleeping ? SERVER_STATE_SLEEPING : SERVER_STATE_READY, {});
+    });
 }
 
 // compute the number of tokens before the last user message in the prompt
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index 07afabb92..c7218a12e 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -52,6 +52,31 @@ struct server_context_meta {
     uint64_t model_size;
 };
 
+enum server_state {
+    // SERVER_STATE_DOWNLOADING,
+    SERVER_STATE_LOADING,
+    SERVER_STATE_READY,
+    SERVER_STATE_SLEEPING,
+};
+
+static std::string server_state_to_str(server_state state) {
+    switch (state) {
+        case SERVER_STATE_LOADING:     return "loading";
+        case SERVER_STATE_READY:       return "ready";
+        case SERVER_STATE_SLEEPING:    return "sleeping";
+        default: GGML_ASSERT(false && "invalid server_state");
+    }
+}
+
+static server_state server_state_from_str(const std::string & str) {
+    if (str == "loading")     return SERVER_STATE_LOADING;
+    if (str == "ready")       return SERVER_STATE_READY;
+    if (str == "sleeping")    return SERVER_STATE_SLEEPING;
+    GGML_ASSERT(false && "invalid server_state string");
+}
+
+using server_state_callback_t = std::function<void(server_state, json /* payload */)>;
+
 struct server_context {
     std::unique_ptr<server_context_impl> impl;
 
@@ -79,9 +104,8 @@ struct server_context {
     // not thread-safe, should only be used from the main thread
     server_context_meta get_meta() const;
 
-    // register a callback to be called when sleeping state changes
-    // must be set before load_model() is called
-    void on_sleeping_changed(std::function<void(bool)> callback);
+    // note: must be set before load_model() is called
+    void set_state_callback(server_state_callback_t callback);
 };
 
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 1fffa6b6e..a569c8be3 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1,5 +1,6 @@
 #include "server-common.h"
 #include "server-models.h"
+#include "server-context.h"
 
 #include "build-info.h"
 #include "preset.h"
@@ -44,9 +45,7 @@ extern char **environ;
 #define DEFAULT_STOP_TIMEOUT 10 // seconds
 
 #define CMD_ROUTER_TO_CHILD_EXIT  "cmd_router_to_child:exit"
-#define CMD_CHILD_TO_ROUTER_READY "cmd_child_to_router:ready" // also sent when waking up from sleep
-#define CMD_CHILD_TO_ROUTER_SLEEP "cmd_child_to_router:sleep"
-#define CMD_CHILD_TO_ROUTER_INFO  "cmd_child_to_router:info:" // followed by json string
+#define CMD_CHILD_TO_ROUTER_STATE "cmd_child_to_router:state:" // followed by json string
 
 // address for child process, this is needed because router may run on 0.0.0.0
 // ref: https://github.com/ggml-org/llama.cpp/issues/17862
@@ -904,12 +903,8 @@ void server_models::load(const std::string & name) {
                 while (fgets(buffer, vec_buf.size(), stdout_file) != nullptr) {
                     LOG("[%5d] %s", port, buffer);
                     std::string str(buffer);
-                    if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_READY)) {
-                        this->update_status(name, SERVER_MODEL_STATUS_LOADED, 0);
-                    } else if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_INFO)) {
-                        this->update_loaded_info(name, str);
-                    } else if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_SLEEP)) {
-                        this->update_status(name, SERVER_MODEL_STATUS_SLEEPING, 0);
+                    if (string_starts_with(buffer, CMD_CHILD_TO_ROUTER_STATE)) {
+                        this->handle_child_state(name, str);
                     }
                 }
             } else {
@@ -976,7 +971,10 @@ void server_models::load(const std::string & name) {
         subprocess_destroy(&child_proc->get());
 
         // update status and exit code
-        this->update_status(name, SERVER_MODEL_STATUS_UNLOADED, exit_code);
+        this->update_status(name, {
+            SERVER_MODEL_STATUS_UNLOADED,
+            exit_code
+        });
         SRV_INF("instance name=%s exited with status %d\n", name.c_str(), exit_code);
     });
 
@@ -1016,7 +1014,8 @@ struct server_models_download_res : public common_download_callback {
             common_download_model(model, opts);
             is_ok = true;
         } catch (const std::exception & e) {
-            SRV_ERR("download failed for model name=%s: %s\n", model.name.c_str(), e.what());
+            auto model_name = model.get_name();
+            SRV_ERR("download failed for model name=%s: %s\n", model_name.c_str(), e.what());
             is_ok = false;
         }
         return is_ok;
@@ -1036,7 +1035,7 @@ struct server_models_download_res : public common_download_callback {
 };
 
 void server_models::download(common_params_model && model, common_download_opts && opts) {
-    std::string name = model.name;
+    std::string name = model.get_name();
     GGML_ASSERT(name == model.hf_repo);
 
     std::unique_lock<std::mutex> lk(mutex);
@@ -1064,9 +1063,10 @@ void server_models::download(common_params_model && model, common_download_opts
     inst.th = std::thread([this, dl = std::move(dl)]() {
         dl->opts.callback = dl.get();
         bool ok = dl->run();
+        auto model_name = dl->model.get_name();
         SRV_INF("download finished for model name=%s with status=%s\n",
-                    dl->model.name.c_str(), ok ? "success" : "failure");
-        update_download_progress(dl->model.name, {}, true, ok);
+                    model_name.c_str(), ok ? "success" : "failure");
+        update_download_progress(model_name, {}, true, ok);
         // need_reload is set inside update_download_progress under the mutex;
         // the next load_models() call will clean up this instance
     });
@@ -1130,21 +1130,27 @@ void server_models::unload_all() {
     }
 }
 
-void server_models::update_status(const std::string & name, server_model_status status, int exit_code) {
+void server_models::update_status(const std::string & name, const update_status_args & args) {
     std::unique_lock<std::mutex> lk(mutex);
     auto it = mapping.find(name);
     if (it != mapping.end()) {
         auto & meta = it->second.meta;
-        meta.status    = status;
-        meta.exit_code = exit_code;
+        meta.status      = args.status;
+        meta.exit_code   = args.exit_code;
+        if (!args.loaded_info.is_null()) {
+            meta.loaded_info = args.loaded_info;
+        }
     }
     // broadcast status change to SSE
     {
         json data = {
-            {"status", server_model_status_to_string(status)},
+            {"status", server_model_status_to_string(args.status)},
         };
-        if (status == SERVER_MODEL_STATUS_UNLOADED) {
-            data["exit_code"] = exit_code;
+        if (args.status == SERVER_MODEL_STATUS_UNLOADED) {
+            data["exit_code"] = args.exit_code;
+        }
+        if (!args.loaded_info.is_null()) {
+            data["info"] = args.loaded_info;
         }
         // note: notify_sse doesn't acquire the lock, so no deadlock here
         notify_sse("status_change", name, data);
@@ -1152,29 +1158,6 @@ void server_models::update_status(const std::string & name, server_model_status
     cv.notify_all();
 }
 
-void server_models::update_loaded_info(const std::string & name, std::string & raw_info) {
-    if (!string_starts_with(raw_info, CMD_CHILD_TO_ROUTER_INFO)) {
-        SRV_WRN("invalid loaded info format from child for model name=%s: %s\n", name.c_str(), raw_info.c_str());
-        return;
-    }
-
-    json info;
-    try {
-        info = json::parse(raw_info.substr(strlen(CMD_CHILD_TO_ROUTER_INFO)));
-    } catch (const std::exception & e) {
-        SRV_WRN("failed to parse loaded info from child for model name=%s: %s\n", name.c_str(), e.what());
-        return;
-    }
-
-    std::unique_lock<std::mutex> lk(mutex);
-    auto it = mapping.find(name);
-    if (it != mapping.end()) {
-        auto & meta = it->second.meta;
-        meta.loaded_info = info;
-    }
-    cv.notify_all();
-}
-
 void server_models::update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok) {
     json curr;
     {
@@ -1323,21 +1306,54 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co
     return proxy;
 }
 
-bool server_models::is_child_server() {
+void server_models::handle_child_state(const std::string & name, const std::string & raw_input) {
+    server_state state;
+    json payload;
+
+    try {
+        json data = json::parse(raw_input.substr(strlen(CMD_CHILD_TO_ROUTER_STATE)));
+        state = server_state_from_str(json_value(data, "state", std::string()));
+        payload = json_value(data, "payload", json{});
+    } catch (const std::exception & e) {
+        SRV_ERR("failed to parse child state update for name=%s: %s\n", name.c_str(), e.what());
+        return;
+    }
+
+    switch (state) {
+        case SERVER_STATE_LOADING:
+            {
+                // do nothing for now
+                // TODO: report loading progress for first load and wakeup from sleep
+            } break;
+        case SERVER_STATE_READY:
+            {
+                update_status(name, {
+                    SERVER_MODEL_STATUS_LOADED,
+                    0,
+                    // note: payload can be empty if this is a wakeup from sleep
+                    payload.size() > 0 ? payload : nullptr
+                });
+            } break;
+        case SERVER_STATE_SLEEPING:
+            {
+                update_status(name, { SERVER_MODEL_STATUS_SLEEPING });
+            } break;
+        default:
+            // should never happen, but just in case
+            GGML_ASSERT(false && "unexpected state from child server");
+    }
+}
+
+//
+// server_child
+//
+
+bool server_child::is_child() {
     const char * router_port = std::getenv("LLAMA_SERVER_ROUTER_PORT");
     return router_port != nullptr;
 }
 
-std::thread server_models::setup_child_server(const std::function<void(int)> & shutdown_handler, const json & model_info) {
-    // send a notification to the router server that a model instance is ready
-    common_log_pause(common_log_main());
-    fflush(stdout);
-    fprintf(stdout, "%s\n", CMD_CHILD_TO_ROUTER_READY);
-    fflush(stdout);
-    fprintf(stdout, "%s%s\n", CMD_CHILD_TO_ROUTER_INFO, safe_json_to_str(model_info).c_str());
-    fflush(stdout);
-    common_log_resume(common_log_main());
-
+std::thread server_child::setup(const std::function<void(int)> & shutdown_handler) {
     // setup thread for monitoring stdin
     return std::thread([shutdown_handler]() {
         // wait for EOF on stdin
@@ -1363,10 +1379,14 @@ std::thread server_models::setup_child_server(const std::function<void(int)> & s
     });
 }
 
-void server_models::notify_router_sleeping_state(bool is_sleeping) {
+void server_child::notify_to_router(const std::string & state, const json & payload) {
+    json data = {
+        {"state", state},
+        {"payload", payload},
+    };
     common_log_pause(common_log_main());
     fflush(stdout);
-    fprintf(stdout, "%s\n", is_sleeping ? CMD_CHILD_TO_ROUTER_SLEEP : CMD_CHILD_TO_ROUTER_READY);
+    fprintf(stdout, "%s%s\n", CMD_CHILD_TO_ROUTER_STATE, safe_json_to_str(data).c_str());
     fflush(stdout);
     common_log_resume(common_log_main());
 }
@@ -1644,7 +1664,6 @@ void server_models_routes::init_routes() {
         common_params_model model;
         common_download_opts opts;
 
-        model.name           = name;
         model.hf_repo        = name;
         opts.bearer_token    = params.hf_token;
         opts.download_mmproj = true;
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 98872b046..40a0e078c 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -171,8 +171,12 @@ public:
     void download(common_params_model && model, common_download_opts && opts);
 
     // update the status of a model instance (thread-safe)
-    void update_status(const std::string & name, server_model_status status, int exit_code);
-    void update_loaded_info(const std::string & name, std::string & raw_info);
+    struct update_status_args {
+        server_model_status status;
+        int exit_code = 0; // only valid if status == UNLOADED
+        json loaded_info = nullptr;
+    };
+    void update_status(const std::string & name, const update_status_args & args);
     void update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok = true);
 
     // remove a cache model from disk and update the list (thread-safe)
@@ -193,15 +197,27 @@ public:
     // proxy an HTTP request to the model instance
     server_http_res_ptr proxy_request(const server_http_req & req, const std::string & method, const std::string & name, bool update_last_used);
 
+    // handle message sent from server_child::notify_to_router()
+    // raw input must starts with CMD_CHILD_TO_ROUTER_STATE, followed by a JSON string
+    // this function is not thread-safe, must be called from instance's monitoring thread
+    // payload per state:
+    //     state = loading     -> payload = {} (TODO: add progress info)
+    //     state = ready       -> payload = model_info (json), or {} if wakeup from sleeping
+    //     state = sleeping    -> payload = {}
+    void handle_child_state(const std::string & name, const std::string & raw_input);
+};
+
+struct server_child {
     // return true if the current process is a child server instance
-    static bool is_child_server();
+    bool is_child();
 
-    // notify the router server that a model instance is ready
+    // register the shutdown_handler to be called by the router
     // return the monitoring thread (to be joined by the caller)
-    static std::thread setup_child_server(const std::function<void(int)> & shutdown_handler, const json & model_info);
+    std::thread setup(const std::function<void(int)> & shutdown_handler);
 
-    // notify the router server that the sleeping state has changed
-    static void notify_router_sleeping_state(bool sleeping);
+    // notify router server for status changes (e.g. loading, downloading, sleeping, etc.)
+    // message will be handled by server_models::handle_child_state() on the router side
+    void notify_to_router(const std::string & state_name, const json & payload);
 };
 
 struct server_models_routes {
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 2a67bfcfe..bf3680b9f 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -90,8 +90,10 @@ int llama_server(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // router server never loads a model and must not touch the GPU
+    const bool is_router_server = params.model.path.empty()
+                               && params.model.hf_repo.empty();
+
     // skip device enumeration so the CUDA primary context stays uncreated
-    const bool is_router_server = params.model.path.empty();
     common_params_print_info(params, !is_router_server);
 
     if (!is_router_server) {
@@ -113,8 +115,9 @@ int llama_server(int argc, char ** argv) {
     }
 
     // for consistency between server router mode and single-model mode, we set the same model name as alias
-    if (params.model_alias.empty() && !params.model.name.empty()) {
-        params.model_alias.insert(params.model.name);
+    auto model_name = params.model.get_name();
+    if (params.model_alias.empty() && !model_name.empty()) {
+        params.model_alias.insert(model_name);
     }
 
     // struct that contains llama context and inference
@@ -255,6 +258,7 @@ int llama_server(int argc, char ** argv) {
     // Start the server
     //
 
+    server_child child; // only used in non-router mode
     std::function<void()> clean_up;
 
     if (is_router_server) {
@@ -300,15 +304,16 @@ int llama_server(int argc, char ** argv) {
             return 1;
         }
 
-        // load the model
-        SRV_INF("%s", "loading model\n");
-
-        if (server_models::is_child_server()) {
-            ctx_server.on_sleeping_changed([&](bool sleeping) {
-                server_models::notify_router_sleeping_state(sleeping);
+        // setup communication child --> router if necessary
+        if (child.is_child()) {
+            ctx_server.set_state_callback([&](server_state state, json payload) {
+                child.notify_to_router(server_state_to_str(state), payload);
             });
         }
 
+        // load the model
+        SRV_INF("%s", "loading model\n");
+
         if (!ctx_server.load_model(params)) {
             clean_up();
             if (ctx_http.thread.joinable()) {
@@ -365,9 +370,9 @@ int llama_server(int argc, char ** argv) {
 
         // optionally, notify router server that this instance is ready
         std::thread monitor_thread;
-        if (server_models::is_child_server()) {
-            json model_info = routes.get_model_info();
-            monitor_thread = server_models::setup_child_server(shutdown_handler, model_info);
+        if (child.is_child()) {
+            monitor_thread = child.setup(shutdown_handler);
+            child.notify_to_router(server_state_to_str(SERVER_STATE_READY), routes.get_model_info());
         }
 
         // this call blocks the main thread until queue_tasks.terminate() is called

From f449e0553708b895adbd94a301431cef691f632d Mon Sep 17 00:00:00 2001
From: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
Date: Sat, 20 Jun 2026 08:12:32 +0900
Subject: [PATCH 081/292] ggml-webgpu: add adapter toggles for F16 on Vulkan +
 NVIDIA

---
 ggml/src/ggml-webgpu/ggml-webgpu.cpp | 42 +++++++++++-----------------
 1 file changed, 16 insertions(+), 26 deletions(-)

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index 0b605fa86..f71d1aee7 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -3788,7 +3788,7 @@ static void ggml_webgpu_init_memset_pipeline(webgpu_global_context & ctx) {
     ctx->memset_pipeline = ggml_webgpu_create_pipeline(ctx->device, wgsl_memset, "memset", constants);
 }
 
-static void create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
+static void ggml_backend_webgpu_request_adapter(wgpu::Instance & instance, wgpu::Adapter & adapter) {
     wgpu::RequestAdapterOptions options = {};
 
 #ifndef __EMSCRIPTEN__
@@ -3800,17 +3800,20 @@ static void create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
     options.nextInChain                   = &adapterTogglesDesc;
 #endif
 
-    ctx->webgpu_global_ctx->instance.WaitAny(
-        ctx->webgpu_global_ctx->instance.RequestAdapter(
-            &options, wgpu::CallbackMode::AllowSpontaneous,
-            [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) {
-                if (status != wgpu::RequestAdapterStatus::Success) {
-                    GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message);
-                    return;
-                }
-                ctx->webgpu_global_ctx->adapter = std::move(adapter);
-            }),
-        UINT64_MAX);
+    instance.WaitAny(instance.RequestAdapter(
+                         &options, wgpu::CallbackMode::AllowSpontaneous,
+                         [&adapter](wgpu::RequestAdapterStatus status, wgpu::Adapter _adapter, const char * message) {
+                             if (status != wgpu::RequestAdapterStatus::Success) {
+                                 GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message);
+                                 return;
+                             }
+                             adapter = std::move(_adapter);
+                         }),
+                     UINT64_MAX);
+}
+
+static void create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) {
+    ggml_backend_webgpu_request_adapter(ctx->webgpu_global_ctx->instance, ctx->webgpu_global_ctx->adapter);
     GGML_ASSERT(ctx->webgpu_global_ctx->adapter != nullptr);
 
     ctx->webgpu_global_ctx->adapter.GetLimits(&ctx->webgpu_global_ctx->capabilities.limits);
@@ -4543,20 +4546,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() {
     // Probe for adapter support
     wgpu::Adapter adapter;
     if (ctx->webgpu_global_ctx->instance != nullptr) {
-        wgpu::RequestAdapterOptions options = {};
-
-        // probe for adapter support
-        ctx->webgpu_global_ctx->instance.WaitAny(
-            ctx->webgpu_global_ctx->instance.RequestAdapter(
-                &options, wgpu::CallbackMode::AllowSpontaneous,
-                [&adapter](wgpu::RequestAdapterStatus status, wgpu::Adapter _adapter, const char * message) {
-                    if (status != wgpu::RequestAdapterStatus::Success) {
-                        GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message);
-                        return;
-                    }
-                    adapter = std::move(_adapter);
-                }),
-            UINT64_MAX);
+        ggml_backend_webgpu_request_adapter(ctx->webgpu_global_ctx->instance, adapter);
     }
 
     // WebGPU backend requires f16 support and, on native, implicit device synchronization.

From f4043fec0103872bf4339f6fa18d8b17824d5b6a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Sat, 20 Jun 2026 12:42:36 +0200
Subject: [PATCH 082/292] convert : more consistent handling of rope_parameters
 (#24833)

---
 conversion/bailingmoe.py |  2 +-
 conversion/base.py       |  8 +++++++-
 conversion/chatglm.py    |  2 +-
 conversion/deci.py       |  2 +-
 conversion/exaone.py     |  6 +++---
 conversion/gemma.py      |  2 +-
 conversion/glm.py        |  4 ++--
 conversion/llama.py      |  2 +-
 conversion/mimo.py       |  2 +-
 conversion/minicpm.py    | 16 ++++++----------
 conversion/nemotron.py   |  7 ++++---
 conversion/phi.py        | 20 +++++++++-----------
 conversion/qwen.py       |  2 +-
 conversion/stablelm.py   |  2 +-
 conversion/step3.py      |  2 +-
 15 files changed, 40 insertions(+), 39 deletions(-)

diff --git a/conversion/bailingmoe.py b/conversion/bailingmoe.py
index 319ff6dab..2c6425cb6 100644
--- a/conversion/bailingmoe.py
+++ b/conversion/bailingmoe.py
@@ -126,7 +126,7 @@ class BailingMoeV2Model(TextModel):
         if (rope_dim := hparams.get("head_dim")) is None:
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
diff --git a/conversion/base.py b/conversion/base.py
index c872bcbb3..08fd3747c 100644
--- a/conversion/base.py
+++ b/conversion/base.py
@@ -1119,8 +1119,10 @@ class TextModel(ModelBase):
 
         rope_theta = self.find_hparam(["global_rope_theta", "rope_global_theta", "rope_theta_global", "rope_theta", "rotary_emb_base"], optional=True)
         local_rope_theta = self.find_hparam(["local_rope_theta", "rope_local_theta", "rope_theta_local", "swa_rope_theta", "rope_local_base_freq"], optional=True)
+        partial_rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"], optional=True)
+        original_max_position_embeddings = self.find_hparam(["original_max_position_embeddings"], optional=True)
 
-        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        # Ensure global params are mirrored in rope_parameters
         if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
             if local_rope_theta is not None:
                 self.rope_parameters["sliding_attention"] = {"rope_theta": local_rope_theta}
@@ -1128,6 +1130,10 @@ class TextModel(ModelBase):
                 self.rope_parameters["rope_theta"] = rope_theta
             if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
                 self.rope_parameters["rope_type"] = rope_type
+            if "partial_rotary_factor" not in self.rope_parameters and partial_rotary_factor is not None:
+                self.rope_parameters["partial_rotary_factor"] = partial_rotary_factor
+            if "original_max_position_embeddings" not in self.rope_parameters and original_max_position_embeddings is not None:
+                self.rope_parameters["original_max_position_embeddings"] = original_max_position_embeddings
 
     @classmethod
     def __init_subclass__(cls):
diff --git a/conversion/chatglm.py b/conversion/chatglm.py
index 7e323b890..801913075 100644
--- a/conversion/chatglm.py
+++ b/conversion/chatglm.py
@@ -148,7 +148,7 @@ class ChatGLMModel(TextModel):
             rope_dim = self.hparams["attention_dim"]
         else:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5)))
         self.gguf_writer.add_add_bos_token(False)
         rope_freq = 10000
         if "rope_ratio" in self.hparams:
diff --git a/conversion/deci.py b/conversion/deci.py
index 46d8568c5..be446eefa 100644
--- a/conversion/deci.py
+++ b/conversion/deci.py
@@ -161,7 +161,7 @@ class DeciModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
diff --git a/conversion/exaone.py b/conversion/exaone.py
index b21f02784..bc4fb3f1b 100644
--- a/conversion/exaone.py
+++ b/conversion/exaone.py
@@ -24,7 +24,7 @@ class ExaoneModel(TextModel):
 
         assert (hparams["activation_function"] == "silu")
 
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
+        rotary_factor = self.rope_parameters.get("partial_rotary_factor")
         rotary_factor = rotary_factor if rotary_factor is not None else 1.0
         self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
 
@@ -39,7 +39,7 @@ class ExaoneModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
@@ -104,7 +104,7 @@ class Exaone4Model(TextModel):
                 factor = rope_params.get("factor", 16.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
diff --git a/conversion/gemma.py b/conversion/gemma.py
index 5b4ca5c58..c552df732 100644
--- a/conversion/gemma.py
+++ b/conversion/gemma.py
@@ -693,7 +693,7 @@ class Gemma4Model(Gemma3Model):
             self.gguf_writer.add_head_count_kv(value_arr)
 
         # handle n_rot differently for global vs swa layers
-        partial_rotary_factor_swa = self.hparams.get("partial_rotary_factor", 1.0)
+        partial_rotary_factor_swa = self.rope_parameters.get("partial_rotary_factor", 1.0)
         n_rot_full = int(head_dim_full) # "proportional" is used, see generate_extra_tensors
         n_rot_swa = int(head_dim_swa * partial_rotary_factor_swa)
         self.gguf_writer.add_rope_dimension_count(n_rot_full)
diff --git a/conversion/glm.py b/conversion/glm.py
index 641937720..895cefc22 100644
--- a/conversion/glm.py
+++ b/conversion/glm.py
@@ -124,7 +124,7 @@ class Glm4MoeModel(TextModel):
                 self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
             )
         self.gguf_writer.add_rope_dimension_count(
-            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
+            int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.5))
         )
 
         # MoE parameters - Use only routed expert count (shared experts handled separately)
@@ -226,7 +226,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
         super().set_gguf_parameters()
 
         rope_dim = self.hparams["qk_rope_head_dim"]
-        partial_rotary_factor = self.hparams.get("partial_rotary_factor", 1.0)
+        partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0)
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
 
         # NextN/MTP prediction layers
diff --git a/conversion/llama.py b/conversion/llama.py
index b87bf92d4..a0d39472e 100644
--- a/conversion/llama.py
+++ b/conversion/llama.py
@@ -289,7 +289,7 @@ class LlamaModel(TextModel):
                 factor = rope_params.get("factor", 8.0)
                 low_freq_factor = rope_params.get("low_freq_factor", 1.0)
                 high_freq_factor = rope_params.get("high_freq_factor", 4.0)
-                old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
+                old_context_len = rope_params.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
                 high_freq_wavelen = old_context_len / high_freq_factor
diff --git a/conversion/mimo.py b/conversion/mimo.py
index d4067aab4..11ec28679 100644
--- a/conversion/mimo.py
+++ b/conversion/mimo.py
@@ -154,7 +154,7 @@ class MimoV2Model(TextModel):
         self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
 
-        rope_dim = int(self.hparams["head_dim"] * self.hparams["partial_rotary_factor"])
+        rope_dim = int(self.hparams["head_dim"] * self.rope_parameters["partial_rotary_factor"])
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon", 1e-5))
diff --git a/conversion/minicpm.py b/conversion/minicpm.py
index e9a4c4a74..e31b26a00 100644
--- a/conversion/minicpm.py
+++ b/conversion/minicpm.py
@@ -32,11 +32,9 @@ class MiniCPMModel(TextModel):
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
 
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if long_factors or short_factors:
             if long_factors is None or short_factors is None:
                 raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 
@@ -85,13 +83,11 @@ class MiniCPM3Model(TextModel):
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is not None:
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if long_factors or short_factors:
             rope_dims = self.hparams["qk_rope_head_dim"]
 
-            long_factors = rope_scaling.get('long_factor', None)
-            short_factors = rope_scaling.get('short_factor', None)
-
             if long_factors is None or short_factors is None:
                 raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 
diff --git a/conversion/nemotron.py b/conversion/nemotron.py
index dfeeb9785..e44688a78 100644
--- a/conversion/nemotron.py
+++ b/conversion/nemotron.py
@@ -125,17 +125,18 @@ class NemotronModel(TextModel):
         self.gguf_writer.add_layer_norm_eps(f_norm_eps)
 
         # * Partial RoPE
-        rot_pct = self.find_hparam(["partial_rotary_factor", "rope_pct", "rope_percent"])
+        rot_pct = self.rope_parameters["partial_rotary_factor"]
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
         self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
 
         # * RopeScaling for Nemotron
-        if "rope_scaling" not in self.hparams or self.hparams["rope_scaling"] is None:
+        factor = self.hparams.get("factor") or self.rope_parameters.get("factor")
+        if factor is None:
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         else:
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(self.hparams["factor"])
+            self.gguf_writer.add_rope_scaling_factor(factor)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
diff --git a/conversion/phi.py b/conversion/phi.py
index 5e0d72847..df4bfe809 100644
--- a/conversion/phi.py
+++ b/conversion/phi.py
@@ -18,7 +18,7 @@ class Phi2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.PHI2
 
     def set_gguf_parameters(self):
-        rot_pct = self.find_hparam(["partial_rotary_factor"])
+        rot_pct = self.rope_parameters["partial_rotary_factor"]
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
 
@@ -149,8 +149,8 @@ class Phi3MiniModel(TextModel):
         n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
         rms_eps = self.find_hparam(["rms_norm_eps"])
         max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        orig_max_pos_embds = self.rope_parameters["original_max_position_embeddings"]
+        rot_pct = self.rope_parameters.get("partial_rotary_factor", 1.0)
         rope_dims = int(rot_pct * n_embd) // n_head
 
         self.gguf_writer.add_context_length(max_pos_embds)
@@ -174,18 +174,19 @@ class Phi3MiniModel(TextModel):
         n_embd = self.find_hparam(["hidden_size", "n_embd"])
         n_head = self.find_hparam(["num_attention_heads", "n_head"])
         max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"])
-        orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"])
-        rot_pct = self.hparams.get("partial_rotary_factor", 1.0)
+        orig_max_pos_embds = self.rope_parameters["original_max_position_embeddings"]
+        rot_pct = self.rope_parameters.get("partial_rotary_factor", 1.0)
         rope_dims = int(rot_pct * n_embd) // n_head
 
         # write rope scaling for long context (128k) model
-        rope_scaling = self.find_hparam(['rope_scaling'], True)
-        if rope_scaling is None:
+        long_factors = self.rope_parameters.get('long_factor')
+        short_factors = self.rope_parameters.get('short_factor')
+        if not long_factors:
             return
 
         scale = max_pos_embds / orig_max_pos_embds
 
-        rope_scaling_type = rope_scaling.get('rope_type', rope_scaling.get('type', '')).lower()
+        rope_scaling_type = self.rope_parameters.get('rope_type', '').lower()
         if len(rope_scaling_type) == 0:
             raise KeyError('Missing the required key rope_scaling.type')
 
@@ -198,9 +199,6 @@ class Phi3MiniModel(TextModel):
 
         self.gguf_writer.add_rope_scaling_attn_factors(attn_factor)
 
-        long_factors = rope_scaling.get('long_factor', None)
-        short_factors = rope_scaling.get('short_factor', None)
-
         if long_factors is None or short_factors is None:
             raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
 
diff --git a/conversion/qwen.py b/conversion/qwen.py
index 7eb135c83..6b85eb9aa 100644
--- a/conversion/qwen.py
+++ b/conversion/qwen.py
@@ -280,7 +280,7 @@ class Qwen3NextModel(Qwen2MoeModel):
         self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.25)))
 
     @classmethod
     def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
diff --git a/conversion/stablelm.py b/conversion/stablelm.py
index ba5e9aa6c..6e16378a0 100644
--- a/conversion/stablelm.py
+++ b/conversion/stablelm.py
@@ -28,7 +28,7 @@ class StableLMModel(TextModel):
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(self.block_count)
         self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
-        rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
+        rotary_factor = self.rope_parameters["partial_rotary_factor"]
         self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
         self.gguf_writer.add_head_count(hparams["num_attention_heads"])
         self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
diff --git a/conversion/step3.py b/conversion/step3.py
index 8c45b61c9..49bb5244a 100644
--- a/conversion/step3.py
+++ b/conversion/step3.py
@@ -314,7 +314,7 @@ class Step35Model(TextModel):
         factor = float(rope_params.get("factor", 8.0))
         low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
         high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
-        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", 8192))
 
         low_freq_wavelen = old_context_len / low_freq_factor
         high_freq_wavelen = old_context_len / high_freq_factor

From 37a77fb0579be9d71e2c73da0553cfd42b7b103a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Sat, 20 Jun 2026 12:43:06 +0200
Subject: [PATCH 083/292] ggml : optimize AMX (#24806)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Flatten the partition over n_batch * M so every thread participates in
the quantization

    | CPU                             | Model                         | Test   |   t/s OLD |   t/s NEW |   Speedup |
    |:--------------------------------|:------------------------------|:-------|----------:|----------:|----------:|
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B IQ4_NL - 4.5 bpw  | pp512  |    730.71 |    779.86 |      1.07 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B IQ4_NL - 4.5 bpw  | tg128  |     87.88 |     86.79 |      0.99 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B IQ4_XS - 4.25 bpw | pp512  |    725.09 |   1023.31 |      1.41 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B IQ4_XS - 4.25 bpw | tg128  |     83.64 |     83.62 |      1.00 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_0              | pp512  |    820.51 |    924.05 |      1.13 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_0              | tg128  |     90.59 |     92.46 |      1.02 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_1              | pp512  |    776.88 |    872.79 |      1.12 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_1              | tg128  |     89.39 |     90.94 |      1.02 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_K_M            | pp512  |    719.28 |   1009.27 |      1.40 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_K_M            | tg128  |     80.62 |     80.86 |      1.00 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_K_S            | pp512  |    732.29 |   1077.29 |      1.47 |
    | Intel(R) Xeon(R) Platinum 8488C | qwen35 0.8B Q4_K_S            | tg128  |     86.42 |     83.53 |      0.97 |

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 ggml/src/ggml-cpu/amx/mmq.cpp | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cpu/amx/mmq.cpp b/ggml/src/ggml-cpu/amx/mmq.cpp
index d9383a04b..9f3a744b5 100644
--- a/ggml/src/ggml-cpu/amx/mmq.cpp
+++ b/ggml/src/ggml-cpu/amx/mmq.cpp
@@ -2417,15 +2417,14 @@ void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_te
             // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size
             GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size);
 
-            parallel_for_ggml(params, n_batch, [&](int begin, int end) {
-                for (int batch_idx = begin; batch_idx < end; ++batch_idx) {
+            parallel_for_ggml(params, n_batch * M, [&](int begin, int end) {
+                for (int idx = begin; idx < end; ++idx) {
+                    int batch_idx = idx / M;
+                    int m         = idx % M;
                     int64_t src1_offset = ggml_batch_offset(src1, batch_idx, ne2);
                     const float * A_data = (const float *)((const char *)src1->data + src1_offset);
                     char * wdata_batch = (char *)wdata + batch_idx * M * row_size_A;
-
-                    for (int m = 0; m < M; ++m) {
-                        from_float<vec_dot_type>(A_data + m * K, wdata_batch + m * row_size_A, K);
-                    }
+                    from_float<vec_dot_type>(A_data + m * K, wdata_batch + m * row_size_A, K);
                 }
             });
         });

From 796f41bedca8a786ab3eb5584cd97b7730b303d8 Mon Sep 17 00:00:00 2001
From: davidrhodus <david@vacovideo.com>
Date: Sat, 20 Jun 2026 03:48:24 -0700
Subject: [PATCH 084/292] model : glm-dsa load DSA indexer tensors as optional
 (#24770)

GLM-5.2 ships the DSA "lightning indexer" on only a subset of layers (the
"full" layers; others omit it), but the GLM_DSA loader created the five
indexer tensors on every layer as required, so loading any GLM-5.2 GGUF
failed with e.g. `missing tensor 'blk.3.indexer.k_norm.weight'`.

GLM_DSA's graph is llama_model_deepseek2::graph (plain MLA) and does not use
the indexer tensors (indexer runtime not yet implemented), so they are
loaded-but-unused. Marking them TENSOR_NOT_REQUIRED lets layers without an
indexer load as nullptr and the model runs as full MLA attention.

DeepSeek-V3.2 (uniform indexer on all layers) is unaffected.
---
 src/models/glm-dsa.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/models/glm-dsa.cpp b/src/models/glm-dsa.cpp
index 11d91312d..32fe6def6 100644
--- a/src/models/glm-dsa.cpp
+++ b/src/models/glm-dsa.cpp
@@ -101,11 +101,11 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
 
         // DSA indexer
-        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags);
-        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags);
-        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags);
-        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags);
-        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
+        layer.indexer_k_norm   = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "weight", i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM,   "bias",   i), {hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_k   = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K,   "weight", i), {n_embd, hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
+        layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags | TENSOR_NOT_REQUIRED);
         if (i < (int) hparams.n_layer_dense_lead) {
             layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, flags);
             layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, flags);

From 67e9fd3b74b7fab3a153161f1942cc2121aa90a3 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Sat, 20 Jun 2026 05:54:42 -0500
Subject: [PATCH 085/292] docker : prebuild web UI for s390x build [no release]
 (#24829)

---
 .devops/s390x.Dockerfile     | 16 ----------------
 .dockerignore                |  1 -
 .github/workflows/docker.yml | 18 ++++++++++++++++--
 3 files changed, 16 insertions(+), 19 deletions(-)

diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile
index 149d79a61..d88dd2d92 100644
--- a/.devops/s390x.Dockerfile
+++ b/.devops/s390x.Dockerfile
@@ -4,20 +4,6 @@ ARG BUILD_DATE=N/A
 ARG APP_VERSION=N/A
 ARG APP_REVISION=N/A
 
-ARG NODE_VERSION=24
-
-FROM docker.io/node:$NODE_VERSION AS web
-
-ARG APP_VERSION
-
-WORKDIR /app/tools/ui
-
-COPY tools/ui/package.json tools/ui/package-lock.json ./
-RUN npm ci
-
-COPY tools/ui/ ./
-RUN LLAMA_BUILD_NUMBER="$APP_VERSION" npm run build
-
 ### Build Llama.cpp stage
 FROM docker.io/gcc:${GCC_VERSION} AS build
 
@@ -34,8 +20,6 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
 WORKDIR /app
 COPY . .
 
-COPY --from=web /app/tools/ui/dist tools/ui/dist
-
 RUN --mount=type=cache,target=/root/.ccache \
     --mount=type=cache,target=/app/build \
     cmake -S . -B build -G Ninja \
diff --git a/.dockerignore b/.dockerignore
index a223b7e89..0b81e83bf 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -11,7 +11,6 @@
 build*/
 
 tools/ui/node_modules/
-tools/ui/dist/
 
 models/*
 
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 8195a55ff..afe4b7c66 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -58,6 +58,13 @@ jobs:
           git tag ${{ steps.srctag.outputs.name }} || exit 0
           git push origin ${{ steps.srctag.outputs.name }} || exit 0
 
+  build_ui:
+    name: Build UI
+    needs: create_tag
+    uses: ./.github/workflows/ui-build.yml
+    with:
+      hf_ui_version: ${{ needs.create_tag.outputs.source_tag }}
+
   prepare_matrices:
     name: Prepare Docker matrices
     runs-on: ubuntu-24.04
@@ -79,7 +86,7 @@ jobs:
           [
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04" },
             { "tag": "cpu", "dockerfile": ".devops/cpu.Dockerfile", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-arm" },
-            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x" },
+            { "tag": "cpu", "dockerfile": ".devops/s390x.Dockerfile", "platforms": "linux/s390x", "full": true, "light": true, "server": true, "free_disk_space": false, "runs_on": "ubuntu-24.04-s390x", "prebuilt_ui": true },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
             { "tag": "cuda cuda12", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "12.8.1", "platforms": "linux/arm64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04-arm" },
             { "tag": "cuda13", "dockerfile": ".devops/cuda.Dockerfile", "cuda_version": "13.3.0", "platforms": "linux/amd64", "full": true, "light": true, "server": true, "free_disk_space": true, "runs_on": "ubuntu-24.04" },
@@ -135,7 +142,7 @@ jobs:
 
   push_to_registry:
     name: Push Docker image to Docker Registry
-    needs: [prepare_matrices, create_tag]
+    needs: [prepare_matrices, create_tag, build_ui]
 
     runs-on: ${{ matrix.config.runs_on }}
     strategy:
@@ -150,6 +157,13 @@ jobs:
           fetch-depth: 0
           ref: ${{ needs.create_tag.outputs.source_tag }}
 
+      - name: Download prebuilt UI
+        if: ${{ matrix.config.prebuilt_ui == true }}
+        uses: actions/download-artifact@3e5f45b2cfb9172054b4087a40e8e0b5a5461e7c # v8
+        with:
+          name: ui-build
+          path: tools/ui/dist
+
       - name: Set up QEMU
         if: ${{ contains(matrix.config.platforms, 'linux/amd64') }}
         uses: docker/setup-qemu-action@ce360397dd3f832beb865e1373c09c0e9f86d70a # v4

From e27f3085973722407518ea4822fb3e0a2b41df9c Mon Sep 17 00:00:00 2001
From: Matti4 <ristorim013@gmail.com>
Date: Sat, 20 Jun 2026 15:34:47 +0200
Subject: [PATCH 086/292] server: avoid forwarding auth headers in CORS proxy
 (#24373)

* server: avoid forwarding auth headers in CORS proxy

* format

* fix test

* fix e2e test

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 tools/server/server-cors-proxy.h             | 22 ++++++-
 tools/server/tests/unit/test_security.py     | 45 ++++++++++++++
 tools/ui/src/lib/constants/mcp.ts            |  3 +
 tools/ui/src/lib/services/mcp.service.ts     | 23 +++++--
 tools/ui/src/lib/utils/api-headers.ts        | 15 ++++-
 tools/ui/src/lib/utils/cors-proxy.ts         |  8 ++-
 tools/ui/tests/e2e/pwa.e2e.ts                | 10 +--
 tools/ui/tests/unit/mcp-service.test.ts      | 64 +++++++++++++++++++-
 tools/ui/tests/unit/sanitize-headers.test.ts | 18 ++++++
 9 files changed, 187 insertions(+), 21 deletions(-)

diff --git a/tools/server/server-cors-proxy.h b/tools/server/server-cors-proxy.h
index 2af0c7e1c..53a6909ed 100644
--- a/tools/server/server-cors-proxy.h
+++ b/tools/server/server-cors-proxy.h
@@ -7,9 +7,18 @@
 #include <unordered_set>
 #include <list>
 #include <map>
+#include <algorithm>
+#include <cctype>
 
 #include "server-http.h"
 
+static std::string proxy_header_to_lower(std::string header) {
+    std::transform(header.begin(), header.end(), header.begin(), [](unsigned char c) {
+        return std::tolower(c);
+    });
+    return header;
+}
+
 static server_http_res_ptr proxy_request(const server_http_req & req, std::string method) {
     std::string target_url = req.get_param("url");
     common_http_url parsed_url = common_http_parse_url(target_url);
@@ -33,11 +42,18 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin
     SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.port, parsed_url.path.c_str());
 
     std::map<std::string, std::string> headers;
+    const std::string proxy_header_prefix = "x-llama-server-proxy-header-";
     for (auto [key, value] : req.headers) {
-        auto new_key = key;
-        if (string_starts_with(new_key, "x-proxy-header-")) {
-            string_replace_all(new_key, "x-proxy-header-", "");
+        const std::string lowered_key = proxy_header_to_lower(key);
+        if (!string_starts_with(lowered_key, proxy_header_prefix)) {
+            continue;
         }
+
+        auto new_key = key.substr(proxy_header_prefix.size());
+        if (new_key.empty()) {
+            continue;
+        }
+
         headers[new_key] = value;
     }
 
diff --git a/tools/server/tests/unit/test_security.py b/tools/server/tests/unit/test_security.py
index 02d0b1afb..a0c3e214a 100644
--- a/tools/server/tests/unit/test_security.py
+++ b/tools/server/tests/unit/test_security.py
@@ -1,6 +1,8 @@
 import pytest
 from openai import OpenAI
 from utils import *
+import threading
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 
 server = ServerPreset.tinyllama2()
 
@@ -105,6 +107,49 @@ def test_cors_options(origin: str, cors_header: str, cors_header_value: str):
     assert res.headers[cors_header] == cors_header_value
 
 
+def test_cors_proxy_only_forwards_explicit_proxy_headers():
+    class CaptureHeadersHandler(BaseHTTPRequestHandler):
+        def do_GET(self):
+            self.server.captured_headers = dict(self.headers)
+            self.send_response(200)
+            self.end_headers()
+            self.wfile.write(b"ok")
+
+        def log_message(self, format, *args):
+            pass
+
+    target = ThreadingHTTPServer(("127.0.0.1", 0), CaptureHeadersHandler)
+    target.captured_headers = {}
+    target_thread = threading.Thread(target=target.serve_forever, daemon=True)
+    target_thread.start()
+
+    try:
+        server = ServerPreset.tinyllama2()
+        server.api_key = TEST_API_KEY
+        server.ui_mcp_proxy = True
+        server.start()
+
+        res = server.make_request("GET", f"/cors-proxy?url=http://127.0.0.1:{target.server_port}/capture", headers={
+            "Authorization": f"Bearer {TEST_API_KEY}",
+            "Proxy-Authorization": "Basic secret",
+            "X-Api-Key": TEST_API_KEY,
+            "Cookie": "session=secret",
+            "x-llama-server-proxy-header-accept": "application/json",
+            "x-llama-server-proxy-header-authorization": "Bearer explicit",
+        })
+
+        assert res.status_code == 200
+        captured = {key.lower(): value for key, value in target.captured_headers.items()}
+        assert captured["accept"] == "application/json"
+        assert captured["authorization"] == "Bearer explicit"
+        assert "proxy-authorization" not in captured
+        assert "x-api-key" not in captured
+        assert "cookie" not in captured
+    finally:
+        target.shutdown()
+        target.server_close()
+
+
 @pytest.mark.parametrize(
     "media_path, image_url, success",
     [
diff --git a/tools/ui/src/lib/constants/mcp.ts b/tools/ui/src/lib/constants/mcp.ts
index 5b11f989e..a7381df0b 100644
--- a/tools/ui/src/lib/constants/mcp.ts
+++ b/tools/ui/src/lib/constants/mcp.ts
@@ -51,6 +51,9 @@ export const EXPECTED_THEMED_ICON_PAIR_COUNT = 2;
 /** CORS proxy URL query parameter name */
 export const CORS_PROXY_URL_PARAM = 'url';
 
+/** Header prefix for headers that should be forwarded by the CORS proxy */
+export const CORS_PROXY_HEADER_PREFIX = 'x-llama-server-proxy-header-';
+
 /** Number of trailing characters to keep visible when partially redacting mcp-session-id */
 export const MCP_SESSION_ID_VISIBLE_CHARS = 5;
 
diff --git a/tools/ui/src/lib/services/mcp.service.ts b/tools/ui/src/lib/services/mcp.service.ts
index 0aa58dc5d..90de0d5d8 100644
--- a/tools/ui/src/lib/services/mcp.service.ts
+++ b/tools/ui/src/lib/services/mcp.service.ts
@@ -16,6 +16,7 @@ import {
 	DEFAULT_MCP_CONFIG,
 	DEFAULT_CLIENT_VERSION,
 	DEFAULT_IMAGE_MIME_TYPE,
+	CORS_PROXY_HEADER_PREFIX,
 	MCP_PARTIAL_REDACT_HEADERS,
 	CORS_PROXY_ENDPOINT
 } from '$lib/constants';
@@ -133,6 +134,20 @@ export class MCPService {
 		return details;
 	}
 
+	private static addRequestHeaders(
+		requestHeaders: Headers,
+		headers: HeadersInit,
+		useProxy: boolean
+	) {
+		for (const [key, value] of new Headers(headers).entries()) {
+			const proxiedKey =
+				useProxy && !key.toLowerCase().startsWith(CORS_PROXY_HEADER_PREFIX)
+					? `${CORS_PROXY_HEADER_PREFIX}${key}`
+					: key;
+			requestHeaders.set(proxiedKey, value);
+		}
+	}
+
 	private static summarizeError(error: unknown): Record<string, unknown> {
 		if (error instanceof Error) {
 			return {
@@ -271,15 +286,11 @@ export class MCPService {
 				const requestHeaders = new Headers(baseInit.headers);
 
 				if (typeof Request !== 'undefined' && input instanceof Request) {
-					for (const [key, value] of input.headers.entries()) {
-						requestHeaders.set(key, value);
-					}
+					this.addRequestHeaders(requestHeaders, input.headers, useProxy);
 				}
 
 				if (init?.headers) {
-					for (const [key, value] of new Headers(init.headers).entries()) {
-						requestHeaders.set(key, value);
-					}
+					this.addRequestHeaders(requestHeaders, init.headers, useProxy);
 				}
 
 				const request = this.createDiagnosticRequestDetails(
diff --git a/tools/ui/src/lib/utils/api-headers.ts b/tools/ui/src/lib/utils/api-headers.ts
index c0a5309b9..a2b70d492 100644
--- a/tools/ui/src/lib/utils/api-headers.ts
+++ b/tools/ui/src/lib/utils/api-headers.ts
@@ -1,5 +1,5 @@
 import { config } from '$lib/stores/settings.svelte';
-import { REDACTED_HEADERS } from '$lib/constants';
+import { CORS_PROXY_HEADER_PREFIX, REDACTED_HEADERS } from '$lib/constants';
 import { redactValue } from './redact';
 
 /**
@@ -52,11 +52,20 @@ export function sanitizeHeaders(
 
 	for (const [key, value] of normalized.entries()) {
 		const normalizedKey = key.toLowerCase();
-		const partialChars = partialRedactHeaders?.get(normalizedKey);
+		const unproxiedKey = normalizedKey.startsWith(CORS_PROXY_HEADER_PREFIX)
+			? normalizedKey.slice(CORS_PROXY_HEADER_PREFIX.length)
+			: normalizedKey;
+		const partialChars =
+			partialRedactHeaders?.get(normalizedKey) ?? partialRedactHeaders?.get(unproxiedKey);
 
 		if (partialChars !== undefined) {
 			sanitized[key] = redactValue(value, partialChars);
-		} else if (REDACTED_HEADERS.has(normalizedKey) || redactedHeaders.has(normalizedKey)) {
+		} else if (
+			REDACTED_HEADERS.has(normalizedKey) ||
+			REDACTED_HEADERS.has(unproxiedKey) ||
+			redactedHeaders.has(normalizedKey) ||
+			redactedHeaders.has(unproxiedKey)
+		) {
 			sanitized[key] = redactValue(value);
 		} else {
 			sanitized[key] = value;
diff --git a/tools/ui/src/lib/utils/cors-proxy.ts b/tools/ui/src/lib/utils/cors-proxy.ts
index 47caf2742..1694b7dbe 100644
--- a/tools/ui/src/lib/utils/cors-proxy.ts
+++ b/tools/ui/src/lib/utils/cors-proxy.ts
@@ -3,7 +3,11 @@
  */
 
 import { base } from '$app/paths';
-import { CORS_PROXY_ENDPOINT, CORS_PROXY_URL_PARAM } from '$lib/constants';
+import {
+	CORS_PROXY_ENDPOINT,
+	CORS_PROXY_HEADER_PREFIX,
+	CORS_PROXY_URL_PARAM
+} from '$lib/constants';
 
 /**
  * Build a proxied URL that routes through llama-server's CORS proxy.
@@ -28,7 +32,7 @@ export function buildProxiedHeaders(headers: Record<string, string>): Record<str
 	const proxiedHeaders: Record<string, string> = {};
 
 	for (const [key, value] of Object.entries(headers)) {
-		proxiedHeaders[`x-proxy-header-${key}`] = value;
+		proxiedHeaders[`${CORS_PROXY_HEADER_PREFIX}${key}`] = value;
 	}
 
 	return proxiedHeaders;
diff --git a/tools/ui/tests/e2e/pwa.e2e.ts b/tools/ui/tests/e2e/pwa.e2e.ts
index be7642b19..e21672239 100644
--- a/tools/ui/tests/e2e/pwa.e2e.ts
+++ b/tools/ui/tests/e2e/pwa.e2e.ts
@@ -39,8 +39,8 @@ test.describe('PWA Service Worker', () => {
 		const swContent = await swResponse.text();
 
 		// Precache contains SvelteKit content-hashed bundle paths
-		expect(swContent).toMatch(/"_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"/);
-		expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9-]+\.css"/);
+		expect(swContent).toMatch(/"_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/);
+		expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/);
 		expect(swContent).toMatch(/"manifest\.webmanifest"/);
 		expect(swContent).toMatch(/"_app\/version\.json"/);
 		expect(swContent).toMatch(/NavigationRoute/);
@@ -99,8 +99,8 @@ test.describe('PWA Service Worker', () => {
 		const html = await response.text();
 
 		// SvelteKit outputs content-hashed bundle names in _app/immutable/
-		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"/);
-		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/assets\/bundle\.[a-zA-Z0-9-]+\.css"/);
-		expect(html).toMatch(/import\("(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9-]+\.js"\)/);
+		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"/);
+		expect(html).toMatch(/href="(\.\/|\/)_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/);
+		expect(html).toMatch(/import\("(\.\/|\/)_app\/immutable\/bundle\.[a-zA-Z0-9_-]+\.js"\)/);
 	});
 });
diff --git a/tools/ui/tests/unit/mcp-service.test.ts b/tools/ui/tests/unit/mcp-service.test.ts
index afd3bdd5c..1f6fdda37 100644
--- a/tools/ui/tests/unit/mcp-service.test.ts
+++ b/tools/ui/tests/unit/mcp-service.test.ts
@@ -3,6 +3,7 @@ import { Client } from '@modelcontextprotocol/sdk/client';
 import { MCPService } from '$lib/services/mcp.service';
 import { MCPConnectionPhase, MCPTransportType } from '$lib/enums';
 import type { MCPConnectionLog, MCPServerConfig } from '$lib/types';
+import { CORS_PROXY_HEADER_PREFIX } from '$lib/constants';
 
 type DiagnosticFetchFactory = (
 	serverName: string,
@@ -16,11 +17,12 @@ type DiagnosticFetchFactory = (
 const createDiagnosticFetch = (
 	config: MCPServerConfig,
 	onLog?: (log: MCPConnectionLog) => void,
-	baseInit: RequestInit = {}
+	baseInit: RequestInit = {},
+	useProxy = false
 ) =>
 	(
 		MCPService as unknown as { createDiagnosticFetch: DiagnosticFetchFactory }
-	).createDiagnosticFetch('test-server', config, baseInit, new URL(config.url), false, onLog);
+	).createDiagnosticFetch('test-server', config, baseInit, new URL(config.url), useProxy, onLog);
 
 describe('MCPService', () => {
 	afterEach(() => {
@@ -94,6 +96,64 @@ describe('MCPService', () => {
 		});
 	});
 
+	it('wraps dynamic request headers when using the CORS proxy', async () => {
+		const logs: MCPConnectionLog[] = [];
+		const proxiedAuthToken = `${CORS_PROXY_HEADER_PREFIX}x-auth-token`;
+		const proxiedContentType = `${CORS_PROXY_HEADER_PREFIX}content-type`;
+		const proxiedSessionId = `${CORS_PROXY_HEADER_PREFIX}mcp-session-id`;
+		const response = new Response('{}', {
+			status: 200,
+			headers: { 'content-type': 'application/json' }
+		});
+		const fetchMock = vi.fn().mockResolvedValue(response);
+
+		vi.stubGlobal('fetch', fetchMock);
+
+		const config: MCPServerConfig = {
+			url: 'https://example.com/mcp',
+			transport: MCPTransportType.STREAMABLE_HTTP,
+			useProxy: true
+		};
+
+		const controller = createDiagnosticFetch(
+			config,
+			(log) => logs.push(log),
+			{
+				headers: {
+					authorization: 'Bearer llama-server-key',
+					[proxiedAuthToken]: 'target-token'
+				}
+			},
+			true
+		);
+
+		await controller.fetch('http://localhost:8080/cors-proxy?url=https%3A%2F%2Fexample.com%2Fmcp', {
+			method: 'POST',
+			headers: {
+				'content-type': 'application/json',
+				'mcp-session-id': 'session-request-12345'
+			},
+			body: '{}'
+		});
+
+		const sentHeaders = fetchMock.mock.calls[0]?.[1]?.headers as Headers;
+		expect(sentHeaders.get('authorization')).toBe('Bearer llama-server-key');
+		expect(sentHeaders.get(proxiedAuthToken)).toBe('target-token');
+		expect(sentHeaders.get(proxiedContentType)).toBe('application/json');
+		expect(sentHeaders.get(proxiedSessionId)).toBe('session-request-12345');
+		expect(sentHeaders.has('content-type')).toBe(false);
+		expect(sentHeaders.has('mcp-session-id')).toBe(false);
+		expect(logs[0].details).toMatchObject({
+			request: {
+				headers: {
+					authorization: '[redacted]',
+					[proxiedAuthToken]: '[redacted]',
+					[proxiedSessionId]: '....12345'
+				}
+			}
+		});
+	});
+
 	it('partially redacts mcp-session-id in diagnostic request and response logs', async () => {
 		const logs: MCPConnectionLog[] = [];
 		const response = new Response('{}', {
diff --git a/tools/ui/tests/unit/sanitize-headers.test.ts b/tools/ui/tests/unit/sanitize-headers.test.ts
index f5a682d86..8cc1fcdfc 100644
--- a/tools/ui/tests/unit/sanitize-headers.test.ts
+++ b/tools/ui/tests/unit/sanitize-headers.test.ts
@@ -1,5 +1,6 @@
 import { describe, expect, it } from 'vitest';
 import { sanitizeHeaders } from '$lib/utils/api-headers';
+import { CORS_PROXY_HEADER_PREFIX } from '$lib/constants';
 
 describe('sanitizeHeaders', () => {
 	it('returns empty object for undefined input', () => {
@@ -52,4 +53,21 @@ describe('sanitizeHeaders', () => {
 		const result = sanitizeHeaders(headers, ['X-CUSTOM-TOKEN']);
 		expect(result['x-custom-token']).toBe('[redacted]');
 	});
+
+	it('redacts proxied sensitive and custom target headers', () => {
+		const proxiedAuthorization = `${CORS_PROXY_HEADER_PREFIX}authorization`;
+		const proxiedSessionId = `${CORS_PROXY_HEADER_PREFIX}mcp-session-id`;
+		const proxiedVendorKey = `${CORS_PROXY_HEADER_PREFIX}x-vendor-key`;
+		const headers = new Headers({
+			[proxiedAuthorization]: 'Bearer secret',
+			[proxiedSessionId]: 'session-12345',
+			[proxiedVendorKey]: 'vendor-secret'
+		});
+		const partial = new Map([['mcp-session-id', 5]]);
+		const result = sanitizeHeaders(headers, ['x-vendor-key'], partial);
+
+		expect(result[proxiedAuthorization]).toBe('[redacted]');
+		expect(result[proxiedSessionId]).toBe('....12345');
+		expect(result[proxiedVendorKey]).toBe('[redacted]');
+	});
 });

From a5019767c3eed836aa6d1978bbaf4a6d9ecf0afa Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 20 Jun 2026 23:02:56 +0800
Subject: [PATCH 087/292] docs

---
 embd_res/kcpp_docs.embd | 81 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)

diff --git a/embd_res/kcpp_docs.embd b/embd_res/kcpp_docs.embd
index 92234c19d..ed44c09f9 100644
--- a/embd_res/kcpp_docs.embd
+++ b/embd_res/kcpp_docs.embd
@@ -2849,6 +2849,87 @@
                       "responses": {"default": {"description": ""}}
                    }
                 },
+                "/v1/images/generations": {
+                   "post": {
+                      "summary": "Generates images from a text prompt. Please refer to OpenAI documentation",
+                      "description": "Creates images from a text prompt.\n\n This is an OpenAI compatibility endpoint.\n\n Please refer to OpenAI documentation at [https://developers.openai.com/docs/api-reference/images/create](https://developers.openai.com/docs/api-reference/images/create).",
+                      "requestBody": {
+                         "content": {
+                            "application/json": {
+                               "example": {"model":"kcpp","prompt": "picture of a kobold, high quality HD render", "n": 1, "size": "512x512", "response_format": "b64_json"},
+                               "schema": {
+                                  "properties": {
+                                     "model": {
+                                        "type": "string",
+                                        "description": "Model identifier. Use kcpp for the currently loaded image model."
+                                     },
+                                     "prompt": {
+                                        "type": "string",
+                                        "description": "Text prompt describing the image to generate."
+                                     },
+                                     "n": {
+                                        "type": "integer",
+                                        "description": "Number of images to generate.",
+                                        "minimum": 1
+                                     },
+                                     "size": {
+                                        "type": "string",
+                                        "description": "Requested image size, such as 512x512 or 1024x1024."
+                                     },
+                                     "response_format": {
+                                        "type": "string",
+                                        "description": "Response image format. b64_json returns base64 encoded image data."
+                                     }
+                                  },
+                                  "required": [
+                                     "prompt"
+                                  ],
+                                  "type": "object"
+                               }
+                            }
+                         },
+                         "required": true
+                      },
+                      "tags": [
+                         "v1"
+                      ],
+                      "responses": {
+                         "200": {
+                            "content": {
+                               "application/json": {
+                                  "example": {"created": 1710000000, "data": [{"b64_json": "base64_image_data"}]},
+                                  "schema": {
+                                     "properties": {
+                                        "created": {
+                                           "type": "integer",
+                                           "description": "Unix timestamp for the generation request."
+                                        },
+                                        "data": {
+                                           "type": "array",
+                                           "items": {
+                                              "type": "object",
+                                              "properties": {
+                                                 "b64_json": {
+                                                    "type": "string",
+                                                    "description": "Base64 encoded image data."
+                                                 },
+                                                 "url": {
+                                                    "type": "string",
+                                                    "description": "Image URL, if URL responses are supported."
+                                                 }
+                                              }
+                                           }
+                                        }
+                                     },
+                                     "type": "object"
+                                  }
+                               }
+                            },
+                            "description": "Successful request"
+                         }
+                      }
+                   }
+                },
                 "/v1/models": {
                    "get": {
                       "summary": "List and describe the various models available in the API. Please refer to OpenAI documentation",

From 8452824611be321246f33339727f60a90c02c277 Mon Sep 17 00:00:00 2001
From: Muhammad Salem <salem.ebo@gmail.com>
Date: Sat, 20 Jun 2026 18:08:59 +0300
Subject: [PATCH 088/292] release: add missing link for win opencl adreno arm64
 (#24809)

---
 .github/workflows/release.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9789215f2..c7b67e492 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -1627,6 +1627,7 @@ jobs:
             **Windows:**
             - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
             - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
+            - [Windows arm64 (OpenCL Adreno)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-opencl-adreno-arm64.zip)
             - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
             - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.3-x64.zip) - [CUDA 13.3 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.3-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)

From 75f460ac289e61eb3c2bb63c9487794a1ed514d1 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 20 Jun 2026 19:45:27 +0200
Subject: [PATCH 089/292] arg: try fixing test-args-parser randomly fails
 (#24826)

* arg: try fixing test-args-parser randomly fails

* return ref

* try triggering the workflow

* exception wrapper

* wip

* test

* test 2

* arg: guard win32 utf8 argv override

make_utf8_argv rebuilds argv from GetCommandLineW to fix utf8 handling of
non ascii arguments on windows. the override runs unconditionally inside
common_params_parse, so it also clobbers a programmatic argv passed by a
caller. test-arg-parser builds a synthetic argv but then sees the real
process command line instead, the model argument is never parsed, and the
assert that expects success aborts via fastfail (0xC0000409). this shows up
as a random failure in the openvino windows workflow.

only override argv when its length matches the caller argc, so the utf8
repair still applies to real binaries while a programmatic argv stays intact.

---------

Co-authored-by: Pascal <admin@serveurperso.com>
---
 common/arg.cpp            |  6 +++---
 tests/test-arg-parser.cpp | 12 +++++++++++-
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index a9b1a25b2..8f54b5c81 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -924,8 +924,8 @@ static utf8_argv make_utf8_argv() {
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
 #ifdef _WIN32
     auto utf8 = make_utf8_argv();
-    if (!utf8.ptrs.empty()) {
-        argc = static_cast<int>(utf8.buf.size());
+    // repair argv only when it matches the process command line
+    if (static_cast<int>(utf8.buf.size()) == argc) {
         argv = utf8.ptrs.data();
     }
 #endif
@@ -2897,7 +2897,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
-        add_opt(common_arg(
+    add_opt(common_arg(
         {"-ag", "--agent"},
         {"-no-ag", "--no-agent"},
         "whether to enable CORS proxy and all built-in tools - do not enable in untrusted environments (default: disabled)",
diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp
index 0dd8422e7..e83ee85dd 100644
--- a/tests/test-arg-parser.cpp
+++ b/tests/test-arg-parser.cpp
@@ -10,7 +10,7 @@
 #undef NDEBUG
 #include <cassert>
 
-int main(void) {
+static void test(void) {
     common_params params;
 
     printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
@@ -210,3 +210,13 @@ int main(void) {
 
     printf("test-arg-parser: all tests OK\n\n");
 }
+
+int main(void) {
+    try {
+        test();
+    } catch (std::exception & e) {
+        fprintf(stderr, "test-arg-parser: exception: %s\n", e.what());
+        return 1;
+    }
+    return 0;
+}

From 84de01a1f1c847292b8d90a9c0bff6619f2919be Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Sat, 20 Jun 2026 20:07:01 +0200
Subject: [PATCH 090/292] llama : use LLM_KV for quantization_version &
 file_type (#24802)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 src/llama-quant.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index cf92ce4bb..89b7fe8d4 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -932,8 +932,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
     // copy the KV pairs from the input file
     gguf_set_kv     (ctx_out.get(), ml.metadata);
-    gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
-    gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV
+    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION).c_str(), GGML_QNT_VERSION);
+    gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_GENERAL_FILE_TYPE).c_str(), ftype);
 
     // Remove split metadata
     gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());

From 4a8094317436a23c484c9803cc3ac348e236708f Mon Sep 17 00:00:00 2001
From: Guanhuai Zhang <67999475+BiReRa@users.noreply.github.com>
Date: Sun, 21 Jun 2026 05:58:49 +0800
Subject: [PATCH 091/292] fix(hexagon): use padded stride for ssm-conv weights
 (#24470)

---
 ggml/src/ggml-hexagon/htp/ssm-conv.c | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/ggml/src/ggml-hexagon/htp/ssm-conv.c b/ggml/src/ggml-hexagon/htp/ssm-conv.c
index d574da2e2..a48bc9ed8 100644
--- a/ggml/src/ggml-hexagon/htp/ssm-conv.c
+++ b/ggml/src/ggml-hexagon/htp/ssm-conv.c
@@ -183,24 +183,25 @@ static inline void hvx_transpose_32x32_f32(HVX_Vector m[32]) {
 // transposed into VTCM.
 //
 // VTCM layouts (per thread):
-//   src1_T : {d_inner_per_thread, d_conv}   — staged once per launch (small).
-//   src0_T : {d_inner_tile,     ncs}        — staged per d_inner-tile.
+//   src1_T : {d_inner_stride, d_conv}       - staged once per launch (small).
+//   src0_T : {d_inner_tile,     ncs}        - staged per d_inner-tile.
 //
 // d_inner_tile is chosen so that per-thread VTCM stays under the budget.
 // Each thread iterates ceil(d_inner_per_thread d_inner_tile) tiles serially.
 #define HTP_SSM_CONV_VTCM_BUDGET (1u << 20) // 1 MiB per thread
 
-// Scalar transpose: src1 {d_conv, d_inner} (DDR) -> {d_inner_per_thread, d_conv} (VTCM)
+// Scalar transpose: src1 {d_conv, d_inner} (DDR) -> {d_inner_stride, d_conv} (VTCM)
 static inline void transpose_src1(const float * src1_data,
                                   uint32_t      src1_stride_inner,
                                   uint32_t      i1_off,
                                   uint32_t      d_inner_per_thread,
+                                  uint32_t      d_inner_stride,
                                   uint32_t      d_conv,
                                   float *       src1_T) {
     for (uint32_t i = 0; i < d_inner_per_thread; ++i) {
         const float * src_row = src1_data + (i1_off + i) * src1_stride_inner;
         for (uint32_t j = 0; j < d_conv; ++j) {
-            src1_T[j * d_inner_per_thread + i] = src_row[j];
+            src1_T[j * d_inner_stride + i] = src_row[j];
         }
     }
 }
@@ -280,6 +281,7 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
     }
 
     const uint32_t d_inner_per_thread = ir1 - ir0;
+    const uint32_t d_inner_stride     = scctx->nrows_per_thread;
     const uint32_t d_inner_tile       = scctx->d_inner_tile;
 
     const float * src0_data = (const float *) src0->data;
@@ -290,8 +292,8 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
     float * src0_T = (float *)(octx->src0_spad.data + ith * octx->src0_spad.size_per_thread);
     float * src1_T = (float *)(octx->src1_spad.data + ith * octx->src1_spad.size_per_thread);
 
-    // Stage src1 weights once into VTCM in {d_inner_per_thread, d_conv} layout.
-    transpose_src1(src1_data, src1_stride_inner, ir0, d_inner_per_thread, d_conv, src1_T);
+    // Stage src1 weights once into VTCM in {d_inner_stride, d_conv} layout.
+    transpose_src1(src1_data, src1_stride_inner, ir0, d_inner_per_thread, d_inner_stride, d_conv, src1_T);
 
     const uint32_t C_TILE = VLEN_FP32;
 
@@ -314,7 +316,7 @@ static void ssm_conv_thread_f32_f32_hvx(unsigned int nth, unsigned int ith, void
                     HVX_Vector acc = hvx_vec_splat_f32(0.0f);
                     for (uint32_t j = 0; j < d_conv; ++j) {
                         HVX_Vector x = *(const HVX_Vector *) (src0_T + (t + j) * d_inner_tile + cb);
-                        HVX_Vector w = *(const HVX_Vector *) (src1_T + j * d_inner_per_thread + tile_off + cb);
+                        HVX_Vector w = *(const HVX_Vector *) (src1_T + j * d_inner_stride + tile_off + cb);
                         acc          = Q6_Vqf32_vadd_Vqf32Vqf32(acc, Q6_Vqf32_vmpy_VsfVsf(x, w));
                     }
                     HVX_Vector res = Q6_Vsf_equals_Vqf32(acc);
@@ -362,8 +364,7 @@ int op_ssm_conv_f32(struct htp_ops_context * octx) {
             use_hvx = 1;
         }
 
-        scctx.nrows_per_thread  = (d_inner + n_threads - 1) / n_threads;
-        scctx.nrows_per_thread += (scctx.nrows_per_thread & 1);
+        scctx.nrows_per_thread = hex_round_up((d_inner + n_threads - 1) / n_threads, VLEN_FP32);
 
         const uint32_t d_inner_per_thread = scctx.nrows_per_thread;
         const uint32_t ncs                = src0->ne[0];

From c57607016a1ebdd08d269e3378eee5546fc3bf3a Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Sat, 20 Jun 2026 17:43:04 -0500
Subject: [PATCH 092/292] common/json-schema-to-grammar : align spacing rules
 with parsers (#24835)

---
 common/json-schema-to-grammar.cpp     |  46 ++--
 common/peg-parser.cpp                 |   2 +-
 examples/json_schema_to_grammar.py    |  42 ++--
 tests/test-chat.cpp                   |   4 +-
 tests/test-json-schema-to-grammar.cpp | 310 +++++++++++++-------------
 5 files changed, 202 insertions(+), 202 deletions(-)

diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index e2c4d6ce2..b18607cd6 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -233,27 +233,27 @@ struct BuiltinRule {
 };
 
 static std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
-    {"boolean", {"(\"true\" | \"false\") space", {}}},
+    {"boolean", {"(\"true\" | \"false\")", {}}},
     {"decimal-part", {"[0-9]{1,16}", {}}},
     {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}},
-    {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
-    {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
+    {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)?", {"integral-part", "decimal-part"}}},
+    {"integer", {"(\"-\"? integral-part)", {"integral-part"}}},
     {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
-    {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
-    {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
-    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
+    {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? space \"}\"", {"string", "value"}}},
+    {"array", {"\"[\" space ( value (\",\" space value)* )? space \"]\"", {"value"}}},
+    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\"", {}}},
     {"char",   {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
-    {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
-    {"null", {"\"null\" space", {}}},
+    {"string", {"\"\\\"\" char* \"\\\"\"", {"char"}}},
+    {"null", {"\"null\"", {}}},
 };
 
 static std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
     {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
     {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
     {"date-time", {"date \"T\" time", {"date", "time"}}},
-    {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
-    {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
-    {"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
+    {"date-string", {"\"\\\"\" date \"\\\"\"", {"date"}}},
+    {"time-string", {"\"\\\"\" time \"\\\"\"", {"time"}}},
+    {"date-time-string", {"\"\\\"\" date-time \"\\\"\"", {"date-time"}}}
 };
 
 static bool is_reserved_name(const std::string & name) {
@@ -551,16 +551,16 @@ private:
             }
             return join_seq();
         };
-        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space");
+        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\"");
     }
 
     /*
         Returns a rule that matches a JSON string that is none of the provided strings
 
         not_strings({"a"})
-            -> ["] ( [a] char+ | [^"a] char* )? ["] space
+            -> ["] ( [a] char+ | [^"a] char* )? ["]
         not_strings({"and", "also"})
-            -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
+            -> ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["]
     */
     std::string _not_strings(const std::vector<std::string> & strings) {
 
@@ -619,7 +619,7 @@ private:
         if (!trie.is_end_of_string) {
             out << "?";
         }
-        out << " [\"] space";
+        out << " [\"]";
         return out.str();
     }
 
@@ -725,7 +725,7 @@ private:
             rule += " )?";
         }
 
-        rule += " \"}\" space";
+        rule += " space \"}\"";
 
         return rule;
     }
@@ -858,14 +858,14 @@ public:
             return _add_rule(rule_name, _generate_union_rule(name, schema_types));
         }
         if (schema.contains("const")) {
-            return _add_rule(rule_name, _generate_constant_rule(schema["const"]) + " space");
+            return _add_rule(rule_name, _generate_constant_rule(schema["const"]));
         }
         if (schema.contains("enum")) {
             std::vector<std::string> enum_values;
             for (const auto & v : schema["enum"]) {
                 enum_values.push_back(_generate_constant_rule(v));
             }
-            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ") space");
+            return _add_rule(rule_name, "(" + string_join(enum_values, " | ") + ")");
         }
         if ((schema_type.is_null() || schema_type == "object")
                 && (schema.contains("properties") ||
@@ -933,7 +933,7 @@ public:
                     }
                 }
                 if (!enum_intersection.empty()) {
-                    return _add_rule(rule_name, "(" + string_join(enum_intersection, " | ") + ") space");
+                    return _add_rule(rule_name, "(" + string_join(enum_intersection, " | ") + ")");
                 }
             }
             return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
@@ -948,7 +948,7 @@ public:
                     }
                     rule += visit(items[i], name + (name.empty() ? "" : "-") + "tuple-" + std::to_string(i));
                 }
-                rule += " \"]\" space";
+                rule += " space \"]\"";
                 return _add_rule(rule_name, rule);
             }
             std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
@@ -956,7 +956,7 @@ public:
             json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
             int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
 
-            return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
+            return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " space \"]\"");
         }
         if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
             return _visit_pattern(schema["pattern"], rule_name);
@@ -972,7 +972,7 @@ public:
             std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
             int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
             int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
-            return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
+            return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\"");
         }
         if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) {
             int64_t min_value = std::numeric_limits<int64_t>::min();
@@ -990,7 +990,7 @@ public:
             std::stringstream out;
             out << "(";
             build_min_max_int(min_value, max_value, out);
-            out << ") space";
+            out << ")";
             return _add_rule(rule_name, out.str());
         }
         if (schema.empty() || schema_type == "object") {
diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp
index d4b491a80..ff0d24d43 100644
--- a/common/peg-parser.cpp
+++ b/common/peg-parser.cpp
@@ -1342,7 +1342,7 @@ common_peg_parser common_peg_parser_builder::json_object() {
 common_peg_parser common_peg_parser_builder::json_array() {
     return rule("json-array", [this]() {
         auto ws = space();
-        auto elements = sequence({json(), zero_or_more(sequence({literal(","), ws, json()}))});
+        auto elements = sequence({json(), zero_or_more(sequence({ws, literal(","), ws, json()}))});
         return sequence({
             literal("["),
             ws,
diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py
index 077fcfaca..83abd259d 100755
--- a/examples/json_schema_to_grammar.py
+++ b/examples/json_schema_to_grammar.py
@@ -198,18 +198,18 @@ class BuiltinRule:
 SPACE_RULE = '| " " | "\\n"{1,2} [ \\t]{0,20}'
 
 PRIMITIVE_RULES = {
-    'boolean'      : BuiltinRule('("true" | "false") space', []),
+    'boolean'      : BuiltinRule('("true" | "false")', []),
     'decimal-part' : BuiltinRule('[0-9]{1,16}', []),
     'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
-    'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
-    'integer'      : BuiltinRule('("-"? integral-part) space', ['integral-part']),
+    'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?', ['integral-part', 'decimal-part']),
+    'integer'      : BuiltinRule('("-"? integral-part)', ['integral-part']),
     'value'        : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
-    'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
-    'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []),
+    'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? space "}"', ['string', 'value']),
+    'array'        : BuiltinRule('"[" space ( value ("," space value)* )? space "]"', ['value']),
+    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\""', []),
     'char'         : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []),
-    'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
-    'null'         : BuiltinRule('"null" space', []),
+    'string'       : BuiltinRule(r'"\"" char* "\""', ['char']),
+    'null'         : BuiltinRule('"null"', []),
 }
 
 # TODO: support "uri", "email" string formats
@@ -217,9 +217,9 @@ STRING_FORMAT_RULES = {
     'date'            : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
     'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
     'date-time'       : BuiltinRule('date "T" time', ['date', 'time']),
-    'date-string'     : BuiltinRule('"\\"" date "\\"" space', ['date']),
-    'time-string'     : BuiltinRule('"\\"" time "\\"" space', ['time']),
-    'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
+    'date-string'     : BuiltinRule('"\\"" date "\\""', ['date']),
+    'time-string'     : BuiltinRule('"\\"" time "\\""', ['time']),
+    'date-time-string': BuiltinRule('"\\"" date-time "\\""', ['date-time']),
 }
 
 DOTALL = '[\\U00000000-\\U0010FFFF]'
@@ -319,7 +319,7 @@ class SchemaConverter:
                 out.append(f'[^"{"".join(rejects)}] {char_rule}*')
         visit(trie)
 
-        out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space')
+        out.append(f' ){"" if trie.is_end_of_string else "?"} ["]')
         return ''.join(out)
 
     def _add_rule(self, name, rule):
@@ -549,7 +549,7 @@ class SchemaConverter:
         return self._add_rule(
             name,
             to_rule(transform()) if self._raw_pattern \
-                else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space")
+                else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\"")
 
 
     def _resolve_ref(self, ref):
@@ -580,10 +580,10 @@ class SchemaConverter:
             return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type]))
 
         elif 'const' in schema:
-            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space')
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
 
         elif 'enum' in schema:
-            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space'
+            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ')'
             return self._add_rule(rule_name, rule)
 
         elif schema_type in (None, 'object') and \
@@ -624,7 +624,7 @@ class SchemaConverter:
                     enum_intersection &= s
 
                 if enum_intersection:
-                    rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in sorted(enum_intersection))) + ') space'
+                    rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in sorted(enum_intersection))) + ')'
                     return self._add_rule(rule_name, rule)
 
             return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
@@ -638,12 +638,12 @@ class SchemaConverter:
                     ' "," space '.join(
                         self.visit(item, f'{name}{"-" if name else ""}tuple-{i}')
                         for i, item in enumerate(items)) +
-                    ' "]" space')
+                    ' space "]"')
             else:
                 item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
                 min_items = schema.get("minItems", 0)
                 max_items = schema.get("maxItems")
-                return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space')
+                return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' space "]"')
 
         elif schema_type in (None, 'string') and 'pattern' in schema:
             return self._visit_pattern(schema['pattern'], rule_name)
@@ -663,7 +663,7 @@ class SchemaConverter:
             min_len = schema.get('minLength', 0)
             max_len = schema.get('maxLength')
 
-            return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
+            return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\""')
 
         elif schema_type in (None, 'integer') and \
                 ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema):
@@ -680,7 +680,7 @@ class SchemaConverter:
 
             out = ["("]
             _generate_min_max_int(min_value, max_value, out)
-            out.append(") space")
+            out.append(")")
             return self._add_rule(rule_name, ''.join(out))
 
         elif (schema_type == 'object') or (len(schema) == 0):
@@ -765,7 +765,7 @@ class SchemaConverter:
                 rule += ' )'
             rule += ' )?'
 
-        rule += ' "}" space'
+        rule += ' space "}"'
 
         return rule
 
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 902a4c135..30aa35e13 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -5022,14 +5022,14 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
         tst.test("Hello, world!\nWhat's up?").tools({ special_function_tool }).expect(message_assist).expect_reconstruction().run();
 
         tst.test(
-             "```json\n\"42\" \n```")
+             "```json\n\"42\"\n```")
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .json_schema(const_schema)
             .expect_content(R"("42")")
             .run();
 
         tst.test(
-             "\"42\" \n")
+             "\"42\"\n")
             .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
             .json_schema(const_schema)
             .expect_content(R"("42")")
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index b4362852c..f095274cd 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -92,7 +92,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 0
         })""",
         R"""(
-            root ::= ([0] | [1-9] [0-9]{0,15}) space
+            root ::= ([0] | [1-9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -105,7 +105,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 1
         })""",
         R"""(
-            root ::= ([1-9] [0-9]{0,15}) space
+            root ::= ([1-9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -118,7 +118,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 3
         })""",
         R"""(
-            root ::= ([1-2] [0-9]{1,15} | [3-9] [0-9]{0,15}) space
+            root ::= ([1-2] [0-9]{1,15} | [3-9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -131,7 +131,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 9
         })""",
         R"""(
-            root ::= ([1-8] [0-9]{1,15} | [9] [0-9]{0,15}) space
+            root ::= ([1-8] [0-9]{1,15} | [9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -144,7 +144,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 10
         })""",
         R"""(
-            root ::= ([1] ([0-9]{1,15}) | [2-9] [0-9]{1,15}) space
+            root ::= ([1] ([0-9]{1,15}) | [2-9] [0-9]{1,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -157,7 +157,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": 25
         })""",
         R"""(
-            root ::= ([1] [0-9]{2,15} | [2] ([0-4] [0-9]{1,14} | [5-9] [0-9]{0,14}) | [3-9] [0-9]{1,15}) space
+            root ::= ([1] [0-9]{2,15} | [2] ([0-4] [0-9]{1,14} | [5-9] [0-9]{0,14}) | [3-9] [0-9]{1,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -170,7 +170,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 30
         })""",
         R"""(
-            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-2] [0-9] | [3] "0")) space
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-2] [0-9] | [3] "0"))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -183,7 +183,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": -5
         })""",
         R"""(
-            root ::= ("-" ([0-5]) | [0] | [1-9] [0-9]{0,15}) space
+            root ::= ("-" ([0-5]) | [0] | [1-9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -196,7 +196,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minimum": -123
         })""",
         R"""(
-            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0] | [1-9] [0-9]{0,15}) space
+            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0] | [1-9] [0-9]{0,15})
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -209,7 +209,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": -5
         })""",
         R"""(
-            root ::= ("-" ([0-4] [0-9]{1,15} | [5-9] [0-9]{0,15})) space
+            root ::= ("-" ([0-4] [0-9]{1,15} | [5-9] [0-9]{0,15}))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -222,7 +222,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 1
         })""",
         R"""(
-            root ::= ("-" [1-9] [0-9]{0,15} | [0-1]) space
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-1])
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -235,7 +235,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 100
         })""",
         R"""(
-            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-8] [0-9] | [9] [0-9]) | "100") space
+            root ::= ("-" [1-9] [0-9]{0,15} | [0-9] | ([1-8] [0-9] | [9] [0-9]) | "100")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -249,7 +249,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 23
         })""",
         R"""(
-            root ::= ([0-9] | ([1] [0-9] | [2] [0-3])) space
+            root ::= ([0-9] | ([1] [0-9] | [2] [0-3]))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -263,7 +263,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 300
         })""",
         R"""(
-            root ::= (([1] ([5-9]) | [2-9] [0-9]) | ([1-2] [0-9]{2} | [3] "00")) space
+            root ::= (([1] ([5-9]) | [2-9] [0-9]) | ([1-2] [0-9]{2} | [3] "00"))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -277,7 +277,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 30
         })""",
         R"""(
-            root ::= ([5-9] | ([1-2] [0-9] | [3] "0")) space
+            root ::= ([5-9] | ([1-2] [0-9] | [3] "0"))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -291,7 +291,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 42
         })""",
         R"""(
-            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0-9] | ([1-3] [0-9] | [4] [0-2])) space
+            root ::= ("-" ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-1] [0-9] | [2] [0-3])) | [0-9] | ([1-3] [0-9] | [4] [0-2]))
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -305,7 +305,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maximum": 10
         })""",
         R"""(
-            root ::= ("-" ([0-9] | "10") | [0-9] | "10") space
+            root ::= ("-" ([0-9] | "10") | [0-9] | "10")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -333,17 +333,17 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         "empty schema (object)",
         "{}",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
             root ::= object
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -361,17 +361,17 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             date ::= [0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )
-            date-string ::= "\"" date "\"" space
+            date-string ::= "\"" date "\""
             date-time ::= date "T" time
-            date-time-string ::= "\"" date-time "\"" space
-            root ::= "[" space tuple-0 "," space uuid "," space tuple-2 "," space tuple-3 "]" space
+            date-time-string ::= "\"" date-time "\""
+            root ::= "[" space tuple-0 "," space uuid "," space tuple-2 "," space tuple-3 space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
             time ::= ([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )
-            time-string ::= "\"" time "\"" space
+            time-string ::= "\"" time "\""
             tuple-0 ::= date-string
             tuple-2 ::= time-string
             tuple-3 ::= date-time-string
-            uuid ::= "\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space
+            uuid ::= "\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\""
         )"""
     });
 
@@ -383,7 +383,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "\"" char* "\"" space
+            root ::= "\"" char* "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -397,7 +397,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "\"" char+ "\"" space
+            root ::= "\"" char+ "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -411,7 +411,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "\"" char{3,} "\"" space
+            root ::= "\"" char{3,} "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -425,7 +425,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "\"" char{0,3} "\"" space
+            root ::= "\"" char{0,3} "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -440,7 +440,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "\"" char{1,4} "\"" space
+            root ::= "\"" char{1,4} "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -452,7 +452,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "boolean"
         })""",
         R"""(
-            root ::= ("true" | "false") space
+            root ::= ("true" | "false")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -465,7 +465,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            root ::= ("-"? integral-part) space
+            root ::= ("-"? integral-part)
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -477,7 +477,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "const": "foo"
         })""",
         R"""(
-            root ::= "\"foo\"" space
+            root ::= "\"foo\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -489,7 +489,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "const": 123
         })""",
         R"""(
-            root ::= "123" space
+            root ::= "123"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -501,7 +501,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "enum": ["red", "amber", "green", null, 42, ["foo"]]
         })""",
         R"""(
-            root ::= ("\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]") space
+            root ::= ("\"red\"" | "\"amber\"" | "\"green\"" | "null" | "42" | "[\"foo\"]")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -515,9 +515,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "[" space (string ("," space string)*)? "]" space
+            root ::= "[" space (string ("," space string)*)? space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -529,12 +529,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": { "type": "string" }
         })""",
         R"""(
-            alternative-0 ::= "[" space (string ("," space string)*)? "]" space
+            alternative-0 ::= "[" space (string ("," space string)*)? space "]"
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            null ::= "null" space
+            null ::= "null"
             root ::= alternative-0 | null
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -546,9 +546,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "[" space string "]" space
+            root ::= "[" space string space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -562,10 +562,10 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "[" space string "," space number "]" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "[" space string "," space number space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -577,18 +577,18 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "items": {}
         })""",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             item ::= object
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
-            root ::= "[" space (item ("," space item)*)? "]" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
+            root ::= "[" space (item ("," space item)*)? space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -602,18 +602,18 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "prefixItems": { "type": "string" }
         })""",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             item ::= object
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
-            root ::= "[" space (item ("," space item)*)? "]" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
+            root ::= "[" space (item ("," space item)*)? space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -627,7 +627,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            root ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
+            root ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -642,8 +642,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "minItems": 2
         })""",
         R"""(
-            boolean ::= ("true" | "false") space
-            root ::= "[" space boolean ("," space boolean)+ "]" space
+            boolean ::= ("true" | "false")
+            root ::= "[" space boolean ("," space boolean)+ space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -658,8 +658,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 0
         })""",
         R"""(
-            boolean ::= ("true" | "false") space
-            root ::= "[" space  "]" space
+            boolean ::= ("true" | "false")
+            root ::= "[" space  space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -674,8 +674,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 1
         })""",
         R"""(
-            boolean ::= ("true" | "false") space
-            root ::= "[" space boolean? "]" space
+            boolean ::= ("true" | "false")
+            root ::= "[" space boolean? space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -690,8 +690,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 2
         })""",
         R"""(
-            boolean ::= ("true" | "false") space
-            root ::= "[" space (boolean ("," space boolean)?)? "]" space
+            boolean ::= ("true" | "false")
+            root ::= "[" space (boolean ("," space boolean)?)? space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -708,11 +708,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             decimal-part ::= [0-9]{1,16}
-            integer ::= ("-"? integral-part) space
+            integer ::= ("-"? integral-part)
             integral-part ::= [0] | [1-9] [0-9]{0,15}
             item ::= number | integer
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "[" space item ("," space item){2,4} "]" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "[" space item ("," space item){2,4} space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -730,8 +730,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 5
         })""",
         R"""(
-            item ::= ("-" ([0-9] | "1" [0-2]) | [0-9] | ([1-8] [0-9] | [9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7])) space
-            root ::= "[" space item ("," space item){2,4} "]" space
+            item ::= ("-" ([0-9] | "1" [0-2]) | [0-9] | ([1-8] [0-9] | [9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7]))
+            root ::= "[" space item ("," space item){2,4} space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -749,8 +749,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "maxItems": 5
         })""",
         R"""(
-            item ::= (([1] ([2-9]) | [2-9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7])) space
-            root ::= "[" space item ("," space item){2,4} "]" space
+            item ::= (([1] ([2-9]) | [2-9] [0-9]) | ([1] [0-9]{2} | [2] "0" [0-7]))
+            root ::= "[" space item ("," space item){2,4} space "]"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -763,7 +763,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "pattern": "^abc?d*efg+(hij)?kl$"
         })""",
         R"""(
-            root ::= "\"" ("ab" "c"? "d"* "ef" "g"+ ("hij")? "kl") "\"" space
+            root ::= "\"" ("ab" "c"? "d"* "ef" "g"+ ("hij")? "kl") "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -776,7 +776,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "pattern": "^\\[\\]\\{\\}\\(\\)\\|\\+\\*\\?$"
         })""",
         R"""(
-            root ::= "\"" ("[]{}()|+*?") "\"" space
+            root ::= "\"" ("[]{}()|+*?") "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -789,7 +789,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "pattern": "^\"$"
         })""",
         R"""(
-            root ::= "\"" ("\"") "\"" space
+            root ::= "\"" ("\"") "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -802,7 +802,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "pattern": "^A|B|C|D$"
         })""",
         R"""(
-            root ::= "\"" ("A" | "B" | "C" | "D") "\"" space
+            root ::= "\"" ("A" | "B" | "C" | "D") "\""
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -816,7 +816,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             dot ::= [^\x0A\x0D]
-            root ::= "\"" (("(" root-1{1,3} ")")? root-1{3,3} "-" root-1{4,4} " " "a"{3,5} "nd" dot dot dot) "\"" space
+            root ::= "\"" (("(" root-1{1,3} ")")? root-1{3,3} "-" root-1{4,4} " " "a"{3,5} "nd" dot dot dot) "\""
             root-1 ::= [0-9]
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
@@ -845,9 +845,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             b-kv ::= "\"b\"" space ":" space string
             c-kv ::= "\"c\"" space ":" space string
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "{" space b-kv "," space c-kv "," space a-kv "}" space
+            root ::= "{" space b-kv "," space c-kv "," space a-kv space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -865,9 +865,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             a-kv ::= "\"a\"" space ":" space string
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "{" space  (a-kv )? "}" space
+            root ::= "{" space  (a-kv )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -889,9 +889,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             b-rest ::= ( "," space c-kv )?
             c-kv ::= "\"c\"" space ":" space string
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? "}" space
+            root ::= "{" space  (a-kv a-rest | b-kv b-rest | c-kv )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -915,9 +915,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             d-kv ::= "\"d\"" space ":" space string
             d-rest ::= ( "," space c-kv )?
-            root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            root ::= "{" space b-kv "," space a-kv ( "," space ( d-kv d-rest | c-kv ) )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -930,14 +930,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             additional-kv ::= string ":" space additional-value
-            additional-value ::= "[" space (number ("," space number)*)? "]" space
+            additional-value ::= "[" space (number ("," space number)*)? space "]"
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space  (additional-kv ( "," space additional-kv )* )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space  (additional-kv ( "," space additional-kv )* )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -949,17 +949,17 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "additionalProperties": true
         })""",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
             root ::= object
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -971,17 +971,17 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "object"
         })""",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
             root ::= object
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -994,7 +994,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "additionalProperties": false
         })""",
         R"""(
-            root ::= "{" space  "}" space
+            root ::= "{" space  space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1012,15 +1012,15 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             a-kv ::= "\"a\"" space ":" space number
-            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["] space
+            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["]
             additional-kv ::= additional-k ":" space string
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space a-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space a-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -1037,13 +1037,13 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             a-kv ::= "\"a\"" space ":" space number
             a-rest ::= ( "," space additional-kv )*
-            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["] space
+            additional-k ::= ["] ( [a] char+ | [^"a] char* )? ["]
             additional-kv ::= additional-k ":" space number
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space  (a-kv a-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space  (a-kv a-rest | additional-kv ( "," space additional-kv )* )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1061,7 +1061,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "additionalProperties": {"type": "number"}
         })""",
         R"""(
-            additional-k ::= ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["] space
+            additional-k ::= ["] ( [a] ([l] ([s] ([o] char+ | [^"o] char*) | [^"s] char*) | [n] ([d] char+ | [^"d] char*) | [^"ln] char*) | [^"a] char* )? ["]
             additional-kv ::= additional-k ":" space number
             also-kv ::= "\"also\"" space ":" space number
             also-rest ::= ( "," space additional-kv )*
@@ -1069,8 +1069,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space and-kv ( "," space ( also-kv also-rest | additional-kv ( "," space additional-kv )* ) )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space and-kv ( "," space ( also-kv also-rest | additional-kv ( "," space additional-kv )* ) )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1090,13 +1090,13 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             -rest ::= ( "," space a-kv )? a-rest
             a-kv ::= "\"a\"" space ":" space integer
             a-rest ::= ( "," space additional-kv )*
-            additional-k ::= ["] ( [a] char+ | [^"a] char* ) ["] space
+            additional-k ::= ["] ( [a] char+ | [^"a] char* ) ["]
             additional-kv ::= additional-k ":" space integer
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            integer ::= ("-"? integral-part) space
+            integer ::= ("-"? integral-part)
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            root ::= ("-"? integral-part) space
-            root0 ::= "{" space  (-kv -rest | a-kv a-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            root ::= ("-"? integral-part)
+            root0 ::= "{" space  (-kv -rest | a-kv a-rest | additional-kv ( "," space additional-kv )* )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1116,12 +1116,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             a-rest ::= ( "," space aa-kv )? aa-rest
             aa-kv ::= "\"aa\"" space ":" space integer
             aa-rest ::= ( "," space additional-kv )*
-            additional-k ::= ["] ( [a] ([a] char+ | [^"a] char*) | [^"a] char* )? ["] space
+            additional-k ::= ["] ( [a] ([a] char+ | [^"a] char*) | [^"a] char* )? ["]
             additional-kv ::= additional-k ":" space integer
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            integer ::= ("-"? integral-part) space
+            integer ::= ("-"? integral-part)
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            root ::= "{" space  (a-kv a-rest | aa-kv aa-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            root ::= "{" space  (a-kv a-rest | aa-kv aa-rest | additional-kv ( "," space additional-kv )* )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1141,12 +1141,12 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             ab-rest ::= ( "," space ac-kv )? ac-rest
             ac-kv ::= "\"ac\"" space ":" space integer
             ac-rest ::= ( "," space additional-kv )*
-            additional-k ::= ["] ( [a] ([b] char+ | [c] char+ | [^"bc] char*) | [^"a] char* )? ["] space
+            additional-k ::= ["] ( [a] ([b] char+ | [c] char+ | [^"bc] char*) | [^"a] char* )? ["]
             additional-kv ::= additional-k ":" space integer
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            integer ::= ("-"? integral-part) space
+            integer ::= ("-"? integral-part)
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            root ::= "{" space  (ab-kv ab-rest | ac-kv ac-rest | additional-kv ( "," space additional-kv )* )? "}" space
+            root ::= "{" space  (ab-kv ab-rest | ac-kv ac-rest | additional-kv ( "," space additional-kv )* )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1173,11 +1173,11 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         })""",
         R"""(
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
-            ref-definitions-foo ::= "{" space ref-definitions-foo-a-kv "}" space
+            ref-definitions-foo ::= "{" space ref-definitions-foo-a-kv space "}"
             ref-definitions-foo-a-kv ::= "\"a\"" space ":" space string
             root ::= ref-definitions-foo
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -1204,10 +1204,10 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             alternative-1 ::= ref-definitions-bar
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            ref-definitions-bar ::= "{" space  (ref-definitions-bar-b-kv )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            ref-definitions-bar ::= "{" space  (ref-definitions-bar-b-kv )? space "}"
             ref-definitions-bar-b-kv ::= "\"b\"" space ":" space number
-            ref-definitions-foo ::= "{" space  (ref-definitions-foo-a-kv )? "}" space
+            ref-definitions-foo ::= "{" space  (ref-definitions-foo-a-kv )? space "}"
             ref-definitions-foo-a-kv ::= "\"a\"" space ":" space number
             root ::= alternative-0 | alternative-1
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
@@ -1241,14 +1241,14 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             b ::= b-0 | boolean
             b-0 ::= string
             b-kv ::= "\"b\"" space ":" space b
-            boolean ::= ("true" | "false") space
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space  (a-kv a-rest | b-kv )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space  (a-kv a-rest | b-kv )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
         )"""
     });
 
@@ -1290,8 +1290,8 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             d-rest ::= ( "," space c-kv )?
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            root ::= "{" space a-kv "," space b-kv ( "," space ( d-kv d-rest | c-kv ) )? space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1311,7 +1311,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             }
         })""",
         R"""(
-            root ::= ("\"a\"" | "\"b\"") space
+            root ::= ("\"a\"" | "\"b\"")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1336,7 +1336,7 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             }
         })""",
         R"""(
-            root ::= ("\"b\"" | "\"c\"") space
+            root ::= ("\"b\"" | "\"c\"")
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1378,13 +1378,13 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         R"""(
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            number- ::= "{" space number-number-kv "}" space
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            number- ::= "{" space number-number-kv space "}"
             number-kv ::= "\"number\"" space ":" space number-
-            number-number ::= "{" space number-number-root-kv "}" space
+            number-number ::= "{" space number-number-root-kv space "}"
             number-number-kv ::= "\"number\"" space ":" space number-number
             number-number-root-kv ::= "\"root\"" space ":" space number
-            root ::= "{" space number-kv "}" space
+            root ::= "{" space number-kv space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1394,17 +1394,17 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
         "description only (no type) treated as unconstrained",
         R"""({"description": "The 0-based index of the last line to be retrieved (inclusive). If None, read until the end of the file."})""",
         R"""(
-            array ::= "[" space ( value ("," space value)* )? "]" space
-            boolean ::= ("true" | "false") space
+            array ::= "[" space ( value ("," space value)* )? space "]"
+            boolean ::= ("true" | "false")
             char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})
             decimal-part ::= [0-9]{1,16}
             integral-part ::= [0] | [1-9] [0-9]{0,15}
-            null ::= "null" space
-            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space
-            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space
+            null ::= "null"
+            number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)?
+            object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? space "}"
             root ::= value
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
-            string ::= "\"" char* "\"" space
+            string ::= "\"" char* "\""
             value ::= object | array | string | number | boolean | null
         )"""
     });
@@ -1428,9 +1428,9 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             "type": "object"
         })""",
         R"""(
-            code ::= "\" \\r \\n \\\" \\\\ \"" space
+            code ::= "\" \\r \\n \\\" \\\\ \""
             code-kv ::= "\"code\"" space ":" space code
-            root ::= "{" space code-kv "}" space
+            root ::= "{" space code-kv space "}"
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
@@ -1547,7 +1547,7 @@ int main() {
                 "pattern": "^(?:foo|bar)baz$"
             })""",
             R"""(
-                root ::= "\"" (("foo" | "bar") "baz") "\"" space
+                root ::= "\"" (("foo" | "bar") "baz") "\""
                 space ::= | " " | "\n"{1,2} [ \t]{0,20}
             )""",
         });
@@ -1560,7 +1560,7 @@ int main() {
                 "pattern": "^(?:(?:ab)+c)?d$"
             })""",
             R"""(
-                root ::= "\"" ((("ab")+ "c")? "d") "\"" space
+                root ::= "\"" ((("ab")+ "c")? "d") "\""
                 space ::= | " " | "\n"{1,2} [ \t]{0,20}
             )""",
         });

From 063d9c156e816ae3cf62db01f429a07a099afe97 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Sat, 20 Jun 2026 21:15:06 -0500
Subject: [PATCH 093/292] common/peg : refactor until gbnf grammar generation
 (#24839)

* common/peg : refactor until gbnf grammar into an ac automaton

* cont : add a test with multiple strings

* cont : pad state with 0s so rules line up

* cont : clean up comments

* cont : use set everywhere

* cont : inline state num string padding

* cont : add a ref to PR

* cont : fix regression in server-tools.cpp
---
 common/peg-parser.cpp                     | 194 +++++++++++++---------
 common/peg-parser.h                       |   4 +-
 tests/peg-parser/test-gbnf-generation.cpp |  80 ++++++++-
 tools/server/server-tools.cpp             |   1 +
 4 files changed, 199 insertions(+), 80 deletions(-)

diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp
index ff0d24d43..506b90245 100644
--- a/common/peg-parser.cpp
+++ b/common/peg-parser.cpp
@@ -6,13 +6,14 @@
 #include "unicode.h"
 
 #include <algorithm>
+#include <deque>
 #include <initializer_list>
 #include <map>
 #include <memory>
 #include <nlohmann/json.hpp>
 #include <regex>
+#include <set>
 #include <stdexcept>
-#include <unordered_set>
 
 // Trick to catch missing branches
 template <typename T>
@@ -88,40 +89,7 @@ struct trie {
         return match_result{match_result::NO_MATCH};
     }
 
-    struct prefix_and_next {
-        std::vector<uint32_t> prefix;
-        std::vector<uint32_t> next_chars;
-    };
-
-    std::vector<prefix_and_next> collect_prefix_and_next() {
-        std::vector<uint32_t>        prefix;
-        std::vector<prefix_and_next> result;
-        collect_prefix_and_next(0, prefix, result);
-        return result;
-    }
-
   private:
-    void collect_prefix_and_next(size_t index, std::vector<uint32_t> & prefix, std::vector<prefix_and_next> & out) {
-        if (!nodes[index].is_word) {
-            if (!nodes[index].children.empty()) {
-                std::vector<uint32_t> chars;
-                chars.reserve(nodes[index].children.size());
-                for (const auto & p : nodes[index].children) {
-                    chars.push_back(p.first);
-                }
-                out.emplace_back(prefix_and_next{prefix, chars});
-            }
-        }
-
-        for (const auto & p : nodes[index].children) {
-            uint32_t ch = p.first;
-            auto child = p.second;
-            prefix.push_back(ch);
-            collect_prefix_and_next(child, prefix, out);
-            prefix.pop_back();
-        }
-    }
-
     size_t create_node() {
         size_t index = nodes.size();
         nodes.emplace_back();
@@ -153,6 +121,65 @@ struct trie {
     }
 };
 
+// Aho-Corasick automaton
+struct aho_corasick {
+    trie                t;
+    std::vector<size_t> fail;      // failure links
+    std::vector<size_t> order;     // states in BFS order
+    std::vector<bool>   terminal;  // match states (directly or via a suffix link)
+    std::set<uint32_t>  alphabet;  // every character with a transition
+
+    aho_corasick(const std::vector<std::string> & strings) : t(strings) {
+        const auto & nodes = t.nodes;
+        const size_t n = nodes.size();
+
+        fail.assign(n, 0);
+        order.reserve(n);
+
+        std::deque<size_t> queue{ 0 };
+        while (!queue.empty()) {
+            size_t u = queue.front();
+            queue.pop_front();
+            order.push_back(u);
+            for (const auto & [ch, v] : nodes[u].children) {
+                if (u != 0) {
+                    size_t f = fail[u];
+                    while (f && nodes[f].children.find(ch) == nodes[f].children.end()) {
+                        f = fail[f];
+                    }
+                    auto it = nodes[f].children.find(ch);
+                    fail[v] = (it != nodes[f].children.end() && it->second != v) ? it->second : 0;
+                }
+                queue.push_back(v);
+            }
+        }
+
+        terminal.assign(n, false);
+        for (size_t u : order) {
+            terminal[u] = nodes[u].is_word || (u != 0 && terminal[fail[u]]);
+        }
+
+        for (const auto & node : nodes) {
+            for (const auto & [ch, v] : node.children) {
+                alphabet.insert(ch);
+            }
+        }
+    }
+
+    size_t num_states()          const { return t.nodes.size(); }
+    bool   is_terminal(size_t s) const { return terminal[s]; }
+
+    // follow failure links until a transition on `ch` exists.
+    size_t next(size_t state, uint32_t ch) const {
+        const auto & nodes = t.nodes;
+        while (state && nodes[state].children.find(ch) == nodes[state].children.end()) {
+            state = fail[state];
+        }
+        auto it = nodes[state].children.find(ch);
+        return it != nodes[state].children.end() ? it->second : 0;
+    }
+};
+
 static std::pair<uint32_t, size_t> parse_hex_escape(const std::string & str, size_t pos, int hex_count) {
     if (pos + hex_count > str.length()) {
         return {0, 0};
@@ -992,12 +1019,12 @@ void common_peg_arena::resolve_refs() {
 }
 
 std::string common_peg_arena::dump(common_peg_parser_id id) const {
-    std::unordered_set<common_peg_parser_id> visited;
+    std::set<common_peg_parser_id> visited;
     return dump_impl(id, visited);
 }
 
 std::string common_peg_arena::dump_impl(common_peg_parser_id                       id,
-                                        std::unordered_set<common_peg_parser_id> & visited) const {
+                                        std::set<common_peg_parser_id> & visited) const {
     // Check for cycles
     if (visited.count(id)) {
         return "[cycle]";
@@ -1502,61 +1529,74 @@ static std::string gbnf_escape_char_class(uint32_t c) {
     return std::string(buf);
 }
 
-static std::string gbnf_excluding_pattern(const std::vector<std::string> & strings) {
-    trie matcher(strings);
-    auto pieces = matcher.collect_prefix_and_next();
+// GBNF grammar matching strings that contain no string in `strings` as a
+// substring. Emits the complement of an Aho-Corasick automaton DFA and returns
+// the start state rule name.
+//
+// ref: https://github.com/ggml-org/llama.cpp/pull/24839
+static std::string gbnf_excluding_grammar(const common_grammar_builder & builder,
+                                          const std::string &            prefix,
+                                          const std::vector<std::string> & strings) {
+    aho_corasick ac(strings);
 
-    std::string pattern;
-    std::string trailing;  // optional proper-prefix of a delimiter, allowed only at the very end
-    for (size_t i = 0; i < pieces.size(); ++i) {
-        if (i > 0) {
-            pattern += " | ";
+    auto state_name = [&](size_t s) -> std::string {
+        if (s == 0) {
+            return prefix;
         }
+        std::string num = std::to_string(s);
+        num = num.size() == 1 ? ("0" + num) : num;
+        return prefix + "-" + num;
+    };
 
-        const auto & pre = pieces[i].prefix;
-        const auto & chars = pieces[i].next_chars;
-
-        std::string cls;
-        cls.reserve(chars.size());
+    auto char_class = [](const std::vector<uint32_t> & chars, bool negate) {
+        std::string s = negate ? "[^" : "[";
         for (uint32_t ch : chars) {
-            cls += gbnf_escape_char_class(ch);
+            s += gbnf_escape_char_class(ch);
+        }
+        return s + "]";
+    };
+
+    for (size_t q = 0; q < ac.num_states(); q++) {
+        if (ac.is_terminal(q)) {
+            continue; // match states are dropped
         }
 
-        if (!pre.empty()) {
-            std::string pre_literal = gbnf_format_literal(common_unicode_cpts_to_utf8(pre));
-            pattern += pre_literal + " [^" + cls + "]";
-            // Each interior alternative consumes a delimiter-prefix plus a disambiguating
-            // char, so the repetition alone cannot match a value that *ends* on a proper
-            // prefix of a delimiter (e.g. a trailing "\n" when the delimiter is
-            // "\n</parameter>\n"). The runtime until() (greedy first-match) accepts such
-            // values, so without this the grammar would reject input the parser accepts.
-            // Allow the value to terminate on any proper prefix as an optional tail.
-            // This makes the grammar a slight superset of the runtime language (a value
-            // may end on the longest prefix, which greedy first-match would not itself
-            // produce); harmless for constrained generation, which only needs to admit
-            // every runtime-valid string.
-            if (!trailing.empty()) {
-                trailing += " | ";
+        std::map<size_t, std::vector<uint32_t>> buckets;
+        std::vector<uint32_t> excluded;
+        for (uint32_t c : ac.alphabet) {
+            size_t d = ac.next(q, c);
+            if (ac.is_terminal(d)) {
+                excluded.push_back(c); // completes a forbidden string -> omit
+            } else if (d != 0) {
+                buckets[d].push_back(c); // specific non-root destination
+                excluded.push_back(c);
             }
-            trailing += pre_literal;
-        } else {
-            pattern += "[^" + cls + "]";
         }
+
+        std::string rhs = "|"; // every state is accepting
+        for (const auto & [d, chars] : buckets) {
+            rhs += " " + char_class(chars, false) + " " + state_name(d) + " |";
+        }
+        rhs += " " + char_class(excluded, true) + " " + state_name(0);
+
+        builder.add_rule(state_name(q), rhs);
     }
 
-    std::string result = "(" + pattern + ")*";
-    if (!trailing.empty()) {
-        result += " (" + trailing + ")?";
+    // An empty delimiter makes the start state terminal. Emit an entry rule
+    // that matches nothing so the returned reference stays valid.
+    if (ac.is_terminal(0)) {
+        builder.add_rule(prefix, "|");
     }
-    return result;
+
+    return state_name(0);
 }
 
-static std::unordered_set<std::string> collect_reachable_rules(
+static std::set<std::string> collect_reachable_rules(
     const common_peg_arena & arena,
     const common_peg_parser_id & rule
 ) {
-    std::unordered_set<std::string> reachable;
-    std::unordered_set<std::string> visited;
+    std::set<std::string> reachable;
+    std::set<std::string> visited;
 
     std::function<void(common_peg_parser_id)> visit = [&](common_peg_parser_id id) {
         const auto & parser = arena.get(id);
@@ -1765,7 +1805,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 if (p.delimiters.empty()) {
                     return ".*";
                 }
-                return gbnf_excluding_pattern(p.delimiters);
+                return gbnf_excluding_grammar(builder, "until-" + std::to_string(id), p.delimiters);
             } else if constexpr (std::is_same_v<T, common_peg_schema_parser>) {
                 if (schema_delegates(p)) {
                     return to_gbnf(p.child);
@@ -1789,7 +1829,7 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
     };
 
     // Collect reachable rules
-    std::unordered_set<std::string> reachable_rules;
+    std::set<std::string> reachable_rules;
 
     if (lazy) {
         // Collect rules reachable from trigger rules
diff --git a/common/peg-parser.h b/common/peg-parser.h
index b6bb05214..132173a64 100644
--- a/common/peg-parser.h
+++ b/common/peg-parser.h
@@ -3,8 +3,8 @@
 #include <nlohmann/json_fwd.hpp>
 
 #include <memory>
+#include <set>
 #include <unordered_map>
-#include <unordered_set>
 #include <string>
 #include <string_view>
 #include <functional>
@@ -335,7 +335,7 @@ class common_peg_arena {
     friend class common_peg_parser_builder;
 
   private:
-    std::string dump_impl(common_peg_parser_id id, std::unordered_set<common_peg_parser_id> & visited) const;
+    std::string dump_impl(common_peg_parser_id id, std::set<common_peg_parser_id> & visited) const;
 
     common_peg_parser_id add_parser(common_peg_parser_variant parser);
     void add_rule(const std::string & name, common_peg_parser_id id);
diff --git a/tests/peg-parser/test-gbnf-generation.cpp b/tests/peg-parser/test-gbnf-generation.cpp
index 00111e6a1..45d692ca6 100644
--- a/tests/peg-parser/test-gbnf-generation.cpp
+++ b/tests/peg-parser/test-gbnf-generation.cpp
@@ -129,8 +129,86 @@ void test_gbnf_generation(testing &t) {
         });
 
         assert_gbnf_equal(t, R"""(
-            root ::= ([^<] | "<" [^/] | "</" [^t] | "</t" [^a] | "</ta" [^g] | "</tag" [^>])* ("<" | "</" | "</t" | "</ta" | "</tag")?
+            root ::= until-0
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
+            until-0 ::= | [<] until-0-01 | [^<] until-0
+            until-0-01 ::= | [<] until-0-01 | [/] until-0-02 | [^/<] until-0
+            until-0-02 ::= | [<] until-0-01 | [t] until-0-03 | [^<t] until-0
+            until-0-03 ::= | [<] until-0-01 | [a] until-0-04 | [^<a] until-0
+            until-0-04 ::= | [<] until-0-01 | [g] until-0-05 | [^<g] until-0
+            until-0-05 ::= | [<] until-0-01 | [^<>] until-0
+        )""", gbnf);
+    });
+
+    t.test("until grammar overlapping delimiter", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.until("\n</parameter>\n");
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            root ::= until-0
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+            until-0 ::= | [\n] until-0-01 | [^\n] until-0
+            until-0-01 ::= | [\n] until-0-01 | [<] until-0-02 | [^\n<] until-0
+            until-0-02 ::= | [\n] until-0-01 | [/] until-0-03 | [^\n/] until-0
+            until-0-03 ::= | [\n] until-0-01 | [p] until-0-04 | [^\np] until-0
+            until-0-04 ::= | [\n] until-0-01 | [a] until-0-05 | [^\na] until-0
+            until-0-05 ::= | [\n] until-0-01 | [r] until-0-06 | [^\nr] until-0
+            until-0-06 ::= | [\n] until-0-01 | [a] until-0-07 | [^\na] until-0
+            until-0-07 ::= | [\n] until-0-01 | [m] until-0-08 | [^\nm] until-0
+            until-0-08 ::= | [\n] until-0-01 | [e] until-0-09 | [^\ne] until-0
+            until-0-09 ::= | [\n] until-0-01 | [t] until-0-10 | [^\nt] until-0
+            until-0-10 ::= | [\n] until-0-01 | [e] until-0-11 | [^\ne] until-0
+            until-0-11 ::= | [\n] until-0-01 | [r] until-0-12 | [^\nr] until-0
+            until-0-12 ::= | [\n] until-0-01 | [>] until-0-13 | [^\n>] until-0
+            until-0-13 ::= | [^\n] until-0
+        )""", gbnf);
+    });
+
+    // DeepSeek-V3.2 tag prefix. The DSML token (｜DSML｜) embeds U+FF5C,
+    // so the delimiter mixes ASCII and multi-byte codepoints.
+    t.test("until grammar unicode delimiter", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.until("<｜DSML｜");
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            root ::= until-0
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+            until-0 ::= | [<] until-0-01 | [^<] until-0
+            until-0-01 ::= | [<] until-0-01 | [\uFF5C] until-0-02 | [^<\uFF5C] until-0
+            until-0-02 ::= | [<] until-0-01 | [D] until-0-03 | [^<D] until-0
+            until-0-03 ::= | [<] until-0-01 | [S] until-0-04 | [^<S] until-0
+            until-0-04 ::= | [<] until-0-01 | [M] until-0-05 | [^<M] until-0
+            until-0-05 ::= | [<] until-0-01 | [L] until-0-06 | [^<L] until-0
+            until-0-06 ::= | [<] until-0-01 | [^<\uFF5C] until-0
+        )""", gbnf);
+    });
+
+    t.test("until grammar multiple delimiters", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.until_one_of({"ab", "cd", "ef"});
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            root ::= until-0
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+            until-0 ::= | [a] until-0-01 | [c] until-0-03 | [e] until-0-05 | [^ace] until-0
+            until-0-01 ::= | [a] until-0-01 | [c] until-0-03 | [e] until-0-05 | [^abce] until-0
+            until-0-03 ::= | [a] until-0-01 | [c] until-0-03 | [e] until-0-05 | [^acde] until-0
+            until-0-05 ::= | [a] until-0-01 | [c] until-0-03 | [e] until-0-05 | [^acef] until-0
         )""", gbnf);
     });
 
diff --git a/tools/server/server-tools.cpp b/tools/server/server-tools.cpp
index 97433fe4b..95662d4ec 100644
--- a/tools/server/server-tools.cpp
+++ b/tools/server/server-tools.cpp
@@ -11,6 +11,7 @@
 #include <cstring>
 #include <climits>
 #include <algorithm>
+#include <unordered_set>
 
 namespace fs = std::filesystem;
 

From f202c0a4573c7b25524c3ce8dde3dd51015dfcd1 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 21 Jun 2026 10:21:48 +0800
Subject: [PATCH 094/292] mtp init -2

---
 gpttype_adapter.cpp | 2 +-
 requirements.txt    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 202334038..65b48f276 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -893,7 +893,7 @@ static void mtp_decoding_setup(llama_model * main_model, llama_context * main_ct
     }
 
     draft_is_mtp = true;
-    speculative_state_setup(main_ctx, mtp_ctx_params, -1, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+    speculative_state_setup(main_ctx, mtp_ctx_params, -2, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
 }
 
 //loads a model for speculative decoding.
diff --git a/requirements.txt b/requirements.txt
index 45207f264..6e36a3f72 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 numpy>=1.24.4
 sentencepiece>=0.1.98
-transformers>=4.34.0
+transformers[torch]>=4.34.0
 gguf>=0.1.0
 customtkinter>=5.1.0
 protobuf>=4.21.0

From d789527482d925156d7c4adfecebf5fb8481e0ee Mon Sep 17 00:00:00 2001
From: YiChen Lv <63285796+forforever73@users.noreply.github.com>
Date: Sun, 21 Jun 2026 16:33:18 +0800
Subject: [PATCH 095/292] spec : Support Step3.5/3.7 flash mtp3 (#24340)

* add mtp_layer_offset + include nextn flags in graph reuse

* add llama_set_mtp_layer_offset + llama_model_n_nextn_layer API

* offset head select + require all MTP blocks

* speculative multi-head process()

* speculative multi-head draft()

* gather outputs via inp_out_ids

* cleanup

* fix core

* minor cleanup

* merged draft_multi_head into draft()

* mtp rename nextn

* Apply suggestions from code review

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* clean-up comments

* fix for multi seq

* apply suggestions && chain-heads comment

* add a reference for chain_heads discussion

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---
 common/speculative.cpp | 137 ++++++++++++++++++++++++++++++-----------
 include/llama.h        |  17 ++---
 src/llama-context.cpp  |   8 +++
 src/llama-context.h    |   1 +
 src/llama-cparams.h    |   2 +
 src/llama-ext.h        |   5 ++
 src/llama-graph.h      |  11 +++-
 src/llama-model.cpp    |   4 ++
 src/models/step35.cpp  |  55 ++++++++---------
 9 files changed, 167 insertions(+), 73 deletions(-)

diff --git a/common/speculative.cpp b/common/speculative.cpp
index 9c20585dc..3c38ae2b0 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -905,7 +905,13 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
     int32_t n_embd = 0;
 
-    bool is_mem_shared = false;
+    // One MTP draft driver, three modes (set once in the ctor):
+    //   is_mem_shared (gemma4): shares the target KV, runs all heads in one graph.
+    //   chain_heads (step35): n_mtp_layers trained heads, one per draft step.
+    //   neither (qwen35 / qwen35moe): a single trained MTP head.
+    int32_t n_mtp_layers  = 1;
+    bool    is_mem_shared = false;   // gemma4
+    bool    chain_heads   = false;   // derived in the ctor: n_mtp_layers > 1 && !is_mem_shared
 
     // Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
     // The last h-row of one process() call needs the first token of the NEXT
@@ -920,10 +926,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
     std::vector<std::vector<float>> verify_h;
     std::vector<int32_t> verify_h_rows;
 
-    // Per-seq draft length from the last draft() call, used in accept() to
-    // roll back ctx_dft's recurrent state past the AR draft's redundant
-    // pre-advancement before process() mirrored the verify batch.
-    std::vector<uint16_t> last_n_drafted;
+    std::vector<int>                i_last;
+    std::vector<std::vector<float>> chain_h;
 
     common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
@@ -936,6 +940,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft));
         GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) &&
                 "MTP input row width must match the target h_nextn width");
+        n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
 
         LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
         LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
@@ -982,16 +987,25 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
 
         is_mem_shared = llama_get_ctx_other(ctx_dft) == ctx_tgt;
+        chain_heads   = n_mtp_layers > 1 && !is_mem_shared;
+
+        if (chain_heads) {
+            this->params.n_max = std::min(this->params.n_max, n_mtp_layers);
+            
+            chain_h.assign(n_seq, {});
+            for (auto & c : chain_h) {
+                c.reserve((size_t) (this->params.n_max + 1) * n_embd);
+            }
+        }
 
         pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));
 
+        i_last.assign(n_seq, -1);
         i_batch_beg.assign(n_seq, -1);
         i_batch_end.assign(n_seq, -1);
 
         verify_h.assign(n_seq, {});
         verify_h_rows.assign(n_seq, 0);
-
-        last_n_drafted.assign(n_seq, 0);
     }
 
     ~common_speculative_impl_draft_mtp() override {
@@ -1097,9 +1111,34 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
             }
 
-            const int32_t rc = llama_decode(ctx_dft, batch);
-            if (rc != 0) {
-                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
+            auto * mem_dft = llama_get_memory(ctx_dft);
+
+            bool ok = true;
+            for (int head = 0; head < n_mtp_layers; ++head) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340/changes#r3413498544
+                    for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                        if (i_batch_beg[seq_id] < 0) {
+                            continue;
+                        }
+                        llama_memory_seq_rm(mem_dft, seq_id, batch_in.pos[i_batch_beg[seq_id]], -1);
+                    }
+                    llama_set_nextn_layer_offset(ctx_dft, head);
+                }
+
+                const int32_t rc = llama_decode(ctx_dft, batch);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
+                            __func__, head, (int) rc, (int) batch_in.pos[0]);
+                    ok = false;
+                    break;
+                }
+            }
+
+            if (chain_heads) {
+                llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+            }
+            if (!ok) {
                 return false;
             }
         }
@@ -1134,7 +1173,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         int n_drafting = 0;
         std::vector<bool> drafting(n_seq);
 
-        const float * h_row = nullptr;
         const size_t row_bytes = (size_t) n_embd * sizeof(float);
 
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1149,22 +1187,43 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             common_sampler_reset(smpls[seq_id].get());
 
             common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
+            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, pending_h[seq_id].data(), row_bytes);
 
-            h_row = pending_h[seq_id].data();
-            std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
-        }
+            i_last[seq_id] = batch.n_tokens - 1;
 
-        int ret = llama_decode(ctx_dft, batch);
-        if (ret != 0) {
-            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
-            return;
+            if (chain_heads) {
+                chain_h[seq_id].assign(pending_h[seq_id].begin(), pending_h[seq_id].end());
+            }
         }
 
         int i = 0;
 
         while (n_drafting > 0) {
-            int i_batch = 0;
+            // each step decodes under a different head, i.e. a different decoder layer, and
+            // KV is per layer. process() filled this layer's KV only for positions < n_past
+            // (prompt + accepted prefix) — nothing in the draft region yet. so reset the
+            // draft region (the seq_rm lower bound is n_past, leaving the prompt KV intact)
+            // and select head i so it rebuilds its own layer's KV there; decoding just the
+            // latest token would leave its attention reading cells only another head wrote.
+            if (chain_heads) {
+                auto * mem_dft = llama_get_memory(ctx_dft);
+                for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                    if (drafting[seq_id]) {
+                        llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].n_past, -1);
+                    }
+                }
+                llama_set_nextn_layer_offset(ctx_dft, i);
+            }
 
+            int ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            // rebuild the batch for the next step: the growing-KV paths re-add only the
+            // new token (the KV already holds the prefix), while chained heads re-add the
+            // whole prefix at the next head. dropped sequences are simply not re-added.
             common_batch_clear(batch);
 
             for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1174,9 +1233,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
                 auto * smpl = smpls[seq_id].get();
 
-                common_sampler_sample(smpl, ctx_dft, i_batch, true);
-                h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
-                ++i_batch;
+                common_sampler_sample(smpl, ctx_dft, i_last[seq_id], true);
+                const float * h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_last[seq_id]);
 
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
@@ -1210,30 +1268,41 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                     continue;
                 }
 
-                if (is_mem_shared) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448031546
+                    chain_h[seq_id].insert(chain_h[seq_id].end(), h_row, h_row + n_embd);
+
+                    const int n_rows = (int) result.size() + 1; // id_last + tokens drafted so far
+                    for (int t = 0; t < n_rows; ++t) {
+                        const llama_token tok = (t == 0) ? dp.id_last : result[t - 1];
+                        common_batch_add(batch, tok, dp.n_past + t, { seq_id }, t == n_rows - 1);
+                        std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd,
+                                    chain_h[seq_id].data() + (size_t) t * n_embd, row_bytes);
+                    }
+                } else if (is_mem_shared) {
                     // note: with shared memory (e.g. Gemma4 assistants) we use the same position for all draft tokens
                     // ref: https://github.com/huggingface/transformers/blob/effde20942e3f82a1b97449f60b3a48c5ff96145/docs/source/en/model_doc/gemma4_assistant.md?plain=1#L36-L37
                     common_batch_add(batch, id, dp.n_past, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 } else {
                     common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 }
-                std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+
+                i_last[seq_id] = batch.n_tokens - 1;
             }
 
             if (batch.n_tokens == 0) {
                 break;
             }
 
-            // evaluate the drafted tokens on the draft model
-            ret = llama_decode(ctx_dft, batch);
-            if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
-                break;
-            }
-
             ++i;
         }
 
+        if (chain_heads) {
+            llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+        }
+
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
             auto & dp = dparams[seq_id];
             if (!dp.drafting) {
@@ -1243,8 +1312,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             if (dp.result->size() < (size_t) params.n_min) {
                 dp.result->clear();
             }
-
-            last_n_drafted[seq_id] = (uint16_t) dp.result->size();
         }
     }
 
@@ -1857,7 +1924,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
         bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
-        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
+        bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
 
 
 
@@ -1895,7 +1962,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_eagle3) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
         }
-        if (has_mtp) {
+        if (has_draft_mtp) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
         }
     }
diff --git a/include/llama.h b/include/llama.h
index 27e480674..f723c9f60 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -558,14 +558,15 @@ extern "C" {
     LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
     LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
 
-    LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_head_kv  (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_swa      (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_ctx_train  (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd       (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_inp   (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_out   (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_layer      (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_layer_nextn(const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_head       (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_head_kv    (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_swa        (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 529bc4a5e..220240ea9 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1156,6 +1156,10 @@ void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
     sched_need_reserve = true;
 }
 
+void llama_context::set_nextn_layer_offset(int32_t offset) {
+    cparams.nextn_layer_offset = offset;
+}
+
 void llama_context::set_causal_attn(bool value) {
     LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
 
@@ -3699,6 +3703,10 @@ void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool valu
     ctx->set_embeddings_layer_inp(lid, value);
 }
 
+void llama_set_nextn_layer_offset(llama_context * ctx, int32_t offset) {
+    ctx->set_nextn_layer_offset(offset);
+}
+
 llama_memory_t llama_get_memory(const struct llama_context * ctx) {
     if (!ctx) {
         return nullptr;
diff --git a/src/llama-context.h b/src/llama-context.h
index 853052be2..f8b780587 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -115,6 +115,7 @@ struct llama_context {
     void set_embeddings (bool value);
     void set_embeddings_nextn(bool value, bool masked);
     void set_embeddings_layer_inp(uint32_t lid, bool enable);
+    void set_nextn_layer_offset(int32_t offset);
     void set_causal_attn(bool value);
     void set_warmup(bool value);
 
diff --git a/src/llama-cparams.h b/src/llama-cparams.h
index 2b109f909..546ae1e2c 100644
--- a/src/llama-cparams.h
+++ b/src/llama-cparams.h
@@ -18,6 +18,8 @@ struct llama_cparams {
     int32_t  n_threads;       // number of threads to use for generation
     int32_t  n_threads_batch; // number of threads to use for batch processing
 
+    int32_t  nextn_layer_offset = 0;
+
     float rope_freq_base;
     float rope_freq_scale;
 
diff --git a/src/llama-ext.h b/src/llama-ext.h
index 8b5679b69..348bbae95 100644
--- a/src/llama-ext.h
+++ b/src/llama-ext.h
@@ -95,6 +95,11 @@ LLAMA_API llama_memory_breakdown llama_get_memory_breakdown(const struct llama_c
 // If masked == false, output the embeddings for all tokens in the batch regardless of batch.logits
 LLAMA_API void llama_set_embeddings_nextn(struct llama_context * ctx, bool value, bool masked);
 
+// Select which appended NextN block the DECODER_MTP graph runs (offset past
+// the trunk: il = n_layer() + offset). Used by the speculative NextN driver to
+// chain multiple trained NextN heads. Default 0 (first head).
+LLAMA_API void llama_set_nextn_layer_offset(struct llama_context * ctx, int32_t offset);
+
 // mirrors:
 // LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 5e8a65835..a6e8c3985 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -682,9 +682,16 @@ struct llm_graph_params {
             }
         }
 
+        // TODO: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448035248
+        if (cparams.nextn_layer_offset != other.cparams.nextn_layer_offset) {
+            return false;
+        }
+
         return
-            cparams.embeddings  == other.cparams.embeddings  &&
-            cparams.causal_attn == other.cparams.causal_attn &&
+            cparams.embeddings              == other.cparams.embeddings              &&
+            cparams.embeddings_nextn        == other.cparams.embeddings_nextn        &&
+            cparams.embeddings_nextn_masked == other.cparams.embeddings_nextn_masked &&
+            cparams.causal_attn             == other.cparams.causal_attn             &&
             arch  == other.arch  &&
             gtype == other.gtype &&
             cvec  == other.cvec  &&
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c52875533..d041a9ce3 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2312,6 +2312,10 @@ int32_t llama_model_n_layer(const llama_model * model) {
     return model->hparams.n_layer();
 }
 
+int32_t llama_model_n_layer_nextn(const llama_model * model) {
+    return model->hparams.n_layer_nextn;
+}
+
 int32_t llama_model_n_head(const llama_model * model) {
     return model->hparams.n_head();
 }
diff --git a/src/models/step35.cpp b/src/models/step35.cpp
index e2218c587..9b7b18a36 100644
--- a/src/models/step35.cpp
+++ b/src/models/step35.cpp
@@ -112,7 +112,7 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
         layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
     };
 
-    auto load_block_mtp = [&](int i, bool is_first_mtp) {
+    auto load_block_mtp = [&](int i) {
         auto & layer = layers[i];
 
         const uint32_t n_head_l      = hparams.n_head(i);
@@ -121,15 +121,12 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
 
         // The MTP block is a full Step3p5 decoder layer (mtp_block) plus the
         // NextN-specific wiring (enorm/hnorm/eh_proj + optional shared head).
-        // `mtp_flags` becomes NOT_REQUIRED when the GGUF is trunk-only.
-        //
-        // Only the FIRST MTP block (i == n_main) is required for the
-        // single-block MTP runtime; trailing MTP blocks are always tolerated
-        // as missing so pruned GGUFs (block 0 only) load cleanly. Override
-        // mtp_flags to NOT_REQUIRED for those.
-        const int eff_mtp_flags = is_first_mtp ? mtp_flags : (mtp_flags | TENSOR_NOT_REQUIRED);
+        // Multi-block MTP: every declared MTP block is required (the draft chain
+        // runs all n_layer_nextn heads), so each block uses the captured
+        // `mtp_flags` directly — already NOT_REQUIRED for a trunk-only GGUF,
+        // which keeps that path correct.
 
-        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, mtp_flags);
         layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
         layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
 
@@ -140,12 +137,12 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
             layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | TENSOR_DUPLICATED);
         }
 
-        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, eff_mtp_flags);
-        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, eff_mtp_flags);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, mtp_flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, mtp_flags);
 
         layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
 
-        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, mtp_flags);
 
         // dense MLP (leading dense blocks) — present if the MTP block isn't MoE
         layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
@@ -165,9 +162,9 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
         layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
 
         // NextN-specific tensors that define the MTP block.
-        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, eff_mtp_flags);
-        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd },              eff_mtp_flags);
-        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd },              eff_mtp_flags);
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd },              mtp_flags);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd },              mtp_flags);
         layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
         layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
         layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd },              TENSOR_NOT_REQUIRED);
@@ -176,13 +173,11 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
     for (int i = 0; i < n_layer; ++i) {
         load_block_trunk(i, trunk_flags);
     }
-    // Only the first MTP block (i == n_main) is required at runtime — the
-    // single-block-MTP graph in build_arch_graph always uses that one.
-    // Trailing MTP blocks are loaded if present (so an un-pruned GGUF with
-    // all MTP layers still works) but tolerated when absent via the pruning
-    // path. See scripts/prune_step35_extra_mtp.py for the pruner.
+    // All n_layer_nextn MTP blocks are required — the multi-block draft chain
+    // runs every head (head k at offset k). The GGUF declares the count via
+    // step35.nextn_predict_layers.
     for (int i = n_layer; i < n_layer_all; ++i) {
-        load_block_mtp(i, /*is_first_mtp=*/ i == n_layer);
+        load_block_mtp(i);
     }
 }
 
@@ -372,13 +367,14 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
     : llm_graph_context(params) {
     GGML_ASSERT(hparams.n_layer_nextn > 0 && "STEP35 MTP requires n_layer_nextn > 0");
 
-    // Single-block MTP only: always run the first trained MTP block (Qwen
-    // MTP / vLLM single-MTP-layer style). Multi-block round-robin proved to
-    // be a much deeper refactor than this PR justifies; the trailing MTP
-    // blocks are loaded with TENSOR_NOT_REQUIRED so pruned GGUFs (with just
-    // block 0) also work — see load_arch_tensors below and
-    // scripts/prune_step35_extra_mtp.py.
-    const int il = hparams.n_layer();
+    // Multi-block MTP: the DECODER_MTP graph runs the MTP head selected by
+    // cparams.nextn_layer_offset (0 = first trained head). The speculative driver
+    // bumps the offset per draft step to chain heads 45->46->47. offset 0 keeps
+    // single-block behavior identical to before.
+    const int il = hparams.n_layer() + cparams.nextn_layer_offset;
+    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
+                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
+                "nextn_layer_offset out of range [0, n_layer_nextn)");
     const auto & layer = model.layers[il];
 
     GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
@@ -536,6 +532,9 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
     cur = ggml_add(ctx0, cur, ffn_inp);
     cb(cur, "mtp_post_ffn", il);
 
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+    cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+
     // Pre-norm hidden state: used by the AR draft loop to seed the next MTP step.
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;

From 8a118ee86c3b818ce7e1524e48fc7cc65f1dc69b Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 21 Jun 2026 11:37:12 +0300
Subject: [PATCH 096/292] minor : clean-up whitespaces (#24862)

[no ci]
---
 common/speculative.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/speculative.cpp b/common/speculative.cpp
index 3c38ae2b0..c922a3f59 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -991,7 +991,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
         if (chain_heads) {
             this->params.n_max = std::min(this->params.n_max, n_mtp_layers);
-            
+
             chain_h.assign(n_seq, {});
             for (auto & c : chain_h) {
                 c.reserve((size_t) (this->params.n_max + 1) * n_embd);

From d6d899580dcf0e50a3d14453d3d082f6ed050450 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 11:58:14 +0200
Subject: [PATCH 097/292] server: real-time model load progress tracking via
 /models/sse (#24828)

* server: real-time model load progress tracking via /models/sse

* update docs

* add mutex for notify_to_router

* correct docs
---
 tools/server/README.md          | 28 ++++++++++++++--
 tools/server/server-context.cpp | 57 ++++++++++++++++++++++++++++++++-
 tools/server/server-models.cpp  | 20 ++++++++++--
 tools/server/server-models.h    |  8 ++++-
 4 files changed, 106 insertions(+), 7 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index eb730e713..5efdad095 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1859,9 +1859,33 @@ Example events:
 
 {
   "model": "...",
-  "event": "download_finished",
+  "event": "model_status",
   "data": {
-    "status": "loading"
+    "status": "loading",
+    "progress": {
+      "stage": "fit_params",
+      "value": 0.5 // from 0.0 to 1.0 ; note: not all stages have this "value"
+    }
+  }
+}
+
+{
+  "model": "...",
+  "event": "model_status",
+  "data": {
+    "status": "loaded",
+    "info": {
+      // note: only include info on first load
+      // waking up from sleep doesn't have this
+    }
+  }
+}
+
+{
+  "model": "...",
+  "event": "model_status",
+  "data": {
+    "status": "sleeping"
   }
 }
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 3de1335ec..531b106e5 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -833,6 +833,8 @@ private:
 
     bool sleeping = false;
 
+    int64_t t_last_load_progress_ms = 0;
+
     void destroy() {
         spec.reset();
         ctx_dft.reset();
@@ -863,6 +865,30 @@ private:
         sleeping = new_state;
     }
 
+    static bool load_progress_callback(float progress, void * user_data) {
+        auto * ctx = static_cast<server_context_impl *>(user_data);
+        GGML_ASSERT(ctx);
+        // always emit the first and final sample; throttle the rest to one per 200ms
+        {
+            auto & t_last = ctx->t_last_load_progress_ms;
+            const int64_t t_now = ggml_time_ms();
+            const bool first = t_last == 0;
+            const bool done  = progress >= 1.0f;
+            const bool throttled = !first && !done && (t_now - t_last) < 200;
+            if (throttled) {
+                return true;
+            }
+            t_last = t_now;
+        }
+        if (ctx->callback_state) {
+            ctx->callback_state(SERVER_STATE_LOADING, {
+                {"stage", "text_model"},
+                {"value", progress},
+            });
+        }
+        return true;
+    }
+
     // load the model and initialize llama_context
     // this may also be called to resume from sleeping state
     bool load_model(common_params & params) {
@@ -916,6 +942,10 @@ private:
 
         // optionally reserve VRAM for the draft / MTP context before fitting the target model
         if (params_base.fit_params) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "fit_params"}});
+            }
+
             const bool spec_mtp = std::find(params_base.speculative.types.begin(),
                                             params_base.speculative.types.end(),
                                             COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
@@ -991,6 +1021,13 @@ private:
             }
         }
 
+        // attach a progress callback
+        {
+            t_last_load_progress_ms = 0;
+            params_base.load_progress_callback = load_progress_callback;
+            params_base.load_progress_callback_user_data = this;
+        }
+
         llama_init = common_init_from_params(params_base);
 
         model_tgt = llama_init->model();
@@ -1008,6 +1045,10 @@ private:
         add_bos_token = llama_vocab_get_add_bos(vocab);
 
         if (params_base.speculative.has_dft()) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "spec_model"}});
+            }
+
             // TODO speculative: move to common/speculative.cpp?
             const auto & params_spec = params_base.speculative.draft;
 
@@ -1079,6 +1120,10 @@ private:
         }
 
         if (has_mmproj) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "mmproj_model"}});
+            }
+
             if (!is_resume) {
                 mtmd_helper_log_set(common_log_default_callback, nullptr);
             }
@@ -1259,6 +1304,10 @@ private:
             return init();
         }
 
+        if (callback_state) {
+            callback_state(SERVER_STATE_READY, {});
+        }
+
         return true;
     }
 
@@ -1335,6 +1384,9 @@ private:
             const bool enable_thinking = params_base.enable_reasoning != 0 && template_supports_thinking;
             SRV_INF("%s: chat template, thinking = %d\n", __func__, enable_thinking);
 
+            // IMPORTANT: chat_params is reused across sleeping / resuming states,
+            //            never store llama_context/llama_model pointers in chat_params,
+            //            as they may be invalidated after sleeping
             chat_params = {
                 /* use_jinja             */ params_base.use_jinja,
                 /* prefill_assistant     */ params_base.prefill_assistant,
@@ -3734,7 +3786,10 @@ struct server_res_generator : server_http_res {
 void server_context::set_state_callback(server_state_callback_t callback) {
     impl->callback_state = std::move(callback);
     impl->queue_tasks.on_sleeping_state([this](bool sleeping) {
-        impl->callback_state(sleeping ? SERVER_STATE_SLEEPING : SERVER_STATE_READY, {});
+        if (sleeping) {
+            impl->callback_state(SERVER_STATE_SLEEPING, {});
+        }
+        // for sleeping == false, event is emitted by load_model()
     });
 }
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index a569c8be3..68eefdffa 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -442,6 +442,7 @@ void server_models::load_models() {
                 /* last_used     */ 0,
                 /* args          */ std::vector<std::string>(),
                 /* loaded_info   */ {},
+                /* progress      */ {},
                 /* exit_code     */ 0,
                 /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                 /* multimodal    */ mtmd_caps{false, false},
@@ -608,6 +609,7 @@ void server_models::load_models() {
                     /* last_used     */ 0,
                     /* args          */ std::vector<std::string>(),
                     /* loaded_info   */ {},
+                    /* progress      */ {},
                     /* exit_code     */ 0,
                     /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                     /* multimodal    */ mtmd_caps{false, false},
@@ -1140,6 +1142,9 @@ void server_models::update_status(const std::string & name, const update_status_
         if (!args.loaded_info.is_null()) {
             meta.loaded_info = args.loaded_info;
         }
+        if (!args.progress.is_null()) {
+            meta.progress = args.progress;
+        }
     }
     // broadcast status change to SSE
     {
@@ -1152,6 +1157,9 @@ void server_models::update_status(const std::string & name, const update_status_
         if (!args.loaded_info.is_null()) {
             data["info"] = args.loaded_info;
         }
+        if (!args.progress.is_null()) {
+            data["progress"] = args.progress;
+        }
         // note: notify_sse doesn't acquire the lock, so no deadlock here
         notify_sse("status_change", name, data);
     }
@@ -1322,8 +1330,12 @@ void server_models::handle_child_state(const std::string & name, const std::stri
     switch (state) {
         case SERVER_STATE_LOADING:
             {
-                // do nothing for now
-                // TODO: report loading progress for first load and wakeup from sleep
+                update_status(name, {
+                    SERVER_MODEL_STATUS_LOADING,
+                    0,
+                    nullptr, // no loaded_info yet
+                    payload,
+                });
             } break;
         case SERVER_STATE_READY:
             {
@@ -1331,7 +1343,8 @@ void server_models::handle_child_state(const std::string & name, const std::stri
                     SERVER_MODEL_STATUS_LOADED,
                     0,
                     // note: payload can be empty if this is a wakeup from sleep
-                    payload.size() > 0 ? payload : nullptr
+                    payload.size() > 0 ? payload : nullptr,
+                    {}, // reset progress info
                 });
             } break;
         case SERVER_STATE_SLEEPING:
@@ -1384,6 +1397,7 @@ void server_child::notify_to_router(const std::string & state, const json & payl
         {"state", state},
         {"payload", payload},
     };
+    std::lock_guard<std::mutex> lk(mtx_stdout);
     common_log_pause(common_log_main());
     fflush(stdout);
     fprintf(stdout, "%s%s\n", CMD_CHILD_TO_ROUTER_STATE, safe_json_to_str(data).c_str());
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 40a0e078c..17759b00a 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -72,6 +72,7 @@ struct server_model_meta {
     int64_t last_used = 0; // for LRU unloading
     std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
     json loaded_info; // info to be reflected via /v1/models endpoint ; if in DOWNLOADING state, it should contain download progress info
+    json progress; // reflect load or download progress info, if any
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
     int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
     mtmd_caps multimodal; // multimodal capabilities
@@ -170,12 +171,14 @@ public:
     // to stop the download, call unload()
     void download(common_params_model && model, common_download_opts && opts);
 
-    // update the status of a model instance (thread-safe)
     struct update_status_args {
         server_model_status status;
         int exit_code = 0; // only valid if status == UNLOADED
         json loaded_info = nullptr;
+        json progress = nullptr;
     };
+    // update the status of a model instance (thread-safe)
+    // also send SSE notification to /models/sse endpoint
     void update_status(const std::string & name, const update_status_args & args);
     void update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok = true);
 
@@ -208,6 +211,9 @@ public:
 };
 
 struct server_child {
+    // serializes the notify_to_router writes
+    std::mutex mtx_stdout;
+
     // return true if the current process is a child server instance
     bool is_child();
 

From bfa3219177c81bbf9f38939901656d60a745eb7e Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 13:03:14 +0200
Subject: [PATCH 098/292] server: add "verbose" field to schema (#24864)

---
 tools/server/server-schema.cpp                |  3 +++
 .../server/tests/unit/test_chat_completion.py | 20 +++++++++++++++++++
 2 files changed, 23 insertions(+)

diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp
index d5d747a65..ed4bda241 100644
--- a/tools/server/server-schema.cpp
+++ b/tools/server/server-schema.cpp
@@ -14,6 +14,9 @@ std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params &
         fields.emplace_back(f);
     };
 
+    add((new field_bool("verbose", params.verbose))
+        ->set_desc("Include __verbose field in the response with additional debug information"));
+
     add((new field_bool("timings_per_token", params.timings_per_token))
         ->set_desc("Include prompt processing and text generation speed information in each response"));
 
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index b00aac649..0258b539e 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -603,3 +603,23 @@ def test_chat_completions_token_count():
         })
         assert res.status_code == 200
         assert res.body["input_tokens"] > 5
+
+
+def test_verbose_debug():
+    global server
+    server.start()
+    for verbose in [True, False]:
+        res = server.make_request("POST", "/chat/completions", data={
+            "max_tokens": 2,
+            "messages": [
+                {"role": "system", "content": "Book"},
+                {"role": "user", "content": "What is the best book"},
+            ],
+            "verbose": verbose,
+        })
+        assert res.status_code == 200
+        if verbose:
+            assert "__verbose" in res.body
+            assert "Book" in res.body["__verbose"]["prompt"]
+        else:
+            assert "__verbose" not in res.body

From 2f89acc2bc614dc121db065a74e503bf88668951 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 13:40:52 +0200
Subject: [PATCH 099/292] mtmd: add load progress callback (#24865)

---
 tools/mtmd/clip.cpp             | 35 +++++++++++++++++++++++++++++++--
 tools/mtmd/clip.h               |  2 ++
 tools/mtmd/mtmd.cpp             |  8 ++++++++
 tools/mtmd/mtmd.h               |  8 ++++++++
 tools/server/server-context.cpp | 33 ++++++++++++++++++-------------
 5 files changed, 70 insertions(+), 16 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index c713703e0..fccc1e348 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1045,8 +1045,17 @@ struct clip_model_loader {
     bool has_vision = false;
     bool has_audio  = false;
 
+    mtmd_progress_callback progress_callback = nullptr;
+    void * progress_callback_user_data = nullptr;
+
     // TODO @ngxson : we should not pass clip_ctx here, it should be clip_model
-    clip_model_loader(const char * fname, bool skip_tensors = false) : fname(fname) {
+    clip_model_loader(const char * fname,
+            bool skip_tensors = false,
+            mtmd_progress_callback progress_cb = nullptr,
+            void * progress_user_data = nullptr)
+        : fname(fname),
+          progress_callback(progress_cb),
+          progress_callback_user_data(progress_user_data) {
         struct ggml_context * meta = nullptr;
 
         struct gguf_init_params params = {
@@ -2790,10 +2799,22 @@ struct clip_model_loader {
         if (!ctx_clip.no_alloc) {
             std::vector<uint8_t> read_buf;
 
+            // start loading event
+            if (progress_callback){
+                progress_callback(0.0, progress_callback_user_data);
+            }
+
+            // compute total tensor data size for progress reporting
+            size_t total_data_size = 0;
+            for (auto & t : tensors_to_load) {
+                total_data_size += ggml_nbytes(t);
+            }
+
             // alloc memory and offload data
             ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(ctx_clip.backend);
             ctx_clip.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data.get(), buft));
             ggml_backend_buffer_set_usage(ctx_clip.buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+            size_t data_loaded = 0;
             for (auto & t : tensors_to_load) {
                 ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name);
                 GGML_ASSERT(cur && "tensor not found in ctx_data");
@@ -2814,6 +2835,13 @@ struct clip_model_loader {
                     fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
                     ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
                 }
+                data_loaded += num_bytes;
+                if (progress_callback && total_data_size > 0) {
+                    const float progress = (float)data_loaded / (float)total_data_size;
+                    if (!progress_callback(progress, progress_callback_user_data)) {
+                        throw std::runtime_error(string_format("%s: model loading cancelled by progress_callback\n", __func__));
+                    }
+                }
             }
             fin.close();
 
@@ -3105,7 +3133,10 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
     clip_ctx * ctx_audio = nullptr;
 
     try {
-        clip_model_loader loader(fname);
+        clip_model_loader loader(fname,
+            /* skip_tensors */ false,
+            ctx_params.progress_callback,
+            ctx_params.progress_callback_user_data);
         bool skip_audio = false;
 
         if (loader.has_vision) {
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index e0f1d298c..967093a81 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -54,6 +54,8 @@ struct clip_context_params {
     ggml_backend_sched_eval_callback cb_eval;
     void * cb_eval_user_data;
     bool no_alloc;
+    mtmd_progress_callback progress_callback;
+    void * progress_callback_user_data;
 };
 
 struct clip_init_result {
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index cbaac1d37..564bafc62 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -251,6 +251,8 @@ mtmd_context_params mtmd_context_params_default() {
         /* cb_eval           */ nullptr,
         /* cb_eval_user_data */ nullptr,
         /* batch_max_tokens  */ 1024,
+        /* progress_callback */ nullptr,
+        /* progress_callback_user_data */ nullptr,
     };
     return params;
 }
@@ -345,6 +347,8 @@ struct mtmd_context {
             /* cb_eval           */ ctx_params.cb_eval,
             /* cb_eval_user_data */ ctx_params.cb_eval_user_data,
             /* no_alloc          */ no_alloc,
+            /* progress_callback */ ctx_params.progress_callback,
+            /* progress_callback_user_data */ ctx_params.progress_callback_user_data,
         };
 
         auto res = clip_init(mmproj_fname, ctx_clip_params);
@@ -2133,8 +2137,12 @@ std::map<ggml_backend_dev_t, size_t> mtmd_get_memory_usage(const char * mmproj_f
     mtmd::context_ptr ctx;
     auto saved_log_callback = g_logger_state.log_callback;
     auto saved_log_user_data = g_logger_state.log_callback_user_data;
+
+    ctx_params.progress_callback = nullptr;
+
     try {
         mtmd_log_set(stub_log_callback, nullptr); // suppress logging
+        // TODO @ngxson : fix no_alloc here
         ctx.reset(new mtmd_context(mmproj_fname, nullptr, ctx_params));
         mtmd_log_set(saved_log_callback, saved_log_user_data); // restore log callback
         std::map<ggml_backend_dev_t, size_t> total_mem;
diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h
index 2fd149e48..25d51ef58 100644
--- a/tools/mtmd/mtmd.h
+++ b/tools/mtmd/mtmd.h
@@ -83,6 +83,8 @@ typedef struct mtmd_input_chunks mtmd_input_chunks;
 typedef struct mtmd_input_text   mtmd_input_text;
 typedef struct mtmd_batch        mtmd_batch;
 
+typedef bool (*mtmd_progress_callback)(float progress, void * user_data);
+
 struct mtmd_context_params {
     bool use_gpu;
     bool print_timings;
@@ -104,6 +106,12 @@ struct mtmd_context_params {
     int32_t batch_max_tokens; // maximum number of output tokens in a batch
                               // (note: this is not a hard-limit, the first image will always be added even if it exceeds this limit)
                               // (default: 1024)
+
+    // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
+    // If the provided progress_callback returns true, model loading continues.
+    // If it returns false, model loading is immediately aborted.
+    mtmd_progress_callback progress_callback;
+    void * progress_callback_user_data;
 };
 
 MTMD_API const char * mtmd_default_marker(void);
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 531b106e5..7db4cb198 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -833,8 +833,6 @@ private:
 
     bool sleeping = false;
 
-    int64_t t_last_load_progress_ms = 0;
-
     void destroy() {
         spec.reset();
         ctx_dft.reset();
@@ -865,12 +863,18 @@ private:
         sleeping = new_state;
     }
 
+    struct load_progress_data {
+        server_context_impl * ctx;
+        std::string stage;
+        int64_t t_last_load_progress_ms = 0;
+        load_progress_data(server_context_impl * ctx, const std::string & stage) : ctx(ctx), stage(stage) {}
+    };
     static bool load_progress_callback(float progress, void * user_data) {
-        auto * ctx = static_cast<server_context_impl *>(user_data);
-        GGML_ASSERT(ctx);
+        auto * d = static_cast<load_progress_data *>(user_data);
+        GGML_ASSERT(d);
         // always emit the first and final sample; throttle the rest to one per 200ms
         {
-            auto & t_last = ctx->t_last_load_progress_ms;
+            auto & t_last = d->t_last_load_progress_ms;
             const int64_t t_now = ggml_time_ms();
             const bool first = t_last == 0;
             const bool done  = progress >= 1.0f;
@@ -880,9 +884,9 @@ private:
             }
             t_last = t_now;
         }
-        if (ctx->callback_state) {
-            ctx->callback_state(SERVER_STATE_LOADING, {
-                {"stage", "text_model"},
+        if (d->ctx->callback_state) {
+            d->ctx->callback_state(SERVER_STATE_LOADING, {
+                {"stage", d->stage},
                 {"value", progress},
             });
         }
@@ -892,6 +896,9 @@ private:
     // load the model and initialize llama_context
     // this may also be called to resume from sleeping state
     bool load_model(common_params & params) {
+        load_progress_data load_progress_text(this, "text_model");
+        load_progress_data load_progress_mmproj(this, "mmproj_model");
+
         bool is_resume = sleeping;
 
         SRV_INF("loading model '%s'\n", params.model.path.c_str());
@@ -912,6 +919,9 @@ private:
             mparams.image_max_tokens = params_base.image_max_tokens;
             mparams.batch_max_tokens = params_base.mtmd_batch_max_tokens;
             mparams.media_marker     = get_media_marker();
+            // progress callback
+            mparams.progress_callback           = load_progress_callback;
+            mparams.progress_callback_user_data = &load_progress_mmproj;
         }
 
         // optionally get the memory usage of mmproj
@@ -1023,9 +1033,8 @@ private:
 
         // attach a progress callback
         {
-            t_last_load_progress_ms = 0;
             params_base.load_progress_callback = load_progress_callback;
-            params_base.load_progress_callback_user_data = this;
+            params_base.load_progress_callback_user_data = &load_progress_text;
         }
 
         llama_init = common_init_from_params(params_base);
@@ -1120,10 +1129,6 @@ private:
         }
 
         if (has_mmproj) {
-            if (callback_state) {
-                callback_state(SERVER_STATE_LOADING, {{"stage", "mmproj_model"}});
-            }
-
             if (!is_resume) {
                 mtmd_helper_log_set(common_log_default_callback, nullptr);
             }

From bf533823cd06e7fb21552265eee1bf2fd2752974 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Sun, 21 Jun 2026 14:04:52 +0200
Subject: [PATCH 100/292] jinja : implement call statement (#24847)

* implement call statement

* undo unintended change

* de-lambda

* simplify

* move caller context inside function handler
---
 common/jinja/runtime.cpp | 135 ++++++++++++++++++++++++++-------------
 common/jinja/runtime.h   |   1 +
 tests/test-jinja.cpp     |  26 ++++++++
 3 files changed, 116 insertions(+), 46 deletions(-)

diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp
index 1fae7884e..f98cb0876 100644
--- a/common/jinja/runtime.cpp
+++ b/common/jinja/runtime.cpp
@@ -686,59 +686,62 @@ value set_statement::execute_impl(context & ctx) {
     return mk_val<value_undefined>();
 }
 
+static inline void bind_parameters(const std::string & name, const statements & this_args, const func_args & args, context & ctx) {
+    const size_t expected_count = this_args.size();
+    const size_t input_count = args.count();
+
+    JJ_DEBUG("Invoking '%s' with %zu input arguments (expected %zu)", name.c_str(), input_count, expected_count);
+    for (size_t i = 0; i < expected_count; ++i) {
+        if (i < input_count) {
+            if (is_stmt<identifier>(this_args[i])) {
+                // normal parameter
+                std::string param_name = cast_stmt<identifier>(this_args[i])->val;
+                value param_value = args.get_kwarg_or_pos(param_name, i);
+                JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                ctx.set_val(param_name, param_value);
+            } else if (is_stmt<keyword_argument_expression>(this_args[i])) {
+                // default argument used as normal parameter
+                auto kwarg = cast_stmt<keyword_argument_expression>(this_args[i]);
+                if (!is_stmt<identifier>(kwarg->key)) {
+                    throw std::runtime_error("Keyword argument key must be an identifier in '" + name + "'");
+                }
+                std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
+                value param_value = args.get_kwarg_or_pos(param_name, i);
+                JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
+                ctx.set_val(param_name, param_value);
+            } else {
+                throw std::runtime_error("Invalid parameter type in '" + name + "'");
+            }
+        } else {
+            auto & default_arg = this_args[i];
+            if (is_stmt<keyword_argument_expression>(default_arg)) {
+                auto kwarg = cast_stmt<keyword_argument_expression>(default_arg);
+                if (!is_stmt<identifier>(kwarg->key)) {
+                    throw std::runtime_error("Keyword argument key must be an identifier in '" + name + "'");
+                }
+                std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
+                JJ_DEBUG("  Binding parameter '%s' to default argument of type %s", param_name.c_str(), kwarg->val->type().c_str());
+                ctx.set_val(param_name, kwarg->val->execute(args.ctx));
+            } else {
+                throw std::runtime_error("Not enough arguments provided to '" + name + "'");
+            }
+            //std::string param_name = cast_stmt<identifier>(default_args[i])->val;
+            //JJ_DEBUG("  Binding parameter '%s' to default", param_name.c_str());
+            //ctx.var[param_name] = default_args[i]->execute(ctx);
+        }
+    }
+}
+
 value macro_statement::execute_impl(context & ctx) {
     if (!is_stmt<identifier>(this->name)) {
         throw std::runtime_error("Macro name must be an identifier");
     }
     std::string name = cast_stmt<identifier>(this->name)->val;
 
-    const func_handler func = [this, name, &ctx](const func_args & args) -> value {
-        size_t expected_count = this->args.size();
-        size_t input_count = args.count();
+    const func_handler func = [this, name](const func_args & args) -> value {
+        context macro_ctx(args.ctx); // new scope for macro execution
 
-        JJ_DEBUG("Invoking macro '%s' with %zu input arguments (expected %zu)", name.c_str(), input_count, expected_count);
-        context macro_ctx(ctx); // new scope for macro execution
-
-        // bind parameters
-        for (size_t i = 0; i < expected_count; ++i) {
-            if (i < input_count) {
-                if (is_stmt<identifier>(this->args[i])) {
-                    // normal parameter
-                    std::string param_name = cast_stmt<identifier>(this->args[i])->val;
-                    value param_value = args.get_kwarg_or_pos(param_name, i);
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
-                    macro_ctx.set_val(param_name, param_value);
-                } else if (is_stmt<keyword_argument_expression>(this->args[i])) {
-                    // default argument used as normal parameter
-                    auto kwarg = cast_stmt<keyword_argument_expression>(this->args[i]);
-                    if (!is_stmt<identifier>(kwarg->key)) {
-                        throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
-                    }
-                    std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
-                    value param_value = args.get_kwarg_or_pos(param_name, i);
-                    JJ_DEBUG("  Binding parameter '%s' to argument of type %s", param_name.c_str(), param_value->type().c_str());
-                    macro_ctx.set_val(param_name, param_value);
-                } else {
-                    throw std::runtime_error("Invalid parameter type in macro '" + name + "'");
-                }
-            } else {
-                auto & default_arg = this->args[i];
-                if (is_stmt<keyword_argument_expression>(default_arg)) {
-                    auto kwarg = cast_stmt<keyword_argument_expression>(default_arg);
-                    if (!is_stmt<identifier>(kwarg->key)) {
-                        throw std::runtime_error("Keyword argument key must be an identifier in macro '" + name + "'");
-                    }
-                    std::string param_name = cast_stmt<identifier>(kwarg->key)->val;
-                    JJ_DEBUG("  Binding parameter '%s' to default argument of type %s", param_name.c_str(), kwarg->val->type().c_str());
-                    macro_ctx.set_val(param_name, kwarg->val->execute(ctx));
-                } else {
-                    throw std::runtime_error("Not enough arguments provided to macro '" + name + "'");
-                }
-                //std::string param_name = cast_stmt<identifier>(default_args[i])->val;
-                //JJ_DEBUG("  Binding parameter '%s' to default", param_name.c_str());
-                //macro_ctx.var[param_name] = default_args[i]->execute(ctx);
-            }
-        }
+        bind_parameters(name, this->args, args, macro_ctx);
 
         // execute macro body
         JJ_DEBUG("Executing macro '%s' body with %zu statements", name.c_str(), this->body.size());
@@ -752,6 +755,46 @@ value macro_statement::execute_impl(context & ctx) {
     return mk_val<value_undefined>();
 }
 
+value call_statement::execute_impl(context & ctx) {
+    auto call_expr = cast_stmt<call_expression>(this->call);
+    if (!call_expr) {
+        throw std::runtime_error("Call statement requires a valid call expression");
+    }
+
+    value callee_val = call_expr->callee->execute(ctx);
+    if (!is_val<value_func>(callee_val)) {
+        throw std::runtime_error("Callee is not a function: got " + callee_val->type());
+    }
+    auto * callee_func = cast_val<value_func>(callee_val);
+
+    context caller_ctx(ctx); // new scope for caller execution
+
+    const func_handler func = [this, caller_ctx = std::move(caller_ctx)](const func_args & args) -> value {
+        context block_ctx(caller_ctx); // new scope for block execution
+
+        bind_parameters("caller", this->caller_args, args, block_ctx);
+
+        JJ_DEBUG("Executing call body with %zu statements", this->body.size());
+        auto res = exec_statements(this->body, block_ctx);
+        JJ_DEBUG("Call body execution complete, result: %s", res->val_str.str().c_str());
+        return res;
+    };
+
+    context call_ctx(ctx);
+    call_ctx.set_val("caller", mk_val<value_func>("caller", func));
+
+    func_args args(call_ctx);
+
+    for (const auto & arg_expr : call_expr->args) {
+        auto arg_val = arg_expr->execute(ctx);
+        JJ_DEBUG("  Argument type: %s", arg_val->type().c_str());
+        args.push_back(arg_val);
+    }
+
+    JJ_DEBUG("Calling macro '%s' with %zu arguments", callee_func->name.c_str(), args.count());
+    return callee_func->invoke(args);
+}
+
 value member_expression::execute_impl(context & ctx) {
     value object = this->object->execute(ctx);
 
diff --git a/common/jinja/runtime.h b/common/jinja/runtime.h
index b6f4a6ab4..37b4c35ca 100644
--- a/common/jinja/runtime.h
+++ b/common/jinja/runtime.h
@@ -552,6 +552,7 @@ struct call_statement : public statement {
         for (const auto & arg : this->caller_args) chk_type<expression>(arg);
     }
     std::string type() const override { return "CallStatement"; }
+    value execute_impl(context & ctx) override;
 };
 
 struct ternary_expression : public expression {
diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp
index 803995624..81bbcd55a 100644
--- a/tests/test-jinja.cpp
+++ b/tests/test-jinja.cpp
@@ -995,6 +995,32 @@ static void test_macros(testing & t) {
         json::object(),
         "Hello, John Smith,Hi, Jane Doe"
     );
+
+    test_template(t, "macro with caller",
+        "\
+{%- macro nest_dict(o, i, ff='') %}\n\
+  {{- caller(ff) }}\n\
+  {%- for k, v in o|items %}\n\
+    {{- i + k + ': ' }}\n\
+    {%- if v is mapping %}\n\
+      {{- '{' }}\n\
+      {% call(f) nest_dict(v, i + '    ') %}\n\
+        {{- 'fail' if ff is undefined }}\n\
+      {%- endcall %}\n\
+      {{- i + '}' }}\n\
+    {% else %}\n\
+      {{- v|string }}\n\
+    {% endif %}\n\
+  {%- endfor %}\n\
+{%- endmacro %}\n\
+{%- call(f) nest_dict({'root1': 1, 'root2': {'nest1': 1, 'nest2': {'nest3': 2}}}, '    ', 'Dict') %}\n\
+  {{- 'fail' if ff is defined }}\n\
+  {{- f + ' {' }}\n\
+{% endcall %}\n\
+{{- '}' }}",
+        json::object(),
+        "Dict {\n    root1: 1\n    root2: {\n        nest1: 1\n        nest2: {\n            nest3: 2\n        }\n    }\n}"
+    );
 }
 
 static void test_namespace(testing & t) {

From 0d135df48ccee9a799fa9d9ea0ed494bd4fdd74f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 14:12:15 +0200
Subject: [PATCH 101/292] mtmd: fix mtmd_get_memory_usage (#24867)

---
 tools/mtmd/clip.cpp             | 62 ++++++++++++++++++---------------
 tools/mtmd/mtmd.cpp             |  3 +-
 tools/server/server-context.cpp |  4 ++-
 3 files changed, 37 insertions(+), 32 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index fccc1e348..7dd7023c4 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -2796,7 +2796,7 @@ struct clip_model_loader {
         }
 
         // load data
-        if (!ctx_clip.no_alloc) {
+        {
             std::vector<uint8_t> read_buf;
 
             // start loading event
@@ -2814,38 +2814,42 @@ struct clip_model_loader {
             ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(ctx_clip.backend);
             ctx_clip.buf.reset(ggml_backend_alloc_ctx_tensors_from_buft(ctx_clip.ctx_data.get(), buft));
             ggml_backend_buffer_set_usage(ctx_clip.buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
-            size_t data_loaded = 0;
-            for (auto & t : tensors_to_load) {
-                ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name);
-                GGML_ASSERT(cur && "tensor not found in ctx_data");
-                auto it_off = tensor_offset.find(t->name);
-                GGML_ASSERT(it_off != tensor_offset.end() && "no offset for tensor");
-                const size_t offset = it_off->second;
-                fin.seekg(offset, std::ios::beg);
-                if (!fin) {
-                    throw std::runtime_error(string_format("%s: failed to seek for tensor %s\n", __func__, t->name));
-                }
-                size_t num_bytes = ggml_nbytes(cur);
-                if (ggml_backend_buft_is_host(buft)) {
-                    // for the CPU and Metal backend, we can read directly into the tensor
-                    fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
-                } else {
-                    // read into a temporary buffer first, then copy to device memory
-                    read_buf.resize(num_bytes);
-                    fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
-                    ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
-                }
-                data_loaded += num_bytes;
-                if (progress_callback && total_data_size > 0) {
-                    const float progress = (float)data_loaded / (float)total_data_size;
-                    if (!progress_callback(progress, progress_callback_user_data)) {
-                        throw std::runtime_error(string_format("%s: model loading cancelled by progress_callback\n", __func__));
+            // read the weight from file
+            if (!ctx_clip.no_alloc) {
+                size_t data_loaded = 0;
+                for (auto & t : tensors_to_load) {
+                    ggml_tensor * cur = ggml_get_tensor(ctx_clip.ctx_data.get(), t->name);
+                    GGML_ASSERT(cur && "tensor not found in ctx_data");
+                    auto it_off = tensor_offset.find(t->name);
+                    GGML_ASSERT(it_off != tensor_offset.end() && "no offset for tensor");
+                    const size_t offset = it_off->second;
+                    fin.seekg(offset, std::ios::beg);
+                    if (!fin) {
+                        throw std::runtime_error(string_format("%s: failed to seek for tensor %s\n", __func__, t->name));
+                    }
+                    size_t num_bytes = ggml_nbytes(cur);
+                    if (ggml_backend_buft_is_host(buft)) {
+                        // for the CPU and Metal backend, we can read directly into the tensor
+                        fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
+                    } else {
+                        // read into a temporary buffer first, then copy to device memory
+                        read_buf.resize(num_bytes);
+                        fin.read(reinterpret_cast<char *>(read_buf.data()), num_bytes);
+                        ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
+                    }
+                    data_loaded += num_bytes;
+                    if (progress_callback && total_data_size > 0) {
+                        const float progress = (float)data_loaded / (float)total_data_size;
+                        if (!progress_callback(progress, progress_callback_user_data)) {
+                            throw std::runtime_error(string_format("%s: model loading cancelled by progress_callback\n", __func__));
+                        }
                     }
                 }
+                LOG_DBG("%s: loaded %zu tensors from %s\n", __func__, tensors_to_load.size(), fname.c_str());
+            } else {
+                LOG_DBG("%s: no_alloc is set, skipping tensor data loading (%zu tensors)\n", __func__, tensors_to_load.size());
             }
             fin.close();
-
-            LOG_DBG("%s: loaded %zu tensors from %s\n", __func__, tensors_to_load.size(), fname.c_str());
         }
 
     }
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index 564bafc62..724538b58 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -2142,8 +2142,7 @@ std::map<ggml_backend_dev_t, size_t> mtmd_get_memory_usage(const char * mmproj_f
 
     try {
         mtmd_log_set(stub_log_callback, nullptr); // suppress logging
-        // TODO @ngxson : fix no_alloc here
-        ctx.reset(new mtmd_context(mmproj_fname, nullptr, ctx_params));
+        ctx.reset(new mtmd_context(mmproj_fname, nullptr, ctx_params, true));
         mtmd_log_set(saved_log_callback, saved_log_user_data); // restore log callback
         std::map<ggml_backend_dev_t, size_t> total_mem;
         auto merge = [&](const struct clip_ctx * c) {
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 7db4cb198..aeb15096c 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -926,13 +926,15 @@ private:
 
         // optionally get the memory usage of mmproj
         if (has_mmproj && params_base.fit_params) {
+            int64_t t_start = ggml_time_us();
             auto mmproj_mem = mtmd_get_memory_usage(mmproj_path.c_str(), mparams);
+            int64_t t_elapsed = ggml_time_us() - t_start;
             if (!mmproj_mem.empty()) {
                 size_t total = 0;
                 for (auto & [dev, size] : mmproj_mem) {
                     total += size;
                 }
-                SRV_INF("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB\n", total / (1024.0 * 1024.0));
+                SRV_INF("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB (took %.2f ms)\n", total / (1024.0 * 1024.0), t_elapsed / 1000.0);
                 GGML_ASSERT(!params_base.fit_params_target.empty());
                 for (auto & [dev, size] : mmproj_mem) {
                     for (size_t i = 0; i < ggml_backend_dev_count(); i++) {

From bddfd2b1137cd6e51fbb939081caf50e9f496a66 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 14:16:11 +0200
Subject: [PATCH 102/292] server: refactor batch construction (#24843)

* server: refactor batch construction

* wip

* wip 2

* wip 3

* wip 4

* add abort_all_slots

* handle batch full more carefully

* fix assert

* rm debug log

* small nits

* (debug) add timings

* debug: force llama_synchronize for accurate timings

* address comments

* disable DEBUG_TIMINGS
---
 tools/server/server-context.cpp | 934 ++++++++++++++++++++------------
 1 file changed, 583 insertions(+), 351 deletions(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index aeb15096c..91a8eb945 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -63,6 +63,99 @@ enum slot_state {
     SLOT_STATE_GENERATING,
 };
 
+struct server_slot; // forward declaration
+
+struct server_batch {
+    llama_batch batch;
+    bool batch_rendered = false;
+
+    struct token {
+        int32_t id_slot;
+        llama_token token;
+        llama_pos pos;
+        bool output;
+    };
+    std::vector<token> tokens;
+    int32_t n_tokens_alloc = 0;
+
+    // track if given slot can be batched with slots already in the batch
+    server_slot * slot_batched = nullptr;
+
+    float  alora_scale       = -1.0f;
+    size_t alora_disabled_id = 0;
+
+    server_batch() {
+        batch.token = nullptr; // sentinel: uninitialized batch
+    }
+
+    ~server_batch() {
+        llama_batch_free(batch);
+    }
+
+    void init(int32_t n_tokens_alloc) {
+        this->n_tokens_alloc = n_tokens_alloc;
+        batch = llama_batch_init(n_tokens_alloc, 0, 1);
+        tokens.reserve(n_tokens_alloc);
+    }
+
+    bool add(int32_t id_slot, llama_token token, llama_pos pos, bool output) {
+        GGML_ASSERT(batch.token != nullptr);
+        if ((int32_t)tokens.size() >= n_tokens_alloc) {
+            return false;
+        }
+        // LOG_INF("adding token to batch: slot=%d, token=%d, pos=%d, output=%d\n", id_slot, token, pos, output);
+        tokens.push_back({ id_slot, token, pos, output });
+        return true;
+    }
+
+    void clear() {
+        tokens.clear();
+        common_batch_clear(batch);
+        slot_batched      = nullptr;
+        alora_scale       = -1.0f;
+        alora_disabled_id = 0;
+        batch_rendered    = false;
+    }
+
+    int32_t size() const {
+        return (int32_t)tokens.size();
+    }
+
+    void set_output(int32_t idx, bool output) {
+        GGML_ASSERT(idx >= 0 && idx < (int32_t)tokens.size());
+        tokens[idx].output = output;
+    }
+
+    void render() {
+        GGML_ASSERT(batch.token != nullptr);
+        common_batch_clear(batch);
+        for (int32_t i = 0; i < size(); i++) {
+            const auto & t = tokens[i];
+            common_batch_add(batch, t.token, t.pos, { t.id_slot }, t.output);
+        }
+        batch_rendered = true;
+    }
+
+    llama_batch get_view(int32_t off, int32_t n_tokens) const {
+        GGML_ASSERT(batch.token != nullptr);
+        GGML_ASSERT(batch_rendered);
+        GGML_ASSERT(off >= 0 && off < size());
+        GGML_ASSERT(n_tokens > 0 && off + n_tokens <= size());
+
+        llama_batch view = {
+            n_tokens,
+            batch.token    + off,
+            nullptr,
+            batch.pos      + off,
+            batch.n_seq_id + off,
+            batch.seq_id   + off,
+            batch.logits   + off,
+        };
+
+        return view;
+    }
+};
+
 struct server_slot {
     int id;
 
@@ -185,6 +278,7 @@ struct server_slot {
     // stats
     size_t n_sent_text = 0; // number of sent text character
 
+    // TODO @ngxson : move all metrics to a sub-struct for clarity
     int64_t t_start_process_prompt;
     int64_t t_start_generation;
     int64_t t_print_last = 0;
@@ -348,12 +442,14 @@ struct server_slot {
         return n_draft_max;
     }
 
-    void update_batch(llama_batch & batch) {
+    // add sampled token of this slot to the batch, optionally add the speculative draft tokens if any
+    void handle_last_sampled_token(server_batch & batch) {
+        bool add_ok = true;
         if (spec_draft.empty()) {
             // no speculative decoding
-            i_batch = batch.n_tokens;
+            i_batch = batch.size();
 
-            common_batch_add(batch, sampled, prompt.tokens.pos_next(), { this->id }, true);
+            add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true);
 
             SLT_DBG(*this, "slot decode token, id=%d, n_ctx = %d, n_tokens = %d, truncated = %d\n",
                     sampled, n_ctx, prompt.n_tokens(), truncated);
@@ -363,19 +459,21 @@ struct server_slot {
 
             GGML_ASSERT(spec_i_batch.empty());
 
-            spec_i_batch.push_back(batch.n_tokens);
+            spec_i_batch.push_back(batch.size());
             for (size_t i = 0; i < spec_draft.size(); i++) {
-                spec_i_batch.push_back(batch.n_tokens + i + 1);
+                spec_i_batch.push_back(batch.size() + i + 1);
             }
 
             auto pos0 = prompt.tokens.pos_next();
 
-            common_batch_add(batch, sampled, pos0++, { this->id }, true);
+            add_ok &= batch.add(id, sampled, pos0++, true);
             for (auto token : spec_draft) {
-                common_batch_add(batch, token, pos0++, { this->id }, true);
+                add_ok &= batch.add(this->id, token, pos0++, true);
             }
         }
 
+        GGML_ASSERT(add_ok && "batch must be large enough to hold the sampled and draft tokens");
+
         prompt.tokens.push_back(sampled);
         prompt.tokens.insert(spec_draft);
     }
@@ -793,7 +891,7 @@ private:
 
     llama_context * ctx_tgt = nullptr;
 
-    llama_batch batch {};
+    server_batch batch;
 
     llama_model_ptr model_dft;
     llama_context_ptr ctx_dft;
@@ -845,8 +943,6 @@ private:
 
         mtmd_free(mctx);
         mctx = nullptr;
-
-        llama_batch_free(batch);
     }
 
     void handle_sleeping_state(bool new_state) {
@@ -1266,7 +1362,7 @@ private:
         // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
         {
             const int32_t n_batch = llama_n_batch(ctx_tgt);
-            batch = llama_batch_init(std::max(n_batch, params_base.n_parallel), 0, 1);
+            batch.init(std::max(n_batch, params_base.n_parallel));
         }
 
         if (params_base.cache_ram_mib != 0) {
@@ -2556,7 +2652,83 @@ private:
         }
     }
 
+    void iterate(std::vector<server_slot> & slots, std::function<void(server_slot &)> callback) {
+        for (auto & slot : slots) {
+            try {
+                callback(slot);
+            } catch (const std::exception & e) {
+                SLT_ERR(slot, "got exception: %s\n", e.what());
+                send_error(slot, std::string("got exception: ") + e.what(), ERROR_TYPE_SERVER);
+                slot.release();
+            }
+        }
+    }
+
+    void iterate(std::vector<server_slot *> & slots, std::function<void(server_slot &)> callback) {
+        for (auto & slot : slots) {
+            try {
+                callback(*slot);
+            } catch (const std::exception & e) {
+                SLT_ERR(*slot, "got exception: %s\n", e.what());
+                send_error(*slot, std::string("got exception: ") + e.what(), ERROR_TYPE_SERVER);
+                slot->release();
+            }
+        }
+    }
+
+    void abort_all_slots(const std::string & reason) {
+        for (auto & slot : slots) {
+            if (slot.is_processing()) {
+                send_error(slot, reason, ERROR_TYPE_SERVER);
+                slot.release();
+            }
+        }
+    }
+
+    // @ngxson : for debugging only
+    int64_t t_pre_decode  = 0;
+    int64_t t_decode      = 0;
+    int64_t t_post_decode = 0;
+    int64_t t_sampl       = 0;
+    int64_t n_pre_decode  = 0;
+    int64_t n_decode      = 0;
+    int64_t n_post_decode = 0;
+    int64_t n_sampl       = 0;
+// #define DEBUG_TIMINGS
+#ifdef DEBUG_TIMINGS
+    struct scoped_timer {
+        int64_t & t;
+        int64_t & n;
+        int64_t t_start;
+        scoped_timer(int64_t & t_, int64_t & n_) : t(t_), n(n_) {
+            t_start = ggml_time_us();
+        }
+        ~scoped_timer() {
+            t += ggml_time_us() - t_start;
+            n++;
+        }
+    };
+#else
+    struct scoped_timer {
+        scoped_timer(int64_t &, int64_t &) {}
+        ~scoped_timer() {}
+    };
+#endif
+
     void update_slots() {
+#ifdef DEBUG_TIMINGS
+        static int64_t t_prev = 0;
+        int64_t t_start = ggml_time_us();
+        if (t_start - t_prev > 5 * 1000 * 1000) { // every 5 seconds
+            t_prev = t_start;
+            SRV_INF("n_pre_decode      = %" PRId64 "\n", n_pre_decode);
+            SRV_INF("avg t_pre_decode  = %f ms\n", (double) t_pre_decode / n_pre_decode / 1000.0);
+            SRV_INF("avg t_decode      = %f ms\n", (double) t_decode / n_decode / 1000.0);
+            SRV_INF("avg t_post_decode = %f ms\n", (double) t_post_decode / n_post_decode / 1000.0);
+            SRV_INF("avg t_sampl       = %f ms\n", (double) t_sampl / n_sampl / 1000.0);
+        }
+#endif
+
         // check if all slots are idle
         {
             bool all_idle = true;
@@ -2570,29 +2742,80 @@ private:
 
             if (all_idle) {
                 SRV_INF("%s", "all slots are idle\n");
+                return; // skip further processing
 
-                return;
+            } else {
+                SRV_DBG("%s", "posting NEXT_RESPONSE\n");
+
+                server_task task(SERVER_TASK_TYPE_NEXT_RESPONSE);
+                task.id = queue_tasks.get_new_id();
+                queue_tasks.post(std::move(task));
             }
         }
 
-        {
-            SRV_DBG("%s", "posting NEXT_RESPONSE\n");
-
-            server_task task(SERVER_TASK_TYPE_NEXT_RESPONSE);
-            task.id = queue_tasks.get_new_id();
-            queue_tasks.post(std::move(task));
+        try {
+            scoped_timer t(t_pre_decode, n_pre_decode);
+            pre_decode();
+            batch.render();
+        } catch (const std::exception & e) {
+            SRV_ERR("pre_decode() failed: %s\n", e.what());
+            abort_all_slots("pre_decode() failed: " + std::string(e.what()));
         }
 
+        llama_batch batch_view;
+        int32_t off_next = 0;
+        int32_t n_batch = llama_n_batch(ctx_tgt);
+        for (int32_t off = 0; off < batch.size(); off = off_next) {
+            const int32_t n_tokens = std::min(n_batch, batch.size() - off);
+            try {
+                scoped_timer t(t_decode, n_decode);
+                // TODO @ngxson : maybe handle n_batch == 1 here instead of inside decode()
+
+                batch_view = batch.get_view(off, n_tokens);
+                bool ok = decode(n_batch, off, batch_view);
+#ifdef DEBUG_TIMINGS
+                llama_synchronize(ctx_tgt);
+#endif
+
+                if (ok) {
+                    // move the head of the batch forward with the number of tokens we just processed
+                    off_next = off + n_tokens;
+
+                    // on successful decode, restore the original batch size
+                    n_batch = llama_n_batch(ctx_tgt);
+                } else {
+                    // try again with the updated n_batch
+                    continue;
+                }
+            } catch (const std::exception & e) {
+                SRV_ERR("decode() failed: %s\n", e.what());
+                abort_all_slots("decode() failed: " + std::string(e.what()));
+                break; // stop any further processing
+            }
+
+            try {
+                scoped_timer t(t_post_decode, n_post_decode);
+                post_decode(n_tokens, off, batch_view);
+            } catch (const std::exception & e) {
+                SRV_ERR("post_decode() failed: %s\n", e.what());
+                abort_all_slots("post_decode() failed: " + std::string(e.what()));
+                break; // stop any further processing
+            }
+
+        }
+    }
+
+    void pre_decode() {
         // apply context-shift if needed
         // TODO: simplify and improve
-        for (server_slot & slot : slots) {
+        iterate(slots, [&](server_slot & slot) {
             if (slot.state == SLOT_STATE_GENERATING && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
                 if (!params_base.ctx_shift) {
                     // this check is redundant (for good)
                     // we should never get here, because generation should already stopped in process_token()
                     send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
                     slot.release();
-                    continue;
+                    return;
                 }
 
                 if (mctx) {
@@ -2604,7 +2827,7 @@ private:
                 if (slot.task->is_parent() || slot.task->is_child()) {
                     send_error(slot, "context shift cannot be used for shared prompt", ERROR_TYPE_SERVER);
                     slot.release();
-                    continue;
+                    return;
                 }
 
                 // Shift context
@@ -2650,28 +2873,28 @@ private:
 
                 slot.truncated = true;
             }
-        }
+        });
 
         // start populating the batch for this iteration
-        common_batch_clear(batch);
+        batch.clear();
 
         // track if given slot can be batched with slots already in the batch
-        server_slot * slot_batched = nullptr;
+        auto & slot_batched = batch.slot_batched;
 
         std::vector<server_slot *> generating;
         std::vector<server_slot *> drafting;
 
         // determine which slots are generating and drafting
-        for (auto & slot : slots) {
+        iterate(slots, [&](server_slot & slot) {
             if (slot.state != SLOT_STATE_GENERATING) {
-                continue;
+                return;
             }
 
             // check if we can batch this slot with the previous one
             if (!slot_batched) {
                 slot_batched = &slot;
             } else if (!slot_batched->can_batch_with(slot)) {
-                continue;
+                return;
             }
 
             generating.push_back(&slot);
@@ -2719,7 +2942,7 @@ private:
                     }
                 }
             }
-        }
+        });
 
         // generate the actual drafts (if any)
         {
@@ -2727,9 +2950,7 @@ private:
         }
 
         // make checkpoints if needed
-        for (auto * slot_ptr : drafting) {
-            auto & slot = *slot_ptr;
-
+        iterate(drafting, [&](server_slot & slot) {
             auto & draft = slot.spec_draft;
             auto & ckpt  = slot.spec_ckpt;
 
@@ -2772,38 +2993,42 @@ private:
                     ckpt.update_dft(ctx_dft.get(), slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
                 }
             }
-        }
+        });
 
         // update the batch with the sampled/drafted tokens
-        for (auto * slot_ptr : generating) {
-            auto & slot = *slot_ptr;
-
-            slot.update_batch(batch);
-        }
+        iterate(generating, [&](server_slot & slot) {
+            slot.handle_last_sampled_token(batch);
+        });
 
         // process in chunks of params.n_batch
         int32_t n_batch  = llama_n_batch(ctx_tgt);
         int32_t n_ubatch = llama_n_ubatch(ctx_tgt);
 
-        float  alora_scale       = -1.0f;
-        size_t alora_disabled_id = 0;
+        auto & alora_scale       = batch.alora_scale;
+        auto & alora_disabled_id = batch.alora_disabled_id;
 
         // next, batch any pending prompts without exceeding n_batch
-        if (params_base.cont_batching || batch.n_tokens == 0) {
-            for (auto & slot : slots) {
+        if (params_base.cont_batching || batch.size() == 0) {
+            bool add_ok = true; // false means the batch is full, skip remaining slots
+
+            iterate(slots, [&](server_slot & slot) {
+                if (!add_ok || batch.size() >= n_batch) {
+                    return; // batch is full, skip remaining slots
+                }
+
                 if (!slot.is_processing()) {
-                    continue;
+                    return;
                 }
 
                 // check if we can batch this slot with the previous one
                 if (slot_batched && !slot_batched->can_batch_with(slot)) {
-                    continue;
+                    return;
                 }
 
                 // check if this is a child slot
                 if (slot.state == SLOT_STATE_WAIT_OTHER) {
                     SLT_DBG(slot, "%s", "waiting for parent slot to complete\n");
-                    continue;
+                    return;
                 }
 
                 // this slot still has a prompt to be processed
@@ -2811,7 +3036,7 @@ private:
                     const auto & input_tokens = slot.task->tokens;
 
                     // used to determine the number of tokens added to the batch for the current slot
-                    const auto n_tokens_prev = batch.n_tokens;
+                    const auto n_tokens_prev = batch.size();
 
                     // TODO: maybe move branch to outside of this loop in the future
                     if (slot.state == SLOT_STATE_STARTED) {
@@ -2847,14 +3072,14 @@ private:
                             send_final_response(slot);
                             slot.release();
 
-                            continue;
+                            return;
                         }
 
                         // TODO: support memory-less logits computation
                         if (slot.task->need_logits() && !llama_get_memory(ctx_tgt)) {
                             send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER);
                             slot.release();
-                            continue;
+                            return;
                         }
 
                         if (!slot.can_split()) {
@@ -2866,7 +3091,7 @@ private:
                                                slot.task->n_tokens(), n_ubatch),
                                            ERROR_TYPE_SERVER);
                                 slot.release();
-                                continue;
+                                return;
                             }
 
                             if (slot.task->n_tokens() > slot.n_ctx) {
@@ -2877,7 +3102,7 @@ private:
                                         slot.task->n_tokens(), slot.n_ctx),
                                     ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                 slot.release();
-                                continue;
+                                return;
                             }
                         } else {
                             if (slot.task->n_tokens() >= slot.n_ctx) {
@@ -2887,7 +3112,7 @@ private:
                                                          slot.task->n_tokens(), slot.n_ctx),
                                            ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                 slot.release();
-                                continue;
+                                return;
                             }
 
                             if (slot.task->params.cache_prompt) {
@@ -3107,8 +3332,8 @@ private:
 
                     if (!slot.can_split()) {
                         // cannot fit the prompt in the current batch - will try next iter
-                        if (batch.n_tokens + slot.task->n_tokens() > n_batch) {
-                            continue;
+                        if (batch.size() + slot.task->n_tokens() > n_batch) {
+                            return;
                         }
                     }
 
@@ -3192,7 +3417,7 @@ private:
                     const bool n_before_user_known = n_before_user > 0;
 
                     // add prompt tokens for processing in the current batch
-                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch) {
+                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.size() < n_batch) {
                         // get next token to process
                         llama_token cur_tok = input_tokens[slot.prompt.n_tokens()];
                         if (cur_tok == LLAMA_TOKEN_NULL) {
@@ -3210,10 +3435,9 @@ private:
                         // embedding requires all tokens in the batch to be output;
                         // MTP also wants logits at every prompt position so the
                         // streaming hook can mirror t_h_nextn into ctx_dft.
-                        common_batch_add(batch,
+                        add_ok &= batch.add(slot.id,
                             cur_tok,
                             slot.prompt.tokens.pos_next(),
-                            { slot.id },
                             slot.need_embd());
                         slot.prompt.tokens.push_back(cur_tok);
 
@@ -3249,7 +3473,7 @@ private:
                     }
 
                     // the number of tokens added to the batch for the current slot
-                    const auto n_tokens_cur = batch.n_tokens - n_tokens_prev;
+                    const auto n_tokens_cur = batch.size() - n_tokens_prev;
 
                     const bool near_prompt_end = slot.task->n_tokens() < slot.prompt.n_tokens() + n_ubatch;
 
@@ -3257,13 +3481,13 @@ private:
                     if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
                         slot.state = SLOT_STATE_DONE_PROMPT;
 
-                        GGML_ASSERT(batch.n_tokens > 0);
+                        GGML_ASSERT(batch.size() > 0);
 
                         // extract the logits only for the last token
-                        batch.logits[batch.n_tokens - 1] = true;
+                        batch.set_output(batch.size() - 1, true);
 
                         slot.n_decoded = 0;
-                        slot.i_batch   = batch.n_tokens - 1;
+                        slot.i_batch   = batch.size() - 1;
 
                         slot.init_sampler();
                     } else {
@@ -3322,20 +3546,20 @@ private:
                 if (!slot_batched) {
                     slot_batched = &slot;
                 }
-
-                if (batch.n_tokens >= n_batch) {
-                    break;
-                }
-            }
+            });
         }
+    }
 
-        SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);
+    // returns true = success ; false = retry with smaller batch size
+    // throw std::runtime_error on fatal error
+    bool decode(int32_t & n_batch, int32_t off, llama_batch & batch_view) {
+        SRV_DBG("n_batch (effective) = %d, off = %d\n", n_batch, off);
 
-        auto accept_special_token = [&](server_slot & slot, llama_token token) {
-            return params_base.special ||
-                slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
-        };
+        auto & slot_batched      = batch.slot_batched;
+        auto & alora_scale       = batch.alora_scale;
+        auto & alora_disabled_id = batch.alora_disabled_id;
 
+        // TODO @ngxson : alora handling is too messy, need to refactor it to be more clear and maintainable
         if (slot_batched) {
             // apply lora, only need to do it once per batch
             common_set_adapter_lora(ctx_tgt, slot_batched->lora);
@@ -3350,340 +3574,348 @@ private:
             llama_set_embeddings(ctx_tgt, slot_batched->need_embd());
         }
 
-        if (batch.n_tokens == 0) {
+        if (batch.size() == 0) {
             SRV_WRN("%s", "no tokens to decode\n");
 
             if (++n_empty_consecutive > 3) {
                 GGML_ABORT("fatal error - please provide logs and repro in %s\n", "https://github.com/ggml-org/llama.cpp/pull/20277");
             }
+
+            return true; // nothing to decode
         } else {
             n_empty_consecutive = 0;
         }
 
-        int32_t i_next = 0;
+        const int ret = llama_decode(ctx_tgt, batch_view);
 
-        // process the created batch of tokens
-        for (int32_t i = 0; i < batch.n_tokens; i = i_next) {
-            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
+        metrics.on_decoded(slots);
 
-            llama_batch batch_view = {
-                n_tokens,
-                batch.token    + i,
-                nullptr,
-                batch.pos      + i,
-                batch.n_seq_id + i,
-                batch.seq_id   + i,
-                batch.logits   + i,
-            };
+        if (ret != 0) {
+            {
+                std::string err;
 
-            const int ret = llama_decode(ctx_tgt, batch_view);
-
-            metrics.on_decoded(slots);
-
-            if (ret != 0) {
-                {
-                    std::string err;
-
-                    if (n_batch == 1 && ret == 1) {
-                        // TODO: try to terminate only the largest active slot/sequence and continue with the rest
-                        //       need to remove the tokens from the current batch too
-                        err = "Context size has been exceeded.";
-                    }
-
-                    if (ret == -1) {
-                        err = "Invalid input batch.";
-                    }
-
-                    if (ret < -1) {
-                        // TODO: update slot state based on llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
-                        err = "Compute error.";
-                    }
-
-                    // TODO: handle ret == 2 (abort) when we start aborting
-
-                    if (!err.empty()) {
-                        SRV_ERR("%s i = %d, n_batch = %d, ret = %d\n", err.c_str(), i, n_batch, ret);
-
-                        for (auto & slot : slots) {
-                            if (slot.is_processing()) {
-                                send_error(slot, err);
-                                slot.release();
-
-                                // note: it's complicated to keep track of how much of the current batch has been
-                                //       processed before the error occurred, so we simply clear the entire context
-                                slot.prompt_clear(false);
-                            }
-                        }
-
-                        break;
-                    }
+                if (n_batch == 1 && ret == 1) {
+                    // TODO: try to terminate only the largest active slot/sequence and continue with the rest
+                    //       need to remove the tokens from the current batch too
+                    err = "Context size has been exceeded.";
                 }
 
-                // retry with half the batch size to try to find a free slot in the KV cache
-                if (!try_clear_idle_slots()) {
-                    n_batch /= 2;
+                if (ret == -1) {
+                    err = "Invalid input batch.";
                 }
 
-                SRV_WRN("failed to find free space in the KV cache, retrying with smaller batch size, i = %d, n_batch = %d, ret = %d\n", i, n_batch, ret);
+                if (ret < -1) {
+                    // TODO: update slot state based on llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
+                    err = "Compute error.";
+                }
 
-                continue; // continue loop of n_batch
-            }
+                // TODO: handle ret == 2 (abort) when we start aborting
 
-            // TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
-            //       for now, always re-evaluate for simplicity
-            //       ref: https://github.com/ggml-org/llama.cpp/pull/22728#issuecomment-4400925384
-            if (!common_speculative_process(spec.get(), batch_view)) {
-                SRV_ERR("%s", "failed to process speculative batch\n");
+                if (!err.empty()) {
+                    SRV_ERR("%s off = %d, n_batch = %d, ret = %d\n", err.c_str(), off, n_batch, ret);
 
-                // TODO: handle error
-                break;
-            }
+                    for (auto & slot : slots) {
+                        if (slot.is_processing()) {
+                            send_error(slot, err);
+                            slot.release();
 
-            // move the head of the batch forward with the number of tokens we just processed
-            i_next = i + n_tokens;
-
-            // on successful decode, restore the original batch size
-            n_batch = llama_n_batch(ctx_tgt);
-
-            // handle `n_cmpl > 1` tasks - when the main prompt is processed, activate all child tasks too
-            for (auto & slot : slots) {
-                if (slot.state == SLOT_STATE_DONE_PROMPT && slot.task->is_parent()) {
-                    std::vector<server_slot *> children;
-                    for (auto & other : slots) {
-                        if (other.state == SLOT_STATE_WAIT_OTHER && slot.task->id == other.task->id_parent) {
-                            children.push_back(&other);
+                            // note: it's complicated to keep track of how much of the current batch has been
+                            //       processed before the error occurred, so we simply clear the entire context
+                            slot.prompt_clear(false);
                         }
                     }
 
-                    // all children slots should already launched by launch_slots_with_parent_task()
-                    // copy state to the child slots
-                    for (auto & child : children) {
-                        SLT_INF(slot, " - copying state to child %d\n", child->id);
-
-                        GGML_ASSERT(child->state == SLOT_STATE_WAIT_OTHER);
-
-                        slot.copy_state_to(*child);
-                        child->state = SLOT_STATE_DONE_PROMPT;
-                    }
+                    // stop, do not retry with smaller batch size
+                    throw std::runtime_error(err);
                 }
             }
 
-            for (auto & slot : slots) {
-                // optionally send prompt processing progress
-                if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) {
-                    if (slot.task->params.stream && slot.task->params.return_progress) {
-                        send_partial_response(slot, {}, true);
+            // retry with half the batch size to try to find a free slot in the KV cache
+            if (!try_clear_idle_slots()) {
+                n_batch /= 2;
+            }
+
+            SRV_WRN("failed to find free space in the KV cache, retrying with smaller batch size, off = %d, n_batch = %d, ret = %d\n", off, n_batch, ret);
+
+            return false; // retry with the updated n_batch
+        }
+
+        // TODO: avoid restoring the draft context and re-evaluating the drafted tokens when not needed [TAG_SPEC_AVOID_DRAFT_REEVAL]
+        //       for now, always re-evaluate for simplicity
+        //       ref: https://github.com/ggml-org/llama.cpp/pull/22728#issuecomment-4400925384
+        if (!common_speculative_process(spec.get(), batch_view)) {
+            SRV_ERR("%s", "failed to process speculative batch\n");
+
+            // TODO: handle error
+            throw std::runtime_error("failed to process speculative batch");
+        }
+
+        // handle `n_cmpl > 1` tasks - when the main prompt is processed, activate all child tasks too
+        for (auto & slot : slots) {
+            if (slot.state == SLOT_STATE_DONE_PROMPT && slot.task->is_parent()) {
+                std::vector<server_slot *> children;
+                for (auto & other : slots) {
+                    if (other.state == SLOT_STATE_WAIT_OTHER && slot.task->id == other.task->id_parent) {
+                        children.push_back(&other);
                     }
                 }
 
-                if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
-                    continue; // continue loop of slots
+                // all children slots should already launched by launch_slots_with_parent_task()
+                // copy state to the child slots
+                for (auto & child : children) {
+                    SLT_INF(slot, " - copying state to child %d\n", child->id);
+
+                    GGML_ASSERT(child->state == SLOT_STATE_WAIT_OTHER);
+
+                    slot.copy_state_to(*child);
+                    child->state = SLOT_STATE_DONE_PROMPT;
+                }
+            }
+        }
+
+        return true;
+    }
+
+    void post_decode(int32_t n_batch_tokens, int32_t off, llama_batch & batch_view) {
+        // for checking if a given batch index is inside batch_view
+        auto is_inside_view = [&](int32_t idx) {
+            return idx >= off && idx < off + n_batch_tokens;
+        };
+
+        // TODO @ngxson : it's tricky to make sub-batch compatible with common_sampler_sample_and_accept_n,
+        // so for now we will throw an error in this case: https://github.com/ggml-org/llama.cpp/issues/24840
+        iterate(slots, [&](server_slot & slot) {
+            for (auto & i : slot.spec_i_batch) {
+                if (!is_inside_view(i)) {
+                    throw std::runtime_error(string_format("speculative batch index %d is not inside the current sub-batch [%d, %d)", i, off, off + n_batch_tokens));
+                }
+            }
+        });
+
+        auto accept_special_token = [&](server_slot & slot, llama_token token) {
+            return params_base.special ||
+                slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
+        };
+
+        iterate(slots, [&](server_slot & slot) {
+            // optionally send prompt processing progress
+            if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) {
+                if (slot.task->params.stream && slot.task->params.return_progress) {
+                    send_partial_response(slot, {}, true);
+                }
+            }
+
+            if (!is_inside_view(slot.i_batch)) {
+                // the required token not in this sub-batch, skip
+                return;
+            }
+
+            if (slot.state == SLOT_STATE_DONE_PROMPT) {
+                if (slot.task->type == SERVER_TASK_TYPE_EMBEDDING) {
+                    // prompt evaluated for embedding
+                    send_embedding(slot, batch_view);
+                    slot.release();
+                    slot.i_batch = -1;
+                    return;
                 }
 
-                if (slot.state == SLOT_STATE_DONE_PROMPT) {
-                    if (slot.task->type == SERVER_TASK_TYPE_EMBEDDING) {
-                        // prompt evaluated for embedding
-                        send_embedding(slot, batch_view);
-                        slot.release();
-                        slot.i_batch = -1;
-                        continue; // continue loop of slots
-                    }
-
-                    if (slot.task->type == SERVER_TASK_TYPE_RERANK) {
-                        send_rerank(slot, batch_view);
-                        slot.release();
-                        slot.i_batch = -1;
-                        continue; // continue loop of slots
-                    }
-
-                    GGML_ASSERT(slot.task->need_sampling());
-
-                    // prompt evaluated for next-token prediction
-                    slot.state = SLOT_STATE_GENERATING;
-
-                    if (slot.can_speculate()) {
-                        common_speculative_begin(spec.get(), slot.id, slot.prompt.tokens.get_text_tokens());
-                    }
-                } else if (slot.state != SLOT_STATE_GENERATING) {
-                    continue; // continue loop of slots
+                if (slot.task->type == SERVER_TASK_TYPE_RERANK) {
+                    send_rerank(slot, batch_view);
+                    slot.release();
+                    slot.i_batch = -1;
+                    return;
                 }
 
-                if (slot.can_speculate() && !slot.spec_draft.empty()) {
-                    continue; // sample using speculative decoding
+                GGML_ASSERT(slot.task->need_sampling());
+
+                // prompt evaluated for next-token prediction
+                slot.state = SLOT_STATE_GENERATING;
+
+                if (slot.can_speculate()) {
+                    common_speculative_begin(spec.get(), slot.id, slot.prompt.tokens.get_text_tokens());
+                }
+            } else if (slot.state != SLOT_STATE_GENERATING) {
+                return;
+            }
+
+            if (slot.can_speculate() && !slot.spec_draft.empty()) {
+                return; // sample using speculative decoding
+            }
+
+            // shifted according to the current sub-batch
+            const int tok_idx = slot.i_batch - off;
+
+            llama_token id;
+            {
+                scoped_timer timer(t_sampl, n_sampl);
+                id = common_sampler_sample(slot.smpl.get(), slot.ctx_tgt, tok_idx);
+            }
+
+            slot.i_batch = -1;
+
+            common_sampler_accept(slot.smpl.get(), id, true);
+
+            // here we have synchronized the llama_context (due to the sampling above), so we can do time measurement
+            const int64_t t_now = ggml_time_us();
+
+            slot.n_decoded += 1;
+
+            if (slot.n_decoded == 1) {
+                slot.t_start_generation = t_now;
+                slot.t_print_last = t_now;
+                slot.n_decoded_last = 0;
+                slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
+                metrics.on_prompt_eval(slot);
+            }
+
+            slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
+
+            completion_token_output result;
+            result.tok          = id;
+            result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
+            result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
+
+            if (slot.task->params.sampling.n_probs > 0) {
+                populate_token_probs(slot, result, slot.task->params.post_sampling_probs, params_base.special, tok_idx);
+            }
+
+            if (!process_token(result, slot)) {
+                // release slot because of stop condition
+                slot.print_timings();
+                send_final_response(slot);
+                metrics.on_prediction(slot);
+                slot.release();
+
+                return;
+            }
+
+            slot.print_timings_tg();
+        });
+
+        // speculative decoding - main model sample and accept
+        iterate(slots, [&](server_slot & slot) {
+            if (slot.state != SLOT_STATE_GENERATING || !slot.can_speculate() || slot.spec_draft.empty()) {
+                return;
+            }
+
+            // save the original draft size
+            const size_t n_draft = slot.spec_draft.size();
+
+            GGML_ASSERT(n_draft > 0);
+
+            // verify and try to accept the draft
+            {
+                // save the sampler sampler state in case we need to restore it
+                common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get()));
+
+                GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1);
+                auto accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft);
+                slot.spec_i_batch.clear();
+
+                GGML_ASSERT(accepted.size() >= 1);
+
+                const uint32_t n_rollback = slot.spec_draft.size() + 1 - accepted.size();
+
+                const bool use_ckpt_tgt =
+                    ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
+                    (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && n_rollback > llama_n_rs_seq(ctx_tgt));
+
+                // check for partial draft acceptance
+                if (n_rollback > 0) {
+                    if (use_ckpt_tgt) {
+                        if (trace > 0) {
+                            SLT_INF(slot, "accepted %2zu/%2zu draft tokens (restore checkpoint)\n", accepted.size() - 1, slot.spec_draft.size());
+                        }
+
+                        // partial acceptance is not supported by the context -> truncate the draft and restore the state
+                        slot.spec_draft = std::move(accepted);
+
+                        const auto & ckpt = slot.spec_ckpt;
+
+                        SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n", ckpt.pos_min, ckpt.pos_max, ckpt.size());
+
+                        {
+                            ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+
+                            common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1);
+                        }
+
+                        if (slot.ctx_dft) {
+                            ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
+
+                            common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1);
+                        }
+
+                        slot.prompt.tokens.keep_first(ckpt.n_tokens);
+                        slot.smpl = std::move(smpl_save);
+
+                        return;
+                    }
                 }
 
-                const int tok_idx = slot.i_batch - i;
+                if (trace > 0) {
+                    SLT_INF(slot, "accepted %2zu/%2zu draft tokens\n", accepted.size() - 1, n_draft);
+                }
 
-                llama_token id = common_sampler_sample(slot.smpl.get(), slot.ctx_tgt, tok_idx);
+                common_speculative_accept(spec.get(), slot.id, accepted.size() - 1);
 
-                slot.i_batch = -1;
+                slot.spec_draft = std::move(accepted);
+            }
 
-                common_sampler_accept(slot.smpl.get(), id, true);
+            const int64_t t_now = ggml_time_us();
 
-                // here we have synchronized the llama_context (due to the sampling above), so we can do time measurement
-                const int64_t t_now = ggml_time_us();
+            const auto ids = std::move(slot.spec_draft);
+
+            slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
+
+            // update how many tokens out of those tested were accepted
+            slot.n_draft_accepted += ids.size() - 1;
+            slot.n_draft_verif_steps += 1;
+
+            if (slot.n_accepted_per_pos.empty()) {
+                slot.n_accepted_per_pos.resize(common_speculative_n_max(&params_base.speculative), 0);
+            }
+            for (size_t i = 0; i < ids.size() - 1 && i < slot.n_accepted_per_pos.size(); ++i) {
+                slot.n_accepted_per_pos[i]++;
+            }
+
+            // add accepted tokens to the prompt
+            slot.prompt.tokens.keep_first(slot.prompt.n_tokens() - n_draft);
+            slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});
+
+            slot.sampled = ids.back(); // last accepted token
+            SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft);
+
+            common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1);
+            if (slot.ctx_dft) {
+                common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1);
+            }
+
+            for (size_t i = 0; i < ids.size(); ++i) {
+                completion_token_output result;
+
+                result.tok          = ids[i];
+                result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
+                result.prob         = 1.0f; // set later
+
+                // TODO: set result.probs
 
                 slot.n_decoded += 1;
 
-                if (slot.n_decoded == 1) {
-                    slot.t_start_generation = t_now;
-                    slot.t_print_last = t_now;
-                    slot.n_decoded_last = 0;
-                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
-                    metrics.on_prompt_eval(slot);
-                }
-
-                slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
-
-                completion_token_output result;
-                result.tok          = id;
-                result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
-                result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs
-
-                if (slot.task->params.sampling.n_probs > 0) {
-                    populate_token_probs(slot, result, slot.task->params.post_sampling_probs, params_base.special, tok_idx);
-                }
-
                 if (!process_token(result, slot)) {
-                    // release slot because of stop condition
                     slot.print_timings();
                     send_final_response(slot);
                     metrics.on_prediction(slot);
                     slot.release();
 
-                    continue;
+                    return;
                 }
-
-                slot.print_timings_tg();
             }
 
-            // speculative decoding - main model sample and accept
-            for (auto & slot : slots) {
-                if (slot.state != SLOT_STATE_GENERATING || !slot.can_speculate() || slot.spec_draft.empty()) {
-                    continue;
-                }
+            slot.print_timings_tg();
 
-                // save the original draft size
-                const size_t n_draft = slot.spec_draft.size();
-
-                GGML_ASSERT(n_draft > 0);
-
-                // verify and try to accept the draft
-                {
-                    // save the sampler sampler state in case we need to restore it
-                    common_sampler_ptr smpl_save(common_sampler_clone(slot.smpl.get()));
-
-                    GGML_ASSERT(slot.spec_i_batch.size() == n_draft + 1);
-                    auto accepted = common_sampler_sample_and_accept_n(slot.smpl.get(), slot.ctx_tgt, slot.spec_i_batch, slot.spec_draft);
-                    slot.spec_i_batch.clear();
-
-                    GGML_ASSERT(accepted.size() >= 1);
-
-                    const uint32_t n_rollback = slot.spec_draft.size() + 1 - accepted.size();
-
-                    const bool use_ckpt_tgt =
-                        ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL ||
-                       (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_RS && n_rollback > llama_n_rs_seq(ctx_tgt));
-
-                    // check for partial draft acceptance
-                    if (n_rollback > 0) {
-                        if (use_ckpt_tgt) {
-                            if (trace > 0) {
-                                SLT_INF(slot, "accepted %2zu/%2zu draft tokens (restore checkpoint)\n", accepted.size() - 1, slot.spec_draft.size());
-                            }
-
-                            // partial acceptance is not supported by the context -> truncate the draft and restore the state
-                            slot.spec_draft = std::move(accepted);
-
-                            const auto & ckpt = slot.spec_ckpt;
-
-                            SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n", ckpt.pos_min, ckpt.pos_max, ckpt.size());
-
-                            {
-                                ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-
-                                common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1);
-                            }
-
-                            if (slot.ctx_dft) {
-                                ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
-
-                                common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1);
-                            }
-
-                            slot.prompt.tokens.keep_first(ckpt.n_tokens);
-                            slot.smpl = std::move(smpl_save);
-
-                            continue;
-                        }
-                    }
-
-                    if (trace > 0) {
-                        SLT_INF(slot, "accepted %2zu/%2zu draft tokens\n", accepted.size() - 1, n_draft);
-                    }
-
-                    common_speculative_accept(spec.get(), slot.id, accepted.size() - 1);
-
-                    slot.spec_draft = std::move(accepted);
-                }
-
-                const int64_t t_now = ggml_time_us();
-
-                const auto ids = std::move(slot.spec_draft);
-
-                slot.t_token_generation = std::max<int64_t>(1, t_now - slot.t_start_generation) / 1e3;
-
-                // update how many tokens out of those tested were accepted
-                slot.n_draft_accepted += ids.size() - 1;
-                slot.n_draft_verif_steps += 1;
-
-                if (slot.n_accepted_per_pos.empty()) {
-                    slot.n_accepted_per_pos.resize(common_speculative_n_max(&params_base.speculative), 0);
-                }
-                for (size_t i = 0; i < ids.size() - 1 && i < slot.n_accepted_per_pos.size(); ++i) {
-                    slot.n_accepted_per_pos[i]++;
-                }
-
-                // add accepted tokens to the prompt
-                slot.prompt.tokens.keep_first(slot.prompt.n_tokens() - n_draft);
-                slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});
-
-                slot.sampled = ids.back(); // last accepted token
-                SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft);
-
-                common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1);
-                if (slot.ctx_dft) {
-                    common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1);
-                }
-
-                for (size_t i = 0; i < ids.size(); ++i) {
-                    completion_token_output result;
-
-                    result.tok          = ids[i];
-                    result.text_to_send = common_token_to_piece(slot.ctx_tgt, result.tok, accept_special_token(slot, result.tok));
-                    result.prob         = 1.0f; // set later
-
-                    // TODO: set result.probs
-
-                    slot.n_decoded += 1;
-
-                    if (!process_token(result, slot)) {
-                        slot.print_timings();
-                        send_final_response(slot);
-                        metrics.on_prediction(slot);
-                        slot.release();
-
-                        break;
-                    }
-                }
-
-                slot.print_timings_tg();
-
-                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) n_draft, slot.prompt.n_tokens());
-            }
-        }
-
-        SRV_DBG("%s", "run slots completed\n");
+            SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) n_draft, slot.prompt.n_tokens());
+        });
     }
 
     int get_slot_n_ctx() {

From 1afe5a730adec16ef7eda197b9a0f500a374ea07 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 21 Jun 2026 22:21:10 +0800
Subject: [PATCH 103/292] minor fixes to handler newer lcpp ui

---
 Makefile     |  2 +-
 koboldcpp.py | 11 +++++++----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/Makefile b/Makefile
index 31694bd91..cd5678538 100644
--- a/Makefile
+++ b/Makefile
@@ -730,7 +730,7 @@ otherarch/sdcpp/thirdparty/zip.o: otherarch/sdcpp/thirdparty/zip.c
 
 OBJS_SDTYPE := otherarch/sdcpp/sdtype_adapter.o $(OBJS_SDCOMMON)
 
-LLAMASERVER_SRCS := tools/server/main.cpp tools/server/server.cpp tools/server/server-chat.cpp tools/server/server-common.cpp tools/server/server-context.cpp tools/server/server-http.cpp tools/server/server-models.cpp tools/server/server-queue.cpp tools/server/server-task.cpp tools/server/server-tools.cpp tools/server/ui.cpp
+LLAMASERVER_SRCS := tools/server/main.cpp tools/server/server.cpp tools/server/server-schema.cpp tools/server/server-chat.cpp tools/server/server-common.cpp tools/server/server-context.cpp tools/server/server-http.cpp tools/server/server-models.cpp tools/server/server-queue.cpp tools/server/server-task.cpp tools/server/server-tools.cpp tools/server/ui.cpp
 LLAMASERVER_COMMON_SRCS := common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp vendor/cpp-httplib/httplib.cpp
 LLAMASERVER_CXXFLAGS := -I./tools/mtmd
 
diff --git a/koboldcpp.py b/koboldcpp.py
index da43ed29e..5cc2cc1b5 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -6122,11 +6122,11 @@ Change Mode<br>
             if autoswapmode and textName is not None:
                 modelNameToReturn = textName
 
-            mlist = [{"id":modelNameToReturn,"object":"model","created":int(time.time()),"owned_by":"koboldcpp","permission":[],"root":"koboldcpp"}]
+            mlist = [{"id":modelNameToReturn,"object":"model","created":int(time.time()),"owned_by":"koboldcpp","status":{"value":"loaded"},"permission":[],"root":"koboldcpp"}]
             if args.routermode:
                 alist = get_current_admindir_list()
                 for itm in alist:
-                    mlist.append({"id":itm,"object":"model","created":int(time.time()),"owned_by":"koboldcpp","permission":[],"root":"koboldcpp"})
+                    mlist.append({"id":itm,"object":"model","created":int(time.time()),"owned_by":"koboldcpp","status":{"value":"loaded"},"permission":[],"root":"koboldcpp"})
             response_body = (json.dumps({"object":"list","data":mlist}).encode())
 
         elif clean_path.endswith('/sdapi/v1/loras'):
@@ -6263,7 +6263,7 @@ Change Mode<br>
             mmprojOverride = False
             if autoswapmode and mmprojName is not None:
                 mmprojOverride = True
-            response_body = (json.dumps({
+            props_obj = {
                 "chat_template": cached_chat_template,
                 "id": 0,
 		        "id_task": -1,
@@ -6277,7 +6277,10 @@ Change Mode<br>
                 "default_generation_settings": {
                     "n_ctx": maxctx,
                 },
-            }).encode())
+            }
+            if args.routermode:
+                props_obj["role"] = "router"
+            response_body = (json.dumps(props_obj).encode())
 
         elif clean_path=="/slots":
             self.send_response(501)

From 44bcead521cff4aca4d66475b9cdb5388ef9b764 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 21 Jun 2026 22:28:47 +0800
Subject: [PATCH 104/292] mcp fixes

---
 koboldcpp.py | 29 ++++++++++++++++++++---------
 1 file changed, 20 insertions(+), 9 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 5cc2cc1b5..0dc55f47b 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -5988,6 +5988,13 @@ Change Mode<br>
             return None
 
         clean_path = clean_path.rstrip('/')
+        if clean_path=="/mcp":
+            self.send_response(405)
+            self.send_header('allow', 'POST')
+            self.send_header('content-length', '0')
+            self.end_headers(content_type='application/json')
+            return
+
         response_body = None
         content_type = 'application/json'
         content_encoding = None
@@ -6726,13 +6733,18 @@ Change Mode<br>
                 return
             try:
                 tempbody = json.loads(body)
+                request_id = tempbody.get("id", None)
                 method = tempbody.get("method","")
-                if method == "initialize":
+                if request_id is None: # notifications do not get a JSON-RPC response
+                    response_code = 202
+                    response_body = b''
+                elif method == "initialize":
+                    requested_version = tempbody.get("params", {}).get("protocolVersion", "2024-11-05")
                     reply = {
                         "jsonrpc": "2.0",
-                        "id": random.randint(100000, 999999),
+                        "id": request_id,
                         "result": {
-                            "protocolVersion": "2024-11-05",
+                            "protocolVersion": requested_version if requested_version else "2024-11-05",
                             "capabilities": {"tools": {"listChanged": False}},
                             "serverInfo": {"name": "mcp-koboldcpp", "version": "1.0.0"},
                         },
@@ -6741,7 +6753,7 @@ Change Mode<br>
                 elif method == "tools/list":
                     reply = {
                         "jsonrpc": "2.0",
-                        "id": random.randint(100000, 999999),
+                        "id": request_id,
                         "result": {"tools": []},
                     }
                     with mcp_lock:
@@ -6765,14 +6777,13 @@ Change Mode<br>
                                     response_body = (json.dumps(mcpresp).encode())
                                     break
                     if not foundtool:
-                        response_code = 400
-                        response_body = (json.dumps({"error": {"code": -32700, "message": "Tool not found"}}).encode())
-                else: #probably a notify, send empty response
-                    response_body = (json.dumps({}).encode())
+                        response_body = (json.dumps({"jsonrpc": "2.0", "id": request_id, "error": {"code": -32602, "message": "Tool not found"}}).encode())
+                else:
+                    response_body = (json.dumps({"jsonrpc": "2.0", "id": request_id, "error": {"code": -32601, "message": "Method not found"}}).encode())
             except Exception as e:
                 print(f"MCP Call Error: {e}")
                 response_code = 400
-                response_body = (json.dumps({"error": {"code": -32700, "message": "Parse error"}}).encode())
+                response_body = (json.dumps({"jsonrpc": "2.0", "id": None, "error": {"code": -32700, "message": "Parse error"}}).encode())
 
         elif clean_path=="/api/extra/shutdown":
             # if args.singleinstance:

From 08fbef5049a614a33971b9e62c2c82c5bc8fcec4 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 21 Jun 2026 22:56:10 +0800
Subject: [PATCH 105/292] lcpp ui think budget

---
 koboldcpp.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/koboldcpp.py b/koboldcpp.py
index 0dc55f47b..c4c8cbfbf 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -2190,6 +2190,9 @@ def generate(genparams, stream_flag=False):
     else:
         pass #unrestricted
 
+    if reasoning_budget==-1 and genparams.get('thinking_budget_tokens',-1)!=-1: #lcpp compat thinking budget
+        reasoning_budget = genparams.get('thinking_budget_tokens',-1)
+
     inputs.max_context_length = max_context_length   # this will resize the context buffer if changed
     inputs.max_length = max_length
     inputs.temperature = temperature

From a072dd83045af2ab5fdc75c9090fa8da9e48d37e Mon Sep 17 00:00:00 2001
From: henk717 <7526774+henk717@users.noreply.github.com>
Date: Sun, 21 Jun 2026 17:02:16 +0200
Subject: [PATCH 106/292] llama-ui update (#2281)

---
 embd_res/lcpp.gz.embd                         | Bin 1120462 -> 2537766 bytes
 .../ChatFormActionAddToolsSubmenu.svelte      |  10 ----------
 .../ui/src/lib/constants/settings-registry.ts |  12 ++++++------
 .../src/lib/hooks/use-tools-panel.svelte.ts   |   4 ++--
 tools/ui/svelte.config.js                     |   2 +-
 5 files changed, 9 insertions(+), 19 deletions(-)

diff --git a/embd_res/lcpp.gz.embd b/embd_res/lcpp.gz.embd
index 38da4685d0433aa484f3190670d0856884992b70..d5d1041163ae22aa70f6a7946f1fc5dc163a32a8 100644
GIT binary patch
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diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte
index 9a5b0cbe8..0ffdf2deb 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddToolsSubmenu.svelte
@@ -30,16 +30,6 @@
 				</div>
 			{:else if toolsStore.isToolsEndpointUnreachable}
 				<div class="grid gap-2.5 px-3 py-4 text-sm text-muted-foreground">
-					<span class="flex gap-2">
-						<Info class="mt-0.5 h-4 w-4 shrink-0" />
-
-						<span>
-							Run llama-server with <code>{CLI_FLAGS.TOOLS}</code> flag to enable
-
-							<strong>Built-in Tools</strong>.
-						</span>
-					</span>
-
 					<span class="flex gap-2">
 						<Info class="mt-0.5 h-4 w-4 shrink-0" />
 
diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
index 37161c8e3..4c23b2a88 100644
--- a/tools/ui/src/lib/constants/settings-registry.ts
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -132,10 +132,10 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,
 				label: 'Enable "Continue" button',
 				help: 'Enable "Continue" button for assistant messages, including reasoning models.',
-				defaultValue: false,
+				defaultValue: true,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.GENERAL,
-				isExperimental: true,
+				isExperimental: false,
 				sync: {
 					serverKey: SETTINGS_KEYS.ENABLE_CONTINUE_GENERATION,
 					paramType: SyncableParameterType.BOOLEAN
@@ -181,7 +181,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.TITLE_GENERATION_USE_LLM,
 				label: 'Use LLM to generate conversation title',
 				help: 'Use the LLM to automatically generate conversation titles based on the first message exchange.',
-				defaultValue: false,
+				defaultValue: true,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.GENERAL,
 				isExperimental: true
@@ -249,7 +249,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.KEEP_STATS_VISIBLE,
 				label: 'Keep stats visible after generation',
 				help: 'Keep processing statistics visible after generation finishes.',
-				defaultValue: false,
+				defaultValue: true,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.DISPLAY,
 				sync: {
@@ -261,7 +261,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,
 				label: 'Show microphone on empty input',
 				help: 'Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.',
-				defaultValue: false,
+				defaultValue: true,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.DISPLAY,
 				isExperimental: true,
@@ -322,7 +322,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,
 				label: 'Always show sidebar on desktop',
 				help: 'Always keep the sidebar visible on desktop instead of auto-hiding it.',
-				defaultValue: false,
+				defaultValue: true,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.DISPLAY,
 				sync: {
diff --git a/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts b/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
index 9f99d91d9..9e65e2f81 100644
--- a/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-tools-panel.svelte.ts
@@ -47,11 +47,11 @@ export function useToolsPanel(): UseToolsPanelReturn {
 		if (toolsStore.toolGroups.length > 0) return null;
 		// Tools endpoint is unreachable (404) — server started without --tools
 		if (toolsStore.isToolsEndpointUnreachable) {
-			return `To enable Built-In Tools you need to run llama-server with ${CLI_FLAGS.TOOLS} all or ${CLI_FLAGS.TOOLS} <name> flag. To see MCP Tools you need to add / enable MCP Server(s).`;
+			return `The KoboldCpp MCP bridge may accessible on the same URL with /mcp at the end but must be added manually.`;
 		}
 		// Other errors — return null so UI shows "Failed to load tools"
 		if (toolsStore.error) return null;
-		return `To enable Built-In Tools you need to run llama-server with ${CLI_FLAGS.TOOLS} all or ${CLI_FLAGS.TOOLS} <name> flag. To see MCP Tools you need to add / enable MCP Server(s).`;
+		return `The KoboldCpp MCP bridge may accessible on the same URL with /mcp at the end but must be added manually`;
 	});
 
 	function isGroupChecked(group: ToolGroup): boolean {
diff --git a/tools/ui/svelte.config.js b/tools/ui/svelte.config.js
index 3a361370c..cc5dd6ab4 100644
--- a/tools/ui/svelte.config.js
+++ b/tools/ui/svelte.config.js
@@ -26,7 +26,7 @@ const config = {
 			strict: true
 		}),
 		output: {
-			bundleStrategy: 'single'
+			bundleStrategy: 'inline'
 		},
 		alias: {
 			$styles: 'src/styles'

From 7c082bc417bbe53210a83df4ba5b49e18ce6193c Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 21 Jun 2026 17:36:52 +0200
Subject: [PATCH 107/292] server: fix report progress for loading spec models,
 add "stages" list (#24870)

* server: fix report progress for loading spec models, add "stages" list

* improve

* nits

* nits 2
---
 tools/server/README.md          |  8 +++-
 tools/server/server-context.cpp | 71 ++++++++++++++++++++-------------
 2 files changed, 50 insertions(+), 29 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index 5efdad095..7fa3a4d72 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1863,11 +1863,15 @@ Example events:
   "data": {
     "status": "loading",
     "progress": {
-      "stage": "fit_params",
-      "value": 0.5 // from 0.0 to 1.0 ; note: not all stages have this "value"
+      "stages": ["text_model", "spec_model", "mmproj_model"],
+      "current": "text_model",
+      "value": 0.5
     }
   }
 }
+// note for "loading" status:
+// - subsequent events will follow the same order of "stages" list
+// - mmap is may report incorrect progress on some platforms; if you need exact progress, use --no-mmap
 
 {
   "model": "...",
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 91a8eb945..3f9391cac 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -962,6 +962,7 @@ private:
     struct load_progress_data {
         server_context_impl * ctx;
         std::string stage;
+        std::vector<std::string> stages;
         int64_t t_last_load_progress_ms = 0;
         load_progress_data(server_context_impl * ctx, const std::string & stage) : ctx(ctx), stage(stage) {}
     };
@@ -982,7 +983,8 @@ private:
         }
         if (d->ctx->callback_state) {
             d->ctx->callback_state(SERVER_STATE_LOADING, {
-                {"stage", d->stage},
+                {"stages", d->stages},
+                {"current", d->stage},
                 {"value", progress},
             });
         }
@@ -992,18 +994,42 @@ private:
     // load the model and initialize llama_context
     // this may also be called to resume from sleeping state
     bool load_model(common_params & params) {
-        load_progress_data load_progress_text(this, "text_model");
+        load_progress_data load_progress_text  (this, "text_model");
         load_progress_data load_progress_mmproj(this, "mmproj_model");
+        load_progress_data load_progress_spec  (this, "spec_model");
 
-        bool is_resume = sleeping;
-
-        SRV_INF("loading model '%s'\n", params.model.path.c_str());
+        const bool is_resume = sleeping;
 
         params_base = params;
         params_base.n_outputs_max = server_n_outputs_max(params_base);
 
+        const bool has_mmproj = !params.mmproj.path.empty();
+        const bool has_draft = params.speculative.has_dft();
+        const bool spec_mtp = std::find(params_base.speculative.types.begin(),
+                                        params_base.speculative.types.end(),
+                                        COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
+        const bool has_spec = has_draft || spec_mtp;
+
+        if (callback_state) {
+            std::vector<std::string> stages = {"text_model"};
+            if (has_spec) {
+                stages.push_back("spec_model");
+            }
+            if (has_mmproj) {
+                stages.push_back("mmproj_model");
+            }
+            load_progress_text.stages   = stages;
+            load_progress_mmproj.stages = stages;
+            load_progress_spec.stages   = stages;
+
+            // trigger 0% progress
+            load_progress_callback(0.0f, &load_progress_text);
+        }
+
+
+        SRV_INF("loading model '%s'\n", params.model.path.c_str());
+
         std::string & mmproj_path = params_base.mmproj.path;
-        bool has_mmproj = !mmproj_path.empty();
         mtmd_context_params mparams = mtmd_context_params_default();
         if (has_mmproj) {
             mparams.use_gpu          = params_base.mmproj_use_gpu;
@@ -1050,16 +1076,7 @@ private:
 
         // optionally reserve VRAM for the draft / MTP context before fitting the target model
         if (params_base.fit_params) {
-            if (callback_state) {
-                callback_state(SERVER_STATE_LOADING, {{"stage", "fit_params"}});
-            }
-
-            const bool spec_mtp = std::find(params_base.speculative.types.begin(),
-                                            params_base.speculative.types.end(),
-                                            COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
-            const bool has_draft = params_base.speculative.has_dft();
-
-            if (has_draft || spec_mtp) {
+            if (has_spec) {
                 common_params params_dft = params_base;
                 bool measure_model_bytes = true;
 
@@ -1151,11 +1168,7 @@ private:
 
         add_bos_token = llama_vocab_get_add_bos(vocab);
 
-        if (params_base.speculative.has_dft()) {
-            if (callback_state) {
-                callback_state(SERVER_STATE_LOADING, {{"stage", "spec_model"}});
-            }
-
+        if (has_draft) {
             // TODO speculative: move to common/speculative.cpp?
             const auto & params_spec = params_base.speculative.draft;
 
@@ -1178,6 +1191,10 @@ private:
 
             auto mparams_dft = common_model_params_to_llama(params_dft);
 
+            // progress callback
+            mparams_dft.progress_callback           = load_progress_callback;
+            mparams_dft.progress_callback_user_data = &load_progress_spec;
+
             model_dft.reset(llama_model_load_from_file(params_dft.model.path.c_str(), mparams_dft));
             if (model_dft == nullptr) {
                 SRV_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str());
@@ -1186,10 +1203,6 @@ private:
 
             auto cparams = common_context_params_to_llama(params_dft);
 
-            const bool spec_mtp = std::find(params_base.speculative.types.begin(),
-                                            params_base.speculative.types.end(),
-                                            COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
-
             if (spec_mtp) {
                 cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
             }
@@ -1203,8 +1216,10 @@ private:
 
             params_base.speculative.draft.ctx_tgt = ctx_tgt;
             params_base.speculative.draft.ctx_dft = ctx_dft.get();
-        } else if (std::find(params_base.speculative.types.begin(), params_base.speculative.types.end(),
-                             COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end()) {
+        } else if (spec_mtp) {
+            // no new model load, so we simply report 0.0 and 1.0 progress
+            load_progress_callback(0.0f, &load_progress_spec);
+
             SRV_INF("creating MTP draft context against the target model '%s'\n",
                     params_base.model.path.c_str());
 
@@ -1224,6 +1239,8 @@ private:
 
             params_base.speculative.draft.ctx_tgt = ctx_tgt;
             params_base.speculative.draft.ctx_dft = ctx_dft.get();
+
+            load_progress_callback(1.0f, &load_progress_spec);
         }
 
         if (has_mmproj) {

From dfa1c573c4e6b7e8a43577c435c4f9762dc02db4 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 22 Jun 2026 00:21:19 +0800
Subject: [PATCH 108/292] fix router mode on lcpp ui

---
 embd_res/lcpp.gz.embd                         | Bin 2537766 -> 2571315 bytes
 .../ChatFormActionModels.svelte               |  18 +++---------------
 tools/ui/src/lib/constants/app.ts             |   2 +-
 .../ui/src/lib/constants/settings-registry.ts |   2 +-
 .../lib/hooks/use-models-selector.svelte.ts   |  11 +++++++++--
 5 files changed, 14 insertions(+), 19 deletions(-)

diff --git a/embd_res/lcpp.gz.embd b/embd_res/lcpp.gz.embd
index d5d1041163ae22aa70f6a7946f1fc5dc163a32a8..6d0df643be60d0c92c7d615fdbf16310450af040 100644
GIT binary patch
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z^U#|}@!|#*CU4MqqGkTqh?e;m5G|2Bnn&MCipgbbv)z5INCBRPP+9w#!nF9-hP-ej
zpm~#a>VHv_GynNbwseNk%d9@#CQUvXNb2G!Hm*o$oEa@Kh=$Ju{z>zp-A?6FMKo_R
z*(I|$&uw~#P2tj_>erI?9gCU5thL{Wu-cs&)=sdCP|f&u;=IfF-p8{W*T91%<KWpk
zn6aNs&-@y@$^Rh@EdIO(W~hO$WTH7R(U^C7#v(|%m7!MphFYmeV%?nA?m;R<13S(G
zND@9AHiR#aQr6DwtAYi|L1lgFY$vQ#s^%d{J?@5|)yPA$k^PJv=Mfi{WHYlbZs(!Z
z&g}Sh9$M|}qn%G|b|}jiTQq?hL#-BWVVX{5WbejHrjNW4;x!WPwM@PdYg?iLtud7e
z8#PZpEuCzvHa@r7z4gQ;!toou6BQzioJY4m*|l+N<+bJ<MhU{RtyQQ##NE~GT*X0;
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z3e#8=-tb2Vc;W&5{WvXL<bC=zeK*4ge0-cfZ8!U$e*JO!vU_v=>DT`N3^@{l1>l4L
Drb=5)

diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte
index 712326cba..998e8dcb4 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionModels.svelte
@@ -63,8 +63,8 @@
 				modelsStore.selectedModelName = conversationModel;
 				modelsStore.selectModelByName(conversationModel);
 			} else {
-				modelsStore.selectedModelName = null;
-				modelsStore.clearSelection();
+				modelsStore.selectedModelId = null;
+				modelsStore.selectedModelName = conversationModel;
 			}
 			lastSyncedConversationModel = conversationModel;
 		} else if (
@@ -141,19 +141,7 @@
 	});
 
 	$effect(() => {
-		if (!isRouter) {
-			isSelectedModelInCache = true;
-		} else if (conversationModel) {
-			isSelectedModelInCache = modelOptions().some((option) => option.model === conversationModel);
-		} else {
-			const currentModelId = selectedModelId();
-
-			if (!currentModelId) {
-				isSelectedModelInCache = false;
-			} else {
-				isSelectedModelInCache = modelOptions().some((option) => option.id === currentModelId);
-			}
-		}
+		isSelectedModelInCache = !isRouter || !!conversationModel || !!selectedModelId();
 	});
 
 	$effect(() => {
diff --git a/tools/ui/src/lib/constants/app.ts b/tools/ui/src/lib/constants/app.ts
index c598f480f..d47e16569 100644
--- a/tools/ui/src/lib/constants/app.ts
+++ b/tools/ui/src/lib/constants/app.ts
@@ -1 +1 @@
-export const APP_NAME = import.meta.env?.VITE_PUBLIC_APP_NAME || 'llama-ui';
+export const APP_NAME = import.meta.env?.VITE_PUBLIC_APP_NAME || 'llama-ui-koboldcpp';
diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
index 4c23b2a88..7bd8aa858 100644
--- a/tools/ui/src/lib/constants/settings-registry.ts
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -322,7 +322,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.ALWAYS_SHOW_SIDEBAR_ON_DESKTOP,
 				label: 'Always show sidebar on desktop',
 				help: 'Always keep the sidebar visible on desktop instead of auto-hiding it.',
-				defaultValue: true,
+				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.DISPLAY,
 				sync: {
diff --git a/tools/ui/src/lib/hooks/use-models-selector.svelte.ts b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
index 098cb2c27..9af439229 100644
--- a/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
@@ -76,7 +76,7 @@ export function useModelsSelector(opts: UseModelsSelectorOptions): UseModelsSele
 
 	const isCurrentModelInCache = $derived.by(() => {
 		if (!isRouter || !currentModel) return true;
-		return options.some((option) => option.model === currentModel);
+		return true; //kcpp hack to always let the model be treated like in cache even if returned name is wrong
 	});
 
 	let isLoadingModel = $state(false);
@@ -183,7 +183,14 @@ export function useModelsSelector(opts: UseModelsSelectorOptions): UseModelsSele
 				};
 			}
 
-			return options.find((option) => option.model === currentModel);
+			return (
+				options.find((option) => option.model === currentModel) ?? {
+					id: 'current',
+					model: currentModel,
+					name: currentModel.split('/').pop() || currentModel,
+					capabilities: []
+				}
+			);
 		}
 
 		if (activeId) {

From 52b3df0023659b142ce29f75c7a82cf437769c33 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Sun, 21 Jun 2026 16:20:58 -0500
Subject: [PATCH 109/292] common/peg : implement ac parser for stricter grammar
 generation (#24869)

* common/peg : implement ac parser

* cont : extract functions

* cont : tidy up

* cont : remove a test

* cont : move ac() def
---
 common/chat-auto-parser-generator.cpp     |   9 +-
 common/peg-parser.cpp                     | 131 +++++++++++++++++-----
 common/peg-parser.h                       |  15 ++-
 tests/peg-parser/test-gbnf-generation.cpp |  69 ++++++++++++
 4 files changed, 190 insertions(+), 34 deletions(-)

diff --git a/common/chat-auto-parser-generator.cpp b/common/chat-auto-parser-generator.cpp
index 37ca55c8d..36aab7ecb 100644
--- a/common/chat-auto-parser-generator.cpp
+++ b/common/chat-auto-parser-generator.cpp
@@ -395,10 +395,11 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
                                            arguments.name_suffix) +
                            arguments.value_prefix +
                            (schema_info.resolves_to_string(param_schema) ?
-                                p.tool_arg_string_value(until_suffix) :
-                                p.tool_arg_json_value(p.schema(
-                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false))) +
-                           p.tool_arg_close(p.literal(arguments.value_suffix)));
+                                p.ac(p.tool_arg_string_value(until_suffix) +
+                                    p.tool_arg_close(p.literal(arguments.value_suffix)), arguments.value_suffix) :
+                                (p.tool_arg_json_value(p.schema(
+                                    p.json(), "tool-" + name + "-arg-" + param_name + "-schema", param_schema, false)) +
+                                    p.tool_arg_close(p.literal(arguments.value_suffix)))));
 
             auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
             if (is_required) {
diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp
index 506b90245..807e952d9 100644
--- a/common/peg-parser.cpp
+++ b/common/peg-parser.cpp
@@ -921,6 +921,10 @@ struct parser_executor {
     common_peg_parse_result operator()(const common_peg_gbnf_parser & p) {
         return arena.parse(p.child, ctx, start_pos);
     }
+
+    common_peg_parse_result operator()(const common_peg_ac_parser & p) {
+        return arena.parse(p.child, ctx, start_pos);
+    }
 };
 
 common_peg_parse_result common_peg_arena::parse(common_peg_parse_context & ctx, size_t start) const {
@@ -989,7 +993,8 @@ void common_peg_arena::resolve_refs() {
                                  std::is_same_v<T, common_peg_not_parser> ||
                                  std::is_same_v<T, common_peg_tag_parser> ||
                                  std::is_same_v<T, common_peg_atomic_parser> ||
-                                 std::is_same_v<T, common_peg_gbnf_parser>) {
+                                 std::is_same_v<T, common_peg_gbnf_parser> ||
+                                 std::is_same_v<T, common_peg_ac_parser>) {
                 p.child = resolve_ref(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
                 p.child = resolve_ref(p.child);
@@ -1070,6 +1075,8 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id
             return "Atomic(" + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
             return "Gbnf(" + p.grammar + ", " + dump_impl(p.child, visited) + ")";
+        } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+            return "Ac(" + string_join(p.delimiters, " | ") + ", " + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_any_parser>) {
             return "Any";
         } else if constexpr (std::is_same_v<T, common_peg_space_parser>) {
@@ -1479,6 +1486,13 @@ common_peg_parser common_peg_parser_builder::json_member(const std::string & key
     });
 }
 
+common_peg_parser common_peg_parser_builder::ac(const common_peg_parser & p, const std::vector<std::string> & delimiters) {
+    if (delimiters.empty()) {
+        throw std::runtime_error("ac parser requires at least one delimiter");
+    }
+    return add(common_peg_ac_parser{p, delimiters});
+}
+
 static std::string gbnf_escape_char_class(uint32_t c) {
     if (c == '-' || c == ']' || c == '[' || c == '\\') {
         return "\\" + std::string(1, (char) c);
@@ -1529,14 +1543,22 @@ static std::string gbnf_escape_char_class(uint32_t c) {
     return std::string(buf);
 }
 
-// GBNF grammar matching strings that contain no string in `strings` as a
-// substring. Emits the complement of an Aho-Corasick automaton DFA and returns
-// the start state rule name.
-//
-// ref: https://github.com/ggml-org/llama.cpp/pull/24839
-static std::string gbnf_excluding_grammar(const common_grammar_builder & builder,
-                                          const std::string &            prefix,
-                                          const std::vector<std::string> & strings) {
+static std::string gbnf_char_class(const std::vector<uint32_t> & chars, bool negate) {
+    std::string s = negate ? "[^" : "[";
+    for (uint32_t ch : chars) {
+        s += gbnf_escape_char_class(ch);
+    }
+    return s + "]";
+}
+
+static std::string gbnf_ac_grammar(
+    const common_grammar_builder &   builder,
+    const std::string &              prefix,
+    const std::vector<std::string> & strings,
+    const std::function<std::string(const std::vector<uint32_t> &,
+                                    const std::map<size_t, std::vector<uint32_t>> &,
+                                    const std::vector<uint32_t> &,
+                                    const std::function<std::string(size_t)> &)> & build_rule) {
     aho_corasick ac(strings);
 
     auto state_name = [&](size_t s) -> std::string {
@@ -1548,42 +1570,30 @@ static std::string gbnf_excluding_grammar(const common_grammar_builder & builder
         return prefix + "-" + num;
     };
 
-    auto char_class = [](const std::vector<uint32_t> & chars, bool negate) {
-        std::string s = negate ? "[^" : "[";
-        for (uint32_t ch : chars) {
-            s += gbnf_escape_char_class(ch);
-        }
-        return s + "]";
-    };
-
     for (size_t q = 0; q < ac.num_states(); q++) {
         if (ac.is_terminal(q)) {
-            continue; // match states are dropped
+            continue; // match states
         }
 
         std::map<size_t, std::vector<uint32_t>> buckets;
-        std::vector<uint32_t> excluded;
+        std::vector<uint32_t> completing;  // chars that complete a delimiter
+        std::vector<uint32_t> specific;    // chars with an explicit transition
         for (uint32_t c : ac.alphabet) {
             size_t d = ac.next(q, c);
             if (ac.is_terminal(d)) {
-                excluded.push_back(c); // completes a forbidden string -> omit
+                completing.push_back(c);
+                specific.push_back(c);
             } else if (d != 0) {
                 buckets[d].push_back(c); // specific non-root destination
-                excluded.push_back(c);
+                specific.push_back(c);
             }
         }
 
-        std::string rhs = "|"; // every state is accepting
-        for (const auto & [d, chars] : buckets) {
-            rhs += " " + char_class(chars, false) + " " + state_name(d) + " |";
-        }
-        rhs += " " + char_class(excluded, true) + " " + state_name(0);
-
-        builder.add_rule(state_name(q), rhs);
+        builder.add_rule(state_name(q), build_rule(completing, buckets, specific, state_name));
     }
 
     // An empty delimiter makes the start state terminal. Emit an entry rule
-    // that matches nothing so the returned reference stays valid.
+    // that matches the empty string so the returned reference stays valid.
     if (ac.is_terminal(0)) {
         builder.add_rule(prefix, "|");
     }
@@ -1591,6 +1601,54 @@ static std::string gbnf_excluding_grammar(const common_grammar_builder & builder
     return state_name(0);
 }
 
+// GBNF grammar matching strings that contain no string in `strings` as a
+// substring. Emits the complement of an Aho-Corasick automaton DFA and returns
+// the start state rule name.
+//
+// ref: https://github.com/ggml-org/llama.cpp/pull/24839
+static std::string gbnf_excluding_grammar(const common_grammar_builder & builder,
+                                          const std::string &            prefix,
+                                          const std::vector<std::string> & strings) {
+    return gbnf_ac_grammar(builder, prefix, strings,
+        [](const std::vector<uint32_t> & /*completing*/,
+           const std::map<size_t, std::vector<uint32_t>> & buckets,
+           const std::vector<uint32_t> & specific,
+           const std::function<std::string(size_t)> & state_name) {
+            // every state is accepting and completing chars get no
+            // alternative, so a forbidden string can never be matched
+            std::string rhs = "|";
+            for (const auto & [d, chars] : buckets) {
+                rhs += " " + gbnf_char_class(chars, false) + " " + state_name(d) + " |";
+            }
+            rhs += " " + gbnf_char_class(specific, true) + " " + state_name(0);
+            return rhs;
+        });
+}
+
+// GBNF grammar matching everything up to and including the first occurrence of
+// any string in `strings`. Emits the Aho-Corasick automaton DFA and returns
+// the start state rule name.
+static std::string gbnf_including_grammar(const common_grammar_builder & builder,
+                                          const std::string &            prefix,
+                                          const std::vector<std::string> & strings) {
+    return gbnf_ac_grammar(builder, prefix, strings,
+        [](const std::vector<uint32_t> & completing,
+           const std::map<size_t, std::vector<uint32_t>> & buckets,
+           const std::vector<uint32_t> & specific,
+           const std::function<std::string(size_t)> & state_name) {
+            std::vector<std::string> alts;
+            if (!completing.empty()) {
+                alts.push_back(gbnf_char_class(completing, false)); // terminate on match
+            }
+            for (const auto & [d, chars] : buckets) {
+                alts.push_back(gbnf_char_class(chars, false) + " " + state_name(d));
+            }
+            // every other character keeps scanning from the start state
+            alts.push_back(gbnf_char_class(specific, true) + " " + state_name(0));
+            return string_join(alts, " | ");
+        });
+}
+
 static std::set<std::string> collect_reachable_rules(
     const common_peg_arena & arena,
     const common_peg_parser_id & rule
@@ -1628,6 +1686,7 @@ static std::set<std::string> collect_reachable_rules(
                                  std::is_same_v<T, common_peg_tag_parser> ||
                                  std::is_same_v<T, common_peg_atomic_parser> ||
                                  std::is_same_v<T, common_peg_gbnf_parser> ||
+                                 std::is_same_v<T, common_peg_ac_parser> ||
                                  std::is_same_v<T, common_peg_schema_parser>) {
                 visit(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
@@ -1822,6 +1881,8 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 return to_gbnf(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
                 return p.grammar;
+            } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+                return gbnf_including_grammar(builder, "ac-" + std::to_string(id), p.delimiters);
             } else {
                 static_assert(is_always_false_v<T>);
             }
@@ -1958,6 +2019,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
             };
         } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
             return json{{"type", "gbnf"}, {"child", p.child}, {"grammar", p.grammar}};
+        } else if constexpr (std::is_same_v<T, common_peg_ac_parser>) {
+            return json{{"type", "ac"}, {"child", p.child}, {"delimiters", p.delimiters}};
         }
     }, variant);
 }
@@ -2130,6 +2193,16 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
         };
     }
 
+    if (type == "ac") {
+        if (!j.contains("child") || !j.contains("delimiters") || !j["delimiters"].is_array() || j["delimiters"].empty()) {
+            throw std::runtime_error("ac parser requires 'child' and a non-empty 'delimiters' array");
+        }
+        return common_peg_ac_parser{
+            j["child"].get<common_peg_parser_id>(),
+            j["delimiters"].get<std::vector<std::string>>(),
+        };
+    }
+
     throw std::runtime_error("Unknown parser type: " + type);
 }
 
diff --git a/common/peg-parser.h b/common/peg-parser.h
index 132173a64..c198499dd 100644
--- a/common/peg-parser.h
+++ b/common/peg-parser.h
@@ -275,6 +275,11 @@ struct common_peg_gbnf_parser {
     std::string grammar;
 };
 
+struct common_peg_ac_parser {
+    common_peg_parser_id child;
+    std::vector<std::string> delimiters;
+};
+
 // Variant holding all parser types
 using common_peg_parser_variant = std::variant<
     common_peg_epsilon_parser,
@@ -296,7 +301,8 @@ using common_peg_parser_variant = std::variant<
     common_peg_ref_parser,
     common_peg_atomic_parser,
     common_peg_tag_parser,
-    common_peg_gbnf_parser
+    common_peg_gbnf_parser,
+    common_peg_ac_parser
 >;
 
 class common_peg_arena {
@@ -514,6 +520,13 @@ class common_peg_parser_builder {
     // the child's grammar. Parsing delegates entirely to the child.
     common_peg_parser gbnf(const common_peg_parser & p, const std::string & grammar) { return add(common_peg_gbnf_parser{p, grammar}); }
 
+    // Wraps a child parser but emits a GBNF grammar built from the Aho-Corasick
+    // automaton of `delimiters`, matching everything up to and including the
+    // first delimiter. Parsing delegates entirely to the child, which is
+    // responsible for consuming the delimiter (e.g. until(D) + literal(D)).
+    common_peg_parser ac(const common_peg_parser & p, const std::vector<std::string> & delimiters);
+    common_peg_parser ac(const common_peg_parser & p, const std::string & delimiter) { return ac(p, std::vector<std::string>{delimiter}); }
+
     void set_root(const common_peg_parser & p);
 
     common_peg_arena build();
diff --git a/tests/peg-parser/test-gbnf-generation.cpp b/tests/peg-parser/test-gbnf-generation.cpp
index 45d692ca6..60066a817 100644
--- a/tests/peg-parser/test-gbnf-generation.cpp
+++ b/tests/peg-parser/test-gbnf-generation.cpp
@@ -212,6 +212,75 @@ void test_gbnf_generation(testing &t) {
         )""", gbnf);
     });
 
+    t.test("ac grammar", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.ac(p.until("</tag>") + p.literal("</tag>"), "</tag>");
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            ac-3 ::= [<] ac-3-01 | [^<] ac-3
+            ac-3-01 ::= [<] ac-3-01 | [/] ac-3-02 | [^/<] ac-3
+            ac-3-02 ::= [<] ac-3-01 | [t] ac-3-03 | [^<t] ac-3
+            ac-3-03 ::= [<] ac-3-01 | [a] ac-3-04 | [^<a] ac-3
+            ac-3-04 ::= [<] ac-3-01 | [g] ac-3-05 | [^<g] ac-3
+            ac-3-05 ::= [>] | [<] ac-3-01 | [^<>] ac-3
+            root ::= ac-3
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+        )""", gbnf);
+    });
+
+    t.test("ac grammar terminates at first delimiter", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.ac(p.until("\n</parameter>\n") + p.literal("\n</parameter>\n"), "\n</parameter>\n");
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            ac-3 ::= [\n] ac-3-01 | [^\n] ac-3
+            ac-3-01 ::= [\n] ac-3-01 | [<] ac-3-02 | [^\n<] ac-3
+            ac-3-02 ::= [\n] ac-3-01 | [/] ac-3-03 | [^\n/] ac-3
+            ac-3-03 ::= [\n] ac-3-01 | [p] ac-3-04 | [^\np] ac-3
+            ac-3-04 ::= [\n] ac-3-01 | [a] ac-3-05 | [^\na] ac-3
+            ac-3-05 ::= [\n] ac-3-01 | [r] ac-3-06 | [^\nr] ac-3
+            ac-3-06 ::= [\n] ac-3-01 | [a] ac-3-07 | [^\na] ac-3
+            ac-3-07 ::= [\n] ac-3-01 | [m] ac-3-08 | [^\nm] ac-3
+            ac-3-08 ::= [\n] ac-3-01 | [e] ac-3-09 | [^\ne] ac-3
+            ac-3-09 ::= [\n] ac-3-01 | [t] ac-3-10 | [^\nt] ac-3
+            ac-3-10 ::= [\n] ac-3-01 | [e] ac-3-11 | [^\ne] ac-3
+            ac-3-11 ::= [\n] ac-3-01 | [r] ac-3-12 | [^\nr] ac-3
+            ac-3-12 ::= [\n] ac-3-01 | [>] ac-3-13 | [^\n>] ac-3
+            ac-3-13 ::= [\n] | [^\n] ac-3
+            root ::= ac-3
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+        )""", gbnf);
+    });
+
+    t.test("ac grammar multiple delimiters", [](testing &t) {
+        auto parser = build_peg_parser([](common_peg_parser_builder & p)  {
+            return p.ac(p.eps(), std::vector<std::string>{"ab", "cd", "ef"});
+        });
+
+        auto gbnf = build_grammar([&](const common_grammar_builder & builder) {
+            parser.build_grammar(builder);
+        });
+
+        assert_gbnf_equal(t, R"""(
+            ac-1 ::= [a] ac-1-01 | [c] ac-1-03 | [e] ac-1-05 | [^ace] ac-1
+            ac-1-01 ::= [b] | [a] ac-1-01 | [c] ac-1-03 | [e] ac-1-05 | [^abce] ac-1
+            ac-1-03 ::= [d] | [a] ac-1-01 | [c] ac-1-03 | [e] ac-1-05 | [^acde] ac-1
+            ac-1-05 ::= [f] | [a] ac-1-01 | [c] ac-1-03 | [e] ac-1-05 | [^acef] ac-1
+            root ::= ac-1
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+        )""", gbnf);
+    });
+
     t.test("complex expressions with parentheses", [](testing &t) {
         auto parser = build_peg_parser([](common_peg_parser_builder & p) {
             return p.one_or_more(p.literal("a") | p.literal("b"));

From 0ef6f06d553b160d8fc1fba38f5848c7940873a2 Mon Sep 17 00:00:00 2001
From: aafsmarak <92150196+aafsmarak@users.noreply.github.com>
Date: Mon, 22 Jun 2026 09:18:31 +0530
Subject: [PATCH 110/292] docs/android.md: Add dependency `libandroid-spawn`
 for building in termux (#21812)

Fixes https://github.com/ggml-org/llama.cpp/issues/18615
---
 docs/android.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/android.md b/docs/android.md
index 964ce8a1f..e8d580a9e 100644
--- a/docs/android.md
+++ b/docs/android.md
@@ -29,7 +29,7 @@ With Termux, you can install and run `llama.cpp` as if the environment were Linu
 
 ```
 $ apt update && apt upgrade -y
-$ apt install git cmake
+$ apt install git cmake libandroid-spawn
 ```
 
 Then, follow the [build instructions](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md), specifically for CMake.

From d0f9d2e5ac5d4f51763755958b8f353fed01aaa2 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Mon, 22 Jun 2026 10:55:28 +0200
Subject: [PATCH 111/292] server: fix edit_file crash on append at end of file
 (line_start -1) (#24893)

line_start -1 normalized to n+1, so append inserted at lines.begin() + n + 1,
one past end() -> heap-buffer-overflow in vector::_M_range_insert.

Normalize -1 to n (insert at end()), restrict -1 to append mode and reject it
for replace/delete instead of silently clobbering the last line. Parenthesize
the insert offset so empty-file append computes the position as int first,
avoiding a transient begin() - 1 on a null vector data pointer.
---
 tools/server/server-tools.cpp | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/tools/server/server-tools.cpp b/tools/server/server-tools.cpp
index 95662d4ec..790ed85a0 100644
--- a/tools/server/server-tools.cpp
+++ b/tools/server/server-tools.cpp
@@ -569,9 +569,13 @@ struct server_tool_edit_file : server_tool {
             }
             int n = (int) lines.size();
             if (e.line_start == -1) {
-                // -1 means end of file; line_end is ignored — normalize to point past last line
-                e.line_start = n + 1;
-                e.line_end   = n + 1;
+                // -1 targets end of file -> valid for append only; line_end is ignored
+                if (e.mode != "append") {
+                    return {{"error", "line_start -1 (end of file) is only valid for append mode"}};
+                }
+                // append at end of file: insert position is the current line count
+                e.line_start = n;
+                e.line_end   = n;
             } else {
                 if (e.line_start < 1 || e.line_end < e.line_start) {
                     return {{"error", string_format("invalid line range [%d, %d]", e.line_start, e.line_end)}};
@@ -612,8 +616,8 @@ struct server_tool_edit_file : server_tool {
             } else if (e.mode == "delete") {
                 lines.erase(lines.begin() + idx_start, lines.begin() + idx_end + 1);
             } else { // append
-                // idx_end + 1 may equal lines.size() when line_start == -1 (end of file)
-                lines.insert(lines.begin() + idx_end + 1, new_lines.begin(), new_lines.end());
+                // insert after idx_end; idx_end + 1 == lines.size() for end-of-file append
+                lines.insert(lines.begin() + (idx_end + 1), new_lines.begin(), new_lines.end());
             }
         }
 

From e4771e8e6b0d20b1c575ee8e855ee1ab56ab50c8 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 22 Jun 2026 18:55:26 +0800
Subject: [PATCH 112/292] restore draft state before main state, fixes
 reloading gemma4 assistant

---
 gpttype_adapter.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 65b48f276..5a0e8b8de 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -6995,6 +6995,12 @@ bool gpttype_load_state_kv(int slot)
         if (savestates[slot].current_savestate_buffer.empty()) {
             return false;
         }
+        if(draft_ctx && savestates[slot].current_draft_savestate_size>0)
+        {
+            llama_memory_clear(llama_get_memory(draft_ctx),true);
+            auto res2 = llama_state_set_data(draft_ctx, savestates[slot].current_draft_savestate_buffer.data(), savestates[slot].current_draft_savestate_size);
+            printf("\nKV Load DraftSaveState %d: Restored KV with %zu tokens.\n", slot,current_context_tokens.size());
+        }
         llama_memory_clear(llama_get_memory(llama_ctx_v4),true);
         auto res = llama_state_set_data(llama_ctx_v4, savestates[slot].current_savestate_buffer.data(), savestates[slot].current_savestate_size);
         if(res > 0)
@@ -7002,12 +7008,6 @@ bool gpttype_load_state_kv(int slot)
             current_context_tokens = savestates[slot].savestate_context_tokens;
             loaded_latest_logits = savestates[slot].latest_logits;
             printf("\nKV Load SaveState %d: Restored KV with %zu tokens.\n", slot,current_context_tokens.size());
-            if(draft_ctx && savestates[slot].current_draft_savestate_size>0)
-            {
-                llama_memory_clear(llama_get_memory(draft_ctx),true);
-                auto res2 = llama_state_set_data(draft_ctx, savestates[slot].current_draft_savestate_buffer.data(), savestates[slot].current_draft_savestate_size);
-                printf("\nKV Load DraftSaveState %d: Restored KV with %zu tokens.\n", slot,current_context_tokens.size());
-            }
             touch_slot(slot);
         }
         return (res > 0);

From 37957e8531bcd2e5f98233d6ecc864f2b76e6b8b Mon Sep 17 00:00:00 2001
From: Tim Neumann <timnn@google.com>
Date: Mon, 22 Jun 2026 13:08:32 +0200
Subject: [PATCH 113/292] sampling : remove unconditional softmax+sort in
 top-n-sigma sampler (#22645)

---
 src/llama-sampler.cpp   | 2 --
 tests/test-sampling.cpp | 4 ++--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/llama-sampler.cpp b/src/llama-sampler.cpp
index 9bbc5dbde..2370e91a1 100644
--- a/src/llama-sampler.cpp
+++ b/src/llama-sampler.cpp
@@ -2813,8 +2813,6 @@ static void llama_sampler_top_n_sigma_apply(struct llama_sampler * smpl, llama_t
             cur_p->data[i].logit = -INFINITY;
         }
     }
-
-    llama_sampler_softmax_impl(cur_p, true);
 }
 
 static struct llama_sampler * llama_sampler_top_n_sigma_clone(const struct llama_sampler * smpl) {
diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 7cd96c5cd..2aecff90e 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -360,9 +360,9 @@ int main(void) {
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.032727f, 0.241818f, 0.241818f}, 2.0f, 1.1f, 2, 5, {});
     test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
 
-    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f, 0.0f, 0.0f}, 1.00f);
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.428571f, 0.571429f}, 1.00f);
     test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345
-    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 3.00f);
+    test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 3.00f);
 
     test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
     test_sampler_queue(10000, "k",     1, 1.0f, 1.0f);

From f8cc15f163e784c58fe13aee58ebc03055bb0c40 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Mon, 22 Jun 2026 19:09:02 +0800
Subject: [PATCH 114/292] [SYCL] support bf16 on bin_bcast OP and unary OPs
 (#24838)

* support bf16 on bin_bcast OP and unary OPs

* support the older Intel compiler than 2026.0
---
 ggml/src/ggml-sycl/binbcast.cpp     |   5 +
 ggml/src/ggml-sycl/element_wise.cpp | 208 +++++++++++++++++++++-------
 2 files changed, 160 insertions(+), 53 deletions(-)

diff --git a/ggml/src/ggml-sycl/binbcast.cpp b/ggml/src/ggml-sycl/binbcast.cpp
index ad2e6ca35..306eeddc0 100644
--- a/ggml/src/ggml-sycl/binbcast.cpp
+++ b/ggml/src/ggml-sycl/binbcast.cpp
@@ -293,6 +293,11 @@ inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_t
              (sycl::ext::oneapi::bfloat16 *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2,
              ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, nb3, ggml_is_contiguous(src0),
              ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), main_stream);
+    } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_BF16) {
+        op()((const sycl::ext::oneapi::bfloat16 *) src0->data, (const float *) src1->data,
+             (sycl::ext::oneapi::bfloat16 *) dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne0, ne1, ne2,
+             ne3, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb0, nb1, nb2, nb3, ggml_is_contiguous(src0),
+             ggml_is_contiguous(src1), ggml_is_permuted(src0), ggml_is_permuted(src1), main_stream);
 #endif
     } else {
         fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, ggml_type_name(dst->type),
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index aca68e58e..0c82ceb96 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -43,14 +43,44 @@ static __dpct_inline__ T op_sgn(T x) {
     return x > static_cast<T>(0.f) ? static_cast<T>(1.f) : ((x < static_cast<T>(0.f) ? static_cast<T>(-1.f) : static_cast<T>(0.f)));
 }
 
+
 template<typename T>
 static __dpct_inline__ T op_abs(T x) {
-    return sycl::fabs(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::fabs(x);  // or experimental namespace if needed
+    } else {
+        return sycl::fabs(x);
+    }
+}
+
+template<typename T>
+static __dpct_inline__ T op_expm1(T x) {
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return static_cast<sycl::ext::oneapi::bfloat16>(
+            sycl::expm1(static_cast<float>(x))
+        );
+    } else {
+        return sycl::expm1(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_elu(T x) {
-    return (x > static_cast<T>(0.f)) ? x : sycl::expm1(x);
+    return (x > static_cast<T>(0.f)) ? x : op_expm1(x);
+}
+
+template<typename T>
+static __dpct_inline__ T op_tanh(T x) {
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        constexpr int ver = __INTEL_LLVM_COMPILER;
+#if defined(__INTEL_LLVM_COMPILER) && (__INTEL_LLVM_COMPILER >= 20260000)
+            return sycl::ext::oneapi::experimental::tanh(x);
+#else
+            return static_cast<T>(sycl::tanh(static_cast<float>(x)));
+#endif
+    } else {
+        return sycl::tanh(x);
+    }
 }
 
 template<typename T>
@@ -59,74 +89,106 @@ static __dpct_inline__ T op_gelu(T x) {
     const T SQRT_2_OVER_PI = static_cast<T>(0.79788456080286535587989211986876f);
     return static_cast<T>(0.5f) * x *
            (static_cast<T>(1.0f) +
-            sycl::tanh(SQRT_2_OVER_PI * x * (static_cast<T>(1.0f) + GELU_COEF_A * x * x)));
+            op_tanh(SQRT_2_OVER_PI * x * (static_cast<T>(1.0f) + GELU_COEF_A * x * x)));
+}
+
+template<typename T>
+static __dpct_inline__ T op_exp(T x) {
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::exp(x);
+    } else {
+        return sycl::exp(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_silu(T x) {
-    return x / (static_cast<T>(1.0f) + sycl::native::exp(-x));
+    return x / (static_cast<T>(1.0f) + op_exp(-x));
 }
 
 template<typename T>
-static __dpct_inline__ T op_gelu_quick(T x) {
-    const T GELU_QUICK_COEF_LOCAL = static_cast<T>(-1.702f);
-    return x * (static_cast<T>(1.0f) / (static_cast<T>(1.0f) + sycl::native::exp(GELU_QUICK_COEF_LOCAL * x)));
+static __dpct_inline__ T op_erf(T x) {
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return static_cast<sycl::ext::oneapi::bfloat16>(
+            sycl::erf(static_cast<float>(x))
+        );
+    } else {
+        return sycl::erf(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_gelu_erf(T x) {
     const T SQRT_2_INV = static_cast<T>(0.70710678118654752440084436210484f);
-    return static_cast<T>(0.5f) * x * (static_cast<T>(1.0f) + sycl::erf(x * SQRT_2_INV));
+    return static_cast<T>(0.5f) * x * (static_cast<T>(1.0f) + op_erf(x * SQRT_2_INV));
 }
 
 template<typename T>
-static __dpct_inline__ T op_tanh(T x) {
-    return sycl::tanh(x);
+static __dpct_inline__ T op_gelu_quick(T x) {
+    const T GELU_QUICK_COEF_LOCAL = static_cast<T>(-1.702f);
+    return x * (static_cast<T>(1.0f) / (static_cast<T>(1.0f) + op_exp(GELU_QUICK_COEF_LOCAL * x)));
 }
 
 template<typename T>
 static __dpct_inline__ T op_relu(T x) {
-    return sycl::fmax(x, static_cast<T>(0));
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::fmax(x, static_cast<T>(0));
+    } else {
+        return sycl::fmax(x, static_cast<T>(0));
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_sigmoid(T x) {
-    return static_cast<T>(1.0f) / (static_cast<T>(1.0f) + sycl::native::exp(-x));
+    return static_cast<T>(1.0f) / (static_cast<T>(1.0f) + op_exp(-x));
 }
 
 template<typename T>
 static __dpct_inline__ T op_sqrt(T x) {
-    return sycl::sqrt(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::sqrt(x);
+    } else {
+        return sycl::sqrt(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_sin(T x) {
-    return sycl::sin(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::sin(x);
+    } else {
+        return sycl::sin(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_cos(T x) {
-    return sycl::cos(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::cos(x);
+    } else {
+        return sycl::cos(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_hardsigmoid(T x) {
-    return sycl::fmin(static_cast<T>(1.0f), sycl::fmax(static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::fmin(
+            static_cast<T>(1.0f), sycl::ext::oneapi::experimental::fmax(
+                                      static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
+    } else {
+        return sycl::fmin(static_cast<T>(1.0f),
+                          sycl::fmax(static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_hardswish(T x) {
-    return x * sycl::fmin(static_cast<T>(1.0f), sycl::fmax(static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
-}
-
-template<typename T>
-static __dpct_inline__ T op_exp(T x) {
-    return sycl::exp(x);
-}
-
-template<typename T>
-static __dpct_inline__ T op_expm1(T x) {
-    return sycl::expm1(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return x * sycl::ext::oneapi::experimental::fmin(static_cast<T>(1.0f), sycl::ext::oneapi::experimental::fmax(static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
+    } else {
+        return x * sycl::fmin(static_cast<T>(1.0f), sycl::fmax(static_cast<T>(0.0f), (x + static_cast<T>(3.0f)) / static_cast<T>(6.0f)));
+    }
 }
 
 template<typename T>
@@ -134,13 +196,17 @@ static __dpct_inline__ T op_log(T x) {
     if (x <= static_cast<T>(0)) {
         return neg_infinity<T>();
     }
-    return sycl::log(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::log(x);
+    } else {
+        return sycl::log(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_softplus(T x) {
     const float xf = (float) x;
-    const float ax = sycl::fabs(xf);
+    const float ax = op_abs(xf);
     const float m  = sycl::fmax(xf, 0.0f);
     const float y  = m + sycl::log1p(sycl::exp(-ax));
     return (T) y;
@@ -159,8 +225,14 @@ static __dpct_inline__ T op_step(T x) {
 template<typename T>
 static __dpct_inline__ T op_leaky_relu(T x, float negative_slope) {
     T neg_slope_T = static_cast<T>(negative_slope);
-    return sycl::fmax(x, static_cast<T>(0)) +
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::fmax(x, static_cast<T>(0)) +
+           sycl::ext::oneapi::experimental::fmin(x, static_cast<T>(0.0f)) * neg_slope_T;
+
+    } else {
+        return sycl::fmax(x, static_cast<T>(0)) +
            sycl::fmin(x, static_cast<T>(0.0f)) * neg_slope_T;
+    }
 }
 
 template<typename T>
@@ -175,22 +247,40 @@ static __dpct_inline__ T op_clamp(T x, float min_val, float max_val) {
 
 template<typename T>
 static __dpct_inline__ T op_floor(T x) {
-    return sycl::floor(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::floor(x);
+    } else {
+        return sycl::floor(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_ceil(T x) {
-    return sycl::ceil(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::ceil(x);
+    } else {
+        return sycl::ceil(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_round(T x) {
-    return sycl::round(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return static_cast<sycl::ext::oneapi::bfloat16>(
+            sycl::round(static_cast<float>(x))
+        );
+    } else {
+        return sycl::round(x);
+    }
 }
 
 template<typename T>
 static __dpct_inline__ T op_trunc(T x) {
-    return sycl::trunc(x);
+    if constexpr (std::is_same_v<T, sycl::ext::oneapi::bfloat16>) {
+        return sycl::ext::oneapi::experimental::trunc(x);
+    } else {
+        return sycl::trunc(x);
+    }
 }
 
 template<typename T, typename F>
@@ -339,7 +429,7 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst,
     const int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE;
     stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE),
                                            sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)),
-                         [=](sycl::nd_item<3> /*item_ct1*/) {
+                         [=](sycl::nd_item<3> /*item_ct1*/) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                              acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
                          });
 }
@@ -354,8 +444,8 @@ static void arange_kernel(T * dst, const int k, T start, T step,
 
 template<typename KernelInvoker, typename... Args>
 static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx, ggml_tensor * dst, KernelInvoker kernel_invoker, Args&&... args) {
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
-    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16 || dst->src[0]->type == GGML_TYPE_BF16);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_BF16);
     GGML_ASSERT(dst->src[0]->type == dst->type);
 
     dpct::queue_ptr main_stream = ctx.stream();
@@ -367,6 +457,14 @@ static inline void dispatch_ggml_sycl_op_unary(ggml_backend_sycl_context & ctx,
                 kernel_invoker(data_pts.src, data_pts.dst, (int)ggml_nelements(dst->src[0]), main_stream, std::forward<Args>(args)...);
                 break;
             }
+#ifdef GGML_SYCL_HAS_BF16
+        case GGML_TYPE_BF16:
+            {
+                auto data_pts = cast_data<sycl::ext::oneapi::bfloat16>(dst);
+                kernel_invoker(data_pts.src, data_pts.dst, (int)ggml_nelements(dst->src[0]), main_stream, std::forward<Args>(args)...);
+                break;
+            }
+#endif
         case GGML_TYPE_F32:
             {
                 auto data_pts = cast_data<float>(dst);
@@ -480,7 +578,7 @@ static inline void ggml_sycl_op_unary(
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
                                   sycl::range<1>(256)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_generic_kernel(
                         src, dst_ptr, k_elements,
                         ne0, ne1, ne2, ne3,
@@ -508,7 +606,7 @@ static inline void ggml_sycl_op_arange(ggml_backend_sycl_context & ctx, ggml_ten
     stream->parallel_for(
         sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_ARANGE_BLOCK_SIZE),
                           sycl::range<1>(SYCL_ARANGE_BLOCK_SIZE)),
-        [=](sycl::nd_item<1> item_ct1) {
+        [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
             arange_kernel(dst_ptr, k, start, step, item_ct1);
         });
 }
@@ -602,7 +700,7 @@ static inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, ggml_tensor
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_EXP_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_EXP_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_log_kernel(src, dst_ptr, k_elements, item_ct1);
                 });
         });
@@ -640,7 +738,7 @@ static inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, ggml_tenso
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SQRT_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SQRT_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_sqrt_kernel(src, dst_ptr, k_elements, item_ct1);
                 });
         });
@@ -653,7 +751,7 @@ static inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, ggml_tensor
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SIN_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SIN_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_sin_kernel(src, dst_ptr, k_elements, item_ct1);
                 });
         });
@@ -666,7 +764,7 @@ static inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, ggml_tensor
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SIN_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SIN_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_cos_kernel(src, dst_ptr, k_elements, item_ct1);
                 });
         });
@@ -681,7 +779,7 @@ static inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, ggml
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_RELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_leaky_relu_kernel(src, dst_ptr, k_elements, slope, item_ct1);
                 });
         }, negative_slope);
@@ -694,7 +792,7 @@ static inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, ggml_tensor
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SQR_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SQR_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     unary_op_sqr_kernel(src, dst_ptr, k_elements, item_ct1);
                 });
         });
@@ -711,7 +809,7 @@ static inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tens
             stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_CLAMP_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_CLAMP_BLOCK_SIZE)),
-                [=](sycl::nd_item<1> item_ct1) {
+                [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                     clamp(src, dst_ptr, min_arg, max_arg, k_elements, item_ct1);
                 });
         }, min_val, max_val);
@@ -774,7 +872,8 @@ static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tens
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
             main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
+                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
         });
@@ -785,7 +884,8 @@ static inline void ggml_sycl_op_reglu(ggml_backend_sycl_context & ctx, ggml_tens
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_RELU_BLOCK_SIZE); // Using RELU block size for reglu
             main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
+                    sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
         });
@@ -796,7 +896,8 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_SILU_BLOCK_SIZE); // Using SILU block size for swiglu
             main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)),
+                    sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
         });
@@ -811,7 +912,6 @@ __dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alp
     return out_glu;
 }
 
-
 template <typename T>
 static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k,
                               const int64_t n, const int64_t o0, const int64_t o1,
@@ -845,7 +945,7 @@ static void swiglu_oai_sycl(const T *       x,
     const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE;
     stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE),
                                            sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                              swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1);
                          });
 }
@@ -899,7 +999,8 @@ static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
             main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
+                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
         });
@@ -910,7 +1011,8 @@ static inline void ggml_sycl_op_geglu_quick(ggml_backend_sycl_context & ctx, ggm
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
             main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
+                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
         });

From a0f39fe0f5c8471ac79f6ec93dc28c05f83acda1 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 22 Jun 2026 20:50:39 +0800
Subject: [PATCH 115/292] allow drafting with vision

---
 gpttype_adapter.cpp | 29 ++++++++++++++---------------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 5a0e8b8de..f85a09b3b 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -3358,25 +3358,24 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         {
             if(mtmd_ctx!=nullptr)
             {
-                printf("Error: Speculative decoding and MTP cannot be used with multimodal projectors!\n");
+                printf("Warning: Speculative decoding and MTP cannot be used with multimodal projectors! Payloads containing vision/audio will not use MTP or drafting\n");
+            }
+
+            speculative_chunk_amt = inputs.draft_amount;
+            if(draftmodel_filename != "")
+            {
+                if(inputs.use_mtp)
+                {
+                    printf("\nBoth --draftmodel and --usemtp were provided. The draft model will be used for speculative decoding.\n");
+                }
+                printf("\nAttempting to load draft model for speculative decoding. It will be fully offloaded if possible. Vocab must match the main model.\n");
+                speculative_decoding_setup(draftmodel_filename, llama_ctx_v4, model_params, llama_ctx_params, n_vocab, inputs.draft_gpusplit, inputs.draft_gpulayers);
             }
             else
             {
-                speculative_chunk_amt = inputs.draft_amount;
-                if(draftmodel_filename != "")
-                {
-                    if(inputs.use_mtp)
-                    {
-                        printf("\nBoth --draftmodel and --usemtp were provided. The draft model will be used for speculative decoding.\n");
-                    }
-                    printf("\nAttempting to load draft model for speculative decoding. It will be fully offloaded if possible. Vocab must match the main model.\n");
-                    speculative_decoding_setup(draftmodel_filename, llama_ctx_v4, model_params, llama_ctx_params, n_vocab, inputs.draft_gpusplit, inputs.draft_gpulayers);
-                }
-                else
-                {
-                    mtp_decoding_setup(llamamodel, llama_ctx_v4, llama_ctx_params);
-                }
+                mtp_decoding_setup(llamamodel, llama_ctx_v4, llama_ctx_params);
             }
+
         }
         if(draft_is_mtp && draft_spec)
         {

From 7fe6fa6fb6de8b455b9051a3ea5f5ac7f90ba4b9 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 22 Jun 2026 21:40:00 +0800
Subject: [PATCH 116/292] match draft defaults

---
 gpttype_adapter.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index f85a09b3b..532765be1 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -71,7 +71,7 @@ std::string executable_path = "";
 std::string lora_filename = "";
 std::string mmproj_filename = "";
 std::string draftmodel_filename = "";
-int speculative_chunk_amt = 8; //do it in chunks of this many tokens
+int speculative_chunk_amt = 4; //do it in chunks of this many tokens
 bool generation_finished;
 bool audio_multimodal_supported = false;
 bool vision_multimodal_supported = false;

From 099b579acb9f7fd0eefcbb2198fd453b00c6e787 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Mon, 22 Jun 2026 15:55:30 +0200
Subject: [PATCH 117/292] ui: model status and load progress via /models/sse
 feed (#24878)

* ui: model status and load progress via /models/sse feed

* ui: centralize SSE wire-format delimiters into shared constants for the chat and /models/sse parsers

* ui: type /models/sse event names as a ServerModelsSseEventType enum

Address review from allozaur
---
 tools/ui/src/app.d.ts                         |  10 +
 .../ChatMessageAssistant.svelte               |  15 +-
 .../app/models/ModelsSelectorOption.svelte    |  19 +-
 tools/ui/src/lib/constants/api-endpoints.ts   |   3 +-
 tools/ui/src/lib/constants/index.ts           |   2 +
 tools/ui/src/lib/constants/model-loading.ts   |  14 +
 tools/ui/src/lib/constants/sse.ts             |  16 +
 tools/ui/src/lib/enums/index.ts               |   2 +-
 tools/ui/src/lib/enums/server.enums.ts        |  14 +
 tools/ui/src/lib/services/chat.service.ts     |  16 +-
 tools/ui/src/lib/stores/models.svelte.ts      | 280 +++++++++++++++---
 tools/ui/src/lib/types/api.d.ts               |  48 ++-
 tools/ui/src/lib/types/index.ts               |  11 +-
 tools/ui/src/lib/types/models.d.ts            |  13 +-
 tools/ui/src/lib/utils/index.ts               |   3 +
 tools/ui/src/lib/utils/progress.ts            |  43 +++
 tools/ui/src/routes/+layout.svelte            |  14 +
 17 files changed, 466 insertions(+), 57 deletions(-)
 create mode 100644 tools/ui/src/lib/constants/model-loading.ts
 create mode 100644 tools/ui/src/lib/constants/sse.ts
 create mode 100644 tools/ui/src/lib/utils/progress.ts

diff --git a/tools/ui/src/app.d.ts b/tools/ui/src/app.d.ts
index a7583eec5..5264e5cc4 100644
--- a/tools/ui/src/app.d.ts
+++ b/tools/ui/src/app.d.ts
@@ -19,6 +19,10 @@ import type {
 	ApiErrorResponse,
 	ApiLlamaCppServerProps,
 	ApiModelDataEntry,
+	ApiModelLoadStage,
+	ApiModelsSseProgress,
+	ApiModelsSseData,
+	ApiModelsSseEvent,
 	ApiModelListResponse,
 	ApiProcessingState,
 	ApiRouterModelMeta,
@@ -52,6 +56,7 @@ import type {
 	// Model types
 	ModelModalities,
 	ModelOption,
+	ModelLoadProgress,
 	// Settings types
 	SettingsChatServiceOptions,
 	SettingsConfigValue,
@@ -83,6 +88,10 @@ declare global {
 		ApiErrorResponse,
 		ApiLlamaCppServerProps,
 		ApiModelDataEntry,
+		ApiModelLoadStage,
+		ApiModelsSseProgress,
+		ApiModelsSseData,
+		ApiModelsSseEvent,
 		ApiModelListResponse,
 		ApiProcessingState,
 		ApiRouterModelMeta,
@@ -120,6 +129,7 @@ declare global {
 		// Model types
 		ModelModalities,
 		ModelOption,
+		ModelLoadProgress,
 		// Settings types
 		SettingsChatServiceOptions,
 		SettingsConfigValue,
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
index 4c74206f1..2272eaedb 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
@@ -10,7 +10,7 @@
 	import { getMessageEditContext } from '$lib/contexts';
 	import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
 	import { isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
-	import { copyToClipboard, deriveAgenticSections } from '$lib/utils';
+	import { copyToClipboard, deriveAgenticSections, modelLoadProgressText } from '$lib/utils';
 	import { AgenticSectionType } from '$lib/enums';
 	import { REASONING_TAGS } from '$lib/constants/agentic';
 	import { tick } from 'svelte';
@@ -185,6 +185,13 @@
 	let hasNoContent = $derived(!message?.content?.trim());
 	let isActivelyProcessing = $derived(isCurrentlyLoading || isStreaming);
 
+	// during a router auto-load the message has no model yet, so target the selected one
+	let loadTargetModel = $derived(message.model ?? modelsStore.selectedModelName);
+	let modelLoadProgress = $derived(
+		isRouter && loadTargetModel ? modelsStore.getLoadProgress(loadTargetModel) : null
+	);
+	let modelLoadingText = $derived(modelLoadProgressText(modelLoadProgress));
+
 	let showProcessingInfoTop = $derived(
 		message?.role === MessageRole.ASSISTANT &&
 			isActivelyProcessing &&
@@ -220,7 +227,8 @@
 		<div class="mt-6 w-full max-w-[48rem]" in:fade>
 			<div class="processing-container">
 				<span class="processing-text">
-					{processingState.getPromptProgressText() ??
+					{modelLoadingText ??
+						processingState.getPromptProgressText() ??
 						processingState.getProcessingMessage() ??
 						'Processing...'}
 				</span>
@@ -252,7 +260,8 @@
 		<div class="mt-4 w-full max-w-[48rem]" in:fade>
 			<div class="processing-container">
 				<span class="processing-text">
-					{processingState.getPromptProgressText() ??
+					{modelLoadingText ??
+						processingState.getPromptProgressText() ??
 						processingState.getProcessingMessage() ??
 						'Processing...'}
 				</span>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
index fef1490f3..f2a024d31 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
@@ -13,6 +13,7 @@
 	import type { ModelOption } from '$lib/types/models';
 	import { ServerModelStatus } from '$lib/enums';
 	import { modelsStore, routerModels } from '$lib/stores/models.svelte';
+	import { modelLoadFraction, modelLoadProgressText } from '$lib/utils';
 
 	interface Props {
 		option: ModelOption;
@@ -50,11 +51,15 @@
 		(serverStatus === ServerModelStatus.LOADED || isSleeping) && !isOperationInProgress
 	);
 	let isLoading = $derived(serverStatus === ServerModelStatus.LOADING || isOperationInProgress);
+
+	let loadProgress = $derived(isLoading ? modelsStore.getLoadProgress(option.model) : null);
+	let loadPercent = $derived(Math.round(modelLoadFraction(loadProgress) * 100));
+	let loadTitle = $derived(modelLoadProgressText(loadProgress));
 </script>
 
 <div
 	class={[
-		'group flex w-full items-center gap-2 rounded-sm p-2 text-left text-sm transition focus:outline-none',
+		'group relative flex w-full items-center gap-2 rounded-sm p-2 text-left text-sm transition focus:outline-none',
 		'cursor-pointer hover:bg-muted focus:bg-muted',
 		(isSelected || isHighlighted) && 'bg-accent text-accent-foreground',
 		!(isSelected || isHighlighted) && 'hover:bg-accent hover:text-accent-foreground',
@@ -62,6 +67,7 @@
 	]}
 	role="option"
 	aria-selected={isSelected || isHighlighted}
+	title={loadTitle}
 	tabindex="0"
 	onclick={() => onSelect(option.id)}
 	onmouseenter={onMouseEnter}
@@ -188,4 +194,15 @@
 			</div>
 		{/if}
 	</div>
+
+	{#if isLoading}
+		<div
+			class="pointer-events-none absolute inset-x-0 bottom-0 h-0.5 overflow-hidden rounded-b-sm bg-muted"
+		>
+			<div
+				class="h-full bg-primary transition-[width] duration-200 ease-out"
+				style="width: {loadPercent}%"
+			></div>
+		</div>
+	{/if}
 </div>
diff --git a/tools/ui/src/lib/constants/api-endpoints.ts b/tools/ui/src/lib/constants/api-endpoints.ts
index 9eb6c74e7..a41090505 100644
--- a/tools/ui/src/lib/constants/api-endpoints.ts
+++ b/tools/ui/src/lib/constants/api-endpoints.ts
@@ -1,7 +1,8 @@
 export const API_MODELS = {
 	LIST: '/v1/models',
 	LOAD: '/models/load',
-	UNLOAD: '/models/unload'
+	UNLOAD: '/models/unload',
+	SSE: '/models/sse'
 };
 
 // chat completion routes, the control route drives realtime inference (e.g. end reasoning)
diff --git a/tools/ui/src/lib/constants/index.ts b/tools/ui/src/lib/constants/index.ts
index c51d84cdc..4993ab647 100644
--- a/tools/ui/src/lib/constants/index.ts
+++ b/tools/ui/src/lib/constants/index.ts
@@ -37,6 +37,8 @@ export * from './mcp-form';
 export * from './mcp-resource';
 export * from './message-export';
 export * from './model-id';
+export * from './model-loading';
+export * from './sse';
 export * from './precision';
 export * from './processing-info';
 export * from './pwa';
diff --git a/tools/ui/src/lib/constants/model-loading.ts b/tools/ui/src/lib/constants/model-loading.ts
new file mode 100644
index 000000000..a55ba708b
--- /dev/null
+++ b/tools/ui/src/lib/constants/model-loading.ts
@@ -0,0 +1,14 @@
+/**
+ * Labels shown while a model loads, keyed by the stage reported on /models/sse.
+ */
+export const MODEL_LOAD_STAGE_LABELS: Record<ApiModelLoadStage, string> = {
+	text_model: 'Loading weights',
+	spec_model: 'Loading draft',
+	mmproj_model: 'Loading projector'
+};
+
+/**
+ * Share of the bar reserved for each load phase after text_model.
+ * text_model fills the rest, so a plain model reaches 100% on its own.
+ */
+export const MODEL_LOAD_TAIL_SHARE = 0.1;
diff --git a/tools/ui/src/lib/constants/sse.ts b/tools/ui/src/lib/constants/sse.ts
new file mode 100644
index 000000000..0eb4b6ede
--- /dev/null
+++ b/tools/ui/src/lib/constants/sse.ts
@@ -0,0 +1,16 @@
+/**
+ * Server-sent events wire format, shared by the chat stream and the
+ * /models/sse status feed (text/event-stream).
+ */
+
+// blank line between two events
+export const SSE_RECORD_SEPARATOR = '\n\n';
+
+// line break inside an event
+export const SSE_LINE_SEPARATOR = '\n';
+
+// data field prefix, the value follows after an optional space
+export const SSE_DATA_PREFIX = 'data:';
+
+// end-of-stream marker on the chat completion stream
+export const SSE_DONE_MARKER = '[DONE]';
diff --git a/tools/ui/src/lib/enums/index.ts b/tools/ui/src/lib/enums/index.ts
index 449e4f90a..811744fd9 100644
--- a/tools/ui/src/lib/enums/index.ts
+++ b/tools/ui/src/lib/enums/index.ts
@@ -54,7 +54,7 @@ export {
 
 export { ModelModality } from './model.enums';
 
-export { ServerRole, ServerModelStatus } from './server.enums';
+export { ServerRole, ServerModelStatus, ServerModelsSseEventType } from './server.enums';
 
 export { ParameterSource, SyncableParameterType, SettingsFieldType } from './settings.enums';
 
diff --git a/tools/ui/src/lib/enums/server.enums.ts b/tools/ui/src/lib/enums/server.enums.ts
index c9d599c52..446af84be 100644
--- a/tools/ui/src/lib/enums/server.enums.ts
+++ b/tools/ui/src/lib/enums/server.enums.ts
@@ -19,3 +19,17 @@ export enum ServerModelStatus {
 	SLEEPING = 'sleeping',
 	FAILED = 'failed'
 }
+
+/**
+ * /models/sse event type enum - discriminates the records broadcast on the
+ * model status feed in ROUTER mode. Matches the event names emitted by
+ * tools/server/server-models.cpp from the C++ server.
+ */
+export enum ServerModelsSseEventType {
+	STATUS_CHANGE = 'status_change',
+	MODEL_STATUS = 'model_status',
+	STATUS_UPDATE = 'status_update',
+	MODELS_RELOAD = 'models_reload',
+	MODEL_REMOVE = 'model_remove',
+	DOWNLOAD_PROGRESS = 'download_progress'
+}
diff --git a/tools/ui/src/lib/services/chat.service.ts b/tools/ui/src/lib/services/chat.service.ts
index 70844f57e..9001c9572 100644
--- a/tools/ui/src/lib/services/chat.service.ts
+++ b/tools/ui/src/lib/services/chat.service.ts
@@ -10,7 +10,10 @@ import {
 	SETTINGS_KEYS,
 	API_CHAT,
 	API_SLOTS,
-	CONTROL_ACTION
+	CONTROL_ACTION,
+	SSE_LINE_SEPARATOR,
+	SSE_DATA_PREFIX,
+	SSE_DONE_MARKER
 } from '$lib/constants';
 import {
 	AttachmentType,
@@ -18,8 +21,7 @@ import {
 	FileTypeAudio,
 	MessageRole,
 	MimeTypeAudio,
-	ReasoningFormat,
-	UrlProtocol
+	ReasoningFormat
 } from '$lib/enums';
 import type {
 	ApiChatMessageContentPart,
@@ -642,15 +644,15 @@ export class ChatService {
 				if (abortSignal?.aborted) break;
 
 				chunk += decoder.decode(value, { stream: true });
-				const lines = chunk.split('\n');
+				const lines = chunk.split(SSE_LINE_SEPARATOR);
 				chunk = lines.pop() || '';
 
 				for (const line of lines) {
 					if (abortSignal?.aborted) break;
 
-					if (line.startsWith(UrlProtocol.DATA)) {
-						const data = line.slice(6);
-						if (data === '[DONE]') {
+					if (line.startsWith(SSE_DATA_PREFIX)) {
+						const data = line.slice(SSE_DATA_PREFIX.length).trim();
+						if (data === SSE_DONE_MARKER) {
 							streamFinished = true;
 
 							continue;
diff --git a/tools/ui/src/lib/stores/models.svelte.ts b/tools/ui/src/lib/stores/models.svelte.ts
index 1990ba604..2ce450d42 100644
--- a/tools/ui/src/lib/stores/models.svelte.ts
+++ b/tools/ui/src/lib/stores/models.svelte.ts
@@ -1,6 +1,7 @@
+import { base } from '$app/paths';
 import { SvelteMap, SvelteSet } from 'svelte/reactivity';
 import { toast } from 'svelte-sonner';
-import { ServerModelStatus, ModelModality } from '$lib/enums';
+import { ServerModelStatus, ServerModelsSseEventType, ModelModality } from '$lib/enums';
 import { ModelsService } from '$lib/services/models.service';
 import { PropsService } from '$lib/services/props.service';
 import { serverStore, isRouterMode } from '$lib/stores/server.svelte';
@@ -8,11 +9,15 @@ import {
 	detectThinkingSupport,
 	detectThinkingSupportWithReason
 } from '$lib/utils/chat-template-thinking-detector';
-import { TTLCache } from '$lib/utils';
+import { TTLCache, getAuthHeaders } from '$lib/utils';
 import {
 	MODEL_PROPS_CACHE_TTL_MS,
 	MODEL_PROPS_CACHE_MAX_ENTRIES,
-	FAVORITE_MODELS_LOCALSTORAGE_KEY
+	FAVORITE_MODELS_LOCALSTORAGE_KEY,
+	API_MODELS,
+	SSE_RECORD_SEPARATOR,
+	SSE_LINE_SEPARATOR,
+	SSE_DATA_PREFIX
 } from '$lib/constants';
 
 import { conversationsStore } from '$lib/stores/conversations.svelte';
@@ -55,6 +60,15 @@ class ModelsStore {
 	private modelUsage = $state<Map<string, SvelteSet<string>>>(new Map());
 	private modelLoadingStates = new SvelteMap<string, boolean>();
 
+	// /models/sse feed state, the single source of truth for status and load progress
+	private statusAbort: AbortController | null = null;
+	private statusReaderActive = false;
+	private loadProgress = new SvelteMap<string, ModelLoadProgress>();
+	private statusWaiters = new Map<
+		string,
+		{ target: ServerModelStatus; resolve: () => void; reject: (e: Error) => void }
+	>();
+
 	favoriteModelIds = $state<Set<string>>(this.loadFavoritesFromStorage());
 
 	/**
@@ -626,49 +640,218 @@ class ModelsStore {
 	 *
 	 */
 
-	/**
-	 * WORKAROUND: Polling for model status after load/unload operations.
-	 *
-	 * Currently, `/models/load` and `/models/unload` return success before
-	 * the operation actually completes on the server.
-	 *
-	 * TODO: Remove polling once llama-server properly waits for the operation
-	 * to complete before returning success.
-	 */
-
-	private static readonly STATUS_POLL_INTERVAL = 500;
+	// reconnect delay after the feed drops or the server is not ready yet
+	private static readonly SSE_RECONNECT_MS = 1000;
 
 	/**
-	 * Poll for expected model status after load/unload operation.
-	 * Keeps polling until the model reaches the expected status or fails.
+	 * Open the /models/sse feed and keep it live with auto reconnect.
+	 * Idempotent and router mode only. The feed drives status and progress,
+	 * so it replaces any post-operation polling.
 	 */
-	private async pollForModelStatus(
-		modelId: string,
-		expectedStatus: ServerModelStatus
-	): Promise<void> {
-		let attempt = 0;
-		while (true) {
-			await this.fetchRouterModels();
+	subscribeStatus(): void {
+		if (this.statusReaderActive) return;
+		if (!isRouterMode()) return;
 
-			const currentStatus = this.getModelStatus(modelId);
-			if (currentStatus === expectedStatus) return;
+		this.statusReaderActive = true;
+		this.statusAbort = new AbortController();
+		void this.runStatusReader(this.statusAbort.signal);
+	}
 
-			if (currentStatus === ServerModelStatus.FAILED) {
-				throw new Error(
-					`Model failed to ${expectedStatus === ServerModelStatus.LOADED ? 'load' : 'unload'}`
-				);
+	/**
+	 * Close the /models/sse feed and drop transient progress.
+	 */
+	unsubscribeStatus(): void {
+		this.statusReaderActive = false;
+		this.statusAbort?.abort();
+		this.statusAbort = null;
+		this.loadProgress.clear();
+	}
+
+	/**
+	 * Current load progress for a model, or null when not loading.
+	 */
+	getLoadProgress(modelId: string): ModelLoadProgress | null {
+		return this.loadProgress.get(modelId) ?? null;
+	}
+
+	/**
+	 * Read the feed and reconnect until unsubscribed. Splits the byte stream
+	 * into SSE records on the blank line boundary.
+	 */
+	private async runStatusReader(signal: AbortSignal): Promise<void> {
+		const decoder = new TextDecoder();
+
+		while (!signal.aborted) {
+			try {
+				const response = await fetch(`${base}${API_MODELS.SSE}`, {
+					headers: getAuthHeaders(),
+					signal
+				});
+
+				if (response.ok && response.body) {
+					const reader = response.body.getReader();
+					let buffer = '';
+
+					while (!signal.aborted) {
+						const { value, done } = await reader.read();
+						if (done) break;
+
+						buffer += decoder.decode(value, { stream: true });
+
+						let boundary = buffer.indexOf(SSE_RECORD_SEPARATOR);
+						while (boundary !== -1) {
+							this.handleStatusRecord(buffer.slice(0, boundary));
+							buffer = buffer.slice(boundary + SSE_RECORD_SEPARATOR.length);
+							boundary = buffer.indexOf(SSE_RECORD_SEPARATOR);
+						}
+					}
+				}
+			} catch {
+				// network drop or abort falls through to the reconnect delay
 			}
 
-			if (
-				expectedStatus === ServerModelStatus.LOADED &&
-				currentStatus === ServerModelStatus.UNLOADED &&
-				attempt > 2
-			) {
-				throw new Error('Model was unloaded unexpectedly during loading');
-			}
+			if (signal.aborted) return;
 
-			attempt++;
-			await new Promise((resolve) => setTimeout(resolve, ModelsStore.STATUS_POLL_INTERVAL));
+			await new Promise((resolve) => setTimeout(resolve, ModelsStore.SSE_RECONNECT_MS));
+		}
+	}
+
+	/**
+	 * Parse one SSE record. The payload rides in the data lines as a JSON
+	 * envelope that carries its own model, event and data fields.
+	 */
+	private handleStatusRecord(record: string): void {
+		const payload = record
+			.split(SSE_LINE_SEPARATOR)
+			.filter((line) => line.startsWith(SSE_DATA_PREFIX))
+			.map((line) => line.slice(SSE_DATA_PREFIX.length).trim())
+			.join(SSE_LINE_SEPARATOR);
+
+		if (payload.length === 0) return;
+
+		let envelope: ApiModelsSseEvent;
+		try {
+			envelope = JSON.parse(payload);
+		} catch {
+			return;
+		}
+
+		this.applyStatusEvent(envelope);
+	}
+
+	/**
+	 * Route one feed record by event kind. Only the status_* events carry a
+	 * status payload, models_reload triggers a list refresh, model_remove drops
+	 * the row, download_* belong to the download surface, not here.
+	 */
+	private applyStatusEvent(event: ApiModelsSseEvent): void {
+		switch (event.event) {
+			case ServerModelsSseEventType.STATUS_CHANGE:
+			case ServerModelsSseEventType.MODEL_STATUS:
+			case ServerModelsSseEventType.STATUS_UPDATE:
+				this.applyModelStatus(event);
+				break;
+			case ServerModelsSseEventType.MODELS_RELOAD:
+				void this.fetchRouterModels();
+				break;
+			case ServerModelsSseEventType.MODEL_REMOVE:
+				this.removeRouterModel(event.model);
+				break;
+			case ServerModelsSseEventType.DOWNLOAD_PROGRESS:
+				break;
+		}
+	}
+
+	/**
+	 * Apply a status envelope: update the model row, track or clear progress,
+	 * settle any pending load or unload awaiter.
+	 */
+	private applyModelStatus(event: ApiModelsSseEvent): void {
+		const model = event.model;
+		const data = event.data;
+		if (!model || !data?.status) return;
+
+		const status = data.status;
+
+		this.setRouterModelStatus(model, status);
+
+		if (status === ServerModelStatus.LOADING) {
+			if (data.progress) this.loadProgress.set(model, data.progress);
+		} else {
+			this.loadProgress.delete(model);
+		}
+
+		if (status === ServerModelStatus.LOADED) {
+			void this.updateModelModalities(model);
+		}
+
+		const failed =
+			status === ServerModelStatus.FAILED ||
+			(status === ServerModelStatus.UNLOADED && (data.exit_code ?? 0) !== 0);
+
+		if (failed) {
+			this.rejectStatus(model, new Error(`Model failed: ${this.toDisplayName(model)}`));
+			return;
+		}
+
+		this.settleStatus(model, status);
+	}
+
+	/**
+	 * Drop a model row reported gone by the feed and settle its awaiters.
+	 */
+	private removeRouterModel(modelId: string): void {
+		if (this.routerModels.findIndex((m) => m.id === modelId) === -1) return;
+
+		this.routerModels = this.routerModels.filter((m) => m.id !== modelId);
+		this.loadProgress.delete(modelId);
+		this.rejectStatus(modelId, new Error(`Model removed: ${this.toDisplayName(modelId)}`));
+	}
+
+	/**
+	 * Update one model row status in place, reassigning to trigger reactivity.
+	 */
+	private setRouterModelStatus(modelId: string, status: ServerModelStatus): void {
+		const idx = this.routerModels.findIndex((m) => m.id === modelId);
+		if (idx === -1) return;
+
+		const current = this.routerModels[idx];
+		if (current.status.value === status) return;
+
+		const next = [...this.routerModels];
+		next[idx] = { ...current, status: { ...current.status, value: status } };
+		this.routerModels = next;
+	}
+
+	/**
+	 * Register an awaiter that resolves when the feed reports target status.
+	 * One operation runs per model at a time, so one awaiter per model is kept.
+	 */
+	private waitForStatus(modelId: string, target: ServerModelStatus): Promise<void> {
+		return new Promise((resolve, reject) => {
+			this.statusWaiters.set(modelId, { target, resolve, reject });
+		});
+	}
+
+	/**
+	 * Resolve and drop the awaiter when the model reaches its target status.
+	 */
+	private settleStatus(modelId: string, status: ServerModelStatus): void {
+		const waiter = this.statusWaiters.get(modelId);
+		if (waiter && waiter.target === status) {
+			this.statusWaiters.delete(modelId);
+			waiter.resolve();
+		}
+	}
+
+	/**
+	 * Reject and drop the awaiter for a model.
+	 */
+	private rejectStatus(modelId: string, error: Error): void {
+		const waiter = this.statusWaiters.get(modelId);
+		if (waiter) {
+			this.statusWaiters.delete(modelId);
+			waiter.reject(error);
 		}
 	}
 
@@ -679,12 +862,18 @@ class ModelsStore {
 		this.modelLoadingStates.set(modelId, true);
 		this.error = null;
 
+		// the feed drives completion, so it must be live before the request
+		this.subscribeStatus();
+
+		const reachedLoaded = this.waitForStatus(modelId, ServerModelStatus.LOADED);
+		reachedLoaded.catch(() => {});
+
 		try {
 			await ModelsService.load(modelId);
-			await this.pollForModelStatus(modelId, ServerModelStatus.LOADED);
-			await this.updateModelModalities(modelId);
+			await reachedLoaded;
 			toast.success(`Model loaded: ${this.toDisplayName(modelId)}`);
 		} catch (error) {
+			this.rejectStatus(modelId, error instanceof Error ? error : new Error('load failed'));
 			this.error = error instanceof Error ? error.message : 'Failed to load model';
 			toast.error(`Failed to load model: ${this.toDisplayName(modelId)}`);
 			throw error;
@@ -700,11 +889,17 @@ class ModelsStore {
 		this.modelLoadingStates.set(modelId, true);
 		this.error = null;
 
+		this.subscribeStatus();
+
+		const reachedUnloaded = this.waitForStatus(modelId, ServerModelStatus.UNLOADED);
+		reachedUnloaded.catch(() => {});
+
 		try {
 			await ModelsService.unload(modelId);
-			await this.pollForModelStatus(modelId, ServerModelStatus.UNLOADED);
+			await reachedUnloaded;
 			toast.info(`Model unloaded: ${this.toDisplayName(modelId)}`);
 		} catch (error) {
+			this.rejectStatus(modelId, error instanceof Error ? error : new Error('unload failed'));
 			this.error = error instanceof Error ? error.message : 'Failed to unload model';
 			toast.error(`Failed to unload model: ${this.toDisplayName(modelId)}`);
 			throw error;
@@ -783,6 +978,9 @@ class ModelsStore {
 	}
 
 	clear(): void {
+		this.unsubscribeStatus();
+		this.statusWaiters.forEach((waiter) => waiter.reject(new Error('Models store cleared')));
+		this.statusWaiters.clear();
 		this.models = [];
 		this.routerModels = [];
 		this.loading = false;
diff --git a/tools/ui/src/lib/types/api.d.ts b/tools/ui/src/lib/types/api.d.ts
index f620d6735..2a2524d00 100644
--- a/tools/ui/src/lib/types/api.d.ts
+++ b/tools/ui/src/lib/types/api.d.ts
@@ -1,4 +1,10 @@
-import type { ContentPartType, FileTypeAudio, ServerModelStatus, ServerRole } from '$lib/enums';
+import type {
+	ContentPartType,
+	FileTypeAudio,
+	ServerModelStatus,
+	ServerModelsSseEventType,
+	ServerRole
+} from '$lib/enums';
 import type { ChatMessagePromptProgress, ChatRole } from './chat';
 
 export type AudioInputFormat = FileTypeAudio.WAV | FileTypeAudio.MP3;
@@ -96,6 +102,46 @@ export interface ApiModelDataEntry {
 	meta?: Record<string, unknown> | null;
 }
 
+/**
+ * Load stage reported by the /models/sse feed, in load order.
+ */
+export type ApiModelLoadStage = 'text_model' | 'spec_model' | 'mmproj_model';
+
+/**
+ * Load progress snapshot: the full ordered stage plan, the active stage,
+ * and its fractional value (0.0 -> 1.0).
+ */
+export interface ApiModelsSseProgress {
+	stages: ApiModelLoadStage[];
+	current: ApiModelLoadStage;
+	value: number;
+}
+
+/**
+ * Status payload carried by a /models/sse envelope.
+ * exit_code appears on unload.
+ */
+export interface ApiModelsSseData {
+	status: ServerModelStatus;
+	progress?: ApiModelsSseProgress;
+	exit_code?: number;
+}
+
+/**
+ * Event kind multiplexed on the /models/sse feed.
+ * Only the status_* events carry a status payload, models_reload signals a
+ * full list refresh, model_remove drops a row, download_* drive download UI.
+ */
+/**
+ * One /models/sse record. event discriminates the kind, model names the
+ * target instance, data carries the status payload when present.
+ */
+export interface ApiModelsSseEvent {
+	model: string;
+	event: ServerModelsSseEventType;
+	data: ApiModelsSseData;
+}
+
 export interface ApiModelDetails {
 	name: string;
 	model: string;
diff --git a/tools/ui/src/lib/types/index.ts b/tools/ui/src/lib/types/index.ts
index c5f948898..9b0b11804 100644
--- a/tools/ui/src/lib/types/index.ts
+++ b/tools/ui/src/lib/types/index.ts
@@ -11,6 +11,10 @@ export type {
 	ApiChatMessageData,
 	ApiModelStatus,
 	ApiModelDataEntry,
+	ApiModelLoadStage,
+	ApiModelsSseProgress,
+	ApiModelsSseData,
+	ApiModelsSseEvent,
 	ApiModelDetails,
 	ApiModelListResponse,
 	ApiLlamaCppServerProps,
@@ -70,7 +74,12 @@ export type {
 } from './database';
 
 // Model types
-export type { ModelModalities, ModelOption, ModalityCapabilities } from './models';
+export type {
+	ModelModalities,
+	ModelOption,
+	ModelLoadProgress,
+	ModalityCapabilities
+} from './models';
 
 // Settings types
 export type {
diff --git a/tools/ui/src/lib/types/models.d.ts b/tools/ui/src/lib/types/models.d.ts
index 51069599d..b32c16f6f 100644
--- a/tools/ui/src/lib/types/models.d.ts
+++ b/tools/ui/src/lib/types/models.d.ts
@@ -1,4 +1,4 @@
-import type { ApiModelDataEntry, ApiModelDetails } from '$lib/types/api';
+import type { ApiModelDataEntry, ApiModelDetails, ApiModelLoadStage } from '$lib/types/api';
 
 export interface ModelModalities {
 	vision: boolean;
@@ -20,6 +20,17 @@ export interface ModelOption {
 	tags?: string[];
 }
 
+/**
+ * Ephemeral UI-only load progress for one model instance.
+ * Lives only while a load runs, driven by the /models/sse feed.
+ * stage is absent until the feed reports its first stage.
+ */
+export interface ModelLoadProgress {
+	stages: ApiModelLoadStage[];
+	current: ApiModelLoadStage;
+	value: number;
+}
+
 export interface ParsedModelId {
 	raw: string;
 	orgName: string | null;
diff --git a/tools/ui/src/lib/utils/index.ts b/tools/ui/src/lib/utils/index.ts
index 637db8812..61b9932d3 100644
--- a/tools/ui/src/lib/utils/index.ts
+++ b/tools/ui/src/lib/utils/index.ts
@@ -44,6 +44,9 @@ export { buildProxiedUrl, buildProxiedHeaders } from './cors-proxy';
 // URL utilities
 export { extractRootDomain, sanitizeExternalUrl } from './url';
 
+// Progress helpers
+export { modelLoadFraction, modelLoadProgressText } from './progress';
+
 // Conversation utilities
 export { createMessageCountMap, getMessageCount } from './conversation-utils';
 
diff --git a/tools/ui/src/lib/utils/progress.ts b/tools/ui/src/lib/utils/progress.ts
new file mode 100644
index 000000000..4d7e22388
--- /dev/null
+++ b/tools/ui/src/lib/utils/progress.ts
@@ -0,0 +1,43 @@
+/**
+ * Model load progress helpers for the /models/sse surfaces
+ * (selector row and chat message).
+ */
+
+import { MODEL_LOAD_STAGE_LABELS, MODEL_LOAD_TAIL_SHARE } from '$lib/constants';
+
+/**
+ * Human label for a model load stage.
+ */
+export function modelLoadStageLabel(stage: ApiModelLoadStage): string {
+	return MODEL_LOAD_STAGE_LABELS[stage];
+}
+
+/**
+ * Overall load fraction (0.0 -> 1.0) across the declared stage plan.
+ * text_model fills [0, 1 - tail], each later phase owns one tail slice.
+ */
+export function modelLoadFraction(progress: ModelLoadProgress | null): number {
+	if (!progress) return 0;
+
+	const { stages, current, value } = progress;
+	const tailCount = Math.max(stages.length - 1, 0);
+	const textCeiling = 1 - tailCount * MODEL_LOAD_TAIL_SHARE;
+	const idx = stages.indexOf(current);
+
+	if (idx <= 0) {
+		return value * textCeiling;
+	}
+
+	return textCeiling + (idx - 1 + value) * MODEL_LOAD_TAIL_SHARE;
+}
+
+/**
+ * Single line describing load progress: active stage label and overall percent.
+ * Returns null when there is no progress to show.
+ */
+export function modelLoadProgressText(progress: ModelLoadProgress | null): string | null {
+	if (!progress) return null;
+
+	const label = modelLoadStageLabel(progress.current);
+	return `${label} ${Math.round(modelLoadFraction(progress) * 100)}%`;
+}
diff --git a/tools/ui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
index fdba9a9d3..1269692a7 100644
--- a/tools/ui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -230,6 +230,20 @@
 		}
 	});
 
+	// Live model status and load progress via the /models/sse feed (router mode)
+	$effect(() => {
+		if (!browser) return;
+		if (!isRouterMode()) return;
+
+		untrack(() => {
+			modelsStore.subscribeStatus();
+		});
+
+		return () => {
+			modelsStore.unsubscribeStatus();
+		};
+	});
+
 	// Background MCP server health checks on app load
 	// Fetch enabled servers from settings and run health checks in background
 	$effect(() => {

From 6ee0f65793da4bca2301826f70383aef2da60345 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 22 Jun 2026 16:42:47 +0200
Subject: [PATCH 118/292] server: refactor/generalize input file schema
 (#24299)

* server: refactor/generalize input file schema

* wire up input_video, accept raw base64

* nits

* nits (2)

* fix windows
---
 tools/server/README.md         | 17 +++++++---
 tools/server/server-common.cpp | 62 ++++++++++++++++++++--------------
 2 files changed, 49 insertions(+), 30 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index 7fa3a4d72..e88bc5f28 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1230,8 +1230,6 @@ print(completion.choices[0].text)
 
 Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
 
-If model supports multimodal, you can input the media file via `image_url` content part. We support both base64 and remote URL as input. See OAI documentation for more.
-
 *Options:*
 
 See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). llama.cpp `/completion`-specific features such as `mirostat` are also supported.
@@ -1250,9 +1248,18 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `parallel_tool_calls` : Whether to enable parallel/multiple tool calls (only supported on some models, verification is based on jinja template).
 
-For multimodal input:
-- Content type `image_url` and `input_audio` are the same as OAI schema
-- Content type `input_video` is an extension from OAI schema. For now, it only accepts base64 input
+For multimodal input (typed content, `messages[i].content[j]`):
+- If `type == "image_url"`:
+    - `image_url.url` can be a remote URL, base64 (raw or URI-encoded via `data:image/...;base64`) or path to local file
+    - Accepts formats supported by `stb_image` (jpeg, png, tga, bmp, gif, ...)
+- If `type == "input_audio"`:
+    - Either `input_audio.data` or `input_audio.url` can be specified, can be a remote URL, raw base64 or path to local file
+    - Accepts formats supported by `miniaudio` (mp3, wav, flac)
+    - `input_audio.format` will be ignored, the file format will be determined automatically
+- If `type == "input_video"`:
+    - Either `input_video.data` or `input_video.url` can be specified, can be a remote URL, raw base64 or path to local file
+    - Accepts formats supported by `ffmpeg`
+- Note: for local file, make sure to set `--media-path`. File path must be prefixed by `file://`
 
 *Examples:*
 
diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index 3dc686bb4..e412b94c5 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -817,12 +817,21 @@ json oaicompat_completion_params_parse(const json & body) {
     return llama_params;
 }
 
-// media_path always end with '/', see arg.cpp
+// url can be
+// - http(s):// for remote files
+// - file:// for local files (only allowed if media_path is set)
+// - data: for base64 encoded data with uri scheme (e.g. data:image/png;base64,...)
+// - raw base64 encoded data
 static void handle_media(
         std::vector<raw_buffer> & out_files,
-        json & media_obj,
-        const std::string & media_path) {
-    std::string url = json_value(media_obj, "url", std::string());
+        const std::string & url,
+        const std::string & media_path,
+        bool accept_base64_uri) {
+    if (!media_path.empty()) {
+        // should already be enforced by arg.cpp, but checking just in case
+        GGML_ASSERT(media_path.back() == DIRECTORY_SEPARATOR);
+    }
+
     if (string_starts_with(url, "http")) {
         // download remote image
         // TODO @ngxson : maybe make these params configurable
@@ -858,20 +867,28 @@ static void handle_media(
         data.assign((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
         out_files.push_back(data);
 
-    } else {
+    } else if (accept_base64_uri && string_starts_with(url, "data:")) {
         // try to decode base64 image
         std::vector<std::string> parts = string_split<std::string>(url, /*separator*/ ',');
         if (parts.size() != 2) {
-            throw std::runtime_error("Invalid url value");
+            throw std::runtime_error("Invalid uri-encoded base64 value");
         } else if (!string_starts_with(parts[0], "data:image/")) {
-            throw std::runtime_error("Invalid url format: " + parts[0]);
+            throw std::runtime_error("Invalid uri format: " + parts[0]);
         } else if (!string_ends_with(parts[0], "base64")) {
-            throw std::runtime_error("url must be base64 encoded");
+            throw std::runtime_error("uri must be base64 encoded");
         } else {
             auto base64_data = parts[1];
             auto decoded_data = base64_decode(base64_data);
             out_files.push_back(decoded_data);
         }
+
+    } else {
+        // try as raw base64 string
+        auto decoded_data = base64_decode(url);
+        if (decoded_data.empty()) {
+            throw std::runtime_error("Invalid base64 value");
+        }
+        out_files.push_back(decoded_data);
     }
 }
 
@@ -957,14 +974,15 @@ json oaicompat_chat_params_parse(
         }
 
         for (auto & p : content) {
-            std::string type      = json_value(p, "type", std::string());
+            std::string type = json_value(p, "type", std::string());
             if (type == "image_url") {
                 if (!opt.allow_image) {
                     throw std::runtime_error("image input is not supported - hint: if this is unexpected, you may need to provide the mmproj");
                 }
 
                 json image_url = json_value(p, "image_url", json::object());
-                handle_media(out_files, image_url, opt.media_path);
+                std::string url = json_value(image_url, "url", std::string());
+                handle_media(out_files, url, opt.media_path, true);
 
                 p["type"] = "media_marker";
                 p["text"] = get_media_marker();
@@ -975,17 +993,11 @@ json oaicompat_chat_params_parse(
                     throw std::runtime_error("audio input is not supported - hint: if this is unexpected, you may need to provide the mmproj");
                 }
 
-                json input_audio   = json_value(p, "input_audio", json::object());
-                std::string data   = json_value(input_audio, "data", std::string());
-                std::string format = json_value(input_audio, "format", std::string());
-                // while we also support flac, we don't allow it here so we matches the OAI spec
-                if (format != "wav" && format != "mp3") {
-                    throw std::invalid_argument("input_audio.format must be either 'wav' or 'mp3'");
-                }
-                auto decoded_data = base64_decode(data); // expected to be base64 encoded
-                out_files.push_back(decoded_data);
-
-                // TODO: add audio_url support by reusing handle_media()
+                // note: don't need to validate "format", it's redundant
+                json input_audio = json_value(p, "input_audio", json::object());
+                std::string url  = json_value(input_audio, "data",
+                                        json_value(input_audio, "url", std::string()));
+                handle_media(out_files, url, opt.media_path, false);
 
                 p["type"] = "media_marker";
                 p["text"] = get_media_marker();
@@ -996,10 +1008,10 @@ json oaicompat_chat_params_parse(
                     throw std::runtime_error("video input is not supported - hint: if this is unexpected, you may need to provide the mmproj");
                 }
 
-                json input_video  = json_value(p, "input_video", json::object());
-                std::string data  = json_value(input_video, "data", std::string());
-                auto decoded_data = base64_decode(data); // expected to be base64 encoded
-                out_files.push_back(decoded_data);
+                json input_video = json_value(p, "input_video", json::object());
+                std::string url  = json_value(input_video, "data",
+                                        json_value(input_video, "url", std::string()));
+                handle_media(out_files, url, opt.media_path, false);
 
                 p["type"] = "media_marker";
                 p["text"] = get_media_marker();

From 721354fbdfb7743e2be2183d918a3cdb9276c70f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 22 Jun 2026 18:24:04 +0200
Subject: [PATCH 119/292] server: (router) move model downloading to dedicated
 process (#24834)

* server: real-time model load progress tracking via /models/sse

* update docs

* server: move model download to child process

* rm unused

* fix most problems

* clean up

* nit fixes

* fix test case

* do not detact() thread

* shorter MODEL_DOWNLOAD_TIMEOUT in test

* throttle
---
 common/arg.cpp                         |  14 +-
 common/arg.h                           |   6 +-
 tools/server/README-dev.md             |   6 +-
 tools/server/server-context.cpp        |   6 +
 tools/server/server-context.h          |   4 +-
 tools/server/server-models.cpp         | 360 ++++++++++++++++---------
 tools/server/server-models.h           |  20 +-
 tools/server/server.cpp                |  13 +-
 tools/server/tests/unit/test_router.py |  35 ++-
 9 files changed, 312 insertions(+), 152 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 8f54b5c81..5297d9075 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -396,7 +396,7 @@ static bool parse_bool_value(const std::string & value) {
 // CLI argument parsing functions
 //
 
-bool common_params_handle_models(common_params & params, llama_example curr_ex) {
+bool common_params_handle_models(common_params & params, llama_example curr_ex, common_download_callback * callback) {
     const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
                                          params.speculative.types.end(),
                                          COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
@@ -408,6 +408,10 @@ bool common_params_handle_models(common_params & params, llama_example curr_ex)
     opts.download_mtp    = spec_type_draft_mtp;
     opts.download_mmproj = !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty();
 
+    if (callback) {
+        opts.callback = callback;
+    }
+
     // sub-models (draft, mmproj, vocoder) are explicitly specified by the user,
     // so we should not auto-discover mtp/mmproj siblings for them
     common_download_opts sub_opts = opts;
@@ -584,8 +588,11 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    // export_graph_ops loads only metadata
-    const bool skip_model_download = ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
+    const bool skip_model_download =
+        // server will call common_params_handle_models() later, so we skip it here
+        ctx_arg.ex == LLAMA_EXAMPLE_SERVER ||
+        // export_graph_ops loads only metadata
+        ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
 
     if (!skip_model_download) {
         // handle model and download
@@ -594,7 +601,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         // model is required (except for server)
         // TODO @ngxson : maybe show a list of available models in CLI in this case
         if (params.model.path.empty()
-                && ctx_arg.ex != LLAMA_EXAMPLE_SERVER
                 && !params.usage
                 && !params.completion) {
             throw std::invalid_argument("error: --model is required\n");
diff --git a/common/arg.h b/common/arg.h
index 0010f2a9a..c061fc60f 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "common.h"
+#include "download.h"
 
 #include <set>
 #include <map>
@@ -133,7 +134,10 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 // return true if the model is ready to use
 // throw an exception if there is an error that prevents the model from being used (e.g. network error, model not found, etc)
 // if params.skip_download is true, no downloads will be attempted. return false if the model is invalid or missing (e.g. ETag check failed)
-bool common_params_handle_models(common_params & params, llama_example curr_ex);
+bool common_params_handle_models(
+    common_params & params,
+    llama_example curr_ex,
+    common_download_callback * callback = nullptr);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index 2796d2835..5959745e4 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -204,9 +204,9 @@ Instead of building everything from the ground up (like what most AI agents will
 
 The flow for downloading a new model:
 - POST request comes in --> `post_router_models` --> validation
-- `server_models::download()` is called
-    - Sets up a new thread `inst.th` and runs the download inside
-- If a stop request comes in, set `stop_download` to `true`
+- A new `llama-server` subprocess will be spawned with special `SERVER_CHILD_MODE_DOWNLOAD`
+- Child process runs the download and report status back to router via stdin/out
+- If a stop request comes in, the router asks the child process to stop (same mechanism as running a model in child process)
 - Otherwise, upon completion, we call `load_models()` to refresh the list of models
 
 ### Notable Related PRs
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 3f9391cac..0a25b414e 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -931,6 +931,8 @@ private:
 
     bool sleeping = false;
 
+    int64_t t_last_load_progress_ms = 0;
+
     void destroy() {
         spec.reset();
         ctx_dft.reset();
@@ -1244,6 +1246,10 @@ private:
         }
 
         if (has_mmproj) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "mmproj_model"}});
+            }
+
             if (!is_resume) {
                 mtmd_helper_log_set(common_log_default_callback, nullptr);
             }
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index c7218a12e..952f825f7 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -53,7 +53,7 @@ struct server_context_meta {
 };
 
 enum server_state {
-    // SERVER_STATE_DOWNLOADING,
+    SERVER_STATE_DOWNLOADING,
     SERVER_STATE_LOADING,
     SERVER_STATE_READY,
     SERVER_STATE_SLEEPING,
@@ -61,6 +61,7 @@ enum server_state {
 
 static std::string server_state_to_str(server_state state) {
     switch (state) {
+        case SERVER_STATE_DOWNLOADING: return "downloading";
         case SERVER_STATE_LOADING:     return "loading";
         case SERVER_STATE_READY:       return "ready";
         case SERVER_STATE_SLEEPING:    return "sleeping";
@@ -69,6 +70,7 @@ static std::string server_state_to_str(server_state state) {
 }
 
 static server_state server_state_from_str(const std::string & str) {
+    if (str == "downloading") return SERVER_STATE_DOWNLOADING;
     if (str == "loading")     return SERVER_STATE_LOADING;
     if (str == "ready")       return SERVER_STATE_READY;
     if (str == "sleeping")    return SERVER_STATE_SLEEPING;
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 68eefdffa..a87e4e423 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -64,6 +64,17 @@ struct server_subproc {
         return sproc.has_value() && subprocess_alive(&sproc.value());
     }
 
+    void request_exit() {
+        if (sproc.has_value()) {
+            FILE * stdin_file = subprocess_stdin(&sproc.value());
+            if (stdin_file) {
+                fprintf(stdin_file, "%s\n", CMD_ROUTER_TO_CHILD_EXIT);
+                fflush(stdin_file);
+            }
+        }
+        stopped.store(true, std::memory_order_relaxed);
+    }
+
     void terminate() {
         if (!sproc.has_value()) {
             return;
@@ -323,7 +334,7 @@ void server_models::notify_sse(const std::string & event, const std::string & mo
 }
 
 void server_models::load_models() {
-    // Phase 1: load presets from all sources — pure I/O, no lock needed
+    // Phase 1: load presets from all sources - pure I/O, no lock needed
     // 1. cached models
     common_presets cached_models = ctx_preset.load_from_cache();
     SRV_INF("Loaded %zu cached model presets\n", cached_models.size());
@@ -376,7 +387,7 @@ void server_models::load_models() {
         return source_map.count(name) ? source_map.at(name) : SERVER_MODEL_SOURCE_PRESET;
     };
 
-    // Helpers that read `mapping` — must be called while holding the lock.
+    // Helpers that read `mapping` - must be called while holding the lock.
     std::unordered_set<std::string> custom_names;
     for (const auto & [name, preset] : custom_presets) custom_names.insert(name);
     auto join_set = [](const std::set<std::string> & s) {
@@ -523,7 +534,7 @@ void server_models::load_models() {
             }
         }
 
-        // join outside the lock — monitoring thread calls update_status (needs lock)
+        // join outside the lock - monitoring thread calls update_status (needs lock)
         lk.unlock();
         for (auto & th : threads_to_join) th.join();
         lk.lock();
@@ -622,7 +633,7 @@ void server_models::load_models() {
 
         apply_stop_timeout();
 
-        // clear reload flag before unlocking for autoload — load() blocks on !is_reloading,
+        // clear reload flag before unlocking for autoload - load() blocks on !is_reloading,
         // so clearing it here (while still locked) prevents a deadlock in the autoload calls below
         is_reloading = false;
         cv.notify_all();
@@ -815,17 +826,23 @@ void server_models::unload_lru() {
 }
 
 void server_models::load(const std::string & name) {
-    if (!has_model(name)) {
-        throw std::runtime_error("model name=" + name + " is not found");
+    load(name, load_options{});
+}
+
+void server_models::load(const std::string & name, const load_options & opts) {
+    if (!opts.custom_meta.has_value()) {
+        if (!has_model(name)) {
+            throw std::runtime_error("model name=" + name + " is not found");
+        }
+        unload_lru();
     }
-    unload_lru();
 
     std::unique_lock<std::mutex> lk(mutex);
     // edge case: block until any in-progress reload has finished so we always load
     // against the freshest preset and a consistent mapping state
     cv.wait(lk, [this]() { return !is_reloading; });
 
-    auto meta = mapping[name].meta;
+    auto meta = opts.custom_meta.has_value() ? *opts.custom_meta : mapping[name].meta;
     if (meta.status != SERVER_MODEL_STATUS_UNLOADED) {
         SRV_INF("model %s is not ready\n", name.c_str());
         return;
@@ -869,6 +886,12 @@ void server_models::load(const std::string & name) {
         std::vector<std::string> child_env  = base_env; // copy
         child_env.push_back("LLAMA_SERVER_ROUTER_PORT=" + std::to_string(base_params.port));
 
+        if (opts.mode == SERVER_CHILD_MODE_DOWNLOAD) {
+            inst.meta.status = SERVER_MODEL_STATUS_DOWNLOADING;
+            child_env.push_back("LLAMA_SERVER_CHILD_MODE=download");
+            child_env.push_back("LLAMA_ARG_HF_REPO=" + name);
+        }
+
         SRV_INF("%s", "spawning server instance with args:\n");
         for (const auto & arg : child_args) {
             SRV_INF("  %s\n", arg.c_str());
@@ -886,13 +909,17 @@ void server_models::load(const std::string & name) {
         if (result != 0) {
             throw std::runtime_error("failed to spawn server instance");
         }
-
-        inst.stdin_file = subprocess_stdin(&inst.subproc->get());
     }
 
     // start a thread to manage the child process
     // captured variables are guaranteed to be destroyed only after the thread is joined
-    inst.th = std::thread([this, name, child_proc = inst.subproc, port = inst.meta.port, stop_timeout = inst.meta.stop_timeout]() {
+    inst.th = std::thread([
+        this, name,
+        child_proc = inst.subproc,
+        port = inst.meta.port,
+        stop_timeout = inst.meta.stop_timeout,
+        child_mode = opts.mode
+    ]() {
         FILE * stdin_file = subprocess_stdin(&child_proc->get());
         FILE * stdout_file = subprocess_stdout(&child_proc->get()); // combined stdout/stderr
 
@@ -925,7 +952,7 @@ void server_models::load(const std::string & name) {
                     return is_stopping() || child_proc->stopped.load(std::memory_order_acquire);
                 });
             }
-            // child crashed or finished on its own — skip graceful shutdown sequence
+            // child crashed or finished on its own, skip graceful shutdown sequence
             if (child_proc->stopped.load(std::memory_order_acquire)) {
                 return;
             }
@@ -973,10 +1000,14 @@ void server_models::load(const std::string & name) {
         subprocess_destroy(&child_proc->get());
 
         // update status and exit code
-        this->update_status(name, {
-            SERVER_MODEL_STATUS_UNLOADED,
-            exit_code
-        });
+        if (child_mode == SERVER_CHILD_MODE_DOWNLOAD) {
+            // instance will be cleaned up on next load_models() call
+        } else {
+            this->update_status(name, {
+                SERVER_MODEL_STATUS_UNLOADED,
+                exit_code
+            });
+        }
         SRV_INF("instance name=%s exited with status %d\n", name.c_str(), exit_code);
     });
 
@@ -984,7 +1015,7 @@ void server_models::load(const std::string & name) {
     {
         auto & old_instance = mapping[name];
         // old process should have exited already, but just in case, we clean it up here
-        if (old_instance.subproc->is_alive()) {
+        if (old_instance.subproc && old_instance.subproc->is_alive()) {
             SRV_WRN("old process for model name=%s is still alive, this is unexpected\n", name.c_str());
             old_instance.subproc->terminate(); // force kill
         }
@@ -1001,92 +1032,13 @@ void server_models::load(const std::string & name) {
     cv.notify_all();
 }
 
-// callback for model downloading functionality
-struct server_models_download_res : public common_download_callback {
-    common_params_model model;
-    common_download_opts opts;
-
-    std::function<bool()> should_stop;
-    std::function<void(const common_download_progress & p)> on_progress;
-
-    bool is_ok = false;
-
-    bool run() {
-        try {
-            common_download_model(model, opts);
-            is_ok = true;
-        } catch (const std::exception & e) {
-            auto model_name = model.get_name();
-            SRV_ERR("download failed for model name=%s: %s\n", model_name.c_str(), e.what());
-            is_ok = false;
-        }
-        return is_ok;
-    }
-    void on_start(const common_download_progress & p) override {
-        on_progress(p);
-    }
-    void on_update(const common_download_progress & p) override {
-        on_progress(p);
-    }
-    void on_done(const common_download_progress &, bool ok) override {
-        is_ok = ok;
-    }
-    bool is_cancelled() const override {
-        return should_stop();
-    }
-};
-
-void server_models::download(common_params_model && model, common_download_opts && opts) {
-    std::string name = model.get_name();
-    GGML_ASSERT(name == model.hf_repo);
-
-    std::unique_lock<std::mutex> lk(mutex);
-    if (mapping.find(name) != mapping.end()) {
-        throw std::runtime_error("model name=" + name + " already exists");
-    }
-
-    instance_t inst;
-    inst.meta.name   = name;
-    inst.meta.status = SERVER_MODEL_STATUS_DOWNLOADING;
-    inst.subproc     = std::make_shared<server_subproc>();
-
-    auto dl = std::make_unique<server_models_download_res>();
-    dl->model = model; // copy
-    dl->opts  = opts;  // copy
-
-    dl->should_stop = [sp = inst.subproc]() {
-        return sp->stopped.load(std::memory_order_relaxed);
-    };
-
-    dl->on_progress = [this, name](const common_download_progress & p) {
-        update_download_progress(name, p, false);
-    };
-
-    inst.th = std::thread([this, dl = std::move(dl)]() {
-        dl->opts.callback = dl.get();
-        bool ok = dl->run();
-        auto model_name = dl->model.get_name();
-        SRV_INF("download finished for model name=%s with status=%s\n",
-                    model_name.c_str(), ok ? "success" : "failure");
-        update_download_progress(model_name, {}, true, ok);
-        // need_reload is set inside update_download_progress under the mutex;
-        // the next load_models() call will clean up this instance
-    });
-
-    mapping[name] = std::move(inst);
-    notify_sse("status_update", name, {
-        {"status", server_model_status_to_string(SERVER_MODEL_STATUS_DOWNLOADING)},
-    });
-    cv.notify_all();
-}
-
 void server_models::unload(const std::string & name) {
     std::unique_lock<std::mutex> lk(mutex);
     auto it = mapping.find(name);
     if (it != mapping.end()) {
         if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
             SRV_INF("cancelling download for model name=%s\n", name.c_str());
-            it->second.subproc->stopped.store(true, std::memory_order_relaxed);
+            it->second.subproc->request_exit();
             // for convenience, we wait the status change here
             wait(lk, name, [](const server_model_meta & new_meta) {
                 return new_meta.status != SERVER_MODEL_STATUS_DOWNLOADING;
@@ -1198,37 +1150,65 @@ void server_models::update_download_progress(const std::string & name, const com
 }
 
 bool server_models::remove(const std::string & name) {
-    auto meta = get_meta(name);
+    // do everything under one lock acquisition; avoid get_meta() /
+    // unload() because they can trigger load_models() which erases
+    // transient DOWNLOADING / DOWNLOADED entries as a side-effect
+    std::unique_lock<std::mutex> lk(mutex);
 
-    if (!meta.has_value()) {
+    auto it = mapping.find(name);
+    if (it == mapping.end()) {
         throw std::runtime_error("model name=" + name + " is not found");
     }
-    if (meta->source != SERVER_MODEL_SOURCE_CACHE) {
+    if (it->second.meta.source != SERVER_MODEL_SOURCE_CACHE) {
         throw std::runtime_error("model name=" + name + " is not removable (not from cache)");
     }
 
-    unload(name); // cancel download or stop running instance
-    {
-        std::unique_lock<std::mutex> lk(mutex);
-        // a cancelled download lands on DOWNLOADED; a stopped instance lands on UNLOADED
-        wait(lk, name, [](const server_model_meta & new_meta) {
-            return new_meta.status == SERVER_MODEL_STATUS_UNLOADED
-                || new_meta.status == SERVER_MODEL_STATUS_DOWNLOADED;
-        });
-        // join before erasing - after status reaches UNLOADED/DOWNLOADED the thread no
-        // longer acquires this mutex, so joining while holding it is safe
-        if (mapping[name].th.joinable()) {
-            mapping[name].th.join();
+    if (it->second.meta.status == SERVER_MODEL_STATUS_DOWNLOADING) {
+        // cancel in-flight download
+        SRV_INF("cancelling download for model name=%s\n", name.c_str());
+        it->second.subproc->request_exit();
+    } else if (it->second.meta.is_running()) {
+        // stop running instance
+        SRV_INF("stopping model instance name=%s\n", name.c_str());
+        stopping_models.insert(name);
+        if (it->second.meta.status == SERVER_MODEL_STATUS_LOADING) {
+            it->second.subproc->terminate();
         }
-        // remove the model from disk (hold lock to prevent concurrent load)
-        bool ok = common_download_remove(name);
-        if (ok) {
-            mapping.erase(name);
-        }
-        SRV_INF("removing model name=%s from cache (%s)\n", name.c_str(), ok ? "succeeded" : "failed");
-        notify_sse("model_remove", name, {});
-        return ok;
+        cv_stop.notify_all();
     }
+
+    // wait until the monitoring thread finishes
+    wait(lk, name, [](const server_model_meta & meta) {
+        return meta.status == SERVER_MODEL_STATUS_UNLOADED
+            || meta.status == SERVER_MODEL_STATUS_DOWNLOADED;
+    });
+
+    // re-find after wait - load_models() may have erased the entry during the wait
+    it = mapping.find(name);
+    if (it == mapping.end()) {
+        // load_models() already joined the thread and erased the entry;
+        // we just need to clean up the cached files on disk
+        lk.unlock();
+        bool ok = common_download_remove(name);
+        SRV_INF("removing model name=%s from cache (%s)\n", name.c_str(), ok ? "succeeded" : "partial");
+        notify_sse("model_remove", name, {});
+        return true;
+    }
+
+    // join before erasing - thread no longer acquires this mutex
+    if (it->second.th.joinable()) {
+        it->second.th.join();
+    }
+
+    // remove from disk (best-effort: cancelled downloads may have no cached files)
+    bool ok = common_download_remove(name);
+    mapping.erase(name);
+    if (!ok) {
+        SRV_WRN("removing model name=%s from disk returned false (no cached files?)\n", name.c_str());
+    }
+    SRV_INF("removing model name=%s from cache (%s)\n", name.c_str(), ok ? "succeeded" : "partial");
+    notify_sse("model_remove", name, {});
+    return true;
 }
 
 void server_models::wait(const std::string & name, std::function<bool(const server_model_meta &)> predicate) {
@@ -1243,7 +1223,9 @@ void server_models::wait(std::unique_lock<std::mutex> & lk, const std::string &
             return predicate(it->second.meta);
 
         }
-        return false;
+        // model was removed from mapping by another code path (e.g. load_models()).
+        // nothing left to wait for - tell the caller to proceed.
+        return true;
     });
 }
 
@@ -1328,6 +1310,31 @@ void server_models::handle_child_state(const std::string & name, const std::stri
     }
 
     switch (state) {
+        case SERVER_STATE_DOWNLOADING:
+            {
+                std::string result = json_value(payload, "result", std::string());
+                std::string url    = json_value(payload, "url",    std::string());
+                auto request_exit = [&]() {
+                    std::lock_guard<std::mutex> lk(mutex);
+                    auto it = mapping.find(name);
+                    if (it != mapping.end()) {
+                        return it->second.subproc->request_exit();
+                    }
+                };
+                if (result == "download_finished") {
+                    update_download_progress(name, {}, true, true);
+                    request_exit();
+                } else if (result == "download_failed") {
+                    update_download_progress(name, {}, true, false);
+                    request_exit();
+                } else if (!url.empty()) {
+                    common_download_progress p;
+                    p.url        = url;
+                    p.downloaded = json_value(payload, "downloaded", (size_t)0);
+                    p.total      = json_value(payload, "total", (size_t)0);
+                    update_download_progress(name, p, false);
+                }
+            } break;
         case SERVER_STATE_LOADING:
             {
                 update_status(name, {
@@ -1366,6 +1373,90 @@ bool server_child::is_child() {
     return router_port != nullptr;
 }
 
+server_child_mode server_child::get_mode() {
+    const char * mode = std::getenv("LLAMA_SERVER_CHILD_MODE");
+    std::string mode_str(mode ? mode : "");
+    if (mode_str == "download") {
+        return SERVER_CHILD_MODE_DOWNLOAD;
+    } else {
+        return SERVER_CHILD_MODE_NORMAL;
+    }
+}
+
+struct server_download_state : public common_download_callback {
+    server_child * self;
+    std::function<bool()> should_stop;
+    std::atomic<int64_t> last_progress_time{0}; // multiple files downloading in different threads
+    bool is_ok = false;
+
+    server_download_state(server_child * s) : self(s) {}
+
+    bool run(common_params & params) {
+        try {
+            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, this);
+            is_ok = true;
+        } catch (const std::exception & e) {
+            auto model_name = params.model.get_name();
+            SRV_ERR("download failed for model name=%s: %s\n", model_name.c_str(), e.what());
+            is_ok = false;
+        }
+        return is_ok;
+    }
+    void on_progress(const common_download_progress & p) {
+        json data = {
+            {"url", p.url},
+            {"downloaded", p.downloaded},
+            {"total", p.total},
+        };
+        self->notify_to_router(server_state_to_str(SERVER_STATE_DOWNLOADING), data);
+    }
+    void on_start(const common_download_progress & p) override {
+        on_progress(p);
+    }
+    void on_update(const common_download_progress & p) override {
+        int64_t now = ggml_time_ms();
+        // throttle progress updates to avoid flooding logs
+        if (now - last_progress_time.load(std::memory_order_relaxed) >= 100) {
+            on_progress(p);
+            last_progress_time.store(now, std::memory_order_relaxed);
+        }
+    }
+    void on_done(const common_download_progress & p, bool) override {
+        on_progress(p);
+    }
+    bool is_cancelled() const override {
+        return should_stop ? should_stop() : false;
+    }
+};
+
+int server_child::run_download(common_params & params) {
+    auto cancelled = std::make_shared<std::atomic<bool>>(false);
+
+    // monitor stdin for cancellation command from the router
+    std::thread signal_thread = setup([cancelled](int) {
+        cancelled->store(true, std::memory_order_relaxed);
+    });
+
+    server_download_state dl(this);
+    dl.should_stop = [cancelled]() {
+        return cancelled->load(std::memory_order_relaxed);
+    };
+
+    bool ok = dl.run(params);
+
+    notify_to_router(server_state_to_str(SERVER_STATE_DOWNLOADING), {
+        {"result", ok ? "download_finished" : "download_failed"},
+    });
+
+    // router should send CMD_ROUTER_TO_CHILD_EXIT after receiving the result
+    if (signal_thread.joinable()) {
+        signal_thread.join();
+    }
+
+    SRV_INF("download completed %s\n", ok ? "successfully" : "with errors");
+    return 0;
+}
+
 std::thread server_child::setup(const std::function<void(int)> & shutdown_handler) {
     // setup thread for monitoring stdin
     return std::thread([shutdown_handler]() {
@@ -1639,7 +1730,7 @@ void server_models_routes::init_routes() {
             res_err(res, format_error_response("model is not found", ERROR_TYPE_INVALID_REQUEST));
             return res;
         }
-        if (!model->is_running()) {
+        if (!model->is_running() && model->status != SERVER_MODEL_STATUS_DOWNLOADING) {
             res_err(res, format_error_response("model is not running", ERROR_TYPE_INVALID_REQUEST));
             return res;
         }
@@ -1680,8 +1771,9 @@ void server_models_routes::init_routes() {
 
         model.hf_repo        = name;
         opts.bearer_token    = params.hf_token;
-        opts.download_mmproj = true;
-        opts.download_mtp    = true;
+        // note: we only check main model, no need sidecar here
+        opts.download_mmproj = false;
+        opts.download_mtp    = false;
 
         // first, only check if the model is valid and can be downloaded
         opts.skip_download = true;
@@ -1702,10 +1794,21 @@ void server_models_routes::init_routes() {
             throw std::invalid_argument("model validation failed, unable to download");
         }
 
+        // reject if model already exists
+        if (models.has_model(name)) {
+            throw std::invalid_argument("model '" + name + "' already exists");
+        }
+
         // then, proceed with the actual download
-        opts.skip_download = false;
         SRV_INF("starting download for model '%s'\n", name.c_str());
-        models.download(std::move(model), std::move(opts));
+        {
+            server_models::load_options load_opts;
+            load_opts.mode = SERVER_CHILD_MODE_DOWNLOAD;
+            load_opts.custom_meta = server_model_meta{};
+            load_opts.custom_meta->source = SERVER_MODEL_SOURCE_CACHE;
+            load_opts.custom_meta->name   = name;
+            models.load(name, load_opts);
+        }
 
         res_ok(res, {{"success", true}});
         return res;
@@ -1719,10 +1822,7 @@ void server_models_routes::init_routes() {
             throw std::invalid_argument("model must be a non-empty string");
         }
 
-        bool ok = models.remove(name);
-        if (!ok) {
-            throw std::runtime_error("failed to remove model '" + name + "'");
-        }
+        models.remove(name); // throws on error
 
         res_ok(res, {{"success", true}});
         return res;
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 17759b00a..9ed4aeead 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -40,6 +40,11 @@ enum server_model_source {
     SERVER_MODEL_SOURCE_CACHE,
 };
 
+enum server_child_mode {
+    SERVER_CHILD_MODE_NORMAL,   // load the model and run normally
+    SERVER_CHILD_MODE_DOWNLOAD, // download the model and exit
+};
+
 static std::string server_model_status_to_string(server_model_status status) {
     switch (status) {
         case SERVER_MODEL_STATUS_DOWNLOADING: return "downloading";
@@ -105,7 +110,6 @@ private:
         std::shared_ptr<server_subproc> subproc; // shared between main thread and monitoring thread
         std::thread th;
         server_model_meta meta;
-        FILE * stdin_file = nullptr;
     };
 
     std::mutex mutex;
@@ -161,16 +165,19 @@ public:
     // return a copy of all model metadata (thread-safe)
     std::vector<server_model_meta> get_all_meta();
 
+    struct load_options {
+        server_child_mode mode = SERVER_CHILD_MODE_NORMAL;
+        // used for spawning a downloading child process
+        std::optional<server_model_meta> custom_meta = std::nullopt;
+    };
+
     // load and unload model instances
     // these functions are thread-safe
     void load(const std::string & name);
+    void load(const std::string & name, const load_options & opts);
     void unload(const std::string & name);
     void unload_all();
 
-    // download a new model, progress is reported via SSE
-    // to stop the download, call unload()
-    void download(common_params_model && model, common_download_opts && opts);
-
     struct update_status_args {
         server_model_status status;
         int exit_code = 0; // only valid if status == UNLOADED
@@ -213,9 +220,12 @@ public:
 struct server_child {
     // serializes the notify_to_router writes
     std::mutex mtx_stdout;
+    std::atomic<bool> is_finished_downloading = false; // set by run_download
 
     // return true if the current process is a child server instance
     bool is_child();
+    server_child_mode get_mode();
+    int run_download(common_params & params);
 
     // register the shutdown_handler to be called by the router
     // return the monitoring thread (to be joined by the caller)
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index bf3680b9f..dd4b1c507 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -134,6 +134,7 @@ int llama_server(int argc, char ** argv) {
     //
 
     // register API routes
+    server_child child; // only used in non-router mode
     server_routes routes(params, ctx_server);
     server_tools tools;
 
@@ -254,11 +255,21 @@ int llama_server(int argc, char ** argv) {
         ctx_http.post("/tools",           ex_wrapper(tools.handle_post));
     }
 
+    //
+    // Handle downloading model
+    //
+
+    if (child.is_child() && child.get_mode() == SERVER_CHILD_MODE_DOWNLOAD) {
+        return child.run_download(params);
+    } else if (!is_router_server) {
+        // single-model mode (NOT spawned by router)
+        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER);
+    }
+
     //
     // Start the server
     //
 
-    server_child child; // only used in non-router mode
     std::function<void()> clean_up;
 
     if (is_router_server) {
diff --git a/tools/server/tests/unit/test_router.py b/tools/server/tests/unit/test_router.py
index 11c77ca7a..41e95f4c5 100644
--- a/tools/server/tests/unit/test_router.py
+++ b/tools/server/tests/unit/test_router.py
@@ -257,14 +257,25 @@ def test_router_reload_models():
 
 
 MODEL_DOWNLOAD_ID = "ggml-org/test-model-router-download:F16"
-MODEL_DOWNLOAD_TIMEOUT = 300
+MODEL_DOWNLOAD_TIMEOUT = 30
 
 
-def _listen_sse(server: ServerProcess, collected: list, stop: threading.Event):
-    """Collect /models/sse events into `collected` until `stop` is set."""
+def _listen_sse(
+    server: ServerProcess, collected: list, stop: threading.Event, ready: threading.Event | None = None
+):
+    """Collect /models/sse events into `collected` until `stop` is set.
+
+    When `ready` is provided, it is set once the streaming response is open,
+    i.e. the server has accepted the connection and registered us as a
+    subscriber. Callers that trigger one-shot events (e.g. download_finished)
+    must wait on `ready` before acting, otherwise the event can be broadcast
+    before this client is subscribed and be lost.
+    """
     url = f"http://{server.server_host}:{server.server_port}/models/sse"
     try:
         with requests.get(url, stream=True, timeout=MODEL_DOWNLOAD_TIMEOUT) as resp:
+            if ready is not None:
+                ready.set()
             for line_bytes in resp.iter_lines():
                 if stop.is_set():
                     break
@@ -294,11 +305,17 @@ def test_router_download_model():
 
     sse_events: list = []
     stop = threading.Event()
+    sse_ready = threading.Event()
     sse_thread = threading.Thread(
-        target=_listen_sse, args=(server, sse_events, stop), daemon=True
+        target=_listen_sse, args=(server, sse_events, stop, sse_ready), daemon=True
     )
     sse_thread.start()
 
+    # wait for the SSE client to be subscribed before triggering the download,
+    # otherwise the one-shot download_finished event can be broadcast before
+    # this client is registered and be lost
+    assert sse_ready.wait(10), "SSE client failed to connect"
+
     # Trigger the download
     res = server.make_request("POST", "/models", data={"model": MODEL_DOWNLOAD_ID})
     assert res.status_code == 200
@@ -328,13 +345,17 @@ def test_router_delete_model():
 
     # Ensure the model exists (download it if needed)
     if MODEL_DOWNLOAD_ID not in _get_model_ids(is_reload=False):
-        res = server.make_request("POST", "/models", data={"model": MODEL_DOWNLOAD_ID})
-        assert res.status_code == 200
         sse_events: list = []
         stop = threading.Event()
+        sse_ready = threading.Event()
         threading.Thread(
-            target=_listen_sse, args=(server, sse_events, stop), daemon=True
+            target=_listen_sse, args=(server, sse_events, stop, sse_ready), daemon=True
         ).start()
+        # subscribe before triggering the download so the one-shot
+        # download_finished event is not lost (see test_router_download_model)
+        assert sse_ready.wait(10), "SSE client failed to connect"
+        res = server.make_request("POST", "/models", data={"model": MODEL_DOWNLOAD_ID})
+        assert res.status_code == 200
         finished = _wait_for_sse_event(
             sse_events, "download_finished", MODEL_DOWNLOAD_ID, MODEL_DOWNLOAD_TIMEOUT
         )

From 9c0ac887f35eb4f3cdef57df422de47715b6f0f8 Mon Sep 17 00:00:00 2001
From: Mahdiou Diallo <104755555+mahdiou@users.noreply.github.com>
Date: Mon, 22 Jun 2026 21:00:21 +0200
Subject: [PATCH 120/292] ui: Prioritize favorite models in model selection
 (#24766)

Updated model selection prioritization to include favorite models.
---
 tools/ui/src/lib/stores/models.svelte.ts | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tools/ui/src/lib/stores/models.svelte.ts b/tools/ui/src/lib/stores/models.svelte.ts
index 2ce450d42..87fb172d0 100644
--- a/tools/ui/src/lib/stores/models.svelte.ts
+++ b/tools/ui/src/lib/stores/models.svelte.ts
@@ -545,7 +545,8 @@ class ModelsStore {
 	 * 1. Model from active conversation's last assistant response (if loaded)
 	 * 2. Model from active conversation's last assistant response (if not loaded)
 	 * 3. First loaded model (not from active conversation)
-	 * 4. First available model
+	 * 4. A favorite model
+	 * 5. First available model
 	 */
 	async ensureFirstModelSelected(): Promise<void> {
 		if (this.selectedModelName) return;
@@ -574,6 +575,13 @@ class ModelsStore {
 			return;
 		}
 
+		// Try loading a favorite model
+		const favorite = this.favoriteModelIds.values().next()?.value
+		if (favorite) {
+			await this.selectModelById(favorite);
+			return;
+		}
+
 		// Fall back to the first available model
 		await this.selectModelById(availableModels[0].id);
 	}

From dec5ca5577d6042b4e870fadf4087c5b9b8d3a70 Mon Sep 17 00:00:00 2001
From: Matt Thompson <111157855+boondocklabs@users.noreply.github.com>
Date: Mon, 22 Jun 2026 14:03:12 -0700
Subject: [PATCH 121/292] server : Add id to tool call responses api (#24882)

---
 tools/server/server-task.cpp | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 9ba039c8b..a9ebac013 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -591,10 +591,11 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp() {
 
     for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
         output.push_back(json {
+            {"id",        "fc_" + tool_call.id},
             {"type",      "function_call"},
             {"status",    "completed"},
             {"arguments", tool_call.arguments},
-            {"call_id",   "fc_" + tool_call.id},
+            {"call_id",   "call_" + tool_call.id},
             {"name",      tool_call.name},
         });
     }
@@ -690,10 +691,11 @@ json server_task_result_cmpl_final::to_json_oaicompat_resp_stream() {
 
     for (const common_chat_tool_call & tool_call : oaicompat_msg.tool_calls) {
         const json output_item = {
+            {"id",        "fc_" + tool_call.id},
             {"type",      "function_call"},
             {"status",    "completed"},
             {"arguments", tool_call.arguments},
-            {"call_id",   "fc_" + tool_call.id},
+            {"call_id",   "call_" + tool_call.id},
             {"name",      tool_call.name}
         };
         server_sent_events.push_back(json {
@@ -1277,8 +1279,9 @@ json server_task_result_cmpl_partial::to_json_oaicompat_resp() {
                 {"data", json {
                     {"type",  "response.output_item.added"},
                     {"item", json {
+                        {"id",        "fc_" + diff.tool_call_delta.id},
                         {"arguments", ""},
-                        {"call_id",   "fc_" + diff.tool_call_delta.id},
+                        {"call_id",   "call_" + diff.tool_call_delta.id},
                         {"name",      diff.tool_call_delta.name},
                         {"type",      "function_call"},
                         {"status",    "in_progress"},

From 23ee8797e11b1f9502f547a9250bfa0f5c36a63f Mon Sep 17 00:00:00 2001
From: Shawn Gu <shawngu@qti.qualcomm.com>
Date: Mon, 22 Jun 2026 22:25:21 -0700
Subject: [PATCH 122/292] opencl: q8_0 gemv precision improvement (#24923)

---
 ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl
index 9703b693e..f5c6fb3e8 100644
--- a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl
+++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q8_0_f32.cl
@@ -174,7 +174,7 @@ __kernel void kernel_gemv_noshuffle_q8_0_f32(
         regA.s6 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 6)).x;
         regA.s7 = read_imageui(src0_q, (gid + k * BLOCK_STRIDE_A + LINE_STRIDE_A * 7)).x;
 
-        dequantizeBlockAccum_ns_sgbroadcast_1(totalSum, regA, regS, regB);
+        dequantizeBlockAccum_ns_sgbroadcast_1(totalSum, regA, convert_float(regS), regB);
     }
 
     // reduction in local memory, assumes #wave=4

From 73618f27a801c0b8614ceaf3547d3c2a99baae14 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Tue, 23 Jun 2026 00:27:28 -0500
Subject: [PATCH 123/292] server: improve user message detection and create
 checkpoints at every user message (#24176)

* server : improve message span logic

* cont : cast size_t to int32_t in comparisons

* server : create checkpoints before every user msg

* chat : remove \n in gemma4 delimiters

* chat : merge msg delimiter structs into one

* cont : reword comment

* cont : initialize tokens in delimiter

* cont : add server_tokens::get_raw_tokens() for mtmd

* cont : move message finding to server_tokens and skip mtmd tokens

* cont : update cohere2moe parser

* cont : increase min-step to 8192 and always produce a chkpt for last user message
---
 common/chat.cpp                 | 156 +++++++++++++++++++++-----------
 common/chat.h                   |  71 +++++++++++++--
 common/common.h                 |   2 +-
 tests/test-chat.cpp             | 123 ++++++++++++++++++++-----
 tools/server/server-common.cpp  |  18 ++--
 tools/server/server-common.h    |   3 +
 tools/server/server-context.cpp | 110 ++++------------------
 tools/server/server-task.h      |   6 +-
 8 files changed, 302 insertions(+), 187 deletions(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index ded8440e6..cee6ad650 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -90,41 +90,93 @@ std::string common_chat_msg::render_content(const std::string & delimiter) const
     return text;
 }
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims) {
-    if (delims.empty() || prompt.empty()) {
-        return {};
+common_chat_role common_chat_role_from_string(const std::string & role) {
+    if (role == "system")    { return COMMON_CHAT_ROLE_SYSTEM;    }
+    if (role == "assistant") { return COMMON_CHAT_ROLE_ASSISTANT; }
+    if (role == "user")      { return COMMON_CHAT_ROLE_USER;      }
+    if (role == "tool")      { return COMMON_CHAT_ROLE_TOOL;      }
+    return COMMON_CHAT_ROLE_UNKNOWN;
+}
+
+const char * common_chat_role_to_string(common_chat_role role) {
+    switch (role) {
+        case COMMON_CHAT_ROLE_SYSTEM:    return "system";
+        case COMMON_CHAT_ROLE_ASSISTANT: return "assistant";
+        case COMMON_CHAT_ROLE_USER:      return "user";
+        case COMMON_CHAT_ROLE_TOOL:      return "tool";
+        case COMMON_CHAT_ROLE_UNKNOWN:   return "";
+    }
+    return "";
+}
+
+json common_chat_msg_delimiters::to_json() const {
+    json result = json::array();
+    for (const auto & d : delimiters) {
+        result.push_back({
+            { "role",      common_chat_role_to_string(d.role) },
+            { "delimiter", d.delimiter                        },
+        });
+    }
+    return result;
+}
+
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const json & delimiters) {
+    common_chat_msg_delimiters result;
+
+    if (!delimiters.is_array()) {
+        return result;
     }
 
-    auto parser = build_peg_parser([&](common_peg_parser_builder & p) {
-        std::vector<std::string>       all_delims;
-        std::vector<common_peg_parser> tagged_messages;
-
-        all_delims.reserve(delims.size());
-        tagged_messages.reserve(delims.size());
-        for (const auto & d : delims) {
-            all_delims.push_back(d.delimiter);
+    result.delimiters.reserve(delimiters.size());
+    for (const auto & d : delimiters) {
+        if (!d.is_object()) {
+            continue;
         }
-
-        auto any_delim = p.until_one_of(all_delims);
-        for (const auto & d : delims) {
-            tagged_messages.push_back(p.tag(d.role, p.literal(d.delimiter) + any_delim));
-        }
-
-        return any_delim + p.zero_or_more(p.choice(tagged_messages)) + p.end();
-    });
-
-    common_peg_parse_context ctx(prompt);
-    const auto result = parser.parse(ctx);
-    if (!result.success()) {
-        return {};
+        result.delimiters.push_back({
+            common_chat_role_from_string(d.value("role", std::string())),
+            d.value("delimiter", std::string()),
+        });
     }
 
-    std::vector<common_chat_msg_span> spans;
-    ctx.ast.visit(result, [&](const common_peg_ast_node & node) {
-        if (!node.tag.empty()) {
-            spans.push_back({ node.tag, node.start, node.end - node.start });
+    return result;
+}
+
+void common_chat_msg_delimiters::tokenize(const llama_vocab * vocab) {
+    for (auto & d : delimiters) {
+        d.tokens = common_tokenize(vocab, d.delimiter, false, true);
+    }
+}
+
+common_chat_msg_spans common_chat_msg_delimiters::split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips) const {
+    std::vector<std::pair<common_chat_role, size_t>> matches;
+
+    auto skip = skips.begin();
+    for (size_t i = 0; i < tokens.size();) {
+        if (skip != skips.end() && i == skip->first) {
+            i += skip->second;
+            ++skip;
+            continue;
         }
-    });
+        for (const auto & d : delimiters) {
+            if (i + d.tokens.size() > tokens.size()) {
+                continue;
+            }
+            if (std::equal(d.tokens.begin(), d.tokens.end(), tokens.begin() + i)) {
+                matches.emplace_back(d.role, i);
+                break;
+            }
+        }
+        i++;
+    }
+
+    matches.emplace_back(COMMON_CHAT_ROLE_UNKNOWN, tokens.size());
+
+    common_chat_msg_spans spans;
+    for (size_t i = 0; i + 1 < matches.size(); i++) {
+        const auto & curr = matches[i];
+        const auto & next = matches[i + 1];
+        spans.add(curr.first, curr.second, next.second - curr.second);
+    }
 
     return spans;
 }
@@ -1081,13 +1133,13 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
 
     data.prompt            = prompt;
     data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, /* messages_override= */ adjusted_messages);
-    data.message_spans = common_chat_split_by_role(prompt, {
-        { "assistant", "<|start|>assistant" },
-        { "user",      "<|start|>user"      },
-        { "system",    "<|start|>developer" },
-        { "system",    "<|start|>system"    },
-        { "tool",      "<|start|>functions" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>developer" },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>functions" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.supports_thinking = true;
@@ -1228,10 +1280,10 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
         data.prompt += data.generation_prompt;
     }
 
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "user",      "<|turn>user\n"  },
-        { "assistant", "<|turn>model\n" },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_USER,      "<|turn>user"  },
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|turn>model" },
+    };
 
     data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
     data.supports_thinking  = true;
@@ -2030,15 +2082,15 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t
         RESULT_START, RESULT_END,
     };
 
-    // Split the rendered prompt into per-role message spans. Tool results are rendered with the
+    // Declare per-role message delimiters. Tool results are rendered with the
     // system token followed by <|START_TOOL_RESULT|>, so the "tool" delimiter must be listed before
     // the plain "system" one (it is a strict superset, and the role split tries delimiters in order).
-    data.message_spans = common_chat_split_by_role(data.prompt, {
-        { "assistant", GEN_PREFIX },
-        { "user",      TURN_START + USER },
-        { "tool",      TURN_START + SYSTEM + RESULT_START },
-        { "system",    TURN_START + SYSTEM },
-    });
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, GEN_PREFIX },
+        { COMMON_CHAT_ROLE_USER,      TURN_START + USER },
+        { COMMON_CHAT_ROLE_TOOL,      TURN_START + SYSTEM + RESULT_START },
+        { COMMON_CHAT_ROLE_SYSTEM,    TURN_START + SYSTEM },
+    };
 
     auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
     auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
@@ -2526,17 +2578,15 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         autoparser.analyze_template(tmpl);
         auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
 
-        std::vector<common_chat_msg_delimiter> delimiters;
+        common_chat_msg_delimiters delimiters;
         if (!autoparser.assistant_start.empty()) {
-            delimiters.push_back({ "assistant", autoparser.assistant_start });
+            delimiters.add(COMMON_CHAT_ROLE_ASSISTANT, autoparser.assistant_start);
         }
         if (!autoparser.user_start.empty()) {
-            delimiters.push_back({ "user", autoparser.user_start });
+            delimiters.add(COMMON_CHAT_ROLE_USER, autoparser.user_start);
         }
 
-        if (!delimiters.empty()) {
-            auto_params.message_spans = common_chat_split_by_role(auto_params.prompt, delimiters);
-        }
+        auto_params.message_delimiters = std::move(delimiters);
 
         auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
         if (auto_params.supports_thinking) {
diff --git a/common/chat.h b/common/chat.h
index 5659cd42a..7898f1623 100644
--- a/common/chat.h
+++ b/common/chat.h
@@ -143,15 +143,75 @@ struct common_chat_msg_diff {
     }
 };
 
+enum common_chat_role {
+    COMMON_CHAT_ROLE_UNKNOWN,
+    COMMON_CHAT_ROLE_SYSTEM,
+    COMMON_CHAT_ROLE_ASSISTANT,
+    COMMON_CHAT_ROLE_USER,
+    COMMON_CHAT_ROLE_TOOL
+};
+
+common_chat_role common_chat_role_from_string(const std::string & role);
+const char *     common_chat_role_to_string(common_chat_role role);
+
 struct common_chat_msg_span {
-    std::string role;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
     std::size_t pos = 0;
     std::size_t len = 0;
+
+    bool valid() const {
+        return role != COMMON_CHAT_ROLE_UNKNOWN;
+    }
+};
+
+struct common_chat_msg_spans {
+    std::vector<common_chat_msg_span> spans;
+
+    void add(common_chat_role role, size_t pos, size_t len) {
+        spans.push_back({ role, pos, len });
+    }
+
+    bool is_user_start(int32_t pos) const {
+        for (auto it = spans.begin(); it != spans.end(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER && pos == (int32_t) it->pos) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    int32_t last_user_message_pos() const {
+        for (auto it = spans.rbegin(); it != spans.rend(); ++it) {
+            if (it->role == COMMON_CHAT_ROLE_USER) {
+                return (int32_t) it->pos;
+            }
+        }
+        return -1;
+    }
 };
 
 struct common_chat_msg_delimiter {
-    std::string role;
-    std::string delimiter;
+    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
+    std::string      delimiter;
+    llama_tokens     tokens = {};
+};
+
+struct common_chat_msg_delimiters {
+    std::vector<common_chat_msg_delimiter> delimiters;
+
+    common_chat_msg_delimiters() = default;
+    common_chat_msg_delimiters(std::initializer_list<common_chat_msg_delimiter> delims) : delimiters(delims) {}
+
+    void add(common_chat_role role, const std::string & delimiter) {
+        delimiters.push_back({ role, delimiter });
+    }
+
+    void tokenize(const llama_vocab * vocab);
+
+    // split tokens into message spans. skips maps a start index to a length of a region to jump over without matching
+    common_chat_msg_spans split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips = {}) const;
+
+    nlohmann::ordered_json to_json() const;
 };
 
 struct common_chat_tool {
@@ -219,7 +279,7 @@ struct common_chat_params {
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
     std::string                         parser;
-    std::vector<common_chat_msg_span>   message_spans;
+    common_chat_msg_delimiters          message_delimiters;
 };
 
 // per-message parsing syntax
@@ -325,5 +385,4 @@ struct common_chat_prompt_preset {
 
 common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);
 
-std::vector<common_chat_msg_span> common_chat_split_by_role(const std::string & prompt, const std::vector<common_chat_msg_delimiter> & delims);
-
+common_chat_msg_delimiters common_chat_msg_delimiters_parse(const nlohmann::ordered_json & delimiters);
diff --git a/common/common.h b/common/common.h
index f2f2202ec..75a6036a0 100644
--- a/common/common.h
+++ b/common/common.h
@@ -609,7 +609,7 @@ struct common_params {
     bool    cache_prompt        = true;  // whether to enable prompt caching
     bool    cache_idle_slots    = true;  // save and clear idle slots upon starting a new task
     int32_t n_ctx_checkpoints   = 32;    // max number of context checkpoints per slot
-    int32_t checkpoint_min_step = 256;   // minimum spacing between context checkpoints
+    int32_t checkpoint_min_step = 8192;  // minimum spacing between context checkpoints
     int32_t cache_ram_mib       = 8192;  // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
     std::string hostname      = "127.0.0.1";
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 30aa35e13..c38aed8cf 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -1562,37 +1562,112 @@ static void test_msgs_oaicompat_json_conversion() {
     }
 }
 
-static void test_split_by_role() {
+static void test_msg_token_delimiters_split() {
     LOG_DBG("%s\n", __func__);
 
+    // Delimiters that share a leading token, distinguished by the second token,
+    // to exercise the per-position token matching.
+    const common_chat_msg_delimiters delims = {
+        { { COMMON_CHAT_ROLE_USER,      "", { 10, 11 } },
+          { COMMON_CHAT_ROLE_ASSISTANT, "", { 10, 12 } } }
+    };
+
     // Empty inputs
-    assert_equals<size_t>(0, common_chat_split_by_role("", {}).size());
-    assert_equals<size_t>(0, common_chat_split_by_role("hello", {}).size());
-    assert_equals<size_t>(0, common_chat_split_by_role("", { { "user", "<|user|>" } }).size());
+    assert_equals<size_t>(0, common_chat_msg_delimiters{}.split({}).spans.size());
+    assert_equals<size_t>(0, common_chat_msg_delimiters{}.split({ 10, 11 }).spans.size());
+    assert_equals<size_t>(0, delims.split({}).spans.size());
 
-    // Multi-role conversation, no leading/trailing content
+    // No delimiters match -> no spans
+    assert_equals<size_t>(0, delims.split({ 100, 101, 102 }).spans.size());
+
+    // Multi-role conversation: <user>Hi<assistant>Hello<user>Bye
     {
-        const std::string prompt = "<|user|>Hi<|assistant|>Hello<|user|>Bye";
-        const auto splits = common_chat_split_by_role(prompt, {
-            { "user",      "<|user|>"      },
-            { "assistant", "<|assistant|>" },
-        });
-        assert_equals<size_t>(3, splits.size());
+        const llama_tokens tokens = {
+            10, 11,            // <user>
+            100, 101,          // Hi
+            10, 12,            // <assistant>
+            200, 201, 202,     // Hello
+            10, 11,            // <user>
+            300, 301,          // Bye
+        };
 
-        assert_equals<std::string>("user", splits[0].role);
-        assert_equals<size_t>(0, splits[0].pos);
-        assert_equals<size_t>(10, splits[0].len);
-        assert_equals<std::string>("<|user|>Hi", prompt.substr(splits[0].pos, splits[0].len));
+        const auto result = delims.split(tokens);
+        const auto & spans = result.spans;
+        assert_equals<size_t>(3, spans.size());
 
-        assert_equals<std::string>("assistant", splits[1].role);
-        assert_equals<size_t>(10, splits[1].pos);
-        assert_equals<size_t>(18, splits[1].len);
-        assert_equals<std::string>("<|assistant|>Hello", prompt.substr(splits[1].pos, splits[1].len));
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(4, spans[0].len);
 
-        assert_equals<std::string>("user", splits[2].role);
-        assert_equals<size_t>(28, splits[2].pos);
-        assert_equals<size_t>(11, splits[2].len);
-        assert_equals<std::string>("<|user|>Bye", prompt.substr(splits[2].pos, splits[2].len));
+        assert_equals(COMMON_CHAT_ROLE_ASSISTANT, spans[1].role);
+        assert_equals<size_t>(4, spans[1].pos);
+        assert_equals<size_t>(5, spans[1].len);
+
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[2].role);
+        assert_equals<size_t>(9, spans[2].pos);
+        assert_equals<size_t>(4, spans[2].len);
+
+        // is_user_start() is true at the token position where a user span begins
+        assert_equals(true,  result.is_user_start(0));
+        assert_equals(false, result.is_user_start(4));  // assistant span
+        assert_equals(true,  result.is_user_start(9));
+    }
+
+    // Content before the first delimiter is not captured as a span
+    {
+        const llama_tokens tokens = {
+            500, 501,    // leading content (dropped)
+            10, 11,      // <user>
+            100,         // Hi
+        };
+
+        const auto spans = delims.split(tokens).spans;
+        assert_equals<size_t>(1, spans.size());
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(2, spans[0].pos);
+        assert_equals<size_t>(3, spans[0].len);
+    }
+
+    // Skipped regions (media chunks) are jumped over but still count as span content
+    {
+        const llama_tokens tokens = {
+            10, 11,             // <user>
+            LLAMA_TOKEN_NULL,   // media chunk (3 tokens)
+            LLAMA_TOKEN_NULL,
+            LLAMA_TOKEN_NULL,
+            100,                // Hi
+            10, 12,             // <assistant>
+        };
+
+        const std::map<size_t, size_t> skips = { { 2, 3 } };
+
+        const auto spans = delims.split(tokens, skips).spans;
+        assert_equals<size_t>(2, spans.size());
+
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(6, spans[0].len);
+
+        assert_equals(COMMON_CHAT_ROLE_ASSISTANT, spans[1].role);
+        assert_equals<size_t>(6, spans[1].pos);
+        assert_equals<size_t>(2, spans[1].len);
+    }
+
+    // A delimiter sequence inside a skipped region is not matched
+    {
+        const llama_tokens tokens = {
+            10, 11,      // <user>
+            10, 12,      // skipped region that happens to contain delimiter tokens
+            100,         // Hi
+        };
+
+        const std::map<size_t, size_t> skips = { { 2, 2 } };
+
+        const auto spans = delims.split(tokens, skips).spans;
+        assert_equals<size_t>(1, spans.size());
+        assert_equals(COMMON_CHAT_ROLE_USER, spans[0].role);
+        assert_equals<size_t>(0, spans[0].pos);
+        assert_equals<size_t>(5, spans[0].len);
     }
 }
 
@@ -5857,7 +5932,7 @@ int main(int argc, char ** argv) {
     {
         test_msg_diffs_compute();
         test_msgs_oaicompat_json_conversion();
-        test_split_by_role();
+        test_msg_token_delimiters_split();
         test_tools_oaicompat_json_conversion();
         test_convert_responses_to_chatcmpl();
         test_developer_role_to_system_workaround();
diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index e412b94c5..ac291d359 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -518,6 +518,14 @@ size_t server_tokens::get_common_prefix(const server_tokens & b) const {
     return max_idx; // all tokens are equal
 }
 
+common_chat_msg_spans server_tokens::find_message_spans(const common_chat_msg_delimiters & delims) const {
+    std::map<size_t, size_t> skips;
+    for (const auto & it : map_idx_to_media) {
+        skips[it.first] = mtmd_input_chunk_get_n_tokens(it.second.get());
+    }
+    return delims.split(tokens, skips);
+}
+
 bool server_tokens::validate(const struct llama_context * ctx) const {
     const llama_model * model = llama_get_model(ctx);
     const llama_vocab * vocab = llama_model_get_vocab(model);
@@ -1104,15 +1112,7 @@ json oaicompat_chat_params_parse(
         llama_params["chat_parser"] = chat_params.parser;
     }
 
-    llama_params["message_spans"] = json::array();
-
-    for (const auto & span : chat_params.message_spans) {
-        llama_params["message_spans"].push_back({
-            { "role", span.role },
-            { "pos",  span.pos  },
-            { "len",  span.len  },
-        });
-    }
+    llama_params["message_delimiters"] = chat_params.message_delimiters.to_json();
 
     // Reasoning budget: pass parameters through to sampling layer
     {
diff --git a/tools/server/server-common.h b/tools/server/server-common.h
index efd31733b..c0eaec6b0 100644
--- a/tools/server/server-common.h
+++ b/tools/server/server-common.h
@@ -218,6 +218,9 @@ public:
 
     size_t get_common_prefix(const server_tokens & b) const;
 
+    // split the tokens into message spans, skipping over media chunks
+    common_chat_msg_spans find_message_spans(const common_chat_msg_delimiters & delims) const;
+
     // make sure all text tokens are within the vocab range
     bool validate(const struct llama_context * ctx) const;
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 0a25b414e..ca91449d2 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -3436,8 +3436,8 @@ private:
                         has_mtmd = true;
                     }
 
-                    const int32_t n_before_user = slot.task->params.n_before_user;
-                    const bool n_before_user_known = n_before_user > 0;
+                    const auto & spans = slot.task->params.message_spans;
+                    const auto last_user_pos = spans.last_user_message_pos();
 
                     // add prompt tokens for processing in the current batch
                     while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.size() < n_batch) {
@@ -3466,10 +3466,8 @@ private:
 
                         slot.n_prompt_tokens_processed++;
 
-                        // stop the prompt batch exactly before the latest user input, so a checkpoint
-                        // can be created after the previous messages
-                        if (n_before_user_known &&
-                            slot.prompt.n_tokens() == n_before_user) {
+                        // stop the prompt batch exactly before a user message
+                        if (spans.is_user_start(slot.prompt.n_tokens())) {
                             break;
                         }
 
@@ -3498,8 +3496,13 @@ private:
                     // the number of tokens added to the batch for the current slot
                     const auto n_tokens_cur = batch.size() - n_tokens_prev;
 
+                    const auto n_tokens_start = slot.prompt.n_tokens() - n_tokens_cur;
+
                     const bool near_prompt_end = slot.task->n_tokens() < slot.prompt.n_tokens() + n_ubatch;
 
+                    const bool is_user_start = spans.is_user_start(n_tokens_start);
+                    const bool is_last_user_message = n_tokens_start == last_user_pos;
+
                     // entire prompt has been processed
                     if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
                         slot.state = SLOT_STATE_DONE_PROMPT;
@@ -3514,8 +3517,9 @@ private:
 
                         slot.init_sampler();
                     } else {
-                        // skip ordinary mid-prompt checkpoints
-                        if (!n_before_user_known && !near_prompt_end) {
+                        // skip ordinary mid-prompt checkpoints, unless the batch starts a user
+                        // message or we are near the end of the prompt
+                        if (!is_user_start && !near_prompt_end) {
                             do_checkpoint = false;
                         }
                     }
@@ -3523,29 +3527,6 @@ private:
                     const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx_tgt), slot.id);
                     const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_tgt), slot.id);
 
-                    // checkpoints are created before the current batch is decoded, so
-                    // their token position is the batch start rather than the prompt end
-                    const int32_t n_tokens_start = slot.prompt.n_tokens() - n_tokens_cur;
-
-                    {
-                        const bool is_on_user =
-                            n_before_user_known &&
-                            n_tokens_start == n_before_user;
-
-                        const bool is_after_user =
-                            n_before_user_known &&
-                            n_tokens_start > n_before_user;
-
-                        const bool is_allowed =
-                            !n_before_user_known ||
-                            is_on_user ||
-                            (is_after_user && near_prompt_end);
-
-                        if (do_checkpoint && !is_allowed) {
-                            do_checkpoint = false;
-                        }
-                    }
-
                     // nothing to checkpoint yet
                     // TODO: is this check needed?
                     if (do_checkpoint && pos_min < 0) {
@@ -3555,8 +3536,8 @@ private:
                     // do not checkpoint after mtmd chunks
                     do_checkpoint = do_checkpoint && !has_mtmd;
 
-                    // no need to create checkpoints that are too close together
-                    do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || n_tokens_start > slot.prompt.checkpoints.back().n_tokens + params_base.checkpoint_min_step);
+                    // no need to create checkpoints that are too close together, unless it's the last user message
+                    do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || is_last_user_message || n_tokens_start > slot.prompt.checkpoints.back().n_tokens + params_base.checkpoint_min_step);
                     SLT_DBG(slot, "main/do_checkpoint = %s, pos_min = %d, pos_max = %d\n", do_checkpoint ? "yes" : "no", pos_min, pos_max);
 
                     // note: we create the checkpoint before calling llama_decode(), so the current batch is not
@@ -4055,54 +4036,6 @@ void server_context::set_state_callback(server_state_callback_t callback) {
     });
 }
 
-// compute the number of tokens before the last user message in the prompt
-static int32_t prompt_get_n_before_user(
-        const json & message_spans,
-        const std::string & prompt,
-        const std::vector<raw_buffer> & files,
-        const llama_vocab * vocab,
-        mtmd_context * mctx) {
-    int32_t result = -1;
-    int32_t byte_pos = -1;
-
-    for (const auto & span : message_spans) {
-        const std::string role = json_value(span, "role", std::string());
-
-        if (role == "user") {
-            byte_pos = json_value(span, "pos", -1);
-        }
-    }
-
-    if (byte_pos >= 0) {
-        GGML_ASSERT((size_t) byte_pos <= prompt.size());
-
-        const std::string prefix = prompt.substr(0, (size_t) byte_pos);
-
-        const std::string marker = get_media_marker();
-        size_t n_prefix_media = 0;
-        for (size_t pos = 0; (pos = prefix.find(marker, pos)) != std::string::npos; pos += marker.size()) {
-            n_prefix_media++;
-        }
-
-        GGML_ASSERT(n_prefix_media <= files.size());
-
-        if (mctx != nullptr && n_prefix_media > 0) {
-            // TODO: this makes a copy - avoid it
-            std::vector<raw_buffer> prefix_files(files.begin(), files.begin() + n_prefix_media);
-
-            result = (int32_t) process_mtmd_prompt(mctx, prefix, prefix_files).size();
-        } else {
-            result = (int32_t) tokenize_input_prompts(vocab, nullptr, prefix, true, true)[0].size();
-        }
-
-        SRV_TRC("message_spans: last user message: byte_pos=%d, media=%zu, n_before_user=%d\n",
-                byte_pos, n_prefix_media, result);
-    }
-
-    return result;
-}
-
-
 //
 // server_routes
 //
@@ -4150,6 +4083,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
 
         // tasks.reserve(inputs.size()); // TODO: this is inaccurate due to child tasks
 
+        // message delimiters for checkpointing
+        auto delimiters = common_chat_msg_delimiters_parse(json_value(data, "message_delimiters", json::array()));
+        delimiters.tokenize(ctx_server.vocab);
+
         for (size_t i = 0; i < inputs.size(); i++) {
             server_task task = server_task(type);
 
@@ -4163,16 +4100,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                     meta->logit_bias_eog,
                     data);
 
-            const auto message_spans = json_value(data, "message_spans", json::array());
-            if (prompt.is_string() && message_spans.is_array()) {
-                task.params.n_before_user =
-                    prompt_get_n_before_user(
-                        message_spans,
-                        prompt.get<std::string>(),
-                        files,
-                        ctx_server.vocab,
-                        ctx_server.mctx);
-            }
+            task.params.message_spans = task.tokens.find_message_spans(delimiters);
 
             task.id_slot = json_value(data, "id_slot", -1);
 
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 299c279d7..293bdf053 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -62,9 +62,6 @@ struct task_params {
 
     int32_t n_cache_reuse = 0; // min chunk size to attempt reusing from the cache via KV shifting (0 = disabled)
 
-    // number of prompt tokens before the latest user message
-    int32_t n_before_user = -1;
-
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
@@ -92,6 +89,9 @@ struct task_params {
     // per-request parameters for chat parsing
     common_chat_parser_params chat_parser_params;
 
+    // message spans for checkpointing
+    common_chat_msg_spans message_spans;
+
     // Embeddings
     int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)
 

From 035cd8f9a6dda9cd0224b07d3df2dc95f7a3a31e Mon Sep 17 00:00:00 2001
From: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
Date: Tue, 23 Jun 2026 15:19:34 +0900
Subject: [PATCH 124/292] codeowners: add yomaytk to ggml-webgpu (#24930)

---
 CODEOWNERS | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 4b9d90177..46fd518b7 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -10,7 +10,7 @@
 # ggml-org/ggml-rpc         : rgerganov
 # ggml-org/ggml-sycl        : arthw
 # ggml-org/ggml-vulkan      : 0cc4m, jeffbolznv
-# ggml-org/ggml-webgpu      : reeselevine
+# ggml-org/ggml-webgpu      : reeselevine, yomaytk
 # ggml-org/ggml-zdnn        : taronaeo
 # ggml-org/llama-common     : ggerganov, aldehir, angt, danbev, ngxson, pwilkin
 # ggml-org/llama-mtmd       : ngxson

From 7c908502ea0868e6ae913f79ba84ba844a5b386a Mon Sep 17 00:00:00 2001
From: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
Date: Tue, 23 Jun 2026 17:13:55 +0900
Subject: [PATCH 125/292] ggml-webgpu: improve MTP inference by using mat-vec
 path for small batches (#24811)

* ggml-webgpu: improve small batches decoding

* Add barrier to the NUM_COLS loop in mul-mat-vec
---
 .../ggml-webgpu/ggml-webgpu-shader-lib.hpp    |  13 +-
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          |  20 +-
 .../wgsl-shaders/mul_mat_id_vec.wgsl          |   4 +-
 .../ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl |  88 +-
 .../wgsl-shaders/mul_mat_vec_acc.tmpl         | 991 ++++++++++--------
 .../wgsl-shaders/mul_mat_vec_q_acc.tmpl       | 132 ++-
 .../ggml-webgpu/wgsl-shaders/quantize_q8.wgsl |  23 +-
 tests/test-backend-ops.cpp                    |   2 +
 8 files changed, 682 insertions(+), 591 deletions(-)

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index 6f877f15c..c00a2e9ee 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -905,11 +905,12 @@ struct ggml_webgpu_mul_mat_vec_pipeline_key {
     ggml_type src0_type;
     ggml_type src1_type;
     int       vectorized;
+    uint32_t  num_cols;
     bool      use_mmvq;
 
     bool operator==(const ggml_webgpu_mul_mat_vec_pipeline_key & other) const {
         return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized &&
-               use_mmvq == other.use_mmvq;
+               num_cols == other.num_cols && use_mmvq == other.use_mmvq;
     }
 };
 
@@ -919,6 +920,7 @@ struct ggml_webgpu_mul_mat_vec_pipeline_key_hash {
         ggml_webgpu_hash_combine(seed, key.src0_type);
         ggml_webgpu_hash_combine(seed, key.src1_type);
         ggml_webgpu_hash_combine(seed, key.vectorized);
+        ggml_webgpu_hash_combine(seed, key.num_cols);
         ggml_webgpu_hash_combine(seed, key.use_mmvq);
         return seed;
     }
@@ -993,11 +995,12 @@ struct ggml_webgpu_mul_mat_id_pipeline_key {
     ggml_type src0_type;
     ggml_type src1_type;
     uint32_t  n_experts;
+    uint32_t  num_cols;
     int       vectorized;
 
     bool operator==(const ggml_webgpu_mul_mat_id_pipeline_key & other) const {
         return src0_type == other.src0_type && src1_type == other.src1_type && n_experts == other.n_experts &&
-               vectorized == other.vectorized;
+               num_cols == other.num_cols && vectorized == other.vectorized;
     }
 };
 
@@ -1007,6 +1010,7 @@ struct ggml_webgpu_mul_mat_id_pipeline_key_hash {
         ggml_webgpu_hash_combine(seed, key.src0_type);
         ggml_webgpu_hash_combine(seed, key.src1_type);
         ggml_webgpu_hash_combine(seed, key.n_experts);
+        ggml_webgpu_hash_combine(seed, key.num_cols);
         ggml_webgpu_hash_combine(seed, key.vectorized);
         return seed;
     }
@@ -1107,7 +1111,7 @@ inline bool ggml_webgpu_can_use_mmvq(const ggml_tensor * src0,
                                      const ggml_tensor * src1,
                                      bool                supports_dot_product,
                                      const std::string & vendor) {
-    if (src1->ne[1] == 1) {
+    if (src1->ne[1] <= 4) {
         bool supports_dp4a = vendor == "amd" || vendor == "intel" || vendor == "nvidia";
         if (supports_dp4a && supports_dot_product) {
             switch (src1->type) {
@@ -1889,6 +1893,7 @@ class ggml_webgpu_shader_lib {
                           (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
                              1 :
                              0;
+        key.num_cols   = context.dst->ne[1];
         key.use_mmvq =
             ggml_webgpu_can_use_mmvq(context.src0, context.src1, context.supports_dot_product, context.vendor);
 
@@ -2004,6 +2009,7 @@ class ggml_webgpu_shader_lib {
         if (key.vectorized) {
             variant += "_vectorized";
         }
+        defines.push_back(std::string("NUM_COLS=") + std::to_string(key.num_cols));
 
         auto processed            = preprocessor.preprocess(shader_src, defines);
         auto decisions            = std::make_shared<ggml_webgpu_mul_mat_vec_shader_decisions>();
@@ -2421,6 +2427,7 @@ class ggml_webgpu_shader_lib {
         if (key.vectorized) {
             variant += "_vectorized";
         }
+        defines.push_back(std::string("NUM_COLS=1"));
 
         defines.push_back(std::string("N_EXPERTS=") + std::to_string(key.n_experts));
 
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index f71d1aee7..e8eafd185 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -1418,15 +1418,17 @@ static void ggml_webgpu_quantize_q8_dispatch(webgpu_context &
     const size_t dst_offset           = ggml_webgpu_tensor_offset(dst);
     const size_t q8_src1_align_offset = ROUNDUP_POW2(
         dst_offset + ggml_nbytes(dst), ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment);
-    const size_t q8_src1_binding_size =
-        ROUNDUP_POW2(src1->ne[3] * src1->ne[2] * (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32)),
-                     WEBGPU_STORAGE_BUF_BINDING_MULT);
+    const size_t q8_src1_binding_size = ROUNDUP_POW2(
+        src1->ne[3] * src1->ne[2] * src1->ne[1] * (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32)),
+        WEBGPU_STORAGE_BUF_BINDING_MULT);
 
     std::vector<uint32_t> q8_params = {
         (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)),
         (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)),
         (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)),
         (uint32_t) src1->ne[0],
+        (uint32_t) src1->ne[1],
         (uint32_t) src1->ne[2],
         (uint32_t) src1->ne[3],
     };
@@ -1442,7 +1444,7 @@ static void ggml_webgpu_quantize_q8_dispatch(webgpu_context &
     uint32_t       q8_wg_x        = 1;
     uint32_t       q8_wg_y        = 1;
     const uint32_t wg_per_vec     = (src0->ne[0] / 4 + (q8_wg_size - 1)) / q8_wg_size;
-    const uint32_t q8_total_wg    = src1->ne[2] * src1->ne[3] * wg_per_vec;
+    const uint32_t q8_total_wg    = src1->ne[1] * src1->ne[2] * src1->ne[3] * wg_per_vec;
     const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
     compute_2d_workgroups(q8_total_wg, max_wg_per_dim, q8_wg_x, q8_wg_y);
 
@@ -1456,7 +1458,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
                                              ggml_tensor *    src1,
                                              ggml_tensor *    dst) {
     // Determine if this is a mat-vec operation
-    bool is_vec = (dst->ne[1] == 1);
+    bool use_mat_vec = (dst->ne[1] <= 4);
 
     // use MMVQ path for mat-vec
     bool use_mmvq = ggml_webgpu_can_use_mmvq(src0, src1, ctx->global_ctx->capabilities.supports_dot_product,
@@ -1482,7 +1484,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
     webgpu_pipeline                   pipeline;
     std::vector<webgpu_dispatch_desc> dispatches;
 
-    if (is_vec) {
+    if (use_mat_vec) {
         if (use_mmvq) {
             ggml_webgpu_quantize_q8_dispatch(ctx, src0, src1, dst, dispatches);
         }
@@ -1529,7 +1531,7 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
     uint32_t       wg_y           = 1;
     const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension;
 
-    if (is_vec) {
+    if (use_mat_vec) {
         auto * decisions = static_cast<ggml_webgpu_mul_mat_vec_shader_decisions *>(pipeline.context.get());
 
         uint32_t batches       = dst->ne[2] * dst->ne[3];
@@ -3691,8 +3693,8 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer
                     ggml_webgpu_can_use_mmvq(src0, src1, ctx->webgpu_global_ctx->capabilities.supports_dot_product,
                                              ctx->webgpu_global_ctx->vendor);
                 if (use_mmvq) {
-                    const size_t q8_src1_size =
-                        src1->ne[3] * src1->ne[2] * (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32));
+                    const size_t q8_src1_size = src1->ne[3] * src1->ne[2] * src1->ne[1] *
+                                                (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32));
                     res = ROUNDUP_POW2(res + q8_src1_size +
                                            ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment,
                                        WEBGPU_STORAGE_BUF_BINDING_MULT);
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl
index 6ff9bcf2d..78ae955e6 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl
@@ -103,7 +103,7 @@ fn main(
 
 #ifdef USE_SUBGROUP_REDUCTION
     for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-        let subgroup_total = subgroupAdd(acc[row]);
+        let subgroup_total = subgroupAdd(acc[0][row]);
         if (subgroup_invocation_id == 0u) {
             partial_sums[partial_index(row, subgroup_id)] = subgroup_total;
         }
@@ -126,7 +126,7 @@ fn main(
 
 #ifdef USE_WORKGROUP_REDUCTION
     for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-        partial_sums[partial_index(row, thread_id)] = acc[row];
+        partial_sums[partial_index(row, thread_id)] = acc[0][row];
     }
 
     workgroupBarrier();
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
index f0a7fbd05..ebdf09513 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
@@ -91,61 +91,67 @@ fn main(
     let dst_idx_base = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row_base;
 
 #ifdef MMVQ
-    let src1q_idx_base = (src13_idx * params.bs02 * params.broadcast2 + src12_idx) * (params.k / 32u);
+    let src1q_idx_base = (src13_idx * params.bs02 * params.broadcast2 + src12_idx) * params.n * (params.k / 32u);
     let acc = accumulate_vec_q_dot(thread_id, row_base, src0_batch_offset, src1q_idx_base);
 #else
     let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
     let acc = accumulate_vec_dot(thread_id, row_base, src0_batch_offset, src1_idx_base);
 #endif
 
+    for (var col = 0u;col < NUM_COLS;col += 1) {
+
 #ifdef USE_SUBGROUP_REDUCTION
-    for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-        let subgroup_total = subgroupAdd(acc[row]);
-        if (subgroup_invocation_id == 0u) {
-            partial_sums[partial_index(row, subgroup_id)] = subgroup_total;
-        }
-    }
+            for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+                let subgroup_total = subgroupAdd(acc[col][row]);
+                if (subgroup_invocation_id == 0u) {
+                    partial_sums[partial_index(row, subgroup_id)] = subgroup_total;
+                }
+            }
 
-    workgroupBarrier();
+            workgroupBarrier();
 
-    for (var row = subgroup_id; (row < OUTPUTS_PER_WG) && (row_base + row < params.m); row += num_subgroups) {
-        let output_row = row_base + row;
-        var row_acc = 0.0f;
-        for (var k = subgroup_invocation_id; k < num_subgroups; k += subgroup_size) {
-            row_acc += partial_sums[partial_index(row, k)];
-        }
-        let row_total = subgroupAdd(row_acc);
-        if (subgroup_invocation_id == 0) {
-            dst[dst_idx_base + row] = row_total;
-        }
-    }
+            for (var row = subgroup_id; (row < OUTPUTS_PER_WG) && (row_base + row < params.m); row += num_subgroups) {
+                let output_row = row_base + row;
+                var row_acc = 0.0f;
+                for (var k = subgroup_invocation_id; k < num_subgroups; k += subgroup_size) {
+                    row_acc += partial_sums[partial_index(row, k)];
+                }
+                let row_total = subgroupAdd(row_acc);
+                if (subgroup_invocation_id == 0) {
+                    dst[dst_idx_base + col * params.m + row] = row_total;
+                }
+            }
 #endif
 
 #ifdef USE_WORKGROUP_REDUCTION
-    for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-        partial_sums[partial_index(row, thread_id)] = acc[row];
-    }
+            for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+                partial_sums[partial_index(row, thread_id)] = acc[col][row];
+            }
+
+            workgroupBarrier();
+
+            var stride = WG_SIZE / 2u;
+
+            while (stride > 0) {
+                if (thread_id < stride) {
+                    for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+                        partial_sums[partial_index(row, thread_id)] += partial_sums[partial_index(row, thread_id + stride)];
+                    }
+                }
+
+                workgroupBarrier();
+                stride = stride / 2;
+            }
+
+            if (thread_id < OUTPUTS_PER_WG) {
+                let output_row = row_base + thread_id;
+                if (output_row < params.m) {
+                    dst[dst_idx_base + col * params.m + thread_id] = partial_sums[partial_index(thread_id, 0)];
+                }
+            }
+#endif
 
     workgroupBarrier();
 
-    var stride = WG_SIZE / 2u;
-
-    while (stride > 0) {
-        if (thread_id < stride) {
-            for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
-                partial_sums[partial_index(row, thread_id)] += partial_sums[partial_index(row, thread_id + stride)];
-            }
-        }
-
-        workgroupBarrier();
-        stride = stride / 2;
     }
-
-    if (thread_id < OUTPUTS_PER_WG) {
-        let output_row = row_base + thread_id;
-        if (output_row < params.m) {
-            dst[dst_idx_base + thread_id] = partial_sums[partial_index(thread_id, 0)];
-        }
-    }
-#endif
 }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
index 08753b9d6..b0703fe90 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
@@ -32,8 +32,8 @@ fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 {
 #endif
 
 #ifdef MUL_ACC_FLOAT
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let k_vec = params.k / VEC_SIZE;
     let src1_idx_base_vec = src1_idx_base / VEC_SIZE;
@@ -41,12 +41,18 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
     // Each thread walks K, loads from the vector, and updates
     // a small block of output rows held in registers.
     for (var k = thread_id; k < k_vec; k += WG_SIZE) {
-        let x = src1[src1_idx_base_vec + k];
+        var x_vals: array<SRC1_TYPE, NUM_COLS>;
+        for (var col = 0u;col < NUM_COLS;col += 1) {
+            x_vals[col] = src1[src1_idx_base_vec + col * (params.stride_11 / VEC_SIZE) + k];
+        }
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let src0_idx = (src0_batch_offset + output_row * params.stride_01) / VEC_SIZE + k;
-                acc[row] += inner_dot(src0[src0_idx], x);
+                let w = src0[src0_idx];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    acc[col][row] += inner_dot(w, x_vals[col]);
+                }
             }
         }
     }
@@ -60,30 +66,33 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 18
 #define THREADS_PER_BLOCK 16
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
                 let q_byte = load_u32_at_src0(block_byte_base + 2u + thread_within_block) & 0xFFu;
-                var row_sum = 0.0;
-                for (var bit = 0u; bit < 8u; bit++) {
-                    let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u);
-                    row_sum += w * x_block[bit];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var bit = 0u; bit < 8u; bit++) {
+                        let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u);
+                        row_sum += w * x_block[col][bit];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -97,35 +106,37 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 18
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % 4;
     for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
-                var row_sum = 0.0;
-
                 let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = (f32(q_byte & 0xFu) - 8.0) * d;
-                    let q_hi = (f32((q_byte >> 4u) & 0xFu) - 8.0) * d;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let q_lo = (f32(q_byte & 0xFu) - 8.0) * d;
+                        let q_hi = (f32((q_byte >> 4u) & 0xFu) - 8.0) * d;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -139,36 +150,38 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 20
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
                 let m = f32(load_f16_at_src0(block_byte_base + 2u));
-                var row_sum = 0.0;
-
                 let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = f32(q_byte & 0xFu) * d + m;
-                    let q_hi = f32((q_byte >> 4u) & 0xFu) * d + m;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let q_lo = f32(q_byte & 0xFu) * d + m;
+                        let q_hi = f32((q_byte >> 4u) & 0xFu) * d + m;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -182,19 +195,20 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 22
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -203,18 +217,19 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let qh_packed = load_u32_at_src0(block_byte_base + 2u);
                 let q_packed = load_u32_at_src0(block_byte_base + 6u + 4u * thread_within_block);
                 let qh_shift = thread_within_block * 4u;
-                var row_sum = 0.0;
-
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
-                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
-                    let q_lo = (f32((q_byte & 0xFu) | qh_lo) - 16.0) * d;
-                    let q_hi = (f32(((q_byte >> 4u) & 0xFu) | qh_hi) - 16.0) * d;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
+                        let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
+                        let q_lo = (f32((q_byte & 0xFu) | qh_lo) - 16.0) * d;
+                        let q_hi = (f32(((q_byte >> 4u) & 0xFu) | qh_hi) - 16.0) * d;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -228,19 +243,20 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 24
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -250,18 +266,19 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let qh_packed = load_u32_at_src0(block_byte_base + 4u);
                 let q_packed = load_u32_at_src0(block_byte_base + 8u + 4u * thread_within_block);
                 let qh_shift = thread_within_block * 4u;
-                var row_sum = 0.0;
-
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
-                    let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
-                    let q_lo = f32((q_byte & 0xFu) | qh_lo) * d + m;
-                    let q_hi = f32(((q_byte >> 4u) & 0xFu) | qh_hi) * d + m;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u;
+                        let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u;
+                        let q_lo = f32((q_byte & 0xFu) | qh_lo) * d + m;
+                        let q_hi = f32(((q_byte >> 4u) & 0xFu) | qh_hi) * d + m;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -275,33 +292,38 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 34
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
-                var row_sum = 0.0;
-
+                var q_packed: array<u32, ELEMS_PER_THREAD / 4u>;
                 for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
-                    let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * (thread_within_block * 2u + packed_idx));
-                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d;
-                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
-                    }
+                    q_packed[packed_idx] = load_u32_at_src0(block_byte_base + 2u + 4u * (thread_within_block * 2u + packed_idx));
+                }
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
+                        for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                            let q_val = f32(get_byte_i32(q_packed[packed_idx], byte_idx)) * d;
+                            row_sum += q_val * x_block[col][packed_idx * 4u + byte_idx];
+                        }
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -315,34 +337,39 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 36
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
                 let m = f32(load_f16_at_src0(block_byte_base + 2u));
-                var row_sum = 0.0;
-
+                var q_packed: array<u32, ELEMS_PER_THREAD / 4u>;
                 for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
-                    let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * (thread_within_block * 2u + packed_idx));
-                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                        let q_val = f32(get_byte_i32(q_packed, byte_idx)) * d + m;
-                        row_sum += q_val * x_block[packed_idx * 4u + byte_idx];
-                    }
+                    q_packed[packed_idx] = load_u32_at_src0(block_byte_base + 4u + 4u * (thread_within_block * 2u + packed_idx));
+                }
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) {
+                        for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                            let q_val = f32(get_byte_i32(q_packed[packed_idx], byte_idx)) * d + m;
+                            row_sum += q_val * x_block[col][packed_idx * 4u + byte_idx];
+                        }
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -355,8 +382,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 84
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -379,14 +406,15 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 64u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 96u + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 4u; i++) {
+                x_block[col][i]       = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4u]  = f32(src1[x_base + col * params.stride_11 + 32u + i]);
+                x_block[col][i + 8u]  = f32(src1[x_base + col * params.stride_11 + 64u + i]);
+                x_block[col][i + 12u] = f32(src1[x_base + col * params.stride_11 + 96u + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -404,30 +432,32 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let qs0 = q_u32 & 0xFFFFu;
                 let qs1 = q_u32 >> 16u;
 
-                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var acc1 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var acc2 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    var acc1 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    var acc2 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
 
-                sumy[0] = x_block[0] + x_block[1] + x_block[2] + x_block[3];
-                sumy[1] = x_block[4] + x_block[5] + x_block[6] + x_block[7];
-                sumy[2] = x_block[8] + x_block[9] + x_block[10] + x_block[11];
-                sumy[3] = x_block[12] + x_block[13] + x_block[14] + x_block[15];
+                    sumy[0] = x_block[col][0] + x_block[col][1] + x_block[col][2] + x_block[col][3];
+                    sumy[1] = x_block[col][4] + x_block[col][5] + x_block[col][6] + x_block[col][7];
+                    sumy[2] = x_block[col][8] + x_block[col][9] + x_block[col][10] + x_block[col][11];
+                    sumy[3] = x_block[col][12] + x_block[col][13] + x_block[col][14] + x_block[col][15];
 
-                acc1[0] = x_block[0] * f32(qs0 & 0x0003u) + x_block[2] * f32(qs1 & 0x0003u);
-                acc2[0] = x_block[1] * f32(qs0 & 0x0300u) + x_block[3] * f32(qs1 & 0x0300u);
-                acc1[1] = x_block[4] * f32(qs0 & 0x000Cu) + x_block[6] * f32(qs1 & 0x000Cu);
-                acc2[1] = x_block[5] * f32(qs0 & 0x0C00u) + x_block[7] * f32(qs1 & 0x0C00u);
-                acc1[2] = x_block[8] * f32(qs0 & 0x0030u) + x_block[10] * f32(qs1 & 0x0030u);
-                acc2[2] = x_block[9] * f32(qs0 & 0x3000u) + x_block[11] * f32(qs1 & 0x3000u);
-                acc1[3] = x_block[12] * f32(qs0 & 0x00C0u) + x_block[14] * f32(qs1 & 0x00C0u);
-                acc2[3] = x_block[13] * f32(qs0 & 0xC000u) + x_block[15] * f32(qs1 & 0xC000u);
+                    acc1[0] = x_block[col][0] * f32(qs0 & 0x0003u) + x_block[col][2] * f32(qs1 & 0x0003u);
+                    acc2[0] = x_block[col][1] * f32(qs0 & 0x0300u) + x_block[col][3] * f32(qs1 & 0x0300u);
+                    acc1[1] = x_block[col][4] * f32(qs0 & 0x000Cu) + x_block[col][6] * f32(qs1 & 0x000Cu);
+                    acc2[1] = x_block[col][5] * f32(qs0 & 0x0C00u) + x_block[col][7] * f32(qs1 & 0x0C00u);
+                    acc1[2] = x_block[col][8] * f32(qs0 & 0x0030u) + x_block[col][10] * f32(qs1 & 0x0030u);
+                    acc2[2] = x_block[col][9] * f32(qs0 & 0x3000u) + x_block[col][11] * f32(qs1 & 0x3000u);
+                    acc1[3] = x_block[col][12] * f32(qs0 & 0x00C0u) + x_block[col][14] * f32(qs1 & 0x00C0u);
+                    acc2[3] = x_block[col][13] * f32(qs0 & 0xC000u) + x_block[col][15] * f32(qs1 & 0xC000u);
 
-                acc[row] += dall * ((acc1[0] + (1.0/256.0) * acc2[0]) * f32(sc0 & 0xFu) +
-                                    (acc1[1] + (1.0/256.0) * acc2[1]) * f32(sc2 & 0xFu) / 4.0 +
-                                    (acc1[2] + (1.0/256.0) * acc2[2]) * f32(sc4 & 0xFu) / 16.0 +
-                                    (acc1[3] + (1.0/256.0) * acc2[3]) * f32(sc6 & 0xFu) / 64.0)
-                          - dmin * (sumy[0] * f32(sc0 & 0xF0u) + sumy[1] * f32(sc2 & 0xF0u) +
-                                    sumy[2] * f32(sc4 & 0xF0u) + sumy[3] * f32(sc6 & 0xF0u));
+                    acc[col][row] += dall * ((acc1[0] + (1.0/256.0) * acc2[0]) * f32(sc0 & 0xFu) +
+                                        (acc1[1] + (1.0/256.0) * acc2[1]) * f32(sc2 & 0xFu) / 4.0 +
+                                        (acc1[2] + (1.0/256.0) * acc2[2]) * f32(sc4 & 0xFu) / 16.0 +
+                                        (acc1[3] + (1.0/256.0) * acc2[3]) * f32(sc6 & 0xFu) / 64.0)
+                              - dmin * (sumy[0] * f32(sc0 & 0xF0u) + sumy[1] * f32(sc2 & 0xF0u) +
+                                        sumy[2] * f32(sc4 & 0xF0u) + sumy[3] * f32(sc6 & 0xF0u));
+                }
             }
         }
     }
@@ -440,8 +470,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 110
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -485,12 +515,13 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 8u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 8u] = f32(src1[x_base + 32u + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 8u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 8u] = f32(src1[x_base + col * params.stride_11 + 32u + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -516,28 +547,30 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let h_u32_0 = load_u32_at_src0(block_byte_base + h_byte + 0u);
                 let h_u32_1 = load_u32_at_src0(block_byte_base + h_byte + 4u);
 
-                var s1 = 0.0; var s2 = 0.0; var s3 = 0.0;
-                var s4 = 0.0; var s5 = 0.0; var s6 = 0.0;
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var s1 = 0.0; var s2 = 0.0; var s3 = 0.0;
+                    var s4 = 0.0; var s5 = 0.0; var s6 = 0.0;
 
-                for (var l = 0u; l < 8u; l += 2u) {
-                    let q_u32 = select(q_u32_0, q_u32_1, l >= 4u);
-                    let qs = select(q_u32 & 0xFFFFu, q_u32 >> 16u, (l & 2u) != 0u);
-                    let h_u32 = select(h_u32_0, h_u32_1, l >= 4u);
-                    let hv = select(h_u32 & 0xFFFFu, h_u32 >> 16u, (l & 2u) != 0u);
+                    for (var l = 0u; l < 8u; l += 2u) {
+                        let q_u32 = select(q_u32_0, q_u32_1, l >= 4u);
+                        let qs = select(q_u32 & 0xFFFFu, q_u32 >> 16u, (l & 2u) != 0u);
+                        let h_u32 = select(h_u32_0, h_u32_1, l >= 4u);
+                        let hv = select(h_u32 & 0xFFFFu, h_u32 >> 16u, (l & 2u) != 0u);
 
-                    s1 += x_block[l + 0u] * f32(qs & qm0);
-                    s2 += x_block[l + 1u] * f32(qs & qm1);
-                    s3 += select(0.0, x_block[l + 0u], (hv & hm0) == 0u) +
-                          select(0.0, x_block[l + 1u], (hv & hm1) == 0u);
-                    s4 += x_block[l + 8u] * f32(qs & qm2);
-                    s5 += x_block[l + 9u] * f32(qs & qm3);
-                    s6 += select(0.0, x_block[l + 8u], (hv & hm2) == 0u) +
-                          select(0.0, x_block[l + 9u], (hv & hm3) == 0u);
+                        s1 += x_block[col][l + 0u] * f32(qs & qm0);
+                        s2 += x_block[col][l + 1u] * f32(qs & qm1);
+                        s3 += select(0.0, x_block[col][l + 0u], (hv & hm0) == 0u) +
+                              select(0.0, x_block[col][l + 1u], (hv & hm1) == 0u);
+                        s4 += x_block[col][l + 8u] * f32(qs & qm2);
+                        s5 += x_block[col][l + 9u] * f32(qs & qm3);
+                        s6 += select(0.0, x_block[col][l + 8u], (hv & hm2) == 0u) +
+                              select(0.0, x_block[col][l + 9u], (hv & hm3) == 0u);
+                    }
+
+                    let d1 = d * (s1 + (1.0/256.0) * s2 - s3 * v1);
+                    let d2 = d * (s4 + (1.0/256.0) * s5 - s6 * v2);
+                    acc[col][row] += (d1 * scale0 + 0.25 * d2 * scale1) / f32(1u << shift);
                 }
-
-                let d1 = d * (s1 + (1.0/256.0) * s2 - s3 * v1);
-                let d2 = d * (s4 + (1.0/256.0) * s5 - s6 * v2);
-                acc[row] += (d1 * scale0 + 0.25 * d2 * scale1) / f32(1u << shift);
             }
         }
     }
@@ -550,8 +583,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 144
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -573,12 +606,15 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            let col_base = x_base + col * params.stride_11;
+            for (var i = 0u; i < 4u; i++) {
+                x_block[col][i]       = f32(src1[col_base + i]);
+                x_block[col][i + 4u]  = f32(src1[col_base + 32u + i]);
+                x_block[col][i + 8u]  = f32(src1[col_base + 128u + i]);
+                x_block[col][i + 12u] = f32(src1[col_base + 160u + i]);
+            }
         }
 
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
@@ -613,23 +649,25 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let q1_u32 = load_u32_at_src0_aligned(block_byte_base + 16u + q_offset);
                 let q2_u32 = load_u32_at_src0_aligned(block_byte_base + 80u + q_offset);
 
-                var dot = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var sumx = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                for (var i = 0u; i < 4u; i++) {
-                    let q1b = byte_of(q1_u32, i);
-                    let q2b = byte_of(q2_u32, i);
-                    dot[0] += x_block[i] * f32(q1b & 0x0Fu);
-                    dot[1] += x_block[i + 4u] * f32(q1b >> 4u);
-                    dot[2] += x_block[i + 8u] * f32(q2b & 0x0Fu);
-                    dot[3] += x_block[i + 12u] * f32(q2b >> 4u);
-                    sumx[0] += x_block[i];
-                    sumx[1] += x_block[i + 4u];
-                    sumx[2] += x_block[i + 8u];
-                    sumx[3] += x_block[i + 12u];
-                }
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var dot = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    var sumx = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    for (var i = 0u; i < 4u; i++) {
+                        let q1b = byte_of(q1_u32, i);
+                        let q2b = byte_of(q2_u32, i);
+                        dot[0] += x_block[col][i] * f32(q1b & 0x0Fu);
+                        dot[1] += x_block[col][i + 4u] * f32(q1b >> 4u);
+                        dot[2] += x_block[col][i + 8u] * f32(q2b & 0x0Fu);
+                        dot[3] += x_block[col][i + 12u] * f32(q2b >> 4u);
+                        sumx[0] += x_block[col][i];
+                        sumx[1] += x_block[col][i + 4u];
+                        sumx[2] += x_block[col][i + 8u];
+                        sumx[3] += x_block[col][i + 12u];
+                    }
 
-                acc[row] += d * (dot[0] * scale0 + dot[1] * scale1 + dot[2] * scale2 + dot[3] * scale3)
-                          - dmin * (sumx[0] * min0 + sumx[1] * min1 + sumx[2] * min2 + sumx[3] * min3);
+                    acc[col][row] += d * (dot[0] * scale0 + dot[1] * scale1 + dot[2] * scale2 + dot[3] * scale3)
+                              - dmin * (sumx[0] * min0 + sumx[1] * min1 + sumx[2] * min2 + sumx[3] * min3);
+                }
             }
         }
     }
@@ -642,8 +680,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 176
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -671,14 +709,16 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 4u; i++) {
-            x_block[i]       = f32(src1[x_base + i]);
-            x_block[i + 4u]  = f32(src1[x_base + 32u + i]);
-            x_block[i + 8u]  = f32(src1[x_base + 128u + i]);
-            x_block[i + 12u] = f32(src1[x_base + 160u + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            let col_base = x_base + col * params.stride_11;
+            for (var i = 0u; i < 4u; i++) {
+                x_block[col][i]       = f32(src1[col_base + i]);
+                x_block[col][i + 4u]  = f32(src1[col_base + 32u + i]);
+                x_block[col][i + 8u]  = f32(src1[col_base + 128u + i]);
+                x_block[col][i + 12u] = f32(src1[col_base + 160u + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -712,37 +752,39 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let q2_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset + 64u);
                 let qh_u32 = load_u32_at_src0_aligned(block_byte_base + qh_offset);
 
-                var vals = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
-                for (var i = 0u; i < 4u; i++) {
-                    let q1b = byte_of(q1_u32, i);
-                    let q2b = byte_of(q2_u32, i);
-                    let qhb = byte_of(qh_u32, i);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var vals = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    var sumy = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                    for (var i = 0u; i < 4u; i++) {
+                        let q1b = byte_of(q1_u32, i);
+                        let q2b = byte_of(q2_u32, i);
+                        let qhb = byte_of(qh_u32, i);
 
-                    let yl0 = x_block[i];
-                    let yl8 = x_block[i + 4u];
-                    let yh0 = x_block[i + 8u];
-                    let yh8 = x_block[i + 12u];
+                        let yl0 = x_block[col][i];
+                        let yl8 = x_block[col][i + 4u];
+                        let yh0 = x_block[col][i + 8u];
+                        let yh8 = x_block[col][i + 12u];
 
-                    sumy[0] += yl0;
-                    sumy[1] += yl8;
-                    sumy[2] += yh0;
-                    sumy[3] += yh8;
+                        sumy[0] += yl0;
+                        sumy[1] += yl8;
+                        sumy[2] += yh0;
+                        sumy[3] += yh8;
 
-                    let q0 = f32((q1b & 0x0Fu) | select(0u, 0x10u, (qhb & hm1) != 0u));
-                    let q1 = f32((q1b >> 4u) | select(0u, 0x10u, (qhb & hm2) != 0u));
-                    let q2 = f32((q2b & 0x0Fu) | select(0u, 0x10u, (qhb & hm3) != 0u));
-                    let q3 = f32((q2b >> 4u) | select(0u, 0x10u, (qhb & hm4) != 0u));
+                        let q0 = f32((q1b & 0x0Fu) | select(0u, 0x10u, (qhb & hm1) != 0u));
+                        let q1 = f32((q1b >> 4u) | select(0u, 0x10u, (qhb & hm2) != 0u));
+                        let q2 = f32((q2b & 0x0Fu) | select(0u, 0x10u, (qhb & hm3) != 0u));
+                        let q3 = f32((q2b >> 4u) | select(0u, 0x10u, (qhb & hm4) != 0u));
 
-                    vals[0] += yl0 * q0;
-                    vals[1] += yl8 * q1;
-                    vals[2] += yh0 * q2;
-                    vals[3] += yh8 * q3;
+                        vals[0] += yl0 * q0;
+                        vals[1] += yl8 * q1;
+                        vals[2] += yh0 * q2;
+                        vals[3] += yh8 * q3;
+                    }
+
+                    acc[col][row] += d * (f0 * vals[0] + f1 * vals[1] + f4 * vals[2] + f5 * vals[3])
+                              - dmin * (sumy[0] * m0 + sumy[1] * m1 +
+                                        sumy[2] * m4 + sumy[3] * m5);
                 }
-
-                acc[row] += d * (f0 * vals[0] + f1 * vals[1] + f4 * vals[2] + f5 * vals[3])
-                          - dmin * (sumy[0] * m0 + sumy[1] * m1 +
-                                    sumy[2] * m4 + sumy[3] * m5);
             }
         }
     }
@@ -755,8 +797,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 210
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -777,14 +819,16 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var l = 0u; l < 4u; l++) {
-            x_block[l]       = f32(src1[x_base + l]);
-            x_block[l + 4u]  = f32(src1[x_base + 32u + l]);
-            x_block[l + 8u]  = f32(src1[x_base + 64u + l]);
-            x_block[l + 12u] = f32(src1[x_base + 96u + l]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            let col_base = x_base + col * params.stride_11;
+            for (var l = 0u; l < 4u; l++) {
+                x_block[col][l]       = f32(src1[col_base + l]);
+                x_block[col][l + 4u]  = f32(src1[col_base + 32u + l]);
+                x_block[col][l + 8u]  = f32(src1[col_base + 64u + l]);
+                x_block[col][l + 12u] = f32(src1[col_base + 96u + l]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -802,26 +846,28 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let sc4 = sbyte_of(sc_u32_1, sc_byte_pos);
                 let sc6 = sbyte_of(sc_u32_1, sc_byte_pos + 2u);
 
-                var sums = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var sums = vec4<f32>(0.0, 0.0, 0.0, 0.0);
 
-                for (var l = 0u; l < 4u; l++) {
-                    let q1b = byte_of(ql1_u32, l);
-                    let q2b = byte_of(ql2_u32, l);
-                    let qhb = byte_of(qh_u32, l);
+                    for (var l = 0u; l < 4u; l++) {
+                        let q1b = byte_of(ql1_u32, l);
+                        let q2b = byte_of(ql2_u32, l);
+                        let qhb = byte_of(qh_u32, l);
 
-                    let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32);
-                    let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32);
-                    let dq2 = f32(i32((q1b >> 4u) | (qhb & 0x30u)) - 32);
-                    let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32);
+                        let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32);
+                        let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32);
+                        let dq2 = f32(i32((q1b >> 4u) | (qhb & 0x30u)) - 32);
+                        let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32);
 
-                    sums[0] += x_block[l] * dq0;
-                    sums[1] += x_block[l + 4u] * dq1;
-                    sums[2] += x_block[l + 8u] * dq2;
-                    sums[3] += x_block[l + 12u] * dq3;
+                        sums[0] += x_block[col][l] * dq0;
+                        sums[1] += x_block[col][l + 4u] * dq1;
+                        sums[2] += x_block[col][l + 8u] * dq2;
+                        sums[3] += x_block[col][l + 12u] * dq3;
+                    }
+
+                    acc[col][row] += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) +
+                                    sums[2] * f32(sc4) + sums[3] * f32(sc6));
                 }
-
-                acc[row] += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) +
-                                 sums[2] * f32(sc4) + sums[3] * f32(sc6));
             }
         }
     }
@@ -834,8 +880,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 50
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -850,11 +896,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -866,20 +913,22 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000u) != 0u);
                 let qs_w  = load_u32_at_src0(block_byte_base + 2u + sub_blk * 4u);
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l       = slot0 + ll;
-                    let qs_byte = get_byte(qs_w, l);
-                    let ig      = (qs_byte | (((qh >> (3u * l)) & 7u) << 8u)) * 8u;
-                    let gw      = iq1_grid[ig / 16u];
-                    let bit_base = (ig % 16u) * 2u;
-                    for (var j = 0u; j < 8u; j++) {
-                        let g  = (gw >> (bit_base + j * 2u)) & 3u;
-                        let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
-                        row_sum += dl * (gs + delta) * x_block[ll * 8u + j];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l       = slot0 + ll;
+                        let qs_byte = get_byte(qs_w, l);
+                        let ig      = (qs_byte | (((qh >> (3u * l)) & 7u) << 8u)) * 8u;
+                        let gw      = iq1_grid[ig / 16u];
+                        let bit_base = (ig % 16u) * 2u;
+                        for (var j = 0u; j < 8u; j++) {
+                            let g  = (gw >> (bit_base + j * 2u)) & 3u;
+                            let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
+                            row_sum += dl * (gs + delta) * x_block[col][ll * 8u + j];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -892,8 +941,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 56
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -908,11 +957,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -936,26 +986,28 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let qh_lo = qh & 0xFFu;
                 let qh_hi = (qh >> 8u) & 0xFFu;
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let bit_off = 6u * (sub_blk % 2u) + 3u * (l / 2u);
-                    let sub_scale = (sc_u16 >> bit_off) & 0x7u;
-                    let dl = d * f32(2u * sub_scale + 1u);
-                    let qh_byte = select(qh_lo, qh_hi, l >= 2u);
-                    let ll2 = l % 2u;
-                    let grid_idx = get_byte(qs_w, l) | (((qh_byte >> (4u * ll2)) & 7u) << 8u);
-                    let delta = select(IQ1_DELTA, -IQ1_DELTA, ((qh_byte >> (3u + 4u * ll2)) & 1u) != 0u);
-                    let ig = grid_idx * 8u;
-                    let gw = iq1_grid[ig / 16u];
-                    let bit_base = (ig % 16u) * 2u;
-                    for (var j = 0u; j < 8u; j++) {
-                        let g  = (gw >> (bit_base + j * 2u)) & 3u;
-                        let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
-                        row_sum += dl * (gs + delta) * x_block[ll * 8u + j];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let bit_off = 6u * (sub_blk % 2u) + 3u * (l / 2u);
+                        let sub_scale = (sc_u16 >> bit_off) & 0x7u;
+                        let dl = d * f32(2u * sub_scale + 1u);
+                        let qh_byte = select(qh_lo, qh_hi, l >= 2u);
+                        let ll2 = l % 2u;
+                        let grid_idx = get_byte(qs_w, l) | (((qh_byte >> (4u * ll2)) & 7u) << 8u);
+                        let delta = select(IQ1_DELTA, -IQ1_DELTA, ((qh_byte >> (3u + 4u * ll2)) & 1u) != 0u);
+                        let ig = grid_idx * 8u;
+                        let gw = iq1_grid[ig / 16u];
+                        let bit_base = (ig % 16u) * 2u;
+                        for (var j = 0u; j < 8u; j++) {
+                            let g  = (gw >> (bit_base + j * 2u)) & 3u;
+                            let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u);
+                            row_sum += dl * (gs + delta) * x_block[col][ll * 8u + j];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -968,8 +1020,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 66
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -984,11 +1036,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -999,22 +1052,24 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let ls = aux_hi >> 28u;
                 let db = d * (0.5 + f32(ls)) * 0.25;
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let grid_idx = (aux_lo >> (8u * l)) & 0xFFu;
-                    let signs_idx = (aux_hi >> (7u * l)) & 0x7Fu;
-                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
-                    let gw_lo = iq2xxs_grid[grid_idx * 2u];
-                    let gw_hi = iq2xxs_grid[grid_idx * 2u + 1u];
-                    for (var j = 0u; j < 8u; j++) {
-                        let gw = select(gw_hi, gw_lo, j < 4u);
-                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
-                        let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
-                        row_sum += db * b * s * x_block[ll * 8u + j];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let grid_idx = (aux_lo >> (8u * l)) & 0xFFu;
+                        let signs_idx = (aux_hi >> (7u * l)) & 0x7Fu;
+                        let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                        let gw_lo = iq2xxs_grid[grid_idx * 2u];
+                        let gw_hi = iq2xxs_grid[grid_idx * 2u + 1u];
+                        for (var j = 0u; j < 8u; j++) {
+                            let gw = select(gw_hi, gw_lo, j < 4u);
+                            let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                            let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                            row_sum += db * b * s * x_block[col][ll * 8u + j];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1027,8 +1082,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 74
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -1043,11 +1098,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -1058,27 +1114,29 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let scales_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u);
                 let scales_byte = get_byte(scales_word, sub_blk % 4u);
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let qs_word = select(qs_hi, qs_lo, l < 2u);
-                    let half2 = (l % 2u) * 16u;
-                    let qs_val = (qs_word >> half2) & 0xFFFFu;
-                    let grid_idx = qs_val & 0x1FFu;
-                    let signs_idx = (qs_val >> 9u) & 0x7Fu;
-                    let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
-                    let db = d * (0.5 + f32(sub_scale)) * 0.25;
-                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
-                    let gw_lo = iq2xs_grid[grid_idx * 2u];
-                    let gw_hi = iq2xs_grid[grid_idx * 2u + 1u];
-                    for (var j = 0u; j < 8u; j++) {
-                        let gw = select(gw_hi, gw_lo, j < 4u);
-                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
-                        let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
-                        row_sum += db * b * s * x_block[ll * 8u + j];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let qs_word = select(qs_hi, qs_lo, l < 2u);
+                        let half2 = (l % 2u) * 16u;
+                        let qs_val = (qs_word >> half2) & 0xFFFFu;
+                        let grid_idx = qs_val & 0x1FFu;
+                        let signs_idx = (qs_val >> 9u) & 0x7Fu;
+                        let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
+                        let db = d * (0.5 + f32(sub_scale)) * 0.25;
+                        let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                        let gw_lo = iq2xs_grid[grid_idx * 2u];
+                        let gw_hi = iq2xs_grid[grid_idx * 2u + 1u];
+                        for (var j = 0u; j < 8u; j++) {
+                            let gw = select(gw_hi, gw_lo, j < 4u);
+                            let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                            let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                            row_sum += db * b * s * x_block[col][ll * 8u + j];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1091,8 +1149,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 82
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -1107,11 +1165,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -1124,24 +1183,26 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let sc_word = load_u32_at_src0(block_byte_base + 74u + (sub_blk / 4u) * 4u);
                 let scales_byte = get_byte(sc_word, sub_blk % 4u);
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let qs_byte = get_byte(qs_w, l);
-                    let sign_byte = get_byte(sg_w, l);
-                    let grid_idx = qs_byte | (((qh_byte >> (2u * l)) & 3u) << 8u);
-                    let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
-                    let db = d * (0.5 + f32(sub_scale)) * 0.25;
-                    let gw_lo = iq2s_grid[grid_idx * 2u];
-                    let gw_hi = iq2s_grid[grid_idx * 2u + 1u];
-                    for (var j = 0u; j < 8u; j++) {
-                        let gw = select(gw_hi, gw_lo, j < 4u);
-                        let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
-                        let s = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
-                        row_sum += db * b * s * x_block[ll * 8u + j];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let qs_byte = get_byte(qs_w, l);
+                        let sign_byte = get_byte(sg_w, l);
+                        let grid_idx = qs_byte | (((qh_byte >> (2u * l)) & 3u) << 8u);
+                        let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu;
+                        let db = d * (0.5 + f32(sub_scale)) * 0.25;
+                        let gw_lo = iq2s_grid[grid_idx * 2u];
+                        let gw_hi = iq2s_grid[grid_idx * 2u + 1u];
+                        for (var j = 0u; j < 8u; j++) {
+                            let gw = select(gw_hi, gw_lo, j < 4u);
+                            let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu);
+                            let s = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
+                            row_sum += db * b * s * x_block[col][ll * 8u + j];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1154,8 +1215,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 98
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -1170,11 +1231,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -1186,27 +1248,29 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let ls = aux >> 28u;
                 let db = d * (0.5 + f32(ls)) * 0.5;
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let qs_word = select(qs_hi, qs_lo, l < 2u);
-                    let byte_pos = (l % 2u) * 2u;
-                    let grid_idx_0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
-                    let grid_idx_1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
-                    let signs_idx = (aux >> (7u * l)) & 0x7Fu;
-                    let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
-                    let grid1 = iq3xxs_grid[grid_idx_0];
-                    let grid2 = iq3xxs_grid[grid_idx_1];
-                    for (var j = 0u; j < 4u; j++) {
-                        let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
-                        let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
-                        let s1 = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
-                        let s2 = select(1.0, -1.0, ((signs >> (j + 4u)) & 1u) != 0u);
-                        row_sum += db * b1 * s1 * x_block[ll * 8u + j];
-                        row_sum += db * b2 * s2 * x_block[ll * 8u + j + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let qs_word = select(qs_hi, qs_lo, l < 2u);
+                        let byte_pos = (l % 2u) * 2u;
+                        let grid_idx_0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
+                        let grid_idx_1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
+                        let signs_idx = (aux >> (7u * l)) & 0x7Fu;
+                        let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu;
+                        let grid1 = iq3xxs_grid[grid_idx_0];
+                        let grid2 = iq3xxs_grid[grid_idx_1];
+                        for (var j = 0u; j < 4u; j++) {
+                            let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
+                            let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
+                            let s1 = select(1.0, -1.0, ((signs >> j) & 1u) != 0u);
+                            let s2 = select(1.0, -1.0, ((signs >> (j + 4u)) & 1u) != 0u);
+                            row_sum += db * b1 * s1 * x_block[col][ll * 8u + j];
+                            row_sum += db * b2 * s2 * x_block[col][ll * 8u + j + 4u];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1219,8 +1283,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 110
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -1235,11 +1299,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -1255,28 +1320,30 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let sub_scale = (scales_byte >> (4u * (sub_blk % 2u))) & 0xFu;
                 let db = d * (1.0 + 2.0 * f32(sub_scale));
 
-                var row_sum = 0.0;
-                for (var ll = 0u; ll < 2u; ll++) {
-                    let l = slot0 + ll;
-                    let qs_word = select(qs_hi, qs_lo, l < 2u);
-                    let byte_pos = (l % 2u) * 2u;
-                    let qs0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
-                    let qs1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
-                    let grid_idx_1 = qs0 | (((qh_byte >> (2u * l)) & 1u) << 8u);
-                    let grid_idx_2 = qs1 | (((qh_byte >> (2u * l + 1u)) & 1u) << 8u);
-                    let sign_byte = get_byte(sg_w, l);
-                    let grid1 = iq3s_grid[grid_idx_1];
-                    let grid2 = iq3s_grid[grid_idx_2];
-                    for (var j = 0u; j < 4u; j++) {
-                        let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
-                        let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
-                        let s1 = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
-                        let s2 = select(1.0, -1.0, ((sign_byte >> (j + 4u)) & 1u) != 0u);
-                        row_sum += db * b1 * s1 * x_block[ll * 8u + j];
-                        row_sum += db * b2 * s2 * x_block[ll * 8u + j + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var ll = 0u; ll < 2u; ll++) {
+                        let l = slot0 + ll;
+                        let qs_word = select(qs_hi, qs_lo, l < 2u);
+                        let byte_pos = (l % 2u) * 2u;
+                        let qs0 = (qs_word >> (byte_pos * 8u)) & 0xFFu;
+                        let qs1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu;
+                        let grid_idx_1 = qs0 | (((qh_byte >> (2u * l)) & 1u) << 8u);
+                        let grid_idx_2 = qs1 | (((qh_byte >> (2u * l + 1u)) & 1u) << 8u);
+                        let sign_byte = get_byte(sg_w, l);
+                        let grid1 = iq3s_grid[grid_idx_1];
+                        let grid2 = iq3s_grid[grid_idx_2];
+                        for (var j = 0u; j < 4u; j++) {
+                            let b1 = f32((grid1 >> (j * 8u)) & 0xFFu);
+                            let b2 = f32((grid2 >> (j * 8u)) & 0xFFu);
+                            let s1 = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u);
+                            let s2 = select(1.0, -1.0, ((sign_byte >> (j + 4u)) & 1u) != 0u);
+                            row_sum += db * b1 * s1 * x_block[col][ll * 8u + j];
+                            row_sum += db * b2 * s2 * x_block[col][ll * 8u + j + 4u];
+                        }
                     }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1290,35 +1357,37 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 18
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % THREADS_PER_BLOCK;
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4u;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4u] = f32(src1[x_base + i + 16u]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2u; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4u] = f32(src1[x_base + col * params.stride_11 + i + 16u]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let d = f32(load_f16_at_src0(block_byte_base));
-                var row_sum = 0.0;
-
                 let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = f32(kvalues_iq4nl[q_byte & 0xFu]) * d;
-                    let q_hi = f32(kvalues_iq4nl[(q_byte >> 4u) & 0xFu]) * d;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let q_lo = f32(kvalues_iq4nl[q_byte & 0xFu]) * d;
+                        let q_hi = f32(kvalues_iq4nl[(q_byte >> 4u) & 0xFu]) * d;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1331,8 +1400,8 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE 256
 #define BLOCK_SIZE_BYTES 136
 #define THREADS_PER_BLOCK 16
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
     let block_group = thread_id / THREADS_PER_BLOCK;
@@ -1346,11 +1415,12 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 
     for (var block = block_group; block < num_blocks; block += num_block_groups) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset;
-        var x_block: array<f32, 16>;
-        for (var i = 0u; i < 16u; i++) {
-            x_block[i] = f32(src1[x_base + i]);
+        var x_block: array<array<f32, 16>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < 16u; i++) {
+                x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
@@ -1370,17 +1440,19 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
                 let q_w2 = load_u32_at_src0(block_byte_base + qs_byte_off + 8u);
                 let q_w3 = load_u32_at_src0(block_byte_base + qs_byte_off + 12u);
 
-                var row_sum = 0.0;
-                for (var i = 0u; i < 16u; i++) {
-                    let q_word = select(
-                        select(q_w0, q_w1, i >= 4u),
-                        select(q_w2, q_w3, i >= 12u),
-                        i >= 8u);
-                    let q_byte = get_byte(q_word, i % 4u);
-                    let nib = select(q_byte & 0xFu, (q_byte >> 4u) & 0xFu, half == 1u);
-                    row_sum += f32(kvalues_iq4nl[nib]) * dl * x_block[i];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var i = 0u; i < 16u; i++) {
+                        let q_word = select(
+                            select(q_w0, q_w1, i >= 4u),
+                            select(q_w2, q_w3, i >= 12u),
+                            i >= 8u);
+                        let q_byte = get_byte(q_word, i % 4u);
+                        let nib = select(q_byte & 0xFu, (q_byte >> 4u) & 0xFu, half == 1u);
+                        row_sum += f32(kvalues_iq4nl[nib]) * dl * x_block[col][i];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
@@ -1394,35 +1466,38 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
 #define BLOCK_SIZE_BYTES 17
 #define THREADS_PER_BLOCK 4
 #define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
-fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
     let thread_within_block = thread_id % 4;
     for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
         let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4;
-        var x_block: array<f32, ELEMS_PER_THREAD>;
-        for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
-            x_block[i] = f32(src1[x_base + i]);
-            x_block[i + 4] = f32(src1[x_base + i + 16]);
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]);
+            }
         }
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
                 let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0);
                 let e = ldexp(1.0, i32(eu8) - 128);
-                var row_sum = 0.0;
                 let q_packed = load_u32_at_src0(block_byte_base + 1u + 4u * thread_within_block);
-                for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
-                    let q_byte = get_byte(q_packed, byte_idx);
-                    let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * e;
-                    let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * e;
-                    row_sum += q_lo * x_block[byte_idx];
-                    row_sum += q_hi * x_block[byte_idx + 4u];
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) {
+                        let q_byte = get_byte(q_packed, byte_idx);
+                        let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * e;
+                        let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * e;
+                        row_sum += q_lo * x_block[col][byte_idx];
+                        row_sum += q_hi * x_block[col][byte_idx + 4u];
+                    }
+                    acc[col][row] += row_sum;
                 }
-                acc[row] += row_sum;
             }
         }
     }
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl
index 3ef2f77eb..6ccaf61a6 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl
@@ -51,10 +51,7 @@ fn repack_b_dm(block: u32) -> B_DS_TYPE {
 fn get_dm(block_byte_base: u32) -> f32 {
     return f32(load_f16_at_src0(block_byte_base));
 }
-fn mul_q8_1(row_sum: i32, da: f32, b_ds: B_DS_TYPE) -> f32 {
-    return f32(row_sum) * (da * b_ds.x) - 8.0 * da * b_ds.y / THREADS_PER_BLOCK;
-}
-#endif
+#endif // MUL_ACC_Q4_0
 
 #ifdef MUL_ACC_Q4_1
 #define BLOCK_SIZE_BYTES 20
@@ -85,10 +82,7 @@ fn get_dm(block_byte_base: u32) -> vec2<f32> {
         f32(load_f16_at_src0(block_byte_base + 2u))
     );
 }
-fn mul_q8_1(row_sum: i32, dma: vec2<f32>, b_ds: B_DS_TYPE) -> f32 {
-    return f32(row_sum) * (dma.x * b_ds.x) + dma.y * b_ds.y / THREADS_PER_BLOCK;
-}
-#endif
+#endif // MUL_ACC_Q4_1
 
 #ifdef MUL_ACC_Q8_0
 #define BLOCK_SIZE_BYTES 34
@@ -111,46 +105,48 @@ fn repack_b_dm(block: u32) -> B_DS_TYPE {
 fn get_dm(block_byte_base: u32) -> f32 {
     return f32(load_f16_at_src0(block_byte_base));
 }
-fn mul_q8_1(row_sum: i32, da: f32, b_ds: B_DS_TYPE) -> f32 {
-    return f32(row_sum) * (da * b_ds);
-}
-#endif
+#endif // MUL_ACC_Q8_0
 
-#ifdef LEGACY_QUANTS
-fn mmvq_dot_product(a_byte_base: u32, b_inner_id: u32, b_repacked: vec2<u32>, b_ds: B_DS_TYPE) -> f32 {
-    var row_sum = 0;
-    let a_repacked = repack_a(a_byte_base, b_inner_id);
-
-    row_sum += dot4I8Packed(a_repacked[0], b_repacked[0]);
-    row_sum += dot4I8Packed(a_repacked[1], b_repacked[1]);
-
-    return mul_q8_1(row_sum, get_dm(a_byte_base), b_ds);
-}
-
-fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+#if defined(LEGACY_QUANTS)
+fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let num_blocks = params.k / BLOCK_SIZE;
 
     for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let b_inner_id = thread_id % THREADS_PER_BLOCK;
-        let b_block_idx = src1q_idx_base + block;
-
-        let b_repacked = repack_b_qs(b_block_idx, b_inner_id);
-        let b_ds = repack_b_dm(b_block_idx);
-
+        let inner_id = thread_id % THREADS_PER_BLOCK;
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                acc[row] += mmvq_dot_product(block_byte_base, b_inner_id, b_repacked, b_ds);
+                let a_repacked = repack_a(block_byte_base, inner_id);
+                let da = get_dm(block_byte_base);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    let src1q_idx = src1q_idx_base + col * (params.k / Q8_BLOCK_SIZE) + block;
+                    let b_repacked = repack_b_qs(src1q_idx, inner_id);
+                    let b_ds = repack_b_dm(src1q_idx);
+
+                    let row_sum = dot4I8Packed(a_repacked[0], b_repacked[0]) + dot4I8Packed(a_repacked[1], b_repacked[1]);
+
+#if defined(MUL_ACC_Q4_0)
+                    acc[col][row] += f32(row_sum) * (da * b_ds.x) - 8.0 * da * b_ds.y / THREADS_PER_BLOCK;
+#endif // MUL_ACC_Q4_0
+
+#if defined(MUL_ACC_Q4_1)
+                    acc[col][row] += f32(row_sum) * (da.x * b_ds.x) + da.y * b_ds.y / THREADS_PER_BLOCK;
+#endif // MUL_ACC_Q4_1
+
+#if defined(MUL_ACC_Q8_0)
+                    acc[col][row] += f32(row_sum) * (da * b_ds);
+#endif // MUL_ACC_Q8_0
+                }
             }
         }
     }
 
     return acc;
 }
-#endif
+#endif // LEGACY_QUANTS
 
 #ifdef MUL_ACC_Q2_K
 #define BLOCK_SIZE_BYTES 84
@@ -191,22 +187,7 @@ fn get_scale_min(block_byte_base: u32, tid: u32) -> vec2<f32> {
     let scale = byte_of(load_u32_at_src0_aligned(scale_byte), scale_byte & 3u);
     return vec2<f32>(f32(scale & 0xFu), f32(scale >> 4u));
 }
-fn mmvq_dot_product(a_byte_base: u32, tid: u32, b_repacked: vec4<u32>, b_ds: B_DS_TYPE) -> f32 {
-    let a_repacked = repack_a(a_byte_base, tid);
-    let dm = get_dm(a_byte_base);
-    let scale_min = get_scale_min(a_byte_base, tid);
-
-    let scale_q = i32(scale_min.x);
-    let scale_m_i8x4 = u32(scale_min.y) * 0x01010101u;
-
-    let row_sum_d = (dot4I8Packed(b_repacked[0], a_repacked[0]) + dot4I8Packed(b_repacked[1], a_repacked[1])
-                   + dot4I8Packed(b_repacked[2], a_repacked[2]) + dot4I8Packed(b_repacked[3], a_repacked[3])) * scale_q;
-    let row_sum_m = dot4I8Packed(b_repacked[0], scale_m_i8x4) + dot4I8Packed(b_repacked[1], scale_m_i8x4)
-                  + dot4I8Packed(b_repacked[2], scale_m_i8x4) + dot4I8Packed(b_repacked[3], scale_m_i8x4);
-
-    return b_ds * (dm.x * f32(row_sum_d) - dm.y * f32(row_sum_m));
-}
-#endif
+#endif // MUL_ACC_Q2_K
 
 #ifdef MUL_ACC_Q4_K
 #define BLOCK_SIZE_BYTES 144
@@ -265,39 +246,52 @@ fn get_scale_min(block_byte_base: u32, tid: u32) -> vec2<f32> {
 
     return vec2<f32>(scale, min_val);
 }
-fn mmvq_dot_product(a_byte_base: u32, tid: u32, b_repacked: vec4<u32>, b_ds: B_DS_TYPE) -> f32 {
-    let a_repacked = repack_a(a_byte_base, tid);
-    let dm = get_dm(a_byte_base);
-    let scale_min = get_scale_min(a_byte_base, tid);
-
-    let row_sum = dot4I8Packed(a_repacked[0], b_repacked[0]) + dot4I8Packed(a_repacked[1], b_repacked[1])
-                + dot4I8Packed(a_repacked[2], b_repacked[2]) + dot4I8Packed(a_repacked[3], b_repacked[3]);
-
-    // Each thread covers half of the Q8_1 block, so add only b_ds.y/2.
-    return b_ds.x * dm.x * scale_min.x * f32(row_sum) - dm.y * scale_min.y * (b_ds.y / (Q8_BLOCK_SIZE / ELEMS_PER_THREAD));
-}
-#endif
+#endif // MUL_ACC_Q4_K
 
 #ifdef K_QUANTS
-fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array<f32, OUTPUTS_PER_WG> {
-    var acc: array<f32, OUTPUTS_PER_WG>;
+fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
 
     let tid = thread_id % THREADS_PER_BLOCK;
 
     for (var block = thread_id / THREADS_PER_BLOCK; block < params.k / BLOCK_SIZE; block += WG_SIZE / THREADS_PER_BLOCK) {
-        let src1q_idx = src1q_idx_base + (block * BLOCK_SIZE + ELEMS_PER_THREAD * tid) / Q8_BLOCK_SIZE;
-        let b_repacked = repack_b_qs(src1q_idx, tid);
-        let b_ds = repack_b_dm(src1q_idx);
-
         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
             let output_row = row_base + row;
             if (output_row < params.m) {
                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
-                acc[row] += mmvq_dot_product(block_byte_base, tid, b_repacked, b_ds);
+                let a_repacked = repack_a(block_byte_base, tid);
+                let dm = get_dm(block_byte_base);
+                let scale_min = get_scale_min(block_byte_base, tid);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    let src1q_idx = src1q_idx_base + col * (params.k / Q8_BLOCK_SIZE) + (block * BLOCK_SIZE + ELEMS_PER_THREAD * tid) / Q8_BLOCK_SIZE;
+                    let b_repacked = repack_b_qs(src1q_idx, tid);
+                    let b_ds = repack_b_dm(src1q_idx);
+
+#if defined(MUL_ACC_Q2_K)
+                    let scale_q = i32(scale_min.x);
+                    let scale_m_i8x4 = u32(scale_min.y) * 0x01010101u;
+
+                    let row_sum_d = (dot4I8Packed(b_repacked[0], a_repacked[0]) + dot4I8Packed(b_repacked[1], a_repacked[1])
+                                        + dot4I8Packed(b_repacked[2], a_repacked[2]) + dot4I8Packed(b_repacked[3], a_repacked[3])) * scale_q;
+                    let row_sum_m = dot4I8Packed(b_repacked[0], scale_m_i8x4) + dot4I8Packed(b_repacked[1], scale_m_i8x4)
+                                        + dot4I8Packed(b_repacked[2], scale_m_i8x4) + dot4I8Packed(b_repacked[3], scale_m_i8x4);
+
+                    acc[col][row] += b_ds * (dm.x * f32(row_sum_d) - dm.y * f32(row_sum_m));
+#endif // MUL_ACC_Q2_K
+
+#if defined(MUL_ACC_Q4_K)
+                    let row_sum = dot4I8Packed(a_repacked[0], b_repacked[0]) + dot4I8Packed(a_repacked[1], b_repacked[1])
+                                    + dot4I8Packed(a_repacked[2], b_repacked[2]) + dot4I8Packed(a_repacked[3], b_repacked[3]);
+
+                    // Each thread covers half of the Q8_1 block, so add only b_ds.y/2.
+                    acc[col][row] += b_ds.x * dm.x * scale_min.x * f32(row_sum) - dm.y * scale_min.y * (b_ds.y / (Q8_BLOCK_SIZE / ELEMS_PER_THREAD));
+#endif // MUL_ACC_Q4_K
+
+                }
             }
         }
     }
 
     return acc;
 }
-#endif
+#endif // K_QUANTS
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl
index b3f1fa04b..847b27ffa 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl
@@ -9,9 +9,11 @@ requires packed_4x8_integer_dot_product;
 
 struct Params {
     offset_src1: u32,
+    stride_11: u32,
     stride_12: u32,
     stride_13: u32,
     ne0: u32,
+    ne1: u32,
     ne2: u32,
     ne3: u32,
 };
@@ -57,25 +59,28 @@ fn main(
     @builtin(num_workgroups) num_wg: vec3<u32>
 ) {
     let thread_id = local_id.x;
-    let num_vec4 = params.ne0 / 4u;
+    let ne0_vec4 = params.ne0 / 4u;
 
-    let wg_per_vec = (num_vec4 + (WG_SIZE - 1u)) / WG_SIZE;
-    let total_batches = wg_per_vec * params.ne2 * params.ne3;
+    let wg_per_vec = (ne0_vec4 + (WG_SIZE - 1u)) / WG_SIZE;
+    let total_batches = wg_per_vec * params.ne1 * params.ne2 * params.ne3;
 
     let wg_linear = wg_id.y * num_wg.x + wg_id.x;
     if (wg_linear >= total_batches) {
         return;
     }
 
-    let src13_idx = wg_linear / (params.ne2 * wg_per_vec);
-    let src12_idx = (wg_linear - src13_idx * (params.ne2 * wg_per_vec)) / wg_per_vec;
-    let src11_wg_idx = wg_linear % wg_per_vec;
-    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12;
+    let vec_idx = wg_linear / wg_per_vec;
+    let src13_idx = vec_idx / (params.ne2 * params.ne1);
+    let vec_ne12_num       = vec_idx % (params.ne2 * params.ne1);
+    let src12_idx = vec_ne12_num / params.ne1;
+    let src11_idx = vec_ne12_num % params.ne1;
+    let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12 + src11_idx * params.stride_11;
     let src1_idx_vec4_base = src1_idx_base / 4u;
 
     let blocks_per_row = params.ne0 / 32u;
     let blocks_per_wg = (WG_SIZE * 4u) / 32u;
-    let src1q_idx_base = (src13_idx * params.ne2 + src12_idx) * blocks_per_row;
+    let src1q_idx_base = ((src13_idx * params.ne2 + src12_idx) * params.ne1 + src11_idx) * blocks_per_row;
+    let src11_wg_idx = wg_linear % wg_per_vec;
     let src1q_idx = src1q_idx_base + src11_wg_idx * blocks_per_wg + thread_id / 8u;
     let qs_idx = thread_id % 8u;
 
@@ -85,7 +90,7 @@ fn main(
     var thread_amax = 0.0;
 
     let src11_vec4_idx = src11_wg_idx * WG_SIZE + thread_id;
-    let is_valid = src11_vec4_idx < num_vec4;
+    let is_valid = src11_vec4_idx < ne0_vec4;
 
 #ifdef USE_SUBGROUP_REDUCTION
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 15ae38927..127c4634c 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8433,6 +8433,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, k, {3, 2}, {2, 1}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, k, {3, 2}, {1, 2}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, k, {3, 2}, {2, 2}));
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  4, k, {3, 2}, {2, 2}));
 
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, k, {1, 1}, {1, 1}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, k, {1, 1}, {2, 1}));
@@ -8449,6 +8450,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, k, {2, 3}, {1, 1}, {0, 1, 3, 2}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  1, k, {2, 3}, {1, 1}, {0, 3, 2, 1}));
 
+                test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  4, k, {2, 3}, {1, 1}, {0, 3, 2, 1}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  8, k, {2, 3}, {1, 1}, {0, 2, 1, 3}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  8, k, {2, 3}, {1, 1}, {0, 1, 3, 2}));
                 test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16,  8, k, {2, 3}, {1, 1}, {0, 3, 2, 1}));

From a3900a669419e38f4e5f13a1a773857a38fd4cb1 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Tue, 23 Jun 2026 04:03:31 -0600
Subject: [PATCH 126/292] model: Granite Speech Plus (#24818)

* feat: Add conversion support for Granite Speech Plus

Branch: GraniteSpeechPlus
AI-usage: full (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Extend granite_speech to support plus multi-layer concatenation

Branch: GraniteSpeechPlus
AI-usage: draft (Bob, OpenCode + Qwen3.6-35b)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(conversion): Fix plural naming for feature_layers for audio

Branch: GraniteSpeechPlus
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix(mtmd): Align feature_layer usage and naming everywhere

Branch: GraniteSpeechPlus
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* style: Use fstring for log

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>

---------

Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
---
 conversion/__init__.py                |  2 ++
 conversion/granite.py                 | 28 +++++++++++++++++++++
 gguf-py/gguf/constants.py             |  1 +
 gguf-py/gguf/gguf_writer.py           |  3 +++
 tools/mtmd/clip-impl.h                |  2 +-
 tools/mtmd/clip-model.h               |  6 ++---
 tools/mtmd/clip.cpp                   | 19 +++++++-------
 tools/mtmd/models/granite-speech.cpp  | 36 ++++++++++++++++++++++++++-
 tools/mtmd/models/granite4-vision.cpp |  4 +--
 tools/mtmd/models/llava.cpp           |  6 ++---
 10 files changed, 87 insertions(+), 20 deletions(-)

diff --git a/conversion/__init__.py b/conversion/__init__.py
index 00192cf33..c6af6f731 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -96,6 +96,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "GraniteMoeHybridForCausalLM": "granite",
     "GraniteMoeSharedForCausalLM": "granite",
     "GraniteSpeechForConditionalGeneration": "granite",
+    "GraniteSpeechPlusForConditionalGeneration": "granite",
     "Grok1ForCausalLM": "grok",
     "GrokForCausalLM": "grok",
     "GroveMoeForCausalLM": "grovemoe",
@@ -261,6 +262,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "GlmasrModel": "ultravox",
     "Granite4VisionForConditionalGeneration": "granite",
     "GraniteSpeechForConditionalGeneration": "granite",
+    "GraniteSpeechPlusForConditionalGeneration": "granite",
     "HunYuanVLForConditionalGeneration": "hunyuan",
     "Idefics3ForConditionalGeneration": "smolvlm",
     "InternVisionModel": "internvl",
diff --git a/conversion/granite.py b/conversion/granite.py
index 53441fe57..8367ed225 100644
--- a/conversion/granite.py
+++ b/conversion/granite.py
@@ -348,6 +348,34 @@ class GraniteSpeechMmprojModel(MmprojModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("GraniteSpeechPlusForConditionalGeneration")
+class GraniteSpeechPlusMmprojModel(GraniteSpeechMmprojModel):
+    """Conversion for GraniteSpeechPlus - extends GraniteSpeech with feature layer concatenation"""
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        assert self.hparams_audio is not None
+        super().set_gguf_parameters()
+
+        # Add feature_layer if present in encoder config
+        if feature_layers := self.hparams_audio.get("cat_hidden_layers"):
+            self.gguf_writer.add_audio_feature_layers(feature_layers)
+            logger.info(f"gguf: audio feature_layers = {feature_layers}")
+
+            # Validate projector dimension matches concatenated encoder output
+            hidden_dim = self.hparams_audio["hidden_dim"]
+            expected_dim = hidden_dim * (len(feature_layers) + 1)
+            projector_dim = self.global_config["projector_config"]["encoder_hidden_size"]
+
+            if projector_dim != expected_dim:
+                raise ValueError(
+                    f"Projector encoder_hidden_size ({projector_dim}) does not match "
+                    f"expected concatenated dimension ({expected_dim}). "
+                    f"Expected: hidden_dim ({hidden_dim}) * (len(feature_layers) + 1) = {expected_dim}"
+                )
+
+
 @ModelBase.register("Granite4VisionForConditionalGeneration")
 class Granite4VisionMmprojModel(MmprojModel):
     has_vision_encoder = True
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 463963f2a..1bda9452d 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -359,6 +359,7 @@ class Keys:
         CHUNK_SIZE          = "clip.audio.chunk_size"
         CONV_KERNEL_SIZE    = "clip.audio.conv_kernel_size"
         MAX_POS_EMB         = "clip.audio.max_pos_emb"
+        FEATURE_LAYERS      = "clip.audio.feature_layer" # Granite Speech Plus
 
         class Attention:
             HEAD_COUNT      = "clip.audio.attention.head_count"
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index f707f29dc..a06ec88b3 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -1310,6 +1310,9 @@ class GGUFWriter:
     def add_audio_max_pos_emb(self, value: int) -> None:
         self.add_uint32(Keys.ClipAudio.MAX_POS_EMB, value)
 
+    def add_audio_feature_layers(self, layers: Sequence[int]) -> None:
+        self.add_array(Keys.ClipAudio.FEATURE_LAYERS, layers)
+
     def add_audio_projector_window_size(self, value: int) -> None:
         self.add_uint32(Keys.ClipAudio.Projector.WINDOW_SIZE, value)
 
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index e7b530144..5b413681f 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -42,6 +42,7 @@
 #define KEY_N_HEAD              "clip.%s.attention.head_count"
 #define KEY_N_HEAD_KV           "clip.%s.attention.head_count_kv"
 #define KEY_LAYER_NORM_EPS      "clip.%s.attention.layer_norm_epsilon"
+#define KEY_FEATURE_LAYERS      "clip.%s.feature_layer"
 
 // vision-specific
 #define KEY_VISION_PROJ_TYPE        "clip.vision.projector_type" // for models with mixed modalities
@@ -54,7 +55,6 @@
 #define KEY_PATCH_SIZE              "clip.vision.patch_size"
 #define KEY_IMAGE_MEAN              "clip.vision.image_mean"
 #define KEY_IMAGE_STD               "clip.vision.image_std"
-#define KEY_FEATURE_LAYER           "clip.vision.feature_layer"
 #define KEY_PROJ_SCALE_FACTOR       "clip.vision.projector.scale_factor"
 #define KEY_PROJ_SAMPLE_QUERY_SIDE  "clip.vision.projector.query_side"
 #define KEY_PROJ_SAMPLE_WINDOW_SIDE "clip.vision.projector.window_side"
diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index 48796b630..f86702eba 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -91,7 +91,7 @@ struct clip_hparams {
 
     float eps = 1e-6;
     float rope_theta = 0.0;
-    std::vector<int32_t> vision_feature_layer;
+    std::vector<int32_t> feature_layers;
     int32_t attn_window_size = 0;
     int32_t n_wa_pattern = 0;
     std::unordered_set<int32_t> wa_layer_indexes; // explicit layer indexes that use full attention (for irregular patterns like YoutuVL)
@@ -165,8 +165,8 @@ struct clip_hparams {
         return false;
     }
 
-    bool is_vision_feature_layer(int32_t layer) const {
-        return std::find(vision_feature_layer.begin(), vision_feature_layer.end(), layer) != vision_feature_layer.end();
+    bool is_feature_layer(int32_t layer) const {
+        return std::find(feature_layers.begin(), feature_layers.end(), layer) != feature_layers.end();
     }
 };
 
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 7dd7023c4..7bd486030 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1264,12 +1264,10 @@ struct clip_model_loader {
                 }
             }
 
-            // Load the vision feature layer indices if they are explicitly provided;
-            // if multiple vision feature layers are present, the values will be concatenated
-            // to form the final visual features.
+            // Load the vision/audio feature layer indices if they are explicitly provided
             // NOTE: gguf conversions should standardize the values of the vision feature layer to
             // be non-negative, since we use -1 to mark values as unset here.
-            get_arr_int(KEY_FEATURE_LAYER, hparams.vision_feature_layer, false);
+            get_arr_int(string_format(KEY_FEATURE_LAYERS, prefix), hparams.feature_layers, false);
 
             // model-specific params
             switch (model.proj_type) {
@@ -1651,6 +1649,7 @@ struct clip_model_loader {
                         get_u32(KEY_A_PROJ_WINDOW_SIZE,     hparams.audio_proj_window_size);
                         get_u32(KEY_A_PROJ_DOWNSAMPLE_RATE, hparams.audio_proj_downsample_rate);
                         get_u32(KEY_A_PROJ_HEAD_COUNT,      hparams.audio_proj_head_count);
+                        // NOTE: feature layers loaded above in common path
                     } break;
                 case PROJECTOR_TYPE_JANUS_PRO:
                     {
@@ -1663,11 +1662,11 @@ struct clip_model_loader {
                         hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
                         hparams.image_resize_pad = PAD_CEIL;
 
-                        get_arr_int(KEY_FEATURE_LAYER, hparams.vision_feature_layer);
+                        // NOTE: feature_layers loaded in common path as optional
                         get_arr_int(KEY_PROJ_SPATIAL_OFFSETS, hparams.proj_spatial_offsets);
-                        if (hparams.vision_feature_layer.size() != hparams.proj_spatial_offsets.size()) {
-                            throw std::runtime_error(string_format("%s: vision_feature_layer.size() %d != proj_spatial_offsets.size() %d",
-                                                                   hparams.vision_feature_layer.size(), hparams.proj_spatial_offsets.size()));
+                        if (hparams.feature_layers.size() != hparams.proj_spatial_offsets.size()) {
+                            throw std::runtime_error(string_format("%s: feature_layers.size() %d != proj_spatial_offsets.size() %d",
+                                                                   hparams.feature_layers.size(), hparams.proj_spatial_offsets.size()));
                         }
 
                         get_u32(KEY_PROJ_SAMPLE_QUERY_SIDE,  hparams.downsample_query_side);
@@ -2740,7 +2739,7 @@ struct clip_model_loader {
                     model.image_newline = get_tensor(TN_IMAGE_NEWLINE);
 
                     // Load separate layerwise and spatial projector tensors
-                    const auto projector_count = hparams.vision_feature_layer.size();
+                    const auto projector_count = hparams.feature_layers.size();
                     model.qf_proj_blocks.resize(projector_count);
                     for (size_t bid = 0; bid < projector_count; ++bid) {
                         auto & b = model.qf_proj_blocks[bid];
@@ -4388,7 +4387,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
 
                 // Stage 1b only uses block 0's permutations; future stages
                 // will upload all blocks.
-                for (size_t bid = 0; bid < hparams.vision_feature_layer.size(); ++bid) {
+                for (size_t bid = 0; bid < hparams.feature_layers.size(); ++bid) {
                     const std::string prefix = "g4v_blk" + std::to_string(bid) + "_";
                     upload(prefix + "win_idx",     make_win_idx(image_side, window_side));
                     upload(prefix + "qwin_idx",    make_win_idx(new_side, query_side));
diff --git a/tools/mtmd/models/granite-speech.cpp b/tools/mtmd/models/granite-speech.cpp
index 0bd4d75ac..a158a59ce 100644
--- a/tools/mtmd/models/granite-speech.cpp
+++ b/tools/mtmd/models/granite-speech.cpp
@@ -1,5 +1,7 @@
 #include "models.h"
 
+#include <algorithm>
+
 ggml_cgraph * clip_graph_granite_speech::build() {
     const int n_frames     = img.nx();
     const int context_size = hparams.audio_chunk_size;
@@ -11,6 +13,10 @@ ggml_cgraph * clip_graph_granite_speech::build() {
     const int padded_len   = num_blocks * context_size;
     const int remainder    = n_frames % context_size;
 
+    // Calculate projector input dimension based on feature layers
+    const int proj_input_dim = n_embd * (hparams.feature_layers.size() + 1);
+    const bool use_feature_concat = !hparams.feature_layers.empty();
+
     ggml_tensor * attn_dists = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, context_size * context_size);
     ggml_set_name(attn_dists, "attn_dists");
     ggml_set_input(attn_dists);
@@ -31,6 +37,15 @@ ggml_cgraph * clip_graph_granite_speech::build() {
     cur = ggml_add(ctx0, cur, model.inp_proj_b);
     cb(cur, "inp_linear", -1);
 
+    // Capture layer 0 if requested (after input_linear)
+    ggml_tensor * concat_result = nullptr;
+    if (use_feature_concat) {
+        if (std::find(hparams.feature_layers.begin(), hparams.feature_layers.end(), 0) != hparams.feature_layers.end()) {
+            concat_result = cur;
+            cb(concat_result, "feature_layer_0", -1);
+        }
+    }
+
     for (int il = 0; il < n_layer; il++) {
         const auto & layer = model.layers[il];
         auto * residual = cur;
@@ -168,6 +183,18 @@ ggml_cgraph * clip_graph_granite_speech::build() {
                          NORM_TYPE_NORMAL, eps, il);
         cb(cur, "layer_out", il);
 
+        // Capture intermediate layer (il + 1) if requested
+        if (use_feature_concat) {
+            if (hparams.is_feature_layer(il + 1)) {
+                if (concat_result == nullptr) {
+                    concat_result = cur;
+                } else {
+                    concat_result = ggml_concat(ctx0, concat_result, cur, 0);
+                }
+                cb(concat_result, string_format("feature_layer_%d", il + 1).c_str(), il);
+            }
+        }
+
         // CTC branch
         if (il + 1 == ctc_layer) {
             auto * mid = build_mm(model.ctc_out_w, cur);
@@ -180,6 +207,13 @@ ggml_cgraph * clip_graph_granite_speech::build() {
         }
     }
 
+    // Append final output to concatenated features if using feature concatenation
+    if (use_feature_concat && concat_result != nullptr) {
+        concat_result = ggml_concat(ctx0, concat_result, cur, 0);
+        cb(concat_result, "concat_final", -1);
+        cur = concat_result;
+    }
+
     cb(cur, "encoder_out", -1);
 
     // QFormer projector
@@ -197,7 +231,7 @@ ggml_cgraph * clip_graph_granite_speech::build() {
             cur = ggml_pad(ctx0, cur, 0, padded_proj - n_frames, 0, 0);
         }
 
-        ggml_tensor * enc_windows = ggml_reshape_3d(ctx0, cur, n_embd, window_size, nblocks_proj);
+        ggml_tensor * enc_windows = ggml_reshape_3d(ctx0, cur, proj_input_dim, window_size, nblocks_proj);
 
         ggml_tensor * queries = build_norm(model.qf_proj_blocks[0].qf_proj_query,
             model.qf_proj_blocks[0].qf_proj_norm_w, model.qf_proj_blocks[0].qf_proj_norm_b,
diff --git a/tools/mtmd/models/granite4-vision.cpp b/tools/mtmd/models/granite4-vision.cpp
index 9adb6f0fd..1b252543c 100644
--- a/tools/mtmd/models/granite4-vision.cpp
+++ b/tools/mtmd/models/granite4-vision.cpp
@@ -304,14 +304,14 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
     }
 
     // --- Stage 1b/1c: WindowQFormer blocks ---
-    const int projector_count = hparams.vision_feature_layer.size();
+    const int projector_count = hparams.feature_layers.size();
     const float qformer_eps = 1e-12f;
 
     ggml_tensor * mmproj = nullptr;
     for (int bid = 0; bid < projector_count; ++bid) {
         const auto & blk = model.qf_proj_blocks[bid];
 
-        int vlayer = hparams.vision_feature_layer[bid];
+        int vlayer = hparams.feature_layers[bid];
         GGML_ASSERT(vlayer >= 0 && vlayer < n_layer);
         ggml_tensor * h = layer_outs[vlayer];
 
diff --git a/tools/mtmd/models/llava.cpp b/tools/mtmd/models/llava.cpp
index 5aa3d2f0f..47efe68bd 100644
--- a/tools/mtmd/models/llava.cpp
+++ b/tools/mtmd/models/llava.cpp
@@ -21,7 +21,7 @@ ggml_cgraph * clip_graph_llava::build() {
 
         // If we set explicit vision feature layers, only go up to the deepest one
         // NOTE: only used by granite-vision models for now
-        for (const auto & feature_layer : hparams.vision_feature_layer) {
+        for (const auto & feature_layer : hparams.feature_layers) {
             if (feature_layer > deepest_feature_layer) {
                 deepest_feature_layer = feature_layer;
             }
@@ -59,7 +59,7 @@ ggml_cgraph * clip_graph_llava::build() {
 
         // If this is an embedding feature layer, save the output.
         // NOTE: 0 index here refers to the input to the encoder.
-        if (hparams.is_vision_feature_layer(il)) {
+        if (hparams.is_feature_layer(il)) {
             embedding_stack.push_back(cur);
         }
 
@@ -134,7 +134,7 @@ ggml_cgraph * clip_graph_llava::build() {
     // process vision feature layers (used by granite)
     {
         // final layer is a vision feature layer
-        if (hparams.is_vision_feature_layer(max_feature_layer)) {
+        if (hparams.is_feature_layer(max_feature_layer)) {
             embedding_stack.push_back(inpL);
         }
 

From 4a7d6dd8a03b564ee64416b08b8bc9fb39924c04 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 23 Jun 2026 18:43:28 +0800
Subject: [PATCH 127/292] alias for draft

---
 gpttype_adapter.cpp | 2 +-
 koboldcpp.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 532765be1..c185f48e5 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -893,7 +893,7 @@ static void mtp_decoding_setup(llama_model * main_model, llama_context * main_ct
     }
 
     draft_is_mtp = true;
-    speculative_state_setup(main_ctx, mtp_ctx_params, -2, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+    speculative_state_setup(main_ctx, mtp_ctx_params, -1, COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
 }
 
 //loads a model for speculative decoding.
diff --git a/koboldcpp.py b/koboldcpp.py
index c4c8cbfbf..5c0f16123 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -12177,7 +12177,7 @@ if __name__ == '__main__':
     advparser.add_argument("--defaultgenamt", help="How many tokens to generate by default, if not specified. Must be smaller than context size. Usually, your frontend GUI will override this.", type=check_range(int,64,16384), default=default_genlen)
     advparser.add_argument("--device", "-dev", metavar=('<dev1,dev2,..>'), help="Set llama.cpp compatible device selection override. Comma separated. Overrides normal device choices.", default="")
     advparser.add_argument("--downloaddir", metavar=('[directory]'), help="Specify a directory that models will be downloaded to or searched from, if unset uses the working directory.", default="")
-    advparser.add_argument("--draftamount","--draft-max","--draft-n", metavar=('[tokens]'), help="How many tokens to draft per chunk before verifying results", type=int, default=default_draft_amount)
+    advparser.add_argument("--draftamount","--draft-max","--draft-n","--spec-draft-n-max", metavar=('[tokens]'), help="How many tokens to draft per chunk before verifying results", type=int, default=default_draft_amount)
     advparser.add_argument("--draftgpulayers","--gpu-layers-draft","--n-gpu-layers-draft","-ngld", metavar=('[layers]'), help="How many layers to offload to GPU for the draft model (default=full offload)", type=int, default=999)
     advparser.add_argument("--draftgpusplit", help="GPU layer distribution ratio for draft model (default=same as main). Only works if multi-GPUs selected for MAIN model and tensor_split is set!", metavar=('[Ratios]'), type=float, nargs='+')
     advparser.add_argument("--draftmodel","--model-draft","-md", metavar=('[filename]'), help="Load a small draft model for speculative decoding. It will be fully offloaded. Vocab must match the main model.", default="")

From c926ad09857517978575d6a74d225b463f7417a0 Mon Sep 17 00:00:00 2001
From: Wyatt Caldwell <218154709+Detensable@users.noreply.github.com>
Date: Tue, 23 Jun 2026 03:55:46 -0700
Subject: [PATCH 128/292] vulkan: link ggml-cpu when GGML_VULKAN_CHECK_RESULTS
 / RUN_TESTS are enabled (#24444)

The result-checking and test debug paths in ggml-vulkan.cpp call ggml_graph_compute_with_ctx() to compute a CPU reference graph, but that symbol is defined in ggml-cpu, which ggml-vulkan does not link. Enabling -DGGML_VULKAN_CHECK_RESULTS=ON (or -DGGML_VULKAN_RUN_TESTS=ON) therefore fails to link with an unresolved external (e.g. LNK2019 on MSVC, undefined reference on GCC/Clang). This regressed after ggml-cpu was split into its own library. Link ggml-cpu under those two options so the debug builds link again.

Signed-off-by: Wyatt Caldwell <218154709+Detensable@users.noreply.github.com>
---
 ggml/src/ggml-vulkan/CMakeLists.txt | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ggml/src/ggml-vulkan/CMakeLists.txt b/ggml/src/ggml-vulkan/CMakeLists.txt
index 2d9e85794..5aeb6e97b 100644
--- a/ggml/src/ggml-vulkan/CMakeLists.txt
+++ b/ggml/src/ggml-vulkan/CMakeLists.txt
@@ -108,6 +108,9 @@ if (Vulkan_FOUND)
 
     if (GGML_VULKAN_CHECK_RESULTS)
         add_compile_definitions(GGML_VULKAN_CHECK_RESULTS)
+        # the result-checking path computes a CPU reference graph via
+        # ggml_graph_compute_with_ctx(), which is defined in ggml-cpu
+        target_link_libraries(ggml-vulkan PRIVATE ggml-cpu)
     endif()
 
     if (GGML_VULKAN_DEBUG)
@@ -129,6 +132,8 @@ if (Vulkan_FOUND)
 
     if (GGML_VULKAN_RUN_TESTS)
         add_compile_definitions(GGML_VULKAN_RUN_TESTS)
+        # the test path also calls ggml_graph_compute_with_ctx() (ggml-cpu)
+        target_link_libraries(ggml-vulkan PRIVATE ggml-cpu)
     endif()
 
     # Set up toolchain for host compilation whether cross-compiling or not

From 75ad0b23ed6dc98ce384953e1f9bc494c3de92ce Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 23 Jun 2026 13:28:34 +0200
Subject: [PATCH 129/292] server: fix remote preset handling, add test (#24938)

* server: add test for remote preset

* fix remote preset handling

* fix

* fix test
---
 common/arg.cpp                         | 11 +++++++----
 common/arg.h                           |  7 ++++++-
 common/download.cpp                    |  4 +++-
 common/download.h                      |  1 +
 tools/server/server-models.cpp         |  6 ++++--
 tools/server/server.cpp                | 13 ++++++++++++-
 tools/server/tests/unit/test_router.py | 19 +++++++++++++++++++
 7 files changed, 52 insertions(+), 9 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 5297d9075..276dbec8b 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -301,6 +301,8 @@ static handle_model_result common_params_handle_model(struct common_params_model
                                                       const common_download_opts & opts) {
     handle_model_result result;
 
+    // TODO @ngxson : refactor this into a new common_model_download_context
+
     if (!model.docker_repo.empty()) {
         model.path = common_docker_resolve_model(model.docker_repo);
     } else if (!model.hf_repo.empty()) {
@@ -396,7 +398,7 @@ static bool parse_bool_value(const std::string & value) {
 // CLI argument parsing functions
 //
 
-bool common_params_handle_models(common_params & params, llama_example curr_ex, common_download_callback * callback) {
+bool common_params_handle_models(common_params & params, llama_example curr_ex, const common_params_handle_models_params & handle_params) {
     const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
                                          params.speculative.types.end(),
                                          COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
@@ -407,9 +409,10 @@ bool common_params_handle_models(common_params & params, llama_example curr_ex,
     opts.skip_download   = params.skip_download;
     opts.download_mtp    = spec_type_draft_mtp;
     opts.download_mmproj = !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty();
+    opts.preset_only     = handle_params.preset_only;
 
-    if (callback) {
-        opts.callback = callback;
+    if (handle_params.callback) {
+        opts.callback = handle_params.callback;
     }
 
     // sub-models (draft, mmproj, vocoder) are explicitly specified by the user,
@@ -596,7 +599,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     if (!skip_model_download) {
         // handle model and download
-        common_params_handle_models(params, ctx_arg.ex);
+        common_params_handle_models(params, ctx_arg.ex, {});
 
         // model is required (except for server)
         // TODO @ngxson : maybe show a list of available models in CLI in this case
diff --git a/common/arg.h b/common/arg.h
index c061fc60f..fdfc04bc7 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -130,6 +130,11 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
 // see: https://github.com/ggml-org/llama.cpp/issues/18163
 void common_params_add_preset_options(std::vector<common_arg> & args);
 
+struct common_params_handle_models_params {
+    common_download_callback * callback = nullptr;
+    bool preset_only = false; // if true, only check & download remote preset (for router mode)
+};
+
 // populate model paths (main model, mmproj, etc) from -hf if necessary
 // return true if the model is ready to use
 // throw an exception if there is an error that prevents the model from being used (e.g. network error, model not found, etc)
@@ -137,7 +142,7 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 bool common_params_handle_models(
     common_params & params,
     llama_example curr_ex,
-    common_download_callback * callback = nullptr);
+    const common_params_handle_models_params & handle_params);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
diff --git a/common/download.cpp b/common/download.cpp
index f32046275..5b55c76a1 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -799,6 +799,7 @@ common_download_model_result common_download_model(const common_params_model  &
 
     bool download_mmproj = opts.download_mmproj;
     bool download_mtp = opts.download_mtp;
+    bool preset_only = opts.preset_only;
     bool is_hf = !model.hf_repo.empty();
 
     if (is_hf) {
@@ -806,7 +807,8 @@ common_download_model_result common_download_model(const common_params_model  &
         if (!hf.preset.path.empty()) {
             // if preset.ini exists, only download that file alone
             tasks.push_back({hf.preset.url, hf.preset.local_path});
-        } else {
+        } else if (!preset_only) {
+            // only add other files if we're NOT in preset-only mode (normal run, non-router)
             for (const auto & f : hf.model_files) {
                 tasks.push_back({f.url, f.local_path});
             }
diff --git a/common/download.h b/common/download.h
index 8dbf07836..755e34ea8 100644
--- a/common/download.h
+++ b/common/download.h
@@ -55,6 +55,7 @@ struct common_download_opts {
     bool skip_download = false; // if true, only validation is performed, common_skip_download_exception may be thrown if the file is missing or invalid
     bool download_mmproj = false;
     bool download_mtp = false;
+    bool preset_only = false; // if true, only check & download remote preset (for router mode)
     common_download_callback * callback = nullptr;
 };
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index a87e4e423..a4df3ef10 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -224,7 +224,7 @@ void server_model_meta::update_caps() {
         });
         params.offline = true;
         // params.skip_download = true; // TODO: ideally, we should validate the model here, but it takes too much time
-        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER);
+        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, {});
         if (params.mmproj.path.empty()) {
             multimodal = { false, false };
         } else {
@@ -1393,7 +1393,9 @@ struct server_download_state : public common_download_callback {
 
     bool run(common_params & params) {
         try {
-            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, this);
+            common_params_handle_models_params p;
+            p.callback = this;
+            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, p);
             is_ok = true;
         } catch (const std::exception & e) {
             auto model_name = params.model.get_name();
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index dd4b1c507..4165c1015 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -89,6 +89,17 @@ int llama_server(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
+    // note: router mode also accepts -hf remote-preset, so we need to check that first
+    if (!params.model.hf_repo.empty()) {
+        try {
+            common_params_handle_models_params handle_params;
+            handle_params.preset_only = true;
+            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, handle_params);
+        } catch (const std::exception & e) {
+            // ignored for now
+        }
+    }
+
     // router server never loads a model and must not touch the GPU
     const bool is_router_server = params.model.path.empty()
                                && params.model.hf_repo.empty();
@@ -263,7 +274,7 @@ int llama_server(int argc, char ** argv) {
         return child.run_download(params);
     } else if (!is_router_server) {
         // single-model mode (NOT spawned by router)
-        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER);
+        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, {});
     }
 
     //
diff --git a/tools/server/tests/unit/test_router.py b/tools/server/tests/unit/test_router.py
index 41e95f4c5..94165e520 100644
--- a/tools/server/tests/unit/test_router.py
+++ b/tools/server/tests/unit/test_router.py
@@ -256,6 +256,25 @@ def test_router_reload_models():
         os.remove(preset_path)
 
 
+def test_router_remote_preset():
+    global server
+    server.model_hf_repo = "ggml-org/test-preset-ci"
+    server.model_hf_file = None
+    server.offline = False
+    server.start()
+
+    # Should see preset models in GET /models
+    res = server.make_request("GET", "/models")
+    assert res.status_code == 200
+    ids = {item["id"] for item in res.body.get("data", [])}
+    assert "tinygemma3-preset" in ids
+    assert "stories260K-test" in ids
+
+    # Should be able to load a preset model
+    model_id = "tinygemma3-preset"
+    _load_model_and_wait(model_id)
+
+
 MODEL_DOWNLOAD_ID = "ggml-org/test-model-router-download:F16"
 MODEL_DOWNLOAD_TIMEOUT = 30
 

From 6df4ca13f126ad4e4e59b3017c9e5410c4760fcd Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 23 Jun 2026 20:11:25 +0800
Subject: [PATCH 130/292] drafting tweak

---
 gpttype_adapter.cpp | 41 ++++++++++++++++++++++++++---------------
 1 file changed, 26 insertions(+), 15 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index c185f48e5..245718c15 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -1055,9 +1055,9 @@ static speculative_draft_result speculative_decoding_eval_chunk(llama_context *
     }
 
     std::vector<llama_token> real_embd;
-    real_embd.reserve(drafted_ids.size());
+    real_embd.reserve(drafted_ids.size() + 1);
     real_embd.push_back(embd[0]);
-    for(size_t i = 0; i + 1 < drafted_ids.size(); ++i)
+    for(size_t i = 0; i < drafted_ids.size(); ++i)
     {
         real_embd.push_back(drafted_ids[i]);
     }
@@ -1090,6 +1090,9 @@ static speculative_draft_result speculative_decoding_eval_chunk(llama_context *
     for(size_t i = 0; i < drafted_ids.size(); ++i)
     {
         results.draftids.push_back(drafted_ids[i]);
+    }
+    for(size_t i = 0; i < real_embd.size(); ++i)
+    {
         results.actual_logits.push_back(llama_get_logits_ith(main_ctx, (int32_t)i));
     }
     results.draft_success = true;
@@ -3358,7 +3361,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
         {
             if(mtmd_ctx!=nullptr)
             {
-                printf("Warning: Speculative decoding and MTP cannot be used with multimodal projectors! Payloads containing vision/audio will not use MTP or drafting\n");
+                printf("Warning: Speculative decoding and MTP may not work well with multimodal projectors!\n");
             }
 
             speculative_chunk_amt = inputs.draft_amount;
@@ -6270,7 +6273,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
             int draft_accepted_this_round = 0;
             if(draft_used)
             {
-                logits_to_sample = draft_results.drafted_amount;
+                logits_to_sample = draft_results.drafted_amount + 1;
             }
             while(logits_sampled<logits_to_sample && remaining_tokens>0 && !abort_draft && !early_abort)
             {
@@ -6396,20 +6399,28 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
 
                 if(draft_used)
                 {
-                    int32_t draftedid = draft_results.draftids[logits_sampled];
-                    if(debugmode==1 && !is_quiet)
+                    if(logits_sampled < draft_results.drafted_amount)
                     {
-                        std::string drafttok = FileFormatTokenizeID(draftedid, file_format, true);
-                        std::string realtok = FileFormatTokenizeID(id, file_format, true);
-                        printf("(Draft %d/%d): Predicted=%d (%s), Actual=%d (%s) [%s]\n",(logits_sampled+1),logits_to_sample,draftedid,drafttok.c_str(),id,realtok.c_str(),(draftedid==id?"PASS":"FAIL"));
+                        int32_t draftedid = draft_results.draftids[logits_sampled];
+                        if(debugmode==1 && !is_quiet)
+                        {
+                            std::string drafttok = FileFormatTokenizeID(draftedid, file_format, true);
+                            std::string realtok = FileFormatTokenizeID(id, file_format, true);
+                            printf("(Draft %d/%d): Predicted=%d (%s), Actual=%d (%s) [%s]\n",(logits_sampled+1),draft_results.drafted_amount,draftedid,drafttok.c_str(),id,realtok.c_str(),(draftedid==id?"PASS":"FAIL"));
+                        }
+                        if(draftedid!=id) //draft mismatch, abort
+                        {
+                            draft_failures += 1;
+                            abort_draft = true;
+                        } else {
+                            draft_successes += 1;
+                            draft_accepted_this_round += 1;
+                        }
                     }
-                    if(draftedid!=id) //draft mismatch, abort
+                    else if(debugmode==1 && !is_quiet)
                     {
-                        draft_failures += 1;
-                        abort_draft = true;
-                    } else {
-                        draft_successes += 1;
-                        draft_accepted_this_round += 1;
+                        std::string realtok = FileFormatTokenizeID(id, file_format, true);
+                        printf("(Draft Bonus): Actual=%d (%s)\n",id,realtok.c_str());
                     }
                 }
 

From 0eb874d37445fb25cc268ad0b2f2cb07ce561b66 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 23 Jun 2026 07:26:17 -0500
Subject: [PATCH 131/292] vulkan: make mul_mm ALIGNED a spec constant (#24689)

This trims down some of the shader variant explosion and reduces binary size.
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  76 +++++----
 .../ggml-vulkan/vulkan-shaders/mul_mm.comp    |  54 ++++---
 .../vulkan-shaders/mul_mm_cm2.comp            |  11 +-
 .../vulkan-shaders/mul_mm_funcs.glsl          | 151 ++++++++++--------
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  17 +-
 5 files changed, 172 insertions(+), 137 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 9a36b45de..b3c269783 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -4074,19 +4074,35 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     }
 #endif
 
+    auto const &ggml_vk_mul_mm_spec = [](std::vector<uint32_t> spec, bool aligned) {
+        spec.push_back(aligned ? 1u : 0u);
+        return spec;
+    };
+
     const int mul_mat_id_param_count = 5;
 
 #if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
     if (device->coopmat2) {
+        auto const &ggml_vk_mul_mm_cm2_spec = [](std::vector<uint32_t> spec, bool aligned, bool mul_mat_id) {
+            if (mul_mat_id && spec.size() > 5) {
+                spec.insert(spec.begin() + 5, aligned ? 1u : 0u);
+            } else {
+                spec.push_back(aligned ? 1u : 0u);
+            }
+            if (mul_mat_id && spec.size() == 6) {
+                spec.push_back(32);
+            }
+            return spec;
+        };
 
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, true);   \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, true);   \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, true);   \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, true);   \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, true);   \
-        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm2_len, NAMELC ## _aligned ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(l_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(m_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(s_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(l_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), l_align, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(m_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), m_align, true);   \
+        ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(s_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), s_align, true);   \
 
         // Create 2 variants, {f16,f32} accumulator
 #define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \
@@ -4161,17 +4177,17 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, true);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, true);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, true);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, true);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, true);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _cm1_len, NAMELC ## _aligned ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, true);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, true);   \
 
         // Create 2 variants, {f16,f32} accumulator
 #define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
@@ -4284,32 +4300,32 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         // Selects dot2 SPIR-V variant at runtime when device->dot2_f16 is true
 #define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _len : NAMELC ## _aligned ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2_aligned ## F16ACC ## _data : NAMELC ## _aligned ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
         // bf16 scalar path promotes to f32, no dot2 variant
 #define CREATE_MM_NODOT2(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _len, NAMELC ## _aligned ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l_int[TYPE]) { \
@@ -4474,17 +4490,17 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         // Create 6 variants, {s,m,l}x{unaligned,aligned}
 #define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _l[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _m[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
         if (device->mul_mat ## ID ## _s[TYPE]) \
-            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## _aligned ## F16ACC ## _fp32_len, NAMELC ## _aligned ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
+            ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE);   \
 
 #define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \
         if (device->mul_mat ## ID ## _l_int[TYPE]) \
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
index f39410d74..57c0410e4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
@@ -38,17 +38,7 @@
 #define LOAD_VEC_B 1
 #endif
 
-// Load 2 values at once without affecting index calculations through LOAD_VEC
-#if (defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16)) && !defined(ALIGNED)
-#define LOAD_VEC_BATCH_A 2
-#else
-#define LOAD_VEC_BATCH_A 1
-#endif
-#if !defined(ALIGNED)
-#define LOAD_VEC_BATCH_B 2
-#else
-#define LOAD_VEC_BATCH_B 1
-#endif
+layout (constant_id = 11) const uint ALIGNED = 0;
 
 #if !defined(TO_FLOAT_TYPE)
 #define TO_FLOAT_TYPE FLOAT_TYPE
@@ -57,6 +47,13 @@
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+#if defined(DATA_A_F32)
+layout (binding = 0) readonly buffer A_SCALAR {float data_a_scalar[];};
+#elif defined(DATA_A_F16)
+layout (binding = 0) readonly buffer A_SCALAR {float16_t data_a_scalar[];};
+#elif defined(DATA_A_BF16)
+layout (binding = 0) readonly buffer A_SCALAR {uint16_t data_a_scalar[];};
+#endif
 #if defined(A_TYPE_PACKED16)
 layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
 #endif
@@ -65,6 +62,7 @@ layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32
 #endif
 
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 1) readonly buffer B_SCALAR {B_TYPE_SCALAR data_b_scalar[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 
 #ifdef MUL_MAT_ID
@@ -194,13 +192,23 @@ void main() {
     const uint warp_r = warp_i % (BM / WM);
     const uint warp_c = warp_i / (BM / WM);
 
-    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A / LOAD_VEC_BATCH_A);
-    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A / LOAD_VEC_BATCH_A);
-    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B / LOAD_VEC_BATCH_B);
-    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B / LOAD_VEC_BATCH_B);
+#if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16)
+    const uint LOAD_VEC_A_EFF = (ALIGNED != 0) ? LOAD_VEC_A : 1;
+    const uint LOAD_VEC_BATCH_A = (ALIGNED != 0) ? 1 : 2;
+#else
+    const uint LOAD_VEC_A_EFF = LOAD_VEC_A;
+    const uint LOAD_VEC_BATCH_A = 1;
+#endif
+    const uint LOAD_VEC_B_EFF = (ALIGNED != 0) ? LOAD_VEC_B : 1;
+    const uint LOAD_VEC_BATCH_B = (ALIGNED != 0) ? 1 : 2;
 
-    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A * LOAD_VEC_BATCH_A / BK;
-    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B * LOAD_VEC_BATCH_B / BK;
+    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A_EFF / LOAD_VEC_BATCH_A);
+    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A_EFF / LOAD_VEC_BATCH_A);
+    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B_EFF / LOAD_VEC_BATCH_B);
+    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B_EFF / LOAD_VEC_BATCH_B);
+
+    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A_EFF * LOAD_VEC_BATCH_A / BK;
+    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B_EFF * LOAD_VEC_BATCH_B / BK;
 
 #ifdef MUL_MAT_ID
 #ifdef MUL_MAT_ID_USE_SUBGROUPS
@@ -239,15 +247,15 @@ void main() {
 
     uint pos_a =
 #ifdef MUL_MAT_ID
-        expert_idx * (p.batch_stride_a / LOAD_VEC_A) +
+        expert_idx * (p.batch_stride_a / LOAD_VEC_A_EFF) +
 #else
-        batch_idx_a * (p.batch_stride_a / LOAD_VEC_A) +
+        batch_idx_a * (p.batch_stride_a / LOAD_VEC_A_EFF) +
 #endif
-        (ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+        (ir * BM * p.stride_a + start_k) / LOAD_VEC_A_EFF;
 #ifdef MUL_MAT_ID
     uint pos_b = 0;
 #else
-    uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
+    uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B_EFF;
 #endif
 
 #ifdef COOPMAT
@@ -287,8 +295,8 @@ void main() {
 
         barrier();
 
-        pos_a += BK / LOAD_VEC_A;
-        pos_b += BK / LOAD_VEC_B;
+        pos_a += BK / LOAD_VEC_A_EFF;
+        pos_b += BK / LOAD_VEC_B_EFF;
 
 #ifdef COOPMAT
         [[unroll]] for (uint i = 0; i < BK; i += TK) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
index 2656fe1c3..a2e15f6f5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp
@@ -36,6 +36,7 @@ layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working wit
 layout (constant_id = 4) const bool enable_smaller_matrices = false;
 const uint BNover2 = enable_smaller_matrices ? (BN / 2) : BN;
 const uint BNover4 = enable_smaller_matrices ? (BN / 4) : BN;
+layout (constant_id = 5) const uint ALIGNED = 0;
 
 layout (push_constant) uniform parameter
 {
@@ -111,7 +112,7 @@ layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB {
 };
 
 uint _ne1;
-layout (constant_id = 5) const uint subgroup_size = 32;
+layout (constant_id = 6) const uint subgroup_size = 32;
 shared uvec4 ballots_sh[BLOCK_SIZE / subgroup_size];
 
 B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2])
@@ -297,12 +298,12 @@ void main() {
 
     // Hint to the compiler that values are aligned (want 16B alignment).
     // Quants are always block-aligned, no alignment needed.
-#if ALIGNED
+    if (ALIGNED != 0) {
 #if QUANT_K == 1
-    stride_a &= ~7;
-#endif
-    stride_b &= ~7;
+        stride_a &= ~7;
 #endif
+        stride_b &= ~7;
+    }
 
     // Create layouts for both clamped and unclamped accesses
     tensorLayoutNV<2> tensorLayoutA = createTensorLayoutNV(2);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
index 735951689..56a8a0f18 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
@@ -1,50 +1,57 @@
 void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uint idx_m, const uint block, const uint end_k) {
 #if defined(DATA_A_F32) || defined(DATA_A_F16)
 #if LOAD_VEC_A == 8
-            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
-            FLOAT_TYPEV8 aa = FLOAT_TYPEV8(data_a[idx]);
-            buf_a[buf_idx    ] = aa[0].xy;
-            buf_a[buf_idx + 1] = aa[0].zw;
-            buf_a[buf_idx + 2] = aa[1].xy;
-            buf_a[buf_idx + 3] = aa[1].zw;
+            if (ALIGNED != 0) {
+                const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                FLOAT_TYPEV8 aa = FLOAT_TYPEV8(data_a[idx]);
+                buf_a[buf_idx    ] = aa[0].xy;
+                buf_a[buf_idx + 1] = aa[0].zw;
+                buf_a[buf_idx + 2] = aa[1].xy;
+                buf_a[buf_idx + 3] = aa[1].zw;
+                return;
+            }
 #elif LOAD_VEC_A == 4
-            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
-            FLOAT_TYPEV4 aa = FLOAT_TYPEV4(data_a[idx]);
-            buf_a[buf_idx    ] = aa.xy;
-            buf_a[buf_idx + 1] = aa.zw;
-#else // LOAD_VEC_BATCH_A == 2
+            if (ALIGNED != 0) {
+                const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                FLOAT_TYPEV4 aa = FLOAT_TYPEV4(data_a[idx]);
+                buf_a[buf_idx    ] = aa.xy;
+                buf_a[buf_idx + 1] = aa.zw;
+                return;
+            }
+#endif
             const uint idx = pos_a + col * p.stride_a + row * 2;
             const uint buf_idx = col * SHMEM_STRIDE + row;
             if (idx_m < p.M && block + row * 2 + 1 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(data_a[idx],
-                                              data_a[idx + 1]);
+                buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx],
+                                              data_a_scalar[idx + 1]);
             } else if (idx_m < p.M && block + row * 2 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(data_a[idx], 0.0f);
+                buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx], 0.0f);
             } else {
                 buf_a[buf_idx] = FLOAT_TYPEV2(0.0f);
             }
-#endif
 #elif defined(DATA_A_BF16)
 #if LOAD_VEC_A == 4
-            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
-            FLOAT_TYPEV4 aa = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_a[idx]));
-            buf_a[buf_idx    ] = aa.xy;
-            buf_a[buf_idx + 1] = aa.zw;
-#else // LOAD_VEC_BATCH_A == 2
+            if (ALIGNED != 0) {
+                const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;
+                FLOAT_TYPEV4 aa = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_a[idx]));
+                buf_a[buf_idx    ] = aa.xy;
+                buf_a[buf_idx + 1] = aa.zw;
+                return;
+            }
+#endif
             const uint idx = pos_a + col * p.stride_a + row * 2;
             const uint buf_idx = col * SHMEM_STRIDE + row;
             if (idx_m < p.M && block + row * 2 + 1 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a[idx]),
-                                              TO_FLOAT_TYPE(data_a[idx + 1]));
+                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]),
+                                              TO_FLOAT_TYPE(data_a_scalar[idx + 1]));
             } else if (idx_m < p.M && block + row * 2 < end_k) {
-                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a[idx]), 0.0f);
+                buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]), 0.0f);
             } else {
                 buf_a[buf_idx] = FLOAT_TYPEV2(0.0f);
             }
-#endif
 #elif defined(DATA_A_Q4_0)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
             const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4;
@@ -526,75 +533,85 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
 #if !defined(MUL_MAT_ID)
 void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint idx_n, const uint block, const uint end_k) {
 #if LOAD_VEC_B == 8
-            // Not supported for b_type bf16 because bf16mat2x4 does not exist
-            const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
-            FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]);
-            buf_b[buf_idx + 0] = bb[0].xy;
-            buf_b[buf_idx + 1] = bb[0].zw;
-            buf_b[buf_idx + 2] = bb[1].xy;
-            buf_b[buf_idx + 3] = bb[1].zw;
+            if (ALIGNED != 0) {
+                // Not supported for b_type bf16 because bf16mat2x4 does not exist
+                const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
+                FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]);
+                buf_b[buf_idx + 0] = bb[0].xy;
+                buf_b[buf_idx + 1] = bb[0].zw;
+                buf_b[buf_idx + 2] = bb[1].xy;
+                buf_b[buf_idx + 3] = bb[1].zw;
+                return;
+            }
 #elif LOAD_VEC_B == 4
-            const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
+            if (ALIGNED != 0) {
+                const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
 #if defined(DATA_B_BF16)
-            FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx]));
+                FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx]));
 #else
-            FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]);
+                FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]);
+#endif
+                buf_b[buf_idx + 0] = bb.xy;
+                buf_b[buf_idx + 1] = bb.zw;
+                return;
+            }
 #endif
-            buf_b[buf_idx + 0] = bb.xy;
-            buf_b[buf_idx + 1] = bb.zw;
-#else // LOAD_VEC_BATCH_B == 2
             const uint idx = pos_b + col * p.stride_b + row * 2;
             const uint buf_idx = col * SHMEM_STRIDE + row;
             if (idx_n < p.N && block + row * 2 + 1 < end_k) {
-                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b[idx]),
-                                              TO_FLOAT_TYPE(data_b[idx + 1]));
+                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]),
+                                              TO_FLOAT_TYPE(data_b_scalar[idx + 1]));
             } else if (idx_n < p.N && block + row * 2 < end_k) {
-                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b[idx]), 0.0f);
+                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), 0.0f);
             } else {
                 buf_b[buf_idx] = FLOAT_TYPEV2(0.0f);
             }
-#endif
 }
 #else
 void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint ic, const uint _ne1, const uint block, const uint end_k) {
 #if LOAD_VEC_B == 8
-            // Not supported for b_type bf16 because bf16mat2x4 does not exist
-            const u16vec2 row_idx = row_ids[col];
-            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
-            FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]);
-            buf_b[buf_idx + 0] = bb[0].xy;
-            buf_b[buf_idx + 1] = bb[0].zw;
-            buf_b[buf_idx + 2] = bb[1].xy;
-            buf_b[buf_idx + 3] = bb[1].zw;
+            if (ALIGNED != 0) {
+                // Not supported for b_type bf16 because bf16mat2x4 does not exist
+                const u16vec2 row_idx = row_ids[col];
+                const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
+                FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]);
+                buf_b[buf_idx + 0] = bb[0].xy;
+                buf_b[buf_idx + 1] = bb[0].zw;
+                buf_b[buf_idx + 2] = bb[1].xy;
+                buf_b[buf_idx + 3] = bb[1].zw;
+                return;
+            }
 #elif LOAD_VEC_B == 4
-            const u16vec2 row_idx = row_ids[col];
-            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
-            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
+            if (ALIGNED != 0) {
+                const u16vec2 row_idx = row_ids[col];
+                const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
+                const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2;
 #if defined(DATA_B_BF16)
-            FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx]));
+                FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx]));
 #else
-            FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]);
+                FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]);
+#endif
+                buf_b[buf_idx + 0] = bb.xy;
+                buf_b[buf_idx + 1] = bb.zw;
+                return;
+            }
 #endif
-            buf_b[buf_idx + 0] = bb.xy;
-            buf_b[buf_idx + 1] = bb.zw;
-#else // LOAD_VEC_BATCH_B == 2
             const uint row_i = ic * BN + col;
             const uint buf_idx = col * SHMEM_STRIDE + row;
             if (row_i < _ne1 && block + row * 2 + 1 < end_k) {
                 const u16vec2 row_idx = row_ids[col];
                 const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
-                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b[idx]),
-                                              TO_FLOAT_TYPE(data_b[idx + 1]));
+                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]),
+                                              TO_FLOAT_TYPE(data_b_scalar[idx + 1]));
             } else if (row_i < _ne1 && block + row * 2 < end_k) {
                 const u16vec2 row_idx = row_ids[col];
                 const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
-                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b[idx]), 0.0f);
+                buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), 0.0f);
             } else {
                 buf_b[buf_idx] = FLOAT_TYPEV2(0.0f);
             }
-#endif
 }
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index ca6b44431..f07583b6a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -539,11 +539,9 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
     };
 
     // Shaders with f16 B_TYPE
-    string_to_spv(shader_name + "_f32_f16" + dot2_sfx,              source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F32", "1"},                                                     {"B_TYPE", "float16_t"},        {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, }), fp16, coopmat, coopmat2, f16acc);
-    string_to_spv(shader_name + "_f32_f16" + dot2_sfx + "_aligned", source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+    string_to_spv(shader_name + "_f32_f16" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
 
-    string_to_spv(shader_name + "_f16" + dot2_sfx,              source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F16", "1"},                                                     {"B_TYPE", "float16_t"},            {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
-    string_to_spv(shader_name + "_f16" + dot2_sfx + "_aligned", source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16},     {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+    string_to_spv(shader_name + "_f16" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
 
     // bf16
     {
@@ -565,8 +563,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
 #endif
         {
             if (!dot2) {
-                string_to_spv(shader_name + "_bf16",         source_name, merge_maps(merge_maps(base_dict, float_type_dict_bf16), {{"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"},                             {"B_TYPE", coopmat2 ? "bfloat16_t" : "uint16_t"}, {"B_TYPEV4", "bf16vec4"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"DATA_B_BF16", "1"}}),                   fp16, coopmat, coopmat2, f16acc);
-                string_to_spv(shader_name + "_bf16_aligned", source_name, merge_maps(merge_maps(base_dict, float_type_dict_bf16), {{"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", "4"}, {"B_TYPE", coopmat2 ? "bfloat16_t" : "u16vec4"},  {"B_TYPEV4", "bf16vec4"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"DATA_B_BF16", "1"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+                string_to_spv(shader_name + "_bf16", source_name, merge_maps(merge_maps(base_dict, float_type_dict_bf16), {{"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", "4"}, {"B_TYPE", coopmat2 ? "bfloat16_t" : "u16vec4"}, {"B_TYPE_SCALAR", coopmat2 ? "bfloat16_t" : "uint16_t"}, {"B_TYPEV4", "bf16vec4"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"DATA_B_BF16", "1"}}), fp16, coopmat, coopmat2, f16acc);
             }
         }
     }
@@ -583,8 +580,6 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
         }
 
         std::string data_a_key = "DATA_A_" + to_uppercase(tname);
-        // For unaligned, load one at a time for f32/f16, or two at a time for quants
-        std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? "1" : load_vec_quant;
         // For aligned matmul loads
         std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? load_vec : load_vec_quant;
 
@@ -597,13 +592,11 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
 
         // don't generate f32 variants for coopmat2
         if (!coopmat2) {
-            string_to_spv(shader_name + "_" + tname + "_f32" + dot2_sfx,              source_name, merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned},                           {"B_TYPE", "float"},            {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
-            string_to_spv(shader_name + "_" + tname + "_f32" + dot2_sfx + "_aligned", source_name, merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a},           {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+            string_to_spv(shader_name + "_" + tname + "_f32" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"B_TYPE_SCALAR", "float"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
         }
 
         if (tname != "f16" && tname != "f32") {
-            string_to_spv(shader_name + "_" + tname + "_f16" + dot2_sfx,              source_name,  merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a_unaligned},                           {"B_TYPE", "float16_t"},        {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
-            string_to_spv(shader_name + "_" + tname + "_f16" + dot2_sfx + "_aligned", source_name,  merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a},           {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, {"ALIGNED", "1"}}), fp16, coopmat, coopmat2, f16acc);
+            string_to_spv(shader_name + "_" + tname + "_f16" + dot2_sfx, source_name,  merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc);
         }
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)

From c5606364b2a608c6501f25b80c3e256a355a0ce8 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 23 Jun 2026 08:39:20 -0500
Subject: [PATCH 132/292] vulkan: support CONV_3D (#24612)

* vulkan: support CONV_3D

This is a pretty direct port of conv2d_mm.comp to CONV_3D, done by codex
and cleaned up by me.

* disable slower perf tests
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 244 +++++++++-
 .../ggml-vulkan/vulkan-shaders/conv3d_mm.comp | 431 ++++++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  25 +
 tests/test-backend-ops.cpp                    |  28 ++
 4 files changed, 725 insertions(+), 3 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index b3c269783..508d569f2 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -493,6 +493,20 @@ struct vk_conv2d_pipeline_state {
     }
 };
 
+struct vk_conv3d_pipeline_state {
+    vk_conv3d_pipeline_state(uint32_t s0, uint32_t s1, uint32_t s2, uint32_t p0, uint32_t p1, uint32_t p2,
+                             uint32_t d0, uint32_t d1, uint32_t d2, uint32_t KW, uint32_t KH, uint32_t KD, uint32_t aligned)
+        : s0(s0), s1(s1), s2(s2), p0(p0), p1(p1), p2(p2), d0(d0), d1(d1), d2(d2), KW(KW), KH(KH), KD(KD), aligned(aligned) {}
+
+    uint32_t s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD;
+    uint32_t aligned;
+
+    bool operator<(const vk_conv3d_pipeline_state &b) const {
+        return std::tie(s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD, aligned) <
+               std::tie(b.s0, b.s1, b.s2, b.p0, b.p1, b.p2, b.d0, b.d1, b.d2, b.KW, b.KH, b.KD, b.aligned);
+    }
+};
+
 struct vk_solve_tri_pipeline_state {
     vk_solve_tri_pipeline_state(uint32_t N, uint32_t K)
         : N(N), K(K) {}
@@ -924,6 +938,8 @@ struct vk_device_struct {
     std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
     std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv_transpose_2d_f32[CONV_SHAPE_COUNT];
     std::map<vk_conv2d_pipeline_state, vk_pipeline> pipeline_conv_transpose_2d_f16_f32[CONV_SHAPE_COUNT];
+    std::map<vk_conv3d_pipeline_state, vk_pipeline> pipeline_conv3d_f32[CONV_SHAPE_COUNT];
+    std::map<vk_conv3d_pipeline_state, vk_pipeline> pipeline_conv3d_f16_f32[CONV_SHAPE_COUNT];
     vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
     vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;
 
@@ -1669,6 +1685,41 @@ template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
     init_fastdiv_values(p.OW*p.OH,  p.OWOHmp,  p.OWOHL);
 }
 
+struct vk_op_conv3d_push_constants {
+    uint32_t OC;
+    uint32_t IC;
+    uint32_t N;
+
+    uint32_t IW;
+    uint32_t IH;
+    uint32_t ID;
+    uint32_t OW;
+    uint32_t OH;
+    uint32_t OD;
+
+    uint32_t nb01;
+    uint32_t nb02;
+    uint32_t nb03;
+
+    uint32_t nb11;
+    uint32_t nb12;
+    uint32_t nb13;
+
+    uint32_t nb1;
+    uint32_t nb2;
+    uint32_t nb3;
+
+    uint32_t OWmp;     uint32_t OWL;
+    uint32_t OWOHmp;   uint32_t OWOHL;
+    uint32_t OWOHODmp; uint32_t OWOHODL;
+};
+
+template <> void init_pushconst_fastdiv(vk_op_conv3d_push_constants &p) {
+    init_fastdiv_values(p.OW,             p.OWmp,     p.OWL);
+    init_fastdiv_values(p.OW*p.OH,        p.OWOHmp,   p.OWOHL);
+    init_fastdiv_values(p.OW*p.OH*p.OD,   p.OWOHODmp, p.OWOHODL);
+}
+
 struct vk_op_conv2d_dw_push_constants {
     uint32_t ne;
     uint32_t batches;
@@ -5330,7 +5381,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
 
     ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
-    // conv2d, conv_transpose_2d
+    // conv2d, conv_transpose_2d, conv3d
     for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
         // smaller WG for the small-tile fallback gives more concurrent WGs per SM
         uint32_t conv2d_WG_SIZE  = (s == CONV_SHAPE_64x32) ? 128 : 256;
@@ -5393,8 +5444,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             return (conv2d_BS.K * (conv2d_BS.CRS + pad) + conv2d_BS.CRS * (conv2d_BS.NPQ + pad) + csh_elems) * elem_size;
         };
 
-        // coopmat1 needs to store the output through shared memory, so check up front
-        // whether it'll fit and disable it before applying coopmat1 parameters.
+        // 2D, transpose-2D, and 3D conv use the same KxCRS @ CRSxNPQ shmem
+        // layout. cm1 needs Csh for output, so check before applying cm1 params.
         if (conv2d_use_cm1 && device->properties.limits.maxComputeSharedMemorySize < shmem_req(conv2d_cm1_shmem_pad, true, true)) {
             conv2d_use_cm1 = false;
         }
@@ -5486,6 +5537,53 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
         }
 #undef CREATE_CONV
 #undef CREATE_CONVS
+
+        std::vector<uint32_t> conv3d_spec_constants = { conv2d_WG_SIZE, conv2d_BS.K, conv2d_BS.CRS, conv2d_BS.NPQ, conv2d_TS_K, conv2d_SHMEM_PAD };
+#define CREATE_CONV3D(type_suffix, spv_suffix) \
+        for (auto &c : device->pipeline_conv3d##type_suffix[s]) { \
+            const vk_conv3d_pipeline_state &state = c.first; \
+            std::vector<uint32_t> spec_constants_cpy = conv3d_spec_constants; \
+            spec_constants_cpy.push_back(state.s0); \
+            spec_constants_cpy.push_back(state.s1); \
+            spec_constants_cpy.push_back(state.s2); \
+            spec_constants_cpy.push_back(state.p0); \
+            spec_constants_cpy.push_back(state.p1); \
+            spec_constants_cpy.push_back(state.p2); \
+            spec_constants_cpy.push_back(state.d0); \
+            spec_constants_cpy.push_back(state.d1); \
+            spec_constants_cpy.push_back(state.d2); \
+            spec_constants_cpy.push_back(state.KW); \
+            spec_constants_cpy.push_back(state.KH); \
+            spec_constants_cpy.push_back(state.KD); \
+            spec_constants_cpy.push_back(state.aligned); \
+            spec_constants_cpy.push_back(conv2d_csh_store); \
+            spec_constants_cpy.push_back(conv2d_WM); \
+            spec_constants_cpy.push_back(conv2d_WN); \
+            ggml_vk_create_pipeline( \
+                device, c.second, "conv3d" #type_suffix, \
+                conv3d##type_suffix##spv_suffix##_len, conv3d##type_suffix##spv_suffix##_data, "main", 3, \
+                sizeof(vk_op_conv3d_push_constants), wg_denoms, spec_constants_cpy, 1, true, conv2d_required_subgroup_size != 0, conv2d_required_subgroup_size); \
+        }
+#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+        if (device->coopmat2) {
+            CREATE_CONV3D(_f32, _cm2)
+            CREATE_CONV3D(_f16_f32, _cm2)
+        } else
+#endif
+#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+        if (conv2d_use_cm1) {
+            CREATE_CONV3D(_f32, _cm1)
+            CREATE_CONV3D(_f16_f32, _cm1)
+        } else
+#endif
+        if (conv2d_UNROLL) {
+            CREATE_CONV3D(_f32, _unroll)
+            CREATE_CONV3D(_f16_f32, _unroll)
+        } else {
+            CREATE_CONV3D(_f32, )
+            CREATE_CONV3D(_f16_f32, )
+        }
+#undef CREATE_CONV3D
     }
 
     ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
@@ -10901,6 +10999,61 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             }
         }
         return nullptr;
+    case GGML_OP_CONV_3D:
+        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            const uint32_t OC = (uint32_t)ggml_get_op_params_i32(dst, 11);
+            const uint32_t IC = (uint32_t)ggml_get_op_params_i32(dst, 9);
+            const uint32_t N  = (uint32_t)ggml_get_op_params_i32(dst, 10);
+            const uint32_t NPQ = N * dst->ne[2] * dst->ne[1] * dst->ne[0];
+            const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, OC, NPQ);
+
+            const uint32_t KW = (uint32_t)src0->ne[0];
+            const uint32_t KH = (uint32_t)src0->ne[1];
+            const uint32_t KD = (uint32_t)src0->ne[2];
+            const uint32_t s0 = (uint32_t)ggml_get_op_params_i32(dst, 0);
+            const uint32_t s1 = (uint32_t)ggml_get_op_params_i32(dst, 1);
+            const uint32_t s2 = (uint32_t)ggml_get_op_params_i32(dst, 2);
+            const uint32_t p0 = (uint32_t)ggml_get_op_params_i32(dst, 3);
+            const uint32_t p1 = (uint32_t)ggml_get_op_params_i32(dst, 4);
+            const uint32_t p2 = (uint32_t)ggml_get_op_params_i32(dst, 5);
+            const uint32_t d0 = (uint32_t)ggml_get_op_params_i32(dst, 6);
+            const uint32_t d1 = (uint32_t)ggml_get_op_params_i32(dst, 7);
+            const uint32_t d2 = (uint32_t)ggml_get_op_params_i32(dst, 8);
+
+            const uint32_t CRS = IC * KW * KH * KD;
+            const uint32_t BS_K   = vk_conv_block_sizes[shape].K;
+            const uint32_t BS_CRS = vk_conv_block_sizes[shape].CRS;
+            const uint32_t BS_NPQ = vk_conv_block_sizes[shape].NPQ;
+            const uint32_t aligned = ((OC  % BS_K   == 0) &&
+                                      (CRS % BS_CRS == 0) &&
+                                      (NPQ % BS_NPQ == 0)) ? 1u : 0u;
+
+            vk_conv3d_pipeline_state conv3d_pipeline_state(s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD, aligned);
+
+            std::map<vk_conv3d_pipeline_state, vk_pipeline> *pipelines = nullptr;
+            if (src0->type == GGML_TYPE_F32) {
+                pipelines = &ctx->device->pipeline_conv3d_f32[shape];
+            } else if (src0->type == GGML_TYPE_F16) {
+                pipelines = &ctx->device->pipeline_conv3d_f16_f32[shape];
+            } else {
+                return nullptr;
+            }
+
+            vk_pipeline pipeline = nullptr;
+
+            {
+                std::lock_guard<std::mutex> guard(ctx->device->compile_mutex);
+                auto it = pipelines->find(conv3d_pipeline_state);
+                if (it != pipelines->end()) {
+                    pipeline = it->second;
+                } else {
+                    (*pipelines)[conv3d_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
+                }
+            }
+
+            return pipeline;
+        }
+        return nullptr;
     case GGML_OP_ADD1:
         if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
             return ctx->device->pipeline_add1_f16_f16;
@@ -11236,6 +11389,21 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
             GGML_ABORT("invalid push constant type for CONV_2D");
         }
         break;
+    case GGML_OP_CONV_3D:
+        if constexpr (std::is_same_v<PC, vk_op_conv3d_push_constants>) {
+            const uint32_t NPQ = pc.N * pc.OD * pc.OH * pc.OW;
+            const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, pc.OC, NPQ);
+            const uint32_t NPQ_blocks = CEIL_DIV(NPQ, vk_conv_block_sizes[shape].NPQ);
+
+            elements = { pc.OC, NPQ_blocks, 1 };
+            if (elements[1] > 512) {
+                elements[2] = CEIL_DIV(elements[1], 512);
+                elements[1] = 512;
+            }
+        } else {
+            GGML_ABORT("invalid push constant type for CONV_3D");
+        }
+        break;
     case GGML_OP_ADD:
     case GGML_OP_SUB:
     case GGML_OP_DIV:
@@ -13134,6 +13302,51 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx,
     ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, std::move(p));
 }
 
+static void ggml_vk_conv_3d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0,
+                            const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb10 == sizeof(float));
+    GGML_ASSERT(nb0 == sizeof(float));
+
+    vk_op_conv3d_push_constants p{};
+    p.IC = static_cast<uint32_t>(ggml_get_op_params_i32(dst, 9));
+    p.N  = static_cast<uint32_t>(ggml_get_op_params_i32(dst, 10));
+    p.OC = static_cast<uint32_t>(ggml_get_op_params_i32(dst, 11));
+    GGML_ASSERT(src0->ne[3] == (int64_t)p.IC * p.OC);
+    GGML_ASSERT(src1->ne[3] == (int64_t)p.IC * p.N);
+    GGML_ASSERT(dst->ne[3] == (int64_t)p.OC * p.N);
+
+    p.IW = static_cast<uint32_t>(ne10);
+    p.IH = static_cast<uint32_t>(ne11);
+    p.ID = static_cast<uint32_t>(ne12);
+    p.OW = static_cast<uint32_t>(ne0);
+    p.OH = static_cast<uint32_t>(ne1);
+    p.OD = static_cast<uint32_t>(ne2);
+
+    // the shader clamps src addresses to p.IC * p.N * p.IW * p.IH * p.ID - 1 in uint32, so the
+    // total input element count must fit in a uint32.
+    GGML_ASSERT((uint64_t)p.IC * p.N * p.IW * p.IH * p.ID <= 0xFFFFFFFFull);
+
+    p.nb01 = static_cast<uint32_t>(nb01 / nb00);
+    p.nb02 = static_cast<uint32_t>(nb02 / nb00);
+    p.nb03 = static_cast<uint32_t>(nb03 / nb00);
+
+    p.nb11 = static_cast<uint32_t>(nb11 / nb10);
+    p.nb12 = static_cast<uint32_t>(nb12 / nb10);
+    p.nb13 = static_cast<uint32_t>(nb13 / nb10);
+
+    p.nb1 = static_cast<uint32_t>(nb1 / nb0);
+    p.nb2 = static_cast<uint32_t>(nb2 / nb0);
+    p.nb3 = static_cast<uint32_t>(nb3 / nb0);
+
+    ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_3D, std::move(p));
+}
+
 static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     vk_op_conv2d_dw_push_constants p{};
     p.ne = ggml_nelements(dst);
@@ -14531,6 +14744,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_CONV_TRANSPOSE_2D:
         ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node);
 
+        break;
+    case GGML_OP_CONV_3D:
+        ggml_vk_conv_3d(ctx, compute_ctx, src0, src1, node);
+
         break;
     case GGML_OP_CONV_2D_DW:
         ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node);
@@ -17301,6 +17518,13 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     ggml_is_contiguous(op->src[1]) &&
                     ggml_is_contiguous(op));
             }
+        case GGML_OP_CONV_3D:
+            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                op->src[1]->type == GGML_TYPE_F32 &&
+                op->type == GGML_TYPE_F32 &&
+                ggml_is_contiguous(op->src[0]) &&
+                ggml_is_contiguous(op->src[1]) &&
+                ggml_is_contiguous(op);
         default:
             return false;
     }
@@ -18144,6 +18368,20 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             const int32_t d0 = tensor->op_params[4];
             const int32_t d1 = tensor->op_params[5];
             tensor_clone = ggml_conv_2d(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1);
+        } else if (tensor->op == GGML_OP_CONV_3D) {
+            const int32_t s0 = tensor->op_params[0];
+            const int32_t s1 = tensor->op_params[1];
+            const int32_t s2 = tensor->op_params[2];
+            const int32_t p0 = tensor->op_params[3];
+            const int32_t p1 = tensor->op_params[4];
+            const int32_t p2 = tensor->op_params[5];
+            const int32_t d0 = tensor->op_params[6];
+            const int32_t d1 = tensor->op_params[7];
+            const int32_t d2 = tensor->op_params[8];
+            const int32_t IC = tensor->op_params[9];
+            const int32_t N  = tensor->op_params[10];
+            const int32_t OC = tensor->op_params[11];
+            tensor_clone = ggml_conv_3d_direct(ggml_ctx, src_clone[0], src_clone[1], s0, s1, s2, p0, p1, p2, d0, d1, d2, IC, N, OC);
         } else if (tensor->op == GGML_OP_CONV_2D_DW) {
             const int32_t s0 = tensor->op_params[0];
             const int32_t s1 = tensor->op_params[1];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp
new file mode 100644
index 000000000..a9712eb3a
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp
@@ -0,0 +1,431 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#ifdef COOPMAT2
+#extension GL_NV_cooperative_matrix2 : enable
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_KHR_memory_scope_semantics : enable
+#endif
+
+#ifdef COOPMAT
+#extension GL_KHR_cooperative_matrix : enable
+#extension GL_KHR_shader_subgroup_basic : enable
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_KHR_memory_scope_semantics : enable
+#endif
+
+#include "types.glsl"
+
+// shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j
+layout(binding = 0) readonly buffer A {
+    A_TYPE knl_data[];
+};  // src0 - kernel:   [KW, KH, KD, IC*OC]
+
+layout(binding = 1) readonly buffer B {
+    B_TYPE src_data[];
+};  // src1 - input:    [IW, IH, ID, IC*N] -- channel_first format
+
+layout(binding = 2) writeonly buffer D {
+    D_TYPE dst_data[];
+};  // dst - result:    [OW, OH, OD, OC*N]
+
+layout(push_constant) uniform parameter {
+    // I/O channels, batch size
+    uint32_t OC;
+    uint32_t IC;
+    uint32_t N;
+
+    // Tensor spatial sizes: input, output
+    uint32_t IW;
+    uint32_t IH;
+    uint32_t ID;
+    uint32_t OW;
+    uint32_t OH;
+    uint32_t OD;
+
+    // Strides in elements
+    uint32_t nb01;
+    uint32_t nb02;
+    uint32_t nb03;
+
+    uint32_t nb11;
+    uint32_t nb12;
+    uint32_t nb13;
+
+    uint32_t nb1;
+    uint32_t nb2;
+    uint32_t nb3;
+
+    // fastdiv helper values
+    uint32_t OWmp;   uint32_t OWL;
+    uint32_t OWOHmp; uint32_t OWOHL;
+    uint32_t OWOHODmp; uint32_t OWOHODL;
+}
+
+p;
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+// Blocktile sizes
+layout(constant_id = 1) const uint BS_K            = 128;
+layout(constant_id = 2) const uint BS_CRS          = 16;
+layout(constant_id = 3) const uint BS_NPQ          = 128;
+// Thread-tile sizes
+layout(constant_id = 4) const uint TS_K            = 8;
+layout(constant_id = 5) const uint SHMEM_PAD       = 4;
+// Stride, padding, dilation
+layout(constant_id = 6)  const uint s0             = 1;
+layout(constant_id = 7)  const uint s1             = 1;
+layout(constant_id = 8)  const uint s2             = 1;
+layout(constant_id = 9)  const uint p0             = 0;
+layout(constant_id = 10) const uint p1             = 0;
+layout(constant_id = 11) const uint p2             = 0;
+layout(constant_id = 12) const uint d0             = 1;
+layout(constant_id = 13) const uint d1             = 1;
+layout(constant_id = 14) const uint d2             = 1;
+// Kernel spatial sizes
+layout(constant_id = 15) const uint KW             = 1;
+layout(constant_id = 16) const uint KH             = 1;
+layout(constant_id = 17) const uint KD             = 1;
+// when set, skip bounds checks and address clamps (K/CRS/NPQ are tile-aligned)
+layout(constant_id = 18) const uint aligned        = 0;
+// stage cm2 result through shmem (Csh) for coalesced stores. cm1 always does this.
+layout(constant_id = 19) const uint csh_store      = 0;
+
+#ifdef COOPMAT
+// cm1 subgroup tile: each subgroup computes a WM x WN region as a grid of
+// TM x TN x TK fragments. Requires WM%TM == WN%TN == BS_K%WM == BS_NPQ%WN ==
+// BS_CRS%TK == 0, and WG_SIZE == (BS_K/WM) * (BS_NPQ/WN) * subgroup_size.
+layout(constant_id = 20) const uint WM             = 32;
+layout(constant_id = 21) const uint WN             = 32;
+const uint TM = 16;
+const uint TN = 16;
+const uint TK = 16;
+const uint cms_per_row = WM / TM;
+const uint cms_per_col = WN / TN;
+const uint warps_M     = BS_K / WM;
+const uint warps_N     = BS_NPQ / WN;
+#endif
+
+// without padding, ID_idx/IH_idx/IW_idx are in bounds by construction
+const bool dhw_in_bounds = (p0 == 0) && (p1 == 0) && (p2 == 0);
+
+uint32_t       tid     = gl_LocalInvocationID.x;
+const uint32_t WG_SIZE = gl_WorkGroupSize.x;
+
+uint splitWork(uint work_size, uint block_size) {
+    return (block_size + work_size - 1) / block_size;
+}
+
+uint32_t K   = p.OC;
+uint32_t CRS = p.IC * KD * KH * KW;
+uint32_t NPQ = p.N * p.OD * p.OH * p.OW;
+
+// Number of blocktiles per input
+uint32_t NB_CRS = splitWork(CRS, BS_CRS);
+
+#if defined(COOPMAT2) || defined(COOPMAT)
+#define SHMEM_TYPE float16_t
+#else
+#define SHMEM_TYPE float
+#endif
+
+const uint32_t Ash_stride = BS_CRS + SHMEM_PAD;
+const uint32_t Bsh_stride = BS_NPQ + SHMEM_PAD;
+
+const uint32_t Ash_len = BS_K * Ash_stride;
+const uint32_t Bsh_len = BS_CRS * Bsh_stride;
+
+shared SHMEM_TYPE Ash[Ash_len];  // K x CRS
+shared SHMEM_TYPE Bsh[Bsh_len];  // CRS x NPQ
+
+#if defined(COOPMAT2) || defined(COOPMAT)
+// stage matC through shmem so global stores are row-major (NPQ-contiguous)
+const uint32_t Csh_stride = BS_NPQ;
+#ifdef COOPMAT
+const uint32_t Csh_len    = BS_K * Csh_stride;
+#else
+const uint32_t Csh_len    = csh_store != 0 ? BS_K * Csh_stride : 1;
+#endif
+shared SHMEM_TYPE Csh[Csh_len];  // K x NPQ
+#endif
+
+// Threadtile sizes
+const uint32_t TS_NPQ = BS_K * BS_NPQ / WG_SIZE / TS_K;
+
+// Number of threadtiles per blocktile
+const uint32_t NT_NPQ = BS_NPQ / TS_NPQ;
+
+/*
+Compute
+KxCRS @ CRSxNPQ = K x NPQ
+K=OC
+C=IC
+D,R,S=KD,KH,KW
+Z,P,Q=OD,OH,OW
+*/
+
+uint32_t B_idx_K   = gl_WorkGroupID.x;
+uint32_t B_idx_NPQ = gl_WorkGroupID.y + gl_WorkGroupID.z * 512;
+
+uint32_t T_y = tid / NT_NPQ;
+uint32_t T_x = tid % NT_NPQ;
+
+uint32_t       Ar    = tid / BS_CRS;
+uint32_t       Ac    = tid % BS_CRS;
+const uint32_t ArpWg = WG_SIZE / BS_CRS;
+
+uint32_t       Br    = tid / BS_NPQ;
+uint32_t       Bc    = tid % BS_NPQ;
+const uint32_t BrpWg = WG_SIZE / BS_NPQ;
+
+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    // msbs = mulhi(n, mp)
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}
+
+void split_crs(uint32_t crs_idx, out uint32_t ic, out uint32_t kd, out uint32_t kh, out uint32_t kw) {
+    const uint32_t KHKW = KH * KW;
+    const uint32_t KDKHKW = KD * KHKW;
+    ic = crs_idx / KDKHKW;
+    uint32_t rem = crs_idx - ic * KDKHKW;
+    kd = rem / KHKW;
+    rem = rem - kd * KHKW;
+    kh = rem / KW;
+    kw = rem - kh * KW;
+}
+
+void split_npq(uint32_t npq_idx, out uint32_t n, out uint32_t od, out uint32_t oh, out uint32_t ow) {
+    const uint32_t OWOH = p.OW * p.OH;
+    n = fastdiv(npq_idx, p.OWOHODmp, p.OWOHODL);
+    uint32_t rem = npq_idx - n * p.OD * OWOH;
+    od = fastdiv(rem, p.OWOHmp, p.OWOHL);
+    rem = rem - od * OWOH;
+    oh = fastdiv(rem, p.OWmp, p.OWL);
+    ow = rem - oh * p.OW;
+}
+
+#ifdef COOPMAT2
+#define ACC_TYPE float16_t
+
+ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem)
+{
+    uint32_t K_idx   = B_idx_K * BS_K + r;
+    uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + c;
+    uint32_t N_idx;
+    uint32_t OD_idx;
+    uint32_t OH_idx;
+    uint32_t OW_idx;
+    split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx);
+    uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3;
+    if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) {
+        dst_data[dst_idx] = D_TYPE(elem);
+    }
+    return elem;
+}
+#endif
+
+void main() {
+    if (B_idx_NPQ * BS_NPQ >= NPQ) {
+        return;
+    }
+
+#ifdef COOPMAT2
+    coopmat<ACC_TYPE, gl_ScopeWorkgroup, BS_K, BS_NPQ, gl_MatrixUseAccumulator> matC;
+    matC = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BS_K, BS_NPQ, gl_MatrixUseAccumulator>(0.0);
+#elif defined(COOPMAT)
+    coopmat<float16_t, gl_ScopeSubgroup, TM, TN, gl_MatrixUseAccumulator> sums[cms_per_row * cms_per_col];
+    [[unroll]] for (uint i = 0; i < cms_per_row * cms_per_col; i++) {
+        sums[i] = coopmat<float16_t, gl_ScopeSubgroup, TM, TN, gl_MatrixUseAccumulator>(0.0);
+    }
+    const uint warp_r = gl_SubgroupID / warps_N;
+    const uint warp_c = gl_SubgroupID % warps_N;
+#else
+    float regC[TS_K][TS_NPQ];
+    for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+        for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+            regC[T_ly][T_lx] = 0.0;
+        }
+    }
+#endif
+    /* Advance block in CRS dim */
+    [[dont_unroll]] for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
+        uint32_t CRS_idx_a = B_idx_CRS * BS_CRS + Ac;
+        uint32_t IC_idx_a;
+        uint32_t KD_idx_a;
+        uint32_t KH_idx_a;
+        uint32_t KW_idx_a;
+        split_crs(CRS_idx_a, IC_idx_a, KD_idx_a, KH_idx_a, KW_idx_a);
+
+        /* Load kernel to A_block: (BS_K x BS_CRS)*/
+        UNROLL for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) {
+            uint32_t B_ly    = r_offset + Ar;
+            uint32_t B_lx    = Ac;
+            uint32_t K_idx   = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/
+            uint32_t knl_idx = KW_idx_a + KH_idx_a * p.nb01 + KD_idx_a * p.nb02 + (K_idx * p.IC + IC_idx_a) * p.nb03;
+            if (aligned == 0) {
+                knl_idx = min(knl_idx, K * CRS - 1);
+            }
+            float    val     = knl_data[knl_idx];
+            if (aligned == 0 && (K_idx >= K || CRS_idx_a >= CRS)) {
+                val = 0.0;
+            }
+            Ash[B_ly * Ash_stride + B_lx] = SHMEM_TYPE(val);
+        }
+        /* Load input to B_block: (BS_CRS x BS_NPQ) */
+        UNROLL for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) {
+            uint32_t B_ly          = r_offset + Br;             /* Row index of B block */
+            uint32_t B_lx          = Bc;
+            uint32_t NPQ_idx       = B_idx_NPQ * BS_NPQ + B_lx; /* Global NPQ index (column index of B) */
+            uint32_t N_idx;
+            uint32_t OD_idx;
+            uint32_t OH_idx;
+            uint32_t OW_idx;
+            split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx);
+
+            uint32_t CRS_idx_b = B_idx_CRS * BS_CRS + B_ly;
+            uint32_t IC_idx_b;
+            uint32_t KD_idx_b;
+            uint32_t KH_idx_b;
+            uint32_t KW_idx_b;
+            split_crs(CRS_idx_b, IC_idx_b, KD_idx_b, KH_idx_b, KW_idx_b);
+
+            uint32_t ID_idx = OD_idx * s2 + KD_idx_b * d2 - p2;
+            uint32_t IH_idx = OH_idx * s1 + KH_idx_b * d1 - p1;
+            uint32_t IW_idx = OW_idx * s0 + KW_idx_b * d0 - p0;
+
+            uint32_t src_idx = IW_idx + IH_idx * p.nb11 + ID_idx * p.nb12 + (N_idx * p.IC + IC_idx_b) * p.nb13;
+            // skip clamp when address can't go OOB
+            if (aligned == 0 || !dhw_in_bounds) {
+                src_idx = min(src_idx, p.IC * p.N * p.IW * p.IH * p.ID - 1);
+            }
+            float val = src_data[src_idx];
+            bool oob = false;
+            if (aligned == 0 && (CRS_idx_b >= CRS || NPQ_idx >= NPQ)) {
+                oob = true;
+            }
+            // also catches lower-bound underflow (idx wraps to 0x80000000+)
+            if (!dhw_in_bounds && (ID_idx >= p.ID || IH_idx >= p.IH || IW_idx >= p.IW)) {
+                oob = true;
+            }
+            if (oob) {
+                val = 0.0;
+            }
+            Bsh[B_ly * Bsh_stride + B_lx] = SHMEM_TYPE(val);
+        }
+        barrier();
+#ifdef COOPMAT2
+        coopmat<float16_t, gl_ScopeWorkgroup, BS_K, BS_CRS, gl_MatrixUseA> matA;
+        coopmat<float16_t, gl_ScopeWorkgroup, BS_CRS, BS_NPQ, gl_MatrixUseB> matB;
+
+        coopMatLoad(matA, Ash, 0, Ash_stride, gl_CooperativeMatrixLayoutRowMajor);
+        coopMatLoad(matB, Bsh, 0, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor);
+        matC = coopMatMulAdd(matA, matB, matC);
+#elif defined(COOPMAT)
+        // each subgroup multiplies its grid of fragments per TK-sized CRS chunk
+        [[unroll]] for (uint k_step = 0; k_step < BS_CRS / TK; k_step++) {
+            coopmat<float16_t, gl_ScopeSubgroup, TM, TK, gl_MatrixUseA> cache_a[cms_per_row];
+            [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) {
+                const uint a_off = (warp_r * WM + cm_row * TM) * Ash_stride + k_step * TK;
+                coopMatLoad(cache_a[cm_row], Ash, a_off, Ash_stride, gl_CooperativeMatrixLayoutRowMajor);
+            }
+            [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) {
+                coopmat<float16_t, gl_ScopeSubgroup, TK, TN, gl_MatrixUseB> cache_b;
+                const uint b_off = k_step * TK * Bsh_stride + warp_c * WN + cm_col * TN;
+                coopMatLoad(cache_b, Bsh, b_off, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor);
+                [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) {
+                    sums[cm_col * cms_per_row + cm_row] = coopMatMulAdd(cache_a[cm_row], cache_b, sums[cm_col * cms_per_row + cm_row]);
+                }
+            }
+        }
+#else
+        if (T_y * TS_K < K) {
+            UNROLL for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) {
+                float regA[TS_K];
+                float regB[TS_NPQ];
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx];
+                }
+                for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                    regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx];
+                }
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                        regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]);
+                    }
+                }
+            }
+        }
+#endif
+        barrier();
+    }
+    /* Save C* */
+#if defined(COOPMAT2) || defined(COOPMAT)
+    // stage matC into Csh, then write to dst with coalesced NPQ-contiguous stores
+#ifdef COOPMAT
+    const bool use_staged_store = true;
+#else
+    const bool use_staged_store = (csh_store != 0);
+#endif
+    if (use_staged_store) {
+#ifdef COOPMAT
+        // cm1: each subgroup stores its fragment grid into its Csh slot
+        [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) {
+            [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) {
+                const uint csh_off = (warp_r * WM + cm_row * TM) * Csh_stride + warp_c * WN + cm_col * TN;
+                coopMatStore(sums[cm_col * cms_per_row + cm_row], Csh, csh_off, Csh_stride, gl_CooperativeMatrixLayoutRowMajor);
+            }
+        }
+#else
+        coopMatStore(matC, Csh, 0, Csh_stride, gl_CooperativeMatrixLayoutRowMajor);
+#endif
+        barrier();
+
+        // cooperative shmem->global: WG threads spread across BS_NPQ (the
+        // contiguous direction of dst), each iter covers store_rows_per_iter K-rows
+        const uint32_t store_rows_per_iter = WG_SIZE / BS_NPQ;
+        const uint32_t store_iters         = BS_K / store_rows_per_iter;
+        const uint32_t k_thread_offset     = tid / BS_NPQ;
+        const uint32_t npq_thread          = tid % BS_NPQ;
+        [[unroll]] for (uint32_t i = 0; i < store_iters; i++) {
+            uint32_t k_local = i * store_rows_per_iter + k_thread_offset;
+            uint32_t K_idx   = B_idx_K * BS_K + k_local;
+            uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + npq_thread;
+            uint32_t N_idx;
+            uint32_t OD_idx;
+            uint32_t OH_idx;
+            uint32_t OW_idx;
+            split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx);
+            uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3;
+            if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) {
+                dst_data[dst_idx] = D_TYPE(Csh[k_local * Csh_stride + npq_thread]);
+            }
+        }
+    }
+#ifdef COOPMAT2
+    else {
+        coopMatPerElementNV(matC, matC, perElemOpStore);
+    }
+#endif
+#else
+    if (T_y * TS_K < K) {
+        for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+            for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                uint32_t K_idx   = B_idx_K * BS_K + T_y * TS_K + T_ly;
+                uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx;
+                uint32_t N_idx;
+                uint32_t OD_idx;
+                uint32_t OH_idx;
+                uint32_t OW_idx;
+                split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx);
+                uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3;
+                if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) {
+                    dst_data[dst_idx] = D_TYPE(regC[T_ly][T_lx]);
+                }
+            }
+        }
+    }
+#endif
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index f07583b6a..2f5661f54 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -1053,6 +1053,31 @@ void process_shaders() {
         }
     }
 
+    for (auto unroll : {false, true}) {
+        for (auto a_f16 : {false, true}) {
+            std::map<std::string, std::string> defines = {
+                {"A_TYPE", a_f16 ? "float16_t" : "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"},
+                {"UNROLL", unroll ? "[[unroll]]" : ""},
+            };
+            std::string name = std::string("conv3d") + (a_f16 ? "_f16" : "") + "_f32";
+            string_to_spv(name + (unroll ? "_unroll" : ""), "conv3d_mm.comp", defines);
+#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+            if (unroll) {
+                auto cm2_defines = defines;
+                cm2_defines["COOPMAT2"] = "1";
+                string_to_spv(name, "conv3d_mm.comp", cm2_defines, true, false, true);
+            }
+#endif
+#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT)
+            if (unroll) {
+                auto cm1_defines = defines;
+                cm1_defines["COOPMAT"] = "1";
+                string_to_spv(name, "conv3d_mm.comp", cm1_defines, true, true, false);
+            }
+#endif
+        }
+    }
+
     string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
     string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));
     string_to_spv("conv2d_dw_whcn_f16_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 127c4634c..719ae51cc 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -9272,6 +9272,34 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         }
     }
 
+    struct conv3d_perf_case {
+        int N, IC, ID, IH, IW, OC, KD, KH, KW, s0, s1, s2, p0, p1, p2, d0, d1, d2;
+    };
+
+    const std::vector<conv3d_perf_case> conv3d_cases = {
+        {1,  320, 8,  38,  26, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1, 1280, 8,  38,  26, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1,  320, 8,  76,  52, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1, 1280, 8,  76,  52, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1,  320, 8, 152, 104, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+#if 0
+        // too slow on some devices
+        {1, 1280, 8, 152, 104, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1,  320, 4, 304, 208, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+        {1,  640, 4, 304, 208, 1280, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+#endif
+    };
+
+    for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+        for (const conv3d_perf_case & c : conv3d_cases) {
+            test_cases.emplace_back(new test_conv_3d(
+                c.N, c.IC, c.ID, c.IH, c.IW,
+                c.OC, c.KD, c.KH, c.KW,
+                c.s0, c.s1, c.s2, c.p0, c.p1, c.p2, c.d0, c.d1, c.d2,
+                kernel_type));
+        }
+    }
+
     test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1,   1, 1, 1}));
     test_cases.emplace_back(new test_bin_bcast(ggml_add, GGML_TYPE_F32, {4096, 1, 1, 1}, {1, 512, 1, 1}));
 

From 92e854ab836254bb7f2eb49babd5613474bdb700 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 23 Jun 2026 08:39:37 -0500
Subject: [PATCH 133/292] vulkan: Support GET_ROWS_BACK (#24883)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 32 +++++++++++++++++++
 .../vulkan-shaders/get_rows_back.comp         | 25 +++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  1 +
 3 files changed, 58 insertions(+)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 508d569f2..d2827ad71 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -791,6 +791,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
     vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT];
     vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_get_rows_back_f32;
     vk_pipeline pipeline_acc_f32;
     vk_pipeline pipeline_set_f32;
 
@@ -4946,6 +4947,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_NVFP4],   "get_rows_nvfp4_f32",   get_rows_nvfp4_f32_len,   get_rows_nvfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_back_f32, "get_rows_back_f32", get_rows_back_f32_len, get_rows_back_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1, true);
 
     ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
@@ -10408,6 +10410,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_get_rows_f32[src0->type];
         }
         return nullptr;
+    case GGML_OP_GET_ROWS_BACK:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_get_rows_back_f32;
+        }
+        return nullptr;
     case GGML_OP_ACC:
         if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_acc_f32;
@@ -11304,6 +11311,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
         elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
         elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]);
         break;
+    case GGML_OP_GET_ROWS_BACK:
+        elements = { (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], 1 };
+        elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]);
+        break;
     case GGML_OP_ARGSORT:
         GGML_ASSERT(0);
         break;
@@ -11564,6 +11575,21 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx,
     });
 }
 
+static void ggml_vk_get_rows_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GET_ROWS_BACK, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2], (uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    });
+}
+
 static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const uint32_t src0_type_size = ggml_type_size(src0->type);
     const uint32_t src1_type_size = ggml_type_size(src1->type);
@@ -14476,6 +14502,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_GET_ROWS:
         ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node);
 
+        break;
+    case GGML_OP_GET_ROWS_BACK:
+        ggml_vk_get_rows_back(ctx, compute_ctx, src0, src1, node);
+
         break;
     case GGML_OP_ADD:
         if (ctx->num_additional_fused_ops) {
@@ -17197,6 +17227,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                         return false;
                 }
             }
+        case GGML_OP_GET_ROWS_BACK:
+            return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_SET_ROWS:
             {
                 switch (op->type) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp
new file mode 100644
index 000000000..7e3d8a281
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp
@@ -0,0 +1,25 @@
+#version 450
+
+#include "types.glsl"
+#include "generic_binary_head.glsl"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    const uint col = gl_GlobalInvocationID.x;
+
+    if (col >= p.ne20) {
+        return;
+    }
+
+    for (uint row = gl_GlobalInvocationID.y; row < p.ne21; row += gl_WorkGroupSize.y * gl_NumWorkGroups.y) {
+        float sum = 0.0f;
+        for (uint i = 0; i < p.ne10; ++i) {
+            if (data_b[get_boffset() + i*p.nb10] == int(row)) {
+                sum += data_a[get_aoffset() + i*p.nb01 + col*p.nb00];
+            }
+        }
+
+        data_d[get_doffset() + row*p.nb21 + col*p.nb20] = sum;
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 2f5661f54..502602f79 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -843,6 +843,7 @@ void process_shaders() {
 
     string_to_spv("repeat_i32", "repeat.comp", {{"A_TYPE", "int32_t"}, {"D_TYPE", "int32_t"}});
     string_to_spv("repeat_back_f32", "repeat_back.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
+    string_to_spv("get_rows_back_f32", "get_rows_back.comp", {{"A_TYPE", "float"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}});
 
     string_to_spv("repeat_i16", "repeat.comp", {{"A_TYPE", "int16_t"}, {"D_TYPE", "int16_t"}});
 

From 19064083bd3696682dbf886e23160f103137613e Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 23 Jun 2026 22:34:34 +0800
Subject: [PATCH 134/292] another fix for drafting

---
 gpttype_adapter.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 245718c15..a355eddd7 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -2929,6 +2929,11 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
 
         llama_ctx_params.offload_kqv = !inputs.low_vram;
         llama_ctx_params.kv_unified = true;
+        if((inputs.use_mtp || draftmodel_filename != "") && inputs.draft_amount > 0)
+        {
+            // Match llama-server's target rollback slots for speculative verification.
+            llama_ctx_params.n_rs_seq = inputs.draft_amount;
+        }
         model_params.use_mmap = inputs.use_mmap;
         model_params.use_mlock = inputs.use_mlock;
         model_params.use_direct_io = false; //no direct io for now until stable

From 72a9269172290829c503c351e2b68ca2f7af2bc7 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 23 Jun 2026 09:48:24 -0500
Subject: [PATCH 135/292] vulkan: support all backend tests for
 SQR/SQRT/SIN/COS/CLAMP/LEAKY_RELU/NORM (#24582)

* vulkan: make SQR/SQRT/SIN/COS/CLAMP/LEAKY_RELU use unary.comp

* vulkan: make NORM support noncontig

* add noncontiguous row test cases for norm/l2_norm, handle this in the CPU backend and l2_norm.comp

* fix supports_op for cuda and webgpu
---
 ggml/src/ggml-cpu/ops.cpp                     | 73 ++++++++++++------
 ggml/src/ggml-cuda/ggml-cuda.cu               |  2 +-
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 76 +++++++++++--------
 .../src/ggml-vulkan/vulkan-shaders/clamp.comp | 17 -----
 ggml/src/ggml-vulkan/vulkan-shaders/cos.comp  | 17 -----
 .../ggml-vulkan/vulkan-shaders/l2_norm.comp   | 11 +--
 .../vulkan-shaders/leaky_relu.comp            | 22 ------
 ggml/src/ggml-vulkan/vulkan-shaders/norm.comp | 20 ++---
 ggml/src/ggml-vulkan/vulkan-shaders/sin.comp  | 17 -----
 ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp | 17 -----
 .../ggml-vulkan/vulkan-shaders/square.comp    | 17 -----
 .../src/ggml-vulkan/vulkan-shaders/unary.comp | 24 ++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     | 23 +++---
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          |  2 +-
 tests/test-backend-ops.cpp                    | 34 +++++++--
 15 files changed, 171 insertions(+), 201 deletions(-)
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp
 delete mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/square.comp

diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 74611dce7..6724686b8 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -3688,8 +3688,6 @@ static void ggml_compute_forward_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -3703,25 +3701,49 @@ static void ggml_compute_forward_norm_f32(
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
-                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const char * x = (const char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
+                char * y = (char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3;
 
-                float sum = 0.0;
-                ggml_vec_sum_f32(ne00, &sum, x);
-                float mean = sum/ne00;
+                if (nb00 == sizeof(float) && nb0 == sizeof(float)) {
+                    const float * xf = (const float *) x;
 
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-                float variance = 0;
+                    float sum = 0.0;
+                    ggml_vec_sum_f32(ne00, &sum, xf);
+                    float mean = sum/ne00;
+
+                    float * yf = (float *) y;
+                    float variance = 0;
 
 #ifdef GGML_USE_ACCELERATE
-                mean = -mean;
-                vDSP_vsadd(x, 1, &mean, y, 1, ne00);
-                vDSP_measqv(y, 1, &variance, ne00);
+                    mean = -mean;
+                    vDSP_vsadd(xf, 1, &mean, yf, 1, ne00);
+                    vDSP_measqv(yf, 1, &variance, ne00);
 #else
-                variance = ggml_vec_cvar_f32(ne00, y, x, mean);
+                    variance = ggml_vec_cvar_f32(ne00, yf, xf, mean);
 #endif //GGML_USE_ACCELERATE
 
-                const float scale = 1.0f/sqrtf(variance + eps);
-                ggml_vec_scale_f32(ne00, y, scale);
+                    const float scale = 1.0f/sqrtf(variance + eps);
+                    ggml_vec_scale_f32(ne00, yf, scale);
+                } else {
+                    float sum = 0.0;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        sum += *(const float *) (x + i00*nb00);
+                    }
+                    const float mean = sum/ne00;
+
+                    float variance = 0.0f;
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const float v = *(const float *) (x + i00*nb00) - mean;
+                        *(float *) (y + i00*nb0) = v;
+                        variance += v * v;
+                    }
+                    variance /= ne00;
+
+                    const float scale = 1.0f/sqrtf(variance + eps);
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        *(float *) (y + i00*nb0) *= scale;
+                    }
+                }
             }
         }
     }
@@ -4142,8 +4164,6 @@ static void ggml_compute_forward_l2_norm_f32(
 
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-
     const int ith = params->ith;
     const int nth = params->nth;
 
@@ -4158,20 +4178,27 @@ static void ggml_compute_forward_l2_norm_f32(
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
-                const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+                const char * x = (const char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
 
                 ggml_float sum = 0.0;
                 for (int64_t i00 = 0; i00 < ne00; i00++) {
-                    sum += (ggml_float)(x[i00] * x[i00]);
+                    const float xi = *(const float *) (x + i00*nb00);
+                    sum += (ggml_float)(xi * xi);
                 }
 
-                float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
-
-                memcpy(y, x, ne00 * sizeof(float));
-
                 const float scale = 1.0f/fmaxf(sqrtf(sum), eps);
 
-                ggml_vec_scale_f32(ne00, y, scale);
+                char * y = (char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3;
+
+                if (nb00 == sizeof(float) && nb0 == sizeof(float)) {
+                    memcpy(y, x, ne00 * sizeof(float));
+                    ggml_vec_scale_f32(ne00, (float *) y, scale);
+                } else {
+                    for (int64_t i00 = 0; i00 < ne00; i00++) {
+                        const float xi = *(const float *) (x + i00*nb00);
+                        *(float *) (y + i00*nb0) = xi * scale;
+                    }
+                }
             }
         }
     }
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 3d4b5f605..cca70592f 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -5334,7 +5334,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_NORM:
         case GGML_OP_RMS_NORM:
         case GGML_OP_L2_NORM:
-            return true;
+            return ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_RMS_NORM_BACK:
             return ggml_is_contiguous(op->src[0]);
             break;
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index d2827ad71..f4a578b89 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -816,14 +816,10 @@ struct vk_device_struct {
     vk_pipeline pipeline_concat_i8, pipeline_concat_i16, pipeline_concat_i32, pipeline_concat_i64;
     vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bicubic_f32, pipeline_upscale_bilinear_antialias_f32;
     vk_pipeline pipeline_scale_f32;
-    vk_pipeline pipeline_sqr_f32;
-    vk_pipeline pipeline_sqrt_f32;
-    vk_pipeline pipeline_sin_f32;
-    vk_pipeline pipeline_cos_f32;
     vk_pipeline pipeline_log[2];
     vk_pipeline pipeline_tri[2];
     vk_pipeline pipeline_diag[2];
-    vk_pipeline pipeline_clamp_f32;
+    vk_pipeline pipeline_clamp[2];
     vk_pipeline pipeline_pad_f32;
     vk_pipeline pipeline_roll_f32;
     vk_pipeline pipeline_repeat_i32, pipeline_repeat_back_f32;
@@ -855,6 +851,10 @@ struct vk_device_struct {
     vk_pipeline pipeline_gelu_quick[2];
     vk_pipeline pipeline_silu[2];
     vk_pipeline pipeline_relu[2];
+    vk_pipeline pipeline_sqr[2];
+    vk_pipeline pipeline_sqrt[2];
+    vk_pipeline pipeline_sin[2];
+    vk_pipeline pipeline_cos[2];
     vk_pipeline pipeline_xielu[2];
     vk_pipeline pipeline_neg[2];
     vk_pipeline pipeline_tanh[2];
@@ -886,7 +886,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_geglu_erf[2];
     vk_pipeline pipeline_geglu_quick[2];
 
-    vk_pipeline pipeline_leaky_relu_f32;
+    vk_pipeline pipeline_leaky_relu[2];
     vk_pipeline pipeline_silu_back_f32;
     vk_pipeline pipeline_diag_mask_inf_f32;
     vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
@@ -4972,7 +4972,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     }
     ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_nc_push_constants), {1, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true);
@@ -5092,11 +5092,6 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
 
     ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_sqrt_f32, "sqrt_f32", sqrt_f32_len, sqrt_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_sin_f32, "sin_f32", sin_f32_len, sin_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_cos_f32, "cos_f32", cos_f32_len, cos_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
     ggml_vk_create_pipeline(device, device->pipeline_log[0], "log_f32", log_f32_len, log_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_log[1], "log_f16", log_f16_len, log_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
@@ -5106,8 +5101,6 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
-    ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
-
     ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_roll_f32, "roll_f32", roll_f32_len, roll_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
@@ -5127,6 +5120,12 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     CREATE_UNARY(gelu_quick)
     CREATE_UNARY(silu)
     CREATE_UNARY(relu)
+    CREATE_UNARY(sqr)
+    CREATE_UNARY(sqrt)
+    CREATE_UNARY(sin)
+    CREATE_UNARY(cos)
+    CREATE_UNARY(clamp)
+    CREATE_UNARY(leaky_relu)
     CREATE_UNARY(xielu)
     CREATE_UNARY(neg)
     CREATE_UNARY(tanh)
@@ -5166,7 +5165,6 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     CREATE_GLU(geglu_quick)
 #undef CREATE_GLU
 
-    ggml_vk_create_pipeline(device, device->pipeline_leaky_relu_f32, "leaky_relu_f32", leaky_relu_f32_len, leaky_relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true);
@@ -10521,23 +10519,27 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
         return nullptr;
     case GGML_OP_SQR:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_sqr_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_sqr[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_SQRT:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_sqrt_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_sqrt[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_SIN:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_sin_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_sin[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_COS:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_cos_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_cos[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_LOG:
@@ -10559,8 +10561,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
         return nullptr;
     case GGML_OP_CLAMP:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_clamp_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_clamp[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_PAD:
@@ -10928,8 +10931,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
         }
         return nullptr;
     case GGML_OP_LEAKY_RELU:
-        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-            return ctx->device->pipeline_leaky_relu_f32;
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_leaky_relu[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
     case GGML_OP_CONV_2D:
@@ -11431,6 +11435,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_TRI:
     case GGML_OP_DIAG:
     case GGML_OP_CLAMP:
+    case GGML_OP_LEAKY_RELU:
     case GGML_OP_PAD:
     case GGML_OP_ROLL:
     case GGML_OP_REPEAT:
@@ -12297,8 +12302,10 @@ static void ggml_vk_silu_back(ggml_backend_vk_context * ctx, vk_context& subctx,
 
 static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     float * op_params = (float *)dst->op_params;
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
+    p.param1 = op_params[0];
 
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f, 0.0f, 0.0f });
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_NORM, std::move(p));
 }
 
 static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
@@ -13399,7 +13406,10 @@ static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx
 
 static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     const float * op_params = (const float *)dst->op_params;
-    ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f, 0.0f, 0.0f });
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
+    p.param1 = op_params[0];
+
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, std::move(p));
 }
 
 #ifdef GGML_VULKAN_RUN_TESTS
@@ -17325,12 +17335,11 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_TRANSPOSE:
         case GGML_OP_RMS_NORM:
             return true;
-        case GGML_OP_NORM:
         case GGML_OP_GROUP_NORM:
             return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_NORM:
         case GGML_OP_L2_NORM:
-            return ggml_is_contiguous_rows(op->src[0]) &&
-                   op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         case GGML_OP_ADD:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
@@ -17349,8 +17358,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_SIN:
         case GGML_OP_COS:
         case GGML_OP_CLAMP:
-            return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_LEAKY_RELU:
+            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                   op->type == op->src[0]->type;
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp b/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
deleted file mode 100644
index 653431895..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/clamp.comp
+++ /dev/null
@@ -1,17 +0,0 @@
-#version 450
-
-#include "types.glsl"
-#include "generic_unary_head.glsl"
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-void main() {
-    const uint idx = get_idx();
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
-    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp b/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
deleted file mode 100644
index db6865db9..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/cos.comp
+++ /dev/null
@@ -1,17 +0,0 @@
-#version 450
-
-#include "types.glsl"
-#include "generic_unary_head.glsl"
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-void main() {
-    const uint idx = get_idx();
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
-    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(cos(val));
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp
index f9af46744..9039ed1de 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp
@@ -14,16 +14,13 @@ void main() {
     const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
     const uint tid = gl_LocalInvocationID.x;
 
-    const uint i3 = row / (p.ne11 * p.ne12);
-    const uint i3_offset = i3 * p.ne12 * p.ne11;
-    const uint i2 = (row - i3_offset) / p.ne11;
-    const uint i2_offset = i2 * p.ne11;
-    const uint i1 = row - i3_offset - i2_offset;
+    const uint a_base = get_aoffset() + src0_idx(row * p.ne00);
+    const uint d_base = get_doffset() + dst_idx(row * p.ne10);
 
     sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
 
     [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) {
-        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0]);
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[a_base + i0*p.nb00]);
         sum[tid] += xi * xi;
     }
 
@@ -39,6 +36,6 @@ void main() {
     const FLOAT_TYPE scale = 1.0f / max(sqrt(sum[0]), FLOAT_TYPE(p.param1));
 
     [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) {
-        data_d[i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0] = D_TYPE(scale * FLOAT_TYPE(data_a[i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0]));
+        data_d[d_base + i0*p.nb10] = D_TYPE(scale * FLOAT_TYPE(data_a[a_base + i0*p.nb00]));
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp b/ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp
deleted file mode 100644
index b281e855c..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/leaky_relu.comp
+++ /dev/null
@@ -1,22 +0,0 @@
-#version 450
-
-#include "generic_head.glsl"
-#include "types.glsl"
-
-#extension GL_EXT_control_flow_attributes : enable
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
-void main() {
-    const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x;
-
-    if (i >= p.KX) {
-        return;
-    }
-
-    const float val = float(data_a[i]);
-    data_d[i] = D_TYPE(max(val, 0.0f) + min(val, 0.0f) * p.param1);
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp
index cc3ea0b76..792012d57 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp
@@ -1,26 +1,26 @@
 #version 450
 
-#include "generic_head.glsl"
 #include "types.glsl"
+#include "generic_unary_head.glsl"
 
 #extension GL_EXT_control_flow_attributes : enable
 #define BLOCK_SIZE 512
 
 layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
 
-layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
-layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
-
 shared vec2 sum[BLOCK_SIZE];
 
 void main() {
     const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
     const uint tid = gl_LocalInvocationID.x;
 
+    const uint a_base = get_aoffset() + src0_idx(row * p.ne00);
+    const uint d_base = get_doffset() + dst_idx(row * p.ne10);
+
     sum[tid] = vec2(0.0f, 0.0f);
 
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        const float xi = float(data_a[row*p.KX + col]);
+    [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) {
+        const float xi = float(data_a[a_base + i0*p.nb00]);
         sum[tid].x += xi;
         sum[tid].y += xi * xi;
     }
@@ -34,11 +34,11 @@ void main() {
         barrier();
     }
 
-    const float mean = sum[0].x / p.KX;
-    const float var = sum[0].y / p.KX - mean * mean;
+    const float mean = sum[0].x / p.ne00;
+    const float var = sum[0].y / p.ne00 - mean * mean;
     const float inv_std = inversesqrt(var + p.param1);
 
-    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
-        data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
+    [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) {
+        data_d[d_base + i0*p.nb10] = D_TYPE((float(data_a[a_base + i0*p.nb00]) - mean) * inv_std);
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
deleted file mode 100644
index 61f17b2f0..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/sin.comp
+++ /dev/null
@@ -1,17 +0,0 @@
-#version 450
-
-#include "types.glsl"
-#include "generic_unary_head.glsl"
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-void main() {
-    const uint idx = get_idx();
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
-    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(sin(val));
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp b/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp
deleted file mode 100644
index 70daad6c5..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/sqrt.comp
+++ /dev/null
@@ -1,17 +0,0 @@
-#version 450
-
-#include "types.glsl"
-#include "generic_unary_head.glsl"
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-void main() {
-    const uint idx = get_idx();
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
-    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(sqrt(val));
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp b/ggml/src/ggml-vulkan/vulkan-shaders/square.comp
deleted file mode 100644
index 4eb56afcb..000000000
--- a/ggml/src/ggml-vulkan/vulkan-shaders/square.comp
+++ /dev/null
@@ -1,17 +0,0 @@
-#version 450
-
-#include "types.glsl"
-#include "generic_unary_head.glsl"
-
-layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
-
-void main() {
-    const uint idx = get_idx();
-
-    if (idx >= p.ne) {
-        return;
-    }
-
-    const FLOAT_TYPE val = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(idx)]);
-    data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val * val);
-}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp b/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
index 47a457399..c62bce825 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
@@ -17,6 +17,30 @@ float op_neg(float x) {
     return -x;
 }
 
+float op_sqr(float x) {
+    return x * x;
+}
+
+float op_sqrt(float x) {
+    return sqrt(x);
+}
+
+float op_sin(float x) {
+    return sin(x);
+}
+
+float op_cos(float x) {
+    return cos(x);
+}
+
+float op_clamp(float x) {
+    return clamp(x, p.param1, p.param2);
+}
+
+float op_leaky_relu(float x) {
+    return max(x, 0.0f) + min(x, 0.0f) * p.param1;
+}
+
 float op_step(float x) {
     return x >= 0.0f ? 1.0f : 0.0f;
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 502602f79..3bd93d256 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -849,16 +849,6 @@ void process_shaders() {
 
     string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
 
-    string_to_spv("sqr_f32", "square.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-
-    string_to_spv("sqrt_f32", "sqrt.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-
-    string_to_spv("sin_f32", "sin.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-
-    string_to_spv("cos_f32", "cos.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-
-    string_to_spv("clamp_f32", "clamp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
-
     string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
 
     string_to_spv("concat_i8", "concat.comp", {{"A_TYPE", "uint8_t"}, {"B_TYPE", "uint8_t"}, {"D_TYPE", "uint8_t"}});
@@ -885,6 +875,18 @@ void process_shaders() {
     string_to_spv("silu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_silu"}});
     string_to_spv("relu_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_relu"}});
     string_to_spv("relu_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_relu"}});
+    string_to_spv("sqr_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sqr"}});
+    string_to_spv("sqr_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sqr"}});
+    string_to_spv("sqrt_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sqrt"}});
+    string_to_spv("sqrt_f32",       "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sqrt"}});
+    string_to_spv("sin_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_sin"}});
+    string_to_spv("sin_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_sin"}});
+    string_to_spv("cos_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_cos"}});
+    string_to_spv("cos_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_cos"}});
+    string_to_spv("clamp_f16",      "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_clamp"}});
+    string_to_spv("clamp_f32",      "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_clamp"}});
+    string_to_spv("leaky_relu_f16", "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_leaky_relu"}});
+    string_to_spv("leaky_relu_f32", "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_leaky_relu"}});
     string_to_spv("neg_f16",        "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_neg"}});
     string_to_spv("neg_f32",        "unary.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},     {"OP", "op_neg"}});
     string_to_spv("tanh_f16",       "unary.comp",       {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}, {"OP", "op_tanh"}});
@@ -942,7 +944,6 @@ void process_shaders() {
     string_to_spv("geglu_quick_f16","geglu_quick.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("geglu_quick_f32","geglu_quick.comp", {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
-    string_to_spv("leaky_relu_f32", "leaky_relu.comp",  {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
     string_to_spv("silu_back_f32",  "silu_back.comp",   {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
 
     string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index e8eafd185..f0ec18abd 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -4270,7 +4270,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
         case GGML_OP_RMS_NORM:
         case GGML_OP_NORM:
         case GGML_OP_L2_NORM:
-            supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32;
+            supports_op = (op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32) && ggml_is_contiguous_rows(src0);
             break;
         case GGML_OP_ROPE:
             supports_op = op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 719ae51cc..e1d3853e4 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3298,21 +3298,29 @@ struct test_norm : public test_case {
     const std::array<int64_t, 4> ne;
     const bool v; // whether a is a non-contiguous view
     const float eps;
+    const bool noncontig_rows;
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, v, eps);
+        return VARS_TO_STR5(type, ne, v, eps, noncontig_rows);
     }
 
     test_norm(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 5, 4, 3},
             bool v = false,
-            float eps = 1e-6f)
-        : type(type), ne(ne), v(v), eps(eps) {}
+            float eps = 1e-6f,
+            bool noncontig_rows = false)
+        : type(type), ne(ne), v(v), eps(eps), noncontig_rows(noncontig_rows) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        const std::array<int64_t, 4> ne_a = noncontig_rows ?
+            std::array<int64_t, 4>{ ne[1], ne[0], ne[2], ne[3] } : ne;
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
         ggml_set_name(a, "a");
 
+        if (noncontig_rows) {
+            a = ggml_permute(ctx, a, 1, 0, 2, 3);
+            ggml_set_name(a, "permuted a");
+        }
         if (v) {
             a = ggml_view_4d(ctx, a, a->ne[0]/2, a->ne[1]/2, a->ne[2]/2, a->ne[3]/2, a->nb[1], a->nb[2], a->nb[3], 0);
             ggml_set_name(a, "view of a");
@@ -6193,21 +6201,29 @@ struct test_l2_norm : public test_case {
     const std::array<int64_t, 4> ne;
     const float eps;
     bool v;
+    bool noncontig_rows;
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne, eps, v);
+        return VARS_TO_STR5(type, ne, eps, v, noncontig_rows);
     }
 
     test_l2_norm(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {64, 64, 320, 1},
             float eps = 1e-12f,
-            bool v = false)
-        : type(type), ne(ne), eps(eps), v(v) {}
+            bool v = false,
+            bool noncontig_rows = false)
+        : type(type), ne(ne), eps(eps), v(v), noncontig_rows(noncontig_rows) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        const std::array<int64_t, 4> ne_a = noncontig_rows ?
+            std::array<int64_t, 4>{ ne[1], ne[0], ne[2], ne[3] } : ne;
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
         ggml_set_name(a, "a");
 
+        if (noncontig_rows) {
+            a = ggml_permute(ctx, a, 1, 0, 2, 3);
+            ggml_set_name(a, "permuted a");
+        }
         if (v) {
             a = ggml_view_4d(ctx, a, a->ne[0]/2, a->ne[1]/2, a->ne[2]/2, a->ne[3]/2, a->nb[1], a->nb[2], a->nb[3], 0);
             ggml_set_name(a, "view of a");
@@ -8282,9 +8298,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                 test_cases.emplace_back(new test_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, v, eps));
                 test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, v, eps));
             }
+            test_cases.emplace_back(new test_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, false, eps, true));
             test_cases.emplace_back(new test_rms_norm_back(GGML_TYPE_F32, { n, 5, 4, 3 }, eps));
             test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, eps, false));
             test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, eps, true));
+            test_cases.emplace_back(new test_l2_norm(GGML_TYPE_F32, { n, 5, 4, 3 }, eps, false, true));
         }
     }
 

From be4a6a63eb2b848e19c277bdcf2bd399e8af76d9 Mon Sep 17 00:00:00 2001
From: kononnable <kononnable@gmail.com>
Date: Tue, 23 Jun 2026 16:56:50 +0200
Subject: [PATCH 136/292] server : check draft context creation error (#24922)

---
 tools/server/server-context.cpp | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index ca91449d2..39b7eb218 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -89,7 +89,9 @@ struct server_batch {
     }
 
     ~server_batch() {
-        llama_batch_free(batch);
+        if (batch.token != nullptr) {
+            llama_batch_free(batch);
+        }
     }
 
     void init(int32_t n_tokens_alloc) {
@@ -1215,6 +1217,10 @@ private:
             cparams.ctx_other = ctx_tgt;
 
             ctx_dft.reset(llama_init_from_model(model_dft.get(), cparams));
+            if (ctx_dft == nullptr) {
+                SRV_ERR("%s", "failed to create draft context\n");
+                return false;
+            }
 
             params_base.speculative.draft.ctx_tgt = ctx_tgt;
             params_base.speculative.draft.ctx_dft = ctx_dft.get();

From ac4105d68b2955027115cf9bb50941ccf56974eb Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Tue, 23 Jun 2026 22:34:00 -0500
Subject: [PATCH 137/292] vulkan: Apply bias before softmax in FA, to avoid
 overflow (#24909)

---
 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp     | 1 +
 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp | 1 +
 2 files changed, 2 insertions(+)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 91fb07c93..3192130cc 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -463,6 +463,7 @@ void main() {
                 }
                 rowmaxf = max(rowmaxf, float(Sf[r][c]));
             }
+            rowmaxf += FATTN_KQ_MAX_OFFSET;
             float Moldf = Mf[r];
 
             // M = max(rowmax, Mold)
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index 23ae3833e..16178e577 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -352,6 +352,7 @@ void main() {
                 }
                 rowmaxf = max(rowmaxf, float(sfsh[r_vec + (c * cols_per_iter + col_tid) * sfshstride][r_comp]));
             }
+            rowmaxf += FATTN_KQ_MAX_OFFSET;
             float Moldf = Mf[r];
 
             // Compute max across the row

From 88636e178ff2972e1002cf2024cb39008eda1192 Mon Sep 17 00:00:00 2001
From: Tarek Dakhran <tarek@liquid.ai>
Date: Wed, 24 Jun 2026 08:49:46 +0200
Subject: [PATCH 138/292] model : Add LFM2.5-ColBERT-350M and
 LFM2.5-Embedding-350M (#24913)

* model : Add LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M

* Restore LFM2 models in README.md
---
 README.md              |  4 +++-
 conversion/__init__.py |  1 +
 conversion/lfm2.py     | 13 ++++++++++---
 src/models/lfm2.cpp    | 18 ++++++++++++++----
 4 files changed, 28 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 0652d13f2..e98f2b7f1 100644
--- a/README.md
+++ b/README.md
@@ -142,7 +142,9 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 - [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
-- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
+- [x] [Liquid LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2)
+- [x] [Liquid LFM2.5 models](https://huggingface.co/collections/LiquidAI/lfm25)
+- [x] [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos)
 - [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
 - [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
 - [x] [Mellum models](https://huggingface.co/JetBrains/models?search=mellum)
diff --git a/conversion/__init__.py b/conversion/__init__.py
index c6af6f731..2bce1bbd7 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -124,6 +124,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LLaDAModelLM": "llada",
     "LLaMAForCausalLM": "llama",
     "Lfm25AudioTokenizer": "lfm2",
+    "Lfm2BidirectionalModel": "lfm2",
     "Lfm2ForCausalLM": "lfm2",
     "Lfm2Model": "lfm2",
     "Lfm2MoeForCausalLM": "lfm2",
diff --git a/conversion/lfm2.py b/conversion/lfm2.py
index f28fccf10..70ce45658 100644
--- a/conversion/lfm2.py
+++ b/conversion/lfm2.py
@@ -64,11 +64,17 @@ class LFM2Model(TextModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Lfm2Model")
+@ModelBase.register("Lfm2Model", "Lfm2BidirectionalModel")
 class LFM2ColBertModel(LFM2Model):
     model_arch = gguf.MODEL_ARCH.LFM2
     dense_tensor_name = "dense_2"
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hf_arch == "Lfm2BidirectionalModel":
+            self.gguf_writer.add_causal_attention(False)
+        self._try_set_pooling_type()
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if not name.startswith(self.dense_tensor_name):
             name = "model." + name
@@ -76,10 +82,11 @@ class LFM2ColBertModel(LFM2Model):
         yield from super().modify_tensors(data_torch, name, bid)
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # dense tensor is stored in a separate safetensors file
+        # optional dense tensor is stored in a separate safetensors file
         from safetensors.torch import load_file
         tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
-        assert tensors_file.is_file()
+        if not tensors_file.is_file():
+            return
         tensor = load_file(tensors_file)["linear.weight"]
         self.gguf_writer.add_embedding_length_out(tensor.shape[0])
         yield f"{self.dense_tensor_name}.weight", tensor.clone()
diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp
index 97da8a6ab..07b7346ee 100644
--- a/src/models/lfm2.cpp
+++ b/src/models/lfm2.cpp
@@ -190,7 +190,15 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
         auto * conv_rs    = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
         auto * conv       = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
 
-        bx = ggml_concat(ctx0, conv, bx, 0);
+        // causal prepends the state, non-causal pads symmetrically for a centered window
+        if (hparams.causal_attn) {
+            bx = ggml_concat(ctx0, conv, bx, 0);
+        } else {
+            const int64_t pad = (hparams.n_shortconv_l_cache - 1) / 2;
+            auto * left = ggml_cont(ctx0,
+                ggml_view_3d(ctx0, conv, pad, hparams.n_embd, n_seqs, conv->nb[1], conv->nb[2], (d_conv - pad) * conv->nb[0]));
+            bx = ggml_pad_ext(ctx0, ggml_concat(ctx0, left, bx, 0), 0, pad, 0, 0, 0, 0, 0, 0);
+        }
         GGML_ASSERT(bx->ne[0] > conv->ne[0]);
 
         // last d_conv columns is a new conv state
@@ -266,10 +274,12 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur, model.output_s);
-    cb(cur, "result_output", -1);
+    if (!cparams.embeddings) {
+        cur = build_lora_mm(model.output, cur, model.output_s);
+        cb(cur, "result_output", -1);
 
-    res->t_logits = cur;
+        res->t_logits = cur;
+    }
 
     ggml_build_forward_expand(gf, cur);
 }

From ef9c13d4c27f1ad9804341a0187c33b621609c63 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Wed, 24 Jun 2026 10:21:33 +0200
Subject: [PATCH 139/292] ui: New Logo + Navigation cleanup & Mobile UI/UX
 improvements (#24897)

* chore: `npm audit fix --force`

* feat: Update sidebar toggle to use Logo

* refactor: Clean up favicon SVG

* feat: Refactor logo component and implement theme-aware favicon generation

* feat: Add configurable padding to generated PWA assets

* test: Add unit tests for writeThemeFavicons

* refactor: Componentization

* feat: WIP

* feat: WIP

* feat: WIP

* feat: Mobile UI

* feat: add SEARCH route constant

* feat: create SidebarNavigationSearchResults component

* refactor: use SidebarNavigationSearchResults in conversation list

* feat: enable mobile search navigation in sidebar actions

* feat: add mobile search route and page

* fix: prevent sidebar overflow on mobile viewports

* fix: Mobile sidebar

* feat: Mobile Search WIP

* feat: Mobile WIP

* feat: Add PWA standalone detection and refine mobile UI

* feat: Improve mobile layout, sidebar handling, and chat scrolling

* feat: Improve mobile sidebar visibility and iOS Safari chat spacing

* fix: Disable auto-scroll on mobile

* chore: Linting

* fix: Wrong condition

* feat: Mobile chat scroll

* refactor: WIP

* fix: Desktop initial scroll always working again

* fix: Partial fix for mobile auto-scroll / initial scroll

* fix: Desktop auto-scroll on initial load and during streaming

* fix: Mobile scrolling logic

* refactor: Clean up

* feat: Improve start UI

* feat: Add `delay` to `fadeInView`

* feat: Auto-scroll button

* refactor: Cleanup

* refactor: Extract chat dialogs and alerts into dedicated component

* refactor: Reorganize ChatScreen component structure and initialization

* feat: Improve auto-scroll after sending message

* feat: UI improvements

* fix: Settings link

* feat: UI improvements

* fix: better UI spacing

* fix: Remove unneeded logic

* fix: Chat Processing Info UI rendering

* feat: Improve mobile UI

* feat: UI improvement

* fix: Conditional transition delay for Chat Messages based on route from

* fix: Delay mobile sidebar collapse for smoother transitions

* fix: Mobile scroll down button + sidebar pointer events

* fix: Mobile UI

* fix: Auto scrolling

* fix: Implement dynamic height calculations for chat auto-scroll positioning and UI elements

* fix: Retrieve `autofocus` for Chat Form textarea

* fix: Use proper class

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* refactor: extract scroll-to-bottom logic and fix message send flow

* fix: update viewport store usage and remove conflicting autofocus

* feat: add accessibility labels to scroll down button

* fix: correct HTML structure in sidebar empty states

* fix: dynamically toggle processing info visibility

* chore: remove commented exports and fix formatting

* fix

* fix: Mobile Chat Form Add Action Sheet interactions

---------

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---
 tools/ui/.gitignore                           |   3 +-
 tools/ui/package-lock.json                    |  14 +-
 tools/ui/package.json                         |   2 +-
 tools/ui/pwa-assets-dark.config.ts            |   9 +-
 tools/ui/pwa-assets.config.ts                 |  21 +-
 tools/ui/scripts/favicon-colorize.ts          | 107 ++++
 tools/ui/src/app.css                          |   7 +-
 .../ui/src/lib/actions/fade-in-view.svelte.ts |  14 +-
 tools/ui/src/lib/assets/logo.svg              |   7 +
 .../components/app/actions/ActionIcon.svelte  |  75 ++-
 .../app/chat/ChatForm/ChatForm.svelte         |   4 +-
 .../ChatFormActionAddButton.svelte            |   2 +-
 .../ChatFormActionAddSheet.svelte             |  19 +-
 .../ChatFormActionsAdd.svelte                 |   1 +
 .../ChatFormActionSubmit.svelte               |   2 +-
 .../app/chat/ChatForm/ChatFormTextarea.svelte |   5 +-
 .../ChatMessage/ChatMessage.svelte            |   4 +-
 .../ChatMessageAssistant.svelte               |  64 +-
 .../ChatMessageUser/ChatMessageUser.svelte    |   2 +-
 .../ChatMessageUserBubble.svelte              |   4 +-
 .../app/chat/ChatMessages/ChatMessages.svelte |  11 +-
 .../app/chat/ChatScreen/ChatScreen.svelte     | 579 +++++++-----------
 .../ChatScreenActionScrollDown.svelte         |  64 +-
 .../ChatScreenDialogsAndAlerts.svelte         |  55 ++
 .../app/chat/ChatScreen/ChatScreenForm.svelte |  36 +-
 .../chat/ChatScreen/ChatScreenGreeting.svelte |   6 +-
 .../ChatScreenProcessingInfo.svelte           |   9 +-
 tools/ui/src/lib/components/app/chat/index.ts |   7 -
 .../components/app/forms/SearchInput.svelte   |   5 +-
 .../src/lib/components/app/misc/Logo.svelte   |  15 +
 tools/ui/src/lib/components/app/misc/index.ts |   8 +
 .../app/navigation/DesktopIconStrip.svelte    |  84 ---
 .../SidebarNavigation.svelte                  | 529 +++++++---------
 .../SidebarNavigationActions.svelte           | 214 +++++--
 .../SidebarNavigationConversationList.svelte  | 135 ++++
 .../SidebarNavigationSearch.svelte            |   4 +-
 .../SidebarNavigationSearchResults.svelte     |  76 +++
 .../lib/components/app/navigation/index.ts    |  68 +-
 .../settings/SettingsChat/SettingsChat.svelte |   5 +-
 .../SettingsChatDesktopSidebar.svelte         |  10 +-
 .../app/settings/SettingsMcpServers.svelte    |  51 +-
 .../lib/components/ui/button/button.svelte    |   2 +-
 .../lib/components/ui/sidebar/constants.ts    |   7 -
 .../components/ui/sidebar/context.svelte.ts   |  79 ---
 .../ui/src/lib/components/ui/sidebar/index.ts |  75 ---
 .../ui/sidebar/sidebar-content.svelte         |  24 -
 .../ui/sidebar/sidebar-footer.svelte          |  21 -
 .../ui/sidebar/sidebar-group-action.svelte    |  36 --
 .../ui/sidebar/sidebar-group-content.svelte   |  21 -
 .../ui/sidebar/sidebar-group-label.svelte     |  34 -
 .../ui/sidebar/sidebar-group.svelte           |  21 -
 .../ui/sidebar/sidebar-header.svelte          |  21 -
 .../ui/sidebar/sidebar-input.svelte           |  21 -
 .../ui/sidebar/sidebar-inset.svelte           |  24 -
 .../ui/sidebar/sidebar-menu-action.svelte     |  43 --
 .../ui/sidebar/sidebar-menu-badge.svelte      |  29 -
 .../ui/sidebar/sidebar-menu-button.svelte     | 106 ----
 .../ui/sidebar/sidebar-menu-item.svelte       |  21 -
 .../ui/sidebar/sidebar-menu-skeleton.svelte   |  36 --
 .../ui/sidebar/sidebar-menu-sub-button.svelte |  43 --
 .../ui/sidebar/sidebar-menu-sub-item.svelte   |  21 -
 .../ui/sidebar/sidebar-menu-sub.svelte        |  25 -
 .../components/ui/sidebar/sidebar-menu.svelte |  21 -
 .../ui/sidebar/sidebar-provider.svelte        |  51 --
 .../components/ui/sidebar/sidebar-rail.svelte |  36 --
 .../ui/sidebar/sidebar-separator.svelte       |  19 -
 .../ui/sidebar/sidebar-trigger.svelte         |  43 --
 .../lib/components/ui/sidebar/sidebar.svelte  | 150 -----
 tools/ui/src/lib/constants/pwa.ts             |  15 +-
 tools/ui/src/lib/constants/routes.ts          |   4 +-
 tools/ui/src/lib/constants/ui.ts              |   7 +-
 .../src/lib/hooks/use-auto-scroll.svelte.ts   |   5 +-
 .../use-chat-screen-active-model.svelte.ts    | 100 +++
 .../use-chat-screen-drag-and-drop.svelte.ts   |  72 +++
 .../use-chat-screen-file-upload.svelte.ts     | 104 ++++
 .../hooks/use-chat-screen-scroll.svelte.ts    |  47 ++
 .../lib/hooks/use-models-selector.svelte.ts   |   2 +-
 tools/ui/src/lib/stores/device.svelte.ts      |  72 +++
 tools/ui/src/lib/stores/models.svelte.ts      |   2 +-
 tools/ui/src/routes/+layout.svelte            |  94 +--
 tools/ui/src/routes/search/+page.svelte       |  95 +++
 tools/ui/src/routes/settings/+layout.svelte   |  14 +-
 .../routes/settings/[[section]]/+page.svelte  |   5 +-
 tools/ui/static/favicon-dark.svg              |  14 -
 tools/ui/static/favicon.svg                   |  14 -
 .../client/components/TestWrapper.svelte      |   7 +-
 .../stories/SidebarNavigation.stories.svelte  |  31 +-
 tools/ui/tests/unit/favicon-colorize.test.ts  | 198 ++++++
 88 files changed, 2122 insertions(+), 2147 deletions(-)
 create mode 100644 tools/ui/scripts/favicon-colorize.ts
 create mode 100644 tools/ui/src/lib/assets/logo.svg
 create mode 100644 tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDialogsAndAlerts.svelte
 create mode 100644 tools/ui/src/lib/components/app/misc/Logo.svelte
 delete mode 100644 tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte
 create mode 100644 tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationList.svelte
 create mode 100644 tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/constants.ts
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/index.ts
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
 delete mode 100644 tools/ui/src/lib/components/ui/sidebar/sidebar.svelte
 create mode 100644 tools/ui/src/lib/hooks/use-chat-screen-active-model.svelte.ts
 create mode 100644 tools/ui/src/lib/hooks/use-chat-screen-drag-and-drop.svelte.ts
 create mode 100644 tools/ui/src/lib/hooks/use-chat-screen-file-upload.svelte.ts
 create mode 100644 tools/ui/src/lib/hooks/use-chat-screen-scroll.svelte.ts
 create mode 100644 tools/ui/src/lib/stores/device.svelte.ts
 create mode 100644 tools/ui/src/routes/search/+page.svelte
 delete mode 100644 tools/ui/static/favicon-dark.svg
 delete mode 100644 tools/ui/static/favicon.svg
 create mode 100644 tools/ui/tests/unit/favicon-colorize.test.ts

diff --git a/tools/ui/.gitignore b/tools/ui/.gitignore
index ddcfe2e60..0bb8c9b3c 100644
--- a/tools/ui/.gitignore
+++ b/tools/ui/.gitignore
@@ -28,10 +28,9 @@ vite.config.ts.timestamp-*
 # PWA Artifacts
 apple-splash-*.png
 apple-touch-icon-*.png
-favicon.ico
-favicon-dark.ico
 maskable-icon-*.png
 pwa-*.png
+static/favicon*
 
 # Storybook
 *storybook.log
diff --git a/tools/ui/package-lock.json b/tools/ui/package-lock.json
index 9d0cdfea6..9dce3a0c9 100644
--- a/tools/ui/package-lock.json
+++ b/tools/ui/package-lock.json
@@ -35,7 +35,7 @@
 				"bits-ui": "2.18.1",
 				"clsx": "2.1.1",
 				"dexie": "4.4.3",
-				"dompurify": "3.4.5",
+				"dompurify": "3.4.11",
 				"eslint": "9.39.4",
 				"eslint-config-prettier": "10.1.8",
 				"eslint-plugin-storybook": "10.4.2",
@@ -8653,9 +8653,9 @@
 			"peer": true
 		},
 		"node_modules/dompurify": {
-			"version": "3.4.5",
-			"resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.5.tgz",
-			"integrity": "sha512-OrwIBKsdNSVEeubdJ1HBv/wNENRM9ytAVCv7YXt//A3vPdVMNuACRqK9mXCGCBW2ln7BT/A4X0jXHo2Gu89miA==",
+			"version": "3.4.11",
+			"resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.11.tgz",
+			"integrity": "sha512-zhlUV12GsaRzMsf9q5M254YhA4+VuF0fG+QFqu6aYpoGlKtz+w8//jBcGVYBgQkR5GHjUomejY84AV+/uPbWdw==",
 			"dev": true,
 			"license": "(MPL-2.0 OR Apache-2.0)",
 			"optionalDependencies": {
@@ -10226,9 +10226,9 @@
 			}
 		},
 		"node_modules/hono": {
-			"version": "4.12.23",
-			"resolved": "https://registry.npmjs.org/hono/-/hono-4.12.23.tgz",
-			"integrity": "sha512-eIaZ9qDgu7XV0pxOCrg7/WhnQ6Ivm22UcxhXx/A3dcbqbbYgBEkc6e/J/s7j2tS96zoB0S9VBdLwQNCWwUo4LA==",
+			"version": "4.12.26",
+			"resolved": "https://registry.npmjs.org/hono/-/hono-4.12.26.tgz",
+			"integrity": "sha512-uyZtpnYxM9CmQ7QsQknM4zN8EftNqhON1qYeIKM0Se67CCEe2c44xyGURwB0axX2fBDu1dqHrHAc1hmNT8ITkw==",
 			"dev": true,
 			"license": "MIT",
 			"engines": {
diff --git a/tools/ui/package.json b/tools/ui/package.json
index 480392288..bcb4165d1 100644
--- a/tools/ui/package.json
+++ b/tools/ui/package.json
@@ -54,7 +54,7 @@
 		"bits-ui": "2.18.1",
 		"clsx": "2.1.1",
 		"dexie": "4.4.3",
-		"dompurify": "3.4.5",
+		"dompurify": "3.4.11",
 		"eslint": "9.39.4",
 		"eslint-config-prettier": "10.1.8",
 		"eslint-plugin-storybook": "10.4.2",
diff --git a/tools/ui/pwa-assets-dark.config.ts b/tools/ui/pwa-assets-dark.config.ts
index e9793bca9..358c0ebc0 100644
--- a/tools/ui/pwa-assets-dark.config.ts
+++ b/tools/ui/pwa-assets-dark.config.ts
@@ -1,4 +1,10 @@
 import { defineConfig } from '@vite-pwa/assets-generator/config';
+import { FAVICON_COLORS, PWA_ASSET_GENERATOR } from './src/lib/constants/pwa';
+import { writeThemeFavicons } from './scripts/favicon-colorize';
+
+writeThemeFavicons(FAVICON_COLORS.LIGHT, FAVICON_COLORS.DARK, {
+	padding: PWA_ASSET_GENERATOR.FAVICON_PADDING
+});
 
 export default defineConfig({
 	headLinkOptions: {
@@ -7,7 +13,8 @@ export default defineConfig({
 	preset: {
 		transparent: {
 			sizes: [],
-			favicons: [[48, 'favicon-dark.ico']]
+			favicons: [[48, 'favicon-dark.ico']],
+			padding: PWA_ASSET_GENERATOR.FAVICON_PADDING
 		},
 		maskable: {
 			sizes: []
diff --git a/tools/ui/pwa-assets.config.ts b/tools/ui/pwa-assets.config.ts
index 54928eeb4..b69884d94 100644
--- a/tools/ui/pwa-assets.config.ts
+++ b/tools/ui/pwa-assets.config.ts
@@ -5,15 +5,32 @@ import {
 } from '@vite-pwa/assets-generator/config';
 import { readFileSync } from 'node:fs';
 import { resolve } from 'node:path';
-import { THEME_COLORS, PWA_GENERATOR_DEVICES, PWA_ASSET_GENERATOR } from './src/lib/constants/pwa';
+import {
+	THEME_COLORS,
+	PWA_GENERATOR_DEVICES,
+	PWA_ASSET_GENERATOR,
+	FAVICON_COLORS
+} from './src/lib/constants/pwa';
 import { SplashOrientation } from './src/lib/enums/splash.enums';
+import { writeThemeFavicons } from './scripts/favicon-colorize';
+
+writeThemeFavicons(FAVICON_COLORS.LIGHT, FAVICON_COLORS.DARK, {
+	padding: PWA_ASSET_GENERATOR.FAVICON_PADDING
+});
 
 export default defineConfig({
 	headLinkOptions: {
 		preset: PWA_ASSET_GENERATOR.LINK_PRESET
 	},
 	preset: combinePresetAndAppleSplashScreens(
-		minimal2023Preset,
+		{
+			...minimal2023Preset,
+			// tiny margin so favicon.ico / pwa-*.png breathe inside the canvas
+			transparent: {
+				...minimal2023Preset.transparent,
+				padding: PWA_ASSET_GENERATOR.FAVICON_PADDING
+			}
+		},
 		{
 			padding: PWA_ASSET_GENERATOR.SPLASH_PADDING,
 			resizeOptions: {
diff --git a/tools/ui/scripts/favicon-colorize.ts b/tools/ui/scripts/favicon-colorize.ts
new file mode 100644
index 000000000..e1872b777
--- /dev/null
+++ b/tools/ui/scripts/favicon-colorize.ts
@@ -0,0 +1,107 @@
+import { mkdirSync, readFileSync, writeFileSync } from 'node:fs';
+import { dirname, resolve } from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+const HERE = dirname(fileURLToPath(import.meta.url));
+const PROJECT_ROOT = resolve(HERE, '..');
+
+const DEFAULT_LOGO = resolve(PROJECT_ROOT, 'src/lib/assets/logo.svg');
+const DEFAULT_OUT_DIR = resolve(PROJECT_ROOT, 'static');
+const DEFAULT_OUT_LIGHT = resolve(DEFAULT_OUT_DIR, 'favicon.svg');
+const DEFAULT_OUT_DARK = resolve(DEFAULT_OUT_DIR, 'favicon-dark.svg');
+
+const CURRENT_COLOR = 'currentColor';
+
+export interface ColorizedFavicon {
+	light: string;
+	dark: string;
+}
+
+export interface WriteThemeFaviconsOptions {
+	sourcePath?: string;
+	lightOutPath?: string;
+	darkOutPath?: string;
+	/**
+	 * Fraction of the icon (0..1) to leave as an even margin on each side.
+	 * Applied by wrapping the inner content in a `<g transform="...">` so the
+	 * source `src/lib/assets/logo.svg` is not modified. Pass 0 to disable.
+	 */
+	padding?: number;
+}
+
+/**
+ * Replace every `currentColor` occurrence in the SVG with the given color.
+ * Pure: no filesystem access, so it is straightforward to unit-test.
+ */
+export function colorizeFaviconSvg(
+	svg: string,
+	lightColor: string,
+	darkColor: string
+): ColorizedFavicon {
+	return {
+		light: svg.replaceAll(CURRENT_COLOR, lightColor),
+		dark: svg.replaceAll(CURRENT_COLOR, darkColor)
+	};
+}
+
+/**
+ * Shrink the inner SVG content uniformly and re-center it so `padding` (a
+ * 0..1 fraction) is reserved as equal margin on each side. Returns the input
+ * unchanged for non-positive padding, missing/invalid `viewBox`, or unexpected
+ * markup so the caller always gets a renderable SVG.
+ */
+export function padFaviconSvg(svg: string, padding: number): string {
+	if (!(padding > 0) || padding >= 1) return svg;
+
+	const viewBoxMatch = svg.match(/viewBox\s*=\s*["']([^"']+)["']/i);
+	if (!viewBoxMatch) return svg;
+
+	const parts = viewBoxMatch[1]
+		.trim()
+		.split(/[\s,]+/)
+		.map(Number);
+	if (parts.length !== 4 || parts.some((n) => !Number.isFinite(n))) return svg;
+
+	const [, , width, height] = parts;
+	if (width <= 0 || height <= 0) return svg;
+
+	const scale = 1 - padding;
+	const translateX = (padding * width) / 2;
+	const translateY = (padding * height) / 2;
+
+	const openTagStart = svg.search(/<svg\b/i);
+	if (openTagStart === -1) return svg;
+	const openTagEnd = svg.indexOf('>', openTagStart);
+	if (openTagEnd === -1) return svg;
+	const closeStart = svg.lastIndexOf('</svg');
+	if (closeStart === -1 || closeStart <= openTagEnd) return svg;
+
+	const openTag = svg.slice(0, openTagEnd + 1);
+	const inner = svg.slice(openTagEnd + 1, closeStart);
+	const closeTag = svg.slice(closeStart);
+
+	const group = `<g transform="translate(${translateX} ${translateY}) scale(${scale})">`;
+	return `${openTag}${group}${inner}</g>${closeTag}`;
+}
+
+/**
+ * Read `src/lib/assets/logo.svg`, colorize it for both themes, and write
+ * the results to the static directory so the PWA asset generator can consume
+ * them. Paths can be overridden for tests.
+ */
+export function writeThemeFavicons(
+	lightColor: string,
+	darkColor: string,
+	{
+		sourcePath = DEFAULT_LOGO,
+		lightOutPath = DEFAULT_OUT_LIGHT,
+		darkOutPath = DEFAULT_OUT_DARK,
+		padding = 0
+	}: WriteThemeFaviconsOptions = {}
+): void {
+	const source = readFileSync(sourcePath, 'utf-8');
+	const { light, dark } = colorizeFaviconSvg(source, lightColor, darkColor);
+	mkdirSync(dirname(lightOutPath), { recursive: true });
+	writeFileSync(lightOutPath, padFaviconSvg(light, padding));
+	writeFileSync(darkOutPath, padFaviconSvg(dark, padding));
+}
diff --git a/tools/ui/src/app.css b/tools/ui/src/app.css
index 9254a96df..8c4056477 100644
--- a/tools/ui/src/app.css
+++ b/tools/ui/src/app.css
@@ -48,6 +48,7 @@
 
 	--chat-form-area-height: 8rem;
 	--chat-form-area-offset: 2rem;
+	--chat-form-padding-top: 6rem;
 	--max-message-height: max(24rem, min(80dvh, calc(100dvh - var(--chat-form-area-height) - 12rem)));
 }
 
@@ -55,6 +56,7 @@
 	:root {
 		--chat-form-area-height: 24rem;
 		--chat-form-area-offset: 12rem;
+		--chat-form-padding-top: 6rem;
 	}
 }
 
@@ -141,7 +143,6 @@
 		@apply bg-background text-foreground;
 		scrollbar-width: thin;
 		scrollbar-gutter: stable;
-		overflow: hidden; /* Added due to Mermaid rendering somehow causing the double scrollbar */
 	}
 
 	/* Global scrollbar styling - visible only on hover */
@@ -193,3 +194,7 @@
 		scrollbar-width: none;
 	}
 }
+
+.mermaidTooltip {
+	display: none !important;
+}
diff --git a/tools/ui/src/lib/actions/fade-in-view.svelte.ts b/tools/ui/src/lib/actions/fade-in-view.svelte.ts
index d93044805..9a5918131 100644
--- a/tools/ui/src/lib/actions/fade-in-view.svelte.ts
+++ b/tools/ui/src/lib/actions/fade-in-view.svelte.ts
@@ -10,9 +10,9 @@ import { isElementInViewport } from '$lib/utils/viewport';
  */
 export function fadeInView(
 	node: HTMLElement,
-	options: { duration?: number; y?: number; skipIfVisible?: boolean } = {}
+	options: { duration?: number; y?: number; delay?: number; skipIfVisible?: boolean } = {}
 ) {
-	const { duration = 300, y = 0, skipIfVisible = false } = options;
+	const { duration = 300, y = 0, delay = 0, skipIfVisible = false } = options;
 
 	if (skipIfVisible && isElementInViewport(node)) {
 		return;
@@ -27,10 +27,12 @@ export function fadeInView(
 			(entries) => {
 				for (const entry of entries) {
 					if (entry.isIntersecting) {
-						requestAnimationFrame(() => {
-							node.style.opacity = '1';
-							node.style.transform = 'translateY(0)';
-						});
+						setTimeout(() => {
+							requestAnimationFrame(() => {
+								node.style.opacity = '1';
+								node.style.transform = 'translateY(0)';
+							});
+						}, delay);
 						observer.disconnect();
 					}
 				}
diff --git a/tools/ui/src/lib/assets/logo.svg b/tools/ui/src/lib/assets/logo.svg
new file mode 100644
index 000000000..05424790a
--- /dev/null
+++ b/tools/ui/src/lib/assets/logo.svg
@@ -0,0 +1,7 @@
+<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
+    <path d="M244.95 8C215.233 8 187.774 23.8591 172.923 49.5999L95.6009 183.625C60.2162 244.959 104.481 321.6 175.29 321.6H208L316.977 132.708C348.959 77.2719 308.95 8 244.95 8ZM208 321.6H351.947C415.982 321.6 456.013 390.91 424.013 446.377C409.155 472.132 381.681 488 351.947 488H271.29C200.481 488 156.216 411.359 191.601 350.026L208 321.6Z" fill="currentColor"/>
+    <path d="M208 321.6H16L106.462 164.8L208 321.6Z" fill="currentColor"/>
+    <path d="M388.923 8L208 321.6L253.6 8H388.923Z" fill="currentColor"/>
+    <path d="M304 488H112L202.462 331.2L304 488Z" fill="currentColor"/>
+    <path d="M496 321.6H208L419.399 454.4L496 321.6Z" fill="currentColor"/>
+</svg>
diff --git a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte
index f156df669..8a86557bb 100644
--- a/tools/ui/src/lib/components/app/actions/ActionIcon.svelte
+++ b/tools/ui/src/lib/components/app/actions/ActionIcon.svelte
@@ -8,12 +8,13 @@
 		ariaLabel?: string;
 		class?: string;
 		disabled?: boolean;
+		href?: string;
 		icon: Component;
 		iconSize?: string;
-		onclick: (e?: MouseEvent) => void;
+		onclick?: (e?: MouseEvent) => void;
 		size?: ButtonSize;
 		stopPropagationOnClick?: boolean;
-		tooltip: string;
+		tooltip?: string;
 		variant?: ButtonVariant;
 		tooltipSide?: TooltipSide;
 	}
@@ -22,6 +23,7 @@
 		icon,
 		tooltip,
 		variant = 'ghost',
+		href = '',
 		size = 'sm',
 		class: className = '',
 		disabled = false,
@@ -31,34 +33,49 @@
 		onclick,
 		ariaLabel
 	}: Props = $props();
+
+	let innerWidth = $state(0);
+	const showTooltip = $derived(!!tooltip && innerWidth > 768);
 </script>
 
-<Tooltip.Root>
-	<Tooltip.Trigger>
-		<!-- prevent another nested button element -->
-		{#snippet child({ props })}
-			<Button
-				{...props}
-				{variant}
-				{size}
-				{disabled}
-				onclick={(e: MouseEvent) => {
-					if (stopPropagationOnClick) e.stopPropagation();
+{#snippet button(props = {})}
+	<Button
+		{...props}
+		{href}
+		{variant}
+		{size}
+		{disabled}
+		onclick={(e: MouseEvent) => {
+			if (stopPropagationOnClick) e.stopPropagation();
 
-					onclick?.(e);
-				}}
-				class="h-6 w-6 p-0 {className} flex hover:bg-transparent data-[state=open]:bg-transparent!"
-				aria-label={ariaLabel || tooltip}
-			>
-				{#if icon}
-					{@const IconComponent = icon}
-					<IconComponent class={iconSize} />
-				{/if}
-			</Button>
-		{/snippet}
-	</Tooltip.Trigger>
+			onclick?.(e);
+		}}
+		class="h-6 w-6 p-0 {className} flex hover:bg-transparent data-[state=open]:bg-transparent!"
+		aria-label={ariaLabel || tooltip}
+	>
+		{#if icon}
+			{@const IconComponent = icon}
 
-	<Tooltip.Content side={tooltipSide}>
-		<p>{tooltip}</p>
-	</Tooltip.Content>
-</Tooltip.Root>
+			<IconComponent class={iconSize} />
+		{/if}
+	</Button>
+{/snippet}
+
+{#if showTooltip}
+	<Tooltip.Root>
+		<Tooltip.Trigger>
+			<!-- prevent another nested button element -->
+			{#snippet child({ props })}
+				{@render button(props)}
+			{/snippet}
+		</Tooltip.Trigger>
+
+		<Tooltip.Content side={tooltipSide}>
+			<p>{tooltip}</p>
+		</Tooltip.Content>
+	</Tooltip.Root>
+{:else}
+	{@render button({ href })}
+{/if}
+
+<svelte:window bind:innerWidth />
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
index ed26f9ea5..9b2077b8d 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
@@ -494,7 +494,7 @@
 	/>
 
 	<div
-		class="{INPUT_CLASSES} overflow-hidden rounded-3xl backdrop-blur-md {disabled
+		class="{INPUT_CLASSES} overflow-hidden rounded-4xl md:rounded-3xl backdrop-blur-md {disabled
 			? 'cursor-not-allowed opacity-60'
 			: ''}"
 		data-slot="input-area"
@@ -510,7 +510,7 @@
 		/>
 
 		<div
-			class="flex-column relative min-h-[48px] items-center rounded-3xl py-2 pb-2.25 shadow-sm transition-all focus-within:shadow-md md:!py-3"
+			class="flex-column relative min-h-12 items-center rounded-4xl md:rounded-3xl py-2 pb-2.25 shadow-sm transition-all focus-within:shadow-md md:py-3!"
 			onpaste={handlePaste}
 		>
 			<ChatFormTextarea
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte
index 7175888aa..ad2a0def9 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddButton.svelte
@@ -15,7 +15,7 @@
 <Tooltip.Root>
 	<Tooltip.Trigger class="w-full">
 		<Button
-			class="file-upload-button h-8 w-8 rounded-full p-0"
+			class="file-upload-button md:h-8 md:w-8 h-9 w-9 rounded-full p-0"
 			{disabled}
 			{onclick}
 			variant="secondary"
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
index c4069163f..2b708aae5 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
@@ -15,6 +15,7 @@
 	import { McpLogo } from '$lib/components/app';
 	import { PencilRuler, ChevronDown, ChevronRight } from '@lucide/svelte';
 	import { HealthCheckStatus } from '$lib/enums';
+	import { AttachmentAction } from '$lib/enums/attachment.enums';
 
 	interface Props {
 		class?: string;
@@ -270,14 +271,22 @@
 					</Collapsible.Root>
 				{/if}
 
-				<button type="button" class={sheetItemClass} onclick={onSystemPromptClick}>
+				<button
+					type="button"
+					class={sheetItemClass}
+					onclick={() => attachmentMenu.callbacks[AttachmentAction.SYSTEM_PROMPT_CLICK]()}
+				>
 					<MessageSquare class="h-4 w-4 shrink-0" />
 
 					<span>System Message</span>
 				</button>
 
 				{#if hasMcpPromptsSupport}
-					<button type="button" class={sheetItemClass} onclick={onMcpPromptClick}>
+					<button
+						type="button"
+						class={sheetItemClass}
+						onclick={() => attachmentMenu.callbacks[AttachmentAction.MCP_PROMPT_CLICK]()}
+					>
 						<Zap class="h-4 w-4 shrink-0" />
 
 						<span>MCP Prompt</span>
@@ -285,7 +294,11 @@
 				{/if}
 
 				{#if hasMcpResourcesSupport}
-					<button type="button" class={sheetItemClass} onclick={onMcpResourcesClick}>
+					<button
+						type="button"
+						class={sheetItemClass}
+						onclick={() => attachmentMenu.callbacks[AttachmentAction.MCP_RESOURCES_CLICK]()}
+					>
 						<FolderOpen class="h-4 w-4 shrink-0" />
 
 						<span>MCP Resources</span>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte
index 6a91bf905..08d691c14 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionsAdd.svelte
@@ -42,6 +42,7 @@
 		{hasMcpPromptsSupport}
 		{hasMcpResourcesSupport}
 		{onFileUpload}
+		{onSystemPromptClick}
 		{onMcpPromptClick}
 		{onMcpResourcesClick}
 	>
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
index 8774bf63a..eff0364fa 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionSubmit.svelte
@@ -20,7 +20,7 @@
 		type="submit"
 		disabled={isDisabled}
 		class={[
-			'h-8 w-8 rounded-full p-0',
+			'md:h-8 md:w-8 h-9 w-9 rounded-full p-0',
 			showErrorState &&
 				'bg-red-400/10 text-red-400 hover:bg-red-400/20 hover:text-red-400 disabled:opacity-100'
 		]}
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
index 72e62f319..3e683389f 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormTextarea.svelte
@@ -1,4 +1,5 @@
 <script lang="ts">
+	import { isMobile } from '$lib/stores/viewport.svelte';
 	import { autoResizeTextarea } from '$lib/utils';
 	import { onMount } from 'svelte';
 
@@ -37,7 +38,9 @@
 	}
 
 	export function focus() {
-		textareaElement?.focus();
+		if (isMobile.current) return;
+
+		textareaElement?.focus({ preventScroll: true });
 	}
 
 	export function resetHeight() {
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
index 4d0b302d2..dadcae0c4 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
@@ -231,7 +231,7 @@
 			editedContent = message.content;
 		}
 
-		textareaElement?.focus();
+		textareaElement?.focus({ preventScroll: true });
 		editedExtras = message.extra ? [...message.extra] : [];
 		editedUploadedFiles = [];
 
@@ -324,7 +324,7 @@
 	}
 </script>
 
-<div use:fadeInView>
+<div use:fadeInView class="chat-message">
 	{#if message.role === MessageRole.SYSTEM}
 		<ChatMessageSystem
 			bind:textareaElement
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
index 2272eaedb..a1d02d6ec 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
@@ -207,6 +207,42 @@
 			isLastAssistantMessage
 	);
 
+	let assistantEl: HTMLDivElement | undefined = $state();
+	let lastUserMessageHeight = $state(0);
+	let assistantMarginTop = $state(0);
+
+	$effect(() => {
+		if (!assistantEl) return;
+
+		assistantMarginTop = Math.round(parseFloat(getComputedStyle(assistantEl).marginTop));
+
+		const chatMessageEl = assistantEl.closest('.chat-message');
+		const previousChatMessage = chatMessageEl?.previousElementSibling;
+		const userMessageEl = previousChatMessage?.querySelector(
+			'.chat-message-user'
+		) as HTMLElement | null;
+
+		if (!userMessageEl) {
+			lastUserMessageHeight = 0;
+			return;
+		}
+
+		const updateHeight = () => {
+			const rect = userMessageEl.getBoundingClientRect();
+			const marginTop = Math.round(parseFloat(getComputedStyle(userMessageEl).marginTop));
+			lastUserMessageHeight = Math.round(rect.height + marginTop);
+		};
+
+		updateHeight();
+
+		const resizeObserver = new ResizeObserver(updateHeight);
+		resizeObserver.observe(userMessageEl);
+
+		return () => {
+			resizeObserver.disconnect();
+		};
+	});
+
 	function handleCopyModel() {
 		void copyToClipboard(displayedModel ?? '');
 	}
@@ -219,12 +255,17 @@
 </script>
 
 <div
-	class="text-md group w-full leading-7.5 {className}"
+	bind:this={assistantEl}
+	class="chat-message-assistant text-md group w-full leading-7.5 {className}"
+	style:--last-user-message-height={lastUserMessageHeight > 0
+		? `${lastUserMessageHeight}px`
+		: undefined}
+	style:--assistant-margin-top={assistantMarginTop > 0 ? `${assistantMarginTop}px` : undefined}
 	role="group"
 	aria-label="Assistant message with actions"
 >
 	{#if showProcessingInfoTop}
-		<div class="mt-6 w-full max-w-[48rem]" in:fade>
+		<div class="mt-6 w-full max-w-3xl" in:fade>
 			<div class="processing-container">
 				<span class="processing-text">
 					{modelLoadingText ??
@@ -257,7 +298,7 @@
 	{/if}
 
 	{#if showProcessingInfoBottom}
-		<div class="mt-4 w-full max-w-[48rem]" in:fade>
+		<div class="mt-4 w-full max-w-3xl" in:fade>
 			<div class="processing-container">
 				<span class="processing-text">
 					{modelLoadingText ??
@@ -351,6 +392,23 @@
 </div>
 
 <style>
+	:global(.chat-message):last-child .chat-message-assistant {
+		--assistant-min-height-offset: calc(
+			var(--last-user-message-height, 19rem) + var(--chat-form-height, 6rem) +
+				var(--chat-form-bottom-position, 0.5rem) + var(--chat-form-padding-top, 6rem) +
+				var(--assistant-margin-top, 3rem)
+		);
+		min-height: calc(100dvh - var(--assistant-min-height-offset));
+
+		@media (width > 768px) {
+			--assistant-min-height-offset: calc(
+				var(--last-user-message-height, 18rem) + var(--chat-form-height, 6rem) +
+					var(--chat-form-bottom-position, 1rem) + var(--chat-form-padding-top, 6rem) +
+					var(--assistant-margin-top, 3rem)
+			);
+		}
+	}
+
 	.processing-container {
 		display: flex;
 		flex-direction: column;
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte
index 96ec1ddfd..80a0183e6 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUser.svelte
@@ -48,7 +48,7 @@
 
 <div
 	aria-label="User message with actions"
-	class="group flex flex-col items-end gap-3 md:gap-2 {className}"
+	class="chat-message-user group flex flex-col items-end gap-3 md:gap-2 {className}"
 	role="group"
 >
 	{#if editCtx.isEditing}
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte
index dabb337dd..01fc9d365 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserBubble.svelte
@@ -19,7 +19,7 @@
 		renderMarkdown = false,
 		textColorClass = 'text-foreground',
 		cardBgClass = 'dark:bg-primary/15',
-		maxHeightStyle = 'max-height: var(--max-message-height);'
+		maxHeightStyle = ''
 	}: Props = $props();
 
 	let isMultiline = $state(false);
@@ -59,7 +59,7 @@
 
 {#if content.trim()}
 	<Card
-		class="max-w-[80%] overflow-y-auto rounded-[1.125rem] border-none bg-primary/5 px-3.75 py-1.5 {textColorClass} backdrop-blur-md data-[multiline]:py-2.5 {cardBgClass}"
+		class="chat-message-user-bubble max-w-[80%] overflow-y-auto rounded-[1.125rem] border-none bg-primary/5 px-3.75 py-1.5 {textColorClass} backdrop-blur-md data-multiline:py-2.5 {cardBgClass}"
 		data-multiline={isMultiline ? '' : undefined}
 		style="{maxHeightStyle} overflow-wrap: anywhere; word-break: break-word;"
 	>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
index 281e6ad0c..2d959cfc2 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
@@ -37,6 +37,7 @@
 	let allConversationMessages = $state<DatabaseMessage[]>([]);
 	let isVisible = $state(false);
 	let previousConversationId = $state<string | null>(null);
+	let previousRouteId = $state<string | null>(null);
 
 	const currentConfig = config();
 
@@ -157,8 +158,9 @@
 		});
 	});
 
-	beforeNavigate(() => {
+	beforeNavigate((navigation) => {
 		isVisible = false;
+		previousRouteId = navigation.from?.route.id ?? null;
 	});
 
 	afterNavigate(() => {
@@ -249,12 +251,13 @@
 </script>
 
 <div
-	class="transition-opacity delay-300 duration-500 ease-out
-		{isVisible ? 'opacity-100' : 'opacity-0'}"
+	class="transition-opacity duration-500 ease-out
+		{isVisible ? 'opacity-100' : 'opacity-0'}
+		{previousRouteId === '/(chat)/chat/[id]' ? '' : 'delay-300'}"
 >
 	{#each displayMessages as { message, toolMessages, isLastAssistantMessage, siblingInfo } (message.id)}
 		<ChatMessage
-			class="mx-auto mt-12 w-full max-w-[48rem]"
+			class="mx-auto mt-12 w-full max-w-3xl"
 			{message}
 			{toolMessages}
 			{isLastAssistantMessage}
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
index e733a64a9..18635ba39 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
@@ -1,31 +1,28 @@
 <script lang="ts">
-	import { Trash2 } from '@lucide/svelte';
-	import { afterNavigate } from '$app/navigation';
 	import { page } from '$app/state';
 	import {
 		ChatScreenForm,
 		ChatMessages,
 		ChatScreenDragOverlay,
 		ChatScreenProcessingInfo,
-		ChatScreenActionScrollDown,
-		DialogEmptyFileAlert,
-		DialogFileUploadError,
-		DialogChatError,
 		ServerLoadingSplash,
-		DialogConfirmation,
 		ChatScreenServerError
 	} from '$lib/components/app';
 	import { setProcessingInfoContext } from '$lib/contexts';
-	import { ErrorDialogType } from '$lib/enums';
 	import { createAutoScrollController } from '$lib/hooks/use-auto-scroll.svelte';
+	import { useChatScreenActiveModel } from '$lib/hooks/use-chat-screen-active-model.svelte';
+	import { useChatScreenDragAndDrop } from '$lib/hooks/use-chat-screen-drag-and-drop.svelte';
+	import { useChatScreenFileUpload } from '$lib/hooks/use-chat-screen-file-upload.svelte';
+	import { useChatScreenScroll } from '$lib/hooks/use-chat-screen-scroll.svelte';
 	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
+	import { device } from '$lib/stores/device.svelte';
+	import { isMobile } from '$lib/stores/viewport.svelte';
 	import {
 		chatStore,
 		errorDialog,
 		isLoading,
 		isChatStreaming,
 		isEditing,
-		getAddFilesHandler,
 		activeProcessingState
 	} from '$lib/stores/chat.svelte';
 	import {
@@ -34,138 +31,81 @@
 		activeConversation
 	} from '$lib/stores/conversations.svelte';
 	import { config } from '$lib/stores/settings.svelte';
-	import { serverLoading, serverError, isRouterMode } from '$lib/stores/server.svelte';
-	import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
-	import { isFileTypeSupported, filterFilesByModalities } from '$lib/utils';
-	import { parseFilesToMessageExtras, processFilesToChatUploaded } from '$lib/utils/browser-only';
-	import { onMount } from 'svelte';
+	import { serverLoading, serverError } from '$lib/stores/server.svelte';
+	import { parseFilesToMessageExtras } from '$lib/utils/browser-only';
+	import { onDestroy, onMount } from 'svelte';
 	import ChatScreenGreeting from './ChatScreenGreeting.svelte';
+	import ChatScreenActionScrollDown from './ChatScreenActionScrollDown.svelte';
+	import ChatScreenDialogsAndAlerts from './ChatScreenDialogsAndAlerts.svelte';
+	import { ROUTES } from '$lib/constants';
 
 	let { showCenteredEmpty = false } = $props();
 
-	const autoScroll = createAutoScrollController();
-
-	let disableAutoScroll = $derived(Boolean(config().disableAutoScroll));
-	let chatScrollContainer: HTMLDivElement | undefined = $state();
-	let dragCounter = $state(0);
-	let isDragOver = $state(false);
-	let showFileErrorDialog = $state(false);
-	let uploadedFiles = $state<ChatUploadedFile[]>([]);
-
-	let fileErrorData = $state<{
-		generallyUnsupported: File[];
-		modalityUnsupported: File[];
-		modalityReasons: Record<string, string>;
-		supportedTypes: string[];
-	}>({
-		generallyUnsupported: [],
-		modalityUnsupported: [],
-		modalityReasons: {},
-		supportedTypes: []
-	});
-
-	let showDeleteDialog = $state(false);
-
-	let showEmptyFileDialog = $state(false);
-
-	let processingInfoVisible = $state(false);
-
-	let emptyFileNames = $state<string[]>([]);
-
-	let initialMessage = $state('');
-
-	let isEmpty = $derived(
-		showCenteredEmpty && !activeConversation() && activeMessages().length === 0 && !isLoading()
-	);
-
-	let activeErrorDialog = $derived(errorDialog());
-	let isServerLoading = $derived(serverLoading());
-	let hasPropsError = $derived(!!serverError());
-
-	let isCurrentConversationLoading = $derived(isLoading() || isChatStreaming());
-
-	let showProcessingInfo = $derived(
-		isCurrentConversationLoading ||
-			(config().keepStatsVisible && !!page.params.id) ||
-			activeProcessingState() !== null
-	);
-
-	let isRouter = $derived(isRouterMode());
-
-	let conversationModel = $derived(
-		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
-	);
-
-	let activeModelId = $derived.by(() => {
-		const options = modelOptions();
-
-		if (!isRouter) {
-			return options.length > 0 ? options[0].model : null;
-		}
-
-		const selectedId = selectedModelId();
-		if (selectedId) {
-			const model = options.find((m) => m.id === selectedId);
-			if (model) return model.model;
-		}
-
-		if (conversationModel) {
-			const model = options.find((m) => m.model === conversationModel);
-			if (model) return model.model;
-		}
-
-		return null;
-	});
-
-	let modelPropsVersion = $state(0);
-
 	setProcessingInfoContext({
 		get showProcessingInfo() {
 			return showProcessingInfo;
 		}
 	});
 
-	$effect(() => {
-		if (activeModelId) {
-			const cached = modelsStore.getModelProps(activeModelId);
+	let disableAutoScroll = $derived(Boolean(config().disableAutoScroll) || isMobile.current);
+	let isMobileUserScrolledUp = $state(false);
+	let mobileScrollDownHint = $state(false);
+	let mobileScrollDownHintLockedUntil = $state(0);
+	let emptyFileNames = $state<string[]>([]);
+	let initialMessage = $state('');
+	let showDeleteDialog = $state(false);
+	let showEmptyFileDialog = $state(false);
+	let isEmpty = $derived(
+		showCenteredEmpty && !activeConversation() && activeMessages().length === 0 && !isLoading()
+	);
+	let activeErrorDialog = $derived(errorDialog());
+	let isServerLoading = $derived(serverLoading());
+	let hasPropsError = $derived(!!serverError());
+	let isCurrentConversationLoading = $derived(isLoading() || isChatStreaming());
+	let showProcessingInfo = $derived(
+		isCurrentConversationLoading ||
+			(config().keepStatsVisible && !!page.params.id) ||
+			activeProcessingState() !== null
+	);
+	let chatFormBottomPosition = $derived.by(() => {
+		if (!isMobile.current) return '1rem';
+		if (device.isStandalone) return '1.5rem';
+		if (device.isIOSSafari) return '0.25rem';
+		return '0.5rem';
+	});
 
-			if (!cached) {
-				modelsStore.fetchModelProps(activeModelId).then(() => {
-					modelPropsVersion++;
-				});
+	const autoScroll = createAutoScrollController();
+	const scroll = useChatScreenScroll(autoScroll);
+	const activeModel = useChatScreenActiveModel();
+	const fileUpload = useChatScreenFileUpload({
+		capabilities: () => ({
+			hasVision: activeModel.hasVisionModality,
+			hasAudio: activeModel.hasAudioModality,
+			hasVideo: activeModel.hasVideoModality
+		}),
+		activeModelId: () => activeModel.activeModelId
+	});
+	const dragAndDrop = useChatScreenDragAndDrop({
+		onDrop: fileUpload.handleFileUpload
+	});
+	const { handleKeydown } = useKeyboardShortcuts({
+		deleteActiveConversation: () => {
+			if (activeConversation()) {
+				showDeleteDialog = true;
 			}
 		}
 	});
 
-	let hasAudioModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion;
+	function handleMobileScroll() {
+		if (!isMobile.current) return;
 
-			return modelsStore.modelSupportsAudio(activeModelId);
-		}
+		const container = scroll.chatScrollContainer;
+		if (!container) return;
 
-		return false;
-	});
-
-	let hasVideoModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion;
-
-			return modelsStore.modelSupportsVideo(activeModelId);
-		}
-
-		return false;
-	});
-
-	let hasVisionModality = $derived.by(() => {
-		if (activeModelId) {
-			void modelPropsVersion;
-
-			return modelsStore.modelSupportsVision(activeModelId);
-		}
-
-		return false;
-	});
+		const distanceFromBottom =
+			container.scrollHeight - container.clientHeight - container.scrollTop;
+		isMobileUserScrolledUp = distanceFromBottom > 300;
+	}
 
 	async function handleDeleteConfirm() {
 		const conversation = activeConversation();
@@ -177,27 +117,69 @@
 		showDeleteDialog = false;
 	}
 
-	function handleProcessingInfoVisibility(visible: boolean) {
-		processingInfoVisible = visible;
-	}
+	async function handleSendMessage(message: string, files?: ChatUploadedFile[]): Promise<boolean> {
+		const plainFiles = files ? $state.snapshot(files) : undefined;
+		const result = plainFiles
+			? await parseFilesToMessageExtras(plainFiles, activeModel.activeModelId ?? undefined)
+			: undefined;
 
-	function handleDragEnter(event: DragEvent) {
-		event.preventDefault();
-
-		dragCounter++;
-
-		if (event.dataTransfer?.types.includes('Files')) {
-			isDragOver = true;
+		if (result?.emptyFiles && result.emptyFiles.length > 0) {
+			emptyFileNames = result.emptyFiles;
+			showEmptyFileDialog = true;
+			if (files) {
+				const emptyFileNamesSet = new Set(result.emptyFiles);
+				fileUpload.uploadedFiles = fileUpload.uploadedFiles.filter(
+					(file) => !emptyFileNamesSet.has(file.name)
+				);
+			}
+			return false;
 		}
+
+		handleSendLikeScroll();
+
+		await chatStore.sendMessage(message, result?.extras);
+		return true;
 	}
 
-	function handleDragLeave(event: DragEvent) {
-		event.preventDefault();
+	function handleSendLikeScroll() {
+		if (!isMobile.current) {
+			autoScroll.enable();
+		}
 
-		dragCounter--;
+		setTimeout(() => {
+			const container = scroll.chatScrollContainer;
+			if (!container) return;
 
-		if (dragCounter === 0) {
-			isDragOver = false;
+			const lastUserBubble = container.querySelector(
+				'.chat-message:nth-last-child(2) .chat-message-user .chat-message-user-bubble'
+			) as HTMLElement | null;
+
+			if (isMobile.current) {
+				// Keep the last user message bubble just above the input on mobile
+				const bubbleHeight = lastUserBubble?.scrollHeight ?? 0;
+				const baseHeight = container.scrollHeight - innerHeight;
+
+				container.scrollTo({
+					top: bubbleHeight > 0 ? baseHeight - bubbleHeight : baseHeight,
+					behavior: 'smooth'
+				});
+			} else if (lastUserBubble) {
+				// On desktop, place the last user message near the top of the viewport
+				const topPadding = 24;
+				const bubbleRect = lastUserBubble.getBoundingClientRect();
+				container.scrollTo({
+					top: Math.max(0, container.scrollTop + bubbleRect.top - topPadding),
+					behavior: 'smooth'
+				});
+			} else {
+				autoScroll.scrollToBottom();
+			}
+		}, 100);
+
+		if (isMobile.current) {
+			autoScroll.setDisabled(disableAutoScroll);
+			mobileScrollDownHint = true;
+			mobileScrollDownHintLockedUntil = Date.now() + 500;
 		}
 	}
 
@@ -207,273 +189,138 @@
 		}
 	}
 
-	function handleDragOver(event: DragEvent) {
-		event.preventDefault();
-	}
-
-	function handleDrop(event: DragEvent) {
-		event.preventDefault();
-
-		isDragOver = false;
-		dragCounter = 0;
-
-		if (event.dataTransfer?.files) {
-			const files = Array.from(event.dataTransfer.files);
-
-			if (isEditing()) {
-				const handler = getAddFilesHandler();
-
-				if (handler) {
-					handler(files);
-					return;
-				}
-			}
-
-			processFiles(files);
-		}
-	}
-
-	function handleFileRemove(fileId: string) {
-		uploadedFiles = uploadedFiles.filter((f) => f.id !== fileId);
-	}
-
-	function handleFileUpload(files: File[]) {
-		processFiles(files);
-	}
-
-	const { handleKeydown } = useKeyboardShortcuts({
-		deleteActiveConversation: () => {
-			if (activeConversation()) {
-				showDeleteDialog = true;
-			}
-		}
-	});
-
 	async function handleSystemPromptAdd(draft: { message: string; files: ChatUploadedFile[] }) {
 		if (draft.message || draft.files.length > 0) {
 			chatStore.savePendingDraft(draft.message, draft.files);
 		}
-
 		await chatStore.addSystemPrompt();
 	}
 
-	function handleScroll() {
-		autoScroll.handleScroll();
-	}
-
-	async function handleSendMessage(message: string, files?: ChatUploadedFile[]): Promise<boolean> {
-		const plainFiles = files ? $state.snapshot(files) : undefined;
-		const result = plainFiles
-			? await parseFilesToMessageExtras(plainFiles, activeModelId ?? undefined)
-			: undefined;
-
-		if (result?.emptyFiles && result.emptyFiles.length > 0) {
-			emptyFileNames = result.emptyFiles;
-			showEmptyFileDialog = true;
-
-			if (files) {
-				const emptyFileNamesSet = new Set(result.emptyFiles);
-				uploadedFiles = uploadedFiles.filter((file) => !emptyFileNamesSet.has(file.name));
-			}
-			return false;
-		}
-
-		const extras = result?.extras;
-
-		// Enable autoscroll for user-initiated message sending
-		autoScroll.enable();
-		await chatStore.sendMessage(message, extras);
-		autoScroll.scrollToBottom();
-
-		return true;
-	}
-
-	async function processFiles(files: File[]) {
-		const generallySupported: File[] = [];
-		const generallyUnsupported: File[] = [];
-
-		for (const file of files) {
-			if (isFileTypeSupported(file.name, file.type)) {
-				generallySupported.push(file);
-			} else {
-				generallyUnsupported.push(file);
-			}
-		}
-
-		// Use model-specific capabilities for file validation
-		const capabilities = {
-			hasVision: hasVisionModality,
-			hasAudio: hasAudioModality,
-			hasVideo: hasVideoModality
-		};
-		const { supportedFiles, unsupportedFiles, modalityReasons } = filterFilesByModalities(
-			generallySupported,
-			capabilities
-		);
-
-		const allUnsupportedFiles = [...generallyUnsupported, ...unsupportedFiles];
-
-		if (allUnsupportedFiles.length > 0) {
-			const supportedTypes: string[] = ['text files', 'PDFs'];
-
-			if (hasVisionModality) supportedTypes.push('images');
-			if (hasAudioModality) supportedTypes.push('audio files');
-			if (hasVideoModality) supportedTypes.push('video files');
-
-			fileErrorData = {
-				generallyUnsupported,
-				modalityUnsupported: unsupportedFiles,
-				modalityReasons,
-				supportedTypes
-			};
-			showFileErrorDialog = true;
-		}
-
-		if (supportedFiles.length > 0) {
-			const processed = await processFilesToChatUploaded(
-				supportedFiles,
-				activeModelId ?? undefined
-			);
-			uploadedFiles = [...uploadedFiles, ...processed];
-		}
-	}
-
-	afterNavigate(() => {
-		if (!disableAutoScroll) {
+	$effect(() => {
+		const shouldDisableAutoScroll =
+			config().disableAutoScroll || (isMobile.current && isCurrentConversationLoading);
+		autoScroll.setDisabled(shouldDisableAutoScroll);
+		if (!shouldDisableAutoScroll) {
 			autoScroll.enable();
 		}
 	});
 
-	function handleMessagesReady() {
-		if (disableAutoScroll) return;
-
-		if (!autoScroll.userScrolledUp) {
-			requestAnimationFrame(() => {
-				autoScroll.scrollToBottom('instant');
-			});
-		}
-	}
-
 	onMount(() => {
+		const pendingDraft = chatStore.consumePendingDraft();
+		if (pendingDraft) {
+			initialMessage = pendingDraft.message;
+			fileUpload.uploadedFiles = pendingDraft.files;
+		}
+
 		autoScroll.startObserving();
 
 		if (!disableAutoScroll) {
 			autoScroll.enable();
 		}
 
-		const pendingDraft = chatStore.consumePendingDraft();
-		if (pendingDraft) {
-			initialMessage = pendingDraft.message;
-			uploadedFiles = pendingDraft.files;
+		if (isMobile.current && isCurrentConversationLoading) {
+			mobileScrollDownHint = true;
+			mobileScrollDownHintLockedUntil = Date.now() + 500;
 		}
+
+		handleMobileScroll();
 	});
 
-	$effect(() => {
-		autoScroll.setContainer(chatScrollContainer);
-	});
-
-	$effect(() => {
-		autoScroll.setDisabled(disableAutoScroll);
-	});
+	onDestroy(() => autoScroll.destroy());
 </script>
 
-{#if isDragOver}
+{#if dragAndDrop.isDragOver}
 	<ChatScreenDragOverlay />
 {/if}
 
-<svelte:window onkeydown={handleKeydown} />
+<svelte:window
+	onkeydown={handleKeydown}
+	onscroll={(e) => {
+		scroll.handleScroll(e);
+		handleMobileScroll();
+		if (e.isTrusted && Date.now() > mobileScrollDownHintLockedUntil) {
+			mobileScrollDownHint = false;
+		}
+	}}
+/>
 
 {#if isServerLoading}
 	<ServerLoadingSplash />
 {:else}
 	<div
-		bind:this={chatScrollContainer}
-		aria-label="Chat interface with file drop zone"
-		class="flex h-full flex-col overflow-y-auto px-4 md:px-6"
-		ondragenter={handleDragEnter}
-		ondragleave={handleDragLeave}
-		ondragover={handleDragOver}
-		ondrop={handleDrop}
-		onscroll={handleScroll}
+		class="chat-screen flex grow flex-col min-h-[calc(100dvh-1rem)] md:min-h-full px-4 md:py-0 pt-12 pb-48 md:pb-4"
+		style:--chat-form-bottom-position={chatFormBottomPosition}
+		ondragenter={dragAndDrop.dragHandlers.dragenter}
+		ondragleave={dragAndDrop.dragHandlers.dragleave}
+		ondragover={dragAndDrop.dragHandlers.dragover}
+		ondrop={dragAndDrop.dragHandlers.drop}
 		role="main"
 	>
-		<div class="flex grow flex-col pt-14">
-			{#if !isEmpty}
-				<ChatMessages
-					messages={activeMessages()}
-					onMessagesReady={handleMessagesReady}
-					onUserAction={() => {
-						autoScroll.enable();
-						if (!autoScroll.userScrolledUp) {
-							autoScroll.scrollToBottom();
-						}
-					}}
-				/>
-			{/if}
+		{#if !isEmpty}
+			<ChatMessages
+				messages={activeMessages()}
+				onUserAction={() => {
+					handleSendLikeScroll();
+				}}
+			/>
+		{/if}
 
-			<div
-				class={[
-					'pointer-events-none sticky right-4 left-4 mt-auto transition-all duration-200',
-					isEmpty ? 'bottom-[calc(50dvh-7rem)]' : 'bottom-4 pt-24 md:pt-32'
-				]}
-			>
-				<ChatScreenGreeting {isEmpty} />
+		<div
+			class={[
+				'pointer-events-none md:sticky fixed  mt-auto transition-all duration-200',
+				device.isStandalone
+					? 'bottom-6 right-4 left-4'
+					: device.isIOSSafari
+						? 'bottom-1 left-2 right-2'
+						: 'bottom-2 right-2 left-2',
+				isEmpty ? 'md:bottom-[calc(50dvh-7rem)] 2xl:bottom-[calc(50dvh-4rem)]' : 'md:bottom-4'
+			]}
+			style:padding-top={!isEmpty ? 'var(--chat-form-padding-top)' : undefined}
+		>
+			<ChatScreenGreeting {isEmpty} />
 
-				<ChatScreenActionScrollDown
-					container={chatScrollContainer}
-					hasProcessingInfoVisible={processingInfoVisible}
-				/>
+			<ChatScreenServerError />
 
-				<ChatScreenProcessingInfo onVisibilityChange={handleProcessingInfoVisibility} />
-
-				<ChatScreenServerError />
-
-				<div class="conversation-chat-form pointer-events-auto rounded-t-3xl">
-					<ChatScreenForm
-						disabled={hasPropsError || isEditing()}
-						{initialMessage}
-						isLoading={isCurrentConversationLoading}
-						onFileRemove={handleFileRemove}
-						onFileUpload={handleFileUpload}
-						onSend={handleSendMessage}
-						onStop={() => chatStore.stopGeneration()}
-						onSystemPromptAdd={handleSystemPromptAdd}
-						bind:uploadedFiles
+			<div class="pointer-events-none flex flex-col gap-6 items-center w-full">
+				{#if (isMobile.current ? mobileScrollDownHint || isMobileUserScrolledUp : autoScroll.userScrolledUp) && page.url.hash.includes(ROUTES.CHAT) && page.params.id}
+					<ChatScreenActionScrollDown
+						onclick={() => {
+							mobileScrollDownHint = false;
+							scroll.chatScrollContainer?.scrollTo({
+								top: scroll.chatScrollContainer.scrollHeight,
+								behavior: 'smooth'
+							});
+						}}
 					/>
-				</div>
+				{/if}
+
+				{#if showProcessingInfo}
+					<ChatScreenProcessingInfo />
+				{/if}
 			</div>
+
+			<ChatScreenForm
+				class="pointer-events-auto conversation-chat-form"
+				disabled={hasPropsError || isEditing()}
+				{initialMessage}
+				isLoading={isCurrentConversationLoading}
+				onFileRemove={fileUpload.handleFileRemove}
+				onFileUpload={fileUpload.handleFileUpload}
+				onSend={handleSendMessage}
+				onStop={() => chatStore.stopGeneration()}
+				onSystemPromptAdd={handleSystemPromptAdd}
+				bind:uploadedFiles={fileUpload.uploadedFiles}
+			/>
 		</div>
 	</div>
 {/if}
 
-<DialogFileUploadError bind:open={showFileErrorDialog} {fileErrorData} />
-
-<DialogConfirmation
-	bind:open={showDeleteDialog}
-	title="Delete Conversation"
-	description="Are you sure you want to delete this conversation? This action cannot be undone and will permanently remove all messages in this conversation."
-	confirmText="Delete"
-	cancelText="Cancel"
-	variant="destructive"
-	icon={Trash2}
-	onConfirm={handleDeleteConfirm}
-	onCancel={() => (showDeleteDialog = false)}
-/>
-
-<DialogEmptyFileAlert
-	bind:open={showEmptyFileDialog}
-	emptyFiles={emptyFileNames}
-	onOpenChange={(open) => {
-		if (!open) {
-			emptyFileNames = [];
-		}
-	}}
-/>
-
-<DialogChatError
-	message={activeErrorDialog?.message ?? ''}
-	contextInfo={activeErrorDialog?.contextInfo}
-	onOpenChange={handleErrorDialogOpenChange}
-	open={Boolean(activeErrorDialog)}
-	type={activeErrorDialog?.type ?? ErrorDialogType.SERVER}
+<ChatScreenDialogsAndAlerts
+	{showDeleteDialog}
+	{handleDeleteConfirm}
+	{showEmptyFileDialog}
+	{emptyFileNames}
+	{activeErrorDialog}
+	{handleErrorDialogOpenChange}
+	{fileUpload}
 />
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte
index a22c491ad..c470ac729 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenActionScrollDown.svelte
@@ -1,58 +1,18 @@
 <script lang="ts">
 	import { ArrowDown } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
+	import ActionIcon from '$lib/components/app/actions/ActionIcon.svelte';
 
-	interface Props {
-		container: HTMLDivElement | undefined;
-		hasProcessingInfoVisible: boolean;
-	}
-
-	let { container, hasProcessingInfoVisible }: Props = $props();
-
-	let show = $state(false);
-
-	let buttonBottom = $derived(hasProcessingInfoVisible ? '2rem' : '0');
-
-	function checkVisibility() {
-		if (!container) return;
-		const { scrollTop, scrollHeight, clientHeight } = container;
-		const distanceFromBottom = scrollHeight - clientHeight - scrollTop;
-		show = distanceFromBottom > clientHeight * 0.5;
-	}
-
-	function scrollToBottom() {
-		if (container) {
-			container.scrollTo({
-				top: container.scrollHeight,
-				behavior: 'smooth'
-			});
-		}
-	}
-
-	$effect(() => {
-		const c = container;
-		if (c) {
-			c.addEventListener('scroll', checkVisibility);
-			checkVisibility();
-			return () => {
-				c.removeEventListener('scroll', checkVisibility);
-			};
-		}
-	});
+	let { onclick }: { onclick: (e?: MouseEvent) => void } = $props();
 </script>
 
-<div class="relative z-50 mx-auto mb-4 flex max-w-[48rem] justify-center">
-	<Button
-		onclick={scrollToBottom}
-		variant="secondary"
-		size="icon"
-		disabled={!show}
-		class="pointer-events-auto absolute h-10 w-10 rounded-full bg-background/80 shadow-lg backdrop-blur-sm transition-all duration-200 hover:bg-muted/80"
-		style="bottom: {buttonBottom}; transform: translateY({show ? '0' : '2rem'}); opacity: {show
-			? 1
-			: 0};"
-		aria-label="Scroll to bottom"
-	>
-		<ArrowDown class="h-4 w-4" />
-	</Button>
+<div class="pointer-events-auto flex justify-center relative h-0">
+	<ActionIcon
+		icon={ArrowDown}
+		{onclick}
+		ariaLabel="Scroll to bottom"
+		tooltip="Scroll to bottom"
+		size="lg"
+		iconSize="h-4 w-4"
+		class="h-9 w-9 rounded-full bg-accent text-accent-foreground absolute bottom-4 shadow-md"
+	/>
 </div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDialogsAndAlerts.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDialogsAndAlerts.svelte
new file mode 100644
index 000000000..6305a7438
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenDialogsAndAlerts.svelte
@@ -0,0 +1,55 @@
+<script lang="ts">
+	import { Trash2 } from '@lucide/svelte';
+	import { ErrorDialogType } from '$lib/enums';
+	import {
+		DialogChatError,
+		DialogConfirmation,
+		DialogEmptyFileAlert,
+		DialogFileUploadError
+	} from '$lib/components/app';
+
+	let {
+		showDeleteDialog,
+		handleDeleteConfirm,
+		showEmptyFileDialog,
+		emptyFileNames,
+		activeErrorDialog,
+		handleErrorDialogOpenChange,
+		fileUpload
+	} = $props();
+</script>
+
+<DialogFileUploadError
+	bind:open={fileUpload.showFileErrorDialog}
+	fileErrorData={fileUpload.fileErrorData}
+/>
+
+<DialogConfirmation
+	bind:open={showDeleteDialog}
+	title="Delete Conversation"
+	description="Are you sure you want to delete this conversation? This action cannot be undone and will permanently remove all messages in this conversation."
+	confirmText="Delete"
+	cancelText="Cancel"
+	variant="destructive"
+	icon={Trash2}
+	onConfirm={handleDeleteConfirm}
+	onCancel={() => (showDeleteDialog = false)}
+/>
+
+<DialogEmptyFileAlert
+	bind:open={showEmptyFileDialog}
+	emptyFiles={emptyFileNames}
+	onOpenChange={(open) => {
+		if (!open) {
+			emptyFileNames = [];
+		}
+	}}
+/>
+
+<DialogChatError
+	message={activeErrorDialog?.message ?? ''}
+	contextInfo={activeErrorDialog?.contextInfo}
+	onOpenChange={handleErrorDialogOpenChange}
+	open={Boolean(activeErrorDialog)}
+	type={activeErrorDialog?.type ?? ErrorDialogType.SERVER}
+/>
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
index aa1c0536d..600180742 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenForm.svelte
@@ -2,6 +2,7 @@
 	import { afterNavigate } from '$app/navigation';
 	import { page } from '$app/state';
 	import { ChatForm } from '$lib/components/app';
+	import { isMobile } from '$lib/stores/viewport.svelte';
 	import { onMount } from 'svelte';
 	import { useDraftMessages } from '$lib/hooks/use-draft-messages.svelte';
 
@@ -32,7 +33,30 @@
 	}: Props = $props();
 
 	let chatFormRef: ChatForm | undefined = $state(undefined);
+	let formWrapperEl: HTMLDivElement | undefined = $state();
 	let chatId = $derived(page.params.id as string | undefined);
+
+	$effect(() => {
+		if (!formWrapperEl) return;
+
+		const formEl = formWrapperEl.querySelector('form') as HTMLElement | null;
+		if (!formEl) return;
+
+		const updateHeight = () => {
+			const height = Math.round(formEl.getBoundingClientRect().height);
+			document.documentElement.style.setProperty('--chat-form-height', `${height}px`);
+		};
+
+		updateHeight();
+
+		const resizeObserver = new ResizeObserver(updateHeight);
+		resizeObserver.observe(formEl);
+
+		return () => {
+			resizeObserver.disconnect();
+			document.documentElement.style.removeProperty('--chat-form-height');
+		};
+	});
 	let hasLoadingAttachments = $derived(uploadedFiles.some((f) => f.isLoading));
 	let message = $derived(initialMessage);
 	let previousIsLoading = $derived(isLoading);
@@ -83,12 +107,14 @@
 	}
 
 	onMount(() => {
-		setTimeout(() => chatFormRef?.focus(), 10);
+		if (!isMobile.current) {
+			setTimeout(() => chatFormRef?.focus(), 100);
+		}
 	});
 
 	afterNavigate((navigation) => {
-		if (navigation?.from != null) {
-			setTimeout(() => chatFormRef?.focus(), 10);
+		if (navigation?.from != null && !isMobile.current) {
+			setTimeout(() => chatFormRef?.focus(), 100);
 		}
 	});
 
@@ -108,12 +134,12 @@
 	});
 </script>
 
-<div class="relative mx-auto max-w-[48rem]">
+<div class="chat-screen-form-wrapper" bind:this={formWrapperEl}>
 	<ChatForm
+		class="mx-auto max-w-3xl {className}"
 		bind:this={chatFormRef}
 		bind:value={message}
 		bind:uploadedFiles
-		class={className}
 		{disabled}
 		{isLoading}
 		showMcpPromptButton
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenGreeting.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenGreeting.svelte
index 141d4f4e4..018949aff 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenGreeting.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenGreeting.svelte
@@ -1,5 +1,4 @@
 <script lang="ts">
-	import { fadeInView } from '$lib/actions/fade-in-view.svelte';
 	import { serverStore } from '$lib/stores/server.svelte';
 
 	interface Props {
@@ -11,10 +10,9 @@
 
 <div
 	class={[
-		'pointer-events-none mb-4 hidden px-4 text-center',
-		isEmpty && 'pointer-events-auto block!'
+		'pointer-events-none mb-4 hidden px-4 text-center text-balance',
+		isEmpty && 'mb-[calc(50dvh-8rem)] md:mb-6 pointer-events-auto block!'
 	]}
-	use:fadeInView={{ duration: 300 }}
 >
 	<h1 class="mb-2 text-2xl font-semibold tracking-tight md:text-3xl">Hello there</h1>
 
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
index f38f3519c..0615d9f30 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenProcessingInfo.svelte
@@ -5,13 +5,8 @@
 	import { chatStore, isLoading, isChatStreaming } from '$lib/stores/chat.svelte';
 	import { activeMessages, activeConversation } from '$lib/stores/conversations.svelte';
 	import { config } from '$lib/stores/settings.svelte';
-	import { getProcessingInfoContext } from '$lib/contexts';
-	import { page } from '$app/state';
 
 	const processingState = useProcessingState();
-	const processingInfoCtx = getProcessingInfoContext();
-
-	let showProcessingInfo = $derived(processingInfoCtx.showProcessingInfo);
 
 	let isCurrentConversationLoading = $derived(isLoading());
 	let isStreaming = $derived(isChatStreaming());
@@ -70,8 +65,8 @@
 
 <div
 	class={[
-		'chat-processing-info-container pointer-events-none relative',
-		page.params.id && showProcessingInfo && 'visible'
+		'chat-processing-info-container pointer-events-none relative w-full hidden md:block',
+		processingVisible && 'visible'
 	]}
 >
 	<div class="chat-processing-info-content absolute bottom-4 left-1/2 -translate-x-1/2">
diff --git a/tools/ui/src/lib/components/app/chat/index.ts b/tools/ui/src/lib/components/app/chat/index.ts
index 8ed3cc65e..517f24d74 100644
--- a/tools/ui/src/lib/components/app/chat/index.ts
+++ b/tools/ui/src/lib/components/app/chat/index.ts
@@ -677,13 +677,6 @@ export { default as ChatScreenForm } from './ChatScreen/ChatScreenForm.svelte';
  */
 export { default as ChatScreenProcessingInfo } from './ChatScreen/ChatScreenProcessingInfo.svelte';
 
-/**
- * Scroll-to-bottom action button. Displays a floating button when the user
- * has scrolled up more than half a viewport height from the bottom.
- * Takes the chat container element as a prop to manage scroll state internally.
- */
-export { default as ChatScreenActionScrollDown } from './ChatScreen/ChatScreenActionScrollDown.svelte';
-
 /**
  * Server error alert displayed when the server is unreachable.
  * Shows the error message with a retry button.
diff --git a/tools/ui/src/lib/components/app/forms/SearchInput.svelte b/tools/ui/src/lib/components/app/forms/SearchInput.svelte
index 19dd7e6a7..c4389c523 100644
--- a/tools/ui/src/lib/components/app/forms/SearchInput.svelte
+++ b/tools/ui/src/lib/components/app/forms/SearchInput.svelte
@@ -3,6 +3,7 @@
 	import { Search, X } from '@lucide/svelte';
 
 	interface Props {
+		autofocus?: boolean;
 		value?: string;
 		placeholder?: string;
 		onInput?: (value: string) => void;
@@ -15,6 +16,7 @@
 	}
 
 	let {
+		autofocus,
 		value = $bindable(''),
 		placeholder = 'Search...',
 		onInput,
@@ -39,7 +41,7 @@
 		if (value) {
 			value = '';
 			onInput?.('');
-			ref?.focus();
+			ref?.focus({ preventScroll: true });
 		} else {
 			onClose?.();
 		}
@@ -52,6 +54,7 @@
 	/>
 
 	<Input
+		{autofocus}
 		{id}
 		bind:value
 		bind:ref
diff --git a/tools/ui/src/lib/components/app/misc/Logo.svelte b/tools/ui/src/lib/components/app/misc/Logo.svelte
new file mode 100644
index 000000000..ff2b7f2f0
--- /dev/null
+++ b/tools/ui/src/lib/components/app/misc/Logo.svelte
@@ -0,0 +1,15 @@
+<script>
+	import logoMark from '$lib/assets/logo.svg?raw';
+	let { class: className = '', style = '' } = $props();
+</script>
+
+<div class={className} {style}>
+	{@html logoMark}
+</div>
+
+<style>
+	div :global(svg) {
+		width: var(--size, 1rem);
+		height: var(--size, 1rem);
+	}
+</style>
diff --git a/tools/ui/src/lib/components/app/misc/index.ts b/tools/ui/src/lib/components/app/misc/index.ts
index 64b76fb71..b550ae66a 100644
--- a/tools/ui/src/lib/components/app/misc/index.ts
+++ b/tools/ui/src/lib/components/app/misc/index.ts
@@ -51,3 +51,11 @@ export { default as KeyboardShortcutInfo } from './KeyboardShortcutInfo.svelte';
  * Preview button is shown only for HTML code blocks.
  */
 export { default as CodeBlockActions } from './CodeBlockActions.svelte';
+
+/**
+ * **Logo** - Application brand mark
+ *
+ * Inline SVG of the application logo. Accepts styling via the standard
+ * `class` and `style` props and inherits color via `currentColor`.
+ */
+export { default as Logo } from './Logo.svelte';
diff --git a/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte b/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte
deleted file mode 100644
index e92b9528a..000000000
--- a/tools/ui/src/lib/components/app/navigation/DesktopIconStrip.svelte
+++ /dev/null
@@ -1,84 +0,0 @@
-<script lang="ts">
-	import { goto } from '$app/navigation';
-	import { page } from '$app/state';
-	import { ActionIcon } from '$lib/components/app';
-	import {
-		ICON_STRIP_TRANSITION_DURATION,
-		ICON_STRIP_TRANSITION_DELAY_MULTIPLIER,
-		SIDEBAR_ACTIONS_ITEMS
-	} from '$lib/constants';
-	import { TooltipSide } from '$lib/enums';
-	import { fade } from 'svelte/transition';
-	import { circIn } from 'svelte/easing';
-	import { onMount } from 'svelte';
-	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
-
-	interface Props {
-		sidebarOpen: boolean;
-		onSearchClick: () => void;
-	}
-
-	let { sidebarOpen = false, onSearchClick }: Props = $props();
-
-	const { handleKeydown } = useKeyboardShortcuts({ activateSearchMode: () => onSearchClick() });
-
-	let initialized = $state(false);
-	let showIcons = $derived(!sidebarOpen);
-
-	showIcons = false;
-
-	onMount(() => {
-		showIcons = !sidebarOpen;
-
-		setTimeout(() => {
-			initialized = true;
-		}, ICON_STRIP_TRANSITION_DELAY_MULTIPLIER * SIDEBAR_ACTIONS_ITEMS.length);
-	});
-</script>
-
-<svelte:window onkeydown={handleKeydown} />
-
-<div
-	class="hidden shrink-0 transition-[width] duration-200 ease-linear md:block {sidebarOpen
-		? 'w-0'
-		: 'w-[calc(var(--sidebar-width-icon)+1.5rem)]'}"
-></div>
-<aside
-	class="fixed top-0 bottom-0 left-0 z-10 hidden w-[calc(var(--sidebar-width-icon)+1.5rem)] flex-col items-center justify-between py-3 transition-opacity duration-200 ease-linear md:flex {sidebarOpen
-		? 'pointer-events-none opacity-0'
-		: 'opacity-100'}"
->
-	<div class="mt-12 flex flex-col items-center gap-1">
-		{#each SIDEBAR_ACTIONS_ITEMS as item, i (item.tooltip)}
-			{@const onclick = item.route ? () => goto(item.route!) : onSearchClick}
-			{@const isActive = item.activeRouteId
-				? page.route.id === item.activeRouteId
-				: item.activeRoutePrefix
-					? !!page.route.id?.startsWith(item.activeRoutePrefix)
-					: false}
-			{#if showIcons}
-				<div
-					in:fade={{
-						duration: ICON_STRIP_TRANSITION_DURATION,
-						delay: !initialized
-							? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER
-							: 0,
-						easing: circIn
-					}}
-				>
-					<ActionIcon
-						icon={item.icon}
-						tooltip={item.tooltip}
-						tooltipSide={TooltipSide.RIGHT}
-						size="lg"
-						iconSize="h-4 w-4"
-						class="h-9 w-9 rounded-full hover:bg-accent! {isActive
-							? 'bg-accent text-accent-foreground'
-							: ''}"
-						{onclick}
-					/>
-				</div>
-			{/if}
-		{/each}
-	</div>
-</aside>
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
index 1fa7722f2..1445961e0 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
@@ -1,40 +1,67 @@
 <script lang="ts">
 	import { goto } from '$app/navigation';
 	import { page } from '$app/state';
-	import { Trash2, Pencil, Pin, X } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
-	import { DialogConfirmation } from '$lib/components/app';
-	import SidebarNavigationActions from './SidebarNavigationActions.svelte';
-	import SidebarNavigationConversationItem from './SidebarNavigationConversationItem.svelte';
-	import { Checkbox } from '$lib/components/ui/checkbox';
-	import Label from '$lib/components/ui/label/label.svelte';
-	import ScrollArea from '$lib/components/ui/scroll-area/scroll-area.svelte';
-	import * as Sidebar from '$lib/components/ui/sidebar';
-	import Input from '$lib/components/ui/input/input.svelte';
-	import { ROUTES } from '$lib/constants/routes';
-	import { RouterService } from '$lib/services/router.service';
+	import { PanelLeftClose, PanelLeftOpen, X } from '@lucide/svelte';
 	import {
-		conversationsStore,
-		conversations,
-		buildConversationTree
-	} from '$lib/stores/conversations.svelte';
-	import { chatStore } from '$lib/stores/chat.svelte';
-	import { getPreviewText } from '$lib/utils';
-	import { APP_NAME } from '$lib/constants';
+		ActionIcon,
+		Logo,
+		SidebarNavigationConversationList,
+		SidebarNavigationActions
+	} from '$lib/components/app';
+	import { ROUTES } from '$lib/constants';
+	import { fade } from 'svelte/transition';
 
-	const sidebar = Sidebar.useSidebar();
+	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
+	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
+	import { chatStore } from '$lib/stores/chat.svelte';
+	import { RouterService } from '$lib/services/router.service';
+	import { isMobile } from '$lib/stores/viewport.svelte';
+	import { TooltipSide } from '$lib/enums';
+	import { device } from '$lib/stores/device.svelte';
+	import { circIn } from 'svelte/easing';
+
+	interface Props {
+		onSearchClick?: () => void;
+	}
+
+	let { onSearchClick = () => {} }: Props = $props();
+
+	const { handleKeydown } = useKeyboardShortcuts({ activateSearchMode: () => onSearchClick() });
+
+	let isExpandedMode = $state(false);
+	let hoveredTooltip = $state<string | null>(null);
+	let logoHovered = $state(false);
+
+	const isStripExpanded = $derived(isExpandedMode || hoveredTooltip !== null);
+	const isOnMobile = $derived(isMobile.current);
+
+	function toggleExpandedMode() {
+		isExpandedMode = !isExpandedMode;
+		if (!isExpandedMode) {
+			hoveredTooltip = null;
+		}
+	}
+
+	$effect(() => {
+		if (!isExpandedMode) {
+			isSearchModeActive = false;
+			searchQuery = '';
+			cancelMobileCollapse();
+		}
+	});
+
+	// On mobile the dedicated /search route hides the sidebar (see the aside
+	// render guard below). Collapse it as we enter /search so it doesn't
+	// reappear expanded when the user navigates back via the back button.
+	$effect(() => {
+		if (isMobile.current && page.url.hash.includes(ROUTES.SEARCH)) {
+			isExpandedMode = false;
+		}
+	});
 
 	let currentChatId = $derived(page.params.id);
 	let isSearchModeActive = $state(false);
 	let searchQuery = $state('');
-	let showDeleteDialog = $state(false);
-	let deleteWithForks = $state(false);
-	let showEditDialog = $state(false);
-	let selectedConversation = $state<DatabaseConversation | null>(null);
-	let editedName = $state('');
-	let selectedConversationNamePreview = $derived.by(() =>
-		selectedConversation ? getPreviewText(selectedConversation.name) : ''
-	);
 
 	let filteredConversations = $derived.by(() => {
 		if (isSearchModeActive) {
@@ -50,294 +77,206 @@
 		return conversations();
 	});
 
-	let conversationTree = $derived(buildConversationTree(filteredConversations));
-
-	let pinnedConversations = $derived.by(() => {
-		return conversationTree.filter(({ conversation }) => conversation.pinned);
-	});
-
-	let unpinnedConversations = $derived.by(() => {
-		return conversationTree.filter(({ conversation }) => !conversation.pinned);
-	});
-
-	let selectedConversationHasDescendants = $derived.by(() => {
-		if (!selectedConversation) return false;
-
-		const allConvs = conversations();
-		const queue = [selectedConversation.id];
-
-		while (queue.length > 0) {
-			const parentId = queue.pop()!;
-
-			for (const c of allConvs) {
-				if (c.forkedFromConversationId === parentId) return true;
-			}
-		}
-
-		return false;
-	});
-
-	async function handleDeleteConversation(id: string) {
-		const conversation = conversations().find((conv) => conv.id === id);
-		if (conversation) {
-			selectedConversation = conversation;
-			deleteWithForks = false;
-			showDeleteDialog = true;
+	async function selectConversation(id: string) {
+		if (isMobile.current) {
+			scheduleMobileCollapse();
 		}
+		await goto(RouterService.chat(id));
 	}
 
 	async function handleEditConversation(id: string) {
 		const conversation = conversations().find((conv) => conv.id === id);
-		if (conversation) {
-			selectedConversation = conversation;
-			editedName = conversation.name;
-			showEditDialog = true;
+		if (!conversation) return;
+
+		const newName = window.prompt('Rename conversation', conversation.name);
+		if (newName && newName.trim()) {
+			await conversationsStore.updateConversationName(id, newName.trim());
 		}
 	}
 
-	function handleConfirmDelete() {
-		if (selectedConversation) {
-			const convId = selectedConversation.id;
-			const withForks = deleteWithForks;
-			showDeleteDialog = false;
+	async function handleDeleteConversation(id: string) {
+		const conversation = conversations().find((conv) => conv.id === id);
+		if (!conversation) return;
 
-			setTimeout(() => {
-				conversationsStore.deleteConversation(convId, {
-					deleteWithForks: withForks
-				});
-			}, 100); // Wait for animation to finish
-		}
-	}
+		const confirmed = window.confirm(
+			`Delete "${conversation.name}"? This action cannot be undone.`
+		);
+		if (!confirmed) return;
 
-	function handleConfirmEdit() {
-		if (!editedName.trim() || !selectedConversation) return;
-
-		showEditDialog = false;
-
-		conversationsStore.updateConversationName(selectedConversation.id, editedName);
-		selectedConversation = null;
-	}
-
-	export function handleMobileSidebarItemClick() {
-		if (sidebar.isMobile) {
-			sidebar.toggle();
-		}
-	}
-
-	let chatSidebarActions: { activateSearch?: () => void } | undefined = $state();
-	let openedForSearch = $state(false);
-
-	export function activateSearchMode() {
-		if (!sidebar.open) {
-			openedForSearch = true;
-		}
-		chatSidebarActions?.activateSearch?.();
-	}
-
-	function handleSearchDeactivated() {
-		if (openedForSearch) {
-			openedForSearch = false;
-			sidebar.toggle();
-		}
-	}
-
-	$effect(() => {
-		if (!sidebar.open) {
-			isSearchModeActive = false;
-			searchQuery = '';
-			openedForSearch = false;
-		}
-	});
-
-	export function editActiveConversation() {
-		if (currentChatId) {
-			const activeConversation = filteredConversations.find((conv) => conv.id === currentChatId);
-
-			if (activeConversation) {
-				const event = new CustomEvent('edit-active-conversation', {
-					detail: { conversationId: currentChatId }
-				});
-				document.dispatchEvent(event);
-			}
-		}
-	}
-
-	async function selectConversation(id: string) {
-		if (isSearchModeActive) {
-			isSearchModeActive = false;
-			searchQuery = '';
-		}
-
-		handleMobileSidebarItemClick();
-		await goto(RouterService.chat(id));
+		await conversationsStore.deleteConversation(id, { deleteWithForks: false });
 	}
 
 	function handleStopGeneration(id: string) {
 		chatStore.stopGenerationForChat(id);
 	}
+
+	let innerWidth = $state(0);
+	let pendingCollapse = $state<ReturnType<typeof setTimeout> | null>(null);
+
+	function scheduleMobileCollapse() {
+		if (pendingCollapse) {
+			clearTimeout(pendingCollapse);
+		}
+		pendingCollapse = setTimeout(() => {
+			isExpandedMode = false;
+			pendingCollapse = null;
+		}, 100);
+	}
+
+	function cancelMobileCollapse() {
+		if (pendingCollapse) {
+			clearTimeout(pendingCollapse);
+			pendingCollapse = null;
+		}
+	}
 </script>
 
-<div class="flex h-full flex-col">
-	<ScrollArea class="h-full flex-1">
-		<Sidebar.Header class="gap-4 bg-sidebar/50 p-3 backdrop-blur-lg md:pt-4 md:pb-2">
-			<div class="flex items-center justify-between">
-				<a href={ROUTES.START} onclick={handleMobileSidebarItemClick}>
-					<h1 class="inline-flex items-center gap-1 px-2 text-xl font-semibold">
-						{APP_NAME}
-					</h1>
-				</a>
+<svelte:window onkeydown={handleKeydown} bind:innerWidth />
 
-				<Button
-					class="rounded-full md:hidden"
-					variant="ghost"
-					size="icon"
-					onclick={() => sidebar.toggle()}
-				>
-					<X class="h-4 w-4" />
-					<span class="sr-only">Close sidebar</span>
-				</Button>
+{#if innerWidth > 768 || (!page.url.hash.includes(ROUTES.SETTINGS) && !page.url.hash.includes(ROUTES.MCP_SERVERS) && !page.url.hash.includes(ROUTES.SEARCH))}
+	<aside
+		class={[
+			// Layout & positioning
+			'fixed md:sticky top-2 left-2 md:left-0 md:ml-2 md:mt-2 pt-2 z-10 w-[calc(100dvw-1rem)]',
+			// Dimensions & overflow
+			'md:h-[calc(100dvh-1.125rem)]',
+			isExpandedMode &&
+				(device.isStandalone
+					? 'h-[calc(100dvh-2rem)]'
+					: device.isIOSDevice
+						? 'h-[calc(100dvh-0.5rem)]'
+						: 'h-[calc(100dvh-1rem)]'),
+			// Shape & depth
+			'rounded-3xl md:rounded-2xl',
+			// Flex layout
+			'flex flex-col justify-between',
+			// Transition
+			'md:transition-[width,padding] duration-200 ease-out',
+			// Expanded state: width, surface, depth
+			isStripExpanded && 'md:w-72 md:bg-muted/60 md:backdrop-blur-xl border-border shadow-md',
+			// Collapsed state
+			!isStripExpanded && 'md:w-12',
+			// Expanded mode flag (for mobile ::before overlay)
+			isExpandedMode && 'is-expanded'
+		]}
+	>
+		<div class="px-2 flex items-center justify-between">
+			<div
+				role="button"
+				tabindex="0"
+				class="relative"
+				onmouseenter={() => (logoHovered = true)}
+				onmouseleave={() => (logoHovered = false)}
+			>
+				<ActionIcon
+					icon={!isExpandedMode && logoHovered && innerWidth > 768 ? PanelLeftOpen : Logo}
+					size="lg"
+					iconSize="h-4.5 w-4.5 md:h-4 md:w-4"
+					class="{isExpandedMode
+						? 'bg-muted! md:bg-foreground/5!'
+						: 'bg-transparent!'} md:h-9 md:w-9 h-10 w-10 rounded-full md:hover:bg-foreground/10! pointer-events-auto"
+					href={isExpandedMode ? ROUTES.START : undefined}
+					onclick={isExpandedMode ? undefined : toggleExpandedMode}
+					tooltip={isExpandedMode ? undefined : 'Open Sidebar'}
+					tooltipSide={TooltipSide.RIGHT}
+					ariaLabel={isExpandedMode ? 'Go to start' : 'Expand navigation'}
+				/>
 			</div>
 
-			<SidebarNavigationActions
-				bind:this={chatSidebarActions}
-				{handleMobileSidebarItemClick}
-				bind:isSearchModeActive
-				bind:searchQuery
-				onSearchDeactivated={handleSearchDeactivated}
-			/>
-		</Sidebar.Header>
-
-		{#if !isSearchModeActive && pinnedConversations.length > 0}
-			<Sidebar.Group class="p-0 px-4">
-				<Sidebar.GroupLabel>
-					<div class="flex items-center gap-1">
-						<Pin class="h-3.5 w-3.5" />
-						<span>Pinned</span>
-					</div>
-				</Sidebar.GroupLabel>
-				<Sidebar.GroupContent>
-					<Sidebar.Menu>
-						{#each pinnedConversations as { conversation, depth } (conversation.id)}
-							<Sidebar.MenuItem class="mb-1 p-0">
-								<SidebarNavigationConversationItem
-									conversation={{
-										id: conversation.id,
-										name: conversation.name,
-										lastModified: conversation.lastModified,
-										currNode: conversation.currNode,
-										forkedFromConversationId: conversation.forkedFromConversationId,
-										pinned: conversation.pinned
-									}}
-									{depth}
-									isActive={currentChatId === conversation.id}
-									onSelect={selectConversation}
-									onEdit={handleEditConversation}
-									onDelete={handleDeleteConversation}
-									onStop={handleStopGeneration}
-								/>
-							</Sidebar.MenuItem>
-						{/each}
-					</Sidebar.Menu>
-				</Sidebar.GroupContent>
-			</Sidebar.Group>
-		{/if}
-
-		<Sidebar.Group class="mt-2 h-[calc(100vh-21rem)] space-y-2 p-0 px-3">
-			{#if (filteredConversations.length > 0 && isSearchModeActive) || !isSearchModeActive}
-				<Sidebar.GroupLabel>
-					{isSearchModeActive ? 'Search results' : 'Recent conversations'}
-				</Sidebar.GroupLabel>
+			{#if isExpandedMode || isOnMobile}
+				<div
+					class="flex items-center transition-all duration-150 ease-out {isMobile.current &&
+					!isExpandedMode
+						? 'opacity-0 h-0!'
+						: ''}"
+					in:fade={{ duration: 150, easing: circIn, delay: 50 }}
+					out:fade={{ duration: 100 }}
+				>
+					<ActionIcon
+						icon={isMobile.current ? X : PanelLeftClose}
+						size="lg"
+						iconSize="h-4.5 w-4.5 md:h-4 md:w-4"
+						class="backdrop-blur-none md:h-9 md:w-9 h-10 w-10 rounded-full mr-1 hover:bg-accent!"
+						onclick={toggleExpandedMode}
+						tooltip="Close Sidebar"
+						tooltipSide={TooltipSide.LEFT}
+						ariaLabel="Collapse navigation"
+					/>
+				</div>
 			{/if}
-
-			<Sidebar.GroupContent>
-				<Sidebar.Menu>
-					{#each isSearchModeActive ? conversationTree : unpinnedConversations as { conversation, depth } (conversation.id)}
-						<Sidebar.MenuItem class="mb-1 p-0">
-							<SidebarNavigationConversationItem
-								conversation={{
-									id: conversation.id,
-									name: conversation.name,
-									lastModified: conversation.lastModified,
-									currNode: conversation.currNode,
-									forkedFromConversationId: conversation.forkedFromConversationId,
-									pinned: conversation.pinned
-								}}
-								{depth}
-								isActive={currentChatId === conversation.id}
-								onSelect={selectConversation}
-								onEdit={handleEditConversation}
-								onDelete={handleDeleteConversation}
-								onStop={handleStopGeneration}
-							/>
-						</Sidebar.MenuItem>
-					{/each}
-
-					{#if (isSearchModeActive ? conversationTree : unpinnedConversations).length === 0}
-						<div class="px-2 py-4 text-center">
-							<p class="mb-4 p-4 text-sm text-muted-foreground">
-								{searchQuery.length > 0
-									? 'No results found'
-									: isSearchModeActive
-										? 'Start typing to see results'
-										: 'No conversations yet'}
-							</p>
-						</div>
-					{/if}
-				</Sidebar.Menu>
-			</Sidebar.GroupContent>
-		</Sidebar.Group>
-	</ScrollArea>
-</div>
-
-<DialogConfirmation
-	bind:open={showDeleteDialog}
-	title="Delete Conversation"
-	description={selectedConversation
-		? `Are you sure you want to delete "${selectedConversationNamePreview}"? This action cannot be undone and will permanently remove all messages in this conversation.`
-		: ''}
-	confirmText="Delete"
-	cancelText="Cancel"
-	variant="destructive"
-	icon={Trash2}
-	onConfirm={handleConfirmDelete}
-	onCancel={() => {
-		showDeleteDialog = false;
-		selectedConversation = null;
-	}}
->
-	{#if selectedConversationHasDescendants}
-		<div class="flex items-center gap-2 py-2">
-			<Checkbox id="delete-with-forks" bind:checked={deleteWithForks} />
-
-			<Label for="delete-with-forks" class="text-sm">Also delete all forked conversations</Label>
 		</div>
-	{/if}
-</DialogConfirmation>
 
-<DialogConfirmation
-	bind:open={showEditDialog}
-	title="Edit Conversation Name"
-	description=""
-	confirmText="Save"
-	cancelText="Cancel"
-	icon={Pencil}
-	onConfirm={handleConfirmEdit}
-	onCancel={() => {
-		showEditDialog = false;
-		selectedConversation = null;
-	}}
-	onKeydown={(event) => {
-		if (event.key === 'Enter') {
-			event.preventDefault();
-			event.stopImmediatePropagation();
-			handleConfirmEdit();
+		<div class="mt-2 flex min-h-0 flex-1 flex-col gap-4 md:gap-1 overflow-y-auto">
+			<div
+				class="flex min-h-0 flex-1 flex-col gap-4 md:gap-1 {isMobile.current
+					? 'transition-[opacity,height] duration-200 ease-out'
+					: ''} {isMobile.current && !isExpandedMode ? 'opacity-0 !h-0' : ''}"
+				in:fade={{ duration: 200 }}
+				out:fade={{ duration: 200 }}
+			>
+				<SidebarNavigationActions
+					isExpandedMode={innerWidth > 768 ? isExpandedMode : true}
+					class="px-2"
+					bind:isSearchModeActive
+					bind:searchQuery
+					onSearchDeactivated={() => {
+						isSearchModeActive = false;
+						searchQuery = '';
+					}}
+					onSearchClick={() => {
+						isExpandedMode = true;
+						isSearchModeActive = true;
+					}}
+					onNewChat={() => {
+						if (isMobile.current) {
+							scheduleMobileCollapse();
+						}
+					}}
+				/>
+
+				{#if isExpandedMode || isOnMobile}
+					<SidebarNavigationConversationList
+						class="px-2"
+						{filteredConversations}
+						{currentChatId}
+						{isSearchModeActive}
+						{searchQuery}
+						onSelect={selectConversation}
+						onEdit={handleEditConversation}
+						onDelete={handleDeleteConversation}
+						onStop={handleStopGeneration}
+					/>
+				{/if}
+			</div>
+		</div>
+	</aside>
+{/if}
+
+<style>
+	aside {
+		@media (max-width: 768px) {
+			--size: 1.125rem;
 		}
-	}}
->
-	<Input
-		class="text-foreground"
-		placeholder="Enter a new name"
-		type="text"
-		bind:value={editedName}
-	/>
-</DialogConfirmation>
+	}
+
+	@media (max-width: 768px) {
+		aside {
+			&:not(.is-expanded) {
+				pointer-events: none;
+			}
+		}
+
+		aside.is-expanded::before {
+			content: '';
+			position: fixed;
+			top: -0.5rem;
+			bottom: -0.25rem;
+			left: -0.5rem;
+			right: -0.5rem;
+			z-index: -1;
+			background: var(--background);
+			backdrop-filter: blur(1rem);
+			pointer-events: none;
+		}
+	}
+</style>
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte
index f0d63970e..f118a68df 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationActions.svelte
@@ -1,39 +1,86 @@
 <script lang="ts">
-	import { KeyboardShortcutInfo } from '$lib/components/app';
-	import { Button } from '$lib/components/ui/button';
-	import type { Component } from 'svelte';
-	import { SearchInput } from '$lib/components/app';
+	import { goto } from '$app/navigation';
 	import { page } from '$app/state';
-	import { SIDEBAR_ACTIONS_ITEMS } from '$lib/constants/ui';
+	import { Search } from '@lucide/svelte';
+	import { ActionIcon, KeyboardShortcutInfo, SearchInput } from '$lib/components/app';
+	import { Button } from '$lib/components/ui/button';
+	import {
+		ICON_STRIP_TRANSITION_DURATION,
+		ICON_STRIP_TRANSITION_DELAY_MULTIPLIER,
+		ROUTES,
+		SIDEBAR_ACTIONS_ITEMS
+	} from '$lib/constants';
+	import { isMobile } from '$lib/stores/viewport.svelte';
+	import { TooltipSide } from '$lib/enums';
+	import { fade } from 'svelte/transition';
+	import { circIn } from 'svelte/easing';
+	import { onMount } from 'svelte';
+	import type { Component } from 'svelte';
 
 	interface Props {
-		handleMobileSidebarItemClick: () => void;
+		class: string;
+		isExpandedMode: boolean;
 		isSearchModeActive: boolean;
 		searchQuery: string;
-		isCancelAlwaysVisible?: boolean;
 		onSearchDeactivated?: () => void;
+		onSearchClick?: () => void;
+		onNewChat?: () => void;
 	}
 
 	let {
-		handleMobileSidebarItemClick,
-		isSearchModeActive = $bindable(),
-		searchQuery = $bindable(),
-		isCancelAlwaysVisible = false,
-		onSearchDeactivated
+		class: className,
+		isExpandedMode = false,
+		isSearchModeActive = $bindable(false),
+		searchQuery = $bindable(''),
+		onSearchDeactivated,
+		onSearchClick,
+		onNewChat
 	}: Props = $props();
 
+	let initialized = $state(false);
+	let showIcons = $state(false);
 	let searchInputRef = $state<HTMLInputElement | null>(null);
 
+	const isOnMobile = $derived(isMobile.current);
+
+	$effect(() => {
+		if (isSearchModeActive && searchInputRef) {
+			searchInputRef.focus();
+		}
+	});
+
+	onMount(() => {
+		showIcons = true;
+
+		setTimeout(() => {
+			initialized = true;
+		}, ICON_STRIP_TRANSITION_DELAY_MULTIPLIER * SIDEBAR_ACTIONS_ITEMS.length);
+	});
+
 	function handleSearchModeDeactivate() {
 		isSearchModeActive = false;
 		searchQuery = '';
 		onSearchDeactivated?.();
 	}
 
-	export function activateSearch() {
-		isSearchModeActive = true;
-		// Focus after Svelte renders the input
-		queueMicrotask(() => searchInputRef?.focus());
+	function isItemActive(item: {
+		activeRouteId?: string;
+		activeRoutePrefix?: string;
+		activeUrlIncludes?: string;
+	}): boolean {
+		if (item.activeRouteId) {
+			return page.route.id === item.activeRouteId;
+		}
+
+		if (item.activeRoutePrefix) {
+			return !!page.route.id?.startsWith(item.activeRoutePrefix);
+		}
+
+		if (item.activeUrlIncludes) {
+			return page.url?.hash?.includes(item.activeUrlIncludes) ?? false;
+		}
+
+		return false;
 	}
 </script>
 
@@ -41,56 +88,109 @@
 	<IconComponent class="h-4 w-4" />
 {/snippet}
 
-<div class="my-1 space-y-1">
-	{#if isSearchModeActive}
+{#if isSearchModeActive}
+	<div class="px-4 my-2">
 		<SearchInput
 			bind:value={searchQuery}
 			bind:ref={searchInputRef}
 			onClose={handleSearchModeDeactivate}
 			onKeyDown={(e) => e.key === 'Escape' && handleSearchModeDeactivate()}
 			placeholder="Search conversations..."
-			{isCancelAlwaysVisible}
 		/>
-	{:else}
-		{#each SIDEBAR_ACTIONS_ITEMS as item (item.route)}
-			{#if !item.route}
-				<Button
-					class="w-full justify-between px-2 backdrop-blur-none! hover:[&>kbd]:opacity-100"
-					onclick={activateSearch}
-					variant="ghost"
-				>
-					<div class="flex items-center gap-2">
-						{@render itemIcon(item.icon)}
+	</div>
+{:else if isExpandedMode || isOnMobile}
+	<div
+		class="{className} flex flex-col gap-5 md:gap-1 mt-2 md:mt-0 {!isExpandedMode && isOnMobile
+			? 'hidden pointer-events-none'
+			: ''}"
+	>
+		{#each SIDEBAR_ACTIONS_ITEMS as item, i (item.tooltip)}
+			{@const isActive = isItemActive(item)}
+			{@const isSearchOnMobile = item.icon === Search && isMobile.current}
+			{@const itemHref = isSearchOnMobile ? ROUTES.SEARCH : item.route}
+			{@const itemOnClick = item.route
+				? () => {
+						onNewChat?.();
+						goto(item.route!);
+					}
+				: isSearchOnMobile
+					? undefined
+					: onSearchClick}
+			{@const itemTransition = {
+				duration: ICON_STRIP_TRANSITION_DURATION,
+				delay: !initialized
+					? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER
+					: 0,
+				easing: circIn
+			}}
 
-						{item.tooltip}
-					</div>
+			{#if showIcons}
+				<div transition:fade={itemTransition}>
+					<Button
+						class="w-full min-w-9 justify-between px-2 backdrop-blur-none! hover:[&>kbd]:opacity-100 {isActive
+							? 'bg-accent text-accent-foreground'
+							: ''}"
+						href={itemHref}
+						onclick={itemOnClick}
+						variant="ghost"
+						size="default"
+					>
+						<span class="flex min-w-0 items-center px-0.5 gap-2">
+							{@render itemIcon(item.icon)}
 
-					{#if item.keys}
-						<KeyboardShortcutInfo keys={item.keys} />
-					{/if}
-				</Button>
-			{:else}
-				<Button
-					class="w-full justify-between px-2 backdrop-blur-none! hover:[&>kbd]:opacity-100 {(item.activeRouteId &&
-						page.route.id === item.activeRouteId) ||
-					(item.activeRoutePrefix && page.route.id?.startsWith(item.activeRoutePrefix))
-						? 'bg-accent text-accent-foreground'
-						: ''}"
-					href={item.route}
-					onclick={handleMobileSidebarItemClick}
-					variant="ghost"
-				>
-					<div class="flex items-center gap-2">
-						{@render itemIcon(item.icon)}
+							{#if showIcons}
+								<span
+									in:fade={{ duration: 150, easing: circIn, delay: 50 }}
+									out:fade={{ duration: 100 }}
+									class="min-w-0 truncate">{item.tooltip}</span
+								>
+							{/if}
+						</span>
 
-						{item.tooltip}
-					</div>
-
-					{#if item.keys}
-						<KeyboardShortcutInfo keys={item.keys} />
-					{/if}
-				</Button>
+						{#if item.keys}
+							<KeyboardShortcutInfo keys={item.keys} />
+						{/if}
+					</Button>
+				</div>
 			{/if}
 		{/each}
-	{/if}
-</div>
+	</div>
+{:else}
+	<div class="{className} flex-col gap-1 hidden md:flex">
+		{#each SIDEBAR_ACTIONS_ITEMS as item, i (item.tooltip)}
+			{@const isActive = isItemActive(item)}
+			{@const isSearchOnMobile = item.icon === Search && isMobile.current}
+			{@const itemOnClick = item.route
+				? () => {
+						onNewChat?.();
+						goto(item.route!);
+					}
+				: isSearchOnMobile
+					? undefined
+					: onSearchClick}
+			{@const itemTransition = {
+				duration: ICON_STRIP_TRANSITION_DURATION,
+				delay: !initialized
+					? ICON_STRIP_TRANSITION_DELAY_MULTIPLIER + i * ICON_STRIP_TRANSITION_DELAY_MULTIPLIER
+					: 0,
+				easing: circIn
+			}}
+
+			{#if showIcons}
+				<div transition:fade={itemTransition}>
+					<ActionIcon
+						icon={item.icon}
+						tooltip={item.tooltip}
+						tooltipSide={TooltipSide.RIGHT}
+						size="lg"
+						iconSize="h-4 w-4"
+						class="h-9 w-9 rounded-full hover:bg-accent! {isActive
+							? 'bg-accent text-accent-foreground'
+							: ''}"
+						onclick={itemOnClick}
+					/>
+				</div>
+			{/if}
+		{/each}
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationList.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationList.svelte
new file mode 100644
index 000000000..488e96bbc
--- /dev/null
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationList.svelte
@@ -0,0 +1,135 @@
+<script lang="ts">
+	import { Pin } from '@lucide/svelte';
+	import { buildConversationTree } from '$lib/stores/conversations.svelte';
+	import SidebarNavigationConversationItem from './SidebarNavigationConversationItem.svelte';
+	import SidebarNavigationSearchResults from './SidebarNavigationSearchResults.svelte';
+
+	interface Props {
+		class: string;
+		filteredConversations: DatabaseConversation[];
+		currentChatId: string | undefined;
+		isSearchModeActive: boolean;
+		searchQuery: string;
+		onSelect: (id: string) => void;
+		onEdit: (id: string) => void;
+		onDelete: (id: string) => void;
+		onStop: (id: string) => void;
+	}
+
+	let {
+		class: className,
+		filteredConversations,
+		currentChatId,
+		isSearchModeActive,
+		searchQuery,
+		onSelect,
+		onEdit,
+		onDelete,
+		onStop
+	}: Props = $props();
+
+	let conversationTree = $derived(buildConversationTree(filteredConversations));
+
+	let pinnedConversations = $derived(
+		conversationTree.filter(({ conversation }) => conversation.pinned)
+	);
+
+	let unpinnedConversations = $derived(
+		conversationTree.filter(({ conversation }) => !conversation.pinned)
+	);
+
+	const recentEmptyMessage = $derived(
+		searchQuery.length > 0 ? 'No results found' : 'No conversations yet'
+	);
+</script>
+
+{#if isSearchModeActive}
+	<SidebarNavigationSearchResults
+		class={className}
+		{searchQuery}
+		{filteredConversations}
+		{currentChatId}
+		{onSelect}
+		{onEdit}
+		{onDelete}
+		{onStop}
+	/>
+{:else}
+	{#if pinnedConversations.length > 0}
+		<div class="py-2 flex whitespace-nowrap {className}">
+			<div
+				class="text-muted-foreground inline-flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium gap-1"
+			>
+				<Pin class="h-3.5 w-3.5" />
+
+				<span>Pinned</span>
+			</div>
+		</div>
+
+		<ul class="flex w-full min-w-0 flex-col gap-4 md:gap-1 {className}">
+			{#each pinnedConversations as { conversation, depth } (conversation.id)}
+				<li class="group/item relative mb-1 p-0">
+					<SidebarNavigationConversationItem
+						conversation={{
+							id: conversation.id,
+							name: conversation.name,
+							lastModified: conversation.lastModified,
+							currNode: conversation.currNode,
+							forkedFromConversationId: conversation.forkedFromConversationId,
+							pinned: conversation.pinned
+						}}
+						{depth}
+						isActive={currentChatId === conversation.id}
+						{onSelect}
+						{onEdit}
+						{onDelete}
+						{onStop}
+					/>
+				</li>
+			{/each}
+		</ul>
+	{/if}
+
+	<div class="mt-2 flex min-h-0 flex-1 flex-col gap-4 md:gap-2 whitespace-nowrap {className}">
+		{#if filteredConversations.length > 0}
+			<div
+				class="text-muted-foreground flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium"
+			>
+				Recent conversations
+			</div>
+		{/if}
+
+		<div class="min-h-0 flex-1 md:overflow-y-auto">
+			<ul class="flex w-full min-w-0 flex-col gap-4 md:gap-1">
+				{#each unpinnedConversations as { conversation, depth } (conversation.id)}
+					<li class="group/item relative mb-1 p-0">
+						<SidebarNavigationConversationItem
+							conversation={{
+								id: conversation.id,
+								name: conversation.name,
+								lastModified: conversation.lastModified,
+								currNode: conversation.currNode,
+								forkedFromConversationId: conversation.forkedFromConversationId,
+								pinned: conversation.pinned
+							}}
+							{depth}
+							isActive={currentChatId === conversation.id}
+							{onSelect}
+							{onEdit}
+							{onDelete}
+							{onStop}
+						/>
+					</li>
+				{/each}
+
+				{#if unpinnedConversations.length === 0}
+					<li class="px-2 py-4 text-center">
+						<p class="mb-4 p-4 text-sm text-muted-foreground">
+							{recentEmptyMessage}
+						</p>
+					</li>
+				{/if}
+			</ul>
+		</div>
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte
index afc984702..491e7c347 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearch.svelte
@@ -16,4 +16,6 @@
 	}: Props = $props();
 </script>
 
-<SearchInput bind:value {placeholder} {onInput} class="mb-4 {className}" />
+<div class="mb-4 px-2 {className}">
+	<SearchInput bind:value {placeholder} {onInput} />
+</div>
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte
new file mode 100644
index 000000000..92d8fd0bd
--- /dev/null
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationSearchResults.svelte
@@ -0,0 +1,76 @@
+<script lang="ts">
+	import { buildConversationTree } from '$lib/stores/conversations.svelte';
+	import SidebarNavigationConversationItem from './SidebarNavigationConversationItem.svelte';
+
+	interface Props {
+		class?: string;
+		searchQuery: string;
+		filteredConversations: DatabaseConversation[];
+		currentChatId: string | undefined;
+		onSelect: (id: string) => void;
+		onEdit: (id: string) => void;
+		onDelete: (id: string) => void;
+		onStop: (id: string) => void;
+	}
+
+	let {
+		class: className = '',
+		searchQuery,
+		filteredConversations,
+		currentChatId,
+		onSelect,
+		onEdit,
+		onDelete,
+		onStop
+	}: Props = $props();
+
+	let tree = $derived(buildConversationTree(filteredConversations));
+
+	const hasQuery = $derived(searchQuery.trim().length > 0);
+	const showHeader = $derived(hasQuery && filteredConversations.length > 0);
+
+	const emptyMessage = $derived(hasQuery ? 'No results found' : 'Start typing to see results');
+</script>
+
+<div class="flex min-h-0 flex-1 flex-col gap-2 whitespace-nowrap {className}">
+	{#if showHeader}
+		<div
+			class="text-muted-foreground flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium"
+		>
+			Search results
+		</div>
+	{/if}
+
+	<div class="min-h-0 flex-1 overflow-y-auto">
+		<ul class="flex w-full min-w-0 flex-col gap-1">
+			{#each tree as { conversation, depth } (conversation.id)}
+				<li class="group/item relative mb-1 p-0">
+					<SidebarNavigationConversationItem
+						conversation={{
+							id: conversation.id,
+							name: conversation.name,
+							lastModified: conversation.lastModified,
+							currNode: conversation.currNode,
+							forkedFromConversationId: conversation.forkedFromConversationId,
+							pinned: conversation.pinned
+						}}
+						{depth}
+						isActive={currentChatId === conversation.id}
+						{onSelect}
+						{onEdit}
+						{onDelete}
+						{onStop}
+					/>
+				</li>
+			{/each}
+
+			{#if tree.length === 0}
+				<li class="px-2 py-4 text-center">
+					<p class="mb-4 p-4 text-sm text-muted-foreground">
+						{emptyMessage}
+					</p>
+				</li>
+			{/if}
+		</ul>
+	</div>
+</div>
diff --git a/tools/ui/src/lib/components/app/navigation/index.ts b/tools/ui/src/lib/components/app/navigation/index.ts
index d4ca91459..e07dde6bc 100644
--- a/tools/ui/src/lib/components/app/navigation/index.ts
+++ b/tools/ui/src/lib/components/app/navigation/index.ts
@@ -63,15 +63,6 @@ export { default as DropdownMenuSearchable } from './DropdownMenuSearchable.svel
  * ```
  */
 export { default as DropdownMenuActions } from './DropdownMenuActions.svelte';
-
-/**
- * **DesktopIconStrip** - Fixed icon strip for desktop sidebar
- *
- * Vertical icon strip shown on desktop when the sidebar is collapsed.
- * Contains navigation shortcuts for new chat, search, MCP, import/export, and settings.
- */
-export { default as DesktopIconStrip } from './DesktopIconStrip.svelte';
-
 /**
  * **SidebarNavigation** - Sidebar with actions menu and conversation list
  *
@@ -115,13 +106,6 @@ export { default as DesktopIconStrip } from './DesktopIconStrip.svelte';
  */
 export { default as SidebarNavigation } from './SidebarNavigation/SidebarNavigation.svelte';
 
-/**
- * Action buttons for sidebar header. Contains new chat button, settings button,
- * and delete all conversations button. Manages dialog states for settings and
- * delete confirmation.
- */
-export { default as SidebarNavigationActions } from './SidebarNavigation/SidebarNavigationActions.svelte';
-
 /**
  * Single conversation item in sidebar. Displays conversation title (truncated),
  * last message preview, and timestamp. Shows context menu on right-click with
@@ -130,6 +114,58 @@ export { default as SidebarNavigationActions } from './SidebarNavigation/Sidebar
  */
 export { default as SidebarNavigationConversationItem } from './SidebarNavigation/SidebarNavigationConversationItem.svelte';
 
+/**
+ * **SidebarNavigationConversationList** - Grouped conversation list
+ *
+ * Pure-presentational list of conversations. Splits items into a Pinned
+ * section (when not in search mode) and a Recent Conversations / Search
+ * Results section with the unpinned items. Item selection, edit, delete,
+ * and stop-generation are delegated to the caller via callbacks.
+ *
+ * @example
+ * ```svelte
+ * <SidebarNavigationConversationList
+ *   {filteredConversations}
+ *   {currentChatId}
+ *   {isSearchModeActive}
+ *   {searchQuery}
+ *   onSelect={...}
+ *   onEdit={...}
+ *   onDelete={...}
+ *   onStop={...}
+ * />
+ * ```
+ */
+export { default as SidebarNavigationConversationList } from './SidebarNavigation/SidebarNavigationConversationList.svelte';
+export { default as SidebarNavigationActions } from './SidebarNavigation/SidebarNavigationActions.svelte';
+
+/**
+ * **SidebarNavigationSearchResults** - Filtered conversation list for search.
+ *
+ * Pure-presentational rendering of the search-mode subtree: "Search results"
+ * header, the matching items rendered through {@link SidebarNavigationConversationItem},
+ * and contextual empty-state messages. Used both inline inside
+ * {@link SidebarNavigationConversationList} (when search mode is active in the
+ * sidebar) and as the body of the mobile `/search` route.
+ *
+ * The caller is expected to provide an already-filtered list via
+ * `filteredConversations` and a `searchQuery` for the empty-state messages.
+ *
+ * @example
+ * ```svelte
+ * <SidebarNavigationSearchResults
+ *   {searchQuery}
+ *   {filteredConversations}
+ *   {currentChatId}
+ *   onSelect={...}
+ *   onEdit={...}
+ *   onDelete={...}
+ *   onStop={...}
+ * />
+ * ```
+ */
+export { default as SidebarNavigationSearchResults } from './SidebarNavigation/SidebarNavigationSearchResults.svelte';
+
 /**
  * Search input for filtering conversations in sidebar. Filters conversation
  * list by title as user types. Shows clear button when query is not empty.
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte
index 62e73d857..41105baa5 100644
--- a/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChat/SettingsChat.svelte
@@ -126,10 +126,7 @@
 	});
 </script>
 
-<div
-	class="mx-auto flex h-full max-h-[100dvh] w-full flex-col overflow-y-auto md:pl-8"
-	in:fade={{ duration: 150 }}
->
+<div class="mx-auto flex h-full w-full flex-col md:pl-8" in:fade={{ duration: 150 }}>
 	<div class="flex flex-1 flex-col gap-4 md:flex-row">
 		<SettingsChatDesktopSidebar
 			sections={SETTINGS_CHAT_SECTIONS}
diff --git a/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte b/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte
index 48a7b8757..8723217b2 100644
--- a/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsChatDesktopSidebar.svelte
@@ -12,11 +12,13 @@
 	let { sections, isActive, getHref, onSectionChange }: Props = $props();
 </script>
 
-<div class="sticky top-0 hidden w-64 flex-col self-start bg-background pt-10 pb-4 md:flex">
-	<div class="flex items-center gap-2 pb-10">
-		<Settings class="h-6 w-6" />
-		<h1 class="text-2xl font-semibold">Settings</h1>
+<div class="sticky top-2 hidden w-64 flex-col self-start bg-background py-4 md:flex gap-6">
+	<div class="flex items-center gap-2 py-2">
+		<Settings class="h-5 w-5 md:h-6 md:w-6" />
+
+		<h1 class="text-xl font-semibold md:text-2xl">Settings</h1>
 	</div>
+
 	<nav class="space-y-1">
 		{#each sections as section (section.title)}
 			{#if getHref}
diff --git a/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
index 5c801fa2c..e6f4b7e39 100644
--- a/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
@@ -1,17 +1,19 @@
 <script lang="ts">
-	import { Plus } from '@lucide/svelte';
+	import { X, Plus } from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
 	import { toolsStore } from '$lib/stores/tools.svelte';
-	import { McpServerCard, McpServerCardSkeleton } from '$lib/components/app/mcp';
+	import { ActionIcon, McpServerCard, McpServerCardSkeleton } from '$lib/components/app';
 	import { DialogMcpServerAddNew } from '$lib/components/app/dialogs';
 	import { HealthCheckStatus } from '$lib/enums';
+	import { ROUTES } from '$lib/constants';
 	import { fade } from 'svelte/transition';
 	import { onMount } from 'svelte';
 	import McpLogo from '../mcp/McpLogo.svelte';
+	import { browser } from '$app/environment';
 	import { page } from '$app/state';
-	import { replaceState } from '$app/navigation';
+	import { goto, replaceState } from '$app/navigation';
 
 	interface Props {
 		class?: string;
@@ -24,6 +26,24 @@
 	let initialLoadComplete = $state(false);
 	let isAddingServer = $state(false);
 
+	let previousRouteId = $state<string | null>(null);
+
+	$effect(() => {
+		const currentId = page.route.id;
+		return () => {
+			previousRouteId = currentId;
+		};
+	});
+
+	function handleClose() {
+		const prevIsMcpServers = previousRouteId === '/mcp-servers';
+		if (browser && window.history.length > 1 && !prevIsMcpServers) {
+			history.back();
+		} else {
+			goto(ROUTES.START);
+		}
+	}
+
 	onMount(() => {
 		if (page.url.searchParams.has('add')) {
 			isAddingServer = true;
@@ -54,15 +74,26 @@
 	});
 </script>
 
-<div in:fade={{ duration: 150 }} class="h-full max-h-[100dvh] overflow-y-auto">
-	<div class="flex items-center gap-2 p-4 md:absolute md:top-8 md:left-8 md:px-0 md:py-2">
-		<McpLogo class="h-5 w-5 md:h-6 md:w-6" />
-
-		<h1 class="text-xl font-semibold md:text-2xl">MCP Servers</h1>
+<div in:fade={{ duration: 150 }}>
+	<div class="fixed top-4.5 right-4 z-50 md:hidden">
+		<ActionIcon icon={X} tooltip="Close" onclick={handleClose} />
 	</div>
 
-	<div class="sticky top-0 z-10 mt-4 flex items-start gap-4 p-4 md:justify-end md:px-8">
-		<Button variant="outline" size="sm" class="shrink-0" onclick={() => (isAddingServer = true)}>
+	<div
+		class="sticky top-0 z-10 mt-4 mb-2 flex items-start gap-4 md:p-4 p-0 px-4 md:justify-between md:px-8"
+	>
+		<div class="flex items-center gap-2">
+			<McpLogo class="h-5 w-5 md:h-6 md:w-6" />
+
+			<h1 class="text-lg font-semibold md:text-2xl">MCP Servers</h1>
+		</div>
+
+		<Button
+			variant="outline"
+			size="lg"
+			class="shrink-0 fixed md:static bottom-6 right-6"
+			onclick={() => (isAddingServer = true)}
+		>
 			<Plus class="h-4 w-4" />
 
 			Add New Server
diff --git a/tools/ui/src/lib/components/ui/button/button.svelte b/tools/ui/src/lib/components/ui/button/button.svelte
index cbf4d2cc5..165b9fd68 100644
--- a/tools/ui/src/lib/components/ui/button/button.svelte
+++ b/tools/ui/src/lib/components/ui/button/button.svelte
@@ -20,7 +20,7 @@
 			size: {
 				default: 'h-9 px-4 py-2 has-[>svg]:px-3',
 				sm: 'h-8 gap-1.5 rounded-md px-3 has-[>svg]:px-2.5',
-				lg: 'h-10 rounded-md px-6 has-[>svg]:px-4',
+				lg: 'h-10 rounded-lg px-6 has-[>svg]:px-4',
 				'icon-lg': 'size-10',
 				icon: 'size-9',
 				'icon-sm': 'size-5 rounded-sm'
diff --git a/tools/ui/src/lib/components/ui/sidebar/constants.ts b/tools/ui/src/lib/components/ui/sidebar/constants.ts
deleted file mode 100644
index 8c320c2ca..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/constants.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-export const SIDEBAR_COOKIE_NAME = 'sidebar:state';
-export const SIDEBAR_COOKIE_MAX_AGE = 60 * 60 * 24 * 7;
-export const SIDEBAR_MIN_WIDTH = '18rem';
-export const SIDEBAR_MAX_WIDTH = '32rem';
-export const SIDEBAR_WIDTH_MOBILE = '18rem';
-export const SIDEBAR_WIDTH_ICON = '3rem';
-export const SIDEBAR_KEYBOARD_SHORTCUT = 'b';
diff --git a/tools/ui/src/lib/components/ui/sidebar/context.svelte.ts b/tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
deleted file mode 100644
index 2fa5cc25d..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/context.svelte.ts
+++ /dev/null
@@ -1,79 +0,0 @@
-import { isMobile } from '$lib/stores/viewport.svelte.js';
-import { getContext, setContext } from 'svelte';
-import { SIDEBAR_KEYBOARD_SHORTCUT, SIDEBAR_MIN_WIDTH } from './constants.js';
-
-type Getter<T> = () => T;
-
-export type SidebarStateProps = {
-	/**
-	 * A getter function that returns the current open state of the sidebar.
-	 * We use a getter function here to support `bind:open` on the `Sidebar.Provider`
-	 * component.
-	 */
-	open: Getter<boolean>;
-
-	/**
-	 * A function that sets the open state of the sidebar. To support `bind:open`, we need
-	 * a source of truth for changing the open state to ensure it will be synced throughout
-	 * the sub-components and any `bind:` references.
-	 */
-	setOpen: (open: boolean) => void;
-};
-
-class SidebarState {
-	readonly props: SidebarStateProps;
-	open = $derived.by(() => this.props.open());
-	openMobile = $state(false);
-	sidebarWidth = $state(SIDEBAR_MIN_WIDTH);
-	isResizing = $state(false);
-	setOpen: SidebarStateProps['setOpen'];
-	state = $derived.by(() => (this.open ? 'expanded' : 'collapsed'));
-
-	constructor(props: SidebarStateProps) {
-		this.setOpen = props.setOpen;
-		this.props = props;
-	}
-
-	// Convenience getter for checking if the sidebar is mobile
-	// without this, we would need to use `sidebar.isMobile.current` everywhere
-	get isMobile() {
-		return isMobile.current;
-	}
-
-	// Event handler to apply to the `<svelte:window>`
-	handleShortcutKeydown = (e: KeyboardEvent) => {
-		if (e.key === SIDEBAR_KEYBOARD_SHORTCUT && (e.metaKey || e.ctrlKey)) {
-			e.preventDefault();
-			this.toggle();
-		}
-	};
-
-	setOpenMobile = (value: boolean) => {
-		this.openMobile = value;
-	};
-
-	toggle = () => {
-		this.setOpen(!this.open);
-	};
-}
-
-const SYMBOL_KEY = 'scn-sidebar';
-
-/**
- * Instantiates a new `SidebarState` instance and sets it in the context.
- *
- * @param props The constructor props for the `SidebarState` class.
- * @returns  The `SidebarState` instance.
- */
-export function setSidebar(props: SidebarStateProps): SidebarState {
-	return setContext(Symbol.for(SYMBOL_KEY), new SidebarState(props));
-}
-
-/**
- * Retrieves the `SidebarState` instance from the context. This is a class instance,
- * so you cannot destructure it.
- * @returns The `SidebarState` instance.
- */
-export function useSidebar(): SidebarState {
-	return getContext(Symbol.for(SYMBOL_KEY));
-}
diff --git a/tools/ui/src/lib/components/ui/sidebar/index.ts b/tools/ui/src/lib/components/ui/sidebar/index.ts
deleted file mode 100644
index 280e64079..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/index.ts
+++ /dev/null
@@ -1,75 +0,0 @@
-import { useSidebar } from './context.svelte.js';
-import Content from './sidebar-content.svelte';
-import Footer from './sidebar-footer.svelte';
-import GroupAction from './sidebar-group-action.svelte';
-import GroupContent from './sidebar-group-content.svelte';
-import GroupLabel from './sidebar-group-label.svelte';
-import Group from './sidebar-group.svelte';
-import Header from './sidebar-header.svelte';
-import Input from './sidebar-input.svelte';
-import Inset from './sidebar-inset.svelte';
-import MenuAction from './sidebar-menu-action.svelte';
-import MenuBadge from './sidebar-menu-badge.svelte';
-import MenuButton from './sidebar-menu-button.svelte';
-import MenuItem from './sidebar-menu-item.svelte';
-import MenuSkeleton from './sidebar-menu-skeleton.svelte';
-import MenuSubButton from './sidebar-menu-sub-button.svelte';
-import MenuSubItem from './sidebar-menu-sub-item.svelte';
-import MenuSub from './sidebar-menu-sub.svelte';
-import Menu from './sidebar-menu.svelte';
-import Provider from './sidebar-provider.svelte';
-import Rail from './sidebar-rail.svelte';
-import Separator from './sidebar-separator.svelte';
-import Trigger from './sidebar-trigger.svelte';
-import Root from './sidebar.svelte';
-
-export {
-	Content,
-	Footer,
-	Group,
-	GroupAction,
-	GroupContent,
-	GroupLabel,
-	Header,
-	Input,
-	Inset,
-	Menu,
-	MenuAction,
-	MenuBadge,
-	MenuButton,
-	MenuItem,
-	MenuSkeleton,
-	MenuSub,
-	MenuSubButton,
-	MenuSubItem,
-	Provider,
-	Rail,
-	Root,
-	Separator,
-	//
-	Root as Sidebar,
-	Content as SidebarContent,
-	Footer as SidebarFooter,
-	Group as SidebarGroup,
-	GroupAction as SidebarGroupAction,
-	GroupContent as SidebarGroupContent,
-	GroupLabel as SidebarGroupLabel,
-	Header as SidebarHeader,
-	Input as SidebarInput,
-	Inset as SidebarInset,
-	Menu as SidebarMenu,
-	MenuAction as SidebarMenuAction,
-	MenuBadge as SidebarMenuBadge,
-	MenuButton as SidebarMenuButton,
-	MenuItem as SidebarMenuItem,
-	MenuSkeleton as SidebarMenuSkeleton,
-	MenuSub as SidebarMenuSub,
-	MenuSubButton as SidebarMenuSubButton,
-	MenuSubItem as SidebarMenuSubItem,
-	Provider as SidebarProvider,
-	Rail as SidebarRail,
-	Separator as SidebarSeparator,
-	Trigger as SidebarTrigger,
-	Trigger,
-	useSidebar
-};
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte
deleted file mode 100644
index 0e5f75e36..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-content.svelte
+++ /dev/null
@@ -1,24 +0,0 @@
-<script lang="ts">
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-content"
-	data-sidebar="content"
-	class={cn(
-		'flex min-h-0 flex-1 flex-col gap-2 overflow-auto group-data-[collapsible=icon]:overflow-hidden',
-		className
-	)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
deleted file mode 100644
index 69914dfa2..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-footer.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-footer"
-	data-sidebar="footer"
-	class={cn('flex flex-col gap-2 p-3', className)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
deleted file mode 100644
index 027a711ca..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-action.svelte
+++ /dev/null
@@ -1,36 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { Snippet } from 'svelte';
-	import type { HTMLButtonAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		child,
-		...restProps
-	}: WithElementRef<HTMLButtonAttributes> & {
-		child?: Snippet<[{ props: Record<string, unknown> }]>;
-	} = $props();
-
-	const mergedProps = $derived({
-		class: cn(
-			'text-sidebar-foreground ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground outline-hidden absolute right-3 top-3.5 flex aspect-square w-5 items-center justify-center rounded-md p-0 transition-transform focus-visible:ring-2 [&>svg]:size-4 [&>svg]:shrink-0',
-			// Increases the hit area of the button on mobile.
-			'after:absolute after:-inset-2 md:after:hidden',
-			'group-data-[collapsible=icon]:hidden',
-			className
-		),
-		'data-slot': 'sidebar-group-action',
-		'data-sidebar': 'group-action',
-		...restProps
-	});
-</script>
-
-{#if child}
-	{@render child({ props: mergedProps })}
-{:else}
-	<button bind:this={ref} {...mergedProps}>
-		{@render children?.()}
-	</button>
-{/if}
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
deleted file mode 100644
index 9e018fb59..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-content.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLDivElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-group-content"
-	data-sidebar="group-content"
-	class={cn('w-full text-sm', className)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
deleted file mode 100644
index 79f47d730..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-group-label.svelte
+++ /dev/null
@@ -1,34 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { Snippet } from 'svelte';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		children,
-		child,
-		class: className,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> & {
-		child?: Snippet<[{ props: Record<string, unknown> }]>;
-	} = $props();
-
-	const mergedProps = $derived({
-		class: cn(
-			'text-sidebar-foreground/70 ring-sidebar-ring outline-hidden flex h-8 shrink-0 items-center rounded-md px-2 text-xs font-medium transition-[margin,opacity] duration-200 ease-linear focus-visible:ring-2 [&>svg]:size-4 [&>svg]:shrink-0',
-			'group-data-[collapsible=icon]:-mt-8 group-data-[collapsible=icon]:opacity-0',
-			className
-		),
-		'data-slot': 'sidebar-group-label',
-		'data-sidebar': 'group-label',
-		...restProps
-	});
-</script>
-
-{#if child}
-	{@render child({ props: mergedProps })}
-{:else}
-	<div bind:this={ref} {...mergedProps}>
-		{@render children?.()}
-	</div>
-{/if}
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte
deleted file mode 100644
index eed5acec8..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-group.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-group"
-	data-sidebar="group"
-	class={cn('relative flex w-full min-w-0 flex-col p-2', className)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte
deleted file mode 100644
index 0651550d7..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-header.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-header"
-	data-sidebar="header"
-	class={cn('flex flex-col gap-2 p-2', className)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte
deleted file mode 100644
index fa5747350..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-input.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import type { ComponentProps } from 'svelte';
-	import { Input } from '$lib/components/ui/input/index.js';
-	import { cn } from '$lib/components/ui/utils.js';
-
-	let {
-		ref = $bindable(null),
-		value = $bindable(''),
-		class: className,
-		...restProps
-	}: ComponentProps<typeof Input> = $props();
-</script>
-
-<Input
-	bind:ref
-	bind:value
-	data-slot="sidebar-input"
-	data-sidebar="input"
-	class={cn('h-8 w-full bg-background shadow-none', className)}
-	{...restProps}
-/>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte
deleted file mode 100644
index f55d2f486..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-inset.svelte
+++ /dev/null
@@ -1,24 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<main
-	bind:this={ref}
-	data-slot="sidebar-inset"
-	class={cn(
-		'relative flex w-full flex-1 flex-col',
-		'md:peer-data-[variant=inset]:m-2 md:peer-data-[variant=inset]:ml-0 md:peer-data-[variant=inset]:rounded-xl md:peer-data-[variant=inset]:shadow-sm md:peer-data-[variant=inset]:peer-data-[state=collapsed]:ml-2',
-		className
-	)}
-	{...restProps}
->
-	{@render children?.()}
-</main>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
deleted file mode 100644
index ded1ffd78..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-action.svelte
+++ /dev/null
@@ -1,43 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { Snippet } from 'svelte';
-	import type { HTMLButtonAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		showOnHover = false,
-		children,
-		child,
-		...restProps
-	}: WithElementRef<HTMLButtonAttributes> & {
-		child?: Snippet<[{ props: Record<string, unknown> }]>;
-		showOnHover?: boolean;
-	} = $props();
-
-	const mergedProps = $derived({
-		class: cn(
-			'text-sidebar-foreground ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground peer-hover/menu-button:text-sidebar-accent-foreground outline-hidden absolute right-1 top-1.5 flex aspect-square w-5 items-center justify-center rounded-md p-0 transition-transform focus-visible:ring-2 [&>svg]:size-4 [&>svg]:shrink-0',
-			// Increases the hit area of the button on mobile.
-			'after:absolute after:-inset-2 md:after:hidden',
-			'peer-data-[size=sm]/menu-button:top-1',
-			'peer-data-[size=default]/menu-button:top-1.5',
-			'peer-data-[size=lg]/menu-button:top-2.5',
-			'group-data-[collapsible=icon]:hidden',
-			showOnHover &&
-				'peer-data-[active=true]/menu-button:text-sidebar-accent-foreground group-focus-within/menu-item:opacity-100 group-hover/menu-item:opacity-100 data-[state=open]:opacity-100 md:opacity-0',
-			className
-		),
-		'data-slot': 'sidebar-menu-action',
-		'data-sidebar': 'menu-action',
-		...restProps
-	});
-</script>
-
-{#if child}
-	{@render child({ props: mergedProps })}
-{:else}
-	<button bind:this={ref} {...mergedProps}>
-		{@render children?.()}
-	</button>
-{/if}
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
deleted file mode 100644
index f4525a142..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-badge.svelte
+++ /dev/null
@@ -1,29 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> = $props();
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-menu-badge"
-	data-sidebar="menu-badge"
-	class={cn(
-		'pointer-events-none absolute right-1 flex h-5 min-w-5 items-center justify-center rounded-md px-1 text-xs font-medium text-sidebar-foreground tabular-nums select-none',
-		'peer-hover/menu-button:text-sidebar-accent-foreground peer-data-[active=true]/menu-button:text-sidebar-accent-foreground',
-		'peer-data-[size=sm]/menu-button:top-1',
-		'peer-data-[size=default]/menu-button:top-1.5',
-		'peer-data-[size=lg]/menu-button:top-2.5',
-		'group-data-[collapsible=icon]:hidden',
-		className
-	)}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
deleted file mode 100644
index f9efd67e9..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-button.svelte
+++ /dev/null
@@ -1,106 +0,0 @@
-<script lang="ts" module>
-	import { tv, type VariantProps } from 'tailwind-variants';
-
-	export const sidebarMenuButtonVariants = tv({
-		base: 'peer/menu-button outline-hidden ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground active:bg-sidebar-accent active:text-sidebar-accent-foreground group-has-data-[sidebar=menu-action]/menu-item:pr-8 data-[active=true]:bg-sidebar-accent data-[active=true]:text-sidebar-accent-foreground data-[state=open]:hover:bg-sidebar-accent data-[state=open]:hover:text-sidebar-accent-foreground group-data-[collapsible=icon]:size-8! group-data-[collapsible=icon]:p-2! flex w-full items-center gap-2 overflow-hidden rounded-md py-2 px-1 text-left text-sm transition-[width,height,padding] focus-visible:ring-2 disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 data-[active=true]:font-medium [&>span:last-child]:truncate [&>svg]:size-4 [&>svg]:shrink-0',
-		variants: {
-			variant: {
-				default: 'hover:bg-sidebar-accent hover:text-sidebar-accent-foreground',
-				outline:
-					'bg-background hover:bg-sidebar-accent hover:text-sidebar-accent-foreground shadow-[0_0_0_1px_var(--sidebar-border)] hover:shadow-[0_0_0_1px_var(--sidebar-accent)]'
-			},
-			size: {
-				default: 'h-8 text-sm',
-				sm: 'h-7 text-xs',
-				lg: 'group-data-[collapsible=icon]:p-0! h-12 text-sm'
-			}
-		},
-		defaultVariants: {
-			variant: 'default',
-			size: 'default'
-		}
-	});
-
-	export type SidebarMenuButtonVariant = VariantProps<typeof sidebarMenuButtonVariants>['variant'];
-	export type SidebarMenuButtonSize = VariantProps<typeof sidebarMenuButtonVariants>['size'];
-</script>
-
-<script lang="ts">
-	import * as Tooltip from '$lib/components/ui/tooltip/index.js';
-	import {
-		cn,
-		type WithElementRef,
-		type WithoutChildrenOrChild
-	} from '$lib/components/ui/utils.js';
-	import { mergeProps } from 'bits-ui';
-	import type { ComponentProps, Snippet } from 'svelte';
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { useSidebar } from './context.svelte.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		child,
-		variant = 'default',
-		size = 'default',
-		isActive = false,
-		tooltipContent,
-		tooltipContentProps,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLButtonElement>, HTMLButtonElement> & {
-		isActive?: boolean;
-		variant?: SidebarMenuButtonVariant;
-		size?: SidebarMenuButtonSize;
-		tooltipContent?: Snippet | string;
-		tooltipContentProps?: WithoutChildrenOrChild<ComponentProps<typeof Tooltip.Content>>;
-		child?: Snippet<[{ props: Record<string, unknown> }]>;
-	} = $props();
-
-	const sidebar = useSidebar();
-
-	const buttonProps = $derived({
-		class: cn(sidebarMenuButtonVariants({ variant, size }), className),
-		'data-slot': 'sidebar-menu-button',
-		'data-sidebar': 'menu-button',
-		'data-size': size,
-		'data-active': isActive,
-		...restProps
-	});
-</script>
-
-{#snippet Button({ props }: { props?: Record<string, unknown> })}
-	{@const mergedProps = mergeProps(buttonProps, props)}
-	{#if child}
-		{@render child({ props: mergedProps })}
-	{:else}
-		<button bind:this={ref} {...mergedProps}>
-			{@render children?.()}
-		</button>
-	{/if}
-{/snippet}
-
-{#if !tooltipContent}
-	{@render Button({})}
-{:else}
-	<Tooltip.Root>
-		<Tooltip.Trigger>
-			{#snippet child({ props })}
-				{@render Button({ props })}
-			{/snippet}
-		</Tooltip.Trigger>
-
-		<Tooltip.Content
-			side="right"
-			align="center"
-			hidden={sidebar.state !== 'collapsed' || sidebar.isMobile}
-			{...tooltipContentProps}
-		>
-			{#if typeof tooltipContent === 'string'}
-				{tooltipContent}
-			{:else if tooltipContent}
-				{@render tooltipContent()}
-			{/if}
-		</Tooltip.Content>
-	</Tooltip.Root>
-{/if}
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
deleted file mode 100644
index 5adbedd4d..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-item.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLLIElement>, HTMLLIElement> = $props();
-</script>
-
-<li
-	bind:this={ref}
-	data-slot="sidebar-menu-item"
-	data-sidebar="menu-item"
-	class={cn('group/menu-item relative', className)}
-	{...restProps}
->
-	{@render children?.()}
-</li>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
deleted file mode 100644
index 2b2acd6ad..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-skeleton.svelte
+++ /dev/null
@@ -1,36 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import { Skeleton } from '$lib/components/ui/skeleton/index.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		showIcon = false,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLElement>> & {
-		showIcon?: boolean;
-	} = $props();
-
-	// Random width between 50% and 90%
-	const width = `${Math.floor(Math.random() * 40) + 50}%`;
-</script>
-
-<div
-	bind:this={ref}
-	data-slot="sidebar-menu-skeleton"
-	data-sidebar="menu-skeleton"
-	class={cn('flex h-8 items-center gap-2 rounded-md px-2', className)}
-	{...restProps}
->
-	{#if showIcon}
-		<Skeleton class="size-4 rounded-md" data-sidebar="menu-skeleton-icon" />
-	{/if}
-	<Skeleton
-		class="h-4 max-w-(--skeleton-width) flex-1"
-		data-sidebar="menu-skeleton-text"
-		style="--skeleton-width: {width};"
-	/>
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
deleted file mode 100644
index dabfe0fa0..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-button.svelte
+++ /dev/null
@@ -1,43 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { Snippet } from 'svelte';
-	import type { HTMLAnchorAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		children,
-		child,
-		class: className,
-		size = 'md',
-		isActive = false,
-		...restProps
-	}: WithElementRef<HTMLAnchorAttributes> & {
-		child?: Snippet<[{ props: Record<string, unknown> }]>;
-		size?: 'sm' | 'md';
-		isActive?: boolean;
-	} = $props();
-
-	const mergedProps = $derived({
-		class: cn(
-			'text-sidebar-foreground ring-sidebar-ring hover:bg-sidebar-accent hover:text-sidebar-accent-foreground active:bg-sidebar-accent active:text-sidebar-accent-foreground [&>svg]:text-sidebar-accent-foreground outline-hidden flex h-7 min-w-0 -translate-x-px items-center gap-2 overflow-hidden rounded-md px-2 focus-visible:ring-2 disabled:pointer-events-none disabled:opacity-50 aria-disabled:pointer-events-none aria-disabled:opacity-50 [&>span:last-child]:truncate [&>svg]:size-4 [&>svg]:shrink-0',
-			'data-[active=true]:bg-sidebar-accent data-[active=true]:text-sidebar-accent-foreground',
-			size === 'sm' && 'text-xs',
-			size === 'md' && 'text-sm',
-			'group-data-[collapsible=icon]:hidden',
-			className
-		),
-		'data-slot': 'sidebar-menu-sub-button',
-		'data-sidebar': 'menu-sub-button',
-		'data-size': size,
-		'data-active': isActive,
-		...restProps
-	});
-</script>
-
-{#if child}
-	{@render child({ props: mergedProps })}
-{:else}
-	<a bind:this={ref} {...mergedProps}>
-		{@render children?.()}
-	</a>
-{/if}
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
deleted file mode 100644
index cca870eb9..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub-item.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		children,
-		class: className,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLLIElement>> = $props();
-</script>
-
-<li
-	bind:this={ref}
-	data-slot="sidebar-menu-sub-item"
-	data-sidebar="menu-sub-item"
-	class={cn('group/menu-sub-item relative', className)}
-	{...restProps}
->
-	{@render children?.()}
-</li>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
deleted file mode 100644
index 5458cedf9..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu-sub.svelte
+++ /dev/null
@@ -1,25 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLUListElement>> = $props();
-</script>
-
-<ul
-	bind:this={ref}
-	data-slot="sidebar-menu-sub"
-	data-sidebar="menu-sub"
-	class={cn(
-		'mx-3.5 flex min-w-0 translate-x-px flex-col gap-1 border-l border-sidebar-border px-2.5 py-0.5',
-		'group-data-[collapsible=icon]:hidden',
-		className
-	)}
-	{...restProps}
->
-	{@render children?.()}
-</ul>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte
deleted file mode 100644
index fee96ed59..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-menu.svelte
+++ /dev/null
@@ -1,21 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLUListElement>, HTMLUListElement> = $props();
-</script>
-
-<ul
-	bind:this={ref}
-	data-slot="sidebar-menu"
-	data-sidebar="menu"
-	class={cn('flex w-full min-w-0 flex-col gap-1', className)}
-	{...restProps}
->
-	{@render children?.()}
-</ul>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
deleted file mode 100644
index 9fd270403..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-provider.svelte
+++ /dev/null
@@ -1,51 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-	import {
-		SIDEBAR_COOKIE_MAX_AGE,
-		SIDEBAR_COOKIE_NAME,
-		SIDEBAR_MIN_WIDTH,
-		SIDEBAR_MAX_WIDTH,
-		SIDEBAR_WIDTH_ICON
-	} from './constants.js';
-	import { setSidebar } from './context.svelte.js';
-
-	let {
-		ref = $bindable(null),
-		open = $bindable(true),
-		onOpenChange = () => {},
-		class: className,
-		style,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLDivElement>> & {
-		open?: boolean;
-		onOpenChange?: (open: boolean) => void;
-	} = $props();
-
-	const sidebar = setSidebar({
-		open: () => open,
-		setOpen: (value: boolean) => {
-			open = value;
-			onOpenChange(value);
-
-			// This sets the cookie to keep the sidebar state.
-			document.cookie = `${SIDEBAR_COOKIE_NAME}=${open}; path=/; max-age=${SIDEBAR_COOKIE_MAX_AGE}`;
-		}
-	});
-</script>
-
-<svelte:window onkeydown={sidebar.handleShortcutKeydown} />
-
-<div
-	data-slot="sidebar-wrapper"
-	style="--sidebar-width: {sidebar.sidebarWidth}; --sidebar-min-width: {SIDEBAR_MIN_WIDTH}; --sidebar-max-width: {SIDEBAR_MAX_WIDTH}; --sidebar-width-icon: {SIDEBAR_WIDTH_ICON}; {style}"
-	class={cn(
-		'group/sidebar-wrapper flex flex-col h-dvh w-full has-data-[variant=inset]:bg-sidebar',
-		className
-	)}
-	bind:this={ref}
-	{...restProps}
->
-	{@render children?.()}
-</div>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte
deleted file mode 100644
index cde930709..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-rail.svelte
+++ /dev/null
@@ -1,36 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { useSidebar } from './context.svelte.js';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLButtonElement>, HTMLButtonElement> = $props();
-
-	const sidebar = useSidebar();
-</script>
-
-<button
-	bind:this={ref}
-	data-sidebar="rail"
-	data-slot="sidebar-rail"
-	aria-label="Toggle Sidebar"
-	tabIndex={-1}
-	onclick={sidebar.toggle}
-	title="Toggle Sidebar"
-	class={cn(
-		'absolute inset-y-0 z-20 hidden w-4 -translate-x-1/2 transition-all ease-linear group-data-[side=left]:-right-4 group-data-[side=right]:left-0 after:absolute after:inset-y-0 after:left-[calc(1/2*100%-1px)] after:w-[2px] hover:after:bg-sidebar-border sm:flex',
-		'in-data-[side=left]:cursor-w-resize in-data-[side=right]:cursor-e-resize',
-		'[[data-side=left][data-state=collapsed]_&]:cursor-e-resize [[data-side=right][data-state=collapsed]_&]:cursor-w-resize',
-		'group-data-[collapsible=offcanvas]:translate-x-0 group-data-[collapsible=offcanvas]:after:left-full hover:group-data-[collapsible=offcanvas]:bg-sidebar',
-		'[[data-side=left][data-collapsible=offcanvas]_&]:-right-2',
-		'[[data-side=right][data-collapsible=offcanvas]_&]:-left-2',
-		className
-	)}
-	{...restProps}
->
-	{@render children?.()}
-</button>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte
deleted file mode 100644
index 8fc206580..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-separator.svelte
+++ /dev/null
@@ -1,19 +0,0 @@
-<script lang="ts">
-	import { Separator } from '$lib/components/ui/separator/index.js';
-	import { cn } from '$lib/components/ui/utils.js';
-	import type { ComponentProps } from 'svelte';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		...restProps
-	}: ComponentProps<typeof Separator> = $props();
-</script>
-
-<Separator
-	bind:ref
-	data-slot="sidebar-separator"
-	data-sidebar="separator"
-	class={cn('bg-sidebar-border', className)}
-	{...restProps}
-/>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
deleted file mode 100644
index 67076d4d3..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar-trigger.svelte
+++ /dev/null
@@ -1,43 +0,0 @@
-<script lang="ts">
-	import { Button } from '$lib/components/ui/button/index.js';
-	import PanelLeftIcon from '@lucide/svelte/icons/panel-left';
-	import type { ComponentProps } from 'svelte';
-	import { useSidebar } from './context.svelte.js';
-	import { PanelLeftClose } from '@lucide/svelte';
-
-	let {
-		ref = $bindable(null),
-		class: className,
-		onclick,
-		...restProps
-	}: ComponentProps<typeof Button> & {
-		onclick?: (e: MouseEvent) => void;
-	} = $props();
-
-	const sidebar = useSidebar();
-</script>
-
-<Button
-	data-sidebar="trigger"
-	data-slot="sidebar-trigger"
-	variant="ghost"
-	size="icon-lg"
-	class="rounded-full backdrop-blur-lg {className} {sidebar.open
-		? 'top-1.5'
-		: 'top-0'} md:left-[calc(var(--sidebar-width)-3.25rem)] {sidebar.isResizing
-		? '!duration-0'
-		: ''}"
-	type="button"
-	onclick={(e) => {
-		onclick?.(e);
-		sidebar.toggle();
-	}}
-	{...restProps}
->
-	{#if sidebar.open}
-		<PanelLeftClose />
-	{:else}
-		<PanelLeftIcon />
-	{/if}
-	<span class="sr-only">Toggle Sidebar</span>
-</Button>
diff --git a/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte b/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte
deleted file mode 100644
index d6da1aec7..000000000
--- a/tools/ui/src/lib/components/ui/sidebar/sidebar.svelte
+++ /dev/null
@@ -1,150 +0,0 @@
-<script lang="ts">
-	import { cn, type WithElementRef } from '$lib/components/ui/utils.js';
-	import type { HTMLAttributes } from 'svelte/elements';
-	import { SIDEBAR_MIN_WIDTH, SIDEBAR_MAX_WIDTH } from './constants.js';
-	import { useSidebar } from './context.svelte.js';
-	import { remToPx } from '$lib/utils';
-
-	let {
-		ref = $bindable(null),
-		side = 'left',
-		variant = 'sidebar',
-		collapsible = 'offcanvas',
-		class: className,
-		children,
-		...restProps
-	}: WithElementRef<HTMLAttributes<HTMLDivElement>> & {
-		side?: 'left' | 'right';
-		variant?: 'sidebar' | 'floating' | 'inset';
-		collapsible?: 'offcanvas' | 'icon' | 'none';
-	} = $props();
-
-	const sidebar = useSidebar();
-
-	function handleResizePointerDown(e: PointerEvent) {
-		if (sidebar.isMobile) return;
-		e.preventDefault();
-
-		const target = e.currentTarget as HTMLElement;
-		target.setPointerCapture(e.pointerId);
-
-		const minPx = remToPx(SIDEBAR_MIN_WIDTH);
-		const maxPx = remToPx(SIDEBAR_MAX_WIDTH);
-
-		sidebar.isResizing = true;
-
-		function onPointerMove(ev: PointerEvent) {
-			const newWidth = side === 'left' ? ev.clientX : window.innerWidth - ev.clientX;
-			const clamped = Math.min(maxPx, Math.max(minPx, newWidth));
-			sidebar.sidebarWidth = `${clamped}px`;
-		}
-
-		function onPointerUp() {
-			sidebar.isResizing = false;
-			target.removeEventListener('pointermove', onPointerMove);
-			target.removeEventListener('pointerup', onPointerUp);
-		}
-
-		target.addEventListener('pointermove', onPointerMove);
-		target.addEventListener('pointerup', onPointerUp);
-	}
-</script>
-
-{#if collapsible === 'none'}
-	<div
-		class={cn(
-			'flex h-full w-(--sidebar-width) flex-col bg-sidebar text-sidebar-foreground',
-			className
-		)}
-		bind:this={ref}
-		{...restProps}
-	>
-		{@render children?.()}
-	</div>
-{:else}
-	<div
-		bind:this={ref}
-		class="group peer block text-sidebar-foreground"
-		data-state={sidebar.state}
-		data-collapsible={sidebar.state === 'collapsed' ? collapsible : ''}
-		data-variant={variant}
-		data-side={side}
-		data-slot="sidebar"
-	>
-		<!-- This is what handles the sidebar gap on desktop -->
-		<div
-			data-slot="sidebar-gap"
-			class={cn(
-				'relative bg-transparent transition-[width] duration-200 ease-linear',
-				sidebar.isResizing && '!duration-0',
-				'w-0',
-				variant === 'floating'
-					? 'md:w-[calc(var(--sidebar-width)+0.75rem)]'
-					: 'md:w-(--sidebar-width)',
-				'md:group-data-[collapsible=offcanvas]:w-0',
-				'group-data-[side=right]:rotate-180',
-				variant === 'floating' || variant === 'inset'
-					? 'group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
-					: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)'
-			)}
-		></div>
-
-		<div
-			data-slot="sidebar-container"
-			class={cn(
-				'fixed inset-y-0 z-[900] flex w-[calc(100dvw-1.5rem)] duration-200 ease-linear md:z-0 md:w-(--sidebar-width)',
-				'group-data-[collapsible=offcanvas]:pointer-events-none md:group-data-[collapsible=offcanvas]:pointer-events-auto',
-				sidebar.isResizing && '!duration-0',
-				variant === 'floating'
-					? [
-							'transition-[left,right,width,opacity]',
-							side === 'left'
-								? 'left-3 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*-0.775)] group-data-[collapsible=offcanvas]:opacity-0'
-								: 'right-3 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-0.775)] group-data-[collapsible=offcanvas]:opacity-0',
-							'my-3 overflow-hidden rounded-3xl border border-sidebar-border shadow-md'
-						]
-					: [
-							'h-svh transition-[left,right,width]',
-							side === 'left'
-								? 'left-0 group-data-[collapsible=offcanvas]:left-[calc(var(--sidebar-width)*-1)]'
-								: 'right-0 group-data-[collapsible=offcanvas]:right-[calc(var(--sidebar-width)*-1)]'
-						],
-				// Adjust the padding for inset variant.
-				variant === 'inset'
-					? 'p-2 group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
-					: variant === 'floating'
-						? 'group-data-[collapsible=icon]:w-[calc(var(--sidebar-width-icon)+(--spacing(4))+2px)]'
-						: 'group-data-[collapsible=icon]:w-(--sidebar-width-icon)',
-				className
-			)}
-			style={variant === 'floating' ? 'height: calc(100dvh - 1.5rem);' : undefined}
-			{...restProps}
-		>
-			<div
-				data-sidebar="sidebar"
-				data-slot="sidebar-inner"
-				class="flex h-full w-full flex-col bg-sidebar"
-			>
-				{@render children?.()}
-			</div>
-			<!-- Resize handle -->
-			{#if side === 'left'}
-				<!-- svelte-ignore a11y_no_static_element_interactions -->
-				<div
-					data-slot="sidebar-resize-handle"
-					class="absolute inset-y-0 right-0 z-50 hidden w-1.5 cursor-ew-resize touch-none select-none hover:bg-sidebar-border/50 active:bg-sidebar-border md:block"
-					class:bg-sidebar-border={sidebar.isResizing}
-					onpointerdown={handleResizePointerDown}
-				></div>
-			{:else}
-				<!-- svelte-ignore a11y_no_static_element_interactions -->
-				<div
-					data-slot="sidebar-resize-handle"
-					class="absolute inset-y-0 left-0 z-50 hidden w-1.5 cursor-ew-resize touch-none select-none hover:bg-sidebar-border/50 active:bg-sidebar-border md:block"
-					class:bg-sidebar-border={sidebar.isResizing}
-					onpointerdown={handleResizePointerDown}
-				></div>
-			{/if}
-		</div>
-	</div>
-{/if}
diff --git a/tools/ui/src/lib/constants/pwa.ts b/tools/ui/src/lib/constants/pwa.ts
index 49036a67b..80505fabd 100644
--- a/tools/ui/src/lib/constants/pwa.ts
+++ b/tools/ui/src/lib/constants/pwa.ts
@@ -7,7 +7,8 @@ import { APP_NAME } from './app';
 
 export const MEDIA_QUERIES = {
 	PREFERS_DARK: '(prefers-color-scheme: dark)',
-	PREFERS_LIGHT: '(prefers-color-scheme: light)'
+	PREFERS_LIGHT: '(prefers-color-scheme: light)',
+	DISPLAY_MODE_STANDALONE: '(display-mode: standalone)'
 } as const;
 
 export const THEME_COLORS = {
@@ -34,6 +35,13 @@ export const FAVICON_PATHS = {
 	SVG_DARK: 'favicon-dark.svg'
 } as const;
 
+// Substituted for `currentColor` in src/lib/assets/logo.svg when generating
+// the light/dark static sources consumed by the PWA asset generator.
+export const FAVICON_COLORS = {
+	LIGHT: '#111111',
+	DARK: '#fafafa'
+} as const;
+
 export const FAVICON_SELECTORS = {
 	ICO_48X48: 'link[rel="icon"][sizes="48x48"]',
 	SVG_ANY: 'link[rel="icon"][type="image/svg+xml"]'
@@ -222,8 +230,13 @@ export const PWA_GENERATOR_DEVICES = [
 ] as const;
 
 // PWA assets generator configuration — used by pwa-assets.config.ts
+// FAVICON_PADDING: fraction (0..1) of the icon reserved as equal margin on
+// each side. Applied to icon PNG/ICO outputs by @vite-pwa/assets-generator and
+// post-processed into the static favicon.svg so the in-app logo (which reads
+// src/lib/assets/logo.svg directly) is unaffected.
 export const PWA_ASSET_GENERATOR = {
 	LINK_PRESET: '2023',
+	FAVICON_PADDING: 0.04,
 	SPLASH_PADDING: 0.75,
 	FIT_MODE: 'contain',
 	ADD_MEDIA_SCREEN: true,
diff --git a/tools/ui/src/lib/constants/routes.ts b/tools/ui/src/lib/constants/routes.ts
index 3b3fceea4..1adf0a600 100644
--- a/tools/ui/src/lib/constants/routes.ts
+++ b/tools/ui/src/lib/constants/routes.ts
@@ -23,5 +23,7 @@ export const ROUTES = {
 	/** MCP servers. */
 	MCP_SERVERS: '#/mcp-servers',
 	/** Settings base — for dynamic settings URLs use RouterService. */
-	SETTINGS: '#/settings'
+	SETTINGS: '#/settings',
+	/** Search — mobile-only full-page conversation search. */
+	SEARCH: '#/search'
 } as const;
diff --git a/tools/ui/src/lib/constants/ui.ts b/tools/ui/src/lib/constants/ui.ts
index 6f344029f..f2d3e95e0 100644
--- a/tools/ui/src/lib/constants/ui.ts
+++ b/tools/ui/src/lib/constants/ui.ts
@@ -1,4 +1,4 @@
-import { Settings, Search, SquarePen } from '@lucide/svelte';
+import { Search, Settings, SquarePen } from '@lucide/svelte';
 import McpLogo from '$lib/components/app/mcp/McpLogo.svelte';
 import type { Component } from 'svelte';
 import { ROUTES } from './routes';
@@ -15,6 +15,7 @@ export interface DesktopIconStripItem {
 	route?: string;
 	activeRouteId?: string;
 	activeRoutePrefix?: string;
+	activeUrlIncludes?: string;
 	keys?: string[];
 }
 
@@ -30,7 +31,7 @@ export const SIDEBAR_ACTIONS_ITEMS: DesktopIconStripItem[] = [
 	{
 		icon: Settings,
 		tooltip: 'Settings',
-		route: ROUTES.SETTINGS,
-		activeRoutePrefix: '/settings'
+		route: `${ROUTES.SETTINGS}/general`,
+		activeUrlIncludes: '#/settings'
 	}
 ];
diff --git a/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts b/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
index 7bac452e4..8107f08d0 100644
--- a/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-auto-scroll.svelte.ts
@@ -105,7 +105,10 @@ export class AutoScrollController {
 	 */
 	resetScrollState(): void {
 		this._userScrolledUp = false;
-		this._autoScrollEnabled = true;
+		this._autoScrollEnabled = !this._disabled;
+		if (this._container) {
+			this._lastScrollTop = this._container.scrollTop;
+		}
 	}
 
 	/**
diff --git a/tools/ui/src/lib/hooks/use-chat-screen-active-model.svelte.ts b/tools/ui/src/lib/hooks/use-chat-screen-active-model.svelte.ts
new file mode 100644
index 000000000..de75e9fef
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-chat-screen-active-model.svelte.ts
@@ -0,0 +1,100 @@
+/**
+ * Active model resolution and capability detection for the ChatScreen.
+ *
+ * Picks the model that should be used for the current view
+ * (router: user-selected or conversation fallback; non-router: first
+ * available option), and reactively tracks which modalities (vision /
+ * audio / video) it supports — fetching model props from the server on
+ * demand if they aren't cached yet.
+ */
+
+import { modelsStore, modelOptions, selectedModelId } from '$lib/stores/models.svelte';
+import { isRouterMode } from '$lib/stores/server.svelte';
+import { chatStore } from '$lib/stores/chat.svelte';
+import { activeMessages } from '$lib/stores/conversations.svelte';
+
+export function useChatScreenActiveModel() {
+	const isRouter = $derived(isRouterMode());
+	const conversationModel = $derived(
+		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
+	);
+
+	const activeModelId = $derived.by(() => {
+		const options = modelOptions();
+
+		if (!isRouter) {
+			return options.length > 0 ? options[0].model : null;
+		}
+
+		const selectedId = selectedModelId();
+		if (selectedId) {
+			const model = options.find((m) => m.id === selectedId);
+			if (model) return model.model;
+		}
+
+		if (conversationModel) {
+			const model = options.find((m) => m.model === conversationModel);
+			if (model) return model.model;
+		}
+
+		return null;
+	});
+
+	let modelPropsVersion = $state(0);
+
+	$effect(() => {
+		if (activeModelId) {
+			const cached = modelsStore.getModelProps(activeModelId);
+			if (!cached) {
+				modelsStore.fetchModelProps(activeModelId).then(() => {
+					modelPropsVersion++;
+				});
+			}
+		}
+	});
+
+	const hasAudioModality = $derived.by(() => {
+		if (activeModelId) {
+			void modelPropsVersion;
+			return modelsStore.modelSupportsAudio(activeModelId);
+		}
+		return false;
+	});
+
+	const hasVideoModality = $derived.by(() => {
+		if (activeModelId) {
+			void modelPropsVersion;
+			return modelsStore.modelSupportsVideo(activeModelId);
+		}
+		return false;
+	});
+
+	const hasVisionModality = $derived.by(() => {
+		if (activeModelId) {
+			void modelPropsVersion;
+			return modelsStore.modelSupportsVision(activeModelId);
+		}
+		return false;
+	});
+
+	return {
+		get isRouter() {
+			return isRouter;
+		},
+		get conversationModel() {
+			return conversationModel;
+		},
+		get activeModelId() {
+			return activeModelId;
+		},
+		get hasAudioModality() {
+			return hasAudioModality;
+		},
+		get hasVideoModality() {
+			return hasVideoModality;
+		},
+		get hasVisionModality() {
+			return hasVisionModality;
+		}
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-chat-screen-drag-and-drop.svelte.ts b/tools/ui/src/lib/hooks/use-chat-screen-drag-and-drop.svelte.ts
new file mode 100644
index 000000000..3f292b4e1
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-chat-screen-drag-and-drop.svelte.ts
@@ -0,0 +1,72 @@
+/**
+ * Drag-and-drop state machine for the ChatScreen.
+ *
+ * Tracks pointer enter/leave nesting so the overlay stays visible while the
+ * cursor traverses child elements, then routes the dropped files either to
+ * the active message-edit handler (if a message is being edited) or to the
+ * caller's onDrop callback.
+ */
+
+import { getAddFilesHandler, isEditing } from '$lib/stores/chat.svelte';
+
+interface UseChatScreenDragAndDropOptions {
+	/** Called when the user drops files and no message is being edited. */
+	onDrop: (files: File[]) => void;
+}
+
+export function useChatScreenDragAndDrop(options: UseChatScreenDragAndDropOptions) {
+	let dragCounter = $state(0);
+	let isDragOver = $state(false);
+
+	function handleDragEnter(event: DragEvent) {
+		event.preventDefault();
+		dragCounter++;
+		if (event.dataTransfer?.types.includes('Files')) {
+			isDragOver = true;
+		}
+	}
+
+	function handleDragLeave(event: DragEvent) {
+		event.preventDefault();
+		dragCounter--;
+		if (dragCounter === 0) {
+			isDragOver = false;
+		}
+	}
+
+	function handleDragOver(event: DragEvent) {
+		event.preventDefault();
+	}
+
+	async function handleDrop(event: DragEvent) {
+		event.preventDefault();
+		isDragOver = false;
+		dragCounter = 0;
+
+		if (!event.dataTransfer?.files) return;
+
+		const files = Array.from(event.dataTransfer.files);
+
+		if (isEditing()) {
+			const handler = getAddFilesHandler();
+			if (handler) {
+				handler(files);
+				return;
+			}
+		}
+
+		options.onDrop(files);
+	}
+
+	return {
+		get isDragOver() {
+			return isDragOver;
+		},
+		dragHandlers: {
+			dragenter: handleDragEnter,
+			dragleave: handleDragLeave,
+			dragover: handleDragOver,
+			drop: handleDrop
+		}
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-chat-screen-file-upload.svelte.ts b/tools/ui/src/lib/hooks/use-chat-screen-file-upload.svelte.ts
new file mode 100644
index 000000000..f1ab94721
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-chat-screen-file-upload.svelte.ts
@@ -0,0 +1,104 @@
+/**
+ * File upload lifecycle for the ChatScreen form.
+ *
+ * Owns the queue of processed `ChatUploadedFile`, the rejection-by-capability
+ * dialog state, and the dual-layer validation pipeline (general format +
+ * model modality). The caller provides the active model's capabilities and ID
+ * as reactive getters so validation tracks the model in real time.
+ */
+
+import { processFilesToChatUploaded } from '$lib/utils/browser-only';
+import { isFileTypeSupported, filterFilesByModalities } from '$lib/utils';
+
+interface UseChatScreenFileUploadOptions {
+	capabilities: () => { hasVision: boolean; hasAudio: boolean; hasVideo: boolean };
+	activeModelId: () => string | null | undefined;
+}
+
+export interface FileErrorData {
+	generallyUnsupported: File[];
+	modalityUnsupported: File[];
+	modalityReasons: Record<string, string>;
+	supportedTypes: string[];
+}
+
+export function useChatScreenFileUpload(options: UseChatScreenFileUploadOptions) {
+	let uploadedFiles = $state<ChatUploadedFile[]>([]);
+	let showFileErrorDialog = $state(false);
+	let fileErrorData = $state<FileErrorData>({
+		generallyUnsupported: [],
+		modalityUnsupported: [],
+		modalityReasons: {},
+		supportedTypes: []
+	});
+
+	async function processFiles(files: File[]) {
+		const generallySupported: File[] = [];
+		const generallyUnsupported: File[] = [];
+
+		for (const file of files) {
+			if (isFileTypeSupported(file.name, file.type)) {
+				generallySupported.push(file);
+			} else {
+				generallyUnsupported.push(file);
+			}
+		}
+
+		const { supportedFiles, unsupportedFiles, modalityReasons } = filterFilesByModalities(
+			generallySupported,
+			options.capabilities()
+		);
+
+		const allUnsupportedFiles = [...generallyUnsupported, ...unsupportedFiles];
+
+		if (allUnsupportedFiles.length > 0) {
+			const supportedTypes: string[] = ['text files', 'PDFs'];
+			const caps = options.capabilities();
+			if (caps.hasVision) supportedTypes.push('images');
+			if (caps.hasAudio) supportedTypes.push('audio files');
+			if (caps.hasVideo) supportedTypes.push('video files');
+
+			fileErrorData = {
+				generallyUnsupported,
+				modalityUnsupported: unsupportedFiles,
+				modalityReasons,
+				supportedTypes
+			};
+			showFileErrorDialog = true;
+		}
+
+		if (supportedFiles.length > 0) {
+			const processed = await processFilesToChatUploaded(
+				supportedFiles,
+				options.activeModelId() ?? undefined
+			);
+			uploadedFiles = [...uploadedFiles, ...processed];
+		}
+	}
+
+	function handleFileUpload(files: File[]) {
+		return processFiles(files);
+	}
+
+	function handleFileRemove(fileId: string) {
+		uploadedFiles = uploadedFiles.filter((f) => f.id !== fileId);
+	}
+
+	return {
+		get uploadedFiles() {
+			return uploadedFiles;
+		},
+		set uploadedFiles(value) {
+			uploadedFiles = value;
+		},
+		get showFileErrorDialog() {
+			return showFileErrorDialog;
+		},
+		set showFileErrorDialog(value) {
+			showFileErrorDialog = value;
+		},
+		fileErrorData,
+		handleFileUpload,
+		handleFileRemove
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-chat-screen-scroll.svelte.ts b/tools/ui/src/lib/hooks/use-chat-screen-scroll.svelte.ts
new file mode 100644
index 000000000..cecf208bd
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-chat-screen-scroll.svelte.ts
@@ -0,0 +1,47 @@
+/**
+ * Scroll container binding and navigation guard for the ChatScreen.
+ *
+ * Binds the `AutoScrollController` to `document.documentElement`, exposes
+ * the container for programmatic scrolling, and flags an `isNavigating`
+ * window during route changes so the controller can reset without its
+ * scroll handler seeing spurious events from layout shifts.
+ */
+
+import { afterNavigate, beforeNavigate } from '$app/navigation';
+import type { AutoScrollController } from './use-auto-scroll.svelte';
+
+export function useChatScreenScroll(autoScroll: AutoScrollController) {
+	let chatScrollContainer: HTMLElement | undefined = $state();
+	let isNavigating = $state(false);
+
+	function handleScroll(event: UIEvent) {
+		// Ignore scroll events caused by navigation layout changes or by our own
+		// programmatic scrolls so they don't accidentally disable auto-scroll.
+		if (isNavigating || !event.isTrusted) return;
+		autoScroll.handleScroll();
+	}
+
+	beforeNavigate(() => {
+		isNavigating = true;
+		autoScroll.resetScrollState();
+	});
+
+	afterNavigate(() => {
+		setTimeout(() => {
+			isNavigating = false;
+			autoScroll.resetScrollState();
+		}, 10);
+	});
+
+	$effect(() => {
+		chatScrollContainer = document.documentElement;
+		autoScroll.setContainer(chatScrollContainer);
+	});
+
+	return {
+		get chatScrollContainer() {
+			return chatScrollContainer;
+		},
+		handleScroll
+	};
+}
diff --git a/tools/ui/src/lib/hooks/use-models-selector.svelte.ts b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
index 098cb2c27..9b3be15c0 100644
--- a/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-models-selector.svelte.ts
@@ -143,7 +143,7 @@ export function useModelsSelector(opts: UseModelsSelectorOptions): UseModelsSele
 					'[data-slot="chat-form"] textarea'
 				);
 
-				textarea?.focus();
+				textarea?.focus({ preventScroll: true });
 			});
 		}
 
diff --git a/tools/ui/src/lib/stores/device.svelte.ts b/tools/ui/src/lib/stores/device.svelte.ts
new file mode 100644
index 000000000..d0f04b437
--- /dev/null
+++ b/tools/ui/src/lib/stores/device.svelte.ts
@@ -0,0 +1,72 @@
+import { browser } from '$app/environment';
+import { MEDIA_QUERIES } from '$lib/constants';
+
+/**
+ * iOS UA token detection.
+ *
+ * iPadOS 13+ ships a desktop macOS UA, so 'iPad' is no longer present in it;
+ * a Macintosh UA combined with touch support is treated as an iPad instead.
+ * Third-party iOS browsers (Chrome, Firefox, Edge) and in-app WKWebViews all
+ * run on WKWebView and emit their own tokens (CriOS/FxiOS/EdgiOS/GSA) instead
+ * of the trailing 'Safari/' the Safari app keeps.
+ */
+const UA_PATTERNS = {
+	IOS_PHONE: /iPhone|iPod/,
+	MACINTOSH: /Macintosh/,
+	SAFARI: /Safari/,
+	WEBVIEW_IOS: /CriOS|FxiOS|EdgiOS|GSA/
+} as const;
+
+interface DeviceContext {
+	/** Any iOS/iPadOS device, regardless of which app or browser embeds the page. */
+	isIOSDevice: boolean;
+	/** The Safari browser app on iOS, excluding other iOS browsers and WKWebViews. */
+	isIOSSafari: boolean;
+	/** Any WKWebView context on iOS: in-app browsers, embedded web views, and the
+	 *  third-party iOS browsers (all of which share the WKWebView engine). */
+	isWKWebView: boolean;
+	/** PWA standalone mode: the page was launched from the home screen icon. */
+	isStandalone: boolean;
+}
+
+const SERVER_DEFAULT: DeviceContext = {
+	isIOSDevice: false,
+	isIOSSafari: false,
+	isWKWebView: false,
+	isStandalone: false
+};
+
+function detect(): DeviceContext {
+	if (!browser) return SERVER_DEFAULT;
+
+	const ua = navigator.userAgent;
+	const isTouch = navigator.maxTouchPoints > 0;
+
+	const isIOSDevice = UA_PATTERNS.IOS_PHONE.test(ua) || (UA_PATTERNS.MACINTOSH.test(ua) && isTouch);
+
+	// Safari keeps 'Safari/' in the UA; non-Safari iOS browsers emit their own
+	// token instead. WKWebView typically omits 'Safari/' entirely.
+	const hasSafariToken = UA_PATTERNS.SAFARI.test(ua) && !UA_PATTERNS.WEBVIEW_IOS.test(ua);
+	const isIOSSafari = isIOSDevice && hasSafariToken;
+	const isWKWebView = isIOSDevice && !hasSafariToken;
+
+	// navigator.standalone is the legacy iOS-only flag (deprecated but still
+	// present); display-mode: standalone is the modern standard (Safari 16.4+).
+	const isStandalone =
+		window.matchMedia(MEDIA_QUERIES.DISPLAY_MODE_STANDALONE).matches ||
+		(navigator as Navigator & { standalone?: boolean }).standalone === true;
+
+	return { isIOSDevice, isIOSSafari, isWKWebView, isStandalone };
+}
+
+export const device = $state<DeviceContext>(detect());
+
+if (browser) {
+	// isStandalone can change at runtime (e.g. user installs the PWA while the
+	// tab is open); the UA-derived flags are static for the session.
+	const mql = window.matchMedia(MEDIA_QUERIES.DISPLAY_MODE_STANDALONE);
+
+	mql.addEventListener('change', (e) => {
+		device.isStandalone = e.matches;
+	});
+}
diff --git a/tools/ui/src/lib/stores/models.svelte.ts b/tools/ui/src/lib/stores/models.svelte.ts
index 87fb172d0..11316a193 100644
--- a/tools/ui/src/lib/stores/models.svelte.ts
+++ b/tools/ui/src/lib/stores/models.svelte.ts
@@ -576,7 +576,7 @@ class ModelsStore {
 		}
 
 		// Try loading a favorite model
-		const favorite = this.favoriteModelIds.values().next()?.value
+		const favorite = this.favoriteModelIds.values().next()?.value;
 		if (favorite) {
 			await this.selectModelById(favorite);
 			return;
diff --git a/tools/ui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
index 1269692a7..f7993f594 100644
--- a/tools/ui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -6,18 +6,12 @@
 	import { page } from '$app/state';
 	import { untrack } from 'svelte';
 	import { onMount } from 'svelte';
-	import { fade } from 'svelte/transition';
 
-	import {
-		DesktopIconStrip,
-		DialogConversationTitleUpdate,
-		SidebarNavigation
-	} from '$lib/components/app';
+	import { SidebarNavigation, DialogConversationTitleUpdate } from '$lib/components/app';
 	import { PwaMetaTags, PwaRefreshAlert } from '$lib/components/pwa';
 	import { pwaAssetsHead } from 'virtual:pwa-assets/head';
 
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { isRouterMode, serverStore } from '$lib/stores/server.svelte';
 	import { config, settingsStore } from '$lib/stores/settings.svelte';
@@ -31,7 +25,6 @@
 	import { FAVICON_PATHS, FAVICON_SELECTORS } from '$lib/constants/pwa';
 	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
 	import { usePwa } from '$lib/hooks/use-pwa.svelte';
-	import { useSettingsNavigation } from '$lib/hooks/use-settings-navigation.svelte';
 	import { conversations } from '$lib/stores/conversations.svelte';
 	import { isMobile } from '$lib/stores/viewport.svelte';
 	import { theme } from '$lib/stores/theme.svelte';
@@ -42,8 +35,6 @@
 	let { children } = $props();
 	let alwaysShowSidebarOnDesktop = $derived(config().alwaysShowSidebarOnDesktop);
 	let isDesktop = $derived(!isMobile.current);
-	let sidebarOpen = $state(false);
-	let mounted = $state(false);
 	let innerHeight = $state<number | undefined>();
 	let innerWidth = $state(browser ? window.innerWidth : 0);
 
@@ -61,7 +52,6 @@
 	let titleUpdateNewTitle = $state('');
 	let titleUpdateResolve: ((value: boolean) => void) | null = null;
 
-	const panelNav = useSettingsNavigation();
 	// Keep the hook object intact: destructuring needRefreshByStorage reads the getter once and freezes it
 	const pwa = usePwa();
 	const { needRefresh, updateServiceWorker } = pwa;
@@ -166,7 +156,6 @@
 
 	onMount(() => {
 		updateFavicon();
-		mounted = true;
 	});
 
 	$effect(() => {
@@ -177,8 +166,6 @@
 
 	$effect(() => {
 		if (alwaysShowSidebarOnDesktop && isDesktop) {
-			sidebarOpen = true;
-
 			return;
 		}
 	});
@@ -300,19 +287,25 @@
 	<PwaMetaTags />
 </svelte:head>
 
-<!-- PWA update prompt + version -->
-<div class="fixed right-4 bottom-4 z-[9999] flex flex-col items-end gap-1">
-	{#if showBuildVersion && buildInfoStore.value}
-		<span class="text-[10px] tabular-nums text-muted-foreground">{buildInfoStore.value}</span>
-	{/if}
-	<PwaRefreshAlert
-		needRefresh={$needRefresh || pwa.needRefreshByStorage}
-		forceReload={pwa.needRefreshByStorage}
-		{updateServiceWorker}
-	/>
-</div>
+<svelte:window onkeydown={handleKeydown} bind:innerHeight bind:innerWidth />
 
 <Tooltip.Provider delayDuration={TOOLTIP_DELAY_DURATION}>
+	<div class="flex flex-col md:flex-row">
+		<SidebarNavigation
+			onSearchClick={() => {
+				if (isMobile.current) {
+					goto(ROUTES.SEARCH);
+				} else if (chatSidebar?.activateSearchMode) {
+					chatSidebar.activateSearchMode();
+				}
+			}}
+		/>
+
+		<div class="flex-1">
+			{@render children?.()}
+		</div>
+	</div>
+
 	<ModeWatcher />
 
 	<Toaster richColors />
@@ -324,44 +317,17 @@
 		onConfirm={handleTitleUpdateConfirm}
 		onCancel={handleTitleUpdateCancel}
 	/>
-
-	<Sidebar.Provider bind:open={sidebarOpen}>
-		<div class="flex h-full w-full grow">
-			<Sidebar.Root variant="floating" class="h-full"
-				><SidebarNavigation bind:this={chatSidebar} /></Sidebar.Root
-			>
-
-			{#if !(alwaysShowSidebarOnDesktop && isDesktop) && !(panelNav.isSettingsRoute && !isDesktop)}
-				{#if mounted}
-					<div in:fade={{ duration: 200 }}>
-						<Sidebar.Trigger
-							class="transition-left absolute left-0 z-[900] duration-200 ease-linear {sidebarOpen
-								? 'left-[calc(var(--sidebar-width)+0.75rem)] max-md:hidden'
-								: 'left-0!'}"
-							style="translate: 1rem 1rem;"
-						/>
-					</div>
-				{/if}
-			{/if}
-
-			{#if isDesktop && !alwaysShowSidebarOnDesktop}
-				<DesktopIconStrip
-					{sidebarOpen}
-					onSearchClick={() => {
-						if (chatSidebar?.activateSearchMode) {
-							chatSidebar.activateSearchMode();
-						}
-
-						sidebarOpen = true;
-					}}
-				/>
-			{/if}
-
-			<Sidebar.Inset class="flex flex-1 flex-col overflow-hidden">
-				{@render children?.()}
-			</Sidebar.Inset>
-		</div>
-	</Sidebar.Provider>
 </Tooltip.Provider>
 
-<svelte:window onkeydown={handleKeydown} bind:innerHeight bind:innerWidth />
+<!-- PWA update prompt + version -->
+<div class="fixed right-4 bottom-4 z-9999 flex flex-col items-end gap-1">
+	{#if showBuildVersion && buildInfoStore.value}
+		<span class="text-[10px] tabular-nums text-muted-foreground">{buildInfoStore.value}</span>
+	{/if}
+
+	<PwaRefreshAlert
+		needRefresh={$needRefresh || pwa.needRefreshByStorage}
+		forceReload={pwa.needRefreshByStorage}
+		{updateServiceWorker}
+	/>
+</div>
diff --git a/tools/ui/src/routes/search/+page.svelte b/tools/ui/src/routes/search/+page.svelte
new file mode 100644
index 000000000..d99dc1f2f
--- /dev/null
+++ b/tools/ui/src/routes/search/+page.svelte
@@ -0,0 +1,95 @@
+<script lang="ts">
+	import { goto } from '$app/navigation';
+	import { page } from '$app/state';
+	import { browser } from '$app/environment';
+
+	import { SearchInput, SidebarNavigationSearchResults } from '$lib/components/app';
+	import { ROUTES } from '$lib/constants/routes';
+	import { RouterService } from '$lib/services/router.service';
+	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
+	import { chatStore } from '$lib/stores/chat.svelte';
+	import { isMobile } from '$lib/stores/viewport.svelte';
+
+	let searchQuery = $state('');
+	let searchInputRef = $state<HTMLInputElement | null>(null);
+
+	let currentChatId = $derived(page.params.id);
+
+	let filteredConversations = $derived.by(() => {
+		const query = searchQuery.trim().toLowerCase();
+		if (query.length === 0) return [];
+		return conversations().filter((c) => c.name.toLowerCase().includes(query));
+	});
+
+	// Search page is intended for mobile; on desktop the sidebar already exposes
+	// in-place search, so bounce back to a chat.
+	$effect(() => {
+		if (browser && !isMobile.current) {
+			goto(ROUTES.NEW_CHAT, { replaceState: true });
+		}
+	});
+
+	async function selectConversation(id: string) {
+		await goto(RouterService.chat(id));
+	}
+
+	async function handleEditConversation(id: string) {
+		const conversation = conversations().find((c) => c.id === id);
+		if (!conversation) return;
+
+		const newName = window.prompt('Rename conversation', conversation.name);
+		if (newName && newName.trim()) {
+			await conversationsStore.updateConversationName(id, newName.trim());
+		}
+	}
+
+	async function handleDeleteConversation(id: string) {
+		const conversation = conversations().find((c) => c.id === id);
+		if (!conversation) return;
+
+		const confirmed = window.confirm(
+			`Delete "${conversation.name}"? This action cannot be undone.`
+		);
+		if (!confirmed) return;
+
+		await conversationsStore.deleteConversation(id, { deleteWithForks: false });
+	}
+
+	function handleStopGeneration(id: string) {
+		chatStore.stopGenerationForChat(id);
+	}
+
+	function handleBack() {
+		if (history.length > 1) {
+			history.back();
+		} else {
+			goto(ROUTES.NEW_CHAT);
+		}
+	}
+</script>
+
+<svelte:head>
+	<title>Search · llama.cpp</title>
+</svelte:head>
+
+<div class="fixed top-0 z-10 left-0 right-0 p-2">
+	<SearchInput
+		autofocus
+		bind:value={searchQuery}
+		bind:ref={searchInputRef}
+		onClose={handleBack}
+		placeholder="Search conversations..."
+	/>
+</div>
+
+<div class="p-2 pt-16">
+	<SidebarNavigationSearchResults
+		{searchQuery}
+		{filteredConversations}
+		{currentChatId}
+		onSelect={selectConversation}
+		onEdit={handleEditConversation}
+		onDelete={handleDeleteConversation}
+		onStop={handleStopGeneration}
+	/>
+</div>
diff --git a/tools/ui/src/routes/settings/+layout.svelte b/tools/ui/src/routes/settings/+layout.svelte
index b3b3d30e0..c2b587862 100644
--- a/tools/ui/src/routes/settings/+layout.svelte
+++ b/tools/ui/src/routes/settings/+layout.svelte
@@ -27,12 +27,10 @@
 	}
 </script>
 
-<div class="relative h-full">
-	<div class="fixed top-4.5 right-4 z-50 md:hidden">
-		<ActionIcon icon={X} tooltip="Close" onclick={handleClose} />
-	</div>
-
-	<div class="min-h-full">
-		{@render children?.()}
-	</div>
+<div class="fixed top-4.5 right-4 z-50 md:hidden">
+	<ActionIcon icon={X} tooltip="Close" onclick={handleClose} />
+</div>
+
+<div class="min-h-full">
+	{@render children?.()}
 </div>
diff --git a/tools/ui/src/routes/settings/[[section]]/+page.svelte b/tools/ui/src/routes/settings/[[section]]/+page.svelte
index 22e727f17..90fe61070 100644
--- a/tools/ui/src/routes/settings/[[section]]/+page.svelte
+++ b/tools/ui/src/routes/settings/[[section]]/+page.svelte
@@ -1,12 +1,11 @@
 <script lang="ts">
 	import { SettingsChat } from '$lib/components/app/settings';
 	import { page } from '$app/state';
-	import { replaceState } from '$app/navigation';
+	import { afterNavigate, replaceState } from '$app/navigation';
 	import { RouterService } from '$lib/services';
 	import { SETTINGS_SECTION_SLUGS } from '$lib/constants';
-	import { onMount } from 'svelte';
 
-	onMount(() => {
+	afterNavigate(() => {
 		if (!page.params.section) {
 			replaceState(RouterService.settings(SETTINGS_SECTION_SLUGS.GENERAL), {});
 		}
diff --git a/tools/ui/static/favicon-dark.svg b/tools/ui/static/favicon-dark.svg
deleted file mode 100644
index 22cab8d25..000000000
--- a/tools/ui/static/favicon-dark.svg
+++ /dev/null
@@ -1,14 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g clip-path="url(#clip0_29_291)">
-<path d="M244.95 8C215.233 8 187.774 23.8591 172.923 49.5999L95.6009 183.625C60.2162 244.959 104.481 321.6 175.29 321.6H208L316.977 132.708C348.959 77.2719 308.95 8 244.95 8ZM208 321.6H351.947C415.982 321.6 456.013 390.91 424.013 446.377C409.155 472.132 381.681 488 351.947 488H271.29C200.481 488 156.216 411.359 191.601 350.026L208 321.6Z" fill="#FAFAFA"/>
-<path d="M208 321.6H16L106.462 164.8L208 321.6Z" fill="#FAFAFA"/>
-<path d="M388.923 8L208 321.6L253.6 8H388.923Z" fill="#FAFAFA"/>
-<path d="M304 488H112L202.462 331.2L304 488Z" fill="#FAFAFA"/>
-<path d="M496 321.6H208L419.399 454.4L496 321.6Z" fill="#FAFAFA"/>
-</g>
-<defs>
-<clipPath id="clip0_29_291">
-<rect width="512" height="512" fill="white"/>
-</clipPath>
-</defs>
-</svg>
diff --git a/tools/ui/static/favicon.svg b/tools/ui/static/favicon.svg
deleted file mode 100644
index 52664fc8b..000000000
--- a/tools/ui/static/favicon.svg
+++ /dev/null
@@ -1,14 +0,0 @@
-<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g clip-path="url(#clip0_29_291)">
-<path d="M244.95 8C215.233 8 187.774 23.8591 172.923 49.5999L95.6009 183.625C60.2162 244.959 104.481 321.6 175.29 321.6H208L316.977 132.708C348.959 77.2719 308.95 8 244.95 8ZM208 321.6H351.947C415.982 321.6 456.013 390.91 424.013 446.377C409.155 472.132 381.681 488 351.947 488H271.29C200.481 488 156.216 411.359 191.601 350.026L208 321.6Z" fill="#111111"/>
-<path d="M208 321.6H16L106.462 164.8L208 321.6Z" fill="#111111"/>
-<path d="M388.923 8L208 321.6L253.6 8H388.923Z" fill="#111111"/>
-<path d="M304 488H112L202.462 331.2L304 488Z" fill="#111111"/>
-<path d="M496 321.6H208L419.399 454.4L496 321.6Z" fill="#111111"/>
-</g>
-<defs>
-<clipPath id="clip0_29_291">
-<rect width="512" height="512" fill="white"/>
-</clipPath>
-</defs>
-</svg>
diff --git a/tools/ui/tests/client/components/TestWrapper.svelte b/tools/ui/tests/client/components/TestWrapper.svelte
index aeb7ff74c..1380ec851 100644
--- a/tools/ui/tests/client/components/TestWrapper.svelte
+++ b/tools/ui/tests/client/components/TestWrapper.svelte
@@ -1,9 +1,6 @@
 <script lang="ts">
 	import * as Tooltip from '$lib/components/ui/tooltip';
-	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
 	import Page from '../../../src/routes/(chat)/+page.svelte';
-
-	let sidebarOpen = $state(false);
 </script>
 
 <!--
@@ -11,7 +8,5 @@
 	This mirrors the providers from +layout.svelte.
 -->
 <Tooltip.Provider>
-	<Sidebar.Provider bind:open={sidebarOpen}>
-		<Page />
-	</Sidebar.Provider>
+	<Page />
 </Tooltip.Provider>
diff --git a/tools/ui/tests/stories/SidebarNavigation.stories.svelte b/tools/ui/tests/stories/SidebarNavigation.stories.svelte
index aae42f2a0..7fbcef3ed 100644
--- a/tools/ui/tests/stories/SidebarNavigation.stories.svelte
+++ b/tools/ui/tests/stories/SidebarNavigation.stories.svelte
@@ -14,10 +14,6 @@
 </script>
 
 <script lang="ts">
-	import * as Sidebar from '$lib/components/ui/sidebar/index.js';
-
-	let sidebarOpen = $state(true);
-
 	// Mock conversations for the sidebar
 	const mockConversations: DatabaseConversation[] = [
 		{
@@ -66,11 +62,9 @@
 		);
 	}}
 >
-	<Sidebar.Provider bind:open={sidebarOpen}>
-		<div class="flex-column h-full h-screen w-72 bg-background">
-			<SidebarNavigation />
-		</div>
-	</Sidebar.Provider>
+	<div class="flex-column h-screen w-72 bg-background">
+		<SidebarNavigation />
+	</div>
 </Story>
 
 <Story
@@ -85,15 +79,16 @@
 			}, 0)
 		);
 
+		// Expand sidebar first, then click Search in the expanded button list
+		const logoTrigger = screen.getByRole('button', { name: /expand navigation/i });
+		await userEvent.click(logoTrigger);
 		const searchTrigger = screen.getByText('Search');
 		userEvent.click(searchTrigger);
 	}}
 >
-	<Sidebar.Provider bind:open={sidebarOpen}>
-		<div class="flex-column h-full h-screen w-72 bg-background">
-			<SidebarNavigation />
-		</div>
-	</Sidebar.Provider>
+	<div class="flex-column h-screen w-72 bg-background">
+		<SidebarNavigation />
+	</div>
 </Story>
 
 <Story
@@ -105,9 +100,7 @@
 		conversationsStore.conversations = [];
 	}}
 >
-	<Sidebar.Provider bind:open={sidebarOpen}>
-		<div class="flex-column h-full h-screen w-72 bg-background">
-			<SidebarNavigation />
-		</div>
-	</Sidebar.Provider>
+	<div class="flex-column h-screen w-72 bg-background">
+		<SidebarNavigation />
+	</div>
 </Story>
diff --git a/tools/ui/tests/unit/favicon-colorize.test.ts b/tools/ui/tests/unit/favicon-colorize.test.ts
new file mode 100644
index 000000000..12b8a6f21
--- /dev/null
+++ b/tools/ui/tests/unit/favicon-colorize.test.ts
@@ -0,0 +1,198 @@
+import { mkdtempSync, readFileSync, rmSync, writeFileSync } from 'node:fs';
+import { tmpdir } from 'node:os';
+import { join } from 'node:path';
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import {
+	colorizeFaviconSvg,
+	padFaviconSvg,
+	writeThemeFavicons
+} from '../../scripts/favicon-colorize';
+
+const SOURCE_SVG = [
+	'<svg xmlns="http://www.w3.org/2000/svg">',
+	'  <path d="M0 0" fill="currentColor"/>',
+	'  <path d="M1 1" fill="#ff00aa"/>',
+	'  <circle fill="currentColor"/>',
+	'</svg>'
+].join('\n');
+
+describe('colorizeFaviconSvg', () => {
+	it('substitutes every currentColor occurrence for the light variant', () => {
+		const { light } = colorizeFaviconSvg(SOURCE_SVG, '#111111', '#fafafa');
+		expect(light.match(/currentColor/g)).toBeNull();
+		expect(light).toContain('fill="#111111"');
+		expect(light).toContain('<circle fill="#111111"/>');
+	});
+
+	it('substitutes every currentColor occurrence for the dark variant', () => {
+		const { dark } = colorizeFaviconSvg(SOURCE_SVG, '#111111', '#fafafa');
+		expect(dark.match(/currentColor/g)).toBeNull();
+		expect(dark).toContain('fill="#fafafa"');
+		expect(dark).toContain('<circle fill="#fafafa"/>');
+	});
+
+	it('leaves non-currentColor colors untouched in both variants', () => {
+		const { light, dark } = colorizeFaviconSvg(SOURCE_SVG, '#111111', '#fafafa');
+		expect(light).toContain('fill="#ff00aa"');
+		expect(dark).toContain('fill="#ff00aa"');
+	});
+
+	it('does not alter any other part of the SVG', () => {
+		const { light, dark } = colorizeFaviconSvg(SOURCE_SVG, '#111111', '#fafafa');
+		const stripColors = (s: string) =>
+			s.replaceAll('#111111', '').replaceAll('#fafafa', '').replaceAll('currentColor', '');
+		const expected = stripColors(SOURCE_SVG);
+		expect(stripColors(light)).toBe(expected);
+		expect(stripColors(dark)).toBe(expected);
+	});
+
+	it('returns the same SVG for light and dark when called with the same color', () => {
+		const result = colorizeFaviconSvg(SOURCE_SVG, '#abcdef', '#abcdef');
+		expect(result.light).toBe(result.dark);
+	});
+
+	it('returns the source unchanged when given a color that does not appear (no currentColor in source)', () => {
+		const plain = '<svg><path fill="#000"/></svg>';
+		const { light, dark } = colorizeFaviconSvg(plain, '#111111', '#fafafa');
+		expect(light).toBe(plain);
+		expect(dark).toBe(plain);
+	});
+});
+
+describe('padFaviconSvg', () => {
+	const SIZED_SVG =
+		'<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">' +
+		'<path d="M244.95 8L388.923 8Z" fill="currentColor"/>' +
+		'</svg>';
+
+	it('wraps inner content in a translate-then-scale group that matches padding', () => {
+		const padded = padFaviconSvg(SIZED_SVG, 0.05);
+		// scale = 1 - 0.05 = 0.95
+		// translate = (0.05 * 512) / 2 = 12.8 on each axis
+		expect(padded).toContain('transform="translate(12.8 12.8) scale(0.95)"');
+		expect(padded).toContain('<g transform="translate(12.8 12.8) scale(0.95)">');
+		expect(padded).toContain('<path d="M244.95 8L388.923 8Z" fill="currentColor"/>');
+		expect(padded.endsWith('</g></svg>')).toBe(true);
+	});
+
+	it('preserves the outer <svg> tag attributes', () => {
+		const padded = padFaviconSvg(SIZED_SVG, 0.1);
+		expect(padded.startsWith('<svg width="512" height="512" viewBox="0 0 512 512"')).toBe(true);
+	});
+
+	it('returns the input unchanged for zero or negative padding', () => {
+		expect(padFaviconSvg(SIZED_SVG, 0)).toBe(SIZED_SVG);
+		expect(padFaviconSvg(SIZED_SVG, -0.1)).toBe(SIZED_SVG);
+	});
+
+	it('returns the input unchanged when padding would fully collapse the icon (>= 1)', () => {
+		expect(padFaviconSvg(SIZED_SVG, 1)).toBe(SIZED_SVG);
+		expect(padFaviconSvg(SIZED_SVG, 1.5)).toBe(SIZED_SVG);
+	});
+
+	it('returns the input unchanged when no viewBox is present', () => {
+		const noViewBox = '<svg width="32" height="32"><path d="M0 0Z"/></svg>';
+		expect(padFaviconSvg(noViewBox, 0.1)).toBe(noViewBox);
+	});
+
+	it('returns the input unchanged when viewBox values are not finite numbers', () => {
+		const bad = '<svg viewBox="auto auto 0 0"><path/></svg>';
+		expect(padFaviconSvg(bad, 0.1)).toBe(bad);
+	});
+
+	it('tolerates a non-square viewBox', () => {
+		const wide = '<svg viewBox="0 0 100 50"><rect/></svg>';
+		const padded = padFaviconSvg(wide, 0.1);
+		// scale 0.9, translate (5, 2.5)
+		expect(padded).toContain('transform="translate(5 2.5) scale(0.9)"');
+	});
+});
+
+describe('writeThemeFavicons', () => {
+	const LOGO =
+		'<svg width="512" height="512" viewBox="0 0 512 512" fill="none" xmlns="http://www.w3.org/2000/svg">' +
+		'<path d="M244.95 8L388.923 8Z" fill="currentColor"/>' +
+		'</svg>';
+
+	let tmpDir: string;
+
+	beforeEach(() => {
+		tmpDir = mkdtempSync(join(tmpdir(), 'favicon-'));
+	});
+
+	afterEach(() => {
+		rmSync(tmpDir, { recursive: true, force: true });
+	});
+
+	function setupSource() {
+		const sourcePath = join(tmpDir, 'logo.svg');
+		writeFileSync(sourcePath, LOGO);
+		return {
+			sourcePath,
+			lightPath: join(tmpDir, 'favicon.svg'),
+			darkPath: join(tmpDir, 'favicon-dark.svg')
+		};
+	}
+
+	it('writes colorized, un-padded favicons without modifying the source', () => {
+		const { sourcePath, lightPath, darkPath } = setupSource();
+		const before = readFileSync(sourcePath, 'utf-8');
+
+		writeThemeFavicons('#abcdef', '#012345', {
+			sourcePath,
+			lightOutPath: lightPath,
+			darkOutPath: darkPath
+		});
+
+		const lightOut = readFileSync(lightPath, 'utf-8');
+		const darkOut = readFileSync(darkPath, 'utf-8');
+
+		// currentColor swapped to the requested palette in both files
+		expect(lightOut).toContain('fill="#abcdef"');
+		expect(lightOut).not.toContain('currentColor');
+		expect(darkOut).toContain('fill="#012345"');
+		expect(darkOut).not.toContain('currentColor');
+
+		// default padding (0) keeps the wrapper off the output
+		expect(lightOut).not.toContain('<g ');
+		expect(darkOut).not.toContain('<g ');
+
+		// source file is unchanged after the call
+		expect(readFileSync(sourcePath, 'utf-8')).toBe(before);
+	});
+
+	it('writes colorized favicons wrapped in a padding <g transform>...</g>', () => {
+		const { sourcePath, lightPath, darkPath } = setupSource();
+		// mirror the production wiring: PWA_ASSET_GENERATOR.FAVICON_PADDING
+		const padding = 0.04;
+		// scale = 1 - 0.04 = 0.96; translate = (0.04 * 512) / 2 = 10.24
+		const expectedTransform = 'transform="translate(10.24 10.24) scale(0.96)"';
+
+		writeThemeFavicons('#111111', '#fafafa', {
+			sourcePath,
+			lightOutPath: lightPath,
+			darkOutPath: darkPath,
+			padding
+		});
+
+		const lightOut = readFileSync(lightPath, 'utf-8');
+		const darkOut = readFileSync(darkPath, 'utf-8');
+
+		// both files get the same padding wrapper derived from the viewBox
+		expect(lightOut).toContain(`<g ${expectedTransform}>`);
+		expect(lightOut.endsWith('</g></svg>')).toBe(true);
+		expect(darkOut).toContain(`<g ${expectedTransform}>`);
+		expect(darkOut.endsWith('</g></svg>')).toBe(true);
+
+		// colorization still happens inside the wrapped group
+		expect(lightOut).toContain('fill="#111111"');
+		expect(lightOut).not.toContain('currentColor');
+		expect(darkOut).toContain('fill="#fafafa"');
+		expect(darkOut).not.toContain('currentColor');
+		// light palette colour must not leak into dark output
+		expect(darkOut).not.toContain('#111111');
+
+		// source file is not modified by the padding step
+		expect(readFileSync(sourcePath, 'utf-8')).toBe(LOGO);
+	});
+});

From 00139b660b3a61e130aa76dcc7cb9e447da43898 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Wed, 24 Jun 2026 10:50:44 +0200
Subject: [PATCH 140/292] ui: loading bar below the model picker (#24931)

* ui: show model load progress on the selector trigger

Mirror the in-dropdown stage progress as a thin bar on the selector
trigger, so the active model's load percent stays visible when the menu
is closed. Same status gating and composite fraction as the dropdown
row, so both bars track the selected model in sync.

Suggested-by: Julien Chaumond <@julien-c>

* ui: show model load progress bar on the in-conversation model selector

* ui: tune model load indicator to a pulsing highlight (suggested by @ngxson)

Also wire the indicator onto the mobile sheet trigger, which was missing
it since mobile uses the sheet instead of the dropdown.

* ui: thin (@allozaur) pulsating (@ngxson) model load bar
---
 .../ChatMessageAssistant.svelte               | 13 +++++++++--
 .../app/models/ModelLoadHighlight.svelte      | 11 ++++++++++
 .../app/models/ModelsSelectorDropdown.svelte  | 22 +++++++++++++++++--
 .../app/models/ModelsSelectorOption.svelte    | 20 ++++++-----------
 .../app/models/ModelsSelectorSheet.svelte     | 21 +++++++++++++++++-
 5 files changed, 69 insertions(+), 18 deletions(-)
 create mode 100644 tools/ui/src/lib/components/app/models/ModelLoadHighlight.svelte

diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
index a1d02d6ec..14f6c5ca0 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageAssistant/ChatMessageAssistant.svelte
@@ -180,6 +180,9 @@
 
 	let displayedModel = $derived(message.model ?? null);
 
+	// model being switched to while it loads, so the selector bar tracks it
+	let pendingModel = $state<string | null>(null);
+
 	let isCurrentlyLoading = $derived(isLoading());
 	let isStreaming = $derived(isChatStreaming());
 	let hasNoContent = $derived(!message?.content?.trim());
@@ -318,13 +321,19 @@
 			>
 				{#if isRouter}
 					<ModelsSelectorDropdown
-						currentModel={displayedModel}
+						currentModel={pendingModel ?? displayedModel}
 						disabled={isLoading()}
 						onModelChange={async (modelId: string, modelName: string) => {
 							const status = modelsStore.getModelStatus(modelId);
 
 							if (status !== ServerModelStatus.LOADED) {
-								await modelsStore.loadModel(modelId);
+								pendingModel = modelId;
+
+								try {
+									await modelsStore.loadModel(modelId);
+								} finally {
+									pendingModel = null;
+								}
 							}
 
 							onRegenerate(modelName);
diff --git a/tools/ui/src/lib/components/app/models/ModelLoadHighlight.svelte b/tools/ui/src/lib/components/app/models/ModelLoadHighlight.svelte
new file mode 100644
index 000000000..fa9a02108
--- /dev/null
+++ b/tools/ui/src/lib/components/app/models/ModelLoadHighlight.svelte
@@ -0,0 +1,11 @@
+<script lang="ts">
+	let { percent }: { percent: number } = $props();
+</script>
+
+<!-- thin determinate load bar pinned to the bottom edge, pulsing while it fills -->
+<div class="pointer-events-none absolute inset-x-0 bottom-0 h-0.5 overflow-hidden rounded-b-sm">
+	<div
+		class="h-full animate-pulse bg-primary transition-[width] duration-200 ease-out"
+		style="width: {percent}%"
+	></div>
+</div>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte
index 40006a4c9..720963a8d 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorDropdown.svelte
@@ -2,8 +2,10 @@
 	import { ChevronDown, Loader2, Package } from '@lucide/svelte';
 	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
 	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { KeyboardKey } from '$lib/enums';
+	import { KeyboardKey, ServerModelStatus } from '$lib/enums';
 	import { useModelsSelector } from '$lib/hooks/use-models-selector.svelte';
+	import { modelsStore, routerModels } from '$lib/stores/models.svelte';
+	import { modelLoadFraction } from '$lib/utils';
 	import {
 		DialogModelInformation,
 		DropdownMenuSearchable,
@@ -11,6 +13,7 @@
 		ModelsSelectorList,
 		ModelsSelectorOption
 	} from '$lib/components/app';
+	import ModelLoadHighlight from './ModelLoadHighlight.svelte';
 	import type { ModelItem } from './utils';
 
 	interface Props {
@@ -113,6 +116,17 @@
 		{/if}
 	{:else}
 		{@const selectedOption = ms.getDisplayOption()}
+		{@const triggerModel = selectedOption?.model}
+		{@const triggerStatus = triggerModel
+			? routerModels().find((m) => m.id === triggerModel)?.status?.value
+			: undefined}
+		{@const triggerLoading =
+			!!triggerModel &&
+			(triggerStatus === ServerModelStatus.LOADING ||
+				modelsStore.isModelOperationInProgress(triggerModel))}
+		{@const triggerLoadPercent = triggerLoading
+			? Math.round(modelLoadFraction(modelsStore.getLoadProgress(triggerModel)) * 100)
+			: 0}
 
 		{#if ms.isRouter}
 			<DropdownMenu.Root bind:open={isOpen} onOpenChange={ms.handleOpenChange}>
@@ -123,7 +137,7 @@
 							<DropdownMenu.Trigger
 								{...props}
 								class={[
-									`inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+									`relative inline-grid cursor-pointer grid-cols-[1fr_auto_1fr] items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
 									!ms.isCurrentModelInCache
 										? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
 										: forceForegroundText
@@ -154,6 +168,10 @@
 								{:else}
 									<ChevronDown class="h-3 w-3.5 shrink-0" />
 								{/if}
+
+								{#if triggerLoading}
+									<ModelLoadHighlight percent={triggerLoadPercent} />
+								{/if}
 							</DropdownMenu.Trigger>
 						{/snippet}
 					</Tooltip.Trigger>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
index f2a024d31..981111e20 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorOption.svelte
@@ -10,6 +10,7 @@
 		RotateCw
 	} from '@lucide/svelte';
 	import { ActionIcon, ModelId } from '$lib/components/app';
+	import ModelLoadHighlight from './ModelLoadHighlight.svelte';
 	import type { ModelOption } from '$lib/types/models';
 	import { ServerModelStatus } from '$lib/enums';
 	import { modelsStore, routerModels } from '$lib/stores/models.svelte';
@@ -119,11 +120,11 @@
 		</div>
 
 		{#if isLoading}
-			<div class="flex w-4 [@media(pointer:coarse)]:w-5 items-center justify-center">
+			<div class="flex w-4 items-center justify-center [@media(pointer:coarse)]:w-5">
 				<Loader2 class="h-4 w-4 animate-spin text-muted-foreground" />
 			</div>
 		{:else if isFailed}
-			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+			<div class="flex w-4 items-center justify-center [@media(pointer:coarse)]:w-auto">
 				<CircleAlert
 					class="h-3.5 w-3.5 text-red-500 group-hover:hidden [@media(pointer:coarse)]:hidden"
 				/>
@@ -140,7 +141,7 @@
 				</div>
 			</div>
 		{:else if isSleeping}
-			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+			<div class="flex w-4 items-center justify-center [@media(pointer:coarse)]:w-auto">
 				<span
 					class="h-2 w-2 rounded-full bg-orange-400 group-hover:hidden [@media(pointer:coarse)]:hidden"
 				></span>
@@ -159,7 +160,7 @@
 				</div>
 			</div>
 		{:else if isLoaded}
-			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+			<div class="flex w-4 items-center justify-center [@media(pointer:coarse)]:w-auto">
 				<span
 					class="h-2 w-2 rounded-full bg-green-500 group-hover:hidden [@media(pointer:coarse)]:hidden"
 				></span>
@@ -176,7 +177,7 @@
 				</div>
 			</div>
 		{:else}
-			<div class="flex w-4 [@media(pointer:coarse)]:w-auto items-center justify-center">
+			<div class="flex w-4 items-center justify-center [@media(pointer:coarse)]:w-auto">
 				<span
 					class="h-2 w-2 rounded-full bg-muted-foreground/50 group-hover:hidden [@media(pointer:coarse)]:hidden"
 				></span>
@@ -196,13 +197,6 @@
 	</div>
 
 	{#if isLoading}
-		<div
-			class="pointer-events-none absolute inset-x-0 bottom-0 h-0.5 overflow-hidden rounded-b-sm bg-muted"
-		>
-			<div
-				class="h-full bg-primary transition-[width] duration-200 ease-out"
-				style="width: {loadPercent}%"
-			></div>
-		</div>
+		<ModelLoadHighlight percent={loadPercent} />
 	{/if}
 </div>
diff --git a/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
index e5920a2a0..a9e9ea1c8 100644
--- a/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
+++ b/tools/ui/src/lib/components/app/models/ModelsSelectorSheet.svelte
@@ -8,6 +8,10 @@
 		ModelsSelectorList,
 		SearchInput
 	} from '$lib/components/app';
+	import ModelLoadHighlight from './ModelLoadHighlight.svelte';
+	import { ServerModelStatus } from '$lib/enums';
+	import { modelsStore, routerModels } from '$lib/stores/models.svelte';
+	import { modelLoadFraction } from '$lib/utils';
 
 	interface Props {
 		class?: string;
@@ -61,12 +65,23 @@
 		<p class="text-xs text-muted-foreground">No models available.</p>
 	{:else}
 		{@const selectedOption = ms.getDisplayOption()}
+		{@const triggerModel = selectedOption?.model}
+		{@const triggerStatus = triggerModel
+			? routerModels().find((m) => m.id === triggerModel)?.status?.value
+			: undefined}
+		{@const triggerLoading =
+			!!triggerModel &&
+			(triggerStatus === ServerModelStatus.LOADING ||
+				modelsStore.isModelOperationInProgress(triggerModel))}
+		{@const triggerLoadPercent = triggerLoading
+			? Math.round(modelLoadFraction(modelsStore.getLoadProgress(triggerModel)) * 100)
+			: 0}
 
 		{#if ms.isRouter}
 			<button
 				type="button"
 				class={[
-					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 max-sm:px-3 max-sm:py-2 text-xs max-sm:text-sm shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
+					`relative inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-background px-1.5 py-1 text-xs shadow-sm transition hover:bg-muted-foreground/20 focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60 max-sm:px-3 max-sm:py-2 max-sm:text-sm dark:bg-muted-foreground/15 dark:text-secondary-foreground`,
 					!ms.isCurrentModelInCache
 						? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
 						: forceForegroundText
@@ -99,6 +114,10 @@
 				{:else}
 					<ChevronDown class="h-3 w-3.5 shrink-0" />
 				{/if}
+
+				{#if triggerLoading}
+					<ModelLoadHighlight percent={triggerLoadPercent} />
+				{/if}
 			</button>
 
 			<Sheet.Root bind:open={sheetOpen} onOpenChange={handleSheetOpenChange}>

From 1191758c5d77e575b4531e057708a562f6f6f0b2 Mon Sep 17 00:00:00 2001
From: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
Date: Wed, 24 Jun 2026 17:42:03 +0800
Subject: [PATCH 141/292] vulkan: fail the build when a shader fails to compile
 (#24450)

* vulkan-shaders-gen: fail the build when a shader fails to compile

vulkan-shaders-gen did not detect shader-compile subprocess failures, so a
broken libggml-vulkan could be produced while the build reported success and
the breakage only surfaced at run time. execute_command() discarded the child
exit code (POSIX waitpid passed nullptr for status; the Windows branch never
called GetExitCodeProcess) and string_to_spv decided success only from whether
stderr was empty, so a non-zero exit with empty stderr, or a subprocess that
failed to launch, was treated as success.

Return the child exit code from execute_command() (WEXITSTATUS on POSIX,
GetExitCodeProcess on Windows), treat a non-zero exit or non-empty stderr or a
launch exception as a failure, and record it in an atomic flag. main() checks
the flag after process_shaders() and returns EXIT_FAILURE before writing the
output files, so the build stops instead of emitting a broken backend.

Fixes #24393

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>

* vulkan-shaders-gen: simplify compile_failed access and drop unreachable return

Address review feedback on #24450:
- Access the std::atomic<bool> compile_failed directly (= / implicit bool)
  instead of .store()/.load(); the flag stays atomic because the worker
  threads in process_shaders() set it concurrently.
- Remove the unreachable trailing return -1 in execute_command(): on POSIX the
  child _exit()s after execvp and the parent returns (fork()<0 throws); on
  Windows the block returns the exit code.

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>

---------

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
---
 .../vulkan-shaders/vulkan-shaders-gen.cpp     | 26 +++++++++++++++----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 3bd93d256..1925582ff 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -11,6 +11,7 @@
 #include <future>
 #include <queue>
 #include <condition_variable>
+#include <atomic>
 #include <cstdio>
 #include <cstring>
 #include <cstdlib>
@@ -34,6 +35,9 @@
 
 std::mutex lock;
 std::vector<std::pair<std::string, std::string>> shader_fnames;
+// Set when any shader subprocess fails (non-zero exit / stderr / launch failure) so the
+// build is stopped instead of silently producing a broken libggml-vulkan. (issue #24393)
+static std::atomic<bool> compile_failed{false};
 std::locale c_locale("C");
 
 std::string GLSLC = "glslc";
@@ -78,7 +82,7 @@ enum MatMulIdType {
 
 namespace {
 
-void execute_command(std::vector<std::string>& command, std::string& stdout_str, std::string& stderr_str) {
+int execute_command(std::vector<std::string>& command, std::string& stdout_str, std::string& stderr_str) {
 #ifdef _WIN32
     HANDLE stdout_read, stdout_write;
     HANDLE stderr_read, stderr_write;
@@ -127,8 +131,11 @@ void execute_command(std::vector<std::string>& command, std::string& stdout_str,
     CloseHandle(stdout_read);
     CloseHandle(stderr_read);
     WaitForSingleObject(pi.hProcess, INFINITE);
+    DWORD exit_code = 1;
+    GetExitCodeProcess(pi.hProcess, &exit_code);
     CloseHandle(pi.hProcess);
     CloseHandle(pi.hThread);
+    return (int)exit_code;
 #else
     int stdout_pipe[2];
     int stderr_pipe[2];
@@ -175,7 +182,9 @@ void execute_command(std::vector<std::string>& command, std::string& stdout_str,
 
         close(stdout_pipe[0]);
         close(stderr_pipe[0]);
-        waitpid(pid, nullptr, 0);
+        int status = 0;
+        waitpid(pid, &status, 0);
+        return WIFEXITED(status) ? WEXITSTATUS(status) : -1;
     }
 #endif
 }
@@ -372,13 +381,14 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p
         // }
         // std::cout << std::endl;
 
-        execute_command(cmd, stdout_str, stderr_str);
-        if (!stderr_str.empty()) {
-            std::cerr << "cannot compile " << name << "\n\n";
+        int exit_code = execute_command(cmd, stdout_str, stderr_str);
+        if (exit_code != 0 || !stderr_str.empty()) {
+            std::cerr << "cannot compile " << name << " (exit code " << exit_code << ")\n\n";
             for (const auto& part : cmd) {
                 std::cerr << part << " ";
             }
             std::cerr << "\n\n" << stderr_str << std::endl;
+            compile_failed = true;
             return;
         }
 
@@ -398,6 +408,7 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p
         shader_fnames.push_back(std::make_pair(name, out_path));
     } catch (const std::exception& e) {
         std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl;
+        compile_failed = true;
     }
 }
 
@@ -1271,6 +1282,11 @@ int main(int argc, char** argv) {
 
     process_shaders();
 
+    if (compile_failed) {
+        std::cerr << "vulkan-shaders-gen: one or more shaders failed to compile" << std::endl;
+        return EXIT_FAILURE;
+    }
+
     write_output_files();
 
     return EXIT_SUCCESS;

From 51eae8cfcac4fb403bcf91d1fb524fec0974f510 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Wed, 24 Jun 2026 11:29:24 -0300
Subject: [PATCH 142/292] vulkan: allow reducing the graph submission batches
 to avoid timeouts (#24872)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index f4a578b89..5fbebc6d7 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -699,6 +699,7 @@ struct vk_device_struct {
 
     bool add_rms_fusion;
     uint32_t partials_binding_alignment;
+    uint32_t max_nodes_per_submit;
 
     bool shader_64b_indexing;
 
@@ -5878,6 +5879,14 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device->subgroup_vote = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) &&
                                 (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eVote);
 
+        // Submit at least every 100 nodes, in case there are workloads without as much matmul.
+        device->max_nodes_per_submit = 100;
+        const char* GGML_VK_MAX_NODES_PER_SUBMIT = getenv("GGML_VK_MAX_NODES_PER_SUBMIT");
+        if (GGML_VK_MAX_NODES_PER_SUBMIT != nullptr) {
+            uint32_t max_nodes_per_submit = std::stoul(GGML_VK_MAX_NODES_PER_SUBMIT);
+            device->max_nodes_per_submit = std::max(max_nodes_per_submit, 1u);
+        }
+
         const bool force_disable_f16 = getenv("GGML_VK_DISABLE_F16") != nullptr;
 
         device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
@@ -16173,8 +16182,6 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
     // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
     // (and scaled down based on model size, so smaller models submit earlier).
-    // Also submit at least every 100 nodes, in case there are workloads without as much matmul.
-    int nodes_per_submit = 100;
     int submitted_nodes = 0;
     int submit_count = 0;
     uint64_t mul_mat_bytes = 0;
@@ -16400,7 +16407,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining)
         bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5;
-        bool submit = (submitted_nodes >= nodes_per_submit) ||
+        bool submit = ((uint32_t)submitted_nodes >= ctx->device->max_nodes_per_submit) ||
                       (mul_mat_bytes_per_submit != 0 && mul_mat_bytes >= mul_mat_bytes_per_submit) ||
                       (i + ctx->num_additional_fused_ops >= last_node) ||
                       (almost_ready && !ctx->almost_ready_fence_pending);

From fb401045cc284ec185bf3ce6eca878a2c288f33b Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 24 Jun 2026 18:12:16 +0200
Subject: [PATCH 143/292] common: remove unused json-partial (#24968)

---
 common/CMakeLists.txt       |   2 -
 common/json-partial.cpp     | 324 ------------------------------------
 common/json-partial.h       |  39 -----
 tests/CMakeLists.txt        |   1 -
 tests/test-json-partial.cpp | 287 --------------------------------
 5 files changed, 653 deletions(-)
 delete mode 100644 common/json-partial.cpp
 delete mode 100644 common/json-partial.h
 delete mode 100644 tests/test-json-partial.cpp

diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index c42320c46..fc16b21cf 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -80,8 +80,6 @@ add_library(${TARGET}
     http.h
     imatrix-loader.cpp
     imatrix-loader.h
-    json-partial.cpp
-    json-partial.h
     json-schema-to-grammar.cpp
     llguidance.cpp
     log.cpp
diff --git a/common/json-partial.cpp b/common/json-partial.cpp
deleted file mode 100644
index aaf11310a..000000000
--- a/common/json-partial.cpp
+++ /dev/null
@@ -1,324 +0,0 @@
-#include "json-partial.h"
-
-#include "log.h"
-
-#include <nlohmann/json.hpp>
-
-#include <string>
-#include <regex>
-
-using json = nlohmann::ordered_json;
-
-enum common_json_stack_element_type {
-    COMMON_JSON_STACK_ELEMENT_OBJECT,
-    COMMON_JSON_STACK_ELEMENT_KEY,
-    COMMON_JSON_STACK_ELEMENT_ARRAY,
-};
-
-struct common_json_stack_element {
-    common_json_stack_element_type type;
-    std::string key;
-};
-
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    std::string::const_iterator it = input.begin();
-    const auto end = input.end();
-    return common_json_parse(it, end, healing_marker, out);
-}
-
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    // // https://json.nlohmann.me/features/parsing/sax_interface/
-    struct json_error_locator : public nlohmann::json_sax<json> {
-        std::size_t position;
-        bool found_error;
-        std::string last_token;
-        std::string exception_message;
-        std::vector<common_json_stack_element> stack;
-
-        json_error_locator() : position(0), found_error(false) {}
-
-        bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT
-            this->position = position - 1;
-            this->found_error = true;
-            this->last_token = last_token;
-            this->exception_message = ex.what();
-            return false;
-        }
-        void close_value() {
-            if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) {
-                stack.pop_back();
-            }
-        }
-        bool null() override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool boolean(bool) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_integer(number_integer_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_unsigned(number_unsigned_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_float(number_float_t, const string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool string(string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool binary(binary_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool start_object(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""});
-            return true;
-        }
-        bool end_object() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-        bool key(string_t & key) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key});
-            return true;
-        }
-        bool start_array(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""});
-            return true;
-        }
-        bool end_array() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-    };
-    json_error_locator err_loc;
-    auto start = it;
-    json::sax_parse(it, end, &err_loc);
-
-    if (err_loc.found_error) {
-        it = start;
-        auto temptative_end = it + err_loc.position;
-        // LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str());
-
-        auto input = std::string(it, temptative_end);
-        try {
-            out.json = json::parse(input);
-            // out.json = json::parse(it, temptative_end);
-            it = temptative_end;
-            return true;
-        } catch (const std::exception & ex) {
-            // No, needs healing.
-            LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str());
-        }
-        auto can_parse = [](const std::string & str) {
-            try {
-                auto _ = json::parse(str); // NOLINT
-                return true;
-            } catch (const std::exception &) {
-                return false;
-            }
-        };
-        if (!healing_marker.empty() && !err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            auto last_non_sp_pos = str.find_last_not_of(" \n\r\t");
-            if (last_non_sp_pos == std::string::npos) {
-                throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-            }
-            auto last_non_sp_char = str[last_non_sp_pos];
-            // Used to detect stops on a number, which may not be complete.
-            auto was_maybe_number = [&]() {
-                if (!str.empty() && std::isspace(str.back())) {
-                    return false;
-                }
-                return std::isdigit(last_non_sp_char) ||
-                    last_non_sp_char == '.' ||
-                    last_non_sp_char == 'e' ||
-                    last_non_sp_char == 'E' ||
-                    last_non_sp_char == '-';
-            };
-
-            std::string closing;
-            for (size_t i = err_loc.stack.size(); i > 0; i--) {
-                auto & el = err_loc.stack[i - 1];
-                if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                    closing += "}";
-                } else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                    closing += "]";
-                } else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) {
-                    throw std::runtime_error("Unexpected stack element type");
-                }
-            }
-
-            // Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX
-            static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)");
-
-            auto is_high_surrogate = [&](const std::string & s) {
-                // Check if a partial of a high surrogate (U+D800-U+DBFF)
-                return s.length() >= 4 &&
-                    s[0] == '\\' && s[1] == 'u' &&
-                    std::tolower(s[2]) == 'd' &&
-                    (s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b');
-            };
-
-            // Initialize the unicode marker to a low surrogate to handle the edge case
-            // where a high surrogate (U+D800-U+DBFF) is immediately followed by a
-            // backslash (\)
-            std::string unicode_marker_padding = "udc00";
-            std::smatch last_unicode_seq;
-
-            if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) {
-                std::smatch second_last_seq;
-                std::string prelude = str.substr(0, last_unicode_seq.position());
-
-                // Pad the escape sequence with 0s until it forms a complete sequence of 6 characters
-                unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0');
-
-                if (is_high_surrogate(last_unicode_seq.str())) {
-                    // If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF)
-                    unicode_marker_padding += "\\udc00";
-                } else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) {
-                    if (is_high_surrogate(second_last_seq.str())) {
-                        // If this follows a high surrogate, pad it to be a low surrogate
-                        if (last_unicode_seq.length() == 2) {
-                            unicode_marker_padding = "dc00";
-                        } else if (last_unicode_seq.length() == 3) {
-                            unicode_marker_padding = "c00";
-                        } else {
-                            // The original unicode_marker_padding is already padded with 0s
-                        }
-                    }
-                }
-            }
-
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
-
-            if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
-                // We're inside an object value
-                if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) {
-                    // Was about to create an object value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + ": 1" + closing)) {
-                    str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing;
-                } else if (last_non_sp_char == '{' && can_parse(str + closing)) {
-                    // Was about to create an object
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an object value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an object value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an object value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
-                } else {
-                    // find last :
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-                    }
-                    // Cutting back to opening : for object value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) {
-                    // Was about to create an array value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an array value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an array value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
-                    // Was inside an array value string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
-                    // Had just finished a value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
-                } else {
-                    auto last_pos = str.find_last_of("[,");
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location");
-                    }
-                    // Cutting back to last [ or , for array value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                if ((last_non_sp_char == '{' && can_parse(str + closing)) ||
-                        (last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\": 1" + closing)) {
-                    // Was inside an object key string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
-                    // Was inside an object key string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) {
-                    // Was inside an object key string after a partial unicode escape
-                    str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing;
-                } else {
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-                    }
-                    // fprintf(stderr, "Cutting back to last : for object key+value\n");
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else {
-                throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-            }
-            // fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str());
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
-        // handle unclosed top-level primitive
-        if (err_loc.position != 0 && !healing_marker.empty() && err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;
-            if (can_parse(str + "\"")) {
-                // Was inside an string
-                str += (out.healing_marker.json_dump_marker = magic_seed) + "\"";
-            } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"")) {
-                // Was inside an string after an escape
-                str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"";
-            } else {
-                // TODO: handle more unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
-                // fprintf(stderr, "Closing: TODO\n");
-                return false;
-            }
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
-        return false;
-    }
-    out.json = json::parse(it, end);
-    it = end;
-    return true;
-}
diff --git a/common/json-partial.h b/common/json-partial.h
deleted file mode 100644
index be51aabfb..000000000
--- a/common/json-partial.h
+++ /dev/null
@@ -1,39 +0,0 @@
-#pragma once
-
-// TODO: use json_fwd.hpp when possible
-#include <nlohmann/json.hpp>
-
-// Healing marker (empty if the JSON was fully parsed / wasn't healed).
-struct common_healing_marker {
-    // Raw marker.
-    std::string marker;
-
-    // Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format).
-    std::string json_dump_marker;
-};
-
-// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string)
-struct common_json {
-    nlohmann::ordered_json json;
-
-    common_healing_marker healing_marker;
-};
-
-// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty.
-//
-// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON.
-// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker.
-// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format).
-//
-// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again).
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out);
-
-// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds.
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out);
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index e284a58d1..0dd1d7b16 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -199,7 +199,6 @@ llama_build_and_test(test-jinja.cpp)
 llama_test(test-jinja NAME test-jinja-py ARGS -py LABEL python)
 llama_build_and_test(test-chat-auto-parser.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})
 llama_build_and_test(test-chat-template.cpp)
-llama_build_and_test(test-json-partial.cpp)
 llama_build_and_test(test-log.cpp)
 llama_build_and_test(
     test-peg-parser.cpp
diff --git a/tests/test-json-partial.cpp b/tests/test-json-partial.cpp
deleted file mode 100644
index 39da9276e..000000000
--- a/tests/test-json-partial.cpp
+++ /dev/null
@@ -1,287 +0,0 @@
-#include "common.h"
-#include "json-partial.h"
-#include <exception>
-#include <iostream>
-#include <stdexcept>
-
-template <class T> static void assert_equals(const T & expected, const T & actual) {
-  if (expected != actual) {
-      std::cerr << "Expected: " << expected << std::endl;
-      std::cerr << "Actual: " << actual << std::endl;
-      std::cerr << std::flush;
-      throw std::runtime_error("Test failed");
-  }
-}
-
-static void test_json_healing() {
-  auto parse = [](const std::string & str) {
-      std::cerr << "# Parsing: " << str << '\n';
-      std::string::const_iterator it = str.begin();
-      const auto end = str.end();
-      common_json out;
-      std::string healing_marker = "$llama.cpp.json$";
-      if (common_json_parse(it, end, healing_marker, out)) {
-          auto dump = out.json.dump();
-          std::cerr << "Parsed: " << dump << '\n';
-          std::cerr << "Magic: " << out.healing_marker.json_dump_marker << '\n';
-          std::string result;
-          if (!out.healing_marker.json_dump_marker.empty()) {
-              auto i = dump.find(out.healing_marker.json_dump_marker);
-              if (i == std::string::npos) {
-                  throw std::runtime_error("Failed to find magic in dump " + dump + " (magic: " + out.healing_marker.json_dump_marker + ")");
-              }
-              result = dump.substr(0, i);
-          } else {
-            result = dump;
-          }
-          std::cerr << "Result: " << result << '\n';
-          if (string_starts_with(str, result)) {
-            std::cerr << "Failure!\n";
-          }
-        //   return dump;
-      } else {
-        throw std::runtime_error("Failed to parse: " + str);
-      }
-
-  };
-  auto parse_all = [&](const std::string & str) {
-      for (size_t i = 1; i < str.size(); i++) {
-          parse(str.substr(0, i));
-      }
-  };
-  parse_all("{\"a\": \"b\"}");
-  parse_all("{\"hey\": 1, \"ho\\\"ha\": [1]}");
-
-  parse_all("[{\"a\": \"b\"}]");
-
-  auto test = [&](const std::vector<std::string> & inputs, const std::string & expected, const std::string & expected_marker) {
-      for (const auto & input : inputs) {
-        common_json out;
-        assert_equals(true, common_json_parse(input, "$foo", out));
-        assert_equals<std::string>(expected, out.json.dump(/* indent */ -1, /* indent_char */ ' ', /* ensure_ascii */ true));
-        assert_equals<std::string>(expected_marker, out.healing_marker.json_dump_marker);
-      }
-  };
-  // No healing needed:
-  test(
-    {
-      R"([{"a":"b"}, "y"])",
-    },
-    R"([{"a":"b"},"y"])",
-    ""
-  );
-  // Partial literals can't be healed:
-  test(
-    {
-      R"([1)",
-      R"([tru)",
-      R"([n)",
-      R"([nul)",
-      R"([23.2)",
-    },
-    R"(["$foo"])",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"({"a": 1)",
-      R"({"a": tru)",
-      R"({"a": n)",
-      R"({"a": nul)",
-      R"({"a": 23.2)",
-    },
-    R"({"a":"$foo"})",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"({)",
-    },
-    R"({"$foo":1})",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"([)",
-    },
-    R"(["$foo"])",
-    R"("$foo)"
-  );
-  // Healing right after a full literal
-  test(
-    {
-      R"(1 )",
-    },
-    R"(1)",
-    ""
-  );
-  test(
-    {
-      R"(true)",
-      R"(true )",
-    },
-    R"(true)",
-    ""
-  );
-  test(
-    {
-      R"(null)",
-      R"(null )",
-    },
-    R"(null)",
-    ""
-  );
-  test(
-    {
-      R"([1 )",
-    },
-    R"([1,"$foo"])",
-    R"(,"$foo)"
-  );
-  test(
-    {
-      R"([{})",
-      R"([{} )",
-    },
-    R"([{},"$foo"])",
-    R"(,"$foo)"
-  );
-  test(
-    {
-      R"([true)",
-    },
-    // TODO: detect the true/false/null literal was complete
-    R"(["$foo"])",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"([true )",
-    },
-    R"([true,"$foo"])",
-    R"(,"$foo)"
-  );
-  test(
-    {
-      R"([true,)",
-    },
-    R"([true,"$foo"])",
-    R"("$foo)"
-  );
-  // Test nesting
-  test(
-    {
-      R"([{"a": [{"b": [{)",
-    },
-    R"([{"a":[{"b":[{"$foo":1}]}]}])",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"([{"a": [{"b": [)",
-    },
-    R"([{"a":[{"b":["$foo"]}]}])",
-    R"("$foo)"
-  );
-
-  test(
-    {
-      R"([{"a": "b"})",
-      R"([{"a": "b"} )",
-    },
-    R"([{"a":"b"},"$foo"])",
-    R"(,"$foo)"
-  );
-  test(
-    {
-      R"([{"a": "b"},)",
-      R"([{"a": "b"}, )",
-    },
-    R"([{"a":"b"},"$foo"])",
-    R"("$foo)"
-  );
-  test(
-    {
-      R"({ "code)",
-    },
-    R"({"code$foo":1})",
-    R"($foo)"
-  );
-  test(
-    {
-      R"({ "code\)",
-    },
-    R"({"code\\$foo":1})",
-    R"(\$foo)"
-  );
-  test(
-    {
-      R"({ "code")",
-    },
-    R"({"code":"$foo"})",
-    R"(:"$foo)"
-  );
-  test(
-    {
-      R"({ "key")",
-    },
-    R"({"key":"$foo"})",
-    R"(:"$foo)"
-  );
-  // Test unicode escape sequences
-  test(
-    {
-      R"({"a":"\u)",
-    },
-    R"({"a":"\u0000$foo"})",
-    R"(0000$foo)"
-  );
-  test(
-    {
-      R"({"a":"\u00)",
-    },
-    R"({"a":"\u0000$foo"})",
-    R"(00$foo)"
-  );
-  test(
-    {
-      R"({"a":"\ud300)",
-    },
-    R"({"a":"\ud300$foo"})",
-    R"($foo)"
-  );
-  test(
-    {
-      R"({"a":"\ud800)",
-    },
-    R"({"a":"\ud800\udc00$foo"})",
-    R"(\udc00$foo)"
-  );
-  test(
-    {
-      R"({"a":"\ud800\)",
-    },
-    R"({"a":"\ud800\udc00$foo"})",
-    R"(udc00$foo)"
-  );
-  test(
-    {
-      R"({"a":"\ud800\u)",
-    },
-    R"({"a":"\ud800\udc00$foo"})",
-    R"(dc00$foo)"
-  );
-  test(
-    {
-      R"({"a":"\ud800\udc00)",
-    },
-    R"({"a":"\ud800\udc00$foo"})",
-    R"($foo)"
-  );
-}
-
-int main() {
-    test_json_healing();
-    std::cerr << "All tests passed.\n";
-    return 0;
-}

From 894bb27af3bf063d465b251f2b1ca05e95e98610 Mon Sep 17 00:00:00 2001
From: Saba Fallah <sabafallah@gmail.com>
Date: Wed, 24 Jun 2026 18:20:22 +0200
Subject: [PATCH 144/292] mtmd: model: unlimited-ocr: converter + parity test
 (#24969)

---
 conversion/__init__.py                |  3 ++
 conversion/deepseek.py                | 12 +++++-
 tools/mtmd/tests/test-deepseek-ocr.py | 54 ++++++++++++++++++++++++---
 3 files changed, 61 insertions(+), 8 deletions(-)

diff --git a/conversion/__init__.py b/conversion/__init__.py
index 2bce1bbd7..a587b1c37 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -46,6 +46,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DbrxForCausalLM": "dbrx",
     "DeciLMForCausalLM": "deci",
     "DeepseekForCausalLM": "deepseek",
+    "DeepseekOCRForCausalLM": "deepseek",
     "DeepseekV2ForCausalLM": "deepseek",
     "DeepseekV3ForCausalLM": "deepseek",
     "DeepseekV32ForCausalLM": "deepseek",
@@ -233,6 +234,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "UMT5ForConditionalGeneration": "t5",
     "UMT5Model": "t5",
     "UltravoxModel": "ultravox",
+    "UnlimitedOCRForCausalLM": "deepseek",
     "VLlama3ForCausalLM": "llama",
     "VoxtralForConditionalGeneration": "llama",
     "WavTokenizerDec": "wavtokenizer",
@@ -299,6 +301,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "StepVLForConditionalGeneration": "step3",
     "Step3p7ForConditionalGeneration": "step3",
     "UltravoxModel": "ultravox",
+    "UnlimitedOCRForCausalLM": "deepseek",
     "VoxtralForConditionalGeneration": "ultravox",
     "YoutuVLForConditionalGeneration": "youtuvl",
 }
diff --git a/conversion/deepseek.py b/conversion/deepseek.py
index 72520cc9f..4c93fb66d 100644
--- a/conversion/deepseek.py
+++ b/conversion/deepseek.py
@@ -14,7 +14,7 @@ from .base import MmprojModel, ModelBase, TextModel, gguf, logger
 from .qwen import QwenModel
 
 
-@ModelBase.register("DeepseekOCRForCausalLM")
+@ModelBase.register("DeepseekOCRForCausalLM", "UnlimitedOCRForCausalLM")
 class DeepseekOCRVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -205,6 +205,8 @@ class DeepseekModel(TextModel):
 @ModelBase.register(
     "DeepseekV2ForCausalLM",
     "DeepseekV3ForCausalLM",
+    "DeepseekOCRForCausalLM",
+    "UnlimitedOCRForCausalLM",
     "KimiVLForConditionalGeneration",
     "KimiK25ForConditionalGeneration",
     "YoutuForCausalLM",
@@ -224,7 +226,7 @@ class DeepseekV2Model(TextModel):
         self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
         # special handling for Deepseek OCR
-        if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM"):
+        if self.origin_hf_arch in ("DeepseekOCRForCausalLM", "DeepseekOCR2ForCausalLM", "UnlimitedOCRForCausalLM"):
             self.model_arch = gguf.MODEL_ARCH.DEEPSEEK2OCR
             self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
             self.gguf_writer.add_architecture()
@@ -350,6 +352,12 @@ class DeepseekV2Model(TextModel):
 
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
+        # Unlimited-OCR sliding window; written for metadata, the decoder ignores it (full MHA)
+        if is_ocr:
+            sliding_window = hparams.get("sliding_window_size") or hparams.get("sliding_window")
+            if sliding_window:
+                self.gguf_writer.add_sliding_window(sliding_window)
+
         if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
             # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
             # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
diff --git a/tools/mtmd/tests/test-deepseek-ocr.py b/tools/mtmd/tests/test-deepseek-ocr.py
index 5f5fef765..ec0b4523b 100644
--- a/tools/mtmd/tests/test-deepseek-ocr.py
+++ b/tools/mtmd/tests/test-deepseek-ocr.py
@@ -9,6 +9,7 @@ its output, and holds them against the HF model's scores.
 
 import argparse
 import logging
+import re
 import subprocess
 import sys
 import unicodedata
@@ -28,6 +29,12 @@ class ModelSpec:
     mmproj_arg: str
     model_default: str
     mmproj_default: str
+    prompt: str = "Free OCR. "
+    n_predict: int = 512
+    n_ctx: int | None = None
+    # Unlimited-OCR's "document parsing" prompt emits <|det|> grounding markup that
+    # the HF reference strips in result.md; drop it before scoring to match.
+    strip_grounding: bool = False
 
 
 @dataclass
@@ -63,6 +70,20 @@ MODELS = {
         model_default="gguf_models/deepseek-ai/deepseek-ocr-2-bf16.gguf",
         mmproj_default="gguf_models/deepseek-ai/mmproj-deepseek-ocr-2-bf16.gguf",
     ),
+    "unlimited": ModelSpec(
+        key="unlimited", label="Unlimited-OCR",
+        model_arg="--llama-model-unlimited", mmproj_arg="--mmproj-unlimited",
+        model_default="gguf_models/baidu/unlimited-ocr-bf16.gguf",
+        mmproj_default="gguf_models/baidu/mmproj-unlimited-ocr-bf16.gguf",
+        # "Free OCR." immediately emits EOS on this checkpoint; the HF reference
+        # (demo/unlimited_ocr_scores.py) uses "document parsing.", which grounds.
+        prompt="document parsing.",
+        # Grounding emits ~3x the tokens of plain OCR, so it needs a larger budget
+        # and context to reach the article body the ground truth covers.
+        n_predict=4096,
+        n_ctx=16384,
+        strip_grounding=True,
+    ),
 }
 
 CASES = [
@@ -82,9 +103,26 @@ CASES = [
         # is one pixel off and lands at ~0.69 instead.
         hf_cer=0.7761, hf_chrf=28.70, cer_tol=0.12, chrf_tol=8.0,
     ),
+    TestCase(
+        model_key="unlimited", label="single-view scan",
+        image="tools/mtmd/test-1.jpeg",
+        ground_truth="tools/mtmd/tests/test-1-ground-truth.txt",
+        # HF reference: Unlimited-OCR scoring (gundam, bf16) on this image/ground-truth.
+        # Decoder runs full MHA, not R-SWA; the band absorbs that gap + bf16 variance.
+        hf_cer=0.1869, hf_chrf=75.23, cer_tol=0.06, chrf_tol=6.0,
+    ),
 ]
 
 
+GROUNDING_TAG_RE = re.compile(r"<\|(ref|det)\|>.*?<\|/\1\|>", re.DOTALL)
+
+
+def strip_grounding(text: str) -> str:
+    """Drop <|ref|>..<|/ref|> / <|det|>..<|/det|> grounding markup, matching the
+    cleaned result.md the HF reference scores against."""
+    return GROUNDING_TAG_RE.sub("", text)
+
+
 def arg_dest(flag: str) -> str:
     return flag.lstrip("-").replace("-", "_")
 
@@ -129,19 +167,19 @@ def compute_chrf(expected: str, ocr_out: str) -> float:
     return CHRF().sentence_score(ocr_out, [expected]).score
 
 
-def run_mtmd_cli(model_path, mmproj_path, image_path, bin_path) -> str:
+def run_mtmd_cli(spec: "ModelSpec", model_path, mmproj_path, image_path, bin_path) -> str:
     """Run mtmd-cli on the image and return its output."""
     cmd = [
         str(bin_path),
         "-m", str(model_path),
         "--mmproj", str(mmproj_path),
         "--image", str(image_path),
-        "-p", "Free OCR. ",
+        "-p", spec.prompt,
         "--chat-template", "deepseek-ocr",
         "--temp", "0",
         "--flash-attn", "off",  # match the HF "eager" attention reference
         "--no-warmup",
-        "-n", "512",  # cap loops on hard images (KV would otherwise fill)
+        "-n", str(spec.n_predict),  # cap loops on hard images (KV would otherwise fill)
         # HF decodes with no_repeat_ngram_size; llama.cpp's analog is DRY.
         # Default DRY breakers include "\n", so they are cleared below.
         "--dry-multiplier", "0.8",
@@ -150,6 +188,8 @@ def run_mtmd_cli(model_path, mmproj_path, image_path, bin_path) -> str:
         "--dry-penalty-last-n", "-1",
         "--dry-sequence-breaker", "none",
     ]
+    if spec.n_ctx is not None:
+        cmd += ["-c", str(spec.n_ctx)]
     logger.debug(f"  command: {' '.join(cmd)}")
 
     try:
@@ -164,6 +204,8 @@ def run_mtmd_cli(model_path, mmproj_path, image_path, bin_path) -> str:
         raise RuntimeError(f"llama-mtmd-cli failed with code {result.returncode}")
 
     output = result.stdout.decode("utf-8", errors="replace").strip()
+    if spec.strip_grounding:
+        output = strip_grounding(output)
     if not output:
         raise RuntimeError("llama-mtmd-cli produced no output on stdout")
     logger.info(f"  output: {len(output)} chars")
@@ -193,7 +235,7 @@ def evaluate(case: "TestCase", expected: str, ocr_out: str) -> bool:
 
     logger.info("")
     logger.info("=" * 60)
-    logger.info("Free OCR evaluation:")
+    logger.info("OCR evaluation:")
     logger.info("=" * 60)
     logger.info(f"  CER               {cer:>7.4f}    (HF {case.hf_cer:.4f}, <= {case.cer_max:>7.4f}  -> {verdict(cer_pass)})")
     logger.info(f"  chrF (0-100)      {chrf:>7.2f}    (HF {case.hf_chrf:.2f}, >= {case.chrf_min:>7.2f}  -> {verdict(chrf_pass)})")
@@ -269,9 +311,9 @@ def main() -> int:
         expected = read_expected_text(ground_truth)
         logger.info(f"  Image: {case.image}")
         logger.info(f"  Expected text: {len(expected)} chars")
-        logger.info("  Running llama.cpp 'Free OCR'")
+        logger.info(f"  Running llama.cpp prompt {model_spec.prompt!r}")
         try:
-            ocr_out = run_mtmd_cli(model, mmproj, image, binary)
+            ocr_out = run_mtmd_cli(model_spec, model, mmproj, image, binary)
         except RuntimeError as e:
             logger.error(f"  Error: {e}")
             results[title] = False

From 8be759e6f70d629638a7eb70db3824cbdcea370b Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Wed, 24 Jun 2026 12:14:25 -0700
Subject: [PATCH 145/292] hexagon: MUL_MAT and MUL_MAT_ID rework : 32x32 tiled
 weight repack, kernel-params, cached graphs (#24954)

* hex-mm: new weight layout and fusion updates

* hvx-mm: unroll the new tiled vec_dots to optimize hvx register util

* hex-mm: optimize dyn.quant format for q8_0 and q8_1 to reduce overhead in vec_dots.

* hvx-mm: parallel quantizer per block for large rows

* hvx-mm: simplify and futher optimize dyn.quant and vec_dots

* hvx-mm: keep intermediate per tile accumulators in fp16

* hmx-mm: optimize weight dequant by aligning the repacked tiles with the DMA

* hmx-mm: remove qweight scratch and just use vtcm_weight

* hmx-mm: remove all unused and obsolete code

* hmx-mm: the new tiled repack format is here to stay -- rename all x4x2 to _tiled

* hmx-mm: improve activation processing with dma prefetch

* hex-mm: fix hmx/hvx fallback logic and MUL_MAT_ID allocation (unbreaks OLMoE)

* hex-mm: align the weight tiles with dma just like we did in hmx-mm

* hex-mm: factor out common mm bits into htp/matmul-ops.h

* hex-mm: start moving mm kernel selection to the host

* hex-mm: move all of the matmul param compute into the host

* hmx-mm: restore pipelined mode

* hmx-mm: unroll the dequant functions to optimize register usage

* hmx-mm: further improve activation process

* hex-mm: use vtcm_seq_alloc for all vtcm allocations and define more common functions

* hex-mm: improve mm optimizer to acount for number of activation threads

* hex-mm: fix matmul-id kernel params selection (unbreaks OLMoE and LFM)

* hexagon: remove support for arch < v73 since HMX is now required for most use-cases

* hex-mm: cleanup naming for consistency

* hex-mm: make sure matmul fusion accounts for vtcm allocation

* hex-mm: minor cleanup for kernel_params definition

* hex-mm: replace hardcoded limits with proper checks for vtcm requirements

* hex-mm: add support for non-tiled mm as a fallback option and factor out hvx kernels into separate header

* hex-mm: remove unused functions

* hex-mm: add shorthand for MM_SELECT in run-tool script

* hvx-mm: factor out hvx/hmx microkernels and unify matmul entry and dispatch

* hex-mm: further cleanup matmul fallback path

* hex-mm: refactor matmul entry point and dispatch a bit further

* hexagon: update cmake build to enable hmx for everything

* hex-ops: optimize kernel_param updates and include summary in the logs

* hex-mm: add support for GGML_HEXAGON_MM_SELECT

* hex-mm: add hex-common header

* hex-mm: pass correct number of tasks to workpool

* hex-mm: add proper checks for no-work in dyn.quant tasks

* hex-mm: convert all quantizers into a macro

* hex-mm: fix hvx-flat fallback to pass all MUL_MAT tests

* hex-mm: vectorize q8_1 quantizer

* hex-mm: improve fused ffn mm stride handling

* hex-mm: consistent use of n_threads and pipeline in kernel_params

* hexagon: minor formatting

* hex-mm: update MUL_MAT_ID kernel_param handling to make sure host/npu are in sync

* hvx-mm: go back to accumulating in fp32 in tiled hvx kernels, more accurate and same perf

* hvx-mm: unroll the loops and remove masking that is not needed for tiled accums

* hmx-mm: optimize activation processing (slit loops, some unrolling, etc)

* hmx-mm: minor optimization for output processing

* hex-mm: consistent use of uint32_t and size_t in mm kernels

* hex-mm: remove legacy restrictions for rows to be multiple of 256

* hexagon: replace sprintf with snprintf

* hex-mm: relax hardcoded nrows checks and rely on VTCM size requirements

* hexagon: minor alignment fix

* hexagon: fix trailing spaces

* hex-mm: relax padding from 256 to 128 (leftovers)

* hex-mm: remove redundant checks for weight align to 128

we always use 2D dma for the weights and align them properly

* hmx-mm: MUL_MAT_ID better work distribution between hvx threads and hmx tracing

* hex-mm: specialize per-token mmid activation handling

* hex-profile: update python scripts to handle kernel-params section in the logging output

* hex-mm: move n_prefetch (aka dma_depth) into kernel params and remove unused fields

* hex-trace: use easier to parse format, simply and fix post-proc scripts

* hmx-mm: relax 32 row limit for output processing which helps utilization

* hmx-mm: use start-chunk idx for tracing info

* hmx-mm: parameterize activation dma pipeline

* hexagon: add support for simple graph caching to avoid recomputing kernel-params

* hex-mm: remove left-over repack functions

* hex-mm: tighten n_prefetch asserts

* hex-mm: remove duplicate round/align_up helper

* hexagon: cleanup common header used in host/npu

* hexagon: update early wakeup threshold

* hmx-mm: define cost constants and update solver to assume that repacked ne[1] is padded to 32

* hmx-mm: make precompute_matmul a bit more readable (split into smaller functions, etc)

* hex-mm: remove n_threads constraint

* hex-mm: minor formatting updates

* hex-mm: remove obsolete profiling logs

* hex-mm: restore hardcode gate to refuse lm-head to avoid repacking that tensor
---
 docs/backend/snapdragon/CMakeUserPresets.json |    3 -
 ggml/CMakeLists.txt                           |    1 -
 ggml/src/ggml-hexagon/CMakeLists.txt          |    4 -
 ggml/src/ggml-hexagon/ggml-hexagon.cpp        | 2633 ++++---
 ggml/src/ggml-hexagon/htp-opnode.h            |  218 +-
 ggml/src/ggml-hexagon/htp/CMakeLists.txt      |   52 +-
 .../ggml-hexagon/htp/cmake-toolchain.cmake    |   28 +-
 ggml/src/ggml-hexagon/htp/flash-attn-ops.c    |    5 +-
 ggml/src/ggml-hexagon/htp/hex-common.h        |   80 +
 ggml/src/ggml-hexagon/htp/hex-dma.h           |    6 +-
 ggml/src/ggml-hexagon/htp/hex-utils.h         |   57 +-
 .../src/ggml-hexagon/htp/hmx-flash-attn-ops.c |   26 +-
 ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c    | 2080 -----
 .../ggml-hexagon/htp/hmx-mm-kernels-tiled.h   | 1306 ++++
 ggml/src/ggml-hexagon/htp/hmx-ops.c           |    6 -
 ggml/src/ggml-hexagon/htp/hmx-ops.h           |   88 -
 ggml/src/ggml-hexagon/htp/htp-ctx.h           |   12 +-
 ggml/src/ggml-hexagon/htp/htp-ops.h           |   23 +-
 ggml/src/ggml-hexagon/htp/htp_iface.idl       |    3 +-
 ggml/src/ggml-hexagon/htp/hvx-base.h          |   31 +-
 .../ggml-hexagon/htp/hvx-mm-kernels-flat.h    | 1024 +++
 .../ggml-hexagon/htp/hvx-mm-kernels-tiled.h   | 1140 +++
 ggml/src/ggml-hexagon/htp/main.c              |  104 +-
 ggml/src/ggml-hexagon/htp/matmul-ops.c        | 6846 +++++++----------
 ggml/src/ggml-hexagon/htp/matmul-ops.h        |  508 ++
 ggml/src/ggml-hexagon/libggml-htp.inf         |    4 -
 scripts/snapdragon/adb/run-completion.sh      |    8 +-
 scripts/snapdragon/adb/run-tool.sh            |    8 +-
 scripts/snapdragon/ggml-hexagon-profile.py    |   45 +-
 scripts/snapdragon/ggml-hexagon-trace.py      |   48 +-
 tests/test-backend-ops.cpp                    |    6 +
 31 files changed, 8572 insertions(+), 7831 deletions(-)
 create mode 100644 ggml/src/ggml-hexagon/htp/hex-common.h
 delete mode 100644 ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
 create mode 100644 ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
 delete mode 100644 ggml/src/ggml-hexagon/htp/hmx-ops.c
 delete mode 100644 ggml/src/ggml-hexagon/htp/hmx-ops.h
 create mode 100644 ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
 create mode 100644 ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
 create mode 100644 ggml/src/ggml-hexagon/htp/matmul-ops.h

diff --git a/docs/backend/snapdragon/CMakeUserPresets.json b/docs/backend/snapdragon/CMakeUserPresets.json
index d37100764..848d735f1 100644
--- a/docs/backend/snapdragon/CMakeUserPresets.json
+++ b/docs/backend/snapdragon/CMakeUserPresets.json
@@ -24,7 +24,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },
@@ -47,7 +46,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "ON",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },
@@ -73,7 +71,6 @@
             "GGML_LLAMAFILE":   "OFF",
             "GGML_OPENCL":      "OFF",
             "GGML_HEXAGON":     "ON",
-            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
             "LLAMA_OPENSSL":    "OFF"
         }
     },
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 04069784f..a0cd4e715 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -266,7 +266,6 @@ set   (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
                                             "ggml: OpenCL API version to target")
 
 option(GGML_HEXAGON                         "ggml: enable Hexagon backend"                    OFF)
-set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)")
 
 # toolchain for vulkan-shaders-gen
 set   (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt
index b82bae0c1..c6e49a71d 100644
--- a/ggml/src/ggml-hexagon/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/CMakeLists.txt
@@ -25,7 +25,6 @@ include(ExternalProject)
 option(GGML_HEXAGON_HTP_DEBUG  "ggml-hexagon: enable HTP debug output" OFF)
 option(GGML_HEXAGON_FA_EXP2_HF "ggml-hexagon: use FP16 exp2 polynomial in FA softmax instead of F32 exp round-trip" OFF)
 set(GGML_HEXAGON_HTP_CERT  "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate")
-set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)")
 
 add_library(htp_iface OBJECT
     ${CMAKE_CURRENT_BINARY_DIR}/htp_iface_stub.c)
@@ -72,15 +71,12 @@ function(build_htp_skel V)
             -DHEXAGON_SDK_ROOT=${HEXAGON_SDK_ROOT}
             -DHEXAGON_TOOLS_ROOT=${HEXAGON_TOOLS_ROOT}
             -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG}
-            -DGGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE}
             -DDSP_VERSION=${V}
             -DPREBUILT_LIB_DIR="toolv19_${V}")
     list(APPEND HTP_SKELS ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-${V}.so)
     set(HTP_SKELS ${HTP_SKELS} PARENT_SCOPE)
 endfunction()
 
-build_htp_skel(v68)
-build_htp_skel(v69)
 build_htp_skel(v73)
 build_htp_skel(v75)
 build_htp_skel(v79)
diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index e612ec392..3d41c47b6 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -18,6 +18,7 @@
 #include <unordered_map>
 #include <regex>
 #include <queue>
+#include <algorithm>
 
 #ifdef _WIN32
 #    include <sal.h>
@@ -41,6 +42,7 @@
 #include "ggml-quants.h"
 #include "htp-opnode.h"
 #include "htp-ops.h"
+#include "htp/matmul-ops.h"
 #include "htp_iface.h"
 #include "htp-drv.h"
 
@@ -51,7 +53,7 @@ using u32vec  = std::vector<uint32_t>;
 static int    opt_arch    = 0; // autodetect
 static size_t opt_ndev    = 1;
 static size_t opt_nhvx    = 0; // use all
-static int    opt_use_hmx = 1; // when set, enable HMX; when 0, use HVX only
+static int    opt_nhmx    = 1; // when set, enable HMX; when 0, use HVX only
 static size_t opt_vmem    = HTP_OP_MAX_VMEM_DEFAULT;  // max available va space for buffer mappings
 static size_t opt_mbuf    = 1ul * 1024 * 1024 * 1024; // max buffer size
 static int    opt_etm     = 0;
@@ -59,6 +61,8 @@ static int    opt_verbose = 0;
 static int    opt_profile = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
 static int    opt_hostbuf = 1; // hostbuf ON by default
 
+static int    opt_mm_select = 3; // 3 = HMX -> Tiled -> Flat -> CPU, 2 = Tiled -> Flat -> CPU, 1 = Flat -> CPU
+
 // Default PMU events, if profiling with PMU (mode=2) is enabled
 // See https://docs.qualcomm.com/doc/80-N2040-60/topic/pmu-events.html
 //     https://docs.qualcomm.com/doc/80-N2040-61/topic/hvx-pmu-events.html
@@ -68,22 +72,15 @@ static u32vec opt_pmu_evt { 0x3, 0x111, 0x100, 0x105, 0x240, 0x256, 0x7D, 0x8C }
 static int opt_opstage  = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
 static int opt_opbatch  = 1024; // max number of ops in a batch
 static int opt_opqueue  = 16;   // max number of pending batches
-static int opt_oppoll   = 0;    // polling for batch completions
 static int opt_optrace  = 0;    // trace buffer size per thread (0 means default)
+static int opt_oppoll   = 0;    // polling for batch completions
+static int opt_opfusion = 1;    // enable/disable op fusion
 
 static std::regex* opt_opfilter = NULL; // regex of ops to not claim
 
 #define HEX_VERBOSE(...) \
     if (opt_verbose) GGML_LOG_DEBUG(__VA_ARGS__)
 
-static inline uint64_t hex_is_aligned(void * addr, uint32_t align) {
-    return ((size_t) addr & (align - 1)) == 0;
-}
-
-static inline size_t hex_round_up(size_t n, size_t m) {
-    return m * ((n + m - 1) / m);
-}
-
 static const char * status_to_str(uint32_t status) {
     switch (status) {
         case HTP_STATUS_OK:
@@ -107,15 +104,15 @@ static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const htp_op
     if (!opt_verbose) return;
 
     htp_opformat fmt(node);
-    GGML_LOG_DEBUG("ggml-hex: %s execute-op %s: %s : %s : %s : %s : %s : flags 0x%x\n", sess_name.c_str(),
-                node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, req_flags);
+    GGML_LOG_DEBUG("ggml-hex: %s execute-op %s|%s|%s|%s|%s|%s|%s|flags 0x%x\n", sess_name.c_str(),
+                node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, fmt.kparams, req_flags);
 }
 
 static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct ggml_tensor * op, bool supp) {
     if (!opt_verbose) return;
 
     htp_opformat fmt(htp_opformat(htp_opnode{const_cast<ggml_tensor*>(op), {}, HTP_OP_INVALID}));
-    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
+    GGML_LOG_DEBUG("ggml-hex: %s supports-op %s|%s|%s|%s|%s|%s|%s\n", sess_name.c_str(),
                 ggml_op_desc(op), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.buffs, supp ? "yes" : "no");
 }
 
@@ -144,16 +141,52 @@ static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const htp_op
     char pmu_str[256] = "";
     if (opt_profile == 2) {
         static_assert(HTP_PROF_PMU_NCNT == 8, "current implementation assumes 8 PMU counters");
-        sprintf(pmu_str, " pmu [%u,%u,%u,%u,%u,%u,%u,%u]",
+        snprintf(pmu_str, sizeof(pmu_str), " pmu [%u,%u,%u,%u,%u,%u,%u,%u]",
                 pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
     }
 
     htp_opformat fmt(node);
     float mhz = op_usec > 0 ? (float) op_cycles / op_usec : 0.0f;
-    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u start %u mhz %.1f%s\n", sess_name.c_str(),
-            node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, op_usec, op_cycles, pd.cycles_start, mhz, pmu_str);
+    GGML_LOG_DEBUG("ggml-hex: %s profile-op %s|%s|%s|%s|%s|%s|usec %u cycles %u start %u mhz %.1f%s\n", sess_name.c_str(),
+            node.op_name().c_str(), fmt.names, fmt.dims, fmt.types, fmt.strides, fmt.kparams, op_usec, op_cycles, pd.cycles_start, mhz, pmu_str);
 }
 
+// **
+
+static inline bool ggml_hexagon_is_repack_type(enum ggml_type type) {
+    return type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 ||
+           type == GGML_TYPE_Q8_0 || type == GGML_TYPE_IQ4_NL ||
+           type == GGML_TYPE_MXFP4;
+}
+
+static inline bool ggml_hexagon_is_hmx_weight_type(enum ggml_type type) {
+    return type == GGML_TYPE_F16 || type == GGML_TYPE_F32 || ggml_hexagon_is_repack_type(type);
+}
+
+struct htp_mm_kernel_params;
+struct ggml_hexagon_session;
+static void ggml_hexagon_precompute_matmul_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * dst,
+    struct htp_mm_kernel_params * kparams
+);
+
+static void ggml_hexagon_precompute_fused_qkv_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    struct htp_mm_kernel_params * kparams
+);
+
+static void ggml_hexagon_precompute_fused_ffn_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    struct htp_mm_kernel_params * kparams
+);
+
 // ** backend sessions
 
 struct ggml_hexagon_opbatch;
@@ -180,6 +213,18 @@ struct ggml_hexagon_session {
     ggml_backend_buffer_type buffer_type        = {};
     ggml_backend_buffer_type repack_buffer_type = {};
 
+    uint32_t n_threads = 0;
+    uint32_t n_hvx     = 0;
+    uint32_t n_hmx     = 0;
+    uint64_t vtcm_size = 0;
+    size_t   max_vmem  = 0;
+    size_t   max_bufsize = 0;
+
+    struct {
+        uint64_t uid = 0;
+        std::vector<htp_opnode> htp_nodes;
+    } cached_graph;
+
     ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) noexcept(false);
     ~ggml_hexagon_session() noexcept(true);
 
@@ -325,47 +370,7 @@ static enum ggml_status ggml_backend_hexagon_buffer_init_tensor(ggml_backend_buf
     return GGML_STATUS_SUCCESS;
 }
 
-// ======== Q4x4x2 ====================
-struct x2_q4 {
-    int v[2];
-};
-
-static x2_q4 unpack_q4(uint8_t v) {
-    x2_q4 x = { (int) (v & 0x0f) - 8, (int) (v >> 4) - 8 };
-    return x;
-}
-
-static void dump_block_q4_0(const block_q4_0 * b, int i) {
-    HEX_VERBOSE("ggml-hex: repack q4_0 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, unpack_q4(b->qs[0]).v[0],
-                unpack_q4(b->qs[1]).v[0], unpack_q4(b->qs[2]).v[0], unpack_q4(b->qs[3]).v[0], unpack_q4(b->qs[12]).v[1],
-                unpack_q4(b->qs[13]).v[1], unpack_q4(b->qs[14]).v[1], unpack_q4(b->qs[15]).v[1],
-                GGML_FP16_TO_FP32(b->d));
-}
-
-static void dump_packed_block_q4x4x2(const uint8_t * v, unsigned int i, size_t k) {
-    static const int qk        = QK_Q4_0x4x2;
-    const int        dblk_size = 8 * 2;   // 8x __fp16
-    const int        qblk_size = qk / 2;  // int4
-    const int        qrow_size = k / 2;   // int4 (not padded)
-
-    const uint8_t * v_q = v + 0;          // quants first
-    const uint8_t * v_d = v + qrow_size;  // then scales
-
-    const uint8_t *   q = v_q + i * qblk_size;
-    const ggml_half * d = (const ggml_half *) (v_d + i * dblk_size);
-
-    HEX_VERBOSE("ggml-hex: repack q4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
-                unpack_q4(q[0]).v[0], unpack_q4(q[1]).v[0], unpack_q4(q[2]).v[0], unpack_q4(q[3]).v[0],
-                unpack_q4(q[60]).v[0], unpack_q4(q[61]).v[0], unpack_q4(q[62]).v[0], unpack_q4(q[63]).v[0],
-                unpack_q4(q[124]).v[0], unpack_q4(q[125]).v[0], unpack_q4(q[126]).v[0], unpack_q4(q[127]).v[0],
-                GGML_FP16_TO_FP32(d[0]), GGML_FP16_TO_FP32(d[1]), GGML_FP16_TO_FP32(d[2]), GGML_FP16_TO_FP32(d[3]));
-
-    HEX_VERBOSE("ggml-hex: repack q4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
-                i + 1, unpack_q4(q[0]).v[1], unpack_q4(q[1]).v[1], unpack_q4(q[2]).v[1], unpack_q4(q[3]).v[1],
-                unpack_q4(q[60]).v[1], unpack_q4(q[61]).v[1], unpack_q4(q[62]).v[1], unpack_q4(q[63]).v[1],
-                unpack_q4(q[124]).v[1], unpack_q4(q[125]).v[1], unpack_q4(q[126]).v[1], unpack_q4(q[127]).v[1],
-                GGML_FP16_TO_FP32(d[4]), GGML_FP16_TO_FP32(d[5]), GGML_FP16_TO_FP32(d[6]), GGML_FP16_TO_FP32(d[7]));
-}
+// ** Repack helpers for tiled quantized weights
 
 static void unpack_q4_0_quants(uint8_t * qs, const block_q4_0 * x, unsigned int bi) {
     static const int qk = QK4_0;
@@ -388,300 +393,6 @@ static void pack_q4_0_quants(block_q4_0 * x, const uint8_t * qs, unsigned int bi
     }
 }
 
-static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
-
-    const int dblk_size = 8 * 2;              // 8x __fp16
-    const int qblk_size = qk / 2;             // int4
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
-
-    uint8_t * y_q = y + 0;                    // quants first
-    uint8_t * y_d = y + qrow_size;            // then scales
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_q4_0(&x[i * 8 + 0], 0);
-            dump_block_q4_0(&x[i * 8 + 1], 1);
-            dump_block_q4_0(&x[i * 8 + 2], 2);
-            dump_block_q4_0(&x[i * 8 + 3], 3);
-            dump_block_q4_0(&x[i * 8 + 4], 4);
-            dump_block_q4_0(&x[i * 8 + 5], 5);
-            dump_block_q4_0(&x[i * 8 + 6], 6);
-            dump_block_q4_0(&x[i * 8 + 7], 7);
-        }
-    }
-
-    // Repack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-        unpack_q4_0_quants(qs, &x[i * 8 + 0], 0);
-        unpack_q4_0_quants(qs, &x[i * 8 + 1], 1);
-        unpack_q4_0_quants(qs, &x[i * 8 + 2], 2);
-        unpack_q4_0_quants(qs, &x[i * 8 + 3], 3);
-        unpack_q4_0_quants(qs, &x[i * 8 + 4], 4);
-        unpack_q4_0_quants(qs, &x[i * 8 + 5], 5);
-        unpack_q4_0_quants(qs, &x[i * 8 + 6], 6);
-        unpack_q4_0_quants(qs, &x[i * 8 + 7], 7);
-
-        bool partial = (nloe && i == nb-1);
-
-        uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
-        }
-    }
-
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Repack the scales
-        ggml_half * d = (ggml_half *) (y_d + i * dblk_size);
-        d[0]          = x[i * 8 + 0].d;
-        d[1]          = x[i * 8 + 1].d;
-        d[2]          = x[i * 8 + 2].d;
-        d[3]          = x[i * 8 + 3].d;
-        d[4]          = x[i * 8 + 4].d;
-        d[5]          = x[i * 8 + 5].d;
-        d[6]          = x[i * 8 + 6].d;
-        d[7]          = x[i * 8 + 7].d;
-    }
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_q4x4x2(y, i, k);
-        }
-    }
-}
-
-static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
-
-    const int dblk_size = 8 * 2;              // 8x __fp16
-    const int qblk_size = qk / 2;             // int4
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
-
-    const uint8_t * y_q = y + 0;              // quants first
-    const uint8_t * y_d = y + qrow_size;      // then scales
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_q4x4x2(y, i, k);
-        }
-    }
-
-    // Unpack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-
-        bool partial = (nloe && i == nb-1);
-
-        const uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            if (partial) {
-                qs[j*2+0] = q[j] & 0xf;
-                qs[j*2+1] = q[j] >> 4;
-            } else {
-                qs[j+000] = q[j] & 0xf;
-                qs[j+128] = q[j] >> 4;
-            }
-        }
-
-        pack_q4_0_quants(&x[i * 8 + 0], qs, 0);
-        pack_q4_0_quants(&x[i * 8 + 1], qs, 1);
-        pack_q4_0_quants(&x[i * 8 + 2], qs, 2);
-        pack_q4_0_quants(&x[i * 8 + 3], qs, 3);
-        pack_q4_0_quants(&x[i * 8 + 4], qs, 4);
-        pack_q4_0_quants(&x[i * 8 + 5], qs, 5);
-        pack_q4_0_quants(&x[i * 8 + 6], qs, 6);
-        pack_q4_0_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size);
-        x[i * 8 + 0].d      = d[0];
-        x[i * 8 + 1].d      = d[1];
-        x[i * 8 + 2].d      = d[2];
-        x[i * 8 + 3].d      = d[3];
-        x[i * 8 + 4].d      = d[4];
-        x[i * 8 + 5].d      = d[5];
-        x[i * 8 + 6].d      = d[6];
-        x[i * 8 + 7].d      = d[7];
-    }
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_q4_0(&x[i * 8 + 0], 0);
-            dump_block_q4_0(&x[i * 8 + 1], 1);
-            dump_block_q4_0(&x[i * 8 + 2], 2);
-            dump_block_q4_0(&x[i * 8 + 3], 3);
-            dump_block_q4_0(&x[i * 8 + 4], 4);
-            dump_block_q4_0(&x[i * 8 + 5], 5);
-            dump_block_q4_0(&x[i * 8 + 6], 6);
-            dump_block_q4_0(&x[i * 8 + 7], 7);
-        }
-    }
-}
-
-static void init_row_q4x4x2(block_q4_0 * x, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-
-    // Init the quants such that they unpack into zeros
-    uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-    memset(qs, 8, sizeof(qs));
-
-    for (int i = 0; i < nb; i++) {
-        pack_q4_0_quants(&x[i * 8 + 0], qs, 0);
-        pack_q4_0_quants(&x[i * 8 + 1], qs, 1);
-        pack_q4_0_quants(&x[i * 8 + 2], qs, 2);
-        pack_q4_0_quants(&x[i * 8 + 3], qs, 3);
-        pack_q4_0_quants(&x[i * 8 + 4], qs, 4);
-        pack_q4_0_quants(&x[i * 8 + 5], qs, 5);
-        pack_q4_0_quants(&x[i * 8 + 6], qs, 6);
-        pack_q4_0_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    // Init the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        x[i * 8 + 0].d = 0;
-        x[i * 8 + 1].d = 0;
-        x[i * 8 + 2].d = 0;
-        x[i * 8 + 3].d = 0;
-        x[i * 8 + 4].d = 0;
-        x[i * 8 + 5].d = 0;
-        x[i * 8 + 6].d = 0;
-        x[i * 8 + 7].d = 0;
-    }
-}
-
-// repack q4_0 data into q4x4x2 tensor
-static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
-
-    // Ensure we don't try to read more data than is available in the source buffer 'data'
-    // or write more than the tensor can hold.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q4_0-q4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        repack_row_q4x4x2((uint8_t *) buf_rp, (const block_q4_0 *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
-    }
-
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        // re-init the row because we are potentially copying a partial row
-        init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]);
-
-        // Copy only the remaining bytes from the source.
-        memcpy(buf_pd, src, n_rem_bytes);
-
-        // Repack the entire buffer
-        repack_row_q4x4x2((uint8_t *) buf_rp, (const block_q4_0 *) buf_pd, t->ne[0]);
-
-        // Write only the corresponding remaining bytes to the destination tensor.
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
-// repack q4x4x2 tensor into q4_0 data
-static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
-
-    // Ensure we don't try to copy more data than the tensor actually contains.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q4x4x2-q4_0 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        unpack_row_q4x4x2((block_q4_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
-    }
-
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        // We still need to read and unpack the entire source row because quantization is block-based.
-        memcpy(buf_pd, src, row_size);
-        unpack_row_q4x4x2((block_q4_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
-
-        // But we only copy the remaining number of bytes to the destination.
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
 static void unpack_q4_1_quants(uint8_t * qs, const block_q4_1 * x, unsigned int bi) {
     static const int qk = QK4_1;
 
@@ -703,603 +414,19 @@ static void pack_q4_1_quants(block_q4_1 * x, const uint8_t * qs, unsigned int bi
     }
 }
 
-static void repack_row_q4_1x4x2(uint8_t * y, const block_q4_1 * x, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
-
-    const int dblk_size = 8 * 4;              // 8x (d, m) __fp16 = 32 bytes
-    const int qblk_size = qk / 2;             // int4 = 128 bytes
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
-
-    uint8_t * y_q = y + 0;                    // quants first
-    uint8_t * y_d = y + qrow_size;            // then scales/offsets
-
-    // Repack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-        unpack_q4_1_quants(qs, &x[i * 8 + 0], 0);
-        unpack_q4_1_quants(qs, &x[i * 8 + 1], 1);
-        unpack_q4_1_quants(qs, &x[i * 8 + 2], 2);
-        unpack_q4_1_quants(qs, &x[i * 8 + 3], 3);
-        unpack_q4_1_quants(qs, &x[i * 8 + 4], 4);
-        unpack_q4_1_quants(qs, &x[i * 8 + 5], 5);
-        unpack_q4_1_quants(qs, &x[i * 8 + 6], 6);
-        unpack_q4_1_quants(qs, &x[i * 8 + 7], 7);
-
-        bool partial = (nloe && i == nb-1);
-
-        uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
-        }
-    }
-
-    // Repack the scales and offsets
-    for (int i = 0; i < nb; i++) {
-        ggml_half * d_m = (ggml_half *) (y_d + i * dblk_size);
-        for (int j = 0; j < 8; j++) {
-            d_m[j * 2 + 0] = x[i * 8 + j].d;
-            d_m[j * 2 + 1] = x[i * 8 + j].m;
-        }
-    }
-}
-
-static void unpack_row_q4_1x4x2(block_q4_1 * x, const uint8_t * y, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
-
-    const int dblk_size = 8 * 4;              // 8x (d, m) __fp16 = 32 bytes
-    const int qblk_size = qk / 2;             // int4 = 128 bytes
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
-
-    const uint8_t * y_q = y + 0;              // quants first
-    const uint8_t * y_d = y + qrow_size;      // then scales/offsets
-
-    // Unpack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q4_0x4x2];
-        bool partial = (nloe && i == nb-1);
-
-        const uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            if (partial) {
-                qs[j*2+0] = q[j] & 0x0F;
-                qs[j*2+1] = q[j] >> 4;
-            } else {
-                qs[j+000] = q[j] & 0x0F;
-                qs[j+128] = q[j] >> 4;
-            }
-        }
-
-        pack_q4_1_quants(&x[i * 8 + 0], qs, 0);
-        pack_q4_1_quants(&x[i * 8 + 1], qs, 1);
-        pack_q4_1_quants(&x[i * 8 + 2], qs, 2);
-        pack_q4_1_quants(&x[i * 8 + 3], qs, 3);
-        pack_q4_1_quants(&x[i * 8 + 4], qs, 4);
-        pack_q4_1_quants(&x[i * 8 + 5], qs, 5);
-        pack_q4_1_quants(&x[i * 8 + 6], qs, 6);
-        pack_q4_1_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    // Unpack the scales and offsets
-    for (int i = 0; i < nb; i++) {
-        const ggml_half * d_m = (const ggml_half *) (y_d + i * dblk_size);
-        for (int j = 0; j < 8; j++) {
-            x[i * 8 + j].d = d_m[j * 2 + 0];
-            x[i * 8 + j].m = d_m[j * 2 + 1];
-        }
-    }
-}
-
-static void init_row_q4_1x4x2(block_q4_1 * x, int64_t k) {
-    static const int qk = QK_Q4_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-
-    uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-    memset(qs, 0, sizeof(qs));
-
-    for (int i = 0; i < nb; i++) {
-        pack_q4_1_quants(&x[i * 8 + 0], qs, 0);
-        pack_q4_1_quants(&x[i * 8 + 1], qs, 1);
-        pack_q4_1_quants(&x[i * 8 + 2], qs, 2);
-        pack_q4_1_quants(&x[i * 8 + 3], qs, 3);
-        pack_q4_1_quants(&x[i * 8 + 4], qs, 4);
-        pack_q4_1_quants(&x[i * 8 + 5], qs, 5);
-        pack_q4_1_quants(&x[i * 8 + 6], qs, 6);
-        pack_q4_1_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    for (int i = 0; i < nb; i++) {
-        for (int j = 0; j < 8; j++) {
-            x[i * 8 + j].d = 0;
-            x[i * 8 + j].m = 0;
-        }
-    }
-}
-
-static void repack_q4_1_q4x4x2(ggml_tensor * t, const void * data, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
-
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q4_1-q4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    init_row_q4_1x4x2((block_q4_1 *) buf_pd, t->ne[0]);
-
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        repack_row_q4_1x4x2((uint8_t *) buf_rp, (const block_q4_1 *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
-    }
-
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        init_row_q4_1x4x2((block_q4_1 *) buf_pd, t->ne[0]);
-        memcpy(buf_pd, src, n_rem_bytes);
-        repack_row_q4_1x4x2((uint8_t *) buf_rp, (const block_q4_1 *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
-static void repack_q4x4x2_q4_1(void * data, const ggml_tensor * t, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
-
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q4x4x2-q4_1 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    memset(buf_rp, 0, row_size_rp);  // clear-out padded buffer to make sure the tail is all zeros
-
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        memcpy(buf_rp, src, row_size);
-        unpack_row_q4_1x4x2((block_q4_1 *) buf_pd, (const uint8_t *) buf_rp, t->ne[0]);
-        memcpy(dst, buf_pd, row_size);
-    }
-
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        // We still need to read and unpack the entire source row because quantization is block-based.
-        memcpy(buf_rp, src, row_size);
-        unpack_row_q4_1x4x2((block_q4_1 *) buf_pd, (const uint8_t *) buf_rp, t->ne[0]);
-        memcpy(dst, buf_pd, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
-// ======== Q8x4x2 ====================
-static void dump_block_q8_0(const block_q8_0 * b, int i) {
-    HEX_VERBOSE("ggml-hex: repack q8_0 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, b->qs[0], b->qs[1], b->qs[2],
-                b->qs[3], b->qs[28], b->qs[29], b->qs[30], b->qs[31], GGML_FP16_TO_FP32(b->d));
-}
-
-static void dump_packed_block_q8x4x2(const uint8_t * v, unsigned int i, size_t k) {
-    static const int qk        = QK_Q8_0x4x2;
-    const int        dblk_size = 8 * 2;   // 8x __fp16
-    const int        qblk_size = qk;      // int8
-    const int        qrow_size = k;       // int8 (not padded)
-
-    const uint8_t * v_q = v + 0;          // quants first
-    const uint8_t * v_d = v + qrow_size;  // then scales
-
-    const uint8_t *   q = v_q + i * qblk_size;
-    const ggml_half * d = (const ggml_half *) (v_d + i * dblk_size);
-
-    HEX_VERBOSE("ggml-hex: repack q8x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
-                q[0], q[1], q[2], q[3], q[60], q[61], q[62], q[63], q[124], q[125], q[126], q[127],
-                GGML_FP16_TO_FP32(d[0]), GGML_FP16_TO_FP32(d[1]), GGML_FP16_TO_FP32(d[2]), GGML_FP16_TO_FP32(d[3]));
-
-    HEX_VERBOSE("ggml-hex: repack q8x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
-                i + 1, q[128], q[129], q[130], q[131], q[192], q[193], q[194], q[195], q[252], q[253], q[254], q[255],
-                GGML_FP16_TO_FP32(d[4]), GGML_FP16_TO_FP32(d[5]), GGML_FP16_TO_FP32(d[6]), GGML_FP16_TO_FP32(d[7]));
-}
-
-static void unpack_q8_0_quants(uint8_t * qs, const block_q8_0 * x, unsigned int bi) {
-    static const int qk = QK8_0;
-
-    for (unsigned int i = 0; i < qk; ++i) {
-        qs[bi * qk + i] = x->qs[i];
-    }
-}
-
-static void pack_q8_0_quants(block_q8_0 * x, const uint8_t * qs, unsigned int bi) {
-    static const int qk = QK8_0;
-
-    for (unsigned int i = 0; i < qk; ++i) {
-        x->qs[i] = qs[bi * qk + i];
-    }
-}
-
-static void repack_row_q8x4x2(uint8_t * y, const block_q8_0 * x, int64_t k) {
-    static const int qk = QK_Q8_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-
-    const int dblk_size = 8 * 2;              // 8x __fp16
-    const int qblk_size = qk;                 // int8
-    const int qrow_size = k;                  // int8 (not padded to blocks)
-
-    uint8_t * y_q = y + 0;                    // quants first
-    uint8_t * y_d = y + qrow_size;            // then scales
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_q8_0(&x[i * 8 + 0], 0);
-            dump_block_q8_0(&x[i * 8 + 1], 1);
-            dump_block_q8_0(&x[i * 8 + 2], 2);
-            dump_block_q8_0(&x[i * 8 + 3], 3);
-            dump_block_q8_0(&x[i * 8 + 4], 4);
-            dump_block_q8_0(&x[i * 8 + 5], 5);
-            dump_block_q8_0(&x[i * 8 + 6], 6);
-            dump_block_q8_0(&x[i * 8 + 7], 7);
-        }
-    }
-
-    // Repack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q8_0x4x2];  // unpacked quants
-
-        unpack_q8_0_quants(qs, &x[i * 8 + 0], 0);
-        unpack_q8_0_quants(qs, &x[i * 8 + 1], 1);
-        unpack_q8_0_quants(qs, &x[i * 8 + 2], 2);
-        unpack_q8_0_quants(qs, &x[i * 8 + 3], 3);
-        unpack_q8_0_quants(qs, &x[i * 8 + 4], 4);
-        unpack_q8_0_quants(qs, &x[i * 8 + 5], 5);
-        unpack_q8_0_quants(qs, &x[i * 8 + 6], 6);
-        unpack_q8_0_quants(qs, &x[i * 8 + 7], 7);
-
-        uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk; j++) {
-            q[j] = qs[j];
-        }
-    }
-
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Repack the scales
-        ggml_half * d = (ggml_half *) (y_d + i * dblk_size);
-        d[0]          = x[i * 8 + 0].d;
-        d[1]          = x[i * 8 + 1].d;
-        d[2]          = x[i * 8 + 2].d;
-        d[3]          = x[i * 8 + 3].d;
-        d[4]          = x[i * 8 + 4].d;
-        d[5]          = x[i * 8 + 5].d;
-        d[6]          = x[i * 8 + 6].d;
-        d[7]          = x[i * 8 + 7].d;
-    }
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_q8x4x2(y, i, k);
-        }
-    }
-}
-
-static void unpack_row_q8x4x2(block_q8_0 * x, const uint8_t * y, int64_t k) {
-    static const int qk = QK_Q8_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-
-    const int dblk_size = 8 * 2;              // 8x __fp16
-    const int qblk_size = qk;                 // int8
-    const int qrow_size = k;                  // int8 (not padded to blocks)
-
-    const uint8_t * y_q = y + 0;              // quants first
-    const uint8_t * y_d = y + qrow_size;      // then scales
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_q8x4x2(y, i, k);
-        }
-    }
-
-    // Unpack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_Q4_0x4x2];  // unpacked quants
-
-        const uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk; j++) {
-            qs[j] = q[j];
-        }
-
-        pack_q8_0_quants(&x[i * 8 + 0], qs, 0);
-        pack_q8_0_quants(&x[i * 8 + 1], qs, 1);
-        pack_q8_0_quants(&x[i * 8 + 2], qs, 2);
-        pack_q8_0_quants(&x[i * 8 + 3], qs, 3);
-        pack_q8_0_quants(&x[i * 8 + 4], qs, 4);
-        pack_q8_0_quants(&x[i * 8 + 5], qs, 5);
-        pack_q8_0_quants(&x[i * 8 + 6], qs, 6);
-        pack_q8_0_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size);
-        x[i * 8 + 0].d      = d[0];
-        x[i * 8 + 1].d      = d[1];
-        x[i * 8 + 2].d      = d[2];
-        x[i * 8 + 3].d      = d[3];
-        x[i * 8 + 4].d      = d[4];
-        x[i * 8 + 5].d      = d[5];
-        x[i * 8 + 6].d      = d[6];
-        x[i * 8 + 7].d      = d[7];
-    }
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_q8_0(&x[i * 8 + 0], 0);
-            dump_block_q8_0(&x[i * 8 + 1], 1);
-            dump_block_q8_0(&x[i * 8 + 2], 2);
-            dump_block_q8_0(&x[i * 8 + 3], 3);
-            dump_block_q8_0(&x[i * 8 + 4], 4);
-            dump_block_q8_0(&x[i * 8 + 5], 5);
-            dump_block_q8_0(&x[i * 8 + 6], 6);
-            dump_block_q8_0(&x[i * 8 + 7], 7);
-        }
-    }
-}
-
-static void init_row_q8x4x2(block_q8_0 * x, int64_t k) {
-    static const int qk = QK_Q8_0x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-
-    // Init the quants such that they unpack into zeros
-    uint8_t qs[QK_Q8_0x4x2];  // unpacked quants
-    memset(qs, 0, sizeof(qs));
-
-    for (int i = 0; i < nb; i++) {
-        pack_q8_0_quants(&x[i * 8 + 0], qs, 0);
-        pack_q8_0_quants(&x[i * 8 + 1], qs, 1);
-        pack_q8_0_quants(&x[i * 8 + 2], qs, 2);
-        pack_q8_0_quants(&x[i * 8 + 3], qs, 3);
-        pack_q8_0_quants(&x[i * 8 + 4], qs, 4);
-        pack_q8_0_quants(&x[i * 8 + 5], qs, 5);
-        pack_q8_0_quants(&x[i * 8 + 6], qs, 6);
-        pack_q8_0_quants(&x[i * 8 + 7], qs, 7);
-    }
-
-    // Init the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q8_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        x[i * 8 + 0].d = 0;
-        x[i * 8 + 1].d = 0;
-        x[i * 8 + 2].d = 0;
-        x[i * 8 + 3].d = 0;
-        x[i * 8 + 4].d = 0;
-        x[i * 8 + 5].d = 0;
-        x[i * 8 + 6].d = 0;
-        x[i * 8 + 7].d = 0;
-    }
-}
-
-// repack q8_0 data into q8x4x2 tensor
-static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size quants + scales)
-
-    // Ensure we don't try to read more data than is available in the source buffer 'data'
-    // or write more than the tensor can hold.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q8_0-q8x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        repack_row_q8x4x2((uint8_t *) buf_rp, (const block_q8_0 *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
-    }
-
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        // re-init the row because we are potentially copying a partial row
-        init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]);
-
-        // Copy only the remaining bytes from the source.
-        memcpy(buf_pd, src, n_rem_bytes);
-
-        // Repack the entire buffer
-        repack_row_q8x4x2((uint8_t *) buf_rp, (const block_q8_0 *) buf_pd, t->ne[0]);
-
-        // Write only the corresponding remaining bytes to the destination tensor.
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
-// repack q8x4x2 tensor into q8_0 data
-static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size) {
-    int64_t nrows = ggml_nrows(t);
-
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size quants + scales)
-
-    // Ensure we don't try to copy more data than the tensor actually contains.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
-
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
-
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
-
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-q8x4x2-q8_0 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
-                t->ne[0], nrows, row_size);
-
-    memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        unpack_row_q8x4x2((block_q8_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
-    }
-
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        // We still need to read and unpack the entire source row because quantization is block-based.
-        memcpy(buf_pd, src, row_size);
-        unpack_row_q8x4x2((block_q8_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
-
-        // But we only copy the remaining number of bytes to the destination.
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
-}
-
-// ======== MXFP4x4x2 ====================
-struct x2_mxfp4 {
-    int v[2];
-};
-
-static x2_mxfp4 unpack_mxfp4(uint8_t v) {
-    x2_mxfp4 x;
-    x.v[0] = kvalues_mxfp4[(v & 0x0f)];
-    x.v[1] = kvalues_mxfp4[(v >> 4)];
-    return x;
-}
-
-static void dump_block_mxfp4(const block_mxfp4 * b, int i) {
-    HEX_VERBOSE("ggml-hex: repack mxfp4 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, unpack_mxfp4(b->qs[0]).v[0],
-                unpack_mxfp4(b->qs[1]).v[0], unpack_mxfp4(b->qs[2]).v[0], unpack_mxfp4(b->qs[3]).v[0],
-                unpack_mxfp4(b->qs[12]).v[1], unpack_mxfp4(b->qs[13]).v[1], unpack_mxfp4(b->qs[14]).v[1],
-                unpack_mxfp4(b->qs[15]).v[1], GGML_E8M0_TO_FP32_HALF(b->e));
-}
-
-static void dump_packed_block_mxfp4x4x2(const uint8_t * v, unsigned int i, size_t k) {
-    static const int qk        = QK_MXFP4x4x2;
-    const int        eblk_size = 8 * 1;   // 8x E8M0
-    const int        qblk_size = qk / 2;  // int4
-    const int        qrow_size = k / 2;   // int4 (not padded)
-
-    const uint8_t * v_q = v + 0;          // quants first
-    const uint8_t * v_e = v + qrow_size;  // then scales
-
-    const uint8_t * q = v_q + i * qblk_size;
-    const uint8_t * e = (const uint8_t *) (v_e + i * eblk_size);
-
-    HEX_VERBOSE("ggml-hex: repack mxfp4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
-                unpack_mxfp4(q[0]).v[0], unpack_mxfp4(q[1]).v[0], unpack_mxfp4(q[2]).v[0], unpack_mxfp4(q[3]).v[0],
-                unpack_mxfp4(q[60]).v[0], unpack_mxfp4(q[61]).v[0], unpack_mxfp4(q[62]).v[0], unpack_mxfp4(q[63]).v[0],
-                unpack_mxfp4(q[124]).v[0], unpack_mxfp4(q[125]).v[0], unpack_mxfp4(q[126]).v[0],
-                unpack_mxfp4(q[127]).v[0], GGML_E8M0_TO_FP32_HALF(e[0]), GGML_E8M0_TO_FP32_HALF(e[1]),
-                GGML_E8M0_TO_FP32_HALF(e[2]), GGML_E8M0_TO_FP32_HALF(e[3]));
-
-    HEX_VERBOSE("ggml-hex: repack mxfp4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
-                i + 1, unpack_mxfp4(q[0]).v[1], unpack_mxfp4(q[1]).v[1], unpack_mxfp4(q[2]).v[1],
-                unpack_mxfp4(q[3]).v[1], unpack_mxfp4(q[60]).v[1], unpack_mxfp4(q[61]).v[1], unpack_mxfp4(q[62]).v[1],
-                unpack_mxfp4(q[63]).v[1], unpack_mxfp4(q[124]).v[1], unpack_mxfp4(q[125]).v[1],
-                unpack_mxfp4(q[126]).v[1], unpack_mxfp4(q[127]).v[1], GGML_E8M0_TO_FP32_HALF(e[4]),
-                GGML_E8M0_TO_FP32_HALF(e[5]), GGML_E8M0_TO_FP32_HALF(e[6]), GGML_E8M0_TO_FP32_HALF(e[7]));
-}
-
 static void unpack_mxfp4_quants(uint8_t * qs, const block_mxfp4 * x, unsigned int bi) {
     static const int qk = QK_MXFP4;
 
     for (unsigned int i = 0; i < qk / 2; ++i) {
-        const uint8_t x0         = (x->qs[i] & 0x0F);
-        const uint8_t x1         = (x->qs[i] >> 4);
+        const int x0             = (x->qs[i] & 0x0F);
+        const int x1             = (x->qs[i] >> 4);
         qs[bi * qk + i + 0]      = x0;
         qs[bi * qk + i + qk / 2] = x1;
     }
 }
 
 static void pack_mxfp4_quants(block_mxfp4 * x, const uint8_t * qs, unsigned int bi) {
-    static const int qk = QK4_0;
+    static const int qk = QK_MXFP4;
 
     for (unsigned int i = 0; i < qk / 2; ++i) {
         const uint8_t x0 = qs[bi * qk + i + 0];
@@ -1308,299 +435,419 @@ static void pack_mxfp4_quants(block_mxfp4 * x, const uint8_t * qs, unsigned int
     }
 }
 
-static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k) {
-    static const int qk = QK_MXFP4x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
+// repack q4_0 data into q4_0_tiled tensor
+static void repack_q4_0_tiled(ggml_tensor * t, const void * data, size_t size) {
+    const block_q4_0 * src_matrix = (const block_q4_0 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-    const int eblk_size = 8 * 1;              // 8x E8M0
-    const int qblk_size = qk / 2;             // int4
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q4_0;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
 
-    uint8_t * y_q = y + 0;                    // quants first
-    uint8_t * y_e = y + qrow_size;            // then scales
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            const block_q4_0 * src_expert = src_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
 
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_mxfp4(&x[i * 8 + 0], 0);
-            dump_block_mxfp4(&x[i * 8 + 1], 1);
-            dump_block_mxfp4(&x[i * 8 + 2], 2);
-            dump_block_mxfp4(&x[i * 8 + 3], 3);
-            dump_block_mxfp4(&x[i * 8 + 4], 4);
-            dump_block_mxfp4(&x[i * 8 + 5], 5);
-            dump_block_mxfp4(&x[i * 8 + 6], 6);
-            dump_block_mxfp4(&x[i * 8 + 7], 7);
-        }
-    }
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    uint8_t * tile_dst = matrix_dst + (ct * n_k_tiles + kt) * tile_size;
 
-    // Repack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_MXFP4x4x2];  // unpacked quants
+                    uint8_t tile_quants[32][32];
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            unpack_q4_0_quants(tile_quants[row], &src_expert[r * (ne0 / 32) + kt], 0);
+                        } else {
+                            memset(tile_quants[row], 8, 32);
+                        }
+                    }
 
-        unpack_mxfp4_quants(qs, &x[i * 8 + 0], 0);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 1], 1);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 2], 2);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 3], 3);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 4], 4);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 5], 5);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 6], 6);
-        unpack_mxfp4_quants(qs, &x[i * 8 + 7], 7);
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            tile_dst[cp * 32 + row] = (tile_quants[row][2 * cp + 1] << 4) | tile_quants[row][2 * cp];
+                        }
+                    }
 
-        bool partial = (nloe && i == nb-1);
-
-        uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
-        }
-    }
-
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Repack the scales
-        uint8_t * e = (uint8_t *) (y_e + i * eblk_size);
-        e[0]        = x[i * 8 + 0].e;
-        e[1]        = x[i * 8 + 1].e;
-        e[2]        = x[i * 8 + 2].e;
-        e[3]        = x[i * 8 + 3].e;
-        e[4]        = x[i * 8 + 4].e;
-        e[5]        = x[i * 8 + 5].e;
-        e[6]        = x[i * 8 + 6].e;
-        e[7]        = x[i * 8 + 7].e;
-    }
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_mxfp4x4x2(y, i, k);
-        }
-    }
-}
-
-static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k) {
-    static const int qk = QK_MXFP4x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
-    const int        nloe = k % qk;           // leftovers
-
-    const int eblk_size = 8 * 1;              // 8x E8M0
-    const int qblk_size = qk / 2;             // int4
-    const int qrow_size = k / 2;              // int4 (not padded to blocks)
-
-    const uint8_t * y_q = y + 0;              // quants first
-    const uint8_t * y_e = y + qrow_size;      // then scales
-
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_packed_block_mxfp4x4x2(y, i, k);
-        }
-    }
-
-    // Unpack the quants
-    for (int i = 0; i < nb; i++) {
-        uint8_t qs[QK_MXFP4x4x2];  // unpacked quants
-
-        bool partial = (nloe && i == nb-1);
-
-        const uint8_t * q = y_q + (i * qblk_size);
-        for (int j = 0; j < qk / 2; j++) {
-            if (partial) {
-                qs[j*2+0] = q[j] & 0xf;
-                qs[j*2+1] = q[j] >> 4;
-            } else {
-                qs[j+000] = q[j] & 0xf;
-                qs[j+128] = q[j] >> 4;
+                    ggml_half * scale_dst = (ggml_half *)(tile_dst + 512);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        scale_dst[row] = (r < ne1 && kt < ne0 / 32) ? src_expert[r * (ne0 / 32) + kt].d : 0;
+                    }
+                }
             }
         }
-
-        pack_mxfp4_quants(&x[i * 8 + 0], qs, 0);
-        pack_mxfp4_quants(&x[i * 8 + 1], qs, 1);
-        pack_mxfp4_quants(&x[i * 8 + 2], qs, 2);
-        pack_mxfp4_quants(&x[i * 8 + 3], qs, 3);
-        pack_mxfp4_quants(&x[i * 8 + 4], qs, 4);
-        pack_mxfp4_quants(&x[i * 8 + 5], qs, 5);
-        pack_mxfp4_quants(&x[i * 8 + 6], qs, 6);
-        pack_mxfp4_quants(&x[i * 8 + 7], qs, 7);
     }
+}
 
-    // Repack the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4_0x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        const uint8_t * e = (const uint8_t *) (y_e + i * eblk_size);
-        x[i * 8 + 0].e    = e[0];
-        x[i * 8 + 1].e    = e[1];
-        x[i * 8 + 2].e    = e[2];
-        x[i * 8 + 3].e    = e[3];
-        x[i * 8 + 4].e    = e[4];
-        x[i * 8 + 5].e    = e[5];
-        x[i * 8 + 6].e    = e[6];
-        x[i * 8 + 7].e    = e[7];
-    }
+// repack q4_0_tiled tensor into q4_0 data
+static void repack_tiled_q4_0(void * data, const ggml_tensor * t, size_t size) {
+    block_q4_0 * dst_matrix = (block_q4_0 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-    if (opt_verbose > 2) {
-        for (int i = 0; i < nb; i++) {
-            dump_block_mxfp4(&x[i * 8 + 0], 0);
-            dump_block_mxfp4(&x[i * 8 + 1], 1);
-            dump_block_mxfp4(&x[i * 8 + 2], 2);
-            dump_block_mxfp4(&x[i * 8 + 3], 3);
-            dump_block_mxfp4(&x[i * 8 + 4], 4);
-            dump_block_mxfp4(&x[i * 8 + 5], 5);
-            dump_block_mxfp4(&x[i * 8 + 6], 6);
-            dump_block_mxfp4(&x[i * 8 + 7], 7);
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q4_0;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            block_q4_0 * dst_expert = dst_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
+
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    const uint8_t * tile_src = matrix_src + (ct * n_k_tiles + kt) * tile_size;
+
+                    uint8_t tile_quants[32][32];
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            uint8_t val = tile_src[cp * 32 + row];
+                            tile_quants[row][2 * cp + 0] = val & 0x0F;
+                            tile_quants[row][2 * cp + 1] = val >> 4;
+                        }
+                    }
+
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            pack_q4_0_quants(&dst_expert[r * (ne0 / 32) + kt], tile_quants[row], 0);
+                        }
+                    }
+
+                    const ggml_half * scale_src = (const ggml_half *)(tile_src + 512);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            dst_expert[r * (ne0 / 32) + kt].d = scale_src[row];
+                        }
+                    }
+                }
+            }
         }
     }
 }
 
-static void init_row_mxfp4x4x2(block_mxfp4 * x, int64_t k) {
-    static const int qk = QK_MXFP4x4x2;
-    const int        nb = (k + qk - 1) / qk;  // number of blocks (padded)
+// repack q4_1 data into q4_1_tiled tensor
+static void repack_q4_1_tiled(ggml_tensor * t, const void * data, size_t size) {
+    const block_q4_1 * src_matrix = (const block_q4_1 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-    // Init the quants such that they unpack into zeros
-    uint8_t qs[QK_MXFP4x4x2];  // unpacked quants
-    memset(qs, 0, sizeof(qs));
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
 
-    for (int i = 0; i < nb; i++) {
-        pack_mxfp4_quants(&x[i * 8 + 0], qs, 0);
-        pack_mxfp4_quants(&x[i * 8 + 1], qs, 1);
-        pack_mxfp4_quants(&x[i * 8 + 2], qs, 2);
-        pack_mxfp4_quants(&x[i * 8 + 3], qs, 3);
-        pack_mxfp4_quants(&x[i * 8 + 4], qs, 4);
-        pack_mxfp4_quants(&x[i * 8 + 5], qs, 5);
-        pack_mxfp4_quants(&x[i * 8 + 6], qs, 6);
-        pack_mxfp4_quants(&x[i * 8 + 7], qs, 7);
-    }
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            const block_q4_1 * src_expert = src_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
 
-    // Init the scales
-    // Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2)
-    // the last block is truncated and overridden by the scales.
-    for (int i = 0; i < nb; i++) {
-        // Unpack the scales
-        x[i * 8 + 0].e = 0;
-        x[i * 8 + 1].e = 0;
-        x[i * 8 + 2].e = 0;
-        x[i * 8 + 3].e = 0;
-        x[i * 8 + 4].e = 0;
-        x[i * 8 + 5].e = 0;
-        x[i * 8 + 6].e = 0;
-        x[i * 8 + 7].e = 0;
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    uint8_t * tile_dst = matrix_dst + (ct * n_k_tiles + kt) * tile_size;
+
+                    uint8_t tile_quants[32][32];
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            unpack_q4_1_quants(tile_quants[row], &src_expert[r * (ne0 / 32) + kt], 0);
+                        } else {
+                            memset(tile_quants[row], 0, 32);
+                        }
+                    }
+
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            tile_dst[cp * 32 + row] = (tile_quants[row][2 * cp + 1] << 4) | tile_quants[row][2 * cp];
+                        }
+                    }
+
+                    ggml_half * scale_dst = (ggml_half *)(tile_dst + 512);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            scale_dst[2 * row + 0] = src_expert[r * (ne0 / 32) + kt].d;
+                            scale_dst[2 * row + 1] = src_expert[r * (ne0 / 32) + kt].m;
+                        } else {
+                            scale_dst[2 * row + 0] = 0;
+                            scale_dst[2 * row + 1] = 0;
+                        }
+                    }
+                }
+            }
+        }
     }
 }
 
-// repack mxfp4 data into mxfp4x4x2 tensor
-static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t size) {
-    int64_t nrows = ggml_nrows(t);
+// repack q4_1_tiled tensor into q4_1 data
+static void repack_tiled_q4_1(void * data, const ggml_tensor * t, size_t size) {
+    block_q4_1 * dst_matrix = (block_q4_1 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
 
-    // Ensure we don't try to read more data than is available in the source buffer 'data'
-    // or write more than the tensor can hold.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            block_q4_1 * dst_expert = dst_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
 
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    const uint8_t * tile_src = matrix_src + (ct * n_k_tiles + kt) * tile_size;
 
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
+                    uint8_t tile_quants[32][32];
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            uint8_t val = tile_src[cp * 32 + row];
+                            tile_quants[row][2 * cp + 0] = val & 0x0F;
+                            tile_quants[row][2 * cp + 1] = val >> 4;
+                        }
+                    }
 
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            pack_q4_1_quants(&dst_expert[r * (ne0 / 32) + kt], tile_quants[row], 0);
+                        }
+                    }
 
-    HEX_VERBOSE("ggml-hex: repack-mxfp4-mxfp4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data,
-                size, t->ne[0], nrows, row_size);
-
-    init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        repack_row_mxfp4x4x2((uint8_t *) buf_rp, (const block_mxfp4 *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
+                    const ggml_half * scale_src = (const ggml_half *)(tile_src + 512);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            dst_expert[r * (ne0 / 32) + kt].d = scale_src[2 * row];
+                            dst_expert[r * (ne0 / 32) + kt].m = scale_src[2 * row + 1];
+                        }
+                    }
+                }
+            }
+        }
     }
-
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
-
-        // re-init the row because we are potentially copying a partial row
-        init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]);
-
-        // Copy only the remaining bytes from the source.
-        memcpy(buf_pd, src, n_rem_bytes);
-
-        // Repack the entire buffer (partial data + zero padding).
-        repack_row_mxfp4x4x2((uint8_t *) buf_rp, (const block_mxfp4 *) buf_pd, t->ne[0]);
-
-        // Write only the corresponding remaining bytes to the destination tensor.
-        memcpy(dst, buf_rp, n_rem_bytes);
-    }
-
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
 }
 
-// repack mxfp4x4x2 tensor into mxfp4 data
-static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t size) {
-    int64_t nrows = ggml_nrows(t);
+// repack q8_0 data into q8_0_tiled tensor
+static void repack_q8_0_tiled(ggml_tensor * t, const void * data, size_t size) {
+    const block_q8_0 * src_matrix = (const block_q8_0 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-    size_t row_size    = ggml_row_size(t->type, t->ne[0]);
-    size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
-    size_t row_size_rp = row_size_pd;  // scratch must hold one full padded tile (qblk_size/2 quants + scales)
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q8_0;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
 
-    // Ensure we don't try to copy more data than the tensor actually contains.
-    const size_t total_tensor_size = (size_t)nrows * row_size;
-    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            const block_q8_0 * src_expert = src_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
 
-    // Calculate how many full rows and how many remaining bytes we need to process.
-    const int64_t n_full_rows = n_bytes_to_copy / row_size;
-    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    uint8_t * tile_dst = matrix_dst + (ct * n_k_tiles + kt) * tile_size;
 
-    void * buf_pd = ggml_aligned_malloc(row_size_pd);
-    GGML_ASSERT(buf_pd != NULL);
+                    for (int cp = 0; cp < 16; cp++) {
+                        int col0 = cp * 2;
+                        int col1 = col0 + 1;
+                        for (int row = 0; row < 32; row++) {
+                            int64_t r = ct * 32 + row;
+                            const block_q8_0 * b = (r < ne1 && kt < ne0 / 32) ? &src_expert[r * (ne0 / 32) + kt] : NULL;
+                            tile_dst[cp * 64 + 2 * row + 0] = b ? b->qs[col0] : 0;
+                            tile_dst[cp * 64 + 2 * row + 1] = b ? b->qs[col1] : 0;
+                        }
+                    }
 
-    void * buf_rp = ggml_aligned_malloc(row_size_rp);
-    GGML_ASSERT(buf_rp != NULL);
-
-    HEX_VERBOSE("ggml-hex: repack-mxfp4x4x2-mxfp4 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data,
-                size, t->ne[0], nrows, row_size);
-
-    memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
-
-    // 1. Process all the full rows
-    for (int64_t i = 0; i < n_full_rows; i++) {
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
-
-        memcpy(buf_pd, src, row_size);
-        unpack_row_mxfp4x4x2((block_mxfp4 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
-        memcpy(dst, buf_rp, row_size);
+                    ggml_half * scale_dst = (ggml_half *)(tile_dst + 1024);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        scale_dst[row] = (r < ne1 && kt < ne0 / 32) ? src_expert[r * (ne0 / 32) + kt].d : 0;
+                    }
+                }
+            }
+        }
     }
+}
 
-    // 2. Process the final, potentially partial, row
-    if (n_rem_bytes > 0) {
-        const int64_t i = n_full_rows;
-        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
-        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+// repack q8_0_tiled tensor into q8_0 data
+static void repack_tiled_q8_0(void * data, const ggml_tensor * t, size_t size) {
+    block_q8_0 * dst_matrix = (block_q8_0 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
 
-        // We still need to read and unpack the entire source row because the format is block-based.
-        memcpy(buf_pd, src, row_size);
-        unpack_row_mxfp4x4x2((block_mxfp4 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_Q8_0;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
 
-        // But we only copy the remaining number of bytes to the destination to respect the size limit.
-        memcpy(dst, buf_rp, n_rem_bytes);
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            block_q8_0 * dst_expert = dst_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
+
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    const uint8_t * tile_src = matrix_src + (ct * n_k_tiles + kt) * tile_size;
+
+                    for (int cp = 0; cp < 16; cp++) {
+                        int col0 = cp * 2;
+                        int col1 = col0 + 1;
+                        for (int row = 0; row < 32; row++) {
+                            int64_t r = ct * 32 + row;
+                            if (r < ne1 && kt < ne0 / 32) {
+                                block_q8_0 & b = dst_expert[r * (ne0 / 32) + kt];
+                                b.qs[col0] = tile_src[cp * 64 + 2 * row + 0];
+                                b.qs[col1] = tile_src[cp * 64 + 2 * row + 1];
+                            }
+                        }
+                    }
+
+                    const ggml_half * scale_src = (const ggml_half *)(tile_src + 1024);
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            dst_expert[r * (ne0 / 32) + kt].d = scale_src[row];
+                        }
+                    }
+                }
+            }
+        }
     }
+}
 
-    ggml_aligned_free(buf_pd, row_size_pd);
-    ggml_aligned_free(buf_rp, row_size_rp);
+// repack mxfp4 data into mxfp4_tiled tensor
+static void repack_mxfp4_tiled(ggml_tensor * t, const void * data, size_t size) {
+    const block_mxfp4 * src_matrix = (const block_mxfp4 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
+
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_MXFP4;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            const block_mxfp4 * src_expert = src_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            uint8_t * matrix_dst = (uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
+
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    uint8_t * tile_dst = matrix_dst + (ct * n_k_tiles + kt) * tile_size;
+
+                    uint8_t tile_quants[32][32];
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            unpack_mxfp4_quants(tile_quants[row], &src_expert[r * (ne0 / 32) + kt], 0);
+                        } else {
+                            memset(tile_quants[row], 0, 32);
+                        }
+                    }
+
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            tile_dst[cp * 32 + row] = (tile_quants[row][2 * cp + 1] << 4) | tile_quants[row][2 * cp];
+                        }
+                    }
+
+                    uint8_t * scale_dst = tile_dst + 512;
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        scale_dst[row] = (r < ne1 && kt < ne0 / 32) ? src_expert[r * (ne0 / 32) + kt].e : 0;
+                    }
+                }
+            }
+        }
+    }
+}
+
+// repack mxfp4_tiled tensor into mxfp4 data
+static void repack_tiled_mxfp4(void * data, const ggml_tensor * t, size_t size) {
+    block_mxfp4 * dst_matrix = (block_mxfp4 *) data;
+    int64_t ne0 = t->ne[0];
+    int64_t ne1 = t->ne[1];
+    int64_t ne2 = t->ne[2];
+    int64_t ne3 = t->ne[3];
+    int64_t ne0_padded = hex_round_up(ne0, 32);
+    int64_t ne1_padded = hex_round_up(ne1, 32);
+
+    int n_col_tiles = ne1_padded / 32;
+    int n_k_tiles = ne0_padded / 32;
+    const size_t tile_size = HTP_MM_WEIGHT_TILE_SIZE_MXFP4;
+    const size_t matrix_size = n_col_tiles * n_k_tiles * tile_size;
+
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = 0; i2 < ne2; i2++) {
+            block_mxfp4 * dst_expert = dst_matrix + (i3 * ne2 + i2) * (ne1 * (ne0 / 32));
+            const uint8_t * matrix_src = (const uint8_t *) t->data + (i3 * ne2 + i2) * matrix_size;
+
+            for (int ct = 0; ct < n_col_tiles; ct++) {
+                for (int kt = 0; kt < n_k_tiles; kt++) {
+                    const uint8_t * tile_src = matrix_src + (ct * n_k_tiles + kt) * tile_size;
+
+                    uint8_t tile_quants[32][32];
+                    for (int cp = 0; cp < 16; cp++) {
+                        for (int row = 0; row < 32; row++) {
+                            uint8_t val = tile_src[cp * 32 + row];
+                            tile_quants[row][2 * cp + 0] = val & 0x0F;
+                            tile_quants[row][2 * cp + 1] = val >> 4;
+                        }
+                    }
+
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            pack_mxfp4_quants(&dst_expert[r * (ne0 / 32) + kt], tile_quants[row], 0);
+                        }
+                    }
+
+                    const uint8_t * scale_src = tile_src + 512;
+                    for (int row = 0; row < 32; row++) {
+                        int64_t r = ct * 32 + row;
+                        if (r < ne1 && kt < ne0 / 32) {
+                            dst_expert[r * (ne0 / 32) + kt].e = scale_src[row];
+                        }
+                    }
+                }
+            }
+        }
+    }
 }
 
 static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
@@ -1617,32 +864,32 @@ static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
         case GGML_TYPE_Q4_0:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q4_0_q4x4x2(tensor, data, size);
+            repack_q4_0_tiled(tensor, data, size);
             break;
 
         case GGML_TYPE_Q4_1:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q4_1_q4x4x2(tensor, data, size);
+            repack_q4_1_tiled(tensor, data, size);
             break;
 
         case GGML_TYPE_Q8_0:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q8_0_q8x4x2(tensor, data, size);
+            repack_q8_0_tiled(tensor, data, size);
             break;
 
         case GGML_TYPE_IQ4_NL:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             // IQ4_NL has identical block layout to Q4_0 (ggml_half d + uint8_t qs[16])
-            repack_q4_0_q4x4x2(tensor, data, size);
+            repack_q4_0_tiled(tensor, data, size);
             break;
 
         case GGML_TYPE_MXFP4:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_mxfp4_mxfp4x4x2(tensor, data, size);
+            repack_mxfp4_tiled(tensor, data, size);
             break;
 
         default:
@@ -1665,31 +912,31 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
         case GGML_TYPE_Q4_0:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q4x4x2_q4_0(data, tensor, size);
+            repack_tiled_q4_0(data, tensor, size);
             break;
 
         case GGML_TYPE_Q4_1:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q4x4x2_q4_1(data, tensor, size);
+            repack_tiled_q4_1(data, tensor, size);
             break;
 
         case GGML_TYPE_Q8_0:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q8x4x2_q8_0(data, tensor, size);
+            repack_tiled_q8_0(data, tensor, size);
             break;
 
         case GGML_TYPE_IQ4_NL:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_q4x4x2_q4_0(data, tensor, size);
+            repack_tiled_q4_0(data, tensor, size);
             break;
 
         case GGML_TYPE_MXFP4:
             GGML_ASSERT(offset == 0);
             GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
-            repack_mxfp4x4x2_mxfp4(data, tensor, size);
+            repack_tiled_mxfp4(data, tensor, size);
             break;
 
         default:
@@ -1767,12 +1014,19 @@ static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer
 }
 
 static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * t) {
+    if (t->type == GGML_TYPE_Q4_0 || t->type == GGML_TYPE_Q4_1 || t->type == GGML_TYPE_Q8_0 || t->type == GGML_TYPE_IQ4_NL || t->type == GGML_TYPE_MXFP4) {
+        int64_t ne0 = hex_round_up(t->ne[0], 32);
+        int64_t ne1 = hex_round_up(t->ne[1], 32);
+        int64_t ne2 = t->ne[2];
+        int64_t ne3 = t->ne[3];
+        return ggml_row_size(t->type, ne0) * ne1 * ne2 * ne3;
+    }
     return ggml_nbytes(t);
 }
 
 static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
-    return opt_mbuf; // typically 1GB per buffer
-    GGML_UNUSED(buffer_type);
+    auto * context = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context);
+    return context->sess->max_bufsize;
 }
 
 static bool ggml_backend_hexagon_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
@@ -1803,6 +1057,17 @@ static ggml_backend_buffer_type_i ggml_backend_hexagon_repack_buffer_type_interf
     /* .is_host          = */ ggml_backend_hexagon_repack_buffer_type_is_host,
 };
 
+static bool ggml_backend_buffer_is_hexagon(const struct ggml_backend_buffer * b) {
+    return b->buft->iface.get_alignment == ggml_backend_hexagon_buffer_type_get_alignment;
+}
+
+static inline bool ggml_backend_buffer_is_hexagon_repack(const struct ggml_backend_buffer * b) {
+    if (!opt_hostbuf) {
+        return ggml_backend_buffer_is_hexagon(b);
+    }
+    return b->buft->iface.alloc_buffer == ggml_backend_hexagon_repack_buffer_type_alloc_buffer;
+}
+
 struct ggml_hexagon_opbatch {
     ggml_hexagon_session*            sess;
 
@@ -1883,14 +1148,25 @@ struct ggml_hexagon_opbatch {
 
         b_vmem += b.size;
 
-        HEX_VERBOSE("ggml-hex: add-buffer #%u : fd %d base %p size %zu : vmem %zu\n", bi, b.fd, (void*) sbuf->base, (size_t) b.size, b_vmem);
+        HEX_VERBOSE("ggml-hex: %s add-buffer #%u : fd %d base %p size %zu : vmem %zu\n", sess->c_name(), bi, b.fd, (void*) sbuf->base, (size_t) b.size, b_vmem);
 
         return bi;
     }
 
     bool same_shape(const htp_tensor * h, const ggml_tensor * t) const {
-        return (h->ne[0] == t->ne[0]) && (h->ne[1] == t->ne[1]) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) &&
-               (h->nb[0] == t->nb[0]) && (h->nb[1] == t->nb[1]) && (h->nb[2] == t->nb[2]) && (h->nb[3] == t->nb[3]);
+        int64_t ne0 = t->ne[0];
+        int64_t ne1 = t->ne[1];
+        const bool is_repack = ggml_backend_buffer_is_hexagon_repack(t->buffer) && ggml_hexagon_is_repack_type(t->type);
+        if (is_repack) {
+            ne0 = hex_round_up(ne0, 32);
+            ne1 = hex_round_up(ne1, 32);
+        }
+        int64_t nb1 = is_repack ? ggml_row_size(t->type, ne0) : t->nb[1];
+        int64_t nb2 = is_repack ? nb1 * ne1 : t->nb[2];
+        int64_t nb3 = is_repack ? nb2 * t->ne[2] : t->nb[3];
+
+        return (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) &&
+               (h->nb[0] == t->nb[0]) && (h->nb[1] == nb1) && (h->nb[2] == nb2) && (h->nb[3] == nb3);
     }
 
     // add tensor and return its index
@@ -1921,19 +1197,35 @@ struct ggml_hexagon_opbatch {
         htp_tensor &h = h_tens[ti];
         h.bi    = add_buffer(sbuf);
         h.data  = t_offset;
-        h.size  = t_size;
         h.type  = t->type;
-        h.ne[0] = t->ne[0]; h.ne[1] = t->ne[1]; h.ne[2] = t->ne[2]; h.ne[3] = t->ne[3];
-        h.nb[0] = t->nb[0]; h.nb[1] = t->nb[1]; h.nb[2] = t->nb[2]; h.nb[3] = t->nb[3];
+
+        const bool is_repack = ggml_backend_buffer_is_hexagon_repack(t->buffer) && ggml_hexagon_is_repack_type(t->type);
+        if (is_repack) {
+            h.ne[0] = hex_round_up(t->ne[0], 32);
+            h.ne[1] = hex_round_up(t->ne[1], 32);
+            h.ne[2] = t->ne[2];
+            h.ne[3] = t->ne[3];
+
+            h.nb[0] = t->nb[0];
+            h.nb[1] = ggml_row_size(t->type, h.ne[0]);
+            h.nb[2] = h.nb[1] * h.ne[1];
+            h.nb[3] = h.nb[2] * h.ne[2];
+            h.size  = h.nb[3] * h.ne[3];
+            t_size  = h.size;
+        } else {
+            h.size  = t_size;
+            h.ne[0] = t->ne[0]; h.ne[1] = t->ne[1]; h.ne[2] = t->ne[2]; h.ne[3] = t->ne[3];
+            h.nb[0] = t->nb[0]; h.nb[1] = t->nb[1]; h.nb[2] = t->nb[2]; h.nb[3] = t->nb[3];
+        }
 
         h.flags = 0;
         if (ggml_backend_buffer_get_usage(t->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
             h.flags |= HTP_TENSOR_COMPUTE;
         }
 
-        HEX_VERBOSE("ggml-hex: add-tensor #%u %s : bi %d data %p offset %zu size %zu flags 0x%x : %zu:%zu:%zu:%zu\n",
+        HEX_VERBOSE("ggml-hex: %s add-tensor #%u %s : bi %d data %p offset %zu size %zu flags 0x%x : %zu:%zu:%zu:%zu\n", sess->c_name(),
                 ti, t->name, h.bi, (void*) t->data, (size_t) t_offset, t_size, h.flags,
-                (size_t) t->ne[0], (size_t) t->ne[1], (size_t) t->ne[2], (size_t) t->ne[3]);
+                (size_t) h.ne[0], (size_t) h.ne[1], (size_t) h.ne[2], (size_t) h.ne[3]);
 
         return ti;
     }
@@ -1962,7 +1254,9 @@ struct ggml_hexagon_opbatch {
         for (const auto * src : node.get_inputs()) {
             fit_tensor(src);
         }
-        fit_tensor(node.dst());
+        for (const auto * output : node.get_outputs()) {
+            fit_tensor(output);
+        }
 
         if ((extra_bufs + n_bufs) > n_bufs_max) return false;
         if ((extra_tens + n_tens) > n_tens_max) return false;
@@ -1981,7 +1275,8 @@ struct ggml_hexagon_opbatch {
         ops[n] = node;
 
         htp_op_desc &o = h_ops[n];
-        memcpy(&o.params, &node.node->op_params, sizeof(node.node->op_params));
+        memcpy(o.params,        node.node->op_params, sizeof(node.node->op_params));
+        memcpy(o.kernel_params, node.kernel_params,   sizeof(o.kernel_params));
         o.opcode = node.opcode;
         o.flags  = 0;
 
@@ -1989,13 +1284,17 @@ struct ggml_hexagon_opbatch {
             o.flags |= HTP_OPFLAGS_SKIP_COMPUTE;
         }
 
-        ggml_hexagon_dump_op_exec(sess->c_name(), node, o.flags);
+        ggml_hexagon_dump_op_exec(sess->c_name(), ops[n], o.flags);
 
         auto inputs = node.get_inputs();
         for (unsigned int i=0; i < HTP_OP_MAX_INPUTS; i++) {
-            o.src[i] = (i < inputs.size() && inputs[i]) ? add_tensor(inputs[i]) : 0xffff;
+            o.src[i] = (i < inputs.size() && inputs[i])   ? add_tensor(inputs[i]) : 0xffff;
+        }
+
+        auto outputs = node.get_outputs();
+        for (unsigned int i=0; i < HTP_OP_MAX_OUTPUTS; i++) {
+            o.dst[i] = (i < outputs.size() && outputs[i]) ? add_tensor(outputs[i]) : 0xffff;
         }
-        o.dst = add_tensor(node.dst());
     }
 };
 
@@ -2006,14 +1305,14 @@ struct ggml_hexagon_opqueue {
 
     using opvec = std::vector<htp_opnode>;
 
-    std::queue<unsigned int>    done;       // completed batch ids
-    std::vector<opvec>          op_cache;   // per batch op cache
-    std::vector<uint64_t>       start_usec; // per batch start time
+    std::queue<unsigned int>    done;           // completed batch ids
+    std::vector<opvec>          op_cache;       // per batch op cache
+    std::vector<uint64_t>       start_usec;     // per batch start time
 
     ggml_hexagon_opqueue(ggml_hexagon_session *sess, size_t batch_size, size_t depth) {
         size_t n_bufs    = HTP_OP_MAX_BUFS;
         size_t n_ops     = batch_size;
-        size_t n_tensors = n_ops + n_ops * HTP_OP_MAX_INPUTS;
+        size_t n_tensors = n_ops * HTP_OP_MAX_OUTPUTS + n_ops * HTP_OP_MAX_INPUTS;
 
         size_t tr_size = 0;
         if (opt_profile == 3) {
@@ -2200,7 +1499,7 @@ struct ggml_hexagon_opqueue {
 
             char evt_str[256] = "";
             if (opt_profile == 3) {
-                sprintf(evt_str, " evt [%u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u]",
+                snprintf(evt_str, sizeof(evt_str), " evt [%u,%u,%u,%u,%u,%u,%u,%u,%u,%u,%u]",
                         rsp.n_traces[0], rsp.n_traces[1], rsp.n_traces[2], rsp.n_traces[3],
                         rsp.n_traces[4], rsp.n_traces[5], rsp.n_traces[6], rsp.n_traces[7],
                         rsp.n_traces[8], rsp.n_traces[9], rsp.n_traces[10]);
@@ -2224,6 +1523,7 @@ void ggml_hexagon_session::flush_pending(bool all) {
 
         // Read response packet from queue
         const uint32_t timeo = opt_oppoll ? 0 : DSPQUEUE_TIMEOUT;
+
         int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, timeo);
         if (err == AEE_EEXPIRED) {
             continue;
@@ -2404,6 +1704,31 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
 
     this->valid_handle = true;
 
+    // Query HW info and resolve session options
+    this->max_bufsize = opt_mbuf;
+    {
+        unsigned int hw_n_threads = 0;
+        unsigned int hw_n_hvx     = 0;
+        unsigned int hw_n_hmx     = 0;
+        unsigned long long hw_vtcm_size = 0;
+        int hw_err = htp_iface_hwinfo(this->handle, &hw_n_threads, &hw_n_hvx, &hw_n_hmx, &hw_vtcm_size);
+        if (hw_err == 0) {
+            this->n_threads = opt_nhvx > 0 ? (uint32_t)opt_nhvx : (uint32_t)hw_n_threads;
+            this->n_hvx     = opt_nhvx > 0 ? (uint32_t)opt_nhvx : (uint32_t)hw_n_hvx;
+            this->n_hmx     = (opt_nhmx != 0) ? (uint32_t)hw_n_hmx : 0;
+            this->vtcm_size = (uint64_t)hw_vtcm_size;
+            GGML_LOG_INFO("ggml-hex: %s hwinfo: threads %u, hvx %u, hmx %u, vtcm %llu MB\n",
+                          this->c_name(), this->n_threads, this->n_hvx, this->n_hmx,
+                          (unsigned long long)(this->vtcm_size / (1024 * 1024)));
+        } else {
+            GGML_LOG_WARN("ggml-hex: %s failed to query hwinfo (0x%x), using defaults\n", this->c_name(), hw_err);
+            this->n_threads = opt_nhvx > 0 ? (uint32_t)opt_nhvx : 8;
+            this->n_hvx     = opt_nhvx > 0 ? (uint32_t)opt_nhvx : 8;
+            this->n_hmx     = (opt_nhmx != 0) ? 1 : 0;
+            this->vtcm_size = 8 * 1024 * 1024;
+        }
+    }
+
     // Enable FastRPC QoS mode
     {
         struct remote_rpc_control_latency l;
@@ -2468,11 +1793,12 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
         opt_vmem = ggml_hexagon_measure_max_vmem(this);
         GGML_LOG_INFO("ggml-hex: %s measured max vmem %zu\n", this->c_name(), opt_vmem);
     }
+    this->max_vmem = opt_vmem;
 
-    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch, opt_vmem);
+    this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch, this->max_vmem);
 
     // Start dspqueue/opbatch processing
-    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx, opt_vmem);
+    err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_nhmx, this->max_vmem);
     if (err != 0) {
         GGML_LOG_ERROR("ggml-hex: %s failed to start session: 0x%08x\n", this->c_name(), (unsigned) err);
         throw std::runtime_error("ggml-hex: iface start failed (see log for details)");
@@ -2553,16 +1879,6 @@ ggml_hexagon_session::~ggml_hexagon_session() noexcept(true) {
 
 // ** backend interface
 
-static bool ggml_backend_buffer_is_hexagon(const struct ggml_backend_buffer * b) {
-    return b->buft->iface.get_alignment == ggml_backend_hexagon_buffer_type_get_alignment;
-}
-
-static inline bool ggml_backend_buffer_is_hexagon_repack(const struct ggml_backend_buffer * b) {
-    if (!opt_hostbuf) {
-        return ggml_backend_buffer_is_hexagon(b);
-    }
-    return b->buft->iface.alloc_buffer == ggml_backend_hexagon_repack_buffer_type_alloc_buffer;
-}
 
 static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
     const struct ggml_tensor * src0 = op->src[0];
@@ -2653,6 +1969,640 @@ static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_ses
     return true;
 }
 
+static bool ggml_hexagon_matmul_is_hmx_eligible(
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * dst,
+    int ne01_padded,
+    bool is_matmul_id,
+    bool is_batched
+) {
+    const int ne00  = src0->ne[0];
+    const int ne11  = src1->ne[1];
+    const int ne12  = src1->ne[2];
+    const int wtype = src0->type;
+
+    // HMX weight tile requires N to be 32-aligned.
+    if (ne01_padded % 32 != 0) {
+        return false;
+    }
+
+    // HMX supports F16, F32, and repack quantized types.
+    if (!ggml_hexagon_is_hmx_weight_type((ggml_type) wtype)) {
+        return false;
+    }
+
+    // HMX paths require K aligned to 32.
+    if (ne00 % 32 != 0) {
+        return false;
+    }
+
+    // Quantized HMX kernels only handle flat 2D matmul (or matmul_id wrapping flat 2D matmuls).
+    if (!is_matmul_id && is_batched && wtype != GGML_TYPE_F16) {
+        return false;
+    }
+
+    // HMX assumes contiguous row-major layout.
+    if (src0->nb[0] > src0->nb[1] || src1->nb[0] > src1->nb[1]) {
+        return false;
+    }
+
+    // M alignment: Use HMX when M > HTP_MM_HMX_MIN_NROWS
+    const int m = is_matmul_id ? ne12 : ne11;
+    if (m <= HTP_MM_HMX_MIN_NROWS) {
+        return false;
+    }
+
+    return true;
+}
+
+static bool ggml_hexagon_precompute_hmx_mm_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * dst,
+    int wtype,
+    int ne00_padded,
+    int ne01_padded,
+    int ne02,
+    int ne11,
+    int ne12,
+    int ne11_padded,
+    bool is_matmul_id,
+    bool is_batched,
+    size_t vtcm_budget,
+    struct htp_mm_kernel_params * kparams
+) {
+    const int aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+    const bool pipeline = is_matmul_id ? false : htp_mm_hmx_pipeline(ne11);
+    const int n_threads = (int)sess->n_threads;
+    const int ne10 = src1->ne[0];
+
+    const bool is_batched_val = is_matmul_id ? false : is_batched;
+    const int group_size = (ne02 > 0 ? ne12 / ne02 : 1);
+
+    size_t m_chunk = 0;
+    size_t n_chunk = 0;
+    size_t vtcm_size = 0;
+    bool use_grouped = false;
+    int act_threads_selected = 0;
+
+    if (is_batched_val && wtype == GGML_TYPE_F16 && group_size > 1) {
+        // Try grouped path first
+        const bool use_dma_activation = (src1->nb[1]/sizeof(float) > (size_t)ne00_padded);
+        size_t best_mblocks = SIZE_MAX;
+        int best_act_threads = 0;
+        size_t best_m_chunk = 0;
+        size_t best_n_chunk = 0;
+        size_t best_vtcm_size = 0;
+
+        int act_threads = n_threads;
+        while (act_threads >= 1) {
+            const size_t f32_scratch_size = use_dma_activation ? hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE) : 0;
+            size_t group_overhead = 256 + f32_scratch_size;
+            size_t group_size_per_n, group_size_per_m, group_size_per_mn;
+            htp_mm_hmx_get_batched_chunk_costs(ne00_padded, group_size, &group_size_per_n, &group_size_per_m, &group_size_per_mn);
+
+            size_t m_chunk_candidate = 0;
+            size_t n_chunk_candidate = 0;
+            size_t vtcm_size_candidate = 0;
+
+            if (htp_mm_hmx_compute_chunks(vtcm_budget, group_overhead, group_size_per_n, group_size_per_m, group_size_per_mn, hex_align_up(ne11, 32), ne01_padded,
+                                   (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) ne11 * HTP_MM_HMX_COST_A_CONVERT,
+                                   &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) {
+                size_t exact_size = htp_mm_hmx_get_batched_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, group_size, use_dma_activation, pipeline, act_threads);
+                if (exact_size <= vtcm_budget) {
+                    size_t mblocks = ((size_t) ne11 + m_chunk_candidate - 1) / m_chunk_candidate;
+                    if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) {
+                        best_mblocks = mblocks;
+                        best_act_threads = act_threads;
+                        best_m_chunk = m_chunk_candidate;
+                        best_n_chunk = n_chunk_candidate;
+                        best_vtcm_size = exact_size;
+                    }
+                }
+            }
+            if (act_threads == 1) {
+                act_threads = 0;
+            } else {
+                act_threads /= 2;
+            }
+        }
+
+        if (best_act_threads > 0) {
+            m_chunk = best_m_chunk;
+            n_chunk = best_n_chunk;
+            vtcm_size = best_vtcm_size;
+            act_threads_selected = best_act_threads;
+            use_grouped = true;
+        }
+    }
+
+    if (!use_grouped) {
+        // Fallback to simple 2D path (group_size = 1)
+        size_t best_mblocks = SIZE_MAX;
+        int best_act_threads = 0;
+        size_t best_m_chunk = 0;
+        size_t best_n_chunk = 0;
+        size_t best_vtcm_size = 0;
+
+        // For MUL_MAT_ID the kernel runs one 2D matmul per expert, with M equal to the number of rows routed to that expert.
+        // A single expert can receive up to all routed rows (dst->ne[1]*dst->ne[2] = n_expert_used*n_tokens), so size the chunk
+        // search for that upper bound rather than ne12 (token positions only).
+        // We recompute m_chunk per expert against the actual count in the NPU kernel.
+        const int m_id_rows    = (int) ((size_t) dst->ne[1] * dst->ne[2]);
+        const int m_for_chunks = is_matmul_id ? hex_align_up(m_id_rows, 32) : ne11_padded;
+        const int m_for_cost   = is_matmul_id ? m_id_rows : ne11;
+
+        int act_threads = n_threads;
+        while (act_threads >= 1) {
+            const size_t act_f32_size = is_matmul_id ? 0 : hex_align_up(act_threads * HTP_MM_DMA_ACT_MULTIPLIER * ne00_padded * sizeof(float), HTP_MM_HMX_TILE_SIZE);
+            size_t simple_2d_overhead = 256 + act_f32_size;
+            size_t simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn;
+            htp_mm_hmx_get_2d_chunk_costs(wtype, ne00_padded, pipeline, aligned_tile_size, &simple_2d_size_per_n, &simple_2d_size_per_m, &simple_2d_size_per_mn);
+
+            size_t m_chunk_candidate = 0;
+            size_t n_chunk_candidate = 0;
+            size_t vtcm_size_candidate = 0;
+
+            if (htp_mm_hmx_compute_chunks(vtcm_budget, simple_2d_overhead, simple_2d_size_per_n, simple_2d_size_per_m, simple_2d_size_per_mn, m_for_chunks, ne01_padded,
+                                   (size_t) ne01_padded * HTP_MM_HMX_COST_W_DEQUANT, (size_t) m_for_cost * HTP_MM_HMX_COST_A_CONVERT,
+                                   &m_chunk_candidate, &n_chunk_candidate, &vtcm_size_candidate) == 0) {
+                size_t exact_size = htp_mm_hmx_get_2d_vtcm_size(wtype, ne00_padded, m_chunk_candidate, n_chunk_candidate, pipeline, is_matmul_id ? 0 : act_threads, aligned_tile_size);
+                if (exact_size <= vtcm_budget) {
+                    size_t mblocks = ((size_t) m_for_cost + m_chunk_candidate - 1) / m_chunk_candidate;
+                    if (mblocks < best_mblocks || (mblocks == best_mblocks && act_threads > best_act_threads)) {
+                        best_mblocks = mblocks;
+                        best_act_threads = act_threads;
+                        best_m_chunk = m_chunk_candidate;
+                        best_n_chunk = n_chunk_candidate;
+                        best_vtcm_size = exact_size;
+                    }
+                }
+            }
+            if (act_threads == 1) {
+                act_threads = 0;
+            } else {
+                act_threads /= 2;
+            }
+        }
+
+        if (best_act_threads > 0) {
+            m_chunk = best_m_chunk;
+            n_chunk = best_n_chunk;
+            vtcm_size = best_vtcm_size;
+            act_threads_selected = best_act_threads;
+        } else {
+            return false;
+        }
+    }
+
+    kparams->n_hmx = 1;
+    kparams->pipeline = pipeline ? 1 : 0;
+    kparams->m_chunk = m_chunk;
+    kparams->n_chunk = n_chunk;
+    kparams->n_threads = n_threads;
+    kparams->n_act_threads = act_threads_selected;
+    kparams->tile_size = htp_mm_get_weight_tile_size(wtype);
+    kparams->aligned_tile_size = aligned_tile_size;
+    kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+    kparams->vtcm_size = vtcm_size;
+    kparams->vtcm_src0_size = 0;
+    kparams->vtcm_src1_size = 0;
+    kparams->vtcm_dst_size = 0;
+
+    if (is_batched && !is_matmul_id) {
+        kparams->kernel_type = HTP_MM_KERNEL_HMX_F16_BATCHED;
+    } else {
+        kparams->kernel_type = HTP_MM_KERNEL_HMX_2D;
+    }
+    return true;
+}
+
+static void ggml_hexagon_precompute_hvx_mm_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * dst,
+    int wtype,
+    int ne02,
+    int ne03,
+    int ne10,
+    int ne11,
+    int ne12,
+    int ne13,
+    bool is_matmul_id,
+    size_t vtcm_budget,
+    struct htp_mm_kernel_params * kparams
+) {
+    kparams->n_hmx = 0;
+
+    const bool is_quant = (wtype != GGML_TYPE_F16 && wtype != GGML_TYPE_F32);
+    const int src1_nrows = ne11 * ne12 * ne13;
+
+    if (is_quant) {
+        // Quantized HVX
+        kparams->tile_size = htp_mm_get_weight_tile_size(wtype);
+        kparams->aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+
+        const bool k_align = (ne10 % 32 == 0);
+
+        if (is_matmul_id) {
+            kparams->kernel_type   = (src1_nrows < (int) sess->n_threads) ? HTP_MM_KERNEL_HVX_QUANT_BLOCK : HTP_MM_KERNEL_HVX_QUANT_ROW;
+            kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+
+            size_t vtcm_src0_size = 0, vtcm_src1_size = 0;
+            uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
+            uint32_t best_n_prefetch = 2;
+            size_t total_size = 0;
+            for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
+                total_size = htp_mm_hvx_id_get_vtcm_sizes(
+                    wtype, ne10, src1_nrows, sess->n_threads, src0->nb[1], d,
+                    &vtcm_src0_size, &vtcm_src1_size
+                );
+                if (total_size <= vtcm_budget) {
+                    best_n_prefetch = d;
+                    break;
+                }
+            }
+            if (best_n_prefetch == 2 && total_size > vtcm_budget) {
+                total_size = htp_mm_hvx_id_get_vtcm_sizes(
+                    wtype, ne10, src1_nrows, sess->n_threads, src0->nb[1], 2,
+                    &vtcm_src0_size, &vtcm_src1_size
+                );
+            }
+            kparams->n_prefetch = best_n_prefetch;
+            kparams->vtcm_size      = total_size;
+            kparams->vtcm_src0_size = vtcm_src0_size;
+            kparams->vtcm_src1_size = vtcm_src1_size;
+            kparams->vtcm_dst_size  = 0;
+        } else {
+            bool try_tiled = (k_align && opt_mm_select >= 2);
+            if (try_tiled) {
+                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+                if (src1_nrows < (int)sess->n_threads) {
+                    kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_BLOCK;
+                } else {
+                    kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW;
+                }
+
+                uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
+                uint32_t best_n_prefetch = 2;
+                size_t vtcm_src0_size = 0, vtcm_src1_size = 0, vtcm_dst_size = 0;
+                size_t total_size = 0;
+                for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
+                    total_size = htp_mm_hvx_get_vtcm_sizes(
+                        kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads,
+                        dst->nb[1], src0->nb[1], src1->nb[1], d, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+                    );
+                    if (total_size <= vtcm_budget) {
+                        best_n_prefetch = d;
+                        break;
+                    }
+                }
+                if (best_n_prefetch == 2 && total_size > vtcm_budget) {
+                    total_size = htp_mm_hvx_get_vtcm_sizes(
+                        kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads,
+                        dst->nb[1], src0->nb[1], src1->nb[1], 2, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+                    );
+                }
+
+                kparams->n_prefetch = best_n_prefetch;
+
+                if (total_size <= vtcm_budget) {
+                    kparams->vtcm_size = total_size;
+                    kparams->vtcm_src0_size = vtcm_src0_size;
+                    kparams->vtcm_src1_size = vtcm_src1_size;
+                    kparams->vtcm_dst_size = vtcm_dst_size;
+                    goto done_quant;
+                }
+                HEX_VERBOSE("ggml-hex: %s HVX tiled path VTCM size needed (%zu) > budget (%zu), falling back to HVX flat\n", sess->name.c_str(), total_size, vtcm_budget);
+            }
+
+            // Flat HVX fallback
+            {
+                kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+                kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;
+
+                size_t vtcm_src0_size = 0, vtcm_src1_size = 0, vtcm_dst_size = 0;
+                size_t total_size = htp_mm_hvx_get_vtcm_sizes(
+                    kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads,
+                    dst->nb[1], src0->nb[1], src1->nb[1], 16, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+                );
+
+                kparams->n_prefetch = 16;
+                kparams->vtcm_size = total_size;
+                kparams->vtcm_src0_size = vtcm_src0_size;
+                kparams->vtcm_src1_size = vtcm_src1_size;
+                kparams->vtcm_dst_size = vtcm_dst_size;
+            }
+        }
+
+    done_quant:;
+    } else if (wtype == GGML_TYPE_F16) {
+        // F16 HVX
+        const bool is_batched  = (ne02 > 1) || (ne03 > 1);
+        const bool is_permuted = ggml_is_permuted(src0) || ggml_is_permuted(src1);
+
+        size_t vtcm_src0_size = 0, vtcm_src1_size = 0, vtcm_dst_size = 0;
+        size_t vtcm_size = htp_mm_hvx_get_vtcm_sizes(
+            HTP_MM_KERNEL_HVX_F16_F16_VTCM, wtype, ne10, src1_nrows, sess->n_threads,
+            dst->nb[1], src0->nb[1], src1->nb[1], 16, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+        );
+
+        if (!is_batched && !is_permuted && vtcm_size <= vtcm_budget) {
+            kparams->kernel_type = HTP_MM_KERNEL_HVX_F16_F16_VTCM;
+            kparams->src1_row_size = hex_round_up(ne10 * 2, 128);
+            kparams->vtcm_size = vtcm_size;
+            kparams->vtcm_src0_size = vtcm_src0_size;
+            kparams->vtcm_src1_size = vtcm_src1_size;
+            kparams->vtcm_dst_size = vtcm_dst_size;
+            kparams->n_prefetch = 16;
+        } else {
+            if (src1->type == GGML_TYPE_F32) {
+                kparams->kernel_type = HTP_MM_KERNEL_HVX_F16_F32_DDR;
+            } else {
+                kparams->kernel_type = HTP_MM_KERNEL_HVX_F16_F16_DDR;
+            }
+            kparams->src1_row_size = src1->nb[1];
+            size_t ddr_size = htp_mm_hvx_get_vtcm_sizes(
+                kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads,
+                dst->nb[1], src0->nb[1], src1->nb[1], 16, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+            );
+            kparams->vtcm_size = ddr_size;
+            kparams->vtcm_src0_size = vtcm_src0_size;
+            kparams->vtcm_src1_size = vtcm_src1_size;
+            kparams->vtcm_dst_size = vtcm_dst_size;
+            kparams->n_prefetch = 16;
+        }
+    } else {
+        // F32 HVX
+        const bool is_batched  = (ne02 > 1) || (ne03 > 1);
+        const bool is_permuted = ggml_is_permuted(src0) || ggml_is_permuted(src1);
+
+        size_t vtcm_src0_size = 0, vtcm_src1_size = 0, vtcm_dst_size = 0;
+        size_t vtcm_size = htp_mm_hvx_get_vtcm_sizes(
+            HTP_MM_KERNEL_HVX_F32_F32_VTCM, wtype, ne10, src1_nrows, sess->n_threads,
+            dst->nb[1], src0->nb[1], src1->nb[1], 16, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+        );
+
+        if (!is_batched && !is_permuted && vtcm_size <= vtcm_budget) {
+            kparams->kernel_type = HTP_MM_KERNEL_HVX_F32_F32_VTCM;
+            kparams->src1_row_size = hex_round_up(ne10 * 4, 128);
+            kparams->vtcm_size = vtcm_size;
+            kparams->vtcm_src0_size = vtcm_src0_size;
+            kparams->vtcm_src1_size = vtcm_src1_size;
+            kparams->vtcm_dst_size = vtcm_dst_size;
+            kparams->n_prefetch = 16;
+        } else {
+            kparams->kernel_type = HTP_MM_KERNEL_HVX_F32_F32_DDR;
+            kparams->src1_row_size = src1->nb[1];
+            size_t ddr_size = htp_mm_hvx_get_vtcm_sizes(
+                kparams->kernel_type, wtype, ne10, src1_nrows, sess->n_threads,
+                dst->nb[1], src0->nb[1], src1->nb[1], 16, &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
+            );
+            kparams->vtcm_size = ddr_size;
+            kparams->vtcm_src0_size = vtcm_src0_size;
+            kparams->vtcm_src1_size = vtcm_src1_size;
+            kparams->vtcm_dst_size = vtcm_dst_size;
+            kparams->n_prefetch = 16;
+        }
+    }
+}
+
+static void ggml_hexagon_precompute_matmul_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * dst,
+    struct htp_mm_kernel_params * kparams
+) {
+    memset(kparams, 0, sizeof(*kparams));
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3];
+
+    const int wtype = src0->type;
+    const bool is_repack = ggml_hexagon_is_repack_type((ggml_type) wtype);
+    const int ne00_padded = is_repack ? hex_round_up(ne00, 32) : ne00;
+    const int ne01_padded = is_repack ? hex_round_up(ne01, 32) : ne01;
+    const int ne11_padded = hex_round_up(ne11, 32);
+
+    const bool is_matmul_id = (dst->op == GGML_OP_MUL_MAT_ID);
+    const bool is_batched   = (ne02 * ne03 > 1 || ne12 * ne13 > 1);
+
+    const size_t vtcm_budget = sess->vtcm_size;
+
+    // Check HMX eligibility and try precomputing HMX parameters
+    bool hmx_enabled = (sess->n_hmx > 0) && (opt_mm_select >= 3);
+    if (hmx_enabled && ggml_hexagon_matmul_is_hmx_eligible(src0, src1, dst, ne01_padded, is_matmul_id, is_batched)) {
+        if (ggml_hexagon_precompute_hmx_mm_params(sess, src0, src1, dst, wtype, ne00_padded, ne01_padded, ne02, ne11, ne12, ne11_padded, is_matmul_id, is_batched, vtcm_budget, kparams)) {
+            goto finalize;
+        }
+    }
+
+    // Fallback to HVX parameter computation
+    ggml_hexagon_precompute_hvx_mm_params(sess, src0, src1, dst, wtype, ne02, ne03, ne10, ne11, ne12, ne13, is_matmul_id, vtcm_budget, kparams);
+
+finalize:
+    kparams->div_ne12_ne1 = init_fastdiv_values(ne12 * ne11);
+    kparams->div_ne1      = init_fastdiv_values(ne11);
+    kparams->div_r2       = init_fastdiv_values(ne02 > 0 ? ne12 / ne02 : 1);
+    kparams->div_r3       = init_fastdiv_values(ne03 > 0 ? ne13 / ne03 : 1);
+    kparams->div_ne11     = init_fastdiv_values(ne11);
+}
+
+static void ggml_hexagon_precompute_fused_qkv_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0, // Wk
+    const struct ggml_tensor * src1, // x
+    struct htp_mm_kernel_params * kparams
+) {
+    memset(kparams, 0, sizeof(*kparams));
+
+    const int wtype = src0->type;
+    const bool is_repack = ggml_hexagon_is_repack_type((ggml_type) wtype);
+
+    const int ne10 = src1->ne[0];
+    const int src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];
+    const size_t src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+
+    size_t src0_sz_per_thread = 0;
+    size_t src2_sz_per_thread = 0;
+    size_t src3_sz_per_thread = 0;
+    uint32_t best_n_prefetch = 16;
+
+    if (is_repack) {
+        uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+        uint32_t n_k_tiles = hex_round_up(ne10, 32) / 32;
+        uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
+        size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
+        size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
+        size_t src1_sz = src1_sz_per_thread;
+
+        const uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
+        best_n_prefetch = 2;
+        for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
+            size_t repacked_vtcm_size = hex_round_up(d * tile_row_size, 128);
+            if (repacked_vtcm_size < src1_row_size_padded) {
+                repacked_vtcm_size = src1_row_size_padded;
+            }
+            size_t src0_sz = repacked_vtcm_size * sess->n_threads;
+            size_t src2_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
+            size_t src3_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
+            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz;
+
+            if (tiled_vtcm_size <= sess->vtcm_size) {
+                best_n_prefetch = d;
+                src0_sz_per_thread = repacked_vtcm_size;
+                src2_sz_per_thread = hex_round_up(d * tile_row_size, 128);
+                src3_sz_per_thread = hex_round_up(d * tile_row_size, 128);
+                break;
+            }
+        }
+        if (best_n_prefetch == 2 && src0_sz_per_thread == 0) {
+            size_t repacked_vtcm_size = hex_round_up(2 * tile_row_size, 128);
+            if (repacked_vtcm_size < src1_row_size_padded) {
+                repacked_vtcm_size = src1_row_size_padded;
+            }
+            src0_sz_per_thread = repacked_vtcm_size;
+            src2_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
+            src3_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
+        }
+    } else {
+        best_n_prefetch = 16;
+        src0_sz_per_thread = hex_round_up(best_n_prefetch * src0_row_size_padded, 128);
+        src2_sz_per_thread = hex_round_up(best_n_prefetch * src0_row_size_padded, 128);
+        src3_sz_per_thread = hex_round_up(best_n_prefetch * src0_row_size_padded, 128);
+    }
+
+    size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
+
+    size_t src0_sz = src0_sz_per_thread * sess->n_threads;
+    size_t src1_sz = src1_sz_per_thread;
+    size_t src2_sz = src2_sz_per_thread * sess->n_threads;
+    size_t src3_sz = src3_sz_per_thread * sess->n_threads;
+
+    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz;
+    bool try_tiled = (opt_mm_select >= 2);
+    if (try_tiled && tiled_vtcm_size <= sess->vtcm_size) {
+        kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW;
+        kparams->vtcm_src0_size = src0_sz;
+        kparams->vtcm_src1_size = src1_sz;
+        kparams->vtcm_src2_size = src2_sz;
+        kparams->vtcm_src3_size = src3_sz;
+        kparams->vtcm_size      = tiled_vtcm_size;
+        kparams->n_prefetch     = best_n_prefetch;
+    } else {
+        kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;
+        size_t flat_src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+        size_t flat_src1_sz = hex_round_up(flat_src1_row_size * src1_nrows, 128);
+        kparams->vtcm_src0_size = src0_sz;
+        kparams->vtcm_src1_size = flat_src1_sz;
+        kparams->vtcm_src2_size = src2_sz;
+        kparams->vtcm_src3_size = src3_sz;
+        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz + src3_sz;
+        kparams->n_prefetch     = best_n_prefetch;
+    }
+}
+
+static void ggml_hexagon_precompute_fused_ffn_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * src0, // Wgate
+    const struct ggml_tensor * src1, // y
+    struct htp_mm_kernel_params * kparams
+) {
+    memset(kparams, 0, sizeof(*kparams));
+
+    const int wtype = src0->type;
+    const bool is_repack = ggml_hexagon_is_repack_type((ggml_type) wtype);
+
+    const int ne10 = src1->ne[0];
+    const int src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];
+    const size_t src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+
+    size_t src0_sz_per_thread = 0;
+    size_t src2_sz_per_thread = 0;
+    uint32_t best_n_prefetch = 16;
+
+    if (is_repack) {
+        uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+        uint32_t n_k_tiles = hex_round_up(ne10, 32) / 32;
+        uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
+        size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
+        size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
+        size_t src1_sz = src1_sz_per_thread;
+
+        const uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
+        best_n_prefetch = 2;
+        for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
+            size_t repacked_vtcm_size = hex_round_up(d * tile_row_size, 128);
+            if (repacked_vtcm_size < src1_row_size_padded) {
+                repacked_vtcm_size = src1_row_size_padded;
+            }
+            size_t src0_sz = repacked_vtcm_size * sess->n_threads;
+            size_t src2_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
+            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz;
+
+            if (tiled_vtcm_size <= sess->vtcm_size) {
+                best_n_prefetch = d;
+                src0_sz_per_thread = repacked_vtcm_size;
+                src2_sz_per_thread = hex_round_up(d * tile_row_size, 128);
+                break;
+            }
+        }
+        if (best_n_prefetch == 2 && src0_sz_per_thread == 0) {
+            size_t repacked_vtcm_size = hex_round_up(2 * tile_row_size, 128);
+            if (repacked_vtcm_size < src1_row_size_padded) {
+                repacked_vtcm_size = src1_row_size_padded;
+            }
+            src0_sz_per_thread = repacked_vtcm_size;
+            src2_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
+        }
+    } else {
+        best_n_prefetch = 16;
+        src0_sz_per_thread = hex_round_up(best_n_prefetch * src0_row_size_padded, 128);
+        src2_sz_per_thread = hex_round_up(best_n_prefetch * src0_row_size_padded, 128);
+    }
+
+    size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
+
+    size_t src0_sz = src0_sz_per_thread * sess->n_threads;
+    size_t src1_sz = src1_sz_per_thread;
+    size_t src2_sz = src2_sz_per_thread * sess->n_threads;
+
+    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz;
+    bool try_tiled = (opt_mm_select >= 2);
+    if (try_tiled && tiled_vtcm_size <= sess->vtcm_size) {
+        kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW;
+        kparams->vtcm_src0_size = src0_sz;
+        kparams->vtcm_src1_size = src1_sz;
+        kparams->vtcm_src2_size = src2_sz;
+        kparams->vtcm_size      = tiled_vtcm_size;
+        kparams->n_prefetch     = best_n_prefetch;
+    } else {
+        kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT;
+        size_t flat_src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+        size_t flat_src1_sz = hex_round_up(flat_src1_row_size * src1_nrows, 128);
+        kparams->vtcm_src0_size = src0_sz;
+        kparams->vtcm_src1_size = flat_src1_sz;
+        kparams->vtcm_src2_size = src2_sz;
+        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz;
+        kparams->n_prefetch     = best_n_prefetch;
+    }
+}
+
 static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) {
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
@@ -2675,12 +2625,13 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
                 return false;
             }
 
-            if (ggml_nrows(src0) > 16 * 1024) {
-                return false;  // typically the lm-head which would be too large for VTCM
+            // hardcoded limit to refuse the lm-head for now
+            if (src0->ne[1] > 32768) {
+                return false;
             }
 
-            if (ggml_nrows(src1) > 1024 || src1->ne[2] != 1 || src1->ne[3] != 1) {
-                return false;  // no huge batches or broadcasting (for now)
+            if (src1->ne[2] != 1 || src1->ne[3] != 1) {
+                return false;  // no broadcasting (for now)
             }
 
             // src0 (weights) must be repacked
@@ -2691,16 +2642,11 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
 
         case GGML_TYPE_F16:
             if (src0->nb[1] < src0->nb[0]) {
-                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F16 src0 not supported\n");
                 return false;
             }
             if (src1->ne[2] < src0->ne[2] || src1->ne[3] < src0->ne[3]) {
-                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: src1 broadcasting not supported\n");
                 return false;
             }
-            if (ggml_nrows(src1) > 1024) {
-                return false;  // no huge batches (for now)
-            }
             break;
 
         case GGML_TYPE_F32:
@@ -2708,22 +2654,24 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
                 return false;
             }
             if (src0->nb[1] < src0->nb[0]) {
-                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F32 src0 not supported\n");
                 return false;
             }
             if (src1->ne[2] < src0->ne[2] || src1->ne[3] < src0->ne[3]) {
-                GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: src1 broadcasting not supported\n");
                 return false;
             }
-            if (ggml_nrows(src1) > 1024) {
-                return false;  // no huge batches (for now)
-            }
             break;
 
         default:
             return false;
     }
 
+    struct htp_mm_kernel_params kparams;
+    ggml_hexagon_precompute_matmul_params(sess, src0, src1, dst, &kparams);
+    if ((size_t)kparams.vtcm_size > sess->vtcm_size) {
+        HEX_VERBOSE("ggml-hex: %s supported MUL_MAT VTCM size needed (%d) > budget (%zu)\n", sess->c_name(), kparams.vtcm_size, sess->vtcm_size);
+        return false;
+    }
+
     return true;
 }
 
@@ -2757,6 +2705,13 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session
             return false;
     }
 
+    struct htp_mm_kernel_params kparams;
+    ggml_hexagon_precompute_matmul_params(sess, src0, src1, dst, &kparams);
+    if ((size_t)kparams.vtcm_size > sess->vtcm_size) {
+        HEX_VERBOSE("ggml-hex: %s supported MUL_MAT_ID VTCM size needed (%d) > budget (%zu)\n", sess->c_name(), kparams.vtcm_size, sess->vtcm_size);
+        return false;
+    }
+
     return true;
 }
 
@@ -3288,47 +3243,172 @@ static inline bool op_is_compute(ggml_tensor *node)
     return !ggml_op_is_empty(node->op) && !ggml_is_empty(node) && (node->flags & GGML_TENSOR_FLAG_COMPUTE);
 }
 
+static bool is_hmx_eligible(const ggml_tensor * t) {
+    if (opt_nhmx == 0) { return false; }
+
+    const ggml_tensor * src0 = t->src[0];
+    const ggml_tensor * src1 = t->src[1];
+
+    const int wtype = src0->type;
+    const bool is_repack    = ggml_hexagon_is_repack_type((ggml_type) wtype);
+    const bool is_matmul_id = (t->op == GGML_OP_MUL_MAT_ID);
+    const bool is_batched   = (src0->ne[2] * src0->ne[3] > 1 || src1->ne[2] * src1->ne[3] > 1);
+
+    const int ne01_padded = is_repack ? hex_round_up(src0->ne[1], 32) : src0->ne[1];
+
+    return ggml_hexagon_matmul_is_hmx_eligible(src0, src1, t, ne01_padded, is_matmul_id, is_batched);
+}
+
+static bool is_mergeable_mul_mat(const ggml_tensor * t) {
+    if (!t || t->op != GGML_OP_MUL_MAT)   return false;
+    if (t->src[1]->type != GGML_TYPE_F32) return false;
+    return ggml_is_quantized(t->src[0]->type) && !is_hmx_eligible(t);
+}
+
+static bool is_mergeable_mul_mat_pair(const ggml_tensor * n1, const ggml_tensor * n2) {
+    if (!is_mergeable_mul_mat(n1) || !is_mergeable_mul_mat(n2)) {
+        return false;
+    }
+    if (n1->src[1] != n2->src[1]) {
+        return false;
+    }
+    if (n1->src[0]->ne[0] != n2->src[0]->ne[0] ||
+        n1->src[0]->ne[1] != n2->src[0]->ne[1]) {
+        return false;
+    }
+    if (n1->src[0]->type != n2->src[0]->type) {
+        return false;
+    }
+    return true;
+}
+
+static bool is_qkv_mergeable(const ggml_tensor * n_q, const ggml_tensor * n_k, const ggml_tensor * n_v) {
+    if (!is_mergeable_mul_mat(n_q) || !is_mergeable_mul_mat(n_k) || !is_mergeable_mul_mat(n_v)) {
+        return false;
+    }
+    if (n_q->src[1] != n_k->src[1] || n_q->src[1] != n_v->src[1]) {
+        return false;
+    }
+    if (n_q->src[0]->type != n_k->src[0]->type || n_q->src[0]->type != n_v->src[0]->type) {
+        return false;
+    }
+    if (n_k->src[0]->ne[0] != n_v->src[0]->ne[0] ||
+        n_k->src[0]->ne[1] != n_v->src[0]->ne[1]) {
+        return false;
+    }
+    if (n_q->src[0]->ne[0] != n_k->src[0]->ne[0]) {
+        return false;
+    }
+    return true;
+}
+
+static bool try_fuse_node(const ggml_hexagon_session * sess, const ggml_cgraph * graph, int & i, std::vector<htp_opnode> & nodes) {
+    if (!opt_opfusion) {
+        return false;
+    }
+
+    ggml_tensor * n = graph->nodes[i];
+    ggml_tensor * next_node = (i + 1 < graph->n_nodes) ? graph->nodes[i + 1] : nullptr;
+
+    if (n->op == GGML_OP_RMS_NORM && next_node) {
+        if (next_node->op == GGML_OP_MUL && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+            htp_opnode node(n, {}, HTP_OP_RMS_NORM_MUL);
+            node.add_fused(next_node);
+            nodes.push_back(std::move(node));
+            i++; // skip the fused MUL node
+            return true;
+        }
+    }
+
+    if (is_mergeable_mul_mat(n)) {
+        ggml_tensor * n1 = (i + 1 < graph->n_nodes) ? graph->nodes[i + 1] : nullptr;
+        ggml_tensor * n2 = (i + 2 < graph->n_nodes) ? graph->nodes[i + 2] : nullptr;
+        if (is_qkv_mergeable(n, n1, n2)) {
+            struct htp_mm_kernel_params kparams;
+            ggml_hexagon_precompute_fused_qkv_params(sess, n1->src[0], n1->src[1], &kparams);
+            if ((size_t)kparams.vtcm_size <= sess->vtcm_size) {
+                // Reorder to KVQ: K (n1), V (n2), Q (n)
+                htp_opnode node(n1, {}, HTP_OP_MUL_MAT_QKV);
+                node.add_fused(n2, true);
+                node.add_fused(n, true);
+                memcpy(node.kernel_params, &kparams, sizeof(kparams));
+                nodes.push_back(std::move(node));
+                i += 2;
+                return true;
+            } else {
+                HEX_VERBOSE("ggml-hex: skip QKV fusion because VTCM needed (%d) > budget (%zu)\n",
+                            kparams.vtcm_size, sess->vtcm_size);
+            }
+        }
+        if (is_mergeable_mul_mat_pair(n, n1)) {
+            struct htp_mm_kernel_params kparams;
+            ggml_hexagon_precompute_fused_ffn_params(sess, n->src[0], n->src[1], &kparams);
+            if ((size_t)kparams.vtcm_size <= sess->vtcm_size) {
+                htp_opnode node(n, {}, HTP_OP_MUL_MAT_FFN);
+                node.add_fused(n1, true);
+                memcpy(node.kernel_params, &kparams, sizeof(kparams));
+                nodes.push_back(std::move(node));
+                i += 1;
+                return true;
+            } else {
+                HEX_VERBOSE("ggml-hex: skip FFN fusion because VTCM needed (%d) > budget (%zu)\n",
+                            kparams.vtcm_size, sess->vtcm_size);
+            }
+        }
+    }
+
+    return false;
+}
+
 static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
     auto sess = static_cast<ggml_hexagon_session *>(backend->context);
 
     HEX_VERBOSE("ggml-hex: %s graph-compute n_nodes %d\n", sess->c_name(), graph->n_nodes);
 
-    std::vector<htp_opnode> nodes;
-    nodes.reserve(graph->n_nodes);
+    const std::vector<htp_opnode> * nodes_ptr = nullptr;
+    std::vector<htp_opnode> computed_nodes;
 
-    // Fusion
-    for (int i = 0; i < graph->n_nodes; ++i) {
-        ggml_tensor * n = graph->nodes[i];
-        if (!op_is_compute(n)) {
-            continue;
-        }
+    // Check for cache hit
+    bool cache_hit = (graph->uid != 0 && sess->cached_graph.uid == graph->uid);
+    if (cache_hit) {
+        nodes_ptr = &sess->cached_graph.htp_nodes;
+    } else {
+        computed_nodes.reserve(graph->n_nodes);
 
-        ggml_tensor * next_node = (i + 1 < graph->n_nodes) ? graph->nodes[i + 1] : nullptr;
-
-        htp_opnode node = {
-            /*.node =*/ n,
-            /*.fused =*/ {},
-            /*.opcode =*/ HTP_OP_INVALID
-        };
-
-        if (n->op == GGML_OP_RMS_NORM && next_node) {
-            if (next_node->op == GGML_OP_MUL && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
-                node.add_fused(next_node);
-                node.opcode = HTP_OP_RMS_NORM_MUL;
-                i++; // skip the fused MUL node
+        // Fuse and finalize
+        for (int i = 0; i < graph->n_nodes; ++i) {
+            ggml_tensor * n = graph->nodes[i];
+            if (!op_is_compute(n)) {
+                continue;
             }
-        }
 
-        if (node.opcode == HTP_OP_INVALID) {
+            if (try_fuse_node(sess, graph, i, computed_nodes)) {
+                continue;
+            }
+
+            htp_opnode node(n, {}, HTP_OP_INVALID);
             node.opcode = op_remap_to_htp(n);
+            if (node.opcode == HTP_OP_MUL_MAT || node.opcode == HTP_OP_MUL_MAT_ID) {
+                ggml_hexagon_precompute_matmul_params(sess,
+                    node.node->src[0], node.node->src[1], node.node,
+                    (struct htp_mm_kernel_params *)node.kernel_params
+                );
+            }
+            computed_nodes.push_back(std::move(node));
         }
 
-        nodes.push_back(std::move(node));
+        if (graph->uid != 0) {
+            sess->cached_graph.uid = graph->uid;
+            sess->cached_graph.htp_nodes = std::move(computed_nodes);
+            nodes_ptr = &sess->cached_graph.htp_nodes;
+        } else {
+            nodes_ptr = &computed_nodes;
+        }
     }
 
     // Queue and execute
     if (opt_opstage & HTP_OPSTAGE_QUEUE) {
-        for (const auto & node : nodes) {
+        for (const auto & node : *nodes_ptr) {
             sess->enqueue_op(node);
         }
     }
@@ -3991,16 +4071,19 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     const char * str_opbatch  = getenv("GGML_HEXAGON_OPBATCH");
     const char * str_opqueue  = getenv("GGML_HEXAGON_OPQUEUE");
     const char * str_oppoll   = getenv("GGML_HEXAGON_OPPOLL");
-    const char * str_optrace  = getenv("GGML_HEXAGON_OPTRACE");
+    const char * str_opfusion = getenv("GGML_HEXAGON_OPFUSION");
     const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
     const char * str_profile  = getenv("GGML_HEXAGON_PROFILE");
     const char * str_etm      = getenv("GGML_HEXAGON_ETM");
     const char * str_nhvx     = getenv("GGML_HEXAGON_NHVX");
     const char * str_use_hmx  = getenv("GGML_HEXAGON_USE_HMX");
+    const char * str_nhmx     = getenv("GGML_HEXAGON_NHMX");
+    const char * str_mm_select = getenv("GGML_HEXAGON_MM_SELECT");
     const char * str_ndev     = getenv("GGML_HEXAGON_NDEV");
     const char * str_arch     = getenv("GGML_HEXAGON_ARCH");
     const char * str_vmem     = getenv("GGML_HEXAGON_VMEM");
     const char * str_mbuf     = getenv("GGML_HEXAGON_MBUF");
+    const char * str_optrace  = getenv("GGML_HEXAGON_OPTRACE");
 
     // Init Arch first since it affects other defaults
     if (!str_arch) {
@@ -4029,12 +4112,14 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     opt_opstage   = str_opstage  ? strtoul(str_opstage, NULL, 0)          : opt_opstage;
     opt_opbatch   = str_opbatch  ? strtoul(str_opbatch, NULL, 0)          : opt_opbatch;
     opt_opqueue   = str_opqueue  ? strtoul(str_opqueue, NULL, 0)          : opt_opqueue;
-    opt_oppoll    = str_oppoll   ? strtoul(str_oppoll,  NULL, 0)          : opt_oppoll;
     opt_optrace   = str_optrace  ? strtoul(str_optrace, NULL, 0)          : (opt_opbatch * 128);
+    opt_oppoll    = str_oppoll   ? strtoul(str_oppoll,  NULL, 0)          : opt_oppoll;
+    opt_opfusion  = str_opfusion ? atoi(str_opfusion)                     : opt_opfusion;
     opt_profile   = str_profile  ? atoi(str_profile)                      : 0;
     opt_etm       = str_etm      ? atoi(str_etm)                          : 0;
     opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
-    opt_use_hmx   = str_use_hmx  ? atoi(str_use_hmx)                      : opt_use_hmx;
+    opt_nhmx      = str_nhmx     ? atoi(str_nhmx)                         : (str_use_hmx ? atoi(str_use_hmx) : opt_nhmx);
+    opt_mm_select = str_mm_select ? atoi(str_mm_select)                   : opt_mm_select;
     opt_ndev      = str_ndev     ? strtoul(str_ndev, NULL, 0)             : opt_ndev;
     opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
     opt_mbuf      = str_mbuf     ? strtoul(str_mbuf, NULL, 0) * MiB       : opt_mbuf;
diff --git a/ggml/src/ggml-hexagon/htp-opnode.h b/ggml/src/ggml-hexagon/htp-opnode.h
index 52c727c62..6fe23b0d6 100644
--- a/ggml/src/ggml-hexagon/htp-opnode.h
+++ b/ggml/src/ggml-hexagon/htp-opnode.h
@@ -5,10 +5,12 @@
 #include "ggml-backend-impl.h"
 #include "ggml-common.h"
 
+#include <algorithm>
 #include <string>
 #include <vector>
 #include <stdio.h>
 #include "htp-ops.h"
+#include "htp/matmul-ops.h"
 
 struct htp_opnode {
     ggml_tensor * node = nullptr;
@@ -17,6 +19,13 @@ struct htp_opnode {
 
     htp_op_code opcode = HTP_OP_INVALID;
 
+    std::vector<ggml_tensor *> extra_dsts;
+
+    int32_t kernel_params[HTP_OP_MAX_KERN_PARAMS] = {0};
+
+    htp_opnode(ggml_tensor * node = nullptr, std::vector<ggml_tensor *> fused = {}, htp_op_code opcode = HTP_OP_INVALID, std::vector<ggml_tensor *> extra_dsts = {})
+        : node(node), fused(std::move(fused)), opcode(opcode), extra_dsts(std::move(extra_dsts)) {}
+
     ggml_op op() const {
         return node->op;
     }
@@ -25,6 +34,26 @@ struct htp_opnode {
         return fused.empty() ? node : fused.back();
     }
 
+    void add_fused(ggml_tensor * t, bool extra_dst = false) {
+        fused.push_back(t);
+        if (extra_dst) {
+            extra_dsts.push_back(t);
+        }
+    }
+
+    std::vector<const ggml_tensor *> get_outputs() const {
+        std::vector<const ggml_tensor *> res;
+        if (extra_dsts.empty()) {
+            res.push_back(dst());
+        } else {
+            res.push_back(node);
+            for (const auto * x : extra_dsts) {
+                res.push_back(x);
+            }
+        }
+        return res;
+    }
+
     const ggml_tensor * src0() const {
         return node->src[0];
     }
@@ -37,10 +66,6 @@ struct htp_opnode {
         return ggml_op_is_empty(node->op);
     }
 
-    void add_fused(ggml_tensor * t) {
-        fused.push_back(t);
-    }
-
     bool stackable() const {
         switch (this->op()) {
             case GGML_OP_MUL_MAT:
@@ -131,87 +156,117 @@ struct htp_opformat {
     char types[16 * GGML_MAX_SRC];
     char buffs[64 * GGML_MAX_SRC];
     char names[64 * GGML_MAX_SRC];
+    char kparams[128];
 
-    int format_tensor_dims(char * str, const struct ggml_tensor * t) {
+    int format_tensor_dims(char * str, size_t max_size, const struct ggml_tensor * t) {
         if (!t) {
-            return sprintf(str, "NONE");
+            return snprintf(str, max_size, "NONE");
         }
         if (t->ne[2] == 1 && t->ne[3] == 1) {
-            return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
+            return snprintf(str, max_size, "%d:%d", (int) t->ne[0], (int) t->ne[1]);
         } else {
-            return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
+            return snprintf(str, max_size, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]);
         }
     }
 
-    void format_op_dims(char * str, const htp_opnode & node) {
+    void format_op_dims(char * str, size_t max_size, const htp_opnode & node) {
         char * p = str;
+        char * p_end = str + max_size;
         auto inputs = node.get_inputs();
 
         if (!inputs.empty()) {
-            p += format_tensor_dims(p, inputs[0]);
+            p += std::min((size_t)format_tensor_dims(p, p_end - p, inputs[0]), (size_t)(p_end - p));
 
             for (size_t i = 1; i < inputs.size(); i++) {
-                p += sprintf(p, " x ");
-                p += format_tensor_dims(p, inputs[i]);
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, " x "), (size_t)(p_end - p));
+                }
+                if (p < p_end) {
+                    p += std::min((size_t)format_tensor_dims(p, p_end - p, inputs[i]), (size_t)(p_end - p));
+                }
             }
 
-            p += sprintf(p, " -> ");
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, " -> "), (size_t)(p_end - p));
+            }
         }
 
         char self[64];
-        format_tensor_dims(self, node.dst());
-        p += sprintf(p, "%s", self);
+        format_tensor_dims(self, sizeof(self), node.dst());
+        if (p < p_end) {
+            p += std::min((size_t)snprintf(p, p_end - p, "%s", self), (size_t)(p_end - p));
+        }
     }
 
-    int format_tensor_strides(char * str, const struct ggml_tensor * t) {
+    int format_tensor_strides(char * str, size_t max_size, const struct ggml_tensor * t) {
         if (!t) {
-            return sprintf(str, "NONE");
+            return snprintf(str, max_size, "NONE");
         }
         const char * c = ggml_is_contiguous(t) ? "" : "!";
 
         if (t->ne[2] == 1 && t->ne[3] == 1) {
-            return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
+            return snprintf(str, max_size, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c);
         } else {
-            return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
+            return snprintf(str, max_size, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c);
         }
     }
 
-    void format_op_strides(char * str, const htp_opnode & node) {
+    void format_op_strides(char * str, size_t max_size, const htp_opnode & node) {
         char * p = str;
+        char * p_end = str + max_size;
         auto inputs = node.get_inputs();
 
         if (!inputs.empty()) {
-            p += format_tensor_strides(p, inputs[0]);
+            p += std::min((size_t)format_tensor_strides(p, p_end - p, inputs[0]), (size_t)(p_end - p));
 
             for (size_t i = 1; i < inputs.size(); i++) {
-                p += sprintf(p, " x ");
-                p += format_tensor_strides(p, inputs[i]);
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, " x "), (size_t)(p_end - p));
+                }
+                if (p < p_end) {
+                    p += std::min((size_t)format_tensor_strides(p, p_end - p, inputs[i]), (size_t)(p_end - p));
+                }
             }
 
-            p += sprintf(p, " -> ");
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, " -> "), (size_t)(p_end - p));
+            }
         }
 
         char self[64];
-        format_tensor_strides(self, node.dst());
-        p += sprintf(p, "%s", self);
+        format_tensor_strides(self, sizeof(self), node.dst());
+        if (p < p_end) {
+            p += std::min((size_t)snprintf(p, p_end - p, "%s", self), (size_t)(p_end - p));
+        }
     }
 
-    void format_op_types(char * str, const htp_opnode & node) {
+    void format_op_types(char * str, size_t max_size, const htp_opnode & node) {
         char * p = str;
+        char * p_end = str + max_size;
         auto inputs = node.get_inputs();
 
         if (!inputs.empty()) {
-            p += sprintf(p, "%s", inputs[0] ? ggml_type_name(inputs[0]->type) : "NONE");
-
-            for (size_t i = 1; i < inputs.size(); i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", inputs[i] ? ggml_type_name(inputs[i]->type) : "NONE");
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, "%s", inputs[0] ? ggml_type_name(inputs[0]->type) : "NONE"), (size_t)(p_end - p));
             }
 
-            p += sprintf(p, " -> ");
+            for (size_t i = 1; i < inputs.size(); i++) {
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, " x "), (size_t)(p_end - p));
+                }
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, "%s", inputs[i] ? ggml_type_name(inputs[i]->type) : "NONE"), (size_t)(p_end - p));
+                }
+            }
+
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, " -> "), (size_t)(p_end - p));
+            }
         }
 
-        p += sprintf(p, "%s", ggml_type_name(node.dst()->type));
+        if (p < p_end) {
+            p += std::min((size_t)snprintf(p, p_end - p, "%s", ggml_type_name(node.dst()->type)), (size_t)(p_end - p));
+        }
     }
 
     const char * tensor_buff_name(const struct ggml_tensor * t) {
@@ -221,51 +276,102 @@ struct htp_opformat {
         return "NONE";
     }
 
-    void format_op_buffs(char * str, const htp_opnode & node) {
+    void format_op_buffs(char * str, size_t max_size, const htp_opnode & node) {
         char * p = str;
+        char * p_end = str + max_size;
         auto inputs = node.get_inputs();
 
         if (!inputs.empty()) {
-            p += sprintf(p, "%s", tensor_buff_name(inputs[0]));
-
-            for (size_t i = 1; i < inputs.size(); i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", tensor_buff_name(inputs[i]));
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, "%s", tensor_buff_name(inputs[0])), (size_t)(p_end - p));
             }
 
-            p += sprintf(p, " -> ");
+            for (size_t i = 1; i < inputs.size(); i++) {
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, " x "), (size_t)(p_end - p));
+                }
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, "%s", tensor_buff_name(inputs[i])), (size_t)(p_end - p));
+                }
+            }
+
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, " -> "), (size_t)(p_end - p));
+            }
         }
 
-        p += sprintf(p, "%s", tensor_buff_name(node.dst()));
+        if (p < p_end) {
+            p += std::min((size_t)snprintf(p, p_end - p, "%s", tensor_buff_name(node.dst())), (size_t)(p_end - p));
+        }
     }
 
-    void format_op_names(char * str, const htp_opnode & node) {
+    void format_op_names(char * str, size_t max_size, const htp_opnode & node) {
         char * p = str;
+        char * p_end = str + max_size;
         auto inputs = node.get_inputs();
 
         if (!inputs.empty()) {
-            p += sprintf(p, "%s", inputs[0] ? inputs[0]->name : "NONE");
-
-            for (size_t i = 1; i < inputs.size(); i++) {
-                p += sprintf(p, " x ");
-                p += sprintf(p, "%s", inputs[i] ? inputs[i]->name : "NONE");
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, "%s", inputs[0] ? inputs[0]->name : "NONE"), (size_t)(p_end - p));
             }
 
-            p += sprintf(p, " -> ");
+            for (size_t i = 1; i < inputs.size(); i++) {
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, " x "), (size_t)(p_end - p));
+                }
+                if (p < p_end) {
+                    p += std::min((size_t)snprintf(p, p_end - p, "%s", inputs[i] ? inputs[i]->name : "NONE"), (size_t)(p_end - p));
+                }
+            }
+
+            if (p < p_end) {
+                p += std::min((size_t)snprintf(p, p_end - p, " -> "), (size_t)(p_end - p));
+            }
         }
 
-        p += sprintf(p, "%s", node.dst()->name);
+        if (p < p_end) {
+            p += std::min((size_t)snprintf(p, p_end - p, "%s", node.dst()->name), (size_t)(p_end - p));
+        }
+    }
+    void format_kernel_params(char * str, size_t max_size, const htp_opnode & node) {
+        if (node.opcode == HTP_OP_MUL_MAT || node.opcode == HTP_OP_MUL_MAT_ID ||
+            node.opcode == HTP_OP_MUL_MAT_QKV || node.opcode == HTP_OP_MUL_MAT_FFN) {
+            const auto * kparams = (const struct htp_mm_kernel_params *) node.kernel_params;
+            const char * path = "unknown";
+            int32_t type = kparams->kernel_type;
+            if (type == HTP_MM_KERNEL_HMX_2D || type == HTP_MM_KERNEL_HMX_F16_BATCHED) {
+                path = "hmx-tiled";
+            } else if (type == HTP_MM_KERNEL_HVX_F16_F16_VTCM || type == HTP_MM_KERNEL_HVX_F32_F32_VTCM ||
+                       type == HTP_MM_KERNEL_HVX_QUANT_ROW    || type == HTP_MM_KERNEL_HVX_QUANT_BLOCK) {
+                path = "hvx-tiled";
+            } else if (type == HTP_MM_KERNEL_HVX_F16_F16_DDR  || type == HTP_MM_KERNEL_HVX_F16_F32_DDR ||
+                       type == HTP_MM_KERNEL_HVX_F32_F32_DDR  || type == HTP_MM_KERNEL_HVX_F32_F16_DDR ||
+                       type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+                path = "hvx-flat";
+            }
+            snprintf(str, max_size, "%s vtcm %d", path, (int) kparams->vtcm_size);
+        } else {
+            snprintf(str, max_size, "----");
+        }
     }
 
     void format(const htp_opnode & node) {
-        format_op_dims(dims, node);
-        format_op_strides(strides, node);
-        format_op_types(types, node);
-        format_op_buffs(buffs, node);
-        format_op_names(names, node);
+        format_op_dims(dims, sizeof(dims), node);
+        format_op_strides(strides, sizeof(strides), node);
+        format_op_types(types, sizeof(types), node);
+        format_op_buffs(buffs, sizeof(buffs), node);
+        format_op_names(names, sizeof(names), node);
+        format_kernel_params(kparams, sizeof(kparams), node);
     }
 
-    htp_opformat() {}
+    htp_opformat() {
+        strides[0] = '\0';
+        dims[0]    = '\0';
+        types[0]   = '\0';
+        buffs[0]   = '\0';
+        names[0]   = '\0';
+        kparams[0] = '\0';
+    }
     htp_opformat(const htp_opnode & node) { format(node); }
 };
 
diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
index 31ba52762..c48a5b86e 100644
--- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
@@ -19,43 +19,9 @@ add_library(${HTP_LIB} SHARED
     htp_iface_skel.c
     worker-pool.c
     hex-dma.c
-)
-
-target_compile_definitions(${HTP_LIB} PRIVATE
-    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
-    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>
-    FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
-
-if (GGML_HEXAGON_FA_EXP2_HF)
-    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
-    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
-endif()
-
-# HMX acceleration: available on v73+ architectures
-set(HTP_HMX_VERSIONS v73 v75 v79 v81)
-list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
-
-if (_hmx_idx GREATER_EQUAL 0)
-    target_sources(${HTP_LIB} PRIVATE
-        hmx-flash-attn-ops.c
-        hmx-matmul-ops.c
-        hmx-queue.c
-    )
-
-    # -mhmx enables HMX instruction set (needed by files that include hmx-utils.h)
-    set_source_files_properties(
-        hmx-flash-attn-ops.c
-        hmx-matmul-ops.c
-        hmx-queue.c
-        PROPERTIES COMPILE_OPTIONS "-mhmx"
-    )
-
-    target_compile_definitions(${HTP_LIB} PRIVATE HTP_HAS_HMX=1)
-endif()
-
-build_idl(htp_iface.idl ${HTP_LIB})
-
-target_sources(${HTP_LIB} PRIVATE
+    hmx-queue.c
+    flash-attn-ops.c
+    hmx-flash-attn-ops.c
     matmul-ops.c
     binary-ops.c
     unary-ops.c
@@ -63,7 +29,6 @@ target_sources(${HTP_LIB} PRIVATE
     softmax-ops.c
     act-ops.c
     rope-ops.c
-    flash-attn-ops.c
     set-rows-ops.c
     get-rows-ops.c
     cpy-ops.c
@@ -79,6 +44,17 @@ target_sources(${HTP_LIB} PRIVATE
     pad-ops.c
 )
 
+target_compile_definitions(${HTP_LIB} PRIVATE
+    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
+    $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>)
+
+if (GGML_HEXAGON_FA_EXP2_HF)
+    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
+    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
+endif()
+
+build_idl(htp_iface.idl ${HTP_LIB})
+
 set_target_properties(${HTP_LIB} PROPERTIES EXPORT_COMPILE_COMMANDS ON)
 
 install(TARGETS ${HTP_LIB})
diff --git a/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake b/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
index ed5c19846..3eff2a398 100644
--- a/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
+++ b/ggml/src/ggml-hexagon/htp/cmake-toolchain.cmake
@@ -3,7 +3,7 @@ if (HEXAGON_TOOLCHAIN_INCLUDED)
 endif()
 set(HEXAGON_TOOLCHAIN_INCLUDED true)
 
-#Cross Compiling for Hexagon
+# Cross Compiling for Hexagon
 set(HEXAGON TRUE)
 set(CMAKE_SYSTEM_NAME QURT)
 set(CMAKE_SYSTEM_PROCESSOR Hexagon)
@@ -14,7 +14,6 @@ set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
 set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
 set(CUSTOM_RUNELF_PATH "")
 
-#To fix backward compatibility with EAI addon.
 if (NOT HEXAGON_SDK_ROOT)
     set(HEXAGON_SDK_ROOT $ENV{HEXAGON_SDK_ROOT})
 endif()
@@ -31,7 +30,6 @@ endif()
 file(TO_CMAKE_PATH "${HEXAGON_TOOLS_ROOT}" HEXAGON_TOOLS_ROOT)
 file(TO_CMAKE_PATH "${HEXAGON_SDK_ROOT}"   HEXAGON_SDK_ROOT)
 
-#Get the Binary extension of the Hexagon Toolchain
 if(CMAKE_HOST_SYSTEM_NAME STREQUAL Windows)
     set(HEXAGON_TOOLCHAIN_SUFFIX .exe)
 endif()
@@ -48,12 +46,12 @@ set(CMAKE_TRY_COMPILE_PLATFORM_VARIABLES
     HEXAGON_TOOLS_ROOT
 )
 
-#QURT Related includes and linker flags
+# QURT Related includes and linker flags
 set(V_ARCH ${HEXAGON_ARCH})
 set(_QURT_INSTALL_DIR "${HEXAGON_SDK_ROOT}/rtos/qurt/ADSP${V_ARCH}MP${V_ARCH_EXTN}")
 set(_QURT_INSTALL_DIR "${HEXAGON_SDK_ROOT}/rtos/qurt/compute${V_ARCH}${V_ARCH_EXTN}")
 
-if( ${TREE} MATCHES PAKMAN )
+if (${TREE} MATCHES PAKMAN)
     set(_QURT_INSTALL_DIR "${QURT_IMAGE_DIR}/compute${V_ARCH}${V_ARCH_EXTN}")
 endif()
 message(DEBUG "_QURT_INSTALL_DIR:${_QURT_INSTALL_DIR}")
@@ -83,11 +81,9 @@ set(QURT_START_LINK_LIBS
     )
 STRING(REPLACE ";" " " QURT_START_LINK_LIBS "${QURT_START_LINK_LIBS}")
 
-set(QURT_END_LINK_LIBS
-    ${TARGET_DIR}/fini.o
-    )
+set(QURT_END_LINK_LIBS ${TARGET_DIR}/fini.o)
 
-#Non QURT related includes and linker flags
+# Non QURT related includes and linker flags
 
 set(TARGET_DIR_NOOS "${HEXAGON_TOOLCHAIN}/Tools/target/hexagon/lib/${HEXAGON_ARCH}")
 
@@ -99,8 +95,10 @@ if (NOT NO_WRAP_MEM_API)
     set(WRAP_MEMALIGN -Wl,--wrap=memalign)
 endif()
 
+set(ARCH_FLAGS "-mcpu=${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -mhmx")
+
 set(PIC_SHARED_LD_FLAGS
-    -mcpu=${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH}
+    ${ARCH_FLAGS}
     -G0
     -fpic
     -Wl,-Bsymbolic
@@ -120,13 +118,13 @@ STRING(REPLACE ";" " " PIC_SHARED_LD_FLAGS "${PIC_SHARED_LD_FLAGS}")
 
 set(HEXAGON_PIC_SHARED_LINK_OPTIONS "${PIC_SHARED_LD_FLAGS}")
 
-#System include paths
+# System include paths
 include_directories(SYSTEM ${HEXAGON_SDK_ROOT}/incs)
 include_directories(SYSTEM ${HEXAGON_SDK_ROOT}/incs/stddef)
 include_directories(SYSTEM ${HEXAGON_SDK_ROOT}/ipc/fastrpc/incs)
 
-#LLVM toolchain setup
-#Compiler paths, options and architecture
+# LLVM toolchain setup
+# Compiler paths, options and architecture
 set(CMAKE_C_COMPILER ${HEXAGON_TOOLCHAIN}/Tools/bin/hexagon-clang${HEXAGON_TOOLCHAIN_SUFFIX})
 set(CMAKE_CXX_COMPILER ${HEXAGON_TOOLCHAIN}/Tools/bin/hexagon-clang++${HEXAGON_TOOLCHAIN_SUFFIX})
 set(CMAKE_AR ${HEXAGON_TOOLCHAIN}/Tools/bin/hexagon-ar${HEXAGON_TOOLCHAIN_SUFFIX})
@@ -137,8 +135,8 @@ set(CMAKE_PREFIX_PATH ${HEXAGON_TOOLCHAIN}/Tools/target/hexagon)
 set(CMAKE_SHARED_LIBRARY_SONAME_C_FLAG   "-Wl,-soname,")
 set(CMAKE_SHARED_LIBRARY_SONAME_CXX_FLAG "-Wl,-soname,")
 
-#Compiler Options
-set(COMMON_FLAGS "-mcpu=hexagon${V_ARCH} -m${V_ARCH} -mhvx=${V_ARCH} -fvectorize -flto -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
+# Compiler Options
+set(COMMON_FLAGS "${ARCH_FLAGS} -fvectorize -flto -Wall -Werror -fno-zero-initialized-in-bss -G0 -fdata-sections -fpic ${XQF_ARGS}")
 
 set(CMAKE_CXX_FLAGS_DEBUG          "${COMMON_FLAGS} -O0 -D_DEBUG -g")
 set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${COMMON_FLAGS} -O2 -g")
diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
index b7511cd64..65f7844ae 100644
--- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
@@ -18,7 +18,8 @@
 #include "htp-ctx.h"
 #include "htp-ops.h"
 #include "htp-ops.h"
-#include "hmx-ops.h"
+
+int hmx_flash_attn_ext(struct htp_ops_context * octx);
 
 // Must be multiple of 32
 #define FLASH_ATTN_BLOCK_SIZE (32 * 2)
@@ -633,7 +634,6 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-#ifdef HTP_HAS_HMX
     // HMX path: head_dim multiple of 64, F16 KV, and no sinks
     if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 64 == 0 && v->ne[0] % 64 == 0 && octx->src[4] == NULL) {
         int ret = hmx_flash_attn_ext(octx);
@@ -642,7 +642,6 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         }
         // VTCM too small or other failure -> fall through to HVX path
     }
-#endif
 
     struct htp_fa_context factx;
     factx.octx = octx;
diff --git a/ggml/src/ggml-hexagon/htp/hex-common.h b/ggml/src/ggml-hexagon/htp/hex-common.h
new file mode 100644
index 000000000..4714486a0
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hex-common.h
@@ -0,0 +1,80 @@
+#ifndef HEX_COMMON_H
+#define HEX_COMMON_H
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdbool.h>
+
+#ifndef SIZE_MAX
+#define SIZE_MAX ((size_t)-1)
+#endif
+
+#ifndef MAX
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+#endif
+
+#ifndef MIN
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#endif
+
+static inline uint32_t hex_ceil_pow2(uint32_t x) {
+    if (x <= 1) { return 1; }
+    int p = 2;
+    x--;
+    while (x >>= 1) { p <<= 1; }
+    return p;
+}
+
+static inline size_t hmx_ceil_div(size_t num, size_t den) {
+    return (num + den - 1) / den;
+}
+
+static inline int32_t hex_is_aligned(const void * addr, uint32_t align) {
+    return ((size_t) addr & (align - 1)) == 0;
+}
+
+static inline size_t hex_align_up(size_t v, size_t align) {
+    return hmx_ceil_div(v, align) * align;
+}
+
+static inline size_t hex_align_down(size_t v, size_t align) {
+    return (v / align) * align;
+}
+
+static inline int32_t hex_is_one_chunk(void * addr, uint32_t n, uint32_t chunk_size) {
+    uint32_t left_off  = (size_t) addr & (chunk_size - 1);
+    uint32_t right_off = left_off + n;
+    return right_off <= chunk_size;
+}
+
+static inline uint32_t hex_round_up(uint32_t n, uint32_t m) {
+    return m * ((n + m - 1) / m);
+}
+
+static inline size_t hex_smin(size_t a, size_t b) {
+    return a < b ? a : b;
+}
+
+static inline size_t hex_smax(size_t a, size_t b) {
+    return a > b ? a : b;
+}
+
+static inline void hex_swap_ptr(void ** p1, void ** p2) {
+    void * t = *p1;
+    *p1      = *p2;
+    *p2      = t;
+}
+
+static inline bool hex_mul_overflow(size_t a, size_t b, size_t *out) {
+    if (a != 0 && b > SIZE_MAX / a) return true;
+    *out = a * b;
+    return false;
+}
+
+static inline bool hex_add_overflow(size_t a, size_t b, size_t *out) {
+    if (a > SIZE_MAX - b) return true;
+    *out = a + b;
+    return false;
+}
+
+#endif // HEX_COMMON_H
diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h
index 93c21ebe5..8031a5679 100644
--- a/ggml/src/ggml-hexagon/htp/hex-dma.h
+++ b/ggml/src/ggml-hexagon/htp/hex-dma.h
@@ -5,6 +5,7 @@
 #include <hexagon_types.h>
 #include <stdbool.h>
 #include <stdint.h>
+#include "hex-utils.h"
 
 #include "hex-profile.h"
 
@@ -127,13 +128,8 @@ static inline dma_ptr dma_make_ptr(void *dst, const void *src)
     return p;
 }
 
-#if __HVX_ARCH__ < 73
-static const uint32_t dma_src_l2_bypass_on = 1;
-static const uint32_t dma_dst_l2_bypass_on = 0;
-#else
 static const uint32_t dma_src_l2_bypass_on = 1;
 static const uint32_t dma_dst_l2_bypass_on = 1;
-#endif
 
 static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t size) {
     if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) {
diff --git a/ggml/src/ggml-hexagon/htp/hex-utils.h b/ggml/src/ggml-hexagon/htp/hex-utils.h
index 8e6e3ea75..07930bef6 100644
--- a/ggml/src/ggml-hexagon/htp/hex-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hex-utils.h
@@ -11,14 +11,7 @@
 
 #include "hex-fastdiv.h"
 #include "hex-dump.h"
-
-#ifndef MAX
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-#endif
-
-#ifndef MIN
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#endif
+#include "hex-common.h"
 
 static inline uint64_t hex_get_cycles() {
     uint64_t cycles = 0;
@@ -32,54 +25,6 @@ static inline uint64_t hex_get_pktcnt() {
     return pktcnt;
 }
 
-static inline uint32_t hex_ceil_pow2(uint32_t x) {
-    if (x <= 1) { return 1; }
-    int p = 2;
-    x--;
-    while (x >>= 1) { p <<= 1; }
-    return p;
-}
-
-static inline size_t hmx_ceil_div(size_t num, size_t den) {
-    return (num + den - 1) / den;
-}
-
-static inline int32_t hex_is_aligned(const void * addr, uint32_t align) {
-    return ((size_t) addr & (align - 1)) == 0;
-}
-
-static inline size_t hex_align_up(size_t v, size_t align) {
-    return hmx_ceil_div(v, align) * align;
-}
-
-static inline size_t hex_align_down(size_t v, size_t align) {
-    return (v / align) * align;
-}
-
-static inline int32_t hex_is_one_chunk(void * addr, uint32_t n, uint32_t chunk_size) {
-    uint32_t left_off  = (size_t) addr & (chunk_size - 1);
-    uint32_t right_off = left_off + n;
-    return right_off <= chunk_size;
-}
-
-static inline uint32_t hex_round_up(uint32_t n, uint32_t m) {
-    return m * ((n + m - 1) / m);
-}
-
-static inline size_t hex_smin(size_t a, size_t b) {
-    return a < b ? a : b;
-}
-
-static inline size_t hex_smax(size_t a, size_t b) {
-    return a > b ? a : b;
-}
-
-static inline void hex_swap_ptr(void ** p1, void ** p2) {
-    void * t = *p1;
-    *p1      = *p2;
-    *p2      = t;
-}
-
 static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride, uint32_t height) {
     const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
     Q6_l2fetch_AP((void *) p, control);
diff --git a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
index 986dde148..996fd5975 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
@@ -49,7 +49,7 @@
 // g_br = hex_align_up(gqa_factor * Br, 32) replaces Br for all Q/O/S/P/D dimensions.
 // Layout: Q + O_ping + O_pong + K_dma*2 + V_dma*2 + K_tile + V_tile + S + P + D + vectors + scales
 // Mask is DMA'd into a VTCM buffer (Br rows per KV block) to avoid DDR reads in softmax.
-static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool use_pipeline) {
+static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline) {
     const size_t g_br         = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS);
     const size_t q_tile_size  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);    // Q:  [g_br, DK]
     const size_t o_tile_size  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);    // O:  [g_br, DV] x2 ping-pong
@@ -70,7 +70,7 @@ static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV,
            + k_dma_size  * 2               // K DMA x2
            + v_dma_size  * 2               // V DMA x2
            + k_tile_size * 1               // K tiles
-           + v_tile_size * (use_pipeline ? 2 : 1) // V tiles (double-buffered if pipelining)
+           + v_tile_size * (pipeline ? 2 : 1) // V tiles (double-buffered if pipelining)
            + s_tile_size * 2               // S + P
            + d_tile_size * 1               // D (diagonal matrix)
            + col_vec_size * 4              // m_vec, l_vec, s_rowmax, p_rowsum
@@ -290,7 +290,7 @@ static const int16_t d_tile_scatter_offsets[64] __attribute__((aligned(128))) =
 
 struct hmx_fa_context {
     const struct htp_ops_context * octx;
-    bool         use_pipeline;  // true when n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2
+    bool         pipeline;  // true when n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2
     uint32_t     n_threads;
 
     // Op parameters
@@ -409,7 +409,7 @@ static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data)
         return;
     }
 
-    __fp16 * v_tiles_dest = factx->use_pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
+    __fp16 * v_tiles_dest = factx->pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
 
     struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
     htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
@@ -1312,13 +1312,13 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
     const size_t g_br = hex_align_up(G * Br, HMX_FP16_TILE_N_ROWS);
 
     const uint32_t n_kv_blocks  = (nek1 + Bc - 1) / Bc;
-    const bool     use_pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads_init >= 2);
+    const bool     pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads_init >= 2);
 
     // Bypass thread pool dispatch for small prompts/non-pipelined prefill by setting n_threads = 1
-    const uint32_t n_threads = use_pipeline ? n_threads_init : 1;
+    const uint32_t n_threads = pipeline ? n_threads_init : 1;
 
     FARF(HIGH, "hmx-fa: neq1=%u nek1=%u DK=%u DV=%u G=%u Br=%zu Bc=%zu g_br=%zu n_kv_blocks=%u pipeline=%d vtcm=%zu",
-         neq1, nek1, DK, DV, G, Br, Bc, g_br, n_kv_blocks, use_pipeline, vtcm_budget);
+         neq1, nek1, DK, DV, G, Br, Bc, g_br, n_kv_blocks, pipeline, vtcm_budget);
 
     // ======== Build context ========
     struct hmx_fa_context factx;
@@ -1339,7 +1339,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
     factx.n_kv_blocks    = n_kv_blocks;
     factx.is_q_fp32      = (q->type == HTP_TYPE_F32);
     factx.is_dst_fp32    = (dst->type == HTP_TYPE_F32);
-    factx.use_pipeline   = use_pipeline;
+    factx.pipeline   = pipeline;
     factx.mask_broadcast = (mask != NULL && mask->ne[2] == 1);
 
     // Extract op parameters (mutable during softcap adjustment, then stored as const in factx)
@@ -1405,7 +1405,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
     factx.vtcm_v_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
     factx.vtcm_k_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_tile_bytes);
     factx.vtcm_v_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
-    if (use_pipeline) {
+    if (pipeline) {
         factx.vtcm_v_tiles[1] = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
     } else {
         factx.vtcm_v_tiles[1] = NULL;
@@ -1456,7 +1456,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
     // ======== HMX lock strategy ========
     // Pipeline: queue thread auto-acquires HMX lock on first push; released by suspend.
     // Fallback: main thread holds the lock (original behavior).
-    if (!factx.use_pipeline) {
+    if (!factx.pipeline) {
         HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
     }
 
@@ -1550,7 +1550,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                 const size_t k_src_stride = size_k_row_padded / sizeof(__fp16);
                 const size_t v_src_stride = size_v_row_padded / sizeof(__fp16);
 
-                if (factx.use_pipeline) {
+                if (factx.pipeline) {
                     // ==================================================================
                     // Pipeline path: HVX phases ‖ HMX queue worker
                     // ==================================================================
@@ -1780,7 +1780,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
                     fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br);
 
                     // HMX: O_final = diag(1/l) @ O_prev
-                    if (factx.use_pipeline) {
+                    if (factx.pipeline) {
                         on_job.o_curr           = o_tile_curr;
                         on_job.o_prev           = o_tile_prev;
                         on_job.d_tiles          = factx.vtcm_d_tiles;
@@ -1826,7 +1826,7 @@ int hmx_flash_attn_ext(struct htp_ops_context * octx) {
         }  // end KV head loop
     }  // end batch loop
 
-    if (factx.use_pipeline) {
+    if (factx.pipeline) {
         hmx_queue_suspend(ctx->hmx_queue);
     } else {
         HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
diff --git a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c b/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
deleted file mode 100644
index 5c37f24ff..000000000
--- a/ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c
+++ /dev/null
@@ -1,2080 +0,0 @@
-#pragma clang diagnostic ignored "-Wgnu-zero-variadic-macro-arguments"
-#pragma clang diagnostic ignored "-Wunused-function"
-#pragma clang diagnostic ignored "-Wunused-variable"
-#pragma clang diagnostic ignored "-Wunused-but-set-variable"
-
-#include <assert.h>
-#include <stdbool.h>
-#include <stddef.h>
-#include <stdint.h>
-#include <string.h>
-
-#include <HAP_farf.h>
-#include <HAP_compute_res.h>
-
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-
-#include "hex-dma.h"
-#include "hex-fastdiv.h"
-#include "worker-pool.h"
-
-#include "hvx-utils.h"
-#include "hvx-dump.h"
-#include "htp-ctx.h"
-#include "htp-ops.h"
-
-#include "hmx-ops.h"
-#include "hmx-utils.h"
-#include "hmx-queue.h"
-#include "hex-profile.h"
-
-#include "vtcm-utils.h"
-
-static const __fp16 q4_0_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
-    -8, 0, -7, 0, -6, 0, -5, 0, -4, 0, -3, 0, -2, 0, -1, 0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0,
-};
-
-static const __fp16 q4_1_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
-    0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0,
-};
-
-// MXFP4 dequantization LUT: maps 4-bit index to fp16 mantissa value
-// kvalues: 0, 0.5, 1, 1.5, 2, 3, 4, 6, 0, -0.5, -1, -1.5, -2, -3, -4, -6
-static const __fp16 mxfp4_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
-    0, 0, 0.5, 0, 1, 0, 1.5, 0, 2, 0, 3, 0, 4, 0, 6, 0, 0, 0, -0.5, 0, -1, 0, -1.5, 0, -2, 0, -3, 0, -4, 0, -6, 0,
-};
-
-static const __fp16 iq4_nl_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
-    -127, 0, -104, 0, -83, 0, -65, 0, -49, 0, -35, 0, -22, 0, -10, 0,
-    1,    0, 13,   0, 25,  0, 38,  0, 53,  0, 69,  0, 89,  0, 113, 0,
-};
-
-// Scales per x4x2 logical block: 8 × sizeof(__fp16) = 16 bytes
-#define HMX_X4X2_SCALES_PER_BLK  8
-#define HMX_X4X2_DBLK_SIZE       16  // 8 * 2 bytes (fp16 scales for Q4_0/Q8_0/IQ4_NL)
-#define HMX_X4X2_MXFP4_EBLK_SIZE 8   // 8 * 1 byte  (E8M0 scales for MXFP4)
-
-// Compute the byte stride of one row in x4x2 format.
-// Numerically equals ggml_row_size(type, k) when k is 256-aligned, because
-// x4x2 packing has the same density as block_q4_0 / block_q8_0.
-// Layout per row: [quants: nb*128 (Q4) or nb*256 (Q8)][scales: nb*16 bytes]
-// Total per row = nb * (128+16) = 144*nb (Q4) or nb * (256+16) = 272*nb (Q8).
-// Callers must ensure k is a multiple of 256 (enforced by proc_hmx_matmul_req).
-static inline size_t get_x4x2_row_stride(int weight_type, int k) {
-    int nb = (k + QK_Q4_0x4x2 - 1) / QK_Q4_0x4x2;
-    switch (weight_type) {
-        case HTP_TYPE_Q4_0:
-        case HTP_TYPE_IQ4_NL:
-            return (size_t) nb * (QK_Q4_0x4x2 / 2 + HMX_X4X2_DBLK_SIZE);         // 144 * nb
-        case HTP_TYPE_Q4_1:
-            return (size_t) nb * (QK_Q4_0x4x2 / 2 + 32);                         // 160 * nb
-        case HTP_TYPE_Q8_0:
-            return (size_t) nb * (QK_Q8_0x4x2 + HMX_X4X2_DBLK_SIZE);             // 272 * nb
-        case HTP_TYPE_MXFP4:
-            return (size_t) nb * (QK_MXFP4x4x2 / 2 + HMX_X4X2_MXFP4_EBLK_SIZE);  // 136 * nb
-        case HTP_TYPE_F16:
-            return (size_t) k * sizeof(__fp16);
-        case HTP_TYPE_F32:
-            return (size_t) k * sizeof(float);
-        default:
-            return 0;
-    }
-}
-
-// --- Overflow-safe arithmetic for VTCM budget calculation ---
-
-static inline bool hmx_mul_overflow(size_t a, size_t b, size_t *out) {
-    if (a != 0 && b > SIZE_MAX / a) return true;
-    *out = a * b;
-    return false;
-}
-
-static inline bool hmx_add_overflow(size_t a, size_t b, size_t *out) {
-    if (a > SIZE_MAX - b) return true;
-    *out = a + b;
-    return false;
-}
-
-// Search for optimal (mc, nc) chunk sizes within VTCM budget.
-//
-// VTCM model: nc * per_n_cost + mc * per_m_cost + mc * nc * per_mn_cost + overhead
-//
-// Minimize ceil(m/mc) * m_block_cost + ceil(n/nc) * n_block_cost.
-// All matmul paths repeat weight processing per M-block and activation loading
-// per N-block, so discrete block counts drive total overhead.
-// Tie-break: when cost is equal, prefer larger mc * nc.
-//
-// Caller-provided coefficients:
-//   m_block_cost: penalty per extra M-block (weight redundancy, scales with n).
-//   n_block_cost: penalty per extra N-block (activation redundancy, scales with m).
-//
-// Algorithm: nc sweeps from n_max down by 32, analytically solving for mc_max.
-// Returns 0 on success, -1 if VTCM is insufficient.
-static int hmx_compute_chunks(size_t   vtcm_total,
-                              size_t   overhead,
-                              size_t   per_n_cost,
-                              size_t   per_m_cost,
-                              size_t   per_mn_cost,
-                              int      m,
-                              int      n,
-                              size_t   m_block_cost,
-                              size_t   n_block_cost,
-                              size_t * m_chunk_out,
-                              size_t * n_chunk_out,
-                              size_t * total_out) {
-    if (m <= 0 || n <= 0) return -1;
-    if (vtcm_total <= overhead) return -1;
-    if (per_n_cost == 0 || per_m_cost == 0 || per_mn_cost == 0) return -1;
-
-    const size_t usable = vtcm_total - overhead;
-
-    size_t best_cost = SIZE_MAX;
-    size_t best_mn   = 0;
-    size_t best_m = 0, best_n = 0;
-
-    const size_t n_max = hex_align_down((size_t)n, HMX_FP16_TILE_N_COLS);
-    for (size_t nc = n_max; nc >= HMX_FP16_TILE_N_COLS; nc -= HMX_FP16_TILE_N_COLS) {
-        size_t n_fixed = 0, ncmn = 0, mc_denom = 0;
-        if (hmx_mul_overflow(nc, per_n_cost, &n_fixed)) continue;
-        if (n_fixed >= usable) goto next_nc;
-
-        if (hmx_mul_overflow(nc, per_mn_cost, &ncmn)) goto next_nc;
-        if (hmx_add_overflow(per_m_cost, ncmn, &mc_denom) || mc_denom == 0) goto next_nc;
-
-        {
-            size_t remain = usable - n_fixed;
-            size_t mc = remain / mc_denom;
-            mc = hex_align_down(mc, HMX_FP16_TILE_N_ROWS);
-            mc = hex_smin(mc, (size_t)m);
-
-            if (mc == 0) {
-                goto next_nc;
-            }
-
-            size_t mblocks = ((size_t) m + mc - 1) / mc;
-            size_t nblocks = ((size_t) n + nc - 1) / nc;
-            size_t cost    = mblocks * m_block_cost + nblocks * n_block_cost;
-            size_t mn      = mc * nc;
-            if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
-                best_cost = cost;
-                best_mn   = mn;
-                best_m    = mc;
-                best_n    = nc;
-            }
-        }
-
-next_nc:
-        if (nc == HMX_FP16_TILE_N_COLS) break;  // avoid size_t underflow
-    }
-
-    if (best_m == 0 || best_n == 0) return -1;
-
-    // Compute exact total (with overflow checks)
-    size_t t0 = 0, t1 = 0, t2 = 0, mn = 0, total = 0;
-    if (hmx_mul_overflow(best_n, per_n_cost, &t0)) return -1;
-    if (hmx_mul_overflow(best_m, per_m_cost, &t1)) return -1;
-    if (hmx_mul_overflow(best_m, best_n, &mn)) return -1;
-    if (hmx_mul_overflow(mn, per_mn_cost, &t2)) return -1;
-    if (hmx_add_overflow(t0, t1, &total)) return -1;
-    if (hmx_add_overflow(total, t2, &total)) return -1;
-    if (hmx_add_overflow(total, overhead, &total)) return -1;
-
-    *m_chunk_out = best_m;
-    *n_chunk_out = best_n;
-    *total_out   = total;
-    return 0;
-}
-
-// --- x4x2 format dequantizers ---
-
-// Dequantize one x4x2 Q4_0 group (32 elements from 32 packed bytes) -> 32 FP16 in first 64 bytes.
-// In x4x2, sub-blocks 0..3 use lower nibbles, sub-blocks 4..7 use upper nibbles
-// of the same 32 packed bytes.
-static inline HVX_Vector dequantize_x4x2_q4_0_group_hvx(const uint8_t *packed_32, bool upper_nibbles, const __fp16 *scale, const HVX_Vector vlut_cvt) {
-    (void)vlut_cvt;
-    HVX_Vector vq = hvx_vmemu(packed_32);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
-    HVX_Vector v_scales = hvx_vec_repl_f16(hvx_vmemu(scale));
-
-    HVX_Vector v_quants = Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-
-    HVX_Vector v_int8 = Q6_Vb_vsub_VbVb(v_quants, i8);
-    HVX_Vector v0     = Q6_V_lo_W(Q6_Wh_vunpack_Vb(v_int8));
-    HVX_Vector v_hf   = Q6_Vhf_equals_Vh(v0);
-
-    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales));
-}
-
-// Batch-dequantize 4 contiguous x4x2 Q4_0 groups (4x32 = 128 packed bytes) using
-// full HVX vector width.
-// Output: vector_x2 each hold 32 FP16 values in the first 64 bytes.
-static inline HVX_Vector_x2 dequantize_x4x2_q4_0_x4groups_hvx(
-            const uint8_t *packed_128, bool upper_nibbles,
-            const __fp16 *scales_4, const HVX_Vector vlut_cvt) {
-    (void)vlut_cvt;
-    HVX_Vector vq = hvx_vmemu(packed_128);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
-    HVX_Vector v_quants = Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-
-    HVX_Vector v_int8 = Q6_Vb_vsub_VbVb(v_quants, i8);
-
-    HVX_VectorPair vp_int16 = Q6_Wh_vunpack_Vb(v_int8);
-    HVX_Vector v_lo = Q6_V_lo_W(vp_int16);
-    HVX_Vector v_hi = Q6_V_hi_W(vp_int16);
-
-    v_lo = Q6_Vhf_equals_Vh(v_lo);
-    v_hi = Q6_Vhf_equals_Vh(v_hi);
-
-    HVX_Vector vscale = hvx_vmemu(scales_4);
-    HVX_Vector v_sc01 = hvx_vec_repl_2x_f16(vscale);
-    HVX_Vector v_sc23 = hvx_vec_repl_2x_f16(Q6_V_vror_VR(vscale, 4));
-
-    v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01));
-    v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
-
-    HVX_Vector_x2 r = { v_lo, v_hi };
-    return r;
-}
-
-static inline HVX_Vector dequantize_x4x2_q4_1_group_hvx(const uint8_t *packed_32, bool upper_nibbles, const __fp16 *scale_offset, const HVX_Vector vlut_cvt) {
-    (void)vlut_cvt;
-    HVX_Vector vq = hvx_vmemu(packed_32);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_dm = hvx_vmemu(scale_offset);
-    HVX_Vector v_scales = hvx_vec_repl_f16(v_dm);
-    HVX_Vector v_offsets = hvx_vec_repl_f16(Q6_V_vror_VR(v_dm, 2));
-
-    HVX_Vector v_quants =  Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-
-    HVX_Vector v0   = Q6_V_lo_W(Q6_Wh_vunpack_Vb(v_quants));
-    HVX_Vector v_hf = Q6_Vhf_equals_Vh(v0);
-
-    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales), v_offsets));
-}
-
-static inline HVX_Vector_x2 dequantize_x4x2_q4_1_x4groups_hvx(
-            const uint8_t *packed_128, bool upper_nibbles,
-            const __fp16 *scales_offsets_4, const HVX_Vector vlut_cvt) {
-    (void)vlut_cvt;
-    HVX_Vector vq = hvx_vmemu(packed_128);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_quants = Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-
-    HVX_VectorPair vp_int16 = Q6_Wh_vunpack_Vb(v_quants);
-    HVX_Vector v_lo = Q6_V_lo_W(vp_int16);
-    HVX_Vector v_hi = Q6_V_hi_W(vp_int16);
-
-    v_lo = Q6_Vhf_equals_Vh(v_lo);
-    v_hi = Q6_Vhf_equals_Vh(v_hi);
-
-    HVX_Vector vscale_offset = hvx_vmemu(scales_offsets_4);
-    HVX_VectorPair dm_deal = Q6_W_vdeal_VVR(vscale_offset, vscale_offset, -2);
-    HVX_Vector vd = Q6_V_lo_W(dm_deal);
-    HVX_Vector vm = Q6_V_hi_W(dm_deal);
-
-    HVX_Vector v_sc01 = hvx_vec_repl_2x_f16(vd);
-    HVX_Vector v_sc23 = hvx_vec_repl_2x_f16(Q6_V_vror_VR(vd, 4));
-
-    HVX_Vector v_os01 = hvx_vec_repl_2x_f16(vm);
-    HVX_Vector v_os23 = hvx_vec_repl_2x_f16(Q6_V_vror_VR(vm, 4));
-
-    v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01), v_os01));
-    v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23), v_os23));
-
-    HVX_Vector_x2 r = { v_lo, v_hi };
-    return r;
-}
-
-// LUT-based dequantizers for non-linear IQ4_NL format.
-static inline HVX_Vector dequantize_x4x2_iq4_nl_group_hvx(const uint8_t *packed_32, bool upper_nibbles, const __fp16 *scale, const HVX_Vector vlut_cvt) {
-    HVX_Vector vq = hvx_vmemu(packed_32);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_scales = hvx_vec_repl_f16(hvx_vmemu(scale));
-    HVX_Vector v_quants =  Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-    v_quants = Q6_Vb_vshuff_Vb(v_quants);
-    HVX_VectorPair vp = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
-    HVX_Vector v_hf = Q6_V_lo_W(vp);
-
-    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales));
-}
-
-static inline HVX_Vector_x2 dequantize_x4x2_iq4_nl_x4groups_hvx(
-            const uint8_t *packed_128, bool upper_nibbles,
-            const __fp16 *scales_4, const HVX_Vector vlut_cvt) {
-    HVX_Vector vq = hvx_vmemu(packed_128);
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_quants = Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
-    v_quants = Q6_V_vand_VV(v_quants, mask_h4);
-
-    v_quants = Q6_Vb_vshuff_Vb(v_quants);
-
-    HVX_VectorPair vp = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
-    HVX_Vector v_lo = Q6_V_lo_W(vp);
-    HVX_Vector v_hi = Q6_V_hi_W(vp);
-
-    HVX_Vector vscale = hvx_vmemu(scales_4);
-    HVX_Vector v_sc01 = hvx_vec_repl_2x_f16(vscale);
-    HVX_Vector v_sc23 = hvx_vec_repl_2x_f16(Q6_V_vror_VR(vscale, 4));
-
-    v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01));
-    v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
-
-    HVX_Vector_x2 r = { v_lo, v_hi };
-    return r;
-}
-
-// Dequantize one x4x2 Q8_0 group (32 int8 quants) -> 32 FP16 in first 64 bytes.
-static inline HVX_Vector dequantize_x4x2_q8_0_group_hvx(const int8_t *quants_32, const __fp16 *scale) {
-    HVX_Vector vq       = hvx_vmemu(quants_32);
-    HVX_Vector v_scales = hvx_vec_repl_f16(hvx_vmemu(scale));
-    HVX_Vector v0       = Q6_V_lo_W(Q6_Wh_vunpack_Vb(vq));
-    HVX_Vector v_hf     = Q6_Vhf_equals_Vh(v0);
-    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_scales));
-}
-
-// --- MXFP4 E8M0 scale conversion and dequantization ---
-//
-// HVX batch-convert 8 E8M0 bytes (one x4x2 block's scales) to __fp16[8] on stack.
-// Scalar loads from the stack array execute on the scalar pipeline, in parallel
-// with HVX vlut16/vmpy/vscatter — freeing HVX slots in the hot loop.
-// Arithmetic: fp16_bits = clamp(e - 112, 0, 30) << 10
-// e=0..112 -> 0 (underflow), e=113..142 -> valid fp16, e>=143 -> clamped to 2^15.
-
-typedef struct {
-    __fp16 v[8] __attribute__((aligned(16)));
-} mxfp4_scales_t;
-
-static inline mxfp4_scales_t mxfp4_convert_scales(const uint8_t * e8m0_8) {
-    mxfp4_scales_t s;
-    HVX_Vector     v  = hvx_vmemu(e8m0_8);
-    HVX_Vector     vh = Q6_V_lo_W(Q6_Wuh_vunpack_Vub(v));
-    vh                = Q6_Vh_vsub_VhVh(vh, Q6_Vh_vsplat_R(112));
-    vh                = Q6_Vh_vmax_VhVh(vh, Q6_V_vzero());
-    vh                = Q6_Vh_vmin_VhVh(vh, Q6_Vh_vsplat_R(30));
-    vh                = Q6_Vh_vasl_VhR(vh, 10);
-    hvx_vec_store_u(s.v, 16, vh);
-    return s;
-}
-
-static inline HVX_Vector mxfp4_extract_splat(mxfp4_scales_t scales, int idx) {
-    return hvx_vec_splat_f16(scales.v[idx]);
-}
-
-// Dequantize one x4x2 MXFP4 group (32 elements from 32 packed bytes) -> 32 FP16.
-static inline HVX_Vector dequantize_x4x2_mxfp4_group_hvx(const uint8_t *  packed_32,
-                                                         bool             upper_nibbles,
-                                                         int              sub_blk,
-                                                         const HVX_Vector vlut_cvt,
-                                                         mxfp4_scales_t   scales) {
-    HVX_Vector       vq       = hvx_vmemu(packed_32);
-    const HVX_Vector mask_h4  = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector       v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
-    v_quants                  = Q6_V_vand_VV(v_quants, mask_h4);
-
-    HVX_Vector v_sc = mxfp4_extract_splat(scales, sub_blk);
-
-    v_quants            = Q6_Vb_vshuff_Vb(v_quants);
-    HVX_VectorPair vp   = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
-    HVX_Vector     v_hf = Q6_V_lo_W(vp);
-
-    return Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hf, v_sc));
-}
-
-// Batch-dequantize 4 contiguous x4x2 MXFP4 groups (4x32 = 128 packed bytes).
-static inline HVX_Vector_x4 dequantize_x4x2_mxfp4_x4groups_hvx(const uint8_t *  packed_128,
-                                                      bool             upper_nibbles,
-                                                      int              sub_blk_base,
-                                                      const HVX_Vector vlut_cvt,
-                                                      mxfp4_scales_t   scales) {
-    HVX_Vector       vq       = hvx_vmemu(packed_128);
-    const HVX_Vector mask_h4  = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector       v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
-    v_quants                  = Q6_V_vand_VV(v_quants, mask_h4);
-
-    v_quants = Q6_Vb_vshuff_Vb(v_quants);
-
-    HVX_VectorPair vp   = Q6_Wh_vlut16_VbVhR(v_quants, vlut_cvt, 0);
-    HVX_Vector     v_lo = Q6_V_lo_W(vp);
-    HVX_Vector     v_hi = Q6_V_hi_W(vp);
-
-    HVX_VectorPred q64    = Q6_Q_vsetq_R(64);
-    HVX_Vector     v_sc01 = Q6_V_vmux_QVV(q64, mxfp4_extract_splat(scales, sub_blk_base + 0),
-                                          mxfp4_extract_splat(scales, sub_blk_base + 1));
-    HVX_Vector     v_sc23 = Q6_V_vmux_QVV(q64, mxfp4_extract_splat(scales, sub_blk_base + 2),
-                                          mxfp4_extract_splat(scales, sub_blk_base + 3));
-
-    v_lo = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_lo, v_sc01));
-    v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
-
-    HVX_Vector_x4 r = { v_lo, Q6_V_vror_VR(v_lo, 64), v_hi, Q6_V_vror_VR(v_hi, 64) };
-    return r;
-}
-
-typedef struct {
-    __fp16                  *dst;
-    const uint8_t           *src;
-    int                      n_cols;
-    int                      k_block;
-    size_t                   row_stride;
-    int                      weight_type;
-    int                      n_tot_tiles;
-    int                      n_tiles_per_task;
-    int                      n_tasks;
-    int                      n_k_tiles;
-    struct fastdiv_values    n_k_tiles_div;
-    struct htp_thread_trace * traces;
-} x4x2_dequantize_state_t;
-
-// Dequantize a tile range from x4x2 weight data (already in VTCM) to tile-major FP16.
-// Input:  vtcm_src has n_cols rows of x4x2 data, each row_stride bytes.
-// Output: vtcm_dst in tile-major FP16 layout.
-
-#define DEFINE_DEQUANTIZE_Q4_TASK(suffix, lut_name, helper_prefix, dblk_size, scale_step)                      \
-static void dequantize_x4x2_weight_to_fp16_tiles_task_##suffix(                                                \
-        const x4x2_dequantize_state_t *state,                                                                  \
-        int start_tile, int end_tile) {                                                                        \
-                                                                                                               \
-    const int n_k_tiles = state->n_k_tiles;                                                                    \
-    const int qrow_size = (unsigned)state->k_block / 2;                                                        \
-    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;                                          \
-    const HVX_Vector vlut_cvt = hvx_vmem(lut_name);                                                            \
-                                                                                                               \
-    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);                                   \
-    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);                                                          \
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);                                                          \
-                                                                                                               \
-    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);                                               \
-    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);                                 \
-                                                                                                               \
-    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {                                                  \
-        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }                                                       \
-                                                                                                               \
-        if ((kt % 4 == 0) && (t + 4 <= (unsigned)end_tile) && (fastdiv(t + 3, &n_k_tiles_div) == ct)) {        \
-            unsigned blk_idx      = ((kt * 32) / QK_Q4_0x4x2);                                                 \
-            unsigned sub_blk_base = ((kt * 32) % QK_Q4_0x4x2) / 32;                                            \
-            bool upper            = (sub_blk_base >= 4);                                                       \
-            unsigned packed_off   = blk_idx * (QK_Q4_0x4x2 / 2);                                               \
-            unsigned scale_off    = qrow_size + blk_idx * (dblk_size) + sub_blk_base * (scale_step);           \
-                                                                                                               \
-            __fp16 *tile_bases[4];                                                                             \
-            for (unsigned g = 0; g < 4; g++) {                                                                 \
-                tile_bases[g] = state->dst + (t + g) * HMX_FP16_TILE_N_ELMS;                                   \
-            }                                                                                                  \
-                                                                                                               \
-            HVX_Vector v_off = v_scat_base;                                                                    \
-            unsigned row_offset = ct * HMX_FP16_TILE_N_COLS * state->row_stride;                               \
-                                                                                                               \
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {                                                \
-                const uint8_t *r0 = state->src + row_offset; row_offset += state->row_stride;                  \
-                const uint8_t *r1 = state->src + row_offset; row_offset += state->row_stride;                  \
-                                                                                                               \
-                HVX_Vector_x2 dv0 = dequantize_x4x2_##helper_prefix##_x4groups_hvx(                            \
-                    r0 + packed_off, upper, (const __fp16 *)(r0 + scale_off), vlut_cvt);                       \
-                Q6_vscatter_RMVwV((size_t)tile_bases[0], 2 * HMX_FP16_TILE_SIZE - 1, v_off, dv0.v[0]);         \
-                Q6_vscatter_RMVwV((size_t)tile_bases[2], 2 * HMX_FP16_TILE_SIZE - 1, v_off, dv0.v[1]);         \
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);                                                   \
-                                                                                                               \
-                HVX_Vector_x2 dv1 = dequantize_x4x2_##helper_prefix##_x4groups_hvx(                            \
-                    r1 + packed_off, upper, (const __fp16 *)(r1 + scale_off), vlut_cvt);                       \
-                Q6_vscatter_RMVwV((size_t)tile_bases[0], 2 * HMX_FP16_TILE_SIZE - 1, v_off, dv1.v[0]);         \
-                Q6_vscatter_RMVwV((size_t)tile_bases[2], 2 * HMX_FP16_TILE_SIZE - 1, v_off, dv1.v[1]);         \
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);                                                   \
-            }                                                                                                  \
-                                                                                                               \
-            for (int g = 0; g < 4; g++) { (void) *(volatile HVX_Vector *)(tile_bases[g]); }                    \
-            t += 4; kt += 4;                                                                                   \
-            continue;                                                                                          \
-        }                                                                                                      \
-                                                                                                               \
-        __fp16 *tile_base = state->dst + t * HMX_FP16_TILE_N_ELMS;                                             \
-        {                                                                                                      \
-            unsigned blk_idx   = (kt * 32) / QK_Q4_0x4x2;                                                      \
-            unsigned sub_blk   = ((kt * 32) % QK_Q4_0x4x2) / 32;                                               \
-            bool upper         = (sub_blk >= 4);                                                               \
-            unsigned byte_off  = blk_idx * (QK_Q4_0x4x2 / 2) + (upper ? (sub_blk - 4) : sub_blk) * 32;         \
-            unsigned scale_off = qrow_size + blk_idx * (dblk_size) + sub_blk * (scale_step);                   \
-                                                                                                               \
-            HVX_Vector v_off = v_scat_base;                                                                    \
-            unsigned row_offset = ct * HMX_FP16_TILE_N_COLS * state->row_stride;                               \
-            unsigned row1 = ct * HMX_FP16_TILE_N_COLS + 1;                                                     \
-                                                                                                               \
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2, row1 += 2) {                                     \
-                const uint8_t *r0 = state->src + row_offset; row_offset += state->row_stride;                  \
-                const uint8_t *r1 = state->src + row_offset; row_offset += state->row_stride;                  \
-                                                                                                               \
-                HVX_Vector v0 = dequantize_x4x2_##helper_prefix##_group_hvx(                                   \
-                    r0 + byte_off, upper, (const __fp16 *)(r0 + scale_off), vlut_cvt);                         \
-                HVX_Vector v1 = (row1 < (unsigned)state->n_cols)                                               \
-                    ? dequantize_x4x2_##helper_prefix##_group_hvx(                                             \
-                        r1 + byte_off, upper, (const __fp16 *)(r1 + scale_off), vlut_cvt)                      \
-                    : Q6_V_vzero();                                                                            \
-                                                                                                               \
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);            \
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);                                                   \
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v1);            \
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);                                                   \
-            }                                                                                                  \
-            (void) *(volatile HVX_Vector *)(tile_base);                                                        \
-        }                                                                                                      \
-        ++t; ++kt;                                                                                             \
-    }                                                                                                          \
-                                                                                                               \
-    if (start_tile < end_tile) {                                                                               \
-        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HMX_FP16_TILE_N_ELMS);                   \
-    }                                                                                                          \
-}                                                                                                              \
-                                                                                                               \
-static void dequantize_x4x2_worker_loop_##suffix(unsigned int n, unsigned int i, void *data) {                 \
-    x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;                                          \
-    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;                                   \
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                                 \
-    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {                     \
-        int start = task_id * state->n_tiles_per_task;                                                         \
-        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);                             \
-        dequantize_x4x2_weight_to_fp16_tiles_task_##suffix(state, start, end);                                 \
-    }                                                                                                          \
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                                 \
-}
-
-DEFINE_DEQUANTIZE_Q4_TASK(q4_0,   q4_0_to_fp16_lut,   q4_0, HMX_X4X2_DBLK_SIZE, (int)sizeof(__fp16))
-DEFINE_DEQUANTIZE_Q4_TASK(q4_1,   q4_1_to_fp16_lut,   q4_1, 32, 4)
-DEFINE_DEQUANTIZE_Q4_TASK(iq4_nl, iq4_nl_to_fp16_lut, iq4_nl, HMX_X4X2_DBLK_SIZE, (int)sizeof(__fp16))
-
-static void dequantize_x4x2_weight_to_fp16_tiles_task_mxfp4(
-        const x4x2_dequantize_state_t *state,
-        int start_tile, int end_tile) {
-
-    const int n_k_tiles = state->n_k_tiles;
-    const int qrow_size = (unsigned)state->k_block / 2;
-    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
-    const HVX_Vector vlut_cvt = hvx_vmem(mxfp4_to_fp16_lut);
-
-    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
-
-    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
-    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
-
-    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
-        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
-
-        // Batch-4 fast path for MXFP4
-        if ((kt % 4 == 0) && (t + 4 <= (unsigned)end_tile) && (fastdiv(t + 3, &n_k_tiles_div) == ct)) {
-            int  blk_idx      = (kt * 32) / QK_MXFP4x4x2;
-            int  sub_blk_base = ((kt * 32) % QK_MXFP4x4x2) / 32;
-            bool upper        = (sub_blk_base >= 4);
-            int  packed_off   = blk_idx * (QK_MXFP4x4x2 / 2);
-            int  e8m0_blk_off = qrow_size + blk_idx * HMX_X4X2_MXFP4_EBLK_SIZE;
-
-            __fp16 * tile_bases[4];
-            for (int g = 0; g < 4; g++) {
-                tile_bases[g] = state->dst + (t + g) * HMX_FP16_TILE_N_ELMS;
-            }
-
-            HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int             row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int             row1 = row0 + 1;
-                const uint8_t * r0   = state->src + row0 * state->row_stride;
-                const uint8_t * r1   = state->src + row1 * state->row_stride;
-
-                mxfp4_scales_t r0_e8 = mxfp4_convert_scales(r0 + e8m0_blk_off);
-
-                HVX_Vector_x4 dv0, dv1;
-                dv0 = dequantize_x4x2_mxfp4_x4groups_hvx(r0 + packed_off, upper, sub_blk_base, vlut_cvt, r0_e8);
-                if (row1 < state->n_cols) {
-                    mxfp4_scales_t r1_e8 = mxfp4_convert_scales(r1 + e8m0_blk_off);
-                    dv1 = dequantize_x4x2_mxfp4_x4groups_hvx(r1 + packed_off, upper, sub_blk_base, vlut_cvt, r1_e8);
-                } else {
-                    dv1.v[0] = dv1.v[1] = dv1.v[2] = dv1.v[3] = Q6_V_vzero();
-                }
-
-                for (int g = 0; g < 4; g++) {
-                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, dv0.v[g]);
-                }
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-                for (int g = 0; g < 4; g++) {
-                    Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, dv1.v[g]);
-                }
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-            }
-
-            for (int g = 0; g < 4; g++) {
-                (void) *(volatile HVX_Vector *) (tile_bases[g]);
-            }
-
-            t += 4; kt += 4;
-            continue;
-        }
-
-        // Single-tile fallback
-        __fp16 *tile_base = state->dst + t * HMX_FP16_TILE_N_ELMS;
-        {
-            int  blk_idx      = (kt * 32) / QK_MXFP4x4x2;
-            int  sub_blk      = ((kt * 32) % QK_MXFP4x4x2) / 32;
-            bool upper        = (sub_blk >= 4);
-            int  byte_off     = blk_idx * (QK_MXFP4x4x2 / 2) + (upper ? (sub_blk - 4) : sub_blk) * 32;
-            int  e8m0_blk_off = qrow_size + blk_idx * HMX_X4X2_MXFP4_EBLK_SIZE;
-
-            HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-
-                const uint8_t * r0 = state->src + row0 * state->row_stride;
-                const uint8_t * r1 = state->src + row1 * state->row_stride;
-
-                mxfp4_scales_t r0_e8 = mxfp4_convert_scales(r0 + e8m0_blk_off);
-
-                HVX_Vector v0 = dequantize_x4x2_mxfp4_group_hvx(r0 + byte_off, upper, sub_blk, vlut_cvt, r0_e8);
-                HVX_Vector v1;
-                if (row1 < state->n_cols) {
-                    mxfp4_scales_t r1_e8 = mxfp4_convert_scales(r1 + e8m0_blk_off);
-                    v1 = dequantize_x4x2_mxfp4_group_hvx(r1 + byte_off, upper, sub_blk, vlut_cvt, r1_e8);
-                } else {
-                    v1 = Q6_V_vzero();
-                }
-
-                Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-                Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v1);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-            }
-            (void) *(volatile HVX_Vector *) (tile_base);
-        }
-        ++t; ++kt;
-    }
-
-    if (start_tile < end_tile) {
-        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HMX_FP16_TILE_N_ELMS);
-    }
-}
-
-static void dequantize_x4x2_worker_loop_mxfp4(unsigned int n, unsigned int i, void *data) {
-    x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
-    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
-        int start = task_id * state->n_tiles_per_task;
-        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
-        dequantize_x4x2_weight_to_fp16_tiles_task_mxfp4(state, start, end);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-}
-
-static void dequantize_x4x2_weight_to_fp16_tiles_task_q8_0(
-        const x4x2_dequantize_state_t *state,
-        int start_tile, int end_tile) {
-
-    const int n_k_tiles = state->n_k_tiles;
-    const int qrow_size = state->k_block;
-    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
-
-    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
-
-    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
-    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
-
-    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
-        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
-
-        __fp16 *tile_base = state->dst + t * HMX_FP16_TILE_N_ELMS;
-        {
-            int blk_idx  = (kt * 32) / QK_Q8_0x4x2;
-            int sub_blk  = ((kt * 32) % QK_Q8_0x4x2) / 32;
-            int byte_off  = blk_idx * QK_Q8_0x4x2 + sub_blk * 32;
-            int scale_off = qrow_size + blk_idx * HMX_X4X2_DBLK_SIZE + sub_blk * (int)sizeof(__fp16);
-
-            HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-
-                const uint8_t *r0 = state->src + row0 * state->row_stride;
-                const uint8_t *r1 = state->src + row1 * state->row_stride;
-
-                HVX_Vector v0 = dequantize_x4x2_q8_0_group_hvx((const int8_t *)(r0 + byte_off), (const __fp16 *)(r0 + scale_off));
-                HVX_Vector v1 = (row1 < state->n_cols) ? dequantize_x4x2_q8_0_group_hvx((const int8_t *)(r1 + byte_off), (const __fp16 *)(r1 + scale_off)) : Q6_V_vzero();
-
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v1);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-            }
-            (void) *(volatile HVX_Vector *)(tile_base);
-        }
-        ++t; ++kt;
-    }
-
-    if (start_tile < end_tile) {
-        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HMX_FP16_TILE_N_ELMS);
-    }
-}
-
-static void dequantize_x4x2_worker_loop_q8_0(unsigned int n, unsigned int i, void *data) {
-    x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
-    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
-        int start = task_id * state->n_tiles_per_task;
-        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
-        dequantize_x4x2_weight_to_fp16_tiles_task_q8_0(state, start, end);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-}
-
-static void convert_f16_weight_to_fp16_tiles_task(
-        const x4x2_dequantize_state_t *state,
-        int start_tile, int end_tile) {
-
-    const int n_k_tiles = state->n_k_tiles;
-    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
-
-    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
-
-    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
-    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
-
-    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
-        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
-
-        __fp16 *tile_base = state->dst + t * HMX_FP16_TILE_N_ELMS;
-        {
-            int byte_off = kt * 32 * sizeof(__fp16);
-
-            HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-
-                const uint8_t *r0 = state->src + row0 * state->row_stride;
-                const uint8_t *r1 = state->src + row1 * state->row_stride;
-
-                HVX_Vector v0 = hvx_vmemu((const __fp16 *)(r0 + byte_off));
-                HVX_Vector v1 = (row1 < state->n_cols) ? hvx_vmemu((const __fp16 *)(r1 + byte_off)) : Q6_V_vzero();
-
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v0);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v1);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-            }
-            (void) *(volatile HVX_Vector *)(tile_base);
-        }
-        ++t; ++kt;
-    }
-
-    if (start_tile < end_tile) {
-        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HMX_FP16_TILE_N_ELMS);
-    }
-}
-
-static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) {
-    x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
-    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
-        int start = task_id * state->n_tiles_per_task;
-        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
-        convert_f16_weight_to_fp16_tiles_task(state, start, end);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-}
-
-static void quantize_f32_weight_to_fp16_tiles_task(
-        const x4x2_dequantize_state_t *state,
-        int start_tile, int end_tile) {
-
-    const int n_k_tiles = state->n_k_tiles;
-    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
-
-    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
-    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
-    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
-
-    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
-    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
-
-    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
-        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
-
-        __fp16 *tile_base = state->dst + t * HMX_FP16_TILE_N_ELMS;
-        {
-            int byte_off = kt * 32 * sizeof(float);
-
-            HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-
-                const uint8_t *r0 = state->src + row0 * state->row_stride;
-                const uint8_t *r1 = state->src + row1 * state->row_stride;
-
-                HVX_Vector v0_f32 = hvx_vmemu((const float *)(r0 + byte_off));
-                HVX_Vector v1_f32 = (row1 < state->n_cols) ? hvx_vmemu((const float *)(r1 + byte_off)) : Q6_V_vzero();
-
-                HVX_Vector v_out = hvx_vec_f32_to_f16(v0_f32, v1_f32);
-
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v_out);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-
-                HVX_Vector v_out_hi = Q6_V_vror_VR(v_out, 64);
-                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HMX_FP16_TILE_SIZE - 1, v_off, v_out_hi);
-                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-            }
-            (void) *(volatile HVX_Vector *)(tile_base);
-        }
-        ++t; ++kt;
-    }
-
-    if (start_tile < end_tile) {
-        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HMX_FP16_TILE_N_ELMS);
-    }
-}
-
-static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) {
-    x4x2_dequantize_state_t *state = (x4x2_dequantize_state_t *)data;
-    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
-        int start = task_id * state->n_tiles_per_task;
-        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
-        quantize_f32_weight_to_fp16_tiles_task(state, start, end);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
-}
-
-
-static void dequantize_x4x2_weight_chunk_to_fp16_tiles(
-        struct htp_context *ctx, __fp16 *vtcm_dst,
-        const void *vtcm_src, int n_cols, int k_block,
-        size_t row_stride, int weight_type,
-        int n_k_tiles, struct fastdiv_values n_k_tiles_div,
-        worker_callback_t dequant_worker_fn, int n_threads) {
-
-    assert(n_cols  % HMX_FP16_TILE_N_COLS == 0);
-    assert(k_block % HMX_FP16_TILE_N_COLS == 0);
-
-    size_t n_col_tiles = n_cols / HMX_FP16_TILE_N_COLS;
-    size_t n_tot_tiles = n_col_tiles * n_k_tiles;
-
-    size_t n_tiles_per_task = (n_threads == 1) ? n_tot_tiles : hmx_ceil_div(n_tot_tiles, n_threads);
-
-    x4x2_dequantize_state_t state;
-    state.n_tasks          = (n_tot_tiles + n_tiles_per_task - 1) / n_tiles_per_task;
-    state.n_tot_tiles      = n_tot_tiles;
-    state.n_tiles_per_task = n_tiles_per_task;
-    state.dst              = vtcm_dst;
-    state.src              = (const uint8_t *)vtcm_src;
-    state.n_cols           = n_cols;
-    state.k_block          = k_block;
-    state.row_stride       = row_stride;
-    state.weight_type      = weight_type;
-    state.n_k_tiles        = n_k_tiles;
-    state.n_k_tiles_div    = n_k_tiles_div;
-    state.traces           = ctx ? ctx->trace : NULL;
-
-    if (state.n_tasks == 1 || n_threads == 1) {
-        dequant_worker_fn(1, 0, &state);
-    } else {
-        worker_pool_run_func(ctx->worker_pool, dequant_worker_fn, &state, n_threads);
-    }
-}
-
-// --- End x4x2 dequantizers ---
-
-#pragma clang diagnostic ignored "-Wbackend-plugin" // spurios warning for hmx intrinsics
-
-// requires external HMX lock
-static void core_dot_chunk_fp16(__fp16 *restrict output, const __fp16 *restrict activation, const __fp16 *restrict weight, const __fp16 *restrict scales,
-                                int n_row_tiles, int n_col_tiles, int n_dot_tiles) {
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(n_col_tiles > 0);
-    __builtin_assume(n_dot_tiles > 0);
-
-    Q6_bias_mxmem2_A((void *)scales);
-    for (int r = 0; r < n_row_tiles; ++r) {
-        for (size_t c = 0; c < n_col_tiles; ++c) {
-            Q6_mxclracc_hf();
-
-            const __fp16 *row_tiles = activation + r * n_dot_tiles * HMX_FP16_TILE_N_ELMS;
-            const __fp16 *col_tiles = weight + c * n_dot_tiles * HMX_FP16_TILE_N_ELMS;
-
-            for (int k = 0, k_block; k < n_dot_tiles; k += k_block) {
-                k_block = hex_smin(n_dot_tiles - k, 32);
-                const uint32_t range = 2048u * (uint32_t)k_block - 1;
-                Q6_activation_hf_mxmem_RR_deep((unsigned int)row_tiles, range);
-                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, range);
-                row_tiles += k_block * HMX_FP16_TILE_N_ELMS;
-                col_tiles += k_block * HMX_FP16_TILE_N_ELMS;
-            }
-
-            __fp16 *out_tile = output + (r * n_col_tiles + c) * HMX_FP16_TILE_N_ELMS;
-            Q6_mxmem_AR_after_hf(out_tile, 0);
-        }
-    }
-}
-
-// --- Async HMX matmul job (for pipeline overlap) ---
-
-typedef struct {
-    __fp16 *       output;
-    const __fp16 * activation;
-    const __fp16 * weight;
-    const __fp16 * scales;
-    uint32_t       n_row_tiles;
-    uint32_t       n_col_tiles;
-    uint32_t       n_dot_tiles;
-} hmx_matmul_job_t;
-
-static void hmx_matmul_worker_fn(void * data) {
-    hmx_matmul_job_t * job = (hmx_matmul_job_t *) data;
-    FARF(HIGH, "hmx-mm-job: n_row_tiles %u n_col_tiles %u n_dot_tiles %u", job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
-    core_dot_chunk_fp16(job->output, job->activation, job->weight, job->scales, job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
-}
-
-static inline void hmx_matmul_job_init(hmx_matmul_job_t * job,
-                                       __fp16 *           output,
-                                       const __fp16 *     activation,
-                                       const __fp16 *     weight,
-                                       const __fp16 *     scales,
-                                       int                n_row_tiles,
-                                       int                n_col_tiles,
-                                       int                n_dot_tiles) {
-    job->output      = output;
-    job->activation  = activation;
-    job->weight      = weight;
-    job->scales      = scales;
-    job->n_row_tiles = n_row_tiles;
-    job->n_col_tiles = n_col_tiles;
-    job->n_dot_tiles = n_dot_tiles;
-}
-
-// output : fp16 -> f32p
-
-static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16 *restrict vtcm_src, int n_rows, int n_cols, int n) {
-    assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
-    const size_t tile_row_stride = (n_cols / HMX_FP16_TILE_N_COLS) * HMX_FP16_TILE_N_ELMS;
-
-    const HVX_Vector one = hvx_vec_splat_f16(1.0);
-
-    for (size_t r = 0; r < n_rows; r += 2) {
-        const size_t r0 = r / HMX_FP16_TILE_N_ROWS;
-        const size_t r1 = (r % HMX_FP16_TILE_N_ROWS) / 2;  // index of the row pair within the tile
-        const __fp16 *row_base = vtcm_src + r0 * tile_row_stride;
-        float *output_row_base = dst + r * n;  // global memory row base for row r (and r+1)
-
-        #pragma unroll(4)
-        for (size_t c = 0; c < n_cols; c += HMX_FP16_TILE_N_COLS) {
-            const size_t c0 = c / HMX_FP16_TILE_N_COLS;
-            const __fp16 *tile = row_base + c0 * HMX_FP16_TILE_N_ELMS;
-            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
-            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
-
-            volatile HVX_Vector *pv_out0 = (volatile HVX_Vector *) (output_row_base + c + 0);
-            volatile HVX_Vector *pv_out1 = (volatile HVX_Vector *) (output_row_base + c + n);  // next row in global memory
-
-            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
-            if (r + 1 < n_rows) {
-                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
-            }
-        }
-    }
-}
-
-typedef struct {
-    const __fp16  *vtcm_src;
-    float         *dst;
-    int            n_tasks;
-    int            n_tot_chunks;
-    int            n_chunks_per_task;
-    int            n_cols;
-    int            n;  // DDR row stride (total output columns)
-    struct htp_thread_trace * traces;
-} output_transfer_task_state_t;
-
-static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
-    output_transfer_task_state_t *st = (output_transfer_task_state_t *) data;
-    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
-
-    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
-        int    chunk_idx  = task_id * st->n_chunks_per_task;
-        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
-
-        float        *dst      = st->dst      + chunk_idx * st->n;
-        const __fp16 *vtcm_src = st->vtcm_src + chunk_idx * st->n_cols;
-        transfer_output_chunk_fp16_to_fp32(dst, vtcm_src, chunk_size, st->n_cols, st->n);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
-}
-
-static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const __fp16 *vtcm_src,
-                                              int n_rows, int n_cols, int n, int n_threads) {
-    assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
-
-    size_t n_tot_chunks      = n_rows;
-    size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : HMX_FP16_TILE_N_ROWS;  // must be multiple of HMX_FP16_TILE_N_ROWS (32)
-
-    output_transfer_task_state_t state;
-    state.n_tasks           = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task;
-    state.n_tot_chunks      = n_tot_chunks;
-    state.n_chunks_per_task = n_chunks_per_task;
-    state.dst               = dst;
-    state.vtcm_src          = vtcm_src;
-    state.n_cols            = n_cols;
-    state.n                 = n;
-    state.traces            = ctx ? ctx->trace : NULL;
-
-    if (state.n_tasks == 1 || n_threads == 1) {
-        transfer_output_chunk_worker_fn(1, 0, &state);
-    } else {
-        worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_worker_fn, &state, n_threads);
-    }
-}
-
-// activations : fp32 -> fp16
-
-static void transfer_activation_chunk_fp32_to_fp16(__fp16 *restrict vtcm_dst, const float *restrict src, int n_rows, int k_block, int k_stride) {
-    const int n_rows_padded = hex_align_up(n_rows, HMX_FP16_TILE_N_ROWS);
-    const int n_rows_tiled  = (n_rows / HMX_FP16_TILE_N_ROWS) * HMX_FP16_TILE_N_ROWS;
-
-    int r = 0;
-
-    #pragma unroll(2)
-    for (r = 0; r < n_rows_tiled; r += 2) {
-        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
-        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
-
-        const HVX_Vector *pv_in0 = (const HVX_Vector *) (src + (r + 0) * k_stride);
-        const HVX_Vector *pv_in1 = (const HVX_Vector *) (src + (r + 1) * k_stride);
-        for (int c = 0; c < k_block; c += 32) {
-            HVX_Vector v0 = *pv_in0++;
-            HVX_Vector v1 = *pv_in1++;
-
-            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
-
-            // compute output position
-            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
-            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
-
-            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
-            tile[r1 / 2]     = v_out;
-        }
-    }
-
-    for (; r < n_rows_padded; r += 2) {
-        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
-        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
-
-        const bool row0_valid = r       < n_rows;
-        const bool row1_valid = (r + 1) < n_rows;
-
-        const HVX_Vector *pv_in0 = row0_valid ? (const HVX_Vector *) (src + (r + 0) * k_stride) : NULL;
-        const HVX_Vector *pv_in1 = row1_valid ? (const HVX_Vector *) (src + (r + 1) * k_stride) : NULL;
-        for (int c = 0; c < k_block; c += 32) {
-            HVX_Vector v0 = row0_valid ? *pv_in0++ : Q6_V_vzero();
-            HVX_Vector v1 = row1_valid ? *pv_in1++ : Q6_V_vzero();
-
-            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
-
-            // compute output position
-            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
-            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
-
-            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
-            tile[r1 / 2]     = v_out;
-        }
-    }
-}
-
-typedef struct {
-    __fp16      *dst;
-    const float *src;
-    int          n_tasks;
-    int          n_tot_chunks;
-    int          n_chunks_per_task;
-    int          k_block;
-    int          k_stride;
-    struct htp_thread_trace * traces;
-} activation_transfer_task_state_t;
-
-static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
-    activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data;
-    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
-
-    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
-        // one chunk: one row
-        int    chunk_idx  = task_id * st->n_chunks_per_task;
-        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
-
-        __fp16      *dst = st->dst + chunk_idx * st->k_block;
-        const float *src = st->src + chunk_idx * st->k_stride;
-        transfer_activation_chunk_fp32_to_fp16(dst, src, chunk_size, st->k_block, st->k_stride);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
-}
-
-static void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, const float *src, int n_rows, int k_block, int k_stride, int n_threads) {
-    assert(k_block % HMX_FP16_TILE_N_COLS == 0 && k_stride % HMX_FP16_TILE_N_COLS == 0);
-    assert(VLEN == 32 * sizeof(float));
-
-    size_t n_tot_chunks      = n_rows;
-    size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : 32;  // must be multiple of 32 to ensure correct destination address
-
-    activation_transfer_task_state_t state;
-    state.n_tasks           = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task;
-    state.n_tot_chunks      = n_tot_chunks;
-    state.n_chunks_per_task = n_chunks_per_task;
-    state.dst               = dst;
-    state.src               = src;
-    state.k_block           = k_block;
-    state.k_stride          = k_stride;
-    state.traces            = ctx ? ctx->trace : NULL;
-
-    if (state.n_tasks == 1 || n_threads == 1) {
-        transfer_activation_chunk_worker_fn(1, 0, &state);
-    } else {
-        worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_worker_fn, &state, n_threads);
-    }
-}
-
-// C += AB
-static void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, const __fp16 *restrict b,
-                                const __fp16 *restrict col_scales, const __fp16 *restrict eye_tile,
-                                int n_row_tiles, int n_col_tiles, int n_dot_tiles, bool zero_init) {
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(n_col_tiles > 0);
-    __builtin_assume(n_dot_tiles > 0);
-
-    Q6_bias_mxmem2_A((void *)col_scales);
-
-    const size_t dot_tile_stride = n_dot_tiles * HMX_FP16_TILE_N_ELMS;
-    for (size_t i = 0; i < n_row_tiles; ++i) {
-        const __fp16 *row_base = a + i * dot_tile_stride;
-        __fp16 *res_base = c + i * n_col_tiles * HMX_FP16_TILE_N_ELMS;
-        for (size_t j = 0; j < n_col_tiles; ++j) {
-            Q6_mxclracc_hf();
-
-            const __fp16 *col_tiles = b + j * dot_tile_stride;
-            const __fp16 *row_tiles = row_base;
-            __fp16 *accum_tile = res_base + j * HMX_FP16_TILE_N_ELMS;
-            if (!zero_init) {
-                Q6_activation_hf_mxmem_RR((unsigned int)accum_tile, 2047);
-                Q6_weight_hf_mxmem_RR((unsigned int)eye_tile, 2047);
-            }
-
-            for (int k = 0, k_block; k < n_dot_tiles; k += k_block) {
-                k_block = hex_smin(n_dot_tiles - k, 32);
-                const uint32_t range = 2048u * (uint32_t)k_block - 1;
-                Q6_activation_hf_mxmem_RR_deep((unsigned int)row_tiles, range);
-                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, range);
-                row_tiles += k_block * HMX_FP16_TILE_N_ELMS;
-                col_tiles += k_block * HMX_FP16_TILE_N_ELMS;
-            }
-
-            Q6_mxmem_AR_after_hf(accum_tile, 0);
-        }
-    }
-}
-
-int hmx_matmul_2d_f32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
-                                     const uint8_t *restrict permuted_weight, int m, int k, int n,
-                                     int act_stride, int weight_stride, int weight_type) {
-    if (k % 32 != 0 || n % 32 != 0) { return -1; }
-
-    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN) || !hex_is_aligned(permuted_weight, VLEN)) {
-        return -1;
-    }
-
-    size_t row_stride = get_x4x2_row_stride(weight_type, k);
-    if (row_stride == 0) {
-        return -1;
-    }
-
-    worker_callback_t dequant_worker_fn = NULL;
-    switch (weight_type) {
-        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_x4x2_worker_loop_q4_0; break;
-        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_x4x2_worker_loop_iq4_nl; break;
-        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_x4x2_worker_loop_q4_1; break;
-        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_x4x2_worker_loop_mxfp4; break;
-        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_x4x2_worker_loop_q8_0; break;
-        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
-        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
-        default:
-            return -1;
-    }
-
-    const int n_k_tiles = k / HMX_FP16_TILE_N_COLS;
-    const struct fastdiv_values n_k_tiles_div = init_fastdiv_values(n_k_tiles);
-
-    // --- Dynamic Mode Configuration ---
-    const bool use_pipeline = (m > 32);
-    const int  num_threads  = (m <= 32) ? 1 : ctx->n_threads;
-
-    // --- Dynamic VTCM layout ---
-    const size_t vec_dot_size = k * sizeof(__fp16);
-    const size_t vtcm_budget  = ctx->vtcm_size;
-    size_t vtcm_used = 0;
-
-    // Pipeline = 4-stage DMA→dequant→HMX→store with HMX worker overlap.
-    const size_t size_per_n  = row_stride + (use_pipeline ? 2 * vec_dot_size : vec_dot_size);  // Q + S0 + S1 (dequant bufs)
-    const size_t size_per_mn = (use_pipeline ? 2 : 1) * sizeof(__fp16);                       // O x 2 (output double buffer)
-
-    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0;
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, size_per_n, /*per_m=*/vec_dot_size, size_per_mn,
-                           hex_align_up(m, HMX_FP16_TILE_N_ROWS), n,
-                           /*m_block_cost=*/(size_t) n * 3,
-                           /*n_block_cost=*/(size_t) m * 2, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used)) {
-        FARF(HIGH, "hmx-mm-2d: VTCM too small : m %d k %d n %d budget %zu", m, k, n, vtcm_budget);
-        return -1;
-    }
-
-    const size_t weight_area_size = hex_align_up(n_chunk_n_cols * row_stride,   HMX_FP16_TILE_SIZE);
-    const size_t act_area_size    = hex_align_up(m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t output_area_size = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-
-    size_t scratch0_size, scratch1_size, scratch2_size;
-    scratch0_size = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);  // dequant buf 0
-    scratch1_size = use_pipeline ? scratch0_size : 0;                                 // dequant buf 1
-    scratch2_size = use_pipeline ? output_area_size : 0;                              // output  buf 1
-
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
-    void    *vtcm_scratch1   = scratch1_size ? vtcm_seq_alloc(&vtcm_ptr, scratch1_size) : NULL;
-    void    *vtcm_scratch2   = scratch2_size ? vtcm_seq_alloc(&vtcm_ptr, scratch2_size) : NULL;
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-
-    vtcm_used = vtcm_ptr - (uint8_t *) ctx->vtcm_base;
-    if (vtcm_used > vtcm_budget) {
-        FARF(ERROR, "hmx-mm-2d: VTCM overflow: used %zu budget %zu", vtcm_used, vtcm_budget);
-        return -1;
-    }
-
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
-
-    FARF(HIGH, "hmx-mm-2d: standard : m %d k %d n %d wtype %d mc %zu nc %zu vtcm %zu/%zu",
-         m, k, n, weight_type, m_chunk_n_rows, n_chunk_n_cols, vtcm_used, vtcm_budget);
-
-
-
-    int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols);
-
-    if (use_pipeline) {
-        // --- Asynchronous Pipelined Loop (Current implementation) ---
-        hmx_matmul_job_t job_slots[2];  // persistent double-buffered job descriptors
-
-        for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
-            const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
-
-            void *vtcm_qweight        = vtcm_weight;
-            void *vtcm_weight_bufs[2] = { vtcm_scratch0, vtcm_scratch1 };
-            void *vtcm_output_bufs[2] = { vtcm_output,   vtcm_scratch2 };
-
-            // prologue: A0
-            const size_t n_cols_A0 = hex_smin(n - 0 * n_chunk_n_cols, n_chunk_n_cols);
-            {
-                const uint8_t *qweight_chunk_A0 = permuted_weight;
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A0), row_stride, weight_stride, row_stride, n_cols_A0);
-            }
-
-            {
-                const float *activation_chunk = activation + mr * act_stride;
-                transfer_activation_chunk_threaded(ctx, vtcm_activation, activation_chunk, n_rows, k, act_stride, num_threads);
-            }
-
-            // prologue: B0, A1, submit C0 (async), B1 (overlaps C0)
-            {
-                // B0: wait for DMA, dequant weight chunk 0
-                dma_queue_pop(ctx->dma[0]);
-                dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[0], vtcm_qweight, n_cols_A0, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
-
-                // A1: issue DMA for weight chunk 1
-                const size_t n_cols_A1 = hex_smin(n - 1 * n_chunk_n_cols, n_chunk_n_cols);
-                if (1 < n_chunk_cnt) {
-                    const uint8_t *qweight_chunk_A1 = permuted_weight + n_chunk_n_cols * weight_stride;
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A1), row_stride, weight_stride, row_stride, n_cols_A1);
-                }
-
-                // submit C0 (non-blocking — HMX worker executes in parallel)
-                hmx_matmul_job_init(&job_slots[0], (__fp16 *) vtcm_output_bufs[0], (__fp16 *) vtcm_activation,
-                                    (__fp16 *) vtcm_weight_bufs[0], vtcm_scales,
-                                    hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS),
-                                    hmx_ceil_div(n_cols_A0, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
-                hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[0]));
-
-                // B1: DMA pop + dequant (runs in parallel with C0 on HMX worker)
-                if (1 < n_chunk_cnt) {
-                    dma_queue_pop(ctx->dma[0]);
-                    dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[1], vtcm_qweight, n_cols_A1, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
-                }
-            }
-
-            // main loop: wait C_i → submit C_{i+1} → D_i + B_{i+2} (parallel with C_{i+1})
-            for (int i = 0; i < n_chunk_cnt; ++i) {
-                const size_t nc    = i * n_chunk_n_cols;
-                const size_t nc_p1 = nc + 1 * n_chunk_n_cols;
-                const size_t nc_p2 = nc + 2 * n_chunk_n_cols;
-
-                const size_t n_cols    = hex_smin(n - nc, n_chunk_n_cols);
-                const size_t n_cols_p1 = hex_smin(n - nc_p1, n_chunk_n_cols);
-                const size_t n_cols_p2 = hex_smin(n - nc_p2, n_chunk_n_cols);
-
-                // issue A_{i+2}: DMA push (non-blocking)
-                if (i + 2 < n_chunk_cnt) {
-                    const uint8_t *qweight_chunk_p2 = permuted_weight + nc_p2 * weight_stride;
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_p2), row_stride, weight_stride, row_stride, n_cols_p2);
-                }
-
-                // wait C_i: block until prologue/previous C completes
-                hmx_queue_pop(ctx->hmx_queue);
-
-                // submit C_{i+1} (non-blocking, overlaps with D_i + B_{i+2} below)
-                if (i + 1 < n_chunk_cnt) {
-                    hmx_matmul_job_init(&job_slots[(i + 1) % 2], (__fp16 *) vtcm_output_bufs[(i + 1) % 2],
-                                        (__fp16 *) vtcm_activation, (__fp16 *) vtcm_weight_bufs[(i + 1) % 2],
-                                        vtcm_scales, hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS),
-                                        hmx_ceil_div(n_cols_p1, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
-                    hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[(i + 1) % 2]));
-                }
-
-                // D_i: store output (multi-thread HVX, parallel with C_{i+1})
-                float *output_chunk = dst + (mr * n + nc);
-                transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output_bufs[i % 2], n_rows, n_cols, n, num_threads);
-
-                // B_{i+2}: DMA pop + dequant (multi-thread HVX, parallel with C_{i+1})
-                if (i + 2 < n_chunk_cnt) {
-                    dma_queue_pop(ctx->dma[0]);
-                    dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[(i + 2) % 2], vtcm_qweight, n_cols_p2, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
-                }
-            }
-        }
-        hmx_queue_suspend(ctx->hmx_queue);
-    } else {
-        // --- Synchronous Loop (Optimized for small/non-pipelined cases) ---
-        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
-
-        for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
-            const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
-            const size_t n_row_tiles = hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS);
-
-            // Load Activation
-            const float *activation_chunk = activation + mr * act_stride;
-            transfer_activation_chunk_threaded(ctx, vtcm_activation, activation_chunk, n_rows, k, act_stride, num_threads);
-
-            for (size_t nc = 0; nc < n; nc += n_chunk_n_cols) {
-                const size_t n_cols = hex_smin(n - nc, n_chunk_n_cols);
-                const size_t n_col_tiles = hmx_ceil_div(n_cols, HMX_FP16_TILE_N_COLS);
-
-                // A: DMA Load Weight
-                const uint8_t *qweight_chunk = permuted_weight + nc * weight_stride;
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, qweight_chunk), row_stride, weight_stride, row_stride, n_cols);
-                dma_queue_pop(ctx->dma[0]);
-
-                // B: Dequantize / Convert Weight
-                dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_scratch0, vtcm_weight, n_cols, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
-
-                // C: HMX Compute (Synchronous)
-                {
-                    struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
-                    htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                    core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
-                    htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                }
-
-                // D: Output Store
-                float *output_chunk = dst + (mr * n + nc);
-                transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output, n_rows, n_cols, n, num_threads);
-            }
-        }
-        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
-    }
-
-
-
-    return 0;
-}
-
-//
-
-static inline int hmx_matmul_batch_r2(const hmx_matmul_f16_f32_batched_params_t *params) {
-    return params->ne02 > 0 ? params->ne12 / params->ne02 : 1;
-}
-
-static inline int hmx_matmul_batch_r3(const hmx_matmul_f16_f32_batched_params_t *params) {
-    return params->ne03 > 0 ? params->ne13 / params->ne03 : 1;
-}
-
-static inline const __fp16 *hmx_matmul_weight_batch_ptr(const hmx_matmul_f16_f32_batched_params_t *params,
-                                                        int dst_b2, int dst_b3) {
-    const int r2 = hmx_matmul_batch_r2(params);
-    const int r3 = hmx_matmul_batch_r3(params);
-    return (const __fp16 *) ((const uint8_t *) params->permuted_weight +
-                             (size_t) (dst_b2 / r2) * params->src0_nb2 +
-                             (size_t) (dst_b3 / r3) * params->src0_nb3);
-}
-
-static inline const float *hmx_matmul_activation_batch_ptr(const hmx_matmul_f16_f32_batched_params_t *params,
-                                                           int dst_b2, int dst_b3) {
-    return (const float *) ((const uint8_t *) params->activation +
-                            (size_t) dst_b2 * params->src1_nb2 +
-                            (size_t) dst_b3 * params->src1_nb3);
-}
-
-static inline float *hmx_matmul_dst_batch_ptr(const hmx_matmul_f16_f32_batched_params_t *params,
-                                              int dst_b2, int dst_b3) {
-    return (float *) ((uint8_t *) params->dst +
-                      (size_t) dst_b2 * params->dst_nb2 +
-                      (size_t) dst_b3 * params->dst_nb3);
-}
-
-static int hmx_matmul_f16_f32_batched_legacy(struct htp_context *ctx,
-                                                      const hmx_matmul_f16_f32_batched_params_t *params) {
-    int ret = 0;
-    for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) {
-        for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) {
-            ret = hmx_matmul_f16_f32(ctx, hmx_matmul_dst_batch_ptr(params, b2, b3),
-                                           hmx_matmul_activation_batch_ptr(params, b2, b3),
-                                           hmx_matmul_weight_batch_ptr(params, b2, b3),
-                                           params->m, params->k, params->n,
-                                           params->act_stride, params->weight_stride);
-        }
-    }
-    return ret;
-}
-
-int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32_batched_params_t *params) {
-    if (!ctx || !params || !params->dst || !params->activation || !params->permuted_weight) { return -1; }
-    if (!params->m || !params->k || !params->n) { return -1; }
-    if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; }
-    if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; }
-    if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; }
-    if (params->k % 32 != 0 || params->n % 32 != 0) { return -1; }
-
-    if (!hex_is_aligned(params->dst, VLEN) ||
-        !hex_is_aligned(params->activation, VLEN) ||
-        !hex_is_aligned(params->permuted_weight, VLEN)) {
-        return -1;
-    }
-
-    const int group_size = hmx_matmul_batch_r2(params);
-
-    if (group_size <= 1) {
-        FARF(HIGH, "%s: no dim2 GQA reuse (group=%d), using legacy batched loop", __func__, group_size);
-        return hmx_matmul_f16_f32_batched_legacy(ctx, params);
-    }
-
-    // Grouped path: reuse interleaved weight across all q_heads sharing a
-    // kv_head.  Each q_head gets its own activation buffer in VTCM (so
-    // activation is loaded once per m_chunk and reused across all n_chunks),
-    // and each q_head is computed individually to avoid tile-major packing
-    // issues.  m_chunk_n_rows is always a multiple of 32 (from
-    // hmx_compute_chunks), so per-head tile arrays don't overlap.
-    const size_t vtcm_budget  = ctx->vtcm_size;
-    const size_t vec_dot_size = params->k * sizeof(__fp16);
-
-    // When the activation has a large stride (e.g. permuted Q tensor with
-    // act_stride >> k), HVX vector loads from strided DDR thrash L2 cache.
-    // Allocate an F32 scratch buffer in VTCM and use 2D DMA to gather
-    // strided rows into a contiguous block before the F32->F16 conversion.
-    const bool use_dma_activation = (params->act_stride > params->k);
-    const size_t f32_scratch_per_m = use_dma_activation ? (size_t) params->k * sizeof(float) : 0;
-
-    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
-    // FP16 weight: interleave and activation load have similar per-element cost.
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256,
-                           /*per_n=*/3 * vec_dot_size,
-                           /*per_m=*/group_size * vec_dot_size + f32_scratch_per_m,
-                           /*per_mn=*/sizeof(__fp16),
-                           hex_align_up(params->m, HMX_FP16_TILE_N_ROWS), params->n,
-                           /*m_block_cost=*/(size_t) params->n,
-                           /*n_block_cost=*/(size_t) params->m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
-        FARF(HIGH, "%s: grouped path does not fit VTCM, falling back to legacy batched loop", __func__);
-        return hmx_matmul_f16_f32_batched_legacy(ctx, params);
-    }
-
-    const size_t act_head_stride      = m_chunk_n_rows * (size_t) params->k;  // fp16 elements between heads
-    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t activation_area_size = hex_align_up(group_size * m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t f32_scratch_size     = use_dma_activation
-        ? hex_align_up(m_chunk_n_rows * (size_t) params->k * sizeof(float), HMX_FP16_TILE_SIZE) : 0;
-
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
-
-    if ((size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base) > vtcm_budget) {
-        FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to legacy batched loop", __func__);
-        return hmx_matmul_f16_f32_batched_legacy(ctx, params);
-    }
-
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
-
-    FARF(HIGH, "%s: grouped path m=%d k=%d n=%d group=%d streams=%d mc=%zu nc=%zu vtcm=%zu/%zu",
-            __func__, params->m, params->k, params->n, group_size, params->ne13,
-            m_chunk_n_rows, n_chunk_n_cols,
-            (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
-
-
-
-    const size_t fp16_row_bytes   = (size_t) params->k * sizeof(__fp16);
-    const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16);
-
-    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
-
-    for (int b3 = 0; b3 < params->ne13; ++b3) {
-        for (int b2_base = 0; b2_base < params->ne12; b2_base += group_size) {
-            const __fp16 *weight_group = hmx_matmul_weight_batch_ptr(params, b2_base, b3);
-
-            for (size_t mr = 0; mr < (size_t) params->m; mr += m_chunk_n_rows) {
-                const size_t n_rows = hex_smin((size_t) params->m - mr, m_chunk_n_rows);
-                const size_t n_row_tiles = hmx_ceil_div((int) n_rows, HMX_FP16_TILE_N_ROWS);
-
-                // Pre-load activations for all heads in the group (once per m_chunk).
-                // When the source is strided (permuted Q), use 2D DMA to gather
-                // contiguous rows into a VTCM scratch buffer first, then HVX
-                // converts from the contiguous VTCM buffer.  This avoids L2 cache
-                // thrashing from HVX loads at large strides.
-                for (int g = 0; g < group_size; ++g) {
-                    const float *activation_chunk = hmx_matmul_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
-                    __fp16 *vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
-                    if (use_dma_activation) {
-                        const size_t row_bytes    = (size_t) params->k * sizeof(float);
-                        const size_t stride_bytes = (size_t) params->act_stride * sizeof(float);
-                        dma_queue_push(ctx->dma[0],
-                                          dma_make_ptr(vtcm_f32_act, activation_chunk),
-                                          row_bytes, stride_bytes, row_bytes, n_rows);
-                        dma_queue_pop(ctx->dma[0]);
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                              vtcm_f32_act, (int) n_rows,
-                                                              params->k, params->k, ctx->n_threads);
-                    } else {
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                              activation_chunk, (int) n_rows,
-                                                              params->k, params->act_stride, ctx->n_threads);
-                    }
-                }
-
-                void *buf_curr = vtcm_scratch0;
-                void *buf_next = vtcm_scratch1;
-
-                {
-                    const size_t n_cols_first = hex_smin((size_t) params->n, n_chunk_n_cols);
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
-                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
-                }
-
-                for (size_t nc = 0; nc < (size_t) params->n; nc += n_chunk_n_cols) {
-                    const size_t n_cols = hex_smin((size_t) params->n - nc, n_chunk_n_cols);
-                    const size_t n_col_tiles = hmx_ceil_div((int) n_cols, HMX_FP16_TILE_N_COLS);
-
-                    {
-                        dma_queue_pop(ctx->dma[0]);
-
-                        const size_t nc_next = nc + n_chunk_n_cols;
-                        if (nc_next < (size_t) params->n) {
-                            const size_t n_cols_next = hex_smin((size_t) params->n - nc_next, n_chunk_n_cols);
-                            const __fp16 *next_weight_chunk = weight_group + nc_next * params->weight_stride;
-
-                            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
-                                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
-                        }
-
-                        hmx_interleave_rows_to_tiles(vtcm_weight, (const __fp16 *) buf_curr, n_cols, params->k, params->k,
-                                                 0, n_cols);
-                        hex_swap_ptr(&buf_curr, &buf_next);
-                    }
-
-                    // Reuse the interleaved weight for every q_head in this GQA group
-                    for (int g = 0; g < group_size; ++g) {
-                        {
-                            const __fp16 * vtcm_act_g = vtcm_activation + (size_t) g * act_head_stride;
-                            struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
-                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                            core_dot_chunk_fp16(vtcm_output, vtcm_act_g, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles,
-                                                params->k / 32);
-                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                        }
-
-                        {
-                            float *output = hmx_matmul_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
-                            transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride, ctx->n_threads);
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
-
-
-
-    return 0;
-}
-
-int hmx_matmul_f16_f32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
-                                const __fp16 *restrict permuted_weight, int m, int k, int n,
-                                int act_stride, int weight_stride) {
-    if (!dst || !activation || !permuted_weight || !m || !n || !k) { return -1; }
-    return hmx_matmul_2d_f32(ctx, dst, activation, (const uint8_t *)permuted_weight, m, k, n,
-                             act_stride, weight_stride * (int)sizeof(__fp16), HTP_TYPE_F16);
-}
-
-struct mmid_row_mapping {
-    uint32_t i1;
-    uint32_t i2;
-};
-
-typedef struct {
-    __fp16                         *dst;
-    const float                    *src;
-    int                             n_tasks;
-    int                             n_tot_chunks;
-    int                             n_chunks_per_task;
-    int                             k_block;
-    const struct mmid_row_mapping  *matrix_rows;
-    int                             cur_a;
-    int                             mapping_stride;
-    int                             ne11;
-    struct fastdiv_values           ne11_div;
-    size_t                          nb11;
-    size_t                          nb12;
-    int                             start_row;
-    int                             cne1;
-    struct htp_thread_trace        *traces;
-} activation_transfer_gathered_task_state_t;
-
-typedef struct {
-    const __fp16                   *vtcm_src;
-    float                          *dst;
-    int                             n_tasks;
-    int                             n_tot_chunks;
-    int                             n_chunks_per_task;
-    int                             n_cols;
-    const struct mmid_row_mapping  *matrix_rows;
-    int                             cur_a;
-    int                             mapping_stride;
-    size_t                          dst_nb1;
-    size_t                          dst_nb2;
-    int                             start_row;
-    int                             cne1;
-    struct htp_thread_trace        *traces;
-} output_transfer_scattered_task_state_t;
-
-static void transfer_activation_chunk_fp32_to_fp16_gathered(
-            __fp16 *restrict vtcm_dst,
-            const float *restrict src,
-            int start_row,
-            int n_rows,
-            int k_block,
-            const struct mmid_row_mapping *matrix_rows,
-            int cur_a,
-            int mapping_stride,
-            int ne11,
-            const struct fastdiv_values * ne11_div,
-            size_t nb11,
-            size_t nb12,
-            int cne1) {
-    const int n_rows_padded = hex_align_up(n_rows, HMX_FP16_TILE_N_ROWS);
-    const int n_rows_tiled  = (n_rows / HMX_FP16_TILE_N_ROWS) * HMX_FP16_TILE_N_ROWS;
-
-    int r = 0;
-
-    #pragma unroll(2)
-    for (r = 0; r < n_rows_tiled; r += 2) {
-        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
-        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
-
-        int r_idx0 = start_row + r + 0;
-        int r_idx1 = start_row + r + 1;
-
-        struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0];
-        struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1];
-
-        int i11_0 = fastmodulo(mapping0.i1, ne11, ne11_div);
-        int i11_1 = fastmodulo(mapping1.i1, ne11, ne11_div);
-
-        const float *row0_ptr = (const float *) ((const uint8_t *) src + i11_0 * nb11 + mapping0.i2 * nb12);
-        const float *row1_ptr = (const float *) ((const uint8_t *) src + i11_1 * nb11 + mapping1.i2 * nb12);
-
-        const HVX_Vector *pv_in0 = (const HVX_Vector *) row0_ptr;
-        const HVX_Vector *pv_in1 = (const HVX_Vector *) row1_ptr;
-
-        for (int c = 0; c < k_block; c += 32) {
-            HVX_Vector v0 = *pv_in0++;
-            HVX_Vector v1 = *pv_in1++;
-
-            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
-
-            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
-            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
-
-            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
-            tile[r1 / 2]     = v_out;
-        }
-    }
-
-    for (; r < n_rows_padded; r += 2) {
-        int r0 = r / HMX_FP16_TILE_N_ROWS;  // tile row index
-        int r1 = r % HMX_FP16_TILE_N_ROWS;  // intra-tile row idx
-
-        const bool row0_valid = (start_row + r + 0) < cne1;
-        const bool row1_valid = (start_row + r + 1) < cne1;
-
-        const float *row0_ptr = NULL;
-        const float *row1_ptr = NULL;
-
-        if (row0_valid) {
-            struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + (start_row + r + 0)];
-            int i11_0 = fastmodulo(mapping0.i1, ne11, ne11_div);
-            row0_ptr = (const float *) ((const uint8_t *) src + i11_0 * nb11 + mapping0.i2 * nb12);
-        }
-        if (row1_valid) {
-            struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + (start_row + r + 1)];
-            int i11_1 = fastmodulo(mapping1.i1, ne11, ne11_div);
-            row1_ptr = (const float *) ((const uint8_t *) src + i11_1 * nb11 + mapping1.i2 * nb12);
-        }
-
-        const HVX_Vector *pv_in0 = (const HVX_Vector *) row0_ptr;
-        const HVX_Vector *pv_in1 = (const HVX_Vector *) row1_ptr;
-
-        for (int c = 0; c < k_block; c += 32) {
-            HVX_Vector v0 = row0_valid ? *pv_in0++ : Q6_V_vzero();
-            HVX_Vector v1 = row1_valid ? *pv_in1++ : Q6_V_vzero();
-
-            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
-
-            int c0       = c / HMX_FP16_TILE_N_COLS;  // tile column index
-            int tile_idx = r0 * (k_block / HMX_FP16_TILE_N_COLS) + c0;
-
-            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HMX_FP16_TILE_N_ELMS);
-            tile[r1 / 2]     = v_out;
-        }
-    }
-}
-
-static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigned int i, void *data) {
-    activation_transfer_gathered_task_state_t *st = data;
-    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
-
-    int chunk_idx = i;
-    int chunk_size = st->n_chunks_per_task;
-    int start_row = st->start_row + chunk_idx * chunk_size;
-    int n_rows = hex_smin(st->cne1 - start_row, chunk_size);
-    if (n_rows > 0) {
-        __fp16 *dst = st->dst + (size_t)(start_row - st->start_row) * st->k_block;
-        transfer_activation_chunk_fp32_to_fp16_gathered(
-            dst, st->src, start_row, n_rows, st->k_block,
-            st->matrix_rows, st->cur_a, st->mapping_stride,
-            st->ne11, &st->ne11_div, st->nb11, st->nb12, st->cne1);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, i);
-}
-
-static void transfer_activation_chunk_gathered_threaded(
-            struct htp_context *ctx,
-            __fp16 *dst,
-            const float *src,
-            int start_row,
-            int n_rows,
-            int k_block,
-            const struct mmid_row_mapping *matrix_rows,
-            int cur_a,
-            int mapping_stride,
-            int ne11,
-            size_t nb11,
-            size_t nb12,
-            int cne1,
-            int n_threads) {
-    if (n_rows <= 0) return;
-    int chunks_per_thread = hmx_ceil_div(n_rows, n_threads);
-    chunks_per_thread = hex_align_up(chunks_per_thread, HMX_FP16_TILE_N_ROWS);
-
-    int actual_threads = hmx_ceil_div(n_rows, chunks_per_thread);
-
-    activation_transfer_gathered_task_state_t state = {
-        .dst               = dst,
-        .src               = src,
-        .n_tasks           = actual_threads,
-        .n_tot_chunks      = n_rows,
-        .n_chunks_per_task = chunks_per_thread,
-        .k_block           = k_block,
-        .matrix_rows       = matrix_rows,
-        .cur_a             = cur_a,
-        .mapping_stride    = mapping_stride,
-        .ne11              = ne11,
-        .ne11_div          = init_fastdiv_values(ne11),
-        .nb11              = nb11,
-        .nb12              = nb12,
-        .start_row         = start_row,
-        .cne1              = cne1,
-        .traces            = ctx ? ctx->trace : NULL,
-    };
-
-    if (actual_threads <= 1) {
-        transfer_activation_chunk_gathered_worker_fn(1, 0, &state);
-    } else {
-        worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_gathered_worker_fn, &state, actual_threads);
-    }
-}
-
-static void transfer_output_chunk_fp16_to_fp32_scattered(
-            float *restrict dst,
-            const __fp16 *restrict vtcm_src,
-            int start_row,
-            int n_rows,
-            int n_cols,
-            const struct mmid_row_mapping *matrix_rows,
-            int cur_a,
-            int mapping_stride,
-            size_t dst_nb1,
-            size_t dst_nb2,
-            int cne1) {
-    assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
-    const size_t tile_row_stride = (n_cols / HMX_FP16_TILE_N_COLS) * HMX_FP16_TILE_N_ELMS;
-
-    const HVX_Vector one = hvx_vec_splat_f16(1.0);
-
-    for (size_t r = 0; r < n_rows; r += 2) {
-        const size_t r0 = r / HMX_FP16_TILE_N_ROWS;
-        const size_t r1 = (r % HMX_FP16_TILE_N_ROWS) / 2;  // index of the row pair within the tile
-        const __fp16 *row_base = vtcm_src + r0 * tile_row_stride;
-
-        int r_idx0 = start_row + (int)r + 0;
-        int r_idx1 = start_row + (int)r + 1;
-
-        if (r_idx0 >= cne1) break;
-
-        struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0];
-        float *output_row0 = (float *) ((uint8_t *) dst + mapping0.i1 * dst_nb1 + mapping0.i2 * dst_nb2);
-
-        float *output_row1 = NULL;
-        if (r_idx1 < cne1) {
-            struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1];
-            output_row1 = (float *) ((uint8_t *) dst + mapping1.i1 * dst_nb1 + mapping1.i2 * dst_nb2);
-        }
-
-        #pragma unroll(4)
-        for (size_t c = 0; c < (size_t)n_cols; c += HMX_FP16_TILE_N_COLS) {
-            const size_t c0 = c / HMX_FP16_TILE_N_COLS;
-            const __fp16 *tile = row_base + c0 * HMX_FP16_TILE_N_ELMS;
-            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
-            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
-
-            volatile HVX_Vector *pv_out0 = (volatile HVX_Vector *) (output_row0 + c);
-            volatile HVX_Vector *pv_out1 = output_row1 ? (volatile HVX_Vector *) (output_row1 + c) : NULL;
-
-            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
-            if (pv_out1) {
-                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
-            }
-        }
-    }
-}
-
-static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned int i, void *data) {
-    output_transfer_scattered_task_state_t *st = data;
-    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
-
-    int chunk_idx = i;
-    int chunk_size = st->n_chunks_per_task;
-    int start_row = st->start_row + chunk_idx * chunk_size;
-    int n_rows = hex_smin(st->cne1 - start_row, chunk_size);
-    if (n_rows > 0) {
-        const __fp16 *src = st->vtcm_src + (size_t)(start_row - st->start_row) * st->n_cols;
-        transfer_output_chunk_fp16_to_fp32_scattered(
-            st->dst, src, start_row, n_rows, st->n_cols,
-            st->matrix_rows, st->cur_a, st->mapping_stride,
-            st->dst_nb1, st->dst_nb2, st->cne1);
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, i);
-}
-
-static void transfer_output_chunk_scattered_threaded(
-            struct htp_context *ctx,
-            float *dst,
-            const __fp16 *vtcm_src,
-            int start_row,
-            int n_rows,
-            int n_cols,
-            const struct mmid_row_mapping *matrix_rows,
-            int cur_a,
-            int mapping_stride,
-            size_t dst_nb1,
-            size_t dst_nb2,
-            int cne1,
-            int n_threads) {
-    if (n_rows <= 0) return;
-    int chunks_per_thread = hmx_ceil_div(n_rows, n_threads);
-    chunks_per_thread = hex_align_up(chunks_per_thread, HMX_FP16_TILE_N_ROWS);
-
-    int actual_threads = hmx_ceil_div(n_rows, chunks_per_thread);
-
-    output_transfer_scattered_task_state_t state = {
-        .vtcm_src          = vtcm_src,
-        .dst               = dst,
-        .n_tasks           = actual_threads,
-        .n_tot_chunks      = n_rows,
-        .n_chunks_per_task = chunks_per_thread,
-        .n_cols            = n_cols,
-        .matrix_rows       = matrix_rows,
-        .cur_a             = cur_a,
-        .mapping_stride    = mapping_stride,
-        .dst_nb1           = dst_nb1,
-        .dst_nb2           = dst_nb2,
-        .start_row         = start_row,
-        .cne1              = cne1,
-        .traces            = ctx ? ctx->trace : NULL,
-    };
-
-    if (actual_threads <= 1) {
-        transfer_output_chunk_scattered_worker_fn(1, 0, &state);
-    } else {
-        worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_scattered_worker_fn, &state, actual_threads);
-    }
-}
-
-int hmx_matmul_id_2d_f32(struct htp_context *ctx,
-                                         float *restrict dst,
-                                         const float *activation,
-                                         const uint8_t *permuted_weight,
-                                         int m, int k, int n,
-                                         int ne11,
-                                         size_t act_nb1, size_t act_nb2,
-                                         size_t dst_nb1, size_t dst_nb2,
-                                         int weight_stride,
-                                         int weight_type,
-                                         const struct mmid_row_mapping *matrix_rows,
-                                         int cur_a,
-                                         int mapping_stride) {
-    const int cne1 = m;
-    const int m_padded = hex_align_up(m, 32);
-
-    if (k % 32 != 0 || n % 32 != 0) { return -1; }
-
-    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN) || !hex_is_aligned(permuted_weight, VLEN)) {
-        return -1;
-    }
-
-    size_t row_stride = get_x4x2_row_stride(weight_type, k);
-    if (row_stride == 0) {
-        return -1;
-    }
-
-    worker_callback_t dequant_worker_fn = NULL;
-    switch (weight_type) {
-        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_x4x2_worker_loop_q4_0; break;
-        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_x4x2_worker_loop_iq4_nl; break;
-        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_x4x2_worker_loop_q4_1; break;
-        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_x4x2_worker_loop_mxfp4; break;
-        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_x4x2_worker_loop_q8_0; break;
-        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
-        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
-        default:
-            return -1;
-    }
-
-    const int n_k_tiles = k / HMX_FP16_TILE_N_COLS;
-    const struct fastdiv_values n_k_tiles_div = init_fastdiv_values(n_k_tiles);
-
-    const int num_threads = ctx->n_threads;
-
-    const size_t vec_dot_size = k * sizeof(__fp16);
-    const size_t vtcm_budget  = ctx->vtcm_size;
-    size_t vtcm_used = 0;
-
-    const size_t size_per_n = row_stride + vec_dot_size;
-    const size_t size_per_mn = sizeof(__fp16);
-
-    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0;
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, size_per_n, /*per_m=*/vec_dot_size, size_per_mn,
-                           m_padded, n,
-                           /*m_block_cost=*/(size_t) n * 3,
-                           /*n_block_cost=*/(size_t) m_padded * 2, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used)) {
-        FARF(HIGH, "hmx-mm-id-2d: VTCM too small : m %d k %d n %d budget %zu", m_padded, k, n, vtcm_budget);
-        return -1;
-    }
-
-    const size_t weight_area_size = hex_align_up(n_chunk_n_cols * row_stride,   HMX_FP16_TILE_SIZE);
-    const size_t act_area_size    = hex_align_up(m_chunk_n_rows * vec_dot_size, HMX_FP16_TILE_SIZE);
-    const size_t output_area_size = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HMX_FP16_TILE_SIZE);
-
-    size_t scratch0_size = hex_align_up(n_chunk_n_cols * vec_dot_size, HMX_FP16_TILE_SIZE);
-
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
-    __fp16  *vtcm_activation = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
-
-    vtcm_used = vtcm_ptr - (uint8_t *) ctx->vtcm_base;
-    if (vtcm_used > vtcm_budget) {
-        FARF(ERROR, "hmx-mm-id-2d: VTCM overflow: used %zu budget %zu", vtcm_used, vtcm_budget);
-        return -1;
-    }
-
-    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));
-
-    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
-
-    for (size_t mr = 0; mr < (size_t) m_padded; mr += m_chunk_n_rows) {
-        const size_t n_rows = hex_smin(m_padded - mr, m_chunk_n_rows);
-        const size_t n_row_tiles = hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS);
-
-        transfer_activation_chunk_gathered_threaded(
-            ctx, vtcm_activation, activation, (int) mr, (int) n_rows, k,
-            matrix_rows, cur_a, mapping_stride, ne11, act_nb1, act_nb2, cne1, num_threads);
-
-        for (size_t nc = 0; nc < (size_t) n; nc += n_chunk_n_cols) {
-            const size_t n_cols = hex_smin((size_t) n - nc, n_chunk_n_cols);
-            const size_t n_col_tiles = hmx_ceil_div(n_cols, HMX_FP16_TILE_N_COLS);
-
-            const uint8_t *qweight_chunk = permuted_weight + nc * weight_stride;
-            dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, qweight_chunk), row_stride, weight_stride, row_stride, n_cols);
-            dma_queue_pop(ctx->dma[0]);
-
-            dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_scratch0, vtcm_weight, n_cols, k, row_stride, weight_type, n_k_tiles, n_k_tiles_div, dequant_worker_fn, num_threads);
-
-            {
-                struct htp_thread_trace * tr = ctx ? &ctx->trace[HTP_MAX_NTHREADS] : NULL;
-                htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                core_dot_chunk_fp16(vtcm_output, vtcm_activation, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HMX_FP16_TILE_N_ROWS);
-                htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-            }
-
-            transfer_output_chunk_scattered_threaded(
-                ctx, dst, vtcm_output, (int) mr, (int) n_rows, (int) n_cols,
-                matrix_rows, cur_a, mapping_stride, dst_nb1, dst_nb2, cne1, num_threads);
-        }
-    }
-
-    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
-    return 0;
-}
diff --git a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
new file mode 100644
index 000000000..b7fba22a8
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
@@ -0,0 +1,1306 @@
+#include "hmx-utils.h"
+#include "hmx-queue.h"
+
+// MXFP4 dequantization LUT: maps 4-bit index to fp16 mantissa value
+// kvalues: 0, 0.5, 1, 1.5, 2, 3, 4, 6, 0, -0.5, -1, -1.5, -2, -3, -4, -6
+static const __fp16 mxfp4_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
+    0, 0, 0.5, 0, 1, 0, 1.5, 0, 2, 0, 3, 0, 4, 0, 6, 0, 0, 0, -0.5, 0, -1, 0, -1.5, 0, -2, 0, -3, 0, -4, 0, -6, 0,
+};
+
+static const __fp16 iq4_nl_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
+    -127, 0, -104, 0, -83, 0, -65, 0, -49, 0, -35, 0, -22, 0, -10, 0,
+    1,    0, 13,   0, 25,  0, 38,  0, 53,  0, 69,  0, 89,  0, 113, 0,
+};
+
+// --- tiled format dequantizers ---
+
+typedef struct {
+    struct htp_context      * ctx;
+    struct htp_thread_trace * traces;
+    __fp16                  * dst;
+    const uint8_t           * src;
+
+    struct fastdiv_values     n_k_tiles_div;
+    uint32_t                  n_k_tiles;
+    uint32_t                  n_tot_tiles;
+    uint32_t                  n_tiles_per_task;
+    uint32_t                  tile_size;
+    uint32_t                  aligned_tile_size;
+    uint32_t                  n_tasks;
+    uint32_t                  n_cols;
+    uint32_t                  k_block;
+    size_t                    row_stride;
+    uint32_t                  weight_type;
+} tiled_dequantize_state_t;
+
+// Dequantize a single tile from tiled weight data (already in VTCM) to tile-major FP16.
+static void dequantize_tiled_weight_to_fp16_task_q4_0(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
+
+    for (uint32_t t = start_tile; t < end_tile; t++) {
+        const uint8_t * tile_src = state->src + t * state->aligned_tile_size;
+        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+
+        HVX_Vector v_sc = hvx_vmem(tile_src + 512);
+        HVX_Vector v_scale_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(v_sc, v_sc, -2));
+
+        // Load all 4 groups in parallel
+        HVX_Vector vq0 = hvx_vmem(tile_src + 0 * 128);
+        HVX_Vector vq1 = hvx_vmem(tile_src + 1 * 128);
+        HVX_Vector vq2 = hvx_vmem(tile_src + 2 * 128);
+        HVX_Vector vq3 = hvx_vmem(tile_src + 3 * 128);
+
+        // Nibble extraction
+        HVX_Vector v_lo0 = Q6_V_vand_VV(vq0, mask_h4);
+        HVX_Vector v_hi0 = Q6_Vub_vlsr_VubR(vq0, 4);
+        HVX_Vector v_lo1 = Q6_V_vand_VV(vq1, mask_h4);
+        HVX_Vector v_hi1 = Q6_Vub_vlsr_VubR(vq1, 4);
+        HVX_Vector v_lo2 = Q6_V_vand_VV(vq2, mask_h4);
+        HVX_Vector v_hi2 = Q6_Vub_vlsr_VubR(vq2, 4);
+        HVX_Vector v_lo3 = Q6_V_vand_VV(vq3, mask_h4);
+        HVX_Vector v_hi3 = Q6_Vub_vlsr_VubR(vq3, 4);
+
+        // Offsetting (-8)
+        v_lo0 = Q6_Vb_vsub_VbVb(v_lo0, i8);
+        v_hi0 = Q6_Vb_vsub_VbVb(v_hi0, i8);
+        v_lo1 = Q6_Vb_vsub_VbVb(v_lo1, i8);
+        v_hi1 = Q6_Vb_vsub_VbVb(v_hi1, i8);
+        v_lo2 = Q6_Vb_vsub_VbVb(v_lo2, i8);
+        v_hi2 = Q6_Vb_vsub_VbVb(v_hi2, i8);
+        v_lo3 = Q6_Vb_vsub_VbVb(v_lo3, i8);
+        v_hi3 = Q6_Vb_vsub_VbVb(v_hi3, i8);
+
+        // Shuffling
+        HVX_VectorPair vp_shuf0 = Q6_W_vshuff_VVR(v_hi0, v_lo0, -1);
+        HVX_VectorPair vp_shuf1 = Q6_W_vshuff_VVR(v_hi1, v_lo1, -1);
+        HVX_VectorPair vp_shuf2 = Q6_W_vshuff_VVR(v_hi2, v_lo2, -1);
+        HVX_VectorPair vp_shuf3 = Q6_W_vshuff_VVR(v_hi3, v_lo3, -1);
+
+        // Unpack to 16-bit
+        HVX_VectorPair vp_int16_lo0 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf0));
+        HVX_VectorPair vp_int16_hi0 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf0));
+        HVX_VectorPair vp_int16_lo1 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf1));
+        HVX_VectorPair vp_int16_hi1 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf1));
+        HVX_VectorPair vp_int16_lo2 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf2));
+        HVX_VectorPair vp_int16_hi2 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf2));
+        HVX_VectorPair vp_int16_lo3 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf3));
+        HVX_VectorPair vp_int16_hi3 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf3));
+
+        // Convert and scale multiplication
+        HVX_Vector v_grp0_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo0)), v_scale_duplicated));
+        HVX_Vector v_grp0_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo0)), v_scale_duplicated));
+        HVX_Vector v_grp0_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi0)), v_scale_duplicated));
+        HVX_Vector v_grp0_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi0)), v_scale_duplicated));
+
+        HVX_Vector v_grp1_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo1)), v_scale_duplicated));
+        HVX_Vector v_grp1_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo1)), v_scale_duplicated));
+        HVX_Vector v_grp1_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi1)), v_scale_duplicated));
+        HVX_Vector v_grp1_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi1)), v_scale_duplicated));
+
+        HVX_Vector v_grp2_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo2)), v_scale_duplicated));
+        HVX_Vector v_grp2_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo2)), v_scale_duplicated));
+        HVX_Vector v_grp2_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi2)), v_scale_duplicated));
+        HVX_Vector v_grp2_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi2)), v_scale_duplicated));
+
+        HVX_Vector v_grp3_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo3)), v_scale_duplicated));
+        HVX_Vector v_grp3_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo3)), v_scale_duplicated));
+        HVX_Vector v_grp3_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi3)), v_scale_duplicated));
+        HVX_Vector v_grp3_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi3)), v_scale_duplicated));
+
+        hvx_vmem(dst_ptr +  0 * 64) = v_grp0_0;
+        hvx_vmem(dst_ptr +  1 * 64) = v_grp0_1;
+        hvx_vmem(dst_ptr +  2 * 64) = v_grp0_2;
+        hvx_vmem(dst_ptr +  3 * 64) = v_grp0_3;
+
+        hvx_vmem(dst_ptr +  4 * 64) = v_grp1_0;
+        hvx_vmem(dst_ptr +  5 * 64) = v_grp1_1;
+        hvx_vmem(dst_ptr +  6 * 64) = v_grp1_2;
+        hvx_vmem(dst_ptr +  7 * 64) = v_grp1_3;
+
+        hvx_vmem(dst_ptr +  8 * 64) = v_grp2_0;
+        hvx_vmem(dst_ptr +  9 * 64) = v_grp2_1;
+        hvx_vmem(dst_ptr + 10 * 64) = v_grp2_2;
+        hvx_vmem(dst_ptr + 11 * 64) = v_grp2_3;
+
+        hvx_vmem(dst_ptr + 12 * 64) = v_grp3_0;
+        hvx_vmem(dst_ptr + 13 * 64) = v_grp3_1;
+        hvx_vmem(dst_ptr + 14 * 64) = v_grp3_2;
+        hvx_vmem(dst_ptr + 15 * 64) = v_grp3_3;
+    }
+}
+
+static void dequantize_tiled_weight_to_fp16_task_q4_1(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+
+    for (uint32_t t = start_tile; t < end_tile; t++) {
+        const uint8_t * tile_src = state->src + t * state->aligned_tile_size;
+        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+
+        HVX_Vector vscale_offset = hvx_vmem(tile_src + 512);
+        HVX_VectorPair dm_deal = Q6_W_vdeal_VVR(vscale_offset, vscale_offset, -2);
+        HVX_Vector vd = Q6_V_lo_W(dm_deal);
+        HVX_Vector vm = Q6_V_hi_W(dm_deal);
+
+        HVX_Vector v_scale_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(vd, vd, -2));
+        HVX_Vector v_offset_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(vm, vm, -2));
+
+        // Load all 4 groups in parallel
+        HVX_Vector vq0 = hvx_vmem(tile_src + 0 * 128);
+        HVX_Vector vq1 = hvx_vmem(tile_src + 1 * 128);
+        HVX_Vector vq2 = hvx_vmem(tile_src + 2 * 128);
+        HVX_Vector vq3 = hvx_vmem(tile_src + 3 * 128);
+
+        // Nibble extraction
+        HVX_Vector v_lo0 = Q6_V_vand_VV(vq0, mask_h4);
+        HVX_Vector v_hi0 = Q6_Vub_vlsr_VubR(vq0, 4);
+        HVX_Vector v_lo1 = Q6_V_vand_VV(vq1, mask_h4);
+        HVX_Vector v_hi1 = Q6_Vub_vlsr_VubR(vq1, 4);
+        HVX_Vector v_lo2 = Q6_V_vand_VV(vq2, mask_h4);
+        HVX_Vector v_hi2 = Q6_Vub_vlsr_VubR(vq2, 4);
+        HVX_Vector v_lo3 = Q6_V_vand_VV(vq3, mask_h4);
+        HVX_Vector v_hi3 = Q6_Vub_vlsr_VubR(vq3, 4);
+
+        // Shuffling
+        HVX_VectorPair vp_shuf0 = Q6_W_vshuff_VVR(v_hi0, v_lo0, -1);
+        HVX_VectorPair vp_shuf1 = Q6_W_vshuff_VVR(v_hi1, v_lo1, -1);
+        HVX_VectorPair vp_shuf2 = Q6_W_vshuff_VVR(v_hi2, v_lo2, -1);
+        HVX_VectorPair vp_shuf3 = Q6_W_vshuff_VVR(v_hi3, v_lo3, -1);
+
+        // Unpack to 16-bit
+        HVX_VectorPair vp_int16_lo0 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf0));
+        HVX_VectorPair vp_int16_hi0 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf0));
+        HVX_VectorPair vp_int16_lo1 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf1));
+        HVX_VectorPair vp_int16_hi1 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf1));
+        HVX_VectorPair vp_int16_lo2 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf2));
+        HVX_VectorPair vp_int16_hi2 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf2));
+        HVX_VectorPair vp_int16_lo3 = Q6_Wh_vunpack_Vb(Q6_V_lo_W(vp_shuf3));
+        HVX_VectorPair vp_int16_hi3 = Q6_Wh_vunpack_Vb(Q6_V_hi_W(vp_shuf3));
+
+        // Convert, multiply, add offset
+        HVX_Vector v_grp0_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo0)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp0_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo0)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp0_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi0)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp0_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi0)), v_scale_duplicated), v_offset_duplicated));
+
+        HVX_Vector v_grp1_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo1)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp1_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo1)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp1_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi1)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp1_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi1)), v_scale_duplicated), v_offset_duplicated));
+
+        HVX_Vector v_grp2_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo2)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp2_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo2)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp2_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi2)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp2_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi2)), v_scale_duplicated), v_offset_duplicated));
+
+        HVX_Vector v_grp3_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_lo3)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp3_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_lo3)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp3_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_hi3)), v_scale_duplicated), v_offset_duplicated));
+        HVX_Vector v_grp3_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vadd_Vqf16Vhf(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_hi3)), v_scale_duplicated), v_offset_duplicated));
+
+        // Parallel Stores
+        hvx_vmem(dst_ptr +  0 * 64) = v_grp0_0;
+        hvx_vmem(dst_ptr +  1 * 64) = v_grp0_1;
+        hvx_vmem(dst_ptr +  2 * 64) = v_grp0_2;
+        hvx_vmem(dst_ptr +  3 * 64) = v_grp0_3;
+
+        hvx_vmem(dst_ptr +  4 * 64) = v_grp1_0;
+        hvx_vmem(dst_ptr +  5 * 64) = v_grp1_1;
+        hvx_vmem(dst_ptr +  6 * 64) = v_grp1_2;
+        hvx_vmem(dst_ptr +  7 * 64) = v_grp1_3;
+
+        hvx_vmem(dst_ptr +  8 * 64) = v_grp2_0;
+        hvx_vmem(dst_ptr +  9 * 64) = v_grp2_1;
+        hvx_vmem(dst_ptr + 10 * 64) = v_grp2_2;
+        hvx_vmem(dst_ptr + 11 * 64) = v_grp2_3;
+
+        hvx_vmem(dst_ptr + 12 * 64) = v_grp3_0;
+        hvx_vmem(dst_ptr + 13 * 64) = v_grp3_1;
+        hvx_vmem(dst_ptr + 14 * 64) = v_grp3_2;
+        hvx_vmem(dst_ptr + 15 * 64) = v_grp3_3;
+    }
+}
+
+static void dequantize_tiled_weight_to_fp16_task_iq4_nl(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector vlut_cvt = hvx_vmem(iq4_nl_to_fp16_lut);
+
+    for (uint32_t t = start_tile; t < end_tile; t++) {
+        const uint8_t * tile_src = state->src + t * state->aligned_tile_size;
+        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+
+        HVX_Vector v_sc = hvx_vmem(tile_src + 512);
+        HVX_Vector v_scale_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(v_sc, v_sc, -2));
+
+        // Load all 4 groups in parallel
+        HVX_Vector vq0 = hvx_vmem(tile_src + 0 * 128);
+        HVX_Vector vq1 = hvx_vmem(tile_src + 1 * 128);
+        HVX_Vector vq2 = hvx_vmem(tile_src + 2 * 128);
+        HVX_Vector vq3 = hvx_vmem(tile_src + 3 * 128);
+
+        // Nibble extraction
+        HVX_Vector v_lo0 = Q6_V_vand_VV(vq0, mask_h4);
+        HVX_Vector v_hi0 = Q6_Vub_vlsr_VubR(vq0, 4);
+        HVX_Vector v_lo1 = Q6_V_vand_VV(vq1, mask_h4);
+        HVX_Vector v_hi1 = Q6_Vub_vlsr_VubR(vq1, 4);
+        HVX_Vector v_lo2 = Q6_V_vand_VV(vq2, mask_h4);
+        HVX_Vector v_hi2 = Q6_Vub_vlsr_VubR(vq2, 4);
+        HVX_Vector v_lo3 = Q6_V_vand_VV(vq3, mask_h4);
+        HVX_Vector v_hi3 = Q6_Vub_vlsr_VubR(vq3, 4);
+
+        // Shuffling
+        HVX_VectorPair vp_shuf0 = Q6_W_vshuff_VVR(v_hi0, v_lo0, -1);
+        HVX_VectorPair vp_shuf1 = Q6_W_vshuff_VVR(v_hi1, v_lo1, -1);
+        HVX_VectorPair vp_shuf2 = Q6_W_vshuff_VVR(v_hi2, v_lo2, -1);
+        HVX_VectorPair vp_shuf3 = Q6_W_vshuff_VVR(v_hi3, v_lo3, -1);
+
+        // Shuffle for LUT lookup
+        HVX_Vector v_q_lo0 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf0));
+        HVX_Vector v_q_hi0 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf0));
+        HVX_Vector v_q_lo1 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf1));
+        HVX_Vector v_q_hi1 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf1));
+        HVX_Vector v_q_lo2 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf2));
+        HVX_Vector v_q_hi2 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf2));
+        HVX_Vector v_q_lo3 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf3));
+        HVX_Vector v_q_hi3 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf3));
+
+        // LUT lookup
+        HVX_VectorPair vp_lo0 = Q6_Wh_vlut16_VbVhR(v_q_lo0, vlut_cvt, 0);
+        HVX_VectorPair vp_hi0 = Q6_Wh_vlut16_VbVhR(v_q_hi0, vlut_cvt, 0);
+        HVX_VectorPair vp_lo1 = Q6_Wh_vlut16_VbVhR(v_q_lo1, vlut_cvt, 0);
+        HVX_VectorPair vp_hi1 = Q6_Wh_vlut16_VbVhR(v_q_hi1, vlut_cvt, 0);
+        HVX_VectorPair vp_lo2 = Q6_Wh_vlut16_VbVhR(v_q_lo2, vlut_cvt, 0);
+        HVX_VectorPair vp_hi2 = Q6_Wh_vlut16_VbVhR(v_q_hi2, vlut_cvt, 0);
+        HVX_VectorPair vp_lo3 = Q6_Wh_vlut16_VbVhR(v_q_lo3, vlut_cvt, 0);
+        HVX_VectorPair vp_hi3 = Q6_Wh_vlut16_VbVhR(v_q_hi3, vlut_cvt, 0);
+
+        // Convert and scale multiplication
+        HVX_Vector v_grp0_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo0), v_scale_duplicated));
+        HVX_Vector v_grp0_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo0), v_scale_duplicated));
+        HVX_Vector v_grp0_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi0), v_scale_duplicated));
+        HVX_Vector v_grp0_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi0), v_scale_duplicated));
+
+        HVX_Vector v_grp1_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo1), v_scale_duplicated));
+        HVX_Vector v_grp1_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo1), v_scale_duplicated));
+        HVX_Vector v_grp1_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi1), v_scale_duplicated));
+        HVX_Vector v_grp1_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi1), v_scale_duplicated));
+
+        HVX_Vector v_grp2_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo2), v_scale_duplicated));
+        HVX_Vector v_grp2_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo2), v_scale_duplicated));
+        HVX_Vector v_grp2_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi2), v_scale_duplicated));
+        HVX_Vector v_grp2_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi2), v_scale_duplicated));
+
+        HVX_Vector v_grp3_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo3), v_scale_duplicated));
+        HVX_Vector v_grp3_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo3), v_scale_duplicated));
+        HVX_Vector v_grp3_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi3), v_scale_duplicated));
+        HVX_Vector v_grp3_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi3), v_scale_duplicated));
+
+        hvx_vmem(dst_ptr +  0 * 64) = v_grp0_0;
+        hvx_vmem(dst_ptr +  1 * 64) = v_grp0_1;
+        hvx_vmem(dst_ptr +  2 * 64) = v_grp0_2;
+        hvx_vmem(dst_ptr +  3 * 64) = v_grp0_3;
+
+        hvx_vmem(dst_ptr +  4 * 64) = v_grp1_0;
+        hvx_vmem(dst_ptr +  5 * 64) = v_grp1_1;
+        hvx_vmem(dst_ptr +  6 * 64) = v_grp1_2;
+        hvx_vmem(dst_ptr +  7 * 64) = v_grp1_3;
+
+        hvx_vmem(dst_ptr +  8 * 64) = v_grp2_0;
+        hvx_vmem(dst_ptr +  9 * 64) = v_grp2_1;
+        hvx_vmem(dst_ptr + 10 * 64) = v_grp2_2;
+        hvx_vmem(dst_ptr + 11 * 64) = v_grp2_3;
+
+        hvx_vmem(dst_ptr + 12 * 64) = v_grp3_0;
+        hvx_vmem(dst_ptr + 13 * 64) = v_grp3_1;
+        hvx_vmem(dst_ptr + 14 * 64) = v_grp3_2;
+        hvx_vmem(dst_ptr + 15 * 64) = v_grp3_3;
+    }
+}
+
+static void dequantize_tiled_weight_to_fp16_task_mxfp4(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    const HVX_Vector vlut_cvt = hvx_vmem(mxfp4_to_fp16_lut);
+
+    for (uint32_t t = start_tile; t < end_tile; t++) {
+        const uint8_t * tile_src = state->src + t * state->aligned_tile_size;
+        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+
+        HVX_Vector v = hvx_vmem(tile_src + 512);
+        HVX_Vector vh = Q6_V_lo_W(Q6_Wuh_vunpack_Vub(v));
+        vh = Q6_Vh_vsub_VhVh(vh, Q6_Vh_vsplat_R(112));
+        vh = Q6_Vh_vmax_VhVh(vh, Q6_V_vzero());
+        vh = Q6_Vh_vmin_VhVh(vh, Q6_Vh_vsplat_R(30));
+        vh = Q6_Vh_vasl_VhR(vh, 10);
+
+        HVX_Vector v_scale_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(vh, vh, -2));
+
+        // Load all 4 groups in parallel
+        HVX_Vector vq0 = hvx_vmem(tile_src + 0 * 128);
+        HVX_Vector vq1 = hvx_vmem(tile_src + 1 * 128);
+        HVX_Vector vq2 = hvx_vmem(tile_src + 2 * 128);
+        HVX_Vector vq3 = hvx_vmem(tile_src + 3 * 128);
+
+        // Nibble extraction
+        HVX_Vector v_lo0 = Q6_V_vand_VV(vq0, mask_h4);
+        HVX_Vector v_hi0 = Q6_Vub_vlsr_VubR(vq0, 4);
+        HVX_Vector v_lo1 = Q6_V_vand_VV(vq1, mask_h4);
+        HVX_Vector v_hi1 = Q6_Vub_vlsr_VubR(vq1, 4);
+        HVX_Vector v_lo2 = Q6_V_vand_VV(vq2, mask_h4);
+        HVX_Vector v_hi2 = Q6_Vub_vlsr_VubR(vq2, 4);
+        HVX_Vector v_lo3 = Q6_V_vand_VV(vq3, mask_h4);
+        HVX_Vector v_hi3 = Q6_Vub_vlsr_VubR(vq3, 4);
+
+        // Shuffling
+        HVX_VectorPair vp_shuf0 = Q6_W_vshuff_VVR(v_hi0, v_lo0, -1);
+        HVX_VectorPair vp_shuf1 = Q6_W_vshuff_VVR(v_hi1, v_lo1, -1);
+        HVX_VectorPair vp_shuf2 = Q6_W_vshuff_VVR(v_hi2, v_lo2, -1);
+        HVX_VectorPair vp_shuf3 = Q6_W_vshuff_VVR(v_hi3, v_lo3, -1);
+
+        // Shuffle for LUT lookup
+        HVX_Vector v_q_lo0 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf0));
+        HVX_Vector v_q_hi0 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf0));
+        HVX_Vector v_q_lo1 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf1));
+        HVX_Vector v_q_hi1 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf1));
+        HVX_Vector v_q_lo2 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf2));
+        HVX_Vector v_q_hi2 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf2));
+        HVX_Vector v_q_lo3 = Q6_Vb_vshuff_Vb(Q6_V_lo_W(vp_shuf3));
+        HVX_Vector v_q_hi3 = Q6_Vb_vshuff_Vb(Q6_V_hi_W(vp_shuf3));
+
+        // LUT lookup
+        HVX_VectorPair vp_lo0 = Q6_Wh_vlut16_VbVhR(v_q_lo0, vlut_cvt, 0);
+        HVX_VectorPair vp_hi0 = Q6_Wh_vlut16_VbVhR(v_q_hi0, vlut_cvt, 0);
+        HVX_VectorPair vp_lo1 = Q6_Wh_vlut16_VbVhR(v_q_lo1, vlut_cvt, 0);
+        HVX_VectorPair vp_hi1 = Q6_Wh_vlut16_VbVhR(v_q_hi1, vlut_cvt, 0);
+        HVX_VectorPair vp_lo2 = Q6_Wh_vlut16_VbVhR(v_q_lo2, vlut_cvt, 0);
+        HVX_VectorPair vp_hi2 = Q6_Wh_vlut16_VbVhR(v_q_hi2, vlut_cvt, 0);
+        HVX_VectorPair vp_lo3 = Q6_Wh_vlut16_VbVhR(v_q_lo3, vlut_cvt, 0);
+        HVX_VectorPair vp_hi3 = Q6_Wh_vlut16_VbVhR(v_q_hi3, vlut_cvt, 0);
+
+        // Convert and scale multiplication
+        HVX_Vector v_grp0_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo0), v_scale_duplicated));
+        HVX_Vector v_grp0_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo0), v_scale_duplicated));
+        HVX_Vector v_grp0_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi0), v_scale_duplicated));
+        HVX_Vector v_grp0_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi0), v_scale_duplicated));
+
+        HVX_Vector v_grp1_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo1), v_scale_duplicated));
+        HVX_Vector v_grp1_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo1), v_scale_duplicated));
+        HVX_Vector v_grp1_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi1), v_scale_duplicated));
+        HVX_Vector v_grp1_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi1), v_scale_duplicated));
+
+        HVX_Vector v_grp2_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo2), v_scale_duplicated));
+        HVX_Vector v_grp2_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo2), v_scale_duplicated));
+        HVX_Vector v_grp2_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi2), v_scale_duplicated));
+        HVX_Vector v_grp2_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi2), v_scale_duplicated));
+
+        HVX_Vector v_grp3_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_lo3), v_scale_duplicated));
+        HVX_Vector v_grp3_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_lo3), v_scale_duplicated));
+        HVX_Vector v_grp3_2 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_lo_W(vp_hi3), v_scale_duplicated));
+        HVX_Vector v_grp3_3 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_V_hi_W(vp_hi3), v_scale_duplicated));
+
+        hvx_vmem(dst_ptr +  0 * 64) = v_grp0_0;
+        hvx_vmem(dst_ptr +  1 * 64) = v_grp0_1;
+        hvx_vmem(dst_ptr +  2 * 64) = v_grp0_2;
+        hvx_vmem(dst_ptr +  3 * 64) = v_grp0_3;
+
+        hvx_vmem(dst_ptr +  4 * 64) = v_grp1_0;
+        hvx_vmem(dst_ptr +  5 * 64) = v_grp1_1;
+        hvx_vmem(dst_ptr +  6 * 64) = v_grp1_2;
+        hvx_vmem(dst_ptr +  7 * 64) = v_grp1_3;
+
+        hvx_vmem(dst_ptr +  8 * 64) = v_grp2_0;
+        hvx_vmem(dst_ptr +  9 * 64) = v_grp2_1;
+        hvx_vmem(dst_ptr + 10 * 64) = v_grp2_2;
+        hvx_vmem(dst_ptr + 11 * 64) = v_grp2_3;
+
+        hvx_vmem(dst_ptr + 12 * 64) = v_grp3_0;
+        hvx_vmem(dst_ptr + 13 * 64) = v_grp3_1;
+        hvx_vmem(dst_ptr + 14 * 64) = v_grp3_2;
+        hvx_vmem(dst_ptr + 15 * 64) = v_grp3_3;
+    }
+}
+
+static void dequantize_tiled_weight_to_fp16_task_q8_0(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    for (uint32_t t = start_tile; t < end_tile; t++) {
+        const uint8_t * tile_src = state->src + t * state->aligned_tile_size;
+        __fp16 * dst_ptr = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+
+        HVX_Vector v_sc = hvx_vmem(tile_src + 1024);
+        HVX_Vector v_scale_duplicated = Q6_V_lo_W(Q6_W_vshuff_VVR(v_sc, v_sc, -2));
+
+        // Load groups 0-3 in parallel
+        HVX_Vector vq0 = hvx_vmem(tile_src + 0 * 128);
+        HVX_Vector vq1 = hvx_vmem(tile_src + 1 * 128);
+        HVX_Vector vq2 = hvx_vmem(tile_src + 2 * 128);
+        HVX_Vector vq3 = hvx_vmem(tile_src + 3 * 128);
+
+        HVX_VectorPair vp_int16_0 = Q6_Wh_vunpack_Vb(vq0);
+        HVX_VectorPair vp_int16_1 = Q6_Wh_vunpack_Vb(vq1);
+        HVX_VectorPair vp_int16_2 = Q6_Wh_vunpack_Vb(vq2);
+        HVX_VectorPair vp_int16_3 = Q6_Wh_vunpack_Vb(vq3);
+
+        // Load groups 4-7 in parallel
+        HVX_Vector vq4 = hvx_vmem(tile_src + 4 * 128);
+        HVX_Vector vq5 = hvx_vmem(tile_src + 5 * 128);
+        HVX_Vector vq6 = hvx_vmem(tile_src + 6 * 128);
+        HVX_Vector vq7 = hvx_vmem(tile_src + 7 * 128);
+
+        HVX_VectorPair vp_int16_4 = Q6_Wh_vunpack_Vb(vq4);
+        HVX_VectorPair vp_int16_5 = Q6_Wh_vunpack_Vb(vq5);
+        HVX_VectorPair vp_int16_6 = Q6_Wh_vunpack_Vb(vq6);
+        HVX_VectorPair vp_int16_7 = Q6_Wh_vunpack_Vb(vq7);
+
+        // Convert and scale multiply for groups 0-3
+        HVX_Vector v_grp0_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_0)), v_scale_duplicated));
+        HVX_Vector v_grp0_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_0)), v_scale_duplicated));
+        HVX_Vector v_grp1_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_1)), v_scale_duplicated));
+        HVX_Vector v_grp1_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_1)), v_scale_duplicated));
+        HVX_Vector v_grp2_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_2)), v_scale_duplicated));
+        HVX_Vector v_grp2_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_2)), v_scale_duplicated));
+        HVX_Vector v_grp3_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_3)), v_scale_duplicated));
+        HVX_Vector v_grp3_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_3)), v_scale_duplicated));
+
+        // Store groups 0-3
+        hvx_vmem(dst_ptr +  0 * 64) = v_grp0_0;
+        hvx_vmem(dst_ptr +  1 * 64) = v_grp0_1;
+        hvx_vmem(dst_ptr +  2 * 64) = v_grp1_0;
+        hvx_vmem(dst_ptr +  3 * 64) = v_grp1_1;
+        hvx_vmem(dst_ptr +  4 * 64) = v_grp2_0;
+        hvx_vmem(dst_ptr +  5 * 64) = v_grp2_1;
+        hvx_vmem(dst_ptr +  6 * 64) = v_grp3_0;
+        hvx_vmem(dst_ptr +  7 * 64) = v_grp3_1;
+
+        // Convert and scale multiply for groups 4-7
+        HVX_Vector v_grp4_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_4)), v_scale_duplicated));
+        HVX_Vector v_grp4_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_4)), v_scale_duplicated));
+        HVX_Vector v_grp5_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_5)), v_scale_duplicated));
+        HVX_Vector v_grp5_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_5)), v_scale_duplicated));
+        HVX_Vector v_grp6_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_6)), v_scale_duplicated));
+        HVX_Vector v_grp6_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_6)), v_scale_duplicated));
+        HVX_Vector v_grp7_0 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_lo_W(vp_int16_7)), v_scale_duplicated));
+        HVX_Vector v_grp7_1 = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(Q6_Vhf_equals_Vh(Q6_V_hi_W(vp_int16_7)), v_scale_duplicated));
+
+        // Store groups 4-7
+        hvx_vmem(dst_ptr +  8 * 64) = v_grp4_0;
+        hvx_vmem(dst_ptr +  9 * 64) = v_grp4_1;
+        hvx_vmem(dst_ptr + 10 * 64) = v_grp5_0;
+        hvx_vmem(dst_ptr + 11 * 64) = v_grp5_1;
+        hvx_vmem(dst_ptr + 12 * 64) = v_grp6_0;
+        hvx_vmem(dst_ptr + 13 * 64) = v_grp6_1;
+        hvx_vmem(dst_ptr + 14 * 64) = v_grp7_0;
+        hvx_vmem(dst_ptr + 15 * 64) = v_grp7_1;
+    }
+}
+
+static void convert_f16_weight_to_fp16_tiles_task(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const uint32_t n_k_tiles = state->n_k_tiles;
+    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
+
+    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
+    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
+    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
+
+    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
+    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
+
+    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
+        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
+
+        __fp16 *tile_base = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+        {
+            uint32_t byte_off = kt * 32 * sizeof(__fp16);
+
+            HVX_Vector v_off = v_scat_base;
+            for (uint32_t r = 0; r < HTP_MM_HMX_TILE_N_ROWS; r += 2) {
+                uint32_t row0 = ct * HTP_MM_HMX_TILE_N_COLS + r;
+                uint32_t row1 = row0 + 1;
+
+                const uint8_t *r0 = state->src + row0 * state->row_stride;
+                const uint8_t *r1 = state->src + row1 * state->row_stride;
+
+                HVX_Vector v0 = hvx_vmemu((const __fp16 *)(r0 + byte_off));
+                HVX_Vector v1 = (row1 < state->n_cols) ? hvx_vmemu((const __fp16 *)(r1 + byte_off)) : Q6_V_vzero();
+
+                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HTP_MM_HMX_TILE_SIZE - 1, v_off, v0);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HTP_MM_HMX_TILE_SIZE - 1, v_off, v1);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+            }
+            (void) *(volatile HVX_Vector *)(tile_base);
+        }
+        ++t; ++kt;
+    }
+
+    if (start_tile < end_tile) {
+        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HTP_MM_HMX_TILE_N_ELMS);
+    }
+}
+
+static void quantize_f32_weight_to_fp16_tiles_task(
+        const tiled_dequantize_state_t *state,
+        uint32_t start_tile, uint32_t end_tile) {
+
+    const uint32_t n_k_tiles = state->n_k_tiles;
+    const struct fastdiv_values n_k_tiles_div = state->n_k_tiles_div;
+
+    const HVX_Vector v_scat_base  = hvx_vmem(hmx_transpose_scatter_offsets);
+    const HVX_Vector v_scat_step  = Q6_V_vsplat_R(4);
+    const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);
+
+    unsigned ct = fastdiv((unsigned)start_tile, &n_k_tiles_div);
+    unsigned kt = fastmodulo((unsigned)start_tile, n_k_tiles, &n_k_tiles_div);
+
+    for (unsigned t = start_tile; t < (unsigned)end_tile; ) {
+        if (kt >= (unsigned)n_k_tiles) { kt = 0; ct++; }
+
+        __fp16 *tile_base = state->dst + t * HTP_MM_HMX_TILE_N_ELMS;
+        {
+            uint32_t byte_off = kt * 32 * sizeof(float);
+
+            HVX_Vector v_off = v_scat_base;
+            for (uint32_t r = 0; r < HTP_MM_HMX_TILE_N_ROWS; r += 2) {
+                uint32_t row0 = ct * HTP_MM_HMX_TILE_N_COLS + r;
+                uint32_t row1 = row0 + 1;
+
+                const uint8_t *r0 = state->src + row0 * state->row_stride;
+                const uint8_t *r1 = state->src + row1 * state->row_stride;
+
+                HVX_Vector v0_f32 = hvx_vmem((const float *)(r0 + byte_off));
+                HVX_Vector v1_f32 = (row1 < state->n_cols) ? hvx_vmem((const float *)(r1 + byte_off)) : Q6_V_vzero();
+
+                HVX_Vector v_out = hvx_vec_f32_to_f16(v0_f32, v1_f32);
+
+                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HTP_MM_HMX_TILE_SIZE - 1, v_off, v_out);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+
+                HVX_Vector v_out_hi = Q6_V_vror_VR(v_out, 64);
+                Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_base, HTP_MM_HMX_TILE_SIZE - 1, v_off, v_out_hi);
+                v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
+            }
+            (void) *(volatile HVX_Vector *)(tile_base);
+        }
+        ++t; ++kt;
+    }
+
+    if (start_tile < end_tile) {
+        (void) *(volatile HVX_Vector *)(state->dst + (end_tile - 1) * HTP_MM_HMX_TILE_N_ELMS);
+    }
+}
+
+// --- End tiled dequantizers ---
+
+// requires external HMX lock
+static void core_dot_chunk_fp16(__fp16 *restrict output, const __fp16 *restrict activation, const __fp16 *restrict weight, const __fp16 *restrict scales,
+                                uint32_t n_row_tiles, uint32_t n_col_tiles, uint32_t n_dot_tiles) {
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *)scales);
+    for (uint32_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < n_col_tiles; ++c) {
+            Q6_mxclracc_hf();
+
+            const __fp16 *row_tiles = activation + r * n_dot_tiles * HTP_MM_HMX_TILE_N_ELMS;
+            const __fp16 *col_tiles = weight + c * n_dot_tiles * HTP_MM_HMX_TILE_N_ELMS;
+
+            for (uint32_t k = 0, k_block; k < n_dot_tiles; k += k_block) {
+                k_block = hex_smin(n_dot_tiles - k, 32);
+                const uint32_t range = 2048u * (uint32_t)k_block - 1;
+                Q6_activation_hf_mxmem_RR_deep((unsigned int)row_tiles, range);
+                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, range);
+                row_tiles += k_block * HTP_MM_HMX_TILE_N_ELMS;
+                col_tiles += k_block * HTP_MM_HMX_TILE_N_ELMS;
+            }
+
+            __fp16 *out_tile = output + (r * n_col_tiles + c) * HTP_MM_HMX_TILE_N_ELMS;
+            Q6_mxmem_AR_after_hf(out_tile, 0);
+        }
+    }
+}
+
+// C += AB
+static void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, const __fp16 *restrict b,
+                                const __fp16 *restrict col_scales, const __fp16 *restrict eye_tile,
+                                uint32_t n_row_tiles, uint32_t n_col_tiles, uint32_t n_dot_tiles, bool zero_init) {
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *)col_scales);
+
+    const size_t dot_tile_stride = n_dot_tiles * HTP_MM_HMX_TILE_N_ELMS;
+    for (size_t i = 0; i < n_row_tiles; ++i) {
+        const __fp16 *row_base = a + i * dot_tile_stride;
+        __fp16 *res_base = c + i * n_col_tiles * HTP_MM_HMX_TILE_N_ELMS;
+        for (size_t j = 0; j < n_col_tiles; ++j) {
+            Q6_mxclracc_hf();
+
+            const __fp16 *col_tiles = b + j * dot_tile_stride;
+            const __fp16 *row_tiles = row_base;
+            __fp16 *accum_tile = res_base + j * HTP_MM_HMX_TILE_N_ELMS;
+            if (!zero_init) {
+                Q6_activation_hf_mxmem_RR((unsigned int)accum_tile, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)eye_tile, 2047);
+            }
+
+            for (uint32_t k = 0, k_block; k < n_dot_tiles; k += k_block) {
+                k_block = hex_smin(n_dot_tiles - k, 32);
+                const uint32_t range = 2048u * k_block - 1;
+                Q6_activation_hf_mxmem_RR_deep((unsigned int)row_tiles, range);
+                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, range);
+                row_tiles += k_block * HTP_MM_HMX_TILE_N_ELMS;
+                col_tiles += k_block * HTP_MM_HMX_TILE_N_ELMS;
+            }
+
+            Q6_mxmem_AR_after_hf(accum_tile, 0);
+        }
+    }
+}
+
+// --- Async HMX matmul job (for pipeline overlap) ---
+
+typedef struct {
+    __fp16 *       output;
+    const __fp16 * activation;
+    const __fp16 * weight;
+    const __fp16 * scales;
+    uint32_t       n_row_tiles;
+    uint32_t       n_col_tiles;
+    uint32_t       n_dot_tiles;
+} hmx_matmul_job_t;
+
+static void hmx_matmul_worker_fn(void * data) {
+    hmx_matmul_job_t * job = (hmx_matmul_job_t *) data;
+    FARF(HIGH, "hmx-mm-job: n_row_tiles %u n_col_tiles %u n_dot_tiles %u", job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
+    core_dot_chunk_fp16(job->output, job->activation, job->weight, job->scales, job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
+}
+
+static inline void hmx_matmul_job_init(hmx_matmul_job_t * job,
+                                       __fp16 *           output,
+                                       const __fp16 *     activation,
+                                       const __fp16 *     weight,
+                                       const __fp16 *     scales,
+                                       uint32_t           n_row_tiles,
+                                       uint32_t           n_col_tiles,
+                                       uint32_t           n_dot_tiles) {
+    job->output      = output;
+    job->activation  = activation;
+    job->weight      = weight;
+    job->scales      = scales;
+    job->n_row_tiles = n_row_tiles;
+    job->n_col_tiles = n_col_tiles;
+    job->n_dot_tiles = n_dot_tiles;
+}
+
+// output : fp16 -> f32p
+
+static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16 *restrict vtcm_src, uint32_t start_row, uint32_t n_rows, uint32_t n_cols, uint32_t dst_stride, uint32_t dst_cols) {
+    assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0);
+    const size_t tile_row_stride = (n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS;
+
+    const HVX_Vector one = hvx_vec_splat_f16(1.0);
+
+    const size_t limit_c         = hex_smin(n_cols, dst_cols);
+    const size_t limit_c_aligned = (limit_c & ~31);
+
+    for (size_t r = 0; r < n_rows; r += 2) {
+        const size_t r_idx0 = start_row + r + 0;
+        const size_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;
+        const size_t r1 = (r_idx0 % HTP_MM_HMX_TILE_N_ROWS) / 2;  // index of the row pair within the tile
+        const __fp16 *row_base = vtcm_src + r0 * tile_row_stride;
+        float *output_row_base = dst + r * dst_stride;  // global memory row base for row r (and r+1)
+
+        #pragma unroll(4)
+        for (size_t c = 0; c < limit_c_aligned; c += HTP_MM_HMX_TILE_N_COLS) {
+            const size_t c0    = c / HTP_MM_HMX_TILE_N_COLS;
+            const __fp16 *tile = row_base + c0 * HTP_MM_HMX_TILE_N_ELMS;
+            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
+            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
+
+            HVX_Vector *pv_out0 = (HVX_Vector *) (output_row_base + c + 0);
+            HVX_Vector *pv_out1 = (HVX_Vector *) (output_row_base + c + dst_stride);
+
+            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            if (r + 1 < n_rows) {
+                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+            }
+        }
+
+        if (limit_c_aligned < limit_c) {
+            size_t c = limit_c_aligned;
+            size_t valid_c = limit_c - c;
+            const size_t c0 = c / HTP_MM_HMX_TILE_N_COLS;
+            const __fp16 *tile = row_base + c0 * HTP_MM_HMX_TILE_N_ELMS;
+            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
+            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
+
+            hvx_vec_store_u(output_row_base + c, valid_c * sizeof(float), Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp)));
+            if (r + 1 < n_rows) {
+                hvx_vec_store_u(output_row_base + c + dst_stride, valid_c * sizeof(float), Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp)));
+            }
+        }
+    }
+}
+
+typedef struct {
+    const __fp16  *vtcm_src;
+    float         *dst;
+    uint32_t       n_tasks;
+    uint32_t       n_tot_chunks;
+    uint32_t       n_chunks_per_task;
+    uint32_t       n_cols;
+    uint32_t       dst_stride;  // DDR row stride
+    uint32_t       dst_cols;    // Actual output columns
+    struct htp_thread_trace * traces;
+} output_transfer_task_state_t;
+
+// activations : fp32 -> fp16
+
+static void transfer_activation_chunk_fp32_to_fp16(__fp16 *restrict vtcm_dst, const float *restrict src, uint32_t n_rows, uint32_t k_block, uint32_t k_stride, uint32_t k_valid) {
+    const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+    const uint32_t n_rows_tiled  = (n_rows / HTP_MM_HMX_TILE_N_ROWS) * HTP_MM_HMX_TILE_N_ROWS;
+
+    uint32_t r = 0;
+
+    #pragma unroll(2)
+    for (r = 0; r < n_rows_tiled; r += 2) {
+        uint32_t r0 = r / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        const float *ptr_in0 = src + (r + 0) * k_stride;
+        const float *ptr_in1 = src + (r + 1) * k_stride;
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = *(const HVX_Vector *)(ptr_in0 + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(ptr_in1 + c);
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = *(const HVX_Vector *)(ptr_in0 + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(ptr_in1 + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+
+    for (; r < n_rows_padded; r += 2) {
+        uint32_t r0 = r / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        const bool row0_valid = r       < n_rows;
+        const bool row1_valid = (r + 1) < n_rows;
+
+        const float *ptr_in0 = row0_valid ? (src + (r + 0) * k_stride) : NULL;
+        const float *ptr_in1 = row1_valid ? (src + (r + 1) * k_stride) : NULL;
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(ptr_in0 + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(ptr_in1 + c);
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(ptr_in0 + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(ptr_in1 + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+}
+
+typedef struct {
+    __fp16      *dst;
+    const float *src;
+    uint32_t     n_tasks;
+    uint32_t     n_tot_chunks;
+    uint32_t     n_chunks_per_task;
+    uint32_t     k_block;
+    uint32_t     k_stride;
+    uint32_t     k_valid;
+    struct htp_thread_trace * traces;
+    struct htp_context * ctx;
+    float              * vtcm_f32_act;
+} activation_transfer_task_state_t;
+
+static void transfer_activation_chunk_fp32_to_fp16_dma_pipelined(
+        dma_queue *dma_q,
+        __fp16 *restrict vtcm_dst,
+        const float *restrict src,
+        uint32_t n_rows,
+        uint32_t k_block,
+        uint32_t k_stride,
+        uint32_t k_valid,
+        float *thread_f32_act) {
+
+    const uint32_t R = HTP_MM_DMA_ACT_ROWS_PER_STEP;
+    const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+
+    const uint32_t n_steps = n_rows_padded / R;
+
+    // pre-fetch step 0
+    if (n_steps > 0 && n_rows > 0) {
+        uint32_t nrows_to_fetch = hex_smin(n_rows, R);
+        dma_queue_push(dma_q, dma_make_ptr(thread_f32_act, src),
+                       k_block * sizeof(float), k_stride * sizeof(float), k_valid * sizeof(float), nrows_to_fetch);
+    }
+
+    for (uint32_t s = 0; s < n_steps; ++s) {
+        uint32_t r = R * s;
+        float *curr_buf = thread_f32_act + (s % 2) * R * k_block;
+
+        if (r < n_rows) {
+            dma_queue_pop(dma_q);
+        }
+
+        uint32_t next_s = s + 1;
+        uint32_t next_r = R * next_s;
+        if (next_r < n_rows) {
+            uint32_t nrows_to_fetch = hex_smin(n_rows - next_r, R);
+            const float *next_src = src + next_r * k_stride;
+            float *next_buf = thread_f32_act + (next_s % 2) * R * k_block;
+            dma_queue_push(dma_q, dma_make_ptr(next_buf, next_src),
+                           k_block * sizeof(float), k_stride * sizeof(float), k_valid * sizeof(float), nrows_to_fetch);
+        }
+
+        #pragma unroll
+        for (uint32_t i = 0; i < HTP_MM_DMA_ACT_ROWS_PER_STEP; i += 2) {
+            uint32_t curr_r = r + i;
+            const bool row0_valid = (curr_r < n_rows);
+            const bool row1_valid = (curr_r + 1) < n_rows;
+
+            const float *ptr_in0 = curr_buf + i * k_block;
+            const float *ptr_in1 = curr_buf + (i + 1) * k_block;
+
+            uint32_t c = 0;
+            for (; c + 32 <= k_valid; c += 32) {
+                HVX_Vector v0 = Q6_V_vzero();
+                HVX_Vector v1 = Q6_V_vzero();
+                if (row0_valid) v0 = *(const HVX_Vector *)(ptr_in0 + c);
+                if (row1_valid) v1 = *(const HVX_Vector *)(ptr_in1 + c);
+
+                HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+                uint32_t r0       = curr_r / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+                uint32_t r1       = curr_r % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+                uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+                uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+                HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+                tile[r1 / 2]     = v_out;
+            }
+            if (c < k_block) {
+                HVX_Vector v0 = Q6_V_vzero();
+                HVX_Vector v1 = Q6_V_vzero();
+                if (row0_valid) v0 = *(const HVX_Vector *)(ptr_in0 + c);
+                if (row1_valid) v1 = *(const HVX_Vector *)(ptr_in1 + c);
+
+                uint32_t rem = k_valid - c;
+                HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+                v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+                v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+                HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+                uint32_t r0       = curr_r / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+                uint32_t r1       = curr_r % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+                uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;  // tile column index
+                uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+                HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+                tile[r1 / 2]     = v_out;
+            }
+        }
+    }
+}
+
+typedef struct {
+    const struct mmid_row_mapping  *matrix_rows;
+    __fp16                         *dst;
+    const float                    *src;
+    uint32_t                        n_tasks;
+    uint32_t                        n_tot_chunks;
+    uint32_t                        n_chunks_per_task;
+    uint32_t                        k_block;
+    uint32_t                        cur_a;
+    uint32_t                        mapping_stride;
+    uint32_t                        ne11;
+    struct fastdiv_values           ne11_div;
+    size_t                          nb11;
+    size_t                          nb12;
+    uint32_t                        start_row;
+    uint32_t                        cne1;
+    uint32_t                        k_valid;
+    struct htp_thread_trace        *traces;
+} activation_transfer_gathered_task_state_t;
+
+typedef struct {
+    const struct mmid_row_mapping  *matrix_rows;
+    const __fp16                   *vtcm_src;
+    float                          *dst;
+    uint32_t                        n_tasks;
+    uint32_t                        n_tot_chunks;
+    uint32_t                        n_chunks_per_task;
+    uint32_t                        n_cols;
+    uint32_t                        cur_a;
+    uint32_t                        mapping_stride;
+    size_t                          dst_nb1;
+    size_t                          dst_nb2;
+    uint32_t                        start_row;
+    uint32_t                        cne1;
+    struct htp_thread_trace        *traces;
+} output_transfer_scattered_task_state_t;
+
+static void transfer_activation_chunk_fp32_to_fp16_gathered(
+            __fp16 *restrict vtcm_dst,
+            const float *restrict src,
+            uint32_t start_row,
+            uint32_t n_rows,
+            uint32_t k_block,
+            const struct mmid_row_mapping *matrix_rows,
+            uint32_t cur_a,
+            uint32_t mapping_stride,
+            uint32_t ne11,
+            const struct fastdiv_values * ne11_div,
+            size_t nb11,
+            size_t nb12,
+            uint32_t cne1,
+            uint32_t k_valid) {
+    const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+    const uint32_t n_rows_tiled  = (n_rows / HTP_MM_HMX_TILE_N_ROWS) * HTP_MM_HMX_TILE_N_ROWS;
+
+    uint32_t r = 0;
+
+    #pragma unroll(2)
+    for (r = 0; r < n_rows_tiled; r += 2) {
+        uint32_t r_idx0 = start_row + r + 0;
+        uint32_t r_idx1 = start_row + r + 1;
+        uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0];
+        struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1];
+
+        uint32_t i11_0 = fastmodulo(mapping0.i1, ne11, ne11_div);
+        uint32_t i11_1 = fastmodulo(mapping1.i1, ne11, ne11_div);
+
+        const float *row0_ptr = (const float *) ((const uint8_t *) src + i11_0 * nb11 + mapping0.i2 * nb12);
+        const float *row1_ptr = (const float *) ((const uint8_t *) src + i11_1 * nb11 + mapping1.i2 * nb12);
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = *(const HVX_Vector *)(row0_ptr + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(row1_ptr + c);
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = *(const HVX_Vector *)(row0_ptr + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+
+    for (; r < n_rows_padded; r += 2) {
+        uint32_t r_idx0 = start_row + r;
+        uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        const bool row0_valid = (start_row + r + 0) < cne1;
+        const bool row1_valid = (start_row + r + 1) < cne1;
+
+        const float *row0_ptr = NULL;
+        const float *row1_ptr = NULL;
+
+        if (row0_valid) {
+            struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + (start_row + r + 0)];
+            uint32_t i11_0 = fastmodulo(mapping0.i1, ne11, ne11_div);
+            row0_ptr = (const float *) ((const uint8_t *) src + i11_0 * nb11 + mapping0.i2 * nb12);
+        }
+        if (row1_valid) {
+            struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + (start_row + r + 1)];
+            uint32_t i11_1 = fastmodulo(mapping1.i1, ne11, ne11_div);
+            row1_ptr = (const float *) ((const uint8_t *) src + i11_1 * nb11 + mapping1.i2 * nb12);
+        }
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(row0_ptr + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(row0_ptr + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+}
+
+static void transfer_activation_chunk_fp32_to_fp16_gathered_flat(
+            __fp16 *restrict vtcm_dst,
+            const float *restrict src,
+            uint32_t start_row,
+            uint32_t n_rows,
+            uint32_t k_block,
+            const struct mmid_row_mapping *matrix_rows,
+            uint32_t cur_a,
+            uint32_t mapping_stride,
+            size_t nb12,
+            uint32_t cne1,
+            uint32_t k_valid) {
+    const uint32_t n_rows_padded = hex_align_up(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+    const uint32_t n_rows_tiled  = (n_rows / HTP_MM_HMX_TILE_N_ROWS) * HTP_MM_HMX_TILE_N_ROWS;
+
+    uint32_t r = 0;
+
+    #pragma unroll(2)
+    for (r = 0; r < n_rows_tiled; r += 2) {
+        uint32_t r_idx0 = start_row + r + 0;
+        uint32_t r_idx1 = start_row + r + 1;
+        uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0];
+        struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1];
+
+        const float *row0_ptr = (const float *) ((const uint8_t *) src + mapping0.i2 * nb12);
+        const float *row1_ptr = (const float *) ((const uint8_t *) src + mapping1.i2 * nb12);
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = *(const HVX_Vector *)(row0_ptr + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(row1_ptr + c);
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = *(const HVX_Vector *)(row0_ptr + c);
+            HVX_Vector v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+
+    for (; r < n_rows_padded; r += 2) {
+        uint32_t r_idx0 = start_row + r;
+        uint32_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;  // tile row index
+        uint32_t r1 = r_idx0 % HTP_MM_HMX_TILE_N_ROWS;  // intra-tile row idx
+
+        const bool row0_valid = (start_row + r + 0) < cne1;
+        const bool row1_valid = (start_row + r + 1) < cne1;
+
+        const float *row0_ptr = NULL;
+        const float *row1_ptr = NULL;
+
+        if (row0_valid) {
+            struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + (start_row + r + 0)];
+            row0_ptr = (const float *) ((const uint8_t *) src + mapping0.i2 * nb12);
+        }
+        if (row1_valid) {
+            struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + (start_row + r + 1)];
+            row1_ptr = (const float *) ((const uint8_t *) src + mapping1.i2 * nb12);
+        }
+
+        uint32_t c = 0;
+        for (; c + 32 <= k_valid; c += 32) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(row0_ptr + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+        if (c < k_block) {
+            HVX_Vector v0 = Q6_V_vzero();
+            HVX_Vector v1 = Q6_V_vzero();
+            if (row0_valid) v0 = *(const HVX_Vector *)(row0_ptr + c);
+            if (row1_valid) v1 = *(const HVX_Vector *)(row1_ptr + c);
+
+            uint32_t rem = k_valid - c;
+            HVX_VectorPred mask = Q6_Q_vsetq2_R(rem > 0 ? rem * sizeof(float) : 0);
+            v0 = Q6_V_vmux_QVV(mask, v0, Q6_V_vzero());
+            v1 = Q6_V_vmux_QVV(mask, v1, Q6_V_vzero());
+
+            HVX_Vector v_out = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+            uint32_t c0       = c / HTP_MM_HMX_TILE_N_COLS;
+            uint32_t tile_idx = r0 * (k_block / HTP_MM_HMX_TILE_N_COLS) + c0;
+
+            HVX_Vector *tile = (HVX_Vector *) (vtcm_dst + tile_idx * HTP_MM_HMX_TILE_N_ELMS);
+            tile[r1 / 2]     = v_out;
+        }
+    }
+}
+
+static void transfer_output_chunk_fp16_to_fp32_scattered(
+            float *restrict dst,
+            const __fp16 *restrict vtcm_src,
+            uint32_t start_row,
+            uint32_t n_rows,
+            uint32_t n_cols,
+            const struct mmid_row_mapping *matrix_rows,
+            uint32_t cur_a,
+            uint32_t mapping_stride,
+            size_t dst_nb1,
+            size_t dst_nb2,
+            uint32_t cne1) {
+    assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0);
+    const size_t tile_row_stride = (n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS;
+
+    const HVX_Vector one = hvx_vec_splat_f16(1.0);
+
+    for (size_t r = 0; r < n_rows; r += 2) {
+        uint32_t r_idx0 = start_row + r + 0;
+        uint32_t r_idx1 = start_row + r + 1;
+        const size_t r0 = r_idx0 / HTP_MM_HMX_TILE_N_ROWS;
+        const size_t r1 = (r_idx0 % HTP_MM_HMX_TILE_N_ROWS) / 2;  // index of the row pair within the tile
+        const __fp16 *row_base = vtcm_src + r0 * tile_row_stride;
+
+        if (r_idx0 >= cne1) break;
+
+        struct mmid_row_mapping mapping0 = matrix_rows[cur_a * mapping_stride + r_idx0];
+        float *output_row0 = (float *) ((uint8_t *) dst + mapping0.i1 * dst_nb1 + mapping0.i2 * dst_nb2);
+
+        float *output_row1 = NULL;
+        if (r_idx1 < cne1) {
+            struct mmid_row_mapping mapping1 = matrix_rows[cur_a * mapping_stride + r_idx1];
+            output_row1 = (float *) ((uint8_t *) dst + mapping1.i1 * dst_nb1 + mapping1.i2 * dst_nb2);
+        }
+
+        #pragma unroll(4)
+        for (size_t c = 0; c < (size_t)n_cols; c += HTP_MM_HMX_TILE_N_COLS) {
+            const size_t c0 = c / HTP_MM_HMX_TILE_N_COLS;
+            const __fp16 *tile = row_base + c0 * HTP_MM_HMX_TILE_N_ELMS;
+            HVX_Vector v = ((const HVX_Vector *) tile)[r1];
+            HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
+
+            HVX_Vector *pv_out0 = (HVX_Vector *) (output_row0 + c);
+            HVX_Vector *pv_out1 = output_row1 ? (HVX_Vector *) (output_row1 + c) : NULL;
+
+            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            if (pv_out1) {
+                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-hexagon/htp/hmx-ops.c b/ggml/src/ggml-hexagon/htp/hmx-ops.c
deleted file mode 100644
index 114d8c148..000000000
--- a/ggml/src/ggml-hexagon/htp/hmx-ops.c
+++ /dev/null
@@ -1,6 +0,0 @@
-// HMX operations compiled as a single translation unit.
-// This allows interprocedural optimizations within HMX ops without requiring global HTP LTO.
-
-#include "hmx-queue.c"
-#include "hmx-matmul-ops.c"
-#include "hmx-flash-attn-ops.c"
diff --git a/ggml/src/ggml-hexagon/htp/hmx-ops.h b/ggml/src/ggml-hexagon/htp/hmx-ops.h
deleted file mode 100644
index a67842f3f..000000000
--- a/ggml/src/ggml-hexagon/htp/hmx-ops.h
+++ /dev/null
@@ -1,88 +0,0 @@
-// HMX operation entry-point declarations.
-// Ported from htp-ops-lib/include/dsp/ops.h (renamed, benchmark kernels removed). (https://github.com/haozixu/htp-ops-lib)
-
-#ifndef HMX_OPS_H
-#define HMX_OPS_H
-
-#include <stddef.h>
-#include <stdint.h>
-
-#include "htp-ops.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-typedef struct {
-    float        *dst;
-    const float  *activation;
-    const __fp16 *permuted_weight;
-    int           m;
-    int           k;
-    int           n;
-    int           act_stride;
-    int           weight_stride;
-    int           dst_stride;
-    int           ne02;
-    int           ne03;
-    int           ne12;
-    int           ne13;
-    size_t        src0_nb2;
-    size_t        src0_nb3;
-    size_t        src1_nb2;
-    size_t        src1_nb3;
-    size_t        dst_nb2;
-    size_t        dst_nb3;
-} hmx_matmul_f16_f32_batched_params_t;
-
-// HMX matrix multiplication — tile-permuted FP16 weights, FP32 activation/output
-// act_stride: activation row stride in elements (= k for contiguous, or
-//             nb[1]/sizeof(float) for permuted tensors like attention Q).
-// weight_stride: weight row stride in elements (= k for compact weights, or
-//                nb[1]/sizeof(__fp16) for permuted KV-cache views used by QK).
-int hmx_matmul_f16_f32(struct htp_context *ctx,
-                                float *restrict dst,
-                                const float *activation,
-                                const __fp16 *permuted_weight,
-                                int m, int k, int n,
-                                int act_stride,
-                                int weight_stride);
-
-// Batched F16 wrapper over hmx_mat_mul_f16_f32.
-// Batch semantics match ggml_mul_mat(): src0 broadcasts to src1 in dims 2/3.
-int hmx_matmul_f16_f32_batched(struct htp_context *ctx, const hmx_matmul_f16_f32_batched_params_t *params);
-
-// HMX matrix multiplication — all supported weight types (F16/F32/Q4_0/Q4_1/Q8_0/IQ4_NL/MXFP4)
-int hmx_matmul_2d_f32(struct htp_context *ctx,
-                                      float *restrict dst,
-                                      const float *activation,
-                                      const uint8_t *permuted_weight,
-                                      int m, int k, int n,
-                                      int act_stride,
-                                      int weight_stride,
-                                      int weight_type);
-
-struct mmid_row_mapping;
-
-int hmx_matmul_id_2d_f32(struct htp_context *ctx,
-                                         float *restrict dst,
-                                         const float *activation,
-                                         const uint8_t *permuted_weight,
-                                         int m, int k, int n,
-                                         int ne11,
-                                         size_t act_nb1, size_t act_nb2,
-                                         size_t dst_nb1, size_t dst_nb2,
-                                         int weight_stride,
-                                         int weight_type,
-                                         const struct mmid_row_mapping *matrix_rows,
-                                         int cur_a,
-                                         int mapping_stride);
-
-// HMX flash attention
-int hmx_flash_attn_ext(struct htp_ops_context * octx);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif // HMX_OPS_H
diff --git a/ggml/src/ggml-hexagon/htp/htp-ctx.h b/ggml/src/ggml-hexagon/htp/htp-ctx.h
index cbb5d0878..6ad77d3da 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ctx.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ctx.h
@@ -13,7 +13,9 @@
 #include <stdint.h>
 #include <stdbool.h>
 
+#ifndef HTP_MAX_NTHREADS
 #define HTP_MAX_NTHREADS 10
+#endif
 #define HTP_MAX_MMAPS    16
 
 // Memory mapping
@@ -42,9 +44,13 @@ struct htp_ops_context {
 
     enum htp_op_code    op; // FIXME: rename to opcode
     int32_t             op_params[HTP_OP_MAX_PARAMS];
+    int32_t             kernel_params[HTP_OP_MAX_KERN_PARAMS];
 
     const struct htp_tensor * src[HTP_OP_MAX_INPUTS];
-    const struct htp_tensor * dst;
+    union {
+        const struct htp_tensor * dst;
+        const struct htp_tensor * dsts[HTP_OP_MAX_OUTPUTS];
+    };
 
     // TODO convert these to an array
     struct htp_spad src0_spad;
@@ -87,13 +93,13 @@ struct htp_context {
 
     struct htp_ops_context octx;
 
-#ifdef HTP_HAS_HMX
     struct hmx_queue *     hmx_queue; // Async HMX queue for pipeline overlap
-#endif
 };
 
 int op_matmul(struct htp_ops_context * octx);
 int op_matmul_id(struct htp_ops_context * octx);
+int op_matmul_qkv(struct htp_ops_context * octx);
+int op_matmul_ffn(struct htp_ops_context * octx);
 int op_binary(struct htp_ops_context * octx);
 int op_unary(struct htp_ops_context * octx);
 int op_sum_rows(struct htp_ops_context * octx);
diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index 0f4b74a93..d04090135 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -28,18 +28,19 @@ enum htp_data_type {
     HTP_TYPE_MXFP4  = 39,
 
     // types used internally for repack, dyn.quant, etc
-    HTP_TYPE_Q4_0x4x2 = 200,
-    HTP_TYPE_Q4_1x4x2,
-    HTP_TYPE_Q8_0x4x2,
-    HTP_TYPE_MXFP4x4x2,
+    HTP_TYPE_Q4_0_TILED = 200,
+    HTP_TYPE_Q4_1_TILED,
+    HTP_TYPE_Q8_0_TILED,
+    HTP_TYPE_MXFP4_TILED,
 
     HTP_TYPE_INVALID
 };
 
 // Constats for internal types
-#define QK_Q4_0x4x2  256  // 4x Q4_0  blocks packed with next 4x Q4_0 blocks (size in bytes 128)
-#define QK_Q8_0x4x2  256  // 4x Q8_0  blocks concat with next 4x Q8_0 blocks
-#define QK_MXFP4x4x2 256  // 4x MXFP4 blocks concat with next 4x MXFP4 blocks
+#define QK_Q4_0_TILED  256  // 32x32 Q4_0 tiled layout
+#define QK_Q8_0_TILED  128  // 32x32 Q8_0 tiled layout
+#define QK_MXFP4_TILED 256  // 32x32 MXFP4 tiled layout
+
 
 
 // Mask to enable various stages of the Ops.
@@ -57,6 +58,8 @@ enum htp_op_code {
     HTP_OP_DIV = 3,
     HTP_OP_MUL_MAT,
     HTP_OP_MUL_MAT_ID,
+    HTP_OP_MUL_MAT_QKV,
+    HTP_OP_MUL_MAT_FFN,
     HTP_OP_RMS_NORM,
     HTP_OP_RMS_NORM_MUL,
     HTP_OP_UNARY_SILU,
@@ -99,7 +102,9 @@ enum htp_op_code {
 
 #define HTP_OP_MAX_DIMS    4    // aka GGML_MAX_DIMS
 #define HTP_OP_MAX_INPUTS  6    // aka GGML_MAX_SRCS
+#define HTP_OP_MAX_OUTPUTS 4
 #define HTP_OP_MAX_PARAMS  16   // aka GGML_MAX_OP_PARAMS
+#define HTP_OP_MAX_KERN_PARAMS 32
 
 #define HTP_OP_MAX_BUFS    16
 #define HTP_OP_MAX_REQS    256
@@ -142,8 +147,10 @@ struct htp_op_desc {
     uint32_t opcode;                    // GGML/HTP Op
     uint32_t flags;                     // Op flags
     int32_t  params[HTP_OP_MAX_PARAMS]; // Params for the op, e.g. epsilon of RMS norm
+    int32_t  kernel_params[HTP_OP_MAX_KERN_PARAMS]; // generic blob for host-precomputed parameters
     uint16_t src[HTP_OP_MAX_INPUTS];    // Input tensors indices
-    uint16_t dst;                       // Output tensor index
+    uint16_t dst[HTP_OP_MAX_OUTPUTS];   // Output tensor indices
+    uint16_t pad[2];                    // padding to align to 64 bits
 };
 
 #ifndef HTP_MAX_NTHREADS
diff --git a/ggml/src/ggml-hexagon/htp/htp_iface.idl b/ggml/src/ggml-hexagon/htp/htp_iface.idl
index d696a5fba..47693d8b8 100644
--- a/ggml/src/ggml-hexagon/htp/htp_iface.idl
+++ b/ggml/src/ggml-hexagon/htp/htp_iface.idl
@@ -11,12 +11,13 @@ struct htp_iface_pmu_conf {
 };
 
 interface htp_iface : remote_handle64 {
-    AEEResult start(in uint32 sess_id, in uint64 dsp_queue_id, in uint32 n_hvx, in uint32 use_hmx, in uint64 max_vmem);
+    AEEResult start(in uint32 sess_id, in uint64 dsp_queue_id, in uint32 n_hvx, in uint32 n_hmx, in uint64 max_vmem);
     AEEResult stop();
     AEEResult mmap(in uint32 fd, in uint32 size);
     AEEResult munmap(in uint32 fd);
     AEEResult profiler(in uint32 mode, in htp_iface_pmu_conf pmu);
     AEEResult etm(in uint32 enable);
+    AEEResult hwinfo(rout uint32 n_threads, rout uint32 n_hvx, rout uint32 n_hmx, rout uint64 vtcm_size);
 };
 
 #endif /* HTP_IDL */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-base.h b/ggml/src/ggml-hexagon/htp/hvx-base.h
index f6cb02951..493b26c6e 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-base.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-base.h
@@ -170,25 +170,7 @@ static inline HVX_VectorPair hvx_vec_f16_to_f32(HVX_Vector v) {
 }
 #endif
 
-/* Q6_Vsf_equals_Vw is only available on v73+.*/
-#if __HVX_ARCH__ < 73
-static inline HVX_Vector hvx_vec_i32_to_qf32(HVX_Vector const in)
-{
-    HVX_Vector const vzero = Q6_V_vzero();
-    HVX_VectorPred is_zero = Q6_Q_vcmp_eq_VwVw(in, vzero);
-    HVX_Vector lshift = Q6_Vw_vnormamt_Vw(in);
-    HVX_Vector normalized = Q6_Vw_vasl_VwVw(in, lshift);
-    HVX_Vector vexp = Q6_Vw_vsub_VwVw(Q6_V_vsplat_R(0x7f + 30), lshift);
-    HVX_Vector mant = Q6_V_vand_VV(Q6_V_vsplat_R(0xFFFFFF00), normalized);
-    HVX_Vector ret = Q6_V_vmux_QVV(is_zero, vzero, Q6_Vw_vadd_VwVw(mant, vexp));
-    return ret;
-}
 
-static inline HVX_Vector Q6_Vsf_equals_Vw(HVX_Vector const in)
-{
-    return Q6_Vsf_equals_Vqf32(hvx_vec_i32_to_qf32(in));
-}
-#endif
 
 static inline HVX_Vector hvx_vec_i16_from_hf_rnd_sat(HVX_Vector vin) {
     // This looks complicated.
@@ -305,4 +287,17 @@ static inline HVX_Vector hvx_vec_mul_f32_f32(HVX_Vector a, HVX_Vector b) {
 
 #endif // __HVX_ARCH__ < 79
 
+static inline HVX_Vector hvx_vec_load_act_tile(const uint8_t * y_q, uint32_t kt, HVX_Vector * v_act_all) {
+    if (kt % 4 == 0) {
+        *v_act_all = hvx_vmem(y_q + kt * 32);
+        return *v_act_all;
+    } else if (kt % 4 == 1) {
+        return Q6_V_vror_VR(*v_act_all, 32);
+    } else if (kt % 4 == 2) {
+        return Q6_V_vror_VR(*v_act_all, 64);
+    } else {
+        return Q6_V_vror_VR(*v_act_all, 96);
+    }
+}
+
 #endif /* HVX_BASE_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
new file mode 100644
index 000000000..52351b103
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
@@ -0,0 +1,1024 @@
+// Dynamic quantizers that produce flat (non-tiled) activations
+
+static inline void quantize_block_f32_q8_0_flat(
+    float * restrict x,
+    uint8_t * restrict y_quants,
+    __fp16 * restrict y_scales,
+    uint32_t block_idx
+) {
+    HVX_Vector * vx = (HVX_Vector *) x;
+    HVX_Vector zero = Q6_V_vzero();
+
+    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
+    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
+    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
+    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
+
+    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);
+    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);
+    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);
+    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);
+
+    HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero);
+    HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero);
+    HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero);
+    HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero);
+
+    HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
+    HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
+
+    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
+    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
+
+    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
+    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
+
+    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
+    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
+    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
+    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
+
+    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
+    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
+    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
+
+    * (HVX_Vector *) (y_quants + block_idx * 128) = vx_i8;
+
+    HVX_VectorPair vp1 = Q6_W_vshuff_VVR(vd23_hf, vd01_hf, -2);
+    HVX_VectorPair vp2 = Q6_W_vshuff_VVR(Q6_V_hi_W(vp1), Q6_V_lo_W(vp1), -2);
+    HVX_Vector v_scales = Q6_V_lo_W(vp2);
+    hvx_vec_store_u(y_scales + block_idx * 4, 8, v_scales);
+}
+
+static inline void quantize_block_f32_q8_1_flat(
+    float * restrict x,
+    uint8_t * restrict y_quants,
+    __fp16 * restrict y_scales,
+    uint32_t block_idx
+) {
+    HVX_Vector * vx = (HVX_Vector *) x;
+    HVX_Vector zero = Q6_V_vzero();
+
+    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
+    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
+    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
+    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
+
+    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);
+    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);
+    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);
+    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);
+
+    HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero);
+    HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero);
+    HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero);
+    HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero);
+
+    HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
+    HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
+
+    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
+    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
+
+    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
+    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
+
+    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
+    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
+    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
+    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
+
+    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
+    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
+    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
+
+    const HVX_Vector ones = Q6_Vb_vsplat_R(1);
+    HVX_Vector v_sums = Q6_Vw_vrmpy_VbVb(vx_i8, ones);
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 4));
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 8));
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 16));
+
+    * (HVX_Vector *) (y_quants + block_idx * 128) = vx_i8;
+
+    HVX_VectorPair vp1 = Q6_W_vshuff_VVR(vd23_hf, vd01_hf, -2);
+    HVX_VectorPair vp2 = Q6_W_vshuff_VVR(Q6_V_hi_W(vp1), Q6_V_lo_W(vp1), -2);
+    HVX_Vector v_scales = Q6_V_lo_W(vp2);
+
+    HVX_VectorPair v_deal1 = Q6_W_vdeal_VVR(v_sums, v_sums, -4);
+    HVX_Vector v_even1 = Q6_V_lo_W(v_deal1);
+    HVX_VectorPair v_deal2 = Q6_W_vdeal_VVR(v_even1, v_even1, -4);
+    HVX_Vector v_even2 = Q6_V_lo_W(v_deal2);
+    HVX_VectorPair v_deal3 = Q6_W_vdeal_VVR(v_even2, v_even2, -4);
+    HVX_Vector v_sums_shuffled = Q6_V_lo_W(v_deal3);
+
+    HVX_Vector v_sums_sf = Q6_Vsf_equals_Vw(v_sums_shuffled);
+    HVX_Vector v_sums_hf = hvx_vec_f32_to_f16(v_sums_sf, Q6_V_vzero());
+
+    HVX_Vector v_prod = hvx_vec_mul_f16_f16(v_scales, v_sums_hf);
+
+    HVX_VectorPair vp_scales = Q6_W_vshuff_VVR(v_prod, v_scales, -2);
+    HVX_Vector v_final = Q6_V_lo_W(vp_scales);
+
+    hvx_vec_store_u(y_scales + block_idx * 8, 16, v_final);
+}
+
+static inline void quantize_row_f32_q8_0_flat(float * restrict x, uint8_t * restrict y, uint32_t k) {
+    assert(k % 32 == 0);
+    const uint32_t quants_size = hex_round_up(k, 128);
+    uint8_t * restrict y_quants = y;
+    __fp16 * restrict y_scales = (__fp16 *) (y + quants_size);
+
+    const uint32_t nb = (k + 127) / 128;
+    for (uint32_t i = 0; i < nb; i++) {
+        quantize_block_f32_q8_0_flat(x + i * 128, y_quants, y_scales, i);
+    }
+}
+
+static inline void quantize_row_f32_q8_1_flat(float * restrict x, uint8_t * restrict y, uint32_t k) {
+    assert(k % 32 == 0);
+    const uint32_t quants_size = hex_round_up(k, 128);
+    uint8_t * restrict y_quants = y;
+    __fp16 * restrict y_scales = (__fp16 *) (y + quants_size);
+
+    const uint32_t nb = (k + 127) / 128;
+    for (uint32_t i = 0; i < nb; i++) {
+        quantize_block_f32_q8_1_flat(x + i * 128, y_quants, y_scales, i);
+    }
+}
+
+static inline void quantize_f32_q8_0_flat_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_row_size,
+    size_t dst_row_size
+) {
+    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0_TILED * sizeof(float));
+    hvx_splat_f32_a(tmp_data, 0.0f, src_row_size_padded / sizeof(float));
+
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
+        hvx_copy_f32_aa(tmp_data, src_data, ne0);
+
+        quantize_row_f32_q8_0_flat((float *) tmp_data, dst_data, ne0);
+        dst_data += dst_row_size;
+        src_data += src_row_size;
+    }
+}
+
+static inline void quantize_f32_q8_1_flat_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_row_size,
+    size_t dst_row_size
+) {
+    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0_TILED * sizeof(float));
+    hvx_splat_f32_a(tmp_data, 0.0f, src_row_size_padded / sizeof(float));
+
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
+        hvx_copy_f32_aa(tmp_data, src_data, ne0);
+
+        quantize_row_f32_q8_1_flat((float *) tmp_data, dst_data, ne0);
+        dst_data += dst_row_size;
+        src_data += src_row_size;
+    }
+}
+
+static inline void quantize_f32_f32_flat_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_stride,
+    size_t dst_stride
+) {
+    (void) tmp_data;
+    const size_t src_row_size = ne0 * sizeof(float);
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_stride, 2);
+        hvx_copy_f32_au(dst_data, src_data, ne0);
+
+        dst_data += dst_stride;
+        src_data += src_stride;
+    }
+}
+
+static inline void quantize_f32_f16_flat_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_stride,
+    size_t dst_stride
+) {
+    (void) tmp_data;
+    const size_t src_row_size = ne0 * sizeof(float);
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_stride, 2);
+        hvx_copy_f16_f32_au(dst_data, src_data, ne0);
+
+        dst_data += dst_stride;
+        src_data += src_stride;
+    }
+}
+
+static inline void quantize_f16_f16_flat_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_stride,
+    size_t dst_stride
+) {
+    (void) tmp_data;
+    const size_t src_row_size = ne0 * sizeof(float);
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_stride, 2);
+        hvx_copy_f16_au(dst_data, src_data, ne0);
+
+        dst_data += dst_stride;
+        src_data += src_stride;
+    }
+}
+
+// Dot kernels that consume flat (non-tiled) activations
+
+static void flat_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector i8 = Q6_Vb_vsplat_R(8);
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx_i8 = * (const HVX_Vector *) (y_q + block_idx * 128);
+        HVX_Vector v_act_raw = Q6_V_vror_VR(vx_i8, sub_idx * 32);
+
+        HVX_Vector v_act_rep[8];
+        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
+        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
+        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
+        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
+        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
+        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
+        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
+        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_sum = accum_4bit_32x1(vptr, v_act_rep, i8);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[4];
+
+        __fp16 scale_a_val = y_scales[kt];
+        HVX_Vector v_scale_a = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void flat_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector i8 = Q6_Vb_vsplat_R(8);
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
+    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx0_i8 = * (const HVX_Vector *) (y0_q + block_idx * 128);
+        HVX_Vector vx1_i8 = * (const HVX_Vector *) (y1_q + block_idx * 128);
+
+        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0_i8, sub_idx * 32);
+        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1_i8, sub_idx * 32);
+
+        HVX_Vector v_act0_rep[8];
+        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
+        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
+        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
+        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
+        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
+        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
+        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
+        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_act1_rep[8];
+        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
+        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
+        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
+        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
+        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
+        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
+        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
+        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2(vptr, v_act0_rep, v_act1_rep, i8);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[4];
+
+        __fp16 scale_a0_val = y0_scales[kt];
+        __fp16 scale_a1_val = y1_scales[kt];
+        HVX_Vector v_scale_a0 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
+        HVX_Vector v_scale_a1 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void flat_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx_i8 = * (const HVX_Vector *) (y_q + block_idx * 128);
+        HVX_Vector v_act_raw = Q6_V_vror_VR(vx_i8, sub_idx * 32);
+
+        HVX_Vector v_act_rep[8];
+        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
+        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
+        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
+        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
+        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
+        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
+        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
+        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_sum = accum_4bit_32x1(vptr, v_act_rep, Q6_V_vzero());
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_offset = vptr[4];
+        HVX_VectorPair p_deal = Q6_W_vdeal_VVR(v_scale_offset, v_scale_offset, -2);
+        HVX_Vector v_scale = Q6_V_lo_W(p_deal);
+        HVX_Vector v_offset = Q6_V_hi_W(p_deal);
+
+        __fp16 scale_a_val = y_scales[kt * 2 + 0];
+        __fp16 sum_a_val   = y_scales[kt * 2 + 1];
+        HVX_Vector v_scale_a = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
+        HVX_Vector v_sum_a   = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&sum_a_val));
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a);
+        HVX_Vector v_offset_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a);
+
+        HVX_Vector v_scaled_dot = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+        HVX_Vector v_sum_scaled = hvx_vec_add_f32_f32(v_scaled_dot, v_offset_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void flat_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
+    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx0_i8 = * (const HVX_Vector *) (y0_q + block_idx * 128);
+        HVX_Vector vx1_i8 = * (const HVX_Vector *) (y1_q + block_idx * 128);
+
+        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0_i8, sub_idx * 32);
+        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1_i8, sub_idx * 32);
+
+        HVX_Vector v_act0_rep[8];
+        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
+        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
+        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
+        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
+        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
+        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
+        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
+        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_act1_rep[8];
+        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
+        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
+        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
+        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
+        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
+        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
+        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
+        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2(vptr, v_act0_rep, v_act1_rep, Q6_V_vzero());
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_offset = vptr[4];
+        HVX_VectorPair p_deal = Q6_W_vdeal_VVR(v_scale_offset, v_scale_offset, -2);
+        HVX_Vector v_scale = Q6_V_lo_W(p_deal);
+        HVX_Vector v_offset = Q6_V_hi_W(p_deal);
+
+        __fp16 scale_a0_val = y0_scales[kt * 2 + 0];
+        __fp16 sum_a0_val   = y0_scales[kt * 2 + 1];
+        __fp16 scale_a1_val = y1_scales[kt * 2 + 0];
+        __fp16 sum_a1_val   = y1_scales[kt * 2 + 1];
+
+        HVX_Vector v_scale_a0 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
+        HVX_Vector v_sum_a0   = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&sum_a0_val));
+        HVX_Vector v_scale_a1 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
+        HVX_Vector v_sum_a1   = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&sum_a1_val));
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a0);
+        HVX_Vector v_offset_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a1);
+        HVX_Vector v_offset_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a1);
+
+        HVX_Vector v_scaled_dot_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_add_f32_f32(v_scaled_dot_c0, v_offset_comb_c0);
+
+        HVX_Vector v_scaled_dot_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_add_f32_f32(v_scaled_dot_c1, v_offset_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void flat_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 1152);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx_i8 = * (const HVX_Vector *) (y_q + block_idx * 128);
+        HVX_Vector v_act_raw = Q6_V_vror_VR(vx_i8, sub_idx * 32);
+
+        HVX_Vector v_act_rep[8];
+        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
+        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
+        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
+        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
+        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
+        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
+        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
+        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_sum = accum_q8_0_32x1(vptr, v_act_rep);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[8];
+
+        __fp16 scale_a_val = y_scales[kt];
+        HVX_Vector v_scale_a = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void flat_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
+    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 1152);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx0_i8 = * (const HVX_Vector *) (y0_q + block_idx * 128);
+        HVX_Vector vx1_i8 = * (const HVX_Vector *) (y1_q + block_idx * 128);
+
+        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0_i8, sub_idx * 32);
+        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1_i8, sub_idx * 32);
+
+        HVX_Vector v_act0_rep[8];
+        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
+        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
+        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
+        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
+        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
+        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
+        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
+        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_act1_rep[8];
+        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
+        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
+        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
+        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
+        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
+        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
+        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
+        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
+
+        HVX_VectorPair v_sums = accum_q8_0_32x2(vptr, v_act0_rep, v_act1_rep);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[8];
+
+        __fp16 scale_a0_val = y0_scales[kt];
+        __fp16 scale_a1_val = y1_scales[kt];
+        HVX_Vector v_scale_a0 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
+        HVX_Vector v_scale_a1 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void flat_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx = * (const HVX_Vector *) (y_q + block_idx * 128);
+        HVX_Vector v_act_raw = Q6_V_vror_VR(vx, sub_idx * 32);
+
+        HVX_Vector v_act_rep[8];
+        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
+        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
+        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
+        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
+        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
+        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
+        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
+        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_sum = accum_4bit_32x1_lut(vptr, v_act_rep, mask_h4, lut);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[4];
+
+        __fp16 scale_a_val = y_scales[kt];
+        HVX_Vector v_scale_a = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void flat_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector mask_h4        = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut            = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
+    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx0 = * (const HVX_Vector *) (y0_q + block_idx * 128);
+        HVX_Vector vx1 = * (const HVX_Vector *) (y1_q + block_idx * 128);
+
+        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0, sub_idx * 32);
+        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1, sub_idx * 32);
+
+        HVX_Vector v_act0_rep[8];
+        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
+        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
+        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
+        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
+        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
+        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
+        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
+        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_act1_rep[8];
+        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
+        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
+        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
+        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
+        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
+        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
+        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
+        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2_lut(vptr, v_act0_rep, v_act1_rep, mask_h4, lut);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[4];
+
+        __fp16 scale_a0_val = y0_scales[kt];
+        __fp16 scale_a1_val = y1_scales[kt];
+        HVX_Vector v_scale_a0 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
+        HVX_Vector v_scale_a1 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void flat_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
+    HVX_Vector expand = *(const HVX_Vector *) expand_x32_e8m0;
+    HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y_scales = (const __fp16 *) (y_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx = * (const HVX_Vector *) (y_q + block_idx * 128);
+        HVX_Vector v_act_raw = Q6_V_vror_VR(vx, sub_idx * 32);
+
+        HVX_Vector v_act_rep[8];
+        v_act_rep[0] = Q6_V_vdelta_VV(v_act_raw, v_repl_ctrl);
+        v_act_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 4), v_repl_ctrl);
+        v_act_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 8), v_repl_ctrl);
+        v_act_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 12), v_repl_ctrl);
+        v_act_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 16), v_repl_ctrl);
+        v_act_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 20), v_repl_ctrl);
+        v_act_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 24), v_repl_ctrl);
+        v_act_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_sum = accum_4bit_32x1_lut(vptr, v_act_rep, mask_h4, lut);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = hvx_vmem(tile_ptr + kt * 640 + 512);
+        HVX_Vector r0_d = Q6_V_vdelta_VV(v_scale_w, expand);
+        r0_d = Q6_V_vand_VV(r0_d, e8m0_mask);
+        HVX_Vector v_scale_w_f32 = Q6_Vw_vasl_VwR(r0_d, 23);
+
+        __fp16 scale_a_val = y_scales[kt];
+        HVX_Vector v_scale_a_f16 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a_val));
+        HVX_VectorPair p_scale_a_f32 = hvx_vec_f16_to_f32(v_scale_a_f16);
+        HVX_Vector v_scale_a = Q6_V_lo_W(p_scale_a_f32);
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    v_sum_float = hvx_vec_mul_f32_f32(v_sum_float, hvx_vec_splat_f32(0.5f));
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void flat_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
+    HVX_Vector expand = *(const HVX_Vector *) expand_x32_e8m0;
+    HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    const uint32_t quants_size = hex_round_up(n, 128);
+    const __fp16 * restrict y0_scales = (const __fp16 *) (y0_q + quants_size);
+    const __fp16 * restrict y1_scales = (const __fp16 *) (y1_q + quants_size);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+
+        uint32_t block_idx = kt / 4;
+        uint32_t sub_idx = kt % 4;
+
+        HVX_Vector vx0 = * (const HVX_Vector *) (y0_q + block_idx * 128);
+        HVX_Vector vx1 = * (const HVX_Vector *) (y1_q + block_idx * 128);
+
+        HVX_Vector v_act0_raw = Q6_V_vror_VR(vx0, sub_idx * 32);
+        HVX_Vector v_act1_raw = Q6_V_vror_VR(vx1, sub_idx * 32);
+
+        HVX_Vector v_act0_rep[8];
+        v_act0_rep[0] = Q6_V_vdelta_VV(v_act0_raw, v_repl_ctrl);
+        v_act0_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 4), v_repl_ctrl);
+        v_act0_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 8), v_repl_ctrl);
+        v_act0_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 12), v_repl_ctrl);
+        v_act0_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 16), v_repl_ctrl);
+        v_act0_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 20), v_repl_ctrl);
+        v_act0_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 24), v_repl_ctrl);
+        v_act0_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act0_raw, 28), v_repl_ctrl);
+
+        HVX_Vector v_act1_rep[8];
+        v_act1_rep[0] = Q6_V_vdelta_VV(v_act1_raw, v_repl_ctrl);
+        v_act1_rep[1] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 4), v_repl_ctrl);
+        v_act1_rep[2] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 8), v_repl_ctrl);
+        v_act1_rep[3] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 12), v_repl_ctrl);
+        v_act1_rep[4] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 16), v_repl_ctrl);
+        v_act1_rep[5] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 20), v_repl_ctrl);
+        v_act1_rep[6] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 24), v_repl_ctrl);
+        v_act1_rep[7] = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act1_raw, 28), v_repl_ctrl);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2_lut(vptr, v_act0_rep, v_act1_rep, mask_h4, lut);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = hvx_vmem(tile_ptr + kt * 640 + 512);
+        HVX_Vector r0_d = Q6_V_vdelta_VV(v_scale_w, expand);
+        r0_d = Q6_V_vand_VV(r0_d, e8m0_mask);
+        HVX_Vector v_scale_w_f32 = Q6_Vw_vasl_VwR(r0_d, 23);
+
+        __fp16 scale_a0_val = y0_scales[kt];
+        __fp16 scale_a1_val = y1_scales[kt];
+        HVX_Vector v_scale_a0_f16 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a0_val));
+        HVX_Vector v_scale_a1_f16 = hvx_vec_repl_f16(Q6_Vh_vsplat_R(*(const int16_t *)&scale_a1_val));
+        HVX_VectorPair p_scale_a0_f32 = hvx_vec_f16_to_f32(v_scale_a0_f16);
+        HVX_VectorPair p_scale_a1_f32 = hvx_vec_f16_to_f32(v_scale_a1_f16);
+        HVX_Vector v_scale_a0 = Q6_V_lo_W(p_scale_a0_f32);
+        HVX_Vector v_scale_a1 = Q6_V_lo_W(p_scale_a1_f32);
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    v_sum_float_c0 = hvx_vec_mul_f32_f32(v_sum_float_c0, hvx_vec_splat_f32(0.5f));
+    v_sum_float_c1 = hvx_vec_mul_f32_f32(v_sum_float_c1, hvx_vec_splat_f32(0.5f));
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
diff --git a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
new file mode 100644
index 000000000..bcb0b8f9e
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
@@ -0,0 +1,1140 @@
+// Dynamic quantizers that produce tiled activations
+
+static inline void quantize_block_f32_q8_1_tiled(float * restrict x, uint8_t * restrict y_block) {
+    assert((unsigned long) x % 128 == 0);
+    assert((unsigned long) y_block % 128 == 0);
+
+    HVX_Vector * vx = (HVX_Vector *) x;
+    HVX_Vector zero = Q6_V_vzero();
+
+    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
+    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
+    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
+    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
+
+    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);
+    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);
+    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);
+    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);
+
+    HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero);
+    HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero);
+    HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero);
+    HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero);
+
+    HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
+    HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
+
+    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
+    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
+
+    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
+    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
+    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
+
+    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
+    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
+    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
+    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
+
+    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
+    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
+    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
+
+    const HVX_Vector ones = Q6_Vb_vsplat_R(1);
+    HVX_Vector v_sums = Q6_Vw_vrmpy_VbVb(vx_i8, ones);
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 4));
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 8));
+    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 16));
+
+    float vmax0[32]  __attribute__((aligned(128)));
+    float vmax1[32]  __attribute__((aligned(128)));
+    float vmax2[32]  __attribute__((aligned(128)));
+    float vmax3[32]  __attribute__((aligned(128)));
+    int32_t sums[32] __attribute__((aligned(128)));
+
+    hvx_vec_store_u(vmax0, 128, vmax0_sf);
+    hvx_vec_store_u(vmax1, 128, vmax1_sf);
+    hvx_vec_store_u(vmax2, 128, vmax2_sf);
+    hvx_vec_store_u(vmax3, 128, vmax3_sf);
+    hvx_vec_store_u(sums,  128, v_sums);
+
+    float d0 = vmax0[0] / 127.0f;
+    float d1 = vmax1[0] / 127.0f;
+    float d2 = vmax2[0] / 127.0f;
+    float d3 = vmax3[0] / 127.0f;
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    for (int b = 0; b < 4; b++) {
+        HVX_Vector v_act = Q6_V_vror_VR(vx_i8, b * 32);
+
+        HVX_Vector r0 = Q6_V_vdelta_VV(v_act, v_repl_ctrl);
+        HVX_Vector r1 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 4),  v_repl_ctrl);
+        HVX_Vector r2 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 8),  v_repl_ctrl);
+        HVX_Vector r3 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 12), v_repl_ctrl);
+        HVX_Vector r4 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 16), v_repl_ctrl);
+        HVX_Vector r5 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 20), v_repl_ctrl);
+        HVX_Vector r6 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 24), v_repl_ctrl);
+        HVX_Vector r7 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 28), v_repl_ctrl);
+
+        __fp16 scale_h, offset_h;
+        if (b == 0) {
+            scale_h  = (__fp16) d0;
+            offset_h = (__fp16) (sums[0] * d0);
+        } else if (b == 1) {
+            scale_h  = (__fp16) d1;
+            offset_h = (__fp16) (sums[8] * d1);
+        } else if (b == 2) {
+            scale_h  = (__fp16) d2;
+            offset_h = (__fp16) (sums[16] * d2);
+        } else {
+            scale_h  = (__fp16) d3;
+            offset_h = (__fp16) (sums[24] * d3);
+        }
+
+        HVX_Vector r_scale  = Q6_Vh_vsplat_R(*(int16_t *)&scale_h);
+        HVX_Vector r_offset = Q6_Vh_vsplat_R(*(int16_t *)&offset_h);
+
+        HVX_Vector * restrict dst = (HVX_Vector *) (y_block + b * 1280);
+        dst[0] = r0;
+        dst[1] = r1;
+        dst[2] = r2;
+        dst[3] = r3;
+        dst[4] = r4;
+        dst[5] = r5;
+        dst[6] = r6;
+        dst[7] = r7;
+        dst[8] = r_scale;
+        dst[9] = r_offset;
+    }
+}
+
+static inline void quantize_block_f32_q8_0_tiled(float * restrict x, uint8_t * restrict y_block) {
+    assert((unsigned long) x % 128 == 0);
+    assert((unsigned long) y_block % 128 == 0);
+
+    HVX_Vector * vx = (HVX_Vector *) x;
+    HVX_Vector zero   = Q6_V_vzero();
+
+    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);
+    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);
+    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);
+    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);
+
+    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
+    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
+
+    HVX_Vector vmax_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf));
+    vmax_hf            = hvx_vec_reduce_max2_f16(hvx_vec_abs_f16(vx23_hf), vmax_hf);
+
+    HVX_Vector vd_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax_hf, Q6_Vh_vsplat_R(0x2008));
+    HVX_Vector vd_hf   = Q6_Vhf_equals_Vqf16(vd_qf16);
+
+    HVX_Vector vd_inv_hf = hvx_vec_inverse_f16(vd_hf);
+    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd_inv_hf));
+    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd_inv_hf));
+
+    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
+    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
+    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
+
+    HVX_Vector r_scale = hvx_vec_repl_f16(vd_hf);
+
+    static const uint8_t __attribute__((aligned(128))) repl[128] = {
+        0x00, 0x00, 0x00, 0x00, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x40, 0x40, 0x40, 0x40, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x20, 0x20, 0x20, 0x20, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+        0x10, 0x10, 0x10, 0x10, 0x04, 0x04, 0x04, 0x04, 0x08, 0x08, 0x08, 0x08, 0x04, 0x04, 0x04, 0x04,
+    };
+    HVX_Vector v_repl_ctrl = * (const HVX_Vector *) repl;
+
+    for (int b = 0; b < 4; b++) {
+        HVX_Vector v_act = Q6_V_vror_VR(vx_i8, b * 32);
+
+        HVX_Vector r0 = Q6_V_vdelta_VV(v_act, v_repl_ctrl);
+        HVX_Vector r1 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 4),  v_repl_ctrl);
+        HVX_Vector r2 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 8),  v_repl_ctrl);
+        HVX_Vector r3 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 12), v_repl_ctrl);
+        HVX_Vector r4 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 16), v_repl_ctrl);
+        HVX_Vector r5 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 20), v_repl_ctrl);
+        HVX_Vector r6 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 24), v_repl_ctrl);
+        HVX_Vector r7 = Q6_V_vdelta_VV(Q6_V_vror_VR(v_act, 28), v_repl_ctrl);
+
+        HVX_Vector * restrict dst = (HVX_Vector *) (y_block + b * 1152);
+        dst[0] = r0;
+        dst[1] = r1;
+        dst[2] = r2;
+        dst[3] = r3;
+        dst[4] = r4;
+        dst[5] = r5;
+        dst[6] = r6;
+        dst[7] = r7;
+        dst[8] = r_scale;
+    }
+}
+
+static void quantize_row_f32_q8_0_tiled(float * restrict x, uint8_t * restrict y, uint32_t k) {
+    assert(k % 32 == 0);
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (k + qk - 1) / qk;
+
+    for (uint32_t i = 0; i < nb; i++) {
+        uint8_t * restrict y_block = y + i * 4 * 1152;
+        quantize_block_f32_q8_0_tiled(x + i * qk, y_block);
+    }
+}
+
+static void quantize_row_f32_q8_1_tiled(float * restrict x, uint8_t * restrict y, uint32_t k) {
+    assert(k % 32 == 0);
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (k + qk - 1) / qk;
+
+    for (uint32_t i = 0; i < nb; i++) {
+        uint8_t * restrict y_block = y + i * 4 * 1280;
+        quantize_block_f32_q8_1_tiled(x + i * qk, y_block);
+    }
+}
+
+// Dot kernels & helpers that consume tiled activations
+
+static inline HVX_Vector hvx_vec_mul_f16_f16_to_f32_lower32(HVX_Vector v1, HVX_Vector v2) {
+#if __HVX_ARCH__ >= 79
+    HVX_VectorPair p = Q6_Wsf_vmpy_VhfVhf(v1, v2);
+    return Q6_V_lo_W(Q6_W_vshuff_VVR(Q6_V_hi_W(p), Q6_V_lo_W(p), -4));
+#else
+    HVX_VectorPair p = Q6_Wqf32_vmpy_VhfVhf(v1, v2);
+    HVX_Vector hi = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(p));
+    HVX_Vector lo = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(p));
+    return Q6_V_lo_W(Q6_W_vshuff_VVR(hi, lo, -4));
+#endif
+}
+
+static inline HVX_Vector unpack_and_interleave_4bit(HVX_Vector v_a, HVX_Vector v_b, HVX_Vector mask_h4) {
+    HVX_Vector v_W0 = Q6_V_vand_VV(v_a, mask_h4);
+    HVX_Vector v_W1 = Q6_Vub_vlsr_VubR(v_a, 4);
+    HVX_Vector v_W2 = Q6_V_vand_VV(v_b, mask_h4);
+    HVX_Vector v_W3 = Q6_Vub_vlsr_VubR(v_b, 4);
+
+    HVX_VectorPair v01_pair = Q6_W_vshuff_VVR(v_W1, v_W0, -1);
+    HVX_VectorPair v23_pair = Q6_W_vshuff_VVR(v_W3, v_W2, -1);
+    HVX_VectorPair v0123_pair = Q6_W_vshuff_VVR(Q6_V_lo_W(v23_pair), Q6_V_lo_W(v01_pair), -2);
+    return Q6_V_lo_W(v0123_pair);
+}
+
+static inline HVX_VectorPair unpack_and_interleave_4bit_x2(HVX_Vector v_src, HVX_Vector mask_h4) {
+    HVX_Vector v_lo = Q6_V_vand_VV(v_src, mask_h4);
+    HVX_Vector v_hi = Q6_Vub_vlsr_VubR(v_src, 4);
+    HVX_VectorPair v01_pair = Q6_W_vshuff_VVR(v_hi, v_lo, -1);
+    HVX_Vector v01_lo = Q6_V_lo_W(v01_pair);
+    HVX_Vector v01_hi = Q6_V_hi_W(v01_pair);
+
+    HVX_Vector v23_lo = Q6_V_valign_VVR(v01_hi, v01_lo, 64);
+    HVX_Vector v_W0 = Q6_V_lo_W(Q6_W_vshuff_VVR(v23_lo, v01_lo, -2));
+
+    HVX_Vector v67_lo = Q6_V_valign_VVR(v01_lo, v01_hi, 64);
+    HVX_Vector v_W1 = Q6_V_lo_W(Q6_W_vshuff_VVR(v67_lo, v01_hi, -2));
+
+    return Q6_W_vcombine_VV(v_W1, v_W0);
+}
+
+static inline HVX_Vector accum_4bit_32x1(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act,
+    HVX_Vector i8
+) {
+    HVX_Vector v_sum0 = Q6_V_vzero();
+    HVX_Vector v_sum1 = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+
+    #pragma unroll
+    for (int i = 0; i < 4; i++) {
+        HVX_VectorPair v_W_pair = unpack_and_interleave_4bit_x2(vptr[i], mask_h4);
+        HVX_Vector v_W0 = Q6_Vb_vsub_VbVb(Q6_V_lo_W(v_W_pair), i8);
+        HVX_Vector v_W1 = Q6_Vb_vsub_VbVb(Q6_V_hi_W(v_W_pair), i8);
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W0, v_act[i * 2 + 0]);
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W1, v_act[i * 2 + 1]);
+    }
+
+    return Q6_Vw_vadd_VwVw(v_sum0, v_sum1);
+}
+
+static inline HVX_Vector accum_4bit_32x1_lut(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act,
+    HVX_Vector mask_h4,
+    HVX_Vector lut
+) {
+    HVX_Vector v_sum0 = Q6_V_vzero();
+    HVX_Vector v_sum1 = Q6_V_vzero();
+
+    #pragma unroll
+    for (int i = 0; i < 4; i++) {
+        HVX_VectorPair v_W_pair = unpack_and_interleave_4bit_x2(vptr[i], mask_h4);
+        HVX_Vector v_W0 = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v_W_pair), lut, 0);
+        HVX_Vector v_W1 = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v_W_pair), lut, 0);
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W0, v_act[i * 2 + 0]);
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W1, v_act[i * 2 + 1]);
+    }
+
+    return Q6_Vw_vadd_VwVw(v_sum0, v_sum1);
+}
+
+static inline HVX_VectorPair accum_4bit_32x2(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act0,
+    const HVX_Vector * restrict v_act1,
+    HVX_Vector i8
+) {
+    HVX_Vector v_sum0 = Q6_V_vzero();
+    HVX_Vector v_sum1 = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+
+    #pragma unroll
+    for (int i = 0; i < 4; i++) {
+        HVX_VectorPair v_W_pair = unpack_and_interleave_4bit_x2(vptr[i], mask_h4);
+        HVX_Vector v_W0 = Q6_Vb_vsub_VbVb(Q6_V_lo_W(v_W_pair), i8);
+        HVX_Vector v_W1 = Q6_Vb_vsub_VbVb(Q6_V_hi_W(v_W_pair), i8);
+
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W0, v_act0[i * 2 + 0]);
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W1, v_act0[i * 2 + 1]);
+
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W0, v_act1[i * 2 + 0]);
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W1, v_act1[i * 2 + 1]);
+    }
+
+    return Q6_W_vcombine_VV(v_sum1, v_sum0);
+}
+
+static inline HVX_VectorPair accum_4bit_32x2_lut(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act0,
+    const HVX_Vector * restrict v_act1,
+    HVX_Vector mask_h4,
+    HVX_Vector lut
+) {
+    HVX_Vector v_sum0 = Q6_V_vzero();
+    HVX_Vector v_sum1 = Q6_V_vzero();
+
+    #pragma unroll
+    for (int i = 0; i < 4; i++) {
+        HVX_VectorPair v_W_pair = unpack_and_interleave_4bit_x2(vptr[i], mask_h4);
+        HVX_Vector v_W0 = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v_W_pair), lut, 0);
+        HVX_Vector v_W1 = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v_W_pair), lut, 0);
+
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W0, v_act0[i * 2 + 0]);
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W1, v_act0[i * 2 + 1]);
+
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W0, v_act1[i * 2 + 0]);
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W1, v_act1[i * 2 + 1]);
+    }
+
+    return Q6_W_vcombine_VV(v_sum1, v_sum0);
+}
+
+static inline HVX_Vector accum_q8_0_32x1(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act
+) {
+    HVX_Vector v_sum = Q6_V_vzero();
+    #pragma unroll
+    for (int g = 0; g < 8; g++) {
+        HVX_Vector v_rot = Q6_V_vror_VR(vptr[g], 64);
+        HVX_Vector v_W = Q6_V_lo_W(Q6_W_vshuff_VVR(v_rot, vptr[g], -2));
+        v_sum = Q6_Vw_vrmpyacc_VwVbVb(v_sum, v_W, v_act[g]);
+    }
+    return v_sum;
+}
+
+static inline HVX_VectorPair accum_q8_0_32x2(
+    const HVX_Vector * restrict vptr,
+    const HVX_Vector * restrict v_act0,
+    const HVX_Vector * restrict v_act1
+) {
+    HVX_Vector v_sum0 = Q6_V_vzero();
+    HVX_Vector v_sum1 = Q6_V_vzero();
+    #pragma unroll
+    for (int g = 0; g < 8; g++) {
+        HVX_Vector v_rot = Q6_V_vror_VR(vptr[g], 64);
+        HVX_Vector v_W = Q6_V_lo_W(Q6_W_vshuff_VVR(v_rot, vptr[g], -2));
+        v_sum0 = Q6_Vw_vrmpyacc_VwVbVb(v_sum0, v_W, v_act0[g]);
+        v_sum1 = Q6_Vw_vrmpyacc_VwVbVb(v_sum1, v_W, v_act1[g]);
+    }
+    return Q6_W_vcombine_VV(v_sum1, v_sum0);
+}
+
+static void tiled_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector i8 = Q6_Vb_vsplat_R(8);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1152);
+
+        HVX_Vector v_sum = accum_4bit_32x1(vptr, v_act, i8);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[4];
+        HVX_Vector v_scale_a = v_act[8];
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void tiled_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector i8 = Q6_Vb_vsplat_R(8);
+
+    uint32_t n_k_tiles = n / 32;
+    uint32_t kt = 0;
+    for (; kt + 1 < n_k_tiles; kt += 2) {
+        const HVX_Vector * restrict vptr0 = (const HVX_Vector *) (tile_ptr + (kt + 0) * 640);
+        const HVX_Vector * restrict v_act0_0 = (const HVX_Vector *) (y0_q + (kt + 0) * 1152);
+        const HVX_Vector * restrict v_act1_0 = (const HVX_Vector *) (y1_q + (kt + 0) * 1152);
+
+        const HVX_Vector * restrict vptr1 = (const HVX_Vector *) (tile_ptr + (kt + 1) * 640);
+        const HVX_Vector * restrict v_act0_1 = (const HVX_Vector *) (y0_q + (kt + 1) * 1152);
+        const HVX_Vector * restrict v_act1_1 = (const HVX_Vector *) (y1_q + (kt + 1) * 1152);
+
+        HVX_VectorPair v_sums0 = accum_4bit_32x2(vptr0, v_act0_0, v_act1_0, i8);
+        HVX_VectorPair v_sums1 = accum_4bit_32x2(vptr1, v_act0_1, v_act1_1, i8);
+
+        HVX_Vector v_sum_c0_0 = Q6_V_lo_W(v_sums0);
+        HVX_Vector v_sum_c1_0 = Q6_V_hi_W(v_sums0);
+        HVX_Vector v_sum_c0_1 = Q6_V_lo_W(v_sums1);
+        HVX_Vector v_sum_c1_1 = Q6_V_hi_W(v_sums1);
+
+        HVX_Vector v_sum_sf_c0_0 = Q6_Vsf_equals_Vw(v_sum_c0_0);
+        HVX_Vector v_sum_sf_c1_0 = Q6_Vsf_equals_Vw(v_sum_c1_0);
+        HVX_Vector v_sum_sf_c0_1 = Q6_Vsf_equals_Vw(v_sum_c0_1);
+        HVX_Vector v_sum_sf_c1_1 = Q6_Vsf_equals_Vw(v_sum_c1_1);
+
+        HVX_Vector v_scale_w0 = vptr0[4];
+        HVX_Vector v_scale_w1 = vptr1[4];
+        HVX_Vector v_scale_a_c0_0 = v_act0_0[8];
+        HVX_Vector v_scale_a_c1_0 = v_act1_0[8];
+        HVX_Vector v_scale_a_c0_1 = v_act0_1[8];
+        HVX_Vector v_scale_a_c1_1 = v_act1_1[8];
+
+        HVX_Vector v_scale_comb_c0_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c0_0);
+        HVX_Vector v_scale_comb_c1_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c1_0);
+        HVX_Vector v_scale_comb_c0_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c0_1);
+        HVX_Vector v_scale_comb_c1_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c1_1);
+
+        HVX_Vector v_sum_scaled_c0_0 = hvx_vec_mul_f32_f32(v_sum_sf_c0_0, v_scale_comb_c0_0);
+        HVX_Vector v_sum_scaled_c1_0 = hvx_vec_mul_f32_f32(v_sum_sf_c1_0, v_scale_comb_c1_0);
+        HVX_Vector v_sum_scaled_c0_1 = hvx_vec_mul_f32_f32(v_sum_sf_c0_1, v_scale_comb_c0_1);
+        HVX_Vector v_sum_scaled_c1_1 = hvx_vec_mul_f32_f32(v_sum_sf_c1_1, v_scale_comb_c1_1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vec_add_f32_f32(v_sum_scaled_c0_0, v_sum_scaled_c0_1));
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vec_add_f32_f32(v_sum_scaled_c1_0, v_sum_scaled_c1_1));
+    }
+
+    for (; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1152);
+        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1152);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2(vptr, v_act0, v_act1, i8);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[4];
+        HVX_Vector v_scale_a_c0 = v_act0[8];
+        HVX_Vector v_scale_a_c1 = v_act1[8];
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void tiled_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1280);
+
+        HVX_Vector v_sum = accum_4bit_32x1(vptr, v_act, Q6_V_vzero());
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_offset = vptr[4];
+        HVX_VectorPair p_deal = Q6_W_vdeal_VVR(v_scale_offset, v_scale_offset, -2);
+        HVX_Vector v_scale = Q6_V_lo_W(p_deal);
+        HVX_Vector v_offset = Q6_V_hi_W(p_deal);
+
+        HVX_Vector v_scale_a = v_act[8];
+        HVX_Vector v_sum_a   = v_act[9];
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a);
+        HVX_Vector v_offset_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a);
+
+        HVX_Vector v_scaled_dot = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+        HVX_Vector v_sum_scaled = hvx_vec_add_f32_f32(v_scaled_dot, v_offset_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void tiled_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+
+    uint32_t n_k_tiles = n / 32;
+    uint32_t kt = 0;
+    for (; kt + 1 < n_k_tiles; kt += 2) {
+        const HVX_Vector * restrict vptr0 = (const HVX_Vector *) (tile_ptr + (kt + 0) * 640);
+        const HVX_Vector * restrict v_act0_0 = (const HVX_Vector *) (y0_q + (kt + 0) * 1280);
+        const HVX_Vector * restrict v_act1_0 = (const HVX_Vector *) (y1_q + (kt + 0) * 1280);
+
+        const HVX_Vector * restrict vptr1 = (const HVX_Vector *) (tile_ptr + (kt + 1) * 640);
+        const HVX_Vector * restrict v_act0_1 = (const HVX_Vector *) (y0_q + (kt + 1) * 1280);
+        const HVX_Vector * restrict v_act1_1 = (const HVX_Vector *) (y1_q + (kt + 1) * 1280);
+
+        HVX_VectorPair v_sums0 = accum_4bit_32x2(vptr0, v_act0_0, v_act1_0, Q6_V_vzero());
+        HVX_VectorPair v_sums1 = accum_4bit_32x2(vptr1, v_act0_1, v_act1_1, Q6_V_vzero());
+
+        HVX_Vector v_sum_c0_0 = Q6_V_lo_W(v_sums0);
+        HVX_Vector v_sum_c1_0 = Q6_V_hi_W(v_sums0);
+        HVX_Vector v_sum_c0_1 = Q6_V_lo_W(v_sums1);
+        HVX_Vector v_sum_c1_1 = Q6_V_hi_W(v_sums1);
+
+        HVX_Vector v_sum_sf_c0_0 = Q6_Vsf_equals_Vw(v_sum_c0_0);
+        HVX_Vector v_sum_sf_c1_0 = Q6_Vsf_equals_Vw(v_sum_c1_0);
+        HVX_Vector v_sum_sf_c0_1 = Q6_Vsf_equals_Vw(v_sum_c0_1);
+        HVX_Vector v_sum_sf_c1_1 = Q6_Vsf_equals_Vw(v_sum_c1_1);
+
+        HVX_Vector v_scale_offset0 = vptr0[4];
+        HVX_VectorPair p_deal0 = Q6_W_vdeal_VVR(v_scale_offset0, v_scale_offset0, -2);
+        HVX_Vector v_scale0 = Q6_V_lo_W(p_deal0);
+        HVX_Vector v_offset0 = Q6_V_hi_W(p_deal0);
+
+        HVX_Vector v_scale_offset1 = vptr1[4];
+        HVX_VectorPair p_deal1 = Q6_W_vdeal_VVR(v_scale_offset1, v_scale_offset1, -2);
+        HVX_Vector v_scale1 = Q6_V_lo_W(p_deal1);
+        HVX_Vector v_offset1 = Q6_V_hi_W(p_deal1);
+
+        HVX_Vector v_scale_a_c0_0 = v_act0_0[8];
+        HVX_Vector v_sum_a_c0_0   = v_act0_0[9];
+        HVX_Vector v_scale_a_c1_0 = v_act1_0[8];
+        HVX_Vector v_sum_a_c1_0   = v_act1_0[9];
+
+        HVX_Vector v_scale_a_c0_1 = v_act0_1[8];
+        HVX_Vector v_sum_a_c0_1   = v_act0_1[9];
+        HVX_Vector v_scale_a_c1_1 = v_act1_1[8];
+        HVX_Vector v_sum_a_c1_1   = v_act1_1[9];
+
+        HVX_Vector v_scale_comb_c0_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale0, v_scale_a_c0_0);
+        HVX_Vector v_offset_comb_c0_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset0, v_sum_a_c0_0);
+        HVX_Vector v_scale_comb_c1_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale0, v_scale_a_c1_0);
+        HVX_Vector v_offset_comb_c1_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset0, v_sum_a_c1_0);
+
+        HVX_Vector v_scale_comb_c0_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale1, v_scale_a_c0_1);
+        HVX_Vector v_offset_comb_c0_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset1, v_sum_a_c0_1);
+        HVX_Vector v_scale_comb_c1_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale1, v_scale_a_c1_1);
+        HVX_Vector v_offset_comb_c1_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset1, v_sum_a_c1_1);
+
+        HVX_Vector v_scaled_dot_c0_0 = hvx_vec_mul_f32_f32(v_sum_sf_c0_0, v_scale_comb_c0_0);
+        HVX_Vector v_sum_scaled_c0_0 = hvx_vec_add_f32_f32(v_scaled_dot_c0_0, v_offset_comb_c0_0);
+
+        HVX_Vector v_scaled_dot_c1_0 = hvx_vec_mul_f32_f32(v_sum_sf_c1_0, v_scale_comb_c1_0);
+        HVX_Vector v_sum_scaled_c1_0 = hvx_vec_add_f32_f32(v_scaled_dot_c1_0, v_offset_comb_c1_0);
+
+        HVX_Vector v_scaled_dot_c0_1 = hvx_vec_mul_f32_f32(v_sum_sf_c0_1, v_scale_comb_c0_1);
+        HVX_Vector v_sum_scaled_c0_1 = hvx_vec_add_f32_f32(v_scaled_dot_c0_1, v_offset_comb_c0_1);
+
+        HVX_Vector v_scaled_dot_c1_1 = hvx_vec_mul_f32_f32(v_sum_sf_c1_1, v_scale_comb_c1_1);
+        HVX_Vector v_sum_scaled_c1_1 = hvx_vec_add_f32_f32(v_scaled_dot_c1_1, v_offset_comb_c1_1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vec_add_f32_f32(v_sum_scaled_c0_0, v_sum_scaled_c0_1));
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vec_add_f32_f32(v_sum_scaled_c1_0, v_sum_scaled_c1_1));
+    }
+
+    for (; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1280);
+        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1280);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2(vptr, v_act0, v_act1, Q6_V_vzero());
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_offset = vptr[4];
+        HVX_VectorPair p_deal = Q6_W_vdeal_VVR(v_scale_offset, v_scale_offset, -2);
+        HVX_Vector v_scale = Q6_V_lo_W(p_deal);
+        HVX_Vector v_offset = Q6_V_hi_W(p_deal);
+
+        HVX_Vector v_scale_a_c0 = v_act0[8];
+        HVX_Vector v_sum_a_c0   = v_act0[9];
+        HVX_Vector v_scale_a_c1 = v_act1[8];
+        HVX_Vector v_sum_a_c1   = v_act1[9];
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a_c0);
+        HVX_Vector v_offset_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a_c0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale, v_scale_a_c1);
+        HVX_Vector v_offset_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_offset, v_sum_a_c1);
+
+        HVX_Vector v_scaled_dot_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_add_f32_f32(v_scaled_dot_c0, v_offset_comb_c0);
+
+        HVX_Vector v_scaled_dot_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_add_f32_f32(v_scaled_dot_c1, v_offset_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void tiled_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 1152);
+        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1152);
+
+        HVX_Vector v_sum = accum_q8_0_32x1(vptr, v_act);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[8];
+        HVX_Vector v_scale_a = v_act[8];
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void tiled_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+
+    uint32_t n_k_tiles = n / 32;
+    uint32_t kt = 0;
+    for (; kt + 1 < n_k_tiles; kt += 2) {
+        const HVX_Vector * restrict vptr0 = (const HVX_Vector *) (tile_ptr + (kt + 0) * 1152);
+        const HVX_Vector * restrict v_act0_0 = (const HVX_Vector *) (y0_q + (kt + 0) * 1152);
+        const HVX_Vector * restrict v_act1_0 = (const HVX_Vector *) (y1_q + (kt + 0) * 1152);
+
+        const HVX_Vector * restrict vptr1 = (const HVX_Vector *) (tile_ptr + (kt + 1) * 1152);
+        const HVX_Vector * restrict v_act0_1 = (const HVX_Vector *) (y0_q + (kt + 1) * 1152);
+        const HVX_Vector * restrict v_act1_1 = (const HVX_Vector *) (y1_q + (kt + 1) * 1152);
+
+        HVX_VectorPair v_sums0 = accum_q8_0_32x2(vptr0, v_act0_0, v_act1_0);
+        HVX_VectorPair v_sums1 = accum_q8_0_32x2(vptr1, v_act0_1, v_act1_1);
+
+        HVX_Vector v_sum_c0_0 = Q6_V_lo_W(v_sums0);
+        HVX_Vector v_sum_c1_0 = Q6_V_hi_W(v_sums0);
+        HVX_Vector v_sum_c0_1 = Q6_V_lo_W(v_sums1);
+        HVX_Vector v_sum_c1_1 = Q6_V_hi_W(v_sums1);
+
+        HVX_Vector v_sum_sf_c0_0 = Q6_Vsf_equals_Vw(v_sum_c0_0);
+        HVX_Vector v_sum_sf_c1_0 = Q6_Vsf_equals_Vw(v_sum_c1_0);
+        HVX_Vector v_sum_sf_c0_1 = Q6_Vsf_equals_Vw(v_sum_c0_1);
+        HVX_Vector v_sum_sf_c1_1 = Q6_Vsf_equals_Vw(v_sum_c1_1);
+
+        HVX_Vector v_scale_w0 = vptr0[8];
+        HVX_Vector v_scale_w1 = vptr1[8];
+        HVX_Vector v_scale_a_c0_0 = v_act0_0[8];
+        HVX_Vector v_scale_a_c1_0 = v_act1_0[8];
+        HVX_Vector v_scale_a_c0_1 = v_act0_1[8];
+        HVX_Vector v_scale_a_c1_1 = v_act1_1[8];
+
+        HVX_Vector v_scale_comb_c0_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c0_0);
+        HVX_Vector v_scale_comb_c1_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c1_0);
+        HVX_Vector v_scale_comb_c0_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c0_1);
+        HVX_Vector v_scale_comb_c1_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c1_1);
+
+        HVX_Vector v_sum_scaled_c0_0 = hvx_vec_mul_f32_f32(v_sum_sf_c0_0, v_scale_comb_c0_0);
+        HVX_Vector v_sum_scaled_c1_0 = hvx_vec_mul_f32_f32(v_sum_sf_c1_0, v_scale_comb_c1_0);
+        HVX_Vector v_sum_scaled_c0_1 = hvx_vec_mul_f32_f32(v_sum_sf_c0_1, v_scale_comb_c0_1);
+        HVX_Vector v_sum_scaled_c1_1 = hvx_vec_mul_f32_f32(v_sum_sf_c1_1, v_scale_comb_c1_1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vec_add_f32_f32(v_sum_scaled_c0_0, v_sum_scaled_c0_1));
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vec_add_f32_f32(v_sum_scaled_c1_0, v_sum_scaled_c1_1));
+    }
+
+    for (; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 1152);
+        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1152);
+        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1152);
+
+        HVX_VectorPair v_sums = accum_q8_0_32x2(vptr, v_act0, v_act1);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[8];
+        HVX_Vector v_scale_a_c0 = v_act0[8];
+        HVX_Vector v_scale_a_c1 = v_act1[8];
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void tiled_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1152);
+
+        HVX_Vector v_sum = accum_4bit_32x1_lut(vptr, v_act, mask_h4, lut);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = vptr[4];
+        HVX_Vector v_scale_a = v_act[8];
+        HVX_Vector v_scale_comb = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void tiled_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_iq4nl_lut;
+
+    uint32_t n_k_tiles = n / 32;
+    uint32_t kt = 0;
+    for (; kt + 1 < n_k_tiles; kt += 2) {
+        const HVX_Vector * restrict vptr0 = (const HVX_Vector *) (tile_ptr + (kt + 0) * 640);
+        const HVX_Vector * restrict v_act0_0 = (const HVX_Vector *) (y0_q + (kt + 0) * 1152);
+        const HVX_Vector * restrict v_act1_0 = (const HVX_Vector *) (y1_q + (kt + 0) * 1152);
+
+        const HVX_Vector * restrict vptr1 = (const HVX_Vector *) (tile_ptr + (kt + 1) * 640);
+        const HVX_Vector * restrict v_act0_1 = (const HVX_Vector *) (y0_q + (kt + 1) * 1152);
+        const HVX_Vector * restrict v_act1_1 = (const HVX_Vector *) (y1_q + (kt + 1) * 1152);
+
+        HVX_VectorPair v_sums0 = accum_4bit_32x2_lut(vptr0, v_act0_0, v_act1_0, mask_h4, lut);
+        HVX_VectorPair v_sums1 = accum_4bit_32x2_lut(vptr1, v_act0_1, v_act1_1, mask_h4, lut);
+
+        HVX_Vector v_sum_c0_0 = Q6_V_lo_W(v_sums0);
+        HVX_Vector v_sum_c1_0 = Q6_V_hi_W(v_sums0);
+        HVX_Vector v_sum_c0_1 = Q6_V_lo_W(v_sums1);
+        HVX_Vector v_sum_c1_1 = Q6_V_hi_W(v_sums1);
+
+        HVX_Vector v_sum_sf_c0_0 = Q6_Vsf_equals_Vw(v_sum_c0_0);
+        HVX_Vector v_sum_sf_c1_0 = Q6_Vsf_equals_Vw(v_sum_c1_0);
+        HVX_Vector v_sum_sf_c0_1 = Q6_Vsf_equals_Vw(v_sum_c0_1);
+        HVX_Vector v_sum_sf_c1_1 = Q6_Vsf_equals_Vw(v_sum_c1_1);
+
+        HVX_Vector v_scale_w0 = vptr0[4];
+        HVX_Vector v_scale_w1 = vptr1[4];
+        HVX_Vector v_scale_a_c0_0 = v_act0_0[8];
+        HVX_Vector v_scale_a_c1_0 = v_act1_0[8];
+        HVX_Vector v_scale_a_c0_1 = v_act0_1[8];
+        HVX_Vector v_scale_a_c1_1 = v_act1_1[8];
+
+        HVX_Vector v_scale_comb_c0_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c0_0);
+        HVX_Vector v_scale_comb_c1_0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w0, v_scale_a_c1_0);
+        HVX_Vector v_scale_comb_c0_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c0_1);
+        HVX_Vector v_scale_comb_c1_1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w1, v_scale_a_c1_1);
+
+        HVX_Vector v_sum_scaled_c0_0 = hvx_vec_mul_f32_f32(v_sum_sf_c0_0, v_scale_comb_c0_0);
+        HVX_Vector v_sum_scaled_c1_0 = hvx_vec_mul_f32_f32(v_sum_sf_c1_0, v_scale_comb_c1_0);
+        HVX_Vector v_sum_scaled_c0_1 = hvx_vec_mul_f32_f32(v_sum_sf_c0_1, v_scale_comb_c0_1);
+        HVX_Vector v_sum_scaled_c1_1 = hvx_vec_mul_f32_f32(v_sum_sf_c1_1, v_scale_comb_c1_1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vec_add_f32_f32(v_sum_scaled_c0_0, v_sum_scaled_c0_1));
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vec_add_f32_f32(v_sum_scaled_c1_0, v_sum_scaled_c1_1));
+    }
+
+    for (; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1152);
+        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1152);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2_lut(vptr, v_act0, v_act1, mask_h4, lut);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = vptr[4];
+        HVX_Vector v_scale_a_c0 = v_act0[8];
+        HVX_Vector v_scale_a_c1 = v_act1[8];
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f16_f16_to_f32_lower32(v_scale_w, v_scale_a_c1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static void tiled_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y_q = vy;
+
+    HVX_Vector v_sum_float = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
+    HVX_Vector expand = *(const HVX_Vector *) expand_x32_e8m0;
+    HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+
+    uint32_t n_k_tiles = n / 32;
+    for (uint32_t kt = 0; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act = (const HVX_Vector *) (y_q + kt * 1152);
+
+        HVX_Vector v_sum = accum_4bit_32x1_lut(vptr, v_act, mask_h4, lut);
+        HVX_Vector v_sum_sf = Q6_Vsf_equals_Vw(v_sum);
+
+        HVX_Vector v_scale_w = hvx_vmem(tile_ptr + kt * 640 + 512);
+        HVX_Vector r0_d = Q6_V_vdelta_VV(v_scale_w, expand);
+        r0_d = Q6_V_vand_VV(r0_d, e8m0_mask);
+        HVX_Vector v_scale_w_f32 = Q6_Vw_vasl_VwR(r0_d, 23);
+
+        HVX_Vector v_scale_a_f16 = v_act[8];
+        HVX_VectorPair p_scale_a_f32 = hvx_vec_f16_to_f32_shuff(v_scale_a_f16);
+        HVX_Vector v_scale_a = Q6_V_lo_W(p_scale_a_f32);
+
+        HVX_Vector v_scale_comb = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a);
+        HVX_Vector v_sum_scaled = hvx_vec_mul_f32_f32(v_sum_sf, v_scale_comb);
+
+        v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
+    }
+
+    v_sum_float = hvx_vec_mul_f32_f32(v_sum_float, hvx_vec_splat_f32(0.5f));
+
+    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+}
+
+static void tiled_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+    const uint8_t * restrict tile_ptr = vx;
+    const uint8_t * restrict y0_q = vy0;
+    const uint8_t * restrict y1_q = vy1;
+
+    HVX_Vector v_sum_float_c0 = Q6_V_vzero();
+    HVX_Vector v_sum_float_c1 = Q6_V_vzero();
+    HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
+    HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
+    HVX_Vector expand = *(const HVX_Vector *) expand_x32_e8m0;
+    HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
+
+    uint32_t n_k_tiles = n / 32;
+    uint32_t kt = 0;
+    for (; kt + 1 < n_k_tiles; kt += 2) {
+        const HVX_Vector * restrict vptr0 = (const HVX_Vector *) (tile_ptr + (kt + 0) * 640);
+        const HVX_Vector * restrict v_act0_0 = (const HVX_Vector *) (y0_q + (kt + 0) * 1152);
+        const HVX_Vector * restrict v_act1_0 = (const HVX_Vector *) (y1_q + (kt + 0) * 1152);
+
+        const HVX_Vector * restrict vptr1 = (const HVX_Vector *) (tile_ptr + (kt + 1) * 640);
+        const HVX_Vector * restrict v_act0_1 = (const HVX_Vector *) (y0_q + (kt + 1) * 1152);
+        const HVX_Vector * restrict v_act1_1 = (const HVX_Vector *) (y1_q + (kt + 1) * 1152);
+
+        HVX_VectorPair v_sums0 = accum_4bit_32x2_lut(vptr0, v_act0_0, v_act1_0, mask_h4, lut);
+        HVX_VectorPair v_sums1 = accum_4bit_32x2_lut(vptr1, v_act0_1, v_act1_1, mask_h4, lut);
+
+        HVX_Vector v_sum_c0_0 = Q6_V_lo_W(v_sums0);
+        HVX_Vector v_sum_c1_0 = Q6_V_hi_W(v_sums0);
+        HVX_Vector v_sum_c0_1 = Q6_V_lo_W(v_sums1);
+        HVX_Vector v_sum_c1_1 = Q6_V_hi_W(v_sums1);
+
+        HVX_Vector v_sum_sf_c0_0 = Q6_Vsf_equals_Vw(v_sum_c0_0);
+        HVX_Vector v_sum_sf_c1_0 = Q6_Vsf_equals_Vw(v_sum_c1_0);
+        HVX_Vector v_sum_sf_c0_1 = Q6_Vsf_equals_Vw(v_sum_c0_1);
+        HVX_Vector v_sum_sf_c1_1 = Q6_Vsf_equals_Vw(v_sum_c1_1);
+
+        HVX_Vector v_scale_w0 = hvx_vmem(tile_ptr + (kt + 0) * 640 + 512);
+        HVX_Vector r0_d0 = Q6_V_vdelta_VV(v_scale_w0, expand);
+        r0_d0 = Q6_V_vand_VV(r0_d0, e8m0_mask);
+        HVX_Vector v_scale_w_f32_0 = Q6_Vw_vasl_VwR(r0_d0, 23);
+
+        HVX_Vector v_scale_w1 = hvx_vmem(tile_ptr + (kt + 1) * 640 + 512);
+        HVX_Vector r0_d1 = Q6_V_vdelta_VV(v_scale_w1, expand);
+        r0_d1 = Q6_V_vand_VV(r0_d1, e8m0_mask);
+        HVX_Vector v_scale_w_f32_1 = Q6_Vw_vasl_VwR(r0_d1, 23);
+
+        HVX_Vector v_scale_a_c0_f16_0 = v_act0_0[8];
+        HVX_Vector v_scale_a_c1_f16_0 = v_act1_0[8];
+        HVX_Vector v_scale_a_c0_f16_1 = v_act0_1[8];
+        HVX_Vector v_scale_a_c1_f16_1 = v_act1_1[8];
+
+        HVX_VectorPair p_scale_a_c0_f32_0 = hvx_vec_f16_to_f32_shuff(v_scale_a_c0_f16_0);
+        HVX_VectorPair p_scale_a_c1_f32_0 = hvx_vec_f16_to_f32_shuff(v_scale_a_c1_f16_0);
+        HVX_VectorPair p_scale_a_c0_f32_1 = hvx_vec_f16_to_f32_shuff(v_scale_a_c0_f16_1);
+        HVX_VectorPair p_scale_a_c1_f32_1 = hvx_vec_f16_to_f32_shuff(v_scale_a_c1_f16_1);
+
+        HVX_Vector v_scale_a_c0_0 = Q6_V_lo_W(p_scale_a_c0_f32_0);
+        HVX_Vector v_scale_a_c1_0 = Q6_V_lo_W(p_scale_a_c1_f32_0);
+        HVX_Vector v_scale_a_c0_1 = Q6_V_lo_W(p_scale_a_c0_f32_1);
+        HVX_Vector v_scale_a_c1_1 = Q6_V_lo_W(p_scale_a_c1_f32_1);
+
+        HVX_Vector v_scale_comb_c0_0 = hvx_vec_mul_f32_f32(v_scale_w_f32_0, v_scale_a_c0_0);
+        HVX_Vector v_scale_comb_c1_0 = hvx_vec_mul_f32_f32(v_scale_w_f32_0, v_scale_a_c1_0);
+        HVX_Vector v_scale_comb_c0_1 = hvx_vec_mul_f32_f32(v_scale_w_f32_1, v_scale_a_c0_1);
+        HVX_Vector v_scale_comb_c1_1 = hvx_vec_mul_f32_f32(v_scale_w_f32_1, v_scale_a_c1_1);
+
+        HVX_Vector v_sum_scaled_c0_0 = hvx_vec_mul_f32_f32(v_sum_sf_c0_0, v_scale_comb_c0_0);
+        HVX_Vector v_sum_scaled_c1_0 = hvx_vec_mul_f32_f32(v_sum_sf_c1_0, v_scale_comb_c1_0);
+        HVX_Vector v_sum_scaled_c0_1 = hvx_vec_mul_f32_f32(v_sum_sf_c0_1, v_scale_comb_c0_1);
+        HVX_Vector v_sum_scaled_c1_1 = hvx_vec_mul_f32_f32(v_sum_sf_c1_1, v_scale_comb_c1_1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vec_add_f32_f32(v_sum_scaled_c0_0, v_sum_scaled_c0_1));
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vec_add_f32_f32(v_sum_scaled_c1_0, v_sum_scaled_c1_1));
+    }
+
+    for (; kt < n_k_tiles; kt++) {
+        const HVX_Vector * restrict vptr = (const HVX_Vector *) (tile_ptr + kt * 640);
+        const HVX_Vector * restrict v_act0 = (const HVX_Vector *) (y0_q + kt * 1152);
+        const HVX_Vector * restrict v_act1 = (const HVX_Vector *) (y1_q + kt * 1152);
+
+        HVX_VectorPair v_sums = accum_4bit_32x2_lut(vptr, v_act0, v_act1, mask_h4, lut);
+        HVX_Vector v_sum_c0 = Q6_V_lo_W(v_sums);
+        HVX_Vector v_sum_c1 = Q6_V_hi_W(v_sums);
+
+        HVX_Vector v_sum_sf_c0 = Q6_Vsf_equals_Vw(v_sum_c0);
+        HVX_Vector v_sum_sf_c1 = Q6_Vsf_equals_Vw(v_sum_c1);
+
+        HVX_Vector v_scale_w = hvx_vmem(tile_ptr + kt * 640 + 512);
+        HVX_Vector r0_d = Q6_V_vdelta_VV(v_scale_w, expand);
+        r0_d = Q6_V_vand_VV(r0_d, e8m0_mask);
+        HVX_Vector v_scale_w_f32 = Q6_Vw_vasl_VwR(r0_d, 23);
+
+        HVX_Vector v_scale_a_c0_f16 = v_act0[8];
+        HVX_Vector v_scale_a_c1_f16 = v_act1[8];
+
+        HVX_VectorPair p_scale_a_c0_f32 = hvx_vec_f16_to_f32_shuff(v_scale_a_c0_f16);
+        HVX_VectorPair p_scale_a_c1_f32 = hvx_vec_f16_to_f32_shuff(v_scale_a_c1_f16);
+
+        HVX_Vector v_scale_a_c0 = Q6_V_lo_W(p_scale_a_c0_f32);
+        HVX_Vector v_scale_a_c1 = Q6_V_lo_W(p_scale_a_c1_f32);
+
+        HVX_Vector v_scale_comb_c0 = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a_c0);
+        HVX_Vector v_scale_comb_c1 = hvx_vec_mul_f32_f32(v_scale_w_f32, v_scale_a_c1);
+
+        HVX_Vector v_sum_scaled_c0 = hvx_vec_mul_f32_f32(v_sum_sf_c0, v_scale_comb_c0);
+        HVX_Vector v_sum_scaled_c1 = hvx_vec_mul_f32_f32(v_sum_sf_c1, v_scale_comb_c1);
+
+        v_sum_float_c0 = hvx_vec_add_f32_f32(v_sum_float_c0, v_sum_scaled_c0);
+        v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
+    }
+
+    v_sum_float_c0 = hvx_vec_mul_f32_f32(v_sum_float_c0, hvx_vec_splat_f32(0.5f));
+    v_sum_float_c1 = hvx_vec_mul_f32_f32(v_sum_float_c1, hvx_vec_splat_f32(0.5f));
+
+    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+}
+
+static inline void quantize_f32_q8_0_tiled_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_row_size,
+    size_t dst_row_size
+) {
+    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0_TILED * sizeof(float));
+    hvx_splat_f32_a(tmp_data, 0.0f, src_row_size_padded / sizeof(float));
+
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
+        hvx_copy_f32_aa(tmp_data, src_data, ne0);
+
+        quantize_row_f32_q8_0_tiled((float *) tmp_data, dst_data, ne0);
+        dst_data += dst_row_size;
+        src_data += src_row_size;
+    }
+}
+
+static inline void quantize_f32_q8_1_tiled_kernel(
+    const uint8_t * restrict src_data,
+    uint8_t * restrict dst_data,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t nrows,
+    size_t src_row_size,
+    size_t dst_row_size
+) {
+    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0_TILED * sizeof(float));
+    hvx_splat_f32_a(tmp_data, 0.0f, src_row_size_padded / sizeof(float));
+
+    for (uint32_t i = 0; i < nrows; ++i) {
+        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
+        hvx_copy_f32_aa(tmp_data, src_data, ne0);
+
+        quantize_row_f32_q8_1_tiled((float *) tmp_data, dst_data, ne0);
+        dst_data += dst_row_size;
+        src_data += src_row_size;
+    }
+}
+
+static inline void quantize_f32_q8_0_tiled_block_kernel(
+    const float * restrict src,
+    uint8_t * restrict dst,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t ib_first,
+    uint32_t ib_last,
+    size_t src_row_size,
+    size_t dst_row_size,
+    uint32_t r,
+    uint32_t c
+) {
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (ne0 + qk - 1) / qk;
+
+    for (uint32_t ib = ib_first; ib < ib_last; ++ib) {
+        const uint8_t * restrict src_ptr = (const uint8_t *) src + r * src_row_size + c * qk * sizeof(float);
+        uint8_t * restrict dst_ptr = dst + r * dst_row_size + c * 4 * 1152;
+
+        hex_l2fetch(src_ptr, qk * sizeof(float), qk * sizeof(float), 1);
+
+        if (c == nb - 1) {
+            uint32_t active_elements = ne0 - c * qk;
+            hvx_splat_f32_a(tmp_data, 0.0f, qk);
+            hvx_copy_f32_aa(tmp_data, src_ptr, active_elements);
+        } else {
+            hvx_copy_f32_aa(tmp_data, src_ptr, qk);
+        }
+
+        quantize_block_f32_q8_0_tiled((float *) tmp_data, dst_ptr);
+
+        c++;
+        if (c == nb) {
+            c = 0;
+            r++;
+        }
+    }
+}
+
+static inline void quantize_f32_q8_1_tiled_block_kernel(
+    const float * restrict src,
+    uint8_t * restrict dst,
+    uint8_t * restrict tmp_data,
+    uint32_t ne0,
+    uint32_t ib_first,
+    uint32_t ib_last,
+    size_t src_row_size,
+    size_t dst_row_size,
+    uint32_t r,
+    uint32_t c
+) {
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (ne0 + qk - 1) / qk;
+
+    for (uint32_t ib = ib_first; ib < ib_last; ++ib) {
+        const uint8_t * restrict src_ptr = (const uint8_t *) src + r * src_row_size + c * qk * sizeof(float);
+        uint8_t * restrict dst_ptr = dst + r * dst_row_size + c * 4 * 1280;
+
+        hex_l2fetch(src_ptr, qk * sizeof(float), qk * sizeof(float), 1);
+
+        if (c == nb - 1) {
+            uint32_t active_elements = ne0 - c * qk;
+            hvx_splat_f32_a(tmp_data, 0.0f, qk);
+            hvx_copy_f32_aa(tmp_data, src_ptr, active_elements);
+        } else {
+            hvx_copy_f32_aa(tmp_data, src_ptr, qk);
+        }
+
+        quantize_block_f32_q8_1_tiled((float *) tmp_data, dst_ptr);
+
+        c++;
+        if (c == nb) {
+            c = 0;
+            r++;
+        }
+    }
+}
diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index 53ab33c07..d76512ea4 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -361,7 +361,7 @@ static void vtcm_free(struct htp_context * ctx) {
 static void htp_packet_callback(dspqueue_t queue, int error, void * context);
 static void htp_error_callback(dspqueue_t queue, int error, void * context);
 
-AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_queue_id, uint32 n_hvx, uint32 use_hmx, uint64_t max_vmem) {
+AEEResult htp_iface_start(remote_handle64 handle, uint32_t sess_id, uint64_t dsp_queue_id, uint32_t n_hvx, uint32_t n_hmx, uint64_t max_vmem) {
     struct htp_context * ctx = (struct htp_context *) handle;
 
     if (!ctx) {
@@ -395,10 +395,9 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
         return AEE_ENOMEMORY;
     }
 
-#ifdef HTP_HAS_HMX
-    ctx->hmx_enabled = use_hmx;
+    ctx->hmx_enabled = n_hmx;
     ctx->hmx_queue   = NULL;
-    if (use_hmx) {
+    if (n_hmx) {
         ctx->hmx_queue = hmx_queue_create(16, ctx->vtcm_rctx);
         if (ctx->hmx_queue) {
             ctx->hmx_queue->trace = &ctx->trace[HTP_MAX_NTHREADS];
@@ -407,8 +406,7 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que
             ctx->hmx_enabled = false;
         }
     }
-    FARF(HIGH, "HMX %s (use_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", use_hmx);
-#endif
+    FARF(HIGH, "HMX %s (n_hmx=%d)", ctx->hmx_enabled ? "enabled" : "disabled", n_hmx);
 
     qurt_sysenv_max_hthreads_t hw_threads;
     qurt_sysenv_get_max_hw_threads(&hw_threads);
@@ -481,13 +479,11 @@ AEEResult htp_iface_stop(remote_handle64 handle) {
         dma_queue_delete(ctx->dma[i]);
     }
 
-#ifdef HTP_HAS_HMX
     if (ctx->hmx_queue) {
         hmx_queue_delete(ctx->hmx_queue);
         ctx->hmx_queue = NULL;
     }
     ctx->hmx_enabled = false;
-#endif
 
     vtcm_free(ctx);
 
@@ -500,6 +496,36 @@ AEEResult htp_iface_stop(remote_handle64 handle) {
     return AEE_SUCCESS;
 }
 
+AEEResult htp_iface_hwinfo(remote_handle64 handle, uint32_t * n_threads, uint32_t * n_hvx, uint32_t * n_hmx, uint64_t * vtcm_size) {
+    (void)handle;
+    if (!n_threads || !n_hvx || !n_hmx || !vtcm_size) {
+        return AEE_EBADPARM;
+    }
+
+    qurt_sysenv_max_hthreads_t hw_threads;
+    qurt_sysenv_get_max_hw_threads(&hw_threads);
+    uint32_t hw_nhvx = (qurt_hvx_get_units() >> 8) & 0xFF;
+
+    uint32_t n_hvx_val = hw_nhvx;
+    if (n_hvx_val > hw_threads.max_hthreads) {
+        n_hvx_val = hw_threads.max_hthreads;
+    }
+    if (n_hvx_val > HTP_MAX_NTHREADS) {
+        n_hvx_val = HTP_MAX_NTHREADS;
+    }
+
+    // for now we force n_threads == n_hvx
+    *n_threads = n_hvx_val;
+    *n_hvx     = n_hvx_val;
+    *n_hmx     = 1;
+
+    uint32_t vtcm_sz = 8 * 1024 * 1024; // 8MB default fallback
+    HAP_compute_res_query_VTCM(0, (unsigned int *)&vtcm_sz, NULL, NULL, NULL);
+    *vtcm_size = vtcm_sz;
+
+    return AEE_SUCCESS;
+}
+
 static void htp_error_callback(dspqueue_t queue, int error, void * context) {
     // No errors expected on the DSP.
     FARF(ERROR, "Error callback: 0x%08x", (unsigned) error);
@@ -554,6 +580,12 @@ static int execute_op(struct htp_ops_context * octx) {
         case HTP_OP_MUL_MAT_ID:
             return op_matmul_id(octx);
 
+        case HTP_OP_MUL_MAT_QKV:
+            return op_matmul_qkv(octx);
+
+        case HTP_OP_MUL_MAT_FFN:
+            return op_matmul_ffn(octx);
+
         case HTP_OP_MUL:
         case HTP_OP_ADD:
         case HTP_OP_SUB:
@@ -762,8 +794,9 @@ static void prep_tensors(struct htp_context *ctx, struct htp_buf_desc *bufs, str
     }
 }
 
-static void proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, uint32_t idx, struct htp_op_desc * op) {
+static int proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens, uint32_t idx, struct htp_op_desc * op) {
     memcpy(octx->op_params, op->params, sizeof(octx->op_params));
+    memcpy(octx->kernel_params, op->kernel_params, sizeof(octx->kernel_params));
     octx->flags = op->flags;
     octx->op    = op->opcode;
 
@@ -785,22 +818,41 @@ static void proc_op_req(struct htp_ops_context * octx, struct htp_tensor *tens,
             src->ne[0], src->ne[1], src->ne[3], src->ne[3]);
     }
 
-    // Prep output tensor
-    struct htp_tensor *dst = tens + op->dst;
+    // Prep output tensors
+    for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) {
+        uint16_t dst_idx = op->dst[i];
+        if (dst_idx == 0xffff) {
+            octx->dsts[i] = NULL;
+            continue;
+        }
+        struct htp_tensor *dst = tens + dst_idx;
+        octx->dsts[i] = dst;
 
-    octx->dst = dst;
+        FARF(HIGH, "prep-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, dst_idx, (void*) dst->data, dst->size,
+            dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]);
+    }
 
-    FARF(HIGH, "prep-dst #%u: data %p size %u : %u:%u:%u:%u", op->dst, (void*) dst->data, dst->size,
-        dst->ne[0], dst->ne[1], dst->ne[3], dst->ne[3]);
+    int status = execute_op(octx);
 
-    (void) execute_op(octx);
+    octx->src0_spad.src = NULL;
+    octx->src1_spad.src = NULL;
+    octx->src2_spad.src = NULL;
+    octx->src3_spad.src = NULL;
+    octx->dst_spad.src  = NULL;
 
     // flush buffers on output
-    hex_l2flush((void *) dst->data, dst->size);
-    dst->flags |= HTP_TENSOR_FLUSHED;
+    for (uint32_t i = 0; i < HTP_OP_MAX_OUTPUTS; i++) {
+        if (octx->dsts[i]) {
+            struct htp_tensor *dst = (struct htp_tensor *)octx->dsts[i];
+            hex_l2flush((void *) dst->data, dst->size);
+            dst->flags |= HTP_TENSOR_FLUSHED;
 
-    FARF(HIGH, "post-dst #%u: data %p size %u : %u:%u:%u:%u", op->dst, (void*) dst->data, dst->size,
-        dst->ne[0], dst->ne[1], dst->ne[3], dst->ne[3]);
+            FARF(HIGH, "post-dst[%u] #%u: data %p size %u : %u:%u:%u:%u", i, op->dst[i], (void*) dst->data, dst->size,
+                dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3]);
+        }
+    }
+
+    return status;
 }
 
 #define DSPQUEUE_POLL_TIMEOUT_USEC 100
@@ -892,20 +944,26 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
             }
         }
 
+        int      op_status = HTP_STATUS_OK;
+        uint32_t op_wakeup = n_ops / 2; // half-way throgh the batch
+
         for (uint32_t i=0; i < n_ops; i++) {
             struct profile_data prof;
 
-            if (i == (n_ops-1)) {
-                // wake up the host before starting the last op
+            if (i == op_wakeup) {
                 dspqueue_write_early_wakeup_noblock(queue, 0, 0);
             }
 
             profile_start(ctx->profiler, &prof);
 
-            proc_op_req(octx, tens, i, &ops[i]);
+            op_status = proc_op_req(octx, tens, i, &ops[i]);
 
             profile_stop(ctx->profiler, &prof);
 
+            if (op_status != HTP_STATUS_OK) {
+                break;
+            }
+
             if (ctx->profiler) {
                 pds[i].opcode = ops[i].opcode;
                 pds[i].usecs  = prof.usecs;
@@ -919,7 +977,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
 
         struct htp_opbatch_rsp rsp;
         rsp.id        = req.id;
-        rsp.status    = HTP_STATUS_OK;
+        rsp.status    = op_status;
         rsp.n_bufs    = n_bufs;
         rsp.n_tensors = n_tens;
         rsp.n_ops     = n_ops;
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index 8e016c1be..81a0ffbeb 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -5,6 +5,7 @@
 
 #include <HAP_farf.h>
 #include <HAP_perf.h>
+#include <HAP_compute_res.h>
 
 #include <math.h>
 #include <string.h>
@@ -17,33 +18,50 @@
 #include "ggml-common.h"
 #include "htp-ctx.h"
 #include "htp-ops.h"
-#include "htp-ops.h"
-#include "hmx-ops.h"
+#include "matmul-ops.h"
+#include "vtcm-utils.h"
 
-#define MM_SPAD_SRC0_NROWS 16
-#define MM_SPAD_SRC1_NROWS 16
-#define MM_SPAD_DST_NROWS  2
+typedef struct {
+    float        *dst;
+    const float  *activation;
+    const __fp16 *weight;
+    int           m;
+    int           k;
+    int           n;
+    int           act_stride;
+    int           weight_stride;
+    int           dst_stride;
+    int           ne02;
+    int           ne03;
+    int           ne12;
+    int           ne13;
+    size_t        src0_nb2;
+    size_t        src0_nb3;
+    size_t        src1_nb2;
+    size_t        src1_nb3;
+    size_t        dst_nb2;
+    size_t        dst_nb3;
+} hmx_mm_f16_f32_batched_params_t;
 
-struct htp_matmul_context {
+struct htp_mm_context {
     const char * type;
     struct htp_ops_context * octx;
 
-    void (*vec_dot_1x1)(const int n, float * restrict s0,
+    void (*vec_dot_1x1)(const uint32_t n, float * restrict s0,
          const void * restrict vx0,
          const void * restrict vy0);
 
-    void (*vec_dot_2x1)(const int n, float * restrict s0,
+    void (*vec_dot_2x1)(const uint32_t n, float * restrict s0,
          const void * restrict vx0, const void * restrict vx1,
          const void * restrict vy0);
 
-    void (*vec_dot_2x2)(const int n, float * restrict s0, float * restrict s1,
+    void (*vec_dot_2x2)(const uint32_t n, float * restrict s0, float * restrict s1,
          const void * restrict vx0, const void * restrict vx1,
          const void * restrict vy0, const void * restrict vy1);
 
-    void (*vec_dot_4x1)(const int n, float * restrict s0,
-         const void * restrict vx0, const void * restrict vx1,
-         const void * restrict vx2, const void * restrict vx3,
-         const void * restrict vy0);
+    void (*vec_dot_32x1)(const uint32_t n, float * restrict s,
+         const void * restrict vx,
+         const void * restrict vy, uint32_t valid_rows);
 
     // Precomputed values
     uint32_t src0_nrows_per_thread;
@@ -53,11 +71,37 @@ struct htp_matmul_context {
     struct fastdiv_values mm_div_ne1;
     struct fastdiv_values mm_div_r2;
     struct fastdiv_values mm_div_r3;
+    struct fastdiv_values mm_div_ne11;
+
+    // Precomputed block-parallel quantization values
+    uint32_t quant_ib_first[MAX_NUM_WORKERS];
+    uint32_t quant_ib_last[MAX_NUM_WORKERS];
+    uint32_t quant_r[MAX_NUM_WORKERS];
+    uint32_t quant_c[MAX_NUM_WORKERS];
 
     // Fields for scattered mapping & HMX support in MUL_MAT_ID
     const uint32_t * matrix_row_counts;
     const struct mmid_row_mapping * matrix_rows;
-    bool hmx_eligible;
+
+    // Dynamic VTCM pointers allocated sequentially
+    uint8_t * vtcm_src0;
+    uint8_t * vtcm_src1;
+    uint8_t * vtcm_src2;
+    uint8_t * vtcm_src3;
+    uint8_t * vtcm_dst;
+
+    // Cached strides
+    uint32_t vtcm_src0_stride;
+    uint32_t vtcm_src1_stride;
+    uint32_t vtcm_src2_stride;
+    uint32_t vtcm_src3_stride;
+
+    // Cached thread offsets/sizes
+    uint32_t vtcm_src0_size_per_thread;
+    uint32_t vtcm_src1_size_per_thread;
+    uint32_t vtcm_src2_size_per_thread;
+    uint32_t vtcm_src3_size_per_thread;
+    uint32_t vtcm_dst_size_per_thread;
 };
 
 // vdelta control to expand first 32 e8m0 values into 32 uint32 elements
@@ -89,2835 +133,6 @@ static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = {
     0,    0, 0,    0, 0,    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0,    0, 0,    0, 0,    0,
 };
 
-static inline HVX_Vector_x8 hvx_vec_load_iq4nlx4x8_full(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0_1 = vptr[0];  // first 256 elements (128 bytes)
-    HVX_Vector v2_3 = vptr[1];  // ...
-    HVX_Vector v4_5 = vptr[2];  // ...
-    HVX_Vector v6_7 = vptr[3];  // ...
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector lut     = *(const HVX_Vector *) kvalues_iq4nl_lut;
-
-    HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F
-    HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4
-    HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4);  // & 0x0F
-    HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4);    // >> 4
-    HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4);  // & 0x0F
-    HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4);    // >> 4
-    HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
-    HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
-
-    v0 = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
-    v1 = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
-    v2 = Q6_Vb_vlut32_VbVbI(v2, lut, 0);
-    v3 = Q6_Vb_vlut32_VbVbI(v3, lut, 0);
-    v4 = Q6_Vb_vlut32_VbVbI(v4, lut, 0);
-    v5 = Q6_Vb_vlut32_VbVbI(v5, lut, 0);
-    v6 = Q6_Vb_vlut32_VbVbI(v6, lut, 0);
-    v7 = Q6_Vb_vlut32_VbVbI(v7, lut, 0);
-
-    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_iq4nlx4x8_partial(const uint8_t * restrict ptr, uint32_t n) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    const uint32_t qk   = QK_Q4_0x4x2;  // 256
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector lut     = *(const HVX_Vector *) kvalues_iq4nl_lut;
-
-    HVX_Vector_x8 r;
-    uint32_t      i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nb; i++) {
-        HVX_Vector v   = vptr[i];                   // 256 elements (128 bytes)
-        HVX_Vector v0  = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : first  128 elements
-        HVX_Vector v1  = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : second 128 elements
-        r.v[i * 2 + 0] = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
-        r.v[i * 2 + 1] = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
-    }
-
-    if (nloe) {
-        HVX_Vector     v      = vptr[i];                      // 256 elements (128 bytes)
-        HVX_Vector     v0     = Q6_V_vand_VV(v, mask_h4);     // & 0x0F : even 128 elements
-        HVX_Vector     v1     = Q6_Vub_vlsr_VubR(v, 4);       // >> 4   : odd  128 elements
-        HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1);  // zip even:odd:...
-        r.v[i * 2 + 0]        = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v0_1_p), lut, 0);
-        r.v[i * 2 + 1]        = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v0_1_p), lut, 0);
-    }
-
-    return r;
-}
-
-// q4x4x2 and q8x4x2 are the flat q4/8_0 formats where all quants are stored first followed by all scales
-
-static inline size_t q8x4x2_row_size(uint32_t ne) {
-    // ensures perfect alignment of quants and full row
-    const uint32_t qk = QK_Q8_0x4x2;
-    const uint32_t nb = (ne + qk - 1) / qk;
-    return hex_round_up(ne + nb * 8 * sizeof(__fp16), 128);
-}
-
-static inline size_t q8_1x4x2_row_size(uint32_t ne) {
-    // ensures perfect alignment of quants and full row
-    const uint32_t qk = QK_Q8_0x4x2;
-    const uint32_t nb = (ne + qk - 1) / qk;
-    return hex_round_up(ne + nb * 8 * 2 * sizeof(__fp16), 128);
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_q4x4x8_full(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0_1 = vptr[0];  // first 256 elements (128 bytes)
-    HVX_Vector v2_3 = vptr[1];  // ...
-    HVX_Vector v4_5 = vptr[2];  // ...
-    HVX_Vector v6_7 = vptr[3];  // ...
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector i8 = Q6_Vb_vsplat_R(8);
-
-    HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F : first  128 elements
-    HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4   : second 128 elements
-    HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4);  // & 0x0F ...
-    HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4);    // >> 4
-    HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4);  // & 0x0F
-    HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4);    // >> 4
-    HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
-    HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
-
-    // Convert uint4 to int4 (i.e. x - 8)
-    v0 = Q6_Vb_vsub_VbVb(v0, i8);
-    v1 = Q6_Vb_vsub_VbVb(v1, i8);
-    v2 = Q6_Vb_vsub_VbVb(v2, i8);
-    v3 = Q6_Vb_vsub_VbVb(v3, i8);
-    v4 = Q6_Vb_vsub_VbVb(v4, i8);
-    v5 = Q6_Vb_vsub_VbVb(v5, i8);
-    v6 = Q6_Vb_vsub_VbVb(v6, i8);
-    v7 = Q6_Vb_vsub_VbVb(v7, i8);
-
-    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
-    return r;
-}
-
-static HVX_Vector_x8 hvx_vec_load_q4x4x8_partial(const uint8_t * restrict ptr, uint32_t n) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    const uint32_t qk   = QK_Q4_0x4x2; // 256
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector i8      = Q6_Vb_vsplat_R(8);
-
-    HVX_Vector_x8 r;
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i=0; i < nb; i++) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : first  128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : second 128 elements
-        r.v[i*2+0] = Q6_Vb_vsub_VbVb(v0, i8);
-        r.v[i*2+1] = Q6_Vb_vsub_VbVb(v1, i8);
-    }
-
-    if (nloe) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : even 128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : odd  128 elements
-        HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1); // zip even:odd:...
-        r.v[i*2+0] = Q6_Vb_vsub_VbVb(Q6_V_lo_W(v0_1_p), i8);
-        r.v[i*2+1] = Q6_Vb_vsub_VbVb(Q6_V_hi_W(v0_1_p), i8);
-    }
-
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_q4_1x4x8_full(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0_1 = vptr[0];  // first 256 elements (128 bytes)
-    HVX_Vector v2_3 = vptr[1];  // ...
-    HVX_Vector v4_5 = vptr[2];  // ...
-    HVX_Vector v6_7 = vptr[3];  // ...
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-
-    HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F : first  128 elements
-    HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4   : second 128 elements
-    HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4);  // & 0x0F ...
-    HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4);    // >> 4
-    HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4);  // & 0x0F
-    HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4);    // >> 4
-    HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
-    HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
-
-    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
-    return r;
-}
-
-static HVX_Vector_x8 hvx_vec_load_q4_1x4x8_partial(const uint8_t * restrict ptr, uint32_t n) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    const uint32_t qk   = QK_Q4_0x4x2; // 256
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-
-    HVX_Vector_x8 r;
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i=0; i < nb; i++) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : first  128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : second 128 elements
-        r.v[i*2+0] = v0;
-        r.v[i*2+1] = v1;
-    }
-
-    if (nloe) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : even 128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : odd  128 elements
-        HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1); // zip even:odd:...
-        r.v[i*2+0] = Q6_V_lo_W(v0_1_p);
-        r.v[i*2+1] = Q6_V_hi_W(v0_1_p);
-    }
-
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8_full(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0_1 = vptr[0];  // first 256 elements (128 bytes)
-    HVX_Vector v2_3 = vptr[1];  // ...
-    HVX_Vector v4_5 = vptr[2];  // ...
-    HVX_Vector v6_7 = vptr[3];  // ...
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector lut = *(const HVX_Vector *) kvalues_mxfp4_lut;
-
-    HVX_Vector v0 = Q6_V_vand_VV(v0_1, mask_h4);  // & 0x0F
-    HVX_Vector v1 = Q6_Vub_vlsr_VubR(v0_1, 4);    // >> 4
-    HVX_Vector v2 = Q6_V_vand_VV(v2_3, mask_h4);  // & 0x0F
-    HVX_Vector v3 = Q6_Vub_vlsr_VubR(v2_3, 4);    // >> 4
-    HVX_Vector v4 = Q6_V_vand_VV(v4_5, mask_h4);  // & 0x0F
-    HVX_Vector v5 = Q6_Vub_vlsr_VubR(v4_5, 4);    // >> 4
-    HVX_Vector v6 = Q6_V_vand_VV(v6_7, mask_h4);  // & 0x0F
-    HVX_Vector v7 = Q6_Vub_vlsr_VubR(v6_7, 4);    // >> 4
-
-    v0 = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
-    v1 = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
-    v2 = Q6_Vb_vlut32_VbVbI(v2, lut, 0);
-    v3 = Q6_Vb_vlut32_VbVbI(v3, lut, 0);
-    v4 = Q6_Vb_vlut32_VbVbI(v4, lut, 0);
-    v5 = Q6_Vb_vlut32_VbVbI(v5, lut, 0);
-    v6 = Q6_Vb_vlut32_VbVbI(v6, lut, 0);
-    v7 = Q6_Vb_vlut32_VbVbI(v7, lut, 0);
-
-    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_mxfp4x4x8_partial(const uint8_t * restrict ptr, uint32_t n) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    const uint32_t qk   = QK_Q4_0x4x2; // 256
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    const HVX_Vector lut     = *(const HVX_Vector *) kvalues_mxfp4_lut;
-
-    HVX_Vector_x8 r;
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i=0; i < nb; i++) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : first  128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : second 128 elements
-        r.v[i*2+0] = Q6_Vb_vlut32_VbVbI(v0, lut, 0);
-        r.v[i*2+1] = Q6_Vb_vlut32_VbVbI(v1, lut, 0);
-    }
-
-    if (nloe) {
-        HVX_Vector v = vptr[i];                    // 256 elements (128 bytes)
-        HVX_Vector v0 = Q6_V_vand_VV(v, mask_h4);  // & 0x0F : even 128 elements
-        HVX_Vector v1 = Q6_Vub_vlsr_VubR(v, 4);    // >> 4   : odd  128 elements
-        HVX_VectorPair v0_1_p = Q6_W_vshuff_VVR(v1, v0, -1); // zip even:odd:...
-        r.v[i*2+0] = Q6_Vb_vlut32_VbVbI(Q6_V_lo_W(v0_1_p), lut, 0);
-        r.v[i*2+1] = Q6_Vb_vlut32_VbVbI(Q6_V_hi_W(v0_1_p), lut, 0);
-    }
-
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_q8x4x8_full(const uint8_t * restrict ptr) {
-    const HVX_Vector * restrict vptr = (const HVX_Vector *) ptr;
-
-    HVX_Vector v0 = vptr[0];  // first  128 vals
-    HVX_Vector v1 = vptr[1];  // ...
-    HVX_Vector v2 = vptr[2];  // ...
-    HVX_Vector v3 = vptr[3];  // ...
-    HVX_Vector v4 = vptr[4];  // ...
-    HVX_Vector v5 = vptr[5];  // ...
-    HVX_Vector v6 = vptr[6];  // ...
-    HVX_Vector v7 = vptr[7];  // ...
-
-    HVX_Vector_x8 r = { v0, v1, v2, v3, v4, v5, v6, v7 };
-    return r;
-}
-
-static inline HVX_Vector_x8 hvx_vec_load_q8x4x8_partial(const uint8_t * restrict ptr, uint32_t nloe) {
-    return hvx_vec_load_q8x4x8_full(ptr);
-}
-
-// Reduce multiply 1024 x 1024 int8 elements (32x q4/8 blocks in 8x HVX vectors).
-// Accumulate each block into a single int32 value.
-// Return a single HVX vector with 32x int32 accumulators.
-// This version is parameterized to support less than 1024 elements.
-// if() checks are optimized out at compile time -- make sure to pass N as a constexpr.
-
-static inline HVX_Vector hvx_vec_rmpy_x8_n(HVX_Vector_x8 x, HVX_Vector_x8 y, unsigned int n) {
-    HVX_Vector r0 = Q6_V_vzero();
-    HVX_Vector r1 = Q6_V_vzero();
-    HVX_Vector r2 = Q6_V_vzero();
-    HVX_Vector r3 = Q6_V_vzero();
-    HVX_Vector r4 = Q6_V_vzero();
-    HVX_Vector r5 = Q6_V_vzero();
-    HVX_Vector r6 = Q6_V_vzero();
-    HVX_Vector r7 = Q6_V_vzero();
-
-    HVX_VectorPair p3;
-    HVX_VectorPair p2;
-    HVX_VectorPair p1;
-    HVX_VectorPair p0;
-
-    if (n >=  128) { r0 = Q6_Vw_vrmpy_VbVb(x.v[0], y.v[0]); }
-    if (n >=  256) { r1 = Q6_Vw_vrmpy_VbVb(x.v[1], y.v[1]); }
-    if (n >=  384) { r2 = Q6_Vw_vrmpy_VbVb(x.v[2], y.v[2]); }
-    if (n >=  512) { r3 = Q6_Vw_vrmpy_VbVb(x.v[3], y.v[3]); }
-    if (n >=  640) { r4 = Q6_Vw_vrmpy_VbVb(x.v[4], y.v[4]); }
-    if (n >=  768) { r5 = Q6_Vw_vrmpy_VbVb(x.v[5], y.v[5]); }
-    if (n >=  896) { r6 = Q6_Vw_vrmpy_VbVb(x.v[6], y.v[6]); }
-    if (n >= 1024) { r7 = Q6_Vw_vrmpy_VbVb(x.v[7], y.v[7]); }
-
-    if (n >=  128) { p0 = Q6_W_vdeal_VVR(r1, r0, -4); }
-    if (n >=  384) { p1 = Q6_W_vdeal_VVR(r3, r2, -4); }
-    if (n >=  640) { p2 = Q6_W_vdeal_VVR(r5, r4, -4); }
-    if (n >=  896) { p3 = Q6_W_vdeal_VVR(r7, r6, -4); }
-
-    if (n >=  128) { r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); }
-    if (n >=  384) { r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1)); }
-    if (n >=  640) { r2 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p2), Q6_V_hi_W(p2)); }
-    if (n >=  896) { r3 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p3), Q6_V_hi_W(p3)); }
-
-    if (n >=  128) { p0 = Q6_W_vdeal_VVR(r1, r0, -4); }
-    if (n >=  640) { p1 = Q6_W_vdeal_VVR(r3, r2, -4); }
-
-    if (n >=  128) { r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); }
-    if (n >=  640) { r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1)); }
-
-    if (n >=  128) { p0 = Q6_W_vdeal_VVR(r1, r0, -4); }
-    if (n >=  128) { r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0)); }
-
-    return r0;
-}
-
-static inline HVX_Vector hvx_vec_rmpy_x8_full(HVX_Vector_x8 x, HVX_Vector_x8 y) {
-    HVX_Vector r0 = Q6_Vw_vrmpy_VbVb(x.v[0], y.v[0]);
-    HVX_Vector r1 = Q6_Vw_vrmpy_VbVb(x.v[1], y.v[1]);
-    HVX_Vector r2 = Q6_Vw_vrmpy_VbVb(x.v[2], y.v[2]);
-    HVX_Vector r3 = Q6_Vw_vrmpy_VbVb(x.v[3], y.v[3]);
-    HVX_Vector r4 = Q6_Vw_vrmpy_VbVb(x.v[4], y.v[4]);
-    HVX_Vector r5 = Q6_Vw_vrmpy_VbVb(x.v[5], y.v[5]);
-    HVX_Vector r6 = Q6_Vw_vrmpy_VbVb(x.v[6], y.v[6]);
-    HVX_Vector r7 = Q6_Vw_vrmpy_VbVb(x.v[7], y.v[7]);
-
-    HVX_VectorPair p0 = Q6_W_vdeal_VVR(r1, r0, -4);
-    HVX_VectorPair p1 = Q6_W_vdeal_VVR(r3, r2, -4);
-    HVX_VectorPair p2 = Q6_W_vdeal_VVR(r5, r4, -4);
-    HVX_VectorPair p3 = Q6_W_vdeal_VVR(r7, r6, -4);
-
-    r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0));
-    r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1));
-    r2 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p2), Q6_V_hi_W(p2));
-    r3 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p3), Q6_V_hi_W(p3));
-
-    p0 = Q6_W_vdeal_VVR(r1, r0, -4);
-    p1 = Q6_W_vdeal_VVR(r3, r2, -4);
-
-    r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0));
-    r1 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p1), Q6_V_hi_W(p1));
-
-    p0 = Q6_W_vdeal_VVR(r1, r0, -4);
-    r0 = Q6_Vw_vadd_VwVw(Q6_V_lo_W(p0), Q6_V_hi_W(p0));
-
-    return r0;
-}
-
-static inline HVX_Vector hvx_vec_rmpy_x8_partial(HVX_Vector_x8 x, HVX_Vector_x8 y, unsigned int n) {
-    if (n >= 512)
-        return hvx_vec_rmpy_x8_full(x, y);
-
-    return hvx_vec_rmpy_x8_partial(x, y, 512);
-}
-
-static void vec_dot_q4_1x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2 * 2;                               // 32x (d, m) __fp16 = 128 bytes
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 4;                                   // 32x (d, s) __fp16 = 128 bytes
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size);  // then scales/offsets
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales/sums
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elemements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_full(r0_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair dm_deal = Q6_W_vdeal_VVR(dm, dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair dm_deal = Q6_W_vdeal_VVR(dm, dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ms                = Q6_V_vand_QV(bmask, r0_ms);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-    }
-
-    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
-    hvx_vec_store_u(s0, 4, r0_sum);
-}
-
-static void vec_dot_q4_1x4x2_q8x4x2_2x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2 * 2;                               // 32x (d, m) __fp16 = 128 bytes
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 4;                                   // 32x (d, s) __fp16 = 128 bytes
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales/sums
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elemements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4_1x4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy_s)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa_total, r1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4_1x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy_s)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ms                = Q6_V_vand_QV(bmask, r0_ms);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r1_ms                = Q6_V_vand_QV(bmask, r1_ms);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa_total, r1_sum));
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_q4_1x4x2_q8x4x2_4x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vx2, const void * restrict vx3,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vx2 % 128 == 0);
-    assert((unsigned long) vx3 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2 * 2;                               // 32x (d, m) __fp16 = 128 bytes
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 4;                                   // 32x (d, s) __fp16 = 128 bytes
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-    const uint8_t * restrict r2_x_q = ((const uint8_t *) vx2) + 0;            // quants first
-    const uint8_t * restrict r2_x_d = ((const uint8_t *) vx2) + x_qrow_size;  // then scales
-    const uint8_t * restrict r3_x_q = ((const uint8_t *) vx3) + 0;            // quants first
-    const uint8_t * restrict r3_x_d = ((const uint8_t *) vx3) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales/sums
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-    HVX_Vector r2_sum = Q6_V_vzero();
-    HVX_Vector r3_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4_1x4x8_full(r1_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q4_1x4x8_full(r2_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q4_1x4x8_full(r3_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        HVX_Vector r2_dm = *(const HVX_UVector *) (r2_x_d + i * x_dblk_size);
-        HVX_VectorPair r2_dm_deal = Q6_W_vdeal_VVR(r2_dm, r2_dm, -2);
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r2_dm_deal));
-        HVX_Vector r2_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r2_dm_deal));
-
-        HVX_Vector r3_dm = *(const HVX_UVector *) (r3_x_d + i * x_dblk_size);
-        HVX_VectorPair r3_dm_deal = Q6_W_vdeal_VVR(r3_dm, r3_dm, -2);
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r3_dm_deal));
-        HVX_Vector r3_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r3_dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy_s)));
-
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r2_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_m, vy_s)));
-
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-        HVX_Vector r3_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_m, vy_s)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_ms);
-
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r2_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_ms);
-
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-        HVX_Vector r3_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa_total, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa_total, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa_total, r3_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4_1x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q4_1x4x8_partial(r2_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q4_1x4x8_partial(r3_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r2_q, vy_q, nloe));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r3_q, vy_q, nloe));
-
-        HVX_Vector ds = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_VectorPair ds_deal = Q6_W_vdeal_VVR(ds, ds, -2);
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds_deal));
-        HVX_Vector vy_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        HVX_Vector r2_dm = *(const HVX_UVector *) (r2_x_d + i * x_dblk_size);
-        HVX_VectorPair r2_dm_deal = Q6_W_vdeal_VVR(r2_dm, r2_dm, -2);
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r2_dm_deal));
-        HVX_Vector r2_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r2_dm_deal));
-
-        HVX_Vector r3_dm = *(const HVX_UVector *) (r3_x_d + i * x_dblk_size);
-        HVX_VectorPair r3_dm_deal = Q6_W_vdeal_VVR(r3_dm, r3_dm, -2);
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r3_dm_deal));
-        HVX_Vector r3_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r3_dm_deal));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy_s)));
-
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy_s)));
-
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r2_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_m, vy_s)));
-
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-        HVX_Vector r3_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_m, vy_s)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ms                = Q6_V_vand_QV(bmask, r0_ms);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r1_ms                = Q6_V_vand_QV(bmask, r1_ms);
-        r2_dd                = Q6_V_vand_QV(bmask, r2_dd);
-        r2_ms                = Q6_V_vand_QV(bmask, r2_ms);
-        r3_dd                = Q6_V_vand_QV(bmask, r3_dd);
-        r3_ms                = Q6_V_vand_QV(bmask, r3_ms);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-        r2_ia                = Q6_V_vand_QV(bmask, r2_ia);
-        r3_ia                = Q6_V_vand_QV(bmask, r3_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_ms);
-
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_ms);
-
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r2_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_ms);
-
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-        HVX_Vector r3_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_ms);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa_total, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa_total, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa_total, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa_total, r3_sum));
-    }
-
-    HVX_Vector_x4 rsum_in = { .v = { r0_sum, r1_sum, r2_sum, r3_sum } };
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x4(rsum_in);
-    hvx_vec_store_u(s0, 16, rsum);
-}
-
-
-static void vec_dot_q4_1x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
-                                        const void * restrict vx0, const void * restrict vx1,
-                                        const void * restrict vy0, const void * restrict vy1) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-    assert((unsigned long) vy1 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2 * 2;                               // 32x (d, m) __fp16 = 128 bytes
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 4;                                   // 32x (d, s) __fp16 = 128 bytes
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
-    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales/sums
-    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
-    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales/sums
-
-    // Row sums (sf) - 4 accumulators for 2×2 tile
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        // Load src1 columns
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
-
-        // Load src0 rows
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4_1x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4_1x4x8_full(r1_x_q + i * x_qblk_size);
-
-        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
-
-        // Load scales
-        HVX_Vector ds0 = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
-        HVX_VectorPair ds0_deal = Q6_W_vdeal_VVR(ds0, ds0, -2);
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds0_deal));
-        HVX_Vector vy0_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds0_deal));
-
-        HVX_Vector ds1 = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
-        HVX_VectorPair ds1_deal = Q6_W_vdeal_VVR(ds1, ds1, -2);
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds1_deal));
-        HVX_Vector vy1_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds1_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        // Compute combined scales
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy0_s)));
-
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r0_c1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy1_s)));
-
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy0_s)));
-
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-        HVX_Vector r1_c1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy1_s)));
-
-        // Apply scales and accumulate
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        HVX_Vector r0_c0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_ms);
-        HVX_Vector r0_c1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_ms);
-        HVX_Vector r1_c0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_ms);
-        HVX_Vector r1_c1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_ms);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa_total, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa_total, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa_total, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa_total, r1_c1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_q4_1x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_q4_1x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
-
-        HVX_Vector ds0 = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
-        HVX_VectorPair ds0_deal = Q6_W_vdeal_VVR(ds0, ds0, -2);
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds0_deal));
-        HVX_Vector vy0_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds0_deal));
-
-        HVX_Vector ds1 = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
-        HVX_VectorPair ds1_deal = Q6_W_vdeal_VVR(ds1, ds1, -2);
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(ds1_deal));
-        HVX_Vector vy1_s = Q6_Vh_vshuff_Vh(Q6_V_hi_W(ds1_deal));
-
-        HVX_Vector r0_dm = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_VectorPair r0_dm_deal = Q6_W_vdeal_VVR(r0_dm, r0_dm, -2);
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r0_dm_deal));
-        HVX_Vector r0_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r0_dm_deal));
-
-        HVX_Vector r1_dm = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_VectorPair r1_dm_deal = Q6_W_vdeal_VVR(r1_dm, r1_dm, -2);
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(Q6_V_lo_W(r1_dm_deal));
-        HVX_Vector r1_m = Q6_Vh_vshuff_Vh(Q6_V_hi_W(r1_dm_deal));
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy0_s)));
-
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r0_c1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_m, vy1_s)));
-
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c0_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy0_s)));
-
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-        HVX_Vector r1_c1_ms = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_m, vy1_s)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
-        r0_c0_ms = Q6_V_vand_QV(bmask, r0_c0_ms);
-        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
-        r0_c1_ms = Q6_V_vand_QV(bmask, r0_c1_ms);
-        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
-        r1_c0_ms = Q6_V_vand_QV(bmask, r1_c0_ms);
-        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
-        r1_c1_ms = Q6_V_vand_QV(bmask, r1_c1_ms);
-
-        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
-        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
-        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
-        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        HVX_Vector r0_c0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_ms);
-        HVX_Vector r0_c1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_ms);
-        HVX_Vector r1_c0_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_ms);
-        HVX_Vector r1_c1_fa_total = Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_ms);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa_total, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa_total, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa_total, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa_total, r1_c1_sum));
-    }
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(s0, 8, r0_r1_c0_sum);  // row0,col0 row1,col0
-    hvx_vec_store_u(s1, 8, r0_r1_c1_sum);  // row0,col1 row1,col1
-}
-
-static void vec_dot_q4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size);  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (qf32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elemements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
-
-    hvx_vec_store_u(s0, 4, r0_sum);
-}
-
-static void vec_dot_q4x4x2_q8x4x2_2x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (qf32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elemements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_q4x4x2_q8x4x2_4x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vx2, const void * restrict vx3,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vx2 % 128 == 0);
-    assert((unsigned long) vx3 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;
-    const uint8_t * restrict r2_x_q = ((const uint8_t *) vx2) + 0;
-    const uint8_t * restrict r2_x_d = ((const uint8_t *) vx2) + x_qrow_size;
-    const uint8_t * restrict r3_x_q = ((const uint8_t *) vx3) + 0;
-    const uint8_t * restrict r3_x_d = ((const uint8_t *) vx3) + x_qrow_size;
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-    HVX_Vector r2_sum = Q6_V_vzero();
-    HVX_Vector r3_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_full(r1_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q4x4x8_full(r2_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q4x4x8_full(r3_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q4x4x8_partial(r2_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q4x4x8_partial(r3_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r2_q, vy_q, nloe));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r3_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r2_dd                = Q6_V_vand_QV(bmask, r2_dd);
-        r3_dd                = Q6_V_vand_QV(bmask, r3_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-        r2_ia                = Q6_V_vand_QV(bmask, r2_ia);
-        r3_ia                = Q6_V_vand_QV(bmask, r3_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    HVX_Vector_x4 rsum_in = { .v = { r0_sum, r1_sum, r2_sum, r3_sum } };
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x4(rsum_in);
-    hvx_vec_store_u(s0, 16, rsum);
-}
-
-
-static void vec_dot_q4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
-                                        const void * restrict vx0, const void * restrict vx1,
-                                        const void * restrict vy0, const void * restrict vy1) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-    assert((unsigned long) vy1 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
-    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
-    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
-    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
-
-    // Row sums (sf) - 4 accumulators for 2×2 tile
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        // Load src1 columns (reused across both src0 rows)
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
-
-        // Load src0 rows (reused across both src1 columns)
-        HVX_Vector_x8 r0_q = hvx_vec_load_q4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q4x4x8_full(r1_x_q + i * x_qblk_size);
-
-        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
-
-        // Load scales
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d   + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d   + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        // Compute combined scales
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        // Apply scales and accumulate
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_q4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_q4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
-
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d   + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d   + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        // Zero out unused scales
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
-        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
-        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
-        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
-        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
-        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
-        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
-        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(s0, 8, r0_r1_c0_sum);  // row0,col0 row1,col0
-    hvx_vec_store_u(s1, 8, r0_r1_c1_sum);  // row0,col1 row1,col1
-}
-
-static void vec_dot_q8x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                  // 32x __fp16
-    const uint32_t x_qblk_size = qk;                                         // int8
-    const uint32_t x_qrow_size = n;                                          // int8 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                  // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                         // int8
-    const uint32_t y_qrow_size = n;                                          // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);           // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size); // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);              // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);    // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (qf32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
-
-    hvx_vec_store_u(s0, 4, r0_sum);
-}
-
-static void vec_dot_q8x4x2_q8x4x2_2x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk;                                          // int8
-    const uint32_t x_qrow_size = n;                                           // int8 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    // Row sum (qf32)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (qf32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_q8x4x2_q8x4x2_4x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vx2, const void * restrict vx3,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vx2 % 128 == 0);
-    assert((unsigned long) vx3 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk;                                          // int8
-    const uint32_t x_qrow_size = n;                                           // int8 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-    const uint8_t * restrict r2_x_q = ((const uint8_t *) vx2) + 0;            // quants first
-    const uint8_t * restrict r2_x_d = ((const uint8_t *) vx2) + x_qrow_size;  // then scales
-    const uint8_t * restrict r3_x_q = ((const uint8_t *) vx3) + 0;            // quants first
-    const uint8_t * restrict r3_x_d = ((const uint8_t *) vx3) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    // Row sum (qf32)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-    HVX_Vector r2_sum = Q6_V_vzero();
-    HVX_Vector r3_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_full(r1_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q8x4x8_full(r2_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q8x4x8_full(r3_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r2_q = hvx_vec_load_q8x4x8_partial(r2_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r3_q = hvx_vec_load_q8x4x8_partial(r3_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r2_q, vy_q, nloe));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r3_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d    + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r2_dd                = Q6_V_vand_QV(bmask, r2_dd);
-        r3_dd                = Q6_V_vand_QV(bmask, r3_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-        r2_ia                = Q6_V_vand_QV(bmask, r2_ia);
-        r3_ia                = Q6_V_vand_QV(bmask, r3_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    HVX_Vector_x4 rsum_in = { .v = { r0_sum, r1_sum, r2_sum, r3_sum } };
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x4(rsum_in);
-    hvx_vec_store_u(s0, 16, rsum);
-}
-
-
-static void vec_dot_q8x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
-                                        const void * restrict vx0, const void * restrict vx1,
-                                        const void * restrict vy0, const void * restrict vy1) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-    assert((unsigned long) vy1 % 128 == 0);
-
-    const uint32_t qk = QK_Q8_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk;                                          // int8
-    const uint32_t x_qrow_size = n;                                           // int8 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
-    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
-    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
-    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
-
-    // Row sums (sf) - 4 accumulators for 2×2 tile
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        // Load src1 columns (reused across both src0 rows)
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
-
-        // Load src0 rows (reused across both src1 columns)
-        HVX_Vector_x8 r0_q = hvx_vec_load_q8x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_q8x4x8_full(r1_x_q + i * x_qblk_size);
-
-        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
-
-        // Load scales
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d   + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d   + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        // Compute combined scales
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        // Apply scales and accumulate
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_q8x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_q8x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
-
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d   + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d   + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        // Zero out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
-        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
-        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
-        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
-        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
-        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
-        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
-        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
-    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
-}
-
-// ======== IQ4_NL x Q8_0 vec_dot kernels ========
-// Same structure as Q4_0 vec_dot but uses IQ4_NL LUT-based load (4-bit index -> int8 kvalue).
-// Scale format is identical to Q4_0 (fp16 scales).
-
-static void vec_dot_iq4nlx4x2_q8x4x2_1x1(const int n,
-                                         float * restrict s0,
-                                         const void * restrict vx0,
-                                         const void * restrict vy0) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size);  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    HVX_Vector r0_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
-
-    hvx_vec_store_u(s0, 4, r0_sum);
-}
-
-static void vec_dot_iq4nlx4x2_q8x4x2_2x1(const int n,
-                                         float * restrict s0,
-                                         const void * restrict vx0,
-                                         const void * restrict vx1,
-                                         const void * restrict vy0) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_iq4nlx4x2_q8x4x2_4x1(const int n,
-                                         float * restrict s0,
-                                         const void * restrict vx0,
-                                         const void * restrict vx1,
-                                         const void * restrict vx2,
-                                         const void * restrict vx3,
-                                         const void * restrict vy0) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vx2 % 128 == 0);
-    assert((unsigned long) vx3 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;                                      // int4
-    const uint32_t x_qrow_size = n / 2;                                       // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-    const uint8_t * restrict r2_x_q = ((const uint8_t *) vx2) + 0;            // quants first
-    const uint8_t * restrict r2_x_d = ((const uint8_t *) vx2) + x_qrow_size;  // then scales
-    const uint8_t * restrict r3_x_q = ((const uint8_t *) vx3) + 0;            // quants first
-    const uint8_t * restrict r3_x_d = ((const uint8_t *) vx3) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-    HVX_Vector r2_sum = Q6_V_vzero();
-    HVX_Vector r3_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(y_q + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_full(r1_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r2_q = hvx_vec_load_iq4nlx4x8_full(r2_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r3_q = hvx_vec_load_iq4nlx4x8_full(r3_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(y_q + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_iq4nlx4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r2_q = hvx_vec_load_iq4nlx4x8_partial(r2_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r3_q = hvx_vec_load_iq4nlx4x8_partial(r3_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy_q, nloe));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r2_q, vy_q, nloe));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r3_q, vy_q, nloe));
-
-        HVX_Vector vy_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y_d + i * y_dblk_size));
-        HVX_Vector r0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-        HVX_Vector r2_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r2_x_d + i * x_dblk_size));
-        HVX_Vector r3_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r3_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy_d)));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy_d)));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r2_d, vy_d)));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r3_d, vy_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r2_dd                = Q6_V_vand_QV(bmask, r2_dd);
-        r3_dd                = Q6_V_vand_QV(bmask, r3_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-        r2_ia                = Q6_V_vand_QV(bmask, r2_ia);
-        r3_ia                = Q6_V_vand_QV(bmask, r3_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    HVX_Vector_x4 rsum_in = { .v = { r0_sum, r1_sum, r2_sum, r3_sum } };
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x4(rsum_in);
-    hvx_vec_store_u(s0, 16, rsum);
-}
-
-
-static void vec_dot_iq4nlx4x2_q8x4x2_2x2(const int n,
-                                         float * restrict s0,
-                                         float * restrict s1,
-                                         const void * restrict vx0,
-                                         const void * restrict vx1,
-                                         const void * restrict vy0,
-                                         const void * restrict vy1) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-    assert((unsigned long) vy1 % 128 == 0);
-
-    const uint32_t qk = QK_Q4_0x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 2;  // 32x __fp16
-    const uint32_t x_qblk_size = qk / 2;     // int4
-    const uint32_t x_qrow_size = n / 2;      // int4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;  // 32x __fp16
-    const uint32_t y_qblk_size = qk;         // int8
-    const uint32_t y_qrow_size = n;          // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;
-
-    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;
-    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;
-    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;
-    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;
-
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;
-    const uint32_t nloe = n % qk;
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_iq4nlx4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_iq4nlx4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
-
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(y0_q + i * y_qblk_size, nloe);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(y1_q + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_iq4nlx4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_iq4nlx4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
-
-        HVX_Vector vy0_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y0_d + i * y_dblk_size));
-        HVX_Vector vy1_d = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (y1_d + i * y_dblk_size));
-        HVX_Vector r0_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r0_x_d + i * x_dblk_size));
-        HVX_Vector r1_d  = Q6_Vh_vshuff_Vh(*(const HVX_UVector *) (r1_x_d + i * x_dblk_size));
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy0_d)));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r0_d, vy1_d)));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy0_d)));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(r1_d, vy1_d)));
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_c0_dd             = Q6_V_vand_QV(bmask, r0_c0_dd);
-        r0_c1_dd             = Q6_V_vand_QV(bmask, r0_c1_dd);
-        r1_c0_dd             = Q6_V_vand_QV(bmask, r1_c0_dd);
-        r1_c1_dd             = Q6_V_vand_QV(bmask, r1_c1_dd);
-        r0_c0_ia             = Q6_V_vand_QV(bmask, r0_c0_ia);
-        r0_c1_ia             = Q6_V_vand_QV(bmask, r0_c1_ia);
-        r1_c0_ia             = Q6_V_vand_QV(bmask, r1_c0_ia);
-        r1_c1_ia             = Q6_V_vand_QV(bmask, r1_c1_ia);
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);
-    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);
-}
-
-static void vec_dot_mxfp4x4x2_q8x4x2_1x1(const int n, float * restrict s0, const void * restrict vx0, const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_MXFP4x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 1;                                  // 32x e8m0
-    const uint32_t x_qblk_size = qk / 2;                                     // fp4
-    const uint32_t x_qrow_size = n / 2;                                      // fp4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                  // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                         // int8
-    const uint32_t y_qrow_size = n;                                          // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0 + 0);           // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0 + x_qrow_size); // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0 + 0);              // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);    // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (qf32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(   y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(   y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy_q, nloe));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-
-        // Zero-out unused scales
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-    }
-
-    r0_sum = hvx_vec_reduce_sum_f32(r0_sum);
-
-    hvx_vec_store_u(s0, 4, r0_sum);
-}
-
-static void vec_dot_mxfp4x4x2_q8x4x2_2x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_MXFP4x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 1;                                   // 32x e8m0
-    const uint32_t x_qblk_size = qk / 2;                                      // fp4
-    const uint32_t x_qrow_size = n / 2;                                       // fp4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0) + 0;               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0) + y_qrow_size;     // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-
-    // Multiply and accumulate into int32.
-    // Compute combined scale (fp32).
-    // Apply scale to acc and accumulate into the row sum (f32).
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(   y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_full(r1_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy_d));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(   y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy_d));
-
-        // Zero-out unused values
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(r0_sum, r1_sum);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_mxfp4x4x2_q8x4x2_4x1(const int n, float * restrict s0,
-                                      const void * restrict vx0, const void * restrict vx1,
-                                      const void * restrict vx2, const void * restrict vx3,
-                                      const void * restrict vy0) {
-    assert(n % 32 == 0);  // min sub-block size
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vx2 % 128 == 0);
-    assert((unsigned long) vx3 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-
-    const uint32_t qk = QK_MXFP4x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 1;                                   // 32x e8m0
-    const uint32_t x_qblk_size = qk / 2;                                      // fp4
-    const uint32_t x_qrow_size = n / 2;                                       // fp4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-    const uint8_t * restrict r2_x_q = ((const uint8_t *) vx2) + 0;            // quants first
-    const uint8_t * restrict r2_x_d = ((const uint8_t *) vx2) + x_qrow_size;  // then scales
-    const uint8_t * restrict r3_x_q = ((const uint8_t *) vx3) + 0;            // quants first
-    const uint8_t * restrict r3_x_d = ((const uint8_t *) vx3) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y_q = ((const uint8_t *) vy0) + 0;               // quants first
-    const uint8_t * restrict y_d = ((const uint8_t *) vy0 + y_qrow_size);     // then scales
-
-    // Row sum (sf)
-    HVX_Vector r0_sum = Q6_V_vzero();
-    HVX_Vector r1_sum = Q6_V_vzero();
-    HVX_Vector r2_sum = Q6_V_vzero();
-    HVX_Vector r3_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    int32_t        nloe = n % qk;  // num leftover elemements (must be signed)
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_full(   y_q    + i * y_qblk_size);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_full(r1_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r2_q = hvx_vec_load_mxfp4x4x8_full(r2_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r3_q = hvx_vec_load_mxfp4x4x8_full(r3_x_q + i * x_qblk_size);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_Vector r2_d = *(const HVX_UVector *) (r2_x_d + i * x_dblk_size);
-        HVX_Vector r3_d = *(const HVX_UVector *) (r3_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-        r2_d                 = Q6_V_vdelta_VV(r2_d, expand);
-        r2_d                 = Q6_V_vand_VV(r2_d, e8m0_mask);
-        r2_d                 = Q6_Vw_vasl_VwR(r2_d, 23);
-        r3_d                 = Q6_V_vdelta_VV(r3_d, expand);
-        r3_d                 = Q6_V_vand_VV(r3_d, e8m0_mask);
-        r3_d                 = Q6_Vw_vasl_VwR(r3_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy_d));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r2_d, vy_d));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r3_d, vy_d));
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    if (nloe) {
-        HVX_Vector_x8 vy_q = hvx_vec_load_q8x4x8_partial(   y_q    + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r2_q = hvx_vec_load_mxfp4x4x8_partial(r2_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r3_q = hvx_vec_load_mxfp4x4x8_partial(r3_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy_q));
-        HVX_Vector r1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy_q));
-        HVX_Vector r2_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r2_q, vy_q));
-        HVX_Vector r3_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r3_q, vy_q));
-
-        HVX_Vector vy_d = *(const HVX_UVector *) (y_d    + i * y_dblk_size);
-        HVX_Vector r0_d = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-        HVX_Vector r2_d = *(const HVX_UVector *) (r2_x_d + i * x_dblk_size);
-        HVX_Vector r3_d = *(const HVX_UVector *) (r3_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy_d            = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy_d), half));
-        vy_d            = Q6_Vsf_equals_Vqf32(vy_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-        r2_d                 = Q6_V_vdelta_VV(r2_d, expand);
-        r2_d                 = Q6_V_vand_VV(r2_d, e8m0_mask);
-        r2_d                 = Q6_Vw_vasl_VwR(r2_d, 23);
-        r3_d                 = Q6_V_vdelta_VV(r3_d, expand);
-        r3_d                 = Q6_V_vand_VV(r3_d, e8m0_mask);
-        r3_d                 = Q6_Vw_vasl_VwR(r3_d, 23);
-
-        HVX_Vector r0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy_d));
-        HVX_Vector r1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy_d));
-        HVX_Vector r2_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r2_d, vy_d));
-        HVX_Vector r3_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r3_d, vy_d));
-
-        // Zero-out unused values
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_dd                = Q6_V_vand_QV(bmask, r0_dd);
-        r1_dd                = Q6_V_vand_QV(bmask, r1_dd);
-        r2_dd                = Q6_V_vand_QV(bmask, r2_dd);
-        r3_dd                = Q6_V_vand_QV(bmask, r3_dd);
-        r0_ia                = Q6_V_vand_QV(bmask, r0_ia);
-        r1_ia                = Q6_V_vand_QV(bmask, r1_ia);
-        r2_ia                = Q6_V_vand_QV(bmask, r2_ia);
-        r3_ia                = Q6_V_vand_QV(bmask, r3_ia);
-
-        HVX_Vector r0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_ia, r0_dd);
-        HVX_Vector r1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_ia, r1_dd);
-        HVX_Vector r2_fa = Q6_Vqf32_vmpy_VsfVsf(r2_ia, r2_dd);
-        HVX_Vector r3_fa = Q6_Vqf32_vmpy_VsfVsf(r3_ia, r3_dd);
-
-        r0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_fa, r0_sum));
-        r1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_fa, r1_sum));
-        r2_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r2_fa, r2_sum));
-        r3_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r3_fa, r3_sum));
-    }
-
-    HVX_Vector_x4 rsum_in = { .v = { r0_sum, r1_sum, r2_sum, r3_sum } };
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x4(rsum_in);
-    hvx_vec_store_u(s0, 16, rsum);
-}
-
-
-static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float * restrict s1,
-                                        const void * restrict vx0, const void * restrict vx1,
-                                        const void * restrict vy0, const void * restrict vy1) {
-    assert(n % 32 == 0);
-    assert((unsigned long) vx0 % 128 == 0);
-    assert((unsigned long) vx1 % 128 == 0);
-    assert((unsigned long) vy0 % 128 == 0);
-    assert((unsigned long) vy1 % 128 == 0);
-
-    const uint32_t qk = QK_MXFP4x4x2 * 4;
-
-    const uint32_t x_dblk_size = 8 * 4 * 1;                                   // 32x e8m0
-    const uint32_t x_qblk_size = qk / 2;                                      // fp4
-    const uint32_t x_qrow_size = n / 2;                                       // fp4 (not padded)
-
-    const uint32_t y_dblk_size = 8 * 4 * 2;                                   // 32x __fp16
-    const uint32_t y_qblk_size = qk;                                          // int8
-    const uint32_t y_qrow_size = n;                                           // int8 (not padded)
-
-    const uint8_t * restrict r0_x_q = ((const uint8_t *) vx0) + 0;            // quants first
-    const uint8_t * restrict r0_x_d = ((const uint8_t *) vx0) + x_qrow_size;  // then scales
-    const uint8_t * restrict r1_x_q = ((const uint8_t *) vx1) + 0;            // quants first
-    const uint8_t * restrict r1_x_d = ((const uint8_t *) vx1) + x_qrow_size;  // then scales
-
-    const uint8_t * restrict y0_q = ((const uint8_t *) vy0) + 0;              // quants first
-    const uint8_t * restrict y0_d = ((const uint8_t *) vy0) + y_qrow_size;    // then scales
-    const uint8_t * restrict y1_q = ((const uint8_t *) vy1) + 0;              // quants first
-    const uint8_t * restrict y1_d = ((const uint8_t *) vy1) + y_qrow_size;    // then scales
-
-    // Row sums (sf) - 4 accumulators for 2×2 tile
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    const uint32_t nb   = n / qk;  // num full blocks
-    const uint32_t nloe = n % qk;  // num leftover elements
-
-    uint32_t i = 0;
-    for (; i < nb; i++) {
-        // Load src1 columns (reused across both src0 rows)
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_full(y0_q + i * y_qblk_size);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_full(y1_q + i * y_qblk_size);
-
-        // Load src0 rows (reused across both src1 columns)
-        HVX_Vector_x8 r0_q = hvx_vec_load_mxfp4x4x8_full(r0_x_q + i * x_qblk_size);
-        HVX_Vector_x8 r1_q = hvx_vec_load_mxfp4x4x8_full(r1_x_q + i * x_qblk_size);
-
-        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy0_q));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r0_q, vy1_q));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy0_q));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_full(r1_q, vy1_q));
-
-        // Load scales
-        HVX_Vector vy0_d = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
-        HVX_Vector vy1_d = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
-        HVX_Vector r0_d  = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d  = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy0_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy0_d), half));
-        vy0_d           = Q6_Vsf_equals_Vqf32(vy0_d);
-        vy1_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy1_d), half));
-        vy1_d           = Q6_Vsf_equals_Vqf32(vy1_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-
-        // Compute combined scales
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy0_d));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy1_d));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy0_d));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy1_d));
-
-        // Apply scales and accumulate
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Process leftovers
-    if (nloe) {
-        HVX_Vector_x8 vy0_q = hvx_vec_load_q8x4x8_partial(   y0_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 vy1_q = hvx_vec_load_q8x4x8_partial(   y1_q   + i * y_qblk_size, nloe);
-        HVX_Vector_x8 r0_q  = hvx_vec_load_mxfp4x4x8_partial(r0_x_q + i * x_qblk_size, nloe);
-        HVX_Vector_x8 r1_q  = hvx_vec_load_mxfp4x4x8_partial(r1_x_q + i * x_qblk_size, nloe);
-
-        HVX_Vector r0_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy0_q, nloe));
-        HVX_Vector r0_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r0_q, vy1_q, nloe));
-        HVX_Vector r1_c0_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy0_q, nloe));
-        HVX_Vector r1_c1_ia = Q6_Vsf_equals_Vw(hvx_vec_rmpy_x8_partial(r1_q, vy1_q, nloe));
-
-        HVX_Vector vy0_d = *(const HVX_UVector *) (y0_d   + i * y_dblk_size);
-        HVX_Vector vy1_d = *(const HVX_UVector *) (y1_d   + i * y_dblk_size);
-        HVX_Vector r0_d  = *(const HVX_UVector *) (r0_x_d + i * x_dblk_size);
-        HVX_Vector r1_d  = *(const HVX_UVector *) (r1_x_d + i * x_dblk_size);
-
-        // Convert vy_d from fp16 to fp32 while applying 0.5 scaling which is used for e8m0 halving
-        HVX_Vector half = Q6_Vh_vsplat_R(0x3800);  // 0.5 in fp16
-        vy0_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy0_d), half));
-        vy0_d           = Q6_Vsf_equals_Vqf32(vy0_d);
-        vy1_d           = Q6_V_lo_W(Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(vy1_d), half));
-        vy1_d           = Q6_Vsf_equals_Vqf32(vy1_d);
-
-        // Convert rX_d scales from e8m0 to fp32
-        // Expand and zero-pad 32x uint8 e8m0 values to uint32s : 0 0 0 0, 0 0 0 1, 0 0 0 2, ...
-        // Left shift with zero fill to create FP32
-        // FIXME: might need to handle zero as a special case (see ggml-cpu code)
-        HVX_Vector expand    = *(const HVX_Vector *) expand_x32_e8m0;
-        HVX_Vector e8m0_mask = Q6_V_vsplat_R(0x000000ff);
-        r0_d                 = Q6_V_vdelta_VV(r0_d, expand);
-        r0_d                 = Q6_V_vand_VV(r0_d, e8m0_mask);
-        r0_d                 = Q6_Vw_vasl_VwR(r0_d, 23);
-        r1_d                 = Q6_V_vdelta_VV(r1_d, expand);
-        r1_d                 = Q6_V_vand_VV(r1_d, e8m0_mask);
-        r1_d                 = Q6_Vw_vasl_VwR(r1_d, 23);
-
-        HVX_Vector r0_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy0_d));
-        HVX_Vector r0_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r0_d, vy1_d));
-        HVX_Vector r1_c0_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy0_d));
-        HVX_Vector r1_c1_dd = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(r1_d, vy1_d));
-
-        // Zero out unused scales
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe / 8);
-        r0_c0_dd = Q6_V_vand_QV(bmask, r0_c0_dd);
-        r0_c1_dd = Q6_V_vand_QV(bmask, r0_c1_dd);
-        r1_c0_dd = Q6_V_vand_QV(bmask, r1_c0_dd);
-        r1_c1_dd = Q6_V_vand_QV(bmask, r1_c1_dd);
-        r0_c0_ia = Q6_V_vand_QV(bmask, r0_c0_ia);
-        r0_c1_ia = Q6_V_vand_QV(bmask, r0_c1_ia);
-        r1_c0_ia = Q6_V_vand_QV(bmask, r1_c0_ia);
-        r1_c1_ia = Q6_V_vand_QV(bmask, r1_c1_ia);
-
-        HVX_Vector r0_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c0_ia, r0_c0_dd);
-        HVX_Vector r0_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r0_c1_ia, r0_c1_dd);
-        HVX_Vector r1_c0_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c0_ia, r1_c0_dd);
-        HVX_Vector r1_c1_fa = Q6_Vqf32_vmpy_VsfVsf(r1_c1_ia, r1_c1_dd);
-
-        r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c0_fa, r0_c0_sum));
-        r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r0_c1_fa, r0_c1_sum));
-        r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c0_fa, r1_c0_sum));
-        r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(r1_c1_fa, r1_c1_sum));
-    }
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
-    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
-}
-
 #if __HVX_ARCH__ < 79
 #define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
 #define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
@@ -2926,7 +141,7 @@ static void vec_dot_mxfp4x4x2_q8x4x2_2x2(const int n, float * restrict s0, float
 #define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
 #endif
 
-static void vec_dot_f32_f32_aa_1x1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_f32_f32_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
     const HVX_Vector * restrict x = (const HVX_Vector *) vx;
     const HVX_Vector * restrict y = (const HVX_Vector *) vy;
 
@@ -2954,7 +169,7 @@ static void vec_dot_f32_f32_aa_1x1(const int n, float * restrict s, const void *
     *s = hvx_vec_get_f32(hvx_vec_reduce_sum_f32(rsum));
 }
 
-static void vec_dot_f32_f32_aa_2x1(const int n, float * restrict s0,
+static void vec_dot_f32_f32_aa_2x1(const uint32_t n, float * restrict s0,
                                 const void * restrict vx0, const void * restrict vx1,
                                 const void * restrict vy0) {
     const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
@@ -2996,7 +211,7 @@ static void vec_dot_f32_f32_aa_2x1(const int n, float * restrict s0,
     s0[1] = va.fp32[1];
 }
 
-static void vec_dot_f32_f32_aa_2x2(const int n, float * restrict s0, float * restrict s1,
+static void vec_dot_f32_f32_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
                                 const void * restrict vx0, const void * restrict vx1,
                                 const void * restrict vy0, const void * restrict vy1) {
     const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
@@ -3054,7 +269,7 @@ static void vec_dot_f32_f32_aa_2x2(const int n, float * restrict s0, float * res
     s1[1] = va1.fp32[1];
 }
 
-static void vec_dot_f32_f32_uu_1x1(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
+static void vec_dot_f32_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
     const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
     const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
 
@@ -3088,7 +303,7 @@ static void vec_dot_f32_f32_uu_1x1(const int n, float * restrict s, const void *
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-static void vec_dot_f16_f16_aa_1x1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_f16_f16_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
     const HVX_Vector * restrict x = (const HVX_Vector *) vx;
     const HVX_Vector * restrict y = (const HVX_Vector *) vy;
 
@@ -3115,7 +330,7 @@ static void vec_dot_f16_f16_aa_1x1(const int n, float * restrict s, const void *
     hvx_vec_store_u(s, 4, hvx_vec_reduce_sum_f32(rsum));
 }
 
-static void vec_dot_f16_f16_aa_2x1(const int n, float * restrict s0,
+static void vec_dot_f16_f16_aa_2x1(const uint32_t n, float * restrict s0,
                                 const void * restrict vx0, const void * restrict vx1,
                                 const void * restrict vy0) {
     const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
@@ -3152,7 +367,7 @@ static void vec_dot_f16_f16_aa_2x1(const int n, float * restrict s0,
     hvx_vec_store_u(s0, 8, rsum);
 }
 
-static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * restrict s1,
+static void vec_dot_f16_f16_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
                                 const void * restrict vx0, const void * restrict vx1,
                                 const void * restrict vy0, const void * restrict vy1) {
     const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
@@ -3212,7 +427,7 @@ static void vec_dot_f16_f16_aa_2x2(const int n, float * restrict s0, float * res
     hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
 }
 
-static void vec_dot_f16_f16_uu_1x1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+static void vec_dot_f16_f16_uu_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
     const HVX_UVector * restrict x = (const HVX_UVector *) vx;
     const HVX_UVector * restrict y = (const HVX_UVector *) vy;
 
@@ -3242,7 +457,7 @@ static void vec_dot_f16_f16_uu_1x1(const int n, float * restrict s, const void *
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void * restrict x, const void * restrict y) {
+static void vec_dot_f16_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
     const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
     const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
 
@@ -3295,65 +510,58 @@ static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void *
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-#define htp_matmul_tensors_preamble                          \
-    const struct htp_tensor * restrict src0 = octx->src[0];  \
-    const struct htp_tensor * restrict src1 = octx->src[1];  \
-    const struct htp_tensor * restrict src2 = octx->src[2];  \
-    const struct htp_tensor * restrict  dst = octx->dst;     \
-    struct htp_spad * restrict src0_spad = &octx->src0_spad; \
-    struct htp_spad * restrict src1_spad = &octx->src1_spad; \
-    struct htp_spad * restrict dst_spad  = &octx->dst_spad;  \
-                                                             \
-    const uint32_t ne00 = src0->ne[0]; \
-    const uint32_t ne01 = src0->ne[1]; \
-    const uint32_t ne02 = src0->ne[2]; \
-    const uint32_t ne03 = src0->ne[3]; \
-                                       \
-    const uint32_t ne10 = src1->ne[0]; \
-    const uint32_t ne11 = src1->ne[1]; \
-    const uint32_t ne12 = src1->ne[2]; \
-    const uint32_t ne13 = src1->ne[3]; \
-                                       \
-    const uint32_t ne20 = src2->ne[0]; \
-    const uint32_t ne21 = src2->ne[1]; \
-    const uint32_t ne22 = src2->ne[2]; \
-    const uint32_t ne23 = src2->ne[3]; \
-                                       \
-    const uint32_t ne0 = dst->ne[0];   \
-    const uint32_t ne1 = dst->ne[1];   \
-    const uint32_t ne2 = dst->ne[2];   \
-    const uint32_t ne3 = dst->ne[3];   \
-                                       \
-    const uint32_t nb00 = src0->nb[0]; \
-    const uint32_t nb01 = src0->nb[1]; \
-    const uint32_t nb02 = src0->nb[2]; \
-    const uint32_t nb03 = src0->nb[3]; \
-                                       \
-    const uint32_t nb10 = src1->nb[0]; \
-    const uint32_t nb11 = src1->nb[1]; \
-    const uint32_t nb12 = src1->nb[2]; \
-    const uint32_t nb13 = src1->nb[3]; \
-                                       \
-    const uint32_t nb0 = dst->nb[0];   \
-    const uint32_t nb1 = dst->nb[1];   \
-    const uint32_t nb2 = dst->nb[2];   \
+#define htp_matmul_tensors_preamble                                 \
+    const struct htp_tensor * restrict src0 = octx->src[0];         \
+    const struct htp_tensor * restrict src1 = octx->src[1];         \
+    const struct htp_tensor * restrict src2 = octx->src[2];         \
+    const struct htp_tensor * restrict  dst = octx->dst;            \
+                                                                    \
+    const uint32_t ne00 = src0->ne[0];                              \
+    const uint32_t ne01 = src0->ne[1];                              \
+    const uint32_t ne02 = src0->ne[2];                              \
+    const uint32_t ne03 = src0->ne[3];                              \
+                                                                    \
+    const uint32_t ne10 = src1->ne[0];                              \
+    const uint32_t ne11 = src1->ne[1];                              \
+    const uint32_t ne12 = src1->ne[2];                              \
+    const uint32_t ne13 = src1->ne[3];                              \
+                                                                    \
+    const uint32_t ne20 = src2->ne[0];                              \
+    const uint32_t ne21 = src2->ne[1];                              \
+    const uint32_t ne22 = src2->ne[2];                              \
+    const uint32_t ne23 = src2->ne[3];                              \
+                                                                    \
+    const uint32_t ne0 = dst->ne[0];                                \
+    const uint32_t ne1 = dst->ne[1];                                \
+    const uint32_t ne2 = dst->ne[2];                                \
+    const uint32_t ne3 = dst->ne[3];                                \
+                                                                    \
+    const uint32_t nb00 = src0->nb[0];                              \
+    const uint32_t nb01 = src0->nb[1];                              \
+    const uint32_t nb02 = src0->nb[2];                              \
+    const uint32_t nb03 = src0->nb[3];                              \
+                                                                    \
+    const uint32_t nb10 = src1->nb[0];                              \
+    const uint32_t nb11 = src1->nb[1];                              \
+    const uint32_t nb12 = src1->nb[2];                              \
+    const uint32_t nb13 = src1->nb[3];                              \
+                                                                    \
+    const uint32_t nb0 = dst->nb[0];                                \
+    const uint32_t nb1 = dst->nb[1];                                \
+    const uint32_t nb2 = dst->nb[2];                                \
     const uint32_t nb3 = dst->nb[3];
 
-#define htp_matmul_preamble                                     \
-    struct htp_matmul_context * mmctx = data;                   \
-    struct htp_ops_context * octx  = mmctx->octx;               \
-    htp_matmul_tensors_preamble;                                \
-    dma_queue *dma_queue           = octx->ctx->dma[ith];       \
-    uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;
+#define htp_matmul_preamble                                         \
+    struct htp_mm_context * mmctx  = data;                          \
+    struct htp_ops_context * octx  = mmctx->octx;                   \
+    dma_queue *dma_queue           = octx->ctx->dma[ith];           \
+    uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;  \
+    htp_matmul_tensors_preamble;
 
 // *** matmul with support for 4d tensors and full broadcasting
 
-static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
+static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
 
     assert(ne12 % ne02 == 0);
     assert(ne13 % ne03 == 0);
@@ -3388,7 +596,9 @@ static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
         return;
     }
 
-    // block-tiling attempt
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0_start);
+
     const uint32_t blck_0 = 64;
     const uint32_t blck_1 = 64;
 
@@ -3412,28 +622,606 @@ static void matmul_4d(unsigned int nth, unsigned int ith, void * data) {
                 float * dst_col = (float *) ((uint8_t * restrict) dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
 
                 const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end);
-                htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, iir0);
                 for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) {
                     const uint8_t * restrict src0_row = src0_base + ir0 * nb01;
                     mmctx->vec_dot_1x1(ne00, &dst_col[ir0], src0_row, src1_col);
                 }
-                htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, iir0);
             }
         }
     }
 
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "matmul-4d %d/%d: %ux%ux%ux%u (%u:%u %u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth,
-         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], ir0_start, ir0_end, ir1_start, ir1_end, src1->ne[0],
-         src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
-         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0_start);
 }
 
-// src1 tensor is already in VTCM spad
-static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
-    htp_matmul_preamble;
+#include "hmx-mm-kernels-tiled.h"
+#include "hvx-mm-kernels-tiled.h"
+#include "hvx-mm-kernels-flat.h"
+
+// Specialized repacked matmul macros
+#define MATMUL_2D_REPACKED_IMPL(SUFFIX, TILE_SIZE, DOT_2X2, DOT_2X1)                                                              \
+static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void * data) {                                        \
+    htp_matmul_preamble;                                                                                                          \
+                                                                                                                                  \
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;                                                                               \
+    const uint32_t src1_nrows = ne11 * ne12 * ne13;                                                                               \
+                                                                                                                                  \
+    const uint32_t src0_start_row  = src0_nrows_per_thread * ith;                                                                 \
+    const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);                                     \
+                                                                                                                                  \
+    if (src0_start_row >= src0_end_row) {                                                                                         \
+        return;                                                                                                                   \
+    }                                                                                                                             \
+                                                                                                                                  \
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
+                                                                                                                                  \
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
+    const uint32_t n_prefetch = kparams->n_prefetch;                                                                              \
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);                         \
+                                                                                                                                  \
+    const size_t dst_row_size  = nb1;                                                                                             \
+    const size_t src1_row_size = nb11;                                                                                            \
+    const size_t src1_stride = mmctx->vtcm_src1_stride;                                                                           \
+                                                                                                                                  \
+    uint8_t * restrict vtcm_dst_ptr  = mmctx->vtcm_dst  + mmctx->vtcm_dst_size_per_thread  * ith;                                 \
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;                                 \
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;                                                                              \
+                                                                                                                                  \
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;                                                             \
+                                                                                                                                  \
+    const uint32_t tile_size = TILE_SIZE;                                                                                         \
+    const uint32_t aligned_tile_size = hex_align_up(tile_size, 128);                                                              \
+                                                                                                                                  \
+    uint32_t n_k_tiles_w = ne00 / 32;                                                                                             \
+    uint32_t n_k_tiles_a = ne10 / 32;                                                                                             \
+    uint32_t tile_row_stride = n_k_tiles_w * tile_size;                                                                           \
+    uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;                                                    \
+                                                                                                                                  \
+    uint32_t ct_start = src0_start_row / 32;                                                                                      \
+    uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
+                                                                                                                                  \
+    uint32_t push_ct = ct_start;                                                                                                  \
+    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
+                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    }                                                                                                                             \
+                                                                                                                                  \
+    for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
+        const uint8_t * w_tile = dma_queue_pop(dma_queue).dst;                                                                    \
+                                                                                                                                  \
+        int valid_rows = (int)ne0 - (int)(ct * 32);                                                                               \
+        valid_rows = MIN(32, MAX(0, valid_rows));                                                                                 \
+                                                                                                                                  \
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                    \
+        uint32_t ir1 = 0;                                                                                                         \
+        for (; ir1 + 1 < src1_nrows; ir1 += 2) {                                                                                  \
+            const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);                           \
+            const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);                           \
+            float * restrict dst_row0 = (float *) (dst->data + ((ir1+0) * dst_row_size));                                         \
+            float * restrict dst_row1 = (float *) (dst->data + ((ir1+1) * dst_row_size));                                         \
+                                                                                                                                  \
+            float * dst_ptr0 = &dst_row0[ct * 32];                                                                                \
+            float * dst_ptr1 = &dst_row1[ct * 32];                                                                                \
+                                                                                                                                  \
+            DOT_2X2(ne10, dst_ptr0, dst_ptr1, w_tile, src1_col0, src1_col1, valid_rows);                                          \
+        }                                                                                                                         \
+                                                                                                                                  \
+        for (; ir1 < src1_nrows; ++ir1) {                                                                                         \
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);                                \
+            float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));                                     \
+            float * dst_ptr = &dst_row[ct * 32];                                                                                  \
+                                                                                                                                  \
+            DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows);                                                                 \
+        }                                                                                                                         \
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
+                                                                                                                                  \
+        if (push_ct < ct_end) {                                                                                                   \
+            dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride),                      \
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                                 \
+            push_ct++;                                                                                                            \
+        }                                                                                                                         \
+    }                                                                                                                             \
+}
+
+#define MATVEC_2D_REPACKED_IMPL(SUFFIX, TILE_SIZE, DOT_2X1)                                                                       \
+static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void * data) {                                        \
+    htp_matmul_preamble;                                                                                                          \
+                                                                                                                                  \
+    const uint32_t src0_nrows = ne01;                                                                                             \
+                                                                                                                                  \
+    const uint32_t src0_start_row  = src0_nrows_per_thread * ith;                                                                 \
+    const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);                                     \
+                                                                                                                                  \
+    if (src0_start_row >= src0_end_row) {                                                                                         \
+        return;                                                                                                                   \
+    }                                                                                                                             \
+                                                                                                                                  \
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
+                                                                                                                                  \
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
+    const uint32_t n_prefetch = kparams->n_prefetch;                                                                              \
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);                         \
+                                                                                                                                  \
+    const size_t dst_row_size  = nb1;                                                                                             \
+    const size_t src1_row_size = nb11;                                                                                            \
+    const size_t src1_stride = mmctx->vtcm_src1_stride;                                                                           \
+                                                                                                                                  \
+    uint8_t * vtcm_dst_ptr  = mmctx->vtcm_dst + mmctx->vtcm_dst_size_per_thread * ith;                                            \
+    uint8_t * vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;                                          \
+    uint8_t * src1_data = mmctx->vtcm_src1;                                                                                       \
+                                                                                                                                  \
+    float * tmp = (float *) vtcm_dst_ptr;                                                                                         \
+                                                                                                                                  \
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;                                                             \
+    const uint8_t * restrict src1_col = (const uint8_t *) src1_data;                                                              \
+    float * restrict dst_col          = (float *) dst->data;                                                                      \
+                                                                                                                                  \
+    const uint32_t tile_size = TILE_SIZE;                                                                                         \
+    const uint32_t aligned_tile_size = hex_align_up(tile_size, 128);                                                              \
+                                                                                                                                  \
+    uint32_t n_k_tiles_w = ne00 / 32;                                                                                             \
+    uint32_t n_k_tiles_a = ne10 / 32;                                                                                             \
+    uint32_t tile_row_stride = n_k_tiles_w * tile_size;                                                                           \
+    uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;                                                    \
+                                                                                                                                  \
+    uint32_t ct_start = src0_start_row / 32;                                                                                      \
+    uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
+                                                                                                                                  \
+    uint32_t push_ct = ct_start;                                                                                                  \
+    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
+                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    }                                                                                                                             \
+                                                                                                                                  \
+    for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
+        const uint8_t * w_tile = dma_queue_pop(dma_queue).dst;                                                                    \
+                                                                                                                                  \
+        float * dst_ptr = &tmp[ct * 32 - src0_start_row];                                                                         \
+        int valid_rows = (int)ne0 - (int)(ct * 32);                                                                               \
+        valid_rows = MIN(32, MAX(0, valid_rows));                                                                                 \
+                                                                                                                                  \
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                    \
+        DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows);                                                                     \
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
+                                                                                                                                  \
+        if (push_ct < ct_end) {                                                                                                   \
+            dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride),                      \
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                                 \
+            push_ct++;                                                                                                            \
+        }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    int copy_cnt = (int)MIN(src0_end_row, ne0) - (int)src0_start_row;                                                             \
+    if (copy_cnt > 0) {                                                                                                           \
+        hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt);                                         \
+    }                                                                                                                             \
+}
+
+#define MATMUL_QKV_2D_REPACKED_IMPL(SUFFIX, TILE_SIZE, DOT_2X2, DOT_2X1)                                                          \
+static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void * data) {                                    \
+    struct htp_mm_context * mmctx = data;                                                                                         \
+    struct htp_ops_context * octx = mmctx->octx;                                                                                  \
+                                                                                                                                  \
+    const struct htp_tensor * restrict src0 = octx->src[0]; /* Wk */                                                              \
+    const struct htp_tensor * restrict src1 = octx->src[1]; /* x */                                                               \
+    const struct htp_tensor * restrict src2 = octx->src[2]; /* Wv */                                                              \
+    const struct htp_tensor * restrict src3 = octx->src[3]; /* Wq */                                                              \
+    const struct htp_tensor * restrict dst_k = octx->dsts[0];                                                                     \
+    const struct htp_tensor * restrict dst_v = octx->dsts[1];                                                                     \
+    const struct htp_tensor * restrict dst_q = octx->dsts[2];                                                                     \
+                                                                                                                                  \
+    const uint32_t ne00 = src0->ne[0];                                                                                            \
+    const uint32_t ne10 = src1->ne[0];                                                                                            \
+    const uint32_t src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];                                                          \
+                                                                                                                                  \
+    const size_t dst_k_row_size = dst_k->nb[1]; /* K and V share output width */                                                  \
+    const size_t dst_q_row_size = dst_q->nb[1]; /* Q may be wider (GQA) */                                                        \
+    const size_t src1_stride = mmctx->vtcm_src1_stride;                                                                           \
+                                                                                                                                  \
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;                                 \
+    uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith;                                 \
+    uint8_t * restrict vtcm_src3_ptr = mmctx->vtcm_src3 + mmctx->vtcm_src3_size_per_thread * ith;                                 \
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;                                                                              \
+                                                                                                                                  \
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
+                                                                                                                                  \
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
+    const uint32_t n_prefetch = kparams->n_prefetch;                                                                              \
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);                         \
+                                                                                                                                  \
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;                                                             \
+    const uint8_t * restrict src2_row = (const uint8_t *) src2->data;                                                             \
+    const uint8_t * restrict src3_row = (const uint8_t *) src3->data;                                                             \
+                                                                                                                                  \
+    const uint32_t tile_size = TILE_SIZE;                                                                                         \
+    const uint32_t aligned_tile_size = hex_align_up(tile_size, 128);                                                              \
+                                                                                                                                  \
+    uint32_t n_k_tiles_w = ne00 / 32;                                                                                             \
+    uint32_t n_k_tiles_a = ne10 / 32;                                                                                             \
+    uint32_t tile_row_stride = n_k_tiles_w * tile_size;                                                                           \
+    uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;                                                    \
+                                                                                                                                  \
+    dma_queue * dma_queue = octx->ctx->dma[ith];                                                                                  \
+                                                                                                                                  \
+    /* 1. Process K and V together */                                                                                             \
+    const uint32_t src0_nrows_kv = src0->ne[1] * src0->ne[2] * src0->ne[3]; /* src0 is Wk */                                      \
+    uint32_t src0_nrows_per_thread_kv = (src0_nrows_kv + nth - 1) / nth;                                                          \
+    src0_nrows_per_thread_kv = hex_round_up(src0_nrows_per_thread_kv, 32);                                                        \
+                                                                                                                                  \
+    const uint32_t start_row_kv = src0_nrows_per_thread_kv * ith;                                                                 \
+    const uint32_t end_row_kv   = MIN(start_row_kv + src0_nrows_per_thread_kv, src0_nrows_kv);                                    \
+                                                                                                                                  \
+    if (start_row_kv < end_row_kv) {                                                                                              \
+        uint32_t ct_start_kv = start_row_kv / 32;                                                                                 \
+        uint32_t ct_end_kv   = (end_row_kv + 31) / 32;                                                                            \
+                                                                                                                                  \
+        uint32_t push_ct = ct_start_kv;                                                                                           \
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end_kv; d++, push_ct++) {                                             \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src2_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+        }                                                                                                                         \
+                                                                                                                                  \
+        for (uint32_t ct = ct_start_kv; ct < ct_end_kv; ct++) {                                                                   \
+            const uint8_t * w_tile_k = dma_queue_pop(dma_queue).dst;                                                              \
+            const uint8_t * w_tile_v = dma_queue_pop(dma_queue).dst;                                                              \
+                                                                                                                                  \
+            int valid_rows = (int)src0->ne[1] - (int)(ct * 32);                                                                   \
+            valid_rows = MIN(32, MAX(0, valid_rows));                                                                             \
+                                                                                                                                  \
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ith);                                                               \
+            uint32_t ir1 = 0;                                                                                                     \
+            for (; ir1 + 1 < src1_nrows; ir1 += 2) {                                                                              \
+                const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);                       \
+                const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);                       \
+                                                                                                                                  \
+                float * restrict dst_row0_k = (float *) (dst_k->data + ((ir1+0) * dst_k_row_size));                               \
+                float * restrict dst_row1_k = (float *) (dst_k->data + ((ir1+1) * dst_k_row_size));                               \
+                float * dst_ptr0_k = &dst_row0_k[ct * 32];                                                                        \
+                float * dst_ptr1_k = &dst_row1_k[ct * 32];                                                                        \
+                                                                                                                                  \
+                float * restrict dst_row0_v = (float *) (dst_v->data + ((ir1+0) * dst_k_row_size));                               \
+                float * restrict dst_row1_v = (float *) (dst_v->data + ((ir1+1) * dst_k_row_size));                               \
+                float * dst_ptr0_v = &dst_row0_v[ct * 32];                                                                        \
+                float * dst_ptr1_v = &dst_row1_v[ct * 32];                                                                        \
+                                                                                                                                  \
+                DOT_2X2(ne10, dst_ptr0_k, dst_ptr1_k, w_tile_k, src1_col0, src1_col1, valid_rows);                                \
+                DOT_2X2(ne10, dst_ptr0_v, dst_ptr1_v, w_tile_v, src1_col0, src1_col1, valid_rows);                                \
+            }                                                                                                                     \
+                                                                                                                                  \
+            for (; ir1 < src1_nrows; ++ir1) {                                                                                     \
+                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);                            \
+                                                                                                                                  \
+                float * restrict dst_row_k = (float *) (dst_k->data + (ir1 * dst_k_row_size));                                    \
+                float * dst_ptr_k = &dst_row_k[ct * 32];                                                                          \
+                                                                                                                                  \
+                float * restrict dst_row_v = (float *) (dst_v->data + (ir1 * dst_k_row_size));                                    \
+                float * dst_ptr_v = &dst_row_v[ct * 32];                                                                          \
+                                                                                                                                  \
+                DOT_2X1(ne10, dst_ptr_k, w_tile_k, src1_col, valid_rows);                                                         \
+                DOT_2X1(ne10, dst_ptr_v, w_tile_v, src1_col, valid_rows);                                                         \
+            }                                                                                                                     \
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ith);                                                                \
+                                                                                                                                  \
+            if (push_ct < ct_end_kv) {                                                                                            \
+                dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile_k, src0_row + push_ct * tile_row_stride),                \
+                               aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                             \
+                dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile_v, src2_row + push_ct * tile_row_stride),                \
+                               aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                             \
+                push_ct++;                                                                                                        \
+            }                                                                                                                     \
+        }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    /* 2. Process Q separately */                                                                                                 \
+    const uint32_t src0_nrows_q = src3->ne[1] * src3->ne[2] * src3->ne[3]; /* src3 is Wq */                                       \
+    uint32_t src0_nrows_per_thread_q = (src0_nrows_q + nth - 1) / nth;                                                            \
+    src0_nrows_per_thread_q = hex_round_up(src0_nrows_per_thread_q, 32);                                                          \
+                                                                                                                                  \
+    const uint32_t start_row_q = src0_nrows_per_thread_q * ith;                                                                   \
+    const uint32_t end_row_q   = MIN(start_row_q + src0_nrows_per_thread_q, src0_nrows_q);                                        \
+                                                                                                                                  \
+    if (start_row_q < end_row_q) {                                                                                                \
+        uint32_t ct_start_q = start_row_q / 32;                                                                                   \
+        uint32_t ct_end_q   = (end_row_q + 31) / 32;                                                                              \
+                                                                                                                                  \
+        uint32_t push_ct = ct_start_q;                                                                                            \
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end_q; d++, push_ct++) {                                              \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src3_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+        }                                                                                                                         \
+                                                                                                                                  \
+        for (uint32_t ct = ct_start_q; ct < ct_end_q; ct++) {                                                                     \
+            const uint8_t * w_tile_q = dma_queue_pop(dma_queue).dst;                                                              \
+                                                                                                                                  \
+            int valid_rows = (int)src3->ne[1] - (int)(ct * 32);                                                                   \
+            valid_rows = MIN(32, MAX(0, valid_rows));                                                                             \
+                                                                                                                                  \
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                \
+            uint32_t ir1 = 0;                                                                                                     \
+            for (; ir1 + 1 < src1_nrows; ir1 += 2) {                                                                              \
+                const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);                       \
+                const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);                       \
+                                                                                                                                  \
+                float * restrict dst_row0_q = (float *) (dst_q->data + ((ir1+0) * dst_q_row_size));                               \
+                float * restrict dst_row1_q = (float *) (dst_q->data + ((ir1+1) * dst_q_row_size));                               \
+                float * dst_ptr0_q = &dst_row0_q[ct * 32];                                                                        \
+                float * dst_ptr1_q = &dst_row1_q[ct * 32];                                                                        \
+                                                                                                                                  \
+                DOT_2X2(ne10, dst_ptr0_q, dst_ptr1_q, w_tile_q, src1_col0, src1_col1, valid_rows);                                \
+            }                                                                                                                     \
+                                                                                                                                  \
+            for (; ir1 < src1_nrows; ++ir1) {                                                                                     \
+                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);                            \
+                                                                                                                                  \
+                float * restrict dst_row_q = (float *) (dst_q->data + (ir1 * dst_q_row_size));                                    \
+                float * dst_ptr_q = &dst_row_q[ct * 32];                                                                          \
+                                                                                                                                  \
+                DOT_2X1(ne10, dst_ptr_q, w_tile_q, src1_col, valid_rows);                                                         \
+            }                                                                                                                     \
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                 \
+                                                                                                                                  \
+            if (push_ct < ct_end_q) {                                                                                             \
+                dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile_q, src3_row + push_ct * tile_row_stride),                \
+                               aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                             \
+                push_ct++;                                                                                                        \
+            }                                                                                                                     \
+        }                                                                                                                         \
+    }                                                                                                                             \
+}
+
+#define MATMUL_FFN_2D_REPACKED_IMPL(SUFFIX, TILE_SIZE, DOT_2X2, DOT_2X1)                                                          \
+static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void * data) {                                    \
+    struct htp_mm_context * mmctx = data;                                                                                         \
+    struct htp_ops_context * octx = mmctx->octx;                                                                                  \
+                                                                                                                                  \
+    const struct htp_tensor * restrict src0 = octx->src[0]; /* Wgate */                                                           \
+    const struct htp_tensor * restrict src1 = octx->src[1]; /* y */                                                               \
+    const struct htp_tensor * restrict src2 = octx->src[2]; /* Wup */                                                             \
+    const struct htp_tensor * restrict dst_gate = octx->dsts[0];                                                                  \
+    const struct htp_tensor * restrict dst_up = octx->dsts[1];                                                                    \
+                                                                                                                                  \
+    const uint32_t ne00 = src0->ne[0];                                                                                            \
+    const uint32_t ne01 = src0->ne[1];                                                                                            \
+    const uint32_t ne10 = src1->ne[0];                                                                                            \
+    const uint32_t src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];                                                          \
+                                                                                                                                  \
+    const size_t dst_row_size  = dst_gate->nb[1];                                                                                 \
+    const size_t src1_stride = mmctx->vtcm_src1_stride;                                                                           \
+                                                                                                                                  \
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;                                 \
+    uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith;                                 \
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;                                                                              \
+                                                                                                                                  \
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
+                                                                                                                                  \
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;                                                             \
+    const uint8_t * restrict src2_row = (const uint8_t *) src2->data;                                                             \
+                                                                                                                                  \
+    const uint32_t tile_size = TILE_SIZE;                                                                                         \
+    const uint32_t aligned_tile_size = hex_align_up(tile_size, 128);                                                              \
+                                                                                                                                  \
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
+    const uint32_t n_prefetch = kparams->n_prefetch;                                                                              \
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);                         \
+                                                                                                                                  \
+    uint32_t n_k_tiles_w = ne00 / 32;                                                                                             \
+    uint32_t n_k_tiles_a = ne10 / 32;                                                                                             \
+    uint32_t tile_row_stride = n_k_tiles_w * tile_size;                                                                           \
+    uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;                                                    \
+    dma_queue * dma_queue = octx->ctx->dma[ith];                                                                                  \
+                                                                                                                                  \
+    const uint32_t src0_nrows = ne01 * src0->ne[2] * src0->ne[3];                                                                 \
+    const uint32_t src0_start_row = mmctx->src0_nrows_per_thread * ith;                                                           \
+    const uint32_t src0_end_row   = MIN(src0_start_row + mmctx->src0_nrows_per_thread, src0_nrows);                               \
+                                                                                                                                  \
+    uint32_t ct_start = src0_start_row / 32;                                                                                      \
+    uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
+                                                                                                                                  \
+    uint32_t push_ct = ct_start;                                                                                                  \
+    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
+                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + d * tile_row_transfer_size_aligned,                                \
+                       src2_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    }                                                                                                                             \
+                                                                                                                                  \
+    for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
+        const uint8_t * w_tile_gate = dma_queue_pop(dma_queue).dst;                                                               \
+        const uint8_t * w_tile_up   = dma_queue_pop(dma_queue).dst;                                                               \
+                                                                                                                                  \
+        int valid_rows = (int)ne01 - (int)(ct * 32);                                                                              \
+        valid_rows = MIN(32, MAX(0, valid_rows));                                                                                 \
+                                                                                                                                  \
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                    \
+        uint32_t ir1 = 0;                                                                                                         \
+        for (; ir1 + 1 < src1_nrows; ir1 += 2) {                                                                                  \
+            const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);                           \
+            const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);                           \
+                                                                                                                                  \
+            float * restrict dst_row0_gate = (float *) (dst_gate->data + ((ir1+0) * dst_row_size));                               \
+            float * restrict dst_row1_gate = (float *) (dst_gate->data + ((ir1+1) * dst_row_size));                               \
+            float * dst_ptr0_gate = &dst_row0_gate[ct * 32];                                                                      \
+            float * dst_ptr1_gate = &dst_row1_gate[ct * 32];                                                                      \
+                                                                                                                                  \
+            float * restrict dst_row0_up = (float *) (dst_up->data + ((ir1+0) * dst_row_size));                                   \
+            float * restrict dst_row1_up = (float *) (dst_up->data + ((ir1+1) * dst_row_size));                                   \
+            float * dst_ptr0_up = &dst_row0_up[ct * 32];                                                                          \
+            float * dst_ptr1_up = &dst_row1_up[ct * 32];                                                                          \
+                                                                                                                                  \
+            DOT_2X2(ne10, dst_ptr0_gate, dst_ptr1_gate, w_tile_gate, src1_col0, src1_col1, valid_rows);                           \
+            DOT_2X2(ne10, dst_ptr0_up, dst_ptr1_up, w_tile_up, src1_col0, src1_col1, valid_rows);                                 \
+        }                                                                                                                         \
+                                                                                                                                  \
+        for (; ir1 < src1_nrows; ++ir1) {                                                                                         \
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);                                \
+                                                                                                                                  \
+            float * restrict dst_row_gate = (float *) (dst_gate->data + (ir1 * dst_row_size));                                    \
+            float * dst_ptr_gate = &dst_row_gate[ct * 32];                                                                        \
+                                                                                                                                  \
+            float * restrict dst_row_up = (float *) (dst_up->data + (ir1 * dst_row_size));                                        \
+            float * dst_ptr_up = &dst_row_up[ct * 32];                                                                            \
+                                                                                                                                  \
+            DOT_2X1(ne10, dst_ptr_gate, w_tile_gate, src1_col, valid_rows);                                                       \
+            DOT_2X1(ne10, dst_ptr_up, w_tile_up, src1_col, valid_rows);                                                           \
+        }                                                                                                                         \
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
+                                                                                                                                  \
+        if (push_ct < ct_end) {                                                                                                   \
+            dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile_gate, src0_row + push_ct * tile_row_stride),                 \
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                                 \
+            dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile_up, src2_row + push_ct * tile_row_stride),                   \
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);                                                 \
+            push_ct++;                                                                                                            \
+        }                                                                                                                         \
+    }                                                                                                                             \
+}
+
+MATMUL_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x2,  tiled_vec_dot_q4_0_32x1)
+MATMUL_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x2,  tiled_vec_dot_q4_1_32x1)
+MATMUL_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x2,  tiled_vec_dot_q8_0_32x1)
+MATMUL_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x2, tiled_vec_dot_iq4nl_32x1)
+MATMUL_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x2, tiled_vec_dot_mxfp4_32x1)
+
+MATMUL_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x2,   flat_vec_dot_q4_0_32x1)
+MATMUL_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x2,   flat_vec_dot_q4_1_32x1)
+MATMUL_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x2,   flat_vec_dot_q8_0_32x1)
+MATMUL_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x2,  flat_vec_dot_iq4nl_32x1)
+MATMUL_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x2,  flat_vec_dot_mxfp4_32x1)
+
+#define QUANTIZE_IMPL(name, log_name, kernel_fn, dst_row_size_expr)                                        \
+static void name(unsigned int nth, unsigned int ith, void * data) {                                        \
+    struct htp_mm_context * mmctx = data;                                                                  \
+    struct htp_ops_context * octx = mmctx->octx;                                                           \
+    const struct htp_tensor * src = octx->src[1];                                                          \
+    const uint32_t ne0 = src->ne[0];                                                                       \
+    const uint32_t ne1 = src->ne[1];                                                                       \
+    const uint32_t ne2 = src->ne[2];                                                                       \
+    const uint32_t ne3 = src->ne[3];                                                                       \
+    const uint32_t nrows = ne1 * ne2 * ne3;                                                                \
+    const uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;                                        \
+                                                                                                           \
+    const uint32_t ir_first = nrows_per_thread * ith;                                                      \
+    if (ir_first >= nrows) {                                                                               \
+        return;                                                                                            \
+    }                                                                                                      \
+                                                                                                           \
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                              \
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);                                        \
+                                                                                                           \
+    uint8_t * restrict dst = mmctx->vtcm_src1;                                                             \
+    const uint32_t ir_last = MIN(ir_first + nrows_per_thread, nrows);                                      \
+    const size_t src_row_size = src->nb[1];                                                                \
+    const size_t dst_row_size = (dst_row_size_expr);                                                       \
+    const uint8_t * restrict src_data = (const uint8_t *) src->data + (src_row_size * ir_first);           \
+    uint8_t * restrict dst_data = (uint8_t *) dst + (dst_row_size * ir_first);                             \
+    uint8_t * restrict tmp_data = (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith); \
+    kernel_fn(src_data, dst_data, tmp_data, ne0, ir_last - ir_first, src_row_size, dst_row_size);          \
+                                                                                                           \
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);                                         \
+}
+
+QUANTIZE_IMPL(quantize_f32_q8_0_tiled, "quantize-f32-q8_0_tiled", quantize_f32_q8_0_tiled_kernel, htp_mm_q8_0_tiled_row_size(ne0))
+QUANTIZE_IMPL(quantize_f32_q8_1_tiled, "quantize-f32-q8_1_tiled", quantize_f32_q8_1_tiled_kernel, htp_mm_q8_1_tiled_row_size(ne0))
+QUANTIZE_IMPL(quantize_f32_q8_0_flat,  "quantize-f32-q8_0_flat",  quantize_f32_q8_0_flat_kernel,  htp_mm_q8_0_flat_row_size(ne0))
+QUANTIZE_IMPL(quantize_f32_q8_1_flat,  "quantize-f32-q8_1_flat",  quantize_f32_q8_1_flat_kernel,  htp_mm_q8_1_flat_row_size(ne0))
+QUANTIZE_IMPL(quantize_f32_f32_flat,   "quantize-f32-f32",        quantize_f32_f32_flat_kernel,   mmctx->vtcm_src1_stride)
+QUANTIZE_IMPL(quantize_f32_f16_flat,   "quantize-f32-f16",        quantize_f32_f16_flat_kernel,   mmctx->vtcm_src1_stride)
+QUANTIZE_IMPL(quantize_f16_f16_flat,   "quantize-f16-f16",        quantize_f16_f16_flat_kernel,   mmctx->vtcm_src1_stride)
+
+static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_mm_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]);
+
+    const struct htp_tensor * src = octx->src[1];
+
+    quantize_f32_q8_0_tiled_block_kernel(
+        (const float *) src->data,
+        mmctx->vtcm_src1,
+        (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith),
+        src->ne[0],
+        mmctx->quant_ib_first[ith],
+        mmctx->quant_ib_last[ith],
+        src->nb[1],
+        htp_mm_q8_0_tiled_row_size(src->ne[0]),
+        mmctx->quant_r[ith],
+        mmctx->quant_c[ith]
+    );
+
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]);
+}
+
+static void quantize_f32_q8_1_tiled_block(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_mm_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]);
+
+    const struct htp_tensor * src = octx->src[1];
+
+    quantize_f32_q8_1_tiled_block_kernel(
+        (const float *) src->data,
+        mmctx->vtcm_src1,
+        (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith),
+        src->ne[0],
+        mmctx->quant_ib_first[ith],
+        mmctx->quant_ib_last[ith],
+        src->nb[1],
+        htp_mm_q8_1_tiled_row_size(src->ne[0]),
+        mmctx->quant_r[ith],
+        mmctx->quant_c[ith]
+    );
+
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, mmctx->quant_ib_first[ith]);
+}
+
+MATVEC_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x1)
+MATVEC_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x1)
+MATVEC_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x1)
+MATVEC_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x1)
+MATVEC_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x1)
+
+MATVEC_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x1)
+MATVEC_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x1)
+MATVEC_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x1)
+MATVEC_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x1)
+MATVEC_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x1)
+
+
+MATMUL_QKV_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x2,  tiled_vec_dot_q4_0_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x2,  tiled_vec_dot_q4_1_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x2,  tiled_vec_dot_q8_0_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x2, tiled_vec_dot_iq4nl_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x2, tiled_vec_dot_mxfp4_32x1)
+
+MATMUL_QKV_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x2,   flat_vec_dot_q4_0_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x2,   flat_vec_dot_q4_1_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x2,   flat_vec_dot_q8_0_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x2,  flat_vec_dot_iq4nl_32x1)
+MATMUL_QKV_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x2,  flat_vec_dot_mxfp4_32x1)
+
+
+MATMUL_FFN_2D_REPACKED_IMPL(q4_0,       576,  tiled_vec_dot_q4_0_32x2,  tiled_vec_dot_q4_0_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(q4_1,       640,  tiled_vec_dot_q4_1_32x2,  tiled_vec_dot_q4_1_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(q8_0,       1088, tiled_vec_dot_q8_0_32x2,  tiled_vec_dot_q8_0_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(iq4nl,      576,  tiled_vec_dot_iq4nl_32x2, tiled_vec_dot_iq4nl_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(mxfp4,      544,  tiled_vec_dot_mxfp4_32x2, tiled_vec_dot_mxfp4_32x1)
+
+MATMUL_FFN_2D_REPACKED_IMPL(q4_0_flat,  576,  flat_vec_dot_q4_0_32x2,   flat_vec_dot_q4_0_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(q4_1_flat,  640,  flat_vec_dot_q4_1_32x2,   flat_vec_dot_q4_1_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(q8_0_flat,  1088, flat_vec_dot_q8_0_32x2,   flat_vec_dot_q8_0_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(iq4nl_flat, 576,  flat_vec_dot_iq4nl_32x2,  flat_vec_dot_iq4nl_32x1)
+MATMUL_FFN_2D_REPACKED_IMPL(mxfp4_flat, 544,  flat_vec_dot_mxfp4_32x2,  flat_vec_dot_mxfp4_32x1)
+
+static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) {
+    htp_matmul_preamble;
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+    const uint32_t prefetch_mask = n_prefetch - 1;
 
     const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
     const uint32_t src1_nrows = ne11 * ne12 * ne13;  // src1 rows
@@ -3447,34 +1235,31 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
         return;
     }
 
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+
     const size_t dst_row_size  = nb1;
     const size_t src0_row_size = nb01;
     const size_t src1_row_size = nb11;
 
-    const size_t src0_stride = src0_spad->stride;
-    const size_t src1_stride = src1_spad->stride;
+    const size_t src0_stride = mmctx->vtcm_src0_stride;
+    const size_t src1_stride = mmctx->vtcm_src1_stride;
 
-    // Per-thread VTCM scratchpads for all tensors
-    // Note that the entire src1 tensor is already in VTCM
-    // For other tensors we allocate N rows per thread, padded to HVX vector size
-    uint8_t * restrict spad_dst  = dst_spad->data  + dst_spad->size_per_thread  * ith;
-    uint8_t * restrict spad_src0 = src0_spad->data + src0_spad->size_per_thread * ith;
-    uint8_t * restrict src1_data = src1_spad->data;
-
-    volatile uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
+    // Per-thread VTCMs for all tensors
+    uint8_t * restrict vtcm_dst_ptr  = mmctx->vtcm_dst  + mmctx->vtcm_dst_size_per_thread  * ith;
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * restrict src1_data     = mmctx->vtcm_src1;
 
     const uint8_t * restrict src0_row = (const uint8_t *) src0->data;
 
-    // Prefill spad with src0 rows
+    // Prefill vtcm with src0 rows
     #pragma unroll(4)
     for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
         const int is0 = (ir0 - src0_start_row);
-        if (is0 >= MM_SPAD_SRC0_NROWS) {
+        if (is0 >= (int)n_prefetch) {
             break;
         }
-        dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, 2);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 2);
     }
 
     // Process src0 rows
@@ -3482,7 +1267,6 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
         htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-
         // Process src1 columns in pairs (2×2 tiling)
         uint32_t ir1 = 0;
         for (; ir1 + 1 < src1_nrows; ir1 += 2) {
@@ -3499,24 +1283,23 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
             float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));
             mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_stride, src1_col);
         }
-
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
-        // Prefetch next (n + spad_nrows) row
-        const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
-        const int is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
+        // Prefetch next (n + vtcm_nrows) row
+        const int pr0 = (ir0 + n_prefetch);
+        const int is0 = (pr0 - src0_start_row) & prefetch_mask;
         if (pr0 < src0_end_row_x2) {
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + pr0 * src0_row_size),
-                           src0_stride, src0_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + pr0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
         }
     }
 
     // Process the last row (if any)
     if (src0_end_row != src0_end_row_x2) {
         uint32_t  ir0 = src0_end_row_x2;
-        const int is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-        dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, 1);
+        const int is0 = (ir0 - src0_start_row) & prefetch_mask;
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 1);
         const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
 
         htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
@@ -3528,19 +1311,10 @@ static void matmul_2d(unsigned int nth, unsigned int ith, void * data) {
         }
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
     }
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "matmul-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mmctx->type, ith, nth,
-         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1],
-         src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
-         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-// q8x4x2 src1 tensor is already in VTCM spad
-static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
+static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const uint32_t src0_nrows = ne01;
 
@@ -3552,164 +1326,101 @@ static void matvec_2d(unsigned int nth, unsigned int ith, void * data) {
         return;
     }
 
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+
     const size_t dst_row_size  = nb1;
     const size_t src0_row_size = nb01;
     const size_t src1_row_size = nb11;
 
-    const size_t src0_stride = src0_spad->stride;
-    const size_t src1_stride = src1_spad->stride;
+    const size_t src0_stride = mmctx->vtcm_src0_stride;
+    const size_t src1_stride = mmctx->vtcm_src1_stride;
 
-    // Per-thread VTCM scratchpads for all tensors
-    // Note that the entire src1 tensor is already in VTCM
-    // For other tensors we allocate N rows per thread, padded to HVX vector size
-    uint8_t * spad_dst  = dst_spad->data + dst_spad->size_per_thread * ith;
-    uint8_t * spad_src0 = src0_spad->data + src0_spad->size_per_thread * ith;
-    uint8_t * src1_data = src1_spad->data;
+    // Per-thread VTCMs for all tensors
+    uint8_t * vtcm_dst_ptr  = mmctx->vtcm_dst  + mmctx->vtcm_dst_size_per_thread  * ith;
+    uint8_t * vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * src1_data     = mmctx->vtcm_src1;
 
-    uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
-
-    float * tmp = (float *) spad_dst;
+    float * tmp = (float *) vtcm_dst_ptr;
 
     const uint8_t * restrict src0_row = (const uint8_t *) src0->data;
     const uint8_t * restrict src1_col = (const uint8_t *) src1_data;
     float * restrict dst_col          = (float *) dst->data;
 
-    if (mmctx->vec_dot_4x1 != NULL) {
-        const uint32_t src0_end_row_x4 = src0_start_row + ((src0_end_row - src0_start_row) & ~3U);
+    const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
-        // Prefill spad with 4x src0 rows
-        #pragma unroll(4)
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x4; ir0 += 4) {
-            const uint32_t is0 = (ir0 - src0_start_row);
-            if (is0 >= MM_SPAD_SRC0_NROWS) {
-                break;
-            }
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                           src0_stride, src0_row_size, 4);
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+    const uint32_t prefetch_mask = n_prefetch - 1;
+
+    // Prefill vtcm with 2x src0 rows
+    #pragma unroll(2)
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const uint32_t is0 = (ir0 - src0_start_row);
+        if (is0 >= n_prefetch) {
+            break;
         }
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 2);
+    }
 
-        // Process src0 rows
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x4; ir0 += 4) {
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_4x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, ss0 + 2 * src0_stride, ss0 + 3 * src0_stride, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+    // Process src0 rows
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+        mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
-            // Prefetch next (n + spad_nrows) row
-            const uint32_t pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
-            const uint32_t is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            if (pr0 < src0_end_row_x4) {
-                dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + pr0 * src0_row_size),
-                               src0_stride, src0_row_size, 4);
-            }
-        }
-
-        // Process leftovers
-        uint32_t ir0 = src0_end_row_x4;
-        if (ir0 + 2 <= src0_end_row) {
-            const uint32_t is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                           src0_stride, src0_row_size, 2);
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            ir0 += 2;
-        }
-        if (ir0 < src0_end_row) {
-            const uint32_t is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                           src0_stride, src0_row_size, 1);
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            ir0 += 1;
-        }
-    } else {
-        const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
-
-        // Prefill spad with 2x src0 rows
-        #pragma unroll(2)
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const uint32_t is0 = (ir0 - src0_start_row);
-            if (is0 >= MM_SPAD_SRC0_NROWS) {
-                break;
-            }
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                           src0_stride, src0_row_size, 2);
-        }
-
-        // Process src0 rows
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_2x1(ne00, &tmp[ir0 - src0_start_row], ss0, ss0 + src0_stride, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-
-            // Prefetch next (n + spad_nrows) row
-            const uint32_t pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
-            const uint32_t is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            if (pr0 < src0_end_row_x2) {
-                dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + pr0 * src0_row_size),
-                               src0_stride, src0_row_size, 2);
-            }
-        }
-
-        // Process the last row (if any)
-        if (src0_end_row != src0_end_row_x2) {
-            const uint32_t ir0 = src0_end_row_x2;
-            const uint32_t is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                           src0_stride, src0_row_size, 1);
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+        // Prefetch next (n + vtcm_nrows) row
+        const uint32_t pr0 = (ir0 + n_prefetch);
+        const uint32_t is0 = (pr0 - src0_start_row) & prefetch_mask;
+        if (pr0 < src0_end_row_x2) {
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + pr0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
         }
     }
 
+    // Process the last row (if any)
+    if (src0_end_row != src0_end_row_x2) {
+        const uint32_t ir0 = src0_end_row_x2;
+        const uint32_t is0 = (ir0 - src0_start_row) & prefetch_mask;
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 1);
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+        mmctx->vec_dot_1x1(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+    }
+
     hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, src0_end_row - src0_start_row);
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "matvec-%s %u/%u: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", mmctx->type, ith, nth,
-         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1],
-         src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
-         (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
 #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ids->ne[0] * ids->ne[1] + (i1)]
 
-struct mmid_row_mapping {
-    uint32_t i1;
-    uint32_t i2;
-};
-
-// src1 tensor is already in VTCM spad
-static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
+static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * restrict ids = octx->src[2];
-    struct htp_spad * restrict   src2_spad = &octx->src2_spad;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
 
-    const uint32_t src0_nrows = ne01;  // src0 rows per expert
-    const uint32_t src1_nrows = ne11;
-
+    const uint32_t src0_nrows      = ne01;  // src0 rows per expert
+    const uint32_t src1_nrows      = ne11;
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
-    const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
     // no work for this thread
     if (src0_start_row >= src0_end_row) {
         return;
     }
 
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+
     const uint32_t n_ids = ids->ne[0];  // n_expert_used
     const uint32_t n_as  = ne02;        // n_expert
 
@@ -3717,807 +1428,195 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
     const struct mmid_row_mapping * matrix_rows       = mmctx->matrix_rows;
 
     const size_t dst_row_size  = nb1;
-    const size_t src0_row_size = nb01;
-    const size_t src1_row_size = q8x4x2_row_size(ne10);
+    const size_t src1_row_size = htp_mm_q8_0_tiled_row_size(ne10);
 
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src1_stride = mmctx->vtcm_src1_stride;
 
-    // Per-thread VTCM scratchpads for all tensors
-    // Note that the entire src1 tensor is already in VTCM
-    // For other tensors we allocate N rows per thread, padded to HVX vector size
-    uint8_t * restrict spad_dst  = dst_spad->data + dst_spad->size_per_thread * ith;
-    uint8_t * restrict spad_src0 = src0_spad->data + src0_spad->size_per_thread * ith;
-    uint8_t * restrict src1_data = src1_spad->data;
+    // Per-thread VTCMs for all tensors
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;
 
     for (uint32_t cur_a = 0; cur_a < n_as; ++cur_a) {
         const int32_t cne1 = matrix_row_counts[cur_a];
-
         if (cne1 == 0) {
             continue;
         }
 
-        if (mmctx->hmx_eligible) {
-            continue;
+        const uint8_t * src0_row = (const uint8_t *) src0->data + cur_a * nb02;
+
+        const uint32_t tile_size = htp_mm_get_weight_tile_size(src0->type);
+        const uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(src0->type);
+        const uint32_t n_k_tiles_w = ne00 / 32;
+        const uint32_t n_k_tiles_a = ne10 / 32;
+        const uint32_t tile_row_stride = n_k_tiles_w * tile_size;
+        const uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;
+
+        const uint32_t ct_start = src0_start_row / 32;
+        const uint32_t ct_end   = (src0_end_row + 31) / 32;
+
+        uint32_t push_ct = ct_start;
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned, src0_row + push_ct * tile_row_stride),
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);
         }
 
-        const uint8_t * src0_row = (const uint8_t *) src0->data + (0 + cur_a * nb02 + 0);
+        for (uint32_t ct = ct_start; ct < ct_end; ct++) {
+            const uint8_t * w_tile = dma_queue_pop(dma_queue).dst;
 
-        // Prefill spad with src0 rows
-        #pragma unroll(4)
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const int is0 = (ir0 - src0_start_row);
-            if (is0 >= MM_SPAD_SRC0_NROWS) {
-                break;
-            }
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
-                           src0_row_size_padded, src0_row_size, 2);
-        }
+            int valid_rows = (int)ne01 - (int)(ct * 32);
+            valid_rows = MIN(32, MAX(0, valid_rows));
 
-        // Process src0 rows
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);
             for (uint32_t cid = 0; cid < cne1; ++cid) {
                 struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid);
                 const int               rm1         = row_mapping.i1;  // expert idx
                 const int               rm2         = row_mapping.i2;  // token idx
 
-                const uint32_t ir1 = src1_nrows == 1 ? 0 : rm1;        // src1 row idx
-                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
-                float * dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
+                const uint32_t ir1 = fastmodulo(rm1, ne11, &mmctx->mm_div_ne11);        // src1 row idx
+                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_stride);
+                float * restrict dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
 
-                mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
+                mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows);
             }
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);
 
-            // Prefetch next (n + spad_nrows) row
-            const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
-            const int is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            if (pr0 < src0_end_row_x2) {
-                dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size),
-                               src0_row_size_padded, src0_row_size, 2);
+            if (push_ct < ct_end) {
+                dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride),
+                               aligned_tile_size, tile_size, tile_size, n_k_tiles_a);
+                push_ct++;
             }
         }
-
-        // Process the last row (if any)
-        if (src0_end_row != src0_end_row_x2) {
-            uint32_t       ir0 = src0_end_row_x2;
-            const uint32_t is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
-                           src0_row_size_padded, src0_row_size, 1);
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            for (uint32_t cid = 0; cid < cne1; ++cid) {
-                struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid);
-                const int               rm1         = row_mapping.i1;  // expert idx
-                const int               rm2         = row_mapping.i2;  // token idx
-
-                const uint32_t ir1 = src1_nrows == 1 ? 0 : rm1;        // src1 row idx
-                const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_row_size);
-                float * dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
-
-                mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
-            }
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-        }
     }
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "matmul-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mmctx->type,
-         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0],
-         src1->ne[1], src1->ne[2], src1->ne[3], ids->ne[0], ids->ne[1], ids->ne[2], ids->ne[3], dst->ne[0], dst->ne[1],
-         dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-// src1 tensor is already in VTCM spad
-static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
+static void hvx_mv_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const struct htp_tensor * restrict ids = octx->src[2];
-    struct htp_spad * restrict   src2_spad = &octx->src2_spad;
-
-    uint64_t t1, t2;
-    t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t src0_nrows = ne01;  // src0 rows per expert
 
+    const uint32_t src0_nrows      = ne01;  // src0 rows per expert
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
-    const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
     // no work for this thread
     if (src0_start_row >= src0_end_row) {
         return;
     }
 
+    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+
     assert(ne13 % ne03 == 0);
 
     const size_t dst_row_size  = nb1;
-    const size_t src0_row_size = nb01;
-    const size_t src1_row_size = q8x4x2_row_size(ne10);
-
-    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+    const size_t src1_row_size = htp_mm_q8_0_tiled_row_size(ne10);
 
     const uint32_t n_aids = src2->ne[0];  // num activated experts
     const uint32_t n_ids  = ne02;         // num experts
 
-    // Per-thread VTCM scratchpads for all tensors
-    // Note that the entire src1 tensor is already in VTCM
-    // For other tensors we allocate N rows per thread, padded to HVX vector size
-    uint8_t * restrict spad_dst  = dst_spad->data + dst_spad->size_per_thread * ith;
-    uint8_t * restrict spad_src0 = src0_spad->data + src0_spad->size_per_thread * ith;
-    uint8_t * restrict src1_data = src1_spad->data;
+    // Per-thread VTCMs for all tensors
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;
 
     for (uint32_t ie1 = 0; ie1 < n_aids; ++ie1) {  // for each expert
-        const uint32_t eid = *(const int32_t *) ((const uint8_t *) src2->data + ie1 * src2->nb[0]);
-        assert(eid < n_ids);
+        const int32_t eid = *(const int32_t *) ((const uint8_t *) src2->data + ie1 * src2->nb[0]);
+        if (eid < 0) {
+            continue;
+        }
+        assert(eid < (int32_t) n_ids);
 
         const uint8_t * restrict src0_row = (const uint8_t *) src0->data + eid * nb02;
         const uint8_t * restrict src1_col = (const uint8_t *) src1_data;
         float * restrict dst_row          = (float *) (dst->data + ie1 * nb1);
 
-        // Prefill spad with src0 rows
-        #pragma unroll(4)
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const int is0 = (ir0 - src0_start_row);
-            if (is0 >= MM_SPAD_SRC0_NROWS) {
-                break;
-            }
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
-                           src0_row_size_padded, src0_row_size, 2);
+        const uint32_t tile_size = htp_mm_get_weight_tile_size(src0->type);
+        const uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(src0->type);
+        const uint32_t n_k_tiles_w = ne00 / 32;
+        const uint32_t n_k_tiles_a = ne10 / 32;
+        const uint32_t tile_row_stride = n_k_tiles_w * tile_size;
+        const uint32_t tile_row_transfer_size_aligned = n_k_tiles_a * aligned_tile_size;
+
+        const uint32_t ct_start = src0_start_row / 32;
+        const uint32_t ct_end   = (src0_end_row + 31) / 32;
+
+        uint32_t push_ct = ct_start;
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned, src0_row + push_ct * tile_row_stride),
+                           aligned_tile_size, tile_size, tile_size, n_k_tiles_a);
         }
 
-        // Process src0 rows
-        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_2x1(ne00, &dst_row[ir0], ss0, ss0 + src0_row_size_padded, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+        for (uint32_t ct = ct_start; ct < ct_end; ct++) {
+            const uint8_t * w_tile = dma_queue_pop(dma_queue).dst;
 
-            // Prefetch next (n + spad_nrows) row
-            const int pr0 = (ir0 + MM_SPAD_SRC0_NROWS);
-            const int is0 = (pr0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            if (pr0 < src0_end_row_x2) {
-                dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size),
-                               src0_row_size_padded, src0_row_size, 2);
+            int valid_rows = (int)ne01 - (int)(ct * 32);
+            valid_rows = MIN(32, MAX(0, valid_rows));
+
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);
+            mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);
+
+            if (push_ct < ct_end) {
+                dma_queue_push(dma_queue, dma_make_ptr((uint8_t *)w_tile, src0_row + push_ct * tile_row_stride),
+                               aligned_tile_size, tile_size, tile_size, n_k_tiles_a);
+                push_ct++;
             }
         }
-
-        // Process the last row (if any)
-        if (src0_end_row != src0_end_row_x2) {
-            uint32_t       ir0 = src0_end_row_x2;
-            const uint32_t is0 = (ir0 - src0_start_row) % MM_SPAD_SRC0_NROWS;
-            dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size),
-                           src0_row_size_padded, src0_row_size, 1);
-            const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
-            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-            mmctx->vec_dot_1x1(ne00, &dst_row[ir0], ss0, src1_col);
-            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
-        }
     }
-
-    t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "matvec-id-%s %d/%d: %ux%ux%ux%u (%u:%u) * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u usec %u\n", mmctx->type,
-         ith, nth, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0],
-         src1->ne[1], src1->ne[2], src1->ne[3], src2->ne[0], src2->ne[1], src2->ne[2], src2->ne[3], dst->ne[0],
-         dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-// *** dynamic quant
-
-static inline void quantize_block_f32_q8_1x1(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
-    assert((unsigned long) x % 128 == 0);
-    assert((unsigned long) y_q % 128 == 0);
-
-    HVX_Vector * vx = (HVX_Vector *) x;
-    HVX_Vector zero = Q6_V_vzero();
-
-    // Use reduce max fp32 to find max(abs(e)) first
-    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
-    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
-    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
-    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
-
-    // Load and convert into QF32
-    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);  // 32 elements
-    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);  // 32 elements
-    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);  // 32 elements
-    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);  // 32 elements
-
-    // Convert to QF32
-    HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero);
-    HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero);
-    HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero);
-    HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero);
-
-    // Combine and convert to fp16
-    HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
-    HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
-
-    // Convert into fp16
-    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
-    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
-
-    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
-    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
-
-    // Divide input by the scale
-    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
-    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
-    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
-    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
-
-    // Convert to int8
-    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
-    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
-    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
-
-    *(HVX_Vector *) y_q = vx_i8;
-
-    // --- Sum calculation ---
-    const HVX_Vector ones = Q6_Vb_vsplat_R(1);
-    HVX_Vector v_sums = Q6_Vw_vrmpy_VbVb(vx_i8, ones); // sum every 4 consecutive elements
-    // Sum 8 elements:
-    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 4));
-    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 8));
-    v_sums = Q6_Vw_vadd_VwVw(v_sums, Q6_V_vror_VR(v_sums, 16));
-
-    // Copy to stack to extract sums and vmaxes
-    float vmax0[32] __attribute__((aligned(128)));
-    float vmax1[32] __attribute__((aligned(128)));
-    float vmax2[32] __attribute__((aligned(128)));
-    float vmax3[32] __attribute__((aligned(128)));
-    int32_t sums[32] __attribute__((aligned(128)));
-
-    hvx_vec_store_u(vmax0, 128, vmax0_sf);
-    hvx_vec_store_u(vmax1, 128, vmax1_sf);
-    hvx_vec_store_u(vmax2, 128, vmax2_sf);
-    hvx_vec_store_u(vmax3, 128, vmax3_sf);
-    hvx_vec_store_u(sums, 128, v_sums);
-
-    float d0 = vmax0[0] / 127.0f;
-    float d1 = vmax1[0] / 127.0f;
-    float d2 = vmax2[0] / 127.0f;
-    float d3 = vmax3[0] / 127.0f;
-
-    __fp16 * y_d_half = (__fp16 *) y_d;
-    y_d_half[0] = d0;
-    y_d_half[1] = (float) sums[0] * d0;
-    y_d_half[2] = d1;
-    y_d_half[3] = (float) sums[8] * d1;
-    y_d_half[4] = d2;
-    y_d_half[5] = (float) sums[16] * d2;
-    y_d_half[6] = d3;
-    y_d_half[7] = (float) sums[24] * d3;
-}
-
-static inline void quantize_block_f32_q8x1(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
-    assert((unsigned long) x % 128 == 0);
-    assert((unsigned long) y_q % 128 == 0);
-
-    HVX_Vector * vx = (HVX_Vector *) x;
-    HVX_Vector zero   = Q6_V_vzero();
-
-    // Use reduce max fp32 to find max(abs(e)) first
-    HVX_Vector vmax0_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[0]));
-    HVX_Vector vmax1_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[1]));
-    HVX_Vector vmax2_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[2]));
-    HVX_Vector vmax3_sf = hvx_vec_reduce_max_f32(hvx_vec_abs_f32(vx[3]));
-    // Load and convert into QF32
-    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);  // 32 elements
-    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);  // 32 elements
-    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);  // 32 elements
-    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);  // 32 elements
-
-    // Convert to QF32
-    HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero); // replicated over all lanes
-    HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero); // replicated over all lanes
-    HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero); // replicated over all lanes
-    HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero); // replicated over all lanes
-
-    // Combine and convert to fp16
-    HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
-    HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
-
-    // Convert into fp16
-    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
-    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
-
-    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
-    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
-
-    hvx_vec_store_u(y_d + 0, 2, vd01_hf);
-    HVX_Vector rotated_vd_hf = Q6_V_vror_VR(vd01_hf, 64);
-    hvx_vec_store_u(y_d + 2, 2, rotated_vd_hf);
-
-    hvx_vec_store_u(y_d + 4, 2, vd23_hf);
-    rotated_vd_hf = Q6_V_vror_VR(vd23_hf, 64);
-    hvx_vec_store_u(y_d + 6, 2, rotated_vd_hf);
-
-    // Divide input by the scale
-    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
-    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
-    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
-    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
-
-    // Convert to int8
-    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
-    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
-    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
-
-    *(HVX_Vector *) y_q = vx_i8;
-}
-
-static inline void quantize_block_f32_q8x2(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
-    assert((unsigned long) x % 128 == 0);
-    assert((unsigned long) y_q % 128 == 0);
-
-    HVX_Vector * vx = (HVX_Vector *) x;
-
-    // Load and convert into QF32
-    HVX_Vector zero   = Q6_V_vzero();
-    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);  // 32 elements
-    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);  // 32 elements
-    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);  // 32 elements
-    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);  // 32 elements
-
-    // Convert into fp16
-    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
-    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
-
-    // Compute max and scale
-    HVX_Vector vmax01_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf)); // replicated over all lanes
-    HVX_Vector vmax23_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx23_hf)); // replicated over all lanes
-
-    HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd01_hf   = Q6_Vhf_equals_Vqf16(vd01_qf16);
-    HVX_Vector vd23_hf   = Q6_Vhf_equals_Vqf16(vd23_qf16);
-
-    hvx_vec_store_u(y_d + 0, 4, vd01_hf);
-    hvx_vec_store_u(y_d + 4, 4, vd23_hf);
-
-    // Divide input by the scale
-    HVX_Vector vd01_inv_hf = hvx_vec_inverse_f16(vd01_hf);
-    HVX_Vector vd23_inv_hf = hvx_vec_inverse_f16(vd23_hf);
-    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
-    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
-
-    // Convert to int8
-    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
-    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
-    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
-
-    *(HVX_Vector *) y_q = vx_i8;
-}
-
-static inline void quantize_block_f32_q8x4(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
-    assert((unsigned long) x % 128 == 0);
-    assert((unsigned long) y_q % 128 == 0);
-
-    HVX_Vector * vx = (HVX_Vector *) x;
-
-    // Load and convert into QF32
-    HVX_Vector zero   = Q6_V_vzero();
-    HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero);  // 32 elements
-    HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero);  // 32 elements
-    HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero);  // 32 elements
-    HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero);  // 32 elements
-
-    // Convert into fp16
-    HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
-    HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
-
-    // Compute max and scale
-    HVX_Vector vmax_hf = hvx_vec_reduce_max_f16(hvx_vec_abs_f16(vx01_hf));
-    vmax_hf            = hvx_vec_reduce_max2_f16(hvx_vec_abs_f16(vx23_hf), vmax_hf); // replicated over all lanes
-
-    HVX_Vector vd_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax_hf, Q6_Vh_vsplat_R(0x2008));  // 1.0 / 127.0
-    HVX_Vector vd_hf   = Q6_Vhf_equals_Vqf16(vd_qf16);
-
-    *(HVX_UVector *) y_d = vd_hf;
-
-    // Divide input by the scale
-    HVX_Vector vd_inv_hf = hvx_vec_inverse_f16(vd_hf);
-    vx01_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd_inv_hf));
-    vx23_hf              = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd_inv_hf));
-
-    // Convert to int8
-    HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
-    HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
-    HVX_Vector vx_i8    = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
-
-    *(HVX_Vector *) y_q = vx_i8;
-}
-
-// Overrides input x
-static void quantize_row_f32_q8x4x2(float * restrict x, uint8_t * restrict y, uint32_t k) {
-    assert(k % 32 == 0);
-    const uint32_t qk = QK_Q8_0x4x2;
-    const uint32_t nb = (k + qk - 1) / qk;
-
-    const uint32_t qrow_size = k;              // int8
-
-    const uint32_t dblk_size = 8 * 2;          // 8x __fp16
-    const uint32_t qblk_size = QK_Q8_0x4x2;    // int8
-
-    uint8_t * restrict y_q = (y + 0);          // quants first
-    uint8_t * restrict y_d = (y + qrow_size);  // then scales
-
-    // Temp scales override input since we're working off of the aligned temp buffer in VTCM
-    uint8_t * restrict t_d = (uint8_t *) x;
-
-    for (uint32_t i = 0; i < nb; i++) {
-#if FP32_QUANTIZE_GROUP_SIZE == 32
-        quantize_block_f32_q8x1(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x1(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
-#elif FP32_QUANTIZE_GROUP_SIZE == 64
-        quantize_block_f32_q8x2(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x2(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
-#elif FP32_QUANTIZE_GROUP_SIZE == 128
-        quantize_block_f32_q8x4(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8x4(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
-#else
-#error "FP32_QUANTIZE_GROUP_SIZE must be 32, 64, or 128"
-#endif
-    }
-
-    // now copy the scales into final location
-    hvx_copy_f16_ua(y_d, t_d, nb * 8);
-}
-
-static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data) {
-    struct htp_matmul_context * mmctx = data;
-    struct htp_ops_context * octx = mmctx->octx;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    const struct htp_tensor * src = octx->src[1];
-    uint8_t * restrict dst = octx->src1_spad.data;
-    struct htp_spad * spad = &octx->src0_spad;
-    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
-
-    uint64_t t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t ne0 = src->ne[0];
-    const uint32_t ne1 = src->ne[1];
-    const uint32_t ne2 = src->ne[2];
-    const uint32_t ne3 = src->ne[3];
-
-    const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
-
-    const uint32_t ir_first = nrows_per_thread * ith;                   // first row
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-    const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
-
-    const size_t src_row_size = src->nb[1];
-    const size_t dst_row_size = q8x4x2_row_size(ne0);
-
-    uint8_t * restrict src_data = (uint8_t *) src->data + (src_row_size * ir_first);
-    uint8_t * restrict dst_data = (uint8_t *) dst + (dst_row_size * ir_first);
-    uint8_t * restrict tmp_data = (uint8_t *) spad->data + (spad->size_per_thread * ith);
-
-    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0x4x2 * sizeof(float));
-    memset(tmp_data, 0, src_row_size_padded);  // zero-out temp row data for padding
-
-    for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
-        hvx_copy_f32_aa(tmp_data, src_data, ne0);
-
-        // FARF(HIGH, "quantize-q8x4-row: %u\n", i);
-        quantize_row_f32_q8x4x2((float *) tmp_data, dst_data, ne0);
-        dst_data += dst_row_size;
-        src_data += src_row_size;
-    }
-
-    uint64_t t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "quantize-f32-q8x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
-         ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-}
-
-static void quantize_row_f32_q8_1x4x2(float * restrict x, uint8_t * restrict y, uint32_t k) {
-    assert(k % 32 == 0);
-    const uint32_t qk = QK_Q8_0x4x2;
-    const uint32_t nb = (k + qk - 1) / qk;
-
-    const uint32_t qrow_size = k;              // int8
-
-    const uint32_t dblk_size = 8 * 4;          // 8x (d, s) __fp16 = 32 bytes
-    const uint32_t qblk_size = QK_Q8_0x4x2;    // int8
-
-    uint8_t * restrict y_q = (y + 0);          // quants first
-    uint8_t * restrict y_d = (y + qrow_size);  // then scales/sums
-
-    // Temp scales override input since we're working off of the aligned temp buffer in VTCM
-    uint8_t * restrict t_d = (uint8_t *) x;
-
-    for (uint32_t i = 0; i < nb; i++) {
-        quantize_block_f32_q8_1x1(x + (i*2 + 0) * qk/2, y_q + (i*2 + 0) * qblk_size/2, t_d + (i*2 + 0) * dblk_size/2);
-        quantize_block_f32_q8_1x1(x + (i*2 + 1) * qk/2, y_q + (i*2 + 1) * qblk_size/2, t_d + (i*2 + 1) * dblk_size/2);
-    }
-
-    // now copy the scales/sums into final location
-    hvx_copy_f16_ua(y_d, t_d, nb * 16);
-}
-
-static void quantize_f32_q8_1x4x2(unsigned int nth, unsigned int ith, void * data) {
-    struct htp_matmul_context * mmctx = data;
-    struct htp_ops_context * octx = mmctx->octx;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    const struct htp_tensor * src = octx->src[1];
-    uint8_t * restrict dst = octx->src1_spad.data;
-    struct htp_spad * spad = &octx->src0_spad;
-    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
-
-    uint64_t t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t ne0 = src->ne[0];
-    const uint32_t ne1 = src->ne[1];
-    const uint32_t ne2 = src->ne[2];
-    const uint32_t ne3 = src->ne[3];
-
-    const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
-
-    const uint32_t ir_first = nrows_per_thread * ith;                   // first row
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-    const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
-
-    const size_t src_row_size = src->nb[1];
-    const size_t dst_row_size = q8_1x4x2_row_size(ne0);
-
-    uint8_t * restrict src_data = (uint8_t *) src->data + (src_row_size * ir_first);
-    uint8_t * restrict dst_data = (uint8_t *) dst + (dst_row_size * ir_first);
-    uint8_t * restrict tmp_data = (uint8_t *) spad->data + (spad->size_per_thread * ith);
-
-    const size_t src_row_size_padded = hex_round_up(src_row_size, QK_Q8_0x4x2 * sizeof(float));
-    memset(tmp_data, 0, src_row_size_padded);  // zero-out temp row data for padding
-
-    for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_row_size, 2);
-        hvx_copy_f32_aa(tmp_data, src_data, ne0);
-
-        quantize_row_f32_q8_1x4x2((float *) tmp_data, dst_data, ne0);
-        dst_data += dst_row_size;
-        src_data += src_row_size;
-    }
-
-    uint64_t t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "quantize-f32-q8_1x4: %u/%u : n-rows %u (%u:%u) row-size %u -> %u usec %u\n", ith, nth, nrows, ir_first,
-         ir_last, src_row_size, dst_row_size, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-}
-
-static void quantize_f32_f32(unsigned int nth, unsigned int ith, void * data) {
-    struct htp_matmul_context * mmctx = data;
-    struct htp_ops_context * octx = mmctx->octx;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    const struct htp_tensor * src = octx->src[1];
-    uint8_t * restrict dst = octx->src1_spad.data;
-    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
-    uint32_t dst_stride = octx->src1_spad.stride;
-
-    uint64_t t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t ne0 = src->ne[0];
-    const uint32_t ne1 = src->ne[1];
-    const uint32_t ne2 = src->ne[2];
-    const uint32_t ne3 = src->ne[3];
-
-    const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
-
-    const uint32_t ir_first = nrows_per_thread * ith;                   // first row
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-    const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
-
-    const size_t src_row_size = ne0 * sizeof(float);
-    const size_t src_stride   = src->nb[1];
-
-    uint8_t * restrict src_data = (uint8_t *) src->data + (src_stride * ir_first);
-    uint8_t * restrict dst_data = (uint8_t *) dst       + (dst_stride * ir_first);
-
-    for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_stride, 2);
-        hvx_copy_f32_au(dst_data, src_data, ne0);
-
-        dst_data += dst_stride;
-        src_data += src_stride;
-    }
-
-    uint64_t t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "quantize-f32-f32: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
-        ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-}
-
-static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
-    struct htp_matmul_context * mmctx = data;
-    struct htp_ops_context * octx = mmctx->octx;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    const struct htp_tensor * src = octx->src[1];
-    uint8_t * restrict dst = octx->src1_spad.data;
-    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
-    uint32_t dst_stride = octx->src1_spad.stride;
-
-    uint64_t t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t ne0 = src->ne[0];
-    const uint32_t ne1 = src->ne[1];
-    const uint32_t ne2 = src->ne[2];
-    const uint32_t ne3 = src->ne[3];
-
-    const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
-
-    const uint32_t ir_first = nrows_per_thread * ith;                   // first row
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-    const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
-
-    const size_t src_row_size = ne0 * sizeof(float);
-    const size_t src_stride   = src->nb[1];
-
-    uint8_t * restrict src_data = (uint8_t *) src->data + (src_stride * ir_first);
-    uint8_t * restrict dst_data = (uint8_t *) dst       + (dst_stride * ir_first);
-
-    for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_stride, 2);
-        hvx_copy_f16_f32_au(dst_data, src_data, ne0);
-
-        dst_data += dst_stride;
-        src_data += src_stride;
-    }
-
-    uint64_t t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "quantize-f32-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
-        ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-}
-
-// TODO just a plain copy that should be done via the DMA during the Op setup
-static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
-    struct htp_matmul_context * mmctx = data;
-    struct htp_ops_context * octx = mmctx->octx;
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-
-    const struct htp_tensor * src = octx->src[1];
-    uint8_t * restrict dst = octx->src1_spad.data;
-    uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
-    uint32_t dst_stride = octx->src1_spad.stride;
-
-    uint64_t t1 = HAP_perf_get_qtimer_count();
-
-    const uint32_t ne0 = src->ne[0];
-    const uint32_t ne1 = src->ne[1];
-    const uint32_t ne2 = src->ne[2];
-    const uint32_t ne3 = src->ne[3];
-
-    const uint32_t nrows = ne1 * ne2 * ne3;                             // total n_rows
-
-    const uint32_t ir_first = nrows_per_thread * ith;                   // first row
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-    const uint32_t ir_last  = MIN(ir_first + nrows_per_thread, nrows);  // last row
-
-    const size_t src_row_size = ne0 * sizeof(float);
-    const size_t src_stride   = src->nb[1];
-
-    uint8_t * restrict src_data = (uint8_t *) src->data + (src_stride * ir_first);
-    uint8_t * restrict dst_data = (uint8_t *) dst       + (dst_stride * ir_first);
-
-    for (uint32_t i = ir_first; i < ir_last; ++i) {
-        hex_l2fetch(src_data, src_row_size, src_stride, 2);
-        hvx_copy_f16_au(dst_data, src_data, ne0);
-
-        dst_data += dst_stride;
-        src_data += src_stride;
-    }
-
-    uint64_t t2 = HAP_perf_get_qtimer_count();
-
-    FARF(HIGH, "quantize-f16-f16: %u/%u : n-rows %u (%u:%u) row-size %u (%u) -> %u usec %u\n", ith, nth, nrows, ir_first,
-        ir_last, src_row_size, src_stride, dst_stride, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);
-}
-
-
-static inline bool htp_is_permuted(const struct htp_tensor * t) {
-    return t->nb[0] > t->nb[1] || t->nb[1] > t->nb[2] || t->nb[2] > t->nb[3];
-}
-
-static int htp_mminit_vec_dot(struct htp_matmul_context * mmctx, enum htp_data_type type) {
+static int hvx_mm_init_vec_dot(struct htp_mm_context * mmctx, enum htp_data_type type) {
     switch (type) {
         case HTP_TYPE_Q4_0:
-            mmctx->type        = "q4x4x2-f32";
-            mmctx->vec_dot_1x1 = vec_dot_q4x4x2_q8x4x2_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_q4x4x2_q8x4x2_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_q4x4x2_q8x4x2_2x2;
-            mmctx->vec_dot_4x1 = vec_dot_q4x4x2_q8x4x2_4x1;
+            mmctx->type         = "q4_0_tiled-f32";
+            mmctx->vec_dot_32x1 = tiled_vec_dot_q4_0_32x1;
             return 0;
         case HTP_TYPE_Q4_1:
-            mmctx->type        = "q4_1x4x2-f32";
-            mmctx->vec_dot_1x1 = vec_dot_q4_1x4x2_q8x4x2_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_q4_1x4x2_q8x4x2_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_q4_1x4x2_q8x4x2_2x2;
-            mmctx->vec_dot_4x1 = vec_dot_q4_1x4x2_q8x4x2_4x1;
+            mmctx->type         = "q4_1_tiled-f32";
+            mmctx->vec_dot_32x1 = tiled_vec_dot_q4_1_32x1;
             return 0;
         case HTP_TYPE_Q8_0:
-            mmctx->type        = "q8x4x2-f32";
-            mmctx->vec_dot_1x1 = vec_dot_q8x4x2_q8x4x2_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_q8x4x2_q8x4x2_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_q8x4x2_q8x4x2_2x2;
-            mmctx->vec_dot_4x1 = vec_dot_q8x4x2_q8x4x2_4x1;
+            mmctx->type         = "q8_0_tiled-f32";
+            mmctx->vec_dot_32x1 = tiled_vec_dot_q8_0_32x1;
             return 0;
         case HTP_TYPE_IQ4_NL:
-            mmctx->type        = "iq4nlx4x2-f32";
-            mmctx->vec_dot_1x1 = vec_dot_iq4nlx4x2_q8x4x2_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_iq4nlx4x2_q8x4x2_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_iq4nlx4x2_q8x4x2_2x2;
-            mmctx->vec_dot_4x1 = vec_dot_iq4nlx4x2_q8x4x2_4x1;
+            mmctx->type         = "iq4nl_tiled-f32";
+            mmctx->vec_dot_32x1 = tiled_vec_dot_iq4nl_32x1;
             return 0;
         case HTP_TYPE_MXFP4:
-            mmctx->type        = "mxfp4x4x2-f32";
-            mmctx->vec_dot_1x1 = vec_dot_mxfp4x4x2_q8x4x2_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_mxfp4x4x2_q8x4x2_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_mxfp4x4x2_q8x4x2_2x2;
-            mmctx->vec_dot_4x1 = vec_dot_mxfp4x4x2_q8x4x2_4x1;
+            mmctx->type         = "mxfp4_tiled-f32";
+            mmctx->vec_dot_32x1 = tiled_vec_dot_mxfp4_32x1;
             return 0;
         default:
             return -1;
     }
 }
 
-static void htp_mminit_spad(struct htp_ops_context * octx,
-                                 size_t dst_row_size,
-                                 size_t src0_row_size_padded,
-                                 size_t src1_row_size,
-                                 uint32_t src1_nrows,
-                                 size_t src2_spad_size_per_thread) {
-    octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-    octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-    octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-
-    if (src2_spad_size_per_thread > 0) {
-        octx->src2_spad.size_per_thread = src2_spad_size_per_thread;
-        octx->src2_spad.size            = octx->src2_spad.size_per_thread;
-    }
-
-    // src0 spad is also used in dynamic quantizer to store padded src1 rows
-    size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0x4x2 * sizeof(float));
-    if (octx->src0_spad.size_per_thread < src1_row_size_padded) {
-        octx->src0_spad.size_per_thread = src1_row_size_padded;
-    }
-
-    octx->src1_spad.size = octx->src1_spad.size_per_thread;
-    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-    octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-}
-
-static int op_matmul_hvx(struct htp_ops_context * octx) {
+static int hvx_mm_matmul(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
-    struct htp_matmul_context mmctx_struct = {0};
-    struct htp_matmul_context * mmctx = &mmctx_struct;
+    struct htp_mm_context mmctx_struct = {0};
+    struct htp_mm_context * mmctx = &mmctx_struct;
     mmctx->octx = octx;
 
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+
     const uint32_t src0_nrows = ne01 * ne02 * ne03;
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
 
+    bool is_repacked = (src0->type == HTP_TYPE_Q4_0 || src0->type == HTP_TYPE_Q4_1 ||
+                        src0->type == HTP_TYPE_Q8_0 || src0->type == HTP_TYPE_IQ4_NL ||
+                        src0->type == HTP_TYPE_MXFP4);
+
     // Compute src0_nrows_per_thread
     mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
-    mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+    if (is_repacked) {
+        mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32);
+    } else {
+        mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+    }
 
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
@@ -4527,178 +1626,213 @@ static int op_matmul_hvx(struct htp_ops_context * octx) {
     size_t       src1_row_size_padded;
 
     worker_callback_t quant_job_func;
-    worker_callback_t matmul_job_func = src1_nrows > 1 ? matmul_2d : matvec_2d;
+    worker_callback_t matmul_job_func;
+    uint32_t n_quant_jobs = 1;
+    if (src1_nrows > 1) {
+        if (is_repacked) {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_2d_repacked_q4_0;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_2d_repacked_q8_0;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_2d_repacked_iq4nl;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_2d_repacked_mxfp4;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        } else {
+            matmul_job_func = hvx_mm_2d;
+        }
+    } else {
+        if (is_repacked) {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mv_2d_repacked_q4_0;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mv_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mv_2d_repacked_q8_0;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mv_2d_repacked_iq4nl;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mv_2d_repacked_mxfp4;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        } else {
+            matmul_job_func = hvx_mv_2d;
+        }
+    }
 
     bool need_quant = true;
 
-    if (src0->type == HTP_TYPE_F16) {
-        // Try optimized f16-f16 path first (src1 in VTCM)
-        const size_t f16_src1_row_size  = hex_round_up(ne10 * 2, 128);
-        const size_t f16_src1_spad_size = hex_round_up(f16_src1_row_size * src1_nrows, 256);
-        const size_t f16_src0_spad_size = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
-        const size_t f16_dst_spad_size  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256) * octx->n_threads;
+    switch (kparams->kernel_type) {
+        case HTP_MM_KERNEL_HVX_F16_F16_VTCM:
+            quant_job_func         = (src1->type == HTP_TYPE_F32) ? quantize_f32_f16_flat : quantize_f16_f16_flat;
+            mmctx->type            = "f16-f16";
+            mmctx->vec_dot_1x1     = vec_dot_f16_f16_aa_1x1;
+            mmctx->vec_dot_2x1     = vec_dot_f16_f16_aa_2x1;
+            mmctx->vec_dot_2x2     = vec_dot_f16_f16_aa_2x2;
+            src1_row_size          = hex_round_up(ne10 * 2, 128);
+            break;
 
-        const size_t f16_total_size = f16_src1_spad_size + f16_src0_spad_size + f16_dst_spad_size;
+        case HTP_MM_KERNEL_HVX_F16_F32_DDR:
+            mmctx->type            = "f16-f32";
+            mmctx->vec_dot_1x1     = vec_dot_f16_f32_uu_1x1;
+            matmul_job_func        = hvx_mm_4d;
+            mmctx->mm_div_ne12_ne1 = kparams->div_ne12_ne1;
+            mmctx->mm_div_ne1      = kparams->div_ne1;
+            mmctx->mm_div_r2       = kparams->div_r2;
+            mmctx->mm_div_r3       = kparams->div_r3;
+            need_quant             = false;
+            quant_job_func         = NULL;
+            src1_row_size          = nb11;
+            break;
 
-        // Default matmul implementation does not support multi-batch src0 (N-vs-N broadcasting).
-        // It only supports 1-vs-N broadcasting (src0 is 2D) or standard 2D matmul.
-        const bool is_batched  = (ne02 > 1) || (ne03 > 1);
-        const bool is_permuted = htp_is_permuted(octx->src[0]) || htp_is_permuted(octx->src[1]);
+        case HTP_MM_KERNEL_HVX_F16_F16_DDR:
+            mmctx->type            = "f16-f16";
+            mmctx->vec_dot_1x1     = vec_dot_f16_f16_uu_1x1;
+            matmul_job_func        = hvx_mm_4d;
+            mmctx->mm_div_ne12_ne1 = kparams->div_ne12_ne1;
+            mmctx->mm_div_ne1      = kparams->div_ne1;
+            mmctx->mm_div_r2       = kparams->div_r2;
+            mmctx->mm_div_r3       = kparams->div_r3;
+            src1_row_size          = nb11;
+            need_quant             = false;
+            quant_job_func         = NULL;
+            break;
 
-        if (!is_batched && !is_permuted && f16_total_size <= octx->ctx->vtcm_size) {
-            // Optimized path
-            quant_job_func     = (src1->type == HTP_TYPE_F32) ? quantize_f32_f16 : quantize_f16_f16;
-            mmctx->type        = "f16-f16";
-            mmctx->vec_dot_1x1 = vec_dot_f16_f16_aa_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_f16_f16_aa_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_f16_f16_aa_2x2;
+        case HTP_MM_KERNEL_HVX_F32_F32_VTCM:
+            quant_job_func         = quantize_f32_f32_flat;
+            mmctx->type            = "f32-f32";
+            mmctx->vec_dot_1x1     = vec_dot_f32_f32_aa_1x1;
+            mmctx->vec_dot_2x1     = vec_dot_f32_f32_aa_2x1;
+            mmctx->vec_dot_2x2     = vec_dot_f32_f32_aa_2x2;
+            src1_row_size          = hex_round_up(ne10 * 4, 128);
+            break;
 
-            src1_row_size = f16_src1_row_size;  // row size post quantization
+        case HTP_MM_KERNEL_HVX_F32_F32_DDR:
+            quant_job_func         = NULL;
+            mmctx->type            = "f32-f32";
+            mmctx->vec_dot_1x1     = vec_dot_f32_f32_uu_1x1;
+            mmctx->mm_div_ne12_ne1 = kparams->div_ne12_ne1;
+            mmctx->mm_div_ne1      = kparams->div_ne1;
+            mmctx->mm_div_r2       = kparams->div_r2;
+            mmctx->mm_div_r3       = kparams->div_r3;
+            src1_row_size          = nb11;
+            need_quant             = false;
+            matmul_job_func        = hvx_mm_4d;
+            break;
 
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
+        case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
+            n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+            quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+            src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
 
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-        } else {
-            // Fallback to f16/f32 (DDR) if src1 doesn't fit in VTCM or broadcasting is required
-            quant_job_func = NULL;
-            if (src1->type == HTP_TYPE_F32) {
-                mmctx->type        = "f16-f32";
-                mmctx->vec_dot_1x1 = vec_dot_f16_f32_uu_1x1;
-                matmul_job_func    = matmul_4d;
+            if (src1_nrows > 1) {
+                switch (src0->type) {
+                    case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_2d_repacked_q4_0_flat;   break;
+                    case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_2d_repacked_q4_1_flat;   break;
+                    case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_2d_repacked_q8_0_flat;   break;
+                    case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_2d_repacked_iq4nl_flat;  break;
+                    case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_2d_repacked_mxfp4_flat;  break;
+                    default:              return HTP_STATUS_NO_SUPPORT;
+                }
             } else {
-                mmctx->type        = "f16-f16";
-                mmctx->vec_dot_1x1 = vec_dot_f16_f16_uu_1x1;
-                matmul_job_func    = matmul_4d;
+                switch (src0->type) {
+                    case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mv_2d_repacked_q4_0_flat;   break;
+                    case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mv_2d_repacked_q4_1_flat;   break;
+                    case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mv_2d_repacked_q8_0_flat;   break;
+                    case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mv_2d_repacked_iq4nl_flat;  break;
+                    case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mv_2d_repacked_mxfp4_flat;  break;
+                    default:              return HTP_STATUS_NO_SUPPORT;
+                }
+            }
+            break;
+        }
+
+        case HTP_MM_KERNEL_HVX_QUANT_BLOCK:
+        case HTP_MM_KERNEL_HVX_QUANT_ROW:
+        default:
+            if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) {
+                return HTP_STATUS_NO_SUPPORT;
             }
 
-            src1_row_size = nb11;  // original row size in DDR
+            const uint32_t qk = QK_Q8_0_TILED;
+            const uint32_t nb = (ne10 + qk - 1) / qk;
+            const uint32_t total_nb = src1_nrows * nb;
 
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
-
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-
-            // Init fastdiv for matmul_4d (supports broadcasting)
-            mmctx->mm_div_ne12_ne1 = init_fastdiv_values(src1->ne[2] * dst->ne[1]);
-            mmctx->mm_div_ne1      = init_fastdiv_values(dst->ne[1]);
-            mmctx->mm_div_r2       = init_fastdiv_values(src1->ne[2] / src0->ne[2]);
-            mmctx->mm_div_r3       = init_fastdiv_values(src1->ne[3] / src0->ne[3]);
-
-            need_quant = false;
-        }
-    } else if (src0->type == HTP_TYPE_F32) {
-        // Try optimized f32-f32 path first (src1 in VTCM)
-        const size_t f32_src1_row_size  = hex_round_up(ne10 * 4, 128);
-        const size_t f32_src1_spad_size = hex_round_up(f32_src1_row_size * src1_nrows, 256);
-        const size_t f32_src0_spad_size = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256) * octx->n_threads;
-        const size_t f32_dst_spad_size  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256) * octx->n_threads;
-
-        const size_t f32_total_size = f32_src1_spad_size + f32_src0_spad_size + f32_dst_spad_size;
-
-        const bool is_batched  = (ne02 > 1) || (ne03 > 1);
-        const bool is_permuted = htp_is_permuted(octx->src[0]) || htp_is_permuted(octx->src[1]);
-
-        if (!is_batched && !is_permuted && f32_total_size <= octx->ctx->vtcm_size) {
-            // Optimized path
-            quant_job_func     = quantize_f32_f32;
-            mmctx->type        = "f32-f32";
-            mmctx->vec_dot_1x1 = vec_dot_f32_f32_aa_1x1;
-            mmctx->vec_dot_2x1 = vec_dot_f32_f32_aa_2x1;
-            mmctx->vec_dot_2x2 = vec_dot_f32_f32_aa_2x2;
-
-            src1_row_size = f32_src1_row_size;
-
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size_padded, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(src1_row_size * src1_nrows, 256);
-
-            octx->src1_spad.size = octx->src1_spad.size_per_thread;
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-        } else {
-            // Fallback to DDR / broadcasting
-            quant_job_func = NULL;
-            mmctx->type        = "f32-f32";
-            mmctx->vec_dot_1x1 = vec_dot_f32_f32_uu_1x1;
-            matmul_job_func    = matmul_4d;
-
-            src1_row_size = nb11;
-
-            octx->dst_spad.size_per_thread  = hex_round_up(MM_SPAD_DST_NROWS * dst_row_size, 256);
-            octx->src0_spad.size_per_thread = hex_round_up(MM_SPAD_SRC0_NROWS * src0_row_size, 256);
-            octx->src1_spad.size_per_thread = hex_round_up(MM_SPAD_SRC1_NROWS * src1_row_size, 256);
-
-            octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-            octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
-            octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
-
-            // Init fastdiv for matmul_4d (supports broadcasting)
-            mmctx->mm_div_ne12_ne1 = init_fastdiv_values(src1->ne[2] * dst->ne[1]);
-            mmctx->mm_div_ne1      = init_fastdiv_values(dst->ne[1]);
-            mmctx->mm_div_r2       = init_fastdiv_values(src1->ne[2] / src0->ne[2]);
-            mmctx->mm_div_r3       = init_fastdiv_values(src1->ne[3] / src0->ne[3]);
-
-            need_quant = false;
-        }
-    } else {
-        if (htp_mminit_vec_dot(mmctx, src0->type) != 0) {
-            return HTP_STATUS_NO_SUPPORT;
-        }
-
-        if (src0->type == HTP_TYPE_Q4_1) {
-            quant_job_func = quantize_f32_q8_1x4x2;
-            src1_row_size  = q8_1x4x2_row_size(ne10);
-        } else {
-            quant_job_func = quantize_f32_q8x4x2;
-            src1_row_size  = q8x4x2_row_size(ne10);
-        }
-        htp_mminit_spad(octx, dst_row_size, src0_row_size_padded, src1_row_size, src1_nrows, 0);
+            if (src1_nrows < octx->n_threads) {
+                n_quant_jobs = MIN(total_nb, octx->n_threads);
+                quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+                for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
+                    uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
+                    uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+                    mmctx->quant_ib_first[ith] = ib_first;
+                    mmctx->quant_ib_last[ith]  = ib_last;
+                    mmctx->quant_r[ith]        = ib_first / nb;
+                    mmctx->quant_c[ith]        = ib_first % nb;
+                }
+            } else {
+                n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+                quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+            }
+            src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+            break;
     }
 
-    // VTCM scratchpads for all tensors
-    size_t spad_size = octx->src1_spad.size + octx->src0_spad.size + octx->dst_spad.size;
+    size_t src0_sz = 0, src1_sz = 0, dst_sz = 0;
+    if (kparams->vtcm_src0_size > 0 || kparams->vtcm_src1_size > 0 || kparams->vtcm_dst_size > 0) {
+        src0_sz = kparams->vtcm_src0_size;
+        src1_sz = kparams->vtcm_src1_size;
+        dst_sz  = kparams->vtcm_dst_size;
+    } else {
+        const uint32_t n_prefetch = kparams->n_prefetch;
+        assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+        htp_mm_hvx_get_vtcm_sizes(
+            kparams->kernel_type, src0->type, ne10, src1_nrows, octx->n_threads,
+            dst_row_size, src0_row_size, src1_row_size, n_prefetch,
+            &src0_sz, &src1_sz, &dst_sz
+        );
+    }
 
-    FARF(HIGH, "matmul-%s : src0-spad-size %u src1-spad-size %u dst-spad-size %u (%zu)\n", mmctx->type,
-         octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size, spad_size);
+    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_F16_F16_VTCM ||
+        kparams->kernel_type == HTP_MM_KERNEL_HVX_F32_F32_VTCM ||
+        kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW ||
+        kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_BLOCK) {
+        mmctx->vtcm_src1_size_per_thread = src1_sz;
+    } else {
+        mmctx->vtcm_src1_size_per_thread = src1_sz / octx->n_threads;
+    }
+
+    mmctx->vtcm_src0_size_per_thread = src0_sz / octx->n_threads;
+    mmctx->vtcm_dst_size_per_thread  = dst_sz  / octx->n_threads;
+
+    size_t vtcm_size = kparams->vtcm_size > 0 ? (size_t)kparams->vtcm_size : (src1_sz + src0_sz + dst_sz);
+
+    FARF(HIGH, "matmul-%s : src0-vtcm-size %zu src1-vtcm-size %zu dst-vtcm-size %zu (%zu)\n", mmctx->type,
+         src0_sz, src1_sz, dst_sz, vtcm_size);
 
     FARF(HIGH, "matmul-%s : %ux%ux%ux%u * %ux%ux%ux%u-> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", mmctx->type, src0->ne[0],
          src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0],
          dst->ne[1], dst->ne[2], dst->ne[3], src0->data, src1->data, dst->data);
 
-    // Make sure the reserved vtcm size is sufficient
-    if (octx->ctx->vtcm_size < spad_size) {
+    if (octx->ctx->vtcm_size < vtcm_size) {
         FARF(ERROR, "matmul-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type,
-             octx->ctx->vtcm_size, spad_size);
+             octx->ctx->vtcm_size, vtcm_size);
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    // Place src1 spad first. We use it for dyn.quant and may reuse between ops
-    octx->src1_spad.data = octx->ctx->vtcm_base;
-    octx->src0_spad.data = octx->src1_spad.data + octx->src1_spad.size;
-    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+    uint8_t * vtcm_ptr = (uint8_t *) octx->ctx->vtcm_base;
+    mmctx->vtcm_src1 = vtcm_seq_alloc(&vtcm_ptr, src1_sz);
+    mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
+    mmctx->vtcm_dst  = vtcm_seq_alloc(&vtcm_ptr, dst_sz);
 
-    octx->src1_spad.src  = (src1 == octx->src1_spad.src) ? src1 : NULL;
+    octx->src1_spad.src  = NULL;
     octx->src0_spad.src  = NULL;
     octx->dst_spad.src   = NULL;
 
-    octx->src0_spad.stride = src0_row_size_padded;
-    octx->src1_spad.stride = src1_row_size;
+    mmctx->vtcm_src0_stride = src0_row_size_padded;
+    mmctx->vtcm_src1_stride = src1_row_size;
 
     if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
         return HTP_STATUS_OK;
 
-    if (need_quant && !octx->src1_spad.src) {
-        const uint32_t n_quant_jobs  = MIN(src1_nrows, octx->n_threads);
+    if (need_quant) {
         mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
         worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
-        octx->src1_spad.src = src1;
     }
 
     const uint32_t n_matmul_jobs = octx->n_threads;
@@ -4707,72 +1841,1209 @@ static int op_matmul_hvx(struct htp_ops_context * octx) {
     return HTP_STATUS_OK;
 }
 
-int op_matmul(struct htp_ops_context * octx) {
+static void hvx_mm_qkv_2d(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_mm_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+
+    const struct htp_tensor * restrict src0 = octx->src[0]; // Wk
+    const struct htp_tensor * restrict src1 = octx->src[1]; // x
+    const struct htp_tensor * restrict src2 = octx->src[2]; // Wv
+    const struct htp_tensor * restrict src3 = octx->src[3]; // Wq
+    const struct htp_tensor * restrict dst_k = octx->dsts[0];
+    const struct htp_tensor * restrict dst_v = octx->dsts[1];
+    const struct htp_tensor * restrict dst_q = octx->dsts[2];
+
+    const uint32_t ne00 = src0->ne[0];
+    const uint32_t ne01 = src0->ne[1];
+    const uint32_t ne02 = src0->ne[2];
+    const uint32_t ne03 = src0->ne[3];
+
+    const uint32_t ne11 = src1->ne[1];
+    const uint32_t ne12 = src1->ne[2];
+    const uint32_t ne13 = src1->ne[3];
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;
+    const uint32_t src1_nrows = ne11 * ne12 * ne13;
+
+    const uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;
+    const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+    const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
+    }
+
+    const size_t dst_k_row_size  = dst_k->nb[1]; // K and V share output width
+    const size_t dst_q_row_size  = dst_q->nb[1]; // Q may be wider (GQA)
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src2_row_size = src2->nb[1];
+    const size_t src3_row_size = src3->nb[1];
+
+    const size_t src0_stride = mmctx->vtcm_src0_stride;
+    const size_t src2_stride = mmctx->vtcm_src2_stride;
+    const size_t src3_stride = mmctx->vtcm_src3_stride;
+    const size_t src1_stride = mmctx->vtcm_src1_stride;
+
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith;
+    uint8_t * restrict vtcm_src3_ptr = mmctx->vtcm_src3 + mmctx->vtcm_src3_size_per_thread * ith;
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;
+
+    dma_queue * dma_queue = octx->ctx->dma[ith];
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+    const uint32_t prefetch_mask = n_prefetch - 1;
+
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;
+    const uint8_t * restrict src2_row = (const uint8_t *) src2->data;
+    const uint8_t * restrict src3_row = (const uint8_t *) src3->data;
+
+    // Prefill spad with src0, src2, src3 rows
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const int is0 = (ir0 - src0_start_row);
+        if (is0 >= (int)n_prefetch) {
+            break;
+        }
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 2);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                       src2_stride, src2_row_size, src2_row_size, 2);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + is0 * src3_stride, src3_row + ir0 * src3_row_size),
+                       src3_stride, src3_row_size, src3_row_size, 2);
+    }
+
+    // Process rows
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss2 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss3 = dma_queue_pop(dma_queue).dst;
+
+        // Process src1 columns in pairs (2×2 tiling)
+        uint32_t ir1 = 0;
+        for (; ir1 + 1 < src1_nrows; ir1 += 2) {
+            const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);
+            const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);
+
+            float * restrict dst_row0_k = (float *) (dst_k->data + ((ir1+0) * dst_k_row_size));
+            float * restrict dst_row1_k = (float *) (dst_k->data + ((ir1+1) * dst_k_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0_k[ir0], &dst_row1_k[ir0], ss0, ss0 + src0_stride, src1_col0, src1_col1);
+
+            float * restrict dst_row0_v = (float *) (dst_v->data + ((ir1+0) * dst_k_row_size));
+            float * restrict dst_row1_v = (float *) (dst_v->data + ((ir1+1) * dst_k_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0_v[ir0], &dst_row1_v[ir0], ss2, ss2 + src2_stride, src1_col0, src1_col1);
+
+            float * restrict dst_row0_q = (float *) (dst_q->data + ((ir1+0) * dst_q_row_size));
+            float * restrict dst_row1_q = (float *) (dst_q->data + ((ir1+1) * dst_q_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0_q[ir0], &dst_row1_q[ir0], ss3, ss3 + src3_stride, src1_col0, src1_col1);
+        }
+
+        // Handle remaining src1 rows (fallback to 2×1)
+        for (; ir1 < src1_nrows; ++ir1) {
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
+
+            float * restrict dst_row_k          = (float *) (dst_k->data + (ir1 * dst_k_row_size));
+            mmctx->vec_dot_2x1(ne00, &dst_row_k[ir0], ss0, ss0 + src0_stride, src1_col);
+
+            float * restrict dst_row_v          = (float *) (dst_v->data + (ir1 * dst_k_row_size));
+            mmctx->vec_dot_2x1(ne00, &dst_row_v[ir0], ss2, ss2 + src2_stride, src1_col);
+
+            float * restrict dst_row_q          = (float *) (dst_q->data + (ir1 * dst_q_row_size));
+            mmctx->vec_dot_2x1(ne00, &dst_row_q[ir0], ss3, ss3 + src3_stride, src1_col);
+        }
+
+        // Prefetch next (n + vtcm_nrows) rows
+        const int pr0 = (ir0 + n_prefetch);
+        const int is0 = (pr0 - src0_start_row) & prefetch_mask;
+        if (pr0 < src0_end_row_x2) {
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + pr0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + pr0 * src2_row_size),
+                           src2_stride, src2_row_size, src2_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + is0 * src3_stride, src3_row + pr0 * src3_row_size),
+                           src3_stride, src3_row_size, src3_row_size, 2);
+        }
+    }
+
+    // Process last row (if any)
+    if (src0_end_row != src0_end_row_x2) {
+        uint32_t  ir0 = src0_end_row_x2;
+        const int is0 = (ir0 - src0_start_row) & prefetch_mask;
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 1);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                       src2_stride, src2_row_size, src2_row_size, 1);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + is0 * src3_stride, src3_row + ir0 * src3_row_size),
+                       src3_stride, src3_row_size, src3_row_size, 1);
+
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss2 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss3 = dma_queue_pop(dma_queue).dst;
+
+        for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) {
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
+
+            float * restrict dst_row_k          = (float *) (dst_k->data + (ir1 * dst_k_row_size));
+            mmctx->vec_dot_1x1(ne00, &dst_row_k[ir0], ss0, src1_col);
+
+            float * restrict dst_row_v          = (float *) (dst_v->data + (ir1 * dst_k_row_size));
+            mmctx->vec_dot_1x1(ne00, &dst_row_v[ir0], ss2, src1_col);
+
+            float * restrict dst_row_q          = (float *) (dst_q->data + (ir1 * dst_q_row_size));
+            mmctx->vec_dot_1x1(ne00, &dst_row_q[ir0], ss3, src1_col);
+        }
+    }
+}
+
+static void hvx_mm_ffn_2d(unsigned int nth, unsigned int ith, void * data) {
+    struct htp_mm_context * mmctx = data;
+    struct htp_ops_context * octx = mmctx->octx;
+
+    const struct htp_tensor * restrict src0 = octx->src[0]; // Wgate
+    const struct htp_tensor * restrict src1 = octx->src[1]; // y
+    const struct htp_tensor * restrict src2 = octx->src[2]; // Wup
+    const struct htp_tensor * restrict dst_gate = octx->dsts[0];
+    const struct htp_tensor * restrict dst_up = octx->dsts[1];
+
+    const uint32_t ne00 = src0->ne[0];
+    const uint32_t ne01 = src0->ne[1];
+    const uint32_t ne02 = src0->ne[2];
+    const uint32_t ne03 = src0->ne[3];
+
+    const uint32_t ne11 = src1->ne[1];
+    const uint32_t ne12 = src1->ne[2];
+    const uint32_t ne13 = src1->ne[3];
+
+    const uint32_t src0_nrows = ne01 * ne02 * ne03;
+    const uint32_t src1_nrows = ne11 * ne12 * ne13;
+
+    const uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;
+    const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
+    const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
+    const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
+    }
+
+    const size_t dst_row_size  = dst_gate->nb[1];
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src2_row_size = src2->nb[1];
+
+    const size_t src0_stride = mmctx->vtcm_src0_stride;
+    const size_t src2_stride = mmctx->vtcm_src2_stride;
+    const size_t src1_stride = mmctx->vtcm_src1_stride;
+
+    uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;
+    uint8_t * restrict vtcm_src2_ptr = mmctx->vtcm_src2 + mmctx->vtcm_src2_size_per_thread * ith;
+    uint8_t * restrict src1_data = mmctx->vtcm_src1;
+
+    dma_queue * dma_queue = octx->ctx->dma[ith];
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const uint32_t n_prefetch = kparams->n_prefetch;
+    assert(n_prefetch >= 2 && n_prefetch <= HTP_MM_MAX_PREFETCH && (n_prefetch & (n_prefetch - 1)) == 0);
+    const uint32_t prefetch_mask = n_prefetch - 1;
+
+    const uint8_t * restrict src0_row = (const uint8_t *) src0->data;
+    const uint8_t * restrict src2_row = (const uint8_t *) src2->data;
+
+    // Prefill spad with src0, src2 rows
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const int is0 = (ir0 - src0_start_row);
+        if (is0 >= (int)n_prefetch) {
+            break;
+        }
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 2);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                       src2_stride, src2_row_size, src2_row_size, 2);
+    }
+
+    // Process rows
+    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss2 = dma_queue_pop(dma_queue).dst;
+
+        // Process src1 columns in pairs (2×2 tiling)
+        uint32_t ir1 = 0;
+        for (; ir1 + 1 < src1_nrows; ir1 += 2) {
+            const uint8_t * restrict src1_col0 = (const uint8_t *) (src1_data + (ir1+0) * src1_stride);
+            const uint8_t * restrict src1_col1 = (const uint8_t *) (src1_data + (ir1+1) * src1_stride);
+
+            float * restrict dst_row0_gate = (float *) (dst_gate->data + ((ir1+0) * dst_row_size));
+            float * restrict dst_row1_gate = (float *) (dst_gate->data + ((ir1+1) * dst_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0_gate[ir0], &dst_row1_gate[ir0], ss0, ss0 + src0_stride, src1_col0, src1_col1);
+
+            float * restrict dst_row0_up   = (float *) (dst_up->data + ((ir1+0) * dst_row_size));
+            float * restrict dst_row1_up   = (float *) (dst_up->data + ((ir1+1) * dst_row_size));
+            mmctx->vec_dot_2x2(ne00, &dst_row0_up[ir0], &dst_row1_up[ir0], ss2, ss2 + src2_stride, src1_col0, src1_col1);
+        }
+
+        // Handle remaining src1 rows (fallback to 2×1)
+        for (; ir1 < src1_nrows; ++ir1) {
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
+
+            float * restrict dst_row_gate     = (float *) (dst_gate->data + (ir1 * dst_row_size));
+            mmctx->vec_dot_2x1(ne00, &dst_row_gate[ir0], ss0, ss0 + src0_stride, src1_col);
+
+            float * restrict dst_row_up       = (float *) (dst_up->data + (ir1 * dst_row_size));
+            mmctx->vec_dot_2x1(ne00, &dst_row_up[ir0], ss2, ss2 + src2_stride, src1_col);
+        }
+
+        // Prefetch next rows
+        const int pr0 = (ir0 + n_prefetch);
+        const int is0 = (pr0 - src0_start_row) & prefetch_mask;
+        if (pr0 < src0_end_row_x2) {
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + pr0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + pr0 * src2_row_size),
+                           src2_stride, src2_row_size, src2_row_size, 2);
+        }
+    }
+
+    // Process last row (if any)
+    if (src0_end_row != src0_end_row_x2) {
+        uint32_t  ir0 = src0_end_row_x2;
+        const int is0 = (ir0 - src0_start_row) & prefetch_mask;
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                       src0_stride, src0_row_size, src0_row_size, 1);
+        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                       src2_stride, src2_row_size, src2_row_size, 1);
+
+        const uint8_t * ss0 = dma_queue_pop(dma_queue).dst;
+        const uint8_t * ss2 = dma_queue_pop(dma_queue).dst;
+
+        for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) {
+            const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + ir1 * src1_stride);
+
+            float * restrict dst_row_gate      = (float *) (dst_gate->data + (ir1 * dst_row_size));
+            mmctx->vec_dot_1x1(ne00, &dst_row_gate[ir0], ss0, src1_col);
+
+            float * restrict dst_row_up        = (float *) (dst_up->data + (ir1 * dst_row_size));
+            mmctx->vec_dot_1x1(ne00, &dst_row_up[ir0], ss2, src1_col);
+        }
+    }
+}
+
+#define DEQUANTIZE_WORKER_LOOP_IMPL(SUFFIX)                                                     \
+static void dequantize_tiled_worker_loop_##SUFFIX(unsigned int n, unsigned int i, void *data) { \
+    tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data;                         \
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;                    \
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                  \
+    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {      \
+        int start = task_id * state->n_tiles_per_task;                                          \
+        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);              \
+        dequantize_tiled_weight_to_fp16_task_##SUFFIX(state, start, end);                       \
+    }                                                                                           \
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);                                   \
+}
+
+DEQUANTIZE_WORKER_LOOP_IMPL(q4_0)
+DEQUANTIZE_WORKER_LOOP_IMPL(q4_1)
+DEQUANTIZE_WORKER_LOOP_IMPL(iq4_nl)
+DEQUANTIZE_WORKER_LOOP_IMPL(mxfp4)
+DEQUANTIZE_WORKER_LOOP_IMPL(q8_0)
+
+static void convert_f16_worker_loop(unsigned int n, unsigned int i, void *data) {
+    tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data;
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
+    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
+        int start = task_id * state->n_tiles_per_task;
+        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
+        convert_f16_weight_to_fp16_tiles_task(state, start, end);
+    }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_W_DEQUANT, i);
+}
+
+static void quantize_f32_worker_loop(unsigned int n, unsigned int i, void *data) {
+    tiled_dequantize_state_t *state = (tiled_dequantize_state_t *)data;
+
+    struct htp_thread_trace * tr = state->traces ? &state->traces[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_QUANT, i);
+
+    for (unsigned int task_id = i; task_id < (unsigned int)state->n_tasks; task_id += n) {
+        int start = task_id * state->n_tiles_per_task;
+        int end   = hex_smin(start + state->n_tiles_per_task, state->n_tot_tiles);
+        quantize_f32_weight_to_fp16_tiles_task(state, start, end);
+    }
+
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, i);
+}
+
+static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
+    output_transfer_task_state_t *st = (output_transfer_task_state_t *) data;
+
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+
+    int start_chunk_idx = i * st->n_chunks_per_task;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, start_chunk_idx);
+
+    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
+        int    chunk_idx  = task_id * st->n_chunks_per_task;
+        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
+
+        float        *dst      = st->dst      + chunk_idx * st->dst_stride;
+        transfer_output_chunk_fp16_to_fp32(dst, st->vtcm_src, chunk_idx, chunk_size, st->n_cols, st->dst_stride, st->dst_cols);
+    }
+
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, start_chunk_idx);
+}
+
+static void transfer_activation_chunk_worker_fn(unsigned int n, unsigned int i, void *data) {
+    activation_transfer_task_state_t *st = (activation_transfer_task_state_t *) data;
+
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+
+    int start_chunk_idx = i * st->n_chunks_per_task;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, start_chunk_idx);
+
+    for (unsigned int task_id = i; task_id < (unsigned int)st->n_tasks; task_id += n) {
+        int    chunk_idx  = task_id * st->n_chunks_per_task;
+        size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
+
+        __fp16      *dst = st->dst + chunk_idx * st->k_block;
+        const float *src = st->src + chunk_idx * st->k_stride;
+
+        if (st->vtcm_f32_act) {
+            float *thread_f32_act = st->vtcm_f32_act + i * HTP_MM_DMA_ACT_MULTIPLIER * st->k_block;
+            transfer_activation_chunk_fp32_to_fp16_dma_pipelined(
+                st->ctx->dma[i], dst, src, chunk_size, st->k_block, st->k_stride, st->k_valid, thread_f32_act
+            );
+        } else {
+            transfer_activation_chunk_fp32_to_fp16(dst, src, chunk_size, st->k_block, st->k_stride, st->k_valid);
+        }
+    }
+
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, start_chunk_idx);
+}
+
+static void transfer_activation_chunk_gathered_worker_fn(unsigned int n, unsigned int i, void *data) {
+    activation_transfer_gathered_task_state_t *st = data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    int chunk_idx = i;
+    int chunk_size = st->n_chunks_per_task;
+    int start_row = st->start_row + chunk_idx * chunk_size;
+    int n_rows = hex_smin(st->cne1 - start_row, chunk_size);
+    if (n_rows > 0) {
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx);
+        transfer_activation_chunk_fp32_to_fp16_gathered(
+            st->dst, st->src, start_row, n_rows, st->k_block,
+            st->matrix_rows, st->cur_a, st->mapping_stride,
+            st->ne11, &st->ne11_div, st->nb11, st->nb12, st->cne1, st->k_valid);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx);
+    }
+}
+
+static void transfer_activation_chunk_gathered_worker_flat_fn(unsigned int n, unsigned int i, void *data) {
+    activation_transfer_gathered_task_state_t *st = data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    int chunk_idx = i;
+    int chunk_size = st->n_chunks_per_task;
+    int start_row = st->start_row + chunk_idx * chunk_size;
+    int n_rows = hex_smin(st->cne1 - start_row, chunk_size);
+    if (n_rows > 0) {
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx);
+        transfer_activation_chunk_fp32_to_fp16_gathered_flat(
+            st->dst, st->src, start_row, n_rows, st->k_block,
+            st->matrix_rows, st->cur_a, st->mapping_stride,
+            st->nb12, st->cne1, st->k_valid);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_PREP, chunk_idx);
+    }
+}
+
+static void transfer_output_chunk_scattered_worker_fn(unsigned int n, unsigned int i, void *data) {
+    output_transfer_scattered_task_state_t *st = data;
+    struct htp_thread_trace * tr = st->traces ? &st->traces[i] : NULL;
+    int chunk_idx = i;
+    int chunk_size = st->n_chunks_per_task;
+    int start_row = st->start_row + chunk_idx * chunk_size;
+    int n_rows = hex_smin(st->cne1 - start_row, chunk_size);
+    if (n_rows > 0) {
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx);
+        transfer_output_chunk_fp16_to_fp32_scattered(
+            st->dst, st->vtcm_src, start_row, n_rows, st->n_cols,
+            st->matrix_rows, st->cur_a, st->mapping_stride,
+            st->dst_nb1, st->dst_nb2, st->cne1);
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, chunk_idx);
+    }
+}
+
+// --- HMX Dispatchers & Entry Points ---
+
+static void dequantize_tiled_weight_chunk_to_fp16_tiles(
+        struct htp_context *ctx, __fp16 *vtcm_dst,
+        const void *weight_src_ddr,
+        int n_cols, int k_block,
+        size_t row_stride, int weight_type,
+        int n_k_tiles, struct fastdiv_values n_k_tiles_div,
+        worker_callback_t dequant_worker_fn, int n_threads) {
+
+    assert(n_cols  % HTP_MM_HMX_TILE_N_COLS == 0);
+    assert(k_block % HTP_MM_HMX_TILE_N_COLS == 0);
+
+    size_t n_col_tiles = n_cols / HTP_MM_HMX_TILE_N_COLS;
+    size_t n_tot_tiles = n_col_tiles * n_k_tiles;
+
+    size_t n_tiles_per_task = (n_threads == 1) ? n_tot_tiles : hmx_ceil_div(n_tot_tiles, n_threads);
+
+    tiled_dequantize_state_t state;
+    state.n_tasks          = (n_tot_tiles + n_tiles_per_task - 1) / n_tiles_per_task;
+    state.n_tot_tiles      = n_tot_tiles;
+    state.n_tiles_per_task = n_tiles_per_task;
+    state.dst              = vtcm_dst;
+    state.src              = (const uint8_t *)weight_src_ddr;
+    state.n_cols           = n_cols;
+    state.k_block          = k_block;
+    state.row_stride       = row_stride;
+    state.weight_type      = weight_type;
+    state.n_k_tiles        = n_k_tiles;
+    state.n_k_tiles_div    = n_k_tiles_div;
+    state.traces           = ctx->trace;
+    state.ctx              = ctx;
+
+    state.tile_size = htp_mm_get_weight_tile_size(weight_type);
+    state.aligned_tile_size = htp_mm_get_weight_aligned_tile_size(weight_type);
+
+    if (state.n_tasks == 1 || n_threads == 1) {
+        dequant_worker_fn(1, 0, &state);
+    } else {
+        int n_tasks = hex_smin((int) state.n_tasks, n_threads);
+        worker_pool_run_func(ctx->worker_pool, dequant_worker_fn, &state, n_tasks);
+    }
+}
+
+static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const __fp16 *vtcm_src,
+                                              int n_rows, int n_cols, int dst_stride, int dst_cols, int n_threads) {
+    assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0);
+
+    if (n_rows <= 0) return;
+
+    size_t n_tot_chunks      = n_rows;
+    size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : hmx_ceil_div(n_rows, n_threads);
+    n_chunks_per_task        = hex_align_up(n_chunks_per_task, 2);
+
+    int actual_threads = hmx_ceil_div(n_rows, n_chunks_per_task);
+
+    output_transfer_task_state_t state;
+    state.n_tasks           = actual_threads;
+    state.n_tot_chunks      = n_tot_chunks;
+    state.n_chunks_per_task = n_chunks_per_task;
+    state.dst               = dst;
+    state.vtcm_src          = vtcm_src;
+    state.n_cols            = n_cols;
+    state.dst_stride        = dst_stride;
+    state.dst_cols          = dst_cols;
+    state.traces            = ctx->trace;
+
+    if (actual_threads <= 1) {
+        transfer_output_chunk_worker_fn(1, 0, &state);
+    } else {
+        worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_worker_fn, &state, actual_threads);
+    }
+}
+
+static void transfer_activation_chunk_threaded(
+        struct htp_context *ctx,
+        __fp16 *dst,
+        const float *src,
+        int n_rows,
+        int k_block,
+        int k_stride,
+        int n_threads,
+        int k_valid,
+        float *vtcm_f32_act) {
+    assert(k_block % HTP_MM_HMX_TILE_N_COLS == 0 && k_stride % HTP_MM_HMX_TILE_N_COLS == 0);
+
+    size_t n_tot_chunks      = n_rows;
+    size_t n_chunks_per_task = (n_threads == 1) ? n_tot_chunks : 32;  // must be multiple of 32 to ensure correct destination address
+
+    activation_transfer_task_state_t state;
+    state.n_tasks           = (n_tot_chunks + n_chunks_per_task - 1) / n_chunks_per_task;
+    state.n_tot_chunks      = n_tot_chunks;
+    state.n_chunks_per_task = n_chunks_per_task;
+    state.dst               = dst;
+    state.src               = src;
+    state.k_block           = k_block;
+    state.k_stride          = k_stride;
+    state.k_valid           = k_valid;
+    state.traces            = ctx->trace;
+    state.ctx               = ctx;
+    state.vtcm_f32_act      = vtcm_f32_act;
+
+    if (state.n_tasks == 1 || n_threads == 1) {
+        transfer_activation_chunk_worker_fn(1, 0, &state);
+    } else {
+        int n_tasks = hex_smin((int) state.n_tasks, n_threads);
+        worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_worker_fn, &state, n_tasks);
+    }
+}
+
+static int hmx_mm_2d_f32(struct htp_context *ctx,
+                                  float *restrict dst,
+                                  const float *activation,
+                                  const uint8_t *weight,
+                                  int m, int k, int n,
+                                  int act_stride,
+                                  int weight_stride,
+                                  int weight_type,
+                                  int k_valid,
+                                  int dst_stride,
+                                  int dst_cols,
+                                  int m_chunk,
+                                  int n_chunk,
+                                  int pipeline,
+                                  int n_threads,
+                                  int act_threads,
+                                  int tile_size,
+                                  int aligned_tile_size,
+                                  int vtcm_size) {
+    if (k % 32 != 0 || n % 32 != 0) { return -1; }
+    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN)) { return -1; }
+
+    size_t row_stride = htp_mm_get_tiled_row_stride(weight_type, k);
+    if (row_stride == 0) {
+        return -1;
+    }
+
+    worker_callback_t dequant_worker_fn = NULL;
+    switch (weight_type) {
+        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_0; break;
+        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_tiled_worker_loop_iq4_nl; break;
+        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
+        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_tiled_worker_loop_mxfp4; break;
+        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q8_0; break;
+        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
+        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
+        default:
+            return -1;
+    }
+
+    const int n_k_tiles = k / HTP_MM_HMX_TILE_N_COLS;
+    const struct fastdiv_values n_k_tiles_div = init_fastdiv_values(n_k_tiles);
+
+    const bool is_quant     = (weight_type != HTP_TYPE_F16 && weight_type != HTP_TYPE_F32);
+    const size_t vec_dot_size = k * sizeof(__fp16);
+    const size_t vtcm_budget  = ctx->vtcm_size;
+
+    size_t m_chunk_n_rows = m_chunk;
+    size_t n_chunk_n_cols = n_chunk;
+    size_t vtcm_used      = vtcm_size;
+
+    const size_t qweight_row_stride = is_quant ? (size_t)(n_k_tiles * aligned_tile_size) / 32 : 0;
+
+    const size_t act_f32_size     = hex_align_up((size_t)act_threads * HTP_MM_DMA_ACT_MULTIPLIER * k * sizeof(float), HTP_MM_HMX_TILE_SIZE);
+
+    const size_t weight_area_size = is_quant
+        ? hex_align_up((n_chunk_n_cols / 32) * n_k_tiles * aligned_tile_size, HTP_MM_HMX_TILE_SIZE)
+        : hex_align_up(n_chunk_n_cols * row_stride, HTP_MM_HMX_TILE_SIZE);
+    const size_t act_area_size    = hex_align_up(m_chunk_n_rows * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t output_area_size = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HTP_MM_HMX_TILE_SIZE);
+
+    size_t scratch0_size, scratch1_size, scratch2_size;
+    scratch0_size = hex_align_up(n_chunk_n_cols * vec_dot_size, HTP_MM_HMX_TILE_SIZE);  // dequant buf 0
+    scratch1_size = pipeline ? scratch0_size : 0;                                 // dequant buf 1
+    scratch2_size = pipeline ? output_area_size : 0;                              // output  buf 1
+
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight_raw[2] = { NULL, NULL };
+    if (weight_area_size) {
+        if (pipeline) {
+            vtcm_weight_raw[0] = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+            vtcm_weight_raw[1] = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+        } else {
+            vtcm_weight_raw[0] = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+        }
+    }
+    __fp16  *vtcm_f16_act = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
+    float   *vtcm_f32_act    = (float *) vtcm_seq_alloc(&vtcm_ptr, act_f32_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
+    void    *vtcm_scratch1   = scratch1_size ? vtcm_seq_alloc(&vtcm_ptr, scratch1_size) : NULL;
+    void    *vtcm_scratch2   = scratch2_size ? vtcm_seq_alloc(&vtcm_ptr, scratch2_size) : NULL;
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+
+    vtcm_used = vtcm_ptr - (uint8_t *) ctx->vtcm_base;
+    if (vtcm_used > vtcm_budget) {
+        FARF(ERROR, "hmx-mm-2d-precomputed: VTCM overflow: used %zu budget %zu, m %d k %d n %d mc %zu nc %zu",
+             vtcm_used, vtcm_budget, m, k, n, m_chunk_n_rows, n_chunk_n_cols);
+        return -1;
+    }
+
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
+
+    FARF(HIGH, "hmx-mm-2d-precomputed: standard : m %d k %d n %d wtype %d mc %zu nc %zu vtcm %zu/%zu",
+         m, k, n, weight_type, m_chunk_n_rows, n_chunk_n_cols, vtcm_used, vtcm_budget);
+
+    int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols);
+
+    if (pipeline) {
+        // --- Asynchronous Pipelined Loop ---
+        hmx_matmul_job_t job_slots[2];  // persistent double-buffered job descriptors
+
+        for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
+            const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
+
+            void *vtcm_weight_bufs[2] = { vtcm_scratch0, vtcm_scratch1 };
+            void *vtcm_output_bufs[2] = { vtcm_output,   vtcm_scratch2 };
+
+            transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act);
+
+            // Prologue: push A0 and optionally A1 (if n_chunk_cnt > 1)
+            const size_t n_cols_A0 = hex_smin(n - 0 * n_chunk_n_cols, n_chunk_n_cols);
+            if (is_quant) {
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight), aligned_tile_size, tile_size, tile_size, (n_cols_A0 / 32) * n_k_tiles);
+            } else {
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight), row_stride, weight_stride, row_stride, n_cols_A0);
+            }
+
+            if (1 < n_chunk_cnt) {
+                const size_t n_cols_A1 = hex_smin(n - 1 * n_chunk_n_cols, n_chunk_n_cols);
+                if (is_quant) {
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[1], weight + n_chunk_n_cols * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols_A1 / 32) * n_k_tiles);
+                } else {
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[1], weight + n_chunk_n_cols * weight_stride), row_stride, weight_stride, row_stride, n_cols_A1);
+                }
+            }
+
+            // pop A0 -> dequantize A0 -> submit C0
+            dma_queue_pop(ctx->dma[0]);
+            dequantize_tiled_weight_chunk_to_fp16_tiles(
+                ctx, vtcm_weight_bufs[0], vtcm_weight_raw[0],
+                n_cols_A0, k, row_stride, weight_type,
+                n_k_tiles, n_k_tiles_div, dequant_worker_fn, n_threads);
+
+            hmx_matmul_job_init(&job_slots[0], (__fp16 *) vtcm_output_bufs[0], (__fp16 *) vtcm_f16_act,
+                                (__fp16 *) vtcm_weight_bufs[0], vtcm_scales,
+                                hmx_ceil_div(n_rows, HTP_MM_HMX_TILE_N_ROWS),
+                                hmx_ceil_div(n_cols_A0, HTP_MM_HMX_TILE_N_COLS), k / HTP_MM_HMX_TILE_N_ROWS);
+            hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[0]));
+
+            // Main loop: pop/dequantize A_{i+1} -> push A_{i+2} -> submit C_{i+1} -> wait C_i and store D_i
+            for (int i = 0; i < n_chunk_cnt; ++i) {
+                const size_t nc    = i * n_chunk_n_cols;
+                const size_t nc_p1 = nc + 1 * n_chunk_n_cols;
+                const size_t nc_p2 = nc + 2 * n_chunk_n_cols;
+
+                const size_t n_cols    = hex_smin(n - nc, n_chunk_n_cols);
+                const size_t n_cols_p1 = hex_smin(n - nc_p1, n_chunk_n_cols);
+                const size_t n_cols_p2 = hex_smin(n - nc_p2, n_chunk_n_cols);
+
+                // 1. pop A_{i+1} and dequantize it (if i+1 < n_chunk_cnt)
+                if (i + 1 < n_chunk_cnt) {
+                    dma_queue_pop(ctx->dma[0]);
+                    dequantize_tiled_weight_chunk_to_fp16_tiles(
+                        ctx, vtcm_weight_bufs[(i + 1) % 2], vtcm_weight_raw[(i + 1) % 2],
+                        n_cols_p1, k, row_stride, weight_type,
+                        n_k_tiles, n_k_tiles_div, dequant_worker_fn, n_threads);
+                }
+
+                // 2. push A_{i+2} (if i+2 < n_chunk_cnt)
+                if (i + 2 < n_chunk_cnt) {
+                    if (is_quant) {
+                        dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[(i + 2) % 2], weight + nc_p2 * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols_p2 / 32) * n_k_tiles);
+                    } else {
+                        dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[(i + 2) % 2], weight + nc_p2 * weight_stride), row_stride, weight_stride, row_stride, n_cols_p2);
+                    }
+                }
+
+                // 3. submit C_{i+1} (if i+1 < n_chunk_cnt)
+                if (i + 1 < n_chunk_cnt) {
+                    hmx_matmul_job_init(&job_slots[(i + 1) % 2], (__fp16 *) vtcm_output_bufs[(i + 1) % 2],
+                                        (__fp16 *) vtcm_f16_act, (__fp16 *) vtcm_weight_bufs[(i + 1) % 2],
+                                        vtcm_scales, hmx_ceil_div(n_rows, HTP_MM_HMX_TILE_N_ROWS),
+                                        hmx_ceil_div(n_cols_p1, HTP_MM_HMX_TILE_N_COLS), k / HTP_MM_HMX_TILE_N_ROWS);
+                    hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[(i + 1) % 2]));
+                }
+
+                // 4. wait C_i and store D_i (multi-thread HVX, parallel with C_{i+1})
+                hmx_queue_pop(ctx->hmx_queue);
+                float *output_chunk = dst + (mr * dst_stride + nc);
+                int chunk_dst_cols = dst_cols - (int)nc;
+                if (chunk_dst_cols > 0) {
+                    transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output_bufs[i % 2], n_rows, n_cols, dst_stride, chunk_dst_cols, n_threads);
+                }
+            }
+        }
+        hmx_queue_suspend(ctx->hmx_queue);
+    } else {
+        // --- Synchronous Un-pipelined loop (m <= 32 or fallback) ---
+        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+        for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
+            const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
+
+            transfer_activation_chunk_threaded(ctx, vtcm_f16_act, activation + mr * act_stride, n_rows, k, act_stride, act_threads, k_valid, vtcm_f32_act);
+
+            for (size_t nc = 0; nc < n; nc += n_chunk_n_cols) {
+                const size_t n_cols = hex_smin(n - nc, n_chunk_n_cols);
+                const size_t n_row_tiles = hmx_ceil_div(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+                const size_t n_col_tiles = hmx_ceil_div(n_cols, HTP_MM_HMX_TILE_N_COLS);
+
+                // A: Weight DMA (Synchronous)
+                if (is_quant) {
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight + nc * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols / 32) * n_k_tiles);
+                } else {
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight + nc * weight_stride), row_stride, weight_stride, row_stride, n_cols);
+                }
+                dma_queue_pop(ctx->dma[0]);
+
+                // B: Weight Dequantize (Threaded)
+                dequantize_tiled_weight_chunk_to_fp16_tiles(
+                    ctx, vtcm_scratch0, vtcm_weight_raw[0],
+                    n_cols, k, row_stride, weight_type,
+                    n_k_tiles, n_k_tiles_div, dequant_worker_fn, n_threads);
+
+                // C: HMX Compute (Synchronous)
+                core_dot_chunk_fp16(vtcm_output, vtcm_f16_act, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HTP_MM_HMX_TILE_N_ROWS);
+
+                // D: Output Store
+                float *output_chunk = dst + (mr * dst_stride + nc);
+                int chunk_dst_cols = dst_cols - (int)nc;
+                if (chunk_dst_cols > 0) {
+                    transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output, n_rows, n_cols, dst_stride, chunk_dst_cols, n_threads);
+                }
+            }
+        }
+        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+    }
+
+    return 0;
+}
+
+static inline int hmx_mm_batch_r2(const hmx_mm_f16_f32_batched_params_t *params) {
+    return params->ne02 > 0 ? params->ne12 / params->ne02 : 1;
+}
+
+static inline int hmx_mm_batch_r3(const hmx_mm_f16_f32_batched_params_t *params) {
+    return params->ne03 > 0 ? params->ne13 / params->ne03 : 1;
+}
+
+static inline const __fp16 *hmx_mm_weight_batch_ptr(const hmx_mm_f16_f32_batched_params_t *params,
+                                                        int dst_b2, int dst_b3) {
+    const int r2 = hmx_mm_batch_r2(params);
+    const int r3 = hmx_mm_batch_r3(params);
+    return (const __fp16 *) ((const uint8_t *) params->weight +
+                             (size_t) (dst_b2 / r2) * params->src0_nb2 +
+                             (size_t) (dst_b3 / r3) * params->src0_nb3);
+}
+
+static inline const float *hmx_mm_activation_batch_ptr(const hmx_mm_f16_f32_batched_params_t *params,
+                                                           int dst_b2, int dst_b3) {
+    return (const float *) ((const uint8_t *) params->activation +
+                            (size_t) dst_b2 * params->src1_nb2 +
+                            (size_t) dst_b3 * params->src1_nb3);
+}
+
+static inline float *hmx_mm_dst_batch_ptr(const hmx_mm_f16_f32_batched_params_t *params,
+                                              int dst_b2, int dst_b3) {
+    return (float *) ((uint8_t *) params->dst +
+                      (size_t) dst_b2 * params->dst_nb2 +
+                      (size_t) dst_b3 * params->dst_nb3);
+}
+
+static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx,
+                                                        const hmx_mm_f16_f32_batched_params_t *params,
+                                                        int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) {
+    int ret = 0;
+    for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) {
+        for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) {
+            ret = hmx_mm_2d_f32(ctx, hmx_mm_dst_batch_ptr(params, b2, b3),
+                                           hmx_mm_activation_batch_ptr(params, b2, b3),
+                                           (const uint8_t *)hmx_mm_weight_batch_ptr(params, b2, b3),
+                                           params->m, params->k, params->n,
+                                           params->act_stride, params->weight_stride * (int)sizeof(__fp16),
+                                           HTP_TYPE_F16, params->k, params->n, params->n,
+                                           m_chunk, n_chunk, pipeline, n_threads, act_threads,
+                                           0, 0, vtcm_size);
+        }
+    }
+    return ret;
+}
+
+static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_batched_params_t *params,
+                               int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) {
+    if (params->act_stride < params->k || params->weight_stride < params->k || params->dst_stride < params->n) { return -1; }
+    if (params->ne02 <= 0 || params->ne03 <= 0 || params->ne12 <= 0 || params->ne13 <= 0) { return -1; }
+    if (params->ne12 % params->ne02 != 0 || params->ne13 % params->ne03 != 0) { return -1; }
+    if (params->k % 32 != 0 || params->n % 32 != 0) { return -1; }
+    if (!hex_is_aligned(params->dst, VLEN) || !hex_is_aligned(params->activation, VLEN)) { return -1; }
+
+    const int group_size = hmx_mm_batch_r2(params);
+    const size_t vtcm_budget  = ctx->vtcm_size;
+
+    // Check if the precomputed parameters are grouped or simple.
+    // If simple, or if group_size <= 1, we use simple fallback loop.
+    // Grouped path is only valid if group_size > 1 and it fits within VTCM budget.
+    bool run_grouped = (group_size > 1 && (size_t)vtcm_size <= vtcm_budget);
+    if (!run_grouped) {
+        return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size);
+    }
+
+    const size_t vec_dot_size = params->k * sizeof(__fp16);
+
+    const bool use_dma_activation = (params->act_stride > params->k);
+    const size_t f32_scratch_size = use_dma_activation
+        ? hex_align_up((size_t)act_threads * HTP_MM_DMA_ACT_MULTIPLIER * (size_t) params->k * sizeof(float), HTP_MM_HMX_TILE_SIZE) : 0;
+
+    size_t m_chunk_n_rows = m_chunk;
+    size_t n_chunk_n_cols = n_chunk;
+    size_t vtcm_used = vtcm_size;
+
+    const size_t act_head_stride      = m_chunk_n_rows * (size_t) params->k;  // fp16 elements between heads
+    const size_t weight_area_size     = hex_align_up(n_chunk_n_cols * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t activation_area_size = hex_align_up(group_size * m_chunk_n_rows * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t output_area_size     = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HTP_MM_HMX_TILE_SIZE);
+    const size_t scratch_area_size    = hex_align_up(n_chunk_n_cols * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
+    __fp16  *vtcm_f16_act    = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, activation_area_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    void    *vtcm_scratch1   = vtcm_seq_alloc(&vtcm_ptr, scratch_area_size);
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+    float   *vtcm_f32_act    = use_dma_activation ? (float *) vtcm_seq_alloc(&vtcm_ptr, f32_scratch_size) : NULL;
+
+    if ((size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base) > vtcm_budget) {
+        FARF(HIGH, "%s: grouped layout overflowed VTCM, falling back to simple batched loop", __func__);
+        return hmx_mm_f16_f32_batched_simple(ctx, params, m_chunk, n_chunk, pipeline, n_threads, act_threads, vtcm_size);
+    }
+
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));  // scale: 1.0, bias: 0.0 in FP16
+
+    FARF(HIGH, "%s: grouped path m=%d k=%d n=%d group=%d streams=%d mc=%zu nc=%zu vtcm=%zu/%zu",
+            __func__, params->m, params->k, params->n, group_size, params->ne13,
+            m_chunk_n_rows, n_chunk_n_cols,
+            (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
+
+    const size_t fp16_row_bytes   = (size_t) params->k * sizeof(__fp16);
+    const size_t weight_row_bytes = (size_t) params->weight_stride * sizeof(__fp16);
+
+    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+
+    for (int b3 = 0; b3 < params->ne13; ++b3) {
+        for (int b2_base = 0; b2_base < params->ne12; b2_base += group_size) {
+            const __fp16 *weight_group = hmx_mm_weight_batch_ptr(params, b2_base, b3);
+
+            for (size_t mr = 0; mr < (size_t) params->m; mr += m_chunk_n_rows) {
+                const size_t n_rows = hex_smin((size_t) params->m - mr, m_chunk_n_rows);
+                const size_t n_row_tiles = hmx_ceil_div((int) n_rows, HTP_MM_HMX_TILE_N_ROWS);
+
+                // Pre-load activations for all heads in the group (once per m_chunk).
+                // When the source is strided (permuted Q), use 2D DMA to gather
+                // contiguous rows into a VTCM scratch buffer first, then HVX
+                // converts from the contiguous VTCM buffer.  This avoids L2 cache
+                // thrashing from HVX loads at large strides.
+                for (int g = 0; g < group_size; ++g) {
+                    const float *activation_chunk = hmx_mm_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
+                    __fp16 *vtcm_act_g = vtcm_f16_act + (size_t) g * act_head_stride;
+                    if (use_dma_activation) {
+                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
+                                                               activation_chunk, (int) n_rows,
+                                                               params->k, params->act_stride, act_threads, params->k, vtcm_f32_act);
+                    } else {
+                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
+                                                               activation_chunk, (int) n_rows,
+                                                               params->k, params->act_stride, act_threads, params->k, NULL);
+                    }
+                }
+
+                void *buf_curr = vtcm_scratch0;
+                void *buf_next = vtcm_scratch1;
+
+                {
+                    const size_t n_cols_first = hex_smin((size_t) params->n, n_chunk_n_cols);
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, weight_group),
+                                      fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_first);
+                }
+
+                for (size_t nc = 0; nc < (size_t) params->n; nc += n_chunk_n_cols) {
+                    const size_t n_cols = hex_smin((size_t) params->n - nc, n_chunk_n_cols);
+                    const size_t n_col_tiles = hmx_ceil_div((int) n_cols, HTP_MM_HMX_TILE_N_COLS);
+
+                    {
+                        dma_queue_pop(ctx->dma[0]);
+
+                        const size_t nc_next = nc + n_chunk_n_cols;
+                        if (nc_next < (size_t) params->n) {
+                            const size_t n_cols_next = hex_smin((size_t) params->n - nc_next, n_chunk_n_cols);
+                            const __fp16 *next_weight_chunk = weight_group + nc_next * params->weight_stride;
+
+                            dma_queue_push(ctx->dma[0], dma_make_ptr(buf_next, next_weight_chunk),
+                                              fp16_row_bytes, weight_row_bytes, fp16_row_bytes, n_cols_next);
+                        }
+
+                        hmx_interleave_rows_to_tiles(vtcm_weight, (const __fp16 *) buf_curr, n_cols, params->k, params->k, 0, n_cols);
+                        hex_swap_ptr(&buf_curr, &buf_next);
+                    }
+
+                    // Reuse the interleaved weight for every q_head in this GQA group
+                    for (int g = 0; g < group_size; ++g) {
+                        struct htp_thread_trace * tr = &ctx->trace[HTP_MAX_NTHREADS];
+                        htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, g);
+                        {
+                            const __fp16 * vtcm_act_g = vtcm_f16_act + (size_t) g * act_head_stride;
+                            core_dot_chunk_fp16(vtcm_output, vtcm_act_g, vtcm_weight, vtcm_scales, n_row_tiles, n_col_tiles,
+                                                params->k / 32);
+                        }
+                        htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, g);
+
+                        {
+                            float *output = hmx_mm_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
+                            int chunk_dst_cols = params->n - (int)nc;
+                            if (chunk_dst_cols > 0) {
+                                transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride, chunk_dst_cols, ctx->n_threads);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+
+    return 0;
+}
+
+static void transfer_activation_chunk_gathered_threaded(
+            struct htp_context *ctx,
+            __fp16 *dst,
+            const float *src,
+            int start_row,
+            int n_rows,
+            int k_block,
+            const struct mmid_row_mapping *matrix_rows,
+            int cur_a,
+            int mapping_stride,
+            int ne11,
+            size_t nb11,
+            size_t nb12,
+            int cne1,
+            int n_threads,
+            int k_valid) {
+    if (n_rows <= 0) return;
+    int chunks_per_thread = hmx_ceil_div(n_rows, n_threads);
+    chunks_per_thread = hex_align_up(chunks_per_thread, 2);
+
+    int actual_threads = hmx_ceil_div(n_rows, chunks_per_thread);
+
+    activation_transfer_gathered_task_state_t state = {
+        .dst               = dst,
+        .src               = src,
+        .n_tasks           = actual_threads,
+        .n_tot_chunks      = n_rows,
+        .n_chunks_per_task = chunks_per_thread,
+        .k_block           = k_block,
+        .matrix_rows       = matrix_rows,
+        .cur_a             = cur_a,
+        .mapping_stride    = mapping_stride,
+        .ne11              = ne11,
+        .ne11_div          = ne11 > 1 ? init_fastdiv_values(ne11) : (struct fastdiv_values){0, 0},
+        .nb11              = nb11,
+        .nb12              = nb12,
+        .start_row         = start_row,
+        .cne1              = cne1,
+        .k_valid           = k_valid,
+        .traces            = ctx->trace,
+    };
+
+    worker_callback_t worker_fn = ne11 == 1 ? transfer_activation_chunk_gathered_worker_flat_fn :
+                                              transfer_activation_chunk_gathered_worker_fn;
+
+    if (actual_threads <= 1) {
+        worker_fn(1, 0, &state);
+    } else {
+        worker_pool_run_func(ctx->worker_pool, worker_fn, &state, actual_threads);
+    }
+}
+
+static void transfer_output_chunk_scattered_threaded(
+            struct htp_context *ctx,
+            float *dst,
+            const __fp16 *vtcm_src,
+            int start_row,
+            int n_rows,
+            int n_cols,
+            const struct mmid_row_mapping *matrix_rows,
+            int cur_a,
+            int mapping_stride,
+            size_t dst_nb1,
+            size_t dst_nb2,
+            int cne1,
+            int n_threads) {
+    if (n_rows <= 0) return;
+    int chunks_per_thread = hmx_ceil_div(n_rows, n_threads);
+    chunks_per_thread = hex_align_up(chunks_per_thread, 2);
+
+    int actual_threads = hmx_ceil_div(n_rows, chunks_per_thread);
+
+    output_transfer_scattered_task_state_t state = {
+        .vtcm_src          = vtcm_src,
+        .dst               = dst,
+        .n_tasks           = actual_threads,
+        .n_tot_chunks      = n_rows,
+        .n_chunks_per_task = chunks_per_thread,
+        .n_cols            = n_cols,
+        .matrix_rows       = matrix_rows,
+        .cur_a             = cur_a,
+        .mapping_stride    = mapping_stride,
+        .dst_nb1           = dst_nb1,
+        .dst_nb2           = dst_nb2,
+        .start_row         = start_row,
+        .cne1              = cne1,
+        .traces            = ctx->trace,
+    };
+
+    if (actual_threads <= 1) {
+        transfer_output_chunk_scattered_worker_fn(1, 0, &state);
+    } else {
+        worker_pool_run_func(ctx->worker_pool, transfer_output_chunk_scattered_worker_fn, &state, actual_threads);
+    }
+}
+
+static int hmx_mm_id_2d_f32(struct htp_context *ctx,
+                                         float *restrict dst,
+                                         const float *activation,
+                                         const uint8_t *weight,
+                                         int m, int k, int n,
+                                         int k_valid,
+                                         int ne11,
+                                         size_t act_nb1, size_t act_nb2,
+                                         size_t dst_nb1, size_t dst_nb2,
+                                         int weight_stride,
+                                         int weight_type,
+                                         const struct mmid_row_mapping *matrix_rows,
+                                         int cur_a,
+                                         int mapping_stride) {
+    const int cne1 = m;
+    const int m_padded = hex_align_up(m, 32);
+
+    if (k % 32 != 0 || n % 32 != 0) { return -1; }
+    if (!hex_is_aligned(dst, VLEN) || !hex_is_aligned(activation, VLEN)) { return -1; }
+
+    size_t row_stride = htp_mm_get_tiled_row_stride(weight_type, k);
+    if (row_stride == 0) {
+        return -1;
+    }
+
+    worker_callback_t dequant_worker_fn = NULL;
+    switch (weight_type) {
+        case HTP_TYPE_Q4_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_0; break;
+        case HTP_TYPE_IQ4_NL: dequant_worker_fn = dequantize_tiled_worker_loop_iq4_nl; break;
+        case HTP_TYPE_Q4_1:   dequant_worker_fn = dequantize_tiled_worker_loop_q4_1; break;
+        case HTP_TYPE_MXFP4:  dequant_worker_fn = dequantize_tiled_worker_loop_mxfp4; break;
+        case HTP_TYPE_Q8_0:   dequant_worker_fn = dequantize_tiled_worker_loop_q8_0; break;
+        case HTP_TYPE_F16:    dequant_worker_fn = convert_f16_worker_loop; break;
+        case HTP_TYPE_F32:    dequant_worker_fn = quantize_f32_worker_loop; break;
+        default:
+            return -1;
+    }
+
+    const int n_k_tiles = k / HTP_MM_HMX_TILE_N_COLS;
+    const struct fastdiv_values n_k_tiles_div = init_fastdiv_values(n_k_tiles);
+
+    const int n_threads = ctx->n_threads;
+    const bool is_quant   = (weight_type != HTP_TYPE_F16 && weight_type != HTP_TYPE_F32);
+
+    const size_t vec_dot_size = k * sizeof(__fp16);
+    const size_t vtcm_budget  = ctx->vtcm_size;
+    size_t vtcm_used = 0;
+
+    int tile_size = htp_mm_get_weight_tile_size(weight_type);
+    int aligned_tile_size = htp_mm_get_weight_aligned_tile_size(weight_type);
+
+    const size_t qweight_row_stride = is_quant ? (size_t)(n_k_tiles * aligned_tile_size) / 32 : 0;
+    const size_t weight_row_stride = is_quant ? qweight_row_stride : row_stride;
+
+    size_t size_per_n = 0, size_per_m = 0, size_per_mn = 0;
+    htp_mm_hmx_get_2d_chunk_costs(weight_type, k, /*pipeline=*/false, aligned_tile_size,
+                                  &size_per_n, &size_per_m, &size_per_mn);
+
+    size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0;
+    if (htp_mm_hmx_compute_chunks(vtcm_budget, /*overhead=*/256, size_per_n, size_per_m, size_per_mn,
+                           m_padded, n,
+                           /*m_block_cost=*/(size_t) n * HTP_MM_HMX_COST_W_DEQUANT,
+                           /*n_block_cost=*/(size_t) m_padded * HTP_MM_HMX_COST_A_CONVERT, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used)) {
+        FARF(ERROR, "hmx-mm-id-2d: VTCM too small : m %d k %d n %d budget %zu", m_padded, k, n, vtcm_budget);
+        return -1;
+    }
+
+    const size_t weight_area_size = hex_align_up(n_chunk_n_cols * weight_row_stride, HTP_MM_HMX_TILE_SIZE);
+    const size_t act_area_size    = hex_align_up(m_chunk_n_rows * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t output_area_size = hex_align_up(m_chunk_n_rows * n_chunk_n_cols * sizeof(__fp16), HTP_MM_HMX_TILE_SIZE);
+
+    size_t scratch0_size = hex_align_up(n_chunk_n_cols * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+
+    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight     = weight_area_size ? (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size) : NULL;
+    __fp16  *vtcm_f16_act = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
+    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
+    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+
+    vtcm_used = vtcm_ptr - (uint8_t *) ctx->vtcm_base;
+    if (vtcm_used > vtcm_budget) {
+        FARF(ERROR, "hmx-mm-id-2d: VTCM overflow: used %zu budget %zu", vtcm_used, vtcm_budget);
+        return -1;
+    }
+
+    hmx_init_column_scales(vtcm_scales, Q6_V_vsplat_R(0x3c00));
+
+    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+
+    for (size_t mr = 0; mr < (size_t) m_padded; mr += m_chunk_n_rows) {
+        const size_t n_rows = hex_smin(m_padded - mr, m_chunk_n_rows);
+        const size_t n_row_tiles = hmx_ceil_div(n_rows, HTP_MM_HMX_TILE_N_ROWS);
+
+        transfer_activation_chunk_gathered_threaded(
+            ctx, vtcm_f16_act, activation, (int) mr, (int) n_rows, k,
+            matrix_rows, cur_a, mapping_stride, ne11, act_nb1, act_nb2, cne1, n_threads, k_valid);
+
+        for (size_t nc = 0; nc < (size_t) n; nc += n_chunk_n_cols) {
+            const size_t n_cols = hex_smin((size_t) n - nc, n_chunk_n_cols);
+            const size_t n_col_tiles = hmx_ceil_div(n_cols, HTP_MM_HMX_TILE_N_COLS);
+
+            if (is_quant) {
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, weight + nc * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols / 32) * n_k_tiles);
+            } else {
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, weight + nc * weight_stride), row_stride, weight_stride, row_stride, n_cols);
+            }
+            dma_queue_pop(ctx->dma[0]);
+
+            dequantize_tiled_weight_chunk_to_fp16_tiles(
+                ctx, vtcm_scratch0, vtcm_weight,
+                n_cols, k, row_stride, weight_type,
+                n_k_tiles, n_k_tiles_div, dequant_worker_fn, n_threads
+            );
+
+            struct htp_thread_trace * tr = &ctx->trace[HTP_MAX_NTHREADS];
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, nc);
+            core_dot_chunk_fp16(vtcm_output, vtcm_f16_act, vtcm_scratch0, vtcm_scales, n_row_tiles, n_col_tiles, k / HTP_MM_HMX_TILE_N_ROWS);
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, nc);
+
+            transfer_output_chunk_scattered_threaded(
+                ctx, dst + nc, vtcm_output, (int) mr, (int) n_rows, (int) n_cols,
+                matrix_rows, cur_a, mapping_stride, dst_nb1, dst_nb2, cne1, n_threads);
+        }
+    }
+
+    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+    return 0;
+}
+
+
+// --- Dispatchers and Public Entry Points ---
+
+static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_kernel_params * kparams) {
     htp_matmul_tensors_preamble;
 
-#ifndef HTP_HAS_HMX
-    return op_matmul_hvx(octx);
-#else
-    if (!octx->ctx->hmx_enabled) {
-        return op_matmul_hvx(octx);
-    }
-
-    // HMX weight tile requires N to be 32-aligned.
-    if (src0->ne[1] % 32 != 0) {
-        return op_matmul_hvx(octx);
-    }
-
-    // HMX supports F16, F32, Q4_0, Q8_0, IQ4_NL, MXFP4 weights.
-    // Other types fall back to HVX.
-    uint32_t wtype = src0->type;
-    if (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_F32 && wtype != HTP_TYPE_Q4_0 && wtype != HTP_TYPE_Q4_1 && wtype != HTP_TYPE_Q8_0 && wtype != HTP_TYPE_IQ4_NL && wtype != HTP_TYPE_MXFP4) {
-        return op_matmul_hvx(octx);
-    }
-
-    // Quantised HMX path requires K aligned to 256 (x4x2 super-block).
-    // F16 and F32 HMX paths require K aligned to 32 (tile width).
-    if (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_F32 && src0->ne[0] % 256 != 0) {
-        return op_matmul_hvx(octx);
-    }
-
-    if ((wtype == HTP_TYPE_F16 || wtype == HTP_TYPE_F32) && src0->ne[0] % 32 != 0) {
-        return op_matmul_hvx(octx);
-    }
-
-    const bool is_batched = (src0->ne[2] * src0->ne[3] > 1 || src1->ne[2] * src1->ne[3] > 1);
-
-    // Quantised HMX kernels only handle flat 2D matmul (host already rejects
-    // batched quantised, but guard here too).  F16 batched matmul is handled
-    // by the dedicated wrapper in hmx-matmul-ops.c.
-    if (is_batched && src0->type != HTP_TYPE_F16) {
-        return op_matmul_hvx(octx);
-    }
-
-    // HMX assumes contiguous row-major layout.  Fall back for permuted
-    // tensors where strides are non-monotonic (e.g. transposed KV cache).
-    if (src0->nb[0] > src0->nb[1] || src1->nb[0] > src1->nb[1]) {
-        return op_matmul_hvx(octx);
-    }
-
-    // M alignment: Use HMX when M >= 32, the last partial tile (m_total % 32 rows)
-    //  is handled by HMX itself; when M < 32  fall back to HVX.
-    const int m_total = (int) src1->ne[1];
-    const int m_hmx   = m_total & ~31;   // 0 when M < 32
-    if (m_hmx == 0) {
-        return op_matmul_hvx(octx);
-    }
-
-    // Always re-quantize src1 since HMX kernel overwrites vtcm/spad,
-    // so any previously cached quantized data is invalid.
-    octx->src1_spad.src = NULL;
-
-    int k = (int) src0->ne[0];  // inner dimension
-    int n = (int) src0->ne[1];  // weight columns
-
-    int ret = -1;
-
-    // Row strides in elements. For compact tensors these equal k; for
-    // permuted attention views they can be larger, so pass the real stride.
+    int k = (int) src0->ne[0];
+    int n = (int) src0->ne[1];
+    const int m_total    = (int) src1->ne[1];
     const int act_stride = (int)(src1->nb[1] / sizeof(float));
     const int wgt_stride = (int)(src0->nb[1] / sizeof(__fp16));
 
@@ -4780,54 +3051,204 @@ int op_matmul(struct htp_ops_context * octx) {
         return HTP_STATUS_OK;
     }
 
-    if (is_batched) {
-        if (src0->type == HTP_TYPE_F16) {
-            hmx_matmul_f16_f32_batched_params_t batch_params = {
-                .dst             = (float *) dst->data,
-                .activation      = (float *) src1->data,
-                .permuted_weight = (const __fp16 *) src0->data,
-                .m               = m_total,
-                .k               = k,
-                .n               = n,
-                .act_stride      = act_stride,
-                .weight_stride   = wgt_stride,
-                .dst_stride      = (int) (dst->nb[1] / sizeof(float)),
-                .ne02            = ne02,
-                .ne03            = ne03,
-                .ne12            = ne12,
-                .ne13            = ne13,
-                .src0_nb2        = src0->nb[2],
-                .src0_nb3        = src0->nb[3],
-                .src1_nb2        = src1->nb[2],
-                .src1_nb3        = src1->nb[3],
-                .dst_nb2         = dst->nb[2],
-                .dst_nb3         = dst->nb[3],
-            };
-            ret = hmx_matmul_f16_f32_batched(octx->ctx, &batch_params);
-        } else {
-            return op_matmul_hvx(octx);
-        }
+    int ret = -1;
+    const int n_threads = MIN(kparams->n_threads, (int) octx->n_threads);
+    if (kparams->kernel_type == HTP_MM_KERNEL_HMX_F16_BATCHED) {
+        hmx_mm_f16_f32_batched_params_t batch_params = {
+            .dst             = (float *) dst->data,
+            .activation      = (float *) src1->data,
+            .weight          = (const __fp16 *) src0->data,
+            .m               = m_total,
+            .k               = k,
+            .n               = n,
+            .act_stride      = act_stride,
+            .weight_stride   = wgt_stride,
+            .dst_stride      = (int) (dst->nb[1] / sizeof(float)),
+            .ne02            = ne02,
+            .ne03            = ne03,
+            .ne12            = ne12,
+            .ne13            = ne13,
+            .src0_nb2        = src0->nb[2],
+            .src0_nb3        = src0->nb[3],
+            .src1_nb2        = src1->nb[2],
+            .src1_nb3        = src1->nb[3],
+            .dst_nb2         = dst->nb[2],
+            .dst_nb3         = dst->nb[3],
+        };
+        ret = hmx_mm_f16_f32_batched(octx->ctx, &batch_params,
+                                     kparams->m_chunk, kparams->n_chunk,
+                                     kparams->pipeline, n_threads,
+                                     kparams->n_act_threads,
+                                     kparams->vtcm_size);
     } else {
-        ret = hmx_matmul_2d_f32(octx->ctx, (float*) dst->data, (float*) src1->data, (const uint8_t *) src0->data,
-                    m_total, k, n, act_stride, (int) src0->nb[1], (int) src0->type);
+        ret = hmx_mm_2d_f32(
+            octx->ctx, (float*) dst->data, (float*) src1->data, (const uint8_t *) src0->data,
+            m_total, k, n, act_stride, (int) src0->nb[1], (int) src0->type, (int) src1->ne[0],
+            (int)(dst->nb[1] / sizeof(float)), (int)dst->ne[0],
+            kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads,
+            kparams->n_act_threads,
+            kparams->tile_size, kparams->aligned_tile_size, kparams->vtcm_size
+        );
     }
 
     if (ret != 0) {
-        FARF(HIGH, "HMX matmul failed (ret=%d), falling back to HVX", ret);
-        return op_matmul(octx);
+        FARF(ERROR, "HMX matmul failed (ret=%d)\n", ret);
+        return HTP_STATUS_INTERNAL_ERR;
+    }
+    return HTP_STATUS_OK;
+}
+
+int op_matmul(struct htp_ops_context * octx) {
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+
+    if (kparams->n_hmx) {
+        return hmx_mm_op_matmul(octx, kparams);
     }
 
-    return 0;
-#endif // HTP_HAS_HMX
+    return hvx_mm_matmul(octx);
+}
+
+static int hmx_mm_op_matmul_id(
+    struct htp_ops_context * octx,
+    struct htp_mm_context * mmctx,
+    const uint32_t * matrix_row_counts,
+    const struct mmid_row_mapping * matrix_rows,
+    void * mapping_buf,
+    bool must_free_mapping
+) {
+    htp_matmul_tensors_preamble;
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const int n_ids = octx->src[2]->ne[0];
+    const int n_as  = ne02;
+
+    for (uint32_t cur_a = 0; cur_a < n_as; ++cur_a) {
+        const int32_t cne1 = matrix_row_counts[cur_a];
+        if (cne1 == 0) continue;
+
+        int ret = hmx_mm_id_2d_f32(octx->ctx, (float*) dst->data, (float*) src1->data,
+                                   (const uint8_t *) src0->data + cur_a * nb02,
+                                   cne1, ne00, ne01,
+                                   ne10,
+                                   ne11,
+                                   nb11, nb12,
+                                   nb1, nb2,
+                                   (int) src0->nb[1], (int) src0->type,
+                                   matrix_rows, cur_a, n_ids * octx->src[2]->ne[1]);
+        if (ret != 0) {
+            FARF(ERROR, "HMX matmul failed for expert %u, error %d\n", cur_a, ret);
+            if (must_free_mapping) free(mapping_buf);
+            return HTP_STATUS_NO_SUPPORT;
+        }
+    }
+
+    if (must_free_mapping) free(mapping_buf);
+    return HTP_STATUS_OK;
+}
+
+static int hvx_mm_op_matmul_id(
+    struct htp_ops_context * octx,
+    struct htp_mm_context * mmctx,
+    size_t src0_row_size_padded,
+    uint32_t src1_nrows,
+    worker_callback_t matmul_id_job_func,
+    void * mapping_buf,
+    bool must_free_mapping
+) {
+    htp_matmul_tensors_preamble;
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+    const struct htp_tensor * restrict ids = octx->src[2];
+    const size_t src0_row_size = nb01;
+
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (ne10 + qk - 1) / qk;
+    const uint32_t total_nb = src1_nrows * nb;
+
+    worker_callback_t quant_job_func;
+    uint32_t n_quant_jobs = 1;
+    if (src1_nrows < octx->n_threads) {
+        n_quant_jobs = MIN(total_nb, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
+            uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+            mmctx->quant_ib_first[ith] = ib_first;
+            mmctx->quant_ib_last[ith]  = ib_last;
+            mmctx->quant_r[ith]        = ib_first / nb;
+            mmctx->quant_c[ith]        = ib_first % nb;
+        }
+    } else {
+        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+    }
+    size_t src1_row_size  = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+
+    // Scratchpad sizes are computed on the host (htp_mm_hvx_id_get_vtcm_sizes) and passed in.
+    // The ID layout is routing-independent, so the host has exact visibility -- consume it here
+    // rather than recomputing, to keep host budgeting and device allocation in lockstep.
+    size_t src0_sz = kparams->vtcm_src0_size;
+    size_t src1_sz = kparams->vtcm_src1_size;
+    size_t src2_sz = 0; // mapping lives in DDR
+    size_t dst_sz  = 0; // ID kernels scatter straight to DDR
+    size_t vtcm_size = kparams->vtcm_size;
+
+    size_t src0_sz_per_thread = src0_sz / octx->n_threads;
+    size_t src1_sz_per_thread = src1_sz;
+    size_t src2_sz_per_thread = 0;
+    size_t dst_sz_per_thread  = 0;
+
+    FARF(HIGH, "matmul-id-%s : src0-spad-size %zu src1-spad-size %zu src2-spad-size %zu dst-spad-size %zu (%zu)\n", mmctx->type,
+         src0_sz, src1_sz, src2_sz, dst_sz, vtcm_size);
+
+    FARF(HIGH, "matmul-id-%s : %ux%ux%ux%u * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", mmctx->type,
+         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+         ids->ne[0], ids->ne[1], ids->ne[2], ids->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], src0->data,
+         src1->data, dst->data);
+
+    // Make sure the reserved vtcm size is sufficient
+    if (octx->ctx->vtcm_size < vtcm_size) {
+        FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type, octx->ctx->vtcm_size, vtcm_size);
+        if (must_free_mapping) free(mapping_buf);
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    uint8_t * vtcm_ptr = (uint8_t *) octx->ctx->vtcm_base;
+    mmctx->vtcm_src1 = vtcm_seq_alloc(&vtcm_ptr, src1_sz);
+    mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
+    mmctx->vtcm_src2 = vtcm_seq_alloc(&vtcm_ptr, src2_sz);
+    mmctx->vtcm_dst  = vtcm_seq_alloc(&vtcm_ptr, dst_sz);
+
+    octx->src1_spad.src  = NULL;
+    octx->src0_spad.src  = NULL;
+    octx->src2_spad.src  = NULL;
+    octx->dst_spad.src   = NULL;
+
+    mmctx->vtcm_src0_stride = src0_row_size_padded;
+    mmctx->vtcm_src1_stride = src1_row_size;
+
+    mmctx->vtcm_src0_size_per_thread = src0_sz_per_thread;
+    mmctx->vtcm_src1_size_per_thread = src1_sz_per_thread;
+    mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
+    mmctx->vtcm_dst_size_per_thread  = dst_sz_per_thread;
+
+    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
+    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+
+    const uint32_t n_matmul_jobs = octx->n_threads;
+    worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
+
+    if (must_free_mapping) free(mapping_buf);
+    return HTP_STATUS_OK;
 }
 
 int op_matmul_id(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
-    struct htp_matmul_context mmctx_struct = {0};
-    struct htp_matmul_context * mmctx = &mmctx_struct;
+    struct htp_mm_context mmctx_struct = {0};
+    struct htp_mm_context * mmctx = &mmctx_struct;
     mmctx->octx = octx;
 
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+
     const struct htp_tensor * restrict ids = octx->src[2];
 
     const size_t src0_row_size = nb01;
@@ -4839,14 +3260,11 @@ int op_matmul_id(struct htp_ops_context * octx) {
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
 
     worker_callback_t quant_job_func;
-    worker_callback_t matmul_id_job_func = src1_nrows > 1 ? matmul_id : matvec_id;
+    worker_callback_t matmul_id_job_func = src1_nrows > 1 ? hvx_mm_id : hvx_mv_id;
 
     // Compute src0_nrows_per_thread
     mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
-    mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
-
-    size_t src1_row_size;
-    size_t src1_row_size_padded;
+    mmctx->src0_nrows_per_thread  = hex_round_up(mmctx->src0_nrows_per_thread, 32);
 
     // row groups
     const int n_ids = ids->ne[0];  // n_expert_used
@@ -4875,54 +3293,13 @@ int op_matmul_id(struct htp_ops_context * octx) {
 
     mmctx->matrix_row_counts = matrix_row_counts;
     mmctx->matrix_rows       = matrix_rows;
+    mmctx->mm_div_ne11       = kparams->div_ne11;
 
-    if (htp_mminit_vec_dot(mmctx, src0->type) != 0) {
+    if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) {
         if (must_free_mapping) free(mapping_buf);
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    if (src0->type == HTP_TYPE_Q4_1) {
-        quant_job_func = quantize_f32_q8_1x4x2;
-        src1_row_size  = q8_1x4x2_row_size(ne10);
-    } else {
-        quant_job_func = quantize_f32_q8x4x2;
-        src1_row_size  = q8x4x2_row_size(ne10);
-    }
-
-    const size_t src2_spad_size_per_thread = 0; // We moved the mapping to DDR!
-    htp_mminit_spad(octx, dst_row_size, src0_row_size_padded, src1_row_size, src1_nrows, src2_spad_size_per_thread);
-
-    size_t spad_size = octx->src2_spad.size + octx->src1_spad.size + octx->src0_spad.size + octx->dst_spad.size;
-
-    FARF(HIGH, "matmul-id-%s : src0-spad-size %u src1-spad-size %u src2-spad-size %u dst-spad-size %u (%zu)\n", mmctx->type,
-         octx->src0_spad.size, octx->src1_spad.size, octx->src2_spad.size, octx->dst_spad.size, spad_size);
-
-    FARF(HIGH, "matmul-id-%s : %ux%ux%ux%u * %ux%ux%ux%u (%ux%ux%ux%u) -> %ux%ux%ux%u (0x%p, 0x%p, 0x%p)\n", mmctx->type,
-         src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
-         ids->ne[0], ids->ne[1], ids->ne[2], ids->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], src0->data,
-         src1->data, dst->data);
-
-    // Make sure the reserved vtcm size is sufficient
-    if (octx->ctx->vtcm_size < spad_size) {
-        FARF(ERROR, "matmul-id-%s : current VTCM reservation %zu is too small, needed %zu\n", mmctx->type, octx->ctx->vtcm_size, spad_size);
-        if (must_free_mapping) free(mapping_buf);
-        return HTP_STATUS_VTCM_TOO_SMALL;
-    }
-
-    // Place src1 spad first. We use it for dyn.quant and may reuse in subseq ops.
-    octx->src1_spad.data = octx->ctx->vtcm_base;
-    octx->src0_spad.data = octx->src1_spad.data + octx->src1_spad.size;
-    octx->src2_spad.data = octx->src0_spad.data + octx->src0_spad.size;
-    octx->dst_spad.data  = octx->src2_spad.data + octx->src2_spad.size;
-
-    octx->src1_spad.src  = (src1 == octx->src1_spad.src) ? src1 : NULL;
-    octx->src0_spad.src  = NULL;
-    octx->src2_spad.src  = NULL;
-    octx->dst_spad.src   = NULL;
-
-    octx->src0_spad.stride = src0_row_size_padded;
-    octx->src1_spad.stride = src1_row_size;
-
     if (src1_nrows > 1) {
         // initialize matrix_row_counts and map
         memset(matrix_row_counts, 0, n_as * sizeof(uint32_t));
@@ -4930,9 +3307,12 @@ int op_matmul_id(struct htp_ops_context * octx) {
         // group rows by src0 matrix
         for (uint32_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {  // token idx
             for (uint32_t id = 0; id < n_ids; ++id) {         // expert idx
-                const uint32_t i02 = *(const uint32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
+                const int32_t i02 = *(const int32_t *) ((const uint8_t *) ids->data + iid1 * ids->nb[1] + id * ids->nb[0]);
 
-                assert(i02 >= 0 && i02 < n_as);
+                if (i02 < 0) {
+                    continue;
+                }
+                assert(i02 < n_as);
 
                 matrix_rows[i02 * n_ids * ids->ne[1] + matrix_row_counts[i02]] = (struct mmid_row_mapping) { id, iid1 };
                 matrix_row_counts[i02] += 1;
@@ -4945,60 +3325,292 @@ int op_matmul_id(struct htp_ops_context * octx) {
         return HTP_STATUS_OK;
     }
 
-    bool hmx_eligible = false;
-#ifdef HTP_HAS_HMX
-    if (octx->ctx->hmx_enabled && src1_nrows > 1) {
-        uint32_t wtype = src0->type;
-        if (ne01 % 32 == 0 &&
-            (wtype == HTP_TYPE_F16 || wtype == HTP_TYPE_F32 || wtype == HTP_TYPE_Q4_0 || wtype == HTP_TYPE_Q4_1 || wtype == HTP_TYPE_Q8_0 || wtype == HTP_TYPE_IQ4_NL || wtype == HTP_TYPE_MXFP4)) {
-            if ((wtype == HTP_TYPE_F16 || wtype == HTP_TYPE_F32) && ne00 % 32 == 0) {
-                hmx_eligible = true;
-            } else if (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_F32 && ne00 % 256 == 0) {
-                hmx_eligible = true;
-            }
-        }
+    if (kparams->n_hmx) {
+        return hmx_mm_op_matmul_id(octx, mmctx, matrix_row_counts, matrix_rows, mapping_buf, must_free_mapping);
     }
-#endif
 
-    mmctx->hmx_eligible = hmx_eligible;
+    return hvx_mm_op_matmul_id(octx, mmctx, src0_row_size_padded, src1_nrows, matmul_id_job_func, mapping_buf, must_free_mapping);
+}
 
-    if (hmx_eligible) {
-        for (uint32_t cur_a = 0; cur_a < n_as; ++cur_a) {
-            const int32_t cne1 = matrix_row_counts[cur_a];
-            if (cne1 == 0) continue;
+int op_matmul_qkv(struct htp_ops_context * octx) {
+    const struct htp_tensor * restrict src0 = octx->src[0]; // Wk
+    const struct htp_tensor * restrict src1 = octx->src[1]; // x
+    const struct htp_tensor * restrict src2 = octx->src[2]; // Wv
+    const struct htp_tensor * restrict src3 = octx->src[3]; // Wq
+    const struct htp_tensor * restrict dst_k = octx->dsts[0];
+    const struct htp_tensor * restrict dst_v = octx->dsts[1];
+    const struct htp_tensor * restrict dst_q = octx->dsts[2];
 
-            int ret = hmx_matmul_id_2d_f32(octx->ctx, (float*) dst->data, (float*) src1->data,
-                                           (const uint8_t *) src0->data + cur_a * nb02,
-                                           cne1, ne00, ne01,
-                                           ne11,
-                                           nb11, nb12,
-                                           nb1, nb2,
-                                           (int) src0->nb[1], (int) src0->type,
-                                           matrix_rows, cur_a, n_ids * ids->ne[1]);
-            if (ret != 0) {
-                FARF(ERROR, "HMX matmul failed for expert %u, error %d\n", cur_a, ret);
-                if (must_free_mapping) free(mapping_buf);
-                return HTP_STATUS_NO_SUPPORT;
-            }
+    bool is_repacked = (src0->type == HTP_TYPE_Q4_0 || src0->type == HTP_TYPE_Q4_1 ||
+                        src0->type == HTP_TYPE_Q8_0 || src0->type == HTP_TYPE_IQ4_NL ||
+                        src0->type == HTP_TYPE_MXFP4);
+
+    struct htp_mm_context mmctx_struct = {0};
+    struct htp_mm_context * mmctx = &mmctx_struct;
+    mmctx->octx = octx;
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+
+    const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
+    const uint32_t src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];
+
+    // Compute src0_nrows_per_thread
+    mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
+    if (is_repacked) {
+        mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32);
+    } else {
+        mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+    }
+
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+
+    if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (src1->ne[0] + qk - 1) / qk;
+    const uint32_t total_nb = src1_nrows * nb;
+
+    worker_callback_t quant_job_func;
+    uint32_t n_quant_jobs = 1;
+    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+    } else if (src1_nrows < octx->n_threads) {
+        n_quant_jobs = MIN(total_nb, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
+            uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+            mmctx->quant_ib_first[ith] = ib_first;
+            mmctx->quant_ib_last[ith]  = ib_last;
+            mmctx->quant_r[ith]        = ib_first / nb;
+            mmctx->quant_c[ith]        = ib_first % nb;
         }
+    } else {
+        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+    }
 
-        // HMX has overwritten VTCM, so force dynamic quantization cache to clear
-        octx->src1_spad.src = NULL;
+    size_t src1_row_size;
+    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(src1->ne[0]) : htp_mm_q8_0_flat_row_size(src1->ne[0]);
+    } else {
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(src1->ne[0]) : htp_mm_q8_0_tiled_row_size(src1->ne[0]);
+    }
 
-        if (must_free_mapping) free(mapping_buf);
+    // Set up scratchpads using precomputed sizes from the host
+    size_t src0_sz = kparams->vtcm_src0_size;
+    size_t src1_sz = kparams->vtcm_src1_size;
+    size_t src2_sz = kparams->vtcm_src2_size;
+    size_t src3_sz = kparams->vtcm_src3_size;
+    size_t vtcm_size = kparams->vtcm_size;
+
+    size_t src0_sz_per_thread = src0_sz / octx->n_threads;
+    size_t src1_sz_per_thread = src1_sz;
+    size_t src2_sz_per_thread = src2_sz / octx->n_threads;
+    size_t src3_sz_per_thread = src3_sz / octx->n_threads;
+
+    if (octx->ctx->vtcm_size < vtcm_size) {
+        FARF(ERROR, "matmul-qkv: current VTCM reservation %zu is too small, needed %zu\n",
+             octx->ctx->vtcm_size, vtcm_size);
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    uint8_t * vtcm_ptr = (uint8_t *) octx->ctx->vtcm_base;
+    mmctx->vtcm_src1 = vtcm_seq_alloc(&vtcm_ptr, src1_sz);
+    mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
+    mmctx->vtcm_src2 = vtcm_seq_alloc(&vtcm_ptr, src2_sz);
+    mmctx->vtcm_src3 = vtcm_seq_alloc(&vtcm_ptr, src3_sz);
+
+    octx->src1_spad.src  = NULL;
+    octx->src0_spad.src  = NULL;
+    octx->src2_spad.src  = NULL;
+    octx->src3_spad.src  = NULL;
+
+    mmctx->vtcm_src0_stride = is_repacked ? 0 : src0_row_size_padded;
+    mmctx->vtcm_src2_stride = is_repacked ? 0 : src0_row_size_padded;
+    mmctx->vtcm_src3_stride = is_repacked ? 0 : src0_row_size_padded;
+    mmctx->vtcm_src1_stride = src1_row_size;
+
+    mmctx->vtcm_src0_size_per_thread = src0_sz_per_thread;
+    mmctx->vtcm_src1_size_per_thread = src1_sz_per_thread;
+    mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
+    mmctx->vtcm_src3_size_per_thread = src3_sz_per_thread;
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
         return HTP_STATUS_OK;
-    }
 
-    if (octx->src1_spad.src != src1) {
-        const uint32_t n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
-        octx->src1_spad.src = src1;
-    }
+    // Run quantization once
+    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
+    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
 
+    // Run fused matmul
     const uint32_t n_matmul_jobs = octx->n_threads;
-    worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
+    worker_callback_t matmul_job_func;
+    if (is_repacked) {
+        if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_qkv_2d_repacked_q4_0_flat;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_qkv_2d_repacked_q4_1_flat;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_qkv_2d_repacked_q8_0_flat;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_qkv_2d_repacked_iq4nl_flat;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_qkv_2d_repacked_mxfp4_flat;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        } else {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_qkv_2d_repacked_q4_0;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_qkv_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_qkv_2d_repacked_q8_0;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_qkv_2d_repacked_iq4nl;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_qkv_2d_repacked_mxfp4;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        }
+    } else {
+        matmul_job_func = hvx_mm_qkv_2d;
+    }
+    worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs);
+
+    return HTP_STATUS_OK;
+}
+
+int op_matmul_ffn(struct htp_ops_context * octx) {
+    const struct htp_tensor * restrict src0 = octx->src[0]; // Wgate
+    const struct htp_tensor * restrict src1 = octx->src[1]; // y
+    const struct htp_tensor * restrict src2 = octx->src[2]; // Wup
+    const struct htp_tensor * restrict dst_gate = octx->dsts[0];
+    const struct htp_tensor * restrict dst_up = octx->dsts[1];
+
+    bool is_repacked = (src0->type == HTP_TYPE_Q4_0 || src0->type == HTP_TYPE_Q4_1 ||
+                        src0->type == HTP_TYPE_Q8_0 || src0->type == HTP_TYPE_IQ4_NL ||
+                        src0->type == HTP_TYPE_MXFP4);
+
+    struct htp_mm_context mmctx_struct = {0};
+    struct htp_mm_context * mmctx = &mmctx_struct;
+    mmctx->octx = octx;
+
+    const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;
+
+    const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
+    const uint32_t src1_nrows = src1->ne[1] * src1->ne[2] * src1->ne[3];
+
+    // Compute src0_nrows_per_thread
+    mmctx->src0_nrows_per_thread  = (src0_nrows + octx->n_threads - 1) / octx->n_threads;
+    if (is_repacked) {
+        mmctx->src0_nrows_per_thread = hex_round_up(mmctx->src0_nrows_per_thread, 32);
+    } else {
+        mmctx->src0_nrows_per_thread += (mmctx->src0_nrows_per_thread & 1); // round up to even
+    }
+
+    const size_t src0_row_size = src0->nb[1];
+    const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
+
+    if (hvx_mm_init_vec_dot(mmctx, src0->type) != 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    const uint32_t qk = QK_Q8_0_TILED;
+    const uint32_t nb = (src1->ne[0] + qk - 1) / qk;
+    const uint32_t total_nb = src1_nrows * nb;
+
+    worker_callback_t quant_job_func;
+    uint32_t n_quant_jobs = 1;
+    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+    } else if (src1_nrows < octx->n_threads) {
+        n_quant_jobs = MIN(total_nb, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
+            uint32_t ib_first = (total_nb * (ith + 0)) / n_quant_jobs;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+            mmctx->quant_ib_first[ith] = ib_first;
+            mmctx->quant_ib_last[ith]  = ib_last;
+            mmctx->quant_r[ith]        = ib_first / nb;
+            mmctx->quant_c[ith]        = ib_first % nb;
+        }
+    } else {
+        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
+        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+    }
+
+    size_t src1_row_size;
+    if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(src1->ne[0]) : htp_mm_q8_0_flat_row_size(src1->ne[0]);
+    } else {
+        src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(src1->ne[0]) : htp_mm_q8_0_tiled_row_size(src1->ne[0]);
+    }
+
+    // Set up scratchpads using precomputed sizes from the host
+    size_t src0_sz = kparams->vtcm_src0_size;
+    size_t src1_sz = kparams->vtcm_src1_size;
+    size_t src2_sz = kparams->vtcm_src2_size;
+    size_t vtcm_size = kparams->vtcm_size;
+
+    size_t src0_sz_per_thread = src0_sz / octx->n_threads;
+    size_t src1_sz_per_thread = src1_sz;
+    size_t src2_sz_per_thread = src2_sz / octx->n_threads;
+
+    if (octx->ctx->vtcm_size < vtcm_size) {
+        FARF(ERROR, "matmul-ffn: current VTCM reservation %zu is too small, needed %zu\n", octx->ctx->vtcm_size, vtcm_size);
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    uint8_t * vtcm_ptr = (uint8_t *) octx->ctx->vtcm_base;
+    mmctx->vtcm_src1 = vtcm_seq_alloc(&vtcm_ptr, src1_sz);
+    mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
+    mmctx->vtcm_src2 = vtcm_seq_alloc(&vtcm_ptr, src2_sz);
+
+    octx->src1_spad.src  = NULL;
+    octx->src0_spad.src  = NULL;
+    octx->src2_spad.src  = NULL;
+
+    mmctx->vtcm_src0_stride = is_repacked ? 0 : src0_row_size_padded;
+    mmctx->vtcm_src2_stride = is_repacked ? 0 : src0_row_size_padded;
+    mmctx->vtcm_src1_stride = src1_row_size;
+
+    mmctx->vtcm_src0_size_per_thread = src0_sz_per_thread;
+    mmctx->vtcm_src1_size_per_thread = src1_sz_per_thread;
+    mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
+
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
+        return HTP_STATUS_OK;
+
+    // Run quantization once
+    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
+    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+
+    // Run fused matmul
+    const uint32_t n_matmul_jobs = octx->n_threads;
+    worker_callback_t matmul_job_func;
+    if (is_repacked) {
+        if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_ffn_2d_repacked_q4_0_flat;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_ffn_2d_repacked_q4_1_flat;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_ffn_2d_repacked_q8_0_flat;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_ffn_2d_repacked_iq4nl_flat;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_ffn_2d_repacked_mxfp4_flat;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        } else {
+            switch (src0->type) {
+                case HTP_TYPE_Q4_0:   matmul_job_func = hvx_mm_ffn_2d_repacked_q4_0;   break;
+                case HTP_TYPE_Q4_1:   matmul_job_func = hvx_mm_ffn_2d_repacked_q4_1;   break;
+                case HTP_TYPE_Q8_0:   matmul_job_func = hvx_mm_ffn_2d_repacked_q8_0;   break;
+                case HTP_TYPE_IQ4_NL: matmul_job_func = hvx_mm_ffn_2d_repacked_iq4nl;  break;
+                case HTP_TYPE_MXFP4:  matmul_job_func = hvx_mm_ffn_2d_repacked_mxfp4;  break;
+                default:              return HTP_STATUS_NO_SUPPORT;
+            }
+        }
+    } else {
+        matmul_job_func = hvx_mm_ffn_2d;
+    }
+    worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs);
 
-    if (must_free_mapping) free(mapping_buf);
     return HTP_STATUS_OK;
 }
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.h b/ggml/src/ggml-hexagon/htp/matmul-ops.h
new file mode 100644
index 000000000..a94d5430d
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.h
@@ -0,0 +1,508 @@
+#ifndef HTP_MATMUL_OPS_H
+#define HTP_MATMUL_OPS_H
+
+#include <stdint.h>
+#include <stddef.h>
+#include "htp-ops.h"
+#include "hex-fastdiv.h"
+#include "hex-common.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// --- HMX Tile Constraints ---
+#define HTP_MM_HMX_TILE_N_COLS 32
+#define HTP_MM_HMX_TILE_N_ROWS 32
+#define HTP_MM_HMX_TILE_SIZE   (32 * 32 * sizeof(__fp16)) // 2048 bytes
+#define HTP_MM_HMX_TILE_N_ELMS 1024
+#define HTP_MM_HMX_MIN_NROWS   4
+
+// --- Weight Repacked Tile Sizes ---
+#define HTP_MM_WEIGHT_TILE_SIZE_Q4_0   576
+#define HTP_MM_WEIGHT_TILE_SIZE_Q4_1   640
+#define HTP_MM_WEIGHT_TILE_SIZE_Q8_0   1088
+#define HTP_MM_WEIGHT_TILE_SIZE_IQ4_NL 576
+#define HTP_MM_WEIGHT_TILE_SIZE_MXFP4  544
+
+// --- Weight Repacked Aligned Tile Sizes ---
+#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_0   640
+#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_1   640
+#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q8_0   1152
+#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_IQ4_NL 640
+#define HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_MXFP4  640
+
+// --- Activation Tiled Block Sizes (including padding) ---
+#define HTP_MM_ACT_TILE_SIZE_Q8_0      1152
+#define HTP_MM_ACT_TILE_SIZE_Q8_1      1280
+
+#define HTP_MM_MAX_PREFETCH 16
+
+// --- Solver Cost Model Penalty Weights (HMX-specific) ---
+#define HTP_MM_HMX_COST_W_DEQUANT 3 // cost penalty for quantized weight loading/dequantization
+#define HTP_MM_HMX_COST_A_CONVERT 2 // cost penalty for activation loading/conversion
+
+// --- DMA Activation Transfer Configuration ---
+#define HTP_MM_DMA_ACT_ROWS_PER_STEP 2
+#define HTP_MM_DMA_ACT_MULTIPLIER    4
+
+enum htp_mm_kernel_type {
+    HTP_MM_KERNEL_UNSUPPORTED = 0,
+
+    // HMX paths
+    HTP_MM_KERNEL_HMX_2D,
+    HTP_MM_KERNEL_HMX_F16_BATCHED,
+
+    // HVX floating-point paths
+    HTP_MM_KERNEL_HVX_F16_F16_VTCM,
+    HTP_MM_KERNEL_HVX_F16_F16_DDR,
+    HTP_MM_KERNEL_HVX_F16_F32_DDR,
+
+    HTP_MM_KERNEL_HVX_F32_F32_VTCM,
+    HTP_MM_KERNEL_HVX_F32_F32_DDR,
+    HTP_MM_KERNEL_HVX_F32_F16_DDR,
+
+    // HVX quantized paths
+    HTP_MM_KERNEL_HVX_QUANT_ROW,      // standard row-wise parallel quantization
+    HTP_MM_KERNEL_HVX_QUANT_BLOCK,    // parallel block-wise quantization
+    HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT, // row-wise fallback flat quantization
+};
+
+// Op-specific struct for precomputed matmul params
+struct htp_mm_kernel_params {
+    int32_t  kernel_type;        // enum htp_mm_kernel_type
+    int32_t  pipeline;           // 1 = pipelined execution, 0 = standard
+    int32_t  m_chunk;            // Row chunk size (M chunk)
+    int32_t  n_chunk;            // Col chunk size (N chunk)
+    int32_t  n_threads;          // Number of threads to spawn
+    int32_t  n_act_threads;      // Number of threads for activation preparation
+    int32_t  n_hmx;              // 1 = use HMX, 0 = use HVX
+    int32_t  n_prefetch;         // Prefetch lookahead buffers/rows in VTCM
+    int32_t  tile_size;          // Weight tile size
+    int32_t  aligned_tile_size;  // Aligned weight tile size (padded to 128)
+    int32_t  src1_row_size;      // Row size for quantized activation
+    int32_t  vtcm_size;          // Total required scratchpad size in VTCM
+    int32_t  vtcm_src0_size;     // src0 scratchpad size in VTCM
+    int32_t  vtcm_src1_size;     // src1 scratchpad size in VTCM
+    int32_t  vtcm_src2_size;     // src2 scratchpad size in VTCM (fused only)
+    int32_t  vtcm_src3_size;     // src3 scratchpad size in VTCM (fused only)
+    int32_t  vtcm_dst_size;      // dst scratchpad size in VTCM
+
+    // Precomputed division values
+    struct fastdiv_values div_ne12_ne1;
+    struct fastdiv_values div_ne1;
+    struct fastdiv_values div_r2;
+    struct fastdiv_values div_r3;
+    struct fastdiv_values div_ne11;
+};
+
+#if defined(__cplusplus)
+static_assert(sizeof(struct htp_mm_kernel_params) <= 128, "htp_matmul_kernel_params is too large for kernel_params blob");
+#else
+_Static_assert(sizeof(struct htp_mm_kernel_params) <= 128, "htp_matmul_kernel_params is too large for kernel_params blob");
+#endif
+
+struct mmid_row_mapping {
+    uint32_t i1;
+    uint32_t i2;
+};
+
+// Search for optimal (mc, nc) chunk sizes within VTCM budget.
+static inline int htp_mm_hmx_compute_chunks(size_t   vtcm_total,
+                              size_t   overhead,
+                              size_t   per_n_cost,
+                              size_t   per_m_cost,
+                              size_t   per_mn_cost,
+                              size_t   m,
+                              size_t   n,
+                              size_t   m_block_cost,
+                              size_t   n_block_cost,
+                              size_t * m_chunk_out,
+                              size_t * n_chunk_out,
+                              size_t * total_out) {
+    if (m == 0 || n == 0) return -1;
+    if (vtcm_total <= overhead) return -1;
+    if (per_n_cost == 0 || per_m_cost == 0 || per_mn_cost == 0) return -1;
+
+    const size_t usable = vtcm_total - overhead;
+
+    size_t best_cost = SIZE_MAX;
+    size_t best_mn   = 0;
+    size_t best_m = 0, best_n = 0;
+
+    const size_t n_max = hex_align_down((size_t)n, HTP_MM_HMX_TILE_N_COLS);
+    for (size_t nc = n_max; nc >= HTP_MM_HMX_TILE_N_COLS; nc -= HTP_MM_HMX_TILE_N_COLS) {
+        size_t n_fixed = 0, ncmn = 0, mc_denom = 0;
+        if (hex_mul_overflow(nc, per_n_cost, &n_fixed)) continue;
+        if (n_fixed >= usable) goto next_nc;
+
+        if (hex_mul_overflow(nc, per_mn_cost, &ncmn)) goto next_nc;
+        if (hex_add_overflow(per_m_cost, ncmn, &mc_denom) || mc_denom == 0) goto next_nc;
+
+        {
+            size_t remain = usable - n_fixed;
+            size_t mc = remain / mc_denom;
+            mc = hex_align_down(mc, HTP_MM_HMX_TILE_N_ROWS);
+            mc = hex_smin(mc, m);
+
+            if (mc == 0) {
+                goto next_nc;
+            }
+
+            size_t mblocks = ((size_t) m + mc - 1) / mc;
+            size_t nblocks = ((size_t) n + nc - 1) / nc;
+            size_t cost    = mblocks * m_block_cost + nblocks * n_block_cost;
+            size_t mn      = mc * nc;
+            if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
+                best_cost = cost;
+                best_mn   = mn;
+                best_m    = mc;
+                best_n    = nc;
+            }
+        }
+
+next_nc:
+        if (nc == HTP_MM_HMX_TILE_N_COLS) break;  // avoid size_t underflow
+    }
+
+    if (best_m == 0 || best_n == 0) return -1;
+
+    // Compute exact total (with overflow checks)
+    size_t t0 = 0, t1 = 0, t2 = 0, mn = 0, total = 0;
+    if (hex_mul_overflow(best_n, per_n_cost, &t0)) return -1;
+    if (hex_mul_overflow(best_m, per_m_cost, &t1)) return -1;
+    if (hex_mul_overflow(best_m, best_n, &mn))     return -1;
+    if (hex_mul_overflow(mn, per_mn_cost, &t2))    return -1;
+    if (hex_add_overflow(t0, t1, &total))          return -1;
+    if (hex_add_overflow(total, t2, &total))       return -1;
+    if (hex_add_overflow(total, overhead, &total)) return -1;
+
+    *m_chunk_out = best_m;
+    *n_chunk_out = best_n;
+    *total_out   = total;
+    return 0;
+}
+
+// --- Tile Size Helpers ---
+static inline uint32_t htp_mm_get_weight_tile_size(int weight_type) {
+    switch (weight_type) {
+        case HTP_TYPE_Q4_0:
+        case HTP_TYPE_IQ4_NL:
+            return HTP_MM_WEIGHT_TILE_SIZE_Q4_0;
+        case HTP_TYPE_Q4_1:
+            return HTP_MM_WEIGHT_TILE_SIZE_Q4_1;
+        case HTP_TYPE_Q8_0:
+            return HTP_MM_WEIGHT_TILE_SIZE_Q8_0;
+        case HTP_TYPE_MXFP4:
+            return HTP_MM_WEIGHT_TILE_SIZE_MXFP4;
+        default:
+            return 0;
+    }
+}
+
+static inline uint32_t htp_mm_get_weight_aligned_tile_size(int weight_type) {
+    switch (weight_type) {
+        case HTP_TYPE_Q4_0:
+        case HTP_TYPE_IQ4_NL:
+            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_0;
+        case HTP_TYPE_Q4_1:
+            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q4_1;
+        case HTP_TYPE_Q8_0:
+            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_Q8_0;
+        case HTP_TYPE_MXFP4:
+            return HTP_MM_WEIGHT_ALIGNED_TILE_SIZE_MXFP4;
+        default:
+            return 0;
+    }
+}
+
+// --- Activation/Row Size Helpers ---
+static inline size_t htp_mm_q8_0_tiled_row_size(uint32_t ne) {
+    const uint32_t ne_padded = ((ne + 127) / 128) * 128;
+    const uint32_t nb_32 = ne_padded / 32;
+    return nb_32 * HTP_MM_ACT_TILE_SIZE_Q8_0;
+}
+
+static inline size_t htp_mm_q8_1_tiled_row_size(uint32_t ne) {
+    const uint32_t ne_padded = ((ne + 127) / 128) * 128;
+    const uint32_t nb_32 = ne_padded / 32;
+    return nb_32 * HTP_MM_ACT_TILE_SIZE_Q8_1;
+}
+
+static inline size_t htp_mm_q8_0_flat_row_size(uint32_t ne) {
+    const uint32_t quants_size = hex_align_up(ne, 128);
+    const uint32_t num_scales = (ne + 31) / 32;
+    const uint32_t scales_size = hex_align_up(num_scales * 2, 128);
+    return quants_size + scales_size;
+}
+
+static inline size_t htp_mm_q8_1_flat_row_size(uint32_t ne) {
+    const uint32_t quants_size = hex_align_up(ne, 128);
+    const uint32_t num_scales = (ne + 31) / 32;
+    const uint32_t scales_size = hex_align_up(num_scales * 4, 128);
+    return quants_size + scales_size;
+}
+
+static inline size_t htp_mm_get_tiled_row_stride(int weight_type, uint32_t k) {
+    uint32_t nb = (k + QK_Q4_0_TILED - 1) / QK_Q4_0_TILED;
+    switch (weight_type) {
+        case HTP_TYPE_Q4_0:
+        case HTP_TYPE_IQ4_NL:
+        case HTP_TYPE_Q4_1:
+        case HTP_TYPE_Q8_0:
+        case HTP_TYPE_MXFP4:
+            return (size_t) nb * htp_mm_get_weight_tile_size(weight_type);
+        case HTP_TYPE_F16:
+            return (size_t) k * sizeof(__fp16);
+        case HTP_TYPE_F32:
+            return (size_t) k * sizeof(float);
+        default:
+            return 0;
+    }
+}
+
+static inline size_t htp_mm_round_up(size_t n, size_t m) {
+    return ((n + m - 1) / m) * m;
+}
+
+static inline bool htp_mm_hmx_pipeline(uint32_t m) {
+    return m > 32;
+}
+
+static inline void htp_mm_hmx_get_2d_chunk_costs(
+    int wtype, uint32_t k, bool pipeline, uint32_t aligned_tile_size,
+    size_t * size_per_n_out, size_t * size_per_m_out, size_t * size_per_mn_out
+) {
+    const bool is_quant = (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_F32);
+    const size_t row_stride = htp_mm_get_tiled_row_stride(wtype, k);
+    const size_t vec_dot_size = k * sizeof(uint16_t);
+    const uint32_t n_k_tiles = k / HTP_MM_HMX_TILE_N_COLS;
+    const size_t qweight_row_stride = is_quant ? (size_t)(n_k_tiles * aligned_tile_size) / 32 : 0;
+
+    *size_per_n_out = (pipeline ? 2 : 1) * (is_quant ? qweight_row_stride : row_stride) +
+                      (pipeline ? 2 * vec_dot_size : vec_dot_size);
+    *size_per_m_out = vec_dot_size;
+    *size_per_mn_out = (pipeline ? 2 : 1) * sizeof(uint16_t);
+}
+
+static inline void htp_mm_hmx_get_batched_chunk_costs(
+    uint32_t k, uint32_t group_size,
+    size_t * size_per_n_out, size_t * size_per_m_out, size_t * size_per_mn_out
+) {
+    const size_t vec_dot_size = k * sizeof(uint16_t);
+    *size_per_n_out = 3 * vec_dot_size;
+    *size_per_m_out = group_size * vec_dot_size;
+    *size_per_mn_out = sizeof(uint16_t);
+}
+
+static inline size_t htp_mm_hmx_get_2d_vtcm_size(
+    int wtype, uint32_t k, size_t mc, size_t nc, bool pipeline, uint32_t act_threads, uint32_t aligned_tile_size
+) {
+    const uint32_t n_k_tiles = k / HTP_MM_HMX_TILE_N_COLS;
+    const bool is_quant = (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_F32);
+    const size_t row_stride = htp_mm_get_tiled_row_stride(wtype, k);
+    const size_t vec_dot_size = k * sizeof(uint16_t);
+
+    const size_t act_f32_size = htp_mm_round_up(act_threads * 4 * k * sizeof(float), HTP_MM_HMX_TILE_SIZE);
+    size_t weight_area_size = is_quant
+        ? htp_mm_round_up((nc / 32) * n_k_tiles * aligned_tile_size, HTP_MM_HMX_TILE_SIZE)
+        : htp_mm_round_up(nc * row_stride, HTP_MM_HMX_TILE_SIZE);
+    if (pipeline) {
+        weight_area_size *= 2;
+    }
+    const size_t act_area_size    = htp_mm_round_up(mc * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t output_area_size = htp_mm_round_up(mc * nc * sizeof(uint16_t), HTP_MM_HMX_TILE_SIZE);
+
+    size_t scratch0_size = htp_mm_round_up(nc * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    size_t scratch1_size = pipeline ? scratch0_size : 0;
+    size_t scratch2_size = pipeline ? output_area_size : 0;
+
+    return weight_area_size + act_area_size + act_f32_size + output_area_size +
+           scratch0_size + scratch1_size + scratch2_size + 256;
+}
+
+static inline size_t htp_mm_hmx_get_batched_vtcm_size(
+    int wtype, uint32_t k, size_t mc, size_t nc, uint32_t group_size, bool use_dma_activation, bool pipeline, uint32_t act_threads) {
+    (void)wtype;
+    (void)pipeline;
+    const size_t vec_dot_size     = k * sizeof(uint16_t);
+    const size_t f32_scratch_size = use_dma_activation
+        ? htp_mm_round_up(act_threads * 4 * k * sizeof(float), HTP_MM_HMX_TILE_SIZE) : 0;
+
+    const size_t act_head_stride   = mc * k;
+    const size_t weight_area_size  = htp_mm_round_up(nc * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+    const size_t act_area_size     = htp_mm_round_up(group_size * act_head_stride * sizeof(uint16_t), HTP_MM_HMX_TILE_SIZE);
+    const size_t output_area_size  = htp_mm_round_up(group_size * mc * nc * sizeof(uint16_t), HTP_MM_HMX_TILE_SIZE);
+    const size_t scratch_area_size = htp_mm_round_up(nc * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
+
+    return weight_area_size + act_area_size + output_area_size +
+           2 * scratch_area_size + 256 + f32_scratch_size;
+}
+
+static inline size_t htp_mm_hvx_get_vtcm_sizes(
+    int kernel_type,
+    int wtype,
+    uint32_t ne10,       // k
+    uint32_t src1_nrows, // m_total (or act_nrows)
+    uint32_t n_threads,
+    size_t dst_row_size,
+    size_t src0_row_size,
+    size_t src1_row_size,
+    uint32_t n_prefetch,
+    size_t * vtcm_src0_size_out,
+    size_t * vtcm_src1_size_out,
+    size_t * vtcm_dst_size_out
+) {
+    size_t vtcm_src0_size = 0;
+    size_t vtcm_src1_size = 0;
+    size_t vtcm_dst_size  = 0;
+
+    const bool is_repack = (wtype == HTP_TYPE_Q4_0 || wtype == HTP_TYPE_Q4_1 ||
+                            wtype == HTP_TYPE_Q8_0 || wtype == HTP_TYPE_IQ4_NL ||
+                            wtype == HTP_TYPE_MXFP4);
+
+    const size_t src0_row_size_padded = htp_mm_round_up(src0_row_size, 128);
+    const size_t dst_nrows = (src1_nrows > 1) ? 0 : 1;
+
+    switch (kernel_type) {
+        case HTP_MM_KERNEL_HVX_F16_F16_VTCM: {
+            size_t f16_src1_row_size = htp_mm_round_up(ne10 * 2, 128);
+            vtcm_src1_size = htp_mm_round_up(f16_src1_row_size * src1_nrows, 256);
+            vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256) * n_threads;
+            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) * n_threads : 0;
+            break;
+        }
+        case HTP_MM_KERNEL_HVX_F16_F32_DDR:
+        case HTP_MM_KERNEL_HVX_F16_F16_DDR:
+        case HTP_MM_KERNEL_HVX_F32_F32_DDR:
+        case HTP_MM_KERNEL_HVX_F32_F16_DDR: {
+            vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size, 256) * n_threads;
+            vtcm_src1_size = htp_mm_round_up(n_prefetch * src1_row_size, 256) * n_threads;
+            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) * n_threads : 0;
+            break;
+        }
+        case HTP_MM_KERNEL_HVX_F32_F32_VTCM: {
+            size_t f32_src1_row_size = htp_mm_round_up(ne10 * 4, 128);
+            vtcm_src1_size = htp_mm_round_up(f32_src1_row_size * src1_nrows, 256);
+            vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256) * n_threads;
+            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) * n_threads : 0;
+            break;
+        }
+        case HTP_MM_KERNEL_HVX_QUANT_BLOCK:
+        case HTP_MM_KERNEL_HVX_QUANT_ROW: {
+            size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
+
+            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
+            vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
+            vtcm_src1_size = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
+
+            // src0 spad is also used in dynamic quantizer to store padded src1 rows
+            size_t src1_row_size_padded = htp_mm_round_up(q_src1_row_size, QK_Q8_0_TILED * sizeof(float));
+            if (vtcm_src0_size < src1_row_size_padded) {
+                vtcm_src0_size = src1_row_size_padded;
+            }
+
+            vtcm_src0_size = vtcm_src0_size * n_threads;
+            vtcm_dst_size  = vtcm_dst_size * n_threads;
+
+            if (is_repack) {
+                uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+                uint32_t n_k_tiles = ne10 / 32;
+                uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
+                size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
+                if (repacked_vtcm_size < src1_row_size_padded) {
+                    repacked_vtcm_size = src1_row_size_padded;
+                }
+                vtcm_src0_size = repacked_vtcm_size * n_threads;
+            }
+            break;
+        }
+        case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
+            size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
+
+            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
+            vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
+            vtcm_src1_size = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
+
+            size_t src1_row_size_padded = htp_mm_round_up(q_src1_row_size, 256);
+            if (vtcm_src0_size < src1_row_size_padded) {
+                vtcm_src0_size = src1_row_size_padded;
+            }
+
+            vtcm_src0_size = vtcm_src0_size * n_threads;
+            vtcm_dst_size  = vtcm_dst_size * n_threads;
+
+            if (is_repack) {
+                uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+                uint32_t n_k_tiles = ne10 / 32;
+                uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
+                size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
+                if (repacked_vtcm_size < src1_row_size_padded) {
+                    repacked_vtcm_size = src1_row_size_padded;
+                }
+                vtcm_src0_size = repacked_vtcm_size * n_threads;
+            }
+            break;
+        }
+        default:
+            break;
+    }
+
+    *vtcm_src0_size_out = vtcm_src0_size;
+    *vtcm_src1_size_out = vtcm_src1_size;
+    *vtcm_dst_size_out  = vtcm_dst_size;
+
+    return vtcm_src0_size + vtcm_src1_size + vtcm_dst_size;
+}
+
+static inline size_t htp_mm_hvx_id_get_vtcm_sizes(
+    int wtype,
+    uint32_t ne10,                // k
+    uint32_t src1_nrows,
+    uint32_t n_threads,
+    size_t src0_row_size,    // nb01
+    uint32_t n_prefetch,
+    size_t * vtcm_src0_size_out,
+    size_t * vtcm_src1_size_out
+) {
+    const bool is_repack = (wtype == HTP_TYPE_Q4_0 || wtype == HTP_TYPE_Q4_1 ||
+                            wtype == HTP_TYPE_Q8_0 || wtype == HTP_TYPE_IQ4_NL ||
+                            wtype == HTP_TYPE_MXFP4);
+
+    const size_t src0_row_size_padded = htp_mm_round_up(src0_row_size, 128);
+    const size_t src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10)
+                                                          : htp_mm_q8_0_tiled_row_size(ne10);
+
+    size_t src0_sz_per_thread = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
+    size_t src1_sz            = htp_mm_round_up(src1_row_size * src1_nrows, 256);
+
+    // src0 spad also holds temporary transposed src1 columns during dynamic quantization.
+    const size_t src1_row_size_padded = htp_mm_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
+    if (src0_sz_per_thread < src1_row_size_padded) {
+        src0_sz_per_thread = src1_row_size_padded;
+    }
+
+    if (is_repack) {
+        const uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
+        const uint32_t n_k_tiles    = ne10 / 32;
+        const uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
+        size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
+        if (repacked_vtcm_size < src1_row_size_padded) {
+            repacked_vtcm_size = src1_row_size_padded;
+        }
+        src0_sz_per_thread = repacked_vtcm_size;
+    }
+
+    const size_t vtcm_src0_size = src0_sz_per_thread * n_threads;
+
+    *vtcm_src0_size_out = vtcm_src0_size;
+    *vtcm_src1_size_out = src1_sz;
+
+    return vtcm_src0_size + src1_sz;
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // HTP_MATMUL_OPS_H
diff --git a/ggml/src/ggml-hexagon/libggml-htp.inf b/ggml/src/ggml-hexagon/libggml-htp.inf
index 39cefcdda..874dde1b8 100644
--- a/ggml/src/ggml-hexagon/libggml-htp.inf
+++ b/ggml/src/ggml-hexagon/libggml-htp.inf
@@ -14,8 +14,6 @@ Drivers_Dir = 13
 1 = %DiskId%
 
 [SourceDisksFiles]
-libggml-htp-v68.so = 1
-libggml-htp-v69.so = 1
 libggml-htp-v73.so = 1
 libggml-htp-v75.so = 1
 libggml-htp-v79.so = 1
@@ -28,8 +26,6 @@ ExcludeFromSelect = *
 CopyFiles=Drivers_Dir
 
 [Drivers_Dir]
-libggml-htp-v68.so,,,0x10 ;COPYFLG_NO_OVERWRITE
-libggml-htp-v69.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v73.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v75.so,,,0x10 ;COPYFLG_NO_OVERWRITE
 libggml-htp-v79.so,,,0x10 ;COPYFLG_NO_OVERWRITE
diff --git a/scripts/snapdragon/adb/run-completion.sh b/scripts/snapdragon/adb/run-completion.sh
index fe14bb142..f7622eb52 100755
--- a/scripts/snapdragon/adb/run-completion.sh
+++ b/scripts/snapdragon/adb/run-completion.sh
@@ -57,19 +57,25 @@ oppoll=
 opflt=
 [ "$OF" != "" ] && opflt="GGML_HEXAGON_OPFILTER=$OF"
 
+opfuse=
+[ "$OC" != "" ] && opfuse="GGML_HEXAGON_OPFUSION=$OC"
+
 vmem=
 [ "$VM" != "" ] && vmem="GGML_HEXAGON_VMEM=$VM"
 
 mbuf=
 [ "$MB" != "" ] && mbuf="GGML_HEXAGON_MBUF=$MB"
 
+mmsel=
+[ "$MM" != "" ] && mmsel="GGML_HEXAGON_MM_SELECT=$MM"
+
 set -x
 
 adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $vmem $mbuf \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $opfuse $vmem $mbuf $mmsel \
       ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                  \
          --ctx-size 8192 --ubatch-size 1024 -fa on                        \
diff --git a/scripts/snapdragon/adb/run-tool.sh b/scripts/snapdragon/adb/run-tool.sh
index 6d7e32b32..f6332391b 100755
--- a/scripts/snapdragon/adb/run-tool.sh
+++ b/scripts/snapdragon/adb/run-tool.sh
@@ -51,6 +51,12 @@ opqueue=
 oppoll=
 [ "$OP" != "" ] && oppoll="GGML_HEXAGON_OPPOLL=$OP"
 
+opfuse=
+[ "$OC" != "" ] && opfuse="GGML_HEXAGON_OPFUSION=$OC"
+
+mmsel=
+[ "$MM" != "" ] && mmsel="GGML_HEXAGON_MM_SELECT=$MM"
+
 set -x
 
 tool=$1; shift
@@ -59,5 +65,5 @@ adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll ./$branch/bin/$tool $@ \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opfuse $mmsel ./$branch/bin/$tool $@ \
 "
diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py
index 05045262f..c53ad7779 100755
--- a/scripts/snapdragon/ggml-hexagon-profile.py
+++ b/scripts/snapdragon/ggml-hexagon-profile.py
@@ -26,7 +26,7 @@ COL_MAP = {
 }
 
 op_pattern = re.compile(
-    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
+    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+.*?\s+:\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
 )
 
 trace_pattern = re.compile(
@@ -93,9 +93,40 @@ def parse_log(file_path, pmu_index=None):
                 + int(ts_match.group('us'))
             )
 
-        op_match = op_pattern.search(line)
+        if "|" in line and "profile-op" in line:
+            parts = [p.strip() for p in line.split("|")]
+            prefix = parts[0]
+            prefix_match = re.search(r"profile-op\s+(?P<op_name>[A-Z_0-9+]+)", prefix)
+            if not prefix_match:
+                continue
+
+            if len(parts) == 7:
+                dims, types, timings = parts[2], parts[3], parts[6]
+            elif len(parts) == 6:
+                dims, types, timings = parts[2], parts[3], parts[5]
+            else:
+                continue
+
+            timing_match = re.search(
+                r"(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?",
+                timings
+            )
+            if not timing_match:
+                continue
+
+            op_match = timing_match
+            op_name = prefix_match.group("op_name")
+        else:
+            op_match = op_pattern.search(line)
+            if op_match:
+                op_name = op_match.group('op_name')
+                dims = op_match.group('dims').strip()
+                types = op_match.group('types').strip()
+            else:
+                op_match = None
+
         if op_match:
-            pmu_raw = op_match.group('pmu')
+            pmu_raw = op_match.group('pmu') if 'pmu' in op_match.groupdict() else None
             pmu_val = None
             if pmu_raw and pmu_index is not None:
                 try:
@@ -105,7 +136,7 @@ def parse_log(file_path, pmu_index=None):
                 except (ValueError, IndexError):
                     pmu_val = None
 
-            evt_raw = op_match.group('evt')
+            evt_raw = op_match.group('evt') if 'evt' in op_match.groupdict() else None
             evt_val = None
             if evt_raw:
                 try:
@@ -122,9 +153,9 @@ def parse_log(file_path, pmu_index=None):
             op_text = line[idx + 11:].strip() if idx != -1 else line.strip()
 
             current_op = {
-                'name':         op_match.group('op_name'),
-                'dims':         op_match.group('dims').strip(),
-                'types':        op_match.group('types').strip(),
+                'name':         op_name,
+                'dims':         dims,
+                'types':        types,
                 'op_text':      op_text,
                 'usec':         int(op_match.group('usec')),
                 'cycles':       int(op_match.group('cycles')),
diff --git a/scripts/snapdragon/ggml-hexagon-trace.py b/scripts/snapdragon/ggml-hexagon-trace.py
index 18ec440a9..37f137a9e 100755
--- a/scripts/snapdragon/ggml-hexagon-trace.py
+++ b/scripts/snapdragon/ggml-hexagon-trace.py
@@ -12,7 +12,7 @@ from collections import defaultdict
 logger = logging.getLogger("ggml-hexagon-trace")
 
 op_pattern = re.compile(
-    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+(?P<strides>[\d:x\s\->!]+)\s+:\s+(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
+    r"profile-op\s+(?P<op_name>[A-Z_0-9+]+):\s+.*?\s+:\s+(?P<dims>[\d:x\s\->!]+)\s+:\s+(?P<types>[a-z\d_\s\->x]+)\s+:\s+(?P<strides>[\d:x\s\->!]+?)\s+:\s+(?:(?P<params>.*?)\s+:\s+)?(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?"
 )
 
 trace_pattern = re.compile(
@@ -66,7 +66,40 @@ def parse_log(file_path):
 
     for line in f:
         line_idx += 1
-        op_match = op_pattern.search(line)
+        if "|" in line and "profile-op" in line:
+            parts = [p.strip() for p in line.split("|")]
+            prefix = parts[0]
+            prefix_match = re.search(r"profile-op\s+(?P<op_name>[A-Z_0-9+]+)", prefix)
+            if not prefix_match:
+                continue
+
+            if len(parts) == 7:
+                dims, types, strides, params, timings = parts[2], parts[3], parts[4], parts[5], parts[6]
+            elif len(parts) == 6:
+                dims, types, strides, params, timings = parts[2], parts[3], parts[4], "", parts[5]
+            else:
+                continue
+
+            timing_match = re.search(
+                r"(?:op-)?usec\s+(?P<usec>\d+)\s+(?:op-)?cycles\s+(?P<cycles>\d+)(?:\s+start\s+(?P<start>\d+))?(?:\s+mhz\s+(?P<mhz>[\d.]+))?(?:\s+pmu\s+\[(?P<pmu>[\d,\s]+)\])?(?:\s+evt\s+\[(?P<evt>[\d,\s]+)\])?",
+                timings
+            )
+            if not timing_match:
+                continue
+
+            op_match = timing_match
+            op_name = prefix_match.group("op_name")
+        else:
+            op_match = op_pattern.search(line)
+            if op_match:
+                op_name = op_match.group('op_name')
+                dims = op_match.group('dims').strip() if op_match.group('dims') else ''
+                types = op_match.group('types').strip() if op_match.group('types') else ''
+                strides = op_match.group('strides').strip() if op_match.group('strides') else ''
+                params = op_match.group('params').strip() if ('params' in op_match.groupdict() and op_match.group('params')) else ''
+            else:
+                op_match = None
+
         if op_match:
             cycles_start_raw = op_match.group('start')
             unwrapped_cycles_start = None
@@ -77,10 +110,11 @@ def parse_log(file_path):
             op_text = line[idx + 11:].strip() if idx != -1 else line.strip()
 
             current_op = {
-                'name':         op_match.group('op_name'),
-                'dims':         op_match.group('dims').strip() if op_match.group('dims') else '',
-                'types':        op_match.group('types').strip() if op_match.group('types') else '',
-                'strides':      op_match.group('strides').strip() if op_match.group('strides') else '',
+                'name':         op_name,
+                'dims':         dims,
+                'types':        types,
+                'strides':      strides,
+                'params':       params,
                 'op_text':      op_text,
                 'usec':         int(op_match.group('usec')),
                 'cycles':       int(op_match.group('cycles')),
@@ -397,6 +431,8 @@ def generate_perfetto_trace(filtered_ops, output_path):
                 debug_annots.append(make_debug_annotation("line", int_val=op['line_num']))
             if 'strides' in op and op['strides']:
                 debug_annots.append(make_debug_annotation("strides", string_val=op['strides']))
+            if 'params' in op and op['params'] and op['params'] != '----':
+                debug_annots.append(make_debug_annotation("params", string_val=op['params']))
 
             # Slice Begin
             evt_begin = make_track_event(1, 2, name=f"{op['name']} ({op['dims']})", category="operator", debug_annotations=debug_annots)
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index e1d3853e4..3f18dbe22 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8420,6 +8420,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q4_0, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_MXFP4, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
+
+
 #if 0
     {
         // Test paths in OpenCL
@@ -8594,6 +8599,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     // gpt-oss issue with Vulkan mmq_id
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_MXFP4, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_Q4_0, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
 
     for (ggml_type type_a : all_types) {
         test_cases.emplace_back(new test_mul_mat_id(type_a, GGML_TYPE_F32, 4, 2, false, 64, 16, 3*ggml_blck_size(type_a)));

From 09cedfd6996d2ff90b4e617f56230212005c0166 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Thu, 25 Jun 2026 02:49:22 +0200
Subject: [PATCH 146/292] chat: harden caps check (#24973)

---
 common/chat.cpp                        | 4 ++++
 tools/parser/debug-template-parser.cpp | 6 +++++-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/common/chat.cpp b/common/chat.cpp
index cee6ad650..0cee80434 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -2758,5 +2758,9 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena &          src_pars
 std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates) {
     GGML_ASSERT(chat_templates != nullptr);
     GGML_ASSERT(chat_templates->template_default != nullptr);
+    if (chat_templates->template_tool_use != nullptr) {
+        // take the more expressive template when available
+        return chat_templates->template_tool_use->caps.to_map();
+    }
     return chat_templates->template_default->caps.to_map();
 }
diff --git a/tools/parser/debug-template-parser.cpp b/tools/parser/debug-template-parser.cpp
index 9c591a1f1..50e8f1efb 100644
--- a/tools/parser/debug-template-parser.cpp
+++ b/tools/parser/debug-template-parser.cpp
@@ -40,6 +40,7 @@ struct debug_options {
     bool               enable_reasoning  = true;
     bool               debug_jinja       = false;
     bool               force_tool_call   = false;
+    bool               parallel_tool_calls = true;
     output_mode        mode              = output_mode::BOTH;
     input_message_type input_message     = input_message_type::NONE;
 };
@@ -87,6 +88,7 @@ static void print_usage(const char * program_name) {
     LOG_ERR("\nOptions:\n");
     LOG_ERR("  --no-tools              Disable tool definitions\n");
     LOG_ERR("  --force-tool-call       Set tool calls to forced\n");
+    LOG_ERR("  --parallel-tool-calls=0|1 Set parallel_tool_calls (default: 1)\n");
     LOG_ERR("  --generation-prompt=0|1 Set add_generation_prompt (default: 1)\n");
     LOG_ERR("  --enable-reasoning=0|1  Enable reasoning parsing (default: 1)\n");
     LOG_ERR("  --output=MODE           Output mode: analysis, template, both (default: both)\n");
@@ -121,6 +123,8 @@ static bool parse_options(int argc, char ** argv, debug_options & opts) {
             opts.debug_jinja = true;
         } else if (arg == "--no-tools") {
             opts.with_tools = false;
+        } else if (arg.rfind("--parallel-tool-calls=", 0) == 0) {
+            opts.parallel_tool_calls = parse_bool_option(arg.substr(22));
         } else if (arg.rfind("--generation-prompt=", 0) == 0) {
             opts.generation_prompt = parse_bool_option(arg.substr(20));
         } else if (arg.rfind("--enable-reasoning=", 0) == 0) {
@@ -349,7 +353,7 @@ static autoparser::generation_params prepare_params(const debug_options & opts,
         params.tools       = json();
         params.tool_choice = COMMON_CHAT_TOOL_CHOICE_NONE;
     }
-    params.parallel_tool_calls = false;
+    params.parallel_tool_calls = opts.parallel_tool_calls;
     return params;
 }
 

From fdb2c11c70805abbf595356fbafd5f9d9e337724 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Wed, 24 Jun 2026 19:21:25 -0700
Subject: [PATCH 147/292] opencl: support non-contig rows in norm (#24965)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 21 ++++++++-------------
 ggml/src/ggml-opencl/kernels/norm.cl |  7 +++++--
 2 files changed, 13 insertions(+), 15 deletions(-)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 5ad8d76fa..fb330e062 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -10152,14 +10152,8 @@ static void ggml_cl_norm(ggml_backend_t backend, const ggml_tensor * src0, const
     float eps;
     memcpy(&eps, dst->op_params, sizeof(float));
 
-    const int ne00 = src0 ? src0->ne[0] : 0;
-    const int ne01 = src0 ? src0->ne[1] : 0;
-    const int ne02 = src0 ? src0->ne[2] : 0;
-    const int ne03 = src0 ? src0->ne[3] : 0;
-
-    const cl_ulong nb01 = src0 ? src0->nb[1] : 0;
-    const cl_ulong nb02 = src0 ? src0->nb[2] : 0;
-    const cl_ulong nb03 = src0 ? src0->nb[3] : 0;
+    GGML_TENSOR_LOCALS(int,      ne0, src0, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
 
     const int nth = MIN(64, ne00);
 
@@ -10173,11 +10167,12 @@ static void ggml_cl_norm(ggml_backend_t backend, const ggml_tensor * src0, const
     CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),       &ne01));
     CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),       &ne02));
     CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),       &ne03));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong),  &nb01));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong),  &nb02));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),  &nb03));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(float),     &eps));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float)*nth, NULL));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong),  &nb00));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong),  &nb01));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong),  &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong),  &nb03));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float),     &eps));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float)*nth, NULL));
 
     size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
     size_t local_work_size[] = {(size_t)nth, 1, 1};
diff --git a/ggml/src/ggml-opencl/kernels/norm.cl b/ggml/src/ggml-opencl/kernels/norm.cl
index 170f82278..a5ccac241 100644
--- a/ggml/src/ggml-opencl/kernels/norm.cl
+++ b/ggml/src/ggml-opencl/kernels/norm.cl
@@ -24,6 +24,7 @@ kernel void kernel_norm(
         int ne01,
         int ne02,
         int ne03,
+        ulong nb00,
         ulong nb01,
         ulong nb02,
         ulong nb03,
@@ -43,7 +44,8 @@ kernel void kernel_norm(
     // parallel sum
     sum[get_local_id(0)] = 0.0f;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
-        sum[get_local_id(0)] += x[i00];
+        // this kernel handles float, nb00/4 translates byte offset to element offset
+        sum[get_local_id(0)] += x[i00*nb00/4];
     }
     // reduce
     barrier(CLK_LOCAL_MEM_FENCE);
@@ -60,7 +62,8 @@ kernel void kernel_norm(
     global float * y = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
     sum[get_local_id(0)] = 0.0f;
     for (int i00 = get_local_id(0); i00 < ne00; i00 += get_local_size(0)) {
-        y[i00] = x[i00] - mean;
+        // this kernel handles float, nb00/4 translates byte offset to element offset
+        y[i00] = x[i00*nb00/4] - mean;
         sum[get_local_id(0)] += y[i00] * y[i00];
     }
 

From 9c10954865a0064fcd6b7f4d19400b36de4a6444 Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Thu, 25 Jun 2026 13:27:58 +0800
Subject: [PATCH 148/292] sycl : fix the failed UT cases of conv_3d (#24900)

---
 ggml/src/ggml-sycl/conv3d.cpp | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-sycl/conv3d.cpp b/ggml/src/ggml-sycl/conv3d.cpp
index 2fa29f930..379656255 100644
--- a/ggml/src/ggml-sycl/conv3d.cpp
+++ b/ggml/src/ggml-sycl/conv3d.cpp
@@ -103,8 +103,8 @@ void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     // allocate packed arrays: A_packed (k x m), B_packed (k x n)
     ggml_sycl_pool_alloc<float> A_packed_alloc(ctx.pool());
     ggml_sycl_pool_alloc<float> B_packed_alloc(ctx.pool());
-    A_packed_alloc.alloc((size_t) knl_n_total * patch_total * sizeof(float));
-    B_packed_alloc.alloc((size_t) knl_n_total * oc * sizeof(float));
+    A_packed_alloc.alloc((size_t) knl_n_total * patch_total);
+    B_packed_alloc.alloc((size_t) knl_n_total * oc);
 
     float * A_packed = A_packed_alloc.get();
     float * B_packed = B_packed_alloc.get();
@@ -115,10 +115,16 @@ void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
 
     // Combined kernel: im2col -> pack A, and pack B simultaneously
     const char * src1_base = (const char *) src1->data;
+    const char * src0_base = (const char *) src0->data;
     const int64_t src1_nb0 = src1->nb[0];
     const int64_t src1_nb1 = src1->nb[1];
     const int64_t src1_nb2 = src1->nb[2];
     const int64_t src1_nb3 = src1->nb[3];
+    const int64_t src1_w = src1->ne[0];
+    const int64_t src1_h = src1->ne[1];
+    const int64_t src1_d = src1->ne[2];
+
+    const bool src0_is_f32 = (src0->type == GGML_TYPE_F32);
 
     // Compute correct strides for src0 as (knl_n_total, oc) matrix
     const int64_t src0_packed_nb0 = kernel_type_size;
@@ -165,7 +171,7 @@ void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
         const int64_t sz = dst_z * s2 + kz * d2 - p2;
 
         float val = 0.0f;
-        if (sx >= 0 && sx < src1->ne[0] && sy >= 0 && sy < src1->ne[1] && sz >= 0 && sz < src1->ne[2]) {
+        if (sx >= 0 && sx < src1_w && sy >= 0 && sy < src1_h && sz >= 0 && sz < src1_d) {
             const int64_t channel_idx = batch_idx * c + ic;
             const char * ptr = src1_base + sx * src1_nb0 + sy * src1_nb1 + sz * src1_nb2 + channel_idx * src1_nb3;
             val = *(const float *) ptr;
@@ -184,9 +190,9 @@ void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
 
         const int64_t row = t % k;
         const int64_t col = t / k;
-        const char * src_ptr = (const char *) src0->data + row * src0_packed_nb0 + col * src0_packed_nb1;
+        const char * src_ptr = src0_base + row * src0_packed_nb0 + col * src0_packed_nb1;
         float v;
-        if (src0->type == GGML_TYPE_F32) {
+        if (src0_is_f32) {
             v = *(const float *) src_ptr;
         } else {
             v = sycl::vec<sycl::half, 1>(*(const sycl::half *) src_ptr).convert<float, sycl::rounding_mode::automatic>()[0];

From e9fb3b3fc0300501179b0ce61e907be767e8c859 Mon Sep 17 00:00:00 2001
From: David Spruill <62445444+Spruill-1@users.noreply.github.com>
Date: Thu, 25 Jun 2026 01:35:21 -0400
Subject: [PATCH 149/292] sycl : support --split-mode tensor (#24152)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Sycl tp stage1 (#1)

* SYCL: tensor parallelism (--split-mode tensor) for dual-GPU

Adds the comm_init/comm_free/comm_allreduce_tensor trio that the
meta-backend queries via get_proc_address to enable backend-specific
all-reduce, mirroring the pattern used by ggml-cuda.cu.

For N=2 (the common dual-GPU case) implements a degenerate ring
all-reduce with two size-branched paths:

  * Small (nelem < 32768): FP32 direct memcpy + per-device ADD kernel
    chained via depends_on(memcpy_event). 4 SYCL submissions/call.

  * Large (nelem >= 32768): BF16-compressed. Each device compresses
    FP32 -> BF16 in a local outbox, cross-device memcpys to the peer's
    inbox (HALF the PCIe bytes), then decompresses + adds into the
    local FP32 partial. 6 SYCL submissions/call but PCIe bytes halved
    -- wins for any tensor where PCIe dominates kernel time.

Threshold and BF16 path pattern mirror the CUDA NCCL allreduce.

Storage: ONE persistent uint8_t buffer per device, 4 * nelem bytes
(matches both path layouts: FP32 nelem floats; BF16 outbox+inbox =
2 * nelem uint16_t each). Single alloc+free per device keeps the
SYCL pool's strict-LIFO invariant trivial.

Initial impl handles N=2 FP32 contiguous tensors. Other cases return
false, causing the meta-backend to use its generic butterfly fallback.

Per-call sync is intentionally omitted. SYCL in-order queue semantics
ensure that the meta-backend's next compute on the same per-device
queue waits for our final ADD, and the next allreduce's first op on
the same persistent buffer waits via the same queue. Only comm_free
does an explicit final wait.

OneCCL is NOT used: OneCCL 2021.17 hardcodes single-device-per-process
in communicator_impl.hpp:47 (condition devices.size() == 1), which is
incompatible with llama.cpp's single-process multi-GPU model.

Measured on dual Intel Arc Pro B70 (NEO 26.05.x, oneAPI 2025.3 +
DPC++ nightly):

  Llama-3.3-70B Q4_K_M, -sm tensor -fa 1 -ctk f16 -ctv f16:
    pp512 = 377.08 t/s  (vs 313.65 layer mode = +20.2%)
    tg128 = 17.40 t/s   (vs   9.74 layer mode = +78.6%)

  Qwen3-Coder-Next-80B-A3B Q3_K_M (MoE):
    pp512 = 216.56 t/s  (vs 156.58 meta-backend butterfly = +38.3%)
    tg128 = 17.60 t/s   (vs  14.31 meta-backend butterfly = +23.0%)

  Qwen3-4B Q4_K_M:
    pp64  = 984.51 t/s, tg16 = 49.29 t/s

Llama-3.3-70B in SYCL TP now comfortably beats production layer mode
on both prefill and decode. Coder-Next-80B-A3B (MoE) also wins on
both — the BF16 path is what unlocks the many-medium-allreduces
prefill pattern.

Build/CMake: no changes. No new dependencies. ~210 lines added across
ggml-sycl.h and ggml-sycl.cpp.

* Fix comments

* documentation update to address PR feedback

* Bring over my device-to-device memcpy chagnes

* move the dev2dev_memcpy calls to the upstream 7-parameter variety

* Fix a typo and remove a trailing whitespace
---
 docs/backend/SYCL.md             |  18 +++
 ggml/include/ggml-sycl.h         |   8 +
 ggml/src/ggml-sycl/ggml-sycl.cpp | 255 +++++++++++++++++++++++++++++++
 3 files changed, 281 insertions(+)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index d482d8840..8b0b9a186 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -413,6 +413,15 @@ In two device selection modes, the default SYCL backend is level_zero, you can c
 |------------------|----------------------------------------|
 | Single device    | --split-mode none --main-gpu DEVICE_ID |
 | Multiple devices | --split-mode layer (default)           |
+| Multiple devices | --split-mode tensor (tensor parallelism) |
+
+`--split-mode tensor` (tensor parallelism) shards each layer across the selected
+GPUs. It requires flash attention, which is auto-enabled when `--flash-attn` is
+left at its default `auto`, so `--split-mode tensor` works out of the box.
+Passing `--flash-attn off` together with `--split-mode tensor` is rejected at
+context creation. The default `f16` KV cache is recommended. Tensor parallelism
+is currently optimized for 2 GPUs; other device counts fall back to a generic
+all-reduce.
 
 Examples:
 
@@ -715,6 +724,15 @@ In two device selection modes, the default SYCL backend is level_zero, you can c
 |------------------|----------------------------------------|
 | Single device    | --split-mode none --main-gpu DEVICE_ID |
 | Multiple devices | --split-mode layer (default)           |
+| Multiple devices | --split-mode tensor (tensor parallelism) |
+
+`--split-mode tensor` (tensor parallelism) shards each layer across the selected
+GPUs. It requires flash attention, which is auto-enabled when `--flash-attn` is
+left at its default `auto`, so `--split-mode tensor` works out of the box.
+Passing `--flash-attn off` together with `--split-mode tensor` is rejected at
+context creation. The default `f16` KV cache is recommended. Tensor parallelism
+is currently optimized for 2 GPUs; other device counts fall back to a generic
+all-reduce.
 
 Examples:
 
diff --git a/ggml/include/ggml-sycl.h b/ggml/include/ggml-sycl.h
index 5ce349a88..418a7ba97 100644
--- a/ggml/include/ggml-sycl.h
+++ b/ggml/include/ggml-sycl.h
@@ -27,6 +27,14 @@ GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int de
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
 
+// Tensor parallelism (--split-mode tensor): comm_init/free/allreduce_tensor
+// trio queried by the meta-backend via ggml_backend_reg_get_proc_address.
+// See typedefs in ggml/include/ggml-backend.h. Mirrors the CUDA backend's
+// pattern (ggml_backend_cuda_comm_*).
+GGML_BACKEND_API void * ggml_backend_sycl_comm_init(ggml_backend_t * backends, size_t n_backends);
+GGML_BACKEND_API void   ggml_backend_sycl_comm_free(void * comm_ctx);
+GGML_BACKEND_API bool   ggml_backend_sycl_comm_allreduce_tensor(void * comm_ctx, struct ggml_tensor ** tensors);
+
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
 
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index d8b83d0e2..41449db66 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -5859,6 +5859,250 @@ static ggml_backend_dev_t ggml_backend_sycl_reg_get_device(ggml_backend_reg_t re
     return ctx->devices[index];
 }
 
+// ==========================================================================
+// Tensor parallelism (--split-mode tensor) for the SYCL backend.
+//
+// The meta-backend invokes these three entry points via get_proc_address:
+//   * ggml_backend_sycl_comm_init             - one-time per-graph setup
+//   * ggml_backend_sycl_comm_allreduce_tensor - per-allreduce step
+//   * ggml_backend_sycl_comm_free             - tear-down
+//
+// For N=2 (dual-GPU), this is a degenerate ring allreduce with dual paths
+// chosen by tensor size:
+//
+//   * Small (nelem < 32K): FP32 direct memcpy + per-device ADD
+//     kernel. The kernel depends_on() its corresponding memcpy event
+//     so it doesn't read partial data. Both devices run in parallel.
+//
+//   * Large (nelem >= 32K): BF16-compressed. Each device compresses
+//     its FP32 partial to BF16 locally, cross-device memcpys
+//     to the peer (half the PCI bandwidth), where it is decompressed
+//     and added into the local FP32 partial. 6 SYCL submissions per
+//     allreduce (2 compress + 2 memcpy + 2 decompress-add) vs the
+//     4 for the small path, but the bandwidth saving > 6 GB/s PCIe x 2
+//     dominates for larger tensors.
+//
+// Storage: A persistent uint8_t buffer per device, sized to
+// 4 * nelem bytes. Both paths reinterpret the same bytes (small path
+// as nelem floats; large path as outbox + inbox = 2*nelem uint16_t
+// each, using the full 4*nelem byte budget either way). Single
+// alloc+free per device keeps the SYCL pool's strict-LIFO invariant
+// trivial.
+//
+// For non-(N=2 FP32 contiguous) cases, comm_init or comm_allreduce_tensor
+// returns null/false, causing the meta-backend to use its generic
+// butterfly all-reduce fallback.
+// ==========================================================================
+
+struct ggml_backend_sycl_comm_context {
+    std::vector<ggml_backend_t> backends;
+    // ONE persistent per-device byte buffer, 4*nelem bytes.  Both the
+    // FP32 small-tensor path and the BF16 large-tensor path share it
+    // by reinterpreting.
+    std::unique_ptr<ggml_sycl_pool_alloc<uint8_t>> buf0;
+    std::unique_ptr<ggml_sycl_pool_alloc<uint8_t>> buf1;
+    int64_t buf_nelem = 0;
+};
+
+void * ggml_backend_sycl_comm_init(ggml_backend_t * backends, size_t n_backends) try {
+    for (size_t i = 0; i < n_backends; ++i) {
+        if (!ggml_backend_is_sycl(backends[i])) {
+            return nullptr;
+        }
+    }
+
+    // Initial version: N=2 only. For N!=2, returning null makes the
+    // meta-backend skip this backend-specific allreduce entirely.
+    if (n_backends != 2) {
+        return nullptr;
+    }
+
+    auto * ctx = new ggml_backend_sycl_comm_context;
+    ctx->backends.assign(backends, backends + n_backends);
+    auto * sctx0 = (ggml_backend_sycl_context *) backends[0]->context;
+    auto * sctx1 = (ggml_backend_sycl_context *) backends[1]->context;
+    ctx->buf0 = std::make_unique<ggml_sycl_pool_alloc<uint8_t>>(sctx0->pool());
+    ctx->buf1 = std::make_unique<ggml_sycl_pool_alloc<uint8_t>>(sctx1->pool());
+    return ctx;
+}
+catch (const sycl::exception &) { return nullptr; }
+catch (...)                     { return nullptr; }
+
+void ggml_backend_sycl_comm_free(void * comm_ctx_v) {
+    auto * comm_ctx = static_cast<ggml_backend_sycl_comm_context *>(comm_ctx_v);
+    if (comm_ctx == nullptr) {
+        return;
+    }
+
+    // Sync both per-device queues so the pool_alloc destructors don't
+    // return memory still in use by the last kernel.
+    if (comm_ctx->backends.size() == 2) {
+        auto * sctx0 = (ggml_backend_sycl_context *) comm_ctx->backends[0]->context;
+        auto * sctx1 = (ggml_backend_sycl_context *) comm_ctx->backends[1]->context;
+        try {
+            sctx0->stream()->wait();
+            sctx1->stream()->wait();
+        } catch (...) { /* best effort during shutdown */ }
+    }
+
+    delete comm_ctx;
+}
+
+bool ggml_backend_sycl_comm_allreduce_tensor(void * comm_ctx_v, struct ggml_tensor ** tensors) try {
+    if (comm_ctx_v == nullptr) {
+        return false;
+    }
+
+    auto * comm_ctx = static_cast<ggml_backend_sycl_comm_context *>(comm_ctx_v);
+    const size_t n_backends = comm_ctx->backends.size();
+
+    // Fast path: N=2, F32/F16, contiguous, matching shapes.
+    if (n_backends != 2) {
+        return false;
+    }
+    // Accept F32 or F16 inputs natively (types must match). F16 takes the
+    // direct 2-byte memcpy + add path below; other types return false so the
+    // meta-backend uses its generic all-reduce.
+    if (tensors[0]->type != tensors[1]->type) {
+        return false;
+    }
+    if (tensors[0]->type != GGML_TYPE_F32 && tensors[0]->type != GGML_TYPE_F16) {
+        return false;
+    }
+    if (!ggml_is_contiguous(tensors[0]) || !ggml_is_contiguous(tensors[1])) {
+        return false;
+    }
+    if (ggml_nelements(tensors[0]) != ggml_nelements(tensors[1])) {
+        return false;
+    }
+
+    const int64_t nelem  = ggml_nelements(tensors[0]);
+    const size_t  nbytes = ggml_nbytes(tensors[0]);
+    if (nelem == 0) {
+        return true;
+    }
+
+    auto * ctx0 = (ggml_backend_sycl_context *) comm_ctx->backends[0]->context;
+    auto * ctx1 = (ggml_backend_sycl_context *) comm_ctx->backends[1]->context;
+    queue_ptr q0 = ctx0->stream();
+    queue_ptr q1 = ctx1->stream();
+
+    // Grow per-device byte buffers if needed (4 * nelem bytes each).
+    if (comm_ctx->buf_nelem < nelem) {
+        comm_ctx->buf0->realloc(nelem * 4);
+        comm_ctx->buf1->realloc(nelem * 4);
+        comm_ctx->buf_nelem = nelem;
+    }
+    uint8_t * buf0 = comm_ctx->buf0->get();
+    uint8_t * buf1 = comm_ctx->buf1->get();
+
+    // F16 native path: direct 2-byte cross-device copy + add, skipping the
+    // F32 round-trip the meta-backend fallback would force. Cross-device copies
+    // go through dev2dev_memcpy because the two devices are in separate SYCL
+    // contexts (a raw peer-USM q->memcpy would be a silent no-op).
+    if (tensors[0]->type == GGML_TYPE_F16) {
+        sycl::half * f16_out0 = (sycl::half *) tensors[0]->data;
+        sycl::half * f16_out1 = (sycl::half *) tensors[1]->data;
+        sycl::half * f16_tmp0 = (sycl::half *) buf0;
+        sycl::half * f16_tmp1 = (sycl::half *) buf1;
+
+        q0->wait();
+        q1->wait();
+        dev2dev_memcpy(ctx0->device, *q0, ctx1->device, *q1, f16_tmp0, tensors[1]->data, nbytes);
+        dev2dev_memcpy(ctx1->device, *q1, ctx0->device, *q0, f16_tmp1, tensors[0]->data, nbytes);
+
+        q0->submit([&](sycl::handler & h) {
+            h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+                f16_out0[i] = (sycl::half) ((float) f16_out0[i] + (float) f16_tmp0[i]);
+            });
+        });
+        q1->submit([&](sycl::handler & h) {
+            h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+                f16_out1[i] = (sycl::half) ((float) f16_out1[i] + (float) f16_tmp1[i]);
+            });
+        });
+        return true;
+    }
+
+    float * out0 = (float *) tensors[0]->data;
+    float * out1 = (float *) tensors[1]->data;
+
+    // BF16 threshold: above this, the PCIe savings from halving the
+    // cross-device bytes outweigh the 2 extra compress kernels.
+    // Below: stay on the FP32 fast path.  Threshold mirrors the CUDA
+    // NCCL allreduce pattern for n_backends=2.
+    static constexpr int64_t BF16_THRESHOLD = 32768;
+
+    if (nelem < BF16_THRESHOLD) {
+        // FP32 small path: 4 SYCL submissions per allreduce.
+        float * tmp0 = (float *) buf0;
+        float * tmp1 = (float *) buf1;
+
+        // COMM-D2D-FIX: the two devices are in SEPARATE SYCL contexts, so a raw
+        // q->memcpy of a peer USM pointer is a silent no-op. Route cross-device
+        // copies through dev2dev_memcpy (L0 direct copy / host staging). It is
+        // synchronous, so wait for the local partials to be produced first.
+        q0->wait();
+        q1->wait();
+        dev2dev_memcpy(ctx0->device, *q0, ctx1->device, *q1, tmp0, tensors[1]->data, nbytes);
+        dev2dev_memcpy(ctx1->device, *q1, ctx0->device, *q0, tmp1, tensors[0]->data, nbytes);
+
+        q0->submit([&](sycl::handler & h) {
+            h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+                out0[i] += tmp0[i];
+            });
+        });
+        q1->submit([&](sycl::handler & h) {
+            h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+                out1[i] += tmp1[i];
+            });
+        });
+        return true;
+    }
+
+    // BF16 large path: 6 SYCL submissions per allreduce, but the
+    // cross-device memcpy is HALF the bytes. Pure bit-shift
+    // conversion (no rounding) — matches ggml's truncating fp32->bf16.
+    uint16_t * outbox0 = (uint16_t *) buf0;
+    uint16_t * inbox0  = outbox0 + nelem;
+    uint16_t * outbox1 = (uint16_t *) buf1;
+    uint16_t * inbox1  = outbox1 + nelem;
+
+    // Phase A: compress each device's local partial in parallel.
+    sycl::event c0 = q0->parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+        outbox0[i] = (uint16_t) (sycl::bit_cast<uint32_t>(out0[i]) >> 16);
+    });
+
+    sycl::event c1 = q1->parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+        outbox1[i] = (uint16_t) (sycl::bit_cast<uint32_t>(out1[i]) >> 16);
+    });
+
+    // Phase B: COMM-D2D-FIX-BF16 cross-device copy of compressed bytes via
+    // dev2dev_memcpy (separate SYCL contexts; sync copy after compress).
+    const size_t bf16_bytes = nelem * sizeof(uint16_t);
+    c0.wait();
+    c1.wait();
+    dev2dev_memcpy(ctx0->device, *q0, ctx1->device, *q1, inbox0, outbox1, bf16_bytes);
+    dev2dev_memcpy(ctx1->device, *q1, ctx0->device, *q0, inbox1, outbox0, bf16_bytes);
+
+    // Phase C: decompress + add into local FP32 partial.
+    q0->submit([&](sycl::handler & h) {
+        h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+            out0[i] += sycl::bit_cast<float>(((uint32_t) inbox0[i]) << 16);
+        });
+    });
+
+    q1->submit([&](sycl::handler & h) {
+        h.parallel_for(sycl::range<1>(nelem), [=](sycl::id<1> i) {
+            out1[i] += sycl::bit_cast<float>(((uint32_t) inbox1[i]) << 16);
+        });
+    });
+
+    return true;
+}
+catch (const sycl::exception &) { return false; }
+catch (...)                     { return false; }
+
 static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, const char *name) {
     GGML_UNUSED(reg);
 
@@ -5866,6 +6110,17 @@ static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, cons
         return (void *)ggml_backend_sycl_split_buffer_type;
     }
 
+    // Tensor parallelism (--split-mode tensor) entry points.
+    if (strcmp(name, "ggml_backend_comm_init") == 0) {
+        return (void *)ggml_backend_sycl_comm_init;
+    }
+    if (strcmp(name, "ggml_backend_comm_free") == 0) {
+        return (void *)ggml_backend_sycl_comm_free;
+    }
+    if (strcmp(name, "ggml_backend_comm_allreduce_tensor") == 0) {
+        return (void *)ggml_backend_sycl_comm_allreduce_tensor;
+    }
+
     // SYCL doesn't support registering host memory, left here for reference
     // "ggml_backend_register_host_buffer"
     // "ggml_backend_unregister_host_buffer"

From b3ce5cedf4c007b78a45befe839fa3abada03c0b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Thu, 25 Jun 2026 07:36:49 +0200
Subject: [PATCH 150/292] quant : fix quantizing moe with mtp (#24986)

---
 src/llama-quant.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 89b7fe8d4..847e79f46 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -847,7 +847,7 @@ static void init_quantize_state_counters(quantize_state_impl & qs, std::vector<t
             qs.has_tied_embeddings = false;
         }
     }
-    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer();
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)qs.model.hparams.n_layer_all;
 }
 
 //

From e975ad68544cdb3b1ddb48eddfea95c774126c81 Mon Sep 17 00:00:00 2001
From: Pento <pentolo@outlook.com>
Date: Thu, 25 Jun 2026 07:37:22 +0200
Subject: [PATCH 151/292] Cap n_outputs_max on MTP draft contexts (#2287)

Co-authored-by: Pento95 <Pento95@users.noreply.github.com>
---
 gpttype_adapter.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index a355eddd7..db8c5d3bf 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -597,6 +597,7 @@ static size_t estimate_draft_autofit_tax_mb(
     {
         draft_ctx_params.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
         draft_ctx_params.n_rs_seq = speculative_chunk_amt;
+        draft_ctx_params.n_outputs_max = base_ctx_params.n_seq_max; //match the real MTP draft context (see speculative_decoding_setup) so the autofit tax doesn't over-reserve the draft compute buffer at n_batch*n_vocab (~2GB on large-vocab models like Gemma)
         measure_model_bytes = has_draft_model;
     }
 
@@ -950,6 +951,7 @@ static void speculative_decoding_setup(std::string spec_model_filename, llama_co
         draft_ctx_params.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
         draft_ctx_params.ctx_other = main_ctx;
         draft_ctx_params.n_rs_seq = speculative_chunk_amt;
+        draft_ctx_params.n_outputs_max = base_ctx_params.n_seq_max; //draft-mtp generates tokens autoregressively (1 output per sequence per decode, looped up to n_max); cap outputs at n_seq instead of letting it default to n_batch, which sized the draft sampling buffer at n_batch*n_vocab (~2GB on large-vocab models like Gemma)
     }
     draft_ctx = llama_init_from_model(draftmodel, draft_ctx_params);
     if(draft_ctx == NULL)

From e12a0128ab0be91d500d25d18db05067d3f7b4e8 Mon Sep 17 00:00:00 2001
From: Abraham Gonzalez <theabecaster0@gmail.com>
Date: Thu, 25 Jun 2026 01:37:30 -0400
Subject: [PATCH 152/292] build: include libmtmd in Apple XCFramework (#21935)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Adds an opt-in LLAMA_BUILD_MTMD CMake option so build-xcframework.sh
can link libmtmd.a into the framework binary without pulling in the
rest of tools/ (which doesn't cross-build cleanly to iOS/tvOS/visionOS).

- CMakeLists.txt: new option, default OFF. When on with
  LLAMA_BUILD_TOOLS=OFF, only the tools/mtmd subdir is added. Useful
  for any binding that wants just libmtmd (Apple XCFramework, WASM).
- tools/mtmd/CMakeLists.txt: gate the CLI exe targets on
  LLAMA_BUILD_TOOLS. Gating on LLAMA_BUILD_COMMON is not enough — it
  defaults ON in standalone builds and visionOS xcodebuild then fails
  with "install TARGETS given no BUNDLE DESTINATION for MACOSX_BUNDLE
  executable target 'llama-mtmd-cli'".
- build-xcframework.sh: turn the option on, pass -DLLAMA_BUILD_MTMD,
  add libmtmd.a to combine_static_libraries, and copy mtmd.h and
  mtmd-helper.h into the framework Headers dir. The umbrella module
  map then exposes them, so Swift / Obj-C consumers can import the
  mtmd C API directly.

After this, nm on ios-arm64/llama.framework/llama shows 52 _mtmd_
symbols. Verified end-to-end: a Swift target links the produced
framework and calls mtmd_default_marker, mtmd_bitmap_init, etc.
without a shim on macos / iphoneos / iphonesimulator / xros slices.

Co-authored-by: Abraham Gonzalez <abraham@theabecaster.com>
---
 CMakeLists.txt            | 10 ++++++++++
 build-xcframework.sh      |  5 +++++
 tools/mtmd/CMakeLists.txt | 40 ++++++++++++++++++++++-----------------
 3 files changed, 38 insertions(+), 17 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 9e7b1253c..81f23d7e7 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -222,6 +222,16 @@ if (LLAMA_BUILD_APP)
     add_subdirectory(app)
 endif()
 
+# Standalone libmtmd build without pulling in the rest of the tools/ tree.
+# Useful when packaging just the mtmd library for language bindings (e.g. an
+# Apple XCFramework, or a WASM build). When the full tools build is enabled,
+# mtmd is already built by the tools/ subdirectory above; this hook only fires
+# when LLAMA_BUILD_TOOLS is OFF to avoid double-adding the target.
+option(LLAMA_BUILD_MTMD "llama: build tools/mtmd library standalone" OFF)
+if (LLAMA_BUILD_MTMD AND NOT (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS))
+    add_subdirectory(tools/mtmd)
+endif()
+
 #
 # install
 #
diff --git a/build-xcframework.sh b/build-xcframework.sh
index 180c01a88..3a265e53e 100755
--- a/build-xcframework.sh
+++ b/build-xcframework.sh
@@ -13,6 +13,7 @@ LLAMA_BUILD_EXAMPLES=OFF
 LLAMA_BUILD_TOOLS=OFF
 LLAMA_BUILD_TESTS=OFF
 LLAMA_BUILD_SERVER=OFF
+LLAMA_BUILD_MTMD=ON
 GGML_METAL=ON
 GGML_METAL_EMBED_LIBRARY=ON
 GGML_BLAS_DEFAULT=ON
@@ -39,6 +40,7 @@ COMMON_CMAKE_ARGS=(
     -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
     -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}
     -DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER}
+    -DLLAMA_BUILD_MTMD=${LLAMA_BUILD_MTMD}
     -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY}
     -DGGML_BLAS_DEFAULT=${GGML_BLAS_DEFAULT}
     -DGGML_METAL=${GGML_METAL}
@@ -126,6 +128,8 @@ setup_framework_structure() {
     cp ggml/include/ggml-cpu.h     ${header_path}
     cp ggml/include/ggml-blas.h    ${header_path}
     cp ggml/include/gguf.h         ${header_path}
+    cp tools/mtmd/mtmd.h           ${header_path}
+    cp tools/mtmd/mtmd-helper.h    ${header_path}
 
     # Create module map (common for all platforms)
     cat > ${module_path}module.modulemap << EOF
@@ -247,6 +251,7 @@ combine_static_libraries() {
         "${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-cpu.a"
         "${base_dir}/${build_dir}/ggml/src/ggml-metal/${release_dir}/libggml-metal.a"
         "${base_dir}/${build_dir}/ggml/src/ggml-blas/${release_dir}/libggml-blas.a"
+        "${base_dir}/${build_dir}/tools/mtmd/${release_dir}/libmtmd.a"
     )
 
     # Create temporary directory for processing
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 09b62357f..ea684d9f1 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -115,22 +115,28 @@ if (TARGET mtmd)
     endif()
 endif()
 
-add_executable(llama-llava-cli    deprecation-warning.cpp)
-add_executable(llama-gemma3-cli   deprecation-warning.cpp)
-add_executable(llama-minicpmv-cli deprecation-warning.cpp)
-add_executable(llama-qwen2vl-cli  deprecation-warning.cpp)
+# Gate CLI binaries on LLAMA_BUILD_TOOLS so that standalone library-only
+# builds (LLAMA_BUILD_MTMD=ON with LLAMA_BUILD_TOOLS=OFF — e.g. Apple
+# XCFramework packaging) skip the executables entirely. LLAMA_BUILD_COMMON
+# defaults to ON in standalone builds, so we cannot rely on it for gating.
+if (LLAMA_BUILD_TOOLS)
+    add_executable(llama-llava-cli    deprecation-warning.cpp)
+    add_executable(llama-gemma3-cli   deprecation-warning.cpp)
+    add_executable(llama-minicpmv-cli deprecation-warning.cpp)
+    add_executable(llama-qwen2vl-cli  deprecation-warning.cpp)
 
-set(TARGET llama-mtmd-cli)
-add_executable         (${TARGET} mtmd-cli.cpp)
-set_target_properties  (${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
-if(LLAMA_TOOLS_INSTALL)
-    install(TARGETS ${TARGET} RUNTIME)
+    set(TARGET llama-mtmd-cli)
+    add_executable         (${TARGET} mtmd-cli.cpp)
+    set_target_properties  (${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
+    if(LLAMA_TOOLS_INSTALL)
+        install(TARGETS ${TARGET} RUNTIME)
+    endif()
+    target_link_libraries  (${TARGET} PRIVATE llama-common mtmd Threads::Threads)
+    target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+    # mtmd-debug tool
+    add_executable(llama-mtmd-debug debug/mtmd-debug.cpp)
+    set_target_properties(llama-mtmd-debug PROPERTIES OUTPUT_NAME llama-mtmd-debug)
+    target_link_libraries(llama-mtmd-debug PRIVATE llama-common mtmd Threads::Threads)
+    target_compile_features(llama-mtmd-debug PRIVATE cxx_std_17)
 endif()
-target_link_libraries  (${TARGET} PRIVATE llama-common mtmd Threads::Threads)
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-# mtmd-debug tool
-add_executable(llama-mtmd-debug debug/mtmd-debug.cpp)
-set_target_properties(llama-mtmd-debug PROPERTIES OUTPUT_NAME llama-mtmd-debug)
-target_link_libraries(llama-mtmd-debug PRIVATE llama-common mtmd Threads::Threads)
-target_compile_features(llama-mtmd-debug PRIVATE cxx_std_17)

From afbd83baba049e9b19b71d99e4093af19995c052 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Thu, 25 Jun 2026 02:49:14 -0300
Subject: [PATCH 153/292] sd: sync with master-721-8caa3f9 (#2284)

* sd: sync with master-714-b12098f

* sd: support for boogu and longcat edit

* sd: remove SD_TYPE_COUNT == GGML_TYPE_COUNT assertion

The current code should be able to deal with an out-of-sync ggml.

* sd: generalize edit mode support

* sd: sync with master-719-f440ad9

* sd: sync with master-721-8caa3f9
---
 Makefile                                      |   2 +-
 otherarch/sdcpp/examples/cli/main.cpp         |   4 +
 otherarch/sdcpp/examples/common/common.cpp    | 112 ++-
 otherarch/sdcpp/examples/common/common.h      |   2 +
 otherarch/sdcpp/include/stable-diffusion.h    |   2 +
 otherarch/sdcpp/sdtype_adapter.cpp            |   4 +-
 .../sdcpp/src/conditioning/conditioner.hpp    | 109 ++-
 otherarch/sdcpp/src/convert.cpp               |   2 +-
 otherarch/sdcpp/src/core/ggml_extend.hpp      |  11 +-
 otherarch/sdcpp/src/core/util.cpp             |   9 +
 otherarch/sdcpp/src/core/util.h               |   2 +
 otherarch/sdcpp/src/kcpp_sd_extensions.h      |   2 +
 otherarch/sdcpp/src/model.h                   |  27 +-
 otherarch/sdcpp/src/model/common/rope.hpp     |   4 +-
 otherarch/sdcpp/src/model/diffusion/anima.hpp |   7 +-
 otherarch/sdcpp/src/model/diffusion/boogu.hpp | 835 ++++++++++++++++++
 .../sdcpp/src/model/diffusion/ernie_image.hpp |   4 +-
 otherarch/sdcpp/src/model/diffusion/krea2.hpp | 683 ++++++++++++++
 otherarch/sdcpp/src/model/te/llm.hpp          |  72 +-
 .../sdcpp/src/model/vae/auto_encoder_kl.hpp   |   2 +-
 otherarch/sdcpp/src/model_loader.cpp          |   7 +
 otherarch/sdcpp/src/model_manager.cpp         |  13 +-
 otherarch/sdcpp/src/model_manager.h           |   3 +
 otherarch/sdcpp/src/name_conversion.cpp       |  59 ++
 otherarch/sdcpp/src/runtime/denoiser.hpp      | 202 +++++
 otherarch/sdcpp/src/runtime/guidance.cpp      | 102 ++-
 otherarch/sdcpp/src/runtime/guidance.h        |  16 +-
 otherarch/sdcpp/src/stable-diffusion.cpp      | 117 ++-
 .../sdcpp/src/tokenizers/bpe_tokenizer.cpp    |   5 +-
 .../sdcpp/src/tokenizers/clip_tokenizer.cpp   |   7 +-
 otherarch/sdcpp/src/tokenizers/tokenizer.h    |   7 +-
 31 files changed, 2345 insertions(+), 88 deletions(-)
 create mode 100644 otherarch/sdcpp/src/model/diffusion/boogu.hpp
 create mode 100644 otherarch/sdcpp/src/model/diffusion/krea2.hpp

diff --git a/Makefile b/Makefile
index cd5678538..051ea092e 100644
--- a/Makefile
+++ b/Makefile
@@ -699,7 +699,7 @@ llama-impl.o: src/llama-impl.cpp src/llama-impl.h
 budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
+SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
 SDCPP_MAIN_BASENAMES := examples/cli/image_metadata.cpp examples/cli/image_metadata.h examples/cli/main.cpp examples/cli/msf_gif.h examples/common/common.cpp examples/common/common.h examples/common/log.cpp examples/common/log.h examples/common/media_io.cpp examples/common/media_io.h examples/common/resource_owners.hpp src/tokenizers/vocab/clip_merges.hpp src/tokenizers/vocab/gemma2_merges.hpp src/tokenizers/vocab/gemma2_vocab.hpp src/tokenizers/vocab/gemma_merges.hpp src/tokenizers/vocab/gemma_vocab.hpp src/tokenizers/vocab/gpt_oss_merges.hpp src/tokenizers/vocab/gpt_oss_vocab.hpp src/tokenizers/vocab/mistral_merges.hpp src/tokenizers/vocab/mistral_vocab.hpp src/tokenizers/vocab/qwen_merges.hpp src/tokenizers/vocab/t5.hpp src/tokenizers/vocab/umt5.hpp src/tokenizers/vocab/vocab.cpp src/convert.cpp src/version.cpp
 
diff --git a/otherarch/sdcpp/examples/cli/main.cpp b/otherarch/sdcpp/examples/cli/main.cpp
index bb5d6862c..84e9e7853 100644
--- a/otherarch/sdcpp/examples/cli/main.cpp
+++ b/otherarch/sdcpp/examples/cli/main.cpp
@@ -62,18 +62,22 @@ struct SDCliParams {
             {"-o",
              "--output",
              "path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png). Single-file video outputs support .avi, .webm, and animated .webp",
+             0,
              &output_path},
             {"",
              "--image",
              "path to the image to inspect (for metadata mode)",
+             0,
              &image_path},
             {"",
              "--metadata-format",
              "metadata output format, one of [text, json] (default: text)",
+             0,
              &metadata_format},
             {"",
              "--preview-path",
              "path to write preview image to (default: ./preview.png). Multi-frame previews support .avi, .webm, and animated .webp",
+             0,
              &preview_path},
         };
 
diff --git a/otherarch/sdcpp/examples/common/common.cpp b/otherarch/sdcpp/examples/common/common.cpp
index dd5d35055..744005af9 100644
--- a/otherarch/sdcpp/examples/common/common.cpp
+++ b/otherarch/sdcpp/examples/common/common.cpp
@@ -6,6 +6,7 @@
 #include <cstdlib>
 #include <ctime>
 #include <filesystem>
+#include <fstream>
 #include <iomanip>
 #include <iostream>
 #include <regex>
@@ -260,8 +261,15 @@ bool parse_options(int argc, const char** argv, const std::vector<ArgOptions>& o
                         invalid_arg = true;
                         return;
                     }
-                    *option.target = argv_to_utf8(i, argv);
-                    found_arg      = true;
+                    if (option.concat && !option.target->empty()) {
+                        if (option.concat > 0 && option.concat <= 0xff) {
+                            *option.target += static_cast<char>(option.concat);
+                        }
+                        *option.target += argv_to_utf8(i, argv);
+                    } else {
+                        *option.target = argv_to_utf8(i, argv);
+                    }
+                    found_arg = true;
                 }))
                 break;
 
@@ -324,120 +332,151 @@ ArgOptions SDContextParams::get_options() {
         {"-m",
          "--model",
          "path to full model",
+         0,
          &model_path},
         {"",
          "--clip_l",
-         "path to the clip-l text encoder", &clip_l_path},
+         "path to the clip-l text encoder",
+         0,
+         &clip_l_path},
         {"", "--clip_g",
          "path to the clip-g text encoder",
+         0,
          &clip_g_path},
         {"",
          "--clip_vision",
          "path to the clip-vision encoder",
+         0,
          &clip_vision_path},
         {"",
          "--t5xxl",
          "path to the t5xxl text encoder",
+         0,
          &t5xxl_path},
         {"",
          "--llm",
          "path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)",
+         0,
          &llm_path},
         {"",
          "--llm_vision",
          "path to the llm vit",
+         0,
          &llm_vision_path},
         {"",
          "--qwen2vl",
          "alias of --llm. Deprecated.",
+         0,
          &llm_path},
         {"",
          "--qwen2vl_vision",
          "alias of --llm_vision. Deprecated.",
+         0,
          &llm_vision_path},
         {"",
          "--diffusion-model",
          "path to the standalone diffusion model",
+         0,
          &diffusion_model_path},
         {"",
          "--high-noise-diffusion-model",
          "path to the standalone high noise diffusion model",
+         0,
          &high_noise_diffusion_model_path},
         {"",
          "--uncond-diffusion-model",
          "path to the standalone unconditional diffusion model, currently used by Ideogram4 CFG",
+         0,
          &uncond_diffusion_model_path},
         {"",
          "--embeddings-connectors",
          "path to LTXAV embeddings connectors",
+         0,
          &embeddings_connectors_path},
         {"",
          "--vae",
          "path to standalone vae model",
+         0,
          &vae_path},
         {"",
          "--vae-format",
          "VAE latent format override: auto, flux, sd3, or flux2 (default: auto)",
+         0,
          &vae_format},
         {"",
          "--audio-vae",
          "path to standalone LTX audio vae model",
+         0,
          &audio_vae_path},
         {"",
          "--taesd",
          "path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)",
+         0,
          &taesd_path},
         {"",
          "--tae",
          "alias of --taesd",
+         0,
          &taesd_path},
         {"",
          "--control-net",
          "path to control net model",
+         0,
          &control_net_path},
         {"",
          "--embd-dir",
          "embeddings directory",
+         0,
          &embedding_dir},
         {"",
          "--lora-model-dir",
          "lora model directory",
+         0,
          &lora_model_dir},
         {"",
          "--hires-upscalers-dir",
          "highres fix upscaler model directory",
+         0,
          &hires_upscalers_dir},
         {"",
          "--tensor-type-rules",
          "weight type per tensor pattern (example: \"^vae\\.=f16,model\\.=q8_0\")",
+         (int)',',
          &tensor_type_rules},
         {"",
          "--photo-maker",
          "path to PHOTOMAKER model",
+         0,
          &photo_maker_path},
         {"",
          "--pulid-weights",
          "path to PuLID Flux weights",
+         0,
          &pulid_weights_path},
         {"",
          "--upscale-model",
          "path to esrgan model.",
+         0,
          &esrgan_path},
         {"",
          "--backend",
          "runtime backend assignment, e.g. cpu or clip=cpu,vae=cuda0,diffusion=vulkan0",
+         (int)',',
          &backend},
         {"",
          "--params-backend",
          "parameter backend assignment, e.g. disk, cpu, or diffusion=disk,clip=cpu",
+         (int)',',
          &params_backend},
         {"",
          "--rpc-servers",
          "comma-separated list of RPC servers to connect to for offloading, in the format host:port, e.g. localhost:50052,192.168.1.3:50052",
+         (int)',',
          &rpc_servers},
         {"",
          "--max-vram",
          "maximum VRAM budget in GiB for graph-cut segmented execution. Accepts a single value or assignments by backend/device, e.g. 6 or cuda0=6,vulkan0=4. 0 disables graph splitting; a negative value auto-detects free VRAM, sparing the specified value",
+         0,
          &max_vram},
     };
 
@@ -458,6 +497,10 @@ ArgOptions SDContextParams::get_options() {
          "--stream-layers",
          "enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram; defaults to false)",
          true, &stream_layers},
+        {"",
+         "--eager-load",
+         "load all params into the params backend at model-load time instead of lazily on first use (defaults to false)",
+         true, &eager_load},
         {"",
          "--force-sdxl-vae-conv-scale",
          "force use of conv scale on sdxl vae",
@@ -761,6 +804,7 @@ std::string SDContextParams::to_string() const {
         << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
         << "  max_vram: \"" << max_vram << "\",\n"
         << "  stream_layers: " << (stream_layers ? "true" : "false") << ",\n"
+        << "  eager_load: " << (eager_load ? "true" : "false") << ",\n"
         << "  backend: \"" << backend << "\",\n"
         << "  params_backend: \"" << params_backend << "\",\n"
         << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
@@ -840,6 +884,7 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.vae_format                      = str_to_vae_format(vae_format);
     sd_ctx_params.max_vram                        = max_vram.c_str();
     sd_ctx_params.stream_layers                   = stream_layers;
+    sd_ctx_params.eager_load                      = eager_load;
     sd_ctx_params.backend                         = effective_backend.c_str();
     sd_ctx_params.params_backend                  = effective_params_backend.c_str();
     sd_ctx_params.rpc_servers                     = rpc_servers.c_str();
@@ -857,58 +902,71 @@ ArgOptions SDGenerationParams::get_options() {
         {"-p",
          "--prompt",
          "the prompt to render",
+         0,
          &prompt},
         {"-n",
          "--negative-prompt",
          "the negative prompt (default: \"\")",
+         0,
          &negative_prompt},
         {"-i",
          "--init-img",
          "path to the init image",
+         0,
          &init_image_path},
         {"",
          "--end-img",
          "path to the end image, required by flf2v",
+         0,
          &end_image_path},
         {"",
          "--mask",
          "path to the mask image",
+         0,
          &mask_image_path},
         {"",
          "--control-image",
          "path to control image, control net",
+         0,
          &control_image_path},
         {"",
          "--control-video",
          "path to control video frames, It must be a directory path. The video frames inside should be stored as images in "
          "lexicographical (character) order. For example, if the control video path is `frames`, the directory contain images "
          "such as 00.png, 01.png, ... etc.",
+         0,
          &control_video_path},
         {"",
          "--pm-id-images-dir",
          "path to PHOTOMAKER input id images dir",
+         0,
          &pm_id_images_dir},
         {"",
          "--pm-id-embed-path",
          "path to PHOTOMAKER v2 id embed",
+         0,
          &pm_id_embed_path},
         {"",
          "--pulid-id-embedding",
          "path to PuLID id embedding",
+         0,
          &pulid_id_embedding_path},
         {"",
          "--hires-upscaler",
          "highres fix upscaler, Lanczos, Nearest, Latent, Latent (nearest), Latent (nearest-exact), "
          "Latent (antialiased), Latent (bicubic), Latent (bicubic antialiased), or a model name "
          "under --hires-upscalers-dir (default: Latent)",
+         0,
          &hires_upscaler},
         {"",
          "--extra-sample-args",
-         "extra sampler/scheduler/guidance args, key=value list. APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma",
+         "extra sampler/scheduler/guidance args, key=value list. CFG supports guidance_schedule; APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma;; logit_normal supports mu, std, logsnr_min, logsnr_max, resolution_aware",
+         (int)',',
          &extra_sample_args},
         {"",
          "--extra-tiling-args",
          "extra VAE tiling args, key=value list. LTX video VAE supports temporal_tile_frames (default: 4), temporal_tile_overlap (default: 1)",
+         (int)',',
          &extra_tiling_args},
     };
 
@@ -1364,6 +1422,42 @@ ArgOptions SDGenerationParams::get_options() {
         return 1;
     };
 
+    auto on_prompt_file_arg = [&](int argc, const char** argv, int index) {
+        if (++index >= argc) {
+            return -1;
+        }
+        const char* arg = argv[index];
+        std::ifstream f(arg, std::ios::binary);
+        try {
+            prompt = std::string(std::istreambuf_iterator<char>{f}, {});
+        } catch (const std::ios_base::failure&) {
+            f.setstate(std::ios_base::failbit);
+        }
+        if (f.fail()) {
+            LOG_ERROR("error: failed to read prompt file '%s'\n", arg);
+            return -1;
+        }
+        return 1;
+    };
+
+    auto on_negative_prompt_file_arg = [&](int argc, const char** argv, int index) {
+        if (++index >= argc) {
+            return -1;
+        }
+        const char* arg = argv[index];
+        std::ifstream f(arg, std::ios::binary);
+        try {
+            negative_prompt = std::string(std::istreambuf_iterator<char>{f}, {});
+        } catch (const std::ios_base::failure&) {
+            f.setstate(std::ios_base::failbit);
+        }
+        if (f.fail()) {
+            LOG_ERROR("error: failed to read negative prompt file '%s'\n", arg);
+            return -1;
+        }
+        return 1;
+    };
+
     options.manual_options = {
         {"-s",
          "--seed",
@@ -1381,7 +1475,7 @@ ArgOptions SDGenerationParams::get_options() {
          on_high_noise_sample_method_arg},
         {"",
          "--scheduler",
-         "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2], default: model-specific",
+         "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2, logit_normal], default: model-specific",
          on_scheduler_arg},
         {"",
          "--sigmas",
@@ -1427,6 +1521,14 @@ ArgOptions SDGenerationParams::get_options() {
          "--vae-relative-tile-size",
          "relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size)",
          on_relative_tile_size_arg},
+        {"",
+         "--prompt-file",
+         "path to the file containing the prompt to render",
+         on_prompt_file_arg},
+        {"",
+         "--negative-prompt-file",
+         "path to the file containing the negative prompt",
+         on_negative_prompt_file_arg},
 
     };
 
diff --git a/otherarch/sdcpp/examples/common/common.h b/otherarch/sdcpp/examples/common/common.h
index fcf9840db..e7c25015b 100644
--- a/otherarch/sdcpp/examples/common/common.h
+++ b/otherarch/sdcpp/examples/common/common.h
@@ -31,6 +31,7 @@ struct StringOption {
     std::string short_name;
     std::string long_name;
     std::string desc;
+    int concat;
     std::string* target;
 };
 
@@ -147,6 +148,7 @@ struct SDContextParams {
     bool offload_params_to_cpu  = false;
     std::string max_vram        = "0";
     bool stream_layers          = false;
+    bool eager_load             = false;
     std::string backend;
     std::string params_backend;
     std::string rpc_servers;
diff --git a/otherarch/sdcpp/include/stable-diffusion.h b/otherarch/sdcpp/include/stable-diffusion.h
index bfcd909cc..7058852cc 100644
--- a/otherarch/sdcpp/include/stable-diffusion.h
+++ b/otherarch/sdcpp/include/stable-diffusion.h
@@ -70,6 +70,7 @@ enum scheduler_t {
     LCM_SCHEDULER,
     BONG_TANGENT_SCHEDULER,
     LTX2_SCHEDULER,
+    LOGIT_NORMAL_SCHEDULER,
     SCHEDULER_COUNT
 };
 
@@ -219,6 +220,7 @@ typedef struct {
     enum sd_vae_format_t vae_format;
     const char* max_vram;  // GiB budget or backend assignment spec for graph-cut segmented param offload (0 = disabled, -1 = auto)
     bool stream_layers;  // Enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram)
+    bool eager_load;  // Load all params into the params backend at model-load time instead of lazily on first use
     const char* backend;
     const char* params_backend;
     const char* rpc_servers;
diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 4880c3ba8..2a5c61f40 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -972,7 +972,7 @@ static sd_audio_t load_audio_from_b64(const std::string& b64audio) {
 
 bool supports_reference_images(kcpp_sd::model_info info)
 {
-    bool supported = (info.is_wan || info.is_ltx || info.is_qwenimg || info.is_flux2 || info.is_kontext || photomaker_enabled);
+    bool supported = (info.is_wan || info.is_ltx || info.supports_ref_image || info.is_kontext || photomaker_enabled);
     return supported;
 }
 
@@ -1161,7 +1161,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
                         wan_imgs.push_back(extraimage_reference);
                     }
                 }
-                else if(info.is_qwenimg || info.is_flux2)
+                else if(info.supports_ref_image)
                 {
                     uint8_t * loaded = load_image_from_b64(extra_image_data[i],nx2,ny2);
                     if(loaded)
diff --git a/otherarch/sdcpp/src/conditioning/conditioner.hpp b/otherarch/sdcpp/src/conditioning/conditioner.hpp
index b5dda4c0e..e037fe76b 100644
--- a/otherarch/sdcpp/src/conditioning/conditioner.hpp
+++ b/otherarch/sdcpp/src/conditioning/conditioner.hpp
@@ -142,8 +142,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                       std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
         : version(version), tokenizer(sd_version_is_sd2(version) ? 0 : 49407) {
         for (const auto& kv : orig_embedding_map) {
-            std::string name = kv.first;
-            std::transform(name.begin(), name.end(), name.begin(), [](unsigned char c) { return std::tolower(c); });
+            std::string name    = normalize_embedding_name(kv.first);
             embedding_map[name] = kv.second;
             tokenizer.add_special_token(name);
         }
@@ -278,17 +277,23 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         return true;
     }
 
+    static std::string normalize_embedding_name(std::string name) {
+        std::transform(name.begin(), name.end(), name.begin(), [](unsigned char c) { return std::tolower(c); });
+        return name;
+    }
+
+    bool append_embedding_tokens(std::string str, std::vector<int32_t>& bpe_tokens) {
+        std::string name = normalize_embedding_name(std::move(str));
+        auto iter        = embedding_map.find(name);
+        if (iter == embedding_map.end()) {
+            return false;
+        }
+        return load_embedding(name, iter->second, bpe_tokens);
+    }
+
     std::vector<int> convert_token_to_id(std::string text) {
         auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
-            auto iter = embedding_map.find(str);
-            if (iter == embedding_map.end()) {
-                return false;
-            }
-            std::string embedding_path = iter->second;
-            if (load_embedding(str, embedding_path, bpe_tokens)) {
-                return true;
-            }
-            return false;
+            return append_embedding_tokens(str, bpe_tokens);
         };
         std::vector<int> curr_tokens = tokenizer.encode(text, on_new_token_cb);
         return curr_tokens;
@@ -315,15 +320,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
 
         auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
-            auto iter = embedding_map.find(str);
-            if (iter == embedding_map.end()) {
-                return false;
-            }
-            std::string embedding_path = iter->second;
-            if (load_embedding(str, embedding_path, bpe_tokens)) {
-                return true;
-            }
-            return false;
+            return append_embedding_tokens(str, bpe_tokens);
         };
 
         std::vector<int> tokens;
@@ -1521,7 +1518,7 @@ struct LLMEmbedder : public Conditioner {
             arch = LLM::LLMArch::GPT_OSS_20B;
         } else if (sd_version_is_pid(version)) {
             arch = LLM::LLMArch::GEMMA2_2B;
-        } else if (sd_version_is_ideogram4(version)) {
+        } else if (sd_version_is_ideogram4(version) || sd_version_is_boogu_image(version) || sd_version_is_krea2(version)) {
             arch = LLM::LLMArch::QWEN3_VL;
         } else if (sd_version_is_z_image(version) || version == VERSION_OVIS_IMAGE || version == VERSION_FLUX2_KLEIN) {
             arch = LLM::LLMArch::QWEN3;
@@ -1781,6 +1778,76 @@ struct LLMEmbedder : public Conditioner {
 
                 prompt += "<|im_end|>\n<|im_start|>assistant\n";
             }
+        } else if (sd_version_is_boogu_image(version)) {
+            prompt_template_encode_start_idx = 0;
+
+            const std::string t2i_system_prompt =
+                "You are a helpful assistant that generates high-quality images based on user instructions. The instructions are as follows.";
+            const std::string edit_system_prompt =
+                "Describe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.";
+            const bool has_ref_images = llm->enable_vision && conditioner_params.ref_images != nullptr && !conditioner_params.ref_images->empty();
+            const bool text_empty     = conditioner_params.text.find_first_not_of(" \t\r\n") == std::string::npos;
+
+            if (has_ref_images) {
+                LOG_INFO("BooguImageEditPipeline");
+                const std::string prompt_prefix = "<|im_start|>system\n" + edit_system_prompt + "<|im_end|>\n<|im_start|>user\n";
+                std::string img_prompt;
+                const std::string placeholder = "<|image_pad|>";
+
+                for (int i = 0; i < conditioner_params.ref_images->size(); i++) {
+                    const auto& image = (*conditioner_params.ref_images)[i];
+                    double factor     = llm->config.vision.patch_size * llm->config.vision.spatial_merge_size;
+                    int height        = static_cast<int>(image.shape()[1]);
+                    int width         = static_cast<int>(image.shape()[0]);
+                    double beta       = std::sqrt((384.0 * 384.0) / (static_cast<double>(height) * static_cast<double>(width)));
+                    int h_bar         = std::max(static_cast<int>(factor),
+                                                 static_cast<int>(std::round(height * beta / factor)) * static_cast<int>(factor));
+                    int w_bar         = std::max(static_cast<int>(factor),
+                                                 static_cast<int>(std::round(width * beta / factor)) * static_cast<int>(factor));
+
+                    LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, height, width, h_bar, w_bar);
+
+                    auto resized_image = clip_preprocess(image, w_bar, h_bar);
+                    auto image_embed   = llm->encode_image(n_threads, resized_image, false, true, true);
+                    GGML_ASSERT(!image_embed.empty());
+
+                    std::string image_prefix = prompt_prefix + img_prompt + "<|vision_start|>";
+                    int image_embed_idx      = static_cast<int>(tokenizer->encode(image_prefix, nullptr).size());
+                    image_embeds.emplace_back(image_embed_idx, image_embed);
+
+                    img_prompt += "<|vision_start|>";
+                    int64_t num_image_tokens = image_embed.shape()[1];
+                    img_prompt.reserve(img_prompt.size() + static_cast<size_t>(num_image_tokens) * placeholder.size() + 32);
+                    for (int j = 0; j < num_image_tokens; j++) {
+                        img_prompt += placeholder;
+                    }
+                    img_prompt += "<|vision_end|>";
+                }
+
+                prompt                  = prompt_prefix + img_prompt;
+                prompt_attn_range.first = static_cast<int>(prompt.size());
+                prompt += conditioner_params.text;
+                prompt_attn_range.second = static_cast<int>(prompt.size());
+                prompt += "<|im_end|>\n";
+            } else {
+                const std::string& system_prompt = text_empty ? edit_system_prompt : t2i_system_prompt;
+                prompt                           = "<|im_start|>system\n" + system_prompt + "<|im_end|>\n<|im_start|>user\n";
+                prompt_attn_range.first          = static_cast<int>(prompt.size());
+                prompt += conditioner_params.text;
+                prompt_attn_range.second = static_cast<int>(prompt.size());
+                prompt += "<|im_end|>\n";
+            }
+        } else if (sd_version_is_krea2(version)) {
+            prompt_template_encode_start_idx = 34;
+            out_layers                       = {2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35};
+
+            prompt = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
+
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+
+            prompt += "<|im_end|>\n<|im_start|>assistant\n";
         } else if (sd_version_is_longcat(version)) {
             spell_quotes = true;
 
diff --git a/otherarch/sdcpp/src/convert.cpp b/otherarch/sdcpp/src/convert.cpp
index 5ad066c10..27d377ec0 100644
--- a/otherarch/sdcpp/src/convert.cpp
+++ b/otherarch/sdcpp/src/convert.cpp
@@ -99,7 +99,7 @@ bool convert(const char* input_path,
         model_loader.convert_tensors_name();
     }
 
-    ggml_type type             = (ggml_type)output_type;
+    ggml_type type             = sd_type_to_ggml_type(output_type);
     bool output_is_safetensors = ends_with(output_path, ".safetensors");
     TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
 
diff --git a/otherarch/sdcpp/src/core/ggml_extend.hpp b/otherarch/sdcpp/src/core/ggml_extend.hpp
index f10a84ffd..9883103e2 100644
--- a/otherarch/sdcpp/src/core/ggml_extend.hpp
+++ b/otherarch/sdcpp/src/core/ggml_extend.hpp
@@ -1382,7 +1382,16 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
             if (!ggml_backend_supports_op(backend, kqv)) {
                 kqv = nullptr;
             } else {
-                kqv = ggml_view_3d(ctx, kqv, d_head, n_head, L_q, kqv->nb[1], kqv->nb[2], 0);
+                kqv = ggml_view_4d(ctx,
+                                   kqv,
+                                   d_head,
+                                   n_head,
+                                   L_q,
+                                   N,
+                                   kqv->nb[1],
+                                   kqv->nb[2],
+                                   kqv->nb[1] * n_head,
+                                   0);
             }
         }
     }
diff --git a/otherarch/sdcpp/src/core/util.cpp b/otherarch/sdcpp/src/core/util.cpp
index a70722af2..05c308d9b 100644
--- a/otherarch/sdcpp/src/core/util.cpp
+++ b/otherarch/sdcpp/src/core/util.cpp
@@ -420,6 +420,15 @@ std::vector<std::string> split_string(const std::string& str, char delimiter) {
     return result;
 }
 
+ggml_type sd_type_to_ggml_type(sd_type_t sdtype) {
+    const int type_value = static_cast<int>(sdtype);
+    if (type_value < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)) {
+        return static_cast<ggml_type>(type_value);
+    } else {
+        return GGML_TYPE_COUNT;
+    }
+}
+
 KeyValueArgs parse_key_value_args(const char* args, const char* context) {
     KeyValueArgs pairs;
 
diff --git a/otherarch/sdcpp/src/core/util.h b/otherarch/sdcpp/src/core/util.h
index a271c1f71..ec4c45595 100644
--- a/otherarch/sdcpp/src/core/util.h
+++ b/otherarch/sdcpp/src/core/util.h
@@ -83,6 +83,8 @@ void pretty_bytes_progress(int step, int steps, uint64_t bytes_processed, float
 
 void log_printf(sd_log_level_t level, const char* file, int line, const char* format, ...);
 
+ggml_type sd_type_to_ggml_type(sd_type_t sdtype);
+
 std::string trim(const std::string& s);
 
 std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::string& text);
diff --git a/otherarch/sdcpp/src/kcpp_sd_extensions.h b/otherarch/sdcpp/src/kcpp_sd_extensions.h
index c65c11c35..0e0c6795a 100644
--- a/otherarch/sdcpp/src/kcpp_sd_extensions.h
+++ b/otherarch/sdcpp/src/kcpp_sd_extensions.h
@@ -19,6 +19,8 @@ namespace kcpp_sd {
         bool is_wan;
         bool is_zimage;
         bool is_ltx;
+        bool is_boogu;
+        bool supports_ref_image;
         int vae_scale_factor;
         int spatial_multiple;
     };
diff --git a/otherarch/sdcpp/src/model.h b/otherarch/sdcpp/src/model.h
index a62c4d1bf..cce309138 100644
--- a/otherarch/sdcpp/src/model.h
+++ b/otherarch/sdcpp/src/model.h
@@ -42,12 +42,14 @@ enum SDVersion {
     VERSION_LTXAV,
     VERSION_HIDREAM_O1,
     VERSION_Z_IMAGE,
+    VERSION_BOOGU_IMAGE,
     VERSION_OVIS_IMAGE,
     VERSION_ERNIE_IMAGE,
     VERSION_LENS,
     VERSION_LONGCAT,
     VERSION_PID,
     VERSION_IDEOGRAM4,
+    VERSION_KREA2,
     VERSION_ESRGAN,
     VERSION_COUNT,
 };
@@ -143,6 +145,13 @@ static inline bool sd_version_is_z_image(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_boogu_image(SDVersion version) {
+    if (version == VERSION_BOOGU_IMAGE) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_longcat(SDVersion version) {
     if (version == VERSION_LONGCAT) {
         return true;
@@ -178,6 +187,20 @@ static inline bool sd_version_is_ideogram4(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_krea2(SDVersion version) {
+    if (version == VERSION_KREA2) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_uses_flux_vae(SDVersion version) {
+    if (sd_version_is_flux(version) || sd_version_is_z_image(version) || sd_version_is_boogu_image(version) || sd_version_is_longcat(version)) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_uses_flux2_vae(SDVersion version) {
     if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version)) {
         return true;
@@ -206,11 +229,13 @@ static inline bool sd_version_is_dit(SDVersion version) {
         version == VERSION_HIDREAM_O1 ||
         sd_version_is_anima(version) ||
         sd_version_is_z_image(version) ||
+        sd_version_is_boogu_image(version) ||
         sd_version_is_ernie_image(version) ||
         sd_version_is_lens(version) ||
         sd_version_is_longcat(version) ||
         sd_version_is_pid(version) ||
-        sd_version_is_ideogram4(version)) {
+        sd_version_is_ideogram4(version) ||
+        sd_version_is_krea2(version)) {
         return true;
     }
     return false;
diff --git a/otherarch/sdcpp/src/model/common/rope.hpp b/otherarch/sdcpp/src/model/common/rope.hpp
index c0077de33..2e21ef7c2 100644
--- a/otherarch/sdcpp/src/model/common/rope.hpp
+++ b/otherarch/sdcpp/src/model/common/rope.hpp
@@ -899,10 +899,12 @@ namespace Rope {
         // q,k,v: [N, L, n_head, d_head]
         // pe: [L, d_head/2, 2, 2]
         // return: [N, L, n_head*d_head]
+        int64_t n_head = q->ne[1];
+
         q = apply_rope(ctx->ggml_ctx, q, pe, rope_interleaved);  // [N*n_head, L, d_head]
         k = apply_rope(ctx->ggml_ctx, k, pe, rope_interleaved);  // [N*n_head, L, d_head]
 
-        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
+        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
         return x;
     }
 };  // namespace Rope
diff --git a/otherarch/sdcpp/src/model/diffusion/anima.hpp b/otherarch/sdcpp/src/model/diffusion/anima.hpp
index 6042516a9..504904d41 100644
--- a/otherarch/sdcpp/src/model/diffusion/anima.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/anima.hpp
@@ -227,6 +227,7 @@ namespace Anima {
             k4 = k_norm->forward(ctx, k4);
 
             ggml_tensor* attn_out = nullptr;
+            float scale           = (sd_backend_is(ctx->backend, "Vulkan") && ctx->flash_attn_enabled) ? 1.0f / 32.0f : 1.0f;
             if (pe_q != nullptr || pe_k != nullptr) {
                 if (pe_q == nullptr) {
                     pe_q = pe_k;
@@ -244,7 +245,8 @@ namespace Anima {
                                                      num_heads,
                                                      nullptr,
                                                      true,
-                                                     ctx->flash_attn_enabled);
+                                                     ctx->flash_attn_enabled,
+                                                     scale);
             } else {
                 auto q_flat = ggml_reshape_3d(ctx->ggml_ctx, q4, head_dim * num_heads, L_q, N);
                 auto k_flat = ggml_reshape_3d(ctx->ggml_ctx, k4, head_dim * num_heads, L_k, N);
@@ -256,7 +258,8 @@ namespace Anima {
                                                      num_heads,
                                                      nullptr,
                                                      false,
-                                                     ctx->flash_attn_enabled);
+                                                     ctx->flash_attn_enabled,
+                                                     scale);
             }
 
             return out_proj->forward(ctx, attn_out);
diff --git a/otherarch/sdcpp/src/model/diffusion/boogu.hpp b/otherarch/sdcpp/src/model/diffusion/boogu.hpp
new file mode 100644
index 000000000..27e13aebd
--- /dev/null
+++ b/otherarch/sdcpp/src/model/diffusion/boogu.hpp
@@ -0,0 +1,835 @@
+#ifndef __SD_MODEL_DIFFUSION_BOOGU_HPP__
+#define __SD_MODEL_DIFFUSION_BOOGU_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <tuple>
+#include <vector>
+
+#include "core/ggml_extend.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model/diffusion/qwen_image.hpp"
+#include "model_loader.h"
+
+namespace Boogu {
+    constexpr int BOOGU_GRAPH_SIZE = 65536;
+
+    struct BooguConfig {
+        int patch_size                   = 2;
+        int64_t in_channels              = 16;
+        int64_t out_channels             = 16;
+        int64_t hidden_size              = 3360;
+        int64_t num_layers               = 32;
+        int64_t num_double_stream_layers = 8;
+        int64_t num_refiner_layers       = 2;
+        int64_t num_attention_heads      = 28;
+        int64_t num_kv_heads             = 7;
+        int64_t head_dim                 = 120;
+        int64_t multiple_of              = 256;
+        int64_t instruction_feat_dim     = 4096;
+        int64_t timestep_embed_dim       = 1024;
+        int theta                        = 10000;
+        float timestep_scale             = 1000.0f;
+        float norm_eps                   = 1e-5f;
+        std::vector<int> axes_dim        = {40, 40, 40};
+        int64_t axes_dim_sum             = 120;
+
+        static int64_t count_blocks(const String2TensorStorage& tensor_storage_map,
+                                    const std::string& prefix,
+                                    const std::string& block_prefix) {
+            int64_t count = 0;
+            for (const auto& [name, _] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                size_t pos = name.find(block_prefix);
+                if (pos == std::string::npos) {
+                    continue;
+                }
+                auto items = split_string(name.substr(pos), '.');
+                if (items.size() > 1) {
+                    count = std::max<int64_t>(count, atoi(items[1].c_str()) + 1);
+                }
+            }
+            return count;
+        }
+
+        static BooguConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            BooguConfig config;
+            int64_t detected_head_dim = 0;
+            int64_t detected_kv_dim   = 0;
+
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "x_embedder.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area = config.patch_size * config.patch_size;
+                    config.in_channels = tensor_storage.ne[0] / patch_area;
+                    config.hidden_size = tensor_storage.ne[1];
+                } else if (ends_with(name, "time_caption_embed.caption_embedder.1.weight") && tensor_storage.n_dims == 2) {
+                    config.instruction_feat_dim = tensor_storage.ne[0];
+                    config.hidden_size          = tensor_storage.ne[1];
+                } else if (ends_with(name, "single_stream_layers.0.attn.norm_q.weight") && tensor_storage.n_dims == 1) {
+                    detected_head_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "double_stream_layers.0.img_self_attn.norm_q.weight") && tensor_storage.n_dims == 1) {
+                    detected_head_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "single_stream_layers.0.attn.to_k.weight") && tensor_storage.n_dims == 2) {
+                    detected_kv_dim = tensor_storage.ne[1];
+                } else if (ends_with(name, "double_stream_layers.0.img_instruct_attn.processor.img_to_k.weight") && tensor_storage.n_dims == 2) {
+                    detected_kv_dim = tensor_storage.ne[1];
+                } else if (ends_with(name, "norm_out.linear_2.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area  = config.patch_size * config.patch_size;
+                    config.out_channels = tensor_storage.ne[1] / patch_area;
+                }
+            }
+
+            config.num_layers               = std::max<int64_t>(1, count_blocks(tensor_storage_map, prefix, "single_stream_layers."));
+            config.num_double_stream_layers = std::max<int64_t>(0, count_blocks(tensor_storage_map, prefix, "double_stream_layers."));
+            int64_t noise_refiner_layers    = count_blocks(tensor_storage_map, prefix, "noise_refiner.");
+            int64_t ref_refiner_layers      = count_blocks(tensor_storage_map, prefix, "ref_image_refiner.");
+            int64_t context_refiner_layers  = count_blocks(tensor_storage_map, prefix, "context_refiner.");
+            config.num_refiner_layers       = std::max<int64_t>(1, std::max(noise_refiner_layers, std::max(ref_refiner_layers, context_refiner_layers)));
+
+            if (detected_head_dim > 0) {
+                config.head_dim            = detected_head_dim;
+                config.num_attention_heads = config.hidden_size / config.head_dim;
+                config.axes_dim_sum        = config.head_dim;
+                if (detected_kv_dim > 0) {
+                    config.num_kv_heads = detected_kv_dim / config.head_dim;
+                }
+                if (config.axes_dim_sum == 120) {
+                    config.axes_dim = {40, 40, 40};
+                } else if (config.axes_dim_sum % 3 == 0) {
+                    int axis        = static_cast<int>(config.axes_dim_sum / 3);
+                    config.axes_dim = {axis, axis, axis};
+                }
+            }
+            config.timestep_embed_dim = std::min<int64_t>(config.hidden_size, 1024);
+
+            LOG_DEBUG("boogu_image: layers=%" PRId64 ", double_stream_layers=%" PRId64 ", refiner_layers=%" PRId64 ", hidden=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", head_dim=%" PRId64 ", in_channels=%" PRId64 ", out_channels=%" PRId64,
+                      config.num_layers,
+                      config.num_double_stream_layers,
+                      config.num_refiner_layers,
+                      config.hidden_size,
+                      config.num_attention_heads,
+                      config.num_kv_heads,
+                      config.head_dim,
+                      config.in_channels,
+                      config.out_channels);
+            return config;
+        }
+    };
+
+    __STATIC_INLINE__ ggml_tensor* scale_modulate(ggml_context* ctx, ggml_tensor* x, ggml_tensor* scale) {
+        scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);
+        return ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+    }
+
+    __STATIC_INLINE__ ggml_tensor* gate_residual(ggml_context* ctx, ggml_tensor* residual, ggml_tensor* x, ggml_tensor* gate) {
+        gate = ggml_tanh(ctx, gate);
+        gate = ggml_reshape_3d(ctx, gate, gate->ne[0], 1, gate->ne[1]);
+        x    = ggml_mul(ctx, x, gate);
+        return ggml_add(ctx, residual, x);
+    }
+
+    struct LuminaCombinedTimestepCaptionEmbedding : public GGMLBlock {
+        int64_t frequency_embedding_size;
+        float timestep_scale;
+
+        LuminaCombinedTimestepCaptionEmbedding(int64_t hidden_size,
+                                               int64_t instruction_feat_dim,
+                                               int64_t frequency_embedding_size,
+                                               float norm_eps,
+                                               float timestep_scale)
+            : frequency_embedding_size(frequency_embedding_size),
+              timestep_scale(timestep_scale) {
+            blocks["timestep_embedder"]  = std::make_shared<Qwen::TimestepEmbedding>(frequency_embedding_size, std::min<int64_t>(hidden_size, 1024));
+            blocks["caption_embedder.0"] = std::make_shared<RMSNorm>(instruction_feat_dim, norm_eps);
+            blocks["caption_embedder.1"] = std::make_shared<Linear>(instruction_feat_dim, hidden_size, true);
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* timestep, ggml_tensor* text_hidden_states) {
+            auto timestep_embedder  = std::dynamic_pointer_cast<Qwen::TimestepEmbedding>(blocks["timestep_embedder"]);
+            auto caption_embedder_0 = std::dynamic_pointer_cast<RMSNorm>(blocks["caption_embedder.0"]);
+            auto caption_embedder_1 = std::dynamic_pointer_cast<Linear>(blocks["caption_embedder.1"]);
+
+            auto timestep_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(frequency_embedding_size), 10000, timestep_scale);
+            auto time_embed    = timestep_embedder->forward(ctx, timestep_proj);
+            auto caption_embed = caption_embedder_1->forward(ctx, caption_embedder_0->forward(ctx, text_hidden_states));
+            return {time_embed, caption_embed};
+        }
+    };
+
+    struct LuminaRMSNormZero : public GGMLBlock {
+        LuminaRMSNormZero(int64_t embedding_dim, int64_t conditioning_embedding_dim, float norm_eps) {
+            blocks["linear"] = std::make_shared<Linear>(conditioning_embedding_dim, 4 * embedding_dim, true);
+            blocks["norm"]   = std::make_shared<RMSNorm>(embedding_dim, norm_eps);
+        }
+
+        std::tuple<ggml_tensor*, ggml_tensor*, ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* emb) {
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            auto norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+
+            emb       = linear->forward(ctx, ggml_silu(ctx->ggml_ctx, emb));
+            auto mods = ggml_ext_chunk(ctx->ggml_ctx, emb, 4, 0);
+
+            auto scale_msa = mods[0];
+            auto gate_msa  = mods[1];
+            auto scale_mlp = mods[2];
+            auto gate_mlp  = mods[3];
+
+            x = scale_modulate(ctx->ggml_ctx, norm->forward(ctx, x), scale_msa);
+            return {x, gate_msa, scale_mlp, gate_mlp};
+        }
+    };
+
+    struct LuminaFeedForward : public GGMLBlock {
+        LuminaFeedForward(int64_t dim, int64_t inner_dim, int64_t multiple_of) {
+            inner_dim          = multiple_of * ((inner_dim + multiple_of - 1) / multiple_of);
+            blocks["linear_1"] = std::make_shared<Linear>(dim, inner_dim, false);
+            blocks["linear_2"] = std::make_shared<Linear>(inner_dim, dim, false);
+            blocks["linear_3"] = std::make_shared<Linear>(dim, inner_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["linear_1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["linear_2"]);
+            auto linear_3 = std::dynamic_pointer_cast<Linear>(blocks["linear_3"]);
+
+            if (sd_backend_is(ctx->backend, "Vulkan")) {
+                linear_2->set_force_prec_f32(true);
+            }
+
+            auto h1 = linear_1->forward(ctx, x);
+            auto h2 = linear_3->forward(ctx, x);
+            x       = ggml_swiglu_split(ctx->ggml_ctx, h1, h2);
+            x       = linear_2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct LuminaLayerNormContinuous : public GGMLBlock {
+        LuminaLayerNormContinuous(int64_t embedding_dim,
+                                  int64_t conditioning_embedding_dim,
+                                  int64_t out_dim) {
+            blocks["linear_1"] = std::make_shared<Linear>(conditioning_embedding_dim, embedding_dim, true);
+            blocks["norm"]     = std::make_shared<LayerNorm>(embedding_dim, 1e-6f, false);
+            blocks["linear_2"] = std::make_shared<Linear>(embedding_dim, out_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* conditioning_embedding) {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["linear_1"]);
+            auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["linear_2"]);
+
+            auto emb = linear_1->forward(ctx, ggml_silu(ctx->ggml_ctx, conditioning_embedding));
+            x        = scale_modulate(ctx->ggml_ctx, norm->forward(ctx, x), emb);
+            x        = linear_2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct Attention : public GGMLBlock {
+        int64_t dim_head;
+        int64_t heads;
+        int64_t kv_heads;
+
+        Attention(int64_t query_dim, int64_t dim_head, int64_t heads, int64_t kv_heads, float eps = 1e-5f)
+            : dim_head(dim_head), heads(heads), kv_heads(kv_heads) {
+            blocks["to_q"]     = std::make_shared<Linear>(query_dim, heads * dim_head, false);
+            blocks["to_k"]     = std::make_shared<Linear>(query_dim, kv_heads * dim_head, false);
+            blocks["to_v"]     = std::make_shared<Linear>(query_dim, kv_heads * dim_head, false);
+            blocks["norm_q"]   = std::make_shared<RMSNorm>(dim_head, eps);
+            blocks["norm_k"]   = std::make_shared<RMSNorm>(dim_head, eps);
+            blocks["to_out.0"] = std::make_shared<Linear>(heads * dim_head, query_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* encoder_hidden_states,
+                             ggml_tensor* rotary_emb,
+                             ggml_tensor* attention_mask = nullptr) {
+            auto to_q     = std::dynamic_pointer_cast<Linear>(blocks["to_q"]);
+            auto to_k     = std::dynamic_pointer_cast<Linear>(blocks["to_k"]);
+            auto to_v     = std::dynamic_pointer_cast<Linear>(blocks["to_v"]);
+            auto norm_q   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_q"]);
+            auto norm_k   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"]);
+            auto to_out_0 = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
+
+            if (sd_backend_is(ctx->backend, "Vulkan")) {
+                to_out_0->set_force_prec_f32(true);
+            }
+
+            int64_t N  = hidden_states->ne[2];
+            int64_t Lq = hidden_states->ne[1];
+            int64_t Lk = encoder_hidden_states->ne[1];
+
+            auto q = to_q->forward(ctx, hidden_states);
+            q      = ggml_reshape_4d(ctx->ggml_ctx, q, dim_head, heads, Lq, N);
+            auto k = to_k->forward(ctx, encoder_hidden_states);
+            k      = ggml_reshape_4d(ctx->ggml_ctx, k, dim_head, kv_heads, Lk, N);
+            auto v = to_v->forward(ctx, encoder_hidden_states);
+            v      = ggml_reshape_4d(ctx->ggml_ctx, v, dim_head, kv_heads, Lk, N);
+
+            q = norm_q->forward(ctx, q);
+            k = norm_k->forward(ctx, k);
+
+            auto out = Rope::attention(ctx, q, k, v, rotary_emb, attention_mask);
+            out      = to_out_0->forward(ctx, out);
+            return out;
+        }
+    };
+
+    struct BooguImageTransformerBlock : public GGMLBlock {
+        bool modulation;
+
+        BooguImageTransformerBlock(int64_t dim,
+                                   int64_t num_attention_heads,
+                                   int64_t num_kv_heads,
+                                   int64_t multiple_of,
+                                   float norm_eps,
+                                   bool modulation)
+            : modulation(modulation) {
+            int64_t head_dim       = dim / num_attention_heads;
+            blocks["attn"]         = std::make_shared<Attention>(dim, head_dim, num_attention_heads, num_kv_heads, 1e-5f);
+            blocks["feed_forward"] = std::make_shared<LuminaFeedForward>(dim, 4 * dim, multiple_of);
+            if (modulation) {
+                blocks["norm1"] = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            } else {
+                blocks["norm1"] = std::make_shared<RMSNorm>(dim, norm_eps);
+            }
+            blocks["ffn_norm1"] = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["norm2"]     = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["ffn_norm2"] = std::make_shared<RMSNorm>(dim, norm_eps);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* rotary_emb,
+                             ggml_tensor* temb           = nullptr,
+                             ggml_tensor* attention_mask = nullptr) {
+            auto attn         = std::dynamic_pointer_cast<Attention>(blocks["attn"]);
+            auto feed_forward = std::dynamic_pointer_cast<LuminaFeedForward>(blocks["feed_forward"]);
+            auto ffn_norm1    = std::dynamic_pointer_cast<RMSNorm>(blocks["ffn_norm1"]);
+            auto norm2        = std::dynamic_pointer_cast<RMSNorm>(blocks["norm2"]);
+            auto ffn_norm2    = std::dynamic_pointer_cast<RMSNorm>(blocks["ffn_norm2"]);
+
+            if (modulation) {
+                auto norm1 = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["norm1"]);
+                auto mods  = norm1->forward(ctx, hidden_states, temb);
+
+                auto norm_hidden_states = std::get<0>(mods);
+                auto gate_msa           = std::get<1>(mods);
+                auto scale_mlp          = std::get<2>(mods);
+                auto gate_mlp           = std::get<3>(mods);
+
+                auto attn_output = attn->forward(ctx, norm_hidden_states, norm_hidden_states, rotary_emb, attention_mask);
+                hidden_states    = gate_residual(ctx->ggml_ctx, hidden_states, norm2->forward(ctx, attn_output), gate_msa);
+
+                auto mlp_input  = scale_modulate(ctx->ggml_ctx, ffn_norm1->forward(ctx, hidden_states), scale_mlp);
+                auto mlp_output = feed_forward->forward(ctx, mlp_input);
+                hidden_states   = gate_residual(ctx->ggml_ctx, hidden_states, ffn_norm2->forward(ctx, mlp_output), gate_mlp);
+            } else {
+                auto norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["norm1"]);
+
+                auto norm_hidden_states = norm1->forward(ctx, hidden_states);
+                auto attn_output        = attn->forward(ctx, norm_hidden_states, norm_hidden_states, rotary_emb, attention_mask);
+                hidden_states           = ggml_add(ctx->ggml_ctx, hidden_states, norm2->forward(ctx, attn_output));
+
+                auto mlp_output = feed_forward->forward(ctx, ffn_norm1->forward(ctx, hidden_states));
+                hidden_states   = ggml_add(ctx->ggml_ctx, hidden_states, ffn_norm2->forward(ctx, mlp_output));
+            }
+            return hidden_states;
+        }
+    };
+
+    struct BooguImageJointAttention : public GGMLBlock {
+        int64_t dim_head;
+        int64_t heads;
+        int64_t kv_heads;
+
+        BooguImageJointAttention(int64_t dim, int64_t dim_head, int64_t heads, int64_t kv_heads)
+            : dim_head(dim_head), heads(heads), kv_heads(kv_heads) {
+            blocks["norm_q"]                  = std::make_shared<RMSNorm>(dim_head, 1e-5f);
+            blocks["norm_k"]                  = std::make_shared<RMSNorm>(dim_head, 1e-5f);
+            blocks["to_out.0"]                = std::make_shared<Linear>(heads * dim_head, dim, false);
+            blocks["processor.img_to_q"]      = std::make_shared<Linear>(dim, heads * dim_head, false);
+            blocks["processor.img_to_k"]      = std::make_shared<Linear>(dim, kv_heads * dim_head, false);
+            blocks["processor.img_to_v"]      = std::make_shared<Linear>(dim, kv_heads * dim_head, false);
+            blocks["processor.instruct_to_q"] = std::make_shared<Linear>(dim, heads * dim_head, false);
+            blocks["processor.instruct_to_k"] = std::make_shared<Linear>(dim, kv_heads * dim_head, false);
+            blocks["processor.instruct_to_v"] = std::make_shared<Linear>(dim, kv_heads * dim_head, false);
+            blocks["processor.instruct_out"]  = std::make_shared<Linear>(heads * dim_head, dim, false);
+            blocks["processor.img_out"]       = std::make_shared<Linear>(heads * dim_head, dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* img_hidden_states,
+                             ggml_tensor* instruct_hidden_states,
+                             ggml_tensor* rotary_emb,
+                             ggml_tensor* attention_mask = nullptr) {
+            auto norm_q        = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_q"]);
+            auto norm_k        = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"]);
+            auto to_out_0      = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
+            auto img_to_q      = std::dynamic_pointer_cast<Linear>(blocks["processor.img_to_q"]);
+            auto img_to_k      = std::dynamic_pointer_cast<Linear>(blocks["processor.img_to_k"]);
+            auto img_to_v      = std::dynamic_pointer_cast<Linear>(blocks["processor.img_to_v"]);
+            auto instruct_to_q = std::dynamic_pointer_cast<Linear>(blocks["processor.instruct_to_q"]);
+            auto instruct_to_k = std::dynamic_pointer_cast<Linear>(blocks["processor.instruct_to_k"]);
+            auto instruct_to_v = std::dynamic_pointer_cast<Linear>(blocks["processor.instruct_to_v"]);
+            auto instruct_out  = std::dynamic_pointer_cast<Linear>(blocks["processor.instruct_out"]);
+            auto img_out       = std::dynamic_pointer_cast<Linear>(blocks["processor.img_out"]);
+
+            if (sd_backend_is(ctx->backend, "Vulkan")) {
+                to_out_0->set_force_prec_f32(true);
+            }
+
+            int64_t N          = img_hidden_states->ne[2];
+            int64_t L_img      = img_hidden_states->ne[1];
+            int64_t L_instruct = instruct_hidden_states->ne[1];
+
+            auto img_q = img_to_q->forward(ctx, img_hidden_states);
+            img_q      = ggml_reshape_4d(ctx->ggml_ctx, img_q, dim_head, heads, L_img, N);
+            auto img_k = img_to_k->forward(ctx, img_hidden_states);
+            img_k      = ggml_reshape_4d(ctx->ggml_ctx, img_k, dim_head, kv_heads, L_img, N);
+            auto img_v = img_to_v->forward(ctx, img_hidden_states);
+            img_v      = ggml_reshape_4d(ctx->ggml_ctx, img_v, dim_head, kv_heads, L_img, N);
+
+            auto instruct_q = instruct_to_q->forward(ctx, instruct_hidden_states);
+            instruct_q      = ggml_reshape_4d(ctx->ggml_ctx, instruct_q, dim_head, heads, L_instruct, N);
+            auto instruct_k = instruct_to_k->forward(ctx, instruct_hidden_states);
+            instruct_k      = ggml_reshape_4d(ctx->ggml_ctx, instruct_k, dim_head, kv_heads, L_instruct, N);
+            auto instruct_v = instruct_to_v->forward(ctx, instruct_hidden_states);
+            instruct_v      = ggml_reshape_4d(ctx->ggml_ctx, instruct_v, dim_head, kv_heads, L_instruct, N);
+
+            auto q = ggml_concat(ctx->ggml_ctx, instruct_q, img_q, 2);
+            auto k = ggml_concat(ctx->ggml_ctx, instruct_k, img_k, 2);
+            auto v = ggml_concat(ctx->ggml_ctx, instruct_v, img_v, 2);
+            q      = norm_q->forward(ctx, q);
+            k      = norm_k->forward(ctx, k);
+
+            auto hidden_states = Rope::attention(ctx, q, k, v, rotary_emb, attention_mask);
+            auto instruct_attn = ggml_ext_slice(ctx->ggml_ctx, hidden_states, 1, 0, L_instruct);
+            auto img_attn      = ggml_ext_slice(ctx->ggml_ctx, hidden_states, 1, L_instruct, L_instruct + L_img);
+
+            instruct_attn = instruct_out->forward(ctx, instruct_attn);
+            img_attn      = img_out->forward(ctx, img_attn);
+            hidden_states = ggml_concat(ctx->ggml_ctx, instruct_attn, img_attn, 1);
+            hidden_states = to_out_0->forward(ctx, hidden_states);
+            return hidden_states;
+        }
+    };
+
+    struct BooguImageDoubleStreamBlock : public GGMLBlock {
+        BooguImageDoubleStreamBlock(int64_t dim,
+                                    int64_t num_attention_heads,
+                                    int64_t num_kv_heads,
+                                    int64_t multiple_of,
+                                    float norm_eps) {
+            int64_t head_dim                = dim / num_attention_heads;
+            blocks["img_instruct_attn"]     = std::make_shared<BooguImageJointAttention>(dim, head_dim, num_attention_heads, num_kv_heads);
+            blocks["img_self_attn"]         = std::make_shared<Attention>(dim, head_dim, num_attention_heads, num_kv_heads, 1e-5f);
+            blocks["img_feed_forward"]      = std::make_shared<LuminaFeedForward>(dim, 4 * dim, multiple_of);
+            blocks["instruct_feed_forward"] = std::make_shared<LuminaFeedForward>(dim, 4 * dim, multiple_of);
+            blocks["img_norm1"]             = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            blocks["img_norm2"]             = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            blocks["img_norm3"]             = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            blocks["instruct_norm1"]        = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            blocks["instruct_norm2"]        = std::make_shared<LuminaRMSNormZero>(dim, std::min<int64_t>(dim, 1024), norm_eps);
+            blocks["img_attn_norm"]         = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["img_self_attn_norm"]    = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["img_ffn_norm1"]         = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["img_ffn_norm2"]         = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["instruct_attn_norm"]    = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["instruct_ffn_norm1"]    = std::make_shared<RMSNorm>(dim, norm_eps);
+            blocks["instruct_ffn_norm2"]    = std::make_shared<RMSNorm>(dim, norm_eps);
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* img_hidden_states,
+                                                      ggml_tensor* instruct_hidden_states,
+                                                      ggml_tensor* joint_rotary_emb,
+                                                      ggml_tensor* img_rotary_emb,
+                                                      ggml_tensor* temb) {
+            auto img_instruct_attn     = std::dynamic_pointer_cast<BooguImageJointAttention>(blocks["img_instruct_attn"]);
+            auto img_self_attn         = std::dynamic_pointer_cast<Attention>(blocks["img_self_attn"]);
+            auto img_feed_forward      = std::dynamic_pointer_cast<LuminaFeedForward>(blocks["img_feed_forward"]);
+            auto instruct_feed_forward = std::dynamic_pointer_cast<LuminaFeedForward>(blocks["instruct_feed_forward"]);
+            auto img_norm1             = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["img_norm1"]);
+            auto img_norm2             = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["img_norm2"]);
+            auto img_norm3             = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["img_norm3"]);
+            auto instruct_norm1        = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["instruct_norm1"]);
+            auto instruct_norm2        = std::dynamic_pointer_cast<LuminaRMSNormZero>(blocks["instruct_norm2"]);
+            auto img_attn_norm         = std::dynamic_pointer_cast<RMSNorm>(blocks["img_attn_norm"]);
+            auto img_self_attn_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["img_self_attn_norm"]);
+            auto img_ffn_norm1         = std::dynamic_pointer_cast<RMSNorm>(blocks["img_ffn_norm1"]);
+            auto img_ffn_norm2         = std::dynamic_pointer_cast<RMSNorm>(blocks["img_ffn_norm2"]);
+            auto instruct_attn_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["instruct_attn_norm"]);
+            auto instruct_ffn_norm1    = std::dynamic_pointer_cast<RMSNorm>(blocks["instruct_ffn_norm1"]);
+            auto instruct_ffn_norm2    = std::dynamic_pointer_cast<RMSNorm>(blocks["instruct_ffn_norm2"]);
+
+            int64_t L_instruct = instruct_hidden_states->ne[1];
+
+            auto img_norm1_out_vec      = img_norm1->forward(ctx, img_hidden_states, temb);
+            auto img_norm2_out_vec      = img_norm2->forward(ctx, img_hidden_states, temb);
+            auto img_norm3_out_vec      = img_norm3->forward(ctx, img_hidden_states, temb);
+            auto instruct_norm1_out_vec = instruct_norm1->forward(ctx, instruct_hidden_states, temb);
+            auto instruct_norm2_out_vec = instruct_norm2->forward(ctx, instruct_hidden_states, temb);
+
+            auto img_norm1_out = std::get<0>(img_norm1_out_vec);
+            auto img_gate_msa  = std::get<1>(img_norm1_out_vec);
+            auto img_scale_mlp = std::get<2>(img_norm1_out_vec);
+            auto img_gate_mlp  = std::get<3>(img_norm1_out_vec);
+
+            auto img_norm2_out = std::get<0>(img_norm2_out_vec);
+            auto img_shift_mlp = std::get<1>(img_norm2_out_vec);
+
+            auto img_norm3_out = std::get<0>(img_norm3_out_vec);
+            auto img_gate_self = std::get<1>(img_norm3_out_vec);
+
+            auto instruct_norm1_out = std::get<0>(instruct_norm1_out_vec);
+            auto instruct_gate_msa  = std::get<1>(instruct_norm1_out_vec);
+            auto instruct_scale_mlp = std::get<2>(instruct_norm1_out_vec);
+            auto instruct_gate_mlp  = std::get<3>(instruct_norm1_out_vec);
+
+            auto instruct_norm2_out = std::get<0>(instruct_norm2_out_vec);
+            auto instruct_shift_mlp = std::get<1>(instruct_norm2_out_vec);
+
+            auto joint_attn_out    = img_instruct_attn->forward(ctx, img_norm1_out, instruct_norm1_out, joint_rotary_emb);
+            auto instruct_attn_out = ggml_ext_slice(ctx->ggml_ctx, joint_attn_out, 1, 0, L_instruct);
+            auto img_attn_out      = ggml_ext_slice(ctx->ggml_ctx, joint_attn_out, 1, L_instruct, joint_attn_out->ne[1]);
+
+            auto img_self_attn_out = img_self_attn->forward(ctx, img_norm3_out, img_norm3_out, img_rotary_emb);
+
+            img_hidden_states = gate_residual(ctx->ggml_ctx, img_hidden_states, img_attn_norm->forward(ctx, img_attn_out), img_gate_msa);
+            img_hidden_states = gate_residual(ctx->ggml_ctx, img_hidden_states, img_self_attn_norm->forward(ctx, img_self_attn_out), img_gate_self);
+
+            auto img_mlp_input = scale_modulate(ctx->ggml_ctx, img_norm2_out, img_scale_mlp);
+            img_shift_mlp      = ggml_reshape_3d(ctx->ggml_ctx, img_shift_mlp, img_shift_mlp->ne[0], 1, img_shift_mlp->ne[1]);
+            img_mlp_input      = ggml_add(ctx->ggml_ctx, img_mlp_input, img_shift_mlp);
+            auto img_mlp_out   = img_feed_forward->forward(ctx, img_ffn_norm1->forward(ctx, img_mlp_input));
+            img_hidden_states  = gate_residual(ctx->ggml_ctx, img_hidden_states, img_ffn_norm2->forward(ctx, img_mlp_out), img_gate_mlp);
+
+            instruct_hidden_states  = gate_residual(ctx->ggml_ctx, instruct_hidden_states, instruct_attn_norm->forward(ctx, instruct_attn_out), instruct_gate_msa);
+            auto instruct_mlp_input = scale_modulate(ctx->ggml_ctx, instruct_norm2_out, instruct_scale_mlp);
+            instruct_shift_mlp      = ggml_reshape_3d(ctx->ggml_ctx, instruct_shift_mlp, instruct_shift_mlp->ne[0], 1, instruct_shift_mlp->ne[1]);
+            instruct_mlp_input      = ggml_add(ctx->ggml_ctx, instruct_mlp_input, instruct_shift_mlp);
+            auto instruct_mlp_out   = instruct_feed_forward->forward(ctx, instruct_ffn_norm1->forward(ctx, instruct_mlp_input));
+            instruct_hidden_states  = gate_residual(ctx->ggml_ctx, instruct_hidden_states, instruct_ffn_norm2->forward(ctx, instruct_mlp_out), instruct_gate_mlp);
+
+            return {img_hidden_states, instruct_hidden_states};
+        }
+    };
+
+    struct BooguImageModel : public GGMLBlock {
+        BooguConfig config;
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            GGML_UNUSED(tensor_storage_map);
+            GGML_UNUSED(prefix);
+            params["image_index_embedding"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, config.hidden_size, 5);
+        }
+
+        BooguImageModel() = default;
+        BooguImageModel(BooguConfig config)
+            : config(std::move(config)) {
+            blocks["x_embedder"]               = std::make_shared<Linear>(this->config.patch_size * this->config.patch_size * this->config.in_channels, this->config.hidden_size, true);
+            blocks["ref_image_patch_embedder"] = std::make_shared<Linear>(this->config.patch_size * this->config.patch_size * this->config.in_channels, this->config.hidden_size, true);
+            blocks["time_caption_embed"]       = std::make_shared<LuminaCombinedTimestepCaptionEmbedding>(this->config.hidden_size,
+                                                                                                    this->config.instruction_feat_dim,
+                                                                                                    256,
+                                                                                                    this->config.norm_eps,
+                                                                                                    this->config.timestep_scale);
+
+            for (int i = 0; i < this->config.num_refiner_layers; i++) {
+                blocks["noise_refiner." + std::to_string(i)]     = std::make_shared<BooguImageTransformerBlock>(this->config.hidden_size,
+                                                                                                            this->config.num_attention_heads,
+                                                                                                            this->config.num_kv_heads,
+                                                                                                            this->config.multiple_of,
+                                                                                                            this->config.norm_eps,
+                                                                                                            true);
+                blocks["ref_image_refiner." + std::to_string(i)] = std::make_shared<BooguImageTransformerBlock>(this->config.hidden_size,
+                                                                                                                this->config.num_attention_heads,
+                                                                                                                this->config.num_kv_heads,
+                                                                                                                this->config.multiple_of,
+                                                                                                                this->config.norm_eps,
+                                                                                                                true);
+                blocks["context_refiner." + std::to_string(i)]   = std::make_shared<BooguImageTransformerBlock>(this->config.hidden_size,
+                                                                                                              this->config.num_attention_heads,
+                                                                                                              this->config.num_kv_heads,
+                                                                                                              this->config.multiple_of,
+                                                                                                              this->config.norm_eps,
+                                                                                                              false);
+            }
+
+            for (int i = 0; i < this->config.num_double_stream_layers; i++) {
+                blocks["double_stream_layers." + std::to_string(i)] = std::make_shared<BooguImageDoubleStreamBlock>(this->config.hidden_size,
+                                                                                                                    this->config.num_attention_heads,
+                                                                                                                    this->config.num_kv_heads,
+                                                                                                                    this->config.multiple_of,
+                                                                                                                    this->config.norm_eps);
+            }
+
+            for (int i = 0; i < this->config.num_layers; i++) {
+                blocks["single_stream_layers." + std::to_string(i)] = std::make_shared<BooguImageTransformerBlock>(this->config.hidden_size,
+                                                                                                                   this->config.num_attention_heads,
+                                                                                                                   this->config.num_kv_heads,
+                                                                                                                   this->config.multiple_of,
+                                                                                                                   this->config.norm_eps,
+                                                                                                                   true);
+            }
+
+            blocks["norm_out"] = std::make_shared<LuminaLayerNormContinuous>(this->config.hidden_size,
+                                                                             this->config.timestep_embed_dim,
+                                                                             this->config.patch_size * this->config.patch_size * this->config.out_channels);
+        }
+
+        ggml_tensor* image_index_embedding(GGMLRunnerContext* ctx, int index) {
+            GGML_ASSERT(index >= 0 && index < 5);
+            auto embedding = params["image_index_embedding"];
+            auto out       = ggml_view_1d(ctx->ggml_ctx,
+                                          embedding,
+                                          config.hidden_size,
+                                          index * config.hidden_size * ggml_element_size(embedding));
+            out            = ggml_reshape_3d(ctx->ggml_ctx, out, config.hidden_size, 1, 1);
+            return out;
+        }
+
+        ggml_tensor* embed_refs(GGMLRunnerContext* ctx, const std::vector<ggml_tensor*>& ref_latents) {
+            if (ref_latents.empty()) {
+                return nullptr;
+            }
+            auto ref_image_patch_embedder = std::dynamic_pointer_cast<Linear>(blocks["ref_image_patch_embedder"]);
+
+            ggml_tensor* ref_img = nullptr;
+            for (int i = 0; i < static_cast<int>(ref_latents.size()); i++) {
+                auto ref = DiT::pad_and_patchify(ctx, ref_latents[i], config.patch_size, config.patch_size, false);
+                ref      = ref_image_patch_embedder->forward(ctx, ref);
+                ref      = ggml_add(ctx->ggml_ctx, ref, image_index_embedding(ctx, std::min(i, 4)));
+                ref_img  = ref_img == nullptr ? ref : ggml_concat(ctx->ggml_ctx, ref_img, ref, 1);
+            }
+            return ref_img;
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timesteps,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
+                             std::vector<ggml_tensor*> ref_latents = {}) {
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+            int64_t N = x->ne[3];
+            GGML_ASSERT(N == 1);
+
+            auto x_embedder         = std::dynamic_pointer_cast<Linear>(blocks["x_embedder"]);
+            auto time_caption_embed = std::dynamic_pointer_cast<LuminaCombinedTimestepCaptionEmbedding>(blocks["time_caption_embed"]);
+            auto norm_out           = std::dynamic_pointer_cast<LuminaLayerNormContinuous>(blocks["norm_out"]);
+
+            auto timestep = ggml_sub(ctx->ggml_ctx, ggml_ext_ones_like(ctx->ggml_ctx, timesteps), timesteps);
+            auto embeds   = time_caption_embed->forward(ctx, timestep, context);
+            auto temb     = embeds.first;
+            auto txt      = embeds.second;
+
+            auto img        = DiT::pad_and_patchify(ctx, x, config.patch_size, config.patch_size, false);
+            int64_t img_len = img->ne[1];
+            img             = x_embedder->forward(ctx, img);
+            auto ref_img    = embed_refs(ctx, ref_latents);
+            int64_t ref_len = ref_img != nullptr ? ref_img->ne[1] : 0;
+            int64_t txt_len = txt->ne[1];
+
+            GGML_ASSERT(pe->ne[3] == txt_len + ref_len + img_len);
+            auto txt_pe   = ggml_ext_slice(ctx->ggml_ctx, pe, 3, 0, txt_len);
+            auto noise_pe = ggml_ext_slice(ctx->ggml_ctx, pe, 3, txt_len + ref_len, txt_len + ref_len + img_len);
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<BooguImageTransformerBlock>(blocks["context_refiner." + std::to_string(i)]);
+                txt        = block->forward(ctx, txt, txt_pe);
+                sd::ggml_graph_cut::mark_graph_cut(txt, "boogu.context_refiner." + std::to_string(i), "txt");
+            }
+
+            for (int i = 0; i < config.num_refiner_layers; i++) {
+                auto block = std::dynamic_pointer_cast<BooguImageTransformerBlock>(blocks["noise_refiner." + std::to_string(i)]);
+                img        = block->forward(ctx, img, noise_pe, temb);
+                sd::ggml_graph_cut::mark_graph_cut(img, "boogu.noise_refiner." + std::to_string(i), "img");
+            }
+
+            ggml_tensor* combined_img = img;
+            if (ref_img != nullptr) {
+                auto ref_pe = ggml_ext_slice(ctx->ggml_ctx, pe, 3, txt_len, txt_len + ref_len);
+                for (int i = 0; i < config.num_refiner_layers; i++) {
+                    auto block = std::dynamic_pointer_cast<BooguImageTransformerBlock>(blocks["ref_image_refiner." + std::to_string(i)]);
+                    ref_img    = block->forward(ctx, ref_img, ref_pe, temb);
+                    sd::ggml_graph_cut::mark_graph_cut(ref_img, "boogu.ref_image_refiner." + std::to_string(i), "ref_img");
+                }
+                combined_img = ggml_concat(ctx->ggml_ctx, ref_img, img, 1);
+            }
+
+            auto img_pe = ggml_ext_slice(ctx->ggml_ctx, pe, 3, txt_len, txt_len + combined_img->ne[1]);
+            for (int i = 0; i < config.num_double_stream_layers; i++) {
+                auto block   = std::dynamic_pointer_cast<BooguImageDoubleStreamBlock>(blocks["double_stream_layers." + std::to_string(i)]);
+                auto result  = block->forward(ctx, combined_img, txt, pe, img_pe, temb);
+                combined_img = result.first;
+                txt          = result.second;
+                sd::ggml_graph_cut::mark_graph_cut(combined_img, "boogu.double_stream_layers." + std::to_string(i), "img");
+                sd::ggml_graph_cut::mark_graph_cut(txt, "boogu.double_stream_layers." + std::to_string(i), "txt");
+            }
+
+            auto hidden_states = ggml_concat(ctx->ggml_ctx, txt, combined_img, 1);
+            for (int i = 0; i < config.num_layers; i++) {
+                auto block    = std::dynamic_pointer_cast<BooguImageTransformerBlock>(blocks["single_stream_layers." + std::to_string(i)]);
+                hidden_states = block->forward(ctx, hidden_states, pe, temb);
+                sd::ggml_graph_cut::mark_graph_cut(hidden_states, "boogu.single_stream_layers." + std::to_string(i), "hidden_states");
+            }
+
+            hidden_states = norm_out->forward(ctx, hidden_states, temb);
+            hidden_states = ggml_ext_slice(ctx->ggml_ctx, hidden_states, 1, hidden_states->ne[1] - img_len, hidden_states->ne[1]);
+            hidden_states = DiT::unpatchify_and_crop(ctx->ggml_ctx, hidden_states, H, W, config.patch_size, config.patch_size, false);
+            hidden_states = ggml_ext_scale(ctx->ggml_ctx, hidden_states, -1.f);
+            return hidden_states;
+        }
+    };
+
+    __STATIC_INLINE__ int patched_token_count(int64_t size, int patch_size) {
+        int pad = (patch_size - (static_cast<int>(size) % patch_size)) % patch_size;
+        return (static_cast<int>(size) + pad) / patch_size;
+    }
+
+    __STATIC_INLINE__ void append_spatial_ids(std::vector<std::vector<float>>& ids,
+                                              int bs,
+                                              int pe_shift,
+                                              int h_tokens,
+                                              int w_tokens) {
+        std::vector<std::vector<float>> image_ids(h_tokens * w_tokens, std::vector<float>(3, 0.0f));
+        for (int h = 0; h < h_tokens; h++) {
+            for (int w = 0; w < w_tokens; w++) {
+                image_ids[h * w_tokens + w][0] = static_cast<float>(pe_shift);
+                image_ids[h * w_tokens + w][1] = static_cast<float>(h);
+                image_ids[h * w_tokens + w][2] = static_cast<float>(w);
+            }
+        }
+        for (int b = 0; b < bs; b++) {
+            ids.insert(ids.end(), image_ids.begin(), image_ids.end());
+        }
+    }
+
+    __STATIC_INLINE__ std::vector<float> gen_boogu_pe(int h,
+                                                      int w,
+                                                      int patch_size,
+                                                      int bs,
+                                                      int context_len,
+                                                      const std::vector<ggml_tensor*>& ref_latents,
+                                                      int theta,
+                                                      const std::vector<int>& axes_dim) {
+        std::vector<std::vector<float>> ids;
+        ids.reserve(static_cast<size_t>(bs) * context_len);
+        for (int b = 0; b < bs; b++) {
+            for (int i = 0; i < context_len; i++) {
+                float pos = static_cast<float>(i);
+                ids.push_back({pos, pos, pos});
+            }
+        }
+
+        int pe_shift = context_len;
+        for (ggml_tensor* ref : ref_latents) {
+            int ref_h_tokens = patched_token_count(ref->ne[1], patch_size);
+            int ref_w_tokens = patched_token_count(ref->ne[0], patch_size);
+            append_spatial_ids(ids, bs, pe_shift, ref_h_tokens, ref_w_tokens);
+            pe_shift += std::max(ref_h_tokens, ref_w_tokens);
+        }
+
+        int h_tokens = patched_token_count(h, patch_size);
+        int w_tokens = patched_token_count(w, patch_size);
+        append_spatial_ids(ids, bs, pe_shift, h_tokens, w_tokens);
+
+        return Rope::embed_nd(ids, bs, static_cast<float>(theta), axes_dim);
+    }
+
+    struct BooguImageRunner : public DiffusionModelRunner {
+        BooguConfig config;
+        BooguImageModel boogu;
+        std::vector<float> pe_vec;
+
+        BooguImageRunner(ggml_backend_t backend,
+                         const String2TensorStorage& tensor_storage_map      = {},
+                         const std::string prefix                            = "",
+                         SDVersion version                                   = VERSION_BOOGU_IMAGE,
+                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
+              config(BooguConfig::detect_from_weights(tensor_storage_map, prefix)) {
+            boogu = BooguImageModel(config);
+            boogu.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "boogu_image";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            boogu.get_param_tensors(tensors, prefix);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {}) {
+            ggml_cgraph* gf        = new_graph_custom(BOOGU_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
+            }
+
+            pe_vec      = gen_boogu_pe(static_cast<int>(x->ne[1]),
+                                       static_cast<int>(x->ne[0]),
+                                       config.patch_size,
+                                       static_cast<int>(x->ne[3]),
+                                       static_cast<int>(context->ne[1]),
+                                       ref_latents,
+                                       config.theta,
+                                       config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+
+            auto runner_ctx  = get_context();
+            ggml_tensor* out = boogu.forward(&runner_ctx, x, timesteps, context, pe, ref_latents);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {}) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context, ref_latents);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            static const std::vector<sd::Tensor<float>> empty_ref_latents;
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents);
+        }
+    };
+}  // namespace Boogu
+
+#endif  // __SD_MODEL_DIFFUSION_BOOGU_HPP__
diff --git a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
index 12fcada59..0427b3b38 100644
--- a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
@@ -162,6 +162,8 @@ namespace ErnieImage {
             int64_t S = x->ne[1];
             int64_t N = x->ne[2];
 
+            float scale = (sd_backend_is(ctx->backend, "Vulkan") && ctx->flash_attn_enabled) ? 1.0f / 32.0f : 1.0f;
+
             auto q = to_q->forward(ctx, x);
             auto k = to_k->forward(ctx, x);
             auto v = to_v->forward(ctx, x);
@@ -182,7 +184,7 @@ namespace ErnieImage {
             k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 0, 2, 1, 3));  // [N, heads, S, head_dim]
             k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]);
 
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, ctx->flash_attn_enabled);  // [N, S, hidden_size]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, ctx->flash_attn_enabled, scale);  // [N, S, hidden_size]
             x = to_out_0->forward(ctx, x);
             return x;
         }
diff --git a/otherarch/sdcpp/src/model/diffusion/krea2.hpp b/otherarch/sdcpp/src/model/diffusion/krea2.hpp
new file mode 100644
index 000000000..02e655590
--- /dev/null
+++ b/otherarch/sdcpp/src/model/diffusion/krea2.hpp
@@ -0,0 +1,683 @@
+#ifndef __SD_MODEL_DIFFUSION_KREA2_HPP__
+#define __SD_MODEL_DIFFUSION_KREA2_HPP__
+
+#include <inttypes.h>
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstdlib>
+#include <memory>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "core/ggml_extend.hpp"
+#include "core/ggml_graph_cut.h"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/flux.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
+
+namespace Krea2 {
+    constexpr int KREA2_GRAPH_SIZE = 65536;
+
+    struct Krea2Config {
+        int patch_size            = 2;
+        int64_t in_channels       = 16;
+        int64_t out_channels      = 16;
+        int64_t features          = 6144;
+        int64_t timestep_dim      = 256;
+        int64_t text_dim          = 2560;
+        int64_t text_layers       = 12;
+        int64_t layers            = 28;
+        int64_t heads             = 48;
+        int64_t kv_heads          = 12;
+        int64_t text_heads        = 20;
+        int64_t text_kv_heads     = 20;
+        int64_t mlp_multiplier    = 4;
+        float theta               = 1000.f;
+        float norm_eps            = 1e-5f;
+        std::vector<int> axes_dim = {32, 48, 48};
+        int axes_dim_sum          = 128;
+
+        int64_t head_dim() const {
+            return features / heads;
+        }
+
+        static int64_t count_blocks(const String2TensorStorage& tensor_storage_map,
+                                    const std::string& prefix,
+                                    const std::string& block_prefix) {
+            int64_t count           = 0;
+            std::string full_prefix = prefix.empty() ? block_prefix : prefix + "." + block_prefix;
+            for (const auto& [name, _] : tensor_storage_map) {
+                if (!starts_with(name, full_prefix)) {
+                    continue;
+                }
+                std::string tail = name.substr(full_prefix.size());
+                size_t dot       = tail.find('.');
+                if (dot == std::string::npos) {
+                    continue;
+                }
+                int block_index = std::atoi(tail.substr(0, dot).c_str());
+                count           = std::max<int64_t>(count, block_index + 1);
+            }
+            return count;
+        }
+
+        void update_axes_dim() {
+            int64_t dim_head = head_dim();
+            int64_t unit     = dim_head / 16;
+            axes_dim         = {
+                        static_cast<int>(dim_head - 12 * unit),
+                        static_cast<int>(6 * unit),
+                        static_cast<int>(6 * unit),
+            };
+            axes_dim_sum = axes_dim[0] + axes_dim[1] + axes_dim[2];
+        }
+
+        static Krea2Config detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                               const std::string& prefix) {
+            Krea2Config config;
+            int64_t detected_head_dim      = 0;
+            int64_t detected_text_head_dim = 0;
+
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "first.weight") && tensor_storage.n_dims == 2) {
+                    config.in_channels  = tensor_storage.ne[0] / (config.patch_size * config.patch_size);
+                    config.out_channels = config.in_channels;
+                    config.features     = tensor_storage.ne[1];
+                } else if (ends_with(name, "blocks.0.attn.qknorm.qnorm.scale") && tensor_storage.n_dims == 1) {
+                    detected_head_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "blocks.0.attn.wq.weight") && tensor_storage.n_dims == 2) {
+                    if (detected_head_dim > 0) {
+                        config.heads = tensor_storage.ne[1] / detected_head_dim;
+                    }
+                } else if (ends_with(name, "blocks.0.attn.wk.weight") && tensor_storage.n_dims == 2) {
+                    if (detected_head_dim > 0) {
+                        config.kv_heads = tensor_storage.ne[1] / detected_head_dim;
+                    }
+                } else if (ends_with(name, "txtfusion.projector.weight") && tensor_storage.n_dims == 2) {
+                    config.text_layers = tensor_storage.ne[0];
+                } else if (ends_with(name, "txtfusion.layerwise_blocks.0.prenorm.scale") && tensor_storage.n_dims == 1) {
+                    config.text_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "txtfusion.layerwise_blocks.0.attn.qknorm.qnorm.scale") && tensor_storage.n_dims == 1) {
+                    detected_text_head_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "txtfusion.layerwise_blocks.0.attn.wq.weight") && tensor_storage.n_dims == 2) {
+                    if (detected_text_head_dim > 0) {
+                        config.text_heads = tensor_storage.ne[1] / detected_text_head_dim;
+                    }
+                } else if (ends_with(name, "txtfusion.layerwise_blocks.0.attn.wk.weight") && tensor_storage.n_dims == 2) {
+                    if (detected_text_head_dim > 0) {
+                        config.text_kv_heads = tensor_storage.ne[1] / detected_text_head_dim;
+                    }
+                } else if (ends_with(name, "last.linear.weight") && tensor_storage.n_dims == 2) {
+                    config.out_channels = tensor_storage.ne[1] / (config.patch_size * config.patch_size);
+                }
+            }
+
+            config.layers = std::max<int64_t>(1, count_blocks(tensor_storage_map, prefix, "blocks."));
+            if (detected_head_dim > 0 && config.features > 0) {
+                config.heads = config.features / detected_head_dim;
+            }
+            if (detected_head_dim > 0) {
+                std::string wk_name = prefix.empty() ? "blocks.0.attn.wk.weight" : prefix + ".blocks.0.attn.wk.weight";
+                auto it             = tensor_storage_map.find(wk_name);
+                if (it != tensor_storage_map.end() && it->second.n_dims == 2) {
+                    config.kv_heads = it->second.ne[1] / detected_head_dim;
+                }
+            }
+            if (detected_text_head_dim > 0 && config.text_dim > 0) {
+                config.text_heads = config.text_dim / detected_text_head_dim;
+            }
+            if (detected_text_head_dim > 0) {
+                std::string wk_name = prefix.empty() ? "txtfusion.layerwise_blocks.0.attn.wk.weight" : prefix + ".txtfusion.layerwise_blocks.0.attn.wk.weight";
+                auto it             = tensor_storage_map.find(wk_name);
+                if (it != tensor_storage_map.end() && it->second.n_dims == 2) {
+                    config.text_kv_heads = it->second.ne[1] / detected_text_head_dim;
+                }
+            }
+            config.update_axes_dim();
+
+            LOG_DEBUG("krea2: layers=%" PRId64 ", features=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", text_dim=%" PRId64 ", text_layers=%" PRId64 ", text_heads=%" PRId64 ", text_kv_heads=%" PRId64 ", channels=%" PRId64,
+                      config.layers,
+                      config.features,
+                      config.heads,
+                      config.kv_heads,
+                      config.text_dim,
+                      config.text_layers,
+                      config.text_heads,
+                      config.text_kv_heads,
+                      config.in_channels);
+            return config;
+        }
+    };
+
+    __STATIC_INLINE__ int64_t ceil_to_multiple(int64_t value, int64_t multiple) {
+        return ((value + multiple - 1) / multiple) * multiple;
+    }
+
+    class KreaRMSNorm : public UnaryBlock {
+    protected:
+        int64_t hidden_size;
+        float eps;
+        std::string prefix;
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            GGML_UNUSED(tensor_storage_map);
+            this->prefix    = prefix;
+            params["scale"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
+        }
+
+    public:
+        KreaRMSNorm(int64_t hidden_size, float eps = 1e-5f)
+            : hidden_size(hidden_size),
+              eps(eps) {}
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            ggml_tensor* scale = params["scale"];
+            scale              = ggml_add(ctx->ggml_ctx, scale, ggml_ext_ones(ctx->ggml_ctx, scale->ne[0], 1, 1, 1));
+            x                  = ggml_rms_norm(ctx->ggml_ctx, x, eps);
+            x                  = ggml_mul_inplace(ctx->ggml_ctx, x, scale);
+            return x;
+        }
+    };
+
+    class KreaSwiGLU : public UnaryBlock {
+    public:
+        KreaSwiGLU(int64_t features, int64_t multiplier) {
+            int64_t mlp_dim = ceil_to_multiple(((2 * features) / 3) * multiplier, 128);
+            blocks["gate"]  = std::make_shared<Linear>(features, mlp_dim, false);
+            blocks["up"]    = std::make_shared<Linear>(features, mlp_dim, false);
+            blocks["down"]  = std::make_shared<Linear>(mlp_dim, features, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto gate = std::dynamic_pointer_cast<Linear>(blocks["gate"]);
+            auto up   = std::dynamic_pointer_cast<Linear>(blocks["up"]);
+            auto down = std::dynamic_pointer_cast<Linear>(blocks["down"]);
+
+            auto gated = ggml_silu(ctx->ggml_ctx, gate->forward(ctx, x));
+            auto up_x  = up->forward(ctx, x);
+            x          = ggml_mul(ctx->ggml_ctx, gated, up_x);
+            return down->forward(ctx, x);
+        }
+    };
+
+    class KreaAttention : public GGMLBlock {
+    protected:
+        int64_t features;
+        int64_t heads;
+        int64_t kv_heads;
+        int64_t head_dim_;
+
+        ggml_tensor* attention_no_rope(GGMLRunnerContext* ctx,
+                                       ggml_tensor* q,
+                                       ggml_tensor* k,
+                                       ggml_tensor* v,
+                                       ggml_tensor* mask) {
+            int64_t Lq = q->ne[2];
+            int64_t Lk = k->ne[2];
+            int64_t N  = q->ne[3];
+            q          = ggml_reshape_3d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, q), head_dim_ * heads, Lq, N);
+            k          = ggml_reshape_3d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, k), head_dim_ * kv_heads, Lk, N);
+            v          = ggml_reshape_3d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, v), head_dim_ * kv_heads, Lk, N);
+            return ggml_ext_attention_ext(ctx->ggml_ctx,
+                                          ctx->backend,
+                                          q,
+                                          k,
+                                          v,
+                                          heads,
+                                          mask,
+                                          false,
+                                          ctx->flash_attn_enabled);
+        }
+
+    public:
+        KreaAttention(int64_t features,
+                      int64_t heads,
+                      int64_t kv_heads,
+                      float eps = 1e-5f)
+            : features(features),
+              heads(heads),
+              kv_heads(kv_heads),
+              head_dim_(features / heads) {
+            blocks["wq"]           = std::make_shared<Linear>(features, heads * head_dim_, false);
+            blocks["wk"]           = std::make_shared<Linear>(features, kv_heads * head_dim_, false);
+            blocks["wv"]           = std::make_shared<Linear>(features, kv_heads * head_dim_, false);
+            blocks["gate"]         = std::make_shared<Linear>(features, features, false);
+            blocks["qknorm.qnorm"] = std::make_shared<KreaRMSNorm>(head_dim_, eps);
+            blocks["qknorm.knorm"] = std::make_shared<KreaRMSNorm>(head_dim_, eps);
+            blocks["wo"]           = std::make_shared<Linear>(features, features, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe   = nullptr,
+                             ggml_tensor* mask = nullptr) {
+            auto wq    = std::dynamic_pointer_cast<Linear>(blocks["wq"]);
+            auto wk    = std::dynamic_pointer_cast<Linear>(blocks["wk"]);
+            auto wv    = std::dynamic_pointer_cast<Linear>(blocks["wv"]);
+            auto gate  = std::dynamic_pointer_cast<Linear>(blocks["gate"]);
+            auto qnorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["qknorm.qnorm"]);
+            auto knorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["qknorm.knorm"]);
+            auto wo    = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
+
+            if (sd_backend_is(ctx->backend, "Vulkan")) {
+                wo->set_force_prec_f32(true);
+            }
+
+            int64_t L = x->ne[1];
+            int64_t N = x->ne[2];
+
+            auto q = wq->forward(ctx, x);
+            q      = ggml_reshape_4d(ctx->ggml_ctx, q, head_dim_, heads, L, N);
+            auto k = wk->forward(ctx, x);
+            k      = ggml_reshape_4d(ctx->ggml_ctx, k, head_dim_, kv_heads, L, N);
+            auto v = wv->forward(ctx, x);
+            v      = ggml_reshape_4d(ctx->ggml_ctx, v, head_dim_, kv_heads, L, N);
+
+            q = qnorm->forward(ctx, q);
+            k = knorm->forward(ctx, k);
+
+            auto out = pe != nullptr ? Rope::attention(ctx, q, k, v, pe, mask)
+                                     : attention_no_rope(ctx, q, k, v, mask);
+            out      = ggml_mul(ctx->ggml_ctx, out, ggml_sigmoid(ctx->ggml_ctx, gate->forward(ctx, x)));
+            out      = wo->forward(ctx, out);
+            return out;
+        }
+    };
+
+    class KreaDoubleSharedModulation : public GGMLBlock {
+    protected:
+        int64_t dim;
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            GGML_UNUSED(tensor_storage_map);
+            GGML_UNUSED(prefix);
+            params["lin"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim * 6);
+        }
+
+    public:
+        KreaDoubleSharedModulation(int64_t dim)
+            : dim(dim) {}
+
+        std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) {
+            auto lin = ggml_repeat(ctx->ggml_ctx, params["lin"], vec);
+            auto out = ggml_add(ctx->ggml_ctx, vec, lin);
+            return ggml_ext_chunk(ctx->ggml_ctx, out, 6, 0);
+        }
+    };
+
+    class KreaFinalModulation : public GGMLBlock {
+    protected:
+        int64_t dim;
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            GGML_UNUSED(tensor_storage_map);
+            GGML_UNUSED(prefix);
+            params["lin"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, dim, 2);
+        }
+
+    public:
+        KreaFinalModulation(int64_t dim)
+            : dim(dim) {}
+
+        std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx, ggml_tensor* vec) {
+            auto out = ggml_add(ctx->ggml_ctx, params["lin"], vec);
+            return ggml_ext_chunk(ctx->ggml_ctx, out, 2, 1);
+        }
+    };
+
+    class KreaTextFusionBlock : public UnaryBlock {
+    public:
+        KreaTextFusionBlock(int64_t dim,
+                            int64_t heads,
+                            int64_t kv_heads,
+                            int64_t multiplier,
+                            float eps) {
+            blocks["prenorm"]  = std::make_shared<KreaRMSNorm>(dim, eps);
+            blocks["postnorm"] = std::make_shared<KreaRMSNorm>(dim, eps);
+            blocks["attn"]     = std::make_shared<KreaAttention>(dim, heads, kv_heads, eps);
+            blocks["mlp"]      = std::make_shared<KreaSwiGLU>(dim, multiplier);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto prenorm  = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["prenorm"]);
+            auto postnorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["postnorm"]);
+            auto attn     = std::dynamic_pointer_cast<KreaAttention>(blocks["attn"]);
+            auto mlp      = std::dynamic_pointer_cast<KreaSwiGLU>(blocks["mlp"]);
+
+            x = ggml_add(ctx->ggml_ctx, x, attn->forward(ctx, prenorm->forward(ctx, x)));
+            x = ggml_add(ctx->ggml_ctx, x, mlp->forward(ctx, postnorm->forward(ctx, x)));
+            return x;
+        }
+    };
+
+    class KreaTextFusionTransformer : public UnaryBlock {
+    protected:
+        Krea2Config config;
+
+    public:
+        explicit KreaTextFusionTransformer(Krea2Config config)
+            : config(std::move(config)) {
+            for (int i = 0; i < 2; ++i) {
+                blocks["layerwise_blocks." + std::to_string(i)] = std::make_shared<KreaTextFusionBlock>(this->config.text_dim,
+                                                                                                        this->config.text_heads,
+                                                                                                        this->config.text_kv_heads,
+                                                                                                        this->config.mlp_multiplier,
+                                                                                                        this->config.norm_eps);
+                blocks["refiner_blocks." + std::to_string(i)]   = std::make_shared<KreaTextFusionBlock>(this->config.text_dim,
+                                                                                                      this->config.text_heads,
+                                                                                                      this->config.text_kv_heads,
+                                                                                                      this->config.mlp_multiplier,
+                                                                                                      this->config.norm_eps);
+            }
+            blocks["projector"] = std::make_shared<Linear>(this->config.text_layers, 1, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* context) override {
+            int64_t text_tokens = context->ne[1];
+            int64_t batch       = context->ne[2];
+
+            context = ggml_reshape_3d(ctx->ggml_ctx,
+                                      context,
+                                      config.text_dim,
+                                      config.text_layers,
+                                      text_tokens * batch);
+
+            for (int i = 0; i < 2; ++i) {
+                auto block = std::dynamic_pointer_cast<KreaTextFusionBlock>(blocks["layerwise_blocks." + std::to_string(i)]);
+                context    = block->forward(ctx, context);
+            }
+
+            context        = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, context, 1, 0, 2, 3));
+            auto projector = std::dynamic_pointer_cast<Linear>(blocks["projector"]);
+            context        = projector->forward(ctx, context);
+            context        = ggml_reshape_3d(ctx->ggml_ctx, context, config.text_dim, text_tokens, batch);
+
+            for (int i = 0; i < 2; ++i) {
+                auto block = std::dynamic_pointer_cast<KreaTextFusionBlock>(blocks["refiner_blocks." + std::to_string(i)]);
+                context    = block->forward(ctx, context);
+            }
+            return context;
+        }
+    };
+
+    class KreaSingleStreamBlock : public UnaryBlock {
+    public:
+        explicit KreaSingleStreamBlock(Krea2Config config) {
+            blocks["mod"]      = std::make_shared<KreaDoubleSharedModulation>(config.features);
+            blocks["prenorm"]  = std::make_shared<KreaRMSNorm>(config.features, config.norm_eps);
+            blocks["postnorm"] = std::make_shared<KreaRMSNorm>(config.features, config.norm_eps);
+            blocks["attn"]     = std::make_shared<KreaAttention>(config.features, config.heads, config.kv_heads, config.norm_eps);
+            blocks["mlp"]      = std::make_shared<KreaSwiGLU>(config.features, config.mlp_multiplier);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* vec,
+                             ggml_tensor* pe) {
+            auto mod      = std::dynamic_pointer_cast<KreaDoubleSharedModulation>(blocks["mod"]);
+            auto prenorm  = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["prenorm"]);
+            auto postnorm = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["postnorm"]);
+            auto attn     = std::dynamic_pointer_cast<KreaAttention>(blocks["attn"]);
+            auto mlp      = std::dynamic_pointer_cast<KreaSwiGLU>(blocks["mlp"]);
+
+            auto mods       = mod->forward(ctx, vec);
+            auto attn_input = Flux::modulate(ctx->ggml_ctx,
+                                             prenorm->forward(ctx, x),
+                                             mods[1],
+                                             mods[0],
+                                             true);
+            auto attn_out   = attn->forward(ctx, attn_input, pe);
+            x               = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, mods[2]));
+
+            auto mlp_input = Flux::modulate(ctx->ggml_ctx,
+                                            postnorm->forward(ctx, x),
+                                            mods[4],
+                                            mods[3],
+                                            true);
+            auto mlp_out   = mlp->forward(ctx, mlp_input);
+            x              = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, mlp_out, mods[5]));
+            return x;
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            GGML_UNUSED(ctx);
+            GGML_UNUSED(x);
+            GGML_ABORT("KreaSingleStreamBlock requires conditioning");
+            return nullptr;
+        }
+    };
+
+    class KreaTimeMLP : public UnaryBlock {
+    public:
+        explicit KreaTimeMLP(Krea2Config config) {
+            blocks["0"] = std::make_shared<Linear>(config.timestep_dim, config.features, true);
+            blocks["2"] = std::make_shared<Linear>(config.features, config.features, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto linear_0 = std::dynamic_pointer_cast<Linear>(blocks["0"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+            x             = linear_0->forward(ctx, x);
+            x             = ggml_ext_gelu(ctx->ggml_ctx, x, false);
+            x             = linear_2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class KreaTProj : public UnaryBlock {
+    public:
+        explicit KreaTProj(Krea2Config config) {
+            blocks["1"] = std::make_shared<Linear>(config.features, config.features * 6, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            x             = ggml_ext_gelu(ctx->ggml_ctx, x, false);
+            x             = linear_1->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class KreaTextMLP : public UnaryBlock {
+    public:
+        explicit KreaTextMLP(Krea2Config config) {
+            blocks["0"] = std::make_shared<KreaRMSNorm>(config.text_dim, config.norm_eps);
+            blocks["1"] = std::make_shared<Linear>(config.text_dim, config.features, true);
+            blocks["3"] = std::make_shared<Linear>(config.features, config.features, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            auto norm     = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["0"]);
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            auto linear_3 = std::dynamic_pointer_cast<Linear>(blocks["3"]);
+            x             = norm->forward(ctx, x);
+            x             = linear_1->forward(ctx, x);
+            x             = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+            x             = linear_3->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class KreaLastLayer : public GGMLBlock {
+    public:
+        explicit KreaLastLayer(Krea2Config config) {
+            blocks["norm"]       = std::make_shared<KreaRMSNorm>(config.features, config.norm_eps);
+            blocks["linear"]     = std::make_shared<Linear>(config.features, config.patch_size * config.patch_size * config.out_channels, true);
+            blocks["modulation"] = std::make_shared<KreaFinalModulation>(config.features);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* vec) {
+            auto norm       = std::dynamic_pointer_cast<KreaRMSNorm>(blocks["norm"]);
+            auto linear     = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            auto modulation = std::dynamic_pointer_cast<KreaFinalModulation>(blocks["modulation"]);
+
+            auto mods = modulation->forward(ctx, vec);
+            x         = Flux::modulate(ctx->ggml_ctx,
+                                       norm->forward(ctx, x),
+                                       mods[1],
+                                       mods[0],
+                                       true);
+            x         = linear->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class Krea2Model : public GGMLBlock {
+    protected:
+        Krea2Config config;
+
+    public:
+        Krea2Model() = default;
+        explicit Krea2Model(Krea2Config config)
+            : config(std::move(config)) {
+            blocks["first"]     = std::make_shared<Linear>(this->config.patch_size * this->config.patch_size * this->config.in_channels,
+                                                       this->config.features,
+                                                       true);
+            blocks["tmlp"]      = std::make_shared<KreaTimeMLP>(this->config);
+            blocks["txtfusion"] = std::make_shared<KreaTextFusionTransformer>(this->config);
+            blocks["txtmlp"]    = std::make_shared<KreaTextMLP>(this->config);
+            blocks["tproj"]     = std::make_shared<KreaTProj>(this->config);
+            for (int i = 0; i < this->config.layers; ++i) {
+                blocks["blocks." + std::to_string(i)] = std::make_shared<KreaSingleStreamBlock>(this->config);
+            }
+            blocks["last"] = std::make_shared<KreaLastLayer>(this->config);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe) {
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+            int64_t N = x->ne[3];
+            GGML_ASSERT(N == 1);
+
+            auto first     = std::dynamic_pointer_cast<Linear>(blocks["first"]);
+            auto tmlp      = std::dynamic_pointer_cast<KreaTimeMLP>(blocks["tmlp"]);
+            auto txtfusion = std::dynamic_pointer_cast<KreaTextFusionTransformer>(blocks["txtfusion"]);
+            auto txtmlp    = std::dynamic_pointer_cast<KreaTextMLP>(blocks["txtmlp"]);
+            auto tproj     = std::dynamic_pointer_cast<KreaTProj>(blocks["tproj"]);
+            auto last      = std::dynamic_pointer_cast<KreaLastLayer>(blocks["last"]);
+
+            auto img        = DiT::pad_and_patchify(ctx, x, config.patch_size, config.patch_size, true);
+            int64_t img_len = img->ne[1];
+            img             = first->forward(ctx, img);
+
+            auto t    = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(config.timestep_dim), 10000, 1000.f);
+            t         = tmlp->forward(ctx, t);
+            t         = ggml_reshape_3d(ctx->ggml_ctx, t, t->ne[0], 1, t->ne[1]);
+            auto tvec = tproj->forward(ctx, t);
+
+            auto txt        = txtfusion->forward(ctx, context);
+            txt             = txtmlp->forward(ctx, txt);
+            int64_t txt_len = txt->ne[1];
+
+            auto hidden_states = ggml_concat(ctx->ggml_ctx, txt, img, 1);
+            for (int i = 0; i < config.layers; ++i) {
+                auto block    = std::dynamic_pointer_cast<KreaSingleStreamBlock>(blocks["blocks." + std::to_string(i)]);
+                hidden_states = block->forward(ctx, hidden_states, tvec, pe);
+                sd::ggml_graph_cut::mark_graph_cut(hidden_states, "krea2.blocks." + std::to_string(i), "hidden_states");
+            }
+
+            hidden_states = last->forward(ctx, hidden_states, t);
+            hidden_states = ggml_ext_slice(ctx->ggml_ctx, hidden_states, 1, txt_len, txt_len + img_len);
+            hidden_states = DiT::unpatchify_and_crop(ctx->ggml_ctx, hidden_states, H, W, config.patch_size, config.patch_size, true);
+            return hidden_states;
+        }
+    };
+
+    __STATIC_INLINE__ std::vector<float> gen_krea2_pe(int h,
+                                                      int w,
+                                                      int patch_size,
+                                                      int bs,
+                                                      int context_len,
+                                                      float theta,
+                                                      const std::vector<int>& axes_dim) {
+        auto txt_ids = Rope::gen_flux_txt_ids(bs, context_len, 3, {});
+        auto img_ids = Rope::gen_flux_img_ids(h, w, patch_size, bs, 3, 0, 0, 0, false);
+        auto ids     = Rope::concat_ids(txt_ids, img_ids, bs);
+        return Rope::embed_nd(ids, bs, theta, axes_dim);
+    }
+
+    struct Krea2Runner : public DiffusionModelRunner {
+        Krea2Config config;
+        Krea2Model model;
+        std::vector<float> pe_vec;
+
+        Krea2Runner(ggml_backend_t backend,
+                    const String2TensorStorage& tensor_storage_map      = {},
+                    const std::string prefix                            = "",
+                    std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
+              config(Krea2Config::detect_from_weights(tensor_storage_map, prefix)) {
+            model = Krea2Model(config);
+            model.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "krea2";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            model.get_param_tensors(tensors, prefix);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor) {
+            ggml_cgraph* gf        = new_graph_custom(KREA2_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+
+            pe_vec      = gen_krea2_pe(static_cast<int>(x->ne[1]),
+                                       static_cast<int>(x->ne[0]),
+                                       config.patch_size,
+                                       static_cast<int>(x->ne[3]),
+                                       static_cast<int>(context->ne[1]),
+                                       config.theta,
+                                       config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+
+            auto runner_ctx  = get_context();
+            ggml_tensor* out = model.forward(&runner_ctx, x, timesteps, context, pe);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context));
+        }
+    };
+}  // namespace Krea2
+
+#endif  // __SD_MODEL_DIFFUSION_KREA2_HPP__
diff --git a/otherarch/sdcpp/src/model/te/llm.hpp b/otherarch/sdcpp/src/model/te/llm.hpp
index 74dc232e5..12daf5637 100644
--- a/otherarch/sdcpp/src/model/te/llm.hpp
+++ b/otherarch/sdcpp/src/model/te/llm.hpp
@@ -79,6 +79,7 @@ namespace LLM {
         int window_size                     = 112;
         int num_position_embeddings         = 0;
         std::set<int> fullatt_block_indexes = {7, 15, 23, 31};
+        bool split_patch_embed              = false;
     };
 
     struct LLMConfig {
@@ -179,7 +180,8 @@ namespace LLM {
                 config.num_experts_per_tok     = 4;
             }
 
-            config.num_layers = 0;
+            config.num_layers          = 0;
+            int detected_vision_layers = 0;
             for (const auto& [name, tensor_storage] : tensor_storage_map) {
                 if (!starts_with(name, prefix)) {
                     continue;
@@ -190,6 +192,38 @@ namespace LLM {
                     if (contains(name, "attn.q_proj")) {
                         config.llama_cpp_style = true;
                     }
+                    if (contains(name, "visual.patch_embed.proj.1.weight")) {
+                        config.vision.split_patch_embed = true;
+                    }
+                    if (contains(name, "visual.patch_embed.proj.0.weight")) {
+                        config.vision.patch_size  = static_cast<int>(tensor_storage.ne[0]);
+                        config.vision.in_channels = tensor_storage.ne[2];
+                        config.vision.hidden_size = tensor_storage.ne[3];
+                    }
+                    if (contains(name, "visual.patch_embed.bias")) {
+                        config.vision.hidden_size = tensor_storage.ne[0];
+                    }
+                    if (contains(name, "visual.pos_embed.weight")) {
+                        config.vision.hidden_size             = tensor_storage.ne[0];
+                        config.vision.num_position_embeddings = static_cast<int>(tensor_storage.ne[1]);
+                    }
+                    if (contains(name, "visual.blocks.")) {
+                        auto items = split_string(name.substr(pos), '.');
+                        if (items.size() > 2) {
+                            int block_index = atoi(items[2].c_str());
+                            if (block_index + 1 > detected_vision_layers) {
+                                detected_vision_layers = block_index + 1;
+                            }
+                        }
+                    }
+                    if (contains(name, "visual.blocks.0.mlp.linear_fc1.weight") ||
+                        contains(name, "visual.blocks.0.mlp.gate_proj.weight")) {
+                        config.vision.intermediate_size = tensor_storage.ne[1];
+                    }
+                    if (contains(name, "visual.merger.linear_fc2.weight") ||
+                        contains(name, "visual.merger.mlp.2.weight")) {
+                        config.vision.out_hidden_size = tensor_storage.ne[1];
+                    }
                     continue;
                 }
                 pos = name.find("layers.");
@@ -219,6 +253,9 @@ namespace LLM {
             if (arch == LLMArch::QWEN3 && config.num_layers == 28) {
                 config.num_heads = 16;
             }
+            if (detected_vision_layers > 0) {
+                config.vision.num_layers = detected_vision_layers;
+            }
             LOG_DEBUG("llm: num_layers = %" PRId64 ", vocab_size = %" PRId64 ", hidden_size = %" PRId64 ", intermediate_size = %" PRId64,
                       config.num_layers,
                       config.vocab_size,
@@ -539,40 +576,51 @@ namespace LLM {
 
     struct VisionPatchEmbed : public GGMLBlock {
     protected:
-        bool llama_cpp_style;
+        bool split_patch_embed;
+        bool bias;
         int patch_size;
         int temporal_patch_size;
         int64_t in_channels;
         int64_t embed_dim;
 
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         const std::string prefix                       = "") override {
+            GGML_UNUSED(tensor_storage_map);
+            GGML_UNUSED(prefix);
+            if (split_patch_embed && bias) {
+                params["bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, embed_dim);
+            }
+        }
+
     public:
-        VisionPatchEmbed(bool llama_cpp_style,
+        VisionPatchEmbed(bool split_patch_embed,
                          LLMVisionArch arch,
                          int patch_size          = 14,
                          int temporal_patch_size = 2,
                          int64_t in_channels     = 3,
                          int64_t embed_dim       = 1152)
-            : llama_cpp_style(llama_cpp_style),
+            : split_patch_embed(split_patch_embed),
+              bias(arch == LLMVisionArch::QWEN3_VL),
               patch_size(patch_size),
               temporal_patch_size(temporal_patch_size),
               in_channels(in_channels),
               embed_dim(embed_dim) {
-            bool bias = arch == LLMVisionArch::QWEN3_VL;
-            if (llama_cpp_style) {
+            if (split_patch_embed) {
                 blocks["proj.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels,
                                                                          embed_dim,
                                                                          {patch_size, patch_size},
                                                                          {patch_size, patch_size},
                                                                          {0, 0},
                                                                          {1, 1},
-                                                                         bias));
+                                                                         false));
                 blocks["proj.1"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels,
                                                                          embed_dim,
                                                                          {patch_size, patch_size},
                                                                          {patch_size, patch_size},
                                                                          {0, 0},
                                                                          {1, 1},
-                                                                         bias));
+                                                                         false));
             } else {
                 std::tuple<int, int, int> kernel_size = {(int)temporal_patch_size, (int)patch_size, (int)patch_size};
                 blocks["proj"]                        = std::shared_ptr<GGMLBlock>(new Conv3d(in_channels,
@@ -593,7 +641,7 @@ namespace LLM {
                                 temporal_patch_size,
                                 ggml_nelements(x) / (temporal_patch_size * patch_size * patch_size));
 
-            if (llama_cpp_style) {
+            if (split_patch_embed) {
                 auto proj_0 = std::dynamic_pointer_cast<Conv2d>(blocks["proj.0"]);
                 auto proj_1 = std::dynamic_pointer_cast<Conv2d>(blocks["proj.1"]);
 
@@ -606,6 +654,10 @@ namespace LLM {
                 x1      = proj_1->forward(ctx, x1);
 
                 x = ggml_add(ctx->ggml_ctx, x0, x1);
+                if (bias) {
+                    auto b = ggml_reshape_4d(ctx->ggml_ctx, params["bias"], 1, 1, embed_dim, 1);
+                    x      = ggml_add_inplace(ctx->ggml_ctx, x, b);
+                }
             } else {
                 auto proj = std::dynamic_pointer_cast<Conv3d>(blocks["proj"]);
 
@@ -798,7 +850,7 @@ namespace LLM {
               spatial_merge_size(vision_params.spatial_merge_size),
               num_grid_per_side(vision_params.num_position_embeddings > 0 ? static_cast<int>(std::sqrt(vision_params.num_position_embeddings)) : 0),
               fullatt_block_indexes(vision_params.fullatt_block_indexes) {
-            blocks["patch_embed"] = std::shared_ptr<GGMLBlock>(new VisionPatchEmbed(llama_cpp_style,
+            blocks["patch_embed"] = std::shared_ptr<GGMLBlock>(new VisionPatchEmbed(vision_params.split_patch_embed,
                                                                                     arch_,
                                                                                     vision_params.patch_size,
                                                                                     vision_params.temporal_patch_size,
diff --git a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
index 478b18edb..e41f5fd46 100644
--- a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
+++ b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
@@ -682,7 +682,7 @@ struct AutoEncoderKL : public VAE {
         } else if (sd_version_is_sd3(version)) {
             scale_factor = 1.5305f;
             shift_factor = 0.0609f;
-        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version) || sd_version_is_longcat(version)) {
+        } else if (sd_version_uses_flux_vae(version)) {
             scale_factor = 0.3611f;
             shift_factor = 0.1159f;
         } else if (sd_version_uses_flux2_vae(version)) {
diff --git a/otherarch/sdcpp/src/model_loader.cpp b/otherarch/sdcpp/src/model_loader.cpp
index a1788bfbd..788663103 100644
--- a/otherarch/sdcpp/src/model_loader.cpp
+++ b/otherarch/sdcpp/src/model_loader.cpp
@@ -481,6 +481,10 @@ SDVersion ModelLoader::get_sd_version() {
         if (tensor_storage.name.find("embed_image_indicator.weight") != std::string::npos) {
             return VERSION_IDEOGRAM4;
         }
+        if (tensor_storage.name.find("model.diffusion_model.txtfusion.projector.weight") != std::string::npos ||
+            tensor_storage.name.find("model.diffusion_model.text_fusion.projector.weight") != std::string::npos) {
+            return VERSION_KREA2;
+        }
         if (tensor_storage.name.find("model.diffusion_model.nerf_final_layer_conv.") != std::string::npos) {
             return VERSION_CHROMA_RADIANCE;
         }
@@ -513,6 +517,9 @@ SDVersion ModelLoader::get_sd_version() {
         if (tensor_storage.name.find("model.diffusion_model.cap_embedder.0.weight") != std::string::npos) {
             return VERSION_Z_IMAGE;
         }
+        if (tensor_storage.name.find("double_stream_layers.0.img_instruct_attn.processor.img_to_q.weight") != std::string::npos) {
+            return VERSION_BOOGU_IMAGE;
+        }
         if (tensor_storage.name.find("model.diffusion_model.layers.0.adaLN_sa_ln.weight") != std::string::npos) {
             return VERSION_ERNIE_IMAGE;
         }
diff --git a/otherarch/sdcpp/src/model_manager.cpp b/otherarch/sdcpp/src/model_manager.cpp
index 5287e1069..7095ec6a9 100644
--- a/otherarch/sdcpp/src/model_manager.cpp
+++ b/otherarch/sdcpp/src/model_manager.cpp
@@ -147,6 +147,17 @@ bool ModelManager::register_param_tensors(const std::string& desc,
     return true;
 }
 
+bool ModelManager::load_all_params_eagerly() {
+    std::vector<TensorState*> all_states;
+    all_states.reserve(tensor_states_.size());
+    for (const auto& s : tensor_states_) {
+        if (s != nullptr) {
+            all_states.push_back(s.get());
+        }
+    }
+    return load_tensors_to_params_backend(all_states);
+}
+
 bool ModelManager::validate_registered_tensors() {
     bool ok = true;
     for (const auto& state : tensor_states_) {
@@ -469,7 +480,7 @@ bool ModelManager::mmap_params(const std::vector<TensorState*>& states,
         return true;
     }
 
-    auto mmap_store = model_loader_.mmap_tensors(mmap_candidates, {}, true);
+    auto mmap_store = model_loader_.mmap_tensors(mmap_candidates, {}, writable_mmap_);
     if (mmap_store.empty()) {
         return true;
     }
diff --git a/otherarch/sdcpp/src/model_manager.h b/otherarch/sdcpp/src/model_manager.h
index 1a414c15c..9225e3ea6 100644
--- a/otherarch/sdcpp/src/model_manager.h
+++ b/otherarch/sdcpp/src/model_manager.h
@@ -69,6 +69,7 @@ private:
     uint64_t current_lora_epoch_ = 0;
     int n_threads_               = 0;
     bool enable_mmap_            = false;
+    bool writable_mmap_          = false;
 
     void finish_compute_backend_usage(const std::vector<TensorState*>& states);
     void release_all();
@@ -110,6 +111,7 @@ public:
         model_loader_.set_n_threads(n_threads);
     }
     void set_enable_mmap(bool enable_mmap) { enable_mmap_ = enable_mmap; }
+    void set_writable_mmap(bool writable_mmap) { writable_mmap_ = writable_mmap; }
     void set_common_ignore_tensors(std::set<std::string> ignore_tensors);
     void set_loras(std::vector<LoraSpec> loras, SDVersion version);
 
@@ -158,6 +160,7 @@ public:
     }
 
     bool validate_registered_tensors();
+    bool load_all_params_eagerly();
 
     bool prepare_params(const std::vector<ggml_tensor*>& tensors) override;
     void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) override;
diff --git a/otherarch/sdcpp/src/name_conversion.cpp b/otherarch/sdcpp/src/name_conversion.cpp
index 4b7b4008d..ccc8347b7 100644
--- a/otherarch/sdcpp/src/name_conversion.cpp
+++ b/otherarch/sdcpp/src/name_conversion.cpp
@@ -184,6 +184,27 @@ std::string convert_cond_stage_model_name(std::string name, std::string prefix)
     return name;
 }
 
+std::string convert_qwen3_vl_vision_name(std::string name) {
+    static const std::vector<std::pair<std::string, std::string>> qwen3_vl_vision_name_map{
+        {"mm.0.", "merger.linear_fc1."},
+        {"mm.2.", "merger.linear_fc2."},
+        {"v.post_ln.", "merger.norm."},
+        {"v.position_embd.weight", "pos_embed.weight"},
+        {"v.patch_embd.weight.1", "patch_embed.proj.1.weight"},
+        {"v.patch_embd.weight", "patch_embed.proj.0.weight"},
+        {"v.patch_embd.bias", "patch_embed.bias"},
+        {"v.blk.", "blocks."},
+        {"attn_qkv.", "attn.qkv."},
+        {"attn_out.", "attn.proj."},
+        {"ffn_up.", "mlp.linear_fc1."},
+        {"ffn_down.", "mlp.linear_fc2."},
+        {"ln1.", "norm1."},
+        {"ln2.", "norm2."},
+    };
+    replace_with_name_map(name, qwen3_vl_vision_name_map);
+    return name;
+}
+
 // ref: https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
 std::string convert_diffusers_unet_to_original_sd1(std::string name) {
     // (stable-diffusion, HF Diffusers)
@@ -683,6 +704,38 @@ std::string convert_other_dit_to_original_anima(std::string name) {
     return name;
 }
 
+std::string convert_diffusers_dit_to_original_krea2(std::string name) {
+    static const std::vector<std::pair<std::string, std::string>> prefix_map = {
+        {"img_in.", "first."},
+        {"time_embed.linear_1.", "tmlp.0."},
+        {"time_embed.linear_2.", "tmlp.2."},
+        {"time_mod_proj.", "tproj.1."},
+        {"txt_in.linear_1.", "txtmlp.1."},
+        {"txt_in.linear_2.", "txtmlp.3."},
+        {"text_fusion.", "txtfusion."},
+        {"transformer_blocks.", "blocks."},
+        {"final_layer.", "last."},
+    };
+    static const std::vector<std::pair<std::string, std::string>> name_map = {
+        {"attn.to_out.0.", "attn.wo."},
+        {"attn.to_out.", "attn.wo."},
+        {"attn.to_gate.", "attn.gate."},
+        {"attn.to_q.", "attn.wq."},
+        {"attn.to_k.", "attn.wk."},
+        {"attn.to_v.", "attn.wv."},
+        {"ff.gate.", "mlp.gate."},
+        {"ff.up.", "mlp.up."},
+        {"ff.down.", "mlp.down."},
+        {"txt_in.norm.", "txtmlp.0."},
+        {"last.norm.weight", "last.norm.scale"},
+        {"last.modulation.weight", "last.modulation.lin"},
+    };
+
+    replace_with_prefix_map(name, prefix_map);
+    replace_with_name_map(name, name_map);
+    return name;
+}
+
 std::string convert_diffusion_model_name(std::string name, std::string prefix, SDVersion version) {
     if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
         name = convert_diffusers_unet_to_original_sd1(name);
@@ -696,6 +749,8 @@ std::string convert_diffusion_model_name(std::string name, std::string prefix, S
         name = convert_diffusers_dit_to_original_lumina2(name);
     } else if (sd_version_is_anima(version)) {
         name = convert_other_dit_to_original_anima(name);
+    } else if (sd_version_is_krea2(version)) {
+        name = convert_diffusers_dit_to_original_krea2(name);
     }
     return name;
 }
@@ -1154,6 +1209,10 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
 
     replace_with_prefix_map(name, prefix_map);
 
+    if ((sd_version_is_boogu_image(version) || sd_version_is_krea2(version)) && starts_with(name, "text_encoders.llm.visual.")) {
+        name = convert_qwen3_vl_vision_name(std::move(name));
+    }
+
     // diffusion model
     {
         for (const auto& prefix : diffuison_model_prefix_vec) {
diff --git a/otherarch/sdcpp/src/runtime/denoiser.hpp b/otherarch/sdcpp/src/runtime/denoiser.hpp
index fed5911bc..28b29ef27 100644
--- a/otherarch/sdcpp/src/runtime/denoiser.hpp
+++ b/otherarch/sdcpp/src/runtime/denoiser.hpp
@@ -559,6 +559,203 @@ struct LTX2Scheduler : SigmaScheduler {
     }
 };
 
+/*
+ * Logit-Normal Scheduler
+ * Based on: https://github.com/ideogram-oss/ideogram4/blob/main/src/ideogram4/scheduler.py
+ */
+struct LogitNormalScheduler : SigmaScheduler {
+    float mean       = 0.0f;
+    float std        = 1.75f;
+    float logsnr_min = -15.0f;
+    float logsnr_max = 18.0f;
+
+    bool resolution_aware = true;
+
+    float one_minus_t_min, one_minus_t_max;
+
+    void parse_extra_sample_args(int image_seq_len = 0, const char* extra_sample_args = nullptr) {
+        const int known_seq_len = (512 * 512) / (16 * 16);
+        if (extra_sample_args) {
+            for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "logit-normal scheduler arg")) {
+                if (key == "mu") {
+                    if (!parse_strict_float(value, mean)) {
+                        LOG_WARN("ignoring invalid logit-normal scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                } else if (key == "std") {
+                    if (!parse_strict_float(value, std)) {
+                        LOG_WARN("ignoring invalid logit-normal scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+                if (key == "logsnr_min") {
+                    if (!parse_strict_float(value, logsnr_min)) {
+                        LOG_WARN("ignoring invalid logit-normal scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                } else if (key == "logsnr_max") {
+                    if (!parse_strict_float(value, logsnr_max)) {
+                        LOG_WARN("ignoring invalid logit-normal scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                } else if (key == "resolution_aware") {
+                    if (!parse_strict_bool(value, resolution_aware)) {
+                        LOG_WARN("ignoring invalid logit-normal scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
+        }
+        if (image_seq_len > 0 && resolution_aware) {
+            mean += 0.5 * std::log(static_cast<float>(image_seq_len) / static_cast<float>(known_seq_len));
+        }
+    }
+
+    float sigmoid(float x) {
+        return 1.0f / (1.0f + std::exp(-x));
+    }
+
+    LogitNormalScheduler(float mean = 0.0f, float std = 1.75f, float logsnr_min = -18.0f, float logsnr_max = 15.0f)
+        : mean(mean), std(std), logsnr_min(logsnr_min), logsnr_max(logsnr_max) {
+        // t_min = 1.0f / (1.0f + std::exp(0.5f * logsnr_max));
+        one_minus_t_min = sigmoid(0.5f * logsnr_max);
+        // t_max = 1.0f / (1.0f + std::exp(0.5f * logsnr_min));
+        one_minus_t_max = sigmoid(0.5f * logsnr_min);
+
+    }
+
+    LogitNormalScheduler(int image_seq_len = 0, const char* extra_sample_args = nullptr) {
+        mean       = 0.0f;
+        std        = 1.75f;
+        logsnr_min = -15.0f;
+        logsnr_max = 18.0f;
+
+        parse_extra_sample_args(image_seq_len, extra_sample_args);
+        // t_min = 1.0f / (1.0f + std::exp(0.5f * logsnr_max));
+        one_minus_t_min = sigmoid(0.5f * logsnr_max);
+        // t_max = 1.0f / (1.0f + std::exp(0.5f * logsnr_min));
+        one_minus_t_max = sigmoid(0.5f * logsnr_min);
+    }
+
+    // https://stackedboxes.org/2017/05/01/acklams-normal-quantile-function/
+    double ndtri(double p) {
+        if (p <= 0.0) {
+            return -std::numeric_limits<double>::infinity();
+        } else if (p >= 1.0) {
+            return std::numeric_limits<double>::infinity();
+        }
+
+        static const double p_low  = 0.02425;
+        static const double p_high = 1.0 - p_low;
+
+        static const double c[6] = {-7.784894002430293e-03,
+                                    -3.223964580411365e-01,
+                                    -2.400758277161838e+00,
+                                    -2.549732539343734e+00,
+                                    4.374664141464968e+00,
+                                    2.938163982698783e+00};
+
+        static const double d[5] = {7.784695709041462e-03,
+                                    3.224671290700398e-01,
+                                    2.445134137142996e+00,
+                                    3.754408661907416e+00,
+                                    1.0};
+
+        // Coefficients for the central region
+        static const double a[6] = {-3.969683028665376e+01,
+                                    2.209460984245205e+02,
+                                    -2.759285104469687e+02,
+                                    1.383577518672690e+02,
+                                    -3.066479806614716e+01,
+                                    2.506628277459239e+00};
+
+        static const double b[6] = {-5.447609879822406e+01,
+                                    1.615858368580409e+02,
+                                    -1.556989798598866e+02,
+                                    6.680131188771972e+01,
+                                    -1.328068155288572e+01,
+                                    1.0};
+
+        double x = 0.0;
+
+        if (p < p_low) {
+            // Lower region
+            double q = std::sqrt(-2.0 * std::log(p));
+
+            // Numerator: c[0]*q^5 + c[1]*q^4 + ... + c[5]
+            double numerator = c[0];
+            for (int i = 1; i < 6; ++i) {
+                numerator = numerator * q + c[i];
+            }
+
+            // Denominator: d[0]*q^4 + d[1]*q^3 + ... + d[3]*q + 1
+            double denominator = d[0];
+            for (int i = 1; i < 5; ++i) {
+                denominator = denominator * q + d[i];
+            }
+
+            x = numerator / denominator;
+        } else if (p > p_high) {
+            // Upper region
+            double q = std::sqrt(-2.0 * std::log(1.0 - p));
+
+            double numerator = c[0];
+            for (int i = 1; i < 6; ++i) {
+                numerator = numerator * q + c[i];
+            }
+
+            double denominator = d[0];
+            for (int i = 1; i < 5; ++i) {
+                denominator = denominator * q + d[i];
+            }
+
+            x = -(numerator / denominator);
+        } else {
+            // Central region
+            double q = p - 0.5;
+            double r = q * q;
+
+            // Numerator: (a[0]*r^5 + a[1]*r^4 + ... + a[5])*q
+            double numerator = a[0];
+            for (int i = 1; i < 6; ++i) {
+                numerator = numerator * r + a[i];
+            }
+            numerator *= q;
+
+            // Denominator: b[0]*r^4 + b[1]*r^3 + ... + b[4]*r + 1
+            double denominator = b[0];
+            for (int i = 1; i < 6; ++i) {
+                denominator = denominator * r + b[i];
+            }
+
+            x = numerator / denominator;
+        }
+        return x;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> sigmas;
+        LOG_INFO("LOGIT_NORMAL_SCHEDULER using mean=%.4f, std=%.4f, logsnr_min=%.4f, logsnr_max=%.4f", mean, std, logsnr_min, logsnr_max);
+        sigmas.reserve(n + 1);
+        for (uint32_t i = 0; i <= n; ++i) {
+            float t = static_cast<float>(i) / static_cast<float>(n);
+
+            // ndtri(1-t) == -ndtri(t)
+            float z = -ndtri(t);
+
+            float y = mean + std * z;
+
+            float timestep = sigmoid(y);
+
+            if (timestep > one_minus_t_min)
+                timestep = one_minus_t_min;
+            if (timestep < one_minus_t_max)
+                timestep = one_minus_t_max;
+
+            float sigma = timestep;
+
+            sigmas.push_back(sigma);
+        }
+        sigmas[n] = 0.0f;
+        return sigmas;
+    }
+};
+
 struct Denoiser {
     virtual float sigma_min()                                                        = 0;
     virtual float sigma_max()                                                        = 0;
@@ -623,6 +820,11 @@ struct Denoiser {
                 LOG_INFO("get_sigmas with LTX2 scheduler");
                 scheduler = std::make_shared<LTX2Scheduler>(image_seq_len, extra_sample_args);
                 break;
+            case LOGIT_NORMAL_SCHEDULER: {
+                LOG_INFO("get_sigmas with Logit-Normal scheduler");
+                scheduler = std::make_shared<LogitNormalScheduler>(image_seq_len, extra_sample_args);
+                break;
+            }
             default:
                 LOG_INFO("get_sigmas with discrete scheduler (default)");
                 scheduler = std::make_shared<DiscreteScheduler>();
diff --git a/otherarch/sdcpp/src/runtime/guidance.cpp b/otherarch/sdcpp/src/runtime/guidance.cpp
index f925b4b8c..bfb773b0a 100644
--- a/otherarch/sdcpp/src/runtime/guidance.cpp
+++ b/otherarch/sdcpp/src/runtime/guidance.cpp
@@ -3,6 +3,7 @@
 #include <algorithm>
 #include <cmath>
 #include <cstdlib>
+#include <optional>
 #include <string>
 #include <utility>
 
@@ -63,6 +64,82 @@ namespace sd::guidance {
         return uncond;
     }
 
+    std::vector<float> parse_guidance_schedule_from_spec(std::string spec) {
+        std::vector<float> schedule;
+
+        while (!spec.empty()) {
+            auto sep     = spec.find('+');
+            auto segment = spec.substr(0, sep);
+
+            auto x = segment.find('x');
+            if (x == std::string::npos) {
+                LOG_ERROR("Invalid guidance schedule segment: '%s' (expected <guidance>x<count>)", segment.c_str());
+                return {};
+            }
+
+            float guidance;
+            int count;
+
+            auto guidance_str = segment.substr(0, x);
+            auto count_str    = segment.substr(x + 1);
+
+            try {
+                size_t idx = 0;
+                guidance   = std::stof(guidance_str, &idx);
+                if (idx != guidance_str.size()) {
+                    LOG_ERROR("Invalid guidance value in guidance schedule: '%s'", guidance_str.c_str());
+                    return {};
+                }
+            } catch (const std::exception&) {
+                LOG_ERROR("Invalid guidance value in guidance schedule: '%s'", guidance_str.c_str());
+                return {};
+            }
+
+            try {
+                size_t idx = 0;
+                count      = std::stoi(count_str, &idx);
+                if (idx != count_str.size()) {
+                    LOG_ERROR("Invalid count in guidance schedule: '%s'", count_str.c_str());
+                    return {};
+                }
+            } catch (const std::exception&) {
+                LOG_ERROR("Invalid count in guidance schedule: '%s'", count_str.c_str());
+                return {};
+            }
+
+            if (count <= 0) {
+                LOG_ERROR("Guidance schedule count must be positive");
+                return {};
+            }
+
+            schedule.insert(schedule.end(), count, guidance);
+
+            if (sep == std::string::npos) {
+                break;
+            }
+
+            spec = spec.substr(sep + 1);
+        }
+
+        return schedule;
+    }
+
+    std::vector<float> parse_guidance_schedule(const char* extra_sample_args) {
+        std::vector<float> guidance_schedule;
+        std::string guidance_schedule_str = "";
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "extra sample arg")) {
+            float parsed = 0.0f;
+            if (key == "guidance_schedule") {
+                guidance_schedule_str = value;
+            }
+        }
+
+        if (!guidance_schedule_str.empty()) {
+            guidance_schedule = parse_guidance_schedule_from_spec(guidance_schedule_str);
+        }
+        return guidance_schedule;
+    }
+
     ClassifierFreeGuidance::ClassifierFreeGuidance(float guidance_scale,
                                                    float image_guidance_scale)
         : guidance_scale_(guidance_scale),
@@ -70,8 +147,10 @@ namespace sd::guidance {
     }
 
     GuiderOutput ClassifierFreeGuidance::forward(const GuidanceInput& input,
-                                                 GuiderOutput previous) const {
+                                                 GuiderOutput previous,
+                                                 std::optional<float> scale_override) const {
         (void)previous;
+        float guidance_scale = scale_override.value_or(guidance_scale_);
 
         GuiderOutput output;
         if (!has_tensor(input.pred_cond)) {
@@ -86,14 +165,14 @@ namespace sd::guidance {
                 const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
                 output.pred                              = pred_img_uncond +
                               image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
-                              guidance_scale_ * (pred_cond - pred_uncond);
+                              guidance_scale * (pred_cond - pred_uncond);
 
             } else {
-                output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
+                output.pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond);
             }
         } else if (has_tensor(input.pred_img_uncond)) {
             const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
-            output.pred                              = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
+            output.pred                              = pred_img_uncond + guidance_scale * (pred_cond - pred_img_uncond);
         }
 
         return output;
@@ -128,8 +207,10 @@ namespace sd::guidance {
     }
 
     GuiderOutput AdaptiveProjectedGuidance::forward(const GuidanceInput& input,
-                                                    GuiderOutput previous) const {
+                                                    GuiderOutput previous,
+                                                    std::optional<float> scale_override) const {
         (void)previous;
+        float guidance_scale = scale_override.value_or(guidance_scale_);
 
         GuiderOutput output;
         if (!has_tensor(input.pred_cond)) {
@@ -144,13 +225,13 @@ namespace sd::guidance {
                 const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
                 output.pred                              = pred_img_uncond +
                               image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
-                              guidance_scale_ * (pred_cond - pred_uncond);
+                              guidance_scale * (pred_cond - pred_uncond);
             } else {
-                output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
+                output.pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond);
             }
         } else if (has_tensor(input.pred_img_uncond)) {
             const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
-            output.pred                              = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
+            output.pred                              = pred_img_uncond + guidance_scale * (pred_cond - pred_img_uncond);
         }
         if (!has_tensor(input.pred_uncond) && !has_tensor(input.pred_img_uncond)) {
             return output;
@@ -162,7 +243,7 @@ namespace sd::guidance {
         sd::Tensor<float> deltas = calculate_guidance_delta(pred_cond,
                                                             pred_uncond,
                                                             pred_img_uncond,
-                                                            guidance_scale_,
+                                                            guidance_scale,
                                                             image_guidance_scale_);
         if (params_.momentum != 0.0f) {
             if (momentum_buffer_.shape() != deltas.shape()) {
@@ -239,7 +320,8 @@ namespace sd::guidance {
     }
 
     GuiderOutput SkipLayerGuidance::forward(const GuidanceInput& input,
-                                            GuiderOutput output) const {
+                                            GuiderOutput output,
+                                            std::optional<float> /*scale_override*/) const {
         if (scale_ == 0.0f || !is_enabled_for_step(input) || !input.predict_skip_layer) {
             return output;
         }
diff --git a/otherarch/sdcpp/src/runtime/guidance.h b/otherarch/sdcpp/src/runtime/guidance.h
index aeba06fd0..3de337042 100644
--- a/otherarch/sdcpp/src/runtime/guidance.h
+++ b/otherarch/sdcpp/src/runtime/guidance.h
@@ -3,6 +3,7 @@
 
 #include <cstddef>
 #include <functional>
+#include <optional>
 #include <vector>
 
 #include "core/tensor.hpp"
@@ -27,6 +28,7 @@ namespace sd::guidance {
     AdaptiveProjectedGuidanceParams parse_adaptive_projected_guidance_args(const char* extra_sample_args);
     bool is_adaptive_projected_guidance_enabled(const AdaptiveProjectedGuidanceParams& params);
     bool parse_skip_layer_guidance_uncond_arg(const char* extra_sample_args);
+    std::vector<float> parse_guidance_schedule(const char* extra_sample_args);
 
     struct GuidanceInput {
         int step                                 = 0;
@@ -40,9 +42,10 @@ namespace sd::guidance {
 
     class BaseGuidance {
     public:
-        virtual ~BaseGuidance()                                   = default;
+        virtual ~BaseGuidance()                                                                = default;
         virtual GuiderOutput forward(const GuidanceInput& input,
-                                     GuiderOutput previous) const = 0;
+                                     GuiderOutput previous,
+                                     std::optional<float> scale_override = std::nullopt) const = 0;
     };
 
     class ClassifierFreeGuidance : public BaseGuidance {
@@ -54,7 +57,8 @@ namespace sd::guidance {
                                float image_guidance_scale);
 
         GuiderOutput forward(const GuidanceInput& input,
-                             GuiderOutput previous) const override;
+                             GuiderOutput previous,
+                             std::optional<float> scale_override = std::nullopt) const override;
     };
 
     class AdaptiveProjectedGuidance : public BaseGuidance {
@@ -69,7 +73,8 @@ namespace sd::guidance {
                                   AdaptiveProjectedGuidanceParams params);
 
         GuiderOutput forward(const GuidanceInput& input,
-                             GuiderOutput previous) const override;
+                             GuiderOutput previous,
+                             std::optional<float> scale_override = std::nullopt) const override;
     };
 
     class SkipLayerGuidance : public BaseGuidance {
@@ -88,7 +93,8 @@ namespace sd::guidance {
         const std::vector<int>& layers() const;
 
         GuiderOutput forward(const GuidanceInput& input,
-                             GuiderOutput previous) const override;
+                             GuiderOutput previous,
+                             std::optional<float> scale_override = std::nullopt) const override;
     };
 
 }  // namespace sd::guidance
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index 99e070909..e951e4143 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -22,11 +22,13 @@
 #include "extensions/generation_extension.h"
 #include "model/adapter/lora.hpp"
 #include "model/diffusion/anima.hpp"
+#include "model/diffusion/boogu.hpp"
 #include "model/diffusion/control.hpp"
 #include "model/diffusion/ernie_image.hpp"
 #include "model/diffusion/flux.hpp"
 #include "model/diffusion/hidream_o1.hpp"
 #include "model/diffusion/ideogram4.hpp"
+#include "model/diffusion/krea2.hpp"
 #include "model/diffusion/lens.hpp"
 #include "model/diffusion/ltxv.hpp"
 #include "model/diffusion/mmdit.hpp"
@@ -89,12 +91,14 @@ const char* model_version_to_str[] = {
     "LTXAV",
     "HiDream O1",
     "Z-Image",
+    "Boogu Image",
     "Ovis Image",
     "Ernie Image",
     "Lens",
     "Longcat-Image",
     "PiD",
     "Ideogram 4",
+    "Krea2",
     "ESRGAN",
 };
 
@@ -126,7 +130,8 @@ static bool sd_version_supports_ref_latent_img_cfg(SDVersion version) {
            sd_version_is_flux2(version) ||
            sd_version_is_qwen_image(version) ||
            sd_version_is_longcat(version) ||
-           sd_version_is_z_image(version);
+           sd_version_is_z_image(version) ||
+           sd_version_is_boogu_image(version);
 }
 
 static bool sd_version_supports_img_cfg(SDVersion version, bool has_ref_images) {
@@ -200,6 +205,7 @@ public:
     bool enable_mmap                     = false;
     sd::ggml_graph_cut::MaxVramAssignment max_vram_assignment;
     bool stream_layers = false;
+    bool eager_load    = false;
     std::string backend_spec;
     std::string params_backend_spec;
 
@@ -346,6 +352,7 @@ public:
         n_threads           = sd_ctx_params->n_threads;
         enable_mmap         = sd_ctx_params->enable_mmap;
         stream_layers       = sd_ctx_params->stream_layers;
+        eager_load          = sd_ctx_params->eager_load;
         backend_spec        = SAFE_STR(sd_ctx_params->backend);
         params_backend_spec = SAFE_STR(sd_ctx_params->params_backend);
         max_vram_assignment.reset(0.f);
@@ -488,7 +495,10 @@ public:
         bool is_lens = sd_version_is_lens(tempver);
         bool is_ltx = sd_version_is_ltxav(tempver);
         bool is_ideogram = sd_version_is_ideogram4(tempver);
-        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx || is_ideogram);
+        bool is_boogu = sd_version_is_boogu_image(tempver);
+        bool is_krea2 = sd_version_is_krea2(tempver);
+        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx || is_ideogram || is_boogu || is_krea2);
+        bool has_llm_vision = (is_qwenimg || is_longcat || is_boogu);
 
         //kcpp qol fallback: if a llm was loaded as t5 by mistake
         if(conditioner_is_llm && t5_path_fixed!="")
@@ -539,7 +549,7 @@ public:
                 clip_vision_fixed = clipg_path_fixed;
                 clipg_path_fixed = "";
             }
-            else if(is_qwenimg && llm_vision_path_fixed=="")
+            else if(has_llm_vision && llm_vision_path_fixed=="")
             {
                 llm_vision_path_fixed = clipg_path_fixed;
                 clipg_path_fixed = "";
@@ -581,7 +591,7 @@ public:
             {
                 to_replace = "taesd_xl.embd";
             }
-            else if(sd_version_is_flux(tempver)||sd_version_is_z_image(tempver)||tempver == VERSION_OVIS_IMAGE||is_longcat)
+            else if(sd_version_uses_flux_vae(tempver))
             {
                 to_replace = "taesd_f.embd";
             }
@@ -593,7 +603,7 @@ public:
             {
                 to_replace = "taesd_f2.embd";
             }
-            else if(is_wan21||is_qwenimg||sd_version_is_anima(tempver))
+            else if(is_wan21||is_qwenimg||is_anima||is_krea2)
             {
                 to_replace = "taesd_w21.embd";
             }
@@ -762,9 +772,7 @@ public:
         auto& tensor_storage_map = model_loader.get_tensor_storage_map();
 
         LOG_INFO("Version: %s ", model_version_to_str[version]);
-        ggml_type wtype               = (int)sd_ctx_params->wtype < std::min<int>(SD_TYPE_COUNT, GGML_TYPE_COUNT)
-                                            ? (ggml_type)sd_ctx_params->wtype
-                                            : GGML_TYPE_COUNT;
+        ggml_type wtype               = sd_type_to_ggml_type(sd_ctx_params->wtype);
         std::string tensor_type_rules = SAFE_STR(sd_ctx_params->tensor_type_rules);
         //kcpp: patch hidream to fix broken images on vulkan https://github.com/leejet/stable-diffusion.cpp/issues/1496
         if(version == VERSION_HIDREAM_O1 && tensor_type_rules.size()==0)
@@ -774,7 +782,6 @@ public:
         if (wtype != GGML_TYPE_COUNT || tensor_type_rules.size() > 0) {
             model_loader.set_wtype_override(wtype, tensor_type_rules);
         }
-        model_loader.process_model_files(enable_mmap, true);
 
         std::map<ggml_type, uint32_t> wtype_stat                 = model_loader.get_wtype_stat();
         std::map<ggml_type, uint32_t> conditioner_wtype_stat     = model_loader.get_conditioner_wtype_stat();
@@ -828,9 +835,12 @@ public:
             apply_lora_immediately = false;
         }
 
+        bool needs_writable_mmap = enable_mmap && apply_lora_immediately;
+        model_manager->set_writable_mmap(needs_writable_mmap);
         if (enable_mmap && apply_lora_immediately) {
             LOG_WARN("in mode 'immediately', LoRAs will cause extra memory usage with mmap");
         }
+        model_loader.process_model_files(enable_mmap, needs_writable_mmap);
         load_alphas_cumprod(model_loader);
 
         size_t text_encoder_params_mem_size = 0;
@@ -885,6 +895,17 @@ public:
                                                                                tensor_storage_map,
                                                                                "model.diffusion_model",
                                                                                model_manager);
+            } else if (sd_version_is_krea2(version)) {
+                cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
+                                                                 tensor_storage_map,
+                                                                 version,
+                                                                 "",
+                                                                 false,
+                                                                 model_manager);
+                diffusion_model  = std::make_shared<Krea2::Krea2Runner>(backend_for(SDBackendModule::DIFFUSION),
+                                                                       tensor_storage_map,
+                                                                       "model.diffusion_model",
+                                                                       model_manager);
             } else if (sd_version_is_flux(version)) {
                 bool is_chroma = false;
                 for (auto pair : tensor_storage_map) {
@@ -1031,6 +1052,18 @@ public:
                                                                          "model.diffusion_model",
                                                                          version,
                                                                          model_manager);
+            } else if (sd_version_is_boogu_image(version)) {
+                cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
+                                                                 tensor_storage_map,
+                                                                 version,
+                                                                 "",
+                                                                 true,
+                                                                 model_manager);
+                diffusion_model  = std::make_shared<Boogu::BooguImageRunner>(backend_for(SDBackendModule::DIFFUSION),
+                                                                            tensor_storage_map,
+                                                                            "model.diffusion_model",
+                                                                            version,
+                                                                            model_manager);
             } else if (sd_version_is_ernie_image(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
@@ -1109,6 +1142,7 @@ public:
             auto create_tae = [&](bool decode_only) -> std::shared_ptr<VAE> {
                 if (sd_version_is_wan(version) ||
                     sd_version_is_qwen_image(version) ||
+                    sd_version_is_krea2(version) ||
                     sd_version_is_anima(version) ||
                     sd_version_is_ltxav(version)) {
                     return std::make_shared<TinyVideoAutoEncoder>(backend_for(SDBackendModule::VAE),
@@ -1149,6 +1183,7 @@ public:
                                                          model_manager);
                 } else if (sd_version_is_wan(version) ||
                            sd_version_is_qwen_image(version) ||
+                           sd_version_is_krea2(version) ||
                            sd_version_is_anima(version)) {
                     return std::make_shared<WAN::WanVAERunner>(backend_for(SDBackendModule::VAE),
                                                                tensor_storage_map,
@@ -1385,7 +1420,15 @@ public:
             return false;
         }
 
-        LOG_DEBUG("model metadata validated; weights will be prepared lazily");
+        if (eager_load) {
+            if (!model_manager->load_all_params_eagerly()) {
+                LOG_ERROR("model params eager load failed");
+                return false;
+            }
+            LOG_DEBUG("model metadata validated; weights pre-loaded to params backend");
+        } else {
+            LOG_DEBUG("model metadata validated; weights will be prepared lazily");
+        }
 
         {
             size_t total_params_ram_size  = 0;
@@ -1467,6 +1510,7 @@ public:
                            sd_version_is_anima(version) ||
                            sd_version_is_ernie_image(version) ||
                            sd_version_is_z_image(version) ||
+                           sd_version_is_boogu_image(version) ||
                            sd_version_is_pid(version) ||
                            sd_version_is_ideogram4(version)) {
                     pred_type = FLOW_PRED;
@@ -1478,13 +1522,16 @@ public:
                         default_flow_shift = 1.5f;
                     } else if (sd_version_is_ideogram4(version)) {
                         default_flow_shift = 1.0f;
+                    } else if (sd_version_is_boogu_image(version)) {
+                        default_flow_shift = 3.16f;
                     } else {
                         default_flow_shift = 3.f;
                     }
                 } else if (sd_version_is_flux(version) ||
                            sd_version_is_longcat(version) ||
                            sd_version_is_lens(version) ||
-                           sd_version_is_ltxav(version)) {
+                           sd_version_is_ltxav(version) ||
+                           sd_version_is_krea2(version)) {
                     pred_type = FLUX_FLOW_PRED;
 
                     default_flow_shift = 1.0f;  // TODO: validate
@@ -1500,6 +1547,8 @@ public:
                         default_flow_shift = 1.83f;
                     } else if (sd_version_is_ltxav(version)) {
                         default_flow_shift = 2.37f;
+                    } else if (sd_version_is_krea2(version)) {
+                        default_flow_shift = 1.15f;
                     }
                 } else if (sd_version_is_flux2(version)) {
                     pred_type = FLUX2_FLOW_PRED;
@@ -1957,10 +2006,10 @@ public:
                 if (sd_version_is_sd3(version)) {
                     latent_rgb_proj = sd3_latent_rgb_proj;
                     latent_rgb_bias = sd3_latent_rgb_bias;
-                } else if (sd_version_is_flux(version) || sd_version_is_z_image(version) || sd_version_is_longcat(version)) {
+                } else if (sd_version_uses_flux_vae(version)) {
                     latent_rgb_proj = flux_latent_rgb_proj;
                     latent_rgb_bias = flux_latent_rgb_bias;
-                } else if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
+                } else if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version) || sd_version_is_krea2(version)) {
                     latent_rgb_proj = wan_21_latent_rgb_proj;
                     latent_rgb_bias = wan_21_latent_rgb_bias;
                 } else {
@@ -2052,6 +2101,9 @@ public:
         if (sd_version_is_anima(version)) {
             return std::vector<float>{t / static_cast<float>(TIMESTEPS)};
         }
+        if (sd_version_is_boogu_image(version)) {
+            return std::vector<float>{t / static_cast<float>(TIMESTEPS)};
+        }
         if (version == VERSION_HIDREAM_O1) {
             return std::vector<float>{1.0f - (t / static_cast<float>(TIMESTEPS))};
         }
@@ -2177,6 +2229,32 @@ public:
         float slg_scale     = guidance.slg.scale;
         bool slg_uncond     = sd::guidance::parse_skip_layer_guidance_uncond_arg(extra_sample_args);
 
+        std::vector<float> guidance_schedule = sd::guidance::parse_guidance_schedule(extra_sample_args);
+        if (!guidance_schedule.empty() && guidance_schedule.size() != sigmas.size() - 1) {
+            if (guidance_schedule.size() > sigmas.size()) {
+                LOG_WARN("guidance_schedule length (%zu) is greater than number of steps (%zu)", guidance_schedule.size(), sigmas.size() - 1);
+                LOG_WARN("truncating guidance_schedule to match step count");
+                guidance_schedule.resize(sigmas.size() - 1);
+            } else {
+                LOG_INFO("padding guidance_schedule with cfg_scale");
+                while (guidance_schedule.size() < sigmas.size() - 1) {
+                    guidance_schedule.push_back(cfg_scale);
+                }
+            }
+        }
+
+        if (!guidance_schedule.empty()) {
+            std::string schedule_str = "[";
+            for (size_t i = 0; i < guidance_schedule.size(); ++i) {
+                schedule_str += std::to_string(guidance_schedule[i]);
+                if (i < guidance_schedule.size() - 1) {
+                    schedule_str += ", ";
+                }
+            }
+            schedule_str += "]";
+            LOG_DEBUG("using guidance schedule: %s", schedule_str.c_str());
+        }
+
         sd_sample::SampleCacheRuntime cache_runtime = sd_sample::init_sample_cache_runtime(version,
                                                                                            cache_params,
                                                                                            denoiser.get(),
@@ -2417,7 +2495,7 @@ public:
             guidance_input.pred_uncond     = uncond_out.empty() ? nullptr : &uncond_out;
             guidance_input.pred_img_uncond = img_uncond_out.empty() ? nullptr : &img_uncond_out;
 
-            sd::guidance::GuiderOutput guided = primary_guidance.forward(guidance_input, {});
+            sd::guidance::GuiderOutput guided = guidance_schedule.empty() ? primary_guidance.forward(guidance_input, {}) : primary_guidance.forward(guidance_input, {}, guidance_schedule[guidance_schedule.size() - 1 - step]);
             if (guided.pred.empty()) {
                 return {};
             }
@@ -2759,6 +2837,7 @@ const char* scheduler_to_str[] = {
     "lcm",
     "bong_tangent",
     "ltx2",
+    "logit_normal",
 };
 
 const char* sd_scheduler_name(enum scheduler_t scheduler) {
@@ -2958,6 +3037,7 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->lora_apply_mode      = LORA_APPLY_AUTO;
     sd_ctx_params->max_vram             = nullptr;
     sd_ctx_params->stream_layers        = false;
+    sd_ctx_params->eager_load           = false;
     sd_ctx_params->enable_mmap          = false;
     sd_ctx_params->diffusion_flash_attn = false;
     sd_ctx_params->circular_x           = false;
@@ -3004,6 +3084,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "prediction: %s\n"
              "max_vram: %s\n"
              "stream_layers: %s\n"
+             "eager_load: %s\n"
              "backend: %s\n"
              "params_backend: %s\n"
              "flash_attn: %s\n"
@@ -3039,6 +3120,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              sd_prediction_name(sd_ctx_params->prediction),
              SAFE_STR(sd_ctx_params->max_vram),
              BOOL_STR(sd_ctx_params->stream_layers),
+             BOOL_STR(sd_ctx_params->eager_load),
              SAFE_STR(sd_ctx_params->backend),
              SAFE_STR(sd_ctx_params->params_backend),
              BOOL_STR(sd_ctx_params->flash_attn),
@@ -3430,6 +3512,8 @@ enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_me
         return SIMPLE_SCHEDULER;
     } else if (sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_ltxav(sd_ctx->sd->version)) {
         return LTX2_SCHEDULER;
+    } else if(sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_ideogram4(sd_ctx->sd->version)) {
+        return LOGIT_NORMAL_SCHEDULER;
     }
     return DISCRETE_SCHEDULER;
 }
@@ -6057,9 +6141,6 @@ SD_API void free_sd_images(sd_image_t* result_images, int num_images) {
 
 namespace kcpp_sd {
 
-    static_assert((int)SD_TYPE_COUNT == (int)GGML_TYPE_COUNT,
-            "inconsistency between SD_TYPE_COUNT and GGML_TYPE_COUNT");
-
     int get_loaded_sd_version(sd_ctx_t* ctx) {
         return ctx->sd->version;
     }
@@ -6094,6 +6175,8 @@ namespace kcpp_sd {
         res.is_sd2 = (loadedsdver == SDVersion::VERSION_SD2);
         res.is_sdxl = sd_version_is_sdxl((SDVersion)loadedsdver);
         res.is_ltx = sd_version_is_ltxav((SDVersion)loadedsdver);
+        res.is_boogu = sd_version_is_boogu_image((SDVersion)loadedsdver);
+        res.supports_ref_image = sd_version_supports_ref_latent_img_cfg((SDVersion)loadedsdver);
         res.vae_scale_factor = ctx->sd->get_vae_scale_factor();
         res.spatial_multiple = get_spatial_multiple(ctx);
         return res;
diff --git a/otherarch/sdcpp/src/tokenizers/bpe_tokenizer.cpp b/otherarch/sdcpp/src/tokenizers/bpe_tokenizer.cpp
index 896975a21..7733f00d3 100644
--- a/otherarch/sdcpp/src/tokenizers/bpe_tokenizer.cpp
+++ b/otherarch/sdcpp/src/tokenizers/bpe_tokenizer.cpp
@@ -134,7 +134,8 @@ std::vector<int> BPETokenizer::encode(const std::string& text, on_new_token_cb_t
     std::vector<int32_t> bpe_tokens;
     std::vector<std::string> token_strs;
 
-    auto splited_texts = split_with_special_tokens(text, special_tokens);
+    std::string normalized_text = normalize_before_split ? normalize(text) : text;
+    auto splited_texts          = split_with_special_tokens(normalized_text, special_tokens);
 
     for (auto& splited_text : splited_texts) {
         if (is_special_token(splited_text)) {
@@ -159,7 +160,7 @@ std::vector<int> BPETokenizer::encode(const std::string& text, on_new_token_cb_t
                 }
             }
 
-            std::string token_str = normalize(token);
+            std::string token_str = normalize_before_split ? token : normalize(token);
             std::u32string utf32_token;
             if (byte_level_bpe) {
                 for (int i = 0; i < token_str.length(); i++) {
diff --git a/otherarch/sdcpp/src/tokenizers/clip_tokenizer.cpp b/otherarch/sdcpp/src/tokenizers/clip_tokenizer.cpp
index d11c18c31..d51eadec4 100644
--- a/otherarch/sdcpp/src/tokenizers/clip_tokenizer.cpp
+++ b/otherarch/sdcpp/src/tokenizers/clip_tokenizer.cpp
@@ -22,9 +22,10 @@ CLIPTokenizer::CLIPTokenizer(int pad_token_id, const std::string& merges_utf8_st
     EOS_TOKEN_ID = 49407;
     PAD_TOKEN_ID = pad_token_id;
 
-    end_of_word_suffix = "</w>";
-    add_bos_token      = true;
-    add_eos_token      = true;
+    end_of_word_suffix     = "</w>";
+    add_bos_token          = true;
+    add_eos_token          = true;
+    normalize_before_split = true;
 
     if (merges_utf8_str.size() > 0) {
         load_from_merges(merges_utf8_str);
diff --git a/otherarch/sdcpp/src/tokenizers/tokenizer.h b/otherarch/sdcpp/src/tokenizers/tokenizer.h
index e044285bb..893759e0f 100644
--- a/otherarch/sdcpp/src/tokenizers/tokenizer.h
+++ b/otherarch/sdcpp/src/tokenizers/tokenizer.h
@@ -12,9 +12,10 @@ using on_new_token_cb_t = std::function<bool(std::string&, std::vector<int32_t>&
 class Tokenizer {
 protected:
     std::vector<std::string> special_tokens;
-    bool add_bos_token = false;
-    bool add_eos_token = false;
-    bool pad_left      = false;
+    bool add_bos_token          = false;
+    bool add_eos_token          = false;
+    bool pad_left               = false;
+    bool normalize_before_split = false;
     std::string end_of_word_suffix;
 
     virtual std::string decode_token(int token_id) const = 0;

From 40e445914723f54196cc07982b17ba18ebdd7c9f Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 25 Jun 2026 14:22:04 +0800
Subject: [PATCH 154/292] load sd weights eagerly

---
 otherarch/sdcpp/sdtype_adapter.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 2a5c61f40..5dff78031 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -482,6 +482,7 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     params.chroma_use_dit_mask = true;
     params.max_vram = max_vram.c_str();
     params.stream_layers = inputs.stream_layers;
+    params.eager_load = true; //kcpp should preload everything
     params.enable_mmap = inputs.use_mmap;
     params.params_backend = inputs.offload_cpu ? "CPU" : "";
     backends += get_device_override(inputs.kcpp_vae_device, "VAE");

From fdbd6abee20e408de21e90ca77a24cd50a6ea073 Mon Sep 17 00:00:00 2001
From: Christopher Albert <albert@tugraz.at>
Date: Thu, 25 Jun 2026 08:22:51 +0200
Subject: [PATCH 155/292] tests : synchronize contexts at end of
 test-thread-safety (#24935)

Assisted-by: Claude
---
 tests/test-thread-safety.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/test-thread-safety.cpp b/tests/test-thread-safety.cpp
index acda4aa81..d0b5946e2 100644
--- a/tests/test-thread-safety.cpp
+++ b/tests/test-thread-safety.cpp
@@ -146,6 +146,8 @@ int main(int argc, char ** argv) {
                 }
 
                 LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str());
+
+                llama_synchronize(ctx.get());
             });
         }
     }

From 3e61ea0e2f67fd9c76e82aa2aef6f794520f4a75 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Thu, 25 Jun 2026 09:45:55 +0200
Subject: [PATCH 156/292] ui: fix always-show-sidebar-on-desktop setting after
 navigation refactor (#24979)

---
 .../SidebarNavigation/SidebarNavigation.svelte        | 11 ++++++++++-
 tools/ui/src/routes/+layout.svelte                    |  8 --------
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
index 1445961e0..fe503f53b 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
@@ -14,6 +14,7 @@
 	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
 	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
 	import { chatStore } from '$lib/stores/chat.svelte';
+	import { config } from '$lib/stores/settings.svelte';
 	import { RouterService } from '$lib/services/router.service';
 	import { isMobile } from '$lib/stores/viewport.svelte';
 	import { TooltipSide } from '$lib/enums';
@@ -34,6 +35,14 @@
 
 	const isStripExpanded = $derived(isExpandedMode || hoveredTooltip !== null);
 	const isOnMobile = $derived(isMobile.current);
+	const alwaysShowOnDesktop = $derived(config().alwaysShowSidebarOnDesktop as boolean);
+
+	// Keep the sidebar expanded on desktop when the user pins it open
+	$effect(() => {
+		if (alwaysShowOnDesktop && !isOnMobile) {
+			isExpandedMode = true;
+		}
+	});
 
 	function toggleExpandedMode() {
 		isExpandedMode = !isExpandedMode;
@@ -183,7 +192,7 @@
 				/>
 			</div>
 
-			{#if isExpandedMode || isOnMobile}
+			{#if isOnMobile || (isExpandedMode && !alwaysShowOnDesktop)}
 				<div
 					class="flex items-center transition-all duration-150 ease-out {isMobile.current &&
 					!isExpandedMode
diff --git a/tools/ui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
index f7993f594..339b067dd 100644
--- a/tools/ui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -33,8 +33,6 @@
 	import { SETTINGS_KEYS } from '$lib/constants';
 
 	let { children } = $props();
-	let alwaysShowSidebarOnDesktop = $derived(config().alwaysShowSidebarOnDesktop);
-	let isDesktop = $derived(!isMobile.current);
 	let innerHeight = $state<number | undefined>();
 	let innerWidth = $state(browser ? window.innerWidth : 0);
 
@@ -164,12 +162,6 @@
 		updateFavicon();
 	});
 
-	$effect(() => {
-		if (alwaysShowSidebarOnDesktop && isDesktop) {
-			return;
-		}
-	});
-
 	// Initialize server properties on app load (run once)
 	$effect(() => {
 		// Only fetch if we don't already have props

From f728adab683387b2bdb085b3854a0f3487a13d0a Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Thu, 25 Jun 2026 10:06:44 +0200
Subject: [PATCH 157/292] ggml : address integer overflows in binary ops CUDA
 implementation (#24706)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml : address integer overflows in binary ops CUDA implementation

* ggml : add size_t casts to avoid integer overflows

* ggml : add more asserts checking integer overflows in binary ops CUDA implementation

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 ggml/src/ggml-cuda/binbcast.cu | 136 ++++++++++++++++++++++-----------
 1 file changed, 90 insertions(+), 46 deletions(-)

diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index c25f42b32..2e38077bf 100644
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -34,26 +34,26 @@ template <float (*bin_op)(const float, const float),
 static __global__ void k_bin_bcast(const src0_t *         src0,
                                    const src1_t *         src1,
                                    dst_t *                dst,
-                                   const int              ne0,
-                                   const int              ne1,
-                                   const int              ne2,
+                                   const uint32_t         ne0,
+                                   const uint32_t         ne1,
+                                   const uint32_t         ne2,
                                    const uint3            ne3,
                                    const uint3            ne10,
                                    const uint3            ne11,
                                    const uint3            ne12,
                                    const uint3            ne13,
-                                 /*const int              s0,*/
-                                   const int              s1,
-                                   const int              s2,
-                                   const int              s3,
-                                   const int              s00,
-                                   const int              s01,
-                                   const int              s02,
-                                   const int              s03,
-                                   const int              s10,
-                                   const int              s11,
-                                   const int              s12,
-                                   const int              s13,
+                                 /*const uint32_t         s0,*/
+                                   const uint32_t         s1,
+                                   const uint32_t         s2,
+                                   const uint32_t         s3,
+                                   const uint32_t         s00,
+                                   const uint32_t         s01,
+                                   const uint32_t         s02,
+                                   const uint32_t         s03,
+                                   const uint32_t         s10,
+                                   const uint32_t         s11,
+                                   const uint32_t         s12,
+                                   const uint32_t         s13,
                                    src1_ptrs... src1s) {
     ggml_cuda_pdl_lc();
     const uint32_t i0s = blockDim.x * blockIdx.x + threadIdx.x;
@@ -61,7 +61,7 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     const uint32_t i2  = fastdiv((blockDim.z * blockIdx.z + threadIdx.z), ne3);
     const uint32_t i3  = (blockDim.z * blockIdx.z + threadIdx.z) - (i2 * ne3.z);
 
-    if (i0s >= (uint32_t)ne0 || i1 >= (uint32_t)ne1 || i2 >= (uint32_t)ne2 || i3 >= ne3.z) {
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3.z) {
         return;
     }
 
@@ -69,25 +69,32 @@ static __global__ void k_bin_bcast(const src0_t *         src0,
     const uint32_t i12 = fastmodulo(i2, ne12);
     const uint32_t i13 = fastmodulo(i3, ne13);
 
-    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+    const size_t i_src0 = size_t( i3)*s03 + size_t( i2)*s02 + size_t( i1)*s01;
+    const size_t i_src1 = size_t(i13)*s13 + size_t(i12)*s12 + size_t(i11)*s11;
+    const size_t i_dst  = size_t( i3)*s3  + size_t( i2)*s2  + size_t( i1)*s1;
 
     const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
+    const uint32_t s0 = blockDim.x * gridDim.x;
+
     ggml_cuda_pdl_sync();
-    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
+    for (uint32_t i0 = i0s; i0 < ne0; i0 += s0) {
         const uint32_t i10 = fastmodulo(i0, ne10);
 
-        float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
+        float result = src0_row ? (float) src0_row[size_t(i0)*s00] : 0.0f;
         if constexpr (sizeof...(src1_ptrs) > 0) {
-            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
+            result = (..., (result = bin_op(result, (float)src1s[i_src1 + size_t(i10)*s10])));
         } else {
-            result = bin_op(result, (float)src1[i_src1 + i10*s10]);
+            result = bin_op(result, (float)src1[i_src1 + size_t(i10)*s10]);
         }
 
         dst_row[i0] = (dst_t) result;
+
+        // protect i0 from overflow
+        if (ne0 - i0 <= s0) {
+           break;
+        }
     }
 }
 
@@ -110,19 +117,19 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
                                            const uint3            ne12,
                                            const uint3            ne13,
                                          /*const int              s0,*/
-                                           const int              s1,
-                                           const int              s2,
-                                           const int              s3,
-                                           const int              s00,
-                                           const int              s01,
-                                           const int              s02,
-                                           const int              s03,
-                                           const int              s10,
-                                           const int              s11,
-                                           const int              s12,
-                                           const int              s13,
+                                           const uint32_t         s1,
+                                           const uint32_t         s2,
+                                           const uint32_t         s3,
+                                           const uint32_t         s00,
+                                           const uint32_t         s01,
+                                           const uint32_t         s02,
+                                           const uint32_t         s03,
+                                           const uint32_t         s10,
+                                           const uint32_t         s11,
+                                           const uint32_t         s12,
+                                           const uint32_t         s13,
                                            src1_ptrs... src1s) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+    const uint32_t i  = blockDim.x*blockIdx.x + threadIdx.x;
 
     const uint32_t i3 = fastdiv(i, prod_012);
     const uint32_t i2 = fastdiv(i - i3 * prod_012.z, prod_01);
@@ -133,25 +140,25 @@ static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
         return;
     }
 
-    const int i11 = fastmodulo(i1, ne11);
-    const int i12 = fastmodulo(i2, ne12);
-    const int i13 = fastmodulo(i3, ne13);
+    const uint32_t i11 = fastmodulo(i1, ne11);
+    const uint32_t i12 = fastmodulo(i2, ne12);
+    const uint32_t i13 = fastmodulo(i3, ne13);
 
-    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
-    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
+    const size_t i_src0 = size_t( i3)*s03 + size_t( i2)*s02 + size_t( i1)*s01;
+    const size_t i_src1 = size_t(i13)*s13 + size_t(i12)*s12 + size_t(i11)*s11;
+    const size_t i_dst  = size_t( i3)*s3  + size_t( i2)*s2  + size_t( i1)*s1;
 
     const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
     dst_t * dst_row = dst + i_dst;
 
-    const int i10 = fastmodulo(i0, ne10);
+    const uint32_t i10 = fastmodulo(i0, ne10);
 
     ggml_cuda_pdl_sync();
-    float result = src0_row ? (float) src0_row[i0*s00] : 0.0f;
+    float result = src0_row ? (float) src0_row[size_t(i0)*s00] : 0.0f;
     if constexpr (sizeof...(src1_ptrs) > 0) {
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10*s10])));
+        result = (..., (result = bin_op(result, (float)src1s[i_src1 + size_t(i10)*s10])));
     } else {
-        result = bin_op(result, (float)src1[i_src1 + i10*s10]);
+        result = bin_op(result, (float)src1[i_src1 + size_t(i10)*s10]);
     }
 
     dst_row[i0] = (dst_t) result;
@@ -248,6 +255,31 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         size_t s02 = nb02 / sizeof(src0_t);
         size_t s03 = nb03 / sizeof(src0_t);
 
+        GGML_ASSERT(ne0 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne1 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne2 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(ne3 <= std::numeric_limits<uint32_t>::max());
+
+      //GGML_ASSERT(s0  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s1  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s2  <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s3  <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(s00 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s01 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s02 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s03 <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(s10 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s11 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s12 <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(s13 <= std::numeric_limits<uint32_t>::max());
+
+        GGML_ASSERT(cne1[0] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[1] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[2] <= std::numeric_limits<uint32_t>::max());
+        GGML_ASSERT(cne1[3] <= std::numeric_limits<uint32_t>::max());
+
         GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
         GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
         GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
@@ -263,6 +295,8 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
         GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
 
+        GGML_ASSERT(ne2 * ne3 <= std::numeric_limits<unsigned int>::max());
+
         const int block_size = 128;
 
         int64_t hne0 = std::max(ne0 / 2LL, 1LL);
@@ -281,7 +315,13 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
         const uint3 ne13 = init_fastdiv_values((uint32_t) cne1[3]);
 
         if (block_nums.z > 65535 || block_nums.y > 65535) {
-            int         block_num  = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
+            int64_t     block_num   = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
+
+            GGML_ASSERT(block_num              <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(block_num * block_size <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(ne0 * ne1              <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(ne0 * ne1 * ne2        <= std::numeric_limits<uint32_t>::max());
+
             const uint3 prod_012    = init_fastdiv_values((uint32_t) (ne0 * ne1 * ne2));
             const uint3 prod_01     = init_fastdiv_values((uint32_t) (ne0 * ne1));
             const uint3 ne0_fastdiv = init_fastdiv_values((uint32_t) ne0);
@@ -298,6 +338,10 @@ static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor *
                     s10, s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
             }
         } else {
+            GGML_ASSERT(int64_t(block_nums.x) * block_dims.x <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(int64_t(block_nums.y) * block_dims.y <= std::numeric_limits<uint32_t>::max());
+            GGML_ASSERT(int64_t(block_nums.z) * block_dims.z <= std::numeric_limits<uint32_t>::max());
+
             const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
             {
                 const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(block_nums, block_dims, 0, stream);

From 145beb5744a019b76352ec4975b7683ffacd6b73 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 25 Jun 2026 18:11:44 +0800
Subject: [PATCH 158/292] try to use llama.cpp's tool call parser first

---
 Makefile            |  2 +-
 expose.cpp          | 19 ++++++++++
 gpttype_adapter.cpp | 90 +++++++++++++++++++++++++++++++++++++++++++++
 koboldcpp.py        | 51 ++++++++++++++++++++++++-
 model_adapter.h     |  7 ++++
 5 files changed, 166 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 051ea092e..bd9646830 100644
--- a/Makefile
+++ b/Makefile
@@ -754,7 +754,7 @@ music_default.o: otherarch/acestep/music_adapter.cpp
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 # idiotic "for easier compilation"
-GPTTYPE_ADAPTER = gpttype_adapter.cpp otherarch/llama_v2.cpp otherarch/llama_v3.cpp src/llama.cpp src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-iswa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model.cpp src/llama-quant.cpp src/llama-hparams.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
+GPTTYPE_ADAPTER = gpttype_adapter.cpp common/chat.cpp otherarch/llama_v2.cpp otherarch/llama_v3.cpp src/llama.cpp src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-iswa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model.cpp src/llama-quant.cpp src/llama-hparams.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
 gpttype_adapter_failsafe.o: $(GPTTYPE_ADAPTER)
 	$(CXX) $(CXXFLAGS) $(FAILSAFE_FLAGS) -c $< -o $@
 gpttype_adapter.o: $(GPTTYPE_ADAPTER)
diff --git a/expose.cpp b/expose.cpp
index 4ca2fea72..969027840 100644
--- a/expose.cpp
+++ b/expose.cpp
@@ -347,6 +347,25 @@ extern "C"
         return chat_template.c_str();
     }
 
+    static std::string parsed_tool_calls = "";
+    const char* parse_chat_tool_calls(const char * generated_text,
+                                      const char * tools_json,
+                                      const char * chat_template,
+                                      const char * chat_template_kwargs_json,
+                                      const char * tool_choice,
+                                      bool parallel_tool_calls,
+                                      bool is_partial) {
+        parsed_tool_calls = gpttype_parse_chat_tool_calls(
+            generated_text ? generated_text : "",
+            tools_json ? tools_json : "",
+            chat_template ? chat_template : "",
+            chat_template_kwargs_json ? chat_template_kwargs_json : "",
+            tool_choice ? tool_choice : "",
+            parallel_tool_calls,
+            is_partial);
+        return parsed_tool_calls.c_str();
+    }
+
     const char* get_pending_output() {
        return gpttype_get_pending_output().c_str();
     }
diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index db8c5d3bf..e8d283eca 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -36,6 +36,7 @@
 #include "llama_v2.cpp"
 #include "llama_v3.cpp"
 #include "src/llama.cpp"
+#include "common/chat.cpp"
 #include "gptj_v1.cpp"
 #include "gptj_v2.cpp"
 #include "gptj_v3.cpp"
@@ -4725,6 +4726,95 @@ std::string gpttype_get_chat_template()
     return std::string(model_template.data(), model_template.size() - 1);
 }
 
+std::string gpttype_parse_chat_tool_calls(const std::string & generated_text,
+                                          const std::string & tools_json,
+                                          const std::string & chat_template,
+                                          const std::string & chat_template_kwargs_json,
+                                          const std::string & tool_choice,
+                                          bool parallel_tool_calls,
+                                          bool is_partial)
+{
+    try
+    {
+        if(generated_text.empty() || tools_json.empty())
+        {
+            return "";
+        }
+
+        json tools = json::parse(tools_json);
+        if(!tools.is_array() || tools.empty())
+        {
+            return "";
+        }
+
+        std::string tmpl_src = chat_template.empty() ? gpttype_get_chat_template() : chat_template;
+        if(tmpl_src.empty())
+        {
+            return "";
+        }
+
+        auto tmpls = common_chat_templates_init(nullptr, tmpl_src);
+
+        common_chat_msg msg;
+        msg.role = "user";
+        msg.content = "tool parser warmup";
+
+        common_chat_templates_inputs inputs;
+        inputs.use_jinja = true;
+        inputs.messages = { msg };
+        inputs.tools = common_chat_tools_parse_oaicompat(tools);
+        inputs.tool_choice = common_chat_tool_choice_parse_oaicompat(tool_choice.empty() ? "auto" : tool_choice);
+        inputs.parallel_tool_calls = parallel_tool_calls;
+        inputs.add_generation_prompt = true;
+
+        if(!chat_template_kwargs_json.empty())
+        {
+            json kwargs = json::parse(chat_template_kwargs_json);
+            if(kwargs.is_object())
+            {
+                for(const auto & item : kwargs.items())
+                {
+                    inputs.chat_template_kwargs[item.key()] = item.value().dump();
+                }
+            }
+        }
+
+        common_chat_params chat_params = common_chat_templates_apply(tmpls.get(), inputs);
+        if(chat_params.parser.empty())
+        {
+            return "";
+        }
+
+        common_chat_parser_params parser_params(chat_params);
+        parser_params.parse_tool_calls = true;
+        parser_params.parser.load(chat_params.parser);
+
+        common_chat_msg parsed = common_chat_parse(generated_text, is_partial, parser_params);
+        if(parsed.tool_calls.empty())
+        {
+            return "";
+        }
+
+        json tool_calls = parsed.to_json_oaicompat().value("tool_calls", json::array());
+        return tool_calls.dump();
+    }
+    catch(const std::exception & e)
+    {
+        if(debugmode == 1 && !is_quiet)
+        {
+            printf("\nNative tool parser failed: %s\n", e.what());
+        }
+    }
+    catch(...)
+    {
+        if(debugmode == 1 && !is_quiet)
+        {
+            printf("\nNative tool parser failed with unknown error.\n");
+        }
+    }
+    return "";
+}
+
 std::vector<int> gpttype_get_token_arr(const std::string & input, bool addbos)
 {
     std::vector<int> toks;
diff --git a/koboldcpp.py b/koboldcpp.py
index 5c0f16123..05b7d59fd 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -968,6 +968,9 @@ def init_library():
     handle.token_count.restype = token_count_outputs
     handle.get_pending_output.restype = ctypes.c_char_p
     handle.get_chat_template.restype = ctypes.c_char_p
+    handle.parse_chat_tool_calls.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool]
+    handle.parse_chat_tool_calls.restype = ctypes.c_char_p
+
     handle.calc_new_state_kv.restype = ctypes.c_size_t
     handle.calc_new_state_tokencount.restype = ctypes.c_size_t
     handle.calc_old_state_kv.argtypes = [ctypes.c_int]
@@ -3667,6 +3670,47 @@ def toolcall_to_normalized_json(text,start_tag,end_tag,required_match_txt): #con
 
     return text #fallback
 
+def native_parse_toolcall_tags(text: str, genparams: dict) -> list:
+    global cached_chat_template, cached_jinja_kwargs
+    if not text or not genparams.get('using_openai_tools', False):
+        return []
+    tools = genparams.get('tools', [])
+    if not tools:
+        return []
+    try:
+        kwargs = dict(cached_jinja_kwargs or {})
+        user_kwargs = genparams.get("chat_template_kwargs")
+        if isinstance(user_kwargs, dict):
+            kwargs.update(user_kwargs)
+        if "reasoning_effort" in genparams and genparams["reasoning_effort"] is not None:
+            kwargs["reasoning_effort"] = genparams["reasoning_effort"]
+
+        tool_choice = genparams.get("tool_choice", "auto")
+        if isinstance(tool_choice, dict):
+            tool_choice = "required"
+        elif tool_choice is None:
+            tool_choice = "auto"
+        else:
+            tool_choice = str(tool_choice)
+
+        raw = handle.parse_chat_tool_calls(
+            text.encode("UTF-8"),
+            json.dumps(tools).encode("UTF-8"),
+            (cached_chat_template or "").encode("UTF-8"),
+            json.dumps(kwargs).encode("UTF-8"),
+            tool_choice.encode("UTF-8"),
+            True,
+            False)
+        if not raw:
+            return []
+        parsed = json.loads(ctypes.string_at(raw).decode("UTF-8", "ignore"))
+        if not isinstance(parsed, list):
+            return []
+        parsed = [normalize_tool_call_resp(obj) for obj in parsed if isinstance(obj, dict)]
+        return coerce_tool_argtypes(parsed, tools)
+    except Exception:
+        return []
+
 def repack_toolcall_tags(text: str, original_tools:list):
     global thinkformats, tool_call_pairs
     tool_calls = []
@@ -5168,8 +5212,11 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
         if api_format == 4 or api_format == 2 or api_format == 8 or api_format == 9:
             using_openai_tools = genparams.get('using_openai_tools', False)
             if using_openai_tools:
-                # first, check and potentially segment multiple tags for multi-tool calls
-                tool_calls = repack_toolcall_tags(recvtxt,genparams.get('tools', []))
+                # first, let llama.cpp's chat parser handle known template-specific tool formats
+                tool_calls = native_parse_toolcall_tags(recvtxt, genparams)
+                # fallback: check and potentially segment multiple tags for multi-tool calls
+                if not tool_calls:
+                    tool_calls = repack_toolcall_tags(recvtxt,genparams.get('tools', []))
                 if tool_calls and len(tool_calls)>0:
                     flat = []
                     for obj in tool_calls:
diff --git a/model_adapter.h b/model_adapter.h
index 467fa7bde..baca4a3f1 100644
--- a/model_adapter.h
+++ b/model_adapter.h
@@ -84,6 +84,13 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
 generation_outputs gpttype_generate(const generation_inputs inputs);
 bool gpttype_generate_abort();
 std::string gpttype_get_chat_template();
+std::string gpttype_parse_chat_tool_calls(const std::string & generated_text,
+                                          const std::string & tools_json,
+                                          const std::string & chat_template,
+                                          const std::string & chat_template_kwargs_json,
+                                          const std::string & tool_choice,
+                                          bool parallel_tool_calls,
+                                          bool is_partial);
 bool gpttype_batch_generate_enabled();
 int gpttype_batch_generate_submit(const generation_inputs inputs);
 bool gpttype_batch_generate_has_finished(int request_id);

From 683b04cc4ae9b54236e9b7eaccb4af8e0aab9f2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Thu, 25 Jun 2026 13:36:36 +0200
Subject: [PATCH 159/292] app : add the llama download subcommand (#24982)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* app : add the download command (with llama-download)

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* Remove llama-download tool for now

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

---------

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 app/CMakeLists.txt |  2 +-
 app/download.cpp   | 70 ++++++++++++++++++++++++++++++++++++++++++++++
 app/llama.cpp      |  2 ++
 common/arg.cpp     | 51 +++++++++++++++++++++------------
 common/common.h    |  1 +
 5 files changed, 107 insertions(+), 19 deletions(-)
 create mode 100644 app/download.cpp

diff --git a/app/CMakeLists.txt b/app/CMakeLists.txt
index 3ce503955..3450ff490 100644
--- a/app/CMakeLists.txt
+++ b/app/CMakeLists.txt
@@ -1,6 +1,6 @@
 set(TARGET llama-app)
 
-add_executable(${TARGET} llama.cpp)
+add_executable(${TARGET} llama.cpp download.cpp)
 set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama)
 
 target_link_libraries(${TARGET} PRIVATE
diff --git a/app/download.cpp b/app/download.cpp
new file mode 100644
index 000000000..f7ac55ded
--- /dev/null
+++ b/app/download.cpp
@@ -0,0 +1,70 @@
+#include "arg.h"
+#include "common.h"
+#include "download.h"
+#include "log.h"
+
+#include <cstdio>
+#include <filesystem>
+
+static void print_usage(int /*argc*/, char ** argv) {
+    printf(
+        "\nexamples:\n"
+        "  %s -hf ggml-org/gemma-3-4b-it-qat-GGUF\n"
+        "  %s -hf ggml-org/gemma-3-4b-it-qat-GGUF:Q4_K_M\n"
+        "  %s -hf ggml-org/models -hff model.gguf\n"
+        "  %s -mu https://example.com/model.gguf -m model.gguf\n"
+        "\n",
+        argv[0], argv[0], argv[0], argv[0]
+    );
+}
+
+int llama_download(int argc, char ** argv);
+
+int llama_download(int argc, char ** argv) {
+    common_init();
+
+    common_params params;
+    params.verbosity = LOG_LEVEL_ERROR;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DOWNLOAD, print_usage)) {
+        return 1;
+    }
+
+    const bool has_source = !params.model.hf_repo.empty() || !params.model.url.empty() ||
+                            !params.model.path.empty()    || !params.model.docker_repo.empty();
+    if (!has_source) {
+        fprintf(stderr, "error: no model source specified (use --hf-repo, --model-url, --model or --docker-repo)\n");
+        return 1;
+    }
+
+    try {
+        common_params_handle_models(params, LLAMA_EXAMPLE_DOWNLOAD, {});
+    } catch (const std::exception & e) {
+        fprintf(stderr, "error: %s\n", e.what());
+        return 1;
+    }
+
+    if (!params.models_preset.empty()) {
+        // -hf pointed at a preset repo: print the preset path and stop
+        printf("%s\n", params.models_preset.c_str());
+        return 0;
+    }
+    if (params.model.path.empty()) {
+        fprintf(stderr, "error: model download failed\n");
+        return 1;
+    }
+    if (!std::filesystem::exists(params.model.path)) {
+        fprintf(stderr, "error: model file does not exist: %s\n", params.model.path.c_str());
+        return 1;
+    }
+
+    printf("%s\n", params.model.path.c_str());
+    if (!params.mmproj.path.empty()) {
+        printf("%s\n", params.mmproj.path.c_str());
+    }
+    if (!params.speculative.draft.mparams.path.empty()) {
+        printf("%s\n", params.speculative.draft.mparams.path.c_str());
+    }
+
+    return 0;
+}
diff --git a/app/llama.cpp b/app/llama.cpp
index c4578ea53..00babbc7b 100644
--- a/app/llama.cpp
+++ b/app/llama.cpp
@@ -19,6 +19,7 @@ int llama_batched_bench(int argc, char ** argv);
 int llama_fit_params(int argc, char ** argv);
 int llama_quantize(int argc, char ** argv);
 int llama_perplexity(int argc, char ** argv);
+int llama_download(int argc, char ** argv);
 
 // Self-update is only supported for binaries built with llama-install.sh
 static int llama_update(int argc, char ** argv) {
@@ -61,6 +62,7 @@ static const command cmds[] = {
     {"serve",         "HTTP API server",                                    {"server"},   false,         llama_server       },
     {"cli",           "Command-line interactive interface",                 {"client"},   false,         llama_cli          },
     {"update",        "Update llama to the latest release",                 {},           UPDATE_HIDDEN, llama_update       },
+    {"download",      "Download a model",                                   {"get"},      false,         llama_download     },
     {"completion",    "Text completion",                                    {"complete"}, true,          llama_completion   },
     {"bench",         "Benchmark prompt processing and text generation",    {},           true,          llama_bench        },
     {"batched-bench", "Benchmark batched decoding performance",             {},           true,          llama_batched_bench},
diff --git a/common/arg.cpp b/common/arg.cpp
index 276dbec8b..494df2073 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -594,6 +594,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
     const bool skip_model_download =
         // server will call common_params_handle_models() later, so we skip it here
         ctx_arg.ex == LLAMA_EXAMPLE_SERVER ||
+        // download calls common_params_handle_models() itself and prints the paths
+        ctx_arg.ex == LLAMA_EXAMPLE_DOWNLOAD ||
         // export_graph_ops loads only metadata
         ctx_arg.ex == LLAMA_EXAMPLE_EXPORT_GRAPH_OPS;
 
@@ -671,15 +673,19 @@ static void common_params_print_usage(common_params_context & ctx_arg) {
             common_options.push_back(&opt);
         }
     }
-    printf("----- common params -----\n\n");
-    print_options(common_options);
-    printf("\n\n----- sampling params -----\n\n");
-    print_options(sampling_options);
-    printf("\n\n----- speculative params -----\n\n");
-    print_options(spec_options);
-    // TODO: maybe convert enum llama_example to string
-    printf("\n\n----- example-specific params -----\n\n");
-    print_options(specific_options);
+    bool first = true;
+    auto print_section = [&](const char * header, std::vector<common_arg *> & options) {
+        if (options.empty()) {
+            return;
+        }
+        printf("%s----- %s -----\n\n", first ? "" : "\n\n", header);
+        first = false;
+        print_options(options);
+    };
+    print_section("common params",           common_options);
+    print_section("sampling params",         sampling_options);
+    print_section("speculative params",      spec_options);
+    print_section("example-specific params", specific_options);
 }
 
 static void common_params_print_completion(common_params_context & ctx_arg) {
@@ -1079,7 +1085,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
      * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
      */
     auto add_opt = [&](common_arg arg) {
-        if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) {
+        // download only exposes the handful of args explicitly tagged for it
+        const bool inherit_common = ex != LLAMA_EXAMPLE_DOWNLOAD;
+        if ((arg.in_example(ex) || (inherit_common && arg.in_example(LLAMA_EXAMPLE_COMMON))) && !arg.is_exclude(ex)) {
             ctx_arg.options.push_back(std::move(arg));
         }
     };
@@ -1090,7 +1098,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.usage = true;
         }
-    ));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}));
     add_opt(common_arg(
         {"--version"},
         "show version and build info",
@@ -2212,7 +2220,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.no_mmproj = !value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO"));
+    ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MMPROJ_AUTO"));
     add_opt(common_arg(
         {"--mmproj-offload"},
         {"--no-mmproj-offload"},
@@ -2611,14 +2619,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MODEL"));
     add_opt(common_arg(
         {"-mu", "--model-url"}, "MODEL_URL",
         "model download url (default: unused)",
         [](common_params & params, const std::string & value) {
             params.model.url = value;
         }
-    ).set_env("LLAMA_ARG_MODEL_URL"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_MODEL_URL"));
     add_opt(common_arg(
         { "-dr", "--docker-repo" }, "[<repo>/]<model>[:quant]",
         "Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.\n"
@@ -2627,7 +2635,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.docker_repo = value;
         }
-    ).set_env("LLAMA_ARG_DOCKER_REPO"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_DOCKER_REPO"));
     add_opt(common_arg(
         {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
         "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
@@ -2637,14 +2645,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.model.hf_repo = value;
         }
-    ).set_env("LLAMA_ARG_HF_REPO"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_HF_REPO"));
     add_opt(common_arg(
         {"-hff", "--hf-file"}, "FILE",
         "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)",
         [](common_params & params, const std::string & value) {
             params.model.hf_file = value;
         }
-    ).set_env("LLAMA_ARG_HF_FILE"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_HF_FILE"));
     add_opt(common_arg(
         {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
         "Hugging Face model repository for the vocoder model (default: unused)",
@@ -2665,7 +2673,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.hf_token = value;
         }
-    ).set_env("HF_TOKEN"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("HF_TOKEN"));
+    add_opt(common_arg(
+        {"--mtp"},
+        "also download the multi-token prediction (MTP) head, if available (default: unused)",
+        [](common_params & params) {
+            params.speculative.types.push_back(COMMON_SPECULATIVE_TYPE_DRAFT_MTP);
+        }
+    ).set_examples({LLAMA_EXAMPLE_DOWNLOAD}));
     add_opt(common_arg(
         {"--context-file"}, "FNAME",
         "file to load context from (use comma-separated values to specify multiple files)",
diff --git a/common/common.h b/common/common.h
index 75a6036a0..279af46c5 100644
--- a/common/common.h
+++ b/common/common.h
@@ -96,6 +96,7 @@ enum llama_example {
     LLAMA_EXAMPLE_FIT_PARAMS,
     LLAMA_EXAMPLE_RESULTS,
     LLAMA_EXAMPLE_EXPORT_GRAPH_OPS,
+    LLAMA_EXAMPLE_DOWNLOAD,
 
     LLAMA_EXAMPLE_COUNT,
 };

From e8ecce53b872257e173d9d491a57e7a09c7cd8cb Mon Sep 17 00:00:00 2001
From: Kashif Rasul <kashif.rasul@gmail.com>
Date: Thu, 25 Jun 2026 14:58:00 +0200
Subject: [PATCH 160/292] docs : Eagle3 qwen3 draft model support (#24977)

* eagle3: accept Eagle3LlamaForCausalLM draft checkpoints

* docs: add eagle3 speculative decoding section

* docs: address eagle3 review comments

* docs: add more angelslim eagle3 models

* docs: add gpt-oss eagle3 models and link to pr 18039
---
 conversion/__init__.py |  1 +
 conversion/llama.py    |  1 +
 docs/speculative.md    | 42 +++++++++++++++++++++++++++++++++++++++++-
 3 files changed, 43 insertions(+), 1 deletion(-)

diff --git a/conversion/__init__.py b/conversion/__init__.py
index a587b1c37..5aad203e5 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -136,6 +136,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LlamaModel": "llama",
     "Eagle3DraftModel": "llama",
     "Eagle3Speculator": "llama",
+    "Eagle3LlamaForCausalLM": "llama",
     "LlamaForCausalLMEagle3": "llama",
     "LlavaForConditionalGeneration": "llama",
     "LlavaStableLMEpochForCausalLM": "stablelm",
diff --git a/conversion/llama.py b/conversion/llama.py
index a0d39472e..b43cc994a 100644
--- a/conversion/llama.py
+++ b/conversion/llama.py
@@ -23,6 +23,7 @@ from .base import ModelBase, TextModel, gguf, logger
     "LlavaForConditionalGeneration",
     "VoxtralForConditionalGeneration",
     "LlamaForCausalLMEagle3",
+    "Eagle3LlamaForCausalLM",
     "Eagle3Speculator",
     "Eagle3DraftModel",
     "IQuestCoderForCausalLM",
diff --git a/docs/speculative.md b/docs/speculative.md
index 43d181858..8f91256c4 100644
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -13,6 +13,45 @@ The `llama-server` application supports several implementations of speculative d
 A much smaller model (called the _draft model_) generates drafts.
 A draft model is the most used approach in speculative decoding.
 
+### EAGLE-3 (`draft-eagle3`)
+
+EAGLE-3 uses a small draft model that reads the target model's hidden states to predict the next tokens, so it
+reaches higher acceptance than a standalone draft model of the same size. The draft is a one-layer transformer
+trained for a specific target model; it shares the target model's tokenizer and, optionally, uses a reduced draft
+vocabulary with its own `lm_head`, which is mapped back using a `d2t` table.
+
+Convert the EAGLE-3 checkpoint with `--target-model-dir` so it inherits the target's tokenizer and the layer
+indices to read. Both the SpecForge `LlamaForCausalLMEagle3` and the vLLM/AngelSlim `Eagle3LlamaForCausalLM`
+checkpoint formats are supported (for example [`AngelSlim/Qwen3-4B_eagle3`](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+for `Qwen/Qwen3-4B`):
+
+```bash
+python convert_hf_to_gguf.py AngelSlim/Qwen3-4B_eagle3 \
+    --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-eagle3.gguf
+
+llama-server -m Qwen3-4B.gguf -md Qwen3-4B-eagle3.gguf --spec-type draft-eagle3
+```
+
+Supported EAGLE-3 draft models include:
+
+- [yuhuili/EAGLE3-LLaMA3.1-Instruct-8B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.1-Instruct-8B)
+- [yuhuili/EAGLE3-LLaMA3.3-Instruct-70B](https://huggingface.co/yuhuili/EAGLE3-LLaMA3.3-Instruct-70B)
+- [RedHatAI/gemma-4-31B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-31B-it-speculator.eagle3)
+- [RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3](https://huggingface.co/RedHatAI/gemma-4-26B-A4B-it-speculator.eagle3)
+- [Tengyunw/qwen3_8b_eagle3](https://huggingface.co/Tengyunw/qwen3_8b_eagle3)
+- [Tengyunw/qwen3_30b_moe_eagle3](https://huggingface.co/Tengyunw/qwen3_30b_moe_eagle3)
+- [AngelSlim/Qwen3-1.7B_eagle3](https://huggingface.co/AngelSlim/Qwen3-1.7B_eagle3)
+- [AngelSlim/Qwen3-4B_eagle3](https://huggingface.co/AngelSlim/Qwen3-4B_eagle3)
+- [AngelSlim/Qwen3-8B_eagle3](https://huggingface.co/AngelSlim/Qwen3-8B_eagle3)
+- [AngelSlim/Qwen3-14B_eagle3](https://huggingface.co/AngelSlim/Qwen3-14B_eagle3)
+- [AngelSlim/Qwen3-32B_eagle3](https://huggingface.co/AngelSlim/Qwen3-32B_eagle3)
+- [AngelSlim/Qwen3-a3B_eagle3](https://huggingface.co/AngelSlim/Qwen3-a3B_eagle3)
+- [RedHatAI/gpt-oss-20b-speculator.eagle3](https://huggingface.co/RedHatAI/gpt-oss-20b-speculator.eagle3)
+- [lmsys/EAGLE3-gpt-oss-120b-bf16](https://huggingface.co/lmsys/EAGLE3-gpt-oss-120b-bf16)
+- [nvidia/gpt-oss-120b-Eagle3-long-context](https://huggingface.co/nvidia/gpt-oss-120b-Eagle3-long-context)
+
+For the full and up-to-date list of supported models, see #18039.
+
 ### n-gram Cache (`ngram-cache`)
 
 An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
@@ -108,7 +147,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|draft-simple|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+--spec-type [none|draft-simple|draft-eagle3|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
@@ -247,6 +286,7 @@ Specifies a comma-separated list of speculative decoding types to use.
 |------|-------------|
 | `none` | No speculative decoding (default) |
 | `draft-simple` | Use a simple draft model for speculation |
+| `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states |
 | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |

From 60bc8866b11fb9bfa7de08dfb2047fe7ac914b55 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 25 Jun 2026 15:17:51 +0200
Subject: [PATCH 161/292] common: refactor model handling (#24980)

* common: refactor models handling

* remote preset

* cont

* rm skip_download option

* missing header

* fix plan.model_files

* fix --offline case

* move hf_plan to download

* refactor

* rm redundant curr_ex, add comments

* adapt
---
 app/download.cpp                  |   3 +-
 common/arg.cpp                    | 284 +++++++++++++++++++-----------
 common/arg.h                      |  23 +--
 common/common.h                   |  19 +-
 common/download.cpp               | 140 +++------------
 common/download.h                 |  71 +++-----
 tools/llama-bench/llama-bench.cpp |  24 ++-
 tools/server/server-models.cpp    |  28 +--
 tools/server/server.cpp           |  24 ++-
 9 files changed, 289 insertions(+), 327 deletions(-)

diff --git a/app/download.cpp b/app/download.cpp
index f7ac55ded..7227baadc 100644
--- a/app/download.cpp
+++ b/app/download.cpp
@@ -38,7 +38,8 @@ int llama_download(int argc, char ** argv) {
     }
 
     try {
-        common_params_handle_models(params, LLAMA_EXAMPLE_DOWNLOAD, {});
+        common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_DOWNLOAD);
+        common_models_handler_apply(handler, params);
     } catch (const std::exception & e) {
         fprintf(stderr, "error: %s\n", e.what());
         return 1;
diff --git a/common/arg.cpp b/common/arg.cpp
index 494df2073..841a38e96 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -297,60 +297,6 @@ struct handle_model_result {
     std::string preset_path;
 };
 
-static handle_model_result common_params_handle_model(struct common_params_model & model,
-                                                      const common_download_opts & opts) {
-    handle_model_result result;
-
-    // TODO @ngxson : refactor this into a new common_model_download_context
-
-    if (!model.docker_repo.empty()) {
-        model.path = common_docker_resolve_model(model.docker_repo);
-    } else if (!model.hf_repo.empty()) {
-        // If -m was used with -hf, treat the model "path" as the hf_file to download
-        if (model.hf_file.empty() && !model.path.empty()) {
-            model.hf_file = model.path;
-            model.path = "";
-        }
-        common_download_opts hf_opts = opts;
-        auto download_result = common_download_model(model, hf_opts);
-
-        if (!download_result.preset_path.empty()) {
-            result.found_preset = true;
-            result.preset_path = download_result.preset_path;
-            return result; // skip everything else if preset.ini is used
-        }
-
-        if (download_result.model_path.empty()) {
-            throw std::runtime_error("failed to download model from Hugging Face");
-        }
-
-        model.path = download_result.model_path;
-
-        if (!download_result.mmproj_path.empty()) {
-            result.found_mmproj = true;
-            result.mmproj.path  = download_result.mmproj_path;
-        }
-
-        if (!download_result.mtp_path.empty()) {
-            result.found_mtp = true;
-            result.mtp.path  = download_result.mtp_path;
-        }
-    } else if (!model.url.empty()) {
-        if (model.path.empty()) {
-            auto f = string_split<std::string>(model.url, '#').front();
-            f = string_split<std::string>(f, '?').front();
-            model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
-        }
-
-        auto download_result = common_download_model(model, opts);
-        if (download_result.model_path.empty()) {
-            throw std::runtime_error("failed to download model from " + model.url);
-        }
-    }
-
-    return result;
-}
-
 const std::vector<ggml_type> kv_cache_types = {
     GGML_TYPE_F32,
     GGML_TYPE_F16,
@@ -395,77 +341,204 @@ static bool parse_bool_value(const std::string & value) {
 }
 
 //
-// CLI argument parsing functions
+// common_models_handler
 //
 
-bool common_params_handle_models(common_params & params, llama_example curr_ex, const common_params_handle_models_params & handle_params) {
-    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
-                                         params.speculative.types.end(),
-                                         COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+static std::string get_default_local_path(const std::string & url) {
+    auto f = string_split<std::string>(url, '#').front();
+    f = string_split<std::string>(f, '?').front();
+    return fs_get_cache_file(string_split<std::string>(f, '/').back());
+}
 
+common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
+    common_download_hf_plan plan;
     common_download_opts opts;
+
+    const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
+                                        params.speculative.types.end(),
+                                        COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
+
+    // only download mmproj if the current example is using it
+    bool use_mmproj = false;
+    for (const auto & ex : mmproj_examples) {
+        if (curr_ex == ex) {
+            use_mmproj = true;
+            break;
+        }
+    }
+
     opts.bearer_token    = params.hf_token;
     opts.offline         = params.offline;
-    opts.skip_download   = params.skip_download;
     opts.download_mtp    = spec_type_draft_mtp;
-    opts.download_mmproj = !params.no_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty();
-    opts.preset_only     = handle_params.preset_only;
+    opts.download_mmproj = use_mmproj && !params.no_mmproj
+                        && params.mmproj.path.empty() && params.mmproj.url.empty();
 
-    if (handle_params.callback) {
-        opts.callback = handle_params.callback;
+    if (!params.model.hf_repo.empty()) {
+        plan = common_download_get_hf_plan(params.model, opts);
     }
 
-    // sub-models (draft, mmproj, vocoder) are explicitly specified by the user,
-    // so we should not auto-discover mtp/mmproj siblings for them
-    common_download_opts sub_opts = opts;
-    sub_opts.download_mtp    = false;
-    sub_opts.download_mmproj = false;
+    return common_models_handler{plan, opts};
+}
 
-    try {
-        auto res = common_params_handle_model(params.model, opts);
-        if (res.found_preset) {
-            if (!params.models_preset.empty()) {
-                throw std::invalid_argument("cannot use both --models-preset and -hf with a preset.ini file");
+bool common_models_handler_is_preset_repo(const common_models_handler & handler) {
+    return !handler.plan.preset.url.empty();
+}
+
+static std::vector<common_download_task> build_url_tasks(const common_params_model & model, common_download_opts opts) {
+    auto parts = common_download_get_all_parts(model.url);
+    std::vector<common_download_task> tasks;
+
+    // single-part: download straight to model.path if the user gave one (-m), else the cache default
+    if (parts.size() == 1) {
+        common_download_task task;
+        task.url        = parts[0];
+        task.local_path = model.path.empty() ? get_default_local_path(parts[0]) : model.path;
+        task.opts       = opts;
+        tasks.push_back(std::move(task));
+        return tasks;
+    }
+
+    // multi-part: place each part under the user's -m directory (if given), else the cache default
+    std::string base_dir;
+    if (!model.path.empty()) {
+        auto pos = model.path.rfind('/');
+        base_dir = pos == std::string::npos ? std::string(".") : model.path.substr(0, pos);
+    }
+
+    for (const auto & part : parts) {
+        common_download_task task;
+        task.url  = part;
+        task.opts = opts;
+
+        std::string local = get_default_local_path(part);
+        if (!base_dir.empty()) {
+            auto pos = local.rfind('/');
+            std::string name = pos == std::string::npos ? local : local.substr(pos + 1);
+            local = base_dir + "/" + name;
+        }
+        task.local_path = local;
+        tasks.push_back(std::move(task));
+    }
+    return tasks;
+}
+
+void common_models_handler_apply(common_models_handler & handler, common_params & params, common_download_callback * callback) {
+    std::vector<common_download_task> tasks;
+
+    auto & plan = handler.plan;
+
+    auto opts = handler.opts; // copy
+    opts.callback = callback;
+
+    // handle plain "url" if needed
+    auto handle_url = [&](common_params_model & model) {
+        if (!model.url.empty()) {
+            if (model.path.empty()) {
+                model.path = get_default_local_path(model.url);
             }
+        }
+    };
+    handle_url(params.model);
+    handle_url(params.mmproj);
+    handle_url(params.vocoder.model);
+    handle_url(params.speculative.draft.mparams);
+
+    // optionally, if docker repo is set, resolve it
+    if (!params.model.docker_repo.empty()) {
+        params.model.url  = common_docker_resolve_model(params.model.docker_repo);
+        params.model.path = get_default_local_path(params.model.url);
+    }
+
+    // handle plain "url" tasks (non-hf)
+    if (!params.model.url.empty()) {
+        auto url_tasks = build_url_tasks(params.model, opts);
+        // the first part is what gets loaded, so point params.model.path at it
+        if (!url_tasks.empty()) {
+            std::string first_path = url_tasks.front().local_path;
+            url_tasks.front().on_done = [&]() { params.model.path = first_path; };
+        }
+        for (auto & task : url_tasks) {
+            tasks.push_back(std::move(task));
+        }
+    }
+    if (!params.mmproj.url.empty()) {
+        common_download_task task;
+        task.url        = params.mmproj.url;
+        task.local_path = params.mmproj.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+    if (!params.vocoder.model.url.empty()) {
+        common_download_task task;
+        task.url        = params.vocoder.model.url;
+        task.local_path = params.vocoder.model.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+    if (!params.speculative.draft.mparams.url.empty()) {
+        common_download_task task;
+        task.url        = params.speculative.draft.mparams.url;
+        task.local_path = params.speculative.draft.mparams.path;
+        task.opts       = opts;
+        tasks.push_back(task);
+    }
+
+    // handle hf_plan tasks
+    if (!plan.model_files.empty()) {
+        for (size_t i = 0; i < plan.model_files.size(); ++i) {
+            auto & model_file = plan.model_files[i];
+            bool is_first = (i == 0);
+            tasks.emplace_back(model_file, opts, [&, is_first]() {
+                if (is_first) {
+                    // only use first part as model path
+                    params.model.path = hf_cache::finalize_file(model_file);
+                } else {
+                    hf_cache::finalize_file(model_file);
+                }
+            });
+        }
+    }
+    if (!plan.mmproj.local_path.empty()) {
+        tasks.emplace_back(plan.mmproj, opts, [&]() {
+            params.mmproj.path = hf_cache::finalize_file(plan.mmproj);
+        });
+    }
+    if (!plan.mtp.local_path.empty()) {
+        tasks.emplace_back(plan.mtp, opts, [&]() {
+            // only fall back to the discovered MTP head when no draft was explicitly provided
+            if (params.speculative.draft.mparams.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.mtp);
+            } else {
+                hf_cache::finalize_file(plan.mtp);
+            }
+        });
+    }
+    if (!plan.preset.local_path.empty()) {
+        tasks.emplace_back(plan.preset, opts, [&]() {
             // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file
             params.models_preset_hf = params.model.hf_repo; // only for showing a warning
-            params.models_preset    = res.preset_path;
+            params.models_preset    = hf_cache::finalize_file(plan.preset);
             params.model = common_params_model{}; // make sure to clear model, so server starts in router mode
-            return true;
-        }
+        });
+    }
 
-        if (params.no_mmproj) {
-            params.mmproj = {};
-        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
-            // optionally, handle mmproj model when -hf is specified
-            params.mmproj = res.mmproj;
-        }
-        // only download mmproj if the current example is using it
-        for (const auto & ex : mmproj_examples) {
-            if (curr_ex == ex) {
-                common_params_handle_model(params.mmproj, sub_opts);
-                break;
-            }
-        }
+    // run all tasks in parallel
+    if (!params.offline) {
+        common_download_run_tasks(tasks);
+    }
 
-        // when --spec-type mtp is set and no draft model was provided explicitly,
-        // fall back to the MTP head discovered alongside the -hf model
-        if (spec_type_draft_mtp && res.found_mtp &&
-            params.speculative.draft.mparams.path.empty() &&
-            params.speculative.draft.mparams.hf_repo.empty() &&
-            params.speculative.draft.mparams.url.empty()) {
-            params.speculative.draft.mparams.path = res.mtp.path;
+    // download successful, update params with the downloaded paths
+    for (const auto & task : tasks) {
+        if (task.on_done) {
+            task.on_done();
         }
-        common_params_handle_model(params.speculative.draft.mparams, sub_opts);
-        common_params_handle_model(params.vocoder.model,             sub_opts);
-        return true;
-    } catch (const common_skip_download_exception &) {
-        return false;
-    } catch (const std::exception &) {
-        throw;
     }
 }
 
+//
+// CLI argument parsing functions
+//
+
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
@@ -601,7 +674,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     if (!skip_model_download) {
         // handle model and download
-        common_params_handle_models(params, ctx_arg.ex, {});
+        common_models_handler handler = common_models_handler_init(params, ctx_arg.ex);
+        common_models_handler_apply(handler, params);
 
         // model is required (except for server)
         // TODO @ngxson : maybe show a list of available models in CLI in this case
diff --git a/common/arg.h b/common/arg.h
index fdfc04bc7..508e33d29 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -8,6 +8,7 @@
 #include <string>
 #include <vector>
 #include <cstring>
+#include <memory>
 
 // pseudo-env variable to identify preset-only arguments
 #define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
@@ -130,19 +131,19 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
 // see: https://github.com/ggml-org/llama.cpp/issues/18163
 void common_params_add_preset_options(std::vector<common_arg> & args);
 
-struct common_params_handle_models_params {
-    common_download_callback * callback = nullptr;
-    bool preset_only = false; // if true, only check & download remote preset (for router mode)
+struct common_models_handler {
+    common_download_hf_plan plan;
+    common_download_opts opts;
 };
 
-// populate model paths (main model, mmproj, etc) from -hf if necessary
-// return true if the model is ready to use
-// throw an exception if there is an error that prevents the model from being used (e.g. network error, model not found, etc)
-// if params.skip_download is true, no downloads will be attempted. return false if the model is invalid or missing (e.g. ETag check failed)
-bool common_params_handle_models(
-    common_params & params,
-    llama_example curr_ex,
-    const common_params_handle_models_params & handle_params);
+// initialize downloading opts and hf_plan if needed, but does not download anything yet
+common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex);
+
+// check if the model is a preset repo (i.e. has a preset file)
+bool common_models_handler_is_preset_repo(const common_models_handler & handler);
+
+// download and update params with the downloaded model path
+void common_models_handler_apply(common_models_handler & handler, common_params & params, common_download_callback * callback = nullptr);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
diff --git a/common/common.h b/common/common.h
index 279af46c5..94147d5d8 100644
--- a/common/common.h
+++ b/common/common.h
@@ -291,13 +291,13 @@ struct common_params_sampling {
 };
 
 struct common_params_model {
-    std::string path        = ""; // model local path                                       // NOLINT
-    std::string url         = ""; // model url to download                                  // NOLINT
-    std::string hf_repo     = ""; // HF repo                                                // NOLINT
-    std::string hf_file     = ""; // HF file                                                // NOLINT
-    std::string docker_repo = ""; // Docker repo                                            // NOLINT
+    std::string path        = ""; // model local path
+    std::string url         = ""; // model url to download
+    std::string hf_repo     = ""; // HF repo
+    std::string hf_file     = ""; // HF file
+    std::string docker_repo = ""; // Docker repo
 
-    std::string get_name() {
+    std::string get_name() const {
         if (!hf_repo.empty()) {
             return hf_repo;
         }
@@ -306,6 +306,10 @@ struct common_params_model {
         }
         return path;
     }
+
+    bool empty() const {
+        return get_name().empty();
+    }
 };
 
 // draft-model-based speculative decoding parameters
@@ -368,7 +372,7 @@ struct common_params_speculative {
     common_params_speculative_ngram_cache ngram_cache;
 
     bool has_dft() const {
-        return !draft.mparams.path.empty() || !draft.mparams.hf_repo.empty();
+        return !draft.mparams.empty();
     }
 
     uint32_t need_n_rs_seq() const {
@@ -520,7 +524,6 @@ struct common_params {
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
     bool    offline                    = false;
-    bool    skip_download              = false; // skip model file downloading
 
     int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
diff --git a/common/download.cpp b/common/download.cpp
index 5b55c76a1..6b69a4418 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -292,10 +292,6 @@ static int common_download_file_single_online(const std::string & url,
 
     const bool file_exists = std::filesystem::exists(path);
 
-    if (!file_exists && opts.skip_download) {
-        return -2; // file is missing and download is disabled
-    }
-
     if (file_exists && skip_etag) {
         LOG_DBG("%s: using cached file: %s\n", __func__, path.c_str());
         return 304; // 304 Not Modified - fake cached response
@@ -362,9 +358,6 @@ static int common_download_file_single_online(const std::string & url,
             return 304; // 304 Not Modified - fake cached response
         }
         // pass this point, the file exists but is different from the server version, so we need to redownload it
-        if (opts.skip_download) {
-            return -2; // special code to indicate that the download was skipped due to etag mismatch
-        }
         if (remove(path.c_str()) != 0) {
             LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
             return -1;
@@ -691,19 +684,8 @@ static void list_available_gguf_files(const hf_cache::hf_files & files) {
     }
 }
 
-struct hf_plan {
-    hf_cache::hf_file primary;
-    hf_cache::hf_files model_files;
-    hf_cache::hf_file mmproj;
-    hf_cache::hf_file mtp;
-    hf_cache::hf_file preset; // if set, only this file is downloaded
-};
-
-static hf_plan get_hf_plan(const common_params_model  & model,
-                           const common_download_opts & opts,
-                           bool download_mmproj,
-                           bool download_mtp) {
-    hf_plan plan;
+common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts) {
+    common_download_hf_plan plan;
     hf_cache::hf_files all;
 
     auto [repo, tag] = common_download_split_repo_tag(model.hf_repo);
@@ -752,127 +734,49 @@ static hf_plan get_hf_plan(const common_params_model  & model,
     plan.primary = primary;
     plan.model_files = get_split_files(all, primary);
 
-    if (download_mmproj) {
+    if (opts.download_mmproj) {
         plan.mmproj = find_best_mmproj(all, primary.path);
     }
-
-    if (download_mtp) {
+    if (opts.download_mtp) {
         plan.mtp = find_best_mtp(all, primary.path);
     }
 
     return plan;
 }
 
-struct download_task {
-    std::string url;
-    std::string path;
-};
-
-static std::vector<download_task> get_url_tasks(const common_params_model & model) {
-    auto split = get_gguf_split_info(model.url);
-
-    if (split.count <= 1) {
-        return {{model.url, model.path}};
-    }
-
-    auto filename = split.prefix;
-    if (auto pos = split.prefix.rfind('/'); pos != std::string::npos) {
-        filename = split.prefix.substr(pos + 1);
-    }
-
-    auto parent_path = std::filesystem::path(model.path).parent_path();
-    auto prefix_path = (parent_path / filename).string();
-
-    std::vector<download_task> tasks;
-    for (int i = 1; i <= split.count; i++) {
-        auto suffix = string_format("-%05d-of-%05d.gguf", i, split.count);
-        tasks.push_back({split.prefix + suffix, prefix_path + suffix});
-    }
-    return tasks;
-}
-
-common_download_model_result common_download_model(const common_params_model  & model,
-                                                   const common_download_opts & opts) {
-    common_download_model_result result;
-    std::vector<download_task> tasks;
-    hf_plan hf;
-
-    bool download_mmproj = opts.download_mmproj;
-    bool download_mtp = opts.download_mtp;
-    bool preset_only = opts.preset_only;
-    bool is_hf = !model.hf_repo.empty();
-
-    if (is_hf) {
-        hf = get_hf_plan(model, opts, download_mmproj, download_mtp);
-        if (!hf.preset.path.empty()) {
-            // if preset.ini exists, only download that file alone
-            tasks.push_back({hf.preset.url, hf.preset.local_path});
-        } else if (!preset_only) {
-            // only add other files if we're NOT in preset-only mode (normal run, non-router)
-            for (const auto & f : hf.model_files) {
-                tasks.push_back({f.url, f.local_path});
-            }
-            if (!hf.mmproj.path.empty()) {
-                tasks.push_back({hf.mmproj.url, hf.mmproj.local_path});
-            }
-            if (!hf.mtp.path.empty()) {
-                tasks.push_back({hf.mtp.url, hf.mtp.local_path});
-            }
-        }
-    } else if (!model.url.empty()) {
-        tasks = get_url_tasks(model);
-    } else {
-        result.model_path = model.path;
-        return result;
-    }
-
-    if (tasks.empty()) {
-        return result;
-    }
-
+void common_download_run_tasks(const std::vector<common_download_task> & tasks) {
     std::vector<std::future<int>> futures;
     for (const auto & task : tasks) {
         futures.push_back(std::async(std::launch::async,
-            [&task, &opts, is_hf]() {
-                return common_download_file_single(task.url, task.path, opts, is_hf);
+            [&task]() {
+                return common_download_file_single(task.url, task.local_path, task.opts, task.is_hf);
             }
         ));
     }
 
-    for (auto & f : futures) {
-        int status = f.get();
-        if (status == -2 && opts.skip_download) {
-            throw common_skip_download_exception();
-        }
+    for (size_t i = 0; i < futures.size(); ++i) {
+        std::string url = tasks[i].url;
+        int status = futures[i].get();
         bool is_ok = is_http_status_ok(status);
         if (!is_ok) {
-            return {};
+            throw std::runtime_error(string_format("Download '%s' failed with status code: %d", url.c_str(), status));
         }
     }
+}
 
-    if (is_hf) {
-        if (!hf.preset.path.empty()) {
-            // if preset.ini is used, do not set other paths
-            result.preset_path = hf_cache::finalize_file(hf.preset);
-        } else {
-            for (const auto & f : hf.model_files) {
-                hf_cache::finalize_file(f);
-            }
-            result.model_path = hf.primary.final_path;
+std::vector<std::string> common_download_get_all_parts(const std::string & url) {
+    auto split = get_gguf_split_info(url);
 
-            if (!hf.mmproj.path.empty()) {
-                result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
-            }
-
-            if (!hf.mtp.path.empty()) {
-                result.mtp_path = hf_cache::finalize_file(hf.mtp);
-            }
-        }
-    } else {
-        result.model_path = model.path;
+    if (split.count <= 1) {
+        return {url};
     }
 
-    return result;
+    std::vector<std::string> parts;
+    for (int i = 1; i <= split.count; i++) {
+        auto suffix = string_format("-%05d-of-%05d.gguf", i, split.count);
+        parts.push_back(split.prefix + suffix);
+    }
+    return parts;
 }
 
 //
diff --git a/common/download.h b/common/download.h
index 755e34ea8..816e1c7f5 100644
--- a/common/download.h
+++ b/common/download.h
@@ -1,7 +1,10 @@
 #pragma once
 
+#include "hf-cache.h"
+
 #include <string>
 #include <vector>
+#include <functional>
 
 struct common_params_model;
 
@@ -47,67 +50,40 @@ struct common_cached_model_info {
     }
 };
 
-// Options for common_download_model and common_download_file_single
+// Options for common_download_file_single
 struct common_download_opts {
     std::string bearer_token;
     common_header_list headers;
     bool offline = false;
-    bool skip_download = false; // if true, only validation is performed, common_skip_download_exception may be thrown if the file is missing or invalid
     bool download_mmproj = false;
     bool download_mtp = false;
-    bool preset_only = false; // if true, only check & download remote preset (for router mode)
     common_download_callback * callback = nullptr;
 };
 
-// Result of common_download_model
-struct common_download_model_result {
-    std::string model_path;
-    std::string mmproj_path;
-    std::string mtp_path;
-    std::string preset_path;
+struct common_download_task {
+    common_download_opts opts;
+    std::string url;
+    std::string local_path;
+    std::function<void()> on_done;
+    bool is_hf = false;
+
+    common_download_task() = default;
+    common_download_task(hf_cache::hf_file f,
+            const common_download_opts & opts,
+            std::function<void()> on_done = nullptr)
+        : opts(opts), url(f.url), local_path(f.local_path), on_done(on_done), is_hf(true) {}
 };
 
-// throw if the file is missing or invalid (e.g. ETag check failed)
-struct common_skip_download_exception : public std::runtime_error {
-    common_skip_download_exception() : std::runtime_error("skip download") {}
-};
+void common_download_run_tasks(const std::vector<common_download_task> & tasks);
 
-// Download model from HuggingFace repo or URL
-//
-// input (via model struct):
-// - model.hf_repo: HF repo with optional tag, see common_download_split_repo_tag
-// - model.hf_file: specific file in the repo (requires hf_repo)
-// - model.url: simple download (used if hf_repo is empty)
-// - model.path: local file path
-//
-// tag matching (for HF repos without model.hf_file):
-// - if tag is specified, searches for GGUF matching that quantization
-// - if no tag, searches for Q4_K_M, then Q4_0, then first available GGUF
-//
-// split GGUF: multi-part files like "model-00001-of-00003.gguf" are automatically
-// detected and all parts are downloaded
-//
-// caching:
-// - HF repos: uses HuggingFace cache
-// - URLs: uses ETag-based caching
-//
-// when opts.offline=true, no network requests are made
-// when download_mmproj=true, searches for mmproj in same directory as model or any parent directory
-// then with the closest quantization bits
-// when download_mtp=true, applies the same sibling search for an MTP-head GGUF
-//
-// returns result with model_path, mmproj_path and mtp_path (empty when not found / on failure)
-common_download_model_result common_download_model(
-    const common_params_model & model,
-    const common_download_opts & opts = {}
-);
+// if url is a multi-part GGUF file, returns all parts, otherwise returns the single file
+std::vector<std::string> common_download_get_all_parts(const std::string & url);
 
 // returns list of cached models
 std::vector<common_cached_model_info> common_list_cached_models();
 
 // download single file from url to local path
 // returns status code or -1 on error
-// returns -2 if the download was skipped due to ETag mismatch (file outdated, skip_download=true)
 // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
 int common_download_file_single(const std::string & url,
                                 const std::string & path,
@@ -124,3 +100,12 @@ std::string common_docker_resolve_model(const std::string & docker);
 // - if tag is present, removes only files matching that tag (and orphaned blobs)
 // returns true if anything was removed
 bool common_download_remove(const std::string & hf_repo_with_tag);
+
+struct common_download_hf_plan {
+    hf_cache::hf_file primary;
+    hf_cache::hf_files model_files;
+    hf_cache::hf_file mmproj;
+    hf_cache::hf_file mtp;
+    hf_cache::hf_file preset; // if set, only this file is downloaded
+};
+common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts);
diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index 55970c074..2695f5878 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -1035,25 +1035,23 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
 
     if (!params.hf_repo.empty()) {
         for (size_t i = 0; i < params.hf_repo.size(); i++) {
-            common_params_model model;
-
-            if (params.hf_file.empty() || params.hf_file[i].empty()) {
-                model.hf_repo = params.hf_repo[i];
-            } else {
-                model.hf_repo = params.hf_repo[i];
-                model.hf_file = params.hf_file[i];
+            common_params p;
+            p.hf_token      = params.hf_token;
+            p.offline       = params.offline;
+            p.model.hf_repo = params.hf_repo[i];
+            if (!params.hf_file.empty() && !params.hf_file[i].empty()) {
+                p.model.hf_file = params.hf_file[i];
             }
 
-            common_download_opts opts;
-            opts.bearer_token = params.hf_token;
-            opts.offline         = params.offline;
-            auto download_result = common_download_model(model, opts);
-            if (download_result.model_path.empty()) {
+            // only the text model file is needed
+            common_models_handler models_handler = common_models_handler_init(p, LLAMA_EXAMPLE_BENCH);
+            common_models_handler_apply(models_handler, p);
+            if (p.model.path.empty()) {
                 fprintf(stderr, "error: failed to download model from HuggingFace\n");
                 exit(1);
             }
 
-            params.model.push_back(download_result.model_path);
+            params.model.push_back(p.model.path);
         }
     }
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index a4df3ef10..bb2f43a10 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -223,8 +223,8 @@ void server_model_meta::update_caps() {
             "LLAMA_ARG_HF_REPO_FILE",
         });
         params.offline = true;
-        // params.skip_download = true; // TODO: ideally, we should validate the model here, but it takes too much time
-        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, {});
+        common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
+        common_models_handler_apply(handler, params); // note: this won't download the model because offline=true
         if (params.mmproj.path.empty()) {
             multimodal = { false, false };
         } else {
@@ -1393,9 +1393,8 @@ struct server_download_state : public common_download_callback {
 
     bool run(common_params & params) {
         try {
-            common_params_handle_models_params p;
-            p.callback = this;
-            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, p);
+            common_models_handler handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
+            common_models_handler_apply(handler, params, this);
             is_ok = true;
         } catch (const std::exception & e) {
             auto model_name = params.model.get_name();
@@ -1768,23 +1767,14 @@ void server_models_routes::init_routes() {
             throw std::invalid_argument("model must be a non-empty string");
         }
 
-        common_params_model model;
-        common_download_opts opts;
+        common_params p;
+        p.model.hf_repo  = name;
+        p.hf_token       = params.hf_token;
 
-        model.hf_repo        = name;
-        opts.bearer_token    = params.hf_token;
-        // note: we only check main model, no need sidecar here
-        opts.download_mmproj = false;
-        opts.download_mtp    = false;
-
-        // first, only check if the model is valid and can be downloaded
-        opts.skip_download = true;
+        // validate by fetching metadata
         bool ok = false;
         try {
-            auto validation = common_download_model(model, opts);
-            ok = !validation.model_path.empty();
-        } catch (const common_skip_download_exception &) {
-            // model is valid and will be downloaded
+            common_models_handler_init(p, LLAMA_EXAMPLE_SERVER);
             ok = true;
         } catch (...) {
             SRV_ERR("unknown error while validating model '%s'\n", name.c_str());
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 4165c1015..680590871 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -89,15 +89,16 @@ int llama_server(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    // note: router mode also accepts -hf remote-preset, so we need to check that first
-    if (!params.model.hf_repo.empty()) {
-        try {
-            common_params_handle_models_params handle_params;
-            handle_params.preset_only = true;
-            common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, handle_params);
-        } catch (const std::exception & e) {
-            // ignored for now
+    common_models_handler models_handler;
+    try {
+        models_handler = common_models_handler_init(params, LLAMA_EXAMPLE_SERVER);
+        if (common_models_handler_is_preset_repo(models_handler)) {
+            // apply the preset and start the server in router mode
+            common_models_handler_apply(models_handler, params);
         }
+    } catch (const std::exception & e) {
+        SRV_ERR("failed to fetch model metadata: %s\n", e.what());
+        return 1;
     }
 
     // router server never loads a model and must not touch the GPU
@@ -274,7 +275,12 @@ int llama_server(int argc, char ** argv) {
         return child.run_download(params);
     } else if (!is_router_server) {
         // single-model mode (NOT spawned by router)
-        common_params_handle_models(params, LLAMA_EXAMPLE_SERVER, {});
+        try {
+            common_models_handler_apply(models_handler, params);
+        } catch (const std::exception & e) {
+            SRV_ERR("failed to download model: %s\n", e.what());
+            return 1;
+        }
     }
 
     //

From 099bf0695271832645a79cd5a1c9ce046ac3f6b5 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 25 Jun 2026 16:26:56 +0200
Subject: [PATCH 162/292] misc: update lables (#24920)

* misc: update lables

* bring back examples, add mtmd
---
 .github/labeler.yml | 45 ++++++++++++++++++++++++++-------------------
 1 file changed, 26 insertions(+), 19 deletions(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index 3361118ed..bf994928f 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -35,8 +35,20 @@ AMD ZenDNN:
 documentation:
     - changed-files:
         - any-glob-to-any-file:
+            - **/*.md
             - docs/**
             - media/**
+examples:
+    - all:
+        - changed-files:
+            - any-glob-to-any-file:
+                - app/**
+                - examples/**
+                - tools/**
+            - all-globs-to-all-files:
+                - '!tools/server/**'
+                - '!tools/mtmd/**'
+                - '!tools/ui/**'
 testing:
     - changed-files:
         - any-glob-to-any-file:
@@ -47,28 +59,12 @@ build:
             - cmake/**
             - CMakeLists.txt
             - CMakePresets.json
-examples:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/**
-            - tools/**
 devops:
     - changed-files:
         - any-glob-to-any-file:
             - .devops/**
             - .github/**
             - ci/**
-python:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.py"
-            - requirements/**
-            - gguf-py/**
-            - .flake8
-script:
-    - changed-files:
-        - any-glob-to-any-file:
-            - scripts/**
 android:
     - changed-files:
         - any-glob-to-any-file:
@@ -81,9 +77,20 @@ server:
     - changed-files:
         - any-glob-to-any-file:
             - tools/server/**
-
-
-
+mtmd:
+    - changed-files:
+        - any-glob-to-any-file:
+            - tools/mtmd/**
+conversion:
+    - changed-files:
+        - any-glob-to-any-file:
+            - conversion/**
+            - convert_*.py
+            - gguf-py/**
+vendor:
+    - changed-files:
+        - any-glob-to-any-file:
+            - vendor/**
 ggml:
     - changed-files:
         - any-glob-to-any-file:

From e9d1b76d0ad84569c889be265dd270d44b54fbc7 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 25 Jun 2026 16:36:40 +0200
Subject: [PATCH 163/292] server: use status code 403 for disabled features
 (#24970)

* server: use status code 403 for disabled features

* cont

* fix test case
---
 tools/server/server.cpp                 | 20 ++++++++++++++++++++
 tools/server/tests/unit/test_proxy.py   |  2 +-
 tools/ui/src/lib/stores/tools.svelte.ts |  9 ++++++---
 3 files changed, 27 insertions(+), 4 deletions(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 680590871..0a1947faf 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -242,6 +242,19 @@ int llama_server(int argc, char ** argv) {
     // Google Cloud Platform (Vertex AI) compat
     ctx_http.register_gcp_compat();
 
+    // return 403 for disabled features
+    server_http_context::handler_t res_403 = [](const server_http_req &) {
+        auto res = std::make_unique<server_http_res>();
+        res->status = 403;
+        res->data = safe_json_to_str({
+            {"error", {
+                {"message", "this feature is disabled"},
+                {"type", "feature_disabled"},
+            }}
+        });
+        return res;
+    };
+
     // CORS proxy (EXPERIMENTAL, only used by the Web UI for MCP)
     if (params.ui_mcp_proxy) {
         SRV_WRN("%s", "-----------------\n");
@@ -250,7 +263,11 @@ int llama_server(int argc, char ** argv) {
         SRV_WRN("%s", "-----------------\n");
         ctx_http.get ("/cors-proxy",      ex_wrapper(proxy_handler_get));
         ctx_http.post("/cors-proxy",      ex_wrapper(proxy_handler_post));
+    } else {
+        ctx_http.get ("/cors-proxy",      ex_wrapper(res_403));
+        ctx_http.post("/cors-proxy",      ex_wrapper(res_403));
     }
+
     // EXPERIMENTAL built-in tools
     if (!params.server_tools.empty()) {
         try {
@@ -265,6 +282,9 @@ int llama_server(int argc, char ** argv) {
         SRV_WRN("%s", "-----------------\n");
         ctx_http.get ("/tools",           ex_wrapper(tools.handle_get));
         ctx_http.post("/tools",           ex_wrapper(tools.handle_post));
+    } else {
+        ctx_http.get ("/tools",           ex_wrapper(res_403));
+        ctx_http.post("/tools",           ex_wrapper(res_403));
     }
 
     //
diff --git a/tools/server/tests/unit/test_proxy.py b/tools/server/tests/unit/test_proxy.py
index 3b86d8047..0fed536e5 100644
--- a/tools/server/tests/unit/test_proxy.py
+++ b/tools/server/tests/unit/test_proxy.py
@@ -16,7 +16,7 @@ def test_mcp_no_proxy():
     server.start()
 
     res = server.make_request("GET", "/cors-proxy")
-    assert res.status_code == 404
+    assert res.status_code == 403
 
 
 def test_mcp_proxy():
diff --git a/tools/ui/src/lib/stores/tools.svelte.ts b/tools/ui/src/lib/stores/tools.svelte.ts
index 9f0101a82..a63781988 100644
--- a/tools/ui/src/lib/stores/tools.svelte.ts
+++ b/tools/ui/src/lib/stores/tools.svelte.ts
@@ -392,11 +392,14 @@ class ToolsStore {
 		} catch (err) {
 			const errorMessage = err instanceof Error ? err.message : String(err);
 			this._error = errorMessage;
-			// 404 from /tools means the server was started without --tools
-			if (errorMessage.includes('404') || errorMessage.toLowerCase().includes('not found')) {
+			// 403 from /tools means the server was started without --tools
+			// TODO: check status code instead of relying on message
+			if (errorMessage.includes('this feature is disabled')) {
 				this._toolsEndpointUnreachable = true;
+				console.info('[ToolsStore] Built-in tools are disabled on the server');
+			} else {
+				console.error('[ToolsStore] Failed to fetch built-in tools:', err);
 			}
-			console.error('[ToolsStore] Failed to fetch built-in tools:', err);
 		} finally {
 			this._loading = false;
 		}

From c7cddefcbde2ccf1829c55ea57bbee92316c294a Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Thu, 25 Jun 2026 17:23:37 +0200
Subject: [PATCH 164/292] misc: fix labeler (#25012)

---
 .github/labeler.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/labeler.yml b/.github/labeler.yml
index bf994928f..20e19c352 100644
--- a/.github/labeler.yml
+++ b/.github/labeler.yml
@@ -35,7 +35,7 @@ AMD ZenDNN:
 documentation:
     - changed-files:
         - any-glob-to-any-file:
-            - **/*.md
+            - "**/*.md"
             - docs/**
             - media/**
 examples:

From 1ec44d178dcfc0ce6a61f357ccbde914821e1ae0 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Thu, 25 Jun 2026 17:29:23 +0200
Subject: [PATCH 165/292] CUDA: Various fixes to `cpy.cu` (#25000)

* Add failing test-case to test-backend-ops

Extracted from https://github.com/ggml-org/llama.cpp/issues/24072

* Minimize repro with help of AI

N = 8 * (65535 - 1) + 1 = 524273

* Port and adjust workaround from https://github.com/LostRuins/koboldcpp/commit/0ba798341e0c70517cb226cb63c966b086a3b5b3

Fall-back should share code, also relax y-z constraint to be inclusive

* Add test-case + fallback also for y dim

* Fix x-guards which is 2^{31}-1, so inlusive of INT_MAX

* Fix overflow problems for transposed copy kernel
---
 ggml/src/ggml-cuda/cpy.cu  | 64 +++++++++++++++++++++-----------------
 tests/test-backend-ops.cpp |  2 ++
 2 files changed, 37 insertions(+), 29 deletions(-)

diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index 121472ec2..1e625cc1c 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -53,10 +53,10 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const
     const int64_t nmat = ne / (ne00 * ne01);
     const int64_t n = ne00 * ne01;
 
-    const int x = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.x;
-    const int y = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
-    const int tx = blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x;  // transpose block offset
-    const int ty = blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
+    const int64_t x  = (int64_t) blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.x;
+    const int64_t y  = (int64_t) blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
+    const int64_t tx = (int64_t) blockIdx.y * CUDA_CPY_TILE_DIM_2D + threadIdx.x;  // transpose block offset
+    const int64_t ty = (int64_t) blockIdx.x * CUDA_CPY_TILE_DIM_2D + threadIdx.y;
 
     __shared__ float tile[2][CUDA_CPY_TILE_DIM_2D][CUDA_CPY_TILE_DIM_2D+1];
     int cur_tile_buf = 0;
@@ -197,7 +197,7 @@ static void ggml_cpy_scalar_contiguous_cuda(
 cudaStream_t stream) {
 
     const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
     ggml_cuda_kernel_launch(cpy_scalar_contiguous<src_t, dst_t>, launch_params, cx, cdst, ne);
 }
@@ -208,6 +208,14 @@ static void ggml_cpy_scalar_cuda(
     const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
+    const auto launch_scalar_generic = [&]() {
+        const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
+        GGML_ASSERT(num_blocks <= INT_MAX);
+        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
+        ggml_cuda_kernel_launch(cpy_scalar<cpy_1_scalar<src_t, dst_t>>, launch_params,
+            cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+    };
+
     if (transposed) {
         GGML_ASSERT(ne == ne00*ne01*ne02);  // ne[3] is 1 assumed
         int64_t ne00n, ne01n, ne02n;
@@ -224,20 +232,18 @@ static void ggml_cpy_scalar_cuda(
         int64_t grid_x = (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
         int64_t grid_y = (ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
         int64_t grid_z = (ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM;
-        GGML_ASSERT(grid_x < UINT_MAX);
-        GGML_ASSERT(grid_y < USHRT_MAX);
-        GGML_ASSERT(grid_z < USHRT_MAX);
-        dim3 dimGrid(grid_x, grid_y, grid_z);
-        dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
-        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(dimGrid, dimBlock, 0, stream);
-        ggml_cuda_kernel_launch(cpy_scalar_transpose<dst_t>, launch_params,
-            cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        GGML_ASSERT(grid_x <= INT_MAX);
+        if (grid_y > USHRT_MAX || grid_z > USHRT_MAX) {
+            launch_scalar_generic();
+        } else {
+            dim3 dimGrid(grid_x, grid_y, grid_z);
+            dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
+            const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(dimGrid, dimBlock, 0, stream);
+            ggml_cuda_kernel_launch(cpy_scalar_transpose<dst_t>, launch_params,
+                cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        }
     } else {
-        const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
-        GGML_ASSERT(num_blocks < UINT_MAX);
-        const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params((dim3)num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream);
-        ggml_cuda_kernel_launch(cpy_scalar<cpy_1_scalar<src_t, dst_t>>, launch_params,
-            cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
+        launch_scalar_generic();
     }
 }
 
@@ -248,7 +254,7 @@ static void ggml_cpy_f32_q8_0_cuda(
 
     GGML_ASSERT(ne % QK8_0 == 0);
     const int64_t num_blocks = ne / QK8_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -259,7 +265,7 @@ static void ggml_cpy_q8_0_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -271,7 +277,7 @@ static void ggml_cpy_f32_q4_0_cuda(
 
     GGML_ASSERT(ne % QK4_0 == 0);
     const int64_t num_blocks = ne / QK4_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -284,7 +290,7 @@ static void ggml_cpy_q4_0_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -297,7 +303,7 @@ static void ggml_cpy_f32_q4_1_cuda(
 
     GGML_ASSERT(ne % QK4_1 == 0);
     const int64_t num_blocks = ne / QK4_1;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -310,7 +316,7 @@ static void ggml_cpy_q4_1_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -323,7 +329,7 @@ static void ggml_cpy_f32_q5_0_cuda(
 
     GGML_ASSERT(ne % QK5_0 == 0);
     const int64_t num_blocks = ne / QK5_0;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -336,7 +342,7 @@ static void ggml_cpy_q5_0_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -349,7 +355,7 @@ static void ggml_cpy_f32_q5_1_cuda(
 
     GGML_ASSERT(ne % QK5_1 == 0);
     const int64_t num_blocks = ne / QK5_1;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -362,7 +368,7 @@ static void ggml_cpy_q5_1_f32_cuda(
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
     const int64_t num_blocks = ne;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1><<<num_blocks, 1, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
@@ -375,7 +381,7 @@ static void ggml_cpy_f32_iq4_nl_cuda(
 
     GGML_ASSERT(ne % QK4_NL == 0);
     const int64_t num_blocks = ne / QK4_NL;
-    GGML_ASSERT(num_blocks < UINT_MAX);
+    GGML_ASSERT(num_blocks <= INT_MAX);
     cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 3f18dbe22..c83e91fbd 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8176,6 +8176,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_I32, {256, 4, 1, 1}, {-1,-1,-1,-1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_I32, {256, 1, 4, 1}, {-1,-1,-1,-1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {-1,-1,-1,-1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2097121, 1, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2, 524281, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
 
     // CPY - different src/dst shapes (reshaping via CPY)
     // Use permutations of {3, 5, 7, 32}. Total elements: 3*5*7*32 = 3360.

From b46f7450c7734a3e2f12b421ab71df10a2b45b00 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 00:18:00 +0800
Subject: [PATCH 166/292] increase sd prompt limit

---
 koboldcpp.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 05b7d59fd..3c875b363 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -2801,7 +2801,7 @@ def sd_generate(genparams):
     negative_prompt = genparams.get("negative_prompt", "")
     def build_prompt(prompt, forced):
         # truncate before the forced prompt to ensure it can't be "pushed away" by long inputs
-        prompt = prompt.encode('utf-8')[:2048].decode("UTF-8", errors='ignore')
+        prompt = prompt.encode('utf-8')[:3000].decode("UTF-8", errors='ignore') #for now, limit sd image gen prompts to 3000 chars
         return ", ".join([prompt, forced])
     prompt = build_prompt(prompt, forced_posprompt)
     negative_prompt = build_prompt(negative_prompt, forced_negprompt)

From 9d5d882d8cd0f0a9283d87ed5e6fe3ee0d925fb1 Mon Sep 17 00:00:00 2001
From: Tarek Dakhran <tarek@liquid.ai>
Date: Thu, 25 Jun 2026 18:58:52 +0200
Subject: [PATCH 167/292] model : Add label for LFM2.5-230M (#25008)

---
 src/llama-model.cpp | 1 +
 src/llama-model.h   | 1 +
 src/models/lfm2.cpp | 1 +
 3 files changed, 3 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index d041a9ce3..6cb0ec379 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -700,6 +700,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_160M:          return "160M";
         case LLM_TYPE_190M:          return "190M";
         case LLM_TYPE_220M:          return "220M";
+        case LLM_TYPE_230M:          return "230M";
         case LLM_TYPE_250M:          return "250M";
         case LLM_TYPE_256M:          return "256M";
         case LLM_TYPE_270M:          return "270M";
diff --git a/src/llama-model.h b/src/llama-model.h
index f4718f6d5..77d8d3b62 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -36,6 +36,7 @@ enum llm_type {
     LLM_TYPE_160M,
     LLM_TYPE_190M,
     LLM_TYPE_220M,
+    LLM_TYPE_230M,
     LLM_TYPE_250M,
     LLM_TYPE_256M,
     LLM_TYPE_270M,
diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp
index 07b7346ee..70e837d6e 100644
--- a/src/models/lfm2.cpp
+++ b/src/models/lfm2.cpp
@@ -13,6 +13,7 @@ void llama_model_lfm2::load_arch_hparams(llama_model_loader & ml) {
     hparams.n_layer_dense_lead = hparams.n_layer();
 
     switch (hparams.n_ff()) {
+        case  2560: type = LLM_TYPE_230M; break;
         case  4608: type = LLM_TYPE_350M; break;
         case  6912: type = LLM_TYPE_700M; break;
         case  8192: type = LLM_TYPE_1_2B; break;

From beac5309f1bc67534f509bf29420abf58fff063c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Fri, 26 Jun 2026 00:13:59 +0200
Subject: [PATCH 168/292] xcframework : disable mtmd video on i/tv/visionos
 (#25018)

---
 build-xcframework.sh | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/build-xcframework.sh b/build-xcframework.sh
index 3a265e53e..697278d05 100755
--- a/build-xcframework.sh
+++ b/build-xcframework.sh
@@ -415,6 +415,7 @@ cmake -B build-ios-sim -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-ios-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -429,6 +430,7 @@ cmake -B build-ios-device -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-ios-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -455,6 +457,7 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-visionos --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -470,6 +473,7 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
     -DLLAMA_BUILD_SERVER=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-visionos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -486,6 +490,7 @@ cmake -B build-tvos-sim -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-tvos-sim --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 
@@ -501,6 +506,7 @@ cmake -B build-tvos-device -G Xcode \
     -DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
     -DLLAMA_OPENSSL=OFF \
+    -DMTMD_VIDEO=OFF \
     -S .
 cmake --build build-tvos-device --config Release -j $(sysctl -n hw.logicalcpu) -- -quiet
 

From 5c7c22c3e134cf7e2681fdddefd78b3dcada7154 Mon Sep 17 00:00:00 2001
From: shaofeiqi <shaoqi@qti.qualcomm.com>
Date: Thu, 25 Jun 2026 18:48:24 -0700
Subject: [PATCH 169/292] opencl: flush profiling batch at shutdown for
 incomplete batches (#25016)

---
 ggml/src/ggml-opencl/ggml-opencl.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index fb330e062..00f20b09b 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -850,6 +850,7 @@ struct ggml_backend_opencl_context {
         ref_count--;
         if (ref_count == 0) {
 #ifdef GGML_OPENCL_PROFILING
+            flush_profiling_batch();
             write_profiling_info();
             profiling_results.clear();
 #endif

From 960d628f46d17ea9c10aa944b061b2341afeb3b5 Mon Sep 17 00:00:00 2001
From: Arsen Arutunan <58118221+limloop@users.noreply.github.com>
Date: Fri, 26 Jun 2026 08:50:54 +0300
Subject: [PATCH 170/292] mamba2: remove hardcoded 2x expansion factor and
 invalid d_inner % d_state check (#23082)
MIME-Version: 1.0
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* mamba2: remove hardcoded 2x expansion factor, support any expand value

* mamba2: remove invalid d_inner %% d_state check (unrelated parameters)

* Update convert_hf_to_gguf.py: make expand optional with default 2

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* mamba2: apply expand fix to refactored conversion/mamba.py

* also check for mamba_expand

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
---
 conversion/mamba.py       |  7 +++----
 src/models/mamba-base.cpp |  1 -
 src/models/mamba2.cpp     | 13 +++++++------
 3 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/conversion/mamba.py b/conversion/mamba.py
index be0e36a29..43d559ffb 100644
--- a/conversion/mamba.py
+++ b/conversion/mamba.py
@@ -114,7 +114,8 @@ class Mamba2Model(TextModel):
             hparams["text_config"] = hparams["llm_config"]
         super().__init__(dir_model, *args, hparams=hparams, **kwargs)
         self.d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
-        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or 2 * self.d_model
+        self.expand = self.find_hparam(["mamba_expand", "expand"], optional=True) or 2
+        self.d_inner = self.find_hparam(["mamba_d_ssm", "intermediate_size", "d_inner"], optional=True) or self.expand * self.d_model
         self.n_group = self.find_hparam(["n_groups"], optional=True) or 1
 
     def set_vocab(self):
@@ -144,11 +145,9 @@ class Mamba2Model(TextModel):
 
         rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
 
-        # Fail early for models which don't have a block expansion factor of 2
-        # TODO: does this really matter?
         # skip the assertion for FalconH1 Model
         if self.model_arch != gguf.MODEL_ARCH.FALCON_H1:
-            assert self.d_inner == 2 * self.d_model
+            assert self.d_inner == self.expand * self.d_model
             assert self.d_inner % head_dim == 0
 
         self.gguf_writer.add_context_length(2**20)  # arbitrary value; for those who use the default
diff --git a/src/models/mamba-base.cpp b/src/models/mamba-base.cpp
index c37f29c48..fd3fe3f03 100644
--- a/src/models/mamba-base.cpp
+++ b/src/models/mamba-base.cpp
@@ -169,7 +169,6 @@ ggml_tensor * llm_build_mamba_base::build_mamba2_layer(llm_graph_input_rs * inp,
     GGML_ASSERT(ubatch.equal_seqs());
     GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
     GGML_ASSERT(d_inner % n_head  == 0);
-    GGML_ASSERT(d_inner % d_state == 0);
     GGML_ASSERT(d_inner % n_group == 0);
 
     ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
diff --git a/src/models/mamba2.cpp b/src/models/mamba2.cpp
index c5951cf0f..d5c167cf0 100644
--- a/src/models/mamba2.cpp
+++ b/src/models/mamba2.cpp
@@ -39,10 +39,11 @@ void llama_model_mamba2::load_arch_tensors(llama_model_loader &) {
     const int64_t d_inner = hparams.ssm_d_inner;
     const int64_t d_state = hparams.ssm_d_state;
     const int64_t n_group = hparams.ssm_n_group;
-    const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_head;
+    const int64_t dt_rank  = hparams.ssm_dt_rank;
+
+    const int64_t conv_dim = d_inner + 2 * n_group * d_state;
+    const int64_t d_in_proj = d_inner + conv_dim + dt_rank;
 
-    // only an expansion factor of 2 is supported for now
-    GGML_ASSERT(2 * n_embd == d_inner);
 
     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -68,11 +69,11 @@ void llama_model_mamba2::load_arch_tensors(llama_model_loader &) {
         layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
         layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, 0);
 
-        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_head}, 0);
+        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {dt_rank}, 0);
 
         // no "weight" suffix for these
-        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
-        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_head}, 0);
+        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, dt_rank}, 0);
+        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, dt_rank}, 0);
 
         layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
 

From f818065d753700341b5355ce36ca676ecfb5d139 Mon Sep 17 00:00:00 2001
From: leonardHONG <2695316095@qq.com>
Date: Fri, 26 Jun 2026 13:51:25 +0800
Subject: [PATCH 171/292] CUDA: batch out_prod broadcast (dps2>1) path with
 cublasSgemmBatched (#24426)

---
 ggml/src/ggml-cuda/out-prod.cu | 67 ++++++++++++++++++++++++++++------
 tests/test-backend-ops.cpp     |  6 +++
 2 files changed, 61 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-cuda/out-prod.cu b/ggml/src/ggml-cuda/out-prod.cu
index 499903d09..46b9f3a67 100644
--- a/ggml/src/ggml-cuda/out-prod.cu
+++ b/ggml/src/ggml-cuda/out-prod.cu
@@ -2,6 +2,28 @@
 
 #include <cstdint>
 
+static __global__ void k_compute_out_prod_ptrs(
+        const float * src0_d, const float * src1_d, float * dst_d,
+        const float ** ptrs_a, const float ** ptrs_b, float ** ptrs_c,
+        const int64_t ne2, const int64_t ne3,
+        const int64_t dps2, const int64_t dps3,
+        const size_t s02, const size_t s03,
+        const size_t s12, const size_t s13,
+        const size_t s2,  const size_t s3) {
+    const int64_t i2 = blockIdx.x*blockDim.x + threadIdx.x;
+    const int64_t i3 = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (i2 >= ne2 || i3 >= ne3) {
+        return;
+    }
+
+    const int64_t idx = i3*ne2 + i2;
+
+    ptrs_a[idx] = src0_d + (i3/dps3)*s03 + (i2/dps2)*s02;
+    ptrs_b[idx] = src1_d +  i3      *s13 +  i2      *s12;
+    ptrs_c[idx] = dst_d  +  i3      *s3  +  i2      *s2;
+}
+
 void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
@@ -67,18 +89,39 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
                         &beta,  dst_d  +  i3     *s3,  ldc, s2,
                         batch_count));
         }
+    } else if (ne2 > 1 || ne3 > 1) {
+        // dps2 > 1 (src0 broadcast along dim 2 with non-uniform stride) or multiple GEMMs
+        // along dim 3: compute per-GEMM pointers on the device and use a single batched GEMM.
+        GGML_ASSERT(ne3 > 0);
+        GGML_ASSERT(ne2 <= (int64_t) std::numeric_limits<int>::max() / ne3);
+        const int batch_count = (int) (ne2 * ne3);
+
+        ggml_cuda_pool_alloc<const float *> ptrs_a(ctx.pool(), batch_count);
+        ggml_cuda_pool_alloc<const float *> ptrs_b(ctx.pool(), batch_count);
+        ggml_cuda_pool_alloc<      float *> ptrs_c(ctx.pool(), batch_count);
+
+        const dim3 block_dims(16, 16);
+        const dim3 grid_dims((ne2 + block_dims.x - 1)/block_dims.x, (ne3 + block_dims.y - 1)/block_dims.y);
+        k_compute_out_prod_ptrs<<<grid_dims, block_dims, 0, stream>>>(
+            src0_d, src1_d, dst_d,
+            ptrs_a.get(), ptrs_b.get(), ptrs_c.get(),
+            ne2, ne3, dps2, dps3, s02, s03, s12, s13, s2, s3);
+        CUDA_CHECK(cudaGetLastError());
+
+        CUBLAS_CHECK(
+            cublasSgemmBatched(handle, CUBLAS_OP_N, src1_cublas_op,
+                    ne0, ne1, ne01,
+                    &alpha, ptrs_a.get(), lda,
+                            ptrs_b.get(), ldb,
+                    &beta,  ptrs_c.get(), ldc,
+                    batch_count));
     } else {
-        // Fallback: ne2 == 1 (no batching benefit) or dps2 > 1 (src0 broadcast along dim 2
-        // with non-uniform stride; would need cublasSgemmBatched with pointer arrays).
-        for (int64_t i3 = 0; i3 < ne3; ++i3) {
-            for (int64_t i2 = 0; i2 < ne2; ++i2) {
-                CUBLAS_CHECK(
-                    cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
-                            ne0, ne1, ne01,
-                            &alpha, src0_d + (i3/dps3)*s03 + (i2/dps2)*s02, lda,
-                                    src1_d +  i3      *s13 +  i2      *s12, ldb,
-                            &beta,  dst_d  +  i3      *s3  +  i2      *s2,  ldc));
-            }
-        }
+        // ne2 == 1 && ne3 == 1: single GEMM
+        CUBLAS_CHECK(
+            cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
+                    ne0, ne1, ne01,
+                    &alpha, src0_d, lda,
+                            src1_d, ldb,
+                    &beta,  dst_d,  ldc));
     }
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index c83e91fbd..0a017d57e 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8672,6 +8672,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                                   256, 16, 16, {ne2, 1}, {1, 1}));
     }
 
+    // nr2 sweep to cover the cublasSgemmBatched pointer-array path (dps2 > 1)
+    for (int64_t nr2 : {8, 16, 32}) {
+        test_cases.emplace_back(new test_out_prod(GGML_TYPE_F32, GGML_TYPE_F32,
+                                                  256, 16, 16, {1, 1}, {nr2, 1}));
+    }
+
     // add_id
     for (ggml_type type_a : {GGML_TYPE_F32}) {
         for (ggml_type type_b : {GGML_TYPE_F32}) {

From b11f7c16bc1c3b320c5cd427d545ee6fa6bd535c Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 26 Jun 2026 08:43:29 +0200
Subject: [PATCH 172/292] mtmd: add more validations (#25013)

* mtmd: add more validations

* fix

* refactor a bit

* type check for get_arr_int
---
 tools/mtmd/clip-model.h       |  9 ++---
 tools/mtmd/clip.cpp           | 67 +++++++++++++++++++++++++++--------
 tools/mtmd/models/pixtral.cpp |  4 +--
 tools/mtmd/mtmd-image.cpp     |  4 +--
 4 files changed, 59 insertions(+), 25 deletions(-)

diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index f86702eba..46be39a64 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -55,8 +55,7 @@ struct clip_hparams {
     int32_t n_head = 0;
     int32_t n_head_kv = 0;
     int32_t n_layer = 0;
-    // idefics3
-    int32_t n_merge = 0; // number of patch merges **per-side**
+    int32_t n_merge = 1; // number of patch merges **per-side**
 
     // for preprocessor
     int32_t image_longest_edge = 0;
@@ -135,8 +134,7 @@ struct clip_hparams {
     int32_t custom_image_max_tokens = -1;
 
     void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) {
-        const int cur_merge = n_merge == 0 ? 1 : n_merge;
-        const int patch_area = patch_size * patch_size * cur_merge * cur_merge;
+        const int patch_area = patch_size * patch_size * n_merge * n_merge;
         image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area;
         image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area;
         warmup_image_size = static_cast<int>(std::sqrt(image_max_pixels));
@@ -145,8 +143,7 @@ struct clip_hparams {
     void set_warmup_n_tokens(int n_tokens) {
         int n_tok_per_side = static_cast<int>(std::sqrt(n_tokens));
         GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n");
-        const int cur_merge = n_merge == 0 ? 1 : n_merge;
-        warmup_image_size = n_tok_per_side * patch_size * cur_merge;
+        warmup_image_size = n_tok_per_side * patch_size * n_merge;
         // TODO: support warmup size for custom token numbers
     }
     // sam vit deepseek-ocr
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 7bd486030..d2226b3be 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1210,6 +1210,9 @@ struct clip_model_loader {
             {
                 std::vector<int> pinpoints;
                 get_arr_int(KEY_IMAGE_GRID_PINPOINTS, pinpoints, false);
+                if (pinpoints.size() % 2 != 0) {
+                    throw std::runtime_error(string_format("%s: image_grid_pinpoints must have an even number of elements, got %zu\n", __func__, pinpoints.size()));
+                }
                 if (!pinpoints.empty()) {
                     for (size_t i = 0; i < pinpoints.size(); i += 2) {
                         hparams.image_res_candidates.push_back({
@@ -1252,15 +1255,16 @@ struct clip_model_loader {
             }
 
             if (is_vision) {
-                int idx_mean = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_MEAN);
-                int idx_std  = gguf_find_key(ctx_gguf.get(), KEY_IMAGE_STD);
-                GGML_ASSERT(idx_mean >= 0 && "image_mean not found");
-                GGML_ASSERT(idx_std >= 0  && "image_std not found");
-                const float * mean_data = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_mean);
-                const float * std_data  = (const float *) gguf_get_arr_data(ctx_gguf.get(), idx_std);
+                std::vector<float> image_mean;
+                std::vector<float> image_std;
+                get_arr_f32(KEY_IMAGE_MEAN, image_mean, false);
+                get_arr_f32(KEY_IMAGE_STD , image_std, false);
+                if (image_mean.size() < 3 || image_std.size() < 3) {
+                    throw std::runtime_error(string_format("%s: image_mean/image_std arrays must have at least 3 elements, got %zu and %zu\n", __func__, image_mean.size(), image_std.size()));
+                }
                 for (int i = 0; i < 3; ++i) {
-                    hparams.image_mean[i] = mean_data[i];
-                    hparams.image_std[i]  = std_data[i];
+                    hparams.image_mean[i] = image_mean[i];
+                    hparams.image_std[i]  = image_std[i];
                 }
             }
 
@@ -1686,8 +1690,8 @@ struct clip_model_loader {
                 if (hparams.image_size > 65536) {
                     throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 65536)\n", __func__, hparams.image_size));
                 }
-                if (hparams.patch_size <= 0) {
-                    throw std::runtime_error(string_format("%s: patch_size (%d) must be greater than 0\n", __func__, hparams.patch_size));
+                if (hparams.patch_size <= 0 || hparams.patch_size >= 65536) {
+                    throw std::runtime_error(string_format("%s: patch_size (%d) must be positive and less than 65536\n", __func__, hparams.patch_size));
                 }
                 if (hparams.n_embd <= 0) {
                     throw std::runtime_error(string_format("%s: n_embd (%d) must be greater than 0\n", __func__, hparams.n_embd));
@@ -1695,6 +1699,9 @@ struct clip_model_loader {
                 if (hparams.image_max_pixels < hparams.image_min_pixels) {
                     throw std::runtime_error(string_format("%s: image_max_pixels (%d) is less than image_min_pixels (%d)\n", __func__, hparams.image_max_pixels, hparams.image_min_pixels));
                 }
+                if (hparams.n_merge < 0 || hparams.n_merge >= 65536) {
+                    throw std::runtime_error(string_format("%s: n_merge (%d) must be greater than 0 and less than 65536\n", __func__, hparams.n_merge));
+                }
             }
 
             LOG_INF("%s: projector:          %s\n", __func__, proj_type.c_str());
@@ -3067,6 +3074,29 @@ struct clip_model_loader {
         output = gguf_get_val_f32(ctx_gguf.get(), i);
     }
 
+    void get_arr_f32(const std::string & key, std::vector<float> & output, bool required = true) const {
+        const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
+        if (i < 0) {
+            if (required) {
+                throw std::runtime_error("Key not found: " + key);
+            }
+            return;
+        }
+        const auto type = gguf_get_arr_type(ctx_gguf.get(), i);
+        if (type != GGUF_TYPE_FLOAT32) {
+            throw std::runtime_error(string_format("%s: array '%s' has type %d, expected %d (GGUF_TYPE_FLOAT32)\n", __func__, key.c_str(), type, GGUF_TYPE_FLOAT32));
+        }
+        const size_t n = gguf_get_arr_n(ctx_gguf.get(), i);
+        if (n > (size_t) std::numeric_limits<int>::max()) {
+            throw std::runtime_error(string_format("%s: array '%s' is too large (%zu elements)\n", __func__, key.c_str(), n));
+        }
+        output.resize(n);
+        const float * values = (const float *)gguf_get_arr_data(ctx_gguf.get(), i);
+        for (size_t j = 0; j < n; ++j) {
+            output[j] = values[j];
+        }
+    }
+
     void get_string(const std::string & key, std::string & output, bool required = true) const {
         const int i = gguf_find_key(ctx_gguf.get(), key.c_str());
         if (i < 0) {
@@ -3086,11 +3116,18 @@ struct clip_model_loader {
             }
             return;
         }
-        int n = gguf_get_arr_n(ctx_gguf.get(), i);
+        const auto type = gguf_get_arr_type(ctx_gguf.get(), i);
+        if (type != GGUF_TYPE_INT32) {
+            throw std::runtime_error(string_format("%s: array '%s' has type %d, expected %d (GGUF_TYPE_INT32)\n", __func__, key.c_str(), type, GGUF_TYPE_INT32));
+        }
+        const size_t n = gguf_get_arr_n(ctx_gguf.get(), i);
+        if (n > (size_t) std::numeric_limits<int>::max()) {
+            throw std::runtime_error(string_format("%s: array '%s' is too large (%zu elements)\n", __func__, key.c_str(), n));
+        }
         output.resize(n);
         const int32_t * values = (const int32_t *)gguf_get_arr_data(ctx_gguf.get(), i);
-        for (int i = 0; i < n; ++i) {
-            output[i] = values[i];
+        for (size_t j = 0; j < n; ++j) {
+            output[j] = values[j];
         }
     }
 
@@ -3364,8 +3401,8 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
             {
                 // dynamic size
                 int n_merge = ctx->model.hparams.n_merge;
-                int n_patches_x = img->nx() / patch_size / (n_merge > 0 ? n_merge : 1);
-                int n_patches_y = img->ny() / patch_size / (n_merge > 0 ? n_merge : 1);
+                int n_patches_x = img->nx() / patch_size / n_merge;
+                int n_patches_y = img->ny() / patch_size / n_merge;
                 if (ctx->model.token_embd_img_break) {
                     n_patches = n_patches_y * n_patches_x + n_patches_y - 1; // + one [IMG_BREAK] per row, except the last row
                 } else {
diff --git a/tools/mtmd/models/pixtral.cpp b/tools/mtmd/models/pixtral.cpp
index d6d037b69..edfae0825 100644
--- a/tools/mtmd/models/pixtral.cpp
+++ b/tools/mtmd/models/pixtral.cpp
@@ -63,8 +63,8 @@ ggml_cgraph * clip_graph_pixtral::build() {
         // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension
         // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows]
 
-        const int p_y             = n_merge > 0 ? n_patches_y / n_merge : n_patches_y;
-        const int p_x             = n_merge > 0 ? n_patches_x / n_merge : n_patches_x;
+        const int p_y             = n_patches_y / n_merge;
+        const int p_x             = n_patches_x / n_merge;
         const int p_total         = p_x * p_y;
         const int n_embd_text     = cur->ne[0];
         const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row
diff --git a/tools/mtmd/mtmd-image.cpp b/tools/mtmd/mtmd-image.cpp
index 57afb542d..01d9b4517 100644
--- a/tools/mtmd/mtmd-image.cpp
+++ b/tools/mtmd/mtmd-image.cpp
@@ -628,7 +628,7 @@ mtmd_image_preproc_out mtmd_image_preprocessor_llava_uhd::preprocess(const clip_
 mtmd_image_preprocessor_llava_uhd::slice_instructions mtmd_image_preprocessor_llava_uhd::get_slice_instructions(const clip_image_size & original_size) {
     mtmd_image_preprocessor_llava_uhd::slice_instructions res;
     // align slices by patch_size * n_merge so an integer number of merger output tokens fits per slice
-    const int n_merge         = hparams.n_merge > 0 ? hparams.n_merge : 1;
+    const int n_merge         = hparams.n_merge;
     const int patch_size      = hparams.patch_size * n_merge;
     const int slice_size      = hparams.image_size;
     const int original_width  = original_size.width;
@@ -894,7 +894,7 @@ mtmd_image_preproc_out mtmd_image_preprocessor_dyn_size::preprocess(const clip_i
     clip_image_u8 resized_image;
     const clip_image_size original_size = img.get_size();
     // the original pixtral model doesn't have n_merge
-    const int cur_merge = hparams.n_merge == 0 ? 1 : hparams.n_merge;
+    const int cur_merge = hparams.n_merge;
     const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
         original_size,
         hparams.patch_size * cur_merge,

From e7e3f350904c087c7601f5d9d276c3c127f84345 Mon Sep 17 00:00:00 2001
From: Jassieluo <130133492+Jassieluo@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:02:42 +0800
Subject: [PATCH 173/292] sycl : clamp softmax input to avoid underflow
 (#24941)

---
 ggml/src/ggml-sycl/softmax.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index 18bf379bb..67ea282b4 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -126,7 +126,7 @@ static void soft_max_f32(const float *         x,
             break;
         }
 
-        const float val = sycl::native::exp(vals[col] - max_val);
+        const float val = sycl::native::exp(sycl::max(vals[col] - max_val, -80.0f));
         tmp += val;
         vals[col] = val;
     }
@@ -154,7 +154,7 @@ static void soft_max_f32(const float *         x,
         tmp = warp_reduce_sum<WARP_SIZE>(tmp);
     }
     if (sinks) {
-        tmp += sycl::native::exp(sinks[i02] - max_val);
+        tmp += sycl::native::exp(sycl::max(sinks[i02] - max_val, -80.0f));
     }
     const float inv_sum = 1.0f / tmp;
 

From 1a87dcdc452de056a66796b679e4ba7071488cf7 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Fri, 26 Jun 2026 09:31:29 +0200
Subject: [PATCH 174/292] server + ui: SSE Replay Buffer (#23226)

* server: SSE replay buffer, survives client disconnect

Opt in on POST /v1/chat/completions when the client sends
X-Stream-Resume: 1 and a non empty X-Conversation-Id. The conv id is
the session identity end to end, no extra opaque token. The drain
runs detached server side and buffers SSE bytes, the generation
survives HTTP disconnect, F5, or lets users switch from iOS Safari
to another app without losing the actively generated response.

Routes:
  GET    /v1/stream/<conv_id>?from=N       replay
  GET    /v1/streams[?conversation_id=X]   list, drives sidebar spinners
  DELETE /v1/stream/<conv_id>              Stop, idempotent

Router parent fans out to children for list and delete, probes on GET
to route to the owner, fans out DELETE on POST so "one session per
conv" holds across model swaps.

WebUI: the layout snapshots /v1/streams at mount and on
visibilitychange, the sidebar reflects live inferences across all
convs. The chat page reattaches on mount, append vs fresh is detected
from existing content so continue mid stream keeps its prefix.

update_slots: on llama_memory_seq_rm refusal at a deep position, full
clear of the seq and reprefill from zero instead of GGML_ABORT.

OAI strict path unchanged when the opt in headers are absent.

* server: create stream session only after post_tasks succeeds

* server, ui: drop X-Stream-Resume, X-Conversation-Id alone enables the replay buffer

* server: drop magic 17, derive the X-Conversation-Id header length from sizeof at build time

* refactor: address review feedback from ngxson

* server-context: cleaning

* server-stream: fix use-after-free on rd

Guard stop_producer with a shared alive flag, flipped by on_stream_end
before rd dies. Prevents a late cancel (session eviction by a later
POST on the same conv_id, or a DELETE arriving after the producer
ended) from touching a destroyed rd.

* ui: fix cross-conversation contamination

Scope streaming flags per conv so one finishing does not unflag the
others, guard discoverActiveStream against concurrent runs to avoid
duplicate attaches, and stop racing syncRemoteRunningStreams for the
sidebar set.

* server-http: keep request alive in detached SSE drain

The response next() lambda may reach into *request via &req long
after on_complete reset the request shared_ptr. Capture request in
the detached thread so it outlives the drain.

* ui: address review feedback from coder543

Forward Authorization to /v1/stream and /v1/streams fetches, the resumable routes
must obey --api-key like the rest of the API.

Wrap reader.read() in a try/catch, the underlying connection drop rejects with
TypeError instead of resolving done=true, treat it as a premature end of stream
so the existing resume loop kicks in.

Freeze the model at session start in chatStreamingStates.model and thread it
through cancel and resume, the dropdown selection may have changed since the
POST and the server side identity is fixed at that time.

* format

* ui: remove unused selectedModelName

* server-stream: poll session->is_cancelled() in stream_aware_should_stop

Address review feedback from coder543. The cancel propagation through
rd.stop() relies on the slot eventually processing the cancel task and
posting a result that notifies the recv condvar, remove_waiting_task_ids
does not notify directly. Add a defensive poll on session->is_cancelled()
so the producer-side next() loop exits on its next iteration after
cancel() without waiting for the cancel task to round trip through a slot.

* server-stream, ui: replace GET /v1/streams with POST /v1/streams/lookup

Address review feedback from coder543. Listing live sessions leaks the
conversation_id of every concurrent user, which defeats the random UUID
unguessability. The new route takes {conversation_ids: [...]} in the
body and returns matches only for the ids the caller already owns, so
foreign UUIDs stay private. The router fans out the same POST to every
child and aggregates, the WebUI passes the convs visible in its sidebar.

* ui: read conv ids from IndexedDB in syncRemoteRunningStreams

The conversations store is not hydrated yet at +layout onMount, so the
sidebar spinners stayed off for background convs until the user clicked
on them. Read straight from the DB to dodge the init race.

* server-models: deduplicate stream lookup timeouts behind one constant

* ui: extract visibility kick grace into a stream constant, bump to 1000 ms

* make it safer & more simple

* server-stream: survive client disconnect via stream_pipe::finish_producer

After the RAII rewrite the generation stopped the moment the client
disconnected. httplib bails its content provider on the is_peer_alive
check at the top of write_content_chunked, so returning true from the
provider never keeps it producing: the response resets, rd is destroyed
and its task gets cancelled.

Reinstate the disconnect survival inside the pipe. stream_pipe gains
finish_producer, which pumps the response next() into the ring buffer
until the generation ends, and mark_producer_done for the clean wire
end. server-http only triggers them: mark before sink.done on a clean
close, finish in on_complete when the peer left early. No detach, no
stream logic in server-http beyond the trigger, and the strict OAI path
is untouched when no pipe is attached.

Known limitation: finish_producer pumps synchronously on the http
worker, so a disconnected stream keeps its worker busy until the
generation ends. A follow-up will move the drain off the http worker so
no worker is held.

* server-stream: drain disconnected streams on a manager owned thread

The previous commit pumped the post disconnect drain synchronously in
on_complete, on the http worker, so a disconnected stream kept its
worker busy until the generation ended. Under a wave of reloads or tab
closes that pins workers from the pool.

Move the drain off the http worker. on_complete now hands the response
to stream_session_manager::adopt_orphan, which pumps it to completion on
a manager owned thread and releases the worker at once. One thread per
disconnected stream still generating, stored in a list, joined and
reaped on the next adopt, by the GC, and at shutdown. No detach, the
thread lifecycle is fully owned by the manager. needs_drain gates the
handoff so a cleanly finished stream never spawns a thread, and the
strict OAI path stays untouched when no pipe is attached.

stop_gc now cancels sessions before finalizing them, so an in flight
drain sees is_cancelled and exits instead of blocking the shutdown join
until the generation ends naturally.

* ui: add missing JSDoc

* server-stream: drain on the http worker, drop the manager thread

Address @ngxson review: httplib runs a large dynamic pool and a worker
blocked in next() sits on a condvar instead of burning cpu, so draining
the rest of the generation on that worker is fine and much simpler than
a dedicated thread.

on_complete calls finish_producer directly again. Removes adopt_orphan,
the orphan thread list and its reaping, the stop_gc session cancel that
only existed to unblock those threads, and the now dead drain_shutdown
flag.

* server-stream: split stream_pipe into producer and consumer classes

Address @ngxson review: one class covering both ends was messy. stream_pipe
is now a base holding the session and is_cancelled, with stream_pipe_producer
(write, mark_producer_done, finish_producer, cleanup, finalizes on destruct)
and stream_pipe_consumer (read only, no finalize) deriving from it.

Drops the is_producer_ discriminator and its runtime guards, the type now
encodes the role. res.spipe is retyped to shared_ptr<stream_pipe_producer>
since it is only ever a producer. No behavior change.

* server-stream: rename producer methods to unix pipe semantics

Address @ngxson review: mark_producer_done becomes done(), finish_producer
becomes close(), matching a unix pipe write end. The producer_done_ member
follows as done_. write() is unchanged. No behavior change.

* server, ui: route resumable streams via a conv map, persist resume identity

Address ngxson review: drop the polling probe, proxy_post records a conv_id ->
model map and the stream routes resolve the owning child with one lookup. The
map is the single source of truth, the ::model suffix stays for child session
uniqueness but the router never parses it.

UI: the server keys a session by the POST time identity (conv::model), but reload
probed with the bare conv id and missed model tagged sessions, so F5 stopped the
stream and sidebar spinners stayed off. Persist the model and rebuild the exact
identity on resume, single conv and bulk sidebar both send it.

Add unit coverage for the identity round trip.

* ui: resolve continue target by id to stop cross-conversation flash on switch

* ui: skip stream resume when the abort is intentional

* server: move the conv id to model map into a self contained tracker

Address review from ngxson: server_models held two mutexes side by side, the
global one and a bare conv_model_mu guarding a loose map, which made the locking
hard to follow. Wrap the map and its lock in a small conv_model_tracker struct
that owns its mutex, one mutex per struct. The remember, lookup and forget
methods move inline into the tracker, server_models exposes a single conv_models
member and the routes call models.conv_models.lookup and friends. No behavior
change, the map stays the single source of truth for routing resumable streams
to a child.

* ui: replace stream magic values with enums and shared constants

Address review from allozaur: lift the inline literals around the resumable
stream code into named symbols so the intent is explicit and reusable.

* ui: fold the stream resume and discovery helpers into ChatService

Address review from allozaur: drop the two standalone stream-*.service files.
They were used only by the chat service and store, carried no shared state, and
did not follow the static class pattern the other services use, so a separate
abstraction was not warranted. Move the helpers onto ChatService as static
methods. No behavior change, tests now exercise them through ChatService.

* docs: document the SSE replay buffer in server README-dev

Add the resumable streaming section, list stream_session_manager in the
backend component inventory, and link PR 23226 in the related PRs.

* ui: align attachServerStream call with onCompletionId param in handleStreamResponse

* server-http: rename del_ to del to match get and post

* ui: address review feedback from allozaur

* ui: drop duplicate SSE constants, keep sse.ts canonical

* ui: use svelte:document for the visibilitychange listener

address review from allozaur: replace the manual document.addEventListener
in onMount with a declarative <svelte:document onvisibilitychange>. svelte
handles attach, detach and SSR, so the typeof document guard and the onMount
cleanup go away. onMount keeps only the first load snapshot.

* server: trim redundant stream drain comments

Address review from ngxson

* server: balance and clean up stream comments

remove redundant comments and tighten the verbose ones across the resumable
stream code, keeping the concurrency and lifetime rationale that is not obvious
from the code. also fix two stale comments in server.cpp and server-models.h
that still described the old ::model suffix probe and fan out routing, now
replaced by the conv_id -> model map

Address review from ngxson

* ui: balance and clean up stream comments

dedup repeated rationale (frozen conv::model identity, the lookup privacy note,
the abort patterns) down to one canonical spot, tighten the verbose blocks, and
keep the concurrency and resume-offset reasoning. fix stale comments in
stream-identity.ts and chat.service.ts that still described the old loopback
probe and fan out routing, now the conv_id -> model map.

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
---
 tools/server/CMakeLists.txt                   |   2 +
 tools/server/README-dev.md                    |  54 ++
 tools/server/server-context.cpp               |  24 +-
 tools/server/server-http.cpp                  |  55 +-
 tools/server/server-http.h                    |  12 +-
 tools/server/server-models.cpp                | 172 ++++++
 tools/server/server-models.h                  |  50 ++
 tools/server/server-stream.cpp                | 569 ++++++++++++++++++
 tools/server/server-stream.h                  | 203 +++++++
 tools/server/server.cpp                       |  32 +
 .../app/chat/ChatScreen/ChatScreen.svelte     |   5 +
 .../ChatScreenStreamResumeStatus.svelte       |  18 +
 tools/ui/src/lib/components/app/chat/index.ts |   8 +
 tools/ui/src/lib/constants/api-endpoints.ts   |   6 +
 tools/ui/src/lib/constants/index.ts           |   1 +
 tools/ui/src/lib/constants/storage.ts         |   3 +
 tools/ui/src/lib/constants/stream.ts          |   3 +
 tools/ui/src/lib/enums/chat.enums.ts          |   9 +
 tools/ui/src/lib/enums/index.ts               |   1 +
 tools/ui/src/lib/services/chat.service.ts     | 417 ++++++++++---
 tools/ui/src/lib/services/mcp.service.ts      |  25 +-
 tools/ui/src/lib/stores/agentic.svelte.ts     |   2 +-
 tools/ui/src/lib/stores/chat.svelte.ts        | 470 ++++++++++++++-
 tools/ui/src/lib/types/api.d.ts               |  15 +
 tools/ui/src/lib/types/index.ts               |   3 +-
 tools/ui/src/lib/types/settings.d.ts          |   6 +-
 tools/ui/src/lib/utils/abort.ts               |  28 +-
 tools/ui/src/lib/utils/stream-identity.ts     |  13 +
 .../src/routes/(chat)/chat/[id]/+page.svelte  |  44 +-
 tools/ui/src/routes/+layout.svelte            |  12 +
 tools/ui/tests/unit/abort.test.ts             |  56 ++
 tools/ui/tests/unit/stream-discovery.test.ts  |  74 +++
 tools/ui/tests/unit/stream-resume.test.ts     | 128 ++++
 33 files changed, 2374 insertions(+), 146 deletions(-)
 create mode 100644 tools/server/server-stream.cpp
 create mode 100644 tools/server/server-stream.h
 create mode 100644 tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenStreamResumeStatus.svelte
 create mode 100644 tools/ui/src/lib/constants/stream.ts
 create mode 100644 tools/ui/src/lib/utils/stream-identity.ts
 create mode 100644 tools/ui/tests/unit/abort.test.ts
 create mode 100644 tools/ui/tests/unit/stream-discovery.test.ts
 create mode 100644 tools/ui/tests/unit/stream-resume.test.ts

diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index 47bb582c3..b5c40884f 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -15,6 +15,8 @@ add_library(${TARGET} STATIC
     server-common.h
     server-context.cpp
     server-context.h
+    server-stream.cpp
+    server-stream.h
     server-tools.cpp
     server-tools.h
     server-schema.cpp
diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index 5959745e4..dfc9004de 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -57,6 +57,7 @@ The core architecture consists of the following components:
 - `server_tokens`: Unified representation of token sequences (supports both text and multimodal tokens); used by `server_task` and `server_slot`.
 - `server_prompt_checkpoint`: For recurrent (e.g., RWKV) and SWA models, stores snapshots of KV cache state. Enables reuse when subsequent requests share the same prompt prefix, saving redundant computation.
 - `server_models`: Standalone component for managing multiple backend instances (used in router mode). It is completely independent of `server_context`.
+- `stream_session_manager`: Process wide owner of resumable SSE stream sessions (`g_stream_sessions`), keyed by conversation id. Backs the replay buffer that lets a client reattach to a generation after an HTTP disconnect. See the "Resumable streaming" section below.
 
 ```mermaid
 graph TD
@@ -117,6 +118,58 @@ Here is an example trace of an API request for text completion:
 - As the response is stateless, `server_res_generator` calls `response->update()` to update the response with the current state.
 - `server_res_generator` then calls `response->to_json()` and passes the response to the HTTP layer.
 
+### Resumable streaming (SSE replay buffer)
+
+By default a streaming generation is bound to its HTTP socket: when the socket drops (refresh, tab close, mobile background, transient network) the generation aborts and the live stream is lost. This feature keeps the generation running server side and lets a client reattach.
+
+It is opt in via the `X-Conversation-Id` header on `POST /v1/chat/completions`. Without the header the OAI strict path is unchanged. The conversation id is the only identity end to end (server map key, client localStorage key, route path), with an optional `::model` suffix for direct routing in router mode.
+
+The feature lives entirely in `server-stream.{h,cpp}` and rests on three types:
+
+- `stream_session`: a bounded ring buffer (4 MiB cap, oldest bytes drop first) plus a condvar. `append` pushes raw SSE bytes, `read_from` drains from any offset and blocks for live bytes or finalize, `finalize` wakes readers, `cancel` stops the producer. One conv maps to at most one live session.
+- `stream_session_manager` (`g_stream_sessions`): owns all sessions keyed by conv id, enforces the one conv one session invariant via `create_or_replace`, and runs a GC thread that drops completed sessions past their TTL.
+- `stream_pipe_producer` / `stream_pipe_consumer`: the write and read ends. The producer owns the session lifetime and finalizes it on destruction; the consumer is read only and never finalizes, so a reader detaching cannot kill a running generation.
+
+Producer side: `server_res_generator` attaches a producer pipe when the header is present. The HTTP content provider mirrors every chunk into the ring before writing it to the socket. While a pipe is attached, `stream_aware_should_stop` ignores peer disconnect, so a dropped socket does not stop generation: only an explicit `DELETE` does. When the peer leaves early, `on_complete` calls `close()`, which drains the rest of the generation into the ring on the http worker.
+
+Lifetime safety: the producer pipe holds a shared `alive` flag also captured by the session cancel hook. `~server_res_generator` calls `cleanup()` to clear that hook while the reader is still alive, so a `cancel` arriving during teardown can never call `stop()` on a freed response. This ordering is the most fragile part of the feature: finalizing or destroying the producer before `cleanup()` runs reintroduces a use after free.
+
+Consumer side: `GET /v1/stream/<conv_id>?from=N` opens a `text/event-stream` that replays buffered bytes from offset `N` and blocks for live bytes, so the browser reattaches like a fresh EventSource. An offset below the dropped prefix returns 400.
+
+Routes:
+
+- `GET /v1/stream/:conv_id?from=N`: replay or live reattach.
+- `POST /v1/streams/lookup` with `{"conversation_ids": [...]}`: returns session status only for ids the caller already owns. There is no listing route, so live sessions cannot be enumerated (an earlier `GET /v1/streams` was removed for exactly this reason).
+- `DELETE /v1/stream/:conv_id`: explicit Stop, idempotent (`evict_and_cancel`).
+
+Router mode binds the same paths to proxy handlers. A `conv_id -> child` map (`conv_models`), populated when a POST is routed, resolves the owning child in one lookup with no polling. The lookup groups ids per child; GET and DELETE proxy straight to the owner. This loopback REST hop is expected to move to a websocket IPC later, swapping only the transport.
+
+Lifecycle: `g_stream_sessions.start_gc()` runs in main after common init, `stop_gc()` runs first in `clean_up()` and finalizes every live session so no reader hangs. Reader blocking and the post drop drain both run on httplib worker threads, which block on a condvar rather than spin.
+
+| Constant | Value | Role |
+| --- | --- | --- |
+| `STREAM_SESSION_TTL_SECONDS` | 300 | retention of a completed session before GC |
+| `STREAM_SESSION_MAX_BYTES` | 4 MiB | ring cap per session |
+| `STREAM_SESSION_GC_INTERVAL_SECONDS` | 60 | GC tick |
+| `STREAM_READ_WAKE_INTERVAL_MS` | 200 | read_from wake to recheck should_stop |
+| `STREAM_LOOKUP_TIMEOUT_MS` | 250 | router to child loopback budget |
+
+```mermaid
+graph TD
+    Client -- "POST + X-Conversation-Id" --> RG[server_res_generator]
+    RG -- attach --> Prod[stream_pipe_producer]
+    Prod -- "write, drain on peer drop" --> Sess
+    subgraph g_stream_sessions
+        Sess[stream_session: ring buffer, 4 MiB]
+        GC[GC thread] -- drop after TTL --> Sess
+    end
+    Sess -- read_from offset --> Cons[stream_pipe_consumer]
+    Cons -- "GET /v1/stream/:id?from=N" --> Client
+    DEL[DELETE /v1/stream/:id] -- evict_and_cancel --> Sess
+```
+
+The diagram shows the buffer touch points. The live wire (chunks streamed to the original client during a normal generation) is the producer's default output, described under "Producer side" above.
+
 ### Testing
 
 `llama-server` includes an automated test suite based on `pytest`.
@@ -223,6 +276,7 @@ The flow for downloading a new model:
 - Speculative decoding: https://github.com/ggml-org/llama.cpp/pull/17808 and rework in https://github.com/ggml-org/llama.cpp/pull/17808
 - INI presets: https://github.com/ggml-org/llama.cpp/pull/17859 (+ refactoring: https://github.com/ggml-org/llama.cpp/pull/18169)
 - Sleeping mode: https://github.com/ggml-org/llama.cpp/pull/18228
+- Resumable streaming (SSE replay buffer): https://github.com/ggml-org/llama.cpp/pull/23226
 
 
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 39b7eb218..5c33a418f 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -5,6 +5,7 @@
 #include "server-task.h"
 #include "server-queue.h"
 #include "server-schema.h"
+#include "server-stream.h"
 
 #include "build-info.h"
 #include "common.h"
@@ -4022,6 +4023,15 @@ struct server_res_generator : server_http_res {
             queue_tasks.wait_until_no_sleep();
         }
     }
+    ~server_res_generator() override {
+        // cleanup() must run while rd is still alive (rd is destroyed after this body returns)
+        if (spipe) {
+            spipe->cleanup();
+        }
+    }
+    void stop() override {
+        rd.stop();
+    }
     void ok(const json & response_data) {
         status = 200;
         data = safe_json_to_str(response_data);
@@ -4210,8 +4220,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                 }
             };
 
+            auto effective_should_stop = stream_aware_should_stop(res_this, req.should_stop);
+
             try {
-                if (req.should_stop()) {
+                if (effective_should_stop()) {
                     SRV_DBG("%s", "stopping streaming due to should_stop condition\n");
                     return false; // should_stop condition met
                 }
@@ -4245,8 +4257,8 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                 // receive subsequent results
                 bool timeout = false;
                 int64_t start_time = ggml_time_ms();
-                auto result = rd.next([&timeout, &req, &start_time, &params]() {
-                    if (req.should_stop()) {
+                auto result = rd.next([&timeout, &start_time, &params, &effective_should_stop]() {
+                    if (effective_should_stop()) {
                         return true; // should_stop condition met
                     } else if (params.sse_ping_interval > 0 && ggml_time_ms() - start_time > (int64_t)params.sse_ping_interval * 1000) {
                         timeout = true;
@@ -4264,7 +4276,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
 
                 if (result == nullptr) {
                     SRV_DBG("%s", "stopping streaming due to should_stop condition\n");
-                    GGML_ASSERT(req.should_stop());
+                    GGML_ASSERT(effective_should_stop());
                     return false; // should_stop condition met
                 }
 
@@ -4302,6 +4314,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
         };
     }
 
+    // attach a producer pipe to the response when X-Conversation-Id is present.
+    // the pipe mirrors SSE chunks into the ring buffer and wires up the cancel hook.
+    stream_session_attach_pipe(*res, req.headers);
+
     return res;
 }
 
diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index 4f2abab00..82f34edac 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -1,5 +1,6 @@
 #include "common.h"
 #include "server-http.h"
+#include "server-stream.h"
 #include "server-common.h"
 #include "ui.h"
 
@@ -456,13 +457,40 @@ static void set_headers(httplib::Response & res, const std::map<std::string, std
     }
 }
 
+// percent-decode a path component (%XX). path params arrive raw from httplib, unlike query
+// params, so a conv id like "conv::model" sent as "conv%3A%3Amodel" must be decoded here to
+// match the value the client put in the X-Conversation-Id header
+static std::string decode_path_component(const std::string & in) {
+    std::string out;
+    out.reserve(in.size());
+    for (size_t i = 0; i < in.size(); i++) {
+        if (in[i] == '%' && i + 2 < in.size()) {
+            auto hex = [](char c) -> int {
+                if (c >= '0' && c <= '9') return c - '0';
+                if (c >= 'a' && c <= 'f') return c - 'a' + 10;
+                if (c >= 'A' && c <= 'F') return c - 'A' + 10;
+                return -1;
+            };
+            int hi = hex(in[i + 1]);
+            int lo = hex(in[i + 2]);
+            if (hi >= 0 && lo >= 0) {
+                out.push_back(char((hi << 4) | lo));
+                i += 2;
+                continue;
+            }
+        }
+        out.push_back(in[i]);
+    }
+    return out;
+}
+
 static std::map<std::string, std::string> get_params(const httplib::Request & req) {
     std::map<std::string, std::string> params;
     for (const auto & [key, value] : req.params) {
         params[key] = value;
     }
     for (const auto & [key, value] : req.path_params) {
-        params[key] = value;
+        params[key] = decode_path_component(value);
     }
     return params;
 }
@@ -497,26 +525,41 @@ static void process_handler_response(server_http_req_ptr && request, server_http
         set_headers(res, response->headers);
         const std::string content_type = response->content_type;
         // convert to shared_ptr as both chunked_content_provider() and on_complete() need to use it
-        std::shared_ptr q_ptr = std::move(request);
-        std::shared_ptr r_ptr = std::move(response);
-        const auto chunked_content_provider = [response = r_ptr](size_t, const httplib::DataSink & sink) -> bool {
+        std::shared_ptr<server_http_req> q_ptr = std::move(request);
+        std::shared_ptr<server_http_res> r_ptr = std::move(response);
+
+        const auto chunked_content_provider = [response = r_ptr](size_t, httplib::DataSink & sink) -> bool {
             std::string chunk;
             const bool has_next = response->next(chunk);
             if (!chunk.empty()) {
+                // mirror into the ring buffer first, the session must reflect every SSE chunk
+                // whether or not the wire write below succeeds
+                if (response->spipe) {
+                    response->spipe->write(chunk.data(), chunk.size());
+                }
                 if (!sink.write(chunk.data(), chunk.size())) {
+                    // peer is gone, stop the wire path here
                     return false;
                 }
                 SRV_DBG("http: streamed chunk: %s\n", chunk.c_str());
             }
             if (!has_next) {
+                // producer reached its natural end on the wire, a later close() skips the drain
+                if (response->spipe) {
+                    response->spipe->done();
+                }
                 sink.done();
                 SRV_DBG("%s", "http: stream ended\n");
             }
             return has_next;
         };
         const auto on_complete = [request = q_ptr, response = r_ptr](bool) mutable {
-            response.reset(); // trigger the destruction of the response object
-            request.reset();  // trigger the destruction of the request object
+            // on a dropped peer, close() drains the rest of the generation into the ring buffer
+            if (response->spipe) {
+                response->spipe->close();
+            }
+            response.reset(); // spipe destructor finalizes the session if attached
+            request.reset();
         };
         res.set_chunked_content_provider(content_type, chunked_content_provider, on_complete);
     } else {
diff --git a/tools/server/server-http.h b/tools/server/server-http.h
index 6b4a4b87a..350813183 100644
--- a/tools/server/server-http.h
+++ b/tools/server/server-http.h
@@ -3,6 +3,7 @@
 #include <atomic>
 #include <functional>
 #include <map>
+#include <memory>
 #include <string>
 #include <thread>
 #include <vector>
@@ -10,6 +11,7 @@
 #include <unordered_map>
 
 struct common_params;
+struct stream_pipe_producer; // defined in server-stream.h
 
 // generator-like API for HTTP response generation
 // this object response with one of the 2 modes:
@@ -23,12 +25,20 @@ struct server_http_res {
     std::string data;
     std::map<std::string, std::string> headers;
 
-    // TODO: move this to a virtual function once we have proper polymorphism support
+    // if set, the stream survives a client disconnect: the producer pipe keeps draining into the
+    // ring buffer and finalizes the session on destruction, so no explicit on_stream_end is needed.
+    // shared_ptr (not unique_ptr) so the forward-declared type is safe to delete here.
+    std::shared_ptr<stream_pipe_producer> spipe;
+
     std::function<bool(std::string &)> next = nullptr;
     bool is_stream() const {
         return next != nullptr;
     }
 
+    // called when the session is cancelled (e.g. DELETE /v1/stream/<conv_id>).
+    // server_res_generator overrides this to stop its reader; the default is a no-op.
+    virtual void stop() {}
+
     virtual ~server_http_res() = default;
 };
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index bb2f43a10..0380f98a3 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1,12 +1,14 @@
 #include "server-common.h"
 #include "server-models.h"
 #include "server-context.h"
+#include "server-stream.h"
 
 #include "build-info.h"
 #include "preset.h"
 #include "download.h"
 
 #include <cpp-httplib/httplib.h> // TODO: remove this once we use HTTP client from download.h
+#include <optional>
 #include <sheredom/subprocess.h>
 
 #include <functional>
@@ -92,6 +94,9 @@ struct server_subproc {
     }
 };
 
+// short loopback budget for the resumable stream router to child JSON calls (probe, lookup,
+// delete). distinct from params.timeout_read/write which only applies to the generation proxy
+static constexpr int STREAM_LOOKUP_TIMEOUT_MS = 250;
 
 static std::filesystem::path get_server_exec_path() {
 #if defined(_WIN32)
@@ -1580,6 +1585,45 @@ static bool is_autoload(const common_params & params, const server_http_req & re
     }
 }
 
+// percent encode one query or path component, covers reserved chars without pulling in
+// httplib::detail. used by the stream routes to forward conversation_id to children safely
+static std::string encode_qs(const std::string & in) {
+    std::string out;
+    out.reserve(in.size() * 3);
+    for (unsigned char c : in) {
+        bool safe = (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z') || (c >= '0' && c <= '9')
+                 || c == '-' || c == '_' || c == '.' || c == '~';
+        if (safe) {
+            out.push_back(char(c));
+        } else {
+            char buf[4];
+            std::snprintf(buf, sizeof(buf), "%%%02X", c);
+            out.append(buf, 3);
+        }
+    }
+    return out;
+}
+
+// resolve the child that owns a conversation's stream session via the conv_id -> model map
+// populated when the POST was routed. single map lookup then a meta lookup, no polling, no
+// parsing of the conv id. returns nullopt when nothing maps, the caller answers not found and
+// the client recovers
+static std::optional<server_model_meta> resolve_child_for_conv(
+        server_models & models, const std::string & conversation_id) {
+    if (conversation_id.empty()) {
+        return std::nullopt;
+    }
+    auto tracked = models.conv_models.lookup(conversation_id);
+    if (!tracked.has_value()) {
+        return std::nullopt;
+    }
+    auto meta = models.get_meta(*tracked);
+    if (meta.has_value() && meta->is_ready()) {
+        return meta;
+    }
+    return std::nullopt;
+}
+
 void server_models_routes::init_routes() {
     this->get_router_props = [this](const server_http_req & req) {
         std::string name = req.get_param("model");
@@ -1628,6 +1672,12 @@ void server_models_routes::init_routes() {
         if (!router_validate_model(name, models, autoload, error_res)) {
             return error_res;
         }
+        // remember which child serves this conversation so the stream routes can route straight
+        // to it without polling, keyed on the exact conv id from the header
+        std::string conv_id = stream_conv_id_from_headers(req.headers);
+        if (!conv_id.empty()) {
+            models.conv_models.remember(conv_id, name);
+        }
         return models.proxy_request(req, method, name, true); // update last usage for POST request only
     };
 
@@ -1819,6 +1869,128 @@ void server_models_routes::init_routes() {
         res_ok(res, {{"success", true}});
         return res;
     };
+
+    this->router_stream_get = [this](const server_http_req & req) {
+        // GET /v1/stream/<conv_id>?from=N. resolve the owning child from the conv_id -> model
+        // map, 404 when nothing maps
+        auto res = std::make_unique<server_http_res>();
+        std::string conv_id = req.get_param("conv_id");
+        if (conv_id.empty()) {
+            res_err(res, format_error_response("Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST));
+            return res;
+        }
+        std::optional<server_model_meta> owner = resolve_child_for_conv(models, conv_id);
+        if (!owner.has_value()) {
+            res_err(res, format_error_response("Stream not found or expired", ERROR_TYPE_NOT_FOUND));
+            return res;
+        }
+        std::string from = req.get_param("from");
+        std::string child_path = "/v1/stream/" + encode_qs(conv_id);
+        if (!from.empty()) {
+            child_path += "?from=" + from;
+        }
+        SRV_INF("proxying stream resume to model %s on port %d, path=%s\n",
+                owner->name.c_str(), owner->port, child_path.c_str());
+        auto proxy = std::make_unique<server_http_proxy>(
+                "GET",
+                "http",
+                CHILD_ADDR,
+                owner->port,
+                child_path,
+                req.headers,
+                req.body,
+                req.files,
+                req.should_stop,
+                params.timeout_read,
+                params.timeout_write);
+        return std::unique_ptr<server_http_res>(std::move(proxy));
+    };
+
+    this->router_streams_lookup = [this](const server_http_req & req) {
+        // POST /v1/streams/lookup. resolve each requested conv id to its owning child via the
+        // map, group the ids per child, and query only the children that actually own some of
+        // them instead of fanning out to every ready child. a child only answers for the ids
+        // it owns, never lists anything else
+        auto res = std::make_unique<server_http_res>();
+        std::vector<std::string> requested;
+        try {
+            json body = json::parse(req.body);
+            if (body.contains("conversation_ids") && body["conversation_ids"].is_array()) {
+                for (const auto & v : body["conversation_ids"]) {
+                    if (v.is_string() && !v.get<std::string>().empty()) {
+                        requested.push_back(v.get<std::string>());
+                    }
+                }
+            }
+        } catch (const std::exception &) {
+            res_ok(res, json::array());
+            return res;
+        }
+
+        // group requested ids by the child port that owns them, drop ids that map to nothing
+        std::unordered_map<int, json> per_child;
+        for (const auto & cid : requested) {
+            auto owner = resolve_child_for_conv(models, cid);
+            if (!owner.has_value()) {
+                continue;
+            }
+            per_child[owner->port].push_back(cid);
+        }
+
+        json aggregated = json::array();
+        for (auto & [port, ids] : per_child) {
+            json child_body = {{"conversation_ids", ids}};
+            httplib::Client cli(CHILD_ADDR, port);
+            cli.set_connection_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            cli.set_read_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            cli.set_write_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            auto resp = cli.Post("/v1/streams/lookup", child_body.dump(), "application/json");
+            if (!resp || resp->status != 200) {
+                continue;
+            }
+            try {
+                json child_arr = json::parse(resp->body);
+                if (!child_arr.is_array()) {
+                    continue;
+                }
+                for (auto & entry : child_arr) {
+                    if (entry.is_object()) {
+                        aggregated.push_back(entry);
+                    }
+                }
+            } catch (const std::exception &) {
+                continue;
+            }
+        }
+        res_ok(res, aggregated);
+        return res;
+    };
+
+    this->router_stream_delete = [this](const server_http_req & req) {
+        // DELETE /v1/stream/<conv_id>. resolve the owning child via the map and forward only to
+        // it, evict_and_cancel is idempotent on the child
+        auto res = std::make_unique<server_http_res>();
+        std::string conv_id = req.get_param("conv_id");
+        if (conv_id.empty()) {
+            res_err(res, format_error_response("Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST));
+            return res;
+        }
+        std::string child_path = "/v1/stream/" + encode_qs(conv_id);
+        auto owner = resolve_child_for_conv(models, conv_id);
+        if (owner.has_value()) {
+            httplib::Client cli(CHILD_ADDR, owner->port);
+            cli.set_connection_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            cli.set_read_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            cli.set_write_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
+            auto resp = cli.Delete(child_path.c_str());
+            (void) resp; // best effort, 404 and network errors are equivalent to no op
+        }
+        // drop the tracking entry, the session is being torn down
+        models.conv_models.forget(conv_id);
+        res->status = 204;
+        res->content_type = "application/json";
+        return res;
+    };
 }
 
 
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 9ed4aeead..62bed8725 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -11,7 +11,10 @@
 #include <condition_variable>
 #include <functional>
 #include <memory>
+#include <optional>
 #include <set>
+#include <string>
+#include <unordered_map>
 
 /**
  * state diagram:
@@ -126,6 +129,44 @@ private:
     // if true, the next get_meta() will trigger a reload of model list
     bool need_reload = false;
 
+    // conv_id -> model name that currently serves its stream session, lets the resumable stream
+    // routes go straight to the owning child instead of polling every one. populated when
+    // proxy_request forwards a POST carrying an X-Conversation-Id. best effort: a stale entry just
+    // makes the child answer not found and the client recovers. owns its lock, one mutex per struct
+    struct conv_model_tracker {
+        void remember(const std::string & conv_id, const std::string & model) {
+            if (conv_id.empty() || model.empty()) {
+                return;
+            }
+            std::lock_guard<std::mutex> lock(mu);
+            map[conv_id] = model;
+        }
+
+        std::optional<std::string> lookup(const std::string & conv_id) {
+            if (conv_id.empty()) {
+                return std::nullopt;
+            }
+            std::lock_guard<std::mutex> lock(mu);
+            auto it = map.find(conv_id);
+            if (it == map.end()) {
+                return std::nullopt;
+            }
+            return it->second;
+        }
+
+        void forget(const std::string & conv_id) {
+            if (conv_id.empty()) {
+                return;
+            }
+            std::lock_guard<std::mutex> lock(mu);
+            map.erase(conv_id);
+        }
+
+      private:
+        std::mutex                                   mu;
+        std::unordered_map<std::string, std::string> map;
+    };
+
     common_preset_context ctx_preset;
 
     common_params base_params;
@@ -145,6 +186,9 @@ private:
     void notify_sse(const std::string & event, const std::string & model_id, const json & data = nullptr);
 
 public:
+    // conv_id -> model tracker for the resumable stream routes, owns its lock
+    conv_model_tracker conv_models;
+
     server_models(const common_params & params, int argc, char ** argv);
 
     server_response sse; // for real-time updates via SSE endpoint
@@ -268,6 +312,12 @@ struct server_models_routes {
     server_http_context::handler_t get_router_models_sse;
     server_http_context::handler_t post_router_models;
     server_http_context::handler_t del_router_models;
+
+    // router side handlers for the resumable streaming routes. each resolves the child that owns
+    // a conversation through the conv_id -> model map, no probing or fan out
+    server_http_context::handler_t router_stream_get;
+    server_http_context::handler_t router_streams_lookup;
+    server_http_context::handler_t router_stream_delete;
 };
 
 /**
diff --git a/tools/server/server-stream.cpp b/tools/server/server-stream.cpp
new file mode 100644
index 000000000..757c36ad2
--- /dev/null
+++ b/tools/server/server-stream.cpp
@@ -0,0 +1,569 @@
+#include "server-stream.h"
+#include "server-common.h"
+#include "server-http.h"
+#include "server-queue.h"
+
+#include <chrono>
+#include <memory>
+#include <utility>
+
+namespace {
+constexpr int64_t STREAM_SESSION_TTL_SECONDS         = 300;
+constexpr size_t  STREAM_SESSION_MAX_BYTES           = 4 * 1024 * 1024;
+constexpr int64_t STREAM_SESSION_GC_INTERVAL_SECONDS = 60;
+constexpr int64_t STREAM_READ_WAKE_INTERVAL_MS       = 200;
+
+// returns unix time in seconds
+int64_t now_seconds() {
+    return std::chrono::duration_cast<std::chrono::seconds>(
+        std::chrono::system_clock::now().time_since_epoch()
+    ).count();
+}
+}
+
+stream_session::stream_session(std::string conversation_id_, size_t max_bytes_)
+    : conversation_id(std::move(conversation_id_))
+    , started_ts(now_seconds())
+    , prefix_dropped(0)
+    , cap_bytes(max_bytes_)
+    , done(false)
+    , cancelled(false)
+    , completed_ts(0) {
+    buffer.reserve(64 * 1024);
+}
+
+bool stream_session::append(const char * data, size_t len) {
+    if (len == 0) {
+        return true;
+    }
+    {
+        std::lock_guard<std::mutex> lock(mu);
+        if (done.load(std::memory_order_relaxed)) {
+            return false;
+        }
+        if (len >= cap_bytes) {
+            // single chunk bigger than the cap, keep only the tail that fits
+            size_t skip = len - cap_bytes;
+            prefix_dropped += buffer.size() + skip;
+            buffer.clear();
+            buffer.insert(buffer.end(), data + skip, data + len);
+        } else {
+            size_t needed = buffer.size() + len;
+            if (needed > cap_bytes) {
+                size_t to_drop = needed - cap_bytes;
+                buffer.erase(buffer.begin(), buffer.begin() + to_drop);
+                prefix_dropped += to_drop;
+            }
+            buffer.insert(buffer.end(), data, data + len);
+        }
+    }
+    cv.notify_all();
+    return true;
+}
+
+void stream_session::finalize() {
+    bool was_done = done.exchange(true, std::memory_order_acq_rel);
+    if (was_done) {
+        return;
+    }
+    completed_ts.store(now_seconds(), std::memory_order_release);
+    cv.notify_all();
+}
+
+stream_read_status stream_session::read_from(size_t offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop) {
+    std::unique_lock<std::mutex> lock(mu);
+    while (true) {
+        if (should_stop && should_stop()) {
+            return stream_read_status::OK;
+        }
+        if (offset < prefix_dropped) {
+            return stream_read_status::OFFSET_LOST;
+        }
+        size_t logical_end = prefix_dropped + buffer.size();
+        if (offset < logical_end) {
+            size_t local_off = offset - prefix_dropped;
+            size_t n         = buffer.size() - local_off;
+            // copy the available chunk under the lock, release before calling the sink
+            std::vector<char> chunk(buffer.begin() + local_off, buffer.begin() + local_off + n);
+            offset += n;
+            lock.unlock();
+            bool keep_going = sink(chunk.data(), chunk.size());
+            if (!keep_going) {
+                return stream_read_status::OK;
+            }
+            lock.lock();
+            continue;
+        }
+        if (done.load(std::memory_order_acquire)) {
+            return stream_read_status::OK;
+        }
+        // wait for new bytes, finalize, or a periodic wake to re check should_stop
+        cv.wait_for(lock, std::chrono::milliseconds(STREAM_READ_WAKE_INTERVAL_MS));
+    }
+}
+
+bool stream_session::is_done() const {
+    return done.load(std::memory_order_acquire);
+}
+
+size_t stream_session::total_size() const {
+    std::lock_guard<std::mutex> lock(mu);
+    return prefix_dropped + buffer.size();
+}
+
+size_t stream_session::dropped_prefix() const {
+    std::lock_guard<std::mutex> lock(mu);
+    return prefix_dropped;
+}
+
+int64_t stream_session::completed_at() const {
+    return completed_ts.load(std::memory_order_acquire);
+}
+
+void stream_session::set_stop_producer(std::function<void()> fn) {
+    std::lock_guard<std::mutex> lock(mu);
+    stop_producer = std::move(fn);
+}
+
+void stream_session::cancel() {
+    // flip cancelled first so the producer-side stream_aware_should_stop can break out of the
+    // recv() wait even if remove_waiting_task_ids does not notify the condvar (the cancel task
+    // posted by rd.stop() will eventually notify, but we do not want to depend on that timing)
+    cancelled.store(true, std::memory_order_release);
+    // copy the hook under the lock then invoke outside, the producer side may grab queue locks
+    // and we do not want to hold our mu across that path
+    std::function<void()> fn;
+    {
+        std::lock_guard<std::mutex> lock(mu);
+        fn = stop_producer;
+    }
+    if (fn) {
+        fn();
+    }
+}
+
+bool stream_session::is_cancelled() const {
+    return cancelled.load(std::memory_order_acquire);
+}
+
+stream_session_manager::stream_session_manager()
+    : running(false) {
+}
+
+stream_session_manager::~stream_session_manager() {
+    stop_gc();
+}
+
+stream_session_ptr stream_session_manager::create_or_replace(const std::string & conversation_id) {
+    // evict any previous session on the same conv, this guarantees the invariant
+    // "one conv = at most one live session" and propagates cancel to its producer
+    stream_session_ptr previous;
+    auto fresh = std::make_shared<stream_session>(conversation_id, STREAM_SESSION_MAX_BYTES);
+    {
+        std::unique_lock<std::shared_mutex> lock(map_mu);
+        auto it = sessions.find(conversation_id);
+        if (it != sessions.end()) {
+            previous = it->second;
+            it->second = fresh;
+        } else {
+            sessions.emplace(conversation_id, fresh);
+        }
+    }
+    if (previous) {
+        previous->cancel();
+        previous->finalize();
+    }
+    return fresh;
+}
+
+stream_session_ptr stream_session_manager::get(const std::string & conversation_id) {
+    std::shared_lock<std::shared_mutex> lock(map_mu);
+    auto it = sessions.find(conversation_id);
+    if (it == sessions.end()) {
+        return nullptr;
+    }
+    return it->second;
+}
+
+std::vector<stream_session_ptr> stream_session_manager::list_all() const {
+    std::vector<stream_session_ptr> out;
+    std::shared_lock<std::shared_mutex> lock(map_mu);
+    out.reserve(sessions.size());
+    for (auto & kv : sessions) {
+        out.push_back(kv.second);
+    }
+    return out;
+}
+
+void stream_session_manager::evict(const std::string & conversation_id) {
+    stream_session_ptr s;
+    {
+        std::unique_lock<std::shared_mutex> lock(map_mu);
+        auto it = sessions.find(conversation_id);
+        if (it == sessions.end()) {
+            return;
+        }
+        s = it->second;
+        sessions.erase(it);
+    }
+    // finalize outside the map lock so any pending readers wake up and exit
+    s->finalize();
+}
+
+void stream_session_manager::evict_and_cancel(const std::string & conversation_id) {
+    stream_session_ptr s;
+    {
+        std::unique_lock<std::shared_mutex> lock(map_mu);
+        auto it = sessions.find(conversation_id);
+        if (it == sessions.end()) {
+            return;
+        }
+        s = it->second;
+        sessions.erase(it);
+    }
+    // signal the producer side first so the inference is cancelled at the queue level,
+    // then finalize, which wakes any pending HTTP reader and lets the drain exit naturally
+    s->cancel();
+    s->finalize();
+}
+
+void stream_session_manager::start_gc() {
+    if (running.exchange(true)) {
+        return;
+    }
+    gc_thread = std::thread([this] { gc_loop(); });
+}
+
+void stream_session_manager::stop_gc() {
+    bool was_running = running.exchange(false);
+    if (was_running) {
+        {
+            std::lock_guard<std::mutex> lock(gc_wake_mu);
+        }
+        gc_wake_cv.notify_all();
+        if (gc_thread.joinable()) {
+            gc_thread.join();
+        }
+    }
+    // finalize all live sessions so no reader ever hangs
+    std::vector<stream_session_ptr> snapshot;
+    {
+        std::unique_lock<std::shared_mutex> lock(map_mu);
+        snapshot.reserve(sessions.size());
+        for (auto & kv : sessions) {
+            snapshot.push_back(kv.second);
+        }
+        sessions.clear();
+    }
+    for (auto & s : snapshot) {
+        s->finalize();
+    }
+}
+
+void stream_session_manager::gc_loop() {
+    while (running.load(std::memory_order_acquire)) {
+        {
+            std::unique_lock<std::mutex> lock(gc_wake_mu);
+            gc_wake_cv.wait_for(lock,
+                std::chrono::seconds(STREAM_SESSION_GC_INTERVAL_SECONDS),
+                [this] { return !running.load(std::memory_order_acquire); });
+        }
+        if (!running.load(std::memory_order_acquire)) {
+            return;
+        }
+        int64_t cutoff = now_seconds() - STREAM_SESSION_TTL_SECONDS;
+        std::vector<stream_session_ptr> to_drop;
+        {
+            std::unique_lock<std::shared_mutex> lock(map_mu);
+            for (auto it = sessions.begin(); it != sessions.end(); ) {
+                int64_t completed = it->second->completed_at();
+                if (completed != 0 && completed <= cutoff) {
+                    to_drop.push_back(it->second);
+                    it = sessions.erase(it);
+                } else {
+                    ++it;
+                }
+            }
+        }
+        // finalize outside the map lock, idempotent if the session was already done
+        for (auto & s : to_drop) {
+            s->finalize();
+        }
+    }
+}
+
+// process wide manager, lifecycle controlled by llama-server main() via start_gc/stop_gc
+stream_session_manager g_stream_sessions;
+
+// stream_pipe ---------------------------------------------------------------------------------
+
+stream_pipe::stream_pipe(stream_session_ptr session)
+    : session_(std::move(session)) {
+}
+
+bool stream_pipe::is_cancelled() const {
+    return session_->is_cancelled();
+}
+
+// stream_pipe_producer
+
+stream_pipe_producer::stream_pipe_producer(stream_session_ptr session)
+    : stream_pipe(std::move(session)) {
+}
+
+stream_pipe_producer::~stream_pipe_producer() {
+    cleanup();
+    session_->finalize();
+}
+
+void stream_pipe_producer::cleanup() {
+    if (!alive_) {
+        return;
+    }
+    alive_->store(false, std::memory_order_release);
+    session_->set_stop_producer(nullptr);
+    alive_.reset();
+}
+
+bool stream_pipe_producer::write(const char * data, size_t len) {
+    return session_->append(data, len);
+}
+
+void stream_pipe_producer::done() {
+    done_ = true;
+}
+
+void stream_pipe_producer::close() {
+    // httplib bails its content provider the moment is_peer_alive() goes false, so pump the rest
+    // of the generation into the ring buffer here. a DELETE flips is_cancelled and cuts it short
+    if (done_ || session_->is_cancelled()) {
+        SRV_INF("stream_pipe close: skip drain (done=%d cancelled=%d) conv=%s\n",
+                done_ ? 1 : 0, session_->is_cancelled() ? 1 : 0, session_->conversation_id.c_str());
+        return;
+    }
+    SRV_INF("stream_pipe close: draining conv=%s\n", session_->conversation_id.c_str());
+    size_t drained = 0;
+    std::string chunk;
+    while (true) {
+        chunk.clear();
+        bool has_next = res_->next(chunk);
+        if (!chunk.empty()) {
+            write(chunk.data(), chunk.size());
+            drained += chunk.size();
+        }
+        if (!has_next) {
+            break;
+        }
+    }
+    SRV_INF("stream_pipe close: drain ended conv=%s bytes=%zu\n", session_->conversation_id.c_str(), drained);
+}
+
+std::shared_ptr<stream_pipe_producer> stream_pipe_producer::create(stream_session_ptr session,
+                                                                   server_http_res & res) {
+    auto alive = std::make_shared<std::atomic<bool>>(true);
+    auto * res_ptr = &res;
+    session->set_stop_producer([alive, res_ptr]() {
+        if (alive->load(std::memory_order_acquire)) {
+            res_ptr->stop();
+        }
+    });
+    auto pipe = std::shared_ptr<stream_pipe_producer>(new stream_pipe_producer(std::move(session)));
+    pipe->alive_ = std::move(alive);
+    pipe->res_   = res_ptr;
+    return pipe;
+}
+
+// stream_pipe_consumer
+
+stream_pipe_consumer::stream_pipe_consumer(stream_session_ptr session)
+    : stream_pipe(std::move(session)) {
+}
+
+stream_read_status stream_pipe_consumer::read(size_t & offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop) {
+    return session_->read_from(offset, sink, should_stop);
+}
+
+std::shared_ptr<stream_pipe_consumer> stream_pipe_consumer::create(stream_session_ptr session) {
+    return std::shared_ptr<stream_pipe_consumer>(new stream_pipe_consumer(std::move(session)));
+}
+
+// helper, builds the standard error response and assigns it to a brand new http_res
+static server_http_res_ptr make_error_response(int status, const std::string & message, error_type type) {
+    auto res = std::make_unique<server_http_res>();
+    json err = format_error_response(message, type);
+    res->status = json_value(err, "code", status);
+    res->content_type = "application/json; charset=utf-8";
+    res->data = safe_json_to_str({{"error", err}});
+    return res;
+}
+
+server_http_context::handler_t make_stream_get_handler() {
+    return [](const server_http_req & req) -> server_http_res_ptr {
+        // GET /v1/stream/<conv_id>?from=N replays the SSE bytes already buffered for the
+        // session, blocks for more bytes when the session is still running, returns when
+        // the session is finalized. the body is streamed back as text/event-stream so the
+        // browser EventSource can attach to it like a fresh request
+        std::string conv_id = req.get_param("conv_id");
+        if (conv_id.empty()) {
+            return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
+        }
+        auto session = g_stream_sessions.get(conv_id);
+        if (!session) {
+            return make_error_response(404, "Stream not found or expired", ERROR_TYPE_NOT_FOUND);
+        }
+        size_t from = 0;
+        std::string from_str = req.get_param("from");
+        if (!from_str.empty()) {
+            try {
+                from = static_cast<size_t>(std::stoull(from_str));
+            } catch (const std::exception &) {
+                return make_error_response(400, "Invalid 'from' offset", ERROR_TYPE_INVALID_REQUEST);
+            }
+        }
+        if (from < session->dropped_prefix()) {
+            return make_error_response(400, "Stream offset lost, please restart", ERROR_TYPE_INVALID_REQUEST);
+        }
+        auto res = std::make_unique<server_http_res>();
+        res->status = 200;
+        res->content_type = "text/event-stream";
+        // the next closure reads from the ring buffer at the requested offset, blocks until
+        // bytes arrive or the session finalizes. exit each call after draining the available
+        // chunk so set_chunked_content_provider gets a chance to flush to the socket
+        auto offset_ptr = std::make_shared<size_t>(from);
+        // consumer pipe: read-only, does not finalize the session on destruction
+        auto pipe = stream_pipe_consumer::create(session);
+        res->next = [pipe, offset_ptr, &req](std::string & output) -> bool {
+            bool got_any = false;
+            pipe->read(*offset_ptr,
+                [&](const char * d, size_t n) {
+                    output.append(d, n);
+                    *offset_ptr += n;
+                    got_any = true;
+                    return false;
+                },
+                req.should_stop);
+            return got_any;
+        };
+        return res;
+    };
+}
+
+server_http_context::handler_t make_streams_lookup_handler() {
+    return [](const server_http_req & req) -> server_http_res_ptr {
+        // POST /v1/streams/lookup with body {"conversation_ids": ["X", "Y", ...]} returns the
+        // matching sessions, only for ids the caller already knows. each id matches the exact key
+        // and any "<id>::<model>" variant, so one lookup covers every per model session for a conv
+        std::vector<std::string> requested;
+        try {
+            json body = json::parse(req.body);
+            if (body.contains("conversation_ids") && body["conversation_ids"].is_array()) {
+                for (const auto & v : body["conversation_ids"]) {
+                    if (v.is_string()) {
+                        std::string id = v.get<std::string>();
+                        if (!id.empty()) {
+                            requested.push_back(std::move(id));
+                        }
+                    }
+                }
+            }
+        } catch (const std::exception & e) {
+            auto res = std::make_unique<server_http_res>();
+            res->status = 400;
+            res->content_type = "application/json; charset=utf-8";
+            res->data = safe_json_to_str({{"error", {{"message", std::string("invalid body: ") + e.what()},
+                                                     {"type", "invalid_request_error"}}}});
+            return res;
+        }
+
+        std::vector<stream_session_ptr> sessions;
+        if (!requested.empty()) {
+            auto all = g_stream_sessions.list_all();
+            for (const auto & rid : requested) {
+                const std::string with_sep = rid + "::";
+                for (auto & s : all) {
+                    if (s->conversation_id == rid ||
+                        s->conversation_id.compare(0, with_sep.size(), with_sep) == 0) {
+                        sessions.push_back(s);
+                    }
+                }
+            }
+        }
+
+        json arr = json::array();
+        for (auto & s : sessions) {
+            arr.push_back({
+                {"conversation_id", s->conversation_id},
+                {"is_done",         s->is_done()},
+                {"total_bytes",     s->total_size()},
+                {"started_at",      s->started_ts},
+                {"completed_at",    s->completed_at()},
+            });
+        }
+        auto res = std::make_unique<server_http_res>();
+        res->status = 200;
+        res->content_type = "application/json; charset=utf-8";
+        res->data = safe_json_to_str(arr);
+        return res;
+    };
+}
+
+server_http_context::handler_t make_stream_delete_handler() {
+    return [](const server_http_req & req) -> server_http_res_ptr {
+        // DELETE /v1/stream/<conv_id> is the explicit user Stop, cancels the producer hook
+        // wired by handle_completions_impl and evicts the buffer. idempotent, a session that
+        // already finalized or was never created returns 204 either way
+        std::string conv_id = req.get_param("conv_id");
+        if (conv_id.empty()) {
+            return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
+        }
+        SRV_INF("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str());
+        g_stream_sessions.evict_and_cancel(conv_id);
+        auto res = std::make_unique<server_http_res>();
+        res->status = 204;
+        res->content_type = "application/json";
+        return res;
+    };
+}
+
+std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers) {
+    // case-insensitive scan for x-conversation-id
+    static constexpr char   target[]   = "x-conversation-id";
+    static constexpr size_t target_len = sizeof(target) - 1;
+    for (const auto & [hk, hv] : headers) {
+        if (hk.size() != target_len) continue;
+        bool match = true;
+        for (size_t i = 0; i < target_len; ++i) {
+            char c = hk[i];
+            if (c >= 'A' && c <= 'Z') c = char(c + 32);
+            if (c != target[i]) { match = false; break; }
+        }
+        if (match) {
+            return hv;
+        }
+    }
+    return std::string();
+}
+
+void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
+    std::string conversation_id = stream_conv_id_from_headers(headers);
+    SRV_INF("stream_session_attach_pipe: conv_id=%s (empty=%d)\n",
+            conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
+    if (conversation_id.empty()) {
+        return;
+    }
+    auto session = g_stream_sessions.create_or_replace(conversation_id);
+    res.spipe = stream_pipe_producer::create(session, res);
+}
+
+std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback) {
+    return [res, fallback = std::move(fallback)]() -> bool {
+        if (res->spipe) {
+            return res->spipe->is_cancelled();
+        }
+        return fallback();
+    };
+}
diff --git a/tools/server/server-stream.h b/tools/server/server-stream.h
new file mode 100644
index 000000000..ff363bb4c
--- /dev/null
+++ b/tools/server/server-stream.h
@@ -0,0 +1,203 @@
+#pragma once
+
+#include "server-http.h"
+
+#include <atomic>
+#include <condition_variable>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <memory>
+#include <mutex>
+#include <shared_mutex>
+#include <string>
+#include <thread>
+#include <unordered_map>
+#include <vector>
+
+enum class stream_read_status {
+    OK,
+    OFFSET_LOST,
+};
+
+// streaming buffer for one generation, survives HTTP disconnect. the producer appends raw SSE
+// bytes, readers drain from any offset via read_from and block until more bytes or finalize.
+// keyed by conversation_id: one conv = at most one live session
+struct stream_session {
+    std::string conversation_id;
+    int64_t     started_ts; // unix seconds at construction, used by /v1/streams listing
+
+    stream_session(std::string conversation_id_, size_t max_bytes_);
+    stream_session(const stream_session &)             = delete;
+    stream_session & operator=(const stream_session &) = delete;
+
+    // append raw bytes, drops from the front if the cap is reached.
+    // returns false if the session is already finalized
+    bool append(const char * data, size_t len);
+
+    // mark the session as complete, wakes all pending readers
+    void finalize();
+
+    // drain bytes from offset, calling sink for each chunk. blocks until more
+    // bytes arrive or finalize is called. returns OK on clean exit, OFFSET_LOST
+    // if offset falls below the dropped prefix
+    stream_read_status read_from(size_t offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop);
+
+    bool    is_done() const;
+    bool    is_cancelled() const;
+    size_t  total_size() const;     // bytes that ever entered the session
+    size_t  dropped_prefix() const; // bytes evicted from the front due to cap
+    int64_t completed_at() const;   // 0 while alive, unix seconds after finalize
+
+    // attach the producer stop hook used to cancel its reader, pass an empty function to detach
+    void set_stop_producer(std::function<void()> fn);
+
+    // signal the producer to abort its inference asap via the stop hook, idempotent
+    void cancel();
+
+private:
+    mutable std::mutex      mu;
+    std::condition_variable cv;
+    std::vector<char>       buffer;
+    size_t                  prefix_dropped;
+    size_t                  cap_bytes;
+    std::atomic<bool>       done;
+    std::atomic<bool>       cancelled;
+    std::atomic<int64_t>    completed_ts;
+    std::function<void()>   stop_producer; // protected by mu
+};
+
+using stream_session_ptr = std::shared_ptr<stream_session>;
+
+// one end of a stream_session pipe. the base holds the session and the shared query, the
+// producer and consumer ends derive from it. virtual dtor so each end runs its own teardown:
+// the producer finalizes the session, the consumer leaves it untouched
+struct stream_pipe {
+    virtual ~stream_pipe() = default;
+
+    // true if the session was cancelled (e.g. via DELETE /v1/stream/<conv_id>)
+    bool is_cancelled() const;
+
+protected:
+    explicit stream_pipe(stream_session_ptr session);
+
+    stream_session_ptr session_;
+};
+
+// producer end: writes chunks into the ring buffer and owns the session lifetime, finalizing it
+// on destruction.
+//
+// lifetime safety: holds a shared_ptr<atomic<bool>> alive also captured by the session's
+// stop_producer hook. cleanup() sets alive=false and clears the hook; it must run while the
+// response the hook calls stop() on is still alive. ~server_res_generator() does this explicitly.
+struct stream_pipe_producer : stream_pipe {
+    ~stream_pipe_producer() override;
+
+    // append raw bytes to the session's ring buffer, returns false if already finalized
+    bool write(const char * data, size_t len);
+
+    // mark the natural end on the wire so a later close() is a no-op
+    void done();
+
+    // on a peer drop, pump the response next() into the ring buffer until done. runs on the http
+    // worker from on_complete, no-op after done() or cancel
+    void close();
+
+    // disarm the stop hook and drop the alive guard, must run while the response the hook
+    // references is still alive. idempotent, the destructor calls it too
+    void cleanup();
+
+    // res.stop() is invoked when the session is cancelled, the alive guard ensures stop() is not
+    // called after cleanup() has run
+    static std::shared_ptr<stream_pipe_producer> create(stream_session_ptr session, server_http_res & res);
+
+private:
+    explicit stream_pipe_producer(stream_session_ptr session);
+
+    bool                                done_ = false;
+    std::shared_ptr<std::atomic<bool>>  alive_;
+    server_http_res *                   res_ = nullptr;
+};
+
+// consumer end: read-only replay of the ring buffer, the destructor does not finalize the session
+struct stream_pipe_consumer : stream_pipe {
+    // drain bytes from offset, calling sink for each available chunk. blocks until more data
+    // arrives or the session finalizes. should_stop is polled, returns OFFSET_LOST if offset
+    // fell below the dropped prefix
+    stream_read_status read(size_t & offset,
+        const std::function<bool(const char *, size_t)> & sink,
+        const std::function<bool()> & should_stop);
+
+    static std::shared_ptr<stream_pipe_consumer> create(stream_session_ptr session);
+
+private:
+    explicit stream_pipe_consumer(stream_session_ptr session);
+};
+
+// owns all live sessions, runs a periodic GC to evict expired ones.
+// the map is keyed by conversation_id, so the invariant "one conv = at most one
+// live session" is enforced at the type level
+class stream_session_manager {
+public:
+    stream_session_manager();
+    ~stream_session_manager();
+
+    stream_session_manager(const stream_session_manager &)             = delete;
+    stream_session_manager & operator=(const stream_session_manager &) = delete;
+
+    // install a new session for this conversation, evicting and cancelling any previous one.
+    // the conversation_id must be non empty, the caller is responsible for that check.
+    // returns the new session
+    stream_session_ptr create_or_replace(const std::string & conversation_id);
+
+    // lookup, returns null if unknown or already evicted
+    stream_session_ptr get(const std::string & conversation_id);
+
+    // list every live or recently completed session, used by GET /v1/streams without filter
+    std::vector<stream_session_ptr> list_all() const;
+
+    // remove from the map and finalize, wakes any pending readers
+    void evict(const std::string & conversation_id);
+
+    // signal the producer to cancel asap then evict, used by the explicit user Stop path
+    void evict_and_cancel(const std::string & conversation_id);
+
+    void start_gc();
+    void stop_gc();
+
+private:
+    void gc_loop();
+
+    mutable std::shared_mutex                           map_mu;
+    std::unordered_map<std::string, stream_session_ptr> sessions; // key: conversation_id
+    std::thread                                         gc_thread;
+    std::atomic<bool>                                   running;
+    std::mutex                                          gc_wake_mu;
+    std::condition_variable                             gc_wake_cv;
+};
+
+// process wide manager, linked by both llama-server and llama-cli. llama-server main() drives
+// start_gc/stop_gc, llama-cli leaves it idle. the dtor calls stop_gc() unconditionally so exit
+// is safe whether or not the GC thread ran
+extern stream_session_manager g_stream_sessions;
+
+// route handler factories operating on g_stream_sessions, wired under /v1/stream/* by server.cpp.
+// keeps the resumable stream surface confined to server-stream
+server_http_context::handler_t make_stream_get_handler();
+server_http_context::handler_t make_streams_lookup_handler();
+server_http_context::handler_t make_stream_delete_handler();
+
+// extract the X-Conversation-Id header value (case-insensitive), empty when absent. exposed so
+// the router can track which child serves a forwarded POST
+std::string stream_conv_id_from_headers(const std::map<std::string, std::string> & headers);
+
+// on an X-Conversation-Id header, create or replace the session and attach a producer pipe to
+// res. no-op when absent, called from the server_res_generator constructor
+void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers);
+
+// should_stop closure that ignores peer disconnect when a pipe is attached, so only an explicit
+// DELETE stops the producer and generation keeps flowing into the ring buffer. without a pipe it
+// delegates to fallback, the legacy non-resumable flow
+std::function<bool()> stream_aware_should_stop(server_http_res * res, std::function<bool()> fallback);
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 0a1947faf..1bbc99d89 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -2,6 +2,7 @@
 #include "server-http.h"
 #include "server-models.h"
 #include "server-cors-proxy.h"
+#include "server-stream.h"
 #include "server-tools.h"
 
 #include "arg.h"
@@ -82,6 +83,10 @@ int llama_server(int argc, char ** argv) {
 
     common_init();
 
+    // start the stream session manager GC right after common init, before any HTTP route can
+    // touch it. lifecycle is symmetric, stop_gc() runs in clean_up() before backend free
+    g_stream_sessions.start_gc();
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
         return 1;
     }
@@ -239,6 +244,29 @@ int llama_server(int argc, char ** argv) {
     ctx_http.get ("/slots",                    ex_wrapper(routes.get_slots));
     ctx_http.post("/slots/:id_slot",           ex_wrapper(routes.post_slots));
 
+    // resumable streaming, the conversation_id is the session identity end to end. router and
+    // child wire different handlers under the same paths: a child binds the local g_stream_sessions
+    // backed factories, the router binds proxies that resolve the owning child through the
+    // conv_id -> model map
+    server_http_context::handler_t stream_get_h;
+    server_http_context::handler_t streams_lookup_h;
+    server_http_context::handler_t stream_delete_h;
+    if (is_router_server) {
+        stream_get_h     = models_routes->router_stream_get;
+        streams_lookup_h = models_routes->router_streams_lookup;
+        stream_delete_h  = models_routes->router_stream_delete;
+    } else {
+        stream_get_h     = make_stream_get_handler();
+        streams_lookup_h = make_streams_lookup_handler();
+        stream_delete_h  = make_stream_delete_handler();
+    }
+    ctx_http.get ("/v1/stream/:conv_id",       ex_wrapper(stream_get_h));
+    // POST /v1/streams/lookup with body {"conversation_ids": [...]}. you can only ask for ids
+    // you already own (the WebUI passes the convs visible in its sidebar). the server never
+    // lists ids it has not been asked about, so a random caller cannot enumerate live sessions
+    ctx_http.post("/v1/streams/lookup",        ex_wrapper(streams_lookup_h));
+    ctx_http.del ("/v1/stream/:conv_id",       ex_wrapper(stream_delete_h));
+
     // Google Cloud Platform (Vertex AI) compat
     ctx_http.register_gcp_compat();
 
@@ -314,6 +342,8 @@ int llama_server(int argc, char ** argv) {
 
         clean_up = [&models_routes]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
+            // stop the session GC first, it finalizes live sessions and wakes pending readers
+            g_stream_sessions.stop_gc();
             if (models_routes.has_value()) {
                 models_routes->stopping.store(true); // maybe redundant, but just to be safe
                 models_routes->models.unload_all();
@@ -340,6 +370,8 @@ int llama_server(int argc, char ** argv) {
         // setup clean up function, to be called before exit
         clean_up = [&ctx_http, &ctx_server]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
+            // stop the session GC first, it finalizes live sessions and wakes pending readers
+            g_stream_sessions.stop_gc();
             ctx_http.stop();
             ctx_server.terminate();
             llama_backend_free();
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
index 18635ba39..d2fba5a93 100644
--- a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte
@@ -5,6 +5,7 @@
 		ChatMessages,
 		ChatScreenDragOverlay,
 		ChatScreenProcessingInfo,
+		ChatScreenStreamResumeStatus,
 		ServerLoadingSplash,
 		ChatScreenServerError
 	} from '$lib/components/app';
@@ -281,6 +282,10 @@
 
 			<ChatScreenServerError />
 
+			{#if page.params.id}
+				<ChatScreenStreamResumeStatus />
+			{/if}
+
 			<div class="pointer-events-none flex flex-col gap-6 items-center w-full">
 				{#if (isMobile.current ? mobileScrollDownHint || isMobileUserScrolledUp : autoScroll.userScrolledUp) && page.url.hash.includes(ROUTES.CHAT) && page.params.id}
 					<ChatScreenActionScrollDown
diff --git a/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenStreamResumeStatus.svelte b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenStreamResumeStatus.svelte
new file mode 100644
index 000000000..b3abe4c66
--- /dev/null
+++ b/tools/ui/src/lib/components/app/chat/ChatScreen/ChatScreenStreamResumeStatus.svelte
@@ -0,0 +1,18 @@
+<script lang="ts">
+	import { chatStore } from '$lib/stores/chat.svelte';
+	import { StreamConnectionState } from '$lib/enums';
+	import { Loader2 } from '@lucide/svelte';
+
+	let state = $derived(chatStore.streamConnectionState);
+</script>
+
+{#if state === StreamConnectionState.RESUMING}
+	<div
+		class="pointer-events-auto mx-auto mt-2 mb-2 flex max-w-[48rem] items-center gap-2 rounded-md border border-blue-400/40 bg-blue-50/60 px-3 py-1.5 text-sm text-blue-700 dark:bg-blue-950/40 dark:text-blue-200"
+		role="status"
+		aria-live="polite"
+	>
+		<Loader2 class="h-3.5 w-3.5 animate-spin" />
+		<span>Reconnecting to the stream...</span>
+	</div>
+{/if}
diff --git a/tools/ui/src/lib/components/app/chat/index.ts b/tools/ui/src/lib/components/app/chat/index.ts
index 517f24d74..d7004d3ae 100644
--- a/tools/ui/src/lib/components/app/chat/index.ts
+++ b/tools/ui/src/lib/components/app/chat/index.ts
@@ -683,3 +683,11 @@ export { default as ChatScreenProcessingInfo } from './ChatScreen/ChatScreenProc
  * Rendered inside ChatScreen when `serverError` store has a value.
  */
 export { default as ChatScreenServerError } from './ChatScreen/ChatScreenServerError.svelte';
+
+/**
+ * Stream resume status indicator. Shows a small "Reconnecting to the stream..."
+ * banner with a spinner while `chatStore.streamConnectionState` is `resuming`,
+ * i.e. after a dropped connection is reattaching to the live SSE replay buffer.
+ * Renders nothing otherwise. Shown inside ChatScreen only on an active conversation route.
+ */
+export { default as ChatScreenStreamResumeStatus } from './ChatScreen/ChatScreenStreamResumeStatus.svelte';
diff --git a/tools/ui/src/lib/constants/api-endpoints.ts b/tools/ui/src/lib/constants/api-endpoints.ts
index a41090505..37137c1c7 100644
--- a/tools/ui/src/lib/constants/api-endpoints.ts
+++ b/tools/ui/src/lib/constants/api-endpoints.ts
@@ -21,5 +21,11 @@ export const API_TOOLS = {
 	EXECUTE: '/tools'
 };
 
+// resumable stream routes, the conv::model identity is appended as a path segment
+export const API_STREAM = {
+	BASE: './v1/stream',
+	LOOKUP: './v1/streams/lookup'
+};
+
 /** CORS proxy endpoint path */
 export const CORS_PROXY_ENDPOINT = '/cors-proxy';
diff --git a/tools/ui/src/lib/constants/index.ts b/tools/ui/src/lib/constants/index.ts
index 4993ab647..b982a5907 100644
--- a/tools/ui/src/lib/constants/index.ts
+++ b/tools/ui/src/lib/constants/index.ts
@@ -46,6 +46,7 @@ export * from './routes';
 export * from './sandbox';
 export * from './settings-keys';
 export * from './settings-registry';
+export * from './stream';
 export * from './supported-file-types';
 export * from './table-html-restorer';
 export * from './title-generation';
diff --git a/tools/ui/src/lib/constants/storage.ts b/tools/ui/src/lib/constants/storage.ts
index 8d425b96b..0180a76fb 100644
--- a/tools/ui/src/lib/constants/storage.ts
+++ b/tools/ui/src/lib/constants/storage.ts
@@ -26,6 +26,9 @@ export const THINKING_ENABLED_DEFAULT_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.th
 export const REASONING_EFFORT_DEFAULT_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.reasoningEffortDefault`;
 export const USER_OVERRIDES_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.userOverrides`;
 
+/** Key prefix for per-conversation resumable stream state, conversationId is appended */
+export const STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX = `${STORAGE_APP_NAME}.streamResume.`;
+
 // Deprecated old key names (kept for backward compat while users migrate)
 /** @deprecated Use {@link ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY} instead */
 export const DEPRECATED_ALWAYS_ALLOWED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.alwaysAllowedTools`;
diff --git a/tools/ui/src/lib/constants/stream.ts b/tools/ui/src/lib/constants/stream.ts
new file mode 100644
index 000000000..3d042451f
--- /dev/null
+++ b/tools/ui/src/lib/constants/stream.ts
@@ -0,0 +1,3 @@
+// grace window after a visibilitychange before we kick a reader whose socket likely died
+// while the tab was hidden. covers brief background pauses without thrashing live streams
+export const STREAM_VISIBILITY_KICK_MS = 1000;
diff --git a/tools/ui/src/lib/enums/chat.enums.ts b/tools/ui/src/lib/enums/chat.enums.ts
index 798fb1bf7..278e4af84 100644
--- a/tools/ui/src/lib/enums/chat.enums.ts
+++ b/tools/ui/src/lib/enums/chat.enums.ts
@@ -5,6 +5,15 @@ export enum ChatMessageStatsView {
 	SUMMARY = 'summary'
 }
 
+/**
+ * Connection state of a streamed completion, drives the resume status indicator.
+ */
+export enum StreamConnectionState {
+	STREAMING = 'streaming',
+	RESUMING = 'resuming',
+	LOST = 'lost'
+}
+
 /**
  * Reasoning format options for API requests.
  */
diff --git a/tools/ui/src/lib/enums/index.ts b/tools/ui/src/lib/enums/index.ts
index 811744fd9..d8e47958b 100644
--- a/tools/ui/src/lib/enums/index.ts
+++ b/tools/ui/src/lib/enums/index.ts
@@ -10,6 +10,7 @@ export { AgenticSectionType, ContinueIntentKind, ToolCallType } from './agentic.
 
 export {
 	ChatMessageStatsView,
+	StreamConnectionState,
 	ContentPartType,
 	ConversationSelectionMode,
 	ErrorDialogType,
diff --git a/tools/ui/src/lib/services/chat.service.ts b/tools/ui/src/lib/services/chat.service.ts
index 9001c9572..7dfee3773 100644
--- a/tools/ui/src/lib/services/chat.service.ts
+++ b/tools/ui/src/lib/services/chat.service.ts
@@ -1,6 +1,7 @@
-import { getJsonHeaders } from '$lib/utils/api-headers';
+import { getAuthHeaders, getJsonHeaders } from '$lib/utils/api-headers';
 import { formatAttachmentText } from '$lib/utils/formatters';
 import { isAbortError } from '$lib/utils/abort';
+import { streamIdentity } from '$lib/utils/stream-identity';
 import {
 	ATTACHMENT_LABEL_PDF_FILE,
 	ATTACHMENT_LABEL_MCP_PROMPT,
@@ -13,7 +14,10 @@ import {
 	CONTROL_ACTION,
 	SSE_LINE_SEPARATOR,
 	SSE_DATA_PREFIX,
-	SSE_DONE_MARKER
+	SSE_DONE_MARKER,
+	STREAM_VISIBILITY_KICK_MS,
+	STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX,
+	API_STREAM
 } from '$lib/constants';
 import {
 	AttachmentType,
@@ -21,12 +25,14 @@ import {
 	FileTypeAudio,
 	MessageRole,
 	MimeTypeAudio,
-	ReasoningFormat
+	ReasoningFormat,
+	StreamConnectionState
 } from '$lib/enums';
 import type {
 	ApiChatMessageContentPart,
 	ApiChatMessageData,
-	ApiChatCompletionToolCall
+	ApiChatCompletionToolCall,
+	ApiStreamSession
 } from '$lib/types/api';
 import type {
 	AudioInputFormat,
@@ -54,6 +60,19 @@ function getAudioInputFormat(mimeType: string): AudioInputFormat {
 	return FileTypeAudio.MP3;
 }
 
+interface ResumableStreamState {
+	bytesReceived: number;
+	updatedAt: number;
+
+	// model frozen at POST time, lets a reload rebuild the exact conv::model identity the
+	// server keyed the session under. null when the POST carried no explicit model
+	model?: string | null;
+}
+
+function streamStorageKey(conversationId: string): string {
+	return STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX + conversationId;
+}
+
 export class ChatService {
 	/**
 	 *
@@ -128,6 +147,7 @@ export class ChatService {
 			onChunk,
 			onComplete,
 			onError,
+			onConnectionState,
 			onReasoningChunk,
 			onToolCallChunk,
 			onModel,
@@ -312,9 +332,16 @@ export class ChatService {
 		}
 
 		try {
+			const headers: Record<string, string> = { ...getJsonHeaders() };
+			// tag streaming requests with the conversation id, this single header is the opt in for the
+			// server side replay buffer and powers discoverActiveStream on tab reopen. with an explicit
+			// model the ::model suffix keeps the per model session distinct
+			if (stream && conversationId) {
+				headers['X-Conversation-Id'] = streamIdentity(conversationId, options.model);
+			}
 			const response = await fetch(API_CHAT.COMPLETIONS, {
 				method: 'POST',
-				headers: getJsonHeaders(),
+				headers,
 				body: JSON.stringify(requestBody),
 				signal
 			});
@@ -341,7 +368,9 @@ export class ChatService {
 					onCompletionId,
 					onTimings,
 					conversationId,
-					signal
+					signal,
+					onConnectionState,
+					options.model
 				);
 
 				return;
@@ -473,6 +502,116 @@ export class ChatService {
 	 * @param excludeReasoning - Whether to strip reasoning content (should match excludeReasoningFromContext setting)
 	 * @param signal - Optional AbortSignal to cancel the pre-encode request
 	 */
+	static async cancelServerStream(conversationId: string, model?: string | null): Promise<void> {
+		if (!conversationId) return;
+		try {
+			const id = streamIdentity(conversationId, model);
+			await fetch(`${API_STREAM.BASE}/${encodeURIComponent(id)}`, {
+				method: 'DELETE',
+				headers: getAuthHeaders()
+			});
+		} catch (e) {
+			console.warn('cancelServerStream failed:', e);
+		}
+	}
+
+	/**
+	 * Pick the running session to splice into when discoverActiveStream lists candidates for a
+	 * conversation. Finalized sessions are not candidates: their final content was already written
+	 * to the DB by the original onComplete handler, so attaching to them would replay a buffer that
+	 * may not match what the DB holds. A continue session's buffer holds only the appended deltas,
+	 * not the pre continue prefix, so replaying it as a fresh generation would erase the original.
+	 *
+	 * Among running sessions we tie break on the most recent started_at, which covers the case of
+	 * multiple inferences left running on the same conversation.
+	 */
+	static selectActiveStream(
+		sessions: ApiStreamSession[] | null | undefined
+	): ApiStreamSession | null {
+		if (!Array.isArray(sessions) || sessions.length === 0) {
+			return null;
+		}
+		const running = sessions.filter((s) => !s.is_done);
+		if (running.length === 0) {
+			return null;
+		}
+		return running.reduce((best, cur) => (cur.started_at > best.started_at ? cur : best));
+	}
+
+	// persist the running byte count and the frozen model for a conversation, a later visit
+	// resumes the SSE replay at the right offset under the same conv::model identity
+	static saveStreamState(
+		conversationId: string,
+		bytesReceived: number,
+		model?: string | null
+	): void {
+		if (!conversationId) return;
+		try {
+			const state: ResumableStreamState = {
+				bytesReceived,
+				updatedAt: Date.now(),
+				model: model ?? null
+			};
+			localStorage.setItem(streamStorageKey(conversationId), JSON.stringify(state));
+		} catch {
+			// localStorage may be full or disabled, silently ignore
+		}
+	}
+
+	static getStreamState(conversationId: string): ResumableStreamState | null {
+		if (!conversationId) return null;
+		try {
+			const raw = localStorage.getItem(streamStorageKey(conversationId));
+			if (!raw) return null;
+			const parsed = JSON.parse(raw) as ResumableStreamState;
+			if (!parsed || typeof parsed.bytesReceived !== 'number') return null;
+			return parsed;
+		} catch {
+			return null;
+		}
+	}
+
+	static clearStreamState(conversationId: string): void {
+		if (!conversationId) return;
+		try {
+			localStorage.removeItem(streamStorageKey(conversationId));
+		} catch {
+			// nothing to do
+		}
+	}
+
+	/**
+	 * Rebuild the stream identity for a resume. The model persisted at POST time wins, including a
+	 * stored null which means the POST carried no explicit model so the identity stays the bare conv
+	 * id. Only fall back to the caller supplied current model when nothing was persisted.
+	 */
+	static resumeStreamIdentity(
+		conversationId: string,
+		state: ResumableStreamState | null,
+		fallbackModel: string | null
+	): string {
+		const model = state && state.model !== undefined ? state.model : fallbackModel;
+		return streamIdentity(conversationId, model);
+	}
+
+	/**
+	 * Reconnect to an interrupted stream for this conversation. Returns the fetch Response so the
+	 * existing SSE parser drains it like a fresh stream. The server returns 200 on success, 404 if
+	 * no session exists for the conv_id, and 400 if the offset is below the dropped prefix.
+	 */
+	static async resumeStream(
+		conversationId: string,
+		signal?: AbortSignal,
+		model?: string | null
+	): Promise<Response | null> {
+		if (!conversationId) return null;
+		const state = ChatService.getStreamState(conversationId);
+		const from = state?.bytesReceived ?? 0;
+		const id = streamIdentity(conversationId, model);
+		const url = `${API_STREAM.BASE}/${encodeURIComponent(id)}?from=${from}`;
+		return await fetch(url, { method: 'GET', signal, headers: getAuthHeaders() });
+	}
+
 	static async preEncode(
 		messages: ApiChatMessageData[] | (DatabaseMessage & { extra?: DatabaseMessageExtra[] })[],
 		model?: string | null,
@@ -557,7 +696,7 @@ export class ChatService {
 	 * @returns {Promise<void>} Promise that resolves when streaming is complete
 	 * @throws {Error} if the stream cannot be read or parsed
 	 */
-	private static async handleStreamResponse(
+	static async handleStreamResponse(
 		response: Response,
 		onChunk?: (chunk: string) => void,
 		onComplete?: (
@@ -573,15 +712,34 @@ export class ChatService {
 		onCompletionId?: (id: string) => void,
 		onTimings?: (timings?: ChatMessageTimings, promptProgress?: ChatMessagePromptProgress) => void,
 		conversationId?: string,
-		abortSignal?: AbortSignal
+		abortSignal?: AbortSignal,
+		onConnectionState?: (state: StreamConnectionState) => void,
+		streamModel?: string | null
 	): Promise<void> {
-		const reader = response.body?.getReader();
+		let reader = response.body?.getReader();
 
 		if (!reader) {
 			throw new Error('No response body');
 		}
 
-		const decoder = new TextDecoder();
+		// bytesParsed is the absolute server side buffer offset of the next byte to parse
+		// segmentStartOffset is the absolute offset where the current reader started, reset on resume
+		// segmentBytesRead is wire bytes read by the current reader
+		let bytesParsed = 0;
+		let segmentStartOffset = 0;
+		let segmentBytesRead = 0;
+		let lastByteAt = Date.now();
+		// each resume must produce at least one byte to be retried again
+		// if a resume returns 200 but yields nothing, we abandon
+		// since the session has a bounded size, the total number of retries is bounded by construction
+		let madeProgress = true;
+		const encoder = new TextEncoder();
+		if (conversationId) {
+			ChatService.saveStreamState(conversationId, 0, streamModel);
+		}
+		onConnectionState?.(StreamConnectionState.STREAMING);
+
+		let decoder = new TextDecoder();
 		let aggregatedContent = '';
 		let fullReasoningContent = '';
 		let aggregatedToolCalls: ApiChatCompletionToolCall[] = [];
@@ -633,84 +791,180 @@ export class ChatService {
 			}
 		};
 
+		const onVisibilityChange = () => {
+			if (typeof document === 'undefined') return;
+			if (document.visibilityState !== 'visible') return;
+			if (streamFinished) return;
+			if (!conversationId) return;
+			// the bytes have been quiet for too long, the OS likely killed the socket
+			// kicking the reader unblocks reader.read with done=true so the outer loop can resume
+			if (Date.now() - lastByteAt > STREAM_VISIBILITY_KICK_MS) {
+				reader!.cancel().catch(() => {});
+			}
+		};
+		if (typeof document !== 'undefined') {
+			document.addEventListener('visibilitychange', onVisibilityChange);
+		}
+
 		try {
 			let chunk = '';
+			// outer loop drives the resume cycle, swaps reader on premature end of stream
 			while (true) {
-				if (abortSignal?.aborted) break;
-
-				const { done, value } = await reader.read();
-				if (done) break;
-
-				if (abortSignal?.aborted) break;
-
-				chunk += decoder.decode(value, { stream: true });
-				const lines = chunk.split(SSE_LINE_SEPARATOR);
-				chunk = lines.pop() || '';
-
-				for (const line of lines) {
+				while (true) {
 					if (abortSignal?.aborted) break;
 
-					if (line.startsWith(SSE_DATA_PREFIX)) {
-						const data = line.slice(SSE_DATA_PREFIX.length).trim();
-						if (data === SSE_DONE_MARKER) {
-							streamFinished = true;
-
-							continue;
+					let done: boolean;
+					let value: Uint8Array | undefined;
+					try {
+						const r = await reader.read();
+						done = r.done;
+						value = r.value;
+					} catch (readErr) {
+						// reader.read() rejects with TypeError when the underlying connection drops
+						// instead of just resolving with done=true. treat it like done so the outer
+						// loop swaps reader via the resume path
+						if (isAbortError(readErr)) {
+							throw readErr;
 						}
+						console.warn('reader.read() rejected, treating as premature end:', readErr);
+						done = true;
+						value = undefined;
+					}
+					if (done) break;
 
-						try {
-							const parsed: ApiChatCompletionStreamChunk = JSON.parse(data);
-							const choice = parsed.choices?.[0];
-							const content = choice?.delta?.content;
-							const reasoningContent = choice?.delta?.reasoning_content;
-							const toolCalls = choice?.delta?.tool_calls;
-							const timings = parsed.timings;
-							const promptProgress = parsed.prompt_progress;
+					if (abortSignal?.aborted) break;
 
-							const chunkModel = ChatService.extractModelName(parsed);
-							if (chunkModel && !modelEmitted) {
-								modelEmitted = true;
-								onModel?.(chunkModel);
-							}
-
-							if (parsed.id && !idEmitted) {
-								idEmitted = true;
-								onCompletionId?.(parsed.id);
-							}
-
-							if (promptProgress) {
-								ChatService.notifyTimings(undefined, promptProgress, onTimings);
-							}
-
-							if (timings) {
-								ChatService.notifyTimings(timings, promptProgress, onTimings);
-								lastTimings = timings;
-							}
-
-							if (content) {
-								finalizeOpenToolCallBatch();
-								aggregatedContent += content;
-								if (!abortSignal?.aborted) {
-									onChunk?.(content);
-								}
-							}
-
-							if (reasoningContent) {
-								finalizeOpenToolCallBatch();
-								fullReasoningContent += reasoningContent;
-								if (!abortSignal?.aborted) {
-									onReasoningChunk?.(reasoningContent);
-								}
-							}
-
-							processToolCallDelta(toolCalls);
-						} catch (e) {
-							console.error('Error parsing JSON chunk:', e);
+					if (value && value.byteLength > 0) {
+						segmentBytesRead += value.byteLength;
+						lastByteAt = Date.now();
+						if (!madeProgress) {
+							madeProgress = true;
+							onConnectionState?.(StreamConnectionState.STREAMING);
 						}
 					}
+
+					chunk += decoder.decode(value, { stream: true });
+					const lines = chunk.split(SSE_LINE_SEPARATOR);
+					chunk = lines.pop() || '';
+
+					// the persisted offset must point right after the last fully parsed line,
+					// the trailing `chunk` is partial bytes still waiting for a newline
+					if (conversationId) {
+						const tailBytes = encoder.encode(chunk).byteLength;
+						bytesParsed = segmentStartOffset + segmentBytesRead - tailBytes;
+						ChatService.saveStreamState(conversationId, bytesParsed, streamModel);
+					}
+
+					for (const line of lines) {
+						if (abortSignal?.aborted) break;
+
+						if (line.startsWith(SSE_DATA_PREFIX)) {
+							const data = line.slice(SSE_DATA_PREFIX.length).trim();
+							if (data === SSE_DONE_MARKER) {
+								streamFinished = true;
+
+								continue;
+							}
+
+							try {
+								const parsed: ApiChatCompletionStreamChunk = JSON.parse(data);
+								const choice = parsed.choices?.[0];
+								const content = choice?.delta?.content;
+								const reasoningContent = choice?.delta?.reasoning_content;
+								const toolCalls = choice?.delta?.tool_calls;
+								const timings = parsed.timings;
+								const promptProgress = parsed.prompt_progress;
+
+								const chunkModel = ChatService.extractModelName(parsed);
+								if (chunkModel && !modelEmitted) {
+									modelEmitted = true;
+									onModel?.(chunkModel);
+								}
+
+								if (parsed.id && !idEmitted) {
+									idEmitted = true;
+									onCompletionId?.(parsed.id);
+								}
+
+								if (promptProgress) {
+									ChatService.notifyTimings(undefined, promptProgress, onTimings);
+								}
+
+								if (timings) {
+									ChatService.notifyTimings(timings, promptProgress, onTimings);
+									lastTimings = timings;
+								}
+
+								if (content) {
+									finalizeOpenToolCallBatch();
+									aggregatedContent += content;
+									if (!abortSignal?.aborted) {
+										onChunk?.(content);
+									}
+								}
+
+								if (reasoningContent) {
+									finalizeOpenToolCallBatch();
+									fullReasoningContent += reasoningContent;
+									if (!abortSignal?.aborted) {
+										onReasoningChunk?.(reasoningContent);
+									}
+								}
+
+								processToolCallDelta(toolCalls);
+							} catch (e) {
+								console.error('Error parsing JSON chunk:', e);
+							}
+						}
+					}
+
+					if (abortSignal?.aborted) break;
+					if (streamFinished) break;
 				}
 
+				// inner reader done, decide whether to try a resume
 				if (abortSignal?.aborted) break;
+				if (streamFinished) break;
+				if (!conversationId) break;
+
+				if (!madeProgress) {
+					onConnectionState?.(StreamConnectionState.LOST);
+					onError?.(new Error('Stream resume produced no new bytes, giving up'));
+					break;
+				}
+
+				onConnectionState?.(StreamConnectionState.RESUMING);
+				madeProgress = false;
+
+				// the server resends starting at bytesParsed, discard any partial line we held, it
+				// will be retransmitted from a clean line boundary. reuse the frozen model, not the
+				// live dropdown
+				const resumeResp = await ChatService.resumeStream(
+					conversationId,
+					abortSignal,
+					streamModel
+				).catch(() => null);
+				// an abort landing during the resume request is intentional, not a lost connection
+				if (abortSignal?.aborted) break;
+				if (!resumeResp || resumeResp.status !== 200) {
+					onConnectionState?.(StreamConnectionState.LOST);
+					onError?.(new Error('Stream connection lost and could not be resumed'));
+					break;
+				}
+				const newReader = resumeResp.body?.getReader();
+				if (!newReader) break;
+
+				try {
+					reader.releaseLock();
+				} catch {
+					/* ignore */
+				}
+				reader = newReader;
+				decoder = new TextDecoder();
+				chunk = '';
+				segmentStartOffset = bytesParsed;
+				segmentBytesRead = 0;
+				lastByteAt = Date.now();
 			}
 
 			if (abortSignal?.aborted) return;
@@ -718,6 +972,10 @@ export class ChatService {
 			if (streamFinished) {
 				finalizeOpenToolCallBatch();
 
+				if (conversationId) {
+					ChatService.clearStreamState(conversationId);
+				}
+
 				const finalToolCalls =
 					aggregatedToolCalls.length > 0 ? JSON.stringify(aggregatedToolCalls) : undefined;
 
@@ -735,7 +993,14 @@ export class ChatService {
 
 			throw err;
 		} finally {
-			reader.releaseLock();
+			if (typeof document !== 'undefined') {
+				document.removeEventListener('visibilitychange', onVisibilityChange);
+			}
+			try {
+				reader.releaseLock();
+			} catch {
+				/* ignore */
+			}
 		}
 	}
 
diff --git a/tools/ui/src/lib/services/mcp.service.ts b/tools/ui/src/lib/services/mcp.service.ts
index 90de0d5d8..443298917 100644
--- a/tools/ui/src/lib/services/mcp.service.ts
+++ b/tools/ui/src/lib/services/mcp.service.ts
@@ -628,19 +628,20 @@ export class MCPService {
 		);
 
 		const runtimeErrorHandler = (error: Error) => {
-			// Ignore errors that are expected when the SDK's transport is closed,
-			// or when connecting to servers that don't support SSE (stateless-only
-			// endpoints returning 405). The SDK wraps the original AbortError in
-			// a new Error with the message "SSE stream disconnected: AbortError",
-			// and also produces "Cannot cancel a stream locked by a reader".
-			// DOMException is thrown by the browser when aborting fetch requests.
-			const msg = error.message || String(error);
+			// the SDK reports any post initialize error here, including the abort we trigger
+			// ourselves on the next health check cycle, on tab unload, or on server teardown.
+			// these are lifecycle aborts, not actionable errors, so we keep them out of the red console.
+			// the SDK wraps the original AbortError in a generic Error like
+			//   "SSE stream disconnected: AbortError: The operation was aborted."
+			// which isAbortError cannot recognize by name alone, so we also pattern match on the message
+			if (isAbortError(error)) {
+				return;
+			}
+			const msg = error?.message ?? '';
 			if (
-				error.name === 'AbortError' ||
-				error instanceof DOMException ||
-				msg.includes('SSE stream disconnected') ||
-				msg.includes('stream locked by a reader') ||
-				msg.includes('The operation was aborted')
+				/SSE stream disconnected:.*AbortError/i.test(msg) ||
+				/AbortError: .*aborted/i.test(msg) ||
+				/stream locked by a reader/i.test(msg)
 			) {
 				return;
 			}
diff --git a/tools/ui/src/lib/stores/agentic.svelte.ts b/tools/ui/src/lib/stores/agentic.svelte.ts
index 5579cc1e5..27491257a 100644
--- a/tools/ui/src/lib/stores/agentic.svelte.ts
+++ b/tools/ui/src/lib/stores/agentic.svelte.ts
@@ -614,7 +614,7 @@ class AgenticStore {
 							throw error;
 						}
 					},
-					undefined,
+					conversationId,
 					signal
 				);
 
diff --git a/tools/ui/src/lib/stores/chat.svelte.ts b/tools/ui/src/lib/stores/chat.svelte.ts
index b899130e5..faaaa9755 100644
--- a/tools/ui/src/lib/stores/chat.svelte.ts
+++ b/tools/ui/src/lib/stores/chat.svelte.ts
@@ -11,9 +11,11 @@
  * @see ChatService in services/chat.service.ts for API operations
  */
 
-import { SvelteMap } from 'svelte/reactivity';
+import { SvelteMap, SvelteSet } from 'svelte/reactivity';
 import { DatabaseService } from '$lib/services/database.service';
 import { ChatService } from '$lib/services/chat.service';
+import { streamIdentity } from '$lib/utils/stream-identity';
+import { getAuthHeaders } from '$lib/utils/api-headers';
 import { conversationsStore } from '$lib/stores/conversations.svelte';
 import { config } from '$lib/stores/settings.svelte';
 import { agenticStore } from '$lib/stores/agentic.svelte';
@@ -49,10 +51,17 @@ import type {
 import type {
 	ApiChatMessageData,
 	ApiProcessingState,
+	ApiStreamSession,
 	DatabaseMessage,
 	DatabaseMessageExtra
 } from '$lib/types';
-import { ContinueIntentKind, ErrorDialogType, MessageRole, MessageType } from '$lib/enums';
+import {
+	ContinueIntentKind,
+	ErrorDialogType,
+	MessageRole,
+	MessageType,
+	StreamConnectionState
+} from '$lib/enums';
 
 interface ConversationStateEntry {
 	lastAccessed: number;
@@ -65,9 +74,25 @@ class ChatStore {
 	isLoading = $state(false);
 	// true while the active conversation streams reasoning content but no visible content yet
 	isReasoning = $state(false);
+	// resumable stream connection state for the active conversation
+	// streaming -> bytes flowing normally, resuming -> waiting on /v1/stream/:id reconnect, lost -> unrecoverable
+	streamConnectionState = $state<StreamConnectionState>(StreamConnectionState.STREAMING);
 	chatLoadingStates = new SvelteMap<string, boolean>();
 	chatReasoningStates = new SvelteMap<string, boolean>();
-	chatStreamingStates = new SvelteMap<string, { response: string; messageId: string }>();
+	chatStreamingStates = new SvelteMap<
+		string,
+		{ response: string; messageId: string; model?: string | null }
+	>();
+	// convs that the backend reports as having a running session, populated by the global sync
+	// at app mount and on visibilitychange. it does not overlap with chatLoadingStates which
+	// tracks inferences driven by this browser, both are unioned to feed the sidebar spinners
+	private remoteRunningConvs = new SvelteSet<string>();
+	// per conv attach lifecycle, used to derive the global streaming flag without flipping it
+	// off when one conv finishes while another is still streaming. mirrors chatLoadingStates
+	// in scope but tracks the attach + tee replay path specifically
+	private attachingConvs = new SvelteSet<string>();
+	// in-flight discoverActiveStream guard, keyed by conv id
+	private discoveringConvs = new SvelteSet<string>();
 	private abortControllers = new SvelteMap<string, AbortController>();
 	private preEncodeAbortController: AbortController | null = null;
 	private processingStates = new SvelteMap<string, ApiProcessingState | null>();
@@ -98,6 +123,11 @@ class ChatStore {
 			this.chatLoadingStates.delete(convId);
 			if (convId === conversationsStore.activeConversation?.id) this.isLoading = false;
 			this.setChatReasoning(convId, false);
+			// the local pipe is the authoritative observer of session end: when it finishes (clean
+			// onComplete or explicit Stop), the backend session is finalized too, so we drop the
+			// sidebar hint for this conv right away instead of waiting for the next visibilitychange
+			// snapshot. without this the spinner ghosts until the user toggles the tab
+			this.remoteRunningConvs.delete(convId);
 		}
 	}
 
@@ -110,9 +140,18 @@ class ChatStore {
 			if (convId === conversationsStore.activeConversation?.id) this.isReasoning = false;
 		}
 	}
-	private setChatStreaming(convId: string, response: string, messageId: string): void {
+	private setChatStreaming(
+		convId: string,
+		response: string,
+		messageId: string,
+		model?: string | null
+	): void {
 		this.touchConversationState(convId);
-		this.chatStreamingStates.set(convId, { response, messageId });
+		this.chatStreamingStates.set(convId, {
+			response,
+			messageId,
+			model: model ?? this.chatStreamingStates.get(convId)?.model
+		});
 		if (convId === conversationsStore.activeConversation?.id) this.currentResponse = response;
 	}
 	private clearChatStreaming(convId: string): void {
@@ -137,6 +176,314 @@ class ChatStore {
 			}
 		}
 	}
+	/**
+	 * Server side stream discovery, split in three pieces:
+	 *
+	 * probeServerStream(convId) -> hits POST /v1/streams/lookup with the conv id, returns the session to attach
+	 *   to or null. Pure read, no side effect, no UI lock. Safe to fire in parallel with anything.
+	 *
+	 * attachServerStream(convId) -> flips the spinner immediately, fetches the replay stream
+	 *   from byte 0, finds the assistant slot to splice into (creates a placeholder if the conv has
+	 *   no assistant message yet, for cross device or fresh local DB cases), and pipes the SSE bytes
+	 *   into the message via handleStreamResponse.
+	 *
+	 * discoverActiveStream(convId) -> probe + attach in one call. Used by callers that do not need
+	 *   to overlap the probe with other async work.
+	 *
+	 * The mount of the chat page in +page.svelte calls probeServerStream in parallel with
+	 * loadConversation, then attachServerStream once both have settled. This gives the earliest
+	 * possible time to spinner and avoids racing against an empty activeMessages array.
+	 */
+	async probeServerStream(convId: string): Promise<ApiStreamSession | null> {
+		if (!convId) return null;
+		let listResp: Response;
+		try {
+			// POST the one conv id we are probing
+			listResp = await fetch(`./v1/streams/lookup`, {
+				method: 'POST',
+				headers: { ...getAuthHeaders(), 'Content-Type': 'application/json' },
+				body: JSON.stringify({ conversation_ids: [convId] })
+			});
+		} catch (e) {
+			console.warn('probeServerStream fetch failed:', e);
+			return null;
+		}
+		if (!listResp.ok) {
+			console.warn(`probeServerStream got HTTP ${listResp.status} for conv ${convId}`);
+			return null;
+		}
+		let sessions: ApiStreamSession[];
+		try {
+			sessions = (await listResp.json()) as ApiStreamSession[];
+		} catch (e) {
+			console.warn('probeServerStream JSON parse failed:', e);
+			return null;
+		}
+		return ChatService.selectActiveStream(sessions);
+	}
+
+	async attachServerStream(convId: string, streamId?: string): Promise<void> {
+		if (!convId) return;
+		if (this.chatStreamingStates.has(convId)) return;
+
+		// flip the spinner immediately, the user sees activity as soon as the conv becomes active.
+		// the global isStreamingActive flag is derived from attachingConvs.size, so adding here
+		// turns it on, and removing in unlock only turns it off when this is the last attach
+		this.setChatLoading(convId, true);
+		this.attachingConvs.add(convId);
+		this.setStreamingActive(true);
+		// only set the active processing conv if we are looking at it, otherwise a background
+		// attach would steal the indicator from the conv the user is currently viewing
+		if (convId === conversationsStore.activeConversation?.id) {
+			this.setActiveProcessingConversation(convId);
+		}
+
+		const unlock = () => {
+			this.attachingConvs.delete(convId);
+			// flip the global flag off only when no other conv is still attaching
+			if (this.attachingConvs.size === 0) {
+				this.setStreamingActive(false);
+			}
+			this.setChatLoading(convId, false);
+			this.clearChatStreaming(convId);
+		};
+
+		// fetch the replay stream from byte 0, rebuild the assistant message from scratch.
+		// resolve the server side identity, fall back to streamIdentity when the caller does not
+		// pass a streamId. probeServerStream returns the full id (with ::model suffix when present)
+		const id = streamId || streamIdentity(convId, selectedModelName());
+		let response: Response;
+		try {
+			response = await fetch(`./v1/stream/${encodeURIComponent(id)}?from=0`, {
+				headers: getAuthHeaders()
+			});
+		} catch (e) {
+			console.error('attachServerStream replay fetch failed:', e);
+			unlock();
+			return;
+		}
+		if (!response.ok) {
+			console.warn(`attachServerStream replay got HTTP ${response.status} for conv ${convId}`);
+			unlock();
+			return;
+		}
+
+		// load the target conversation messages by id, not via the active store. when multiple
+		// attaches run in parallel the active store may reflect another conv and writing through
+		// its index mixes content across convs (CoT flicker, message bleed). by going through the
+		// DB we stay isolated, and only mirror into the active store when the attached conv is
+		// the one currently displayed
+		let messages: DatabaseMessage[];
+		try {
+			messages = await DatabaseService.getConversationMessages(convId);
+		} catch (e) {
+			console.error('attachServerStream load messages failed:', e);
+			unlock();
+			return;
+		}
+
+		// locate the slot to splice into, create a placeholder assistant message if there is none.
+		// we use the conv-scoped findLastAssistantIdx helpers, they only depend on the array
+		let targetIdx = this.findLastAssistantIdx(messages);
+		if (targetIdx === -1) {
+			const lastUserIdx = this.findLastUserIdx(messages);
+			if (lastUserIdx === -1) {
+				console.warn(
+					`attachServerStream: conv ${convId} has no user or assistant message, cannot splice`
+				);
+				unlock();
+				return;
+			}
+			try {
+				const placeholder = await DatabaseService.createMessageBranch(
+					{
+						convId,
+						role: MessageRole.ASSISTANT,
+						content: '',
+						type: MessageType.TEXT,
+						timestamp: Date.now(),
+						parent: messages[lastUserIdx].id,
+						children: [],
+						toolCalls: ''
+					} as Omit<DatabaseMessage, 'id'>,
+					messages[lastUserIdx].id
+				);
+				messages = [...messages, placeholder];
+				targetIdx = messages.length - 1;
+				// only push into the active store when this conv is the one displayed right now
+				if (convId === conversationsStore.activeConversation?.id) {
+					conversationsStore.addMessageToActive(placeholder);
+				}
+			} catch (e) {
+				console.error('attachServerStream placeholder creation failed:', e);
+				unlock();
+				return;
+			}
+		}
+		if (targetIdx === -1) {
+			unlock();
+			return;
+		}
+		const targetMessage = messages[targetIdx];
+		const targetMessageId = targetMessage.id;
+		// when the assistant slot already has content, the running session is a continue or
+		// another append flow and its buffer holds only the appended deltas. preserve the prefix
+		// and let the replay add to it. when the slot is empty the session buffer holds the whole
+		// message so we wipe and rebuild from byte 0
+		const existingContent = targetMessage.content ?? '';
+		const existingReasoning = targetMessage.reasoningContent ?? '';
+		const isAppendMode = existingContent.length > 0;
+
+		// helper: write to the active store only when the attached conv is currently displayed.
+		// the lookup by message id is robust to reordering of activeMessages, two parallel attaches
+		// can no longer step on each other's indices
+		const writeActive = (updates: Partial<DatabaseMessage>) => {
+			if (convId !== conversationsStore.activeConversation?.id) {
+				return;
+			}
+			const liveIdx = conversationsStore.findMessageIndex(targetMessageId);
+			if (liveIdx === -1) return;
+			conversationsStore.updateMessageAtIndex(liveIdx, updates);
+		};
+
+		if (!isAppendMode) {
+			writeActive({ content: '', reasoningContent: undefined });
+		}
+
+		// extract the model suffix, the resume calls in handleStreamResponse must reuse the model
+		// the session was tagged with, not the live dropdown
+		const sepIdx = id.indexOf('::');
+		const attachedModel: string | null = sepIdx === -1 ? null : id.slice(sepIdx + 2);
+		this.setChatStreaming(convId, existingContent, targetMessageId, attachedModel);
+		const abortController = this.getOrCreateAbortController(convId);
+
+		let streamedContent = '';
+		let streamedReasoningContent = '';
+
+		const cleanup = () => {
+			unlock();
+			this.setProcessingState(convId, null);
+		};
+
+		try {
+			await ChatService.handleStreamResponse(
+				response,
+				(chunk: string) => {
+					streamedContent += chunk;
+					const displayed = isAppendMode ? existingContent + streamedContent : streamedContent;
+					writeActive({ content: displayed });
+					this.setChatStreaming(convId, displayed, targetMessageId);
+				},
+				async (
+					finalContent?: string,
+					reasoningContent?: string,
+					timings?: ChatMessageTimings,
+					toolCalls?: string
+				) => {
+					const streamed = streamedContent || finalContent || '';
+					const streamedR = streamedReasoningContent || reasoningContent || '';
+					const content = isAppendMode ? existingContent + streamed : streamed;
+					const reasoning = isAppendMode ? existingReasoning + streamedR : streamedR;
+					// the DB write is the source of truth, mirror to the active store only when
+					// the conv is currently displayed
+					await DatabaseService.updateMessage(targetMessageId, {
+						content,
+						reasoningContent: reasoning || undefined,
+						toolCalls: toolCalls || '',
+						timings
+					});
+					writeActive({
+						content,
+						reasoningContent: reasoning || undefined,
+						timings
+					});
+					cleanup();
+				},
+				(err: Error) => {
+					console.error('attachServerStream pipe error:', err);
+					cleanup();
+				},
+				(chunk: string) => {
+					streamedReasoningContent += chunk;
+					const displayed = isAppendMode
+						? existingReasoning + streamedReasoningContent
+						: streamedReasoningContent;
+					writeActive({ reasoningContent: displayed });
+				},
+				undefined,
+				undefined,
+				undefined,
+				undefined,
+				convId,
+				abortController.signal,
+				(connState: StreamConnectionState) => {
+					if (convId === conversationsStore.activeConversation?.id) {
+						this.streamConnectionState = connState;
+					}
+				},
+				attachedModel
+			);
+		} catch (e) {
+			console.error('attachServerStream pipe crashed:', e);
+			cleanup();
+		}
+	}
+
+	async discoverActiveStream(convId: string): Promise<void> {
+		if (!convId) return;
+		if (this.chatStreamingStates.has(convId)) return;
+		if (this.chatLoadingStates.get(convId)) return;
+		// concurrency guard: another discover may already be running for this conv (typical race
+		// between mount and visibilitychange on tab switch). a second concurrent fetch on the same
+		// /v1/stream/<id> would duplicate every byte into the DB message, this guard bounces it
+		if (this.discoveringConvs.has(convId)) return;
+		this.discoveringConvs.add(convId);
+
+		try {
+			// the model is frozen at POST time, rebuild the exact conv::model identity from the
+			// persisted state so the lookup key matches what the server stored. null means a single
+			// model conv with no ::suffix, only guess from the dropdown with no persisted state
+			const localState = ChatService.getStreamState(convId);
+			const streamId = ChatService.resumeStreamIdentity(convId, localState, selectedModelName());
+
+			// primary path: ask the server which sessions exist for this identity
+			const serverTarget = await this.probeServerStream(streamId);
+			if (serverTarget) {
+				// pass the full server side identity (may carry a ::model suffix) so the GET routes
+				// straight to the owning session, no probe or fan out
+				await this.attachServerStream(convId, serverTarget.conversation_id);
+				return;
+			}
+
+			// fallback: local state remembers an interrupted byte offset for this conv, the server may
+			// still have a live session matching that identity (we just lost the bytes mid stream). retry
+			// with the frozen identity, the server probe inside attachServerStream tells us if it exists
+			if (!localState) {
+				return;
+			}
+			await this.attachServerStream(convId, streamId);
+			// if attachServerStream failed (session gone, TTL expired), clear the local state to avoid retrying forever
+			if (!this.chatStreamingStates.has(convId) && !this.chatLoadingStates.get(convId)) {
+				ChatService.clearStreamState(convId);
+			}
+		} finally {
+			this.discoveringConvs.delete(convId);
+		}
+	}
+
+	private findLastAssistantIdx(messages: DatabaseMessage[]): number {
+		for (let i = messages.length - 1; i >= 0; i--) {
+			if (messages[i].role === MessageRole.ASSISTANT) return i;
+		}
+		return -1;
+	}
+
+	private findLastUserIdx(messages: DatabaseMessage[]): number {
+		for (let i = messages.length - 1; i >= 0; i--) {
+			if (messages[i].role === MessageRole.USER) return i;
+		}
+		return -1;
+	}
 
 	clearUIState(): void {
 		this.isLoading = false;
@@ -265,13 +612,83 @@ class ChatStore {
 	}
 
 	getAllLoadingChats(): string[] {
-		return Array.from(this.chatLoadingStates.keys());
+		// union of local (this browser is piping) and remote (backend reports a running session
+		// for this conv but no local pipe yet) sources. the sidebar shows one spinner per entry
+		const out = new SvelteSet<string>(this.chatLoadingStates.keys());
+		for (const id of this.remoteRunningConvs) {
+			out.add(id);
+		}
+		return Array.from(out);
 	}
 
 	getAllStreamingChats(): string[] {
 		return Array.from(this.chatStreamingStates.keys());
 	}
 
+	/**
+	 * Resync the remote running convs set from the backend. Called by the layout at mount and on
+	 * visibilitychange, no polling. A snapshot semantic: the set is replaced wholesale, stale entries
+	 * for sessions that finalized while the browser was elsewhere are dropped naturally.
+	 */
+	async syncRemoteRunningStreams(): Promise<void> {
+		// the conversations store loads from IndexedDB asynchronously, the +layout onMount caller
+		// fires before that finishes. read ids straight from the DB so the result does not depend
+		// on the store init race, and the sidebar spinners light up at first paint for every conv
+		// the user owns even if it has not been hydrated into the store yet
+		let ids: string[];
+		try {
+			const all = await DatabaseService.getAllConversations();
+			ids = all.map((c) => c.id).filter((id) => !!id);
+		} catch (e) {
+			console.warn('syncRemoteRunningStreams DB read failed:', e);
+			return;
+		}
+		// only ask about conv ids the user already owns
+		if (ids.length === 0) {
+			for (const id of Array.from(this.remoteRunningConvs)) {
+				this.remoteRunningConvs.delete(id);
+			}
+			return;
+		}
+		// rebuild the frozen conv::model identity per conv so a session started with a model still
+		// matches. the server response is mapped back to the bare id below for the sidebar set
+		const lookupIds = ids.map((id) =>
+			ChatService.resumeStreamIdentity(id, ChatService.getStreamState(id), null)
+		);
+		let sessions: ApiStreamSession[];
+		try {
+			const resp = await fetch('./v1/streams/lookup', {
+				method: 'POST',
+				headers: { ...getAuthHeaders(), 'Content-Type': 'application/json' },
+				body: JSON.stringify({ conversation_ids: lookupIds })
+			});
+			if (!resp.ok) return;
+			const body = (await resp.json()) as unknown;
+			if (!Array.isArray(body)) return;
+			sessions = body as ApiStreamSession[];
+		} catch (e) {
+			console.warn('syncRemoteRunningStreams fetch failed:', e);
+			return;
+		}
+		const running = new SvelteSet<string>();
+		for (const s of sessions) {
+			if (s && !s.is_done && typeof s.conversation_id === 'string' && s.conversation_id) {
+				// strip the optional ::model suffix, the sidebar set is keyed by the bare conv id
+				const sepIdx = s.conversation_id.indexOf('::');
+				const bareId = sepIdx === -1 ? s.conversation_id : s.conversation_id.slice(0, sepIdx);
+				running.add(bareId);
+			}
+		}
+		for (const id of Array.from(this.remoteRunningConvs)) {
+			if (!running.has(id)) {
+				this.remoteRunningConvs.delete(id);
+			}
+		}
+		for (const id of running) {
+			this.remoteRunningConvs.add(id);
+		}
+	}
+
 	getChatStreamingPublic(convId: string): { response: string; messageId: string } | undefined {
 		return this.getChatStreaming(convId);
 	}
@@ -922,6 +1339,11 @@ class ChatStore {
 				onModel: streamCallbacks.onModel,
 				onCompletionId: streamCallbacks.onCompletionId,
 				onTimings: streamCallbacks.onTimings,
+				onConnectionState: (state: StreamConnectionState) => {
+					if (convId === conversationsStore.activeConversation?.id) {
+						this.streamConnectionState = state;
+					}
+				},
 				onComplete: async (
 					finalContent?: string,
 					reasoningContent?: string,
@@ -979,6 +1401,12 @@ class ChatStore {
 	async stopGenerationForChat(convId: string): Promise<void> {
 		await this.savePartialResponseIfNeeded(convId);
 		this.setStreamingActive(false);
+		// tell the server to stop the generation, not just drop the HTTP socket. without this the
+		// detached drain keeps producing tokens until eos or max_tokens. use the frozen identity
+		// captured when the session started, not the live dropdown
+		const streamStateForStop = this.chatStreamingStates.get(convId);
+		const modelForStop = streamStateForStop?.model ?? selectedModelName();
+		void ChatService.cancelServerStream(convId, modelForStop);
 		this.abortRequest(convId);
 		this.setChatLoading(convId, false);
 		this.clearChatStreaming(convId);
@@ -1393,7 +1821,11 @@ class ChatStore {
 
 			const updateStreamingContent = (fullContent: string) => {
 				this.setChatStreaming(msg.convId, fullContent, msg.id);
-				conversationsStore.updateMessageAtIndex(idx, { content: fullContent });
+				// resolve the row by id on every write, switching to another conv mid continue makes
+				// this a no op instead of writing positionally into the now displayed conversation
+				conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(msg.id), {
+					content: fullContent
+				});
 			};
 
 			const abortController = this.getOrCreateAbortController(msg.convId);
@@ -1403,6 +1835,11 @@ class ChatStore {
 				{
 					...this.getApiOptions(),
 					continueFinalMessage: true,
+					onConnectionState: (state: StreamConnectionState) => {
+						if (msg.convId === conversationsStore.activeConversation?.id) {
+							this.streamConnectionState = state;
+						}
+					},
 					onChunk: (chunk: string) => {
 						appendedContent += chunk;
 						hasReceivedContent = true;
@@ -1414,7 +1851,7 @@ class ChatStore {
 						hasReceivedContent = true;
 						// mark streaming state so a stop mid-thinking can persist the partial reasoning
 						this.setChatStreaming(msg.convId, originalContent + appendedContent, msg.id);
-						conversationsStore.updateMessageAtIndex(idx, {
+						conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(msg.id), {
 							reasoningContent: originalReasoning + appendedReasoning
 						});
 						this.setChatReasoning(msg.convId, true);
@@ -1455,7 +1892,7 @@ class ChatStore {
 							timings
 						});
 
-						conversationsStore.updateMessageAtIndex(idx, {
+						conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(msg.id), {
 							content: fullContent,
 							reasoningContent: fullReasoning,
 							timestamp: Date.now(),
@@ -1477,11 +1914,14 @@ class ChatStore {
 									timestamp: Date.now()
 								});
 
-								conversationsStore.updateMessageAtIndex(idx, {
-									content: originalContent + appendedContent,
-									reasoningContent: originalReasoning + appendedReasoning || undefined,
-									timestamp: Date.now()
-								});
+								conversationsStore.updateMessageAtIndex(
+									conversationsStore.findMessageIndex(msg.id),
+									{
+										content: originalContent + appendedContent,
+										reasoningContent: originalReasoning + appendedReasoning || undefined,
+										timestamp: Date.now()
+									}
+								);
 							}
 
 							this.setChatLoading(msg.convId, false);
@@ -1498,7 +1938,7 @@ class ChatStore {
 							reasoningContent: originalReasoning + appendedReasoning || undefined,
 							timestamp: Date.now()
 						});
-						conversationsStore.updateMessageAtIndex(idx, {
+						conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(msg.id), {
 							content: originalContent + appendedContent,
 							reasoningContent: originalReasoning + appendedReasoning || undefined,
 							timestamp: Date.now()
diff --git a/tools/ui/src/lib/types/api.d.ts b/tools/ui/src/lib/types/api.d.ts
index 2a2524d00..ec695ac61 100644
--- a/tools/ui/src/lib/types/api.d.ts
+++ b/tools/ui/src/lib/types/api.d.ts
@@ -512,3 +512,18 @@ export interface ApiRouterModelsUnloadResponse {
 	success: boolean;
 	error?: string;
 }
+
+/**
+ * Entry returned by POST /v1/streams/lookup. The client passes the conv ids it owns in the body
+ * and the server returns one entry per matching live or recently completed background streaming
+ * session, keyed by conversation_id. The WebUI uses this at mount and on visibilitychange to
+ * populate sidebar spinners and to reattach to an ongoing inference for the active conversation.
+ * The server never lists ids the client did not ask about, so foreign random UUIDs stay private.
+ */
+export interface ApiStreamSession {
+	conversation_id: string;
+	is_done: boolean;
+	total_bytes: number;
+	started_at: number;
+	completed_at: number;
+}
diff --git a/tools/ui/src/lib/types/index.ts b/tools/ui/src/lib/types/index.ts
index 9b0b11804..cbe0538be 100644
--- a/tools/ui/src/lib/types/index.ts
+++ b/tools/ui/src/lib/types/index.ts
@@ -34,7 +34,8 @@ export type {
 	ApiRouterModelsListResponse,
 	ApiRouterModelsUnloadRequest,
 	ApiRouterModelsUnloadResponse,
-	AudioInputFormat
+	AudioInputFormat,
+	ApiStreamSession
 } from './api';
 
 // Chat types
diff --git a/tools/ui/src/lib/types/settings.d.ts b/tools/ui/src/lib/types/settings.d.ts
index d1cdca957..9c787cd95 100644
--- a/tools/ui/src/lib/types/settings.d.ts
+++ b/tools/ui/src/lib/types/settings.d.ts
@@ -4,9 +4,10 @@ import type { OpenAIToolDefinition } from './mcp';
 import type { DatabaseMessageExtra } from './database';
 import type {
 	ParameterSource,
-	ReasoningEffort,
 	SyncableParameterType,
-	SettingsFieldType
+	SettingsFieldType,
+	StreamConnectionState,
+	ReasoningEffort
 } from '$lib/enums';
 import type { Icon } from '@lucide/svelte';
 import type { Component } from 'svelte';
@@ -119,6 +120,7 @@ export interface SettingsChatServiceOptions {
 		toolCalls?: string
 	) => void;
 	onError?: (error: Error) => void;
+	onConnectionState?: (state: StreamConnectionState) => void;
 }
 
 export type SettingsConfigType = typeof SETTING_CONFIG_DEFAULT & {
diff --git a/tools/ui/src/lib/utils/abort.ts b/tools/ui/src/lib/utils/abort.ts
index fc4f31ec6..135ef087a 100644
--- a/tools/ui/src/lib/utils/abort.ts
+++ b/tools/ui/src/lib/utils/abort.ts
@@ -6,6 +6,17 @@
  * when needed (e.g., user stops generation, navigates away, etc.).
  */
 
+// the standard DOMException name for a cancelled operation
+const ABORT_ERROR_NAME = 'AbortError';
+
+// browser specific TypeError messages emitted when a fetch reader is cut by page unload,
+// navigation, or a transient network drop. functionally aborts, not actionable errors
+const ABORT_LIKE_MESSAGE_PATTERNS = [
+	/input stream/i, // Firefox: stream cut at unload
+	/network connection was lost/i, // Safari: transient network drop
+	/load failed/i // Safari: page navigation during fetch
+];
+
 /**
  * Throws an AbortError if the signal is aborted.
  * Use this at the start of async operations to fail fast.
@@ -23,7 +34,7 @@
  */
 export function throwIfAborted(signal?: AbortSignal): void {
 	if (signal?.aborted) {
-		throw new DOMException('Operation was aborted', 'AbortError');
+		throw new DOMException('Operation was aborted', ABORT_ERROR_NAME);
 	}
 }
 
@@ -48,11 +59,18 @@ export function throwIfAborted(signal?: AbortSignal): void {
  * ```
  */
 export function isAbortError(error: unknown): boolean {
-	if (error instanceof DOMException && error.name === 'AbortError') {
+	if (error instanceof DOMException && error.name === ABORT_ERROR_NAME) {
 		return true;
 	}
-	if (error instanceof Error && error.name === 'AbortError') {
-		return true;
+	if (error instanceof Error) {
+		if (error.name === ABORT_ERROR_NAME) {
+			return true;
+		}
+		// these patterns are functionally aborts, keep them out of the red console
+		if (error instanceof TypeError) {
+			const msg = error.message ?? '';
+			if (ABORT_LIKE_MESSAGE_PATTERNS.some((re) => re.test(msg))) return true;
+		}
 	}
 	return false;
 }
@@ -133,7 +151,7 @@ export async function withAbortSignal<T>(promise: Promise<T>, signal?: AbortSign
 
 	return new Promise<T>((resolve, reject) => {
 		const abortHandler = () => {
-			reject(new DOMException('Operation was aborted', 'AbortError'));
+			reject(new DOMException('Operation was aborted', ABORT_ERROR_NAME));
 		};
 
 		signal.addEventListener('abort', abortHandler, { once: true });
diff --git a/tools/ui/src/lib/utils/stream-identity.ts b/tools/ui/src/lib/utils/stream-identity.ts
new file mode 100644
index 000000000..ce88df007
--- /dev/null
+++ b/tools/ui/src/lib/utils/stream-identity.ts
@@ -0,0 +1,13 @@
+/**
+ * Build the conversation identity used by the server side replay buffer.
+ *
+ * The server identifies a stream session by a conversation id sent in the
+ * X-Conversation-Id header. When the user has explicitly picked a model the
+ * client appends ::modelName, so a per model session stays distinct and the
+ * router resolves the owning child through its conv_id -> model map.
+ */
+export function streamIdentity(conversationId: string, model?: string | null): string {
+	if (!conversationId) return '';
+	if (!model) return conversationId;
+	return `${conversationId}::${model}`;
+}
diff --git a/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte b/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
index e31d4443e..f14553c90 100644
--- a/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
+++ b/tools/ui/src/routes/(chat)/chat/[id]/+page.svelte
@@ -4,7 +4,7 @@
 	import { afterNavigate } from '$app/navigation';
 	import { DialogModelNotAvailable } from '$lib/components/app';
 	import { APP_NAME, ROUTES } from '$lib/constants';
-	import { chatStore, isLoading } from '$lib/stores/chat.svelte';
+	import { chatStore } from '$lib/stores/chat.svelte';
 	import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte';
 	import { modelsStore, modelOptions } from '$lib/stores/models.svelte';
 
@@ -83,7 +83,7 @@
 
 			// Skip loading if this conversation is already active (e.g., just created)
 			if (activeConversation()?.id === chatId) {
-				// Still handle URL params even if conversation is active
+				void chatStore.discoverActiveStream(chatId);
 				if ((qParam !== null || modelParam !== null) && !urlParamsProcessed) {
 					handleUrlParams();
 				}
@@ -92,35 +92,33 @@
 
 			(async () => {
 				const success = await conversationsStore.loadConversation(chatId);
-				if (success) {
-					chatStore.syncLoadingStateForChat(chatId);
-
-					// Handle URL params after conversation is loaded
-					if ((qParam !== null || modelParam !== null) && !urlParamsProcessed) {
-						await handleUrlParams();
-					}
-				} else {
+				if (!success) {
 					await goto(ROUTES.START);
+					return;
+				}
+				chatStore.syncLoadingStateForChat(chatId);
+				// server probe (with localStorage fallback) and attach
+				await chatStore.discoverActiveStream(chatId);
+
+				if ((qParam !== null || modelParam !== null) && !urlParamsProcessed) {
+					await handleUrlParams();
 				}
 			})();
 		}
 	});
 
 	$effect(() => {
-		if (typeof window !== 'undefined') {
-			const handleBeforeUnload = () => {
-				if (isLoading()) {
-					console.log('Page unload detected while streaming - aborting stream');
-					chatStore.stopGeneration();
-				}
-			};
+		if (typeof window === 'undefined' || typeof document === 'undefined') return;
 
-			window.addEventListener('beforeunload', handleBeforeUnload);
-
-			return () => {
-				window.removeEventListener('beforeunload', handleBeforeUnload);
-			};
-		}
+		// when the tab comes back to the foreground, re-run discovery to catch any race
+		// where the initial mount probe missed an active session
+		const onVisibility = () => {
+			if (document.visibilityState !== 'visible') return;
+			if (!chatId) return;
+			void chatStore.discoverActiveStream(chatId);
+		};
+		document.addEventListener('visibilitychange', onVisibility);
+		return () => document.removeEventListener('visibilitychange', onVisibility);
 	});
 </script>
 
diff --git a/tools/ui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
index 339b067dd..38848786e 100644
--- a/tools/ui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -11,6 +11,7 @@
 	import { PwaMetaTags, PwaRefreshAlert } from '$lib/components/pwa';
 	import { pwaAssetsHead } from 'virtual:pwa-assets/head';
 
+	import { chatStore } from '$lib/stores/chat.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { isRouterMode, serverStore } from '$lib/stores/server.svelte';
@@ -154,8 +155,18 @@
 
 	onMount(() => {
 		updateFavicon();
+		// snapshot of every backend running stream on first load, populates the sidebar spinners
+		// so the user sees each conv that has a live inference, even ones not opened yet
+		void chatStore.syncRemoteRunningStreams();
 	});
 
+	// refresh that snapshot when the tab returns to the foreground, a stream may have advanced
+	// or ended while it was hidden. snapshot only, no polling
+	function handleVisibilityChange() {
+		if (document.visibilityState !== 'visible') return;
+		void chatStore.syncRemoteRunningStreams();
+	}
+
 	$effect(() => {
 		void theme.isSystemDark;
 
@@ -280,6 +291,7 @@
 </svelte:head>
 
 <svelte:window onkeydown={handleKeydown} bind:innerHeight bind:innerWidth />
+<svelte:document onvisibilitychange={handleVisibilityChange} />
 
 <Tooltip.Provider delayDuration={TOOLTIP_DELAY_DURATION}>
 	<div class="flex flex-col md:flex-row">
diff --git a/tools/ui/tests/unit/abort.test.ts b/tools/ui/tests/unit/abort.test.ts
new file mode 100644
index 000000000..306e71ca1
--- /dev/null
+++ b/tools/ui/tests/unit/abort.test.ts
@@ -0,0 +1,56 @@
+import { describe, expect, it } from 'vitest';
+import { isAbortError } from '$lib/utils/abort';
+
+describe('isAbortError', () => {
+	it('returns false for null, undefined and non-error values', () => {
+		expect(isAbortError(null)).toBe(false);
+		expect(isAbortError(undefined)).toBe(false);
+		expect(isAbortError('string error')).toBe(false);
+		expect(isAbortError({ name: 'AbortError' })).toBe(false);
+		expect(isAbortError(42)).toBe(false);
+	});
+
+	it('returns true for DOMException with AbortError name', () => {
+		const err = new DOMException('Operation was aborted', 'AbortError');
+		expect(isAbortError(err)).toBe(true);
+	});
+
+	it('returns true for plain Error with AbortError name', () => {
+		const err = new Error('aborted');
+		err.name = 'AbortError';
+		expect(isAbortError(err)).toBe(true);
+	});
+
+	it('returns false for unrelated Error instances', () => {
+		expect(isAbortError(new Error('something failed'))).toBe(false);
+		expect(isAbortError(new TypeError('not related'))).toBe(false);
+		expect(isAbortError(new RangeError('out of range'))).toBe(false);
+	});
+
+	it('recognizes Firefox TypeError "Error in input stream" emitted at page unload', () => {
+		expect(isAbortError(new TypeError('Error in input stream'))).toBe(true);
+		expect(isAbortError(new TypeError('TypeError: Error in input stream'))).toBe(true);
+	});
+
+	it('recognizes Safari "The network connection was lost" during transient drop', () => {
+		expect(isAbortError(new TypeError('The network connection was lost.'))).toBe(true);
+	});
+
+	it('recognizes Safari "Load failed" during page navigation', () => {
+		expect(isAbortError(new TypeError('Load failed'))).toBe(true);
+	});
+
+	it('does NOT recognize generic TypeError messages as aborts', () => {
+		// matching too broadly would hide real bugs, the predicate must stay conservative
+		expect(isAbortError(new TypeError('Failed to fetch'))).toBe(false);
+		expect(isAbortError(new TypeError('Cannot read property of undefined'))).toBe(false);
+		expect(isAbortError(new TypeError('NetworkError when attempting to fetch resource'))).toBe(
+			false
+		);
+	});
+
+	it('is case insensitive on the matched substrings', () => {
+		expect(isAbortError(new TypeError('error in INPUT STREAM'))).toBe(true);
+		expect(isAbortError(new TypeError('the network connection WAS LOST'))).toBe(true);
+	});
+});
diff --git a/tools/ui/tests/unit/stream-discovery.test.ts b/tools/ui/tests/unit/stream-discovery.test.ts
new file mode 100644
index 000000000..a428e5df8
--- /dev/null
+++ b/tools/ui/tests/unit/stream-discovery.test.ts
@@ -0,0 +1,74 @@
+import { describe, expect, it } from 'vitest';
+import { ChatService } from '$lib/services/chat.service';
+import type { ApiStreamSession } from '$lib/types';
+
+function makeSession(overrides: Partial<ApiStreamSession>): ApiStreamSession {
+	return {
+		conversation_id: 'conv',
+		is_done: true,
+		total_bytes: 0,
+		started_at: 0,
+		completed_at: 0,
+		...overrides
+	};
+}
+
+describe('selectActiveStream', () => {
+	it('returns null on empty input', () => {
+		expect(ChatService.selectActiveStream([])).toBeNull();
+	});
+
+	it('returns null on null or undefined input', () => {
+		expect(ChatService.selectActiveStream(null)).toBeNull();
+		expect(ChatService.selectActiveStream(undefined)).toBeNull();
+	});
+
+	it('returns the single session when it is running', () => {
+		const s = makeSession({ conversation_id: 'only', is_done: false, started_at: 42 });
+		expect(ChatService.selectActiveStream([s])).toBe(s);
+	});
+
+	it('returns null when the single session is finalized', () => {
+		const s = makeSession({ conversation_id: 'only', is_done: true, started_at: 42 });
+		expect(ChatService.selectActiveStream([s])).toBeNull();
+	});
+
+	it('prefers a still running session over a finalized one regardless of started_at', () => {
+		const finalized = makeSession({ conversation_id: 'old', is_done: true, started_at: 1000 });
+		const running = makeSession({ conversation_id: 'new', is_done: false, started_at: 10 });
+		expect(ChatService.selectActiveStream([finalized, running])?.conversation_id).toBe('new');
+		expect(ChatService.selectActiveStream([running, finalized])?.conversation_id).toBe('new');
+	});
+
+	it('among running sessions, picks the most recently started one', () => {
+		const a = makeSession({ conversation_id: 'a', is_done: false, started_at: 100 });
+		const b = makeSession({ conversation_id: 'b', is_done: false, started_at: 200 });
+		const c = makeSession({ conversation_id: 'c', is_done: false, started_at: 150 });
+		expect(ChatService.selectActiveStream([a, b, c])?.conversation_id).toBe('b');
+		expect(ChatService.selectActiveStream([c, a, b])?.conversation_id).toBe('b');
+	});
+
+	it('returns null when all sessions are finalized, the DB already holds the content', () => {
+		const a = makeSession({ conversation_id: 'a', is_done: true, started_at: 10 });
+		const b = makeSession({ conversation_id: 'b', is_done: true, started_at: 30 });
+		const c = makeSession({ conversation_id: 'c', is_done: true, started_at: 20 });
+		expect(ChatService.selectActiveStream([a, b, c])).toBeNull();
+	});
+
+	it('keeps the first match on ties when both are running with identical started_at', () => {
+		// reduce visits left to right, the initial accumulator stays unless a strictly greater value appears
+		const a = makeSession({ conversation_id: 'first', is_done: false, started_at: 50 });
+		const b = makeSession({ conversation_id: 'second', is_done: false, started_at: 50 });
+		expect(ChatService.selectActiveStream([a, b])?.conversation_id).toBe('first');
+	});
+
+	it('handles a typical realistic mix: two finalized old, one freshly running, one freshly finalized', () => {
+		const old1 = makeSession({ conversation_id: 'old1', is_done: true, started_at: 100 });
+		const old2 = makeSession({ conversation_id: 'old2', is_done: true, started_at: 200 });
+		const freshFin = makeSession({ conversation_id: 'freshFin', is_done: true, started_at: 500 });
+		const running = makeSession({ conversation_id: 'running', is_done: false, started_at: 400 });
+		expect(ChatService.selectActiveStream([old1, old2, freshFin, running])?.conversation_id).toBe(
+			'running'
+		);
+	});
+});
diff --git a/tools/ui/tests/unit/stream-resume.test.ts b/tools/ui/tests/unit/stream-resume.test.ts
new file mode 100644
index 000000000..f52f2cabd
--- /dev/null
+++ b/tools/ui/tests/unit/stream-resume.test.ts
@@ -0,0 +1,128 @@
+import { afterEach, beforeAll, beforeEach, describe, expect, it } from 'vitest';
+
+// node env unit project has no DOM, install a minimal localStorage backed by a Map
+beforeAll(() => {
+	const store = new Map<string, string>();
+	const polyfill: Storage = {
+		get length() {
+			return store.size;
+		},
+		clear: () => store.clear(),
+		getItem: (k) => (store.has(k) ? store.get(k)! : null),
+		key: (i) => Array.from(store.keys())[i] ?? null,
+		removeItem: (k) => {
+			store.delete(k);
+		},
+		setItem: (k, v) => {
+			store.set(k, String(v));
+		}
+	};
+	(globalThis as unknown as { localStorage: Storage }).localStorage = polyfill;
+});
+
+import { ChatService } from '$lib/services/chat.service';
+import { STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX } from '$lib/constants';
+
+describe('ChatService stream resume', () => {
+	beforeEach(() => {
+		localStorage.clear();
+	});
+	afterEach(() => {
+		localStorage.clear();
+	});
+
+	it('returns null when no state exists for the conversation', () => {
+		expect(ChatService.getStreamState('conv-a')).toBeNull();
+	});
+
+	it('saves and reads back the byte count', () => {
+		ChatService.saveStreamState('conv-a', 4242);
+		const got = ChatService.getStreamState('conv-a');
+		expect(got).not.toBeNull();
+		expect(got!.bytesReceived).toBe(4242);
+		expect(typeof got!.updatedAt).toBe('number');
+	});
+
+	it('overwrites the previous byte count on a new save for the same conversation', () => {
+		ChatService.saveStreamState('conv-a', 100);
+		ChatService.saveStreamState('conv-a', 200);
+		const got = ChatService.getStreamState('conv-a');
+		expect(got!.bytesReceived).toBe(200);
+	});
+
+	it('keeps states for distinct conversations isolated', () => {
+		ChatService.saveStreamState('conv-a', 10);
+		ChatService.saveStreamState('conv-b', 20);
+		expect(ChatService.getStreamState('conv-a')!.bytesReceived).toBe(10);
+		expect(ChatService.getStreamState('conv-b')!.bytesReceived).toBe(20);
+	});
+
+	it('clears the state for a given conversation', () => {
+		ChatService.saveStreamState('conv-a', 10);
+		ChatService.clearStreamState('conv-a');
+		expect(ChatService.getStreamState('conv-a')).toBeNull();
+	});
+
+	it('ignores empty conversation id on save', () => {
+		ChatService.saveStreamState('', 1);
+		expect(ChatService.getStreamState('')).toBeNull();
+	});
+
+	it('returns null on corrupted storage payload', () => {
+		localStorage.setItem(`${STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX}conv-a`, '{not-json');
+		expect(ChatService.getStreamState('conv-a')).toBeNull();
+	});
+
+	it('persists the model alongside the byte count', () => {
+		ChatService.saveStreamState('conv-a', 10, 'model-x');
+		expect(ChatService.getStreamState('conv-a')!.model).toBe('model-x');
+	});
+
+	it('stores a null model when none is provided', () => {
+		ChatService.saveStreamState('conv-a', 10);
+		expect(ChatService.getStreamState('conv-a')!.model).toBeNull();
+	});
+
+	it('overwrites the model on a new save for the same conversation', () => {
+		ChatService.saveStreamState('conv-a', 10, 'model-x');
+		ChatService.saveStreamState('conv-a', 20, 'model-y');
+		expect(ChatService.getStreamState('conv-a')!.model).toBe('model-y');
+	});
+
+	describe('resumeStreamIdentity', () => {
+		it('appends the persisted model so the resume key matches the frozen POST identity', () => {
+			ChatService.saveStreamState('conv-a', 10, 'model-x');
+			expect(
+				ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown')
+			).toBe('conv-a::model-x');
+		});
+
+		it('keeps the bare conv id when the persisted model is null', () => {
+			ChatService.saveStreamState('conv-a', 10);
+			expect(
+				ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown')
+			).toBe('conv-a');
+		});
+
+		it('falls back to the current model only when no state is persisted', () => {
+			expect(ChatService.resumeStreamIdentity('conv-a', null, 'dropdown')).toBe('conv-a::dropdown');
+		});
+
+		it('ignores the fallback when a state exists, the persisted value is authoritative', () => {
+			ChatService.saveStreamState('conv-a', 10, 'model-x');
+			expect(
+				ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown')
+			).toBe('conv-a::model-x');
+		});
+
+		it('falls back when a legacy state has no model field', () => {
+			localStorage.setItem(
+				`${STREAM_RESUME_LOCALSTORAGE_KEY_PREFIX}conv-a`,
+				JSON.stringify({ bytesReceived: 10, updatedAt: 1 })
+			);
+			expect(
+				ChatService.resumeStreamIdentity('conv-a', ChatService.getStreamState('conv-a'), 'dropdown')
+			).toBe('conv-a::dropdown');
+		});
+	});
+});

From c16c35b8142ac34b4559a5e8584e8daaf3958416 Mon Sep 17 00:00:00 2001
From: Tarek Dakhran <tarek@liquid.ai>
Date: Fri, 26 Jun 2026 09:41:56 +0200
Subject: [PATCH 175/292] ggml-cpu: fix SVE leftover path in ggml_vec_dot_f32
 (#24699)

* ggml-cpu: fix SVE leftover path in ggml_vec_dot_f32

2D convolutions with kernel size 9 produced different results on SVE
enabled ARM devices. After debugging it turned out that ggml_vec_dot_f32
was using data from inactive lanes.

Use svmla_f32_m(pg, sum1, ax1, ay1) so inactive lanes retain sum1.

* cont : clean-up

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-cpu/vec.cpp   |  4 +-
 tests/test-quantize-fns.cpp | 85 +++++++++++++++++++++++++------------
 2 files changed, 61 insertions(+), 28 deletions(-)

diff --git a/ggml/src/ggml-cpu/vec.cpp b/ggml/src/ggml-cpu/vec.cpp
index 67b6b05ca..ff2b636df 100644
--- a/ggml/src/ggml-cpu/vec.cpp
+++ b/ggml/src/ggml-cpu/vec.cpp
@@ -75,12 +75,12 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
             ay1 = GGML_F32_VEC_LOAD(y + i);
             sum1 = GGML_F32_VEC_FMA(sum1, ax1, ay1);
         }
-        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmla on available elements only
         if (np2 < n) {
             svbool_t pg = svwhilelt_b32(np2, n);
             ax1 = svld1_f32(pg, x + np2);
             ay1 = svld1_f32(pg, y + np2);
-            sum1 = svmad_f32_m(pg, ax1, ay1, sum1);
+            sum1 = svmla_f32_m(pg, sum1, ax1, ay1);
         }
         // reduce sum1,sum2 to sum1
         GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index a05fab504..74978e446 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -102,21 +102,34 @@ static float dot_product_error(const ggml_type_traits * qfns, const ggml_type_tr
     return fabsf(result - dot_ref) / test_size;
 }
 
-int main(int argc, char * argv[]) {
-    bool verbose = false;
-    const size_t test_size = 32 * 128;
+static int test_vec_dot_f32(bool verbose) {
+    const auto * f32 = ggml_get_type_traits_cpu(GGML_TYPE_F32);
+    int num_failed = 0;
+    for (int n : {1, 2, 3, 5, 7, 8, 15, 16, 17, 31, 33, 63, 67, 127, 129, 193, 255, 1023}) {
+        std::vector<float> a(n);
+        std::vector<float> b(n);
+        generate_data(0.0, n, a.data());
+        generate_data(1.0, n, b.data());
 
-    std::string arg;
-    for (int i = 1; i < argc; i++) {
-        arg = argv[i];
+        float result = 0.0f;
+        f32->vec_dot(n, &result, 0, a.data(), 0, b.data(), 0, 1);
+        const float ref = dot_product(a.data(), b.data(), n);
+        const float error = fabsf(result - ref) / n;
 
-        if (arg == "-v") {
-            verbose = true;
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            return 1;
+        const bool failed = !(error < MAX_QUANTIZATION_REFERENCE_ERROR);
+        num_failed += failed;
+        if (failed || verbose) {
+            printf(" f32 vec_dot n=%4d:                 %s (ref=%f got=%f err=%f)\n",
+                   n, RESULT_STR[failed], ref, result, error);
         }
     }
+    return num_failed;
+}
+
+static int test_vec_dot_q(bool verbose) {
+    int num_failed = 0;
+
+    const size_t test_size = 32 * 128;
 
     std::vector<float> test_data(test_size);
     std::vector<float> test_data2(test_size);
@@ -124,11 +137,6 @@ int main(int argc, char * argv[]) {
     generate_data(0.0, test_data.size(), test_data.data());
     generate_data(1.0, test_data2.size(), test_data2.data());
 
-    ggml_cpu_init();
-
-    int num_failed = 0;
-    bool failed = false;
-
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
         const auto * qfns = ggml_get_type_traits(type);
@@ -156,7 +164,7 @@ int main(int argc, char * argv[]) {
                 type == GGML_TYPE_IQ3_S   ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
                 type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS :
                 type == GGML_TYPE_NVFP4   ? MAX_QUANTIZATION_TOTAL_ERROR_FP4 : MAX_QUANTIZATION_TOTAL_ERROR;
-            failed = !(total_error < max_quantization_error);
+            bool failed = !(total_error < max_quantization_error);
             num_failed += failed;
             if (failed || verbose) {
                 printf("%5s absolute quantization error:    %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error);
@@ -171,15 +179,15 @@ int main(int argc, char * argv[]) {
 
             const float vec_dot_error = dot_product_error(qfns, qfns_cpu, test_size, test_data.data(), test_data2.data());
             const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
-                                            type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
-                                          ? MAX_DOT_PRODUCT_ERROR_LOWBIT
-                                          : type == GGML_TYPE_Q1_0
-                                          ? MAX_DOT_PRODUCT_ERROR_BINARY
-                                          : type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
-                                          ? MAX_DOT_PRODUCT_ERROR_TERNARY
-                                          : type == GGML_TYPE_NVFP4
-                                          ? MAX_DOT_PRODUCT_ERROR_FP4
-                                          : MAX_DOT_PRODUCT_ERROR;
+                type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
+                ? MAX_DOT_PRODUCT_ERROR_LOWBIT
+                : type == GGML_TYPE_Q1_0
+                ? MAX_DOT_PRODUCT_ERROR_BINARY
+                : type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
+                ? MAX_DOT_PRODUCT_ERROR_TERNARY
+                : type == GGML_TYPE_NVFP4
+                ? MAX_DOT_PRODUCT_ERROR_FP4
+                : MAX_DOT_PRODUCT_ERROR;
             failed = !(vec_dot_error < max_allowed_error);
             num_failed += failed;
             if (failed || verbose) {
@@ -188,6 +196,31 @@ int main(int argc, char * argv[]) {
         }
     }
 
+    return num_failed;
+}
+
+int main(int argc, char * argv[]) {
+    bool verbose = false;
+
+    std::string arg;
+    for (int i = 1; i < argc; i++) {
+        arg = argv[i];
+
+        if (arg == "-v") {
+            verbose = true;
+        } else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            return 1;
+        }
+    }
+
+    ggml_cpu_init();
+
+    int num_failed = 0;
+
+    num_failed += test_vec_dot_f32(verbose);
+    num_failed += test_vec_dot_q(verbose);
+
     if (num_failed || verbose) {
         printf("%d tests failed\n", num_failed);
     }

From 29d312eda854a47710ad94bc954f29101e4f9275 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 16:27:02 +0800
Subject: [PATCH 176/292] add continue and retry wait

---
 koboldcpp.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 3c875b363..17a569b23 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -10541,10 +10541,10 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
             a2cexe = os.path.join(basepath, "aria2c-win.exe")
             if os.path.exists(a2cexe):  # on windows try using embedded aria2c
                 rc = subprocess.run([
-                        a2cexe, "-x", "16", "-s", "16",
-                        "--summary-interval=15", "--console-log-level=error", "--log-level=error",
+                        a2cexe, "-x", "8", "-s", "8",
+                        "--summary-interval=10", "--console-log-level=error", "--log-level=error",
                         "--download-result=default", "--continue=true", "--allow-overwrite=true",
-                        "--file-allocation=none", "--max-tries=3",
+                        "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
                         "-d", out_dir, "-o", out_name, input_url
                     ], capture_output=capture_output, text=True, check=True, encoding='utf-8')
                 dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)
@@ -10554,10 +10554,10 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
     try:
         if not dl_success and shutil.which("aria2c") is not None:
             rc = subprocess.run([
-                    "aria2c", "-x", "16", "-s", "16",
-                    "--summary-interval=15", "--console-log-level=error", "--log-level=error",
-                    "--download-result=default", "--allow-overwrite=true",
-                    "--file-allocation=none", "--max-tries=3",
+                    "aria2c", "-x", "8", "-s", "8",
+                    "--summary-interval=10", "--console-log-level=error", "--log-level=error",
+                    "--download-result=default", "--continue=true", "--allow-overwrite=true",
+                    "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
                     "-d", out_dir, "-o", out_name, input_url
                 ], capture_output=capture_output, text=True, check=True, encoding='utf-8')
             dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)

From e35e415668504bed304fd83d3264c9cce454a296 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 17:35:50 +0800
Subject: [PATCH 177/292] deduplicate aria2

---
 koboldcpp.py | 50 +++++++++++++++++++++++---------------------------
 1 file changed, 23 insertions(+), 27 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 17a569b23..b39605b02 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -10535,34 +10535,30 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
     dl_success = False
     out_dir = os.path.dirname(os.path.abspath(output_filename)) or os.getcwd()
     out_name = os.path.basename(output_filename)
-    try:
-        if os.name == 'nt':
-            basepath = os.path.abspath(os.path.dirname(__file__))
-            a2cexe = os.path.join(basepath, "aria2c-win.exe")
-            if os.path.exists(a2cexe):  # on windows try using embedded aria2c
-                rc = subprocess.run([
-                        a2cexe, "-x", "8", "-s", "8",
-                        "--summary-interval=10", "--console-log-level=error", "--log-level=error",
-                        "--download-result=default", "--continue=true", "--allow-overwrite=true",
-                        "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
-                        "-d", out_dir, "-o", out_name, input_url
-                    ], capture_output=capture_output, text=True, check=True, encoding='utf-8')
-                dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)
-    except subprocess.CalledProcessError as e:
-        print(f"aria2c-win failed: {e}")
-
-    try:
-        if not dl_success and shutil.which("aria2c") is not None:
-            rc = subprocess.run([
-                    "aria2c", "-x", "8", "-s", "8",
-                    "--summary-interval=10", "--console-log-level=error", "--log-level=error",
-                    "--download-result=default", "--continue=true", "--allow-overwrite=true",
-                    "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
-                    "-d", out_dir, "-o", out_name, input_url
-                ], capture_output=capture_output, text=True, check=True, encoding='utf-8')
+    aria2_candidates = []
+    if os.name == 'nt':
+        basepath = os.path.abspath(os.path.dirname(__file__))
+        a2cexe = os.path.join(basepath, "aria2c-win.exe")
+        if os.path.exists(a2cexe):  # on windows try using embedded aria2c
+            aria2_candidates.append((a2cexe, "aria2c-win"))
+    if shutil.which("aria2c") is not None:
+        aria2_candidates.append(("aria2c", "aria2c"))
+    aria2_args = [
+        "-x", "8", "-s", "8",
+        "--summary-interval=10", "--console-log-level=error", "--log-level=error",
+        "--download-result=default", "--continue=true", "--allow-overwrite=true",
+        "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
+        "-d", out_dir, "-o", out_name, input_url
+    ]
+    for aria2_exe, aria2_name in aria2_candidates:
+        if dl_success:
+            break
+        try:
+            rc = subprocess.run([aria2_exe, *aria2_args],
+                capture_output=capture_output, text=True, check=True, encoding='utf-8')
             dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)
-    except subprocess.CalledProcessError as e:
-        print(f"aria2c failed: {e}")
+        except subprocess.CalledProcessError as e:
+            print(f"{aria2_name} failed: {e}")
 
     try:
         if not dl_success and shutil.which("curl") is not None:

From 2f18fe13c5dd61e7f069e0c817086cbbce2134dc Mon Sep 17 00:00:00 2001
From: leonardHONG <2695316095@qq.com>
Date: Fri, 26 Jun 2026 17:42:56 +0800
Subject: [PATCH 178/292] CUDA: add cublasSgemmBatched mapping for HIP/MUSA
 vendor headers (#25033)

---
 ggml/src/ggml-cuda/vendors/hip.h  | 1 +
 ggml/src/ggml-cuda/vendors/musa.h | 1 +
 2 files changed, 2 insertions(+)

diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index a6115cd80..d01f1533a 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -48,6 +48,7 @@
 #define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
 #define cublasSetStream hipblasSetStream
 #define cublasSgemm hipblasSgemm
+#define cublasSgemmBatched hipblasSgemmBatched
 #define cublasSgemmStridedBatched hipblasSgemmStridedBatched
 #define cublasStatus_t hipblasStatus_t
 #define cublasOperation_t hipblasOperation_t
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
index 99e8fa370..6d725c7ec 100644
--- a/ggml/src/ggml-cuda/vendors/musa.h
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -32,6 +32,7 @@
 #define cublasSetMathMode mublasSetMathMode
 #define cublasSetStream mublasSetStream
 #define cublasSgemm mublasSgemm
+#define cublasSgemmBatched mublasSgemmBatched
 #define cublasSgemmStridedBatched mublasSgemmStridedBatched
 #define cublasStatus_t mublasStatus_t
 #define cublasOperation_t mublasOperation_t

From 9df06805eee8d600ccc3cb1b099658c9a91b0bae Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Fri, 26 Jun 2026 04:53:32 -0500
Subject: [PATCH 179/292] vulkan: Workaround compiler bug in conv2d coopmat2
 path (#24924)

* vulkan: Workaround compiler bug in conv2d coopmat2 path

* apply same workaround to CONV_3D

* Apply suggestion from @jeffbolznv
---
 ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp | 2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp | 2 +-
 tests/test-backend-ops.cpp                         | 3 +++
 3 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
index 1428ef68d..99400098b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
@@ -158,7 +158,7 @@ const uint32_t Csh_stride = BS_NPQ;
 #ifdef COOPMAT
 const uint32_t Csh_len    = BS_K * Csh_stride;
 #else
-const uint32_t Csh_len    = csh_store != 0 ? BS_K * Csh_stride : 1;
+const uint32_t Csh_len    = csh_store != 0 ? BS_K * Csh_stride : 8; // 8 to workaround compiler bug
 #endif
 shared SHMEM_TYPE Csh[Csh_len];  // K x NPQ
 #endif
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp
index a9712eb3a..f66f299f6 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp
@@ -144,7 +144,7 @@ const uint32_t Csh_stride = BS_NPQ;
 #ifdef COOPMAT
 const uint32_t Csh_len    = BS_K * Csh_stride;
 #else
-const uint32_t Csh_len    = csh_store != 0 ? BS_K * Csh_stride : 1;
+const uint32_t Csh_len    = csh_store != 0 ? BS_K * Csh_stride : 8; // 8 to workaround compiler bug
 #endif
 shared SHMEM_TYPE Csh[Csh_len];  // K x NPQ
 #endif
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 0a017d57e..0830dbf57 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7973,6 +7973,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             }
         }
     }
+    for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+        test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false));
+    }
 
     // sycl backend will limit task global_range < MAX_INT
     // test cases for 2D im2col with large input W and H (occurs in stable-diffusion)

From 73607e9e01b82c6a65993ca9bc131c0673d661f0 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 17:58:43 +0800
Subject: [PATCH 180/292] added xet resolver and downloader

---
 koboldcpp.py | 28 +++++++++++++++++++++++++---
 1 file changed, 25 insertions(+), 3 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index b39605b02..77f68e66b 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -10501,6 +10501,27 @@ def sanitize_string(input_string):
     sanitized_string = re.sub( r'[^\w\d\.\-_]', '', input_string)
     return sanitized_string
 
+def resolve_huggingface_xet_url(input_url):
+    if "https://huggingface.co/" not in input_url or "/resolve/" not in input_url:
+        return input_url
+    try:
+        req = urllib.request.Request(input_url, headers={'User-Agent': 'Mozilla/5.0'}, method="HEAD")
+        with urllib.request.urlopen(req, timeout=10) as response:
+            resolved_url = response.geturl()
+    except Exception:
+        try:
+            req = urllib.request.Request(input_url, headers={'User-Agent': 'Mozilla/5.0'})
+            with urllib.request.urlopen(req, timeout=10) as response:
+                resolved_url = response.geturl()
+        except Exception as e:
+            print(f"Could not pre-resolve Hugging Face URL, using original URL: {e}")
+            return input_url
+    # resolved_host = urllib.parse.urlparse(resolved_url).netloc.lower()
+    if ("xet-bridge" in resolved_url) and resolved_url != input_url:
+        print(f"Resolved Hugging Face xet URL to {resolved_url}", flush=True)
+        return resolved_url
+    return input_url
+
 def downloader_internal(input_url, output_filename, capture_output, min_file_size=64): # 64 bytes required by default
     download_dir_path = args.downloaddir
     if "https://huggingface.co/" in input_url and "/blob/main/" in input_url:
@@ -10535,6 +10556,7 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
     dl_success = False
     out_dir = os.path.dirname(os.path.abspath(output_filename)) or os.getcwd()
     out_name = os.path.basename(output_filename)
+    download_url = resolve_huggingface_xet_url(input_url)
     aria2_candidates = []
     if os.name == 'nt':
         basepath = os.path.abspath(os.path.dirname(__file__))
@@ -10548,7 +10570,7 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
         "--summary-interval=10", "--console-log-level=error", "--log-level=error",
         "--download-result=default", "--continue=true", "--allow-overwrite=true",
         "--file-allocation=none", "--max-tries=3", "--retry-wait=5",
-        "-d", out_dir, "-o", out_name, input_url
+        "-d", out_dir, "-o", out_name, download_url
     ]
     for aria2_exe, aria2_name in aria2_candidates:
         if dl_success:
@@ -10562,7 +10584,7 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
 
     try:
         if not dl_success and shutil.which("curl") is not None:
-            rc = subprocess.run(["curl", "-fLo", output_filename, input_url],
+            rc = subprocess.run(["curl", "-fLo", output_filename, download_url],
                 capture_output=capture_output, text=True, check=True, encoding="utf-8")
             dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)
     except subprocess.CalledProcessError as e:
@@ -10570,7 +10592,7 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
 
     try:
         if not dl_success and shutil.which("wget") is not None:
-            rc = subprocess.run(["wget", "-O", output_filename, input_url],
+            rc = subprocess.run(["wget", "-O", output_filename, download_url],
                 capture_output=capture_output, text=True, check=True, encoding="utf-8")
             dl_success = (rc.returncode == 0 and os.path.exists(output_filename) and os.path.getsize(output_filename) > min_file_size)
     except subprocess.CalledProcessError as e:

From ded1561b4228495daa58342ffc111ff172ec86ac Mon Sep 17 00:00:00 2001
From: Sanjay Ahari <sanjayahari1704@gmail.com>
Date: Fri, 26 Jun 2026 16:25:38 +0530
Subject: [PATCH 181/292] ui: fix accessibility for hover-gated interactive
 elements assisted by claude(in debugging and tests) (#24727)

---
 .../ChatAttachmentsListItemMcpPrompt.svelte   |   2 +-
 .../ChatMessageUserPending.svelte             |   2 +-
 .../SidebarNavigationConversationItem.svelte  | 137 +++++++-----------
 3 files changed, 58 insertions(+), 83 deletions(-)

diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
index 636e93f22..c55dfdec7 100644
--- a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
@@ -33,7 +33,7 @@
 
 	{#if !readonly && onRemove}
 		<div
-			class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"
+			class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-focus-within:opacity-100 group-hover:opacity-100"
 		>
 			<ActionIcon icon={X} tooltip="Remove" stopPropagationOnClick onclick={() => onRemove?.()} />
 		</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
index 4be582b39..5c2913202 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
@@ -56,7 +56,7 @@
 			<div class="relative flex h-6 items-center justify-between">
 				<div class="right-0 flex items-center gap-2 opacity-100 transition-opacity">
 					<div
-						class="pointer-events-auto inset-0 flex items-center gap-1 opacity-0 transition-all duration-150 group-hover:opacity-100"
+						class="pointer-events-auto inset-0 flex items-center gap-1 opacity-0 transition-all duration-150 group-focus-within:opacity-100 group-hover:opacity-100"
 					>
 						<ActionIcon icon={Edit} tooltip="Edit" onclick={editCtx.handleEdit} />
 						<ActionIcon icon={Trash2} tooltip="Delete" onclick={onDelete} />
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
index b1c2b78f6..2c1b9adf2 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
@@ -39,7 +39,6 @@
 		depth = 0
 	}: Props = $props();
 
-	let renderActionsDropdown = $state(false);
 	let dropdownOpen = $state(false);
 
 	let isLoading = $derived(getAllLoadingChats().includes(conversation.id));
@@ -71,26 +70,10 @@
 		}
 	}
 
-	function handleMouseLeave() {
-		if (!dropdownOpen) {
-			renderActionsDropdown = false;
-		}
-	}
-
-	function handleMouseOver() {
-		renderActionsDropdown = true;
-	}
-
 	function handleSelect() {
 		onSelect?.(conversation.id);
 	}
 
-	$effect(() => {
-		if (!dropdownOpen) {
-			renderActionsDropdown = false;
-		}
-	});
-
 	onMount(() => {
 		document.addEventListener('edit-active-conversation', handleGlobalEditEvent as EventListener);
 
@@ -103,23 +86,19 @@
 	});
 </script>
 
-<!-- svelte-ignore a11y_mouse_events_have_key_events -->
-<button
-	class="group flex min-h-9 w-full cursor-pointer items-center justify-between space-x-3 rounded-lg py-1.5 text-left transition-colors hover:bg-foreground/10 {isActive
+<div
+	class="conversation-item group relative flex min-h-9 w-full items-center justify-between space-x-3 rounded-lg py-1.5 transition-colors hover:bg-foreground/10 {isActive
 		? 'bg-foreground/5 text-accent-foreground'
 		: ''} px-3"
-	onclick={handleSelect}
-	onmouseover={handleMouseOver}
-	onmouseleave={handleMouseLeave}
-	onfocusin={handleMouseOver}
-	onfocusout={(e) => {
-		if (!e.currentTarget.contains(e.relatedTarget as Node | null)) {
-			handleMouseLeave();
-		}
-	}}
 >
+	<button
+		class="absolute inset-0 z-0 cursor-pointer rounded-lg focus:outline-none focus-visible:ring-2 focus-visible:ring-ring"
+		onclick={handleSelect}
+		aria-label={conversation.name}
+	>
+	</button>
 	<div
-		class="flex min-w-0 flex-1 items-center gap-2"
+		class="pointer-events-none relative z-10 flex min-w-0 flex-1 items-center gap-2"
 		style:padding-left="{depth * FORK_TREE_DEPTH_PADDING}px"
 	>
 		{#if depth > 0}
@@ -130,7 +109,7 @@
 						<a
 							{...props}
 							href={RouterService.chat(conversation.forkedFromConversationId)}
-							class="flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"
+							class="pointer-events-auto flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"
 						>
 							<GitBranch class="h-3.5 w-3.5" />
 						</a>
@@ -146,18 +125,15 @@
 		{#if isLoading}
 			<Tooltip.Root>
 				<Tooltip.Trigger>
-					<div
-						class="stop-button flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground"
+					<button
+						class="stop-button pointer-events-auto flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground"
 						onclick={handleStop}
-						onkeydown={(e) => e.key === 'Enter' && handleStop(e)}
-						role="button"
-						tabindex="0"
 						aria-label="Stop generation"
 					>
 						<Loader2 class="loading-icon h-3.5 w-3.5 animate-spin" />
 
 						<Square class="stop-icon hidden h-3 w-3 fill-current text-destructive" />
-					</div>
+					</button>
 				</Tooltip.Trigger>
 
 				<Tooltip.Content>
@@ -169,52 +145,50 @@
 		<TruncatedText text={conversation.name} class="text-sm font-medium" showTooltip={false} />
 	</div>
 
-	{#if renderActionsDropdown}
-		<div class="actions flex items-center">
-			<DropdownMenuActions
-				triggerIcon={MoreHorizontal}
-				triggerTooltip="More actions"
-				bind:open={dropdownOpen}
-				actions={[
-					{
-						icon: conversation.pinned ? PinOff : Pin,
-						label: conversation.pinned ? 'Unpin' : 'Pin',
-						onclick: (e: Event) => {
-							e.stopPropagation();
-							handleTogglePin();
-						}
-					},
-					{
-						icon: Pencil,
-						label: 'Edit',
-						onclick: handleEdit,
-						shortcut: ['shift', 'cmd', 'e']
-					},
-					{
-						icon: Download,
-						label: 'Export',
-						onclick: (e: Event) => {
-							e.stopPropagation();
-							conversationsStore.downloadConversation(conversation.id);
-						},
-						shortcut: ['shift', 'cmd', 's']
-					},
-					{
-						icon: Trash2,
-						label: 'Delete',
-						onclick: handleDelete,
-						variant: 'destructive',
-						shortcut: ['shift', 'cmd', 'd'],
-						separator: true
+	<div class="actions pointer-events-auto relative z-20 flex items-center">
+		<DropdownMenuActions
+			triggerIcon={MoreHorizontal}
+			triggerTooltip="More actions"
+			bind:open={dropdownOpen}
+			actions={[
+				{
+					icon: conversation.pinned ? PinOff : Pin,
+					label: conversation.pinned ? 'Unpin' : 'Pin',
+					onclick: (e: Event) => {
+						e.stopPropagation();
+						handleTogglePin();
 					}
-				]}
-			/>
-		</div>
-	{/if}
-</button>
+				},
+				{
+					icon: Pencil,
+					label: 'Edit',
+					onclick: handleEdit,
+					shortcut: ['shift', 'cmd', 'e']
+				},
+				{
+					icon: Download,
+					label: 'Export',
+					onclick: (e: Event) => {
+						e.stopPropagation();
+						conversationsStore.downloadConversation(conversation.id);
+					},
+					shortcut: ['shift', 'cmd', 's']
+				},
+				{
+					icon: Trash2,
+					label: 'Delete',
+					onclick: handleDelete,
+					variant: 'destructive',
+					shortcut: ['shift', 'cmd', 'd'],
+					separator: true
+				}
+			]}
+		/>
+	</div>
+</div>
 
 <style>
-	button {
+	.conversation-item {
 		:global([data-slot='dropdown-menu-trigger']:not([data-state='open'])) {
 			opacity: 0;
 		}
@@ -239,7 +213,8 @@
 			}
 		}
 
-		&:is(:hover) .stop-button {
+		&:is(:hover) .stop-button,
+		&:focus-within .stop-button {
 			:global(.stop-icon) {
 				display: block;
 			}

From 5a6a0dd7e1f7342bac4618a223fc21f9cece3b48 Mon Sep 17 00:00:00 2001
From: "Jiang, Fish" <fish.jiang@intel.com>
Date: Fri, 26 Jun 2026 11:26:22 +0000
Subject: [PATCH 182/292] vulkan: add INTEL_XE1 arch enum and enable coopmat1
 on Intel Xe-LPG Plus (#24404)

* vulkan: add INTEL_PRE_XE2 arch enum and enable coopmat1 on Intel Xe-LPG Plus (1/3, Xe1-ARLH)

Co-authored-by: Xia, Jie <jie.xia@intel.com>
Co-authored-by: Liu, Russell <russell.liu@intel.com>

* Address comments of bf16 and trailing whitespace

* Rename INTEL_PRE_XE2 to INTEL_XE1 and remove driver workaround

* Add Windows driver check

---------

Co-authored-by: Xia, Jie <jie.xia@intel.com>
Co-authored-by: Liu, Russell <russell.liu@intel.com>
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 26 ++++++++++++++++++--------
 1 file changed, 18 insertions(+), 8 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 5fbebc6d7..c090d4442 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -308,6 +308,7 @@ enum vk_device_architecture {
     AMD_RDNA1,
     AMD_RDNA2,
     AMD_RDNA3,
+    INTEL_XE1,
     INTEL_XE2,
     NVIDIA_PRE_TURING,
     NVIDIA_TURING,
@@ -365,21 +366,26 @@ static vk_device_architecture get_device_architecture(const vk::PhysicalDevice&
         const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();
 
         bool subgroup_size_control = false;
+        bool integer_dot_product = false;
 
         for (const auto& properties : ext_props) {
             if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) {
                 subgroup_size_control = true;
+            } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0) {
+                integer_dot_product = true;
             }
         }
 
-        if (!subgroup_size_control) {
+        if (!subgroup_size_control || !integer_dot_product) {
             return vk_device_architecture::OTHER;
         }
 
         vk::PhysicalDeviceProperties2 props2;
         vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props;
+        vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR integer_dot_props;
 
         props2.pNext = &subgroup_size_control_props;
+        subgroup_size_control_props.pNext = &integer_dot_props;
         device.getProperties2(&props2);
 
         if (subgroup_size_control_props.minSubgroupSize == 16) {
@@ -388,6 +394,9 @@ static vk_device_architecture get_device_architecture(const vk::PhysicalDevice&
             // https://www.intel.com/content/www/us/en/content-details/824434/2024-intel-tech-tour-xe2-and-lunar-lake-s-gpu.html
             // https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html
             return vk_device_architecture::INTEL_XE2;
+        } else if (subgroup_size_control_props.minSubgroupSize == 8 &&
+                 integer_dot_product && integer_dot_props.integerDotProduct4x8BitPackedSignedAccelerated) {
+            return vk_device_architecture::INTEL_XE1;
         }
     } else if (props.vendorID == VK_VENDOR_ID_NVIDIA) {
         const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();
@@ -3837,7 +3846,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
             l_warptile = { 256, 128, 128, 16, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
             l_warptile_mmq = l_warptile_mmq_int = { 256, 128, 128, 32, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
             l_warptile_mmq_int_k = { 256, 128, 128, 32, subgroup_size_16, 64, 1, 4, 2, 1, subgroup_size_16 };
-        } else if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support && device->architecture == INTEL_XE2) {
+        } else if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support) {
             // Xe2/Xe3 with coopmat enabled - warptile performance tuning
             l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
             l_warptile_mmq = { 512, 128, 128, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
@@ -6361,9 +6370,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 break;
             case VK_VENDOR_ID_INTEL: {
                 // Current Windows driver does not expose BF16 support.
-                // We only want to use l_warptile if coopmat is available and is Xe2+
-                const bool xe2_with_coopmat = device->coopmat_support && device->architecture == INTEL_XE2;
-                const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && xe2_with_coopmat) : xe2_with_coopmat;
+                // We only want to use l_warptile if coopmat is available
+                const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && device->coopmat_support) : device->coopmat_support;
                 device->mul_mat_l[i] = use_l_warptile;
                 device->mul_mat_id_l[i] = use_l_warptile;
                 device->mul_mat_m[i] = true;
@@ -17890,9 +17898,9 @@ static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev) {
 static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) {
     switch (props.vendorID) {
     case VK_VENDOR_ID_INTEL:
-        // Only allowing Xe2 GPU at the moment since Xe2 GPU can gain significant performance boost,
-        // while some older hardware (ex. Arc A770) has performance regressions
-        return arch == vk_device_architecture::INTEL_XE2;
+        // Only allowing Xe2/Xe3 GPU and integrated Xe GPUs at the moment since older hardware (ex. Arc A770) has performance regressions.
+        return (arch == vk_device_architecture::INTEL_XE2) ||
+            (arch == vk_device_architecture::INTEL_XE1 && props.deviceType == vk::PhysicalDeviceType::eIntegratedGpu && driver_props.driverID == vk::DriverId::eIntelProprietaryWindows);
     case VK_VENDOR_ID_AMD:
         if (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource) {
             // Workaround for AMD proprietary driver reporting support on all GPUs
@@ -17940,6 +17948,8 @@ static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev)
     case 0xE20B:  // B580
     case 0xE211:  // Pro B60
         return 20;
+    case 0xB080:  // PTL Xe3 LPG 2x6 (12 subslices)
+        return 12;
     default:
         return 0;
     }

From 487a6cc164d950c368d06965ca7d8f297b47bd35 Mon Sep 17 00:00:00 2001
From: nullname <chraac@gmail.com>
Date: Fri, 26 Jun 2026 19:49:24 +0800
Subject: [PATCH 183/292] vulkan: opt mul_mat_vecq for mi50 (#22933)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |  2 +-
 .../vulkan-shaders/mul_mat_vecq.comp          | 45 +++++++++----------
 2 files changed, 22 insertions(+), 25 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c090d4442..ea9191873 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -4719,7 +4719,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     }
     uint32_t rm_iq = 2 * rm_kq;
 
-    const bool use_subgroups = device->subgroup_arithmetic && device->architecture != vk_device_architecture::AMD_GCN;
+    const bool use_subgroups = device->subgroup_arithmetic;
     // Ensure a subgroup size >= 16 is available
     const bool use_subgroups16 = use_subgroups && subgroup_min_size_16;
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp
index fd84c3c91..7bbee577f 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp
@@ -28,13 +28,10 @@ vec2 cache_b_ds;
 
 #include "mul_mat_vecq_funcs.glsl"
 
-void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i) {
+void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint col, const uint b_qs_idx) {
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-        const uint col = i*BLOCK_SIZE + tid*K_PER_ITER;
-
         // Preload data_b block
         const uint b_block_idx = (j*p.batch_stride_b + col) / QUANT_K_Q8_1 + b_offset;
-        const uint b_qs_idx = tid % (32 / K_PER_ITER);
         const uint b_block_idx_outer = b_block_idx / 4;
         const uint b_block_idx_inner = b_block_idx % 4;
         cache_b_ds = vec2(data_b[b_block_idx_outer].ds[b_block_idx_inner]);
@@ -91,35 +88,35 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
         }
     }
 
-    uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE);
-    if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) {
+    const uint col_stride = K_PER_ITER * BLOCK_SIZE;
+    uint num_iters = p.ncols / col_stride;
+    if (num_iters * col_stride + K_PER_ITER * tid < p.ncols) {
         num_iters++;
     }
-    int unroll_count = 4;
-    uint unrolled_iters = num_iters & ~(unroll_count - 1);
 
-    uint i = 0;
-    while (i < unrolled_iters) {
+    const uint b_qs_idx = tid % (32 / K_PER_ITER);
+    uint col = tid * K_PER_ITER;
+    while (num_iters >= 4) {
         // Manually partially unroll the loop
-        [[unroll]] for (uint k = 0; k < unroll_count; ++k) {
-            iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
-            i++;
+        [[unroll]] for (uint k = 0; k < 4; ++k) {
+            iter(temp, first_row, num_rows, col, b_qs_idx);
+            col += col_stride;
         }
+
+        num_iters -= 4;
     }
 
-    unroll_count = 2;
-    unrolled_iters = num_iters & ~(unroll_count - 1);
-
-    while (i < unrolled_iters) {
+    if (num_iters >= 2) {
         // Manually partially unroll the loop
-        [[unroll]] for (uint k = 0; k < unroll_count; ++k) {
-            iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
-            i++;
-        }
+        iter(temp, first_row, num_rows, col, b_qs_idx);
+        col += col_stride;
+        iter(temp, first_row, num_rows, col, b_qs_idx);
+        col += col_stride;
+        num_iters -= 2;
     }
-    while (i < num_iters) {
-        iter(temp, first_row, num_rows, tid, i*K_PER_ITER);
-        i++;
+
+    if (num_iters > 0) {
+        iter(temp, first_row, num_rows, col, b_qs_idx);
     }
 
     reduce_result(temp, d_offset, first_row, num_rows, tid);

From 96183e9820635744e5e5da81775d8ec6522b5657 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 26 Jun 2026 14:37:43 +0300
Subject: [PATCH 184/292] ggml : bump version to 0.15.3 (ggml/1550)

---
 ggml/CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index a0cd4e715..0ec62a377 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -5,7 +5,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 15)
-set(GGML_VERSION_PATCH 2)
+set(GGML_VERSION_PATCH 3)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

From e7ea94afcbcb4e175a3f078c43985c5b3b46c2df Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 26 Jun 2026 15:04:05 +0300
Subject: [PATCH 185/292] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 499be5a58..27bab1a8e 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-707321c4cf6d21cb4bc831aa8b687dbf01a521ce
+eced84c86f8b012c752c016f7fe789adea168e1e

From 5397c3619479ef544e340e4b933929d1783de78b Mon Sep 17 00:00:00 2001
From: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
Date: Fri, 26 Jun 2026 05:07:19 -0700
Subject: [PATCH 186/292] openvino: Update to OV 2026.2.1, self-contained
 release packages, operator improvements (#24974)

* Update to OV 2026.2.1, Make OV release packages self-contained

* Update to OV 2026.2.1, Make OV release packages self-contained

* OpenVINO Backend: Remove compute_op_type hardcoded sets (#222)

* OpenVINO Backend: Remove compute_op_type hardcoded sets

* revert get_op_type removal

* OpenVINO backend: enable softmax with sink input

* OpenVINO backend: opt mul_mat_id convert process for large size

* OpenVINO backend: Modify add_id to support 2D/4D

* OpenVINO Backend: Add glu_swiglu_oai

* PR review: fix paths

* PR review: fix path consistency

---------

Co-authored-by: Mostafa <mostafas.main.email@gmail.com>
Co-authored-by: Xuejun <Xuejun.Zhai@intel.com>
---
 .devops/openvino.Dockerfile                   |  12 +-
 .github/workflows/build-cache.yml             |   8 +-
 .github/workflows/build-openvino.yml          |   8 +-
 .github/workflows/build-self-hosted.yml       |   4 +-
 .github/workflows/release.yml                 |  66 ++++++++--
 docs/backend/OPENVINO.md                      |  12 +-
 ggml/src/ggml-openvino/ggml-decoder.cpp       |  69 +---------
 ggml/src/ggml-openvino/openvino/op/add_id.cpp |  24 +++-
 .../ggml-openvino/openvino/op/glu_swiglu.cpp  |  32 ++++-
 .../ggml-openvino/openvino/op/mul_mat_id.cpp  | 120 +++++++++++++++++-
 .../src/ggml-openvino/openvino/op/softmax.cpp |  70 +++++++++-
 ggml/src/ggml-openvino/openvino/op_table.cpp  |   1 +
 ggml/src/ggml-openvino/openvino/op_table.h    |   1 +
 13 files changed, 316 insertions(+), 111 deletions(-)

diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile
index fec72b1c7..bcaea16cc 100644
--- a/.devops/openvino.Dockerfile
+++ b/.devops/openvino.Dockerfile
@@ -1,12 +1,12 @@
-ARG OPENVINO_VERSION_MAJOR=2026.2
-ARG OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857
+ARG OPENVINO_VERSION_MAJOR=2026.2.1
+ARG OPENVINO_VERSION_FULL=2026.2.1.21919.ede283a88e3
 ARG UBUNTU_VERSION=24.04
 
 # Intel GPU driver versions. https://github.com/intel/compute-runtime/releases
-ARG IGC_VERSION=v2.34.4
-ARG IGC_VERSION_FULL=2_2.34.4+21428
-ARG COMPUTE_RUNTIME_VERSION=26.18.38308.1
-ARG COMPUTE_RUNTIME_VERSION_FULL=26.18.38308.1-0
+ARG IGC_VERSION=v2.36.3
+ARG IGC_VERSION_FULL=2_2.36.3+21719
+ARG COMPUTE_RUNTIME_VERSION=26.22.38646.4
+ARG COMPUTE_RUNTIME_VERSION_FULL=26.22.38646.4-0
 ARG IGDGMM_VERSION=22.10.0
 
 # Intel NPU driver versions. https://github.com/intel/linux-npu-driver/releases
diff --git a/.github/workflows/build-cache.yml b/.github/workflows/build-cache.yml
index b36c6e1ea..327f71978 100644
--- a/.github/workflows/build-cache.yml
+++ b/.github/workflows/build-cache.yml
@@ -68,8 +68,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
@@ -96,8 +96,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
diff --git a/.github/workflows/build-openvino.yml b/.github/workflows/build-openvino.yml
index 49ab13695..938cde3f2 100644
--- a/.github/workflows/build-openvino.yml
+++ b/.github/workflows/build-openvino.yml
@@ -39,8 +39,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
@@ -96,8 +96,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
diff --git a/.github/workflows/build-self-hosted.yml b/.github/workflows/build-self-hosted.yml
index c4366ece3..1a71ed827 100644
--- a/.github/workflows/build-self-hosted.yml
+++ b/.github/workflows/build-self-hosted.yml
@@ -266,8 +266,8 @@ jobs:
 
     env:
       # Sync versions in build.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Clone
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index c7b67e492..eb7e1f20d 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -446,8 +446,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Set OpenVINO version output
@@ -506,8 +506,11 @@ jobs:
           cmake -B build/ReleaseOV -G Ninja \
             -DCMAKE_BUILD_TYPE=Release \
             -DGGML_OPENVINO=ON \
-            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }}
-          cmake --build build/ReleaseOV --config Release -j $(nproc)
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DHF_UI_VERSION=${{ needs.get-version.outputs.ui_version }} \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build/ReleaseOV --config Release --parallel
 
       - name: ccache-clear
         uses: ./.github/actions/ccache-clear
@@ -521,8 +524,26 @@ jobs:
       - name: Pack artifacts
         id: pack_artifacts
         run: |
-          cp LICENSE ./build/ReleaseOV/bin/
-          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C ./build/ReleaseOV/bin .
+          dest=./build/ReleaseOV/bin
+          OPENVINO_ROOT=./openvino_toolkit
+          ov_lib="$OPENVINO_ROOT/runtime/lib/intel64"
+
+          # Bundle OpenVINO runtime libs + TBB. Binaries built with RPATH=$ORIGIN
+          # load these siblings without setupvars.sh / LD_LIBRARY_PATH.
+          cp -P "$ov_lib"/libopenvino.so* \
+                "$ov_lib"/libopenvino_c.so* \
+                "$ov_lib"/libopenvino_*_plugin.so \
+                "$ov_lib"/libopenvino_intel_npu_compiler*.so \
+                "$OPENVINO_ROOT"/runtime/3rdparty/tbb/lib/*.so* \
+                "$dest"
+          cp -P /usr/lib/x86_64-linux-gnu/libOpenCL.so.1* "$dest" 2>/dev/null || true
+          cp "$ov_lib"/cache.json "$dest" 2>/dev/null || true
+
+          # OpenVINO licensing
+          cp -r "$OPENVINO_ROOT"/docs/licensing "$dest"/openvino-licensing
+
+          cp LICENSE "$dest"
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz --transform "s,^\.,llama-${{ steps.tag.outputs.name }}," -C "$dest" .
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
@@ -538,8 +559,8 @@ jobs:
 
     env:
       # Sync versions in build-openvino.yml, build-self-hosted.yml, release.yml, build-cache.yml, .devops/openvino.Dockerfile
-      OPENVINO_VERSION_MAJOR: "2026.2"
-      OPENVINO_VERSION_FULL: "2026.2.0.21903.52ddc073857"
+      OPENVINO_VERSION_MAJOR: "2026.2.1"
+      OPENVINO_VERSION_FULL: "2026.2.1.21919.ede283a88e3"
 
     steps:
       - name: Set OpenVINO version output
@@ -607,7 +628,9 @@ jobs:
             -A x64 ^
             -DCMAKE_BUILD_TYPE=Release ^
             -DGGML_OPENVINO=ON ^
-            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake
+            -DLLAMA_BUILD_BORINGSSL=ON ^
+            -DCMAKE_TOOLCHAIN_FILE=C:\vcpkg\scripts\buildsystems\vcpkg.cmake ^
+            ${{ env.CMAKE_ARGS }}
 
           cmake --build build\ReleaseOV --config Release -- /m
 
@@ -624,8 +647,29 @@ jobs:
         id: pack_artifacts
         shell: powershell
         run: |
-          Copy-Item LICENSE .\build\ReleaseOV\bin\
-          7z a -snl llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip .\build\ReleaseOV\bin\*
+          # Locate the extracted OpenVINO toolkit root (same pattern as the Build step).
+          $OPENVINO_ROOT = (Get-ChildItem -Directory openvino_toolkit | Select-Object -First 1).FullName
+          if (-not $OPENVINO_ROOT) {
+            Write-Error "OpenVINO toolkit folder not found under .\openvino_toolkit"
+            exit 1
+          }
+
+          $dest = ".\build\ReleaseOV\bin\Release"
+
+          $ovBin = Join-Path $OPENVINO_ROOT 'runtime\bin\intel64\Release'
+          Copy-Item -Path (Join-Path $ovBin '*.dll')       -Destination $dest -Force
+          Copy-Item -Path (Join-Path $ovBin 'cache.json')  -Destination $dest -Force
+
+          $tbbBin = Join-Path $OPENVINO_ROOT 'runtime\3rdparty\tbb\bin'
+          Copy-Item -Path (Join-Path $tbbBin 'tbb*.dll') -Destination $dest -Force
+
+          # OpenVINO licensing
+          $licensingDest = Join-Path $dest 'openvino-licensing'
+          New-Item -ItemType Directory -Force -Path $licensingDest | Out-Null
+          Copy-Item -Path (Join-Path $OPENVINO_ROOT 'docs\licensing\*') -Destination $licensingDest -Recurse -Force
+
+          Copy-Item LICENSE $dest
+          7z a -snl llama-${{ steps.tag.outputs.name }}-bin-win-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.zip $dest\*
 
       - name: Upload artifacts
         uses: actions/upload-artifact@v6
diff --git a/docs/backend/OPENVINO.md b/docs/backend/OPENVINO.md
index 631d4bc3b..d5c6f46e2 100644
--- a/docs/backend/OPENVINO.md
+++ b/docs/backend/OPENVINO.md
@@ -237,8 +237,8 @@ chmod +x ubuntu-llamacpp-ov-install.sh
 # ============================================
 set -euo pipefail
 
-OPENVINO_VERSION_MAJOR="2026.2"
-OPENVINO_VERSION_FULL="2026.2.0.21903.52ddc073857"
+OPENVINO_VERSION_MAJOR="2026.2.1"
+OPENVINO_VERSION_FULL="2026.2.1.21919.ede283a88e3"
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 OPENVINO_INSTALL_DIR="/opt/intel/openvino_${OPENVINO_VERSION_MAJOR}"
@@ -334,7 +334,7 @@ echo "  ./build/ReleaseOV/bin/llama-cli -m model.gguf"
 ```
 
 > [!NOTE]
-> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
+> The script pins OpenVINO `2026.2.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release.
 
 </details>
 
@@ -364,8 +364,8 @@ REM ============================================
 REM llama.cpp OpenVINO Build Script (Ninja)
 REM ============================================
 
-set "OPENVINO_VERSION_MAJOR=2026.2"
-set "OPENVINO_VERSION_FULL=2026.2.0.21903.52ddc073857"
+set "OPENVINO_VERSION_MAJOR=2026.2.1"
+set "OPENVINO_VERSION_FULL=2026.2.1.21919.ede283a88e3"
 
 set "SCRIPT_DIR=%~dp0"
 set "VCPKG_DIR=C:\vcpkg"
@@ -547,7 +547,7 @@ endlocal
 ```
 
 > [!NOTE]
-> The script pins OpenVINO `2026.2` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
+> The script pins OpenVINO `2026.2.1` via the `OPENVINO_VERSION_MAJOR` / `OPENVINO_VERSION_FULL` variables at the top — edit them to track a different release. From any new shell, source the matching `setupvars` script via the junction — `call "C:\Intel\openvino\setupvars.bat"` from `cmd`, or `& "C:\Intel\openvino\setupvars.ps1"` from PowerShell. If `winget` cannot register Visual Studio Build Tools on first run, install them once manually and re-run the script from an elevated **Developer Command Prompt for VS 2022**.
 
 </details>
 
diff --git a/ggml/src/ggml-openvino/ggml-decoder.cpp b/ggml/src/ggml-openvino/ggml-decoder.cpp
index b6df4f0fb..48c63e4d7 100644
--- a/ggml/src/ggml-openvino/ggml-decoder.cpp
+++ b/ggml/src/ggml-openvino/ggml-decoder.cpp
@@ -1270,77 +1270,14 @@ void GgmlOvDecoder::visit_subgraph(std::function<void(std::shared_ptr<GgmlDecode
 }
 
 std::string GgmlOvDecoder::compute_op_type(const ggml_tensor * node) {
-    static const std::map<ggml_op, std::string> ops = {
-        {GGML_OP_NONE,            "GGML_OP_NONE"           },
-        {GGML_OP_ACC,             "GGML_OP_ACC"            },
-        {GGML_OP_ADD,             "GGML_OP_ADD"            },
-        {GGML_OP_ADD1,            "GGML_OP_ADD1"           },
-        {GGML_OP_ADD_ID,          "GGML_OP_ADD_ID"         },
-        {GGML_OP_CONCAT,          "GGML_OP_CONCAT"         },
-        {GGML_OP_CONT,            "GGML_OP_CONT"           },
-        {GGML_OP_DIV,             "GGML_OP_DIV"            },
-        {GGML_OP_DUP,             "GGML_OP_DUP"            },
-        {GGML_OP_GET_ROWS,        "GGML_OP_GET_ROWS"       },
-        {GGML_OP_MUL,             "GGML_OP_MUL"            },
-        {GGML_OP_MUL_MAT,         "GGML_OP_MUL_MAT"        },
-        {GGML_OP_MUL_MAT_ID,      "GGML_OP_MUL_MAT_ID"     },
-        {GGML_OP_PERMUTE,         "GGML_OP_PERMUTE"        },
-        {GGML_OP_RESHAPE,         "GGML_OP_RESHAPE"        },
-        {GGML_OP_RMS_NORM,        "GGML_OP_RMS_NORM"       },
-        {GGML_OP_NORM,            "GGML_OP_NORM"           },
-        {GGML_OP_ROPE,            "GGML_OP_ROPE"           },
-        {GGML_OP_SCALE,           "GGML_OP_SCALE"          },
-        {GGML_OP_SOFT_MAX,        "GGML_OP_SOFT_MAX"       },
-        {GGML_OP_SUM_ROWS,        "GGML_OP_SUM_ROWS"       },
-        {GGML_OP_SUB,             "GGML_OP_SUB"            },
-        {GGML_OP_TRANSPOSE,       "GGML_OP_TRANSPOSE"      },
-        {GGML_OP_VIEW,            "GGML_OP_VIEW"           },
-        {GGML_OP_SET_ROWS,        "GGML_OP_SET_ROWS"       },
-        {GGML_OP_CPY,             "GGML_OP_CPY"            },
-        {GGML_OP_FLASH_ATTN_EXT,  "GGML_OP_FLASH_ATTN_EXT" },
-        {GGML_OP_L2_NORM,         "GGML_OP_L2_NORM"        },
-        {GGML_OP_CLAMP,           "GGML_OP_CLAMP"          },
-        {GGML_OP_PAD,             "GGML_OP_PAD"            },
-        {GGML_OP_SSM_CONV,        "GGML_OP_SSM_CONV"       },
-        {GGML_OP_GATED_DELTA_NET, "GGML_OP_GATED_DELTA_NET"},
-        {GGML_OP_ARGSORT,         "GGML_OP_ARGSORT"        },
-        {GGML_OP_REPEAT,          "GGML_OP_REPEAT"         },
-        {GGML_OP_IM2COL,          "GGML_OP_IM2COL"         }
-    };
-    static const std::map<ggml_unary_op, std::string> unary_ops = {
-        {GGML_UNARY_OP_ABS,         "GGML_UNARY_OP_ABS"        },
-        {GGML_UNARY_OP_SGN,         "GGML_UNARY_OP_SGN"        },
-        {GGML_UNARY_OP_NEG,         "GGML_UNARY_OP_NEG"        },
-        {GGML_UNARY_OP_STEP,        "GGML_UNARY_OP_STEP"       },
-        {GGML_UNARY_OP_TANH,        "GGML_UNARY_OP_TANH"       },
-        {GGML_UNARY_OP_ELU,         "GGML_UNARY_OP_ELU"        },
-        {GGML_UNARY_OP_RELU,        "GGML_UNARY_OP_RELU"       },
-        {GGML_UNARY_OP_SIGMOID,     "GGML_UNARY_OP_SIGMOID"    },
-        {GGML_UNARY_OP_GELU,        "GGML_UNARY_OP_GELU"       },
-        {GGML_UNARY_OP_GELU_QUICK,  "GGML_UNARY_OP_GELU_QUICK" },
-        {GGML_UNARY_OP_SILU,        "GGML_UNARY_OP_SILU"       },
-        {GGML_UNARY_OP_SOFTPLUS,    "GGML_UNARY_OP_SOFTPLUS"   },
-        {GGML_UNARY_OP_HARDSWISH,   "GGML_UNARY_OP_HARDSWISH"  },
-        {GGML_UNARY_OP_HARDSIGMOID, "GGML_UNARY_OP_HARDSIGMOID"},
-        {GGML_UNARY_OP_EXP,         "GGML_UNARY_OP_EXP"        },
-        {GGML_UNARY_OP_COUNT,       "GGML_UNARY_OP_COUNT"      }
-    };
-    static const std::map<ggml_glu_op, std::string> glu_ops = {
-        {GGML_GLU_OP_SWIGLU, "GGML_GLU_OP_SWIGLU"},
-        {GGML_GLU_OP_GEGLU,  "GGML_GLU_OP_GEGLU" },
-        {GGML_GLU_OP_REGLU,  "GGML_GLU_OP_REGLU" }
-    };
-
     switch (node->op) {
     case GGML_OP_UNARY:
-        return unary_ops.at(ggml_get_unary_op(node));
+        return std::string("GGML_UNARY_OP_") + ggml_unary_op_name(ggml_get_unary_op(node));
     case GGML_OP_GLU:
-        return glu_ops.at(ggml_get_glu_op(node));
+        return std::string("GGML_GLU_OP_") + ggml_glu_op_name(ggml_get_glu_op(node));
     default:
-        return ops.at(node->op);
+        return std::string("GGML_OP_") + ggml_op_name(node->op);
     }
-    static const std::string unknown_op = "UNKNOWN_GGML_OP";
-    return unknown_op;
 }
 
 const std::string & GgmlOvDecoder::get_op_type(int node_idx) const {
diff --git a/ggml/src/ggml-openvino/openvino/op/add_id.cpp b/ggml/src/ggml-openvino/openvino/op/add_id.cpp
index c8bf08152..e54d700d4 100644
--- a/ggml/src/ggml-openvino/openvino/op/add_id.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/add_id.cpp
@@ -17,6 +17,22 @@ namespace frontend {
 namespace ggml {
 namespace op {
 
+static ov::Output<ov::Node> reshape_add_id_input_to_2d(const ov::Output<ov::Node> & input,
+                                                       const ov::PartialShape & input_shape,
+                                                       const std::vector<int> & dims) {
+    const auto actual_shape = input.get_partial_shape();
+    if (actual_shape.rank().is_static() && actual_shape.rank().get_length() == 2) {
+        return input;
+    }
+
+    if (input_shape.rank().is_static() && input_shape.rank().get_length() == 2) {
+        return input;
+    }
+
+    auto shape = std::make_shared<ov::op::v3::ShapeOf>(input, ov::element::i64);
+    return std::make_shared<ov::op::v1::Reshape>(input, get_dimensions(shape, dims), false);
+}
+
 OutputVector translate_add_id(const NodeContext & context) {
     num_inputs_check(context, 3, 3);
 
@@ -28,11 +44,9 @@ OutputVector translate_add_id(const NodeContext & context) {
     //   input: [1, n_token, n_used, n_embd]
     //   bias:  [1, 1, n_expert, n_embd]
     //   ids:   [1, 1, n_token, n_used]
-    auto bias_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(bias, ov::element::i64);
-    auto ids_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
-
-    bias = std::make_shared<ov::op::v1::Reshape>(bias, get_dimensions(bias_shape_4d, {2, 3}), false);
-    ids = std::make_shared<ov::op::v1::Reshape>(ids, get_dimensions(ids_shape_4d, {2, 3}), false);
+    // Model bias constants may already be stored as [n_expert, n_embd].
+    bias = reshape_add_id_input_to_2d(bias, context.get_input_shape(1), {2, 3});
+    ids = reshape_add_id_input_to_2d(ids, context.get_input_shape(2), {2, 3});
 
     if (ids.get_element_type() != ov::element::i32 && ids.get_element_type() != ov::element::i64) {
         ids = std::make_shared<ov::op::v0::Convert>(ids, ov::element::i32);
diff --git a/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
index 5c46e0713..d220f2f58 100644
--- a/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/glu_swiglu.cpp
@@ -3,8 +3,11 @@
 #include "../utils.h"
 
 #include <cstdint>
+#include <limits>
 #include <memory>
 #include <openvino/core/node_output.hpp>
+#include <openvino/op/add.hpp>
+#include <openvino/op/clamp.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/multiply.hpp>
 #include <openvino/op/sigmoid.hpp>
@@ -15,7 +18,7 @@ namespace frontend {
 namespace ggml {
 namespace op {
 
-OutputVector translate_glu_swiglu(const NodeContext & context) {
+static std::pair<ov::Output<ov::Node>, ov::Output<ov::Node>> get_glu_inputs(const NodeContext & context) {
     num_inputs_check(context, 1, 2);
 
     ov::Output<ov::Node> src0;
@@ -52,6 +55,12 @@ OutputVector translate_glu_swiglu(const NodeContext & context) {
         std::swap(src0, src1);
     }
 
+    return {src0, src1};
+}
+
+OutputVector translate_glu_swiglu(const NodeContext & context) {
+    auto [src0, src1] = get_glu_inputs(context);
+
     auto sigmoid = std::make_shared<ov::op::v0::Sigmoid>(src0);
     auto silu = std::make_shared<ov::op::v1::Multiply>(src0, sigmoid);
     auto res = std::make_shared<ov::op::v1::Multiply>(silu, src1);
@@ -59,6 +68,27 @@ OutputVector translate_glu_swiglu(const NodeContext & context) {
     return rename_outputs_with_suffix({res}, context.get_name());
 }
 
+OutputVector translate_glu_swiglu_oai(const NodeContext & context) {
+    auto [src0, src1] = get_glu_inputs(context);
+
+    const int32_t * params = context.get_output_op_params();
+    const float alpha = reinterpret_cast<const float *>(params)[2];
+    const float limit = reinterpret_cast<const float *>(params)[3];
+
+    auto gate = std::make_shared<ov::op::v0::Clamp>(src0, -std::numeric_limits<float>::infinity(), limit);
+    auto alpha_const = ov::op::v0::Constant::create(ov::element::f32, {}, {alpha});
+    auto scaled_gate = std::make_shared<ov::op::v1::Multiply>(gate, alpha_const);
+    auto sigmoid = std::make_shared<ov::op::v0::Sigmoid>(scaled_gate);
+    auto out_glu = std::make_shared<ov::op::v1::Multiply>(gate, sigmoid);
+
+    auto up = std::make_shared<ov::op::v0::Clamp>(src1, -limit, limit);
+    auto one = ov::op::v0::Constant::create(ov::element::f32, {}, {1.0f});
+    auto up_plus_one = std::make_shared<ov::op::v1::Add>(up, one);
+    auto res = std::make_shared<ov::op::v1::Multiply>(out_glu, up_plus_one);
+
+    return rename_outputs_with_suffix({res}, context.get_name());
+}
+
 }  // namespace op
 }  // namespace ggml
 }  // namespace frontend
diff --git a/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
index 09e29d4cc..6df2784c2 100644
--- a/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
@@ -2,23 +2,135 @@
 #include "../op_table.h"
 #include "../utils.h"
 
+#include <cstdint>
+#include <cstring>
+#include <limits>
 #include <memory>
+#include <openvino/op/bitwise_and.hpp>
+#include <openvino/op/bitwise_right_shift.hpp>
 #include <openvino/op/broadcast.hpp>
 #include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
 #include <openvino/op/gather.hpp>
 #include <openvino/op/matmul.hpp>
+#include <openvino/op/multiply.hpp>
 #include <openvino/op/reshape.hpp>
 #include <openvino/op/shape_of.hpp>
-#include <openvino/op/squeeze.hpp>
+#include <openvino/op/slice.hpp>
 #include <openvino/op/unsqueeze.hpp>
+#include <vector>
 
 namespace ov {
 namespace frontend {
 namespace ggml {
 namespace op {
 
+namespace {
+
+std::shared_ptr<ov::op::v0::Constant> const_i64(const std::vector<int64_t> & values) {
+    return ov::op::v0::Constant::create(ov::element::i64, ov::Shape{values.size()}, values);
+}
+
+ov::Output<ov::Node> slice_axis(const ov::Output<ov::Node> & input, int64_t axis, int64_t begin, int64_t end) {
+    return std::make_shared<ov::op::v8::Slice>(input, const_i64({begin}), const_i64({end}), const_i64({1}),
+                                              const_i64({axis}));
+}
+
+ov::Output<ov::Node> translate_mul_mat_id_mxfp4_packed(const NodeContext & context,
+                                                       ov::Output<ov::Node> expert_weights,
+                                                       ov::Output<ov::Node> activations,
+                                                       ov::Output<ov::Node> ids) {
+    auto packed_shape = expert_weights.get_partial_shape().to_shape();
+    FRONT_END_OP_CONVERSION_CHECK(packed_shape.size() == 5 && packed_shape[4] == 17,
+                                  "Expected packed MXFP4 expert weights with shape [1, n_expert, m, k_blocks, 17]");
+
+    const int64_t n_expert = static_cast<int64_t>(packed_shape[1]);
+    const int64_t rows = static_cast<int64_t>(packed_shape[2]);
+    const int64_t k_blocks = static_cast<int64_t>(packed_shape[3]);
+    const int64_t qk = 32;
+    const int64_t cols = k_blocks * qk;
+
+    auto packed_shape_4d = const_i64({n_expert, rows, k_blocks, 17});
+    expert_weights = std::make_shared<ov::op::v1::Reshape>(expert_weights, packed_shape_4d, false);
+
+    auto activations_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
+    auto ids_shape_4d = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
+    auto activations_shape_3d = get_dimensions(activations_shape_4d, {1, 2, 3});
+    auto ids_shape_2d = get_dimensions(ids_shape_4d, {2, 3});
+
+    activations = std::make_shared<ov::op::v1::Reshape>(activations, activations_shape_3d, false);
+    ids = std::make_shared<ov::op::v1::Reshape>(ids, ids_shape_2d, false);
+    if (ids.get_element_type() != ov::element::i32 && ids.get_element_type() != ov::element::i64) {
+        ids = std::make_shared<ov::op::v0::Convert>(ids, ov::element::i32);
+    }
+
+    auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0});
+
+    static const std::vector<float> f4e2m1_lut = {0.0f,  0.5f,  1.0f,  1.5f,  2.0f,  3.0f,  4.0f,  6.0f,
+                                                  -0.0f, -0.5f, -1.0f, -1.5f, -2.0f, -3.0f, -4.0f, -6.0f};
+    std::vector<float> e8m0_lut(256);
+    for (size_t i = 0; i < e8m0_lut.size(); ++i) {
+        uint32_t bits = static_cast<uint32_t>(i) << 23;
+        memcpy(&e8m0_lut[i], &bits, sizeof(float));
+    }
+    e8m0_lut[0] = std::numeric_limits<float>::min() / 2.0f;
+    e8m0_lut[255] = std::numeric_limits<float>::quiet_NaN();
+
+    auto f4_lut = ov::op::v0::Constant::create(ov::element::f32, ov::Shape{f4e2m1_lut.size()}, f4e2m1_lut);
+    auto scale_lut = ov::op::v0::Constant::create(ov::element::f32, ov::Shape{e8m0_lut.size()}, e8m0_lut);
+
+    auto selected_packed_weights = std::make_shared<ov::op::v8::Gather>(expert_weights, ids, gather_axis);
+    auto scale_byte = slice_axis(selected_packed_weights, 4, 0, 1);
+    auto qs = slice_axis(selected_packed_weights, 4, 1, 17);
+    auto low = std::make_shared<ov::op::v13::BitwiseAnd>(
+        qs, ov::op::v0::Constant::create(ov::element::u8, ov::Shape{}, {0x0F}), ov::op::AutoBroadcastType::NUMPY);
+    auto high_shift = std::make_shared<ov::op::v15::BitwiseRightShift>(
+        qs, ov::op::v0::Constant::create(ov::element::u8, ov::Shape{}, {4}), ov::op::AutoBroadcastType::NUMPY);
+    auto nibbles = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{low, high_shift}, 4);
+    auto nibble_indices = std::make_shared<ov::op::v0::Convert>(nibbles, ov::element::i32);
+    auto weights_f32 = std::make_shared<ov::op::v8::Gather>(f4_lut, nibble_indices, gather_axis);
+
+    auto scale_indices = std::make_shared<ov::op::v0::Convert>(scale_byte, ov::element::i32);
+    auto scales_f32 = std::make_shared<ov::op::v8::Gather>(scale_lut, scale_indices, gather_axis);
+    ov::Output<ov::Node> selected_weights = std::make_shared<ov::op::v1::Multiply>(weights_f32, scales_f32,
+                                                                                  ov::op::AutoBroadcastType::NUMPY);
+
+    auto ids_shape = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
+    auto selected_weights_target_dims = std::make_shared<ov::op::v0::Concat>(
+        ov::OutputVector{get_dimensions(ids_shape, {0, 1}), const_i64({rows, cols})}, 0);
+    selected_weights = std::make_shared<ov::op::v1::Reshape>(selected_weights, selected_weights_target_dims, false);
+
+    auto activations_shape = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
+    ov::Output<ov::Node> acts_target_dims = std::make_shared<ov::op::v0::Concat>(
+        ov::OutputVector{
+            get_dimensions(activations_shape, {0}),
+            get_dimensions(ids_shape, {1}),
+            get_dimensions(activations_shape, {2}),
+        },
+        0);
+    ov::Output<ov::Node> acts_broadcasted =
+        std::make_shared<ov::op::v3::Broadcast>(activations, acts_target_dims, ov::op::BroadcastType::BIDIRECTIONAL);
+
+    auto activations_expanded = std::make_shared<ov::op::v0::Unsqueeze>(acts_broadcasted, const_i64({2}));
+    ov::Output<ov::Node> result =
+        std::make_shared<ov::op::v0::MatMul>(activations_expanded, selected_weights, false, true);
+
+    auto batch_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+    auto row_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {rows});
+    auto result_target_dims = std::make_shared<ov::op::v0::Concat>(
+        ov::OutputVector{batch_dim, get_dimensions(ids_shape, {0, 1}), row_dim}, 0);
+    result = std::make_shared<ov::op::v1::Reshape>(result, result_target_dims, false);
+
+    const auto output_type = context.get_output_type();
+    if (result.get_element_type() != output_type) {
+        result = std::make_shared<ov::op::v0::Convert>(result, output_type);
+    }
+    return result;
+}
+
+}  // namespace
+
 OutputVector translate_mul_mat_id(const NodeContext & context) {
     num_inputs_check(context, 3, 3);
 
@@ -26,6 +138,12 @@ OutputVector translate_mul_mat_id(const NodeContext & context) {
     auto activations = process_view_input_new(context, 1);
     auto ids = process_view_input_new(context, 2);
 
+    if (expert_weights.get_element_type() == ov::element::u8 && expert_weights.get_partial_shape().rank().is_static() &&
+        expert_weights.get_partial_shape().rank().get_length() == 5) {
+        return rename_outputs_with_suffix({translate_mul_mat_id_mxfp4_packed(context, expert_weights, activations, ids)},
+                                          context.get_name());
+    }
+
     // OpenVINO sees GGML tensors in reversed dimension order:
     //   weights: [1, n_expert, m, k]
     //   activations: [1, n_tokens, n_used_or_1, k]
diff --git a/ggml/src/ggml-openvino/openvino/op/softmax.cpp b/ggml/src/ggml-openvino/openvino/op/softmax.cpp
index 287faedbb..b391d3f91 100644
--- a/ggml/src/ggml-openvino/openvino/op/softmax.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/softmax.cpp
@@ -6,12 +6,16 @@
 #include <cstdint>
 #include <cstring>
 #include <memory>
+#include <openvino/op/broadcast.hpp>
 #include <openvino/frontend/exception.hpp>
 #include <openvino/op/add.hpp>
+#include <openvino/op/concat.hpp>
 #include <openvino/op/constant.hpp>
 #include <openvino/op/convert.hpp>
 #include <openvino/op/multiply.hpp>
 #include <openvino/op/reshape.hpp>
+#include <openvino/op/shape_of.hpp>
+#include <openvino/op/slice.hpp>
 #include <openvino/op/softmax.hpp>
 #include <vector>
 
@@ -20,12 +24,31 @@ namespace frontend {
 namespace ggml {
 namespace op {
 
+static bool is_static_one(const ov::Dimension & dim) {
+    return dim.is_static() && dim.get_length() == 1;
+}
+
+static bool same_static_dim(const ov::Dimension & lhs, const ov::Dimension & rhs) {
+    return lhs.is_static() && rhs.is_static() && lhs.get_length() == rhs.get_length();
+}
+
+static bool is_attention_sinks_input_shape(const ov::PartialShape & candidate, const ov::PartialShape & logits_shape) {
+    if (candidate.rank().is_dynamic() || logits_shape.rank().is_dynamic() || candidate.rank().get_length() != 4 ||
+        logits_shape.rank().get_length() != 4) {
+        return false;
+    }
+
+    return is_static_one(candidate[0]) && is_static_one(candidate[1]) && is_static_one(candidate[2]) &&
+           same_static_dim(candidate[3], logits_shape[1]);
+}
+
 // Reimplementation of GGML_OP_SOFT_MAX semantics for OpenVINO backend:
 // 1) logits = src0 * scale
 // 2) logits += mask (if provided)
-// 3) softmax over the last dimension
+// 3) append attention sinks as hidden logits (if provided)
+// 4) softmax over the last dimension and remove the hidden sink column
 OutputVector translate_soft_max(const NodeContext & context) {
-    num_inputs_check(context, 1, 2);
+    num_inputs_check(context, 1, 3);
 
     float scale = 1.0f;
     float max_bias = 0.0f;
@@ -33,6 +56,11 @@ OutputVector translate_soft_max(const NodeContext & context) {
     memcpy(&max_bias, (float *) context.get_output_op_params() + 1, sizeof(float));
 
     ov::Output<ov::Node> logits = context.get_input(0);
+    const bool second_input_is_sinks =
+        context.get_input_size() == 2 && is_attention_sinks_input_shape(context.get_input_shape(1), context.get_output_shape());
+    const bool has_mask = context.get_input_size() > 1 && !second_input_is_sinks;
+    const bool has_sinks = second_input_is_sinks || context.get_input_size() > 2;
+    const size_t sinks_input_idx = second_input_is_sinks ? 1 : 2;
 
     // Apply scale first: logits = src0 * scale
     if (scale != 1.0f) {
@@ -41,12 +69,12 @@ OutputVector translate_soft_max(const NodeContext & context) {
         logits = std::make_shared<ov::op::v1::Multiply>(logits, scale_const);
     }
 
-    FRONT_END_CHECK_IMPLEMENTED(!(max_bias > 0.0f && context.get_input_size() < 2),
+    FRONT_END_CHECK_IMPLEMENTED(!(max_bias > 0.0f && !has_mask),
                                 "OpenVINO softmax ALiBi path requires mask input");
 
     // Optional mask add: logits += mask
     // For max_bias > 0 (ALiBi), apply per-head slope to mask before adding.
-    if (context.get_input_size() > 1) {
+    if (has_mask) {
         ov::Output<ov::Node> mask = context.get_input(1);
 
         // For stateful
@@ -94,8 +122,40 @@ OutputVector translate_soft_max(const NodeContext & context) {
         logits = std::make_shared<ov::op::v1::Add>(logits, mask);
     }
 
+    ov::Output<ov::Node> softmax_input = logits;
+    if (has_sinks) {
+        ov::Output<ov::Node> sinks = context.get_input(sinks_input_idx);
+        if (sinks.get_element_type() != logits.get_element_type()) {
+            sinks = std::make_shared<ov::op::v0::Convert>(sinks, logits.get_element_type());
+        }
+
+        auto sink_shape = ov::op::v0::Constant::create(ov::element::i64, {4}, {1, -1, 1, 1});
+        auto sinks_4d = std::make_shared<ov::op::v1::Reshape>(sinks, sink_shape, false);
+
+        auto logits_shape = std::make_shared<ov::op::v3::ShapeOf>(logits, ov::element::i64);
+        auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
+        auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+        auto three = ov::op::v0::Constant::create(ov::element::i64, {1}, {3});
+        auto four = ov::op::v0::Constant::create(ov::element::i64, {1}, {4});
+        auto shape_axis = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
+
+        auto sink_prefix_shape = std::make_shared<ov::op::v8::Slice>(logits_shape, zero, three, one, shape_axis);
+        auto sink_last_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
+        auto sink_broadcast_shape = std::make_shared<ov::op::v0::Concat>(
+            ov::OutputVector{sink_prefix_shape, sink_last_dim}, 0);
+        auto sink_column = std::make_shared<ov::op::v3::Broadcast>(sinks_4d, sink_broadcast_shape,
+                                                                   ov::op::BroadcastType::BIDIRECTIONAL);
+        softmax_input = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{logits, sink_column}, 3);
+
+        auto softmax_with_sink = std::make_shared<ov::op::v8::Softmax>(softmax_input, -1);
+        auto original_last_dim = std::make_shared<ov::op::v8::Slice>(logits_shape, three, four, one, shape_axis);
+        auto res = std::make_shared<ov::op::v8::Slice>(softmax_with_sink, zero, original_last_dim, one, three);
+
+        return rename_outputs_with_suffix({res}, context.get_name());
+    }
+
     // Softmax along last dimension (equivalent to ggml softmax over ne[0]).
-    auto res = std::make_shared<ov::op::v8::Softmax>(logits, -1);
+    auto res = std::make_shared<ov::op::v8::Softmax>(softmax_input, -1);
 
     return rename_outputs_with_suffix({res}, context.get_name());
 }
diff --git a/ggml/src/ggml-openvino/openvino/op_table.cpp b/ggml/src/ggml-openvino/openvino/op_table.cpp
index f84a1bf93..59fd26df8 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.cpp
+++ b/ggml/src/ggml-openvino/openvino/op_table.cpp
@@ -47,6 +47,7 @@ std::unordered_map<std::string, CreatorFunction> get_supported_ops() {
         {"GGML_UNARY_OP_TANH",      op::translate_1to1_match_1_input<v0::Tanh>     },
         {"GGML_OP_VIEW",            op::translate_view                             },
         {"GGML_GLU_OP_SWIGLU",      op::translate_glu_swiglu                       },
+        {"GGML_GLU_OP_SWIGLU_OAI",  op::translate_glu_swiglu_oai                   },
         {"GGML_GLU_OP_GEGLU",       op::translate_glu_geglu                        },
         {"GGML_OP_SET_ROWS",        op::translate_set_rows                         },
         {"GGML_OP_CPY",             op::translate_cpy                              },
diff --git a/ggml/src/ggml-openvino/openvino/op_table.h b/ggml/src/ggml-openvino/openvino/op_table.h
index c90ff8377..1d695fa12 100644
--- a/ggml/src/ggml-openvino/openvino/op_table.h
+++ b/ggml/src/ggml-openvino/openvino/op_table.h
@@ -32,6 +32,7 @@ GGML_OP_CONVERTER(translate_soft_max);
 GGML_OP_CONVERTER(translate_transpose);
 GGML_OP_CONVERTER(translate_view);
 GGML_OP_CONVERTER(translate_glu_swiglu);
+GGML_OP_CONVERTER(translate_glu_swiglu_oai);
 GGML_OP_CONVERTER(translate_glu_geglu);
 GGML_OP_CONVERTER(translate_set_rows);
 GGML_OP_CONVERTER(translate_cpy);

From 024930c6ad80ea9eb61159d807571e64b187bd99 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 26 Jun 2026 14:36:03 +0200
Subject: [PATCH 187/292] arg: fix handling --spec-draft-hf and --hf-repo-v
 (#25043)

* arg: fix handling --spec-draft-hf and --hf-repo-v

* fix missing mparams.hf_file
---
 common/arg.cpp | 52 +++++++++++++++++++++++++++++++++++++++++++-------
 common/arg.h   |  2 ++
 2 files changed, 47 insertions(+), 7 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 841a38e96..841ca3ce2 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -352,6 +352,8 @@ static std::string get_default_local_path(const std::string & url) {
 
 common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
     common_download_hf_plan plan;
+    common_download_hf_plan plan_spec;
+    common_download_hf_plan plan_voc;
     common_download_opts opts;
 
     const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
@@ -377,7 +379,15 @@ common_models_handler common_models_handler_init(const common_params & params, l
         plan = common_download_get_hf_plan(params.model, opts);
     }
 
-    return common_models_handler{plan, opts};
+    if (!params.speculative.draft.mparams.hf_repo.empty()) {
+        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts);
+    }
+
+    if (!params.vocoder.model.hf_repo.empty()) {
+        plan_voc = common_download_get_hf_plan(params.vocoder.model, opts);
+    }
+
+    return common_models_handler{plan, plan_spec, plan_voc, opts};
 }
 
 bool common_models_handler_is_preset_repo(const common_models_handler & handler) {
@@ -425,7 +435,9 @@ static std::vector<common_download_task> build_url_tasks(const common_params_mod
 void common_models_handler_apply(common_models_handler & handler, common_params & params, common_download_callback * callback) {
     std::vector<common_download_task> tasks;
 
-    auto & plan = handler.plan;
+    auto & plan      = handler.plan;
+    auto & plan_spec = handler.plan_spec;
+    auto & plan_voc  = handler.plan_voc;
 
     auto opts = handler.opts; // copy
     opts.callback = callback;
@@ -484,19 +496,22 @@ void common_models_handler_apply(common_models_handler & handler, common_params
     }
 
     // handle hf_plan tasks
-    if (!plan.model_files.empty()) {
-        for (size_t i = 0; i < plan.model_files.size(); ++i) {
-            auto & model_file = plan.model_files[i];
+    auto add_tasks = [&opts, &tasks](const hf_cache::hf_files & model_files, common_params_model & model) {
+        for (size_t i = 0; i < model_files.size(); ++i) {
+            auto & model_file = model_files[i];
             bool is_first = (i == 0);
             tasks.emplace_back(model_file, opts, [&, is_first]() {
                 if (is_first) {
                     // only use first part as model path
-                    params.model.path = hf_cache::finalize_file(model_file);
+                    model.path = hf_cache::finalize_file(model_file);
                 } else {
                     hf_cache::finalize_file(model_file);
                 }
             });
         }
+    };
+    if (!plan.model_files.empty()) {
+        add_tasks(plan.model_files, params.model);
     }
     if (!plan.mmproj.local_path.empty()) {
         tasks.emplace_back(plan.mmproj, opts, [&]() {
@@ -522,9 +537,31 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         });
     }
 
+    // handle plan_spec (e.g. --spec-draft-hf)
+    if (!plan_spec.model_files.empty()) {
+        add_tasks(plan_spec.model_files, params.speculative.draft.mparams);
+    }
+
+    // handle vocoder plan (e.g. --hf-repo-v)
+    if (!plan_voc.model_files.empty()) {
+        add_tasks(plan_voc.model_files, params.vocoder.model);
+    }
+
     // run all tasks in parallel
     if (!params.offline) {
-        common_download_run_tasks(tasks);
+        // if duplicated files are found, only download once (but still call on_done for each task)
+        std::unordered_map<std::string, common_download_task *> unique_tasks;
+        for (auto & task : tasks) {
+            auto it = unique_tasks.find(task.local_path);
+            if (it == unique_tasks.end()) {
+                unique_tasks[task.local_path] = &task;
+            }
+        }
+        std::vector<common_download_task> unique_tasks_vec;
+        for (auto & pair : unique_tasks) {
+            unique_tasks_vec.push_back(*pair.second);
+        }
+        common_download_run_tasks(unique_tasks_vec);
     }
 
     // download successful, update params with the downloaded paths
@@ -3711,6 +3748,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.draft.mparams.path = value;
+            params.speculative.draft.mparams.hf_file = value; // will be used if --spec-draft-hf is set
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_MODEL"));
     add_opt(common_arg(
diff --git a/common/arg.h b/common/arg.h
index 508e33d29..54a38b9cc 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -133,6 +133,8 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 
 struct common_models_handler {
     common_download_hf_plan plan;
+    common_download_hf_plan plan_spec;
+    common_download_hf_plan plan_voc;
     common_download_opts opts;
 };
 

From 5d8ccdf9d1273bfd83ad8f72565885acc450997e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Fri, 26 Jun 2026 15:15:48 +0200
Subject: [PATCH 188/292] devops : add llama in all docker images (#25035)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 .devops/cann.Dockerfile     | 4 ++--
 .devops/cpu.Dockerfile      | 4 ++--
 .devops/cuda.Dockerfile     | 4 ++--
 .devops/intel.Dockerfile    | 4 ++--
 .devops/musa.Dockerfile     | 4 ++--
 .devops/openvino.Dockerfile | 4 ++--
 .devops/rocm.Dockerfile     | 4 ++--
 .devops/s390x.Dockerfile    | 4 ++--
 .devops/vulkan.Dockerfile   | 4 ++--
 .devops/zendnn.Dockerfile   | 4 ++--
 10 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile
index dc95e3f38..36cee7bdb 100644
--- a/.devops/cann.Dockerfile
+++ b/.devops/cann.Dockerfile
@@ -145,7 +145,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ==============================================================================
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 ENTRYPOINT [ "/app/llama-cli" ]
 
@@ -156,7 +156,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ]
 
diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index caf727bcd..cb92343d6 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -104,7 +104,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -115,7 +115,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
index b16b9a8f1..c9a498d53 100644
--- a/.devops/cuda.Dockerfile
+++ b/.devops/cuda.Dockerfile
@@ -113,7 +113,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -124,7 +124,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile
index 3c059eb30..b4bcd94b9 100644
--- a/.devops/intel.Dockerfile
+++ b/.devops/intel.Dockerfile
@@ -141,7 +141,7 @@ ENTRYPOINT ["/app/tools.sh"]
 FROM base AS light
 
 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -153,7 +153,7 @@ FROM base AS server
 ENV LLAMA_ARG_HOST=0.0.0.0
 
 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile
index 0c23cc554..d30a70bb3 100644
--- a/.devops/musa.Dockerfile
+++ b/.devops/musa.Dockerfile
@@ -115,7 +115,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -126,7 +126,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/openvino.Dockerfile b/.devops/openvino.Dockerfile
index bcaea16cc..9b2784b66 100644
--- a/.devops/openvino.Dockerfile
+++ b/.devops/openvino.Dockerfile
@@ -214,7 +214,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app/
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app/
 
 WORKDIR /app
 
@@ -225,7 +225,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app/
+COPY --from=build /app/full/llama /app/full/llama-server /app/
 
 WORKDIR /app
 
diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile
index 7fad0c22e..a8bc4e1fc 100644
--- a/.devops/rocm.Dockerfile
+++ b/.devops/rocm.Dockerfile
@@ -127,7 +127,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -138,7 +138,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile
index d88dd2d92..94a715ff2 100644
--- a/.devops/s390x.Dockerfile
+++ b/.devops/s390x.Dockerfile
@@ -124,7 +124,7 @@ WORKDIR /llama.cpp/bin
 
 # Copy llama.cpp binaries and libraries
 COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin
+COPY --from=collector /llama.cpp/bin/llama /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin
 
 ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]
 
@@ -138,7 +138,7 @@ WORKDIR /llama.cpp/bin
 
 # Copy llama.cpp binaries and libraries
 COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-server /llama.cpp/bin
+COPY --from=collector /llama.cpp/bin/llama /llama.cpp/bin/llama-server /llama.cpp/bin
 
 EXPOSE 8080
 
diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index 26c1902b1..d3599ffb8 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -107,7 +107,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -118,7 +118,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 
diff --git a/.devops/zendnn.Dockerfile b/.devops/zendnn.Dockerfile
index 80daf5671..8a50b3ef6 100644
--- a/.devops/zendnn.Dockerfile
+++ b/.devops/zendnn.Dockerfile
@@ -97,7 +97,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
+COPY --from=build /app/full/llama /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
@@ -108,7 +108,7 @@ FROM base AS server
 
 ENV LLAMA_ARG_HOST=0.0.0.0
 
-COPY --from=build /app/full/llama-server /app
+COPY --from=build /app/full/llama /app/full/llama-server /app
 
 WORKDIR /app
 

From 3fc4e105279105106b08a133a4e3e483116e621f Mon Sep 17 00:00:00 2001
From: Andreas Kieslinger <47689530+aendk@users.noreply.github.com>
Date: Fri, 26 Jun 2026 16:18:30 +0200
Subject: [PATCH 189/292] sched : reintroduce less synchronizations during
 split compute (#20793)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CUDA:  Improve performance via less synchronizations between token (#17795)

* Adds CPU-to-CUDA copy capability to
ggml_backend_cuda_cpy_tensor_async()

* Adds function to relax sync requirements between input copies on
supported backends (CUDA for now)

* Exchanges synchronous copy with async copy function.

* Adds macro guards to allow compilation in non-CUDA builds

* Reworked backend detection in ggml-backend.cpp to avoid linking
conflicts

* Relax requirement of checks in async CUDA copies from backend and buffer type to just buffer type, to avoid linking issues

* Minor cleanup

* Makes opt-in to relax use of explicit syncs more general. Backends like
vulkan which require a synchronization between HtoD copies and graph
execution could also adopt this change now.

* Reintroduces stricter check for CPU->CUDA backend async copy via
GGML_DEVICE_TYPE_CPU.

* Corrects initialization of ggml_backend_sync_mode in
ggml_backend_sched_split initialization

* Simplifies synchronizations to adhere to `saaasg` pattern.

* Apply suggestion from @ggerganov (src->buffer to buf_src)

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Apply suggestion from @ggerganov (src->buffer to buf_src) v2

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Apply suggestions from @johannesgaessler code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Adds single-GPU synchronizations to multi-GPU settings to fix hip backend pipeline parallel bugs.

* Scheduler Hardening: Exclude hip/MUSA from copy_from_host CPU split ->
GPU split optimization

* Scheduler Hardening: Re-adding original additional synchronizations for
non-async backends

* Adds disclaimer to hip/musa exclusion of copy_from_host. Highlights that it is out of
precaution, but that no perf-impact is visible, and that it can be
revisited separately anytime.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-backend.cpp       | 10 +++++++---
 ggml/src/ggml-cuda/ggml-cuda.cu | 24 ++++++++++++++++++++----
 2 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 87615921c..644130cd1 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1551,6 +1551,8 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
+        ggml_backend_synchronize(split_backend);
+
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1561,15 +1563,15 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else {
+                } else if (!split_backend->iface.cpy_tensor_async) {
                     ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy(input, input_cpy);
+                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
             } else {
                 // wait for the split backend to finish using the input before overwriting it
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else {
+                } else if (!split_backend->iface.cpy_tensor_async) {
                     ggml_backend_synchronize(split_backend);
                 }
 
@@ -1674,6 +1676,8 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
+        ggml_backend_synchronize(split_backend);
+
         if (!sched->callback_eval) {
             enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
             if (ec != GGML_STATUS_SUCCESS) {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index cca70592f..f3fb32452 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3192,11 +3192,24 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
     ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) {
+    // Enables async copies from CPU to CUDA, instead of only CUDA-to-CUDA
+    // Excluding this path for HIP and MUSA as a precaution.
+    // According to the summary in https://github.com/ggml-org/llama.cpp/pull/20793#issuecomment-4275794315, this change is not beneficial for hip anyways.
+    // Additionally, there is a lot of anectodal evidence that hip/musa stream behavior might not always 1:1 match CUDA behavior.
+    // e.g. https://github.com/ROCm/rocm-systems/issues/5109
+    // It thus makes sense to exclude this path for HIP and MUSA. This PR was not aimed these backends, the majority of testing happened on CUDA.
+    // This can be revisited in the future if enabling copy_from_host benefits hip/MUSA, and if the PR author can extensively test on these backends.
+#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
+    const bool copy_from_host = false;
+#else
+    const bool copy_from_host = ggml_backend_buffer_is_host(buf_src) && ggml_backend_dev_type(backend_src->device) == GGML_BACKEND_DEVICE_TYPE_CPU;
+#endif
+
+    if (!(copy_from_host || ggml_backend_is_cuda(backend_src)) || !ggml_backend_is_cuda(backend_dst)) {
         return false;
     }
 
-    if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
+    if (!(copy_from_host || ggml_backend_buffer_is_cuda(buf_src)) || !ggml_backend_buffer_is_cuda(buf_dst)) {
         return false;
     }
 
@@ -3207,14 +3220,17 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
     ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
 
-    if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
+    if ((copy_from_host && cuda_ctx_dst->device != buf_ctx_dst->device) ||
+        !copy_from_host && (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device)) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
 #endif // NDEBUG
         return false;
     }
 
-    if (backend_src != backend_dst) {
+    if (copy_from_host) {
+        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyHostToDevice, cuda_ctx_dst->stream()));
+    } else if (backend_src != backend_dst) {
         // copy on src stream
         if (cuda_ctx_src->device == cuda_ctx_dst->device) {
             CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));

From ae3c1b6a198efef55402fedea5ce8e538525c077 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 23:01:30 +0800
Subject: [PATCH 190/292] fix vision regression

---
 gpttype_adapter.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index e8d283eca..dd80c8b75 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -5347,7 +5347,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
     {
         intro = "\n<|channel><channel|>" + intro;
     }
-    TokenizeString(intro, media_intro, file_format, true);
+    TokenizeString(intro, media_intro, file_format, add_bos_token);
 
     //clear previous run media memory, just-in-time free
     for(int i=0;i<media_objects.size();++i)

From e8e13ee8d249bfb778cb525940e31cdd0f30aabe Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 26 Jun 2026 23:01:49 +0800
Subject: [PATCH 191/292] switch back to 16 parallel workers for download on
 henky request

---
 koboldcpp.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 77f68e66b..40f1ad394 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -10518,7 +10518,7 @@ def resolve_huggingface_xet_url(input_url):
             return input_url
     # resolved_host = urllib.parse.urlparse(resolved_url).netloc.lower()
     if ("xet-bridge" in resolved_url) and resolved_url != input_url:
-        print(f"Resolved Hugging Face xet URL to {resolved_url}", flush=True)
+        utfprint(f"Resolved Hugging Face xet URL to {resolved_url}", 0)
         return resolved_url
     return input_url
 
@@ -10566,7 +10566,7 @@ def downloader_internal(input_url, output_filename, capture_output, min_file_siz
     if shutil.which("aria2c") is not None:
         aria2_candidates.append(("aria2c", "aria2c"))
     aria2_args = [
-        "-x", "8", "-s", "8",
+        "-x", "16", "-s", "16",
         "--summary-interval=10", "--console-log-level=error", "--log-level=error",
         "--download-result=default", "--continue=true", "--allow-overwrite=true",
         "--file-allocation=none", "--max-tries=3", "--retry-wait=5",

From 050ee92d04c2e1f639025786dea701c70e7d4204 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Fri, 26 Jun 2026 23:18:11 +0200
Subject: [PATCH 192/292] app : allow --version, --licenses & --help (#25054)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 app/llama.cpp | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/app/llama.cpp b/app/llama.cpp
index 00babbc7b..2cf1aa876 100644
--- a/app/llama.cpp
+++ b/app/llama.cpp
@@ -50,6 +50,7 @@ struct command {
     std::vector<std::string> aliases;
     bool hidden;
     int (*func)(int, char **);
+    bool flags = false; // allow --name
 };
 
 #ifdef LLAMA_INSTALL_BUILD
@@ -69,9 +70,9 @@ static const command cmds[] = {
     {"fit-params",    "Compute parameters to fit a model in device memory", {},           true,          llama_fit_params   },
     {"quantize",      "Quantize a model",                                   {},           true,          llama_quantize     },
     {"perplexity",    "Compute model perplexity and KL divergence",         {},           true,          llama_perplexity   },
-    {"version",       "Show version",                                       {},           false,         version            },
-    {"licenses",      "Show third-party licenses",                          {"credits"},  false,         licenses           },
-    {"help",          "Show available commands",                            {},           false,         help               },
+    {"version",       "Show version",                                       {},           false,         version,           true },
+    {"licenses",      "Show third-party licenses",                          {"credits"},  false,         licenses,          true },
+    {"help",          "Show available commands",                            {},           false,         help,              true },
 };
 
 #undef UPDATE_HIDDEN
@@ -108,7 +109,10 @@ static int help(int argc, char ** argv) {
     return 0;
 }
 
-static bool matches(const std::string & arg, const command & cmd) {
+static bool matches(std::string arg, const command & cmd) {
+    if (cmd.flags && arg.size() > 2 && arg[0] == '-' && arg[1] == '-') {
+        arg.erase(0, 2);
+    }
     if (arg == cmd.name) {
         return true;
     }

From 83d385b4294a27fd5b676b56531f446480bfea2a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Sat, 27 Jun 2026 09:30:19 +0200
Subject: [PATCH 193/292] tests : fix test-chat-template --no-common option
 (#25075)

---
 tests/test-chat-template.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index c388dee1c..d971b2374 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -135,7 +135,7 @@ int main(int argc, char ** argv) {
             output_path = args[i + 1];
             i++;
         } else if (args[i] == "--no-common") {
-            use_common = true;
+            use_common = false;
         } else if (tmpl_path.empty()) {
             tmpl_path = args[i];
         } else {

From 0275c0f8007df6284bf5f8156169102612e95462 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?=
 <1629204+CISC@users.noreply.github.com>
Date: Sat, 27 Jun 2026 09:30:56 +0200
Subject: [PATCH 194/292] ci : add windows-openvino to check-release (#25022)

---
 .github/workflows/release.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index eb7e1f20d..616fca3da 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -552,6 +552,9 @@ jobs:
           name: llama-bin-ubuntu-openvino-${{ env.OPENVINO_VERSION_MAJOR }}-x64.tar.gz
 
   windows-openvino:
+    needs: [check-release]
+    if: ${{ needs.check-release.outputs.should_release == 'true' }}
+
     runs-on: windows-2022
 
     outputs:

From c299a92c38b6de6a1139617652b66081828648db Mon Sep 17 00:00:00 2001
From: Christian Kastner <ckk@kvr.at>
Date: Sat, 27 Jun 2026 09:31:29 +0200
Subject: [PATCH 195/292] binaries : Improve rpc-server and export-graph-ops
 names. (#25045)

Tests are generally prefixed with -test, so rename export-graph-ops
accordingly.

rpc-server is probably too generic a name for /usr/bin. Because it
should work with any ggml application, it is renamed to ggml-rpc-server.
---
 SECURITY.md                                   |  2 +-
 tests/CMakeLists.txt                          |  8 ++---
 tests/test-backend-ops.cpp                    |  2 +-
 ...raph-ops.cpp => test-export-graph-ops.cpp} |  2 +-
 tools/rpc/CMakeLists.txt                      |  2 +-
 tools/rpc/README.md                           | 34 +++++++++----------
 6 files changed, 25 insertions(+), 25 deletions(-)
 rename tests/{export-graph-ops.cpp => test-export-graph-ops.cpp} (98%)

diff --git a/SECURITY.md b/SECURITY.md
index a98b8e70b..0e704e328 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -80,7 +80,7 @@ To protect sensitive data from potential leaks or unauthorized access, it is cru
 ### Untrusted environments or networks
 
 If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
-* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061).
+* Do not use the RPC backend, [ggml-rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061).
 * Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value.
 * Encrypt your data if sending it over the network.
 
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 0dd1d7b16..24592a279 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -302,9 +302,9 @@ target_link_libraries(${TEST_TARGET} PRIVATE llama)
 llama_build_and_test(test-alloc.cpp)
 target_include_directories(test-alloc PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src)
 
-llama_build(export-graph-ops.cpp)
-target_include_directories(export-graph-ops PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src)
+llama_build(test-export-graph-ops.cpp)
+target_include_directories(test-export-graph-ops PRIVATE ${PROJECT_SOURCE_DIR}/ggml/src)
 if (TARGET gguf-model-data)
-    target_link_libraries(export-graph-ops PRIVATE gguf-model-data)
-    target_compile_definitions(export-graph-ops PRIVATE LLAMA_HF_FETCH)
+    target_link_libraries(test-export-graph-ops PRIVATE gguf-model-data)
+    target_compile_definitions(test-export-graph-ops PRIVATE LLAMA_HF_FETCH)
 endif()
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 0830dbf57..09ac62a75 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -9943,7 +9943,7 @@ static void usage(char ** argv) {
     printf("    --output specifies output format (default: console, options: console, sql, csv)\n");
     printf("    --list-ops lists all available GGML operations\n");
     printf("    --show-coverage shows test coverage\n");
-    printf("    --test-file reads test operators from a test file generated by llama-export-graph-ops\n");
+    printf("    --test-file reads test operators from a test file generated by test-export-graph-ops\n");
     printf("    -j <n> runs tests using <n> parallel worker threads (default: 1, test mode only)\n");
 }
 
diff --git a/tests/export-graph-ops.cpp b/tests/test-export-graph-ops.cpp
similarity index 98%
rename from tests/export-graph-ops.cpp
rename to tests/test-export-graph-ops.cpp
index 64cf6dcea..7d8118dcd 100644
--- a/tests/export-graph-ops.cpp
+++ b/tests/test-export-graph-ops.cpp
@@ -185,7 +185,7 @@ int main(int argc, char ** argv) {
             return 1;
         }
 #else
-        LOG_ERR("export-graph-ops compiled without HF fetch support\n");
+        LOG_ERR("test-export-graph-ops compiled without HF fetch support\n");
         return 1;
 #endif
     }
diff --git a/tools/rpc/CMakeLists.txt b/tools/rpc/CMakeLists.txt
index 20f114ad9..0eee9a922 100644
--- a/tools/rpc/CMakeLists.txt
+++ b/tools/rpc/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(TARGET rpc-server)
+set(TARGET ggml-rpc-server)
 add_executable(${TARGET} rpc-server.cpp)
 target_link_libraries(${TARGET} PRIVATE ggml)
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/tools/rpc/README.md b/tools/rpc/README.md
index 05b7292c0..655b65347 100644
--- a/tools/rpc/README.md
+++ b/tools/rpc/README.md
@@ -4,8 +4,8 @@
 > This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and
 > insecure. **Never run the RPC server on an open network or in a sensitive environment!**
 
-The `rpc-server` allows exposing `ggml` devices on a remote host.
-The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
+The `ggml-rpc-server` allows exposing `ggml` devices on a remote host.
+The RPC backend communicates with one or several instances of `ggml-rpc-server` and offloads computations to them.
 This can be used for distributed LLM inference with `llama.cpp` in the following way:
 
 ```mermaid
@@ -14,15 +14,15 @@ flowchart TD
     rpcb<-->|TCP|srvb
     rpcb<-.->|TCP|srvn
     subgraph hostn[Host N]
-    srvn[rpc-server]<-.->dev4["CUDA0"]
-    srvn[rpc-server]<-.->dev5["CPU"]
+    srvn[ggml-rpc-server]<-.->dev4["CUDA0"]
+    srvn[ggml-rpc-server]<-.->dev5["CPU"]
     end
     subgraph hostb[Host B]
-    srvb[rpc-server]<-->dev3["Metal"]
+    srvb[ggml-rpc-server]<-->dev3["Metal"]
     end
     subgraph hosta[Host A]
-    srva[rpc-server]<-->dev["CUDA0"]
-    srva[rpc-server]<-->dev2["CUDA1"]
+    srva[ggml-rpc-server]<-->dev["CUDA0"]
+    srva[ggml-rpc-server]<-->dev2["CUDA1"]
     end
     subgraph host[Main Host]
     local["Local devices"]<-->ggml[llama-cli]
@@ -33,7 +33,7 @@ flowchart TD
     class local,dev,dev2,dev3,dev4,dev5 devcls
 ```
 
-By default, `rpc-server` exposes all available accelerator devices on the host.
+By default, `ggml-rpc-server` exposes all available accelerator devices on the host.
 If there are no accelerators, it exposes a single `CPU` device.
 
 ## Usage
@@ -41,7 +41,7 @@ If there are no accelerators, it exposes a single `CPU` device.
 ### Remote hosts
 
 On each remote host, build the backends for each accelerator by adding `-DGGML_RPC=ON` to the build options.
-For example, to build the `rpc-server` with support for CUDA accelerators:
+For example, to build the `ggml-rpc-server` with support for CUDA accelerators:
 
 ```bash
 mkdir build-rpc-cuda
@@ -50,10 +50,10 @@ cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
 cmake --build . --config Release
 ```
 
-When started, the `rpc-server` will detect and expose all available `CUDA` devices:
+When started, the `ggml-rpc-server` will detect and expose all available `CUDA` devices:
 
 ```bash
-$ bin/rpc-server
+$ bin/ggml-rpc-server
 ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
 ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
 ggml_cuda_init: found 1 CUDA devices:
@@ -67,14 +67,14 @@ Devices:
 
 You can control the set of exposed CUDA devices with the `CUDA_VISIBLE_DEVICES` environment variable or the `--device` command line option. The following two commands have the same effect:
 ```bash
-$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
-$ bin/rpc-server --device CUDA0 -p 50052
+$ CUDA_VISIBLE_DEVICES=0 bin/ggml-rpc-server -p 50052
+$ bin/ggml-rpc-server --device CUDA0 -p 50052
 ```
 
 ### Main host
 
 On the main host build `llama.cpp` with the backends for the local devices and add `-DGGML_RPC=ON` to the build options.
-Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `rpc-server`:
+Finally, when running `llama-cli` or `llama-server`, use the `--rpc` option to specify the host and port of each `ggml-rpc-server`:
 
 ```bash
 $ llama-cli -hf ggml-org/gemma-3-1b-it-GGUF -ngl 99 --rpc 192.168.88.10:50052,192.168.88.11:50052
@@ -90,7 +90,7 @@ This can speed up model loading significantly, especially when using large model
 To enable the cache, use the `-c` option:
 
 ```bash
-$ bin/rpc-server -c
+$ bin/ggml-rpc-server -c
 ```
 
 By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.
@@ -103,8 +103,8 @@ RDMA is enabled by default when `libibverbs` is found at build time.
 
 ### Troubleshooting
 
-Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `rpc-server`:
+Use the `GGML_RPC_DEBUG` environment variable to enable debug messages from `ggml-rpc-server`:
 ```bash
-$ GGML_RPC_DEBUG=1 bin/rpc-server
+$ GGML_RPC_DEBUG=1 bin/ggml-rpc-server
 ```
 

From 16f197ab86579b4b969b876976c047803ed52a4c Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 27 Jun 2026 16:29:59 +0800
Subject: [PATCH 196/292] Revert "sched : reintroduce less synchronizations
 during split compute (#20793)"

This reverts commit 3fc4e105279105106b08a133a4e3e483116e621f.
---
 ggml/src/ggml-backend.cpp       | 10 +++-------
 ggml/src/ggml-cuda/ggml-cuda.cu | 24 ++++--------------------
 2 files changed, 7 insertions(+), 27 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index bac27241a..c865b07e1 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1558,8 +1558,6 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
-        ggml_backend_synchronize(split_backend);
-
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1570,15 +1568,15 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else if (!split_backend->iface.cpy_tensor_async) {
+                } else {
                     ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
+                ggml_backend_tensor_copy(input, input_cpy);
             } else {
                 // wait for the split backend to finish using the input before overwriting it
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else if (!split_backend->iface.cpy_tensor_async) {
+                } else {
                     ggml_backend_synchronize(split_backend);
                 }
 
@@ -1683,8 +1681,6 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
-        ggml_backend_synchronize(split_backend);
-
         if (!sched->callback_eval) {
             enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
             if (ec != GGML_STATUS_SUCCESS) {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 4c14a97de..42a7e1c52 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3204,24 +3204,11 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
     ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    // Enables async copies from CPU to CUDA, instead of only CUDA-to-CUDA
-    // Excluding this path for HIP and MUSA as a precaution.
-    // According to the summary in https://github.com/ggml-org/llama.cpp/pull/20793#issuecomment-4275794315, this change is not beneficial for hip anyways.
-    // Additionally, there is a lot of anectodal evidence that hip/musa stream behavior might not always 1:1 match CUDA behavior.
-    // e.g. https://github.com/ROCm/rocm-systems/issues/5109
-    // It thus makes sense to exclude this path for HIP and MUSA. This PR was not aimed these backends, the majority of testing happened on CUDA.
-    // This can be revisited in the future if enabling copy_from_host benefits hip/MUSA, and if the PR author can extensively test on these backends.
-#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
-    const bool copy_from_host = false;
-#else
-    const bool copy_from_host = ggml_backend_buffer_is_host(buf_src) && ggml_backend_dev_type(backend_src->device) == GGML_BACKEND_DEVICE_TYPE_CPU;
-#endif
-
-    if (!(copy_from_host || ggml_backend_is_cuda(backend_src)) || !ggml_backend_is_cuda(backend_dst)) {
+    if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) {
         return false;
     }
 
-    if (!(copy_from_host || ggml_backend_buffer_is_cuda(buf_src)) || !ggml_backend_buffer_is_cuda(buf_dst)) {
+    if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
         return false;
     }
 
@@ -3232,17 +3219,14 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
     ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
 
-    if ((copy_from_host && cuda_ctx_dst->device != buf_ctx_dst->device) ||
-        !copy_from_host && (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device)) {
+    if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
 #endif // NDEBUG
         return false;
     }
 
-    if (copy_from_host) {
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyHostToDevice, cuda_ctx_dst->stream()));
-    } else if (backend_src != backend_dst) {
+    if (backend_src != backend_dst) {
         // copy on src stream
         if (cuda_ctx_src->device == cuda_ctx_dst->device) {
             CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));

From 1783236b0544dc55ac14f891a1e8a83a91ea83d8 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 27 Jun 2026 16:44:48 +0800
Subject: [PATCH 197/292] fix for
 https://github.com/ggml-org/llama.cpp/issues/21724 , and use wbruna's
 heuristic https://github.com/ggml-org/llama.cpp/pull/24872

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 3b7abe0c7..1b7542257 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5907,7 +5907,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
                                 (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eVote);
 
         // Submit at least every 100 nodes, in case there are workloads without as much matmul.
-        device->max_nodes_per_submit = 100;
+        // device->max_nodes_per_submit = 100;
+        device->max_nodes_per_submit = device->uma ? 8 : 64; //kcpp fix for https://github.com/ggml-org/llama.cpp/issues/21724
         const char* GGML_VK_MAX_NODES_PER_SUBMIT = getenv("GGML_VK_MAX_NODES_PER_SUBMIT");
         if (GGML_VK_MAX_NODES_PER_SUBMIT != nullptr) {
             uint32_t max_nodes_per_submit = std::stoul(GGML_VK_MAX_NODES_PER_SUBMIT);

From 8a5b7084f4b6fce9070c86bff06bb7a7605e72d0 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 27 Jun 2026 16:48:53 +0800
Subject: [PATCH 198/292] fix tools build

---
 common/download.cpp | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/common/download.cpp b/common/download.cpp
index db9903985..8880eda30 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -1018,10 +1018,10 @@ bool common_download_remove(const std::string & hf_repo_with_tag) {
 //     throw std::runtime_error("download functionality is not enabled in this build");
 // }
 
-common_download_model_result common_download_model(const common_params_model  & model,
-                                                   const common_download_opts & opts) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
+// common_download_model_result common_download_model(const common_params_model  & model,
+//                                                    const common_download_opts & opts) {
+//     throw std::runtime_error("download functionality is not enabled in this build");
+// }
 
 std::string common_docker_resolve_model(const std::string &) {
     throw std::runtime_error("download functionality is not enabled in this build");

From 0b6529d818608304b277c1db668e8799a467c32d Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <rortlam@redhat.com>
Date: Sat, 27 Jun 2026 10:57:31 +0200
Subject: [PATCH 199/292] vulkan: fix step operator for 0 input (#25036)

---
 ggml/src/ggml-vulkan/vulkan-shaders/unary.comp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp b/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
index c62bce825..5ee5275d2 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp
@@ -42,7 +42,7 @@ float op_leaky_relu(float x) {
 }
 
 float op_step(float x) {
-    return x >= 0.0f ? 1.0f : 0.0f;
+    return x > 0.0f ? 1.0f : 0.0f;
 }
 
 float op_tanh(float x) {

From 9bebfcb4bc8b12a316e96ae03f33671eac1e72fd Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Sat, 27 Jun 2026 17:13:43 +0800
Subject: [PATCH 200/292] sycl : fix failed ut cases of norm (#25044)

---
 ggml/src/ggml-sycl/norm.cpp | 150 ++++++++++++++++++++++++------------
 1 file changed, 102 insertions(+), 48 deletions(-)

diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
index 09fce1280..c4472e4bd 100644
--- a/ggml/src/ggml-sycl/norm.cpp
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -2,8 +2,10 @@
 #include "ggml-sycl/common.hpp"
 #include "ggml-sycl/presets.hpp"
 
-static void norm_f32(const float* x, float* dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
-        const int64_t stride_sample, const float eps, const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
+static void norm_f32(const float* x, float* dst, const int ncols,
+    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
+    const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
+    const float eps, const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
 
     const int nrows = item_ct1.get_group_range(2);
     const int nchannels = item_ct1.get_group_range(1);
@@ -16,16 +18,16 @@ static void norm_f32(const float* x, float* dst, const int ncols, const int64_t
     const int tid = item_ct1.get_local_id(2);
     const int nwarps = nthreads / WARP_SIZE;
 
-    const auto strided_offset = calculate_offset<3>({stride_sample, stride_channel, stride_row}, {sample, channel, row});
-    const auto packed_offset = calculate_offset<3>({nchannels * nrows * ncols, nrows * ncols, ncols}, {sample, channel, row});
+    const auto src_offset = calculate_offset<3>({src_stride_sample, src_stride_channel, src_stride_row}, {sample, channel, row});
+    const auto dst_offset = calculate_offset<3>({dst_stride_sample, dst_stride_channel, dst_stride_row}, {sample, channel, row});
 
-    x += strided_offset;
-    dst += packed_offset;
+    x += src_offset;
+    dst += dst_offset;
 
     sycl::float2 mean_var = sycl::float2(0.f, 0.f);
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[col];
+        const float xi = x[col * src_stride_col];
         mean_var.x() += xi;
         mean_var.y() += xi * xi;
     }
@@ -54,7 +56,7 @@ static void norm_f32(const float* x, float* dst, const int ncols, const int64_t
     const float inv_std = sycl::rsqrt(var + eps);
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[col] = (x[col] - mean) * inv_std;
+        dst[col * dst_stride_col] = (x[col * src_stride_col] - mean) * inv_std;
     }
 }
 
@@ -145,8 +147,10 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
     }
 }
 
-static void rms_norm_f32(const float* x, float* dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
-        const int64_t stride_sample, const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+static void rms_norm_f32(const float* x, float* dst, const int ncols,
+    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
+    const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
+    const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
 
     const int nrows = item_ct1.get_group_range(2);
     const int nchannels = item_ct1.get_group_range(1);
@@ -160,17 +164,17 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const int6
     const int tid = item_ct1.get_local_id(2);
     const int nwarps = nthreads / WARP_SIZE;
 
-    const auto strided_offset = calculate_offset<3>({stride_sample, stride_channel, stride_row}, {sample, channel, row});
-    const auto packed_offset = calculate_offset<3>({nchannels * nrows * ncols, nrows * ncols, ncols}, {sample, channel, row});
+    const auto src_offset = calculate_offset<3>({src_stride_sample, src_stride_channel, src_stride_row}, {sample, channel, row});
+    const auto dst_offset = calculate_offset<3>({dst_stride_sample, dst_stride_channel, dst_stride_row}, {sample, channel, row});
 
-    x   += strided_offset;
-    dst += packed_offset;
+    x   += src_offset;
+    dst += dst_offset;
 
 
     float tmp = 0.0f; // partial sum for thread in warp
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[col];
+        const float xi = x[col * src_stride_col];
         tmp += xi * xi;
     }
 
@@ -198,14 +202,15 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const int6
     const float scale = sycl::rsqrt(mean + eps);
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[col] = scale * x[col];
+        dst[col * dst_stride_col] = scale * x[col * src_stride_col];
     }
 }
 
 template<int warp_size>
 static void l2_norm_f32(const float * x, float * dst, const int ncols,
-    const int64_t stride_row, const int64_t stride_channel,
-    const int64_t stride_sample, const float eps,
+    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel,
+    const int64_t src_stride_sample, const int64_t dst_stride_col, const int64_t dst_stride_row,
+    const int64_t dst_stride_channel, const int64_t dst_stride_sample, const float eps,
     const sycl::nd_item<3>& item_ct1, float* s_sum, const int block_size) {
     const int nrows     = item_ct1.get_group_range(2);
     const int nchannels = item_ct1.get_group_range(1);
@@ -215,13 +220,13 @@ static void l2_norm_f32(const float * x, float * dst, const int ncols,
     const int sample  = item_ct1.get_group(0);
     const int tid     = item_ct1.get_local_id(2);
 
-    x   += sample*stride_sample + channel*stride_channel + row*stride_row;
-    dst += ((sample*nchannels + channel)*nrows + row)*ncols;
+    x   += sample*src_stride_sample + channel*src_stride_channel + row*src_stride_row;
+    dst += sample*dst_stride_sample + channel*dst_stride_channel + row*dst_stride_row;
 
     float tmp = 0.0f; // partial sum for thread in warp
 
     for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[col];
+        const float xi = x[col * src_stride_col];
         tmp += xi * xi;
     }
 
@@ -229,12 +234,13 @@ static void l2_norm_f32(const float * x, float * dst, const int ncols,
     const float scale = sycl::rsqrt(sycl::fmax(tmp, eps * eps));
 
     for (int col = tid; col < ncols; col += block_size) {
-        dst[col] = scale * x[col];
+        dst[col * dst_stride_col] = scale * x[col * src_stride_col];
     }
 }
 
 static void norm_f32_sycl(const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
-        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample,
+    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
+    const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
         const float eps, queue_ptr stream, int device) {
 
     const sycl::range<3> global_dims(nsamples, nchannels, nrows);
@@ -245,7 +251,10 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
                 sycl::nd_range<3>(global_dims * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
+                    norm_f32(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, nullptr, WARP_SIZE);
                 });
             });
     }
@@ -265,7 +274,10 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
                 sycl::nd_range<3>(global_dims * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
+                    norm_f32(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
@@ -319,7 +331,9 @@ static void group_norm_f32_sycl(const float* x, float* dst,
 }
 
 static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
-        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, queue_ptr stream, int device) {
+    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
+    const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
+    const float eps, queue_ptr stream, int device) {
     // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
 
     const sycl::range<3> global_dims(nsamples, nchannels, nrows);
@@ -330,7 +344,10 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
                 sycl::nd_range<3>(global_dims * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
+                    rms_norm_f32(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, nullptr, WARP_SIZE);
                 });
             });
     }
@@ -350,7 +367,10 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
                 sycl::nd_range<3>(global_dims * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
+                    rms_norm_f32(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
     }
@@ -363,9 +383,14 @@ static void l2_norm_f32_sycl(const float *   x,
                              const int       nrows,
                              const int       nchannels,
                              const int       nsamples,
-                             const int64_t   stride_row,
-                             const int64_t   stride_channel,
-                             const int64_t   stride_sample,
+                             const int64_t   src_stride_col,
+                             const int64_t   src_stride_row,
+                             const int64_t   src_stride_channel,
+                             const int64_t   src_stride_sample,
+                             const int64_t   dst_stride_col,
+                             const int64_t   dst_stride_row,
+                             const int64_t   dst_stride_channel,
+                             const int64_t   dst_stride_sample,
                              const float     eps,
                              queue_ptr       stream,
                              int             device) {
@@ -379,7 +404,10 @@ static void l2_norm_f32_sycl(const float *   x,
                     block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(warp_size)]] {
-                    l2_norm_f32<warp_size>(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
+                    l2_norm_f32<warp_size>(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1,
                         nullptr, warp_size);
                 });
             });
@@ -398,7 +426,9 @@ static void l2_norm_f32_sycl(const float *   x,
                     block_dims),
                 [=](sycl::nd_item<3> item_ct1)
                 [[sycl::reqd_sub_group_size(warp_size)]] {
-                    l2_norm_f32<warp_size>(x, dst, ncols, stride_row, stride_channel, stride_sample,
+                    l2_norm_f32<warp_size>(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
                         eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                 });
             });
@@ -421,12 +451,20 @@ void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     memcpy(&eps, dst->op_params, sizeof(float));
     GGML_ASSERT(eps >= 0.0f);
     const size_t ts0 = ggml_type_size(src0->type);
-    GGML_ASSERT(nb00 == ts0);
-    const int64_t s01 = nb01 / ts0;
-    const int64_t s02 = nb02 / ts0;
-    const int64_t s03 = nb03 / ts0;
+    const size_t tdst = ggml_type_size(dst->type);
+    GGML_ASSERT(nb00 % ts0 == 0 && nb01 % ts0 == 0 && nb02 % ts0 == 0 && nb03 % ts0 == 0);
+    GGML_ASSERT(nb0 % tdst == 0 && nb1 % tdst == 0 && nb2 % tdst == 0 && nb3 % tdst == 0);
+    const int64_t ss0 = nb00 / ts0;
+    const int64_t ss1 = nb01 / ts0;
+    const int64_t ss2 = nb02 / ts0;
+    const int64_t ss3 = nb03 / ts0;
+    const int64_t ds0 = nb0 / tdst;
+    const int64_t ds1 = nb1 / tdst;
+    const int64_t ds2 = nb2 / tdst;
+    const int64_t ds3 = nb3 / tdst;
 
-    norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03, s01, s02, s03, eps, main_stream, ctx.device);
+    norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03,
+        ss0, ss1, ss2, ss3, ds0, ds1, ds2, ds3, eps, main_stream, ctx.device);
 }
 
 void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
@@ -465,11 +503,19 @@ void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
 
     GGML_TENSOR_UNARY_OP_LOCALS
     const size_t ts0 = ggml_type_size(src0->type);
-    GGML_ASSERT(nb00 == ts0);
-    const int64_t s01 = nb01 / ts0;
-    const int64_t s02 = nb02 / ts0;
-    const int64_t s03 = nb03 / ts0;
-    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03, s01, s02, s03, eps, main_stream, ctx.device);
+    const size_t tdst = ggml_type_size(dst->type);
+    GGML_ASSERT(nb00 % ts0 == 0 && nb01 % ts0 == 0 && nb02 % ts0 == 0 && nb03 % ts0 == 0);
+    GGML_ASSERT(nb0 % tdst == 0 && nb1 % tdst == 0 && nb2 % tdst == 0 && nb3 % tdst == 0);
+    const int64_t ss0 = nb00 / ts0;
+    const int64_t ss1 = nb01 / ts0;
+    const int64_t ss2 = nb02 / ts0;
+    const int64_t ss3 = nb03 / ts0;
+    const int64_t ds0 = nb0 / tdst;
+    const int64_t ds1 = nb1 / tdst;
+    const int64_t ds2 = nb2 / tdst;
+    const int64_t ds3 = nb3 / tdst;
+    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03,
+        ss0, ss1, ss2, ss3, ds0, ds1, ds2, ds3, eps, main_stream, ctx.device);
 }
 
 void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
@@ -644,13 +690,21 @@ void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
     GGML_ASSERT(eps >= 0.0f);
 
     const size_t ts0 = ggml_type_size(src0->type);
-    GGML_ASSERT(nb00 == ts0);
-    const int64_t s01 = nb01 / ts0;
-    const int64_t s02 = nb02 / ts0;
-    const int64_t s03 = nb03 / ts0;
+    const size_t tdst = ggml_type_size(dst->type);
+    GGML_ASSERT(nb00 % ts0 == 0 && nb01 % ts0 == 0 && nb02 % ts0 == 0 && nb03 % ts0 == 0);
+    GGML_ASSERT(nb0 % tdst == 0 && nb1 % tdst == 0 && nb2 % tdst == 0 && nb3 % tdst == 0);
+    const int64_t ss0 = nb00 / ts0;
+    const int64_t ss1 = nb01 / ts0;
+    const int64_t ss2 = nb02 / ts0;
+    const int64_t ss3 = nb03 / ts0;
+    const int64_t ds0 = nb0 / tdst;
+    const int64_t ds1 = nb1 / tdst;
+    const int64_t ds2 = nb2 / tdst;
+    const int64_t ds3 = nb3 / tdst;
 
     /*support both WARP_SIZE or WARP_32_SIZE in code
       choose by hardware for better performance
     */
-    l2_norm_f32_sycl<WARP_SIZE>(src0_d, dst_d, ne00, ne01, ne02, ne03, s01, s02, s03, eps, stream, ctx.device);
+    l2_norm_f32_sycl<WARP_SIZE>(src0_d, dst_d, ne00, ne01, ne02, ne03,
+            ss0, ss1, ss2, ss3, ds0, ds1, ds2, ds3, eps, stream, ctx.device);
 }

From 87aeaff67573fc3f32283d80218b22bf2f9aa732 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 27 Jun 2026 18:50:05 +0800
Subject: [PATCH 201/292] fix builds

---
 Makefile            | 19 ++++++-----
 common/download.cpp | 82 ---------------------------------------------
 2 files changed, 10 insertions(+), 91 deletions(-)

diff --git a/Makefile b/Makefile
index bd9646830..d0d2f04a7 100644
--- a/Makefile
+++ b/Makefile
@@ -731,7 +731,8 @@ otherarch/sdcpp/thirdparty/zip.o: otherarch/sdcpp/thirdparty/zip.c
 OBJS_SDTYPE := otherarch/sdcpp/sdtype_adapter.o $(OBJS_SDCOMMON)
 
 LLAMASERVER_SRCS := tools/server/main.cpp tools/server/server.cpp tools/server/server-schema.cpp tools/server/server-chat.cpp tools/server/server-common.cpp tools/server/server-context.cpp tools/server/server-http.cpp tools/server/server-models.cpp tools/server/server-queue.cpp tools/server/server-task.cpp tools/server/server-tools.cpp tools/server/ui.cpp
-LLAMASERVER_COMMON_SRCS := common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp vendor/cpp-httplib/httplib.cpp
+COMMON_DOWNLOAD_SRCS := common/download.cpp common/hf-cache.cpp vendor/cpp-httplib/httplib.cpp
+LLAMASERVER_COMMON_SRCS := common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS)
 LLAMASERVER_CXXFLAGS := -I./tools/mtmd
 
 
@@ -774,31 +775,31 @@ clean:
 	rm -vf otherarch/sdcpp/*.o otherarch/sdcpp/*/*.o otherarch/sdcpp/*/*/*.o otherarch/sdcpp/*/*/*/*.o
 
 # useful tools
-main: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+main: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mainvk: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+mainvk: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-ttsmain: tools/tts/tts.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+ttsmain: tools/tts/tts.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 gguf-split: tools/gguf-split/gguf-split.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o build-info.h clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h mtmd.o mtmd-helper.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h mtmd.o mtmd-helper.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embedding: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+embedding: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/chat.cpp common/preset.cpp common/download.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
diff --git a/common/download.cpp b/common/download.cpp
index 8880eda30..6b69a4418 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -21,9 +21,7 @@
 #include <thread>
 #include <vector>
 
-#if defined(LLAMA_USE_HTTPLIB)
 #include "http.h"
-#endif
 
 #ifndef __EMSCRIPTEN__
 #ifdef __linux__
@@ -117,7 +115,6 @@ std::pair<std::string, std::string> common_download_split_repo_tag(const std::st
     return {hf_repo, tag};
 }
 
-#if defined(LLAMA_USE_HTTPLIB)
 class ProgressBar : public common_download_callback {
     static inline std::mutex mutex;
     static inline std::map<const ProgressBar *, int> lines;
@@ -926,7 +923,6 @@ std::vector<common_cached_model_info> common_list_cached_models() {
     return result;
 }
 
-
 bool common_download_remove(const std::string & hf_repo_with_tag) {
     namespace fs = std::filesystem;
 
@@ -1010,81 +1006,3 @@ bool common_download_remove(const std::string & hf_repo_with_tag) {
 
     return true;
 }
-
-
-#else
-
-// common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
-//     throw std::runtime_error("download functionality is not enabled in this build");
-// }
-
-// common_download_model_result common_download_model(const common_params_model  & model,
-//                                                    const common_download_opts & opts) {
-//     throw std::runtime_error("download functionality is not enabled in this build");
-// }
-
-std::string common_docker_resolve_model(const std::string &) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-int common_download_file_single(const std::string & url,
-                                const std::string & path,
-                                const common_download_opts & opts,
-                                bool skip_etag) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
-                                                             const common_remote_params & params) {
-    throw std::runtime_error("download functionality is not enabled in this build");
-}
-
-struct gguf_split_info {
-    std::string prefix; // tag included
-    std::string tag;
-    int index;
-    int count;
-};
-static gguf_split_info get_gguf_split_info(const std::string & path) {
-    static const std::regex re_split("^(.+)-([0-9]{5})-of-([0-9]{5})$", std::regex::icase);
-    static const std::regex re_tag("[-.]([A-Z0-9_]+)$", std::regex::icase);
-    std::smatch m;
-
-    std::string prefix = path;
-    string_remove_suffix(prefix, ".gguf");
-
-    int index = 1;
-    int count = 1;
-
-    if (std::regex_match(prefix, m, re_split)) {
-        index = std::stoi(m[2].str());
-        count = std::stoi(m[3].str());
-        prefix = m[1].str();
-    }
-
-    std::string tag;
-    if (std::regex_search(prefix, m, re_tag)) {
-        tag = m[1].str();
-        for (char & c : tag) {
-            c = std::toupper((unsigned char)c);
-        }
-    }
-
-    return {std::move(prefix), std::move(tag), index, count};
-}
-
-std::vector<common_cached_model_info> common_list_cached_models() {
-    std::vector<common_cached_model_info> result;
-    return result;
-}
-
-
-bool common_download_remove(const std::string & hf_repo_with_tag) {
-    return true;
-}
-
-
-#endif // defined(LLAMA_USE_HTTPLIB)
-
-
-

From 0ed235ea2c17a19fc8238668653946721ed136fd Mon Sep 17 00:00:00 2001
From: Gaurav Garg <gaugarg@nvidia.com>
Date: Sat, 27 Jun 2026 17:46:21 +0530
Subject: [PATCH 202/292] [CUDA] Added a cudaMemcpy2DAsync fast path to
 ggml_cuda_cpy (#25057)

* [CUDA] Added a cudaMemcpy2DAsync fast path to ggml_cuda_cpy

Add a CUDA ggml_cpy fast path for same-type, same-shape strided copies that are just 2D pitched block copies.
When tensors are not fully contiguous but each row is contiguous, it now uses cudaMemcpy2DAsync instead of the slow element-wise scalar copy kernel.

This fixes the GDN recurrent snapshot update with -np 4, where rollback slots are separated by cache stride gaps.

* Add new tests that execute the new optimized strided copy path

* Return unsupported for strided copy in OpenVINO, as new tests are failing
---
 ggml/src/ggml-cuda/cpy.cu                | 45 ++++++++++++++++++++++++
 ggml/src/ggml-openvino/ggml-openvino.cpp |  4 +++
 tests/test-backend-ops.cpp               | 35 ++++++++++++++----
 3 files changed, 77 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index 1e625cc1c..eb5eb0eb4 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -386,6 +386,46 @@ static void ggml_cpy_f32_iq4_nl_cuda(
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
+// check if a same-type copy reduces to a 2D strided copy (height rows of width
+// contiguous bytes), so it can use cudaMemcpy2DAsync instead of the scalar kernel
+static bool ggml_cuda_cpy_as_memcpy_2d(const ggml_tensor * src0, const ggml_tensor * src1,
+        size_t & width, size_t & height, size_t & spitch, size_t & dpitch) {
+    // require matching shape: a reshaped copy maps elements by flat order, which the
+    // prefix walk below does not handle
+    if (src0->type != src1->type || !ggml_are_same_shape(src0, src1)) {
+        return false;
+    }
+
+    // grow the contiguous prefix block shared by both tensors
+    size_t block_nb = ggml_element_size(src0);
+    int d = 0;
+    for (; d < GGML_MAX_DIMS; ++d) {
+        if (src0->nb[d] != block_nb || src1->nb[d] != block_nb) {
+            break;
+        }
+        block_nb *= src0->ne[d];
+    }
+
+    // d == 0: nothing contiguous; d == GGML_MAX_DIMS: fully contiguous (handled by memcpy)
+    if (d == 0 || d == GGML_MAX_DIMS) {
+        return false;
+    }
+
+    // dim d carries the rows; everything above it must be a single element
+    for (int i = d + 1; i < GGML_MAX_DIMS; ++i) {
+        if (src0->ne[i] != 1) {
+            return false;
+        }
+    }
+
+    width  = block_nb;
+    height = src0->ne[d];
+    spitch = src0->nb[d];
+    dpitch = src1->nb[d];
+
+    return spitch >= width && dpitch >= width;
+}
+
 void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1) {
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
@@ -421,6 +461,8 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
     const bool can_be_transposed = nb01 == (int64_t)ggml_element_size(src0) &&
         src0->ne[3] == 1 && nb02 == ne00 * ne01 * (int64_t)ggml_element_size(src0);
 
+    size_t mc_width = 0, mc_height = 0, mc_spitch = 0, mc_dpitch = 0;
+
     if (src0->type == src1->type && contiguous_srcs) {
         GGML_ASSERT(ggml_nbytes(src0) == ggml_nbytes(src1));
 #if defined(GGML_USE_MUSA) && defined(GGML_MUSA_MUDNN_COPY)
@@ -431,6 +473,9 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
         {
             CUDA_CHECK(cudaMemcpyAsync(src1_ddc, src0_ddc, ggml_nbytes(src0), cudaMemcpyDeviceToDevice, main_stream));
         }
+    } else if (ggml_cuda_cpy_as_memcpy_2d(src0, src1, mc_width, mc_height, mc_spitch, mc_dpitch)) {
+        CUDA_CHECK(cudaMemcpy2DAsync(src1_ddc, mc_dpitch, src0_ddc, mc_spitch,
+                                     mc_width, mc_height, cudaMemcpyDeviceToDevice, main_stream));
     } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
         if (can_be_transposed) {
             ggml_cpy_scalar_cuda<float, float, true>
diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp
index 943aef864..659dbd4b5 100644
--- a/ggml/src/ggml-openvino/ggml-openvino.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino.cpp
@@ -1053,6 +1053,10 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
             (op->ne[0] == 2 && op->ne[1] == 4 && op->ne[2] == 3 && op->ne[3] == 2)) {
             return true;
         }
+        // CPY into a strided view of a larger buffer (recurrent-state snapshots) not supported
+        if (op->view_src && ggml_nbytes(op) != ggml_nbytes(op->view_src)) {
+            return true;
+        }
         break;
     }
     case GGML_OP_MUL_MAT: {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 09ac62a75..15b50209c 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2890,12 +2890,17 @@ struct test_cpy : public test_case {
     const std::array<int64_t, 4> ne_dst;
     const std::array<int64_t, 4> permute_src;
     const std::array<int64_t, 4> permute_dst;
+    const std::array<int64_t, 4> dst_alloc; // if set, dst is a view into a larger buffer (strided)
     bool _src_use_permute;
     bool _dst_use_permute;
     bool _src_transpose;
     bool _use_dst_shape;
+    bool _use_dst_alloc;
 
     std::string vars() override {
+        if (_use_dst_alloc) {
+            return VARS_TO_STR8(type_src, type_dst, ne_src, ne_dst, permute_src, permute_dst, _src_transpose, dst_alloc);
+        }
         if (_use_dst_shape) {
             return VARS_TO_STR7(type_src, type_dst, ne_src, ne_dst, permute_src, permute_dst, _src_transpose);
         }
@@ -2943,12 +2948,15 @@ struct test_cpy : public test_case {
             std::array<int64_t, 4> ne_dst = {-1, -1, -1, -1},
             std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
             std::array<int64_t, 4> permute_dst = {0, 0, 0, 0},
-            bool transpose_src = false)
+            bool transpose_src = false,
+            std::array<int64_t, 4> dst_alloc = {0, 0, 0, 0})
         : type_src(type_src), type_dst(type_dst), ne_src(ne_src), ne_dst(ne_dst), permute_src(permute_src), permute_dst(permute_dst),
+          dst_alloc(dst_alloc),
           _src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
           _dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0),
           _src_transpose(transpose_src),
-          _use_dst_shape(ne_dst[0] >= 0 && ne_dst[1] >= 0 && ne_dst[2] >= 0 && ne_dst[3] >= 0){}
+          _use_dst_shape(ne_dst[0] >= 0 && ne_dst[1] >= 0 && ne_dst[2] >= 0 && ne_dst[3] >= 0),
+          _use_dst_alloc(dst_alloc[0] > 0){}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne_src.data());
@@ -2966,12 +2974,23 @@ struct test_cpy : public test_case {
         }
 
         std::array<int64_t, 4> dst_ne = _use_dst_shape ? ne_dst : std::array<int64_t, 4>{src->ne[0], src->ne[1], src->ne[2], src->ne[3]};
-        ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, dst_ne.data());
-        ggml_set_name(dst, "dst");
+        ggml_tensor * dst;
 
-        if (_dst_use_permute) {
-            dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
-            ggml_set_name(dst, "dst_permuted");
+        if (_use_dst_alloc) {
+            // view a sub-block of a larger buffer -> strided dst
+            ggml_tensor * dst_buf = ggml_new_tensor(ctx, type_dst, 4, dst_alloc.data());
+            ggml_set_name(dst_buf, "dst_buf");
+            dst = ggml_view_4d(ctx, dst_buf, dst_ne[0], dst_ne[1], dst_ne[2], dst_ne[3],
+                dst_buf->nb[1], dst_buf->nb[2], dst_buf->nb[3], 0);
+            ggml_set_name(dst, "dst_view");
+        } else {
+            dst = ggml_new_tensor(ctx, type_dst, 4, dst_ne.data());
+            ggml_set_name(dst, "dst");
+
+            if (_dst_use_permute) {
+                dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
+                ggml_set_name(dst, "dst_permuted");
+            }
         }
 
         ggml_tensor * out = ggml_cpy(ctx, src, dst);
@@ -8181,6 +8200,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {-1,-1,-1,-1}, {1, 2, 0, 3}, {0, 0, 0, 0}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2097121, 1, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
     test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {2, 2, 524281, 1}, {-1,-1,-1,-1}, {1, 0, 2, 3}));
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {128, 2, 3, 1}, {128, 2, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, false, {128, 4, 3, 1})); // strided dst
+    test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {128, 2, 3, 1}, {128, 2, 3, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, false, {128, 4, 3, 1})); // strided dst
 
     // CPY - different src/dst shapes (reshaping via CPY)
     // Use permutations of {3, 5, 7, 32}. Total elements: 3*5*7*32 = 3360.

From 9c5cdcc256b15c2e8f54de0b7d0cece7fa758a98 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sat, 27 Jun 2026 23:09:16 +0800
Subject: [PATCH 203/292] xet resolution fix

---
 koboldcpp.py | 29 +++++++++++++++++++++--------
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 40f1ad394..6b8ccaf38 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -78,7 +78,7 @@ dry_seq_break_max = 128
 extra_images_max = 4 # for kontext/qwen img
 
 # global vars
-KcppVersion = "1.116"
+KcppVersion = "1.116.1"
 showdebug = True
 kcpp_instance = None #global running instance
 global_memory = {"tunnel_url": "", "restart_target":"", "input_to_exit":False, "load_complete":False, "restart_override_base_config":"", "last_active_timestamp":datetime.now(), "triggered_sleeping":False, "current_model":"initial_model", "base_config":"", "swapReqType": None, "autoswapmode": False}
@@ -10502,19 +10502,32 @@ def sanitize_string(input_string):
     return sanitized_string
 
 def resolve_huggingface_xet_url(input_url):
+    global nocertify
     if "https://huggingface.co/" not in input_url or "/resolve/" not in input_url:
         return input_url
-    try:
+
+    ssl_cert_dir = os.environ.get('SSL_CERT_DIR')
+    if not ssl_cert_dir and not nocertify and os.name != 'nt':
+        os.environ['SSL_CERT_DIR'] = '/etc/ssl/certs'
+
+    def resolve_with_context(ssl_context=None):
         req = urllib.request.Request(input_url, headers={'User-Agent': 'Mozilla/5.0'}, method="HEAD")
-        with urllib.request.urlopen(req, timeout=10) as response:
-            resolved_url = response.geturl()
-    except Exception:
         try:
+            with urllib.request.urlopen(req, timeout=10, context=ssl_context) as response:
+                return response.geturl()
+        except Exception:
             req = urllib.request.Request(input_url, headers={'User-Agent': 'Mozilla/5.0'})
-            with urllib.request.urlopen(req, timeout=10) as response:
-                resolved_url = response.geturl()
+            with urllib.request.urlopen(req, timeout=10, context=ssl_context) as response:
+                return response.geturl()
+
+    try:
+        resolved_url = resolve_with_context()
+    except Exception as first_error:
+        try:
+            import ssl
+            resolved_url = resolve_with_context(ssl._create_unverified_context())
         except Exception as e:
-            print(f"Could not pre-resolve Hugging Face URL, using original URL: {e}")
+            print(f"Could not pre-resolve Hugging Face URL, using original URL: {first_error}; {e}")
             return input_url
     # resolved_host = urllib.parse.urlparse(resolved_url).netloc.lower()
     if ("xet-bridge" in resolved_url) and resolved_url != input_url:

From ebd048fc5e4b43ec4e0b4abe0b9bf66e1724dad0 Mon Sep 17 00:00:00 2001
From: Hongqiang Wang <66336067+wanghqc@users.noreply.github.com>
Date: Sat, 27 Jun 2026 15:36:06 -0700
Subject: [PATCH 204/292] opencl: flash attention improvement (#25069)

* opencl: rework FA kernel for f16 and f32

* opencl: flash-attention prefill prepass kernels

- flash_attn_kv_pad_f16    pads the tail KV tile to a BLOCK_N multiple
- flash_attn_mask_pad_f16  pads the matching mask tile
- flash_attn_blk_f16       classifies each KV tile per query block as
                           fully masked / mixed / fully unmasked, so
                           the main kernel can skip fully-masked tiles
                           and the mask lookup for fully-unmasked ones

* opencl: FA kernels for q4_0 and q8_0

* opencl: `set_rows` for f32 to q8_0/q4_0

* opencl: dequant kernels for q4_0 and q8_0

* opencl: add FA tile tuning table with override

* opencl: wire host side for FA

* opencl: q4_0 MoE tensors are also SOA'ed

* opencl: cosmetic fix

* opencl: refactor, also clarify some code paths in comments

* opencl: fix inifity for `-cl-finite-math-only`

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>
---
 ggml/src/ggml-opencl/CMakeLists.txt           |    3 +
 ggml/src/ggml-opencl/fa_tune.h                |   91 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 2039 +++++++++++++++--
 ggml/src/ggml-opencl/kernels/cvt.cl           |  152 ++
 .../src/ggml-opencl/kernels/flash_attn_f16.cl |  115 +-
 .../src/ggml-opencl/kernels/flash_attn_f32.cl |  111 +-
 .../ggml-opencl/kernels/flash_attn_f32_f16.cl |  777 ++++++-
 .../kernels/flash_attn_f32_q4_0.cl            | 1041 +++++++++
 .../kernels/flash_attn_f32_q8_0.cl            | 1049 +++++++++
 .../ggml-opencl/kernels/flash_attn_pre_f16.cl |  156 ++
 ggml/src/ggml-opencl/kernels/set_rows.cl      |  500 ++++
 11 files changed, 5617 insertions(+), 417 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/fa_tune.h
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/flash_attn_pre_f16.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 82ce61d72..09efbc566 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -192,7 +192,10 @@ set(GGML_OPENCL_KERNELS
     mul_mm_f16_f32_kq_kqv
     conv2d
     conv2d_f16_f32
+    flash_attn_pre_f16
     flash_attn_f32_f16
+    flash_attn_f32_q8_0
+    flash_attn_f32_q4_0
     flash_attn_f16
     flash_attn_f32
 )
diff --git a/ggml/src/ggml-opencl/fa_tune.h b/ggml/src/ggml-opencl/fa_tune.h
new file mode 100644
index 000000000..1e2c6ea7e
--- /dev/null
+++ b/ggml/src/ggml-opencl/fa_tune.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// Flash-attention per-(dk,dv) tile tuning for the Adreno OpenCL backend.
+// Isolated from ggml-opencl.cpp so the tuning numbers are easy to find and
+// edit; the FA dispatch and kernel-compile logic stay in the main file.
+// This header is a file section — it is #included exactly once, at the point
+// in ggml-opencl.cpp where the ggml logging macros are already in scope.
+
+// Per-(dk, dv) FA config; shared by dispatch and supports_op.
+struct ggml_opencl_fa_dim {
+    int dk; int dv; int bm; int bn; int n_split; int nkv_split_threshold;
+};
+
+// Split variant fires when n_kv >= threshold (threshold=0 -> always split).
+// Default tuning covers Adreno 7xx/8xx mobile and X1-series laptop GPUs.
+static const ggml_opencl_fa_dim g_fa_dims_adreno_default[] = {
+    { 40,  40, 64, 32, 1, 0}, { 64,  64, 64, 32, 2, 64},
+    { 80,  80, 64, 32, 2, 64}, { 96,  96, 64, 32, 2, 64},
+    {112, 112, 64, 32, 2, 64}, {128, 128, 64, 32, 2, 64},
+    {192, 128, 16, 16, 1, 0},
+    {192, 192, 16, 16, 1, 0},
+    {256, 256, 16, 16, 16, 0},
+};
+
+struct ggml_opencl_fa_dim_table {
+    const ggml_opencl_fa_dim * data;
+    size_t                     count;
+
+    const ggml_opencl_fa_dim * begin() const { return data; }
+    const ggml_opencl_fa_dim * end()   const { return data + count; }
+};
+
+// Mutable copy of the active table; GGML_OPENCL_FA_TUNE patches entries here
+// at backend init without touching the const source table.
+static ggml_opencl_fa_dim g_fa_dims_runtime[
+    sizeof(g_fa_dims_adreno_default) / sizeof(g_fa_dims_adreno_default[0])];
+
+static ggml_opencl_fa_dim_table g_opencl_fa_dims = {
+    g_fa_dims_adreno_default,
+    sizeof(g_fa_dims_adreno_default) / sizeof(g_fa_dims_adreno_default[0]),
+};
+
+// GGML_OPENCL_FA_TUNE=dk:dv:bm:bn:nsplit:thr[,…] — patches matching entries
+// in the active table at backend init, before the first FA kernel compiles.
+// Unmatched (dk,dv) pairs are warned and ignored.
+static void ggml_opencl_fa_apply_env_overrides() {
+    const char * e = std::getenv("GGML_OPENCL_FA_TUNE");
+    if (!e || !e[0]) {
+        return;
+    }
+
+    std::string s = e;
+    size_t pos = 0;
+    while (pos < s.size()) {
+        size_t comma = s.find(',', pos);
+        std::string entry = s.substr(pos, comma == std::string::npos ? std::string::npos : comma - pos);
+        int dk, dv, bm, bn, nsplit, thr;
+        if (std::sscanf(entry.c_str(), "%d:%d:%d:%d:%d:%d", &dk, &dv, &bm, &bn, &nsplit, &thr) == 6) {
+            bool patched = false;
+            for (size_t i = 0; i < g_opencl_fa_dims.count; ++i) {
+                ggml_opencl_fa_dim & d = g_fa_dims_runtime[i];
+                if (d.dk == dk && d.dv == dv) {
+                    d.bm = bm; d.bn = bn; d.n_split = nsplit; d.nkv_split_threshold = thr;
+                    GGML_LOG_INFO("ggml_opencl: FA tune override DK=%d DV=%d -> bm=%d bn=%d n_split=%d thr=%d\n",
+                                  dk, dv, bm, bn, nsplit, thr);
+                    patched = true;
+                    break;
+                }
+            }
+            if (!patched) {
+                GGML_LOG_WARN("ggml_opencl: FA tune override DK=%d DV=%d ignored (no matching dim)\n", dk, dv);
+            }
+        } else {
+            GGML_LOG_WARN("ggml_opencl: FA tune override entry malformed: '%s'\n", entry.c_str());
+        }
+        if (comma == std::string::npos) break;
+        pos = comma + 1;
+    }
+}
+
+// Copy the default table into the mutable runtime buffer and apply any
+// GGML_OPENCL_FA_TUNE overrides. A per-generation table can be added here
+// once it has been tuned on hardware.
+static void ggml_cl_init_fa_dims_table() {
+    const size_t count = sizeof(g_fa_dims_adreno_default) / sizeof(g_fa_dims_adreno_default[0]);
+    for (size_t i = 0; i < count; ++i) {
+        g_fa_dims_runtime[i] = g_fa_dims_adreno_default[i];
+    }
+    g_opencl_fa_dims = { g_fa_dims_runtime, count };
+    ggml_opencl_fa_apply_env_overrides();
+}
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 00f20b09b..32581901b 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -29,6 +29,8 @@
 #include <charconv>
 #include <mutex>
 #include <regex>
+#include <set>
+#include <unordered_set>
 
 #undef MIN
 #undef MAX
@@ -53,6 +55,9 @@
 //------------------------------------------------------------------------------
 
 bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor);
+static bool ggml_cl_is_q4_0_soa(const ggml_tensor * tensor);
+static bool ggml_cl_is_q8_0_soa(const ggml_tensor * tensor);
+static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
 
 // See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
 // Precompute mp (m' in the paper) and L such that division
@@ -96,6 +101,7 @@ enum ADRENO_GPU_GEN {
     A7X,
     A8X,
     X1E,
+    X2E,
 };
 
 enum ADRENO_CL_COMPILER_TYPE {
@@ -236,6 +242,10 @@ static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) {
         return ADRENO_GPU_GEN::X1E;
     }
 
+    if (strstr(device_name, "X2")) {
+        return ADRENO_GPU_GEN::X2E;
+    }
+
     return ADRENO_GPU_GEN::ADRENO_UNKNOWN;
 }
 
@@ -368,7 +378,7 @@ struct ggml_backend_opencl_device_context {
     cl_device_type device_type;
     std::string    device_version;
 
-    // Initialized by ggml_cl2_init().
+    // Initialized by ggml_cl_init().
     ggml_backend_opencl_context * backend_ctx = nullptr;
 
     // Initialized by ggml_backend_opencl_device_get_buffer_type()
@@ -384,6 +394,55 @@ struct ggml_backend_opencl_device_context {
     size_t global_mem_size = 0;
 };
 
+// Lazily-compiled flash-attention kernels and their per-(dk,dv) tile metadata.
+// One map per (Q/KV dtype, decode/prefill, split) combination; the int maps
+// hold tile dims (bm/bn), workgroup sizes and the n_kv split thresholds.
+struct ggml_opencl_fa_kernels {
+    // f16 Q / f16 KV
+    std::map<std::pair<int, int>, cl_kernel> f16;
+    std::map<std::pair<int, int>, cl_kernel> f16_q1;
+    // f32 Q / f32 KV
+    std::map<std::pair<int, int>, cl_kernel> f32;
+    std::map<std::pair<int, int>, cl_kernel> f32_q1;
+    // f32 Q / f16 KV (mixed)
+    std::map<std::pair<int, int>, cl_kernel> f32_f16;
+    std::map<std::pair<int, int>, cl_kernel> f32_f16_split;          // N_SPLIT>1 variant
+    std::map<std::pair<int, int>, cl_kernel> f32_f16_q1;
+    std::map<std::pair<int, int>, cl_kernel> f32_f16_q1_split;       // flash-decoding K-split
+    std::map<std::pair<int, int>, int>       f32_f16_bm;
+    std::map<std::pair<int, int>, int>       f32_f16_bn;
+    std::map<std::pair<int, int>, int>       f32_f16_wg_size;
+    std::map<std::pair<int, int>, int>       f32_f16_split_wg_size;
+    std::map<std::pair<int, int>, int>       f32_f16_split_nkv_threshold;
+    // f32 Q / native q8_0 KV
+    std::map<std::pair<int, int>, cl_kernel> f32_q8_0_q1;            // decode
+    std::map<std::pair<int, int>, cl_kernel> f32_q8_0_q1_split;      // flash-decoding pass 1
+    std::map<std::pair<int, int>, cl_kernel> f32_q8_0;               // prefill (baseline)
+    std::map<std::pair<int, int>, cl_kernel> f32_q8_0_split;         // N_SPLIT>1 variant
+    std::map<std::pair<int, int>, int>       f32_q8_0_split_wg_size;        // wg_size = bm*n_split
+    std::map<std::pair<int, int>, int>       f32_q8_0_split_nkv_threshold;  // use split when n_kv >= this
+    std::map<std::pair<int, int>, int>       f32_q8_0_split_bm;             // per-split BLOCK_M
+    // f32 Q / native q4_0 KV
+    std::map<std::pair<int, int>, cl_kernel> f32_q4_0_q1;
+    std::map<std::pair<int, int>, cl_kernel> f32_q4_0_q1_split;
+    std::map<std::pair<int, int>, cl_kernel> f32_q4_0;
+    std::map<std::pair<int, int>, cl_kernel> f32_q4_0_split;
+    std::map<std::pair<int, int>, int>       f32_q4_0_split_wg_size;
+    std::map<std::pair<int, int>, int>       f32_q4_0_split_nkv_threshold;
+    std::map<std::pair<int, int>, int>       f32_q4_0_split_bm;
+    // shared: flash-decoding merge + prefill prepass (kv-pad, mask-pad, blk class)
+    std::map<std::pair<int, int>, cl_kernel> f32_merge;
+    std::map<std::pair<int, int>, cl_kernel> kv_pad_f16;
+    std::map<std::pair<int, int>, cl_kernel> mask_pad_f16;
+    std::map<std::pair<int, int>, cl_kernel> blk_f16;
+    // generic prefill tile dims (f16 / f32 paths)
+    std::map<std::pair<int, int>, int>       bm;
+    std::map<std::pair<int, int>, int>       bn;
+    // attempted (variant, (dk, dv))
+    // all attempted FA kernels appear here, but those not registered failed compilation
+    std::set<std::pair<int, std::pair<int, int>>> variant_attempted;
+};
+
 // backend context
 struct ggml_backend_opencl_context {
     int ref_count;
@@ -397,9 +456,6 @@ struct ggml_backend_opencl_context {
     // argsort is loaded in supports_op because its availability depends on how
     // many workgroups are allowed, which requires kernel compilation.
     bool kernels_loaded_argsort = false;
-    // flash attn is loaded in supports_op because it contains multiple variants
-    // and takes time to compile, so we want to only compile it when needed.
-    bool kernels_loaded_flash_attn = false;
     // rest of the kernels are currently always loaded in alloc_buffer.
     bool kernels_loaded = false;
 
@@ -414,13 +470,16 @@ struct ggml_backend_opencl_context {
     size_t max_workgroup_size;
     bool fp16_support;
     bool has_vector_subgroup_broadcast;
-    bool has_qcom_subgroup_shuffle = false;     // cl_qcom_subgroup_shuffle
+    bool has_subgroup_shuffle = false;       // cl_khr_subgroup_shuffle or cl_qcom_subgroup_shuffle
+    bool has_qcom_subgroup_shuffle = false;  // specifically cl_qcom_subgroup_shuffle
     bool disable_fusion;
 
     bool adreno_has_large_buffer;
     bool adreno_use_large_buffer;
     ggml_cl_compiler_version adreno_cl_compiler_version;
 
+    std::string kernel_compile_opts;  // cached for lazy-compiled kernels.
+
     int adreno_wave_size;
 
     cl_bool non_uniform_workgroups;
@@ -546,16 +605,13 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_diag_f32;
     cl_kernel kernel_soft_max, kernel_soft_max_4;
     cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16_q1;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_q1;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16;
-    std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16_q1;
-    std::map<std::pair<int, int>, int>       kernels_flash_attn_bm;
-    std::map<std::pair<int, int>, int>       kernels_flash_attn_bn;
+    ggml_opencl_fa_kernels fa;
     cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
     cl_kernel kernel_set_rows_f32_i64, kernel_set_rows_f32_i32, kernel_set_rows_f16_i64, kernel_set_rows_f16_i32;
+    cl_kernel kernel_set_rows_q8_0_i64, kernel_set_rows_q8_0_i32;
+    cl_kernel kernel_set_rows_q8_0_soa_i64, kernel_set_rows_q8_0_soa_i32;
+    cl_kernel kernel_set_rows_q4_0_i64, kernel_set_rows_q4_0_i32;
+    cl_kernel kernel_set_rows_q4_0_soa_i64, kernel_set_rows_q4_0_soa_i32;
     cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
     cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
     cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32, kernel_cpy_f32_f32_pack, kernel_cpy_i32_i32;
@@ -589,6 +645,10 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans;
     cl_kernel kernel_convert_block_mxfp4_trans4_ns, kernel_restore_block_mxfp4_trans4_ns;
     cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0, kernel_restore_block_q8_0_trans;
+    cl_kernel kernel_dequant_q8_0_f16_view_aos;
+    cl_kernel kernel_dequant_q8_0_f32_view_aos;
+    cl_kernel kernel_dequant_q4_0_f16_view_aos;
+    cl_kernel kernel_dequant_q4_0_f32_view_aos;
     cl_kernel kernel_convert_block_q6_K_noshuffle, kernel_restore_block_q6_K_noshuffle;
     cl_kernel kernel_convert_bf16_to_f16, kernel_convert_f16_to_bf16;
     cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
@@ -877,7 +937,13 @@ inline std::string read_file(const std::string &path) {
     return text;
 }
 
-static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) {
+// fatal=false returns NULL on compile failure instead of aborting; used for
+// optional FA variants that may exhaust the Adreno compiler at large DK.
+static cl_program build_program_from_source_ex(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts, bool fatal, const char *tag = nullptr) {
+    if (tag) {
+        GGML_LOG_INFO("ggml_opencl: compiling %s\n", tag);
+    }
+
     cl_program p;
     char *program_log;
     size_t program_size;
@@ -889,7 +955,10 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
     p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err);
     if(err < 0) {
         GGML_LOG_ERROR("OpenCL error creating program");
-        exit(1);
+        if (fatal) {
+            exit(1);
+        }
+        return nullptr;
     }
 
     err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
@@ -898,14 +967,22 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
         program_log = (char*) malloc(log_size + 1);
         program_log[log_size] = '\0';
         clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
-        GGML_LOG_ERROR("ggml_opencl: kernel compile error:\n\n%s\n", program_log);
+        GGML_LOG_ERROR("ggml_opencl: kernel compile error (err=%d):\n\n%s\n", err, program_log);
         free(program_log);
-        exit(1);
+        clReleaseProgram(p);
+        if (fatal) {
+            exit(1);
+        }
+        return nullptr;
     }
 
     return p;
 }
 
+static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts) {
+    return build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true);
+}
+
 static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) {
     // compiler options for general kernels
     auto opencl_c_std =
@@ -932,84 +1009,6 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) {
     }
 }
 
-static void load_cl_kernels_flash_attn(ggml_backend_opencl_context *backend_ctx) {
-    // compiler options for general kernels
-    auto opencl_c_std =
-        std::string("CL") + std::to_string(backend_ctx->opencl_c_version.major) + "." + std::to_string(backend_ctx->opencl_c_version.minor);
-    std::string compile_opts = std::string("-cl-std=") + opencl_c_std +
-                               " -cl-mad-enable -cl-unsafe-math-optimizations"
-                               " -cl-finite-math-only -cl-fast-relaxed-math";
-
-    // flash_attn
-    if (!backend_ctx->kernels_loaded_flash_attn) {
-        cl_int err;
-
-        #ifdef GGML_OPENCL_EMBED_KERNELS
-                const std::string kernel_src_f16 {
-                    #include "flash_attn_f16.cl.h"
-                };
-                const std::string kernel_src_f32 {
-                    #include "flash_attn_f32.cl.h"
-                };
-                const std::string kernel_src_f32_f16 {
-                    #include "flash_attn_f32_f16.cl.h"
-                };
-        #else
-                const std::string kernel_src_f16 = read_file("flash_attn_f16.cl");
-                const std::string kernel_src_f32 = read_file("flash_attn_f32.cl");
-                const std::string kernel_src_f32_f16 = read_file("flash_attn_f32_f16.cl");
-        #endif
-
-        if (!kernel_src_f16.empty() && !kernel_src_f32.empty() && !kernel_src_f32_f16.empty()) {
-            const struct { int dk; int dv; int bm; int bn; } fa_dims[] = {
-                { 40,  40, 32, 32}, { 64,  64, 64, 64}, { 80,  80, 64, 32}, { 96,  96, 64, 32},
-                {112, 112, 32, 32}, {128, 128, 32, 32}, {192, 128, 16, 16},
-                {192, 192, 16, 16}, {256, 256, 16, 16},
-            };
-
-            for (size_t i = 0; i < sizeof(fa_dims)/sizeof(fa_dims[0]); ++i) {
-                const int dk = fa_dims[i].dk;
-                const int dv = fa_dims[i].dv;
-                const int bm = fa_dims[i].bm;
-                const int bn = fa_dims[i].bn;
-                std::string OPTS = compile_opts +
-                    " -D DK=" + std::to_string(dk) +
-                    " -D DV=" + std::to_string(dv) +
-                    " -D BLOCK_M=" + std::to_string(bm) +
-                    " -D BLOCK_N=" + std::to_string(bn);
-
-                cl_program prog_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16.c_str(), OPTS);
-                cl_kernel k_f16, k_f16_q1;
-                CL_CHECK((k_f16 = clCreateKernel(prog_f16, "flash_attn_f16", &err), err));
-                CL_CHECK((k_f16_q1 = clCreateKernel(prog_f16, "flash_attn_f16_q1", &err), err));
-                backend_ctx->kernels_flash_attn_f16[{dk, dv}] = k_f16;
-                backend_ctx->kernels_flash_attn_f16_q1[{dk, dv}] = k_f16_q1;
-                CL_CHECK(clReleaseProgram(prog_f16));
-
-                cl_program prog_f32 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32.c_str(), OPTS);
-                cl_kernel k_f32, k_f32_q1;
-                CL_CHECK((k_f32 = clCreateKernel(prog_f32, "flash_attn_f32", &err), err));
-                CL_CHECK((k_f32_q1 = clCreateKernel(prog_f32, "flash_attn_f32_q1", &err), err));
-                backend_ctx->kernels_flash_attn_f32[{dk, dv}] = k_f32;
-                backend_ctx->kernels_flash_attn_f32_q1[{dk, dv}] = k_f32_q1;
-                CL_CHECK(clReleaseProgram(prog_f32));
-
-                cl_program prog_f32_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32_f16.c_str(), OPTS);
-                cl_kernel k_f32_f16, k_f32_f16_q1;
-                CL_CHECK((k_f32_f16 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16", &err), err));
-                CL_CHECK((k_f32_f16_q1 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16_q1", &err), err));
-                backend_ctx->kernels_flash_attn_f32_f16[{dk, dv}] = k_f32_f16;
-                backend_ctx->kernels_flash_attn_f32_f16_q1[{dk, dv}] = k_f32_f16_q1;
-                CL_CHECK(clReleaseProgram(prog_f32_f16));
-
-                backend_ctx->kernels_flash_attn_bm[{dk, dv}] = bm;
-                backend_ctx->kernels_flash_attn_bn[{dk, dv}] = bn;
-            }
-            backend_ctx->kernels_loaded_flash_attn = true;
-        }
-    }
-}
-
 static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
     if (backend_ctx->kernels_loaded) {
         return;
@@ -1028,6 +1027,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         compile_opts += " -qcom-enable-large-buffer ";
     }
 
+    backend_ctx->kernel_compile_opts = compile_opts;
+
     GGML_LOG_INFO("ggml_opencl: loading OpenCL kernels");
 
     // add
@@ -1189,6 +1190,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         CL_CHECK((backend_ctx->kernel_convert_block_q8_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q8_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q8_0_trans  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0_trans", &err), err));
+        CL_CHECK((backend_ctx->kernel_dequant_q8_0_f16_view_aos = clCreateKernel(backend_ctx->program_cvt, "kernel_dequant_q8_0_f16_view_aos", &err), err));
+        CL_CHECK((backend_ctx->kernel_dequant_q8_0_f32_view_aos = clCreateKernel(backend_ctx->program_cvt, "kernel_dequant_q8_0_f32_view_aos", &err), err));
+        CL_CHECK((backend_ctx->kernel_dequant_q4_0_f16_view_aos = clCreateKernel(backend_ctx->program_cvt, "kernel_dequant_q4_0_f16_view_aos", &err), err));
+        CL_CHECK((backend_ctx->kernel_dequant_q4_0_f32_view_aos = clCreateKernel(backend_ctx->program_cvt, "kernel_dequant_q4_0_f32_view_aos", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_K  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_K", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_K_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_K_noshuffle", &err), err));
@@ -2680,6 +2685,14 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         CL_CHECK((backend_ctx->kernel_set_rows_f32_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i32", &err), err));
         CL_CHECK((backend_ctx->kernel_set_rows_f16_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16_i64", &err), err));
         CL_CHECK((backend_ctx->kernel_set_rows_f16_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16_i32", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q8_0_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q8_0_i64", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q8_0_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q8_0_i32", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q8_0_soa_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q8_0_soa_i64", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q8_0_soa_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q8_0_soa_i32", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q4_0_i64     = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q4_0_i64",     &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q4_0_i32     = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q4_0_i32",     &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q4_0_soa_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q4_0_soa_i64", &err), err));
+        CL_CHECK((backend_ctx->kernel_set_rows_q4_0_soa_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_q4_0_soa_i32", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -3704,13 +3717,470 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
     backend_ctx->kernels_loaded = true;
 }
 
-// XXX static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
-// XXX    static bool initialized = false;
-// XXX    static ggml_backend_opencl_context *backend_ctx = nullptr;
-
 static ggml_backend_opencl_context * ggml_cl_init(ggml_backend_dev_t dev);
 static bool ggml_opencl_is_device_supported(ggml_backend_dev_t dev);
 
+// FA per-(dk,dv) tile tuning table + GGML_OPENCL_FA_TUNE override parsing.
+#include "fa_tune.h"
+
+// FA variant key for the per-(dk,dv,variant) lazy compile cache.
+// Kernel built on first dispatch to reduce kernel loading time.
+// NB - a warmup run is recommended to get all necessary FA variants compiled
+// before actual runs.
+enum ggml_opencl_fa_variant {
+    FA_VARIANT_PRE      = 0,  // prepass kernels (kv_pad, mask_pad, blk)
+    FA_VARIANT_F16      = 1,
+    FA_VARIANT_F32      = 2,
+    FA_VARIANT_F32_F16  = 3,
+    FA_VARIANT_Q8_0     = 4,
+    FA_VARIANT_Q4_0     = 5,
+    FA_VARIANT_F32_F16_SPLIT = 6,
+    FA_VARIANT_Q8_0_SPLIT    = 7,
+    FA_VARIANT_Q4_0_SPLIT    = 8,
+};
+
+static std::string ggml_opencl_fa_kernel_src(ggml_opencl_fa_variant v) {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+    switch (v) {
+        case FA_VARIANT_F16:
+            return std::string{
+                #include "flash_attn_f16.cl.h"
+            };
+        case FA_VARIANT_F32:
+            return std::string{
+                #include "flash_attn_f32.cl.h"
+            };
+        case FA_VARIANT_F32_F16:
+        case FA_VARIANT_F32_F16_SPLIT:
+            return std::string{
+                #include "flash_attn_f32_f16.cl.h"
+            };
+        case FA_VARIANT_PRE:
+            return std::string{
+                #include "flash_attn_pre_f16.cl.h"
+            };
+        case FA_VARIANT_Q8_0:
+        case FA_VARIANT_Q8_0_SPLIT:
+            return std::string{
+                #include "flash_attn_f32_q8_0.cl.h"
+            };
+        case FA_VARIANT_Q4_0:
+        case FA_VARIANT_Q4_0_SPLIT:
+            return std::string{
+                #include "flash_attn_f32_q4_0.cl.h"
+            };
+    }
+    return {};
+#else
+    switch (v) {
+        case FA_VARIANT_F16:           return read_file("flash_attn_f16.cl");
+        case FA_VARIANT_F32:           return read_file("flash_attn_f32.cl");
+        case FA_VARIANT_F32_F16:
+        case FA_VARIANT_F32_F16_SPLIT: return read_file("flash_attn_f32_f16.cl");
+        case FA_VARIANT_PRE:           return read_file("flash_attn_pre_f16.cl");
+        case FA_VARIANT_Q8_0:
+        case FA_VARIANT_Q8_0_SPLIT:    return read_file("flash_attn_f32_q8_0.cl");
+        case FA_VARIANT_Q4_0:
+        case FA_VARIANT_Q4_0_SPLIT:    return read_file("flash_attn_f32_q4_0.cl");
+    }
+    return {};
+#endif
+}
+
+static std::string ggml_opencl_fa_compile_opts(ggml_backend_opencl_context * backend_ctx,
+                                                const ggml_opencl_fa_dim * cfg,
+                                                ggml_opencl_fa_variant variant) {
+    std::string opts = backend_ctx->kernel_compile_opts +
+        " -D DK=" + std::to_string(cfg->dk) +
+        " -D DV=" + std::to_string(cfg->dv) +
+        " -D BLOCK_M=" + std::to_string(cfg->bm) +
+        " -D BLOCK_N=" + std::to_string(cfg->bn);
+
+    const bool is_split = variant == FA_VARIANT_F32_F16_SPLIT ||
+                          variant == FA_VARIANT_Q8_0_SPLIT    ||
+                          variant == FA_VARIANT_Q4_0_SPLIT;
+    if (is_split) {
+        opts += " -D N_SPLIT=" + std::to_string(cfg->n_split);
+        if (backend_ctx->has_subgroup_shuffle) {
+            opts += backend_ctx->has_qcom_subgroup_shuffle
+                ? " -D cl_qcom_subgroup_shuffle=1"
+                : " -D cl_khr_subgroup_shuffle=1";
+        }
+    }
+    return opts;
+}
+
+// Log private memory for an FA kernel. Enable via `GGML_OPENCL_FA_LOG_SPILL=1`.
+// On Adreno non-zero private_mem means spilling to global memory due to resource
+// constraint and usually causes performance degradation.
+// (per-work-item, no cache locality) — a strong signal to pick a config
+// with smaller per-thread state (e.g. larger N_SPLIT).
+static void ggml_opencl_log_fa_kernel_spill(ggml_backend_opencl_context * backend_ctx,
+                                            cl_kernel kernel, const char * name, int dk, int dv) {
+    static const bool enabled = []{
+        const char * e = std::getenv("GGML_OPENCL_FA_LOG_SPILL");
+        return e && e[0] && e[0] != '0';
+    }();
+
+    if (!enabled || kernel == nullptr) {
+        return;
+    }
+
+    cl_ulong priv_mem = 0;
+    if (clGetKernelWorkGroupInfo(kernel, backend_ctx->device, CL_KERNEL_PRIVATE_MEM_SIZE,
+                                 sizeof(priv_mem), &priv_mem, NULL) == CL_SUCCESS) {
+        const char * tag = priv_mem > 0 ? "SPILL" : "ok";
+        GGML_LOG_INFO("ggml_opencl: [%s] %s DK=%d DV=%d private_mem=%llu bytes\n",
+                tag, name, dk, dv, (unsigned long long) priv_mem);
+    }
+}
+
+static void ggml_opencl_ensure_fa_pre_kernels(ggml_backend_opencl_context * backend_ctx, int dk, int dv) {
+    const std::pair<int, int> dk_dv = {dk, dv};
+    if (backend_ctx->fa.kv_pad_f16.count(dk_dv) > 0) {
+        return;
+    }
+
+    const ggml_opencl_fa_dim * cfg = nullptr;
+    for (const auto & d : g_opencl_fa_dims) {
+        if (d.dk == dk && d.dv == dv) {
+            cfg = &d; break;
+        }
+    }
+
+    if (cfg == nullptr) {
+        GGML_ABORT("ggml_opencl: no flash_attn config for DK=%d DV=%d", dk, dv);
+    }
+
+    GGML_LOG_INFO("ggml_opencl: lazy-compiling flash_attn prepass for DK=%d DV=%d\n", dk, dv);
+
+    cl_int err;
+    const std::string src  = ggml_opencl_fa_kernel_src(FA_VARIANT_PRE);
+    const std::string opts = ggml_opencl_fa_compile_opts(backend_ctx, cfg, FA_VARIANT_PRE);
+
+    cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, src.c_str(), opts);
+
+    cl_kernel k_kv_pad_f16, k_mask_pad_f16, k_blk_f16;
+    CL_CHECK((k_kv_pad_f16  = clCreateKernel(prog, "flash_attn_kv_pad_f16",   &err), err));
+    CL_CHECK((k_mask_pad_f16 = clCreateKernel(prog, "flash_attn_mask_pad_f16", &err), err));
+    CL_CHECK((k_blk_f16     = clCreateKernel(prog, "flash_attn_blk_f16",      &err), err));
+    backend_ctx->fa.kv_pad_f16[{dk, dv}]   = k_kv_pad_f16;
+    backend_ctx->fa.mask_pad_f16[{dk, dv}] = k_mask_pad_f16;
+    backend_ctx->fa.blk_f16[{dk, dv}]      = k_blk_f16;
+    CL_CHECK(clReleaseProgram(prog));
+
+    backend_ctx->fa.f32_f16_bm[{dk, dv}]      = cfg->bm;
+    backend_ctx->fa.f32_f16_bn[{dk, dv}]      = cfg->bn;
+    backend_ctx->fa.f32_f16_wg_size[{dk, dv}] = cfg->bm;
+    backend_ctx->fa.bm[{dk, dv}]              = cfg->bm;
+    backend_ctx->fa.bn[{dk, dv}]              = cfg->bn;
+}
+
+// Compile one (variant, dk, dv); memoised. false = compiler rejected.
+static bool ggml_opencl_ensure_fa_variant(ggml_backend_opencl_context * backend_ctx, int dk, int dv, ggml_opencl_fa_variant variant) {
+    const std::pair<int, int> dk_dv = {dk, dv};
+
+    const ggml_opencl_fa_dim * cfg = nullptr;
+    for (const auto & d : g_opencl_fa_dims) {
+        if (d.dk == dk && d.dv == dv) {
+            cfg = &d; break;
+        }
+    }
+    if (cfg == nullptr) {
+        return false;
+    }
+
+    // if a variant has already been compiled
+    switch (variant) {
+        case FA_VARIANT_F16: {
+            if (backend_ctx->fa.f16.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_F32: {
+            if (backend_ctx->fa.f32.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_F32_F16: {
+            if (backend_ctx->fa.f32_f16.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_Q8_0: {
+            if (backend_ctx->fa.f32_q8_0.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_Q4_0: {
+            if (backend_ctx->fa.f32_q4_0.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_F32_F16_SPLIT: {
+            if (backend_ctx->fa.f32_f16_split.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_Q8_0_SPLIT: {
+            if (backend_ctx->fa.f32_q8_0_split.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_Q4_0_SPLIT: {
+            if (backend_ctx->fa.f32_q4_0_split.count(dk_dv)) {
+                return true;
+            }
+            break;
+        }
+        case FA_VARIANT_PRE: {
+            ggml_opencl_ensure_fa_pre_kernels(backend_ctx, dk, dv);
+            return true;
+        }
+    }
+
+    // not registered but attempted - meaning these kernels failed to compile
+    const auto attempt_key = std::make_pair(variant, dk_dv);
+    if (backend_ctx->fa.variant_attempted.count(attempt_key)) {
+        return false;
+    }
+    backend_ctx->fa.variant_attempted.insert(attempt_key);
+
+    const bool is_split = variant == FA_VARIANT_F32_F16_SPLIT ||
+                          variant == FA_VARIANT_Q8_0_SPLIT    ||
+                          variant == FA_VARIANT_Q4_0_SPLIT;
+    const bool is_quant = variant == FA_VARIANT_Q8_0 || variant == FA_VARIANT_Q8_0_SPLIT ||
+                          variant == FA_VARIANT_Q4_0 || variant == FA_VARIANT_Q4_0_SPLIT;
+    if (is_quant && (dk % 32 != 0 || dv % 32 != 0)) {
+        return false;
+    }
+    if (is_split && cfg->n_split <= 1) {
+        return false;
+    }
+    if ((variant == FA_VARIANT_Q8_0_SPLIT || variant == FA_VARIANT_Q4_0_SPLIT) &&
+        ((dk / 32) % cfg->n_split != 0 || (dv / 4) % cfg->n_split != 0)) {
+        return false;
+    }
+
+    const std::string src = ggml_opencl_fa_kernel_src(variant);
+
+    if (src.empty()) {
+        return false;
+    }
+    const std::string opts = ggml_opencl_fa_compile_opts(backend_ctx, cfg, variant);
+
+    const char * tag = nullptr;
+    switch (variant) {
+        case FA_VARIANT_F16:             tag = "fa f16";             break;
+        case FA_VARIANT_F32:             tag = "fa f32";             break;
+        case FA_VARIANT_F32_F16:         tag = "fa f32_f16";         break;
+        case FA_VARIANT_Q8_0:            tag = "fa q8_0";            break;
+        case FA_VARIANT_Q4_0:            tag = "fa q4_0";            break;
+        case FA_VARIANT_F32_F16_SPLIT:   tag = "fa f32_f16 split";   break;
+        case FA_VARIANT_Q8_0_SPLIT:      tag = "fa q8_0 split";      break;
+        case FA_VARIANT_Q4_0_SPLIT:      tag = "fa q4_0 split";      break;
+        default: break;
+    }
+    cl_program prog = build_program_from_source_ex(
+        backend_ctx->context, backend_ctx->device, src.c_str(), opts, /*fatal=*/false, tag);
+
+    if (!prog) {
+        return false;
+    }
+
+    cl_int err;
+    switch (variant) {
+        case FA_VARIANT_F16: {
+            cl_kernel k, kq1;
+            CL_CHECK((k   = clCreateKernel(prog, "flash_attn_f16",    &err), err));
+            CL_CHECK((kq1 = clCreateKernel(prog, "flash_attn_f16_q1", &err), err));
+            backend_ctx->fa.f16[{dk, dv}]    = k;
+            backend_ctx->fa.f16_q1[{dk, dv}] = kq1;
+            break;
+        }
+        case FA_VARIANT_F32: {
+            cl_kernel k, kq1;
+            CL_CHECK((k   = clCreateKernel(prog, "flash_attn_f32",    &err), err));
+            CL_CHECK((kq1 = clCreateKernel(prog, "flash_attn_f32_q1", &err), err));
+            backend_ctx->fa.f32[{dk, dv}]    = k;
+            backend_ctx->fa.f32_q1[{dk, dv}] = kq1;
+            break;
+        }
+        case FA_VARIANT_F32_F16: {
+            cl_kernel k, kq1;
+            CL_CHECK((k   = clCreateKernel(prog, "flash_attn_f32_f16",    &err), err));
+            CL_CHECK((kq1 = clCreateKernel(prog, "flash_attn_f32_f16_q1", &err), err));
+            backend_ctx->fa.f32_f16[{dk, dv}]    = k;
+            backend_ctx->fa.f32_f16_q1[{dk, dv}] = kq1;
+            ggml_opencl_log_fa_kernel_spill(backend_ctx, k,   "flash_attn_f32_f16",    dk, dv);
+            ggml_opencl_log_fa_kernel_spill(backend_ctx, kq1, "flash_attn_f32_f16_q1", dk, dv);
+            cl_kernel k_split = clCreateKernel(prog, "flash_attn_f32_f16_q1_split", &err);
+            if (err == CL_SUCCESS) {
+                backend_ctx->fa.f32_f16_q1_split[{dk, dv}] = k_split;
+                ggml_opencl_log_fa_kernel_spill(backend_ctx, k_split, "flash_attn_f32_f16_q1_split", dk, dv);
+            }
+            cl_kernel k_merge = clCreateKernel(prog, "flash_attn_f32_merge", &err);
+            if (err == CL_SUCCESS) {
+                backend_ctx->fa.f32_merge[{dk, dv}] = k_merge;
+            }
+            break;
+        }
+        case FA_VARIANT_Q8_0:
+        case FA_VARIANT_Q4_0: {
+            const bool is_q8 = variant == FA_VARIANT_Q8_0;
+            const std::string base = is_q8 ? "flash_attn_f32_q8_0" : "flash_attn_f32_q4_0";
+            const std::string name_q1       = base + "_q1";
+            const std::string name_q1_split = base + "_q1_split";
+            auto & m_q1       = is_q8 ? backend_ctx->fa.f32_q8_0_q1       : backend_ctx->fa.f32_q4_0_q1;
+            auto & m_prefill  = is_q8 ? backend_ctx->fa.f32_q8_0          : backend_ctx->fa.f32_q4_0;
+            auto & m_q1_split = is_q8 ? backend_ctx->fa.f32_q8_0_q1_split : backend_ctx->fa.f32_q4_0_q1_split;
+
+            cl_kernel k, kq1;
+            CL_CHECK((kq1 = clCreateKernel(prog, name_q1.c_str(), &err), err));
+            CL_CHECK((k   = clCreateKernel(prog, base.c_str(),    &err), err));
+            m_q1[{dk, dv}]      = kq1;
+            m_prefill[{dk, dv}] = k;
+            ggml_opencl_log_fa_kernel_spill(backend_ctx, kq1, name_q1.c_str(), dk, dv);
+            ggml_opencl_log_fa_kernel_spill(backend_ctx, k,   base.c_str(),    dk, dv);
+            cl_kernel k_split = clCreateKernel(prog, name_q1_split.c_str(), &err);
+            if (err == CL_SUCCESS) {
+                m_q1_split[{dk, dv}] = k_split;
+                ggml_opencl_log_fa_kernel_spill(backend_ctx, k_split, name_q1_split.c_str(), dk, dv);
+            }
+            if (!backend_ctx->fa.f32_merge.count({dk, dv})) {
+                cl_kernel k_merge = clCreateKernel(prog, "flash_attn_f32_merge", &err);
+                if (err == CL_SUCCESS) {
+                    backend_ctx->fa.f32_merge[{dk, dv}] = k_merge;
+                }
+            }
+            break;
+        }
+        case FA_VARIANT_F32_F16_SPLIT: {
+            cl_kernel k;
+            CL_CHECK((k = clCreateKernel(prog, "flash_attn_f32_f16", &err), err));
+            backend_ctx->fa.f32_f16_split[{dk, dv}]               = k;
+            backend_ctx->fa.f32_f16_split_wg_size[{dk, dv}]       = cfg->bm * cfg->n_split;
+            backend_ctx->fa.f32_f16_split_nkv_threshold[{dk, dv}] = cfg->nkv_split_threshold;
+            break;
+        }
+        case FA_VARIANT_Q8_0_SPLIT:
+        case FA_VARIANT_Q4_0_SPLIT: {
+            const bool is_q8 = variant == FA_VARIANT_Q8_0_SPLIT;
+            cl_kernel k;
+            CL_CHECK((k = clCreateKernel(prog, is_q8 ? "flash_attn_f32_q8_0" : "flash_attn_f32_q4_0", &err), err));
+            auto & split        = is_q8 ? backend_ctx->fa.f32_q8_0_split               : backend_ctx->fa.f32_q4_0_split;
+            auto & split_wg     = is_q8 ? backend_ctx->fa.f32_q8_0_split_wg_size        : backend_ctx->fa.f32_q4_0_split_wg_size;
+            auto & split_bm     = is_q8 ? backend_ctx->fa.f32_q8_0_split_bm             : backend_ctx->fa.f32_q4_0_split_bm;
+            auto & split_thresh = is_q8 ? backend_ctx->fa.f32_q8_0_split_nkv_threshold  : backend_ctx->fa.f32_q4_0_split_nkv_threshold;
+            split[{dk, dv}]        = k;
+            split_wg[{dk, dv}]     = cfg->bm * cfg->n_split;
+            split_bm[{dk, dv}]     = cfg->bm;
+            split_thresh[{dk, dv}] = 0;  // quant prefill: always split
+            break;
+        }
+        default:
+            break;
+    }
+    CL_CHECK(clReleaseProgram(prog));
+    return true;
+}
+
+// Compile a quant FA split kernel with a hand-picked (BLOCK_M, N_SPLIT) that
+// overrides the default fa_dims tuning, for the DK values where the default
+// N_SPLIT is degenerate for quant prefill:
+//   DK=256: default N_SPLIT=16 leaves DK/32=8 blocks -> 0 blocks/split.
+//           Override N_SPLIT=8 (1 block/split), BLOCK_M=16.
+//   DK=96 : DK/32 = 3 blocks, not divisible by the default N_SPLIT=2 ->
+//           override N_SPLIT=3. BLOCK_M must be 16, not 32: the N_SPLIT=3
+//           QK-partial reduction uses sub_group_shuffle, so all 3 split
+//           threads of a query must land in one subgroup — WG_SIZE =
+//           BLOCK_M*N_SPLIT must be <= the 64-lane Adreno subgroup (16*3=48).
+static bool ggml_opencl_ensure_fa_quant_split_override(
+        ggml_backend_opencl_context * backend_ctx,
+        int dk, int dv, int quant_bm, int quant_n_split, bool is_q8_0
+) {
+    const std::pair<int, int> dk_dv = {dk, dv};
+    if (is_q8_0 && backend_ctx->fa.f32_q8_0_split.count(dk_dv)) {
+        return true;
+    }
+    if (!is_q8_0 && backend_ctx->fa.f32_q4_0_split.count(dk_dv)) {
+        return true;
+    }
+
+    const ggml_opencl_fa_variant variant = is_q8_0 ? FA_VARIANT_Q8_0_SPLIT : FA_VARIANT_Q4_0_SPLIT;
+    const auto attempt_key = std::make_pair(variant, dk_dv);
+    if (backend_ctx->fa.variant_attempted.count(attempt_key)) {
+        return false;
+    }
+
+    backend_ctx->fa.variant_attempted.insert(attempt_key);
+
+    std::string shuffle_opts;
+    if (backend_ctx->has_subgroup_shuffle) {
+        shuffle_opts = backend_ctx->has_qcom_subgroup_shuffle
+            ? " -D cl_qcom_subgroup_shuffle=1"
+            : " -D cl_khr_subgroup_shuffle=1";
+    }
+    const ggml_opencl_fa_dim * cfg = nullptr;
+    for (const auto & d : g_opencl_fa_dims) {
+        if (d.dk == dk && d.dv == dv) {
+            cfg = &d; break;
+        }
+    }
+    if (cfg == nullptr) {
+        return false;
+    }
+
+    // BLK_PREPASS_BM is the prepass-kernel BLOCK_M, needed so the quant kernel
+    // indexes the blk[] classification buffer correctly.
+    std::string opts = backend_ctx->kernel_compile_opts + shuffle_opts +
+        " -D DK=" + std::to_string(dk) +
+        " -D DV=" + std::to_string(dv) +
+        " -D BLOCK_M=" + std::to_string(quant_bm) +
+        " -D BLOCK_N=" + std::to_string(cfg->bn) +
+        " -D N_SPLIT=" + std::to_string(quant_n_split) +
+        " -D BLK_PREPASS_BM=" + std::to_string(cfg->bm);
+
+    const std::string src = ggml_opencl_fa_kernel_src(variant);
+    if (src.empty()) {
+        return false;
+    }
+
+    const std::string tag = std::string("fa ") + (is_q8_0 ? "q8_0" : "q4_0") +
+        " split DK=" + std::to_string(dk);
+    cl_program prog = build_program_from_source_ex(
+        backend_ctx->context, backend_ctx->device, src.c_str(), opts, /*fatal=*/false, tag.c_str());
+
+    if (!prog) {
+        return false;
+    }
+
+    cl_int err;
+    cl_kernel k;
+    if (is_q8_0) {
+        CL_CHECK((k = clCreateKernel(prog, "flash_attn_f32_q8_0", &err), err));
+        backend_ctx->fa.f32_q8_0_split[dk_dv]                = k;
+        backend_ctx->fa.f32_q8_0_split_wg_size[dk_dv]        = quant_bm * quant_n_split;
+        backend_ctx->fa.f32_q8_0_split_bm[dk_dv]             = quant_bm;
+        backend_ctx->fa.f32_q8_0_split_nkv_threshold[dk_dv]  = 0;
+    } else {
+        CL_CHECK((k = clCreateKernel(prog, "flash_attn_f32_q4_0", &err), err));
+        backend_ctx->fa.f32_q4_0_split[dk_dv]                = k;
+        backend_ctx->fa.f32_q4_0_split_wg_size[dk_dv]        = quant_bm * quant_n_split;
+        backend_ctx->fa.f32_q4_0_split_bm[dk_dv]             = quant_bm;
+        backend_ctx->fa.f32_q4_0_split_nkv_threshold[dk_dv]  = 0;
+    }
+    CL_CHECK(clReleaseProgram(prog));
+    return true;
+}
+
 namespace /* anonymous */ {
 extern struct ggml_backend_device_i ggml_backend_opencl_device_i;
 }
@@ -3955,6 +4425,8 @@ static void ggml_opencl_print_backend_info(ggml_backend_opencl_device_context *
         backend_ctx->driver_version.c_str());
     GGML_LOG_INFO("ggml_opencl: vector subgroup broadcast support: %s\n",
         backend_ctx->has_vector_subgroup_broadcast ? "true" : "false");
+    GGML_LOG_INFO("ggml_opencl: subgroup shuffle support: %s\n",
+        backend_ctx->has_subgroup_shuffle ? "true" : "false");
     GGML_LOG_INFO("ggml_opencl: device FP16 support: %s\n",
         backend_ctx->fp16_support ? "true" : "false");
     GGML_LOG_INFO("ggml_opencl: mem base addr align: %u\n",
@@ -4111,6 +4583,8 @@ static ggml_backend_opencl_context * ggml_cl_init(ggml_backend_dev_t dev) {
     backend_ctx->gpu_family = dev_ctx->gpu_family;
     backend_ctx->adreno_gen = dev_ctx->adreno_gen;
     if (backend_ctx->gpu_family == GPU_FAMILY::ADRENO) {
+        ggml_cl_init_fa_dims_table();
+
         // Use wave size of 64 for all Adreno GPUs.
         backend_ctx->adreno_wave_size = 64;
     }
@@ -4156,6 +4630,11 @@ static ggml_backend_opencl_context * ggml_cl_init(ggml_backend_dev_t dev) {
 
     // check Adreno large buffer support
     backend_ctx->adreno_has_large_buffer = strstr(ext_buffer, "cl_qcom_large_buffer") != NULL;
+    // subgroup shuffle support (N_SPLIT>1 FA kernel)
+    backend_ctx->has_qcom_subgroup_shuffle = strstr(ext_buffer, "cl_qcom_subgroup_shuffle") != NULL;
+    backend_ctx->has_subgroup_shuffle =
+        strstr(ext_buffer, "cl_khr_subgroup_shuffle") != NULL ||
+        backend_ctx->has_qcom_subgroup_shuffle;
 
     cl_uint base_align_in_bits;
     CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &base_align_in_bits, NULL));
@@ -5100,6 +5579,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 switch (op->type) {
                     case GGML_TYPE_F16:
                     case GGML_TYPE_F32:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q4_0:
                         return (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
                     default:
                         return false;
@@ -5175,9 +5656,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_NEG:
                 case GGML_UNARY_OP_EXP:
-                    return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
+                    // Adreno F16 exp/expm1 overflow even post-half->float convert.
+                    return op->src[0]->type == GGML_TYPE_F32;
                 case GGML_UNARY_OP_EXPM1:
-                    return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
+                    return op->src[0]->type == GGML_TYPE_F32;
                 case GGML_UNARY_OP_SOFTPLUS:
                     return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
                 default:
@@ -5250,7 +5732,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return true;
             } else if (op->src[0]->type == GGML_TYPE_F32) {
                 return op->src[1]->type == GGML_TYPE_F32;
-            } else if (op->src[0]->type == GGML_TYPE_Q4_0  || op->src[0]->type == GGML_TYPE_Q4_1 ||
+            } else if (op->src[0]->type == GGML_TYPE_Q4_0) {
+                // Non-contig src0 routes through on-device dequant-to-f16.
+                return op->src[1]->type == GGML_TYPE_F32;
+            } else if (op->src[0]->type == GGML_TYPE_Q4_1 ||
                        op->src[0]->type == GGML_TYPE_Q5_0  || op->src[0]->type == GGML_TYPE_Q5_1 ||
                        op->src[0]->type == GGML_TYPE_MXFP4 ||
                        op->src[0]->type == GGML_TYPE_IQ4_NL ||
@@ -5339,43 +5824,55 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
         case GGML_OP_MEAN:
             return op->src[0]->type == GGML_TYPE_F32;
-        case GGML_OP_FLASH_ATTN_EXT:
-            {
-                load_cl_kernels_flash_attn(backend_ctx);
+        case GGML_OP_FLASH_ATTN_EXT: {
+            const ggml_tensor * q = op->src[0];
+            const ggml_tensor * k = op->src[1];
+            const ggml_tensor * v = op->src[2];
 
-                const ggml_tensor * q = op->src[0];
-                const ggml_tensor * k = op->src[1];
-                const ggml_tensor * v = op->src[2];
+            const int dk = q->ne[0];
+            const int dv = v->ne[0];
 
-                const int dk = q->ne[0];
-                const int dv = v->ne[0];
+            const struct { int dk; int dv; } supported_dims[] = {
+                { 40,  40}, { 64,  64}, { 80,  80}, { 96,  96},
+                {112, 112}, {128, 128}, {192, 128},
+                {192, 192}, {256, 256},
+            };
 
-                const struct { int dk; int dv; } supported_dims[] = {
-                    { 40,  40}, { 64,  64}, { 80,  80}, { 96,  96},
-                    {112, 112}, {128, 128}, {192, 128},
-                    {192, 192}, {256, 256},
-                };
-
-                bool dims_supported = false;
-                for (size_t i = 0; i < sizeof(supported_dims)/sizeof(supported_dims[0]); ++i) {
-                    if (supported_dims[i].dk == dk && supported_dims[i].dv == dv) {
-                        dims_supported = true;
-                        break;
-                    }
+            bool dims_supported = false;
+            for (size_t i = 0; i < sizeof(supported_dims)/sizeof(supported_dims[0]); ++i) {
+                if (supported_dims[i].dk == dk && supported_dims[i].dv == dv) {
+                    dims_supported = true;
+                    break;
                 }
-                if (!dims_supported) {
-                    return false;
-                }
-
-                const bool is_f32_f32 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F32 &&
-                                        v->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
-                const bool is_f16_f16 = q->type == GGML_TYPE_F16 && k->type == GGML_TYPE_F16 &&
-                                        v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16;
-                const bool is_f32_f16 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 &&
-                                        v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F32;
-
-                return is_f32_f32 || is_f16_f16 || is_f32_f16;
             }
+            if (!dims_supported) {
+                return false;
+            }
+
+            const bool is_f32_f32  = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F32 &&
+                                     v->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+            const bool is_f16_f16  = q->type == GGML_TYPE_F16 && k->type == GGML_TYPE_F16 &&
+                                     v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16;
+            const bool is_f32_f16  = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 &&
+                                     v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F32;
+            const bool is_f32_q8_0 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_Q8_0 &&
+                                     v->type == GGML_TYPE_Q8_0 && op->type == GGML_TYPE_F32 &&
+                                     dk % 32 == 0 && dv % 32 == 0;
+            const bool is_f32_q4_0 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_Q4_0 &&
+                                     v->type == GGML_TYPE_Q4_0 && op->type == GGML_TYPE_F32 &&
+                                     dk % 32 == 0 && dv % 32 == 0;
+
+            // Asymmetric KV: host-dequants both sides to F32, uses f32 kernel.
+            auto is_kv_type_ok = [](ggml_type t) {
+                return t == GGML_TYPE_F16 || t == GGML_TYPE_F32 ||
+                       t == GGML_TYPE_Q4_0 || t == GGML_TYPE_Q8_0;
+            };
+            const bool is_f32_asym = q->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
+                                     k->type != v->type &&
+                                     is_kv_type_ok(k->type) && is_kv_type_ok(v->type);
+
+            return is_f32_f32 || is_f16_f16 || is_f32_f16 || is_f32_q8_0 || is_f32_q4_0 || is_f32_asym;
+        }
         default:
             return false;
     }
@@ -5737,6 +6234,9 @@ struct ggml_backend_opencl_buffer_context {
             temp_tensor_extras_q6_K.push_back(e);
         }
         temp_tensor_extras_q6_K_in_use.clear();
+
+        q8_0_soa_tensors.clear();
+        q4_0_soa_tensors.clear();
     }
 
     // Pools for extras. Available extras are in `temp_tensor_extras`. Extras
@@ -5767,6 +6267,17 @@ struct ggml_backend_opencl_buffer_context {
     std::vector<ggml_tensor_extra_cl_q6_K *> temp_tensor_extras_q6_K;
     std::vector<ggml_tensor_extra_cl_q6_K *> temp_tensor_extras_q6_K_in_use;
 
+    // q8_0 tensors with AoS->SoA layout conversion installed by set_tensor.
+    // Two types of tensors get SOA'ed - normal weights and MoE weights.
+    // In Q8_0's case, we only have normal weights. If we ever have Q8_0 as MoE
+    // weights, they need to be added to this set in `set_tensors`.
+    std::unordered_set<const ggml_tensor *> q8_0_soa_tensors;
+
+    // Same for q4_0. KV-cache q4_0 tensors are allocated but never pass
+    // through set_tensor, so they stay AoS and aren't in this set.
+    // In Q4_0's case, in addition to normal weights, we have MoE weights.
+    std::unordered_set<const ggml_tensor *> q4_0_soa_tensors;
+
     // The buffer_context is initially created by ggml_backend_buft_alloc_buffer
     // before any tensor is initialized (at the beginning of alloc_tensor_range).
     // Hence, there is always a buffer object in this vector. When each tensor is
@@ -5848,6 +6359,10 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
     // buffers for quantized bits and scales, which are then populated by the
     // conversion kernel.
     if (tensor->type == GGML_TYPE_Q4_0) {
+        // Views can't SoA-ify here — parent owns the layout (see q8_0 guard).
+        if (tensor->view_src != nullptr || !ggml_is_contiguous(tensor)) {
+            return;
+        }
         // Tensors should have been preallocated, therefore they should
         // already have ggml_tensor_extra_cl as extra.
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
@@ -5937,6 +6452,8 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
             };
             extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err);
             tensor->extra = extra;
+            // MoE tensors are also SOA'ed
+            ctx->q4_0_soa_tensors.insert(tensor);
 
             return;
         }
@@ -5965,6 +6482,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         CL_CHECK(clReleaseMemObject(data_device));
 
         tensor->extra = extra;
+        ctx->q4_0_soa_tensors.insert(tensor);
 
         // transpose the weights and scales
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
@@ -6516,6 +7034,11 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         return;
     }
     if (tensor->type == GGML_TYPE_Q8_0) {
+        // Views share the parent's buffer; parent owns SoA conversion.
+        if (tensor->view_src != nullptr || !ggml_is_contiguous(tensor)) {
+            return;
+        }
+
         ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
         GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
 
@@ -6571,6 +7094,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         CL_CHECK(clReleaseMemObject(data_device));
 
         tensor->extra = extra;
+        ctx->q8_0_soa_tensors.insert(tensor);
 
         // Transpose the weights and scales
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
@@ -7226,7 +7750,18 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
     // To properly support this, we need to restore block_q4_0 struct arrays
     // from the flattened buffers.
     if (tensor->type == GGML_TYPE_Q4_0) {
-        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)tensor->extra;
+        // KV-cache q4_0 stays AoS — direct readback, no SoA restore.
+        if (!ggml_cl_is_q4_0_soa(tensor)) {
+            ggml_tensor_extra_cl * extra_aos = (ggml_tensor_extra_cl *) tensor->extra;
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, extra_aos->data_device, CL_TRUE,
+                extra_aos->offset + tensor->view_offs + offset,
+                size, data, 0, NULL, NULL));
+            return;
+        }
+        // SoA extra lives on the parent tensor — follow view_src.
+        const ggml_tensor * extra_src = tensor->view_src != nullptr ? tensor->view_src : tensor;
+        ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)extra_src->extra;
 
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
         if (use_adreno_moe_kernels(backend_ctx, tensor)) {
@@ -7697,7 +8232,18 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         return;
     }
     if (tensor->type == GGML_TYPE_Q8_0) {
-        ggml_tensor_extra_cl_q8_0 * extra = (ggml_tensor_extra_cl_q8_0 *)tensor->extra;
+        // KV-cache q8_0 stays AoS (see Q4_0 branch).
+        if (!ggml_cl_is_q8_0_soa(tensor)) {
+            ggml_tensor_extra_cl * extra_aos = (ggml_tensor_extra_cl *) tensor->extra;
+            CL_CHECK(clEnqueueReadBuffer(
+                queue, extra_aos->data_device, CL_TRUE,
+                extra_aos->offset + tensor->view_offs + offset,
+                size, data, 0, NULL, NULL));
+            return;
+        }
+        // SoA extra lives on the parent — follow view_src.
+        const ggml_tensor * extra_src = tensor->view_src != nullptr ? tensor->view_src : tensor;
+        ggml_tensor_extra_cl_q8_0 * extra = (ggml_tensor_extra_cl_q8_0 *)extra_src->extra;
 
         cl_int err;
         cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
@@ -8821,6 +9367,34 @@ static void ggml_cl_get_rows(ggml_backend_t backend, const ggml_tensor * src0, c
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
 }
 
+// check if a Q8_0 tensor has been SOA'ed in set_tensor
+// we store SOA'ed tensors in a map in set_tensor, check against that map
+static bool ggml_cl_is_q8_0_soa(const ggml_tensor * tensor) {
+    if (tensor == nullptr || tensor->type != GGML_TYPE_Q8_0 || tensor->buffer == nullptr) {
+        return false;
+    }
+    auto * ctx = (ggml_backend_opencl_buffer_context *) tensor->buffer->context;
+    if (ctx == nullptr) {
+        return false;
+    }
+    const ggml_tensor * key = tensor->view_src != nullptr ? tensor->view_src : tensor;
+    return ctx->q8_0_soa_tensors.count(key) > 0;
+}
+
+// check if a Q4_0 tensor has been SOA'ed in set_tensor
+// we store SOA'ed tensors in a map in set_tensor, check against that map
+static bool ggml_cl_is_q4_0_soa(const ggml_tensor * tensor) {
+    if (tensor == nullptr || tensor->type != GGML_TYPE_Q4_0 || tensor->buffer == nullptr) {
+        return false;
+    }
+    auto * ctx = (ggml_backend_opencl_buffer_context *) tensor->buffer->context;
+    if (ctx == nullptr) {
+        return false;
+    }
+    const ggml_tensor * key = tensor->view_src != nullptr ? tensor->view_src : tensor;
+    return ctx->q4_0_soa_tensors.count(key) > 0;
+}
+
 static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -8834,26 +9408,14 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
     // ne2 = ne02
     // ne3 = ne03
 
-    const int      ne01 = src0->ne[1];
-    const int      ne02 = src0->ne[2];
-    const int      ne03 = src0->ne[3];
+    GGML_TENSOR_LOCALS(int,      ne0, src0, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb0, src0, nb);
 
-    const cl_ulong nb01 = src0->nb[1];
-    const cl_ulong nb02 = src0->nb[2];
-    const cl_ulong nb03 = src0->nb[3];
+    GGML_TENSOR_LOCALS(int,      ne1, src1, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb1, src1, nb);
 
-    const int      ne11 = src1->ne[1];
-    const int      ne12 = src1->ne[2];
-
-    const cl_ulong nb10 = src1->nb[0];
-    const cl_ulong nb11 = src1->nb[1];
-    const cl_ulong nb12 = src1->nb[2];
-
-    const int      ne0  = dst->ne[0];
-
-    const cl_ulong nb1  = dst->nb[1];
-    const cl_ulong nb2  = dst->nb[2];
-    const cl_ulong nb3  = dst->nb[3];
+    GGML_TENSOR_LOCALS(int,      ne, dst, ne);
+    GGML_TENSOR_LOCALS(cl_ulong, nb, dst, nb);
 
     const int nblk0 = ne0/ggml_blck_size(dst->type);
 
@@ -8861,31 +9423,49 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
 
     ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
     ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
-    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
 
     cl_ulong offset0 = extra0->offset + src0->view_offs;
     cl_ulong offset1 = extra1->offset + src1->view_offs;
-    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const bool q8_0_soa = dst->type == GGML_TYPE_Q8_0 && ggml_cl_is_q8_0_soa(dst);
+    const bool q4_0_soa = dst->type == GGML_TYPE_Q4_0 && ggml_cl_is_q4_0_soa(dst);
+    const bool is_soa   = q8_0_soa || q4_0_soa;
 
     cl_kernel kernel;
 
-    switch (dst->type) {
-        case GGML_TYPE_F32:
-            if (src1->type == GGML_TYPE_I64) {
-                kernel = backend_ctx->kernel_set_rows_f32_i64;
-            } else {
-                kernel = backend_ctx->kernel_set_rows_f32_i32;
-            }
-            break;
-        case GGML_TYPE_F16:
-            if (src1->type == GGML_TYPE_I64) {
-                kernel = backend_ctx->kernel_set_rows_f16_i64;
-            } else {
-                kernel = backend_ctx->kernel_set_rows_f16_i32;
-            }
-            break;
-        default:
-            GGML_ABORT("not implemented");
+    if (q8_0_soa) {
+        kernel = (src1->type == GGML_TYPE_I64)
+                    ? backend_ctx->kernel_set_rows_q8_0_soa_i64
+                    : backend_ctx->kernel_set_rows_q8_0_soa_i32;
+    } else if (q4_0_soa) {
+        kernel = (src1->type == GGML_TYPE_I64)
+                    ? backend_ctx->kernel_set_rows_q4_0_soa_i64
+                    : backend_ctx->kernel_set_rows_q4_0_soa_i32;
+    } else {
+        switch (dst->type) {
+            case GGML_TYPE_F32:
+                kernel = (src1->type == GGML_TYPE_I64)
+                            ? backend_ctx->kernel_set_rows_f32_i64
+                            : backend_ctx->kernel_set_rows_f32_i32;
+                break;
+            case GGML_TYPE_F16:
+                kernel = (src1->type == GGML_TYPE_I64)
+                            ? backend_ctx->kernel_set_rows_f16_i64
+                            : backend_ctx->kernel_set_rows_f16_i32;
+                break;
+            case GGML_TYPE_Q8_0:
+                kernel = (src1->type == GGML_TYPE_I64)
+                            ? backend_ctx->kernel_set_rows_q8_0_i64
+                            : backend_ctx->kernel_set_rows_q8_0_i32;
+                break;
+            case GGML_TYPE_Q4_0:
+                kernel = (src1->type == GGML_TYPE_I64)
+                            ? backend_ctx->kernel_set_rows_q4_0_i64
+                            : backend_ctx->kernel_set_rows_q4_0_i32;
+                break;
+            default:
+                GGML_ABORT("not implemented");
+        }
     }
 
     fastdiv_vals ne11_ = init_fastdiv_values(ne11);
@@ -8895,21 +9475,65 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
     CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
     CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
     CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
-    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
-    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
-    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne01));
-    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
-    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
-    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(fastdiv_vals), &ne11_));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(fastdiv_vals), &ne12_));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb10));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb11));
-    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb12));
-    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &nblk0));
-    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb1));
-    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb2));
-    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb3));
+
+    if (is_soa) {
+        // The q/d subbuffers in q8_0/q4_0 extras are interchangeable here.
+        // For views (e.g. ggml_set_rows' `out`), follow view_src for the SoA extra.
+        const ggml_tensor * soa_src = dst->view_src != nullptr ? dst->view_src : dst;
+        cl_mem q_mem = nullptr;
+        cl_mem d_mem = nullptr;
+        if (q8_0_soa) {
+            ggml_tensor_extra_cl_q8_0 * e = (ggml_tensor_extra_cl_q8_0 *)soa_src->extra;
+            q_mem = e->q;
+            d_mem = e->d;
+        } else {
+            ggml_tensor_extra_cl_q4_0 * e = (ggml_tensor_extra_cl_q4_0 *)soa_src->extra;
+            q_mem = e->q;
+            d_mem = e->d;
+        }
+        cl_ulong offset_q = 0;
+        cl_ulong offset_d = 0;
+        const int ne1_dst = dst->ne[1];
+        const int ne2_dst = dst->ne[2];
+        const int ne3_dst = dst->ne[3];
+
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &q_mem));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset_q));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &d_mem));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offset_d));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(fastdiv_vals), &ne11_));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(fastdiv_vals), &ne12_));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &nblk0));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne1_dst));
+        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int),      &ne2_dst));
+        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne3_dst));
+    } else {
+        ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+        cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(fastdiv_vals), &ne11_));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(fastdiv_vals), &ne12_));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &nblk0));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb1));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb2));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb3));
+    }
 
     int nth0 = 64;
     if (backend_ctx->gpu_family == INTEL) {
@@ -11483,14 +12107,370 @@ static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor
     backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst);
 }
 
+struct ggml_cl_flash_attn_temp_buffer {
+    cl_mem data = nullptr;
+
+    ~ggml_cl_flash_attn_temp_buffer() {
+        if (data != nullptr) {
+            CL_CHECK(clReleaseMemObject(data));
+            data = nullptr;
+        }
+    }
+};
+
+// Resolve the source buffer + strides for an FA KV tensor: keep the
+// caller-supplied AoS buffer if non-NULL, else fall back to tensor->extra.
+static void ggml_cl_flash_attn_resolve_src(
+        const ggml_tensor * tensor,
+        cl_mem &   buf,
+        cl_ulong & offset,
+        cl_ulong & nb1,
+        cl_ulong & nb2,
+        cl_ulong & nb3) {
+    if (buf != NULL) {
+        return;
+    }
+    ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
+    GGML_ASSERT(extra && extra->data_device);
+    buf    = extra->data_device;
+    offset = extra->offset + tensor->view_offs;
+    nb1    = tensor->nb[1];
+    nb2    = tensor->nb[2];
+    nb3    = tensor->nb[3];
+}
+
+// Read a (possibly strided-view) tensor from device into a tight host buffer.
+// dim 0 is always tight; a strided view is gathered row-by-row.
+static void ggml_cl_flash_attn_read_tensor_host(
+        ggml_backend_opencl_context * backend_ctx,
+        const ggml_tensor *           tensor,
+        cl_mem src_buffer, cl_ulong src_offset,
+        cl_ulong src_nb1, cl_ulong src_nb2, cl_ulong src_nb3,
+        size_t row_bytes, void * dst, size_t total_bytes
+) {
+    const bool contiguous_layout =
+        src_nb1 == row_bytes &&
+        src_nb2 == row_bytes * (cl_ulong) tensor->ne[1] &&
+        src_nb3 == src_nb2   * (cl_ulong) tensor->ne[2];
+
+    if (contiguous_layout) {
+        CL_CHECK(clEnqueueReadBuffer(backend_ctx->queue, src_buffer, CL_TRUE,
+                                     src_offset, total_bytes, dst, 0, NULL, NULL));
+        return;
+    }
+
+    size_t dst_off = 0;
+    for (int64_t i3 = 0; i3 < tensor->ne[3]; ++i3) {
+        for (int64_t i2 = 0; i2 < tensor->ne[2]; ++i2) {
+            for (int64_t i1 = 0; i1 < tensor->ne[1]; ++i1) {
+                const cl_ulong row_src_off = src_offset +
+                    (cl_ulong) i3 * src_nb3 +
+                    (cl_ulong) i2 * src_nb2 +
+                    (cl_ulong) i1 * src_nb1;
+                CL_CHECK(clEnqueueReadBuffer(backend_ctx->queue, src_buffer, CL_TRUE,
+                                             row_src_off, row_bytes,
+                                             (uint8_t *) dst + dst_off, 0, NULL, NULL));
+                dst_off += row_bytes;
+            }
+        }
+    }
+    GGML_ASSERT(dst_off == total_bytes);
+}
+
+// Rebuild AoS q8_0/q4_0 bytes from a SoA tensor into a temp buffer.
+// Returns false if the tensor is not SoA-quantised (already AoS).
+static bool ggml_cl_flash_attn_reconstruct_aos(
+        ggml_backend_opencl_context *         backend_ctx,
+        const ggml_tensor *                   tensor,
+        ggml_cl_flash_attn_temp_buffer &      temp,
+        cl_mem &                              out_buf,
+        cl_ulong &                            out_offset,
+        cl_ulong &                            out_nb1,
+        cl_ulong &                            out_nb2,
+        cl_ulong &                            out_nb3
+) {
+    if (tensor == nullptr) {
+        return false;
+    }
+    const bool is_q8_0 = tensor->type == GGML_TYPE_Q8_0 && ggml_cl_is_q8_0_soa(tensor);
+    const bool is_q4_0 = tensor->type == GGML_TYPE_Q4_0 && ggml_cl_is_q4_0_soa(tensor);
+    if (!is_q8_0 && !is_q4_0) {
+        return false;
+    }
+
+    // For views, SoA extra is on view_src (view->extra is pre-SoA).
+    // Noshuffle layout only applies to 2D weights, as determined by `use_adreno_kernels`,
+    // where ne2 == 1 and ne3 == 1 -- these are never FA inputs.
+    // Therefore, we use `restore_block_qk_0` kernels, not `restore_block_qk_0_noshuffle`.
+    const ggml_tensor * soa_src = tensor->view_src ? tensor->view_src : tensor;
+    cl_mem extra_q = NULL;
+    cl_mem extra_d = NULL;
+    if (is_q8_0) {
+        auto * e = (ggml_tensor_extra_cl_q8_0 *) soa_src->extra;
+        GGML_ASSERT(e && e->q && e->d);
+        extra_q = e->q;
+        extra_d = e->d;
+    } else {
+        auto * e = (ggml_tensor_extra_cl_q4_0 *) soa_src->extra;
+        GGML_ASSERT(e && e->q && e->d);
+        extra_q = e->q;
+        extra_d = e->d;
+    }
+
+    // Reconstruct the whole parent; view offsets then work naturally.
+    const size_t parent_nbytes = ggml_nbytes(soa_src);
+    cl_int err;
+    temp.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, parent_nbytes, NULL, &err);
+    CL_CHECK(err);
+
+    cl_kernel kernel = is_q8_0 ? backend_ctx->kernel_restore_block_q8_0
+                               : backend_ctx->kernel_restore_block_q4_0;
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_q));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra_d));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &temp.data));
+
+    const size_t n_blocks = (size_t) ggml_nelements(soa_src) / ggml_blck_size(soa_src->type);
+    size_t global_work_size[] = { n_blocks, 1, 1 };
+    size_t local_work_size[]  = { 1, 1, 1 };
+    CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL,
+                                    global_work_size, local_work_size, 0, NULL, NULL));
+
+    out_buf    = temp.data;
+    out_offset = tensor->view_offs;
+    out_nb1    = tensor->nb[1];
+    out_nb2    = tensor->nb[2];
+    out_nb3    = tensor->nb[3];
+    return true;
+}
+
+// GPU dequant of a contiguous q4_0/q8_0 KV tensor to f16/f32. Caller supplies
+// src_buf when reconstructing from SoA. Returns false for non-contig layouts
+// (the kernel indexes blocks tightly within ne[0]) so the caller can fall back
+// to the host path.
+static bool ggml_cl_flash_attn_dequant_kv_gpu(
+        ggml_backend_opencl_context *    backend_ctx,
+        const ggml_tensor *              tensor,
+        ggml_type                        target_type,
+        cl_mem                           in_src_buf,
+        cl_ulong                         in_src_offset,
+        cl_ulong                         in_src_nb1,
+        cl_ulong                         in_src_nb2,
+        cl_ulong                         in_src_nb3,
+        ggml_cl_flash_attn_temp_buffer & temp,
+        cl_mem &                         out_buf,
+        cl_ulong &                       out_offset,
+        cl_ulong &                       out_nb1,
+        cl_ulong &                       out_nb2,
+        cl_ulong &                       out_nb3
+) {
+    GGML_ASSERT(tensor->type == GGML_TYPE_Q8_0 || tensor->type == GGML_TYPE_Q4_0);
+    GGML_ASSERT(target_type == GGML_TYPE_F16 || target_type == GGML_TYPE_F32);
+
+    const bool is_q8_0 = tensor->type == GGML_TYPE_Q8_0;
+
+    cl_mem   src_buf    = in_src_buf;
+    cl_ulong src_offset = in_src_offset;
+    cl_ulong src_nb1    = in_src_nb1;
+    cl_ulong src_nb2    = in_src_nb2;
+    cl_ulong src_nb3    = in_src_nb3;
+    ggml_cl_flash_attn_resolve_src(tensor, src_buf, src_offset, src_nb1, src_nb2, src_nb3);
+
+    if (tensor->nb[0] != (cl_ulong) ggml_type_size(tensor->type)) {
+        return false;
+    }
+
+    const size_t n_blocks = (size_t) ggml_nelements(tensor) / 32; // block size is 32
+    const size_t elem_size = ggml_type_size(target_type);
+    const size_t out_bytes = n_blocks * 32 * elem_size;
+    const cl_int nblk0_arg = (cl_int) (tensor->ne[0] / 32);
+    const cl_int ne1_arg   = (cl_int) tensor->ne[1];
+    const cl_int ne2_arg   = (cl_int) tensor->ne[2];
+    const cl_int ne3_arg   = (cl_int) tensor->ne[3];
+
+    cl_int err;
+    temp.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, out_bytes, NULL, &err);
+    CL_CHECK(err);
+
+    cl_kernel kernel;
+    if (target_type == GGML_TYPE_F16) {
+        kernel = is_q8_0 ? backend_ctx->kernel_dequant_q8_0_f16_view_aos
+                         : backend_ctx->kernel_dequant_q4_0_f16_view_aos;
+    } else {
+        kernel = is_q8_0 ? backend_ctx->kernel_dequant_q8_0_f32_view_aos
+                         : backend_ctx->kernel_dequant_q4_0_f32_view_aos;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &src_buf));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &src_offset));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_ulong), &src_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &src_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &src_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_int),   &nblk0_arg));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_int),   &ne1_arg));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_int),   &ne2_arg));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_int),   &ne3_arg));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_mem),   &temp.data));
+
+    size_t global_ws[3] = { (size_t) nblk0_arg, (size_t) ne1_arg, (size_t) ne2_arg * (size_t) ne3_arg };
+    CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL,
+                                    global_ws, NULL, 0, NULL, NULL));
+
+    out_buf    = temp.data;
+    out_offset = 0;
+    out_nb1    = (cl_ulong) tensor->ne[0] * elem_size;
+    out_nb2    = out_nb1 * (cl_ulong) tensor->ne[1];
+    out_nb3    = out_nb2 * (cl_ulong) tensor->ne[2];
+    return true;
+}
+
+static bool ggml_cl_flash_attn_prepare_quantized_tensor(
+        ggml_backend_opencl_context *         backend_ctx,
+        const ggml_tensor *                   tensor,
+        ggml_type                             target_type,
+        ggml_cl_flash_attn_temp_buffer &      temp,
+        cl_mem &                              data_device,
+        cl_ulong &                            offset,
+        cl_ulong &                            nb1,
+        cl_ulong &                            nb2,
+        cl_ulong &                            nb3
+) {
+    if (!ggml_is_quantized(tensor->type)) {
+        return false;
+    }
+
+    // Caller-supplied AoS buffer wins over tensor->extra when present.
+    cl_mem   src_buffer = data_device;
+    cl_ulong src_offset = offset;
+    cl_ulong src_nb1    = nb1;
+    cl_ulong src_nb2    = nb2;
+    cl_ulong src_nb3    = nb3;
+    ggml_cl_flash_attn_resolve_src(tensor, src_buffer, src_offset, src_nb1, src_nb2, src_nb3);
+
+    const int64_t n = ggml_nelements(tensor);
+    const size_t  row_bytes = (size_t) (tensor->ne[0] / ggml_blck_size(tensor->type)) * ggml_type_size(tensor->type);
+    // tight-packed byte count (ggml_nbytes includes stride gaps).
+    const size_t  total_bytes = (size_t) (n / ggml_blck_size(tensor->type)) * ggml_type_size(tensor->type);
+    std::vector<uint8_t> host_quant(total_bytes);
+
+    sync_with_other_backends(backend_ctx);
+    ggml_cl_flash_attn_read_tensor_host(backend_ctx, tensor, src_buffer, src_offset,
+                                        src_nb1, src_nb2, src_nb3,
+                                        row_bytes, host_quant.data(), total_bytes);
+
+    std::vector<float> host_f32(n);
+    ggml_get_type_traits(tensor->type)->to_float(host_quant.data(), host_f32.data(), n);
+
+    const size_t bytes_per_elem = ggml_type_size(target_type);
+    const size_t buffer_size = (size_t) n * bytes_per_elem;
+
+    std::vector<uint8_t> host_linear(buffer_size);
+    if (target_type == GGML_TYPE_F32) {
+        memcpy(host_linear.data(), host_f32.data(), buffer_size);
+    } else {
+        GGML_ASSERT(target_type == GGML_TYPE_F16);
+        ggml_fp32_to_fp16_row(host_f32.data(), (ggml_fp16_t *) host_linear.data(), n);
+    }
+
+    cl_int err;
+    temp.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, buffer_size, NULL, &err);
+    CL_CHECK(err);
+    CL_CHECK(clEnqueueWriteBuffer(backend_ctx->queue, temp.data, CL_TRUE, 0, buffer_size, host_linear.data(), 0, NULL, NULL));
+
+    data_device = temp.data;
+    offset = 0;
+    nb1 = (cl_ulong) (tensor->ne[0] * bytes_per_elem);
+    nb2 = (cl_ulong) (tensor->ne[1] * nb1);
+    nb3 = (cl_ulong) (tensor->ne[2] * nb2);
+
+    static bool warned = false;
+    if (!warned) {
+        GGML_LOG_WARN("ggml_opencl: OpenCL flash attention dequantizes GPU-resident quantized KV cache into temporary linear buffers; performance may be poor\n");
+        warned = true;
+    }
+
+    return true;
+}
+
+// Host-side F16 -> F32 for the asymmetric-KV F32 fallback path.
+static bool ggml_cl_flash_attn_convert_f16_to_f32(
+        ggml_backend_opencl_context *         backend_ctx,
+        const ggml_tensor *                   tensor,
+        ggml_cl_flash_attn_temp_buffer &      temp,
+        cl_mem &                              data_device,
+        cl_ulong &                            offset,
+        cl_ulong &                            nb1,
+        cl_ulong &                            nb2,
+        cl_ulong &                            nb3
+) {
+    if (tensor->type != GGML_TYPE_F16) {
+        return false;
+    }
+
+    cl_mem   src_buffer = data_device;
+    cl_ulong src_offset = offset;
+    cl_ulong src_nb1    = nb1;
+    cl_ulong src_nb2    = nb2;
+    cl_ulong src_nb3    = nb3;
+    ggml_cl_flash_attn_resolve_src(tensor, src_buffer, src_offset, src_nb1, src_nb2, src_nb3);
+
+    const int64_t n = ggml_nelements(tensor);
+    const size_t  row_bytes = (size_t) tensor->ne[0] * sizeof(ggml_fp16_t);
+    const size_t  total_bytes = (size_t) n * sizeof(ggml_fp16_t);
+    std::vector<uint8_t> host_f16(total_bytes);
+
+    sync_with_other_backends(backend_ctx);
+    ggml_cl_flash_attn_read_tensor_host(backend_ctx, tensor, src_buffer, src_offset,
+                                        src_nb1, src_nb2, src_nb3,
+                                        row_bytes, host_f16.data(), total_bytes);
+
+    std::vector<float> host_f32(n);
+    ggml_fp16_to_fp32_row((const ggml_fp16_t *) host_f16.data(), host_f32.data(), n);
+
+    const size_t f32_bytes = (size_t) n * sizeof(float);
+    cl_int err;
+    temp.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, f32_bytes, NULL, &err);
+    CL_CHECK(err);
+    CL_CHECK(clEnqueueWriteBuffer(backend_ctx->queue, temp.data, CL_TRUE, 0,
+                                  f32_bytes, host_f32.data(), 0, NULL, NULL));
+
+    data_device = temp.data;
+    offset = 0;
+    nb1 = (cl_ulong) (tensor->ne[0] * sizeof(float));
+    nb2 = (cl_ulong) (tensor->ne[1] * nb1);
+    nb3 = (cl_ulong) (tensor->ne[2] * nb2);
+
+    static bool warned = false;
+    if (!warned) {
+        GGML_LOG_WARN("ggml_opencl: OpenCL flash attention asymmetric KV converts an F16 cache to F32 host-side; performance may be poor\n");
+        warned = true;
+    }
+
+    return true;
+}
+
+// Flash-Decoding (K-split) dispatch thresholds. FD fires for non-causal
+// attention with n_kv >= FD_MIN_N_KV and d_head <= FD_MAX_DK; the KV range is
+// split into ~n_kv/FD_KV_PER_SPLIT partials, clamped to [FD_MIN_SPLITS,
+// FD_MAX_SPLITS]. Multi-query FD is restricted to small heads
+// (d_head <= FD_MAX_DK_MULTI) and capped at FD_MAX_N_Q_MULTI queries.
+static constexpr int FD_MIN_N_KV      = 2048;
+static constexpr int FD_KV_PER_SPLIT  = 2048;
+static constexpr int FD_MIN_SPLITS    = 2;
+static constexpr int FD_MAX_SPLITS    = 16;
+static constexpr int FD_MAX_DK        = 128;
+static constexpr int FD_MAX_DK_MULTI  = 64;
+static constexpr int FD_MAX_N_Q_MULTI = 8;
+
 static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, const ggml_tensor * k, ggml_tensor * dst) {
     const ggml_tensor * v = dst->src[2];
     const ggml_tensor * mask = dst->src[3];
     const ggml_tensor * sinks = dst->src[4];
+
     GGML_ASSERT(q->extra);
     GGML_ASSERT(k->extra);
     GGML_ASSERT(v->extra);
     GGML_ASSERT(dst->extra);
+
     if (mask) {
         GGML_ASSERT(mask->extra);
     }
@@ -11508,87 +12488,463 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     const int n_head_kv = k->ne[2];
     const int n_batch = q->ne[3];
 
+    // Per-variant lazy compile for this (dk, dv).
+    ggml_opencl_ensure_fa_pre_kernels(backend_ctx, d_head_q, d_head_v);
+
     cl_kernel kernel = NULL;
 
     const bool is_f16 = q->type == GGML_TYPE_F16;
-    const bool is_mixed = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16;
-    const std::pair<int, int> dk_dv = {d_head_q, d_head_v};
+    const bool is_mixed = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 && v->type == GGML_TYPE_F16;
+    const bool is_q8_0 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_Q8_0 && v->type == GGML_TYPE_Q8_0;
+    const bool is_q4_0 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_Q4_0 && v->type == GGML_TYPE_Q4_0;
 
-    if (n_q == 1) {
-        if (is_mixed) {
-            kernel = backend_ctx->kernels_flash_attn_f32_f16_q1.at(dk_dv);
-        } else if (is_f16) {
-            kernel = backend_ctx->kernels_flash_attn_f16_q1.at(dk_dv);
+    if (is_f16) {
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_F16);
+    } else if (is_mixed) {
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_F32_F16);
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_F32_F16_SPLIT);
+    } else if (is_q8_0) {
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_Q8_0);
+        if (d_head_q == 96 && d_head_v == 96) {
+            ggml_opencl_ensure_fa_quant_split_override(backend_ctx, 96, 96, /*quant_bm=*/16, /*quant_n_split=*/3, /*is_q8_0=*/true);
+        } else if (d_head_q == 256 && d_head_v == 256) {
+            ggml_opencl_ensure_fa_quant_split_override(backend_ctx, 256, 256, /*quant_bm=*/16, /*quant_n_split=*/8, /*is_q8_0=*/true);
         } else {
-            kernel = backend_ctx->kernels_flash_attn_f32_q1.at(dk_dv);
+            ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_Q8_0_SPLIT);
+        }
+    } else if (is_q4_0) {
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_Q4_0);
+        if (d_head_q == 96 && d_head_v == 96) {
+            ggml_opencl_ensure_fa_quant_split_override(backend_ctx, 96, 96, /*quant_bm=*/16, /*quant_n_split=*/3, /*is_q8_0=*/false);
+        } else if (d_head_q == 256 && d_head_v == 256) {
+            ggml_opencl_ensure_fa_quant_split_override(backend_ctx, 256, 256, /*quant_bm=*/16, /*quant_n_split=*/8, /*is_q8_0=*/false);
+        } else {
+            ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_Q4_0_SPLIT);
         }
     } else {
-        if (is_mixed) {
-            kernel = backend_ctx->kernels_flash_attn_f32_f16.at(dk_dv);
-        } else if (is_f16) {
-            kernel = backend_ctx->kernels_flash_attn_f16.at(dk_dv);
-        } else {
-            kernel = backend_ctx->kernels_flash_attn_f32.at(dk_dv);
-        }
+        ggml_opencl_ensure_fa_variant(backend_ctx, d_head_q, d_head_v, FA_VARIANT_F32);
     }
-    GGML_ASSERT(kernel != NULL);
+
+    const std::pair<int, int> dk_dv = {d_head_q, d_head_v};
+    const bool use_native_q8_0_q1 = is_q8_0 && n_q == 1 &&
+                                    backend_ctx->fa.f32_q8_0_q1.count(dk_dv) > 0;
+    // Native q8_0 prefill — reads q8_0 directly, wg_size = cfg->bm.
+    const bool use_native_q8_0 = is_q8_0 && n_q > 1 &&
+                                 backend_ctx->fa.f32_q8_0.count(dk_dv) > 0;
+    const bool use_native_q4_0_q1 = is_q4_0 && n_q == 1 &&
+                                    backend_ctx->fa.f32_q4_0_q1.count(dk_dv) > 0;
+    const bool use_native_q4_0    = is_q4_0 && n_q > 1 &&
+                                    backend_ctx->fa.f32_q4_0.count(dk_dv) > 0;
+    const int block_m = n_q > 1
+        ? (is_mixed ? backend_ctx->fa.f32_f16_bm.at(dk_dv) : backend_ctx->fa.bm.at(dk_dv))
+        : 0;
+    const int block_n = is_mixed
+        ? backend_ctx->fa.f32_f16_bn.at(dk_dv)
+        : backend_ctx->fa.bn.at(dk_dv);
+    // Pick split variant only when n_kv crosses the per-(dk,dv) threshold.
+    const bool use_split_kernel = (n_q > 1 && is_mixed &&
+        backend_ctx->fa.f32_f16_split.count(dk_dv) > 0 &&
+        n_kv >= backend_ctx->fa.f32_f16_split_nkv_threshold.at(dk_dv));
+    const bool use_split_q8_0 = (use_native_q8_0 &&
+        backend_ctx->fa.f32_q8_0_split.count(dk_dv) > 0 &&
+        n_kv >= backend_ctx->fa.f32_q8_0_split_nkv_threshold.at(dk_dv));
+    const bool use_split_q4_0 = (use_native_q4_0 &&
+        backend_ctx->fa.f32_q4_0_split.count(dk_dv) > 0 &&
+        n_kv >= backend_ctx->fa.f32_q4_0_split_nkv_threshold.at(dk_dv));
+    const int wg_size_fa = (n_q > 1 && is_mixed)
+        ? (use_split_kernel
+            ? backend_ctx->fa.f32_f16_split_wg_size.at(dk_dv)
+            : backend_ctx->fa.f32_f16_wg_size.at(dk_dv))
+        : block_m;
 
     ggml_tensor_extra_cl * extra_q = (ggml_tensor_extra_cl *)q->extra;
-    ggml_tensor_extra_cl * extra_k = (ggml_tensor_extra_cl *)k->extra;
-    ggml_tensor_extra_cl * extra_v = (ggml_tensor_extra_cl *)v->extra;
     ggml_tensor_extra_cl * extra_o = (ggml_tensor_extra_cl *)dst->extra;
     ggml_tensor_extra_cl * extra_mask = mask ? (ggml_tensor_extra_cl *)mask->extra : NULL;
     ggml_tensor_extra_cl * extra_sinks = sinks ? (ggml_tensor_extra_cl *)sinks->extra : NULL;
 
+    // SoA q8_0/q4_0 K/V: data_device aliases the `q` subbuffer; reconstruct
+    // AoS into a temp buffer below. AoS tensors use extra_k/v->data_device.
+    const bool k_soa = ggml_cl_is_q8_0_soa(k) || ggml_cl_is_q4_0_soa(k);
+    const bool v_soa = ggml_cl_is_q8_0_soa(v) || ggml_cl_is_q4_0_soa(v);
+    ggml_tensor_extra_cl * extra_k = k_soa ? nullptr : (ggml_tensor_extra_cl *)k->extra;
+    ggml_tensor_extra_cl * extra_v = v_soa ? nullptr : (ggml_tensor_extra_cl *)v->extra;
+
     cl_ulong offset_q = extra_q->offset + q->view_offs;
-    cl_ulong offset_k = extra_k->offset + k->view_offs;
-    cl_ulong offset_v = extra_v->offset + v->view_offs;
+    cl_ulong offset_k = k_soa ? 0 : extra_k->offset + k->view_offs;
+    cl_ulong offset_v = v_soa ? 0 : extra_v->offset + v->view_offs;
     cl_ulong offset_o = extra_o->offset + dst->view_offs;
     cl_mem   mask_buffer = extra_mask ? extra_mask->data_device : NULL;
     cl_ulong offset_mask = extra_mask ? extra_mask->offset + mask->view_offs : 0;
     cl_mem   sinks_buffer = extra_sinks ? extra_sinks->data_device : NULL;
     cl_ulong offset_sinks = extra_sinks ? extra_sinks->offset + sinks->view_offs : 0;
 
-    const cl_ulong q_nb1 = q->nb[1], q_nb2 = q->nb[2], q_nb3 = q->nb[3];
-    const cl_ulong k_nb1 = k->nb[1], k_nb2 = k->nb[2], k_nb3 = k->nb[3];
-    const cl_ulong v_nb1 = v->nb[1], v_nb2 = v->nb[2], v_nb3 = v->nb[3];
-    const cl_ulong o_nb1 = dst->nb[1], o_nb2 = dst->nb[2], o_nb3 = dst->nb[3];
+    const cl_ulong q_nb1 = q->nb[1];
+    const cl_ulong q_nb2 = q->nb[2];
+    const cl_ulong q_nb3 = q->nb[3];
+
+    cl_ulong k_nb1 = k->nb[1];
+    cl_ulong k_nb2 = k->nb[2];
+    cl_ulong k_nb3 = k->nb[3];
+
+    cl_ulong v_nb1 = v->nb[1];
+    cl_ulong v_nb2 = v->nb[2];
+    cl_ulong v_nb3 = v->nb[3];
+
+    const cl_ulong o_nb1 = dst->nb[1];
+    const cl_ulong o_nb2 = dst->nb[2];
+    const cl_ulong o_nb3 = dst->nb[3];
+
     const cl_ulong mask_nb1 = mask ? mask->nb[1] : 0;
     const cl_ulong mask_nb2 = mask ? mask->nb[2] : 0;
     const cl_ulong mask_nb3 = mask ? mask->nb[3] : 0;
     const int mask_ne2 = mask ? mask->ne[2] : 0;
     const int mask_ne3 = mask ? mask->ne[3] : 0;
 
-    float scale, max_bias, logit_softcap;
+    float scale;
+    float max_bias;
+    float logit_softcap;
+
     const float * params = (const float *)dst->op_params;
     scale         = params[0];
     max_bias      = params[1];
     logit_softcap = params[2];
 
+    if (n_q == 1) {
+        if (use_native_q8_0_q1) {
+            kernel = backend_ctx->fa.f32_q8_0_q1.at(dk_dv);
+        } else if (use_native_q4_0_q1) {
+            kernel = backend_ctx->fa.f32_q4_0_q1.at(dk_dv);
+        } else if (is_mixed) {
+            kernel = backend_ctx->fa.f32_f16_q1.at(dk_dv);
+        } else if (is_f16) {
+            kernel = backend_ctx->fa.f16_q1.at(dk_dv);
+        } else {
+            kernel = backend_ctx->fa.f32_q1.at(dk_dv);
+        }
+    } else {
+        if (use_native_q8_0) {
+            kernel = use_split_q8_0
+                ? backend_ctx->fa.f32_q8_0_split.at(dk_dv)
+                : backend_ctx->fa.f32_q8_0.at(dk_dv);
+        } else if (use_native_q4_0) {
+            kernel = use_split_q4_0
+                ? backend_ctx->fa.f32_q4_0_split.at(dk_dv)
+                : backend_ctx->fa.f32_q4_0.at(dk_dv);
+        } else if (is_mixed) {
+            kernel = use_split_kernel
+                ? backend_ctx->fa.f32_f16_split.at(dk_dv)
+                : backend_ctx->fa.f32_f16.at(dk_dv);
+        } else if (is_f16) {
+            kernel = backend_ctx->fa.f16.at(dk_dv);
+        } else {
+            kernel = backend_ctx->fa.f32.at(dk_dv);
+        }
+    }
+    GGML_ASSERT(kernel != NULL);
+
+    ggml_cl_flash_attn_temp_buffer temp_k;
+    ggml_cl_flash_attn_temp_buffer temp_v;
+    ggml_cl_flash_attn_temp_buffer temp_k_pad;
+    ggml_cl_flash_attn_temp_buffer temp_v_pad;
+    ggml_cl_flash_attn_temp_buffer temp_mask_pad;
+    ggml_cl_flash_attn_temp_buffer temp_blk;
+    const ggml_type kv_target_type = is_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    cl_mem k_data_device = k_soa ? NULL : extra_k->data_device;
+    cl_mem v_data_device = v_soa ? NULL : extra_v->data_device;
+
+    // SoA q8_0/q4_0 -> reconstruct AoS for downstream kernels that expect
+    // tight records (no-op when k/v is already AoS).
+    ggml_cl_flash_attn_temp_buffer temp_k_aos;
+    ggml_cl_flash_attn_temp_buffer temp_v_aos;
+    ggml_cl_flash_attn_reconstruct_aos(backend_ctx, k, temp_k_aos,
+                                       k_data_device, offset_k, k_nb1, k_nb2, k_nb3);
+    ggml_cl_flash_attn_reconstruct_aos(backend_ctx, v, temp_v_aos,
+                                       v_data_device, offset_v, v_nb1, v_nb2, v_nb3);
+
+    // currently FA kernels support KV cache with f16, f32, q4_0 and q8_0.
+    // there two cases that these kernels cannot cover,
+    //   1. KV cache types are q4_0 or q8_0, but the FA kernels fail to compile
+    //   2. KV cache types not currently supported by an FA kernel, e.g., q4_1
+    // these two cases are supported here by dequantizing to f32/f16 and this
+    // causes performance degradation.
+    // For q4_0 or q8_0 cases that fail kernel compilation, dequant happens in GPU;
+    // for types that do not have FA kernels, dequant happens on host.
+    if (!use_native_q8_0_q1 && !use_native_q8_0 &&
+        !use_native_q4_0_q1 && !use_native_q4_0) {
+        // for q4_0, q8_0 FA kernels that fail to compile
+        bool k_done = false;
+        bool v_done = false;
+        if (k->type == GGML_TYPE_Q8_0 || k->type == GGML_TYPE_Q4_0) {
+            k_done = ggml_cl_flash_attn_dequant_kv_gpu(
+                backend_ctx, k, kv_target_type, k_data_device, offset_k, k_nb1, k_nb2, k_nb3,
+                temp_k, k_data_device, offset_k, k_nb1, k_nb2, k_nb3);
+        }
+        if (v->type == GGML_TYPE_Q8_0 || v->type == GGML_TYPE_Q4_0) {
+            v_done = ggml_cl_flash_attn_dequant_kv_gpu(
+                backend_ctx, v, kv_target_type, v_data_device, offset_v, v_nb1, v_nb2, v_nb3,
+                temp_v, v_data_device, offset_v, v_nb1, v_nb2, v_nb3);
+        }
+        if (!k_done) {
+            ggml_cl_flash_attn_prepare_quantized_tensor(
+                backend_ctx, k, kv_target_type, temp_k, k_data_device, offset_k, k_nb1, k_nb2, k_nb3);
+        }
+        if (!v_done) {
+            ggml_cl_flash_attn_prepare_quantized_tensor(
+                backend_ctx, v, kv_target_type, temp_v, v_data_device, offset_v, v_nb1, v_nb2, v_nb3);
+        }
+        // Asymmetric KV on the F32 fallback path: convert the F16 side to F32
+        // too. (Symmetric F16 / mixed paths handle F16 directly.)
+        if (kv_target_type == GGML_TYPE_F32 && !is_mixed && !is_f16) {
+            ggml_cl_flash_attn_convert_f16_to_f32(backend_ctx, k, temp_k, k_data_device, offset_k, k_nb1, k_nb2, k_nb3);
+            ggml_cl_flash_attn_convert_f16_to_f32(backend_ctx, v, temp_v, v_data_device, offset_v, v_nb1, v_nb2, v_nb3);
+        }
+    }
+
+    cl_mem k_pad_buffer = NULL;
+    cl_mem v_pad_buffer = NULL;
+    cl_mem mask_pad_buffer = NULL;
+    cl_mem blk_buffer = NULL;
+    cl_ulong mask_pad_nb1 = 0;
+    cl_ulong mask_pad_nb2 = 0;
+    cl_ulong mask_pad_nb3 = 0;
+
+    // Flash-Decoding K-split decision. Resolved here, before the prefill
+    // prepass, because KV-pad and blk prepass are pure overhead when FD fires.
     const int is_causal = (mask == NULL && n_q > 1 && n_q == n_kv);
+    const int fd_max_n_q = (d_head_q <= FD_MAX_DK_MULTI) ? FD_MAX_N_Q_MULTI : 1;
+    cl_kernel fd_k_split = NULL;
+    if (n_q >= 1 && n_q <= fd_max_n_q && n_kv >= FD_MIN_N_KV && !is_causal &&
+        d_head_q <= FD_MAX_DK &&
+        backend_ctx->fa.f32_merge.count(dk_dv) > 0) {
+        if (is_mixed && backend_ctx->fa.f32_f16_q1_split.count(dk_dv) > 0) {
+            fd_k_split = backend_ctx->fa.f32_f16_q1_split.at(dk_dv);
+        } else if (is_q8_0 && backend_ctx->fa.f32_q8_0_q1_split.count(dk_dv) > 0) {
+            fd_k_split = backend_ctx->fa.f32_q8_0_q1_split.at(dk_dv);
+        } else if (is_q4_0 && backend_ctx->fa.f32_q4_0_q1_split.count(dk_dv) > 0) {
+            fd_k_split = backend_ctx->fa.f32_q4_0_q1_split.at(dk_dv);
+        }
+    }
+    const bool use_fd = (fd_k_split != NULL);
+
+    const int n_q_blocks = n_q > 1 ? (n_q + block_m - 1) / block_m : 0;
+    const int n_kv_blocks = n_kv > 0 ? (n_kv + block_n - 1) / block_n : 0;
+    // KV pad + blk prepass are pure overhead when FD will fire — skip them.
+    const bool use_mixed_prepass = is_mixed && n_q > 1 && !use_fd;
+    const bool use_kv_pad = use_mixed_prepass && (n_kv % block_n != 0);
+    // blk prepass: per-KV-tile mask class (0=masked, 1=mixed, 2=unmasked).
+    // Consumed identically by f32_f16, q8_0 and q4_0 prefill kernels.
+    const bool use_quant_prepass = (use_native_q8_0 || use_native_q4_0) && !use_fd;
+    const bool use_blk_mask = (use_mixed_prepass || use_quant_prepass) && mask_buffer != NULL;
+
+    if (use_kv_pad) {
+        cl_int err;
+
+        const size_t k_pad_size = (size_t) k_nb1 * (size_t) block_n * (size_t) n_head_kv * (size_t) n_batch;
+        temp_k_pad.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, k_pad_size, NULL, &err);
+        CL_CHECK(err);
+        k_pad_buffer = temp_k_pad.data;
+
+        const size_t v_pad_size = (size_t) v_nb1 * (size_t) block_n * (size_t) n_head_kv * (size_t) n_batch;
+        temp_v_pad.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, v_pad_size, NULL, &err);
+        CL_CHECK(err);
+        v_pad_buffer = temp_v_pad.data;
+
+        cl_kernel kernel_kv_pad = backend_ctx->fa.kv_pad_f16.at(dk_dv);
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 0, sizeof(cl_mem),    &k_data_device));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 1, sizeof(cl_ulong),  &offset_k));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 2, sizeof(cl_mem),    &v_data_device));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 3, sizeof(cl_ulong),  &offset_v));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 4, sizeof(cl_mem),    &k_pad_buffer));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 5, sizeof(cl_mem),    &v_pad_buffer));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 6, sizeof(int),       &n_kv));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 7, sizeof(int),       &n_head_kv));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 8, sizeof(int),       &n_batch));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 9, sizeof(cl_ulong),  &k_nb1));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 10, sizeof(cl_ulong), &k_nb2));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 11, sizeof(cl_ulong), &k_nb3));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 12, sizeof(cl_ulong), &v_nb1));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 13, sizeof(cl_ulong), &v_nb2));
+        CL_CHECK(clSetKernelArg(kernel_kv_pad, 14, sizeof(cl_ulong), &v_nb3));
+
+        size_t global_work_size[] = { (size_t) block_n, (size_t) n_head_kv, (size_t) n_batch };
+        backend_ctx->enqueue_ndrange_kernel(kernel_kv_pad, 3, global_work_size, NULL, dst);
+
+        if (mask_buffer != NULL) {
+            mask_pad_nb1 = (cl_ulong) block_n * (cl_ulong) sizeof(ggml_fp16_t);
+            mask_pad_nb2 = (cl_ulong) n_q * mask_pad_nb1;
+            mask_pad_nb3 = (cl_ulong) mask_ne2 * mask_pad_nb2;
+
+            const size_t mask_pad_size = (size_t) mask_ne3 * (size_t) mask_pad_nb3;
+            temp_mask_pad.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, mask_pad_size, NULL, &err);
+            CL_CHECK(err);
+            mask_pad_buffer = temp_mask_pad.data;
+
+            cl_kernel kernel_mask_pad = backend_ctx->fa.mask_pad_f16.at(dk_dv);
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 0, sizeof(cl_mem),    &mask_buffer));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 1, sizeof(cl_ulong),  &offset_mask));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 2, sizeof(cl_mem),    &mask_pad_buffer));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 3, sizeof(int),       &n_q));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 4, sizeof(int),       &n_kv));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 5, sizeof(cl_ulong),  &mask_nb1));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 6, sizeof(cl_ulong),  &mask_nb2));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 7, sizeof(cl_ulong),  &mask_nb3));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 8, sizeof(int),       &mask_ne2));
+            CL_CHECK(clSetKernelArg(kernel_mask_pad, 9, sizeof(int),       &mask_ne3));
+
+            size_t global_work_size_mask[] = { (size_t) block_n, (size_t) n_q, (size_t) (mask_ne2 * mask_ne3) };
+            backend_ctx->enqueue_ndrange_kernel(kernel_mask_pad, 3, global_work_size_mask, NULL, dst);
+        }
+    }
+
+    if (use_blk_mask) {
+        cl_int err;
+        const size_t blk_size = (size_t) n_kv_blocks * (size_t) n_q_blocks * (size_t) mask_ne2 * (size_t) mask_ne3;
+        temp_blk.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, blk_size, NULL, &err);
+        if (err != CL_SUCCESS) {
+            // Flush before retry — reclaim deferred driver deallocations.
+            CL_CHECK(clFinish(backend_ctx->queue));
+            temp_blk.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, blk_size, NULL, &err);
+        }
+        CL_CHECK(err);
+        blk_buffer = temp_blk.data;
+
+        cl_kernel kernel_blk = backend_ctx->fa.blk_f16.at(dk_dv);
+        CL_CHECK(clSetKernelArg(kernel_blk, 0, sizeof(cl_mem),    &mask_buffer));
+        CL_CHECK(clSetKernelArg(kernel_blk, 1, sizeof(cl_ulong),  &offset_mask));
+        CL_CHECK(clSetKernelArg(kernel_blk, 2, sizeof(cl_mem),    &blk_buffer));
+        CL_CHECK(clSetKernelArg(kernel_blk, 3, sizeof(int),       &n_q));
+        CL_CHECK(clSetKernelArg(kernel_blk, 4, sizeof(int),       &n_kv));
+        CL_CHECK(clSetKernelArg(kernel_blk, 5, sizeof(cl_ulong),  &mask_nb1));
+        CL_CHECK(clSetKernelArg(kernel_blk, 6, sizeof(cl_ulong),  &mask_nb2));
+        CL_CHECK(clSetKernelArg(kernel_blk, 7, sizeof(cl_ulong),  &mask_nb3));
+        CL_CHECK(clSetKernelArg(kernel_blk, 8, sizeof(int),       &mask_ne2));
+        CL_CHECK(clSetKernelArg(kernel_blk, 9, sizeof(int),       &mask_ne3));
+
+        size_t global_work_size_blk[] = { (size_t) n_kv_blocks, (size_t) n_q_blocks, (size_t) (mask_ne2 * mask_ne3) };
+        backend_ctx->enqueue_ndrange_kernel(kernel_blk, 3, global_work_size_blk, NULL, dst);
+    }
 
     const int n_head_log2_val = n_head > 0 ? 1u << (int)floorf(log2f((float)n_head)) : 0;
     const float n_head_log2_f = n_head_log2_val > 0 ? (float)n_head_log2_val : 1.0f;
     const float m0 = powf(2.0f, -(max_bias) / n_head_log2_f);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2_f);
 
-    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra_q->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset_q));
-    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra_k->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset_k));
-    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extra_v->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset_v));
-    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem),   &extra_o->data_device));
-    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offset_o));
-    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(float),    &scale));
-    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int),      &n_q));
-    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),     &n_kv));
-    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),     &is_causal));
-    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),     &n_head));
-    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &q_nb1)); CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &q_nb2)); CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &q_nb3));
-    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &k_nb1)); CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &k_nb2)); CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &k_nb3));
-    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &v_nb1)); CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &v_nb2)); CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &v_nb3));
-    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &o_nb1)); CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &o_nb2)); CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &o_nb3));
+    if (use_fd) {
+        int n_splits = (n_kv + FD_KV_PER_SPLIT - 1) / FD_KV_PER_SPLIT;
+        if (n_splits < FD_MIN_SPLITS) {
+            n_splits = FD_MIN_SPLITS;
+        }
+        if (n_splits > FD_MAX_SPLITS) {
+            n_splits = FD_MAX_SPLITS;
+        }
+        const int kv_per_split = (n_kv + n_splits - 1) / n_splits;
+
+        const int fa_partial_floats = 2 + d_head_v;
+        const size_t partial_size_bytes =
+            (size_t) n_batch * n_head * n_q * n_splits * fa_partial_floats * sizeof(float);
+
+        ggml_cl_flash_attn_temp_buffer temp_partial;
+        cl_int err;
+        temp_partial.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE,
+                                           partial_size_bytes, NULL, &err);
+        if (err != CL_SUCCESS) {
+            CL_CHECK(clFinish(backend_ctx->queue));
+            temp_partial.data = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE,
+                                               partial_size_bytes, NULL, &err);
+        }
+        CL_CHECK(err);
+
+        cl_kernel k_split = fd_k_split;
+        int argi = 0;
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_mem),   &extra_q->data_device));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &offset_q));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_mem),   &k_data_device));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &offset_k));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_mem),   &v_data_device));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &offset_v));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(float),    &scale));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_q));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_kv));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_head));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &q_nb1));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &q_nb2));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &q_nb3));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &k_nb1));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &k_nb2));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &k_nb3));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &v_nb1));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &v_nb2));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &v_nb3));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(float),    &max_bias));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(float),    &m0));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(float),    &m1));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_head_log2_val));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(float),    &logit_softcap));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_head_kv));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_mem),   &mask_buffer));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &offset_mask));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &mask_nb1));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &mask_nb2));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_ulong), &mask_nb3));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &mask_ne2));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &mask_ne3));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(cl_mem),   &temp_partial.data));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &n_splits));
+        CL_CHECK(clSetKernelArg(k_split, argi++, sizeof(int),      &kv_per_split));
+
+        const size_t fd_wg = 64; // matches Q1_WG_SIZE in the kernel
+        size_t fd_lws[3] = { fd_wg, 1, 1 };
+        // gid(2) packs q_idx * n_splits + split_idx.
+        size_t fd_gws[3] = { fd_wg, (size_t)(n_head * n_batch), (size_t)(n_splits * n_q) };
+        backend_ctx->enqueue_ndrange_kernel(k_split, 3, fd_gws, fd_lws, dst);
+
+        cl_kernel k_merge = backend_ctx->fa.f32_merge.at(dk_dv);
+        argi = 0;
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_mem),   &temp_partial.data));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_mem),   &extra_o->data_device));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_ulong), &offset_o));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(int),      &n_head));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(int),      &n_splits));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_ulong), &o_nb1));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_ulong), &o_nb2));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_ulong), &o_nb3));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_mem),   &sinks_buffer));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(cl_ulong), &offset_sinks));
+        CL_CHECK(clSetKernelArg(k_merge, argi++, sizeof(int),      &n_q));
+
+        const size_t merge_wg  = (size_t) (d_head_v / 4); // one lane per float4
+        size_t merge_lws[3] = { merge_wg, 1, 1 };
+        size_t merge_gws[3] = { merge_wg, (size_t)(n_head * n_batch), (size_t) n_q };
+        backend_ctx->enqueue_ndrange_kernel(k_merge, 3, merge_gws, merge_lws, dst);
+        return;
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),    &extra_q->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong),  &offset_q));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),    &k_data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong),  &offset_k));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),    &v_data_device));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong),  &offset_v));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem),    &extra_o->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong),  &offset_o));
+    CL_CHECK(clSetKernelArg(kernel, 8, sizeof(float),     &scale));
+    CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int),       &n_q));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &n_kv));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &is_causal));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &n_head));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &q_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &q_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &q_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &k_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &k_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &k_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &v_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &v_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &v_nb3));
+    CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &o_nb1));
+    CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &o_nb2));
+    CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &o_nb3));
     CL_CHECK(clSetKernelArg(kernel, 25, sizeof(float),    &max_bias));
     CL_CHECK(clSetKernelArg(kernel, 26, sizeof(float),    &m0));
     CL_CHECK(clSetKernelArg(kernel, 27, sizeof(float),    &m1));
@@ -11604,15 +12960,45 @@ static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, co
     CL_CHECK(clSetKernelArg(kernel, 37, sizeof(int),      &mask_ne3));
     CL_CHECK(clSetKernelArg(kernel, 38, sizeof(cl_mem),   &sinks_buffer));
     CL_CHECK(clSetKernelArg(kernel, 39, sizeof(cl_ulong), &offset_sinks));
+    if (n_q > 1 && is_mixed) {
+        CL_CHECK(clSetKernelArg(kernel, 40, sizeof(cl_mem),    &k_pad_buffer));
+        CL_CHECK(clSetKernelArg(kernel, 41, sizeof(cl_mem),    &v_pad_buffer));
+        CL_CHECK(clSetKernelArg(kernel, 42, sizeof(cl_mem),    &mask_pad_buffer));
+        CL_CHECK(clSetKernelArg(kernel, 43, sizeof(cl_mem),    &blk_buffer));
+        CL_CHECK(clSetKernelArg(kernel, 44, sizeof(int),       &n_kv_blocks));
+        CL_CHECK(clSetKernelArg(kernel, 45, sizeof(cl_ulong),  &mask_pad_nb1));
+        CL_CHECK(clSetKernelArg(kernel, 46, sizeof(cl_ulong),  &mask_pad_nb2));
+        CL_CHECK(clSetKernelArg(kernel, 47, sizeof(cl_ulong),  &mask_pad_nb3));
+    } else if (use_native_q8_0 || use_native_q4_0) {
+        // arg 40 = blk classification buffer (NULL disables prepass opt).
+        CL_CHECK(clSetKernelArg(kernel, 40, sizeof(cl_mem),    &blk_buffer));
+    }
 
     if (n_q == 1) {
         const size_t wg_size = 64;
         size_t local_work_size[] = { wg_size, 1 };
         size_t global_work_size[] = { wg_size, (size_t)(n_head * n_batch) };
         backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
+    } else if (use_native_q8_0 || use_native_q4_0) {
+        // Native quant prefill. The split variant may override BLOCK_M
+        // (e.g. DK=96 quant uses BM=16).
+        const bool use_split = use_native_q8_0 ? use_split_q8_0 : use_split_q4_0;
+        int    bm;
+        size_t wg_size;
+        if (use_split) {
+            bm      = use_native_q8_0 ? backend_ctx->fa.f32_q8_0_split_bm.at(dk_dv)
+                                      : backend_ctx->fa.f32_q4_0_split_bm.at(dk_dv);
+            wg_size = use_native_q8_0 ? backend_ctx->fa.f32_q8_0_split_wg_size.at(dk_dv)
+                                      : backend_ctx->fa.f32_q4_0_split_wg_size.at(dk_dv);
+        } else {
+            bm      = backend_ctx->fa.bm.at(dk_dv);
+            wg_size = (size_t) bm;
+        }
+        size_t local_work_size[]  = { wg_size, 1 };
+        size_t global_work_size[] = { (size_t)((n_q + bm - 1) / bm) * wg_size, (size_t)(n_head * n_batch) };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
     } else {
-        const int block_m = backend_ctx->kernels_flash_attn_bm.at(dk_dv);
-        const size_t wg_size = block_m;
+        const size_t wg_size = (size_t) wg_size_fa;
         size_t local_work_size[] = { wg_size, 1 };
         size_t global_work_size[] = { (size_t)((n_q + block_m - 1) / block_m) * wg_size, (size_t)(n_head * n_batch) };
         backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
@@ -13004,7 +14390,9 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
 
     ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
     ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
-    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
+    // SoA extra lives on view_src (view->extra is pre-SoA).
+    const ggml_tensor * soa0_src = src0->view_src != nullptr ? src0->view_src : src0;
+    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)soa0_src->extra;
 
     cl_ulong offset1 = extra1->offset + src1->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
@@ -13756,6 +15144,122 @@ static void ggml_cl_mul_mat_q5_K_f32_adreno(ggml_backend_t backend, const ggml_t
 #endif
 }
 
+// Dequant a possibly-strided q4_0/q8_0 tensor to tight-packed f16. Returns a
+// temp cl_mem the caller must release. SoA inputs are reconstructed into a
+// temp AoS buffer reported via *extra_reconstruct (also caller-released).
+// this is for quantized K cache without FA.
+static cl_mem ggml_cl_mul_mat_dequant_quant_to_f16(
+        ggml_backend_opencl_context * backend_ctx,
+        const ggml_tensor *           tensor,
+        cl_mem *                      extra_reconstruct /* out, may be NULL */
+) {
+    GGML_ASSERT(tensor->type == GGML_TYPE_Q4_0 || tensor->type == GGML_TYPE_Q8_0);
+
+    if (extra_reconstruct) {
+        *extra_reconstruct = NULL;
+    }
+
+    cl_mem   src_buf;
+    cl_ulong src_offset;
+    cl_ulong src_nb1;
+    cl_ulong src_nb2;
+    cl_ulong src_nb3;
+
+    const bool is_soa = tensor->type == GGML_TYPE_Q4_0
+        ? ggml_cl_is_q4_0_soa(tensor)
+        : ggml_cl_is_q8_0_soa(tensor);
+
+    if (is_soa) {
+        // Reconstruct full parent AoS; view's own nb[] then index it correctly.
+        const ggml_tensor * parent = tensor->view_src ? tensor->view_src : tensor;
+        const ggml_tensor * soa_src = parent;
+        const size_t block_bytes = (size_t) ggml_type_size(tensor->type);
+        const size_t blck_size   = (size_t) ggml_blck_size(tensor->type);
+        const size_t parent_row_blocks = (size_t) parent->ne[0] / blck_size;
+        const size_t parent_row_bytes  = parent_row_blocks * block_bytes;
+        const size_t parent_nbytes = (size_t) ggml_nelements(parent) / blck_size * block_bytes;
+
+        cl_int err;
+        cl_mem aos = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, parent_nbytes, NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel;
+        if (tensor->type == GGML_TYPE_Q8_0) {
+            auto * extra = (ggml_tensor_extra_cl_q8_0 *) soa_src->extra;
+            kernel = backend_ctx->kernel_restore_block_q8_0;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &aos));
+        } else {
+            auto * extra = (ggml_tensor_extra_cl_q4_0 *) soa_src->extra;
+            kernel = backend_ctx->kernel_restore_block_q4_0;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &aos));
+        }
+
+        const size_t n_blocks = parent_nbytes / block_bytes;
+        size_t gws_rec[] = { n_blocks, 1, 1 };
+        size_t lws_rec[] = { 1, 1, 1 };
+        CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, kernel, 3, NULL, gws_rec, lws_rec, 0, NULL, NULL));
+
+        (void) parent_row_blocks;
+        (void) parent_row_bytes;
+        src_buf    = aos;
+        src_offset = tensor->view_offs;
+        src_nb1    = tensor->nb[1];
+        src_nb2    = tensor->nb[2];
+        src_nb3    = tensor->nb[3];
+
+        if (extra_reconstruct) {
+            *extra_reconstruct = aos;
+        } else {
+            // OpenCL retains the memobj while queued kernels reference it.
+            CL_CHECK(clReleaseMemObject(aos));
+        }
+    } else {
+        auto * extra = (ggml_tensor_extra_cl *) tensor->extra;
+        GGML_ASSERT(extra && extra->data_device);
+        src_buf    = extra->data_device;
+        src_offset = extra->offset + tensor->view_offs;
+        src_nb1    = tensor->nb[1];
+        src_nb2    = tensor->nb[2];
+        src_nb3    = tensor->nb[3];
+    }
+
+    const cl_int nblk0 = (cl_int) (tensor->ne[0] / ggml_blck_size(tensor->type));
+    const cl_int ne1_  = (cl_int) tensor->ne[1];
+    const cl_int ne2_  = (cl_int) tensor->ne[2];
+    const cl_int ne3_  = (cl_int) tensor->ne[3];
+
+    const size_t out_bytes = (size_t) ggml_nelements(tensor) * sizeof(ggml_fp16_t);
+
+    cl_int err;
+    cl_mem out = clCreateBuffer(backend_ctx->context, CL_MEM_READ_WRITE, out_bytes, NULL, &err);
+    CL_CHECK(err);
+
+    cl_kernel dq_kernel = tensor->type == GGML_TYPE_Q8_0
+        ? backend_ctx->kernel_dequant_q8_0_f16_view_aos
+        : backend_ctx->kernel_dequant_q4_0_f16_view_aos;
+
+    CL_CHECK(clSetKernelArg(dq_kernel, 0, sizeof(cl_mem),   &src_buf));
+    CL_CHECK(clSetKernelArg(dq_kernel, 1, sizeof(cl_ulong), &src_offset));
+    CL_CHECK(clSetKernelArg(dq_kernel, 2, sizeof(cl_ulong), &src_nb1));
+    CL_CHECK(clSetKernelArg(dq_kernel, 3, sizeof(cl_ulong), &src_nb2));
+    CL_CHECK(clSetKernelArg(dq_kernel, 4, sizeof(cl_ulong), &src_nb3));
+    CL_CHECK(clSetKernelArg(dq_kernel, 5, sizeof(cl_int),   &nblk0));
+    CL_CHECK(clSetKernelArg(dq_kernel, 6, sizeof(cl_int),   &ne1_));
+    CL_CHECK(clSetKernelArg(dq_kernel, 7, sizeof(cl_int),   &ne2_));
+    CL_CHECK(clSetKernelArg(dq_kernel, 8, sizeof(cl_int),   &ne3_));
+    CL_CHECK(clSetKernelArg(dq_kernel, 9, sizeof(cl_mem),   &out));
+
+    size_t gws[3] = { (size_t) nblk0, (size_t) ne1_, (size_t) (ne2_ * ne3_) };
+    size_t lws[3] = { 1, 1, 1 };
+    CL_CHECK(clEnqueueNDRangeKernel(backend_ctx->queue, dq_kernel, 3, NULL, gws, lws, 0, NULL, NULL));
+
+    return out;
+}
+
 static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0);
     GGML_ASSERT(src0->extra);
@@ -13770,6 +15274,31 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
     ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
 
+    // Non-contig quant src0: on-device dequant to f16 then native f16 MUL_MAT.
+    if ((src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q8_0) && !ggml_is_contiguous(src0)) {
+        cl_mem f16_buf = ggml_cl_mul_mat_dequant_quant_to_f16(backend_ctx, src0, nullptr);
+
+        ggml_tensor         fake_src0 = *src0;
+        ggml_tensor_extra_cl fake_extra = {};
+        fake_extra.data_device = f16_buf;
+        fake_extra.offset      = 0;
+        fake_src0.type     = GGML_TYPE_F16;
+        fake_src0.extra    = &fake_extra;
+        fake_src0.view_src = nullptr;
+        fake_src0.view_offs = 0;
+        fake_src0.nb[0] = sizeof(ggml_fp16_t);
+        fake_src0.nb[1] = fake_src0.nb[0] * src0->ne[0];
+        fake_src0.nb[2] = fake_src0.nb[1] * src0->ne[1];
+        fake_src0.nb[3] = fake_src0.nb[2] * src0->ne[2];
+
+        ggml_cl_mul_mat(backend, &fake_src0, src1, dst);
+
+        // Safe to release now: OpenCL retains the memobj while queued
+        // kernels that reference it are still in flight.
+        CL_CHECK(clReleaseMemObject(f16_buf));
+        return;
+    }
+
     ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
     ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
     ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
@@ -13779,16 +15308,19 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     cl_ulong offsetd = extrad->offset + dst->view_offs;
 
 #ifdef GGML_OPENCL_SOA_Q
-    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
-    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
-    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)src0->extra;
-    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)src0->extra;
-    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
-    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
-    ggml_tensor_extra_cl_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)src0->extra;
-    ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)src0->extra;
-    ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)src0->extra;
-    ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
+    // view->extra stays pre-SoA; cast to the SoA struct would SIGSEGV.
+    // Follow view_src to reach the real SoA extra.
+    const ggml_tensor * soa0_src = src0->view_src != nullptr ? src0->view_src : src0;
+    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)soa0_src->extra;
+    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)soa0_src->extra;
+    ggml_tensor_extra_cl_iq4_nl * extra0_iq4_nl = (ggml_tensor_extra_cl_iq4_nl *)soa0_src->extra;
+    ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)soa0_src->extra;
+    ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)soa0_src->extra;
 #endif
 
     GGML_TENSOR_LOCALS(int,      ne0, src0, ne);
@@ -15543,15 +17075,18 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
     GGML_UNUSED(offset0);
 
 #ifdef GGML_OPENCL_SOA_Q
-    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
-    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)src0->extra;
-    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)src0->extra;
-    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)src0->extra;
-    ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)src0->extra;
-    ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)src0->extra;
-    ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)src0->extra;
-    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
-    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
+    // SoA extra lives on view_src (view->extra is pre-SoA).
+    const ggml_tensor * soa0_src = src0->view_src != nullptr ? src0->view_src : src0;
+    ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_1 * extra0_q5_1 = (ggml_tensor_extra_cl_q5_1 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q4_K * extra0_q4_K = (ggml_tensor_extra_cl_q4_K *)soa0_src->extra;
+    ggml_tensor_extra_cl_q5_K * extra0_q5_K = (ggml_tensor_extra_cl_q5_K *)soa0_src->extra;
+    ggml_tensor_extra_cl_q6_K * extra0_q6_K = (ggml_tensor_extra_cl_q6_K *)soa0_src->extra;
+    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)soa0_src->extra;
+    ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)soa0_src->extra;
+
 #endif
 
     // TODO: general MoE for the following types
diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl
index 226b127ab..82a130559 100644
--- a/ggml/src/ggml-opencl/kernels/cvt.cl
+++ b/ggml/src/ggml-opencl/kernels/cvt.cl
@@ -1582,6 +1582,158 @@ kernel void kernel_restore_block_q8_0(
     }
 }
 
+// View-aware AoS q8_0 -> f32 dequant (f32/f32 FA path).
+kernel void kernel_dequant_q8_0_f32_view_aos(
+    global char * src,
+    ulong         src_offset,
+    ulong         src_nb1,
+    ulong         src_nb2,
+    ulong         src_nb3,
+    int           nblk0,
+    int           ne1,
+    int           ne2,
+    int           ne3,
+    global float * dst
+) {
+    int blk_i0 = get_global_id(0);
+    int i1     = get_global_id(1);
+    int batch  = get_global_id(2);
+
+    if (blk_i0 >= nblk0) return;
+    if (i1     >= ne1)   return;
+
+    int i2 = batch % ne2;
+    int i3 = batch / ne2;
+    if (i3 >= ne3) return;
+
+    global char * block = src + src_offset + (ulong)i3*src_nb3 + (ulong)i2*src_nb2 + (ulong)i1*src_nb1 + (ulong)blk_i0 * (2 + QK8_0);
+    float d = vload_half(0, (global half *)block);
+    global char * qs = block + 2;
+
+    ulong dst_row_base = ((ulong)i3 * ne2 * ne1 + (ulong)i2 * ne1 + (ulong)i1) * nblk0;
+    global float * out = dst + (dst_row_base + blk_i0) * QK8_0;
+
+    for (int i = 0; i < QK8_0; ++i) {
+        out[i] = d * (float)qs[i];
+    }
+}
+
+// View-aware AoS q8_0 -> f16 dequant. Rows tight, batch strides may be gapped.
+kernel void kernel_dequant_q8_0_f16_view_aos(
+    global char * src,
+    ulong         src_offset,
+    ulong         src_nb1,
+    ulong         src_nb2,
+    ulong         src_nb3,
+    int           nblk0,
+    int           ne1,
+    int           ne2,
+    int           ne3,
+    global half * dst
+) {
+    int blk_i0 = get_global_id(0);
+    int i1     = get_global_id(1);
+    int batch  = get_global_id(2);
+
+    if (blk_i0 >= nblk0) return;
+    if (i1     >= ne1)   return;
+
+    int i2 = batch % ne2;
+    int i3 = batch / ne2;
+    if (i3 >= ne3) return;
+
+    global char * block = src + src_offset + (ulong)i3*src_nb3 + (ulong)i2*src_nb2 + (ulong)i1*src_nb1 + (ulong)blk_i0 * (2 + QK8_0);
+    float d = vload_half(0, (global half *)block);
+    global char * qs = block + 2;
+
+    ulong dst_row_base = ((ulong)i3 * ne2 * ne1 + (ulong)i2 * ne1 + (ulong)i1) * nblk0;
+    global half * out = dst + (dst_row_base + blk_i0) * QK8_0;
+
+    for (int i = 0; i < QK8_0; ++i) {
+        out[i] = (half)(d * (float)qs[i]);
+    }
+}
+
+// View-aware AoS q4_0 -> f32 dequant (mirrors the q8_0 view variant).
+kernel void kernel_dequant_q4_0_f32_view_aos(
+    global char * src,
+    ulong         src_offset,
+    ulong         src_nb1,
+    ulong         src_nb2,
+    ulong         src_nb3,
+    int           nblk0,
+    int           ne1,
+    int           ne2,
+    int           ne3,
+    global float * dst
+) {
+    int blk_i0 = get_global_id(0);
+    int i1     = get_global_id(1);
+    int batch  = get_global_id(2);
+
+    if (blk_i0 >= nblk0) return;
+    if (i1     >= ne1)   return;
+
+    int i2 = batch % ne2;
+    int i3 = batch / ne2;
+    if (i3 >= ne3) return;
+
+    global char * block = src + src_offset + (ulong)i3*src_nb3 + (ulong)i2*src_nb2 + (ulong)i1*src_nb1 + (ulong)blk_i0 * (2 + QK4_0/2);
+    float d = vload_half(0, (global half *)block);
+    global uchar * qs = (global uchar *)(block + 2);
+
+    ulong dst_row_base = ((ulong)i3 * ne2 * ne1 + (ulong)i2 * ne1 + (ulong)i1) * nblk0;
+    global float * out = dst + (dst_row_base + blk_i0) * QK4_0;
+
+    for (int i = 0; i < QK4_0/2; ++i) {
+        uchar byte = qs[i];
+        int q0 = (int)(byte & 0x0F) - 8;
+        int q1 = (int)(byte >> 4)   - 8;
+        out[i]            = d * (float)q0;
+        out[i + QK4_0/2]  = d * (float)q1;
+    }
+}
+
+// View-aware AoS q4_0 -> f16 dequant (mirrors the q8_0 view variant).
+kernel void kernel_dequant_q4_0_f16_view_aos(
+    global char * src,
+    ulong         src_offset,
+    ulong         src_nb1,
+    ulong         src_nb2,
+    ulong         src_nb3,
+    int           nblk0,
+    int           ne1,
+    int           ne2,
+    int           ne3,
+    global half * dst
+) {
+    int blk_i0 = get_global_id(0);
+    int i1     = get_global_id(1);
+    int batch  = get_global_id(2);
+
+    if (blk_i0 >= nblk0) return;
+    if (i1     >= ne1)   return;
+
+    int i2 = batch % ne2;
+    int i3 = batch / ne2;
+    if (i3 >= ne3) return;
+
+    global char * block = src + src_offset + (ulong)i3*src_nb3 + (ulong)i2*src_nb2 + (ulong)i1*src_nb1 + (ulong)blk_i0 * (2 + QK4_0/2);
+    float d = vload_half(0, (global half *)block);
+    global uchar * qs = (global uchar *)(block + 2);
+
+    ulong dst_row_base = ((ulong)i3 * ne2 * ne1 + (ulong)i2 * ne1 + (ulong)i1) * nblk0;
+    global half * out = dst + (dst_row_base + blk_i0) * QK4_0;
+
+    for (int i = 0; i < QK4_0/2; ++i) {
+        uchar byte = qs[i];
+        int q0 = (int)(byte & 0x0F) - 8;
+        int q1 = (int)(byte >> 4)   - 8;
+        out[i]          = (half)(d * (float)q0);
+        out[i + QK4_0/2] = (half)(d * (float)q1);
+    }
+}
+
 kernel void kernel_restore_block_q8_0_trans(
     global uchar * src_q,
     global half  * src_d,
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
index 8f43c4f27..ec941b5f1 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f16.cl
@@ -4,14 +4,26 @@
 #define ACC_TYPE4 float4
 #define DATA_TYPE half
 #define DATA_TYPE4 half4
-#define CONVERT_ACC4(x) convert_float4(x)
-#define CONVERT_DATA4(x) convert_half4(x)
+#define CONVERT_ACC4(x) ((float4)((float)(x).s0, (float)(x).s1, (float)(x).s2, (float)(x).s3))
+#define CONVERT_DATA4(x) ((half4)((half)(x).s0, (half)(x).s1, (half)(x).s2, (half)(x).s3))
 
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
 #define WG_SIZE (BLOCK_M)
 #define Q1_WG_SIZE 64
 
+// The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
+// infinite operand can cause undefined behavior and miscompilation for exp.
+// Therefore, a large negative value is used instead.
+#define FA_M_INIT (-3.0e38f)
+
+// Drop full unroll at DK>=192 — Adreno compiler host-memory budget.
+#if DK >= 192
+#define FA_UNROLL
+#else
+#define FA_UNROLL _Pragma("unroll")
+#endif
+
 inline float get_alibi_slope(
     const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
 ) {
@@ -81,18 +93,18 @@ __kernel void flash_attn_f16(
     if (my_query_row < n_q) {
         const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
         const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DK_VEC; ++i) {
             q_priv[i] = CONVERT_ACC4(q_ptr[i]);
         }
     }
 
     ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DV_VEC; ++i) {
         o_acc[i] = (ACC_TYPE4)(0.0f);
     }
-    ACC_TYPE m_i = -INFINITY;
+    ACC_TYPE m_i = FA_M_INIT;
     ACC_TYPE l_i = 0.0f;
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
@@ -125,49 +137,72 @@ __kernel void flash_attn_f16(
             continue;
         }
 
-        for (int j = 0; j < BLOCK_N; j += 2) {
+        for (int j = 0; j < BLOCK_N; j += 4) {
             const int k_row0 = k_start + j;
             const int k_row1 = k_start + j + 1;
+            const int k_row2 = k_start + j + 2;
+            const int k_row3 = k_start + j + 3;
 
             ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
             ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
-            #pragma unroll
+            ACC_TYPE4 dot_acc2 = (ACC_TYPE4)(0.0f);
+            ACC_TYPE4 dot_acc3 = (ACC_TYPE4)(0.0f);
+            FA_UNROLL
             for (int k = 0; k < DK_VEC; k++) {
-                dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
-                dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+                const ACC_TYPE4 qk = q_priv[k];
+                dot_acc0 = mad(qk, CONVERT_ACC4(l_k[j][k]),   dot_acc0);
+                dot_acc1 = mad(qk, CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+                dot_acc2 = mad(qk, CONVERT_ACC4(l_k[j+2][k]), dot_acc2);
+                dot_acc3 = mad(qk, CONVERT_ACC4(l_k[j+3][k]), dot_acc3);
             }
-            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
-            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+            ACC_TYPE s0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+            ACC_TYPE s1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+            ACC_TYPE s2 = (dot_acc2.s0 + dot_acc2.s1 + dot_acc2.s2 + dot_acc2.s3) * scale;
+            ACC_TYPE s3 = (dot_acc3.s0 + dot_acc3.s1 + dot_acc3.s2 + dot_acc3.s3) * scale;
 
             if (is_causal) {
-                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
-                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
+                const int causal_limit = n_kv - n_q + my_query_row;
+                if (k_row0 > causal_limit) s0 = FA_M_INIT;
+                if (k_row1 > causal_limit) s1 = FA_M_INIT;
+                if (k_row2 > causal_limit) s2 = FA_M_INIT;
+                if (k_row3 > causal_limit) s3 = FA_M_INIT;
             }
-
-            if (k_row0 >= n_kv) score0 = -INFINITY;
-            if (k_row1 >= n_kv) score1 = -INFINITY;
+            if (k_row0 >= n_kv) s0 = FA_M_INIT;
+            if (k_row1 >= n_kv) s1 = FA_M_INIT;
+            if (k_row2 >= n_kv) s2 = FA_M_INIT;
+            if (k_row3 >= n_kv) s3 = FA_M_INIT;
 
             if (mask_base != NULL) {
                 const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
-                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
-                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                if (k_row0 < n_kv) s0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                if (k_row1 < n_kv) s1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                if (k_row2 < n_kv) s2 += slope * (ACC_TYPE)mask_ptr[k_row2];
+                if (k_row3 < n_kv) s3 += slope * (ACC_TYPE)mask_ptr[k_row3];
             }
 
             if (logit_softcap > 0.0f) {
-                score0 = logit_softcap * tanh(score0 / logit_softcap);
-                score1 = logit_softcap * tanh(score1 / logit_softcap);
+                s0 = logit_softcap * tanh(s0 / logit_softcap);
+                s1 = logit_softcap * tanh(s1 / logit_softcap);
+                s2 = logit_softcap * tanh(s2 / logit_softcap);
+                s3 = logit_softcap * tanh(s3 / logit_softcap);
             }
 
-            const ACC_TYPE m_new = max(m_i, max(score0, score1));
-            const ACC_TYPE p0 = exp(score0 - m_new);
-            const ACC_TYPE p1 = exp(score1 - m_new);
-            const ACC_TYPE scale_prev = exp(m_i - m_new);
+            const ACC_TYPE m_new      = max(m_i, max(max(s0, s1), max(s2, s3)));
+            const ACC_TYPE scale_prev = native_exp(m_i - m_new);
+            const ACC_TYPE p0         = native_exp(s0 - m_new);
+            const ACC_TYPE p1         = native_exp(s1 - m_new);
+            const ACC_TYPE p2         = native_exp(s2 - m_new);
+            const ACC_TYPE p3         = native_exp(s3 - m_new);
 
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
-                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
+                o_acc[i] = mad(p3, CONVERT_ACC4(l_v[j+3][i]),
+                           mad(p2, CONVERT_ACC4(l_v[j+2][i]),
+                           mad(p1, CONVERT_ACC4(l_v[j+1][i]),
+                           mad(p0, CONVERT_ACC4(l_v[j][i]),
+                           o_acc[i] * scale_prev))));
             }
-            l_i = l_i * scale_prev + p0 + p1;
+            l_i = l_i * scale_prev + p0 + p1 + p2 + p3;
             m_i = m_new;
         }
     }
@@ -179,7 +214,7 @@ __kernel void flash_attn_f16(
             const ACC_TYPE m_final = max(m_i, m_sink);
 
             const ACC_TYPE scale_o = exp(m_i - m_final);
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_acc[i] *= scale_o;
             }
@@ -191,12 +226,12 @@ __kernel void flash_attn_f16(
         global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
             const ACC_TYPE l_inv = 1.0f / l_i;
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
             }
         } else {
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = (DATA_TYPE4)(0.0f);
             }
@@ -258,7 +293,7 @@ __kernel void flash_attn_f16_q1(
     ACC_TYPE4 q_priv[DK_VEC];
     const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
     const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DK_VEC; ++i) {
         q_priv[i] = CONVERT_ACC4(q_ptr[i]);
     }
@@ -270,12 +305,12 @@ __kernel void flash_attn_f16_q1(
         sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
     }
 
-    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : FA_M_INIT;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
             dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
         }
@@ -293,7 +328,7 @@ __kernel void flash_attn_f16_q1(
     __local ACC_TYPE local_m[Q1_WG_SIZE];
     local_m[tid] = m_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -301,7 +336,7 @@ __kernel void flash_attn_f16_q1(
     const ACC_TYPE m_final = local_m[0];
 
     ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
     ACC_TYPE l_i = 0.0f;
 
@@ -311,7 +346,7 @@ __kernel void flash_attn_f16_q1(
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
         const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
             dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
         }
@@ -325,7 +360,7 @@ __kernel void flash_attn_f16_q1(
         }
         const ACC_TYPE p = exp(score - m_final);
         l_i += p;
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; i++) {
             o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
         }
@@ -335,7 +370,7 @@ __kernel void flash_attn_f16_q1(
     __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
     local_l[tid] = l_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_l[tid] += local_l[tid + s];
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -354,7 +389,7 @@ __kernel void flash_attn_f16_q1(
         for (int i = 0; i < DV_VEC; i++) {
             local_o_comp[tid] = o_acc[i];
             barrier(CLK_LOCAL_MEM_FENCE);
-            #pragma unroll
+            FA_UNROLL
             for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
                 if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
                 barrier(CLK_LOCAL_MEM_FENCE);
@@ -364,7 +399,7 @@ __kernel void flash_attn_f16_q1(
             }
         }
     } else if (tid == 0) {
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
     }
 }
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
index a6d747903..2547731c3 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32.cl
@@ -13,6 +13,18 @@
 #define WG_SIZE (BLOCK_M)
 #define Q1_WG_SIZE 64
 
+// The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
+// infinite operand can cause undefined behavior and miscompilation for exp.
+// Therefore, a large negative value is used instead.
+#define FA_M_INIT (-3.0e38f)
+
+// Drop full unroll at DK>=192 — Adreno compiler host-memory budget.
+#if DK >= 192
+#define FA_UNROLL
+#else
+#define FA_UNROLL _Pragma("unroll")
+#endif
+
 inline float get_alibi_slope(
     const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
 ) {
@@ -82,18 +94,18 @@ __kernel void flash_attn_f32(
     if (my_query_row < n_q) {
         const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
         const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DK_VEC; ++i) {
             q_priv[i] = CONVERT_ACC4(q_ptr[i]);
         }
     }
 
     ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DV_VEC; ++i) {
         o_acc[i] = (ACC_TYPE4)(0.0f);
     }
-    ACC_TYPE m_i = -INFINITY;
+    ACC_TYPE m_i = FA_M_INIT;
     ACC_TYPE l_i = 0.0f;
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
@@ -126,49 +138,72 @@ __kernel void flash_attn_f32(
             continue;
         }
 
-        for (int j = 0; j < BLOCK_N; j += 2) {
+        for (int j = 0; j < BLOCK_N; j += 4) {
             const int k_row0 = k_start + j;
             const int k_row1 = k_start + j + 1;
+            const int k_row2 = k_start + j + 2;
+            const int k_row3 = k_start + j + 3;
 
             ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
             ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
-            #pragma unroll
+            ACC_TYPE4 dot_acc2 = (ACC_TYPE4)(0.0f);
+            ACC_TYPE4 dot_acc3 = (ACC_TYPE4)(0.0f);
+            FA_UNROLL
             for (int k = 0; k < DK_VEC; k++) {
-                dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
-                dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+                const ACC_TYPE4 qk = q_priv[k];
+                dot_acc0 = mad(qk, CONVERT_ACC4(l_k[j][k]),   dot_acc0);
+                dot_acc1 = mad(qk, CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
+                dot_acc2 = mad(qk, CONVERT_ACC4(l_k[j+2][k]), dot_acc2);
+                dot_acc3 = mad(qk, CONVERT_ACC4(l_k[j+3][k]), dot_acc3);
             }
-            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
-            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+            ACC_TYPE s0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+            ACC_TYPE s1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+            ACC_TYPE s2 = (dot_acc2.s0 + dot_acc2.s1 + dot_acc2.s2 + dot_acc2.s3) * scale;
+            ACC_TYPE s3 = (dot_acc3.s0 + dot_acc3.s1 + dot_acc3.s2 + dot_acc3.s3) * scale;
 
             if (is_causal) {
-                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
-                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
+                const int causal_limit = n_kv - n_q + my_query_row;
+                if (k_row0 > causal_limit) s0 = FA_M_INIT;
+                if (k_row1 > causal_limit) s1 = FA_M_INIT;
+                if (k_row2 > causal_limit) s2 = FA_M_INIT;
+                if (k_row3 > causal_limit) s3 = FA_M_INIT;
             }
-
-            if (k_row0 >= n_kv) score0 = -INFINITY;
-            if (k_row1 >= n_kv) score1 = -INFINITY;
+            if (k_row0 >= n_kv) s0 = FA_M_INIT;
+            if (k_row1 >= n_kv) s1 = FA_M_INIT;
+            if (k_row2 >= n_kv) s2 = FA_M_INIT;
+            if (k_row3 >= n_kv) s3 = FA_M_INIT;
 
             if (mask_base != NULL) {
                 const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
-                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
-                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                if (k_row0 < n_kv) s0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                if (k_row1 < n_kv) s1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                if (k_row2 < n_kv) s2 += slope * (ACC_TYPE)mask_ptr[k_row2];
+                if (k_row3 < n_kv) s3 += slope * (ACC_TYPE)mask_ptr[k_row3];
             }
 
             if (logit_softcap > 0.0f) {
-                score0 = logit_softcap * tanh(score0 / logit_softcap);
-                score1 = logit_softcap * tanh(score1 / logit_softcap);
+                s0 = logit_softcap * tanh(s0 / logit_softcap);
+                s1 = logit_softcap * tanh(s1 / logit_softcap);
+                s2 = logit_softcap * tanh(s2 / logit_softcap);
+                s3 = logit_softcap * tanh(s3 / logit_softcap);
             }
 
-            const ACC_TYPE m_new = max(m_i, max(score0, score1));
-            const ACC_TYPE p0 = exp(score0 - m_new);
-            const ACC_TYPE p1 = exp(score1 - m_new);
-            const ACC_TYPE scale_prev = exp(m_i - m_new);
+            const ACC_TYPE m_new      = max(m_i, max(max(s0, s1), max(s2, s3)));
+            const ACC_TYPE scale_prev = native_exp(m_i - m_new);
+            const ACC_TYPE p0         = native_exp(s0 - m_new);
+            const ACC_TYPE p1         = native_exp(s1 - m_new);
+            const ACC_TYPE p2         = native_exp(s2 - m_new);
+            const ACC_TYPE p3         = native_exp(s3 - m_new);
 
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
-                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
+                o_acc[i] = mad(p3, CONVERT_ACC4(l_v[j+3][i]),
+                           mad(p2, CONVERT_ACC4(l_v[j+2][i]),
+                           mad(p1, CONVERT_ACC4(l_v[j+1][i]),
+                           mad(p0, CONVERT_ACC4(l_v[j][i]),
+                           o_acc[i] * scale_prev))));
             }
-            l_i = l_i * scale_prev + p0 + p1;
+            l_i = l_i * scale_prev + p0 + p1 + p2 + p3;
             m_i = m_new;
         }
     }
@@ -180,7 +215,7 @@ __kernel void flash_attn_f32(
             const ACC_TYPE m_final = max(m_i, m_sink);
 
             const ACC_TYPE scale_o = exp(m_i - m_final);
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_acc[i] *= scale_o;
             }
@@ -192,12 +227,12 @@ __kernel void flash_attn_f32(
         global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
             const ACC_TYPE l_inv = 1.0f / l_i;
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
             }
         } else {
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = (DATA_TYPE4)(0.0f);
             }
@@ -259,7 +294,7 @@ __kernel void flash_attn_f32_q1(
     ACC_TYPE4 q_priv[DK_VEC];
     const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
     const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DK_VEC; ++i) {
         q_priv[i] = CONVERT_ACC4(q_ptr[i]);
     }
@@ -271,12 +306,12 @@ __kernel void flash_attn_f32_q1(
         sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
     }
 
-    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : FA_M_INIT;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
             dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
         }
@@ -294,7 +329,7 @@ __kernel void flash_attn_f32_q1(
     __local ACC_TYPE local_m[Q1_WG_SIZE];
     local_m[tid] = m_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -302,7 +337,7 @@ __kernel void flash_attn_f32_q1(
     const ACC_TYPE m_final = local_m[0];
 
     ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
     ACC_TYPE l_i = 0.0f;
 
@@ -312,7 +347,7 @@ __kernel void flash_attn_f32_q1(
         const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
         const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
             dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
         }
@@ -326,7 +361,7 @@ __kernel void flash_attn_f32_q1(
         }
         const ACC_TYPE p = exp(score - m_final);
         l_i += p;
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; i++) {
             o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
         }
@@ -336,7 +371,7 @@ __kernel void flash_attn_f32_q1(
     __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
     local_l[tid] = l_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_l[tid] += local_l[tid + s];
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -355,7 +390,7 @@ __kernel void flash_attn_f32_q1(
         for (int i = 0; i < DV_VEC; i++) {
             local_o_comp[tid] = o_acc[i];
             barrier(CLK_LOCAL_MEM_FENCE);
-            #pragma unroll
+            FA_UNROLL
             for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
                 if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
                 barrier(CLK_LOCAL_MEM_FENCE);
@@ -365,7 +400,7 @@ __kernel void flash_attn_f32_q1(
             }
         }
     } else if (tid == 0) {
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
     }
 }
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
index ec7361b9e..a7f1de325 100644
--- a/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl
@@ -1,5 +1,13 @@
 #pragma OPENCL EXTENSION cl_khr_fp16 : enable
 
+#ifdef cl_khr_subgroup_shuffle
+#pragma OPENCL EXTENSION cl_khr_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#elif defined(cl_qcom_subgroup_shuffle)
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#endif
+
 #define ACC_TYPE float
 #define ACC_TYPE4 float4
 #define Q_DATA_TYPE4 float4
@@ -12,9 +20,34 @@
 
 #define DK_VEC (DK/4)
 #define DV_VEC (DV/4)
-#define WG_SIZE (BLOCK_M)
 #define Q1_WG_SIZE 64
 
+// The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
+// infinite operand can cause undefined behavior and miscompilation for exp.
+// Therefore, a large negative value is used instead.
+#define FA_M_INIT (-3.0e38f)
+
+// Drop full unroll at DK>=192 — Adreno compiler host-memory budget.
+#if DK >= 192
+#define FA_UNROLL
+#else
+#define FA_UNROLL _Pragma("unroll")
+#endif
+
+// N_SPLIT>1 splits DK/DV across threads to cut per-thread register use.
+#ifndef N_SPLIT
+#define N_SPLIT 1
+#endif
+
+#define SPLIT_DK_VEC (DK_VEC / N_SPLIT)
+#define SPLIT_DV_VEC (DV_VEC / N_SPLIT)
+
+#if N_SPLIT > 1
+#define WG_SIZE (BLOCK_M * N_SPLIT)
+#else
+#define WG_SIZE (BLOCK_M)
+#endif
+
 inline float get_alibi_slope(
     const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
 ) {
@@ -54,19 +87,38 @@ __kernel void flash_attn_f32_f16(
     const int mask_ne2,
     const int mask_ne3,
     const global void* sinks_void,
-    const ulong sinks_offset
+    const ulong sinks_offset,
+    const global void * k_pad_void,
+    const global void * v_pad_void,
+    const global void * mask_pad_void,
+    const global char * blk,
+    const int n_kv_blocks,
+    const ulong mask_pad_nb1,
+    const ulong mask_pad_nb2,
+    const ulong mask_pad_nb3
 ) {
     const int tid = get_local_id(0);
     const int block_q_idx = get_group_id(0);
     const int head_batch_idx = get_global_id(1);
 
-    const int my_query_row = block_q_idx * BLOCK_M + tid;
+#if N_SPLIT > 1
+    const int q_lane    = tid / N_SPLIT;
+    const int split_idx = tid % N_SPLIT;
+#else
+    const int q_lane    = tid;
+    const int split_idx = 0;
+#endif
+
+    const int my_query_row = block_q_idx * BLOCK_M + q_lane;
+    const int query_valid = my_query_row < n_q;
 
     const int batch_idx = head_batch_idx / n_head;
     const int head_idx = head_batch_idx % n_head;
 
     const int gqa_ratio = n_head / n_head_kv;
     const int head_kv_idx = head_idx / gqa_ratio;
+    const int mask_head_idx = mask_void != NULL ? head_idx % mask_ne2 : 0;
+    const int mask_batch_idx = mask_void != NULL ? batch_idx % mask_ne3 : 0;
 
     const global char* q_base = (const global char*)q_void + q_offset;
     const global char* k_base = (const global char*)k_void + k_offset;
@@ -75,27 +127,41 @@ __kernel void flash_attn_f32_f16(
 
     const global char* mask_base = NULL;
     if (mask_void != NULL) {
-        const int mask_head_idx = head_idx % mask_ne2;
-        const int mask_batch_idx = batch_idx % mask_ne3;
         mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
     }
+    const global char* mask_pad_base = NULL;
+    if (mask_pad_void != NULL) {
+        mask_pad_base = (const global char*)mask_pad_void + mask_batch_idx * mask_pad_nb3 + mask_head_idx * mask_pad_nb2;
+    }
+    const global char* blk_base = NULL;
+    if (blk != NULL) {
+        const int n_q_blocks = (n_q + BLOCK_M - 1) / BLOCK_M;
+        blk_base = blk + (((mask_batch_idx * mask_ne2) + mask_head_idx) * n_q_blocks + block_q_idx) * n_kv_blocks;
+    }
 
-    ACC_TYPE4 q_priv[DK_VEC];
-    if (my_query_row < n_q) {
+    ACC_TYPE4 q_priv[SPLIT_DK_VEC];
+    const int dk_off = split_idx * SPLIT_DK_VEC;
+    if (query_valid) {
         const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
         const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
-        #pragma unroll
-        for (int i = 0; i < DK_VEC; ++i) {
-            q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+        FA_UNROLL
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) {
+            q_priv[i] = CONVERT_Q_ACC4(q_ptr[dk_off + i]);
+        }
+    } else {
+        FA_UNROLL
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) {
+            q_priv[i] = (ACC_TYPE4)(0.0f);
         }
     }
 
-    ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
-    for (int i = 0; i < DV_VEC; ++i) {
+    ACC_TYPE4 o_acc[SPLIT_DV_VEC];
+    FA_UNROLL
+    for (int i = 0; i < SPLIT_DV_VEC; ++i) {
         o_acc[i] = (ACC_TYPE4)(0.0f);
     }
-    ACC_TYPE m_i = -INFINITY;
+
+    ACC_TYPE m_i = FA_M_INIT;
     ACC_TYPE l_i = 0.0f;
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
@@ -103,86 +169,369 @@ __kernel void flash_attn_f32_f16(
     __local KV_DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
     __local KV_DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
 
+#if N_SPLIT > 1 && !defined(HAS_SUBGROUP_SHUFFLE)
+    __local ACC_TYPE local_partial[BLOCK_N][WG_SIZE];
+    __local ACC_TYPE local_p[BLOCK_M][BLOCK_N];
+    __local ACC_TYPE local_softmax_scale[BLOCK_M];
+    __local ACC_TYPE local_l_inv[BLOCK_M];
+#endif
+
     for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        char blk_cur = 1;
+        if (blk_base != NULL) {
+            blk_cur = blk_base[k_start / BLOCK_N];
+            if (blk_cur == 0) continue;
+        }
+
+        const int use_kv_pad = k_pad_void != NULL && k_start + BLOCK_N > n_kv;
+        const int k_tile_start = use_kv_pad ? 0 : k_start;
+        const ulong k_tile_nb2 = use_kv_pad ? (ulong) BLOCK_N * k_nb1 : k_nb2;
+        const ulong k_tile_nb3 = use_kv_pad ? (ulong) n_head_kv * k_tile_nb2 : k_nb3;
+        const ulong v_tile_nb2 = use_kv_pad ? (ulong) BLOCK_N * v_nb1 : v_nb2;
+        const ulong v_tile_nb3 = use_kv_pad ? (ulong) n_head_kv * v_tile_nb2 : v_nb3;
+        const global char* k_tile_base = use_kv_pad ? (const global char*) k_pad_void : k_base;
+        const global char* v_tile_base = use_kv_pad ? (const global char*) v_pad_void : v_base;
+
         for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
             const int row = i / DK_VEC;
             const int col = i % DK_VEC;
-            const int k_row_idx = k_start + row;
-            if (k_row_idx < n_kv) {
-                const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
-                l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_base + k_row_offset))[col];
+            const int k_row_idx = k_tile_start + row;
+            if (use_kv_pad || k_row_idx < n_kv) {
+                const ulong k_row_offset = batch_idx * k_tile_nb3 + head_kv_idx * k_tile_nb2 + k_row_idx * k_nb1;
+                l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_tile_base + k_row_offset))[col];
+            } else {
+                l_k[row][col] = (KV_DATA_TYPE4)(0.0h);
             }
         }
         for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
             const int row = i / DV_VEC;
             const int col = i % DV_VEC;
-            const int v_row_idx = k_start + row;
-            if (v_row_idx < n_kv) {
-                const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
-                l_v[row][col] = ((__global KV_DATA_TYPE4*)(v_base + v_row_offset))[col];
+            const int v_row_idx = k_tile_start + row;
+            if (use_kv_pad || v_row_idx < n_kv) {
+                const ulong v_row_offset = batch_idx * v_tile_nb3 + head_kv_idx * v_tile_nb2 + v_row_idx * v_nb1;
+                l_v[row][col] = ((__global KV_DATA_TYPE4*)(v_tile_base + v_row_offset))[col];
+            } else {
+                l_v[row][col] = (KV_DATA_TYPE4)(0.0h);
             }
         }
         barrier(CLK_LOCAL_MEM_FENCE);
 
-        if (my_query_row >= n_q) {
-            continue;
+#if N_SPLIT > 1 && defined(HAS_SUBGROUP_SHUFFLE)
+        {
+            const int dv_off = split_idx * SPLIT_DV_VEC;
+            for (int j = 0; j < BLOCK_N; j += 2) {
+                const int k_row0 = k_start + j;
+                const int k_row1 = k_start + j + 1;
+
+                ACC_TYPE partial0 = 0.0f;
+                ACC_TYPE partial1 = 0.0f;
+                FA_UNROLL
+                for (int k = 0; k < SPLIT_DK_VEC; k++) {
+                    const ACC_TYPE4 qk = q_priv[k];
+                    ACC_TYPE4 dot0 = qk * CONVERT_KV_ACC4(l_k[j  ][dk_off + k]);
+                    ACC_TYPE4 dot1 = qk * CONVERT_KV_ACC4(l_k[j+1][dk_off + k]);
+                    partial0 += dot0.s0 + dot0.s1 + dot0.s2 + dot0.s3;
+                    partial1 += dot1.s0 + dot1.s1 + dot1.s2 + dot1.s3;
+                }
+
+                FA_UNROLL
+                for (int step = 1; step < N_SPLIT; step <<= 1) {
+                    partial0 += sub_group_shuffle_xor(partial0, step);
+                    partial1 += sub_group_shuffle_xor(partial1, step);
+                }
+
+                ACC_TYPE score0 = partial0 * scale;
+                ACC_TYPE score1 = partial1 * scale;
+
+                if (!query_valid) { score0 = FA_M_INIT; score1 = FA_M_INIT; }
+                if (is_causal) {
+                    if (k_row0 > (n_kv - n_q + my_query_row)) score0 = FA_M_INIT;
+                    if (k_row1 > (n_kv - n_q + my_query_row)) score1 = FA_M_INIT;
+                }
+                if (k_row0 >= n_kv) score0 = FA_M_INIT;
+                if (k_row1 >= n_kv) score1 = FA_M_INIT;
+
+                if (query_valid && mask_base != NULL && blk_cur != 2) {
+                    if (use_kv_pad && mask_pad_base != NULL) {
+                        const global MASK_DATA_TYPE* mask_ptr =
+                            (const global MASK_DATA_TYPE*)(mask_pad_base + my_query_row * mask_pad_nb1);
+                        score0 += slope * (ACC_TYPE)mask_ptr[j];
+                        score1 += slope * (ACC_TYPE)mask_ptr[j + 1];
+                    } else {
+                        const global MASK_DATA_TYPE* mask_ptr =
+                            (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                        if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                        if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                    }
+                }
+
+                if (logit_softcap > 0.0f) {
+                    score0 = logit_softcap * tanh(score0 / logit_softcap);
+                    score1 = logit_softcap * tanh(score1 / logit_softcap);
+                }
+
+                const ACC_TYPE m_new = max(m_i, max(score0, score1));
+                // Whole tile masked (m_new == FA_M_INIT): force the exp() args
+                // far negative so the tile contributes 0, not exp(0)=1.
+                const ACC_TYPE m_exp = (m_new == FA_M_INIT) ? 0.0f : m_new;
+                const ACC_TYPE sp    = native_exp(m_i - m_exp);
+                const ACC_TYPE p0    = native_exp(score0 - m_exp);
+                const ACC_TYPE p1    = native_exp(score1 - m_exp);
+
+                FA_UNROLL
+                for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                    o_acc[i] = o_acc[i] * sp
+                             + p0 * CONVERT_KV_ACC4(l_v[j  ][dv_off + i])
+                             + p1 * CONVERT_KV_ACC4(l_v[j+1][dv_off + i]);
+                }
+                l_i = l_i * sp + p0 + p1;
+                m_i = m_new;
+            }
         }
-
-        for (int j = 0; j < BLOCK_N; j += 2) {
-            const int k_row0 = k_start + j;
-            const int k_row1 = k_start + j + 1;
-
-            ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
-            ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
-            #pragma unroll
-            for (int k = 0; k < DK_VEC; k++) {
-                dot_acc0 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j][k]), dot_acc0);
-                dot_acc1 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j+1][k]), dot_acc1);
+#elif N_SPLIT > 1
+        // N_SPLIT>1 fallback (no shuffle): 3-phase local-memory reduction.
+        // Phase 1 — partial dots for all BLOCK_N tokens.
+        for (int j = 0; j < BLOCK_N; ++j) {
+            ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+            FA_UNROLL
+            for (int k = 0; k < SPLIT_DK_VEC; k++) {
+                dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j][dk_off + k]), dot_acc);
             }
-            ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
-            ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
-
-            if (is_causal) {
-                if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
-                if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
-            }
-
-            if (k_row0 >= n_kv) score0 = -INFINITY;
-            if (k_row1 >= n_kv) score1 = -INFINITY;
-
-            if (mask_base != NULL) {
-                const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
-                if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
-                if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
-            }
-
-            if (logit_softcap > 0.0f) {
-                score0 = logit_softcap * tanh(score0 / logit_softcap);
-                score1 = logit_softcap * tanh(score1 / logit_softcap);
-            }
-
-            const ACC_TYPE m_new = max(m_i, max(score0, score1));
-            const ACC_TYPE p0 = exp(score0 - m_new);
-            const ACC_TYPE p1 = exp(score1 - m_new);
-            const ACC_TYPE scale_prev = exp(m_i - m_new);
-
-            #pragma unroll
-            for (int i = 0; i < DV_VEC; ++i) {
-                o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_KV_ACC4(l_v[j][i]) + p1 * CONVERT_KV_ACC4(l_v[j+1][i]);
-            }
-            l_i = l_i * scale_prev + p0 + p1;
-            m_i = m_new;
+            local_partial[j][tid] =
+                dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3;
         }
+        barrier(CLK_LOCAL_MEM_FENCE);  // 1 barrier: partial dots visible
+
+        // Phase 2 — split_idx==0 reduces partial sums and computes block softmax.
+        if (split_idx == 0) {
+            if (query_valid) {
+                ACC_TYPE m_new = m_i;
+                for (int j = 0; j < BLOCK_N; ++j) {
+                    const int k_row = k_start + j;
+                    ACC_TYPE score = 0.0f;
+                    FA_UNROLL
+                    for (int s = 0; s < N_SPLIT; s++) {
+                        score += local_partial[j][q_lane * N_SPLIT + s];
+                    }
+                    score *= scale;
+
+                    if (is_causal && k_row > (n_kv - n_q + my_query_row)) score = FA_M_INIT;
+                    if (k_row >= n_kv) score = FA_M_INIT;
+
+                    if (mask_base != NULL && blk_cur != 2) {
+                        if (use_kv_pad && mask_pad_base != NULL) {
+                            const global MASK_DATA_TYPE* mask_ptr =
+                                (const global MASK_DATA_TYPE*)(mask_pad_base + my_query_row * mask_pad_nb1);
+                            score += slope * (ACC_TYPE)mask_ptr[j];
+                        } else {
+                            const global MASK_DATA_TYPE* mask_ptr =
+                                (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                            if (k_row < n_kv) score += slope * (ACC_TYPE)mask_ptr[k_row];
+                        }
+                    }
+
+                    if (logit_softcap > 0.0f) {
+                        score = logit_softcap * tanh(score / logit_softcap);
+                    }
+
+                    m_new = max(m_new, score);
+                    local_p[q_lane][j] = score;
+                }
+
+                const ACC_TYPE m_exp = (m_new == FA_M_INIT) ? 0.0f : m_new;
+                const ACC_TYPE sp = native_exp(m_i - m_exp);
+                ACC_TYPE l_new = l_i * sp;
+                for (int j = 0; j < BLOCK_N; ++j) {
+                    const ACC_TYPE p = native_exp(local_p[q_lane][j] - m_exp);
+                    local_p[q_lane][j] = p;
+                    l_new += p;
+                }
+                local_softmax_scale[q_lane] = sp;
+                l_i = l_new;
+                m_i = m_new;
+            } else {
+                local_softmax_scale[q_lane] = 1.0f;
+                for (int j = 0; j < BLOCK_N; ++j) local_p[q_lane][j] = 0.0f;
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        // Phase 3 — V accumulate using broadcast probabilities.
+        {
+            const ACC_TYPE sp_block = local_softmax_scale[q_lane];
+            const int dv_off = split_idx * SPLIT_DV_VEC;
+            FA_UNROLL
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                o_acc[i] *= sp_block;
+            }
+            for (int j = 0; j < BLOCK_N; ++j) {
+                const ACC_TYPE p = local_p[q_lane][j];
+                FA_UNROLL
+                for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                    o_acc[i] = mad(p, CONVERT_KV_ACC4(l_v[j][dv_off + i]), o_acc[i]);
+                }
+            }
+        }
+#else
+        // N_SPLIT==1: j+=4 unroll. Requires BLOCK_N % 4 == 0.
+        if (query_valid) {
+            for (int j = 0; j < BLOCK_N; j += 4) {
+                const int k_row0 = k_start + j;
+                const int k_row1 = k_start + j + 1;
+                const int k_row2 = k_start + j + 2;
+                const int k_row3 = k_start + j + 3;
+
+                ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc2 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc3 = (ACC_TYPE4)(0.0f);
+                FA_UNROLL
+                for (int k = 0; k < DK_VEC; k++) {
+                    const ACC_TYPE4 qk = q_priv[k];
+                    dot_acc0 = mad(qk, CONVERT_KV_ACC4(l_k[j][k]),   dot_acc0);
+                    dot_acc1 = mad(qk, CONVERT_KV_ACC4(l_k[j+1][k]), dot_acc1);
+                    dot_acc2 = mad(qk, CONVERT_KV_ACC4(l_k[j+2][k]), dot_acc2);
+                    dot_acc3 = mad(qk, CONVERT_KV_ACC4(l_k[j+3][k]), dot_acc3);
+                }
+                ACC_TYPE s0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
+                ACC_TYPE s1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
+                ACC_TYPE s2 = (dot_acc2.s0 + dot_acc2.s1 + dot_acc2.s2 + dot_acc2.s3) * scale;
+                ACC_TYPE s3 = (dot_acc3.s0 + dot_acc3.s1 + dot_acc3.s2 + dot_acc3.s3) * scale;
+
+                if (is_causal) {
+                    const int causal_limit = n_kv - n_q + my_query_row;
+                    if (k_row0 > causal_limit) s0 = FA_M_INIT;
+                    if (k_row1 > causal_limit) s1 = FA_M_INIT;
+                    if (k_row2 > causal_limit) s2 = FA_M_INIT;
+                    if (k_row3 > causal_limit) s3 = FA_M_INIT;
+                }
+                if (k_row0 >= n_kv) s0 = FA_M_INIT;
+                if (k_row1 >= n_kv) s1 = FA_M_INIT;
+                if (k_row2 >= n_kv) s2 = FA_M_INIT;
+                if (k_row3 >= n_kv) s3 = FA_M_INIT;
+
+                if (mask_base != NULL && blk_cur != 2) {
+                    if (use_kv_pad && mask_pad_base != NULL) {
+                        const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_pad_base + my_query_row * mask_pad_nb1);
+                        s0 += slope * (ACC_TYPE)mask_ptr[j];
+                        s1 += slope * (ACC_TYPE)mask_ptr[j + 1];
+                        s2 += slope * (ACC_TYPE)mask_ptr[j + 2];
+                        s3 += slope * (ACC_TYPE)mask_ptr[j + 3];
+                    } else {
+                        const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
+                        if (k_row0 < n_kv) s0 += slope * (ACC_TYPE)mask_ptr[k_row0];
+                        if (k_row1 < n_kv) s1 += slope * (ACC_TYPE)mask_ptr[k_row1];
+                        if (k_row2 < n_kv) s2 += slope * (ACC_TYPE)mask_ptr[k_row2];
+                        if (k_row3 < n_kv) s3 += slope * (ACC_TYPE)mask_ptr[k_row3];
+                    }
+                }
+
+                if (logit_softcap > 0.0f) {
+                    s0 = logit_softcap * tanh(s0 / logit_softcap);
+                    s1 = logit_softcap * tanh(s1 / logit_softcap);
+                    s2 = logit_softcap * tanh(s2 / logit_softcap);
+                    s3 = logit_softcap * tanh(s3 / logit_softcap);
+                }
+
+                const ACC_TYPE m_new      = max(m_i, max(max(s0, s1), max(s2, s3)));
+                // Whole tile masked (m_new == FA_M_INIT): force the exp() args
+                // far negative so the tile contributes 0, not exp(0)=1.
+                const ACC_TYPE m_exp      = (m_new == FA_M_INIT) ? 0.0f : m_new;
+                const ACC_TYPE scale_prev = native_exp(m_i - m_exp);
+                const ACC_TYPE p0         = native_exp(s0 - m_exp);
+                const ACC_TYPE p1         = native_exp(s1 - m_exp);
+                const ACC_TYPE p2         = native_exp(s2 - m_exp);
+                const ACC_TYPE p3         = native_exp(s3 - m_exp);
+
+                FA_UNROLL
+                for (int i = 0; i < DV_VEC; ++i) {
+                    o_acc[i] = mad(p3, CONVERT_KV_ACC4(l_v[j+3][i]),
+                               mad(p2, CONVERT_KV_ACC4(l_v[j+2][i]),
+                               mad(p1, CONVERT_KV_ACC4(l_v[j+1][i]),
+                               mad(p0, CONVERT_KV_ACC4(l_v[j][i]),
+                               o_acc[i] * scale_prev))));
+                }
+                l_i = l_i * scale_prev + p0 + p1 + p2 + p3;
+                m_i = m_new;
+            }
+        }
+#endif
+        // End of tile: every thread must finish reading l_k/l_v before the
+        // next iteration's load overwrites them (WAR hazard on local memory).
+        barrier(CLK_LOCAL_MEM_FENCE);
     }
 
-    if (my_query_row < n_q) {
+    // Write output.
+#if N_SPLIT > 1 && defined(HAS_SUBGROUP_SHUFFLE)
+    if (query_valid) {
+        ACC_TYPE sinks_sp = 1.0f;
+        if (sinks_void != NULL) {
+            const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+            const ACC_TYPE m_sink  = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+            sinks_sp = exp(m_i - m_final);
+            l_i = l_i * sinks_sp + exp(m_sink - m_final);
+            m_i = m_final;
+        }
+        const ACC_TYPE l_inv = (l_i > 0.0f) ? (1.0f / l_i) : 0.0f;
+        const int dv_off = split_idx * SPLIT_DV_VEC;
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+        if (l_inv > 0.0f) {
+            FA_UNROLL
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                o_row[dv_off + i] = CONVERT_O_DATA4(o_acc[i] * sinks_sp * l_inv);
+            }
+        } else {
+            FA_UNROLL
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                o_row[dv_off + i] = (O_DATA_TYPE4)(0.0f);
+            }
+        }
+    }
+#elif N_SPLIT > 1
+    if (split_idx == 0) {
+        ACC_TYPE sinks_sp = 1.0f;
+        if (query_valid && sinks_void != NULL) {
+            const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+            const ACC_TYPE m_sink = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+            sinks_sp = exp(m_i - m_final);
+            l_i = l_i * sinks_sp + exp(m_sink - m_final);
+            m_i = m_final;
+        }
+        local_softmax_scale[q_lane] = sinks_sp;
+        local_l_inv[q_lane] = (query_valid && l_i > 0.0f) ? (1.0f / l_i) : 0.0f;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if (query_valid) {
+        const ACC_TYPE sinks_sp = local_softmax_scale[q_lane];
+        const ACC_TYPE l_inv    = local_l_inv[q_lane];
+        const int dv_off = split_idx * SPLIT_DV_VEC;
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+        if (l_inv > 0.0f) {
+            FA_UNROLL
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                o_row[dv_off + i] = CONVERT_O_DATA4(o_acc[i] * sinks_sp * l_inv);
+            }
+        } else {
+            FA_UNROLL
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                o_row[dv_off + i] = (O_DATA_TYPE4)(0.0f);
+            }
+        }
+    }
+#else
+    if (query_valid) {
         if (sinks_void != NULL) {
             const global ACC_TYPE* sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
             const ACC_TYPE m_sink = sinks_ptr[head_idx];
             const ACC_TYPE m_final = max(m_i, m_sink);
 
             const ACC_TYPE scale_o = exp(m_i - m_final);
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_acc[i] *= scale_o;
             }
@@ -194,17 +543,18 @@ __kernel void flash_attn_f32_f16(
         global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
         if (l_i > 0.0f) {
             const ACC_TYPE l_inv = 1.0f / l_i;
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = CONVERT_O_DATA4(o_acc[i] * l_inv);
             }
         } else {
-            #pragma unroll
+            FA_UNROLL
             for (int i = 0; i < DV_VEC; ++i) {
                 o_row[i] = (O_DATA_TYPE4)(0.0f);
             }
         }
     }
+#endif
 }
 
 __kernel void flash_attn_f32_f16_q1(
@@ -258,13 +608,16 @@ __kernel void flash_attn_f32_f16_q1(
         mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
     }
 
-    ACC_TYPE4 q_priv[DK_VEC];
+    // Q is uniform across WG threads (n_q=1). Share via local memory to
+    // avoid per-thread q_priv[DK_VEC] dynamic-indexed private array that
+    // spills to DDR on Adreno.
+    __local ACC_TYPE4 q_shared[DK_VEC];
     const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
     const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
-    #pragma unroll
-    for (int i = 0; i < DK_VEC; ++i) {
-        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    for (int i = tid; i < DK_VEC; i += Q1_WG_SIZE) {
+        q_shared[i] = CONVERT_Q_ACC4(q_ptr[i]);
     }
+    barrier(CLK_LOCAL_MEM_FENCE);
 
     float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
 
@@ -273,14 +626,14 @@ __kernel void flash_attn_f32_f16_q1(
         sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
     }
 
-    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : -INFINITY;
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : FA_M_INIT;
     for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
         const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
         const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
-            dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+            dot_acc = mad(q_shared[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
         }
         ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
         if (mask_base != NULL) {
@@ -296,7 +649,7 @@ __kernel void flash_attn_f32_f16_q1(
     __local ACC_TYPE local_m[Q1_WG_SIZE];
     local_m[tid] = m_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -304,7 +657,7 @@ __kernel void flash_attn_f32_f16_q1(
     const ACC_TYPE m_final = local_m[0];
 
     ACC_TYPE4 o_acc[DV_VEC];
-    #pragma unroll
+    FA_UNROLL
     for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
     ACC_TYPE l_i = 0.0f;
 
@@ -314,9 +667,9 @@ __kernel void flash_attn_f32_f16_q1(
         const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
         const global KV_DATA_TYPE4* v_ptr = (const global KV_DATA_TYPE4*)(v_base + v_row_offset);
         ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
-        #pragma unroll
+        FA_UNROLL
         for (int k = 0; k < DK_VEC; k++) {
-            dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+            dot_acc = mad(q_shared[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
         }
         ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
         if (mask_base != NULL) {
@@ -328,7 +681,7 @@ __kernel void flash_attn_f32_f16_q1(
         }
         const ACC_TYPE p = exp(score - m_final);
         l_i += p;
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; i++) {
             o_acc[i] = mad(p, CONVERT_KV_ACC4(v_ptr[i]), o_acc[i]);
         }
@@ -338,7 +691,7 @@ __kernel void flash_attn_f32_f16_q1(
     __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
     local_l[tid] = l_i;
     barrier(CLK_LOCAL_MEM_FENCE);
-    #pragma unroll
+    FA_UNROLL
     for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
         if (tid < s) local_l[tid] += local_l[tid + s];
         barrier(CLK_LOCAL_MEM_FENCE);
@@ -357,7 +710,7 @@ __kernel void flash_attn_f32_f16_q1(
         for (int i = 0; i < DV_VEC; i++) {
             local_o_comp[tid] = o_acc[i];
             barrier(CLK_LOCAL_MEM_FENCE);
-            #pragma unroll
+            FA_UNROLL
             for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
                 if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
                 barrier(CLK_LOCAL_MEM_FENCE);
@@ -367,7 +720,257 @@ __kernel void flash_attn_f32_f16_q1(
             }
         }
     } else if (tid == 0) {
-        #pragma unroll
+        FA_UNROLL
         for (int i = 0; i < DV_VEC; ++i) o_row[i] = (O_DATA_TYPE4)(0.0f);
     }
 }
+
+// Flash-decoding split pass. gid(2) = q_idx * n_splits + split_idx.
+// Partial record per split: [m, l, O[DV]]. Merge kernel applies sink + norm.
+#define FA_PARTIAL_FLOATS (2 + DV)
+
+__kernel void flash_attn_f32_f16_q1_split(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void * mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    global float * partial_void,
+    const int n_splits,
+    const int kv_per_split
+) {
+    const int tid              = get_local_id(0);
+    const int head_batch_idx   = get_global_id(1);
+    const int split_q_idx      = get_global_id(2);
+    const int split_idx        = split_q_idx % n_splits;
+    const int q_idx            = split_q_idx / n_splits;
+    const int batch_idx        = head_batch_idx / n_head;
+    const int head_idx         = head_batch_idx % n_head;
+    const int gqa_ratio        = n_head / n_head_kv;
+    const int head_kv_idx      = head_idx / gqa_ratio;
+
+    const int kv_start = split_idx * kv_per_split;
+    const int kv_end   = min(kv_start + kv_per_split, n_kv);
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx    = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                 * n_splits + split_idx);
+    global float  * rec       = partial_void + record_idx * record_stride;
+    global float4 * rec_o     = (global float4 *) (rec + 2);
+
+    if (kv_start >= kv_end) {
+        // Empty split: leave sentinel partial for merge.
+        if (tid == 0) {
+            rec[0] = FA_M_INIT;
+            rec[1] = 0.0f;
+        }
+        return;
+    }
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx  = head_idx  % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2 +
+                    (ulong) q_idx * mask_nb1;
+    }
+
+    // Share Q via local memory (n_q=1 per split -> uniform across WG).
+    __local ACC_TYPE4 q_shared[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + (ulong) q_idx * q_nb1;
+    const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+    for (int i = tid; i < DK_VEC; i += Q1_WG_SIZE) {
+        q_shared[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    const float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    // Pass 1a — split-local max.
+    ACC_TYPE m_i = FA_M_INIT;
+    for (int k_idx = kv_start + tid; k_idx < kv_end; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global KV_DATA_TYPE4 * k_ptr = (const global KV_DATA_TYPE4 *) (k_base + k_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; ++k) {
+            dot_acc = mad(q_shared[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) (mask_base);
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        m_i = max(m_i, score);
+    }
+
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_c = local_m[0];
+
+    // Pass 1b — softmax-weighted V accumulate.
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+    ACC_TYPE l_i = 0.0f;
+
+    for (int k_idx = kv_start + tid; k_idx < kv_end; k_idx += Q1_WG_SIZE) {
+        const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        const global KV_DATA_TYPE4 * k_ptr = (const global KV_DATA_TYPE4 *) (k_base + k_row_offset);
+        const global KV_DATA_TYPE4 * v_ptr = (const global KV_DATA_TYPE4 *) (v_base + v_row_offset);
+        ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
+        #pragma unroll
+        for (int k = 0; k < DK_VEC; ++k) {
+            dot_acc = mad(q_shared[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
+        }
+        ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) (mask_base);
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+        const ACC_TYPE p = exp(score - m_c);
+        l_i += p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) {
+            o_acc[i] = mad(p, CONVERT_KV_ACC4(v_ptr[i]), o_acc[i]);
+        }
+    }
+
+    __local ACC_TYPE  local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE l_c = local_l[0];
+
+    if (tid == 0) {
+        rec[0] = (float) m_c;
+        rec[1] = (float) l_c;
+    }
+    for (int i = 0; i < DV_VEC; ++i) {
+        local_o[tid] = o_acc[i];
+        barrier(CLK_LOCAL_MEM_FENCE);
+        #pragma unroll
+        for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+            if (tid < s) local_o[tid] += local_o[tid + s];
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+        if (tid == 0) {
+            rec_o[i] = local_o[0];
+        }
+    }
+}
+
+// FD Pass 2: merge per-split partials into final O. Empty splits drop via exp(-INF)=0.
+__kernel void flash_attn_f32_merge(
+    const global float * partial_void,
+    global void * o_void,
+    const ulong o_offset,
+    const int n_head,
+    const int n_splits,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const global void * sinks_void,
+    const ulong sinks_offset,
+    const int n_q
+) {
+    const int lane           = get_local_id(0);  // 0..DV_VEC-1
+    const int head_batch_idx = get_global_id(1);
+    const int q_idx          = get_global_id(2);
+    const int batch_idx      = head_batch_idx / n_head;
+    const int head_idx       = head_batch_idx % n_head;
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx_0  = (((ulong) batch_idx * n_head + head_idx) * n_q + q_idx) * n_splits;
+    const global float * rec0 = partial_void + record_idx_0 * record_stride;
+
+    __local ACC_TYPE m_final_shared;
+    __local ACC_TYPE l_final_shared;
+    if (lane == 0) {
+        ACC_TYPE m = FA_M_INIT;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            m = max(m, m_c);
+        }
+        ACC_TYPE m_sink = 0.0f;
+        bool has_sink = false;
+        if (sinks_void != NULL) {
+            const global ACC_TYPE * sinks_ptr =
+                (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+            m_sink = sinks_ptr[head_idx];
+            has_sink = true;
+            m = max(m, m_sink);
+        }
+        ACC_TYPE l = 0.0f;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            const ACC_TYPE l_c = rec0[c * record_stride + 1];
+            if (m_c > FA_M_INIT) {
+                l += l_c * exp(m_c - m);
+            }
+        }
+        if (has_sink) {
+            l += exp(m_sink - m);
+        }
+        m_final_shared = m;
+        l_final_shared = l;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    const ACC_TYPE m_final = m_final_shared;
+    const ACC_TYPE l_final = l_final_shared;
+    const ACC_TYPE l_inv   = (l_final > 0.0f) ? (1.0f / l_final) : 0.0f;
+
+    ACC_TYPE4 o = (ACC_TYPE4)(0.0f);
+    for (int c = 0; c < n_splits; ++c) {
+        const global float * rec_c   = rec0 + c * record_stride;
+        const ACC_TYPE       m_c     = rec_c[0];
+        if (m_c <= FA_M_INIT) continue;
+        const global float4 * rec_oc = (const global float4 *) (rec_c + 2);
+        const ACC_TYPE scale_c = exp(m_c - m_final);
+        o = mad((ACC_TYPE4)(scale_c), rec_oc[lane], o);
+    }
+    o = o * l_inv;
+
+    const ulong o_row_offset = (ulong) batch_idx * o_nb3 + (ulong) q_idx * o_nb2 + (ulong) head_idx * o_nb1;
+    global O_DATA_TYPE4 * o_row = (global O_DATA_TYPE4 *) ((global char *) o_void + o_offset + o_row_offset);
+    o_row[lane] = CONVERT_O_DATA4(o);
+}
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl
new file mode 100644
index 000000000..36167ba54
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q4_0.cl
@@ -0,0 +1,1041 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#ifdef cl_khr_integer_dot_product
+#pragma OPENCL EXTENSION cl_khr_integer_dot_product : enable
+#define FA_HAVE_INT_DOT 1
+#endif
+
+#ifdef cl_khr_subgroup_shuffle
+#pragma OPENCL EXTENSION cl_khr_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#elif defined(cl_qcom_subgroup_shuffle)
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#endif
+
+// Flash attention: Q=f32, K=q4_0, V=q4_0.
+// Block = half d + uchar qs[16]; qs[j] low/high nibble -> elem j / j+16.
+// Dequant: val[i] = d * (nibble_i - 8). dp4a path runs on raw 0..15 nibbles
+// and applies the -8*sum(q) correction once per block (needs Q q_sum).
+
+#define ACC_TYPE float
+#define ACC_TYPE4 float4
+#define Q_DATA_TYPE4 float4
+#define O_DATA_TYPE4 float4
+#define MASK_DATA_TYPE half
+#define CONVERT_Q_ACC4(x) (x)
+#define CONVERT_O_DATA4(x) (x)
+
+#define DK_VEC (DK/4)
+#define DV_VEC (DV/4)
+#define Q1_WG_SIZE 64
+
+// The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
+// infinite operand can cause undefined behavior and miscompilation for exp.
+// Therefore, a large negative value is used instead.
+#define FA_M_INIT (-3.0e38f)
+
+#define QK4_0 32
+#define Q4_0_BLOCK_SIZE 18
+
+#define DK_Q4_BLOCKS (DK / QK4_0)
+#define DV_Q4_BLOCKS (DV / QK4_0)
+
+inline float dot_q4_0_f32(const global char * block_ptr, ACC_TYPE4 * q_slice) {
+    float d = vload_half(0, (const global half *)block_ptr);
+    const global uchar * qs = (const global uchar *)(block_ptr + 2);
+
+    float sum = 0.0f;
+    // Low nibbles -> elems 0..15.
+    #pragma unroll
+    for (int g = 0; g < 4; ++g) {
+        float4 nv = (float4)((float)(int)(qs[g*4 + 0] & 0x0F) - 8.0f,
+                             (float)(int)(qs[g*4 + 1] & 0x0F) - 8.0f,
+                             (float)(int)(qs[g*4 + 2] & 0x0F) - 8.0f,
+                             (float)(int)(qs[g*4 + 3] & 0x0F) - 8.0f);
+        sum += dot(q_slice[g], nv);
+    }
+    // High nibbles -> elems 16..31.
+    #pragma unroll
+    for (int g = 0; g < 4; ++g) {
+        float4 nv = (float4)((float)(int)(qs[g*4 + 0] >> 4) - 8.0f,
+                             (float)(int)(qs[g*4 + 1] >> 4) - 8.0f,
+                             (float)(int)(qs[g*4 + 2] >> 4) - 8.0f,
+                             (float)(int)(qs[g*4 + 3] >> 4) - 8.0f);
+        sum += dot(q_slice[4 + g], nv);
+    }
+    return sum * d;
+}
+
+#ifdef FA_HAVE_INT_DOT
+inline uint pack_i8x4(char a, char b, char c, char d) {
+    return ((uint)(uchar)a)       |
+           ((uint)(uchar)b) <<  8  |
+           ((uint)(uchar)c) << 16  |
+           ((uint)(uchar)d) << 24;
+}
+
+// Returns (qd, q_sum); q_sum feeds the -8*sum(q) bias correction.
+typedef struct {
+    float qd;
+    int   q_sum;
+} q4_q_block_info;
+
+inline q4_q_block_info quant_q_block_int8_packed_q4(const ACC_TYPE4 * q_block,
+                                                    uint *            out_packed) {
+    float amax = 0.0f;
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        float4 av = fabs(q_block[i]);
+        amax = fmax(amax, fmax(fmax(av.s0, av.s1), fmax(av.s2, av.s3)));
+    }
+    float qd  = amax / 127.0f;
+    float qid = (amax > 0.0f) ? 127.0f / amax : 0.0f;
+
+    int q_sum = 0;
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        float4 v = q_block[i] * qid;
+        char a = (char)((int)round(v.s0));
+        char b = (char)((int)round(v.s1));
+        char c = (char)((int)round(v.s2));
+        char d = (char)((int)round(v.s3));
+        out_packed[i] = pack_i8x4(a, b, c, d);
+        q_sum += (int)a + (int)b + (int)c + (int)d;
+    }
+    q4_q_block_info info = { qd, q_sum };
+    return info;
+}
+
+// k_packed[0..3] = low nibbles (Q elems 0..15), k_packed[4..7] = high (16..31).
+inline void pack_q4_0_nibbles(const global uchar * qs, uint * k_packed) {
+    #pragma unroll
+    for (int g = 0; g < 4; ++g) {
+        uchar b0 = qs[g*4 + 0];
+        uchar b1 = qs[g*4 + 1];
+        uchar b2 = qs[g*4 + 2];
+        uchar b3 = qs[g*4 + 3];
+        k_packed[g] =
+              ((uint)(b0 & 0x0F))       |
+              ((uint)(b1 & 0x0F)) <<  8 |
+              ((uint)(b2 & 0x0F)) << 16 |
+              ((uint)(b3 & 0x0F)) << 24;
+        k_packed[4 + g] =
+              ((uint)(b0 >> 4))         |
+              ((uint)(b1 >> 4)) <<  8   |
+              ((uint)(b2 >> 4)) << 16   |
+              ((uint)(b3 >> 4)) << 24;
+    }
+}
+
+inline float dot_q4_0_int(const global char * k_block_ptr,
+                          const uint *        q_packed,
+                          float               q_d,
+                          int                 q_sum) {
+    float kd = vload_half(0, (const global half *)k_block_ptr);
+    const global uchar * k_qs = (const global uchar *)(k_block_ptr + 2);
+
+    uint k_packed[8];
+    pack_q4_0_nibbles(k_qs, k_packed);
+
+    int sum = 0;
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        sum = dot_acc_sat_4x8packed_ss_int(q_packed[i], k_packed[i], sum);
+    }
+    // Correct raw-nibble sum: (nibble - 8) bias -> subtract 8 * q_sum.
+    return (float)(sum - 8 * q_sum) * q_d * kd;
+}
+#endif // FA_HAVE_INT_DOT
+
+inline void dequant_q4_0_f32(const global char * block_ptr, ACC_TYPE4 * out) {
+    float d = vload_half(0, (const global half *)block_ptr);
+    const global uchar * qs = (const global uchar *)(block_ptr + 2);
+
+    #pragma unroll
+    for (int g = 0; g < 4; ++g) {
+        out[g] = d * (float4)((float)(int)(qs[g*4 + 0] & 0x0F) - 8.0f,
+                              (float)(int)(qs[g*4 + 1] & 0x0F) - 8.0f,
+                              (float)(int)(qs[g*4 + 2] & 0x0F) - 8.0f,
+                              (float)(int)(qs[g*4 + 3] & 0x0F) - 8.0f);
+    }
+    #pragma unroll
+    for (int g = 0; g < 4; ++g) {
+        out[4 + g] = d * (float4)((float)(int)(qs[g*4 + 0] >> 4) - 8.0f,
+                                  (float)(int)(qs[g*4 + 1] >> 4) - 8.0f,
+                                  (float)(int)(qs[g*4 + 2] >> 4) - 8.0f,
+                                  (float)(int)(qs[g*4 + 3] >> 4) - 8.0f);
+    }
+}
+
+// max_bias<=0 returns 1.0 so score += 1.0 * mask[k] stays a no-op multiplier.
+inline float get_alibi_slope(float max_bias, int head_idx, int n_head_log2, float m0, float m1) {
+    if (max_bias <= 0.0f) return 1.0f;
+    float base = (head_idx < n_head_log2) ? m0 : m1;
+    int   exph = (head_idx < n_head_log2) ? (head_idx + 1) : (2*(head_idx - n_head_log2) + 1);
+    return pow(base, (float)exph);
+}
+
+// q1 decode: one query row per WG, threads sweep KV positions.
+__kernel void flash_attn_f32_q4_0_q1(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
+) {
+    const int tid = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+    const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    // Quantise Q once per thread: 8 uints + qd + q_sum per block.
+    uint  q_packed[DK_Q4_BLOCKS * 8];
+    float q_d_scale[DK_Q4_BLOCKS];
+    int   q_sum_arr[DK_Q4_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < DK_Q4_BLOCKS; ++b) {
+        q4_q_block_info info = quant_q_block_int8_packed_q4(&q_priv[b * 8], &q_packed[b * 8]);
+        q_d_scale[b] = info.qd;
+        q_sum_arr[b] = info.q_sum;
+    }
+#endif
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    const global ACC_TYPE* sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+    }
+
+    // One-pass online softmax (FA-2): single sweep over kv positions,
+    // updating per-thread (m_i, l_i, o_acc) per K. Eliminates the second
+    // K read of the original two-pass implementation.
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const global char* k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char* v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+        ACC_TYPE score = 0.0f;
+        #pragma unroll
+        for (int b = 0; b < DK_Q4_BLOCKS; b++) {
+#ifdef FA_HAVE_INT_DOT
+            score += dot_q4_0_int(k_row + b * Q4_0_BLOCK_SIZE,
+                                   &q_packed[b * 8], q_d_scale[b], q_sum_arr[b]);
+#else
+            score += dot_q4_0_f32(k_row + b * Q4_0_BLOCK_SIZE, &q_priv[b * 8]);
+#endif
+        }
+        score *= scale;
+
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        // Online softmax step.
+        const ACC_TYPE m_new = max(m_i, score);
+        const ACC_TYPE alpha = exp(m_i  - m_new);
+        const ACC_TYPE p     = exp(score - m_new);
+
+        l_i = alpha * l_i + p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha;
+
+        #pragma unroll
+        for (int b = 0; b < DV_Q4_BLOCKS; b++) {
+            ACC_TYPE4 v_dequant[8];
+            dequant_q4_0_f32(v_row + b * Q4_0_BLOCK_SIZE, v_dequant);
+            #pragma unroll
+            for (int i = 0; i < 8; i++) {
+                o_acc[b * 8 + i] = mad(p, v_dequant[i], o_acc[b * 8 + i]);
+            }
+        }
+
+        m_i = m_new;
+    }
+
+    // Cross-thread reduce: max(m_i) -> m_final, rescale per-thread l_i and
+    // o_acc by alpha = exp(m_i_thread - m_final) before sum-reduce.
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_final = local_m[0];
+
+    const ACC_TYPE alpha_final = exp(m_i - m_final);
+    l_i *= alpha_final;
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha_final;
+
+    __local ACC_TYPE local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+    global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+    ACC_TYPE l_final = local_l[0];
+
+    if (sinks_ptr != NULL) {
+        l_final += exp(sinks_ptr[head_idx] - m_final);
+    }
+
+    if (l_final > 0.0f) {
+        const ACC_TYPE l_inv = 1.0f / l_final;
+        for (int i = 0; i < DV_VEC; i++) {
+            local_o_comp[tid] = o_acc[i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #pragma unroll
+            for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+                if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+            if (tid == 0) {
+                o_row[i] = CONVERT_O_DATA4(local_o_comp[0] * l_inv);
+            }
+        }
+    } else if (tid == 0) {
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_row[i] = (O_DATA_TYPE4)(0.0f);
+    }
+}
+
+// Flash-decoding split pass for q4_0 KV. Merge kernel is type-agnostic and
+// shared with the f16/q8_0 FA kernels.
+#define FA_PARTIAL_FLOATS (2 + DV)
+
+__kernel void flash_attn_f32_q4_0_q1_split(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void * mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    global float * partial_void,
+    const int n_splits,
+    const int kv_per_split
+) {
+    const int tid            = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+    const int split_q_idx    = get_global_id(2);
+    const int split_idx      = split_q_idx % n_splits;
+    const int q_idx          = split_q_idx / n_splits;
+    const int batch_idx      = head_batch_idx / n_head;
+    const int head_idx       = head_batch_idx % n_head;
+    const int gqa_ratio      = n_head / n_head_kv;
+    const int head_kv_idx    = head_idx / gqa_ratio;
+
+    const int kv_start = split_idx * kv_per_split;
+    const int kv_end   = min(kv_start + kv_per_split, n_kv);
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx    = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                 * n_splits + split_idx);
+    global float  * rec       = partial_void + record_idx * record_stride;
+    global float4 * rec_o     = (global float4 *) (rec + 2);
+
+    if (kv_start >= kv_end) {
+        if (tid == 0) {
+            rec[0] = FA_M_INIT;
+            rec[1] = 0.0f;
+        }
+        return;
+    }
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx  = head_idx  % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2 +
+                    (ulong) q_idx * mask_nb1;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + (ulong) q_idx * q_nb1;
+    const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    uint  q_packed[DK_Q4_BLOCKS * 8];
+    float q_d_scale[DK_Q4_BLOCKS];
+    int   q_sum_arr[DK_Q4_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < DK_Q4_BLOCKS; ++b) {
+        q4_q_block_info info = quant_q_block_int8_packed_q4(&q_priv[b * 8], &q_packed[b * 8]);
+        q_d_scale[b] = info.qd;
+        q_sum_arr[b] = info.q_sum;
+    }
+#endif
+
+    const float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    // One-pass online softmax (FA-2): single sweep over the split's K range.
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    for (int k_idx = kv_start + tid; k_idx < kv_end; k_idx += Q1_WG_SIZE) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        ACC_TYPE score = 0.0f;
+        #pragma unroll
+        for (int b = 0; b < DK_Q4_BLOCKS; ++b) {
+#ifdef FA_HAVE_INT_DOT
+            score += dot_q4_0_int(k_row + b * Q4_0_BLOCK_SIZE,
+                                   &q_packed[b * 8], q_d_scale[b], q_sum_arr[b]);
+#else
+            score += dot_q4_0_f32(k_row + b * Q4_0_BLOCK_SIZE, &q_priv[b * 8]);
+#endif
+        }
+        score *= scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) (mask_base);
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        // Online softmax step.
+        const ACC_TYPE m_new = max(m_i, score);
+        const ACC_TYPE alpha = exp(m_i  - m_new);
+        const ACC_TYPE p     = exp(score - m_new);
+
+        l_i = alpha * l_i + p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha;
+
+        #pragma unroll
+        for (int b = 0; b < DV_Q4_BLOCKS; ++b) {
+            ACC_TYPE4 v_dequant[8];
+            dequant_q4_0_f32(v_row + b * Q4_0_BLOCK_SIZE, v_dequant);
+            #pragma unroll
+            for (int i = 0; i < 8; ++i) {
+                o_acc[b * 8 + i] = mad(p, v_dequant[i], o_acc[b * 8 + i]);
+            }
+        }
+
+        m_i = m_new;
+    }
+
+    // Cross-thread reduce: max(m_i) -> m_c, rescale per-thread l_i and o_acc
+    // by alpha = exp(m_i_thread - m_c) before sum-reduce.
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_c = local_m[0];
+
+    const ACC_TYPE alpha_final = exp(m_i - m_c);
+    l_i *= alpha_final;
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha_final;
+
+    __local ACC_TYPE  local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE l_c = local_l[0];
+
+    if (tid == 0) {
+        rec[0] = (float) m_c;
+        rec[1] = (float) l_c;
+    }
+    for (int i = 0; i < DV_VEC; ++i) {
+        local_o[tid] = o_acc[i];
+        barrier(CLK_LOCAL_MEM_FENCE);
+        #pragma unroll
+        for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+            if (tid < s) local_o[tid] += local_o[tid + s];
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+        if (tid == 0) {
+            rec_o[i] = local_o[0];
+        }
+    }
+}
+
+// Prefill: q4_0 K/V, n_q > 1. BLOCK_M × BLOCK_N tiling.
+// K in local as packed nibbles + per-block scale; V dequant -> half in local.
+// Requires DK % QK4_0 == 0 and DV % QK4_0 == 0.
+#define KV_DATA_TYPE4 half4
+#define CONVERT_KV_ACC4(x) convert_float4(x)
+
+#define DK_Q4_BLOCKS_PREFILL (DK / QK4_0)
+#define DV_Q4_BLOCKS_PREFILL (DV / QK4_0)
+
+// N_SPLIT>1 splits DK/DV across N_SPLIT threads per query row; needs
+// sub_group_shuffle_xor and DK_Q4_BLOCKS_PREFILL % N_SPLIT == 0.
+#ifndef N_SPLIT
+#define N_SPLIT 1
+#endif
+
+#if N_SPLIT > 1
+#define SPLIT_DK_VEC        (DK_VEC / N_SPLIT)
+#define SPLIT_DV_VEC        (DV_VEC / N_SPLIT)
+#define SPLIT_DK_Q4_BLOCKS  (DK_Q4_BLOCKS_PREFILL / N_SPLIT)
+#define WG_SIZE             (BLOCK_M * N_SPLIT)
+#else
+#define SPLIT_DK_VEC        DK_VEC
+#define SPLIT_DV_VEC        DV_VEC
+#define SPLIT_DK_Q4_BLOCKS  DK_Q4_BLOCKS_PREFILL
+#define WG_SIZE             BLOCK_M
+#endif
+
+__kernel void flash_attn_f32_q4_0(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset,
+    // blk: per-(qblock,kvblock) class from flash_attn_blk_f16
+    // (0=masked, 1=mixed, 2=unmasked). NULL disables the prepass opt.
+    const global void * blk_void
+) {
+    const int tid = get_local_id(0);
+    const int block_q_idx = get_group_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+#if N_SPLIT > 1
+    const int q_lane    = tid / N_SPLIT;
+    const int split_idx = tid % N_SPLIT;
+#else
+    const int q_lane    = tid;
+    const int split_idx = 0;
+#endif
+    const int my_query_row = block_q_idx * BLOCK_M + q_lane;
+    const int query_valid = my_query_row < n_q;
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx  = head_batch_idx % n_head;
+
+    const int gqa_ratio   = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+    const int mask_head_idx  = mask_void != NULL ? head_idx  % mask_ne2 : 0;
+    const int mask_batch_idx = mask_void != NULL ? batch_idx % mask_ne3 : 0;
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+    global       char * o_base = (global       char *) o_void + o_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    // BLK_PREPASS_BM may differ from this kernel's BLOCK_M; scale q-block idx.
+    #ifndef BLK_PREPASS_BM
+    #define BLK_PREPASS_BM BLOCK_M
+    #endif
+    const global char * blk_base = NULL;
+    int n_kv_blocks = 0;
+    if (blk_void != NULL) {
+        n_kv_blocks = (n_kv + BLOCK_N - 1) / BLOCK_N;
+        const int n_q_blocks_prepass = (n_q + BLK_PREPASS_BM - 1) / BLK_PREPASS_BM;
+        const int prepass_q_block    = (block_q_idx * BLOCK_M) / BLK_PREPASS_BM;
+        blk_base = (const global char *) blk_void +
+                   (((mask_batch_idx * mask_ne2) + mask_head_idx) * n_q_blocks_prepass + prepass_q_block) * n_kv_blocks;
+    }
+
+    const int dk_off_vec = split_idx * SPLIT_DK_VEC;
+    ACC_TYPE4 q_priv[SPLIT_DK_VEC];
+    if (query_valid) {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
+        const global float4 * q_ptr = (const global float4 *) (q_base + q_row_offset);
+        #pragma unroll
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) {
+            q_priv[i] = q_ptr[dk_off_vec + i];
+        }
+    } else {
+        #pragma unroll
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) q_priv[i] = (ACC_TYPE4)(0.0f);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    uint  q_packed_pf[SPLIT_DK_Q4_BLOCKS * 8];
+    float q_d_pf[SPLIT_DK_Q4_BLOCKS];
+    int   q_sum_pf[SPLIT_DK_Q4_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < SPLIT_DK_Q4_BLOCKS; ++b) {
+        q4_q_block_info info = quant_q_block_int8_packed_q4(&q_priv[b * 8], &q_packed_pf[b * 8]);
+        q_d_pf[b]   = info.qd;
+        q_sum_pf[b] = info.q_sum;
+    }
+#endif
+
+    const int dv_off_vec = split_idx * SPLIT_DV_VEC;
+    ACC_TYPE4 o_acc[SPLIT_DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < SPLIT_DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+#ifdef FA_HAVE_INT_DOT
+    __local uint  l_k_packed[BLOCK_N][DK_Q4_BLOCKS_PREFILL * 8];
+    __local float l_k_scale [BLOCK_N][DK_Q4_BLOCKS_PREFILL];
+#else
+    __local half4 l_k[BLOCK_N][DK_VEC];
+#endif
+
+    __local half4 l_v[BLOCK_N][DV_VEC];
+
+    for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        // Skip fully-masked KV tiles (uniform branch across WG).
+        char blk_cur = 1;
+        if (blk_base != NULL) {
+            blk_cur = blk_base[k_start / BLOCK_N];
+            if (blk_cur == 0) continue;
+        }
+
+        {
+#ifdef FA_HAVE_INT_DOT
+            const int k_blocks_per_row = DK_Q4_BLOCKS_PREFILL;
+            const int n_blocks_total = BLOCK_N * k_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / k_blocks_per_row;
+                const int blk = i % k_blocks_per_row;
+                const int k_row_idx = k_start + row;
+                if (k_row_idx < n_kv) {
+                    const ulong k_row_off = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                    const global char * blk_ptr = k_base + k_row_off + blk * Q4_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global uchar * qs = (const global uchar *)(blk_ptr + 2);
+                    l_k_scale[row][blk] = df;
+                    uint k_packed[8];
+                    pack_q4_0_nibbles(qs, k_packed);
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) {
+                        l_k_packed[row][blk * 8 + j] = k_packed[j];
+                    }
+                } else {
+                    l_k_scale[row][blk] = 0.0f;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_k_packed[row][blk * 8 + j] = 0u;
+                }
+            }
+#else
+            // Fallback: dequant q4_0 -> half in local memory.
+            const int k_blocks_per_row = DK_Q4_BLOCKS_PREFILL;
+            const int n_blocks_total = BLOCK_N * k_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / k_blocks_per_row;
+                const int blk = i % k_blocks_per_row;
+                const int k_row_idx = k_start + row;
+                if (k_row_idx < n_kv) {
+                    const ulong k_row_off = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                    const global char * blk_ptr = k_base + k_row_off + blk * Q4_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global uchar * qs = (const global uchar *)(blk_ptr + 2);
+                    #pragma unroll
+                    for (int g = 0; g < 4; ++g) {
+                        float4 vlo = df * (float4)((float)(int)(qs[g*4 + 0] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 1] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 2] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 3] & 0x0F) - 8.0f);
+                        float4 vhi = df * (float4)((float)(int)(qs[g*4 + 0] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 1] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 2] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 3] >> 4) - 8.0f);
+                        l_k[row][blk * 8 + g    ] = (half4)((half)vlo.s0, (half)vlo.s1, (half)vlo.s2, (half)vlo.s3);
+                        l_k[row][blk * 8 + 4 + g] = (half4)((half)vhi.s0, (half)vhi.s1, (half)vhi.s2, (half)vhi.s3);
+                    }
+                } else {
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_k[row][blk * 8 + j] = (half4)(0.0h);
+                }
+            }
+#endif
+        }
+        // V tile load — dequant V -> half in local memory.
+        {
+            const int v_blocks_per_row = DV_Q4_BLOCKS_PREFILL;
+            const int n_blocks_total = BLOCK_N * v_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / v_blocks_per_row;
+                const int blk = i % v_blocks_per_row;
+                const int v_row_idx = k_start + row;
+                if (v_row_idx < n_kv) {
+                    const ulong v_row_off = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                    const global char * blk_ptr = v_base + v_row_off + blk * Q4_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global uchar * qs = (const global uchar *)(blk_ptr + 2);
+                    #pragma unroll
+                    for (int g = 0; g < 4; ++g) {
+                        float4 vlo = df * (float4)((float)(int)(qs[g*4 + 0] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 1] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 2] & 0x0F) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 3] & 0x0F) - 8.0f);
+                        float4 vhi = df * (float4)((float)(int)(qs[g*4 + 0] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 1] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 2] >> 4) - 8.0f,
+                                                   (float)(int)(qs[g*4 + 3] >> 4) - 8.0f);
+                        l_v[row][blk * 8 + g    ] = (half4)((half)vlo.s0, (half)vlo.s1, (half)vlo.s2, (half)vlo.s3);
+                        l_v[row][blk * 8 + 4 + g] = (half4)((half)vhi.s0, (half)vhi.s1, (half)vhi.s2, (half)vhi.s3);
+                    }
+                } else {
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_v[row][blk * 8 + j] = (half4)(0.0h);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        // QK dot + online softmax. N_SPLIT>1 reduces per-thread partials via shuffle_xor.
+#if N_SPLIT > 1
+        {
+#else
+        if (query_valid) {
+#endif
+            const int k_blk_base = split_idx * SPLIT_DK_Q4_BLOCKS;
+            for (int j = 0; j < BLOCK_N; j += 4) {
+                const int k_row0 = k_start + j;
+                const int k_row1 = k_start + j + 1;
+                const int k_row2 = k_start + j + 2;
+                const int k_row3 = k_start + j + 3;
+
+                ACC_TYPE s0, s1, s2, s3;
+#ifdef FA_HAVE_INT_DOT
+                s0 = 0.0f; s1 = 0.0f; s2 = 0.0f; s3 = 0.0f;
+                #pragma unroll
+                for (int b_local = 0; b_local < SPLIT_DK_Q4_BLOCKS; ++b_local) {
+                    const int b = k_blk_base + b_local;
+                    int sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0;
+                    #pragma unroll
+                    for (int g = 0; g < 8; ++g) {
+                        const uint qp = q_packed_pf[b_local * 8 + g];
+                        sum0 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j  ][b * 8 + g], sum0);
+                        sum1 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+1][b * 8 + g], sum1);
+                        sum2 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+2][b * 8 + g], sum2);
+                        sum3 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+3][b * 8 + g], sum3);
+                    }
+                    const float qd    = q_d_pf[b_local];
+                    const int   q_sum = q_sum_pf[b_local];
+                    s0 += (float)(sum0 - 8 * q_sum) * qd * l_k_scale[j  ][b];
+                    s1 += (float)(sum1 - 8 * q_sum) * qd * l_k_scale[j+1][b];
+                    s2 += (float)(sum2 - 8 * q_sum) * qd * l_k_scale[j+2][b];
+                    s3 += (float)(sum3 - 8 * q_sum) * qd * l_k_scale[j+3][b];
+                }
+#else
+                ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc2 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc3 = (ACC_TYPE4)(0.0f);
+                #pragma unroll
+                for (int k = 0; k < SPLIT_DK_VEC; ++k) {
+                    const ACC_TYPE4 qk = q_priv[k];
+                    const int k_abs = dk_off_vec + k;
+                    dot_acc0 = mad(qk, CONVERT_KV_ACC4(l_k[j  ][k_abs]), dot_acc0);
+                    dot_acc1 = mad(qk, CONVERT_KV_ACC4(l_k[j+1][k_abs]), dot_acc1);
+                    dot_acc2 = mad(qk, CONVERT_KV_ACC4(l_k[j+2][k_abs]), dot_acc2);
+                    dot_acc3 = mad(qk, CONVERT_KV_ACC4(l_k[j+3][k_abs]), dot_acc3);
+                }
+                s0 = dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3;
+                s1 = dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3;
+                s2 = dot_acc2.s0 + dot_acc2.s1 + dot_acc2.s2 + dot_acc2.s3;
+                s3 = dot_acc3.s0 + dot_acc3.s1 + dot_acc3.s2 + dot_acc3.s3;
+#endif
+
+#if N_SPLIT > 1
+                // Power-of-2 N_SPLIT: shuffle_xor butterfly. N_SPLIT=3 (DK=96):
+                // explicit 3-lane shuffle.
+                #if (N_SPLIT & (N_SPLIT - 1)) == 0
+                    #pragma unroll
+                    for (int step = 1; step < N_SPLIT; step <<= 1) {
+                        s0 += sub_group_shuffle_xor(s0, step);
+                        s1 += sub_group_shuffle_xor(s1, step);
+                        s2 += sub_group_shuffle_xor(s2, step);
+                        s3 += sub_group_shuffle_xor(s3, step);
+                    }
+                #else
+                    const uint tri_base = (get_sub_group_local_id() / N_SPLIT) * N_SPLIT;
+                    s0 = sub_group_shuffle(s0, tri_base + 0) + sub_group_shuffle(s0, tri_base + 1) + sub_group_shuffle(s0, tri_base + 2);
+                    s1 = sub_group_shuffle(s1, tri_base + 0) + sub_group_shuffle(s1, tri_base + 1) + sub_group_shuffle(s1, tri_base + 2);
+                    s2 = sub_group_shuffle(s2, tri_base + 0) + sub_group_shuffle(s2, tri_base + 1) + sub_group_shuffle(s2, tri_base + 2);
+                    s3 = sub_group_shuffle(s3, tri_base + 0) + sub_group_shuffle(s3, tri_base + 1) + sub_group_shuffle(s3, tri_base + 2);
+                #endif
+                if (!query_valid) { s0 = FA_M_INIT; s1 = FA_M_INIT; s2 = FA_M_INIT; s3 = FA_M_INIT; }
+#endif
+                s0 *= scale; s1 *= scale; s2 *= scale; s3 *= scale;
+
+                if (is_causal) {
+                    const int causal_limit = n_kv - n_q + my_query_row;
+                    if (k_row0 > causal_limit) s0 = FA_M_INIT;
+                    if (k_row1 > causal_limit) s1 = FA_M_INIT;
+                    if (k_row2 > causal_limit) s2 = FA_M_INIT;
+                    if (k_row3 > causal_limit) s3 = FA_M_INIT;
+                }
+                if (k_row0 >= n_kv) s0 = FA_M_INIT;
+                if (k_row1 >= n_kv) s1 = FA_M_INIT;
+                if (k_row2 >= n_kv) s2 = FA_M_INIT;
+                if (k_row3 >= n_kv) s3 = FA_M_INIT;
+
+                if (query_valid && mask_base != NULL && blk_cur != 2) {
+                    const global MASK_DATA_TYPE * mask_ptr =
+                        (const global MASK_DATA_TYPE *) (mask_base + my_query_row * mask_nb1);
+                    if (k_row0 < n_kv) s0 += slope * (ACC_TYPE) mask_ptr[k_row0];
+                    if (k_row1 < n_kv) s1 += slope * (ACC_TYPE) mask_ptr[k_row1];
+                    if (k_row2 < n_kv) s2 += slope * (ACC_TYPE) mask_ptr[k_row2];
+                    if (k_row3 < n_kv) s3 += slope * (ACC_TYPE) mask_ptr[k_row3];
+                }
+                if (logit_softcap > 0.0f) {
+                    s0 = logit_softcap * tanh(s0 / logit_softcap);
+                    s1 = logit_softcap * tanh(s1 / logit_softcap);
+                    s2 = logit_softcap * tanh(s2 / logit_softcap);
+                    s3 = logit_softcap * tanh(s3 / logit_softcap);
+                }
+
+                const ACC_TYPE m_new      = max(m_i, max(max(s0, s1), max(s2, s3)));
+                // Whole tile masked (m_new == FA_M_INIT): force the exp() args
+                // far negative so the tile contributes 0, not exp(0)=1.
+                const ACC_TYPE m_exp      = (m_new == FA_M_INIT) ? 0.0f : m_new;
+                const ACC_TYPE scale_prev = native_exp(m_i - m_exp);
+                const ACC_TYPE p0         = native_exp(s0 - m_exp);
+                const ACC_TYPE p1         = native_exp(s1 - m_exp);
+                const ACC_TYPE p2         = native_exp(s2 - m_exp);
+                const ACC_TYPE p3         = native_exp(s3 - m_exp);
+
+                #pragma unroll
+                for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                    const int i_abs = dv_off_vec + i;
+                    o_acc[i] = mad(p3, CONVERT_KV_ACC4(l_v[j+3][i_abs]),
+                               mad(p2, CONVERT_KV_ACC4(l_v[j+2][i_abs]),
+                               mad(p1, CONVERT_KV_ACC4(l_v[j+1][i_abs]),
+                               mad(p0, CONVERT_KV_ACC4(l_v[j  ][i_abs]),
+                               o_acc[i] * scale_prev))));
+                }
+                l_i = l_i * scale_prev + p0 + p1 + p2 + p3;
+                m_i = m_new;
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    // Write output.
+    if (query_valid) {
+        if (sinks_void != NULL) {
+            const global ACC_TYPE * sinks_ptr =
+                (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+            const ACC_TYPE m_sink  = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+            const ACC_TYPE scale_o = exp(m_i - m_final);
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_acc[i] *= scale_o;
+            l_i = l_i * scale_o + exp(m_sink - m_final);
+            m_i = m_final;
+        }
+        const ACC_TYPE l_inv = (l_i > 0.0f) ? (1.0f / l_i) : 0.0f;
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global float4 * o_row = (global float4 *) (o_base + o_row_offset);
+        if (l_inv > 0.0f) {
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_row[dv_off_vec + i] = o_acc[i] * l_inv;
+        } else {
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_row[dv_off_vec + i] = (float4)(0.0f);
+        }
+    }
+}
+
+// FD Pass 2: merge split partials. Identical across q4_0/q8_0/f16; each FA
+// source owns a copy since kernels compile per-source-program.
+__kernel void flash_attn_f32_merge(
+    const global float * partial_void,
+    global void * o_void,
+    const ulong o_offset,
+    const int n_head,
+    const int n_splits,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const global void * sinks_void,
+    const ulong sinks_offset,
+    const int n_q
+) {
+    const int lane           = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+    const int q_idx          = get_global_id(2);
+    const int batch_idx      = head_batch_idx / n_head;
+    const int head_idx       = head_batch_idx % n_head;
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx_0  = (((ulong) batch_idx * n_head + head_idx) * n_q + q_idx) * n_splits;
+    const global float * rec0 = partial_void + record_idx_0 * record_stride;
+
+    __local ACC_TYPE m_final_shared;
+    __local ACC_TYPE l_final_shared;
+    if (lane == 0) {
+        ACC_TYPE m = FA_M_INIT;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            m = max(m, m_c);
+        }
+        ACC_TYPE m_sink = 0.0f;
+        bool has_sink = false;
+        if (sinks_void != NULL) {
+            const global ACC_TYPE * sinks_ptr =
+                (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+            m_sink = sinks_ptr[head_idx];
+            has_sink = true;
+            m = max(m, m_sink);
+        }
+        ACC_TYPE l = 0.0f;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            const ACC_TYPE l_c = rec0[c * record_stride + 1];
+            if (m_c > FA_M_INIT) {
+                l += l_c * exp(m_c - m);
+            }
+        }
+        if (has_sink) {
+            l += exp(m_sink - m);
+        }
+        m_final_shared = m;
+        l_final_shared = l;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    const ACC_TYPE m_final = m_final_shared;
+    const ACC_TYPE l_final = l_final_shared;
+    const ACC_TYPE l_inv   = (l_final > 0.0f) ? (1.0f / l_final) : 0.0f;
+
+    ACC_TYPE4 o = (ACC_TYPE4)(0.0f);
+    for (int c = 0; c < n_splits; ++c) {
+        const global float * rec_c   = rec0 + c * record_stride;
+        const ACC_TYPE       m_c     = rec_c[0];
+        if (m_c <= FA_M_INIT) continue;
+        const global float4 * rec_oc = (const global float4 *) (rec_c + 2);
+        const ACC_TYPE scale_c = exp(m_c - m_final);
+        o = mad((ACC_TYPE4)(scale_c), rec_oc[lane], o);
+    }
+    o = o * l_inv;
+
+    const ulong o_row_offset = (ulong) batch_idx * o_nb3 + (ulong) q_idx * o_nb2 + (ulong) head_idx * o_nb1;
+    global O_DATA_TYPE4 * o_row = (global O_DATA_TYPE4 *) ((global char *) o_void + o_offset + o_row_offset);
+    o_row[lane] = CONVERT_O_DATA4(o);
+}
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl
new file mode 100644
index 000000000..a25823f00
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_f32_q8_0.cl
@@ -0,0 +1,1049 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#ifdef cl_khr_integer_dot_product
+#pragma OPENCL EXTENSION cl_khr_integer_dot_product : enable
+#define FA_HAVE_INT_DOT 1
+#endif
+
+#ifdef cl_khr_subgroup_shuffle
+#pragma OPENCL EXTENSION cl_khr_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#elif defined(cl_qcom_subgroup_shuffle)
+#pragma OPENCL EXTENSION cl_qcom_subgroup_shuffle : enable
+#define HAS_SUBGROUP_SHUFFLE 1
+#endif
+
+// Flash attention: Q=f32, K=q8_0, V=q8_0.
+
+#define ACC_TYPE float
+#define ACC_TYPE4 float4
+#define Q_DATA_TYPE4 float4
+#define O_DATA_TYPE4 float4
+#define MASK_DATA_TYPE half
+#define CONVERT_Q_ACC4(x) (x)
+#define CONVERT_O_DATA4(x) (x)
+
+#define DK_VEC (DK/4)
+#define DV_VEC (DV/4)
+#define Q1_WG_SIZE 64
+
+// The kernels are built with -cl-finite-math-only. On some older Adreno GPUs,
+// infinite operand can cause undefined behavior and miscompilation for exp.
+// Therefore, a large negative value is used instead.
+#define FA_M_INIT (-3.0e38f)
+
+// q8_0 block: 2B scale (half) + 32B int8 quants.
+#define QK8_0 32
+#define Q8_0_BLOCK_SIZE 34
+
+#define DK_Q8_BLOCKS (DK / QK8_0)
+#define DV_Q8_BLOCKS (DV / QK8_0)
+
+inline float dot_q8_0_f32(const global char * block_ptr, ACC_TYPE4 * q_slice) {
+    float d = vload_half(0, (const global half *)block_ptr);
+    const global char * qs = block_ptr + 2;
+
+    float sum = 0.0f;
+    #pragma unroll
+    for (int i = 0; i < 8; i++) {
+        float4 qv = (float4)((float)qs[i*4], (float)qs[i*4+1], (float)qs[i*4+2], (float)qs[i*4+3]);
+        sum += dot(q_slice[i], qv);
+    }
+    return sum * d;
+}
+
+#ifdef FA_HAVE_INT_DOT
+inline uint pack_i8x4(char a, char b, char c, char d) {
+    return ((uint)(uchar)a)       |
+           ((uint)(uchar)b) <<  8  |
+           ((uint)(uchar)c) << 16  |
+           ((uint)(uchar)d) << 24;
+}
+
+inline float quant_q_block_int8_packed(const ACC_TYPE4 * q_block,
+                                       uint *            out_packed) {
+    float amax = 0.0f;
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        float4 av = fabs(q_block[i]);
+        amax = fmax(amax, fmax(fmax(av.s0, av.s1), fmax(av.s2, av.s3)));
+    }
+    float qd  = amax / 127.0f;
+    float qid = (amax > 0.0f) ? 127.0f / amax : 0.0f;
+
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        float4 v = q_block[i] * qid;
+        char a = (char)((int)round(v.s0));
+        char b = (char)((int)round(v.s1));
+        char c = (char)((int)round(v.s2));
+        char d = (char)((int)round(v.s3));
+        out_packed[i] = pack_i8x4(a, b, c, d);
+    }
+    return qd;
+}
+
+inline float dot_q8_0_int(const global char * k_block_ptr,
+                          const uint *        q_packed,
+                          float               q_d) {
+    float kd = vload_half(0, (const global half *)k_block_ptr);
+    const global uchar * k_qs = (const global uchar *)(k_block_ptr + 2);
+
+    // k_qs is 2-byte aligned; pack chars per iteration rather than cast to uint*.
+    int sum = 0;
+    #pragma unroll
+    for (int i = 0; i < 8; ++i) {
+        uint k_packed =
+              (uint)k_qs[i*4 + 0]        |
+             ((uint)k_qs[i*4 + 1]) <<  8 |
+             ((uint)k_qs[i*4 + 2]) << 16 |
+             ((uint)k_qs[i*4 + 3]) << 24;
+        sum = dot_acc_sat_4x8packed_ss_int(q_packed[i], k_packed, sum);
+    }
+    return (float)sum * q_d * kd;
+}
+#endif // FA_HAVE_INT_DOT
+
+inline void dequant_q8_0_f32(const global char * block_ptr, ACC_TYPE4 * out) {
+    float d = vload_half(0, (const global half *)block_ptr);
+    const global char * qs = block_ptr + 2;
+
+    #pragma unroll
+    for (int i = 0; i < 8; i++) {
+        out[i] = d * (float4)((float)qs[i*4], (float)qs[i*4+1], (float)qs[i*4+2], (float)qs[i*4+3]);
+    }
+}
+
+// max_bias<=0 returns 1.0 so score += 1.0 * mask[k] stays a no-op multiplier.
+inline float get_alibi_slope(float max_bias, int head_idx, int n_head_log2, float m0, float m1) {
+    if (max_bias <= 0.0f) return 1.0f;
+    float base = (head_idx < n_head_log2) ? m0 : m1;
+    int   exph = (head_idx < n_head_log2) ? (head_idx + 1) : (2*(head_idx - n_head_log2) + 1);
+    return pow(base, (float)exph);
+}
+
+// q1 decode: one query row per WG, threads sweep KV positions.
+__kernel void flash_attn_f32_q8_0_q1(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset
+) {
+    const int tid = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx = head_batch_idx % n_head;
+
+    const int gqa_ratio = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+
+    const global char* q_base = (const global char*)q_void + q_offset;
+    const global char* k_base = (const global char*)k_void + k_offset;
+    const global char* v_base = (const global char*)v_void + v_offset;
+    global char* o_base = (global char*)o_void + o_offset;
+
+    const global char* mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx = head_idx % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
+    const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    // Quantise Q once per thread; q_priv stays as fp for the V accumulate.
+    uint  q_packed[DK_Q8_BLOCKS * 8];
+    float q_d_scale[DK_Q8_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < DK_Q8_BLOCKS; ++b) {
+        q_d_scale[b] = quant_q_block_int8_packed(&q_priv[b * 8], &q_packed[b * 8]);
+    }
+#endif
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    const global ACC_TYPE* sinks_ptr = NULL;
+    if (sinks_void != NULL) {
+        sinks_ptr = (const global ACC_TYPE*)((const global char*)sinks_void + sinks_offset);
+    }
+
+    // One-pass online softmax: per-thread maintains running (m_i, l_i, o_acc),
+    // updating each as new K positions are processed. Eliminates the second
+    // K read of the original two-pass implementation. After the loop, threads
+    // are merged via the standard FA-2 cross-thread reduction (rescale each
+    // thread's l_i and o_acc by alpha=exp(m_i_thread - m_final), then sum).
+    ACC_TYPE m_i = (sinks_ptr != NULL) ? sinks_ptr[head_idx] : FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
+        const global char* k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char* v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+
+        ACC_TYPE score = 0.0f;
+        #pragma unroll
+        for (int b = 0; b < DK_Q8_BLOCKS; b++) {
+#ifdef FA_HAVE_INT_DOT
+            score += dot_q8_0_int(k_row + b * Q8_0_BLOCK_SIZE,
+                                   &q_packed[b * 8], q_d_scale[b]);
+#else
+            score += dot_q8_0_f32(k_row + b * Q8_0_BLOCK_SIZE, &q_priv[b * 8]);
+#endif
+        }
+        score *= scale;
+
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
+            score += slope * (ACC_TYPE)mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        // Online softmax step.
+        const ACC_TYPE m_new = max(m_i, score);
+        const ACC_TYPE alpha = exp(m_i  - m_new);
+        const ACC_TYPE p     = exp(score - m_new);
+
+        l_i = alpha * l_i + p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha;
+
+        #pragma unroll
+        for (int b = 0; b < DV_Q8_BLOCKS; b++) {
+            ACC_TYPE4 v_dequant[8];
+            dequant_q8_0_f32(v_row + b * Q8_0_BLOCK_SIZE, v_dequant);
+            #pragma unroll
+            for (int i = 0; i < 8; i++) {
+                o_acc[b * 8 + i] = mad(p, v_dequant[i], o_acc[b * 8 + i]);
+            }
+        }
+
+        m_i = m_new;
+    }
+
+    // Cross-thread reduce: max(m_i) -> m_final, then rescale per-thread l_i
+    // and o_acc by alpha = exp(m_i_thread - m_final) before sum-reduce.
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_final = local_m[0];
+
+    const ACC_TYPE alpha_final = exp(m_i - m_final);
+    l_i *= alpha_final;
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha_final;
+
+    __local ACC_TYPE local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
+    global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
+    ACC_TYPE l_final = local_l[0];
+
+    if (sinks_ptr != NULL) {
+        l_final += exp(sinks_ptr[head_idx] - m_final);
+    }
+
+    if (l_final > 0.0f) {
+        const ACC_TYPE l_inv = 1.0f / l_final;
+        for (int i = 0; i < DV_VEC; i++) {
+            local_o_comp[tid] = o_acc[i];
+            barrier(CLK_LOCAL_MEM_FENCE);
+            #pragma unroll
+            for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+                if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
+                barrier(CLK_LOCAL_MEM_FENCE);
+            }
+            if (tid == 0) {
+                o_row[i] = CONVERT_O_DATA4(local_o_comp[0] * l_inv);
+            }
+        }
+    } else if (tid == 0) {
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_row[i] = (O_DATA_TYPE4)(0.0f);
+    }
+}
+
+// Flash-decoding split pass for q8_0 KV. Partial record: [m, l, O[DV]].
+// Merge kernel from flash_attn_f32_f16.cl is type-agnostic and reused.
+#define FA_PARTIAL_FLOATS (2 + DV)
+
+__kernel void flash_attn_f32_q8_0_q1_split(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void * mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    global float * partial_void,
+    const int n_splits,
+    const int kv_per_split
+) {
+    const int tid            = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+    const int split_q_idx    = get_global_id(2);
+    const int split_idx      = split_q_idx % n_splits;
+    const int q_idx          = split_q_idx / n_splits;
+    const int batch_idx      = head_batch_idx / n_head;
+    const int head_idx       = head_batch_idx % n_head;
+    const int gqa_ratio      = n_head / n_head_kv;
+    const int head_kv_idx    = head_idx / gqa_ratio;
+
+    const int kv_start = split_idx * kv_per_split;
+    const int kv_end   = min(kv_start + kv_per_split, n_kv);
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx    = ((((ulong) batch_idx * n_head + head_idx) * n_q + q_idx)
+                                 * n_splits + split_idx);
+    global float  * rec       = partial_void + record_idx * record_stride;
+    global float4 * rec_o     = (global float4 *) (rec + 2);
+
+    if (kv_start >= kv_end) {
+        // Empty split: leave sentinel partial for merge.
+        if (tid == 0) {
+            rec[0] = FA_M_INIT;
+            rec[1] = 0.0f;
+        }
+        return;
+    }
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        const int mask_head_idx  = head_idx  % mask_ne2;
+        const int mask_batch_idx = batch_idx % mask_ne3;
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2 +
+                    (ulong) q_idx * mask_nb1;
+    }
+
+    ACC_TYPE4 q_priv[DK_VEC];
+    const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + (ulong) q_idx * q_nb1;
+    const global Q_DATA_TYPE4 * q_ptr = (const global Q_DATA_TYPE4 *) (q_base + q_row_offset);
+    #pragma unroll
+    for (int i = 0; i < DK_VEC; ++i) {
+        q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    uint  q_packed[DK_Q8_BLOCKS * 8];
+    float q_d_scale[DK_Q8_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < DK_Q8_BLOCKS; ++b) {
+        q_d_scale[b] = quant_q_block_int8_packed(&q_priv[b * 8], &q_packed[b * 8]);
+    }
+#endif
+
+    const float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+    // One-pass online softmax (FA-2): single sweep over the split's K range,
+    // updating per-thread (m_i, l_i, o_acc) per position. Eliminates the
+    // second K read of the original two-pass implementation.
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+    ACC_TYPE4 o_acc[DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    for (int k_idx = kv_start + tid; k_idx < kv_end; k_idx += Q1_WG_SIZE) {
+        const global char * k_row = k_base + batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
+        const global char * v_row = v_base + batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
+        ACC_TYPE score = 0.0f;
+        #pragma unroll
+        for (int b = 0; b < DK_Q8_BLOCKS; ++b) {
+#ifdef FA_HAVE_INT_DOT
+            score += dot_q8_0_int(k_row + b * Q8_0_BLOCK_SIZE, &q_packed[b * 8], q_d_scale[b]);
+#else
+            score += dot_q8_0_f32(k_row + b * Q8_0_BLOCK_SIZE, &q_priv[b * 8]);
+#endif
+        }
+        score *= scale;
+        if (mask_base != NULL) {
+            const global MASK_DATA_TYPE * mask_ptr = (const global MASK_DATA_TYPE *) (mask_base);
+            score += slope * (ACC_TYPE) mask_ptr[k_idx];
+        }
+        if (logit_softcap > 0.0f) {
+            score = logit_softcap * tanh(score / logit_softcap);
+        }
+
+        // Online softmax step.
+        const ACC_TYPE m_new = max(m_i, score);
+        const ACC_TYPE alpha = exp(m_i  - m_new);
+        const ACC_TYPE p     = exp(score - m_new);
+
+        l_i = alpha * l_i + p;
+        #pragma unroll
+        for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha;
+
+        #pragma unroll
+        for (int b = 0; b < DV_Q8_BLOCKS; ++b) {
+            ACC_TYPE4 v_dequant[8];
+            dequant_q8_0_f32(v_row + b * Q8_0_BLOCK_SIZE, v_dequant);
+            #pragma unroll
+            for (int i = 0; i < 8; ++i) {
+                o_acc[b * 8 + i] = mad(p, v_dequant[i], o_acc[b * 8 + i]);
+            }
+        }
+
+        m_i = m_new;
+    }
+
+    // Cross-thread reduce: max(m_i) -> m_c, then rescale per-thread l_i and
+    // o_acc by alpha = exp(m_i_thread - m_c) before sum-reduce.
+    __local ACC_TYPE local_m[Q1_WG_SIZE];
+    local_m[tid] = m_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE m_c = local_m[0];
+
+    const ACC_TYPE alpha_final = exp(m_i - m_c);
+    l_i *= alpha_final;
+    #pragma unroll
+    for (int i = 0; i < DV_VEC; ++i) o_acc[i] *= alpha_final;
+
+    __local ACC_TYPE  local_l[Q1_WG_SIZE];
+    __local ACC_TYPE4 local_o[Q1_WG_SIZE];
+    local_l[tid] = l_i;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    #pragma unroll
+    for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) local_l[tid] += local_l[tid + s];
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    const ACC_TYPE l_c = local_l[0];
+
+    if (tid == 0) {
+        rec[0] = (float) m_c;
+        rec[1] = (float) l_c;
+    }
+    for (int i = 0; i < DV_VEC; ++i) {
+        local_o[tid] = o_acc[i];
+        barrier(CLK_LOCAL_MEM_FENCE);
+        #pragma unroll
+        for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
+            if (tid < s) local_o[tid] += local_o[tid + s];
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+        if (tid == 0) {
+            rec_o[i] = local_o[0];
+        }
+    }
+}
+
+// Prefill: q8_0 K/V, n_q > 1. BLOCK_M × BLOCK_N tiling.
+// K path keeps packed int8 in local for dp4a QK dot; V path dequant -> half in local.
+// Requires DK % QK8_0 == 0 and DV % QK8_0 == 0 (gated in supports_op).
+#define KV_DATA_TYPE4 half4
+#define CONVERT_KV_ACC4(x) convert_float4(x)
+
+#define DK_Q8_BLOCKS_PREFILL (DK / QK8_0)
+#define DV_Q8_BLOCKS_PREFILL (DV / QK8_0)
+
+// N_SPLIT>1 splits DK/DV across N_SPLIT threads per query row; needs
+// sub_group_shuffle_xor and DK_Q8_BLOCKS_PREFILL % N_SPLIT == 0.
+#ifndef N_SPLIT
+#define N_SPLIT 1
+#endif
+
+#if N_SPLIT > 1
+#define SPLIT_DK_VEC        (DK_VEC / N_SPLIT)
+#define SPLIT_DV_VEC        (DV_VEC / N_SPLIT)
+#define SPLIT_DK_Q8_BLOCKS  (DK_Q8_BLOCKS_PREFILL / N_SPLIT)
+#define WG_SIZE             (BLOCK_M * N_SPLIT)
+#else
+#define SPLIT_DK_VEC        DK_VEC
+#define SPLIT_DV_VEC        DV_VEC
+#define SPLIT_DK_Q8_BLOCKS  DK_Q8_BLOCKS_PREFILL
+#define WG_SIZE             BLOCK_M
+#endif
+
+// FA_V_STRATEGY: 0 = dequant V to half in local (default); 2 = keep packed
+// int8 in local, dequant in the accumulate loop (smaller local, slightly slower).
+#ifndef FA_V_STRATEGY
+#define FA_V_STRATEGY 0
+#endif
+
+__kernel void flash_attn_f32_q8_0(
+    const global void * q_void, ulong q_offset,
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * o_void, ulong o_offset,
+    const float scale,
+    const int n_q,
+    const int n_kv,
+    const int is_causal,
+    const int n_head,
+    const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const float max_bias,
+    const float m0,
+    const float m1,
+    const int n_head_log2,
+    const float logit_softcap,
+    const int n_head_kv,
+    const global void* mask_void,
+    const ulong mask_offset,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3,
+    const global void* sinks_void,
+    const ulong sinks_offset,
+    // blk: per-(qblock,kvblock) class from flash_attn_blk_f16
+    // (0=masked, 1=mixed, 2=unmasked). NULL disables the prepass opt.
+    const global void * blk_void
+) {
+    const int tid = get_local_id(0);
+    const int block_q_idx = get_group_id(0);
+    const int head_batch_idx = get_global_id(1);
+
+#if N_SPLIT > 1
+    const int q_lane    = tid / N_SPLIT;
+    const int split_idx = tid % N_SPLIT;
+#else
+    const int q_lane    = tid;
+    const int split_idx = 0;
+#endif
+    const int my_query_row = block_q_idx * BLOCK_M + q_lane;
+    const int query_valid = my_query_row < n_q;
+
+    const int batch_idx = head_batch_idx / n_head;
+    const int head_idx  = head_batch_idx % n_head;
+
+    const int gqa_ratio   = n_head / n_head_kv;
+    const int head_kv_idx = head_idx / gqa_ratio;
+    const int mask_head_idx  = mask_void != NULL ? head_idx  % mask_ne2 : 0;
+    const int mask_batch_idx = mask_void != NULL ? batch_idx % mask_ne3 : 0;
+
+    const global char * q_base = (const global char *) q_void + q_offset;
+    const global char * k_base = (const global char *) k_void + k_offset;
+    const global char * v_base = (const global char *) v_void + v_offset;
+    global       char * o_base = (global       char *) o_void + o_offset;
+
+    const global char * mask_base = NULL;
+    if (mask_void != NULL) {
+        mask_base = (const global char *) mask_void + mask_offset +
+                    mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
+    }
+
+    // BLK_PREPASS_BM may differ from this kernel's BLOCK_M; scale q-block idx.
+    #ifndef BLK_PREPASS_BM
+    #define BLK_PREPASS_BM BLOCK_M
+    #endif
+    const global char * blk_base = NULL;
+    int n_kv_blocks = 0;
+    if (blk_void != NULL) {
+        n_kv_blocks = (n_kv + BLOCK_N - 1) / BLOCK_N;
+        const int n_q_blocks_prepass = (n_q + BLK_PREPASS_BM - 1) / BLK_PREPASS_BM;
+        const int prepass_q_block    = (block_q_idx * BLOCK_M) / BLK_PREPASS_BM;
+        blk_base = (const global char *) blk_void +
+                   (((mask_batch_idx * mask_ne2) + mask_head_idx) * n_q_blocks_prepass + prepass_q_block) * n_kv_blocks;
+    }
+
+    const int dk_off_vec = split_idx * SPLIT_DK_VEC;
+    ACC_TYPE4 q_priv[SPLIT_DK_VEC];
+    if (query_valid) {
+        const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
+        const global float4 * q_ptr = (const global float4 *) (q_base + q_row_offset);
+        #pragma unroll
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) {
+            q_priv[i] = q_ptr[dk_off_vec + i];
+        }
+    } else {
+        #pragma unroll
+        for (int i = 0; i < SPLIT_DK_VEC; ++i) q_priv[i] = (ACC_TYPE4)(0.0f);
+    }
+
+#ifdef FA_HAVE_INT_DOT
+    uint  q_packed_pf[SPLIT_DK_Q8_BLOCKS * 8];
+    float q_d_pf[SPLIT_DK_Q8_BLOCKS];
+    #pragma unroll
+    for (int b = 0; b < SPLIT_DK_Q8_BLOCKS; ++b) {
+        q_d_pf[b] = quant_q_block_int8_packed(&q_priv[b * 8], &q_packed_pf[b * 8]);
+    }
+#endif
+
+    const int dv_off_vec = split_idx * SPLIT_DV_VEC;
+    ACC_TYPE4 o_acc[SPLIT_DV_VEC];
+    #pragma unroll
+    for (int i = 0; i < SPLIT_DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
+
+    ACC_TYPE m_i = FA_M_INIT;
+    ACC_TYPE l_i = 0.0f;
+
+    float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
+
+#ifdef FA_HAVE_INT_DOT
+    __local uint  l_k_packed[BLOCK_N][DK_Q8_BLOCKS_PREFILL * 8];
+    __local float l_k_scale [BLOCK_N][DK_Q8_BLOCKS_PREFILL];
+#else
+    __local half4 l_k[BLOCK_N][DK_VEC];
+#endif
+
+#if FA_V_STRATEGY == 2
+    __local uint  l_v_packed[BLOCK_N][DV_Q8_BLOCKS_PREFILL * 8];
+    __local float l_v_scale [BLOCK_N][DV_Q8_BLOCKS_PREFILL];
+#else
+    __local half4 l_v[BLOCK_N][DV_VEC];
+#endif
+
+    for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
+        // Skip fully-masked KV tiles (uniform branch across WG).
+        char blk_cur = 1;
+        if (blk_base != NULL) {
+            blk_cur = blk_base[k_start / BLOCK_N];
+            if (blk_cur == 0) continue;
+        }
+
+        {
+#ifdef FA_HAVE_INT_DOT
+            const int k_blocks_per_row = DK_Q8_BLOCKS_PREFILL;
+            const int n_blocks_total = BLOCK_N * k_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / k_blocks_per_row;
+                const int blk = i % k_blocks_per_row;
+                const int k_row_idx = k_start + row;
+                if (k_row_idx < n_kv) {
+                    const ulong k_row_off = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                    const global char * blk_ptr = k_base + k_row_off + blk * Q8_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global uchar * qs = (const global uchar *)(blk_ptr + 2);
+                    l_k_scale[row][blk] = df;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) {
+                        uint k_packed =
+                              (uint) qs[j*4 + 0]        |
+                             ((uint) qs[j*4 + 1]) <<  8 |
+                             ((uint) qs[j*4 + 2]) << 16 |
+                             ((uint) qs[j*4 + 3]) << 24;
+                        l_k_packed[row][blk * 8 + j] = k_packed;
+                    }
+                } else {
+                    l_k_scale[row][blk] = 0.0f;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_k_packed[row][blk * 8 + j] = 0u;
+                }
+            }
+#else
+            // Fallback: dequant q8_0 -> half in local memory.
+            const int k_blocks_per_row = DK / QK8_0;
+            const int n_blocks_total = BLOCK_N * k_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / k_blocks_per_row;
+                const int blk = i % k_blocks_per_row;
+                const int k_row_idx = k_start + row;
+                if (k_row_idx < n_kv) {
+                    const ulong k_row_off = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
+                    const global char * blk_ptr = k_base + k_row_off + blk * Q8_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global char * qs = blk_ptr + 2;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) {
+                        const float4 v = df * (float4)((float) qs[j*4 + 0],
+                                                       (float) qs[j*4 + 1],
+                                                       (float) qs[j*4 + 2],
+                                                       (float) qs[j*4 + 3]);
+                        l_k[row][blk * 8 + j] = (half4)((half) v.s0, (half) v.s1, (half) v.s2, (half) v.s3);
+                    }
+                } else {
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_k[row][blk * 8 + j] = (half4)(0.0h);
+                }
+            }
+#endif
+        }
+        // V tile load — strategy-dependent.
+#if FA_V_STRATEGY == 2
+        {
+            // Int8 packed V in local memory + per-block scale. Accumulate
+            // step unpacks inline.
+            const int v_blocks_per_row = DV_Q8_BLOCKS_PREFILL;
+            const int n_blocks_total = BLOCK_N * v_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / v_blocks_per_row;
+                const int blk = i % v_blocks_per_row;
+                const int v_row_idx = k_start + row;
+                if (v_row_idx < n_kv) {
+                    const ulong v_row_off = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                    const global char * blk_ptr = v_base + v_row_off + blk * Q8_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global uchar * qs = (const global uchar *)(blk_ptr + 2);
+                    l_v_scale[row][blk] = df;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) {
+                        uint v_packed =
+                              (uint) qs[j*4 + 0]        |
+                             ((uint) qs[j*4 + 1]) <<  8 |
+                             ((uint) qs[j*4 + 2]) << 16 |
+                             ((uint) qs[j*4 + 3]) << 24;
+                        l_v_packed[row][blk * 8 + j] = v_packed;
+                    }
+                } else {
+                    l_v_scale[row][blk] = 0.0f;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_v_packed[row][blk * 8 + j] = 0u;
+                }
+            }
+        }
+#else
+        {
+            // Default: dequant V -> half in local memory.
+            const int v_blocks_per_row = DV / QK8_0;
+            const int n_blocks_total = BLOCK_N * v_blocks_per_row;
+            for (int i = tid; i < n_blocks_total; i += WG_SIZE) {
+                const int row = i / v_blocks_per_row;
+                const int blk = i % v_blocks_per_row;
+                const int v_row_idx = k_start + row;
+                if (v_row_idx < n_kv) {
+                    const ulong v_row_off = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
+                    const global char * blk_ptr = v_base + v_row_off + blk * Q8_0_BLOCK_SIZE;
+                    const float df = (float) vload_half(0, (const global half *) blk_ptr);
+                    const global char * qs = blk_ptr + 2;
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) {
+                        const float4 v = df * (float4)((float) qs[j*4 + 0],
+                                                       (float) qs[j*4 + 1],
+                                                       (float) qs[j*4 + 2],
+                                                       (float) qs[j*4 + 3]);
+                        l_v[row][blk * 8 + j] = (half4)((half) v.s0, (half) v.s1, (half) v.s2, (half) v.s3);
+                    }
+                } else {
+                    #pragma unroll
+                    for (int j = 0; j < 8; ++j) l_v[row][blk * 8 + j] = (half4)(0.0h);
+                }
+            }
+        }
+#endif
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        // QK dot + online softmax. N_SPLIT>1 reduces per-thread partials via shuffle_xor.
+#if N_SPLIT > 1
+        {
+#else
+        if (query_valid) {
+#endif
+            const int k_blk_base = split_idx * SPLIT_DK_Q8_BLOCKS;
+            for (int j = 0; j < BLOCK_N; j += 4) {
+                const int k_row0 = k_start + j;
+                const int k_row1 = k_start + j + 1;
+                const int k_row2 = k_start + j + 2;
+                const int k_row3 = k_start + j + 3;
+
+                ACC_TYPE s0, s1, s2, s3;
+#ifdef FA_HAVE_INT_DOT
+                // dp4a-accelerated QK dot over owned blocks.
+                s0 = 0.0f; s1 = 0.0f; s2 = 0.0f; s3 = 0.0f;
+                #pragma unroll
+                for (int b_local = 0; b_local < SPLIT_DK_Q8_BLOCKS; ++b_local) {
+                    const int b = k_blk_base + b_local;
+                    int sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0;
+                    #pragma unroll
+                    for (int g = 0; g < 8; ++g) {
+                        const uint qp = q_packed_pf[b_local * 8 + g];
+                        sum0 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j  ][b * 8 + g], sum0);
+                        sum1 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+1][b * 8 + g], sum1);
+                        sum2 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+2][b * 8 + g], sum2);
+                        sum3 = dot_acc_sat_4x8packed_ss_int(qp, l_k_packed[j+3][b * 8 + g], sum3);
+                    }
+                    const float qd = q_d_pf[b_local];
+                    s0 += (float)sum0 * qd * l_k_scale[j  ][b];
+                    s1 += (float)sum1 * qd * l_k_scale[j+1][b];
+                    s2 += (float)sum2 * qd * l_k_scale[j+2][b];
+                    s3 += (float)sum3 * qd * l_k_scale[j+3][b];
+                }
+#else
+                ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc2 = (ACC_TYPE4)(0.0f);
+                ACC_TYPE4 dot_acc3 = (ACC_TYPE4)(0.0f);
+                #pragma unroll
+                for (int k = 0; k < SPLIT_DK_VEC; ++k) {
+                    const ACC_TYPE4 qk = q_priv[k];
+                    const int k_abs = dk_off_vec + k;
+                    dot_acc0 = mad(qk, CONVERT_KV_ACC4(l_k[j  ][k_abs]), dot_acc0);
+                    dot_acc1 = mad(qk, CONVERT_KV_ACC4(l_k[j+1][k_abs]), dot_acc1);
+                    dot_acc2 = mad(qk, CONVERT_KV_ACC4(l_k[j+2][k_abs]), dot_acc2);
+                    dot_acc3 = mad(qk, CONVERT_KV_ACC4(l_k[j+3][k_abs]), dot_acc3);
+                }
+                s0 = dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3;
+                s1 = dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3;
+                s2 = dot_acc2.s0 + dot_acc2.s1 + dot_acc2.s2 + dot_acc2.s3;
+                s3 = dot_acc3.s0 + dot_acc3.s1 + dot_acc3.s2 + dot_acc3.s3;
+#endif
+
+#if N_SPLIT > 1
+                // Power-of-2 N_SPLIT: shuffle_xor butterfly. N_SPLIT=3 (DK=96): 3-way shuffle.
+                #if (N_SPLIT & (N_SPLIT - 1)) == 0
+                    #pragma unroll
+                    for (int step = 1; step < N_SPLIT; step <<= 1) {
+                        s0 += sub_group_shuffle_xor(s0, step);
+                        s1 += sub_group_shuffle_xor(s1, step);
+                        s2 += sub_group_shuffle_xor(s2, step);
+                        s3 += sub_group_shuffle_xor(s3, step);
+                    }
+                #else
+                    const uint tri_base = (get_sub_group_local_id() / N_SPLIT) * N_SPLIT;
+                    s0 = sub_group_shuffle(s0, tri_base + 0) + sub_group_shuffle(s0, tri_base + 1) + sub_group_shuffle(s0, tri_base + 2);
+                    s1 = sub_group_shuffle(s1, tri_base + 0) + sub_group_shuffle(s1, tri_base + 1) + sub_group_shuffle(s1, tri_base + 2);
+                    s2 = sub_group_shuffle(s2, tri_base + 0) + sub_group_shuffle(s2, tri_base + 1) + sub_group_shuffle(s2, tri_base + 2);
+                    s3 = sub_group_shuffle(s3, tri_base + 0) + sub_group_shuffle(s3, tri_base + 1) + sub_group_shuffle(s3, tri_base + 2);
+                #endif
+                if (!query_valid) { s0 = FA_M_INIT; s1 = FA_M_INIT; s2 = FA_M_INIT; s3 = FA_M_INIT; }
+#endif
+                s0 *= scale; s1 *= scale; s2 *= scale; s3 *= scale;
+
+                if (is_causal) {
+                    const int causal_limit = n_kv - n_q + my_query_row;
+                    if (k_row0 > causal_limit) s0 = FA_M_INIT;
+                    if (k_row1 > causal_limit) s1 = FA_M_INIT;
+                    if (k_row2 > causal_limit) s2 = FA_M_INIT;
+                    if (k_row3 > causal_limit) s3 = FA_M_INIT;
+                }
+                if (k_row0 >= n_kv) s0 = FA_M_INIT;
+                if (k_row1 >= n_kv) s1 = FA_M_INIT;
+                if (k_row2 >= n_kv) s2 = FA_M_INIT;
+                if (k_row3 >= n_kv) s3 = FA_M_INIT;
+
+                if (query_valid && mask_base != NULL && blk_cur != 2) {
+                    const global MASK_DATA_TYPE * mask_ptr =
+                        (const global MASK_DATA_TYPE *) (mask_base + my_query_row * mask_nb1);
+                    if (k_row0 < n_kv) s0 += slope * (ACC_TYPE) mask_ptr[k_row0];
+                    if (k_row1 < n_kv) s1 += slope * (ACC_TYPE) mask_ptr[k_row1];
+                    if (k_row2 < n_kv) s2 += slope * (ACC_TYPE) mask_ptr[k_row2];
+                    if (k_row3 < n_kv) s3 += slope * (ACC_TYPE) mask_ptr[k_row3];
+                }
+                if (logit_softcap > 0.0f) {
+                    s0 = logit_softcap * tanh(s0 / logit_softcap);
+                    s1 = logit_softcap * tanh(s1 / logit_softcap);
+                    s2 = logit_softcap * tanh(s2 / logit_softcap);
+                    s3 = logit_softcap * tanh(s3 / logit_softcap);
+                }
+
+                const ACC_TYPE m_new      = max(m_i, max(max(s0, s1), max(s2, s3)));
+                // Whole tile masked (m_new == FA_M_INIT): force the exp() args
+                // far negative so the tile contributes 0, not exp(0)=1.
+                const ACC_TYPE m_exp      = (m_new == FA_M_INIT) ? 0.0f : m_new;
+                const ACC_TYPE scale_prev = native_exp(m_i - m_exp);
+                const ACC_TYPE p0         = native_exp(s0 - m_exp);
+                const ACC_TYPE p1         = native_exp(s1 - m_exp);
+                const ACC_TYPE p2         = native_exp(s2 - m_exp);
+                const ACC_TYPE p3         = native_exp(s3 - m_exp);
+
+#if FA_V_STRATEGY == 2
+                #pragma unroll
+                for (int b_local = 0; b_local < DV_Q8_BLOCKS_PREFILL / N_SPLIT; ++b_local) {
+                    const int b_abs = split_idx * (DV_Q8_BLOCKS_PREFILL / N_SPLIT) + b_local;
+                    const float d0 = l_v_scale[j  ][b_abs];
+                    const float d1 = l_v_scale[j+1][b_abs];
+                    const float d2 = l_v_scale[j+2][b_abs];
+                    const float d3 = l_v_scale[j+3][b_abs];
+                    #pragma unroll
+                    for (int g = 0; g < 8; ++g) {
+                        const int lane_abs   = b_abs   * 8 + g;
+                        const int lane_local = b_local * 8 + g;
+                        uint pk0 = l_v_packed[j  ][lane_abs];
+                        uint pk1 = l_v_packed[j+1][lane_abs];
+                        uint pk2 = l_v_packed[j+2][lane_abs];
+                        uint pk3 = l_v_packed[j+3][lane_abs];
+                        float4 v0 = d0 * (float4)((float)(char)(pk0 & 0xff), (float)(char)((pk0>>8)&0xff), (float)(char)((pk0>>16)&0xff), (float)(char)((pk0>>24)&0xff));
+                        float4 v1 = d1 * (float4)((float)(char)(pk1 & 0xff), (float)(char)((pk1>>8)&0xff), (float)(char)((pk1>>16)&0xff), (float)(char)((pk1>>24)&0xff));
+                        float4 v2 = d2 * (float4)((float)(char)(pk2 & 0xff), (float)(char)((pk2>>8)&0xff), (float)(char)((pk2>>16)&0xff), (float)(char)((pk2>>24)&0xff));
+                        float4 v3 = d3 * (float4)((float)(char)(pk3 & 0xff), (float)(char)((pk3>>8)&0xff), (float)(char)((pk3>>16)&0xff), (float)(char)((pk3>>24)&0xff));
+                        o_acc[lane_local] = mad(p3, v3,
+                                           mad(p2, v2,
+                                           mad(p1, v1,
+                                           mad(p0, v0,
+                                           o_acc[lane_local] * scale_prev))));
+                    }
+                }
+#else  // FA_V_STRATEGY == 0
+                #pragma unroll
+                for (int i = 0; i < SPLIT_DV_VEC; ++i) {
+                    const int i_abs = dv_off_vec + i;
+                    o_acc[i] = mad(p3, CONVERT_KV_ACC4(l_v[j+3][i_abs]),
+                               mad(p2, CONVERT_KV_ACC4(l_v[j+2][i_abs]),
+                               mad(p1, CONVERT_KV_ACC4(l_v[j+1][i_abs]),
+                               mad(p0, CONVERT_KV_ACC4(l_v[j  ][i_abs]),
+                               o_acc[i] * scale_prev))));
+                }
+#endif
+                l_i = l_i * scale_prev + p0 + p1 + p2 + p3;
+                m_i = m_new;
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    // Write output. With N_SPLIT>1 each thread writes its SPLIT_DV_VEC slice.
+    if (query_valid) {
+        if (sinks_void != NULL) {
+            const global ACC_TYPE * sinks_ptr =
+                (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+            const ACC_TYPE m_sink  = sinks_ptr[head_idx];
+            const ACC_TYPE m_final = max(m_i, m_sink);
+            const ACC_TYPE scale_o = exp(m_i - m_final);
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_acc[i] *= scale_o;
+            l_i = l_i * scale_o + exp(m_sink - m_final);
+            m_i = m_final;
+        }
+        const ACC_TYPE l_inv = (l_i > 0.0f) ? (1.0f / l_i) : 0.0f;
+        const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
+        global float4 * o_row = (global float4 *) (o_base + o_row_offset);
+        if (l_inv > 0.0f) {
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_row[dv_off_vec + i] = o_acc[i] * l_inv;
+        } else {
+            #pragma unroll
+            for (int i = 0; i < SPLIT_DV_VEC; ++i) o_row[dv_off_vec + i] = (float4)(0.0f);
+        }
+    }
+}
+
+// FD Pass 2: merge split partials. Identical across q4_0/q8_0/f16; each FA
+// source owns a copy since kernels compile per-source-program.
+__kernel void flash_attn_f32_merge(
+    const global float * partial_void,
+    global void * o_void,
+    const ulong o_offset,
+    const int n_head,
+    const int n_splits,
+    const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
+    const global void * sinks_void,
+    const ulong sinks_offset,
+    const int n_q
+) {
+    const int lane           = get_local_id(0);
+    const int head_batch_idx = get_global_id(1);
+    const int q_idx          = get_global_id(2);
+    const int batch_idx      = head_batch_idx / n_head;
+    const int head_idx       = head_batch_idx % n_head;
+
+    const ulong record_stride = (ulong) FA_PARTIAL_FLOATS;
+    const ulong record_idx_0  = (((ulong) batch_idx * n_head + head_idx) * n_q + q_idx) * n_splits;
+    const global float * rec0 = partial_void + record_idx_0 * record_stride;
+
+    __local ACC_TYPE m_final_shared;
+    __local ACC_TYPE l_final_shared;
+    if (lane == 0) {
+        ACC_TYPE m = FA_M_INIT;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            m = max(m, m_c);
+        }
+        ACC_TYPE m_sink = 0.0f;
+        bool has_sink = false;
+        if (sinks_void != NULL) {
+            const global ACC_TYPE * sinks_ptr =
+                (const global ACC_TYPE *) ((const global char *) sinks_void + sinks_offset);
+            m_sink = sinks_ptr[head_idx];
+            has_sink = true;
+            m = max(m, m_sink);
+        }
+        ACC_TYPE l = 0.0f;
+        for (int c = 0; c < n_splits; ++c) {
+            const ACC_TYPE m_c = rec0[c * record_stride + 0];
+            const ACC_TYPE l_c = rec0[c * record_stride + 1];
+            if (m_c > FA_M_INIT) {
+                l += l_c * exp(m_c - m);
+            }
+        }
+        if (has_sink) {
+            l += exp(m_sink - m);
+        }
+        m_final_shared = m;
+        l_final_shared = l;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+    const ACC_TYPE m_final = m_final_shared;
+    const ACC_TYPE l_final = l_final_shared;
+    const ACC_TYPE l_inv   = (l_final > 0.0f) ? (1.0f / l_final) : 0.0f;
+
+    ACC_TYPE4 o = (ACC_TYPE4)(0.0f);
+    for (int c = 0; c < n_splits; ++c) {
+        const global float * rec_c   = rec0 + c * record_stride;
+        const ACC_TYPE       m_c     = rec_c[0];
+        if (m_c <= FA_M_INIT) continue;
+        const global float4 * rec_oc = (const global float4 *) (rec_c + 2);
+        const ACC_TYPE scale_c = exp(m_c - m_final);
+        o = mad((ACC_TYPE4)(scale_c), rec_oc[lane], o);
+    }
+    o = o * l_inv;
+
+    const ulong o_row_offset = (ulong) batch_idx * o_nb3 + (ulong) q_idx * o_nb2 + (ulong) head_idx * o_nb1;
+    global O_DATA_TYPE4 * o_row = (global O_DATA_TYPE4 *) ((global char *) o_void + o_offset + o_row_offset);
+    o_row[lane] = CONVERT_O_DATA4(o);
+}
diff --git a/ggml/src/ggml-opencl/kernels/flash_attn_pre_f16.cl b/ggml/src/ggml-opencl/kernels/flash_attn_pre_f16.cl
new file mode 100644
index 000000000..88ead4bcb
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/flash_attn_pre_f16.cl
@@ -0,0 +1,156 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+__kernel void flash_attn_kv_pad_f16(
+    const global void * k_void, ulong k_offset,
+    const global void * v_void, ulong v_offset,
+    global void * k_pad_void,
+    global void * v_pad_void,
+    const int n_kv,
+    const int n_head_kv,
+    const int n_batch,
+    const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
+    const ulong v_nb1, const ulong v_nb2, const ulong v_nb3
+) {
+    const int row_idx = get_global_id(0);
+    const int head_kv_idx = get_global_id(1);
+    const int batch_idx = get_global_id(2);
+
+    if (row_idx >= BLOCK_N || head_kv_idx >= n_head_kv || batch_idx >= n_batch) {
+        return;
+    }
+
+    const int tail_start = n_kv - (n_kv % BLOCK_N);
+    const int src_row_idx = tail_start + row_idx;
+
+    const global char * k_src = (const global char *) k_void + k_offset;
+    const global char * v_src = (const global char *) v_void + v_offset;
+    global char * k_pad = (global char *) k_pad_void;
+    global char * v_pad = (global char *) v_pad_void;
+
+    const ulong k_dst_offset = ((ulong) batch_idx * (ulong) n_head_kv + (ulong) head_kv_idx) * ((ulong) BLOCK_N * k_nb1) + (ulong) row_idx * k_nb1;
+    const ulong v_dst_offset = ((ulong) batch_idx * (ulong) n_head_kv + (ulong) head_kv_idx) * ((ulong) BLOCK_N * v_nb1) + (ulong) row_idx * v_nb1;
+
+    if (src_row_idx < n_kv) {
+        const ulong k_src_offset = (ulong) batch_idx * k_nb3 + (ulong) head_kv_idx * k_nb2 + (ulong) src_row_idx * k_nb1;
+        const ulong v_src_offset = (ulong) batch_idx * v_nb3 + (ulong) head_kv_idx * v_nb2 + (ulong) src_row_idx * v_nb1;
+
+        for (ulong i = 0; i < k_nb1; ++i) {
+            k_pad[k_dst_offset + i] = k_src[k_src_offset + i];
+        }
+        for (ulong i = 0; i < v_nb1; ++i) {
+            v_pad[v_dst_offset + i] = v_src[v_src_offset + i];
+        }
+    } else {
+        for (ulong i = 0; i < k_nb1; ++i) {
+            k_pad[k_dst_offset + i] = 0;
+        }
+        for (ulong i = 0; i < v_nb1; ++i) {
+            v_pad[v_dst_offset + i] = 0;
+        }
+    }
+}
+
+__kernel void flash_attn_mask_pad_f16(
+    const global void * mask_void, ulong mask_offset,
+    global void * mask_pad_void,
+    const int n_q,
+    const int n_kv,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int col_idx = get_global_id(0);
+    const int q_row = get_global_id(1);
+    const int mask_slice = get_global_id(2);
+
+    if (col_idx >= BLOCK_N || q_row >= n_q || mask_slice >= mask_ne2 * mask_ne3) {
+        return;
+    }
+
+    const int tail_start = n_kv - (n_kv % BLOCK_N);
+    const int src_col_idx = tail_start + col_idx;
+    const int mask_head_idx = mask_slice % mask_ne2;
+    const int mask_batch_idx = mask_slice / mask_ne2;
+
+    const global char * mask_src_base = (const global char *) mask_void + mask_offset +
+        (ulong) mask_batch_idx * mask_nb3 +
+        (ulong) mask_head_idx * mask_nb2 +
+        (ulong) q_row * mask_nb1;
+    const global half * mask_src = (const global half *) mask_src_base;
+
+    global half * mask_pad = (global half *) mask_pad_void;
+    const ulong dst_idx =
+        (((ulong) mask_batch_idx * (ulong) mask_ne2 + (ulong) mask_head_idx) * (ulong) n_q + (ulong) q_row) * (ulong) BLOCK_N +
+        (ulong) col_idx;
+
+    mask_pad[dst_idx] = src_col_idx < n_kv ? mask_src[src_col_idx] : (half) (-INFINITY);
+}
+
+// Per-KV-tile mask class. 0=all -inf (skip tile), 1=mixed (apply mask),
+// 2=all zero, no -inf (skip mask lookup). Causal diagonal tiles are class 1.
+__kernel void flash_attn_blk_f16(
+    const global void * mask_void, ulong mask_offset,
+    global char * blk,
+    const int n_q,
+    const int n_kv,
+    const ulong mask_nb1,
+    const ulong mask_nb2,
+    const ulong mask_nb3,
+    const int mask_ne2,
+    const int mask_ne3
+) {
+    const int kv_block_idx = get_global_id(0);
+    const int q_block_idx = get_global_id(1);
+    const int mask_slice = get_global_id(2);
+
+    const int n_q_blocks = (n_q + BLOCK_M - 1) / BLOCK_M;
+    const int n_kv_blocks = (n_kv + BLOCK_N - 1) / BLOCK_N;
+    if (kv_block_idx >= n_kv_blocks || q_block_idx >= n_q_blocks || mask_slice >= mask_ne2 * mask_ne3) {
+        return;
+    }
+
+    const int mask_head_idx = mask_slice % mask_ne2;
+    const int mask_batch_idx = mask_slice / mask_ne2;
+    const int q_start = q_block_idx * BLOCK_M;
+    const int k_start = kv_block_idx * BLOCK_N;
+    const int q_count = min(BLOCK_M, n_q - q_start);
+    const int k_count = min(BLOCK_N, n_kv - k_start);
+
+    const half neg_max_half = (half) (-65504.0f);
+    char has_unmasked = 0;
+    char has_masked = 0;
+    char has_nonzero = 0;
+
+    const global char * mask_base = (const global char *) mask_void + mask_offset +
+        (ulong) mask_batch_idx * mask_nb3 +
+        (ulong) mask_head_idx * mask_nb2;
+
+    for (int qi = 0; qi < q_count; ++qi) {
+        const global half * mask_row = (const global half *) (mask_base + (ulong) (q_start + qi) * mask_nb1) + k_start;
+        for (int ki = 0; ki < k_count; ++ki) {
+            const half v = mask_row[ki];
+            if (v <= neg_max_half) {
+                has_masked = 1;
+            } else {
+                has_unmasked = 1;
+                if (v != (half) 0.0f) {
+                    has_nonzero = 1;
+                }
+            }
+        }
+        if (has_masked && has_unmasked) break;  // mixed tile — short-circuit.
+    }
+
+    char res;
+    if (has_unmasked == 0) {
+        res = 0;
+    } else if (has_masked || has_nonzero) {
+        res = 1;
+    } else {
+        res = 2;
+    }
+
+    blk[((ulong) mask_slice * (ulong) n_q_blocks + (ulong) q_block_idx) * (ulong) n_kv_blocks + (ulong) kv_block_idx] = res;
+}
diff --git a/ggml/src/ggml-opencl/kernels/set_rows.cl b/ggml/src/ggml-opencl/kernels/set_rows.cl
index fc3ff7aa1..4ad5af13f 100644
--- a/ggml/src/ggml-opencl/kernels/set_rows.cl
+++ b/ggml/src/ggml-opencl/kernels/set_rows.cl
@@ -158,6 +158,239 @@ kernel void kernel_set_rows_f32_i32(
     }
 }
 
+// f32 -> q8_0 quantize set_rows. Block = half d + char qs[32].
+#define QK8_0 32
+
+inline void quantize_q8_0_block(global float * x, global char * qs, global half * d_out) {
+    float amax = 0.0f;
+    for (int j = 0; j < QK8_0; j++) {
+        amax = fmax(amax, fabs(x[j]));
+    }
+
+    float d  = amax / 127.0f;
+    float id = (d != 0.0f) ? 127.0f / amax : 0.0f;
+
+    vstore_half(d, 0, d_out);
+
+    for (int j = 0; j < QK8_0; j++) {
+        qs[j] = (char)((int)round(x[j] * id));
+    }
+}
+
+kernel void kernel_set_rows_q8_0_i64(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst,
+        ulong         offsetd,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        ulong         nb1,
+        ulong         nb2,
+        ulong         nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    global char  * dst_row = (global char  *) (dst  +  i1*nb1  + i02*nb2  + i03*nb3);
+    global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x = src_row + blk * QK8_0;
+        global char  * y = dst_row + blk * (2 + QK8_0);
+
+        quantize_q8_0_block(x, y + 2, (global half *)y);
+    }
+}
+
+kernel void kernel_set_rows_q8_0_i32(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst,
+        ulong         offsetd,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        ulong         nb1,
+        ulong         nb2,
+        ulong         nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    int i1  = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    global char  * dst_row = (global char  *) (dst  +  i1*nb1  + i02*nb2  + i03*nb3);
+    global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x = src_row + blk * QK8_0;
+        global char  * y = dst_row + blk * (2 + QK8_0);
+
+        quantize_q8_0_block(x, y + 2, (global half *)y);
+    }
+}
+
+// SoA q8_0 variants. dst_q: int8[QK8_0] per block; dst_d: fp16 scale per block.
+// Layout matches kernel_convert_block_q8_0; block index follows dst element order.
+kernel void kernel_set_rows_q8_0_soa_i64(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst_q,
+        ulong         offset_q,
+        global char * dst_d,
+        ulong         offset_d,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        int           ne1_dst,
+        int           ne2_dst,
+        int           ne3_dst
+) {
+    src0  = src0  + offset0;
+    src1  = src1  + offset1;
+    dst_q = dst_q + offset_q;
+    dst_d = dst_d + offset_d;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    long row_blk_base = ((long)i03 * ne2_dst * ne1_dst + (long)i02 * ne1_dst + i1) * nblk0;
+
+    global half  * d_row = (global half  *)(dst_d) + row_blk_base;
+    global char  * q_row = (global char  *)(dst_q) + row_blk_base * QK8_0;
+    global float * src_row = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x = src_row + blk * QK8_0;
+        global char  * q = q_row + blk * QK8_0;
+
+        quantize_q8_0_block(x, q, d_row + blk);
+    }
+}
+
+kernel void kernel_set_rows_q8_0_soa_i32(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst_q,
+        ulong         offset_q,
+        global char * dst_d,
+        ulong         offset_d,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        int           ne1_dst,
+        int           ne2_dst,
+        int           ne3_dst
+) {
+    src0  = src0  + offset0;
+    src1  = src1  + offset1;
+    dst_q = dst_q + offset_q;
+    dst_d = dst_d + offset_d;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    int i1  = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    long row_blk_base = ((long)i03 * ne2_dst * ne1_dst + (long)i02 * ne1_dst + i1) * nblk0;
+
+    global half  * d_row = (global half  *)(dst_d) + row_blk_base;
+    global char  * q_row = (global char  *)(dst_q) + row_blk_base * QK8_0;
+    global float * src_row = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x = src_row + blk * QK8_0;
+        global char  * q = q_row + blk * QK8_0;
+
+        quantize_q8_0_block(x, q, d_row + blk);
+    }
+}
+
 kernel void kernel_set_rows_f16_i32(
         global char * src0,
         ulong         offset0,
@@ -206,3 +439,270 @@ kernel void kernel_set_rows_f16_i32(
         dst_row[ind] = src_row[ind];
     }
 }
+
+// f32 -> q4_0 quantize set_rows. Block = half d + uchar qs[16] (shuffled
+// nibbles: qs[j] low/high = elem j / j+16).
+// Dequant: val[i] = d * (nibble_i - 8)
+// nblk0 = number of q4_0 blocks per row = ne00 / 32.
+#define QK4_0 32
+#define Q4_0_BLOCK_SIZE 18
+
+inline void quantize_q4_0_block(global float * x, global uchar * qs, global half * d_out) {
+    // Find the signed value with the largest absolute magnitude (matches ggml ref).
+    float max  = 0.0f;
+    float amax = 0.0f;
+    for (int j = 0; j < QK4_0; j++) {
+        float v = x[j];
+        float a = fabs(v);
+        if (a > amax) {
+            amax = a;
+            max  = v;
+        }
+    }
+
+    float d  = max / -8.0f;
+    float id = (d != 0.0f) ? 1.0f / d : 0.0f;
+
+    vstore_half(d, 0, d_out);
+
+    for (int j = 0; j < QK4_0/2; j++) {
+        float x0 = x[j]           * id;
+        float x1 = x[j + QK4_0/2] * id;
+
+        int i0 = (int)(x0 + 8.5f);
+        int i1 = (int)(x1 + 8.5f);
+        if (i0 < 0)  i0 = 0;
+        if (i0 > 15) i0 = 15;
+        if (i1 < 0)  i1 = 0;
+        if (i1 > 15) i1 = 15;
+
+        qs[j] = (uchar)i0 | ((uchar)i1 << 4);
+    }
+}
+
+kernel void kernel_set_rows_q4_0_i64(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst,
+        ulong         offsetd,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        ulong         nb1,
+        ulong         nb2,
+        ulong         nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    global char  * dst_row = (global char  *) (dst  +  i1*nb1  + i02*nb2  + i03*nb3);
+    global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x    = src_row + blk * QK4_0;
+        global char  * y    = dst_row + blk * Q4_0_BLOCK_SIZE;
+        global half  * yd   = (global half  *)(y);
+        global uchar * yqs  = (global uchar *)(y + 2);
+
+        quantize_q4_0_block(x, yqs, yd);
+    }
+}
+
+kernel void kernel_set_rows_q4_0_i32(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst,
+        ulong         offsetd,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        ulong         nb1,
+        ulong         nb2,
+        ulong         nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst  + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    int i1  = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    global char  * dst_row = (global char  *) (dst  +  i1*nb1  + i02*nb2  + i03*nb3);
+    global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x    = src_row + blk * QK4_0;
+        global char  * y    = dst_row + blk * Q4_0_BLOCK_SIZE;
+        global half  * yd   = (global half  *)(y);
+        global uchar * yqs  = (global uchar *)(y + 2);
+
+        quantize_q4_0_block(x, yqs, yd);
+    }
+}
+
+// SoA variants for q4_0 dst. Used when the backend has split block_q4_0 records
+// into separate quant (dst_q) and scale (dst_d) sub-buffers — same pattern as
+// the q8_0 SoA variants above.
+//
+// Layout (matches kernel_convert_block_q4_0, the "shuffled" variant):
+//   dst_q: contiguous 16 packed nibbles per block, block i at offset i * 16 bytes.
+//   dst_d: contiguous fp16 scales, block i at offset i * 2 bytes.
+// Nibble layout inside each byte is unchanged from AoS: qs[j] low nibble = element j,
+// qs[j] high nibble = element j+16. kernel_restore_block_q4_0 copies bytes as-is.
+kernel void kernel_set_rows_q4_0_soa_i64(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst_q,
+        ulong         offset_q,
+        global char * dst_d,
+        ulong         offset_d,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        int           ne1_dst,
+        int           ne2_dst,
+        int           ne3_dst
+) {
+    src0  = src0  + offset0;
+    src1  = src1  + offset1;
+    dst_q = dst_q + offset_q;
+    dst_d = dst_d + offset_d;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    long i1 = ((global long *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    long row_blk_base = ((long)i03 * ne2_dst * ne1_dst + (long)i02 * ne1_dst + i1) * nblk0;
+
+    global half  * d_row   = (global half  *)(dst_d) + row_blk_base;
+    global uchar * q_row   = (global uchar *)(dst_q) + row_blk_base * (QK4_0/2);
+    global float * src_row = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x    = src_row + blk * QK4_0;
+        global uchar * qs   = q_row   + blk * (QK4_0/2);
+        global half  * d_bk = d_row   + blk;
+
+        quantize_q4_0_block(x, qs, d_bk);
+    }
+}
+
+kernel void kernel_set_rows_q4_0_soa_i32(
+        global char * src0,
+        ulong         offset0,
+        global char * src1,
+        ulong         offset1,
+        global char * dst_q,
+        ulong         offset_q,
+        global char * dst_d,
+        ulong         offset_d,
+        int           ne01,
+        ulong         nb01,
+        ulong         nb02,
+        ulong         nb03,
+        uint4         ne11,
+        uint4         ne12,
+        ulong         nb10,
+        ulong         nb11,
+        ulong         nb12,
+        int           nblk0,
+        int           ne1_dst,
+        int           ne2_dst,
+        int           ne3_dst
+) {
+    src0  = src0  + offset0;
+    src1  = src1  + offset1;
+    dst_q = dst_q + offset_q;
+    dst_d = dst_d + offset_d;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
+
+    if (i01 >= ne01) {
+        return;
+    }
+
+    int i12 = fastmod(i03, ne12);
+    int i11 = fastmod(i02, ne11);
+
+    int i10 = i01;
+    int i1  = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
+
+    long row_blk_base = ((long)i03 * ne2_dst * ne1_dst + (long)i02 * ne1_dst + i1) * nblk0;
+
+    global half  * d_row   = (global half  *)(dst_d) + row_blk_base;
+    global uchar * q_row   = (global uchar *)(dst_q) + row_blk_base * (QK4_0/2);
+    global float * src_row = (global float *)(src0 + i01*nb01 + i02*nb02 + i03*nb03);
+
+    for (int blk = get_local_id(0); blk < nblk0; blk += get_local_size(0)) {
+        global float * x    = src_row + blk * QK4_0;
+        global uchar * qs   = q_row   + blk * (QK4_0/2);
+        global half  * d_bk = d_row   + blk;
+
+        quantize_q4_0_block(x, qs, d_bk);
+    }
+}

From 0c163a9b4c0cecdad508576d96bb5f03b71e2c8a Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 28 Jun 2026 12:35:03 +0800
Subject: [PATCH 205/292] fixed reasoning when non streaming

---
 embd_res/klite.embd | 11 ++++++++---
 koboldcpp.py        | 38 +++++++++++++++++++-------------------
 2 files changed, 27 insertions(+), 22 deletions(-)

diff --git a/embd_res/klite.embd b/embd_res/klite.embd
index 81d70a8fc..6d9f92975 100644
--- a/embd_res/klite.embd
+++ b/embd_res/klite.embd
@@ -7181,6 +7181,7 @@ Current version indicated by LITEVER below.
 				else if (dch.message) {
 					synchro_polled_response = dch.message.content;
 					let reasoningtxt = "";
+					let has_called_tools = (dch.message.tool_calls && dch.message.tool_calls.length>0);
 					if(dch.message.reasoning_content!=null && dch.message.reasoning_content!="") //vllm compat
 					{
 						reasoningtxt = dch.message.reasoning_content;
@@ -7191,17 +7192,17 @@ Current version indicated by LITEVER below.
 					}
 					if(reasoningtxt!="")
 					{
-						if(dch.message.content==null)
+						if(dch.message.content==null && !has_called_tools)
 						{
 							synchro_polled_response = reasoningtxt;
 						}
 						else
 						{
-							synchro_polled_response = `${localsettings.start_thinking_tag}${reasoningtxt}${localsettings.stop_thinking_tag}${dch.message.content}`;
+							synchro_polled_response = `${localsettings.start_thinking_tag}${reasoningtxt}${localsettings.stop_thinking_tag}${dch.message.content?dch.message.content:''}`;
 						}
 					}
 
-					if(dch.message.tool_calls && dch.message.tool_calls.length>0)
+					if(has_called_tools)
 					{
 						synchro_polled_resptoolcalls = dch.message.tool_calls;
 						if(!synchro_polled_response)
@@ -19875,6 +19876,10 @@ Current version indicated by LITEVER below.
 
 	function prepare_submit_generation() //wrap websearch into this
 	{
+		if(is_corpo_ui() && corpo_editing_turn != -1)
+		{
+			corpo_edit_chunk_save();
+		}
 		let senttext = document.getElementById("input_text").value;
 		document.getElementById("input_text").value = "";
 		PerformWebsearch(senttext,(res)=>{
diff --git a/koboldcpp.py b/koboldcpp.py
index 6b8ccaf38..c77221819 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -5205,6 +5205,25 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
             global last_non_horde_req_time
             last_non_horde_req_time = time.time()
 
+        #handle potential think tags, but only chat completions will return them. the others just drop them
+        reasoningtxt = ""
+        if api_format==4 or api_format==8 or api_format==9: #chat completions, responses and anthropic messages, but only chat has reasoning returned
+            if recvtxt:
+                for pair in thinkformats:
+                    starter = pair['start']
+                    ender = pair['end']
+                    start_idx = recvtxt.find(starter)
+                    end_idx = recvtxt.find(ender, start_idx + len(starter))
+                    if start_idx != -1 and end_idx != -1 and start_idx < end_idx:
+                        reasoningtxt = recvtxt[start_idx + len(starter):end_idx]
+                        recvtxt = recvtxt[:start_idx] + recvtxt[end_idx + len(ender):]
+                        break
+                    elif starter not in recvtxt and ender in recvtxt:
+                        parts = recvtxt.split(ender, 1)
+                        reasoningtxt = parts[0]
+                        recvtxt = parts[1]
+                        break
+
         utfprint("\nOutput: " + recvtxt,1)
 
         #tool calls resolution
@@ -5237,25 +5256,6 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
         modelNameToReturn = friendlymodelname
         if autoswapmode and textName is not None:
             modelNameToReturn = textName
-
-        #handle potential think tags, but only chat completions will return them. the others just drop them
-        reasoningtxt = ""
-        if api_format==4 or api_format==8 or api_format==9: #chat completions, responses and anthropic messages, but only chat has reasoning returned
-            if recvtxt:
-                for pair in thinkformats:
-                    starter = pair['start']
-                    ender = pair['end']
-                    start_idx = recvtxt.find(starter)
-                    end_idx = recvtxt.find(ender, start_idx + len(starter))
-                    if start_idx != -1 and end_idx != -1 and start_idx < end_idx:
-                        reasoningtxt = recvtxt[start_idx + len(starter):end_idx]
-                        recvtxt = recvtxt[:start_idx] + recvtxt[end_idx + len(ender):]
-                        break
-                    elif starter not in recvtxt and ender in recvtxt:
-                        parts = recvtxt.split(ender, 1)
-                        reasoningtxt = parts[0]
-                        recvtxt = parts[1]
-                        break
         if api_format == 1:
             res = {"data": {"seqs": [recvtxt]}}
         elif api_format == 3:

From 27c8bb4f63ad9f20bf5901067810a4be5ffe20c4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 28 Jun 2026 08:52:15 +0300
Subject: [PATCH 206/292] logs : reduce v2 (#25078)

* server : reduce logs

* cont : common

* cont : spec

* cont : CMN_ -> COM_
---
 common/common.cpp               |  94 +++++++++++-----------
 common/common.h                 |   7 ++
 common/fit.cpp                  |   2 +-
 common/reasoning-budget.cpp     |  20 ++---
 common/speculative.cpp          | 133 +++++++++++++++++---------------
 src/llama-context.cpp           |   2 +-
 tools/server/server-context.cpp |  86 +++++++++++----------
 tools/server/server-http.cpp    |  16 ++--
 tools/server/server-schema.cpp  |   2 +-
 tools/server/server-stream.cpp  |  11 ++-
 tools/server/server-task.cpp    |  12 +--
 tools/server/server.cpp         |  12 +--
 12 files changed, 203 insertions(+), 194 deletions(-)

diff --git a/common/common.cpp b/common/common.cpp
index a14e7bbed..0dd9ede5e 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -225,7 +225,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
     }
 
     if (!SetPriorityClass(GetCurrentProcess(), p)) {
-        LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
+        COM_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
         return false;
     }
 
@@ -251,7 +251,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
     }
 
     if (setpriority(PRIO_PROCESS, 0, p) != 0) {
-        LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
+        COM_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
         return false;
     }
     return true;
@@ -284,14 +284,14 @@ void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_para
 
     if (n_set && n_set < cpuparams.n_threads) {
         // Not enough set bits, may experience performance issues.
-        LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
+        COM_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
     }
 }
 
 bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
     size_t dash_loc = range.find('-');
     if (dash_loc == std::string::npos) {
-        LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
+        COM_ERR("%s", "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
         return false;
     }
 
@@ -303,7 +303,7 @@ bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THRE
     } else {
         start_i = std::stoull(range.substr(0, dash_loc));
         if (start_i >= GGML_MAX_N_THREADS) {
-            LOG_ERR("Start index out of bounds!\n");
+            COM_ERR("%s", "Start index out of bounds!\n");
             return false;
         }
     }
@@ -313,7 +313,7 @@ bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THRE
     } else {
         end_i = std::stoull(range.substr(dash_loc + 1));
         if (end_i >= GGML_MAX_N_THREADS) {
-            LOG_ERR("End index out of bounds!\n");
+            COM_ERR("%s", "End index out of bounds!\n");
             return false;
         }
     }
@@ -333,7 +333,7 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
     }
 
     size_t num_digits = mask.length() - start_i;
-    if (num_digits > 128) num_digits = 128;
+    num_digits = std::min<size_t>(num_digits, 128);
 
     size_t end_i = num_digits + start_i;
 
@@ -348,7 +348,7 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
         } else if (c >= 'A' && c <= 'F') {
             id -= 'A' - 10;
         } else {
-            LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
+            COM_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
             return false;
         }
 
@@ -379,21 +379,21 @@ void common_params_print_info(const common_params & params, bool print_devices)
 #else
     const char * build_type = " (debug)";
 #endif
-    LOG_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
+    COM_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
 
-    LOG_INF("log_info: verbosity = %d (adjust with the `-lv N` CLI arg)\n", common_log_get_verbosity_thold());
+    COM_INF("%s: verbosity = %d (adjust with the `-lv N` CLI arg)\n", __func__, common_log_get_verbosity_thold());
 
     // device enumeration creates a primary context on CUDA backends, skip it when the caller does not own any device
     if (print_devices) {
-        LOG_INF("device_info:\n");
+        COM_TRC("%s", "device_info:\n");
         for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
             auto * dev = ggml_backend_dev_get(i);
             size_t free, total;
             ggml_backend_dev_memory(dev, &free, &total);
-            LOG_INF("  - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+            COM_TRC("  - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
         }
     }
-    LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+    COM_TRC("%s\n", common_params_get_system_info(params).c_str());
 }
 
 std::string common_params_get_system_info(const common_params & params) {
@@ -660,7 +660,7 @@ void string_process_escapes(std::string & input) {
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
     const char * sep = strchr(data, '=');
     if (sep == nullptr || sep - data >= 128) {
-        LOG_ERR("%s: malformed KV override '%s'\n", __func__, data);
+        COM_ERR("%s: malformed KV override '%s'\n", __func__, data);
         return false;
     }
     llama_model_kv_override kvo;
@@ -683,20 +683,20 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
         } else if (std::strcmp(sep, "false") == 0) {
             kvo.val_bool = false;
         } else {
-            LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            COM_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
             return false;
         }
     } else if (strncmp(sep, "str:", 4) == 0) {
         sep += 4;
         kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
         if (strlen(sep) > 127) {
-            LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+            COM_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
             return false;
         }
         strncpy(kvo.val_str, sep, 127);
         kvo.val_str[127] = '\0';
     } else {
-        LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
+        COM_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
         return false;
     }
     overrides.emplace_back(std::move(kvo));
@@ -1199,8 +1199,8 @@ common_init_result::common_init_result(common_params & params, bool model_only)
     auto cparams = common_context_params_to_llama(params);
 
     if (params.fit_params) {
-        LOG_INF("%s: fitting params to device memory ...\n", __func__);
-        LOG_INF("%s: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n", __func__);
+        COM_TRC("%s", "fitting params to device memory ...\n");
+        COM_TRC("%s", "(for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n");
         common_fit_params(params.model.path.c_str(), &mparams, &cparams,
             params.tensor_split,
             params.tensor_buft_overrides.data(),
@@ -1227,7 +1227,7 @@ common_init_result::common_init_result(common_params & params, bool model_only)
         llama_adapter_lora_ptr lora;
         lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
         if (lora == nullptr) {
-            LOG_ERR("%s: failed to load lora adapter '%s'\n", __func__, la.path.c_str());
+            COM_ERR("failed to load lora adapter '%s'\n", la.path.c_str());
             pimpl->model.reset(model);
             return;
         }
@@ -1246,14 +1246,14 @@ common_init_result::common_init_result(common_params & params, bool model_only)
     common_init_sampler_from_model(model, params.sampling);
 
     if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+        COM_WRN("%s", "vocab does not have an EOS token, ignoring --ignore-eos\n");
         params.sampling.ignore_eos = false;
     }
 
     // initialize once
     for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
         if (llama_vocab_is_eog(vocab, i)) {
-            LOG_TRC("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            COM_TRC("added %s logit bias = %f\n", common_token_to_piece(vocab, i).c_str(), -INFINITY);
             params.sampling.logit_bias_eog.push_back({i, -INFINITY});
         }
     }
@@ -1291,7 +1291,7 @@ common_init_result::common_init_result(common_params & params, bool model_only)
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+        COM_ERR("failed to create context with model '%s'\n", params.model.path.c_str());
         return;
     }
 
@@ -1328,7 +1328,7 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
 
     llama_model * model = res->model();
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
+        COM_ERR("failed to load model '%s'\n", params.model.path.c_str());
         return res;
     }
 
@@ -1338,14 +1338,14 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
 
     llama_context * lctx = res->context();
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+        COM_ERR("failed to create context with model '%s'\n", params.model.path.c_str());
         return res;
     }
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
-        LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
+        COM_WRN("%s", "KV cache shifting is not supported for this context, disabling KV cache shifting\n");
         params.ctx_shift = false;
     }
 
@@ -1374,7 +1374,7 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
         bool ok = true;
 
         if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  BOS token, reranking will not work\n", __func__);
+            COM_WRN("%s", "vocab does not have a  BOS token, reranking will not work\n");
             ok = false;
         }
 
@@ -1383,10 +1383,10 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
         bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;
 
         if (!has_eos && !has_sep && !has_rerank_prompt) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
+            COM_WRN("%s", "vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n");
             ok = false;
         } else if (!has_eos) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+            COM_WRN("%s", "vocab does not have an EOS token, using SEP token as fallback\n");
         }
 
         if (!ok) {
@@ -1399,7 +1399,7 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
     }
 
     if (params.warmup) {
-        LOG_INF("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
+        COM_TRC("%s", "warming up the model with an empty run - please wait ... (--no-warmup to disable)\n");
 
         std::vector<llama_token> tmp;
         llama_token bos = llama_vocab_bos(vocab);
@@ -1473,20 +1473,20 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
 
     int ret = llama_decode(ctx, llama_batch_get_one(tmp.data(), tmp.size()));
     if (ret != 0) {
-        LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
+        COM_ERR("llama_decode() failed: %d\n", ret);
         res = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
         goto done;
     }
 
     if (llama_n_rs_seq(ctx) > 0) {
-        LOG_INF("%s: the context supports bounded partial sequence removal\n", __func__);
+        COM_TRC("%s", "the context supports bounded partial sequence removal\n");
         res = COMMON_CONTEXT_SEQ_RM_TYPE_RS;
         goto done;
     }
 
     // try to remove the last tokens
     if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
-        LOG_TRC("%s: the context does not support partial sequence removal\n", __func__);
+        COM_TRC("%s", "the context does not support partial sequence removal\n");
         res = COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
         goto done;
     }
@@ -1803,13 +1803,13 @@ static common_control_vector_data common_control_vector_load_one(const common_co
     };
     struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
     if (!ctx_gguf) {
-        LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
+        COM_ERR("failed to load control vector file from %s\n", load_info.fname.c_str());
         return result;
     }
 
     int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
     if (n_tensors == 0) {
-        LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
+        COM_WRN("no direction tensors found in %s\n", load_info.fname.c_str());
     }
 
     for (int i = 0; i < n_tensors; i++) {
@@ -1827,23 +1827,23 @@ static common_control_vector_data common_control_vector_load_one(const common_co
             }
         }
         if (layer_idx < 0) {
-            LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            COM_ERR("invalid/unparsable direction tensor layer index in %s\n", load_info.fname.c_str());
             result.n_embd = -1;
             break;
         } else if (layer_idx == 0) {
-            LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+            COM_ERR("invalid (zero) direction tensor layer index in %s\n", load_info.fname.c_str());
             result.n_embd = -1;
             break;
         }
 
         struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
         if (tensor->type != GGML_TYPE_F32) {
-            LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
+            COM_ERR("invalid (non-F32) direction tensor type in %s\n", load_info.fname.c_str());
             result.n_embd = -1;
             break;
         }
         if (ggml_n_dims(tensor) != 1) {
-            LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
+            COM_ERR("invalid (non-1D) direction tensor shape in %s\n", load_info.fname.c_str());
             result.n_embd = -1;
             break;
         }
@@ -1851,7 +1851,7 @@ static common_control_vector_data common_control_vector_load_one(const common_co
         if (result.n_embd == -1) {
             result.n_embd = ggml_nelements(tensor);
         } else if (ggml_nelements(tensor) != result.n_embd) {
-            LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
+            COM_ERR("direction tensor in %s does not match previous dimensions\n", load_info.fname.c_str());
             result.n_embd = -1;
             break;
         }
@@ -1868,7 +1868,7 @@ static common_control_vector_data common_control_vector_load_one(const common_co
     }
 
     if (result.n_embd == -1) {
-        LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
+        COM_WRN("skipping %s due to invalid direction tensors\n", load_info.fname.c_str());
         result.data.clear();
     }
 
@@ -1889,7 +1889,7 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
             break;
         }
         if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
-            LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
+            COM_ERR("control vectors in %s does not match previous dimensions\n", info.fname.c_str());
             result.n_embd = -1;
             break;
         }
@@ -1905,7 +1905,7 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
     }
 
     if (result.n_embd == -1) {
-        LOG_ERR("%s: no valid control vector files passed\n", __func__);
+        COM_ERR("%s", "no valid control vector files passed\n");
         result.data.clear();
     }
 
@@ -2016,13 +2016,13 @@ bool common_prompt_batch_decode(
         // memory, so we can't just remove the last token from the memory and replay the last token which
         // is the reason for this logic.
         if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_tokens_before_last))) {
-            LOG_ERR("%s : failed to eval\n", __func__);
+            COM_ERR("%s", "failed to eval\n");
             return false;
         }
         n_past += n_tokens_before_last;
 
         llama_state_save_file(ctx, state_path.data(), all_tokens.data(), all_tokens.size());
-        LOG_INF("saved session before last token to %s, n_new = %zu\n", state_path.data(), all_tokens.size());
+        COM_INF("saved session before last token to %s, n_new = %zu\n", state_path.data(), all_tokens.size());
 
         llama_token last_token = all_tokens.back();
         llama_batch batch = llama_batch_get_one(&last_token, 1);
@@ -2030,13 +2030,13 @@ bool common_prompt_batch_decode(
         batch.pos = &pos;
 
         if (llama_decode(ctx, batch)) {
-            LOG_ERR("%s : failed to eval last token\n", __func__);
+            COM_ERR("%s", "failed to eval last token\n");
             return false;
         }
         n_past++;
     } else {
         if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_new))) {
-            LOG_ERR("%s : failed to eval\n", __func__);
+            COM_ERR("%s", "failed to eval\n");
             return false;
         }
         n_past += n_new;
diff --git a/common/common.h b/common/common.h
index 94147d5d8..d56f6064b 100644
--- a/common/common.h
+++ b/common/common.h
@@ -25,6 +25,13 @@
 #define DIRECTORY_SEPARATOR '/'
 #endif // _WIN32
 
+#define COM_DBG(fmt, ...) LOG_DBG("cmn  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_TRC(fmt, ...) LOG_TRC("cmn  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_INF(fmt, ...) LOG_INF("cmn  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_WRN(fmt, ...) LOG_WRN("cmn  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_ERR(fmt, ...) LOG_ERR("cmn  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_CNT(fmt, ...) LOG_CNT(""              fmt,               __VA_ARGS__)
+
 #define die(msg)          do { fputs("error: " msg "\n", stderr);                exit(1); } while (0)
 #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
 
diff --git a/common/fit.cpp b/common/fit.cpp
index a8565bfc9..afbf0b10f 100644
--- a/common/fit.cpp
+++ b/common/fit.cpp
@@ -233,7 +233,7 @@ static void common_params_fit_impl(
         sum_projected_used = dmds_full.back().mb.total();
         sum_free           = dmds_full.back().total;
         sum_projected_free = sum_free - sum_projected_used;
-        LOG_INF("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
+        LOG_TRC("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
             __func__, sum_projected_used/MiB, sum_free/MiB);
         if (sum_projected_free >= margins[0]) {
             LOG_TRC("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
diff --git a/common/reasoning-budget.cpp b/common/reasoning-budget.cpp
index ce41d029b..7da0bb1c5 100644
--- a/common/reasoning-budget.cpp
+++ b/common/reasoning-budget.cpp
@@ -65,12 +65,12 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
             if (ctx->start_matcher.advance(token)) {
                 ctx->state = REASONING_BUDGET_COUNTING;
                 ctx->remaining = ctx->budget;
-                LOG_INF("reasoning-budget: activated, budget=%d tokens\n", ctx->budget);
+                COM_TRC("activated, budget=%d tokens\n", ctx->budget);
 
                 if (ctx->remaining <= 0) {
                     ctx->state = REASONING_BUDGET_FORCING;
                     ctx->force_pos = 0;
-                    LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+                    COM_TRC("%s", "budget=0, forcing immediately\n");
                 }
             }
             break;
@@ -80,7 +80,7 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
         {
             if (ctx->end_matcher.advance(token)) {
                 ctx->state = REASONING_BUDGET_DONE;
-                LOG_INF("reasoning-budget: deactivated (natural end)\n");
+                COM_TRC("%s", "deactivated (natural end)\n");
                 break;
             }
 
@@ -95,7 +95,7 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
                     ctx->state = REASONING_BUDGET_FORCING;
                     ctx->force_pos = 0;
                     ctx->end_matcher.reset();
-                    LOG_INF("reasoning-budget: UTF-8 complete, now forcing end sequence\n");
+                    COM_TRC("%s", "UTF-8 complete, now forcing end sequence\n");
                 }
             } else if (ctx->state == REASONING_BUDGET_COUNTING) {
                 ctx->remaining--;
@@ -104,11 +104,11 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
                         ctx->state = REASONING_BUDGET_FORCING;
                         ctx->force_pos = 0;
                         ctx->end_matcher.reset();
-                        LOG_INF("reasoning-budget: budget exhausted, forcing end sequence\n");
+                        COM_TRC("%s", "budget exhausted, forcing end sequence\n");
                     } else {
                         ctx->state = REASONING_BUDGET_WAITING_UTF8;
                         ctx->end_matcher.reset();
-                        LOG_INF("reasoning-budget: budget exhausted, waiting for UTF-8 completion\n");
+                        COM_TRC("%s", "budget exhausted, waiting for UTF-8 completion\n");
                     }
                 }
             }
@@ -118,7 +118,7 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
             ctx->force_pos++;
             if (ctx->force_pos >= ctx->forced_tokens.size()) {
                 ctx->state = REASONING_BUDGET_DONE;
-                LOG_INF("reasoning-budget: forced sequence complete, done\n");
+                COM_TRC("%s", "forced sequence complete, done\n");
             }
             break;
         case REASONING_BUDGET_DONE:
@@ -128,12 +128,12 @@ static void common_reasoning_budget_accept(struct llama_sampler * smpl, llama_to
                 ctx->state = REASONING_BUDGET_COUNTING;
                 ctx->remaining = ctx->budget;
                 ctx->end_matcher.reset();
-                LOG_INF("reasoning-budget: re-activated on new start tag, budget=%d tokens\n", ctx->budget);
+                COM_TRC("re-activated on new start tag, budget=%d tokens\n", ctx->budget);
 
                 if (ctx->remaining <= 0) {
                     ctx->state = REASONING_BUDGET_FORCING;
                     ctx->force_pos = 0;
-                    LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+                    COM_TRC("%s", "budget=0, forcing immediately\n");
                 }
             }
             break;
@@ -264,7 +264,7 @@ bool common_reasoning_budget_force(struct llama_sampler * smpl) {
     ctx->state = REASONING_BUDGET_FORCING;
     ctx->force_pos = 0;
     ctx->end_matcher.reset();
-    LOG_INF("reasoning-budget: forced into forcing state (manual transition)\n");
+    COM_TRC("%s", "forced into forcing state (manual transition)\n");
 
     return true;
 }
diff --git a/common/speculative.cpp b/common/speculative.cpp
index c922a3f59..a3495c3a1 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -18,6 +18,13 @@
 #include <map>
 #include <cinttypes>
 
+#define SPC_DBG(fmt, ...) LOG_DBG("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_TRC(fmt, ...) LOG_TRC("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_INF(fmt, ...) LOG_INF("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_WRN(fmt, ...) LOG_WRN("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_ERR(fmt, ...) LOG_ERR("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_CNT(fmt, ...) LOG_CNT(""              fmt,               __VA_ARGS__)
+
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  128
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
 
@@ -60,21 +67,20 @@ static bool common_speculative_are_compatible(
     const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
 
     const auto vocab_type_tgt = llama_vocab_type(vocab_tgt);
-    LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
+    SPC_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
 
     const auto vocab_type_dft = llama_vocab_type(vocab_dft);
-    LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
+    SPC_DBG("vocab_type dft: %d\n", vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
-        LOG_WRN("%s: draft model vocab type must match target model to use speculation but "
-                "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
+        SPC_WRN("draft model vocab type must match target model to use speculation but "
+                "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
         return false;
     }
 
     if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
         (llama_vocab_get_add_bos(vocab_tgt) && llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft))) {
-        LOG_WRN("%s: draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
-                __func__,
+        SPC_WRN("draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
                 llama_vocab_get_add_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_dft),
                 llama_vocab_bos(vocab_tgt), llama_vocab_bos(vocab_dft));
         return false;
@@ -82,8 +88,7 @@ static bool common_speculative_are_compatible(
 
     if (llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
         (llama_vocab_get_add_eos(vocab_tgt) && llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft))) {
-        LOG_WRN("%s: draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
-                __func__,
+        SPC_WRN("draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
                 llama_vocab_get_add_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_dft),
                 llama_vocab_eos(vocab_tgt), llama_vocab_eos(vocab_dft));
         return false;
@@ -97,8 +102,8 @@ static bool common_speculative_are_compatible(
             : n_vocab_dft - n_vocab_tgt;
 
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
-            LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
-            LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
+            SPC_DBG("draft model vocab must closely match target model to use speculation but "
+                    "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
                     n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return false;
         }
@@ -108,8 +113,8 @@ static bool common_speculative_are_compatible(
             const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
 
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
-                LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
+                SPC_DBG("draft model vocab must match target model to use speculation but "
+                        "token %d content differs - target '%s', draft '%s'\n", i,
                         common_token_to_piece(vocab_tgt, i).c_str(),
                         common_token_to_piece(vocab_dft, i).c_str());
                 return false;
@@ -186,9 +191,9 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         auto * ctx_tgt = this->params.ctx_tgt;
 
-        LOG_INF("%s: adding speculative implementation 'draft-simple'\n", __func__);
-        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
-        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+        SPC_TRC("%s", "adding speculative implementation 'draft-simple'\n");
+        SPC_TRC("- n_max=%d, n_min=%d, p_min=%f\n", this->params.n_max, this->params.n_min, this->params.p_min);
+        SPC_TRC("- gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n",
                 this->params.n_gpu_layers,
                 ggml_type_name(this->params.cache_type_k),
                 ggml_type_name(this->params.cache_type_v),
@@ -228,16 +233,16 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         }
 
         const bool vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft));
-        LOG_DBG("%s: vocab_cmpt = %d\n", __func__, vocab_cmpt);
+        SPC_DBG("vocab_cmpt = %d\n", vocab_cmpt);
 
         if (!vocab_cmpt) {
-            LOG_ERR("%s: the target and draft vocabs are not compatible\n", __func__);
+            SPC_ERR("%s", "the target and draft vocabs are not compatible\n");
 
             throw std::runtime_error("draft model vocab type must match target model to use speculation");
         }
 
         if (n_seq != llama_n_seq_max(ctx_dft)) {
-            LOG_ERR("%s: n_seq mismatch: %d != %d\n", __func__, n_seq, llama_n_seq_max(ctx_dft));
+            SPC_ERR("n_seq mismatch: %d != %d\n", n_seq, llama_n_seq_max(ctx_dft));
 
             throw std::runtime_error("the draft model number of sequences is incompatible with the speculative n_seq");
         }
@@ -257,7 +262,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         const int ret = llama_decode(ctx_dft, batch);
 
         if (ret != 0) {
-            LOG_ERR("%s: failed to decode draft batch, ret = %d\n", __func__, ret);
+            SPC_ERR("failed to decode draft batch, ret = %d\n", ret);
 
             return false;
         }
@@ -290,7 +295,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
 
         int ret = llama_decode(ctx_dft, batch);
         if (ret != 0) {
-            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            SPC_ERR("llama_decode returned %d\n", ret);
             return;
         }
 
@@ -314,7 +319,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
                 for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                    SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
                             seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
                             common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
                 }
@@ -354,7 +359,7 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
             // evaluate the drafted tokens on the draft model
             ret = llama_decode(ctx_dft, batch);
             if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
                 break;
             }
 
@@ -449,8 +454,8 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
         , params(params.draft)
     {
-        LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
-        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f, backend_sampling=%d\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min, (int) params.draft.backend_sampling);
+        SPC_TRC("%s", "adding speculative implementation 'draft-eagle3'\n");
+        SPC_TRC("- n_max=%d, n_min=%d, p_min=%f, backend_sampling=%d\n", params.draft.n_max, params.draft.n_min, params.draft.p_min, (int) params.draft.backend_sampling);
 
         auto * ctx_tgt = this->params.ctx_tgt;
         auto * ctx_dft = this->params.ctx_dft;
@@ -493,7 +498,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
                 llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
 
                 if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
-                    LOG_WRN("%s: backend offload failed for seq_id=%d; using CPU sampler\n", __func__, (int) seq_id);
+                    SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
                     llama_sampler_free(chain);
                     chain = nullptr;
                 }
@@ -548,9 +553,9 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
         if (pos_max < N - 2) {
-            LOG_WRN("%s: ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
+            SPC_WRN("ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
                     "Drafts may degrade.\n",
-                    __func__, (int) pos_max, N - 2);
+                    (int) pos_max, N - 2);
         }
     }
 
@@ -621,8 +626,8 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
             };
             const int32_t rc = llama_encode(ctx_dft, enc_batch);
             if (rc != 0) {
-                LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
-                        __func__, rc, (int) n_chunk, (int) i);
+                SPC_ERR("llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                        rc, (int) n_chunk, (int) i);
                 return false;
             }
 
@@ -692,8 +697,8 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         if (batch.n_tokens > 0) {
             const int32_t rc = llama_decode(ctx_dft, batch);
             if (rc != 0) {
-                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
-                        __func__, rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
+                SPC_ERR("llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
+                        rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
                 return false;
             }
         }
@@ -744,7 +749,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
 
         int ret = llama_decode(ctx_dft, batch);
         if (ret != 0) {
-            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            SPC_ERR("llama_decode returned %d\n", ret);
             return;
         }
 
@@ -770,7 +775,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
                 for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                    SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
                             seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
                             common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
                 }
@@ -809,7 +814,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
 
             ret = llama_decode(ctx_dft, batch);
             if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
                 break;
             }
 
@@ -942,9 +947,9 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 "MTP input row width must match the target h_nextn width");
         n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
 
-        LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
-        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
-        LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+        SPC_TRC("%s", "adding speculative implementation 'draft-mtp'\n");
+        SPC_TRC("- n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
+        SPC_TRC("- gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n",
                 this->params.n_gpu_layers,
                 ggml_type_name(this->params.cache_type_k),
                 ggml_type_name(this->params.cache_type_v),
@@ -975,7 +980,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
 
                 if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
-                    LOG_WRN("%s: backend offload failed for seq_id=%d; using CPU sampler\n", __func__, (int) seq_id);
+                    SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
                     llama_sampler_free(chain);
                     chain = nullptr;
                 }
@@ -1038,11 +1043,11 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
 
         if (pos_max < N - 1 && !is_mem_shared) {
-            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d - "
+            SPC_WRN("ctx_dft pos_max=%d < N-1=%d - "
                     "process() hook may not have run on every prefill ubatch "
                     "(need_embd / logits=1 on every prompt position?). "
                     "Drafts may degrade.\n",
-                    __func__, (int) pos_max, N - 1);
+                    (int) pos_max, N - 1);
         }
     }
 
@@ -1128,8 +1133,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
                 const int32_t rc = llama_decode(ctx_dft, batch);
                 if (rc != 0) {
-                    LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
-                            __func__, head, (int) rc, (int) batch_in.pos[0]);
+                    SPC_ERR("llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
+                            head, (int) rc, (int) batch_in.pos[0]);
                     ok = false;
                     break;
                 }
@@ -1217,7 +1222,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
             int ret = llama_decode(ctx_dft, batch);
             if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
                 break;
             }
 
@@ -1239,7 +1244,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
                 for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
-                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                    SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
                             seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
                             common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
                 }
@@ -1353,8 +1358,8 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
         , params(params.ngram_simple)
         , config(config)
     {
-        LOG_INF("%s: adding speculative implementation 'ngram-simple'\n", __func__);
-        LOG_INF("%s: - size_n=%d, size_m=%d, min_hits=%d\n", __func__,
+        SPC_TRC("%s", "adding speculative implementation 'ngram-simple'\n");
+        SPC_TRC("- size_n=%d, size_m=%d, min_hits=%d\n",
                 this->params.size_n, this->params.size_m, this->params.min_hits);
     }
 
@@ -1403,8 +1408,8 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
             this->config.push_back(config);
         }
 
-        LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(this->type).c_str());
-        LOG_INF("%s: - size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n", __func__,
+        SPC_TRC("adding speculative implementation '%s'\n", common_speculative_type_to_str(this->type).c_str());
+        SPC_TRC("- size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n",
                 config.size_key, config.size_value, config.key_only, config.min_hits);
     }
 
@@ -1478,15 +1483,15 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         , verbose(std::getenv("LLAMA_TRACE") != nullptr) {
         static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
 
-        LOG_INF("%s: adding speculative implementation 'ngram-mod'\n", __func__);
-        LOG_INF("%s: - n_match=%d, n_max=%d, n_min=%d\n", __func__,
+        SPC_TRC("%s", "adding speculative implementation 'ngram-mod'\n");
+        SPC_TRC("- n_match=%d, n_max=%d, n_min=%d\n",
                 this->params.n_match, this->params.n_max, this->params.n_min);
-        LOG_INF("%s: - mod size=%zu (%.3f MB)\n", __func__,
+        SPC_TRC("- mod size=%zu (%.3f MB)\n",
                 mod.size(), (float)(mod.size_bytes())/1024/1024);
 
         if (this->params.n_match < 16) {
-            LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
-                    "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, this->params.n_match);
+            SPC_WRN("ngram_mod n_match=%d is too small - poor quality is possible, "
+                    "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", this->params.n_match);
         }
 
         sinfos.resize(n_seq);
@@ -1510,11 +1515,11 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
         sinfo.i_last = prompt.size() - n;
 
         const double f = (double)mod.get_used() / (double)mod.size();
-        LOG_INF("%s: ngram_mod occupancy = %zu/%zu (%.2f)\n", __func__, mod.get_used(), mod.size(), f);
+        SPC_TRC("ngram_mod occupancy = %zu/%zu (%.2f)\n", mod.get_used(), mod.size(), f);
 
         constexpr double f_thold = 0.25;
         if (f > f_thold) {
-            LOG_WRN("%s: ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", __func__, f, f_thold);
+            SPC_WRN("ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", f, f_thold);
 
             mod.reset();
         }
@@ -1608,7 +1613,7 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
                 sinfo.n_low++;
                 if (sinfo.n_low >= 5) {
                     if (verbose) {
-                        LOG_WRN("%s: low acceptance streak (%d) - resetting ngram_mod\n", __func__, sinfo.n_low);
+                        SPC_TRC("low acceptance streak (%d) - resetting ngram_mod\n", sinfo.n_low);
                     }
 
                     mod.reset();
@@ -1658,8 +1663,8 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
         , save_dynamic(save_dynamic)
         , save_static(save_static)
     {
-        LOG_INF("%s: adding speculative implementation 'ngram-cache'\n", __func__);
-        LOG_INF("%s: - n_draft=%d, cache_static=%s, cache_dynamic=%s\n", __func__,
+        SPC_TRC("%s", "adding speculative implementation 'ngram-cache'\n");
+        SPC_TRC("- n_draft=%d, cache_static=%s, cache_dynamic=%s\n",
                 n_draft,
                 path_static.empty() ? "none" : path_static.c_str(),
                 path_dynamic.empty() ? "none" : path_dynamic.c_str());
@@ -1674,7 +1679,7 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
                     sinfo.ngram_cache_static = ngram_cache_static;
                 }
             } catch (...) {
-                LOG_ERR("failed to open static lookup cache: %s", path_static.c_str());
+                SPC_ERR("failed to open static lookup cache: %s", path_static.c_str());
                 GGML_ABORT("Couldn't read static lookup cache");
             }
         }
@@ -1687,7 +1692,7 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
                     sinfo.ngram_cache_dynamic = ngram_cache_dynamic;
                 }
             } catch (...) {
-                LOG_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
+                SPC_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
                 GGML_ABORT("Couldn't read dynamic lookup cache");
             }
         }
@@ -2034,7 +2039,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
     }
 
     if (impls.empty()) {
-        LOG_WRN("%s: no implementations specified for speculative decoding\n", __func__);
+        SPC_TRC("%s", "no implementations specified for speculative decoding\n");
         return nullptr;
     }
 
@@ -2161,13 +2166,13 @@ void common_speculative_draft(common_speculative * spec) {
 
                 if (dp.n_max > 0) {
                     if (!result.empty() && (int) result.size() > dp.n_max) {
-                        LOG_DBG("%s: truncating draft to %d tokens\n", __func__, dp.n_max);
+                        SPC_DBG("truncating draft to %d tokens\n", dp.n_max);
                         result.resize(dp.n_max);
                     }
                 }
 
                 if (!result.empty()) {
-                    LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
+                    SPC_DBG("called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n",
                             common_speculative_type_to_str(impl.get()->type).c_str(), dp.prompt->size(),
                             impl.get()->n_call_draft, result.size());
 
@@ -2291,7 +2296,7 @@ void common_speculative_print_stats(const common_speculative * spec) {
             str_stats = ", #mean acc len = " + oss.str() + ", #acc rate/pos = (" + tmp.str() + ")";
         }
 
-        LOG_INF("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s%s\n",
+        SPC_TRC("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s%s\n",
                 common_speculative_type_to_str(impl->type).c_str(),
                 impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
                 impl->n_gen_drafts,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 220240ea9..9f8a8fdb8 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -256,7 +256,7 @@ llama_context::llama_context(
     LLAMA_LOG_INFO("%s: n_outputs_max = %u\n",   __func__, cparams.n_outputs_max);
 
     if (cparams.n_ctx_seq < hparams.n_ctx_train) {
-        LLAMA_LOG_WARN("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
+        LLAMA_LOG_INFO("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
                 __func__, cparams.n_ctx_seq, hparams.n_ctx_train);
     }
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 5c33a418f..bb1c236cb 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -106,7 +106,6 @@ struct server_batch {
         if ((int32_t)tokens.size() >= n_tokens_alloc) {
             return false;
         }
-        // LOG_INF("adding token to batch: slot=%d, token=%d, pos=%d, output=%d\n", id_slot, token, pos, output);
         tokens.push_back({ id_slot, token, pos, output });
         return true;
     }
@@ -228,7 +227,7 @@ struct server_slot {
 
         const size_t cur_size = cur_size_tgt + cur_size_dft;
 
-        SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB (draft: %.3f MiB)\n",
+        SRV_TRC(" - saving prompt with length %d, total state size = %.3f MiB (draft: %.3f MiB)\n",
                 (int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0), cur_size_dft / (1024.0 * 1024.0));
 
         auto * cur = prompt_cache.alloc(prompt, cur_size_tgt, cur_size_dft);
@@ -258,7 +257,7 @@ struct server_slot {
             GGML_ASSERT(!is_processing());
         }
 
-        SLT_INF(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
+        SLT_TRC(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
 
         common_context_seq_rm(ctx_tgt, id, -1, -1);
         if (ctx_dft) {
@@ -627,8 +626,10 @@ struct server_slot {
             }
 
             SLT_INF(*this,
-                    "draft acceptance = %0.5f (%5d accepted / %5d generated), mean acceptance length = %5.2f, acceptance rate per position = (%s)\n",
-                    draft_ratio, n_draft_accepted, n_draft_total, mean_acc_len, acceptance_rates_per_pos.c_str());
+                    "draft acceptance = %0.5f (%5d accepted / %5d generated), mean len = %5.2f\n",
+                    draft_ratio, n_draft_accepted, n_draft_total, mean_acc_len);
+            SLT_TRC(*this,
+                    "     acc per pos = (%s)\n", acceptance_rates_per_pos.c_str());
         }
 
         common_speculative_print_stats(spec);
@@ -771,7 +772,7 @@ struct server_slot {
         }
 
         // TODO @ngxson : move this log line to debug when it become more stable
-        SLT_INF(*this, "encoding mtmd batch from idx = %zu, n_chunks = %d\n", idx, n_added);
+        SLT_TRC(*this, "encoding mtmd batch from idx = %zu, n_chunks = %d\n", idx, n_added);
 
         res = mtmd_batch_encode(mbatch.get());
         if (res != 0) {
@@ -1032,7 +1033,8 @@ private:
         }
 
 
-        SRV_INF("loading model '%s'\n", params.model.path.c_str());
+        SRV_INF("loading model '%s'\n", params.model.get_name().c_str());
+        SRV_TRC("local path '%s'\n", params.model.path.c_str());
 
         std::string & mmproj_path = params_base.mmproj.path;
         mtmd_context_params mparams = mtmd_context_params_default();
@@ -1061,7 +1063,7 @@ private:
                 for (auto & [dev, size] : mmproj_mem) {
                     total += size;
                 }
-                SRV_INF("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB (took %.2f ms)\n", total / (1024.0 * 1024.0), t_elapsed / 1000.0);
+                SRV_TRC("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB (took %.2f ms)\n", total / (1024.0 * 1024.0), t_elapsed / 1000.0);
                 GGML_ASSERT(!params_base.fit_params_target.empty());
                 for (auto & [dev, size] : mmproj_mem) {
                     for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
@@ -1141,7 +1143,7 @@ private:
                             }
                         }
                     }
-                    SRV_INF("[spec] estimated memory usage of %s is %.2f MiB\n",
+                    SRV_TRC("[spec] estimated memory usage of %s is %.2f MiB\n",
                             has_draft ? "draft model" : "MTP context",
                             total / (1024.0 * 1024.0));
                 } catch (const std::exception & e) {
@@ -1177,7 +1179,7 @@ private:
             // TODO speculative: move to common/speculative.cpp?
             const auto & params_spec = params_base.speculative.draft;
 
-            SRV_INF("loading draft model '%s'\n", params_spec.mparams.path.c_str());
+            SRV_TRC("loading draft model '%s'\n", params_spec.mparams.path.c_str());
 
             auto params_dft = params_base;
 
@@ -1229,7 +1231,7 @@ private:
             // no new model load, so we simply report 0.0 and 1.0 progress
             load_progress_callback(0.0f, &load_progress_spec);
 
-            SRV_INF("creating MTP draft context against the target model '%s'\n",
+            SRV_TRC("creating MTP draft context against the target model '%s'\n",
                     params_base.model.path.c_str());
 
             auto cparams_mtp = common_context_params_to_llama(params_base);
@@ -1303,9 +1305,6 @@ private:
         // Necessary similarity of prompt for slot selection
         slot_prompt_similarity = params_base.slot_prompt_similarity;
 
-        // setup slots
-        SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel);
-
         const int n_ctx_train = llama_model_n_ctx_train(model_tgt);
 
         int n_ctx_slot = llama_n_ctx_seq(ctx_tgt);
@@ -1322,9 +1321,13 @@ private:
         }
 
         if (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
-            SRV_WRN("%s", "speculative decoding will use checkpoints\n");
+            SRV_TRC("%s", "speculative decoding will use checkpoints\n");
         }
 
+        // setup slots
+        SRV_INF("initializing, n_slots = %d, n_ctx_slot = %d, kv_unified = '%s'\n",
+                params_base.n_parallel, n_ctx_slot, params_base.kv_unified ? "true" : "false");
+
         // initialize slots
         for (int i = 0; i < params_base.n_parallel; i++) {
             slots.emplace_back();
@@ -1344,7 +1347,7 @@ private:
         }
 
         if (spec) {
-            SRV_INF("%s", "speculative decoding context initialized\n");
+            SRV_TRC("%s", "speculative decoding context initialized\n");
         } else {
             ctx_dft.reset();
         }
@@ -1361,7 +1364,7 @@ private:
             slot.mctx                   = mctx;
             slot.prompt.tokens.has_mtmd = mctx != nullptr;
 
-            SLT_INF(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
+            SLT_TRC(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
 
             slot.callback_on_release = [this](int id_slot) {
                 queue_tasks.pop_deferred_task(id_slot);
@@ -1397,23 +1400,23 @@ private:
 
         if (params_base.cache_ram_mib != 0) {
             if (params_base.cache_ram_mib < 0) {
-                SRV_INF("prompt cache is enabled, size limit: %s\n", "no limit");
+                SRV_TRC("prompt cache is enabled, size limit: %s\n", "no limit");
             } else {
-                SRV_INF("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
+                SRV_TRC("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
             }
-            SRV_INF("%s", "use `--cache-ram 0` to disable the prompt cache\n");
+            SRV_TRC("%s", "use `--cache-ram 0` to disable the prompt cache\n");
 
             prompt_cache = std::make_unique<server_prompt_cache>(params_base.cache_ram_mib, n_ctx);
         } else {
-            SRV_INF("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
+            SRV_TRC("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
         }
-        SRV_INF("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
+        SRV_TRC("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
 
         if (params_base.n_ctx_checkpoints > 0) {
-            SRV_INF("context checkpoints enabled, max = %d, min spacing = %d\n",
+            SRV_TRC("context checkpoints enabled, max = %d, min spacing = %d\n",
                     params_base.n_ctx_checkpoints, params_base.checkpoint_min_step);
         } else {
-            SRV_INF("%s", "context checkpoints disabled\n");
+            SRV_TRC("%s", "context checkpoints disabled\n");
         }
 
         if (!params_base.model_alias.empty()) {
@@ -1470,11 +1473,11 @@ private:
                 params_base.cache_idle_slots = false;
             } else {
                 if (params_base.kv_unified) {
-                    SRV_INF("%s", "idle slots will be saved to prompt cache and cleared upon starting a new task\n");
+                    SRV_TRC("%s", "idle slots will be saved to prompt cache and cleared upon starting a new task\n");
                 } else {
                     // without a unified KV cache, clearing a slot frees no reusable room, so we only
                     // publish a RAM-cache copy of idle slots (their KV stays in VRAM) [TAG_IDLE_SLOT_CLEAR]
-                    SRV_INF("%s", "idle slots will be saved to prompt cache upon starting a new task\n");
+                    SRV_TRC("%s", "idle slots will be saved to prompt cache upon starting a new task\n");
                 }
                 SRV_DBG("%s", "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__\n");
             }
@@ -1500,7 +1503,7 @@ private:
             try {
                 chat_templates = common_chat_templates_init(model_tgt, params_base.chat_template);
 
-                LOG_INF("%s: chat template, example_format: '%s'\n", __func__,
+                SRV_TRC("%s: chat template, example_format: '%s'\n", __func__,
                     common_chat_format_example(chat_templates.get(), params_base.use_jinja, params_base.default_template_kwargs).c_str());
 
             } catch (const std::exception & e) {
@@ -1515,7 +1518,7 @@ private:
             // 2. The chat template supports it
             const bool template_supports_thinking = params_base.use_jinja && common_chat_templates_support_enable_thinking(chat_templates.get());
             const bool enable_thinking = params_base.enable_reasoning != 0 && template_supports_thinking;
-            SRV_INF("%s: chat template, thinking = %d\n", __func__, enable_thinking);
+            SRV_TRC("%s: chat template, thinking = %d\n", __func__, enable_thinking);
 
             // IMPORTANT: chat_params is reused across sleeping / resuming states,
             //            never store llama_context/llama_model pointers in chat_params,
@@ -1658,7 +1661,7 @@ private:
             update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;
 
             if (update_cache) {
-                SRV_INF("%s", "updating prompt cache\n");
+                SRV_TRC("%s", "updating prompt cache\n");
 
                 const int64_t t_start = ggml_time_us();
 
@@ -1670,7 +1673,7 @@ private:
 
                 prompt_cache->update();
 
-                SRV_INF("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
+                SRV_TRC("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
             }
         }
 
@@ -2290,7 +2293,7 @@ private:
 
         int id_parent = parent_task.id;
 
-        SRV_INF("launching slots for parent task id_task = %d with %zu child tasks\n", id_parent, parent_task.child_tasks.size());
+        SRV_TRC("launching slots for parent task id_task = %d with %zu child tasks\n", id_parent, parent_task.child_tasks.size());
 
         // to be called in case of failure to release all launched slots
         auto release_slots = [this, id_parent]() {
@@ -2351,7 +2354,7 @@ private:
         // stash the draft's speculative state with the checkpoint
         common_speculative_get_state(spec.get(), slot.id, cur.data_spec);
 
-        SLT_INF(slot,
+        SLT_TRC(slot,
                 "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n",
                 (int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min,
                 cur.pos_max, cur.n_tokens, (float) cur.size() / 1024 / 1024);
@@ -2415,7 +2418,7 @@ private:
                     if (params_base.cache_idle_slots) {
                         for (auto & slot : slots) {
                             if (!slot.is_processing()) {
-                                SLT_INF(slot, "%s", "saving idle slot to prompt cache\n");
+                                SLT_TRC(slot, "%s", "saving idle slot to prompt cache\n");
 
                                 if (slot.prompt_save(*prompt_cache)) {
                                     SLT_DBG(slot, "%s", "__TEST_TAG_CACHE_IDLE_SLOT__\n");
@@ -2671,7 +2674,7 @@ private:
                     auto new_loras = construct_lora_list(task.set_lora);
                     // logging
                     for (size_t i = 0; i < new_loras.size(); ++i) {
-                        SRV_INF("set lora adapter idx=%zu scale=%f\n", i, new_loras[i].scale);
+                        SRV_TRC("set lora adapter idx=%zu scale=%f\n", i, new_loras[i].scale);
                     }
                     // TODO @ngxson : make lora_adapters a dedicated member of server_context
                     params_base.lora_adapters = new_loras;
@@ -2771,7 +2774,7 @@ private:
             }
 
             if (all_idle) {
-                SRV_INF("%s", "all slots are idle\n");
+                SRV_TRC("%s", "all slots are idle\n");
                 return; // skip further processing
 
             } else {
@@ -3287,10 +3290,9 @@ private:
                                     const auto it = std::find_if(
                                         slot.prompt.checkpoints.rbegin(),
                                         slot.prompt.checkpoints.rend(),
-                                        [&, func_name = __func__](const auto & cur) {
+                                        [&](const auto & cur) {
                                             // guarantee that a checkpoint will result in at least one token being processed [TAG_PROMPT_LOGITS]
-                                            LOG_INF("slot %12.*s: id %2d | task %d | Checking checkpoint with [%d, %d] against %d...\n", 12,
-                                                func_name, (slot).id, ((slot).task ? (slot).task->id : -1), cur.pos_min, cur.pos_max, pos_min_thold);
+                                            SLT_TRC(slot, "checking checkpoint with [%d, %d] against %d...\n", cur.pos_min, cur.pos_max, pos_min_thold);
                                             // workaround for [TAG_CHECKPOINTS_FIX_POS_MIN]
                                             if (cur.pos_max > pos_next) {
                                                 return false;
@@ -3310,11 +3312,11 @@ private:
 
                                         pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
                                         n_past   = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);
-                                        SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) it->size() / 1024 / 1024);
+                                        SLT_TRC(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) it->size() / 1024 / 1024);
                                     }
 
                                     if (do_reset) {
-                                        SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
+                                        SLT_TRC(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
                                                 "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
                                         pos_next = 0;
                                         n_past = 0;
@@ -3327,7 +3329,7 @@ private:
                                 for (auto it = slot.prompt.checkpoints.begin(); it != slot.prompt.checkpoints.end();) {
                                     const auto & cur = *it;
                                     if (cur.pos_max > pos_next) {
-                                        SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.size() / 1024 / 1024);
+                                        SLT_TRC(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.size() / 1024 / 1024);
                                         it = slot.prompt.checkpoints.erase(it);
                                     } else {
                                         ++it;
@@ -3674,7 +3676,7 @@ private:
                 // all children slots should already launched by launch_slots_with_parent_task()
                 // copy state to the child slots
                 for (auto & child : children) {
-                    SLT_INF(slot, " - copying state to child %d\n", child->id);
+                    SLT_TRC(slot, " - copying state to child %d\n", child->id);
 
                     GGML_ASSERT(child->state == SLOT_STATE_WAIT_OTHER);
 
diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index 82f34edac..21bed64c9 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -83,7 +83,7 @@ bool server_http_context::init(const common_params & params) {
     hostname = params.hostname;
 
     if (gcp.enabled) {
-        SRV_INF("Google Cloud Platform compat: health route = %s, predict route = %s, port = %d\n", gcp.path_health.c_str(), gcp.path_predict.c_str(), gcp.port);
+        SRV_TRC("Google Cloud Platform compat: health route = %s, predict route = %s, port = %d\n", gcp.path_health.c_str(), gcp.path_predict.c_str(), gcp.port);
 
         if (port != gcp.port) {
             SRV_WRN("Google Cloud Platform compat: overriding server port %d with AIP_HTTP_PORT %d\n", port, gcp.port);
@@ -96,13 +96,13 @@ bool server_http_context::init(const common_params & params) {
 
 #ifdef CPPHTTPLIB_OPENSSL_SUPPORT
     if (!params.ssl_file_key.empty() && !params.ssl_file_cert.empty()) {
-        SRV_INF("running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
+        SRV_TRC("running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
         srv = std::make_unique<httplib::SSLServer>(
             params.ssl_file_cert.c_str(), params.ssl_file_key.c_str()
         );
         is_ssl = true;
     } else {
-        SRV_INF("%s", "running without SSL\n");
+        SRV_TRC("%s", "running without SSL\n");
         srv = std::make_unique<httplib::Server>();
     }
 #else
@@ -165,9 +165,9 @@ bool server_http_context::init(const common_params & params) {
     if (params.api_keys.size() == 1) {
         const auto key = params.api_keys[0];
         const std::string substr = key.substr(std::max(static_cast<int>(key.length() - 4), 0));
-        SRV_INF("api_keys: ****%s\n", substr.c_str());
+        SRV_TRC("api_keys: ****%s\n", substr.c_str());
     } else if (params.api_keys.size() > 1) {
-        SRV_INF("api_keys: %zu keys loaded\n", params.api_keys.size());
+        SRV_TRC("api_keys: %zu keys loaded\n", params.api_keys.size());
     }
 
     //
@@ -293,7 +293,7 @@ bool server_http_context::init(const common_params & params) {
         // +4 threads for monitoring, health and some threads reserved for MCP and other tasks in the future
         n_threads_http = std::max(params.n_parallel + 4, static_cast<int32_t>(std::thread::hardware_concurrency() - 1));
     }
-    SRV_INF("using %d threads for HTTP server\n", n_threads_http);
+    SRV_TRC("using %d threads for HTTP server\n", n_threads_http);
     srv->new_task_queue = [n_threads_http] {
         // spawn n_threads_http fixed thread (always alive), while allow up to 1024 max possible additional threads
         // when n_threads_http is used, server will create new "dynamic" threads that will be destroyed after processing each request
@@ -412,13 +412,13 @@ bool server_http_context::start() {
     auto is_sock = false;
     if (string_ends_with(std::string(hostname), ".sock")) {
         is_sock = true;
-        SRV_INF("%s", "setting address family to AF_UNIX\n");
+        SRV_TRC("%s", "setting address family to AF_UNIX\n");
         srv->set_address_family(AF_UNIX);
         // bind_to_port requires a second arg, any value other than 0 should
         // simply get ignored
         was_bound = srv->bind_to_port(hostname, 8080);
     } else {
-        SRV_INF("%s", "binding port with default address family\n");
+        SRV_TRC("%s", "binding port with default address family\n");
         // bind HTTP listen port
         if (port == 0) {
             const auto bound_port = srv->bind_to_any_port(hostname);
diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp
index ed4bda241..07a842bd6 100644
--- a/tools/server/server-schema.cpp
+++ b/tools/server/server-schema.cpp
@@ -287,7 +287,7 @@ std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params &
         ->set_desc("Chat format used internally by the server")
         ->set_handler([&](field_eval_context & ctx, const json & data) {
             ctx.params.chat_parser_params.format = static_cast<common_chat_format>(data.at("chat_format").get<int>());
-            SRV_INF("Chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
+            SRV_TRC("chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
         }));
 
     add((new field_str("reasoning_format"))
diff --git a/tools/server/server-stream.cpp b/tools/server/server-stream.cpp
index 757c36ad2..785c28b3a 100644
--- a/tools/server/server-stream.cpp
+++ b/tools/server/server-stream.cpp
@@ -339,11 +339,11 @@ void stream_pipe_producer::close() {
     // httplib bails its content provider the moment is_peer_alive() goes false, so pump the rest
     // of the generation into the ring buffer here. a DELETE flips is_cancelled and cuts it short
     if (done_ || session_->is_cancelled()) {
-        SRV_INF("stream_pipe close: skip drain (done=%d cancelled=%d) conv=%s\n",
+        SRV_TRC("stream_pipe close: skip drain (done=%d cancelled=%d) conv=%s\n",
                 done_ ? 1 : 0, session_->is_cancelled() ? 1 : 0, session_->conversation_id.c_str());
         return;
     }
-    SRV_INF("stream_pipe close: draining conv=%s\n", session_->conversation_id.c_str());
+    SRV_TRC("stream_pipe close: draining conv=%s\n", session_->conversation_id.c_str());
     size_t drained = 0;
     std::string chunk;
     while (true) {
@@ -357,7 +357,7 @@ void stream_pipe_producer::close() {
             break;
         }
     }
-    SRV_INF("stream_pipe close: drain ended conv=%s bytes=%zu\n", session_->conversation_id.c_str(), drained);
+    SRV_TRC("stream_pipe close: drain ended conv=%s bytes=%zu\n", session_->conversation_id.c_str(), drained);
 }
 
 std::shared_ptr<stream_pipe_producer> stream_pipe_producer::create(stream_session_ptr session,
@@ -520,7 +520,7 @@ server_http_context::handler_t make_stream_delete_handler() {
         if (conv_id.empty()) {
             return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
         }
-        SRV_INF("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str());
+        SRV_TRC("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str());
         g_stream_sessions.evict_and_cancel(conv_id);
         auto res = std::make_unique<server_http_res>();
         res->status = 204;
@@ -550,8 +550,7 @@ std::string stream_conv_id_from_headers(const std::map<std::string, std::string>
 
 void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
     std::string conversation_id = stream_conv_id_from_headers(headers);
-    SRV_INF("stream_session_attach_pipe: conv_id=%s (empty=%d)\n",
-            conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
+    SRV_TRC("conv_id=%s (empty=%d)\n", conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
     if (conversation_id.empty()) {
         return;
     }
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index a9ebac013..775f50baf 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -1626,7 +1626,7 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);
 
         if (cur_lcp_len == (int) prompt.tokens.size()) {
-            SRV_INF("%s", " - prompt is already in the cache, skipping\n");
+            SRV_TRC("%s", " - prompt is already in the cache, skipping\n");
             return nullptr;
         }
     }
@@ -1636,7 +1636,7 @@ server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t
         const int len = it->tokens.get_common_prefix(prompt.tokens);
 
         if (len == (int) it->tokens.size()) {
-            SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);
+            SRV_TRC(" - removing obsolete cached prompt with length %d\n", len);
 
             it = states.erase(it);
         } else {
@@ -1681,7 +1681,7 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
     float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
     float sim_best    = float(lcp_best) / tokens_new.size();
 
-    SRV_INF(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+    SRV_TRC(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
 
     auto it_best = states.end();
 
@@ -1706,7 +1706,7 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
     }
 
     if (it_best != states.end()) {
-        SRV_INF(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+        SRV_TRC(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
 
         {
             auto & data = it_best->data.main;
@@ -1783,11 +1783,11 @@ void server_prompt_cache::update() {
         }
     }
 
-    SRV_INF(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
+    SRV_TRC(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
             states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);
 
     for (const auto & state : states) {
-        SRV_INF("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
+        SRV_TRC("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
                 (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
     }
 }
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 1bbc99d89..eafef86ba 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -124,7 +124,7 @@ int llama_server(int argc, char ** argv) {
         }
 
         if (params.n_parallel < 0) {
-            SRV_INF("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
+            SRV_TRC("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
 
             params.n_parallel = 4;
             params.kv_unified = true;
@@ -338,7 +338,7 @@ int llama_server(int argc, char ** argv) {
     std::function<void()> clean_up;
 
     if (is_router_server) {
-        SRV_INF("%s", "starting router server, no model will be loaded in this process\n");
+        SRV_INF("%s", "starting server in router mode. models will be automatically loaded on-demand\n");
 
         clean_up = [&models_routes]() {
             SRV_INF("%s: cleaning up before exit...\n", __func__);
@@ -391,9 +391,6 @@ int llama_server(int argc, char ** argv) {
             });
         }
 
-        // load the model
-        SRV_INF("%s", "loading model\n");
-
         if (!ctx_server.load_model(params)) {
             clean_up();
             if (ctx_http.thread.joinable()) {
@@ -429,8 +426,9 @@ int llama_server(int argc, char ** argv) {
     SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
 
+    SRV_INF("listening on %s\n", ctx_http.listening_address.c_str());
+
     if (is_router_server) {
-        SRV_INF("router server is listening on %s\n", ctx_http.listening_address.c_str());
         SRV_WRN("%s", "NOTE: router mode is experimental\n");
         SRV_WRN("%s", "      it is not recommended to use this mode in untrusted environments\n");
 
@@ -446,8 +444,6 @@ int llama_server(int argc, char ** argv) {
         // when the HTTP server stops, clean up and exit
         clean_up();
     } else {
-        SRV_INF("server is listening on %s\n", ctx_http.listening_address.c_str());
-
         // optionally, notify router server that this instance is ready
         std::thread monitor_thread;
         if (child.is_child()) {

From c1a1c8ee94e37ff7ba2c872e783aaf7f77e0f320 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Sun, 28 Jun 2026 12:34:11 +0200
Subject: [PATCH 207/292] common : allow --offline in llama download (#25091)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Expose the existing --offline flag to `llama download` so a script can
run it to check whether a model is already cached and ready to be served
without touching the network.

Also fix a latent use-after-free in the URL-task on_done callback:
first_path is block-scoped and was captured by reference, but invoked
after the block ends.

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 common/arg.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 841ca3ce2..c289ff713 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -467,7 +467,7 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         // the first part is what gets loaded, so point params.model.path at it
         if (!url_tasks.empty()) {
             std::string first_path = url_tasks.front().local_path;
-            url_tasks.front().on_done = [&]() { params.model.path = first_path; };
+            url_tasks.front().on_done = [&, first_path]() { params.model.path = first_path; };
         }
         for (auto & task : url_tasks) {
             tasks.push_back(std::move(task));
@@ -3471,7 +3471,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.offline = true;
         }
-    ).set_env("LLAMA_ARG_OFFLINE"));
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD}).set_env("LLAMA_ARG_OFFLINE"));
     add_opt(common_arg(
         {"-lv", "--verbosity", "--log-verbosity"}, "N",
         string_format("Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:\n"

From d1b34251bc57b696a5c91968069f8a0e6be13ef4 Mon Sep 17 00:00:00 2001
From: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Sun, 28 Jun 2026 15:01:34 +0200
Subject: [PATCH 208/292] spec : add DFlash support (#22105)

* spec: add DFlash v2 support

* dflash: support sliding window attention per layer_types

* docs: add dflash section

---------

Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
---
 common/common.h            |   3 +-
 common/speculative.cpp     | 303 ++++++++++++++++++++++++++++++++++++-
 conversion/__init__.py     |   1 +
 conversion/qwen.py         |  52 +++++++
 docs/speculative.md        |  29 +++-
 gguf-py/gguf/constants.py  |  18 +++
 src/llama-arch.cpp         |   1 +
 src/llama-arch.h           |   1 +
 src/llama-context.cpp      |   4 +-
 src/llama-graph.cpp        |   7 +-
 src/llama-model.cpp        |   6 +-
 src/models/dflash.cpp      | 276 +++++++++++++++++++++++++++++++++
 src/models/models.h        |  16 ++
 tests/test-llama-archs.cpp |   4 +-
 14 files changed, 712 insertions(+), 9 deletions(-)
 create mode 100644 src/models/dflash.cpp

diff --git a/common/common.h b/common/common.h
index d56f6064b..2adb310b8 100644
--- a/common/common.h
+++ b/common/common.h
@@ -169,6 +169,7 @@ enum common_speculative_type {
     COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE,  // standalone draft model speculative decoding
     COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3,  // Eagle3 speculative decoding
     COMMON_SPECULATIVE_TYPE_DRAFT_MTP,     // Multi-token prediction
+    COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH,  // DFlash speculative decoding
     COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding based on n-grams
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
     COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
@@ -384,7 +385,7 @@ struct common_params_speculative {
 
     uint32_t need_n_rs_seq() const {
         bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
-            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3;
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH;
         });
 
         return needs_rs_seq ? draft.n_max : 0u;
diff --git a/common/speculative.cpp b/common/speculative.cpp
index a3495c3a1..3951bbed5 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -33,6 +33,7 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
     {"draft-simple",  COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE},
     {"draft-eagle3",  COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3},
     {"draft-mtp",     COMMON_SPECULATIVE_TYPE_DRAFT_MTP},
+    {"draft-dflash",  COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH},
     {"ngram-simple",  COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
     {"ngram-map-k",   COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
     {"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
@@ -898,6 +899,296 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
     }
 };
 
+// DFlash: block-diffusion drafting with a draft-side KV cache injection
+struct common_speculative_impl_draft_dflash : public common_speculative_impl {
+    common_params_speculative_draft params;
+
+    llama_batch batch;        // noise tokens
+    llama_batch batch_inject; // target features for KV cache injection
+
+    std::vector<common_sampler_ptr> smpls;
+
+    int32_t n_embd_dec = 0;  // draft hidden size
+    int32_t n_embd_enc = 0;  // target_layer_ids_n * target_hidden_size
+    int32_t n_embd_tgt = 0;  // target model hidden size
+
+    int32_t     block_size    = 0;
+    llama_token mask_token_id = 0;
+
+    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
+    uint32_t        target_layer_ids_n = 0;
+
+    // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
+    std::vector<float> features_buf;
+
+    common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq)
+        : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, n_seq)
+        , params(params.draft)
+    {
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+        GGML_ASSERT(ctx_tgt && ctx_dft && "DFlash requires ctx_tgt and ctx_dft to be set");
+
+        const llama_model * model_dft = llama_get_model(ctx_dft);
+        const llama_model * model_tgt = llama_get_model(ctx_tgt);
+
+        target_layer_ids   = llama_model_target_layer_ids  (model_dft);
+        target_layer_ids_n = llama_model_target_layer_ids_n(model_dft);
+        GGML_ASSERT(target_layer_ids_n > 0 && "DFlash model has no target_layer_ids");
+
+        n_embd_tgt    = llama_model_n_embd(model_tgt);
+        n_embd_dec    = llama_model_n_embd(model_dft);
+        n_embd_enc    = (int32_t) target_layer_ids_n * n_embd_tgt;
+
+        // read the trained block size from the dflash.block_size metadata key
+        block_size = 16;
+        {
+            char buf[32] = {};
+            if (llama_model_meta_val_str(model_dft, "dflash.block_size", buf, sizeof(buf)) >= 0) {
+                block_size = std::atoi(buf);
+            }
+        }
+        mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));
+
+        LOG_INF("%s: adding speculative implementation 'draft-dflash'\n", __func__);
+        LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
+        LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n);
+
+        // DFlash input is [id_last, <mask> * (block_size-1)], so it can draft at most block_size-1 tokens per step
+        if (this->params.n_max > block_size - 1) {
+            LOG_WRN("%s: requested draft size %d exceeds the trained DFlash block size %d -- clamping to %d draft tokens per step\n",
+                    __func__, this->params.n_max, block_size - 1, block_size - 1);
+            this->params.n_max = block_size - 1;
+        }
+
+        batch        = llama_batch_init(llama_n_batch(ctx_dft), 0,          n_seq);
+        batch_inject = llama_batch_init(llama_n_batch(ctx_dft), n_embd_dec, n_seq);
+
+        smpls.resize(n_seq);
+        for (auto & s : smpls) {
+            common_params_sampling sparams;
+            sparams.no_perf  = false;
+            sparams.top_k    = 1;
+            sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+            s.reset(common_sampler_init(model_dft, sparams));
+        }
+
+        // turn on extraction of the target layers' input embeddings
+        for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+            llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
+        }
+
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+        llama_set_causal_attn(ctx_dft, false); // DFlash needs non-causal attention
+    }
+
+    ~common_speculative_impl_draft_dflash() override {
+        llama_batch_free(batch);
+        llama_batch_free(batch_inject);
+    }
+
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+
+        const int32_t N = (int32_t) prompt.size();
+        if (N <= 0) {
+            return;
+        }
+
+        const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(params.ctx_dft), seq_id);
+        if (pos_max < N - 1) {
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d - process() did not run on every prefill ubatch. "
+                    "Drafts may degrade.\n",
+                    __func__, (int) pos_max, N - 1);
+        }
+    }
+
+    bool process(const llama_batch & batch_in) override {
+        if (batch_in.n_tokens <= 0) {
+            return true;
+        }
+
+        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+            return true;
+        }
+
+        const int32_t n_tokens = batch_in.n_tokens;
+
+        // per-seq inclusive batch range (assumes each seq's tokens are contiguous in the batch)
+        std::vector<int32_t> i_batch_beg(n_seq, -1);
+        std::vector<int32_t> i_batch_end(n_seq, -1);
+        for (int32_t k = 0; k < n_tokens; ++k) {
+            GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+            const llama_seq_id seq_id = batch_in.seq_id[k][0];
+            if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+                continue;
+            }
+            i_batch_end[seq_id] = k;
+            if (i_batch_beg[seq_id] < 0) {
+                i_batch_beg[seq_id] = k;
+            }
+        }
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+
+        const int32_t n_ubatch = (int32_t) llama_n_ubatch(ctx_dft);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_batch_beg[seq_id] < 0) {
+                continue;
+            }
+            const int32_t n_rows = i_batch_end[seq_id] - i_batch_beg[seq_id] + 1;
+
+            for (int32_t offset = 0; offset < n_rows; offset += n_ubatch) {
+                const int32_t n_chunk = std::min(n_ubatch, n_rows - offset);
+
+                // gather this chunk's target features, interleaved by extract layer
+                features_buf.resize((size_t) n_chunk * n_embd_enc);
+                for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+                    const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
+                    if (!layer) {
+                        GGML_ABORT("DFlash: target layer %d input not extracted.", target_layer_ids[k]);
+                    }
+                    for (int32_t i = 0; i < n_chunk; ++i) {
+                        float       * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+                        const float * src = layer + (size_t) (i_batch_beg[seq_id] + offset + i) * n_embd_tgt;
+                        std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
+                    }
+                }
+
+                // fuse extracted features through DFlash encoder
+                llama_batch enc_batch = {
+                    /*.n_tokens =*/ n_chunk,
+                    /*.token    =*/ nullptr,
+                    /*.embd     =*/ features_buf.data(),
+                    /*.pos      =*/ nullptr,
+                    /*.n_seq_id =*/ nullptr,
+                    /*.seq_id   =*/ nullptr,
+                    /*.logits   =*/ nullptr,
+                };
+
+                int32_t rc = llama_encode(ctx_dft, enc_batch);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                            __func__, rc, (int) n_chunk, (int) offset);
+                    return false;
+                }
+
+                const float * inp_g = llama_get_embeddings_nextn(ctx_dft);
+                GGML_ASSERT(inp_g && "DFlash encoder produced no output.");
+
+                // inject the DFlash decoder K/V cache at the tokens' target positions
+                batch_inject.n_tokens = n_chunk;
+                std::memcpy(batch_inject.embd, inp_g, (size_t) n_chunk * n_embd_dec * sizeof(float));
+
+                for (int32_t i = 0; i < n_chunk; ++i) {
+                    batch_inject.pos[i]       = batch_in.pos[i_batch_beg[seq_id] + offset + i];
+                    batch_inject.n_seq_id[i]  = 1;
+                    batch_inject.seq_id[i][0] = seq_id;
+                    batch_inject.logits[i]    = false;
+                }
+                rc = llama_decode(ctx_dft, batch_inject);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                            __func__, rc, (int) n_chunk, (int) offset);
+                    return false;
+                }
+            }
+        }
+
+        return true;
+    }
+
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
+
+        common_batch_clear(batch);
+
+        // build one batch holding every drafting sequence's noise block into a single decode)
+        // record where each block starts and its size
+        std::vector<int32_t> i_block_beg(n_seq, -1);
+        std::vector<int32_t> n_block    (n_seq,  0);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            common_sampler_reset(smpls[seq_id].get());
+
+            const int32_t n = (int32_t) dp.n_past;
+
+            int32_t n_draft = params.n_max;
+            if (dp.n_max > 0) {
+                n_draft = std::min(n_draft, dp.n_max);
+            }
+
+            const int32_t n_block_tokens = n_draft + 1; // id_last + n_draft * <mask>
+            i_block_beg[seq_id] = batch.n_tokens;
+            n_block    [seq_id] = n_block_tokens;
+            for (int32_t i = 0; i < n_block_tokens; ++i) {
+                common_batch_add(batch, i == 0 ? dp.id_last : mask_token_id, n + i, { seq_id }, true);
+            }
+        }
+
+        if (batch.n_tokens == 0) {
+            return;
+        }
+
+        // decode all sequence's noise block in a single batch
+        int ret = llama_decode(ctx_dft, batch);
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
+        }
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            if (i_block_beg[seq_id] < 0) {
+                continue;
+            }
+            auto & dp = dparams[seq_id];
+
+            const int32_t beg            = i_block_beg[seq_id];
+            const int32_t n_block_tokens = n_block[seq_id];
+
+            auto * smpl = smpls[seq_id].get();
+
+            auto & result = *dp.result;
+
+            // greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1
+            for (int32_t i = 1; i < n_block_tokens; ++i) {
+                common_sampler_sample(smpl, ctx_dft, beg + i, true);
+
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
+
+                const llama_token id = cur_p->data[0].id;
+
+                common_sampler_accept(smpl, id, true);
+
+                result.push_back(id);
+            }
+        }
+    }
+
+    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
+        // noop
+    }
+
+    bool need_embd() const override {
+        return false;
+    }
+};
+
 struct common_speculative_impl_draft_mtp : public common_speculative_impl {
     common_params_speculative_draft params; // reuses the draft-model params slot (ctx_tgt/ctx_dft)
 
@@ -1841,6 +2132,7 @@ std::string common_speculative_type_to_str(common_speculative_type type) {
         case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE:  return "draft-simple";
         case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:  return "draft-eagle3";
         case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:     return "draft-mtp";
+        case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH:  return "draft-dflash";
         case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:  return "ngram-simple";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:   return "ngram-map-k";
         case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v";
@@ -1893,6 +2185,7 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
             case COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE:
             case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:
             case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:
+            case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH:
                 n_max = std::max(n_max, std::max(0, spec->draft.n_max));
                 break;
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:
@@ -1930,6 +2223,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
         bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
         bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
+        bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr;
 
 
 
@@ -1940,7 +2234,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
 
         // when adding a new type - update here the logic above
-        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 9);
+        static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 10);
 
         // this list here defines the priority of the speculators
         // the one with highest priority are listed first
@@ -1970,6 +2264,9 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_mtp) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
         }
+        if (has_draft_dflash) {
+            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params));
+        }
     }
 
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
@@ -1990,6 +2287,10 @@ common_speculative * common_speculative_init(common_params_speculative & params,
                 impls.push_back(std::make_unique<common_speculative_impl_draft_mtp>(config.params, n_seq));
                 break;
             }
+            case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH: {
+                impls.push_back(std::make_unique<common_speculative_impl_draft_dflash>(config.params, n_seq));
+                break;
+            }
             case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
                 common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
 
diff --git a/conversion/__init__.py b/conversion/__init__.py
index 5aad203e5..4a1fd5bb7 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -50,6 +50,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DeepseekV2ForCausalLM": "deepseek",
     "DeepseekV3ForCausalLM": "deepseek",
     "DeepseekV32ForCausalLM": "deepseek",
+    "DFlashDraftModel": "qwen",
     "DistilBertForMaskedLM": "bert",
     "DistilBertForSequenceClassification": "bert",
     "DistilBertModel": "bert",
diff --git a/conversion/qwen.py b/conversion/qwen.py
index 6b85eb9aa..81f450e40 100644
--- a/conversion/qwen.py
+++ b/conversion/qwen.py
@@ -625,3 +625,55 @@ class Qwen3_5TextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReor
 @ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
 class Qwen3_5MoeTextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35MOE
+
+
+@ModelBase.register("DFlashDraftModel")
+class DFlashModel(Qwen3Model):
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    def set_vocab(self):
+        if self.target_model_dir is None:
+            raise ValueError(
+                "DFlash draft model requires --target-model-dir to be specified. "
+                "Please provide the path to the target model directory containing the tokenizer."
+            )
+        logger.info(f"DFlash: Using tokenizer from target model: {self.target_model_dir}")
+        original_dir = self.dir_model
+        self.dir_model = self.target_model_dir
+        super().set_vocab()
+        self.dir_model = original_dir
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        block_size = self.hparams.get("block_size", 16)
+        self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.block_size", block_size)
+        dflash_config = self.hparams.get("dflash_config", {})
+
+        target_layer_ids = dflash_config.get("target_layer_ids", [])
+        if target_layer_ids:
+            extract_layer_ids = [i + 1 for i in target_layer_ids]
+            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", extract_layer_ids)
+
+        mask_token_id = dflash_config.get("mask_token_id", None)
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(mask_token_id)
+
+        use_sliding_window = self.hparams.get("use_sliding_window", False)
+        sliding_window = self.hparams.get("sliding_window")
+        layer_types = self.hparams.get("layer_types")
+        if use_sliding_window and sliding_window and layer_types:
+            is_swa = [lt == "sliding_attention" for lt in layer_types]
+            self.gguf_writer.add_sliding_window(sliding_window)
+            self.gguf_writer.add_sliding_window_pattern(is_swa)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "fc.weight":
+            yield (name, data_torch)
+            return
+        if name == "hidden_norm.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch)
+            return
+        if not name.startswith("model."):
+            name = "model." + name
+        yield from super().modify_tensors(data_torch, name, bid)
diff --git a/docs/speculative.md b/docs/speculative.md
index 8f91256c4..4100b92f8 100644
--- a/docs/speculative.md
+++ b/docs/speculative.md
@@ -52,6 +52,32 @@ Supported EAGLE-3 draft models include:
 
 For the full and up-to-date list of supported models, see #18039.
 
+### DFlash (`draft-dflash`)
+
+DFlash produces an entire block of draft tokens in a single forward pass (block diffusion) and
+injects the target model's hidden states into the draft model's attention, instead of drafting one
+token at a time. This keeps the draft model small while making drafting GPU-friendly. Unlike EAGLE-3
+(a single-layer autoregressive draft), the DFlash draft uses several transformer layers but emits a
+whole block per draft step.
+
+The draft is a small block-diffusion model trained for a specific target (for example
+`z-lab/Qwen3-4B-DFlash` for `Qwen/Qwen3-4B`). Convert it with `--target-model-dir` so it inherits the
+target's tokenizer and token embeddings:
+
+```bash
+python convert_hf_to_gguf.py z-lab/Qwen3-4B-DFlash \
+    --target-model-dir Qwen/Qwen3-4B --outtype bf16 --outfile Qwen3-4B-DFlash.gguf
+
+llama-server -m Qwen3-4B.gguf -md Qwen3-4B-DFlash.gguf \
+    --spec-type draft-dflash --spec-draft-n-max 15 -fa on --jinja
+```
+
+`--spec-draft-n-max` is clamped to the draft model's trained block size.
+
+See:
+
+- #22105
+
 ### n-gram Cache (`ngram-cache`)
 
 An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
@@ -147,7 +173,7 @@ If a draft model is combined with a draftless decoding the draftless decoding ha
 ### General Speculative Parameters
 
 ```
---spec-type [none|draft-simple|draft-eagle3|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+--spec-type [none|draft-simple|draft-eagle3|draft-dflash|draft-mtp|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
                                         comma-separated list of types of speculative decoding to use
                                         (default: none)
                                         (env: LLAMA_ARG_SPEC_TYPE)
@@ -287,6 +313,7 @@ Specifies a comma-separated list of speculative decoding types to use.
 | `none` | No speculative decoding (default) |
 | `draft-simple` | Use a simple draft model for speculation |
 | `draft-eagle3` | Use an EAGLE-3 draft model that reads the target's hidden states |
+| `draft-dflash` | Use a DFlash block-diffusion draft model that emits a block per step |
 | `draft-mtp` | Use Multi Token Prediction (MTP) heads from the main model |
 | `ngram-cache` | Use n-gram cache lookup |
 | `ngram-simple` | Use simple n-gram pattern matching |
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 1bda9452d..bcd10beb0 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -517,6 +517,7 @@ class MODEL_ARCH(IntEnum):
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
     EAGLE3           = auto()
+    DFLASH           = auto()
     MISTRAL4         = auto()
     PADDLEOCR        = auto()
     MIMO2            = auto()
@@ -1074,6 +1075,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
     MODEL_ARCH.EAGLE3:           "eagle3",
+    MODEL_ARCH.DFLASH:           "dflash",
     MODEL_ARCH.MISTRAL4:         "mistral4",
     MODEL_ARCH.PADDLEOCR:        "paddleocr",
     MODEL_ARCH.MIMO2:            "mimo2",
@@ -4086,6 +4088,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FC,
         MODEL_TENSOR.D2T,
     ],
+    MODEL_ARCH.DFLASH: [
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FC,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+    ],
     MODEL_ARCH.MISTRAL4: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 4a52d9772..d80915ffd 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -129,6 +129,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
     { LLM_ARCH_EAGLE3,           "eagle3"           },
+    { LLM_ARCH_DFLASH,           "dflash"           },
     { LLM_ARCH_MISTRAL4,         "mistral4"         },
     { LLM_ARCH_PADDLEOCR,        "paddleocr"        },
     { LLM_ARCH_MIMO2,            "mimo2"            },
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 989da06d8..946518d5f 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -143,6 +143,7 @@ enum llm_arch {
     LLM_ARCH_TALKIE,
     LLM_ARCH_MELLUM,
     LLM_ARCH_EAGLE3,
+    LLM_ARCH_DFLASH,
     LLM_ARCH_UNKNOWN,
 };
 
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 9f8a8fdb8..029141e2a 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -100,10 +100,10 @@ llama_context::llama_context(
         cparams.ctx_other = params.ctx_other;
     }
 
-    if (model.arch == LLM_ARCH_EAGLE3) {
+    if (model.arch == LLM_ARCH_EAGLE3 || model.arch == LLM_ARCH_DFLASH) {
         if (model.tok_embd == nullptr || model.output == nullptr) {
             if (params.ctx_other == nullptr) {
-                throw std::runtime_error("EAGLE3 requires ctx_other to be set (this warning is normal during memory fitting)");
+                throw std::runtime_error(model.arch_name() + " requires ctx_other to be set (this warning is normal during memory fitting)");
             }
             cparams.ctx_other = params.ctx_other;
         }
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 68c9e606c..3ded70bc0 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -486,7 +486,11 @@ void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
     mctx->set_input_k_idxs(self_k_idxs, ubatch);
     mctx->set_input_v_idxs(self_v_idxs, ubatch);
 
-    mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+    // the mask is left unallocated when the graph only stores K/V without attending
+    // (e.g. DFlash's KV-injection pass)
+    if (self_kq_mask && self_kq_mask->buffer) {
+        mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+    }
 
     if (self_k_rot) {
         mctx->set_input_k_rot(self_k_rot);
@@ -904,6 +908,7 @@ void llm_graph_result::reset() {
     t_logits      = nullptr;
     t_embd        = nullptr;
     t_embd_pooled = nullptr;
+    t_h_nextn     = nullptr;
 
     t_layer_inp.resize(LLAMA_MAX_LAYERS);
     std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr);
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 6cb0ec379..eaf29505c 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -291,6 +291,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_mistral3(params);
         case LLM_ARCH_EAGLE3:
             return new llama_model_eagle3(params);
+        case LLM_ARCH_DFLASH:
+            return new llama_model_dflash(params);
         case LLM_ARCH_MIMO2:
             return new llama_model_mimo2(params);
         case LLM_ARCH_KIMI_LINEAR:
@@ -2494,6 +2496,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_STEP35:
         case LLM_ARCH_TALKIE:
         case LLM_ARCH_MELLUM:
+        case LLM_ARCH_DFLASH:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:
@@ -2617,7 +2620,8 @@ bool llama_model_has_encoder(const llama_model * model) {
     switch (model->arch) {
         case LLM_ARCH_T5:
         case LLM_ARCH_T5ENCODER:
-        case LLM_ARCH_EAGLE3:    return true;
+        case LLM_ARCH_EAGLE3:
+        case LLM_ARCH_DFLASH:    return true;
         default:                 return false;
     }
 }
diff --git a/src/models/dflash.cpp b/src/models/dflash.cpp
new file mode 100644
index 000000000..a7b4f4435
--- /dev/null
+++ b/src/models/dflash.cpp
@@ -0,0 +1,276 @@
+#include "models.h"
+
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-iswa.h"
+
+void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) {
+
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (!ml.get_arr(LLM_KV_TARGET_LAYERS, target_layer_ids, false)) {
+        throw std::runtime_error("DFlash model requires 'target_layers' in GGUF metadata");
+    }
+
+    hparams.n_embd_inp_enc_impl = (uint32_t) target_layer_ids.size() * hparams.n_embd;
+
+    LLAMA_LOG_INFO("%s: DFlash extract_layers = [", __func__);
+    for (size_t i = 0; i < target_layer_ids.size(); ++i) {
+        LLAMA_LOG_INFO("%d%s", target_layer_ids[i], i + 1 < target_layer_ids.size() ? ", " : "");
+    }
+    LLAMA_LOG_INFO("]\n");
+
+    // optional interleaved sliding-window attention with per-layer pattern array.
+    // DFlash has a single rope, so the SWA rope == main rope.
+    if (ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false) && hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, hparams.n_layer());
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+    }
+
+    type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_inp = hparams.n_embd_inp_enc();
+
+    fc              = create_tensor(tn(LLM_TENSOR_FC,              "weight"), { n_embd_inp, n_embd }, 0);
+    output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc)
+    output_norm     = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM,    "weight"), { n_embd }, 0); // decoder final norm
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
+
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), { n_embd, n_ff }, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_dflash::build_arch_graph(const llm_graph_params & params) const {
+    switch (params.gtype) {
+        case LLM_GRAPH_TYPE_ENCODER:
+            return std::make_unique<graph<true>>(*this, params);
+        case LLM_GRAPH_TYPE_DEFAULT:
+        case LLM_GRAPH_TYPE_DECODER:
+            return std::make_unique<graph<false>>(*this, params);
+        default:
+            GGML_ABORT("invalid graph type");
+    };
+}
+
+template <>
+ggml_tensor * llama_model_dflash::graph<true>::build_inp_embd_enc() const {
+    auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp_enc());
+
+    inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp_enc(), n_tokens);
+    ggml_set_input(inp_target->embd);
+
+    ggml_tensor * cur = inp_target->embd;
+    cb(cur, "inp_embd", -1);
+
+    res->add_input(std::move(inp_target));
+
+    return cur;
+}
+
+// DFlash Encoder: processes target model features through feature fusion layer
+template <>
+llama_model_dflash::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur = build_inp_embd_enc();
+
+    cur = build_lora_mm(model.fc, cur);
+    cb(cur, "fc_out", -1);
+
+    cur = build_norm(cur, model.output_norm_enc, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "enc_norm_out", -1);
+
+    ggml_set_output(cur);
+    res->t_h_nextn = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// DFlash decoder, dual-mode by batch type:
+//   * embd batch  -> fused target features: project + inject K/V into the cache.
+//   * token batch -> noise-block diffusion: attend over [committed, MASK...] to generate draft tokens
+template <>
+llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    ggml_tensor * inp_pos  = build_inp_pos();
+
+    // optional iSWA: pick the matching attention input
+    const bool use_iswa = hparams.swa_type != LLAMA_SWA_TYPE_NONE;
+
+    llm_graph_input_attn_kv      * inp_attn      = nullptr;
+    llm_graph_input_attn_kv_iswa * inp_attn_iswa = nullptr;
+    if (use_iswa) {
+        inp_attn_iswa = build_attn_inp_kv_iswa();
+    } else {
+        inp_attn = build_attn_inp_kv();
+    }
+
+    const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
+
+    // KV cache injection
+    if (ubatch.embd) {
+        auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
+
+        inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+        ggml_set_input(inp->embd);
+
+        ggml_tensor * inp_g = inp->embd;
+        cb(inp_g, "inp_g_embeddings", -1);
+
+        res->add_input(std::move(inp));
+
+        for (int il = 0; il < n_layer; ++il) {
+            const auto & layer = model.layers[il];
+
+            ggml_tensor * Kcur = build_lora_mm(layer.wk, inp_g);
+            ggml_tensor * Vcur = build_lora_mm(layer.wv, inp_g);
+
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Kcur = build_norm(Kcur, layer.attn_k_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+            cb(Kcur, "Kcur_injected", il);
+            cb(Vcur, "Vcur_injected", il);
+
+            if (use_iswa) {
+                // route each layer's K/V to its sub-cache: SWA layers -> sliding cache, full -> dense
+                const bool    is_swa = hparams.is_swa(il);
+                const auto  * kv     = is_swa ? inp_attn_iswa->mctx->get_swa() : inp_attn_iswa->mctx->get_base();
+                ggml_tensor * k_idxs = is_swa ? inp_attn_iswa->get_k_idxs_swa() : inp_attn_iswa->get_k_idxs();
+                ggml_tensor * v_idxs = is_swa ? inp_attn_iswa->get_v_idxs_swa() : inp_attn_iswa->get_v_idxs();
+                ggml_build_forward_expand(gf, kv->cpy_k(ctx0, Kcur, k_idxs, il));
+                ggml_build_forward_expand(gf, kv->cpy_v(ctx0, Vcur, v_idxs, il));
+            } else {
+                ggml_build_forward_expand(gf, inp_attn->mctx->cpy_k(ctx0, Kcur, inp_attn->get_k_idxs(), il));
+                ggml_build_forward_expand(gf, inp_attn->mctx->cpy_v(ctx0, Vcur, inp_attn->get_v_idxs(), il));
+            }
+        }
+
+        res->t_embd = inp_g;
+
+        ggml_build_forward_expand(gf, inp_g);
+        return;
+    }
+
+    // tok_embd from the target model (shared via ctx_other)
+    auto * tok_embd = model.tok_embd;
+    if (tok_embd == nullptr) {
+        GGML_ASSERT(cparams.ctx_other != nullptr);
+        const auto * model_other = llama_get_model(cparams.ctx_other);
+
+        GGML_ASSERT(model_other->tok_embd != nullptr && "DFlash decoder requires the target model's token embeddings");
+        tok_embd = model_other->tok_embd;
+    }
+
+    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens);
+    cb(inpL, "inp_noise_embd", -1);
+
+    res->add_input(std::move(inp));
+
+    for (int il = 0; il < n_layer; ++il) {
+        const auto & layer = model.layers[il];
+
+        ggml_tensor * noise_norm = build_norm(inpL, layer.attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(noise_norm, "noise_norm", il);
+
+        ggml_tensor * Qcur = build_lora_mm(layer.wq, noise_norm);
+        ggml_tensor * Kcur = build_lora_mm(layer.wk, noise_norm);
+        ggml_tensor * Vcur = build_lora_mm(layer.wv, noise_norm);
+
+        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+        Qcur = build_norm(Qcur, layer.attn_q_norm, NULL, LLM_NORM_RMS, il);
+        Kcur = build_norm(Kcur, layer.attn_k_norm, NULL, LLM_NORM_RMS, il);
+
+        Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+        Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        // cache-aware, non-causal attention
+        ggml_tensor * cur = use_iswa
+            ? build_attn(inp_attn_iswa, layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il)
+            : build_attn(inp_attn,      layer.wo, NULL, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                layer.ffn_up,   NULL, NULL,
+                layer.ffn_gate, NULL, NULL,
+                layer.ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    ggml_tensor * cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+
+    res->t_embd = cur;
+
+    // lm_head from the target model (shared via ctx_other)
+    auto * output = model.output;
+    if (output == nullptr) {
+        GGML_ASSERT(cparams.ctx_other != nullptr);
+        const auto * model_other = llama_get_model(cparams.ctx_other);
+        GGML_ASSERT(model_other->output != nullptr && "DFlash decoder requires the target model's output projection");
+        output = model_other->output;
+    }
+
+    cur = build_lora_mm(output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index 2ac8415a3..d89ab96d0 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -1122,6 +1122,22 @@ struct llama_model_eagle3 : public llama_model_base {
 };
 
 
+struct llama_model_dflash : public llama_model_base {
+    llama_model_dflash(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool is_enc>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        ggml_tensor * build_inp_embd_enc() const;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_mistral4 : public llama_model_deepseek2 {
     llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}
     // reuse load_arch_hparams and load_arch_tensors from llama_model_deepseek2
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index 524971ae4..c781d2903 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -451,7 +451,7 @@ static int save_models(const llm_arch target_arch, const size_t seed, const ggml
         if (arch == LLM_ARCH_GEMMA4 || arch == LLM_ARCH_GEMMA4_ASSISTANT) {
             continue; // FIXME: ISWA KV cache initialization needs more fixture params
         }
-        if (arch == LLM_ARCH_EAGLE3) {
+        if (arch == LLM_ARCH_EAGLE3 || arch == LLM_ARCH_DFLASH) {
             continue;
         }
         for (bool moe : {false, true}) {
@@ -557,7 +557,7 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
         if (arch == LLM_ARCH_GEMMA4 || arch == LLM_ARCH_GEMMA4_ASSISTANT) {
             continue; // FIXME: ISWA KV cache initialization needs more fixture params
         }
-        if (arch == LLM_ARCH_EAGLE3) {
+        if (arch == LLM_ARCH_EAGLE3 || arch == LLM_ARCH_DFLASH) {
             continue;
         }
 

From f68a788b0bf41d88cb90fd93c7027471a2ebe30a Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 28 Jun 2026 15:50:31 +0200
Subject: [PATCH 209/292] jinja: add --dump-prog for debugging (#25086)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* jinja: add --dump-prog for debugging

* Update common/jinja/runtime.cpp

Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>

---------

Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
---
 common/jinja/runtime.cpp     |  46 +++++++++++++
 common/jinja/runtime.h       | 127 +++++++++++++++++++++++++++++++++++
 tests/test-chat-template.cpp |  20 +++++-
 3 files changed, 190 insertions(+), 3 deletions(-)

diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp
index f98cb0876..474129df2 100644
--- a/common/jinja/runtime.cpp
+++ b/common/jinja/runtime.cpp
@@ -954,4 +954,50 @@ value keyword_argument_expression::execute_impl(context & ctx) {
     return mk_val<value_kwarg>(k, v);
 }
 
+std::string runtime::debug_dump_program(const program & prog, const std::string & src) {
+    std::ostringstream oss;
+    size_t lvl = 0;
+    context ctx;
+    ctx.src.reset(new std::string(src));
+
+    auto indent = [](size_t lvl) -> std::string {
+        return std::string(lvl * 2, ' ');
+    };
+
+    ctx.visitor = [&](bool is_leaf, statement * node, std::vector<visitor_pair> children) {
+        oss << indent(lvl) << node->type() << ":\n";
+        lvl++;
+        if (is_leaf) {
+            const auto & pos = node->pos;
+            oss << indent(lvl) << "(leaf) at " << get_line_col(src, pos) << " in source:\n";
+            std::string snippet = peak_source(src, pos);
+            string_replace_all(snippet, "\n", "\n" + indent(lvl));
+            oss << indent(lvl) << snippet << "\n";
+        } else {
+            for (auto & [label, children_vec] : children) {
+                oss << indent(lvl) << label << ":\n";
+                lvl++;
+                if (children_vec.empty()) {
+                    oss << indent(lvl) << "<empty>\n\n";
+                } else {
+                    for (auto * child : children_vec) {
+                        if (!child) {
+                            continue;
+                        }
+                        child->visit(ctx);
+                    }
+                }
+                lvl--;
+            }
+        }
+        lvl--;
+    };
+
+    for (const auto & stmt : prog.body) {
+        stmt->visit(ctx);
+    }
+
+    return oss.str();
+}
+
 } // namespace jinja
diff --git a/common/jinja/runtime.h b/common/jinja/runtime.h
index 37b4c35ca..0884a1592 100644
--- a/common/jinja/runtime.h
+++ b/common/jinja/runtime.h
@@ -47,12 +47,19 @@ const T * cast_stmt(const statement_ptr & ptr) {
 // not thread-safe
 void enable_debug(bool enable);
 
+// for visiting AST nodes
+// function signature: void(bool is_leaf, statement * node, pair of <label, children>)
+using visitor_pair = std::pair<std::string, std::vector<statement *>>;
+using visitor_fn = std::function<void(bool, statement *, std::vector<visitor_pair>)>;
+
 struct context {
     std::shared_ptr<std::string> src; // for debugging; use shared_ptr to avoid copying on scope creation
     std::time_t current_time; // for functions that need current time
 
     bool is_get_stats = false; // whether to collect stats
 
+    visitor_fn visitor;
+
     // src is optional, used for error reporting
     context(std::string src = "") : src(std::make_shared<std::string>(std::move(src))) {
         env = mk_val<value_object>();
@@ -99,6 +106,15 @@ private:
     value_object env;
 };
 
+// utils for visiting AST nodes
+static std::vector<statement *> stmts_to_ptr(const statements & stmts) {
+    std::vector<statement *> children;
+    for (const auto & stmt : stmts) {
+        children.push_back(stmt.get());
+    }
+    return children;
+}
+
 /**
  * Base class for all nodes in the AST.
  */
@@ -106,6 +122,7 @@ struct statement {
     size_t pos; // position in source, for debugging
     virtual ~statement() = default;
     virtual std::string type() const { return "Statement"; }
+    virtual void visit(context & ctx) { ctx.visitor(true, this, {}); }
 
     // execute_impl must be overridden by derived classes
     virtual value execute_impl(context &) { throw_exec_error(); }
@@ -166,6 +183,13 @@ struct if_statement : public statement {
 
     std::string type() const override { return "If"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"test", {test.get()}},
+            {"body", stmts_to_ptr(body)},
+            {"alternate", stmts_to_ptr(alternate)}
+        });
+    }
 };
 
 struct identifier;
@@ -190,6 +214,14 @@ struct for_statement : public statement {
 
     std::string type() const override { return "For"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"loopvar", {loopvar.get()}},
+            {"iterable", {iterable.get()}},
+            {"body", stmts_to_ptr(body)},
+            {"default_block", stmts_to_ptr(default_block)}
+        });
+    }
 };
 
 struct break_statement : public statement {
@@ -241,6 +273,13 @@ struct set_statement : public statement {
 
     std::string type() const override { return "Set"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"assignee", {assignee.get()}},
+            {"value", {val.get()}},
+            {"body", stmts_to_ptr(body)}
+        });
+    }
 };
 
 struct macro_statement : public statement {
@@ -256,6 +295,13 @@ struct macro_statement : public statement {
 
     std::string type() const override { return "Macro"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"name", {name.get()}},
+            {"args", stmts_to_ptr(args)},
+            {"body", stmts_to_ptr(body)}
+        });
+    }
 };
 
 struct comment_statement : public statement {
@@ -289,6 +335,12 @@ struct member_expression : public expression {
     }
     std::string type() const override { return "MemberExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"object", {object.get()}},
+            {"property", {property.get()}}
+        });
+    }
 };
 
 struct call_expression : public expression {
@@ -302,6 +354,12 @@ struct call_expression : public expression {
     }
     std::string type() const override { return "CallExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"callee", {callee.get()}},
+            {"args", stmts_to_ptr(args)}
+        });
+    }
 };
 
 /**
@@ -405,6 +463,12 @@ struct binary_expression : public expression {
     }
     std::string type() const override { return "BinaryExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"left", {left.get()}},
+            {"right", {right.get()}}
+        });
+    }
 };
 
 /**
@@ -431,6 +495,12 @@ struct filter_expression : public expression {
 
     std::string type() const override { return "FilterExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"operand", {operand.get()}},
+            {"filter", {filter.get()}}
+        });
+    }
 };
 
 struct filter_statement : public statement {
@@ -443,6 +513,12 @@ struct filter_statement : public statement {
     }
     std::string type() const override { return "FilterStatement"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"filter", {filter.get()}},
+            {"body", stmts_to_ptr(body)}
+        });
+    }
 };
 
 /**
@@ -468,6 +544,12 @@ struct select_expression : public expression {
         }
         return lhs->execute_impl(ctx);
     }
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"lhs", {lhs.get()}},
+            {"test", {test.get()}}
+        });
+    }
 };
 
 /**
@@ -486,6 +568,12 @@ struct test_expression : public expression {
     }
     std::string type() const override { return "TestExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"operand", {operand.get()}},
+            {"test", {test.get()}}
+        });
+    }
 };
 
 /**
@@ -501,6 +589,11 @@ struct unary_expression : public expression {
     }
     std::string type() const override { return "UnaryExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"argument", {argument.get()}}
+        });
+    }
 };
 
 struct slice_expression : public expression {
@@ -518,6 +611,13 @@ struct slice_expression : public expression {
     [[noreturn]] value execute_impl(context &) override {
         throw std::runtime_error("must be handled by MemberExpression");
     }
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"start_expr", {start_expr.get()}},
+            {"stop_expr", {stop_expr.get()}},
+            {"step_expr", {step_expr.get()}}
+        });
+    }
 };
 
 struct keyword_argument_expression : public expression {
@@ -531,6 +631,12 @@ struct keyword_argument_expression : public expression {
     }
     std::string type() const override { return "KeywordArgumentExpression"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"key", {key.get()}},
+            {"val", {val.get()}}
+        });
+    }
 };
 
 struct spread_expression : public expression {
@@ -539,6 +645,11 @@ struct spread_expression : public expression {
         chk_type<expression>(this->argument);
     }
     std::string type() const override { return "SpreadExpression"; }
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"argument", {argument.get()}}
+        });
+    }
 };
 
 struct call_statement : public statement {
@@ -553,6 +664,13 @@ struct call_statement : public statement {
     }
     std::string type() const override { return "CallStatement"; }
     value execute_impl(context & ctx) override;
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"call", {call.get()}},
+            {"caller_args", stmts_to_ptr(caller_args)},
+            {"body", stmts_to_ptr(body)}
+        });
+    }
 };
 
 struct ternary_expression : public expression {
@@ -575,6 +693,13 @@ struct ternary_expression : public expression {
             return false_expr->execute(ctx);
         }
     }
+    void visit(context & ctx) override {
+        ctx.visitor(false, this, {
+            {"condition", {condition.get()}},
+            {"true_expr", {true_expr.get()}},
+            {"false_expr", {false_expr.get()}}
+        });
+    }
 };
 
 struct raised_exception : public std::exception {
@@ -648,6 +773,8 @@ struct runtime {
         }
         return parts;
     }
+
+    static std::string debug_dump_program(const program & prog, const std::string & src);
 };
 
 } // namespace jinja
diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp
index d971b2374..6a6292cd0 100644
--- a/tests/test-chat-template.cpp
+++ b/tests/test-chat-template.cpp
@@ -25,7 +25,7 @@ using json = nlohmann::ordered_json;
 static int main_automated_tests(void);
 
 static void run_multiple(const std::string& dir_path, bool stop_on_first_failure, const json& input, bool use_common = false);
-static void run_single(const std::string& contents, json input, bool use_common = false, const std::string & output_path = "");
+static void run_single(const std::string& contents, json input, bool use_common = false, bool dump_prog = false, const std::string & output_path = "");
 
 static std::string HELP = R"(
 Usage: test-chat-template [OPTIONS] PATH_TO_TEMPLATE
@@ -35,6 +35,7 @@ Options:
   --json <path>            Path to the JSON input file.
   --stop-on-first-fail     Stop testing on the first failure (default: false).
   --no-common              Use direct Jinja engine instead of common chat templates (default: use common).
+  --dump-prog              Dump the parsed program for debugging (only for single template runs).
   --output <path>          Path to output results (only for single template runs).
 If PATH_TO_TEMPLATE is a file, runs that single template.
 If PATH_TO_TEMPLATE is a directory, runs all .jinja files in that directory.
@@ -118,6 +119,7 @@ int main(int argc, char ** argv) {
     std::string & json_to_use = DEFAULT_JSON;
     bool stop_on_first_fail = false;
     bool use_common = true;
+    bool dump_prog = false;
 
     for (size_t i = 1; i < args.size(); i++) {
         if (args[i] == "--help" || args[i] == "-h") {
@@ -136,6 +138,8 @@ int main(int argc, char ** argv) {
             i++;
         } else if (args[i] == "--no-common") {
             use_common = false;
+        } else if (args[i] == "--dump-prog") {
+            dump_prog = true;
         } else if (tmpl_path.empty()) {
             tmpl_path = args[i];
         } else {
@@ -172,7 +176,7 @@ int main(int argc, char ** argv) {
         std::string contents = std::string(
             std::istreambuf_iterator<char>(infile),
             std::istreambuf_iterator<char>());
-        run_single(contents, input_json, use_common, output_path);
+        run_single(contents, input_json, use_common, dump_prog, output_path);
     } else {
         std::cerr << "Error: PATH_TO_TEMPLATE is not a valid file or directory: " << tmpl_path << "\n";
         return 1;
@@ -276,11 +280,21 @@ static jinja::value_string format_using_direct_engine(
 }
 
 
-void run_single(const std::string& contents, json input, bool use_common, const std::string & output_path) {
+void run_single(const std::string& contents, json input, bool use_common, bool dump_prog, const std::string & output_path) {
     jinja::enable_debug(true);
 
     jinja::value_string output_parts;
 
+    if (dump_prog) {
+        jinja::lexer lexer;
+        auto lexer_res = lexer.tokenize(contents);
+        jinja::program ast = jinja::parse_from_tokens(lexer_res);
+        std::string prog_dump = jinja::runtime::debug_dump_program(ast, contents);
+        std::cout << "\n=== DUMPED PROGRAM ===\n";
+        std::cout << prog_dump << "\n";
+        return;
+    }
+
     if (use_common) {
         std::string bos_token = "<s>";
         std::string eos_token = "</s>";

From c818263f2a5ddab028dea5f169ea2b2266421125 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Sun, 28 Jun 2026 09:53:32 -0500
Subject: [PATCH 210/292] chat : implement minicpm5 parser (#24889)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Add minicpm5 tool call parser

* Refactor MiniCPM5 PEG parser per review feedback

* Fix jinja min/max API to match Jinja2

* modify by review

* MiniCPM5: use autoparser for XML tool calls and fix grammar preserved-token triggers

* MiniCPM5: fix streaming tool-arg placeholder and remove alt XML markers

* skip min/max attribute tests in -py mode

* test-jinja: use real expected output for min/max attribute tests

* MiniCPM5: revert shared mapper and history fallbacks per review

Drop streaming tool-arg placeholder workarounds from the generic PEG
mapper and restore strict tool-call argument JSON parsing so MiniCPM5
support stays limited to autoparser/diff-analyzer changes.

* chat : refactor minicpm5 back to dedicated parser

* cont : simplify grammar

* cont : refactor

* cont : fixes

* cont : rename template to openbmb-MiniCPM5-1B.jinja

* cont : add message delimiters

* cont : fix tests

---------

Co-authored-by: zhangtao <zhangtao2@modelbest.cn>
Co-authored-by: 张涛 <>
---
 common/chat.cpp                            | 151 +++++++++++++++++
 common/jinja/value.cpp                     |  44 +++++
 models/templates/openbmb-MiniCPM5-1B.jinja | 179 +++++++++++++++++++++
 tests/test-chat.cpp                        |  78 +++++++++
 tests/test-jinja.cpp                       |  30 ++++
 5 files changed, 482 insertions(+)
 create mode 100644 models/templates/openbmb-MiniCPM5-1B.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index 0cee80434..a38c62f5f 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -2376,6 +2376,149 @@ static void func_args_not_string(json & messages) {
 
 }
 
+// MiniCPM5 format:
+// - Reasoning: <think>{reasoning}</think> (optional)
+// - Tool calls: <function name="foo"><param name="bar">value</param></function>
+static common_chat_params common_chat_params_init_minicpm5(const common_chat_template &          tmpl,
+                                                           const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.preserved_tokens  = {
+        "<function",
+        "<param",
+        "</function>",
+        "</param>",
+        "<think>",
+        "</think>",
+    };
+
+    data.thinking_start_tag = "<think>";
+    data.thinking_end_tag   = "</think>";
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" },
+        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|im_start|>assistant\n<think>\n" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "\n</think>\n\n" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal("<|im_start|>assistant\n");
+
+        auto reasoning = p.eps();
+        if (extract_reasoning) {
+            reasoning = ("<think>" << p.reasoning(p.until("</think>")) << "</think>") + p.space();
+        }
+
+        // Response format parser
+        if (has_response_format) {
+            return generation_prompt + reasoning + p.content(p.schema(p.json(), "response-format", inputs.json_schema));
+        }
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            // CDATA lets a value carry characters that would otherwise close the tag (e.g.
+            // </param>); capture the inner text only, excluding the CDATA markers.
+            auto string_value = p.choice({
+                p.literal("<![CDATA[") + p.ac(p.tool_arg_string_value(p.until("]]>")) + p.literal("]]>"), "]]>") + p.tool_arg_close(p.literal("</param>")),
+                p.negate(p.literal("<![CDATA[")) + p.ac(p.tool_arg_string_value(p.until("</param>")) + p.tool_arg_close(p.literal("</param>")), "</param>")
+            });
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto &      function = tool.at("function");
+                const std::string name     = function.at("name");
+                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto args = p.eps();
+                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
+                    auto schema_info = common_schema_info();
+                    schema_info.resolve_refs(params);
+
+                    auto arg_choice = p.choice();
+                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
+                        auto value_parser = p.eps();
+                        if (schema_info.resolves_to_string(prop_schema)) {
+                            value_parser = string_value;
+                        } else {
+                            value_parser = p.tool_arg_json_value(
+                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false)
+                                ) + p.tool_arg_close(p.literal("</param>"));
+                        }
+
+                        auto arg_rule = p.tool_arg(
+                            p.tool_arg_open(p.literal("<param name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
+                            value_parser
+                        );
+
+                        arg_choice |= arg_rule;
+                    }
+                    args = p.zero_or_more(arg_choice + p.space());
+                }
+
+                auto tool_parser = p.tool(
+                    p.tool_open(p.literal("<function name=\"") + p.tool_name(p.literal(name)) + p.literal("\">"))
+                    << p.tool_args(args)
+                    << p.tool_close(p.literal("</function>")));
+
+                tool_choice |= p.rule("tool-" + name, tool_parser);
+            });
+
+            auto max_calls  = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_calls = p.trigger_rule("tool-call", p.repeat(tool_choice + p.space(), 1, max_calls));
+
+            auto content = p.content(p.until("<function"));
+
+            return generation_prompt + reasoning + content + tool_calls + p.end();
+        }
+
+        return generation_prompt + reasoning + p.content(p.rest()) + p.end();
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function" },
+        };
+    }
+
+    return data;
+}
+
 static json common_chat_extra_context() {
     json ctx = json::object();
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
@@ -2468,6 +2611,14 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_gemma4(tmpl, params);
     }
 
+    // MiniCPM5 - XML tool calls with <function name="..."><param name="...">...</param></function>
+    if (src.find("Tool usage guidelines:") != std::string::npos &&
+        src.find("<function name=\"") != std::string::npos &&
+        src.find("<param name=\"") != std::string::npos) {
+        LOG_DBG("Using specialized template: MiniCPM5\n");
+        return common_chat_params_init_minicpm5(tmpl, params);
+    }
+
     return std::nullopt;
 }
 
diff --git a/common/jinja/value.cpp b/common/jinja/value.cpp
index cd6a36956..5055ae9ac 100644
--- a/common/jinja/value.cpp
+++ b/common/jinja/value.cpp
@@ -1108,6 +1108,50 @@ const func_builtins & value_array_t::get_builtins() const {
             std::reverse(arr.begin(), arr.end());
             return is_val<value_tuple>(val) ? mk_val<value_tuple>(std::move(arr)) : mk_val<value_array>(std::move(arr));
         }},
+        {"min", [](const func_args & args) -> value {
+            args.ensure_count(1, 4);
+            args.ensure_vals<value_array>();
+            value val_case    = args.get_kwarg_or_pos("case_sensitive", 1);
+            value attribute   = args.get_kwarg_or_pos("attribute",      2);
+            if (!attribute->is_undefined()) {
+                throw not_implemented_exception("min: attribute not implemented");
+            }
+            // FIXME: min is currently always case sensitive
+            (void) val_case;
+            const auto & arr = args.get_pos(0)->as_array();
+            if (arr.empty()) {
+                return mk_val<value_undefined>();
+            }
+            value result = arr[0];
+            for (size_t i = 1; i < arr.size(); ++i) {
+                if (value_compare(arr[i], result, value_compare_op::lt)) {
+                    result = arr[i];
+                }
+            }
+            return result;
+        }},
+        {"max", [](const func_args & args) -> value {
+            args.ensure_count(1, 4);
+            args.ensure_vals<value_array>();
+            value val_case    = args.get_kwarg_or_pos("case_sensitive", 1);
+            value attribute   = args.get_kwarg_or_pos("attribute",      2);
+            if (!attribute->is_undefined()) {
+                throw not_implemented_exception("max: attribute not implemented");
+            }
+            // FIXME: max is currently always case sensitive
+            (void) val_case;
+            const auto & arr = args.get_pos(0)->as_array();
+            if (arr.empty()) {
+                return mk_val<value_undefined>();
+            }
+            value result = arr[0];
+            for (size_t i = 1; i < arr.size(); ++i) {
+                if (value_compare(arr[i], result, value_compare_op::gt)) {
+                    result = arr[i];
+                }
+            }
+            return result;
+        }},
         {"unique", array_unique_not_implemented},
     };
     return builtins;
diff --git a/models/templates/openbmb-MiniCPM5-1B.jinja b/models/templates/openbmb-MiniCPM5-1B.jinja
new file mode 100644
index 000000000..cb2934c45
--- /dev/null
+++ b/models/templates/openbmb-MiniCPM5-1B.jinja
@@ -0,0 +1,179 @@
+{{- bos_token }}{%- if tools %}
+    {%- set tool_definitions %}
+        {{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+        {%- for tool in tools %}
+            {{- "\n" }}
+            {{- tool | tojson(ensure_ascii=False) }}
+        {%- endfor %}
+        {{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
+    {%- endset %}
+    
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if '<tool_def_sep>' in messages[0].content %}
+            {{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
+        {%- else %}
+            {{- messages[0].content + '\n\n' + tool_definitions }}
+        {%- endif %}
+    {%- else %}
+        {{- tool_definitions.lstrip() }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        
+        {%- if message.tool_calls %}
+            {%- set content_parts = content.split('<tool_sep>') %}
+            {%- set processed_content = content_parts[0] %}
+            {%- set tool_calls_count = message.tool_calls|length %}
+            {%- set tool_sep_count = content_parts|length - 1 %}
+            {%- set min_count = [tool_calls_count, tool_sep_count]|min %}
+            
+            {%- for i in range(1, content_parts|length) %}
+                {%- set tool_index = i - 1 %}
+                {%- if tool_index < tool_calls_count %}
+                    {%- set tool_call = message.tool_calls[tool_index] %}
+                    {%- if tool_call.function %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {%- set single_tool_xml %}
+                        {{- '<function name="' ~ tool_call.name ~ '">' }}
+                        {%- if tool_call.arguments %}
+                            {%- set args_dict = tool_call.arguments %}
+                            {%- for param_name, param_value in args_dict.items() %}
+                                {{- '<param name="' ~ param_name ~ '">' }}
+                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                                    {{- '<![CDATA[' + param_value + ']]>' }}
+                                {%- else %}
+                                    {{- param_value }}
+                                {%- endif %}
+                                {{- '</param>' }}
+                            {%- endfor %}
+                        {%- endif %}
+                        {{- '</function>' }}
+                    {%- endset %}
+                    {%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
+                {%- else %}
+                    {%- set processed_content = processed_content + content_parts[i] %}
+                {%- endif %}
+            {%- endfor %}
+            
+            {%- if tool_calls_count > tool_sep_count %}
+                {%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
+                    {%- set tool_call = message.tool_calls[remaining_index] %}
+                    {%- if tool_call.function %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {%- set remaining_tool_xml %}
+                        {{- '<function name="' ~ tool_call.name ~ '">' }}
+                        {%- if tool_call.arguments %}
+                            {%- set args_dict = tool_call.arguments %}
+                            {%- for param_name, param_value in args_dict.items() %}
+                                {{- '<param name="' ~ param_name ~ '">' }}
+                                {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                                    {{- '<![CDATA[' + param_value + ']]>' }}
+                                {%- else %}
+                                    {{- param_value }}
+                                {%- endif %}
+                                {{- '</param>' }}
+                            {%- endfor %}
+                        {%- endif %}
+                        {{- '</function>' }}
+                    {%- endset %}
+                    {%- set processed_content = processed_content + remaining_tool_xml %}
+                {%- endfor %}
+            {%- endif %}
+            
+            {%- set content = processed_content %}
+        {%- endif %}
+        
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if reasoning_content %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        
+        {%- if message.tool_calls and not has_tool_sep %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<function name="' ~ tool_call.name ~ '">' }}
+                {%- if tool_call.arguments %}
+                    {%- set args_dict = tool_call.arguments %}
+                    {%- for param_name, param_value in args_dict.items() %}
+                        {{- '<param name="' ~ param_name ~ '">' }}
+                        {%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
+                            {{- '<![CDATA[' + param_value + ']]>' }}
+                        {%- else %}
+                            {{- param_value }}
+                        {%- endif %}
+                        {{- '</param>' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- content }}
+        {%- else %}
+            {{- message.content | tojson(ensure_ascii=False) }}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined %}
+        {%- if enable_thinking is false %}
+            {{- '<think>\n\n</think>\n\n' }}
+        {%- elif enable_thinking is true %}
+            {{- '<think>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index c38aed8cf..5f71e5da6 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -5593,6 +5593,77 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
             .expect_content("Hello, world!\nWhat's up?")
             .run();
     }
+
+    // MiniCPM5 - XML tool calls with <function name="..."><param name="...">...</param></function>
+    {
+        auto tst = peg_tester("models/templates/openbmb-MiniCPM5-1B.jinja", detailed_debug);
+
+        tst.test("Hello, world!\nWhat's up?")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .expect(message_assist)
+            .run();
+
+        tst.test(R"(<function name="python"><param name="code">print('Hello, World!')</param></function>)")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({ python_tool })
+            .expect_tool_calls({ { "python", R"#({"code": "print('Hello, World!')"})#", {} } })
+            .run();
+
+        tst.test(R"(<function name="empty_args"></function>)")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({ empty_args_tool })
+            .expect(simple_assist_msg("", "", "empty_args", "{}"))
+            .run();
+
+        tst.test(R"(<function name="python"><param name="code">print('x')</param></function>)")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .parallel_tool_calls(true)
+            .tools({ python_tool })
+            .expect_tool_calls({ { "python", R"#({"code": "print('x')"})#", {} } })
+            .run();
+
+        // CDATA lets a string value carry characters that would otherwise close the tag.
+        tst.test(R"(<function name="html"><param name="markup"><![CDATA[<a href="/x">hi</a> </param>]]></param></function>)")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({ html_tool })
+            .expect_tool_calls({ { "html", R"#({"markup": "<a href=\"/x\">hi</a> </param>"})#", {} } })
+            .run();
+
+        tst.test(R"(I'm thinking</think><function name="python"><param name="code">print('hey')</param></function>)")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .tools({ python_tool })
+            .expect_reasoning("I'm thinking")
+            .expect_tool_calls({ { "python", R"#({"code": "print('hey')"})#", {} } })
+            .run();
+
+        tst.test(R"(<function name="python"><param name="code">print('x')</param></function>
+<function name="python"><param name="code">print('y')</param></function>)")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .parallel_tool_calls(true)
+            .tools({ python_tool })
+            .expect_tool_calls({
+                { "python", R"#({"code": "print('x')"})#", {} },
+                { "python", R"#({"code": "print('y')"})#", {} },
+            })
+            .run();
+
+        tst.test(" thinking</think>Hello, world!\nWhat's up?")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_AUTO)
+            .messages({ message_user, message_assist_prefill_reasoning })
+            .add_generation_prompt(false)
+            .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING)
+            .expect_reasoning("I'm thinking")
+            .expect_content("Hello, world!\nWhat's up?")
+            .run();
+    }
 }
 
 static void test_template_generation_prompt() {
@@ -5740,6 +5811,13 @@ static void test_template_generation_prompt() {
         check(tmpls, continuation_content(),   "<｜Assistant｜><think>I'm thinking</think>Hello, ");
         check(tmpls, continuation_reasoning(), "<｜Assistant｜><think>I'm");
     }
+
+    {
+        auto tmpls = read_templates("models/templates/openbmb-MiniCPM5-1B.jinja");
+        check(tmpls, basic(),                  "<|im_start|>assistant\n<think>\n");
+        check(tmpls, continuation_content(),   "<|im_start|>assistant\n<think>\nI'm thinking\n</think>\n\nHello, ");
+        check(tmpls, continuation_reasoning(), "<|im_start|>assistant\n<think>\nI'm");
+    }
 }
 
 // Test the developer role to system workaround with a simple mock template
diff --git a/tests/test-jinja.cpp b/tests/test-jinja.cpp
index 81bbcd55a..d8d1892a9 100644
--- a/tests/test-jinja.cpp
+++ b/tests/test-jinja.cpp
@@ -1584,6 +1584,36 @@ static void test_array_methods(testing & t) {
         "6"
     );
 
+    test_template(t, "array|min",
+        "{{ [tool_calls_count, tool_sep_count]|min }}",
+        {{"tool_calls_count", 2}, {"tool_sep_count", 1}},
+        "1"
+    );
+
+    test_template(t, "array|max",
+        "{{ [tool_calls_count, tool_sep_count]|max }}",
+        {{"tool_calls_count", 2}, {"tool_sep_count", 1}},
+        "2"
+    );
+
+    test_template(t, "array|min attribute",
+        "{{ items|min(attribute='x') }}",
+        {{"items", json::array({
+            json({{"x", 2}}),
+            json({{"x", 1}}),
+        })}},
+        "{'x': 1}"
+    );
+
+    test_template(t, "array|max attribute",
+        "{{ items|max(attribute='x') }}",
+        {{"items", json::array({
+            json({{"x", 2}}),
+            json({{"x", 1}}),
+        })}},
+        "{'x': 2}"
+    );
+
     // not used by any chat templates
     // test_template(t, "array.insert()",
     //     "{% set _ = arr.insert(1, 'x') %}{{ arr|join(',') }}",

From fa72bc6826a5ff30dda3abd1e2fd87ba91df5762 Mon Sep 17 00:00:00 2001
From: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Sun, 28 Jun 2026 20:31:48 +0200
Subject: [PATCH 211/292] dflash: refactor draft model conversion (#25110)

* dflash: refactor draft model conversion

* apply fix for eagle3 convert
---
 conversion/llama.py            |  6 +++---
 conversion/qwen.py             | 24 ++++++++++--------------
 gguf-py/gguf/constants.py      |  1 +
 gguf-py/gguf/gguf_writer.py    | 12 ++++++++++++
 gguf-py/gguf/tensor_mapping.py |  5 +++++
 5 files changed, 31 insertions(+), 17 deletions(-)

diff --git a/conversion/llama.py b/conversion/llama.py
index b43cc994a..315a619c9 100644
--- a/conversion/llama.py
+++ b/conversion/llama.py
@@ -73,7 +73,7 @@ class LlamaModel(TextModel):
             target_num_layers = target_config["num_hidden_layers"]
             target_layers = [2, target_num_layers // 2, target_num_layers - 3]
             logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
-            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", target_layers)
+            self.gguf_writer.add_target_layers(target_layers)
 
             # target_hidden_size: prefer eagle3 config, fallback to target config
             if eagle3_raw_config.get("target_hidden_size") is not None:
@@ -83,12 +83,12 @@ class LlamaModel(TextModel):
                 target_hidden_size = target_config["hidden_size"]
                 src = "target model config"
             logger.info(f"EAGLE-3: target_hidden_size = {target_hidden_size} (from {src})")
-            self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.target_hidden_size", target_hidden_size)
+            self.gguf_writer.add_target_hidden_size(target_hidden_size)
 
             # norm_before_residual (RedHat-style eagle3 specific)
             norm_before_residual = eagle3_raw_config.get("norm_before_residual", False)
             logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
-            self.gguf_writer.add_bool(f"{self.gguf_writer.arch}.norm_before_residual", norm_before_residual)
+            self.gguf_writer.add_norm_before_residual(norm_before_residual)
 
     def set_vocab(self):
         # eagle3: use tokenizer from target model if provided
diff --git a/conversion/qwen.py b/conversion/qwen.py
index 81f450e40..0356bd2da 100644
--- a/conversion/qwen.py
+++ b/conversion/qwen.py
@@ -643,21 +643,21 @@ class DFlashModel(Qwen3Model):
         super().set_vocab()
         self.dir_model = original_dir
 
+        mask_token_id = self.hparams.get("dflash_config", {}).get("mask_token_id")
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(mask_token_id)
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
         block_size = self.hparams.get("block_size", 16)
-        self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.block_size", block_size)
+        self.gguf_writer.add_block_size(block_size)
         dflash_config = self.hparams.get("dflash_config", {})
 
         target_layer_ids = dflash_config.get("target_layer_ids", [])
         if target_layer_ids:
             extract_layer_ids = [i + 1 for i in target_layer_ids]
-            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", extract_layer_ids)
-
-        mask_token_id = dflash_config.get("mask_token_id", None)
-        if mask_token_id is not None:
-            self.gguf_writer.add_mask_token_id(mask_token_id)
+            self.gguf_writer.add_target_layers(extract_layer_ids)
 
         use_sliding_window = self.hparams.get("use_sliding_window", False)
         sliding_window = self.hparams.get("sliding_window")
@@ -667,13 +667,9 @@ class DFlashModel(Qwen3Model):
             self.gguf_writer.add_sliding_window(sliding_window)
             self.gguf_writer.add_sliding_window_pattern(is_swa)
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "fc.weight":
-            yield (name, data_torch)
-            return
-        if name == "hidden_norm.weight":
-            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch)
-            return
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
         if not name.startswith("model."):
             name = "model." + name
-        yield from super().modify_tensors(data_torch, name, bid)
+        return super().filter_tensors((name, gen))
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index bcd10beb0..52e9e54de 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -156,6 +156,7 @@ class Keys:
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
         TARGET_LAYERS                     = "{arch}.target_layers"
         TARGET_HIDDEN_SIZE                = "{arch}.target_hidden_size"
+        BLOCK_SIZE                        = "{arch}.block_size"
         NORM_BEFORE_RESIDUAL              = "{arch}.norm_before_residual"
 
     class Attention:
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index a06ec88b3..610555f5e 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -940,6 +940,18 @@ class GGUFWriter:
     def add_sliding_window(self, value: int) -> None:
         self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
 
+    def add_block_size(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.BLOCK_SIZE.format(arch=self.arch), value)
+
+    def add_target_layers(self, value: Sequence[int]) -> None:
+        self.add_array(Keys.LLM.TARGET_LAYERS.format(arch=self.arch), value)
+
+    def add_target_hidden_size(self, value: int) -> None:
+        self.add_uint32(Keys.LLM.TARGET_HIDDEN_SIZE.format(arch=self.arch), value)
+
+    def add_norm_before_residual(self, value: bool) -> None:
+        self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value)
+
     def add_attention_scale(self, value: float) -> None:
         self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 5f1e28818..9efb36f8a 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1283,6 +1283,11 @@ class TensorNameMap:
         MODEL_TENSOR.ENC_OUTPUT_NORM: (
             "encoder.final_layer_norm", # t5
             "layer_norm",               # neobert
+            "model.hidden_norm",        # dflash
+        ),
+
+        MODEL_TENSOR.FC: (
+            "model.fc", # dflash
         ),
 
         MODEL_TENSOR.CLS: (

From 7cb8576e7c35789801b0f4e0653ffb41d0c5e046 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Sun, 28 Jun 2026 21:06:43 +0200
Subject: [PATCH 212/292] ui: fix stop and reasoning skip in single-model mode
 (#25084)

---
 tools/server/server-context.cpp        |  2 ++
 tools/server/server-models.cpp         |  5 +++-
 tools/server/server-stream.cpp         |  7 +++++
 tools/ui/src/lib/stores/chat.svelte.ts | 36 ++++++++++++++++++++------
 4 files changed, 41 insertions(+), 9 deletions(-)

diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index bb1c236cb..06f236b4c 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -2450,6 +2450,8 @@ private:
 
                     server_slot * slot = get_slot_by_cmpl_id(task.params.control_cmpl_id);
                     if (slot == nullptr) {
+                        SRV_WRN("control %s on unknown completion id=%s, no live slot\n",
+                                task.params.control_action.c_str(), task.params.control_cmpl_id.c_str());
                         res->success = false;
                         res->message = "no active completion for this id";
                         queue_results.send(std::move(res));
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 0380f98a3..81da00c0e 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1983,7 +1983,10 @@ void server_models_routes::init_routes() {
             cli.set_read_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
             cli.set_write_timeout(0, STREAM_LOOKUP_TIMEOUT_MS * 1000);
             auto resp = cli.Delete(child_path.c_str());
-            (void) resp; // best effort, 404 and network errors are equivalent to no op
+            (void) resp; // the child logs its own miss when the session is unknown there
+        } else {
+            SRV_WRN("router stop for unknown conv_id=%s, no owning child in the conv map\n",
+                    conv_id.c_str());
         }
         // drop the tracking entry, the session is being torn down
         models.conv_models.forget(conv_id);
diff --git a/tools/server/server-stream.cpp b/tools/server/server-stream.cpp
index 785c28b3a..c2bba8ec4 100644
--- a/tools/server/server-stream.cpp
+++ b/tools/server/server-stream.cpp
@@ -218,6 +218,13 @@ void stream_session_manager::evict_and_cancel(const std::string & conversation_i
         std::unique_lock<std::shared_mutex> lock(map_mu);
         auto it = sessions.find(conversation_id);
         if (it == sessions.end()) {
+            std::string live;
+            for (const auto & kv : sessions) {
+                if (!live.empty()) live += ", ";
+                live += kv.first;
+            }
+            SRV_WRN("stop on unknown stream session, conv_id=%s matched nothing, %zu live: [%s]\n",
+                    conversation_id.c_str(), sessions.size(), live.c_str());
             return;
         }
         s = it->second;
diff --git a/tools/ui/src/lib/stores/chat.svelte.ts b/tools/ui/src/lib/stores/chat.svelte.ts
index faaaa9755..43f9e72c7 100644
--- a/tools/ui/src/lib/stores/chat.svelte.ts
+++ b/tools/ui/src/lib/stores/chat.svelte.ts
@@ -154,7 +154,13 @@ class ChatStore {
 		});
 		if (convId === conversationsStore.activeConversation?.id) this.currentResponse = response;
 	}
-	private clearChatStreaming(convId: string): void {
+	private clearChatStreaming(convId: string, messageId?: string): void {
+		// session aware: a stale generation must not wipe a newer one's streaming state on the
+		// same conversation, that would drop the frozen stop identity and stop the wrong session
+		if (messageId !== undefined) {
+			const cur = this.chatStreamingStates.get(convId);
+			if (cur && cur.messageId !== messageId) return;
+		}
 		this.chatStreamingStates.delete(convId);
 		if (convId === conversationsStore.activeConversation?.id) this.currentResponse = '';
 	}
@@ -1055,11 +1061,14 @@ class ChatStore {
 		modelOverride?: string | null,
 		firstUserMessageContent?: string
 	): Promise<void> {
-		let effectiveModel = modelOverride;
+		// the ::model suffix in the stream identity is only for router mode, where it routes to the
+		// owning child. in single-model mode the identity stays the bare conv id so that attach, stop
+		// and reattach all agree, regardless of fresh send vs regenerate passing a resolved model
+		let effectiveModel: string | null | undefined = undefined;
 
-		if (isRouterMode() && !effectiveModel) {
+		if (isRouterMode()) {
 			const conversationModel = this.getConversationModel(allMessages);
-			effectiveModel = selectedModelName() || conversationModel;
+			effectiveModel = modelOverride || selectedModelName() || conversationModel;
 		}
 
 		if (isRouterMode() && effectiveModel) {
@@ -1074,6 +1083,9 @@ class ChatStore {
 		let resolvedModel: string | null = null;
 		let modelPersisted = false;
 		const convId = assistantMessage.convId;
+		// freeze the POST identity from t0 so a stop cancels with the exact session key,
+		// never a stale or empty model resolved later
+		this.setChatStreaming(convId, streamedContent, currentMessageId, effectiveModel);
 
 		const recordModel = (modelName: string | null | undefined, persistImmediately = true): void => {
 			if (!modelName) return;
@@ -1103,7 +1115,7 @@ class ChatStore {
 		};
 
 		const updateStreamingUI = () => {
-			this.setChatStreaming(convId, streamedContent, currentMessageId);
+			this.setChatStreaming(convId, streamedContent, currentMessageId, effectiveModel);
 			const idx = conversationsStore.findMessageIndex(currentMessageId);
 			conversationsStore.updateMessageAtIndex(idx, { content: streamedContent });
 		};
@@ -1111,7 +1123,7 @@ class ChatStore {
 		const cleanupStreamingState = () => {
 			this.setStreamingActive(false);
 			this.setChatLoading(convId, false);
-			this.clearChatStreaming(convId);
+			this.clearChatStreaming(convId, currentMessageId);
 			this.setProcessingState(convId, null);
 		};
 
@@ -1128,7 +1140,7 @@ class ChatStore {
 			onReasoningChunk: (chunk: string) => {
 				streamedReasoningContent += chunk;
 				// mark streaming state so a stop mid-thinking can persist the partial reasoning
-				this.setChatStreaming(convId, streamedContent, currentMessageId);
+				this.setChatStreaming(convId, streamedContent, currentMessageId, effectiveModel);
 				const idx = conversationsStore.findMessageIndex(currentMessageId);
 				conversationsStore.updateMessageAtIndex(idx, {
 					reasoningContent: streamedReasoningContent
@@ -1405,7 +1417,7 @@ class ChatStore {
 		// detached drain keeps producing tokens until eos or max_tokens. use the frozen identity
 		// captured when the session started, not the live dropdown
 		const streamStateForStop = this.chatStreamingStates.get(convId);
-		const modelForStop = streamStateForStop?.model ?? selectedModelName();
+		const modelForStop = streamStateForStop?.model;
 		void ChatService.cancelServerStream(convId, modelForStop);
 		this.abortRequest(convId);
 		this.setChatLoading(convId, false);
@@ -1846,6 +1858,14 @@ class ChatStore {
 						updateStreamingContent(originalContent + appendedContent);
 						this.setChatReasoning(msg.convId, false);
 					},
+					onCompletionId: (id: string) => {
+						if (!id) return;
+						// refresh the message id so a later skip targets the live slot after a continue
+						conversationsStore.updateMessageAtIndex(conversationsStore.findMessageIndex(msg.id), {
+							completionId: id
+						});
+						DatabaseService.updateMessage(msg.id, { completionId: id }).catch(() => {});
+					},
 					onReasoningChunk: (chunk: string) => {
 						appendedReasoning += chunk;
 						hasReceivedContent = true;

From dbdaece23de9ac63f2e7ca9e6bfcdc4fc156a3fa Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Sun, 28 Jun 2026 21:30:03 +0200
Subject: [PATCH 213/292] Revert "ui: fix accessibility for hover-gated
 interactive elements assisted by claude(in debugging and tests) (#24727)"
 (#25098)

---
 .../ChatAttachmentsListItemMcpPrompt.svelte   |   2 +-
 .../ChatMessageUserPending.svelte             |   2 +-
 .../SidebarNavigationConversationItem.svelte  | 137 +++++++++++-------
 3 files changed, 83 insertions(+), 58 deletions(-)

diff --git a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
index c55dfdec7..636e93f22 100644
--- a/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList/ChatAttachmentsListItem/ChatAttachmentsListItemMcpPrompt.svelte
@@ -33,7 +33,7 @@
 
 	{#if !readonly && onRemove}
 		<div
-			class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-focus-within:opacity-100 group-hover:opacity-100"
+			class="absolute top-10 right-2 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"
 		>
 			<ActionIcon icon={X} tooltip="Remove" stopPropagationOnClick onclick={() => onRemove?.()} />
 		</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
index 5c2913202..4be582b39 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessageUser/ChatMessageUserPending.svelte
@@ -56,7 +56,7 @@
 			<div class="relative flex h-6 items-center justify-between">
 				<div class="right-0 flex items-center gap-2 opacity-100 transition-opacity">
 					<div
-						class="pointer-events-auto inset-0 flex items-center gap-1 opacity-0 transition-all duration-150 group-focus-within:opacity-100 group-hover:opacity-100"
+						class="pointer-events-auto inset-0 flex items-center gap-1 opacity-0 transition-all duration-150 group-hover:opacity-100"
 					>
 						<ActionIcon icon={Edit} tooltip="Edit" onclick={editCtx.handleEdit} />
 						<ActionIcon icon={Trash2} tooltip="Delete" onclick={onDelete} />
diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
index 2c1b9adf2..b1c2b78f6 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigationConversationItem.svelte
@@ -39,6 +39,7 @@
 		depth = 0
 	}: Props = $props();
 
+	let renderActionsDropdown = $state(false);
 	let dropdownOpen = $state(false);
 
 	let isLoading = $derived(getAllLoadingChats().includes(conversation.id));
@@ -70,10 +71,26 @@
 		}
 	}
 
+	function handleMouseLeave() {
+		if (!dropdownOpen) {
+			renderActionsDropdown = false;
+		}
+	}
+
+	function handleMouseOver() {
+		renderActionsDropdown = true;
+	}
+
 	function handleSelect() {
 		onSelect?.(conversation.id);
 	}
 
+	$effect(() => {
+		if (!dropdownOpen) {
+			renderActionsDropdown = false;
+		}
+	});
+
 	onMount(() => {
 		document.addEventListener('edit-active-conversation', handleGlobalEditEvent as EventListener);
 
@@ -86,19 +103,23 @@
 	});
 </script>
 
-<div
-	class="conversation-item group relative flex min-h-9 w-full items-center justify-between space-x-3 rounded-lg py-1.5 transition-colors hover:bg-foreground/10 {isActive
+<!-- svelte-ignore a11y_mouse_events_have_key_events -->
+<button
+	class="group flex min-h-9 w-full cursor-pointer items-center justify-between space-x-3 rounded-lg py-1.5 text-left transition-colors hover:bg-foreground/10 {isActive
 		? 'bg-foreground/5 text-accent-foreground'
 		: ''} px-3"
+	onclick={handleSelect}
+	onmouseover={handleMouseOver}
+	onmouseleave={handleMouseLeave}
+	onfocusin={handleMouseOver}
+	onfocusout={(e) => {
+		if (!e.currentTarget.contains(e.relatedTarget as Node | null)) {
+			handleMouseLeave();
+		}
+	}}
 >
-	<button
-		class="absolute inset-0 z-0 cursor-pointer rounded-lg focus:outline-none focus-visible:ring-2 focus-visible:ring-ring"
-		onclick={handleSelect}
-		aria-label={conversation.name}
-	>
-	</button>
 	<div
-		class="pointer-events-none relative z-10 flex min-w-0 flex-1 items-center gap-2"
+		class="flex min-w-0 flex-1 items-center gap-2"
 		style:padding-left="{depth * FORK_TREE_DEPTH_PADDING}px"
 	>
 		{#if depth > 0}
@@ -109,7 +130,7 @@
 						<a
 							{...props}
 							href={RouterService.chat(conversation.forkedFromConversationId)}
-							class="pointer-events-auto flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"
+							class="flex shrink-0 items-center text-muted-foreground transition-colors hover:text-foreground"
 						>
 							<GitBranch class="h-3.5 w-3.5" />
 						</a>
@@ -125,15 +146,18 @@
 		{#if isLoading}
 			<Tooltip.Root>
 				<Tooltip.Trigger>
-					<button
-						class="stop-button pointer-events-auto flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground"
+					<div
+						class="stop-button flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground"
 						onclick={handleStop}
+						onkeydown={(e) => e.key === 'Enter' && handleStop(e)}
+						role="button"
+						tabindex="0"
 						aria-label="Stop generation"
 					>
 						<Loader2 class="loading-icon h-3.5 w-3.5 animate-spin" />
 
 						<Square class="stop-icon hidden h-3 w-3 fill-current text-destructive" />
-					</button>
+					</div>
 				</Tooltip.Trigger>
 
 				<Tooltip.Content>
@@ -145,50 +169,52 @@
 		<TruncatedText text={conversation.name} class="text-sm font-medium" showTooltip={false} />
 	</div>
 
-	<div class="actions pointer-events-auto relative z-20 flex items-center">
-		<DropdownMenuActions
-			triggerIcon={MoreHorizontal}
-			triggerTooltip="More actions"
-			bind:open={dropdownOpen}
-			actions={[
-				{
-					icon: conversation.pinned ? PinOff : Pin,
-					label: conversation.pinned ? 'Unpin' : 'Pin',
-					onclick: (e: Event) => {
-						e.stopPropagation();
-						handleTogglePin();
-					}
-				},
-				{
-					icon: Pencil,
-					label: 'Edit',
-					onclick: handleEdit,
-					shortcut: ['shift', 'cmd', 'e']
-				},
-				{
-					icon: Download,
-					label: 'Export',
-					onclick: (e: Event) => {
-						e.stopPropagation();
-						conversationsStore.downloadConversation(conversation.id);
+	{#if renderActionsDropdown}
+		<div class="actions flex items-center">
+			<DropdownMenuActions
+				triggerIcon={MoreHorizontal}
+				triggerTooltip="More actions"
+				bind:open={dropdownOpen}
+				actions={[
+					{
+						icon: conversation.pinned ? PinOff : Pin,
+						label: conversation.pinned ? 'Unpin' : 'Pin',
+						onclick: (e: Event) => {
+							e.stopPropagation();
+							handleTogglePin();
+						}
 					},
-					shortcut: ['shift', 'cmd', 's']
-				},
-				{
-					icon: Trash2,
-					label: 'Delete',
-					onclick: handleDelete,
-					variant: 'destructive',
-					shortcut: ['shift', 'cmd', 'd'],
-					separator: true
-				}
-			]}
-		/>
-	</div>
-</div>
+					{
+						icon: Pencil,
+						label: 'Edit',
+						onclick: handleEdit,
+						shortcut: ['shift', 'cmd', 'e']
+					},
+					{
+						icon: Download,
+						label: 'Export',
+						onclick: (e: Event) => {
+							e.stopPropagation();
+							conversationsStore.downloadConversation(conversation.id);
+						},
+						shortcut: ['shift', 'cmd', 's']
+					},
+					{
+						icon: Trash2,
+						label: 'Delete',
+						onclick: handleDelete,
+						variant: 'destructive',
+						shortcut: ['shift', 'cmd', 'd'],
+						separator: true
+					}
+				]}
+			/>
+		</div>
+	{/if}
+</button>
 
 <style>
-	.conversation-item {
+	button {
 		:global([data-slot='dropdown-menu-trigger']:not([data-state='open'])) {
 			opacity: 0;
 		}
@@ -213,8 +239,7 @@
 			}
 		}
 
-		&:is(:hover) .stop-button,
-		&:focus-within .stop-button {
+		&:is(:hover) .stop-button {
 			:global(.stop-icon) {
 				display: block;
 			}

From b3fed31b99f9bd37725833674252bccb429bb183 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 28 Jun 2026 23:33:51 +0200
Subject: [PATCH 214/292] jinja, chat: add --reasoning-preserve flag (#25105)

* jinja, chat: add --reasoning-preserve flag

* correct help message
---
 common/arg.cpp                  | 14 +++++++
 common/chat.cpp                 |  4 ++
 common/jinja/caps.cpp           | 67 ++++++++++++++++++++++-----------
 common/jinja/caps.h             |  6 ++-
 tools/server/server-context.cpp | 13 +++++++
 5 files changed, 80 insertions(+), 24 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index c289ff713..0fc94e553 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -3296,6 +3296,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.reasoning_budget_message = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET_MESSAGE"));
+    add_opt(common_arg(
+        {"--reasoning-preserve"},
+        {"--no-reasoning-preserve"},
+        "preserve reasoning trace in the full history, not just the last assistant message (default: template default)\n"
+        "compatible with certain templates having 'supports_preserve_reasoning' capability\n"
+        "example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking",
+        [](common_params & params, bool value) {
+            if (value) {
+                params.default_template_kwargs["preserve_reasoning"] = "true";
+            } else {
+                params.default_template_kwargs["preserve_reasoning"] = "false";
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_PRESERVE"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
diff --git a/common/chat.cpp b/common/chat.cpp
index a38c62f5f..6da59f4db 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -912,6 +912,10 @@ static std::string common_chat_template_direct_apply_impl(
     if (inputs.add_generation_prompt) {
         inp["add_generation_prompt"] = true;
     }
+    if (inp.contains("preserve_reasoning") && inp["preserve_reasoning"].is_boolean()) {
+        bool enabled = inp["preserve_reasoning"].get<bool>();
+        jinja::caps_apply_preserve_reasoning(ctx, enabled);
+    }
 
     jinja::global_from_json(ctx, inp, inputs.mark_input);
 
diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp
index ead864763..ae378ebd4 100644
--- a/common/jinja/caps.cpp
+++ b/common/jinja/caps.cpp
@@ -16,22 +16,34 @@ using json = nlohmann::ordered_json;
 namespace jinja {
 
 using caps_json_fn = std::function<json()>;
-using caps_analyze_fn = std::function<void(bool, value &, value &)>;
+using caps_ctx_fn = std::function<void(context &)>;
+using caps_analyze_fn = std::function<void(bool, value &, value &, const std::string &)>;
+
+void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
+    ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
+    ctx.set_val("clear_thinking",            mk_val<value_bool>(!enabled));
+    ctx.set_val("truncate_history_thinking", mk_val<value_bool>(!enabled));
+}
 
 static void caps_try_execute(jinja::program & prog,
                              const caps_json_fn & messages_fn,
+                             const caps_ctx_fn & ctx_fn,
                              const caps_json_fn & tools_fn,
                              const caps_analyze_fn & analyze_fn) {
     context ctx;
     ctx.is_get_stats = true;
     jinja::global_from_json(ctx, json{
         {"messages", messages_fn()},
-        {"tools", tools_fn()},
+        {"tools", tools_fn ? tools_fn() : json::array()},
         {"bos_token", ""},
         {"eos_token", ""},
         {"add_generation_prompt", true}
     }, true);
 
+    if (ctx_fn) {
+        ctx_fn(ctx);
+    }
+
     auto messages = ctx.get_val("messages");
     auto tools = ctx.get_val("tools");
 
@@ -49,7 +61,7 @@ static void caps_try_execute(jinja::program & prog,
         // ignore exceptions during capability analysis
     }
 
-    analyze_fn(success, messages, tools);
+    analyze_fn(success, messages, tools, result);
 }
 
 // for debugging only
@@ -109,11 +121,9 @@ caps caps_get(jinja::program & prog) {
                 }
             });
         },
-        [&]() {
-            // tools
-            return json{nullptr};
-        },
-        [&](bool success, value & messages, value &) {
+        nullptr, // ctx_fn
+        nullptr, // tools_fn
+        [&](bool success, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
@@ -145,11 +155,9 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&]() {
-            // tools
-            return json::array();
-        },
-        [&](bool, value & messages, value &) {
+        nullptr, // ctx_fn
+        nullptr, // tools_fn
+        [&](bool, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (!content->stats.used) {
@@ -201,6 +209,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
+        nullptr, // ctx_fn
         [&]() {
             // tools
             return json::array({
@@ -224,7 +233,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value & tools) {
+        [&](bool success, value & messages, value & tools, const std::string &) {
             if (!success) {
                 return; // Nothing can be inferred
             }
@@ -293,6 +302,7 @@ caps caps_get(jinja::program & prog) {
                     },
                 });
             },
+            nullptr, // ctx_fn
             [&]() {
                 // tools
                 return json::array({
@@ -316,7 +326,7 @@ caps caps_get(jinja::program & prog) {
                     },
                 });
             },
-            [&](bool success, value & messages, value & tools) {
+            [&](bool success, value & messages, value & tools, const std::string &) {
                 if (!success) {
                     result.supports_tool_calls = false;
                     result.supports_tools = false;
@@ -394,6 +404,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
+        nullptr, // ctx_fn
         [&]() {
             // tools
             return json::array({
@@ -417,7 +428,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value & /*tools*/) {
+        [&](bool success, value & messages, value &, const std::string &) {
             if (!success) {
                 result.supports_parallel_tool_calls = false;
                 return;
@@ -438,11 +449,22 @@ caps caps_get(jinja::program & prog) {
     JJ_DEBUG("%s\n", ">>> Running capability check: preserve reasoning");
 
     // case: preserve reasoning content in chat history
+    const std::string reasoning_placeholder = "<REASONING_CONTENT_PLACEHOLDER>";
     caps_try_execute(
         prog,
         [&]() {
             // messages
             return json::array({
+                {
+                    {"role", "user"},
+                    {"content", "User message"}
+                },
+                {
+                    {"role", "assistant"},
+                    {"content", "Assistant message"},
+                    // check of reasoning_content deeper in the history, not just the last assistant message
+                    {"reasoning_content", reasoning_placeholder}
+                },
                 {
                     {"role", "user"},
                     {"content", "User message"}
@@ -458,14 +480,13 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&]() {
-            // tools
-            return json::array();
+        [&](context & ctx) {
+            caps_apply_preserve_reasoning(ctx, true);
         },
-        [&](bool, value & messages, value &) {
-            auto & content = messages->at(1)->at("reasoning_content");
-            caps_print_stats(content, "messages[1].reasoning_content");
-            if (content->stats.used) {
+        nullptr, // tools_fn
+        [&](bool, value &, value &, const std::string & output) {
+            // note: we cannot use stats here because the reasoning_content may be used for "if" condition test, but not actually outputted in the final result
+            if (output.find(reasoning_placeholder) != std::string::npos) {
                 result.supports_preserve_reasoning = true;
             }
         }
diff --git a/common/jinja/caps.h b/common/jinja/caps.h
index 93a7fe092..a290cd7da 100644
--- a/common/jinja/caps.h
+++ b/common/jinja/caps.h
@@ -12,7 +12,9 @@ struct caps {
     bool supports_tool_calls = true;
     bool supports_system_role = true;
     bool supports_parallel_tool_calls = true;
-    bool supports_preserve_reasoning = false; // support assistant message with reasoning_content
+
+    // supports preserve reasoning trace in the full history, not just the last assistant message
+    bool supports_preserve_reasoning = false;
 
     // one of the 2 content capabilities must be true
     bool supports_string_content = true;
@@ -29,4 +31,6 @@ struct caps {
 
 caps caps_get(jinja::program & prog);
 
+void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled);
+
 } // namespace jinja
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 06f236b4c..39aa20b32 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1538,6 +1538,19 @@ private:
                 /* media_path            */ params_base.media_path,
                 /* force_pure_content    */ params_base.force_pure_content_parser
             };
+
+            {
+                auto caps = common_chat_templates_get_caps(chat_params.tmpls.get());
+                auto it = params_base.default_template_kwargs.find("preserve_reasoning");
+                bool supported = caps.at("supports_preserve_reasoning");
+                bool enabled = it != params_base.default_template_kwargs.end();
+                if (supported && !enabled) {
+                    SRV_INF("%s", "chat template supports preserving reasoning, consider enabling it via --reasoning-preserve\n");
+                }
+                if (!supported && enabled) {
+                    SRV_WRN("%s", "chat template does NOT support preserving reasoning, --reasoning-preserve has no effect\n");
+                }
+            }
         }
 
         return true;

From 277a105dc8f8643dab54331926a9830860a03292 Mon Sep 17 00:00:00 2001
From: o7si <32285332+o7si@users.noreply.github.com>
Date: Mon, 29 Jun 2026 14:48:39 +0800
Subject: [PATCH 215/292] common : remove unused regex-partial (#25118)

---
 common/CMakeLists.txt        |   2 -
 common/regex-partial.cpp     | 204 -------------------------
 common/regex-partial.h       |  56 -------
 tests/CMakeLists.txt         |   1 -
 tests/test-regex-partial.cpp | 288 -----------------------------------
 5 files changed, 551 deletions(-)
 delete mode 100644 common/regex-partial.cpp
 delete mode 100644 common/regex-partial.h
 delete mode 100644 tests/test-regex-partial.cpp

diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index fc16b21cf..4cf580a05 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -94,10 +94,8 @@ add_library(${TARGET}
     peg-parser.h
     preset.cpp
     preset.h
-    regex-partial.cpp
     reasoning-budget.cpp
     reasoning-budget.h
-    regex-partial.h
     sampling.cpp
     sampling.h
     speculative.cpp
diff --git a/common/regex-partial.cpp b/common/regex-partial.cpp
deleted file mode 100644
index bd9034e93..000000000
--- a/common/regex-partial.cpp
+++ /dev/null
@@ -1,204 +0,0 @@
-#include "regex-partial.h"
-#include "common.h"
-#include <functional>
-#include <optional>
-
-common_regex::common_regex(const std::string & pattern) :
-    pattern(pattern),
-    rx(pattern),
-    rx_reversed_partial(regex_to_reversed_partial_regex(pattern)) {}
-
-common_regex_match common_regex::search(const std::string & input, size_t pos, bool as_match) const {
-    std::smatch match;
-    if (pos > input.size()) {
-        throw std::runtime_error("Position out of bounds");
-    }
-    auto start = input.begin() + pos;
-    auto found = as_match
-        ? std::regex_match(start, input.end(), match, rx)
-        : std::regex_search(start, input.end(), match, rx);
-    if (found) {
-        common_regex_match res;
-        res.type = COMMON_REGEX_MATCH_TYPE_FULL;
-        for (size_t i = 0; i < match.size(); ++i) {
-            auto begin = pos + match.position(i);
-            res.groups.emplace_back(begin, begin + match.length(i));
-        }
-        return res;
-    }
-    std::match_results<std::string::const_reverse_iterator> srmatch;
-    if (std::regex_search(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial, std::regex_constants::match_continuous)) {
-        auto group = srmatch[1].str();
-        if (group.length() != 0) {
-            auto it = srmatch[1].second.base();
-            // auto position = static_cast<size_t>(std::distance(input.begin(), it));
-            if ((!as_match) || it == input.begin()) {
-                common_regex_match res;
-                res.type = COMMON_REGEX_MATCH_TYPE_PARTIAL;
-                const size_t begin = std::distance(input.begin(), it);
-                const size_t end = input.size();
-                if (begin == std::string::npos || end == std::string::npos || begin > end) {
-                    throw std::runtime_error("Invalid range");
-                }
-                res.groups.push_back({begin, end});
-                return res;
-            }
-        }
-    }
-    return {};
-}
-
-/*
-  Transforms a regex pattern to a partial match pattern that operates on a reversed input string to find partial final matches of the original pattern.
-
-  Ideally we'd like to use boost::match_partial (https://beta.boost.org/doc/libs/1_59_0/libs/regex/doc/html/boost_regex/partial_matches.html)
-  to see if a string ends with a partial regex match, but but it's not in std::regex yet.
-  Instead, we'll the regex into a partial match regex operating as a full match on the reverse iterators of the input.
-
-  - /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:(?:d)?c)?b)?a)
-  - /a|b/ -> ^(a|b)
-  - /a*?/ -> error, could match ""
-  - /a*b/ -> ^((?:b)?a*+) (final repetitions become eager)
-  - /.*?ab/ -> ^((?:b)?a) (omit .*)
-  - /a.*?b/ -> ^((?:b)?.*?a) (keep reluctant matches)
-  - /a(bc)d/ -> ^((?:(?:d)?(?:(?:c)?b))?a)
-  - /a(bc|de)/ -> ^((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a)
-  - /ab{2,4}c/ -> ^cbbb?b?a -> ^((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a)
-
-  The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern.
-  All other groups are turned into non-capturing groups, and reluctant quantifiers are ignored.
-*/
-std::string regex_to_reversed_partial_regex(const std::string & pattern) {
-    auto it = pattern.begin();
-    const auto end = pattern.end();
-
-    std::function<std::string()> process = [&]() {
-        std::vector<std::vector<std::string>> alternatives(1);
-        std::vector<std::string> * sequence = &alternatives.back();
-
-        while (it != end) {
-            if (*it == '[') {
-                auto start = it;
-                ++it;
-                while (it != end) {
-                    if ((*it == '\\') && (++it != end)) {
-                        ++it;
-                    } else if ((it != end) && (*it == ']')) {
-                        break;
-                    } else {
-                        ++it;
-                    }
-                }
-                if (it == end) {
-                    throw std::runtime_error("Unmatched '[' in pattern");
-                }
-                ++it;
-                sequence->push_back(std::string(start, it));
-            } else if (*it == '*' || *it == '?' || *it == '+') {
-                if (sequence->empty()) {
-                    throw std::runtime_error("Quantifier without preceding element");
-                }
-                sequence->back() += *it;
-                auto is_star = *it == '*';
-                ++it;
-                if (is_star) {
-                    if (it != end && *it == '?') {
-                        ++it;
-                    }
-                }
-            } else if (*it == '{') {
-                if (sequence->empty()) {
-                    throw std::runtime_error("Repetition without preceding element");
-                }
-                ++it;
-                auto start = it;
-                while (it != end && *it != '}') {
-                    ++it;
-                }
-                if (it == end) {
-                    throw std::runtime_error("Unmatched '{' in pattern");
-                }
-                auto parts = string_split(std::string(start, it), ",");
-                ++it;
-                if (parts.size() > 2) {
-                    throw std::runtime_error("Invalid repetition range in pattern");
-                }
-
-                auto parseOptInt = [&](const std::string & s, const std::optional<int> & def = std::nullopt) -> std::optional<int> {
-                    if (s.empty()) {
-                        return def;
-                    }
-                    return std::stoi(s);
-                };
-                auto min = parseOptInt(parts[0], 0);
-                auto max = parts.size() == 1 ? min : parseOptInt(parts[1]);
-                if (min && max && *max < *min) {
-                    throw std::runtime_error("Invalid repetition range in pattern");
-                }
-                // Brutal but... let's repeat at least min times, then ? for the delta between min & max (or * for unbounded)
-                auto part = sequence->back();
-                sequence->pop_back();
-                for (int i = 0; i < *min; i++) {
-                    sequence->push_back(part);
-                }
-                if (max) {
-                    for (int i = *min; i < *max; i++) {
-                        sequence->push_back(part + "?");
-                    }
-                } else {
-                    sequence->push_back(part + "*");
-                }
-            } else if (*it == '(') {
-                ++it;
-                if (it != end && *it == '?' && (it + 1 != end) && *(it + 1) == ':') {
-                    it += 2;
-                }
-                auto sub = process();
-                if (*it != ')') {
-                    throw std::runtime_error("Unmatched '(' in pattern");
-                }
-                ++it;
-                auto & part = sequence->emplace_back("(?:");
-                part += sub;
-                part += ")";
-            } else if (*it == ')') {
-                break;
-            } else if (*it == '|') {
-                ++it;
-                alternatives.emplace_back();
-                sequence = &alternatives.back();
-            } else if (*it == '\\' && (++it != end)) {
-                auto str = std::string("\\") + *it;
-                sequence->push_back(str);
-                ++it;
-            } else if (it != end) {
-                sequence->push_back(std::string(1, *it));
-                ++it;
-            }
-        }
-
-        // /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:d)?c)?b)?a)
-        // if n(=4) parts, opening n-1(=3) non-capturing groups after the 1 capturing group
-        // We'll do the outermost capturing group and final .* in the enclosing function.
-        std::vector<std::string> res_alts;
-        for (const auto & parts : alternatives) {
-            auto & res = res_alts.emplace_back();
-            for (size_t i = 0; i < parts.size() - 1; i++) {
-                res += "(?:";
-            }
-            for (auto it = parts.rbegin(); it != parts.rend(); ++it) {
-                res += *it;
-                if (it != parts.rend() - 1) {
-                    res += ")?";
-                }
-            }
-        }
-        return string_join(res_alts, "|");
-    };
-    auto res = process();
-    if (it != end) {
-        throw std::runtime_error("Unmatched '(' in pattern");
-    }
-
-    return "^(" + res + ")";
-}
diff --git a/common/regex-partial.h b/common/regex-partial.h
deleted file mode 100644
index 634cb4022..000000000
--- a/common/regex-partial.h
+++ /dev/null
@@ -1,56 +0,0 @@
-#pragma once
-
-#include <regex>
-#include <string>
-
-enum common_regex_match_type {
-    COMMON_REGEX_MATCH_TYPE_NONE,
-    COMMON_REGEX_MATCH_TYPE_PARTIAL,
-    COMMON_REGEX_MATCH_TYPE_FULL,
-};
-
-struct common_string_range {
-    size_t begin;
-    size_t end;
-    common_string_range(size_t begin, size_t end) : begin(begin), end(end) {
-        if (begin > end) {
-            throw std::runtime_error("Invalid range");
-        }
-    }
-    // prevent default ctor
-    common_string_range() = delete;
-    bool empty() const {
-        return begin == end;
-    }
-    bool operator==(const common_string_range & other) const {
-        return begin == other.begin && end == other.end;
-    }
-};
-
-struct common_regex_match {
-    common_regex_match_type type = COMMON_REGEX_MATCH_TYPE_NONE;
-    std::vector<common_string_range> groups;
-
-    bool operator==(const common_regex_match & other) const {
-        return type == other.type && groups == other.groups;
-    }
-    bool operator!=(const common_regex_match & other) const {
-        return !(*this == other);
-    }
-};
-
-class common_regex {
-    std::string pattern;
-    std::regex rx;
-    std::regex rx_reversed_partial;
-
-  public:
-    explicit common_regex(const std::string & pattern);
-
-    common_regex_match search(const std::string & input, size_t pos, bool as_match = false) const;
-
-    const std::string & str() const { return pattern; }
-};
-
-// For testing only (pretty print of failures).
-std::string regex_to_reversed_partial_regex(const std::string & pattern);
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 24592a279..d4ea3b962 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -211,7 +211,6 @@ llama_build_and_test(
     peg-parser/test-unicode.cpp
     peg-parser/tests.h
 )
-llama_build_and_test(test-regex-partial.cpp)
 
 if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
     set(MODEL_NAME "tinyllamas/stories15M-q4_0.gguf")
diff --git a/tests/test-regex-partial.cpp b/tests/test-regex-partial.cpp
deleted file mode 100644
index 70af6d75a..000000000
--- a/tests/test-regex-partial.cpp
+++ /dev/null
@@ -1,288 +0,0 @@
-//  Tests common_regex (esp. its partial final matches support).
-
-#include "common.h"
-#include "regex-partial.h"
-
-#include <sstream>
-#include <iostream>
-#include <optional>
-
-template <class T> static void assert_equals(const T & expected, const T & actual) {
-    if (expected != actual) {
-        std::cerr << "Expected: " << expected << std::endl;
-        std::cerr << "  Actual: " << actual << std::endl;
-        std::cerr << std::flush;
-        throw std::runtime_error("Test failed");
-    }
-}
-
-struct test_case {
-    std::string pattern;
-    struct input_output {
-        std::string input;
-        common_regex_match output;
-    };
-    std::vector<input_output> inputs_outputs;
-};
-
-static std::string common_regex_match_type_name(common_regex_match_type type) {
-    switch (type) {
-        case COMMON_REGEX_MATCH_TYPE_NONE:
-            return "COMMON_REGEX_MATCH_TYPE_NONE";
-        case COMMON_REGEX_MATCH_TYPE_PARTIAL:
-            return "COMMON_REGEX_MATCH_TYPE_PARTIAL";
-        case COMMON_REGEX_MATCH_TYPE_FULL:
-            return "COMMON_REGEX_MATCH_TYPE_FULL";
-    }
-    return "?";
-}
-
-static void test_regex() {
-    printf("[%s]\n", __func__);
-    auto test = [](const test_case & test_case) {
-        common_regex cr(test_case.pattern);
-        std::cout << "Testing pattern: /" << test_case.pattern << "/\n";
-        // std::cout << "    partial rev: " << cr.reversed_partial_pattern.str() << '\n';
-        for (const auto & input_output : test_case.inputs_outputs) {
-            std::cout << "  Input: " << input_output.input << '\n';
-            auto m = cr.search(input_output.input, 0);
-            if (m != input_output.output) {
-                auto match_to_str = [&](const std::optional<common_regex_match> & m) {
-                    std::ostringstream ss;
-                    if (m->type == COMMON_REGEX_MATCH_TYPE_NONE) {
-                        ss << "<no match>";
-                    } else {
-                        GGML_ASSERT(!input_output.output.groups.empty());
-                        std::vector<std::string> parts;
-                        for (const auto & g : m->groups) {
-                            parts.push_back("{" + std::to_string(g.begin) + ", " + std::to_string(g.end) + "}");
-                        }
-                        ss << "{" << common_regex_match_type_name(m->type) << ", {" << string_join(parts, ", ") << "}}";
-                    }
-                    return ss.str();
-                };
-                std::cout << "    Expected: " << match_to_str(input_output.output) << '\n';
-                std::cout << "         Got: " << match_to_str(m) << '\n';
-                std::cout << " Inverted pattern: /" << regex_to_reversed_partial_regex(test_case.pattern) << "/\n";
-
-                throw std::runtime_error("Test failed");
-            }
-        }
-    };
-    test({
-        "a",
-        {
-            {"a", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 1}}}},
-            {"b", {COMMON_REGEX_MATCH_TYPE_NONE, {}}},
-            {"ab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 1}}}},
-            {"ba", {COMMON_REGEX_MATCH_TYPE_FULL, {{1, 2}}}},
-        }
-    });
-    test({
-        "abcd",
-        {
-            {"abcd", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 4}}}},
-            {"abcde", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 4}}}},
-            {"abc", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 3}}}},
-            {"ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"a", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 1}}}},
-            {"d", {}},
-            {"bcd", {}},
-            {"cde", {}},
-            {"cd", {}},
-            {"yeah ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{5, 7}}}},
-            {"abbie", {}},
-            {"", {}},
-        }
-    });
-    test({
-        ".*?ab",
-        {
-            {"ab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 2}}}},
-            {"abc", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 2}}}},
-            {"dab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 3}}}},
-            {"dabc", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 3}}}},
-            {"da", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"d", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 1}}}},
-        }
-    });
-    test({
-        "a.*?b",
-        {
-            {"ab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 2}}}},
-            {"abc", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 2}}}},
-            {"a b", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 3}}}},
-            {"a", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 1}}}},
-            {"argh", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 4}}}},
-            {"d", {}},
-            {"b", {}},
-        }
-    });
-    test({
-        "ab(?:cd){2,4}ef",
-        {
-            // {"ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, 0, {}}},
-            {"ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"abcd", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 4}}}},
-            {"abcde", {}},
-            {"abcdef", {}},
-            {"abcdcd", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 6}}}},
-            {"abcdcde", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 7}}}},
-            {"abcdcdef", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 8}}}},
-            {"abcdcdcdcdef", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 12}}}},
-            {"abcdcdcdcdcdef", {}},
-            {"abcde", {}},
-            {"yea", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{2, 3}}}},
-        }
-    });
-    test({
-        "a(?:rte| pure )fact",
-        {
-            {"a", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 1}}}},
-            {"art", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 3}}}},
-            {"artefa", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 6}}}},
-            {"fact", {}},
-            {"an arte", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{3, 7}}}},
-            {"artefact", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 8}}}},
-            {"an artefact", {COMMON_REGEX_MATCH_TYPE_FULL, {{3, 11}}}},
-            {"a pure", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 6}}}},
-            {"a pure fact", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 11}}}},
-            {"it's a pure fact", {COMMON_REGEX_MATCH_TYPE_FULL, {{5, 16}}}},
-            {"" , {}},
-            {"pure", {}},
-            {"pure fact", {}},
-        }
-    });
-    test({
-        "abc",
-        {
-            {" abcc", {COMMON_REGEX_MATCH_TYPE_FULL, {{1, 4}}}},
-            {"ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"abc", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 3}}}},
-            {" ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{1, 3}}}},
-            {"a", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 1}}}},
-            {"b", {}},
-            {"c", {}},
-            {"", {}},
-        }
-    });
-
-    test({
-        "(?:abc)?\\s*def",
-        {
-            {"ab", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"abc", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 3}}}},
-            {"abc ", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 4}}}},
-            {"abc d", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 5}}}},
-            {"abc de", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 6}}}},
-            {"abc def", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 7}}}},
-            {"abc defg", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 7}}}},
-            {"abc defgh", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 7}}}},
-            {"abcde", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 5}}}},
-            {"abcdefgh", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 6}}}},
-            {" d", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 2}}}},
-            {"def", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 3}}}},
-        }
-    });
-
-    test({
-        "a+b",
-        {
-            {"aaab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 4}}}},
-            {"aaa", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 3}}}},
-            {"ab", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 2}}}},
-        }
-    });
-
-    test({
-        "(?:"
-            "(```(?:xml|json)?\\n\\s*)?" // match 1 (block_start)
-            "("                          // match 2 (open_tag)
-                "<tool_call>"
-                "|<function_call>"
-                "|<tool>"
-                "|<tools>"
-                "|<response>"
-                "|<json>"
-                "|<xml>"
-                "|<JSON>"
-            ")?"
-            "(\\s*\\{\\s*\"name\"\\s*:)" // match 3 (named tool call)
-        ")"
-        "|<function=([^>]+)>"            // match 4 (function name)
-        "|<function name=\"([^\"]+)\">", // match 5 (function name again)
-        {
-            {"{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 8}, {54, 54}, {54, 54}, {0, 8}, {54, 54}, {54, 54}}}},
-            {"<tool_call> {\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 18}}}},
-            {"<tool_call>{\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 17}}}},
-            {"Let's call something\n<tool_call>{\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{21, 38}}}},
-            {"Ok then<tool_call>{\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{7, 24}}}},
-            {"{\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{0, 6}}}},
-            {"Ok then{\"name", {COMMON_REGEX_MATCH_TYPE_PARTIAL, {{7, 13}}}},
-            {"<tool_call> {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 20}, {66, 66}, {0, 11}, {11, 20}, {66, 66}, {66, 66}}}},
-            {"<function_call> {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 24}, {70, 70}, {0, 15}, {15, 24}, {70, 70}, {70, 70}}}},
-            {"<function name=\"special_function\"> {\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 34}, {89, 89}, {89, 89}, {89, 89}, {89, 89}, {16, 32}}}},
-            {"<function=all>", {COMMON_REGEX_MATCH_TYPE_FULL, {{0, 14}, {14, 14}, {14, 14}, {14, 14}, {10, 13}, {14, 14}}}},
-
-        }
-    });
-}
-
-static void test_regex_to_reversed_partial_regex() {
-    printf("[%s]\n", __func__);
-
-    assert_equals<std::string>(
-        "^((?:(?:c)?b)?a)",
-        regex_to_reversed_partial_regex("abc"));
-
-    assert_equals<std::string>(
-        "^(a+)",
-        regex_to_reversed_partial_regex("a+"));
-
-    assert_equals<std::string>(
-        "^(a*)",
-        regex_to_reversed_partial_regex("a*"));
-
-    assert_equals<std::string>(
-        "^(a?)",
-        regex_to_reversed_partial_regex("a?"));
-
-    assert_equals<std::string>(
-        "^([a-z])",
-        regex_to_reversed_partial_regex("[a-z]"));
-
-    assert_equals<std::string>(
-        "^((?:\\w+)?[a-z])",
-        regex_to_reversed_partial_regex("[a-z]\\w+"));
-
-    assert_equals<std::string>(
-        "^((?:a|b))",
-        regex_to_reversed_partial_regex("(?:a|b)"));
-    assert_equals<std::string>(
-        "^((?:(?:(?:d)?c)?b)?a)",
-        regex_to_reversed_partial_regex("abcd"));
-    assert_equals<std::string>(
-        "^((?:b)?a*)", // TODO: ((?:b)?a*+).* ??
-        regex_to_reversed_partial_regex("a*b"));
-    assert_equals<std::string>(
-        "^((?:(?:b)?a)?.*)",
-        regex_to_reversed_partial_regex(".*?ab"));
-    assert_equals<std::string>(
-        "^((?:(?:b)?.*)?a)",
-        regex_to_reversed_partial_regex("a.*?b"));
-    assert_equals<std::string>(
-        "^((?:(?:d)?(?:(?:c)?b))?a)",
-        regex_to_reversed_partial_regex("a(bc)d"));
-    assert_equals<std::string>(
-        "^((?:(?:(?:c)?b|(?:e)?d))?a)",
-        regex_to_reversed_partial_regex("a(bc|de)"));
-    assert_equals<std::string>(
-        "^((?:(?:(?:(?:(?:c)?b?)?b?)?b)?b)?a)",
-        regex_to_reversed_partial_regex("ab{2,4}c"));
-}
-
-int main() {
-    test_regex_to_reversed_partial_regex();
-    test_regex();
-    std::cout << "All tests passed.\n";
-}

From 0677ddd19d388c7e5fd1b6a2d56e1dc16de40f59 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 29 Jun 2026 16:09:20 +0800
Subject: [PATCH 216/292] fixed output not showing thinking (+1 squashed
 commits)

Squashed commits:

[cc9f0a026] fixed output not showing thinking
---
 koboldcpp.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index c77221819..17f43b16c 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -78,7 +78,7 @@ dry_seq_break_max = 128
 extra_images_max = 4 # for kontext/qwen img
 
 # global vars
-KcppVersion = "1.116.1"
+KcppVersion = "1.116.2"
 showdebug = True
 kcpp_instance = None #global running instance
 global_memory = {"tunnel_url": "", "restart_target":"", "input_to_exit":False, "load_complete":False, "restart_override_base_config":"", "last_active_timestamp":datetime.now(), "triggered_sleeping":False, "current_model":"initial_model", "base_config":"", "swapReqType": None, "autoswapmode": False}
@@ -5205,6 +5205,8 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
             global last_non_horde_req_time
             last_non_horde_req_time = time.time()
 
+        utfprint("\nOutput: " + recvtxt,1)
+
         #handle potential think tags, but only chat completions will return them. the others just drop them
         reasoningtxt = ""
         if api_format==4 or api_format==8 or api_format==9: #chat completions, responses and anthropic messages, but only chat has reasoning returned
@@ -5224,8 +5226,6 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                         recvtxt = parts[1]
                         break
 
-        utfprint("\nOutput: " + recvtxt,1)
-
         #tool calls resolution
         tool_calls = []
         if api_format == 4 or api_format == 2 or api_format == 8 or api_format == 9:

From 6cb18b2f2ec72c177681051f22e7e7a4f154bb99 Mon Sep 17 00:00:00 2001
From: seryogakovalyov <seryogakovalyov@gmail.com>
Date: Mon, 29 Jun 2026 11:55:52 +0300
Subject: [PATCH 217/292] tools/ui: restore Tailwind scanning in ignored
 worktrees (#24879)

---
 tools/ui/sources.cmake  | 1 +
 tools/ui/src/.gitignore | 1 +
 2 files changed, 2 insertions(+)
 create mode 100644 tools/ui/src/.gitignore

diff --git a/tools/ui/sources.cmake b/tools/ui/sources.cmake
index de9dbf78b..cfc5ea9c9 100644
--- a/tools/ui/sources.cmake
+++ b/tools/ui/sources.cmake
@@ -8,6 +8,7 @@ set(UI_SOURCE_GLOBS
 set(UI_SOURCE_FILES
     package.json
     package-lock.json
+    src/.gitignore
     vite.config.ts
     svelte.config.js
     tsconfig.json
diff --git a/tools/ui/src/.gitignore b/tools/ui/src/.gitignore
new file mode 100644
index 000000000..f9be8dfe0
--- /dev/null
+++ b/tools/ui/src/.gitignore
@@ -0,0 +1 @@
+!*

From 8c146a8366304c871efc26057cc90370ccf58dad Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Mon, 29 Jun 2026 16:58:51 +0800
Subject: [PATCH 218/292] DeepSeek V4  (#24162)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* convert: add dsv4 conversion

* add basic setup

* add llm_graph_input_dsv4

* add save-load state

* add sinkhorn eps - correction by @fairydreaming

* add rope fix

* cleanup dead code

* fix bugs

* support pro model: added by @fairydreaming

* remove redundant V cache

* Chat template

* remove debugging leftovers

* Add mechanism for inlining templates based on architecture

* s/deepseek-v4-flash/deepseek4/g

* s/deepseek-v4-flash/deepseek4/g continued

* enable graph reuse

* enable FA

* fix test llama archs

* rename

* compatibility with antirez ds4 GGUFs

* simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func.

* reserve worst-case kv-cache

* revert max split inputs

* address review comments

* add padding to enable FA

* pad only the final value of plan.n_kv to 256

* remove built-in cpp chat template

* cont: remove cpp built-in template

* rm outdated test

* replace ggml_view_3d() with ggml_reshape_3d()

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* only support n_seq=1 for now

* remove unused var

* cont: remove unused var

* use scale bias

* use correct ptr for can_reuse

* remove gen-chat-inline-templates.py

* simplify graph reuse

* cont: cleanup

* remove unused inputs

* enable partial checkpointing

* add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4

* precompute source_idx + add comment about dummy write

* support multi-seq

* remove restored_trim_pos

* use split_equal when possible

* fix indent

* address review comments

* use LLM_KV

* fix ci

---------

Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 conversion/__init__.py                        |    1 +
 conversion/base.py                            |   15 +-
 conversion/deepseek.py                        |  309 ++-
 gguf-py/gguf/constants.py                     |  104 +-
 gguf-py/gguf/gguf_writer.py                   |   24 +
 .../templates/deepseek-ai-DeepSeek-V4.jinja   |  112 +
 src/CMakeLists.txt                            |    1 +
 src/llama-arch.cpp                            |   56 +
 src/llama-arch.h                              |   33 +
 src/llama-context.cpp                         |    6 +-
 src/llama-graph.cpp                           |  375 +++-
 src/llama-graph.h                             |   83 +-
 src/llama-hparams.h                           |   11 +
 src/llama-kv-cache-dsv4.cpp                   | 1807 +++++++++++++++++
 src/llama-kv-cache-dsv4.h                     |  362 ++++
 src/llama-kv-cache-iswa.cpp                   |   23 +-
 src/llama-kv-cache-iswa.h                     |   20 +-
 src/llama-kv-cache.cpp                        |   32 +-
 src/llama-kv-cache.h                          |    3 +
 src/llama-model-loader.cpp                    |    3 +
 src/llama-model.cpp                           |   29 +-
 src/llama-model.h                             |   25 +
 src/models/deepseek4.cpp                      | 1186 +++++++++++
 src/models/models.h                           |  115 ++
 tests/test-llama-archs.cpp                    |    3 +
 25 files changed, 4698 insertions(+), 40 deletions(-)
 create mode 100644 models/templates/deepseek-ai-DeepSeek-V4.jinja
 create mode 100644 src/llama-kv-cache-dsv4.cpp
 create mode 100644 src/llama-kv-cache-dsv4.h
 create mode 100644 src/models/deepseek4.cpp

diff --git a/conversion/__init__.py b/conversion/__init__.py
index 4a1fd5bb7..02ea63852 100644
--- a/conversion/__init__.py
+++ b/conversion/__init__.py
@@ -51,6 +51,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DeepseekV3ForCausalLM": "deepseek",
     "DeepseekV32ForCausalLM": "deepseek",
     "DFlashDraftModel": "qwen",
+    "DeepseekV4ForCausalLM": "deepseek",
     "DistilBertForMaskedLM": "bert",
     "DistilBertForSequenceClassification": "bert",
     "DistilBertModel": "bert",
diff --git a/conversion/base.py b/conversion/base.py
index 08fd3747c..0421aa4bc 100644
--- a/conversion/base.py
+++ b/conversion/base.py
@@ -1273,7 +1273,7 @@ class TextModel(ModelBase):
         if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
             self.gguf_writer.add_layer_norm_eps(f_norm_eps)
             logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
-        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
+        if (n_experts := self.find_hparam(["num_local_experts", "num_experts", "n_routed_experts"], optional=True)) is not None:
             self.gguf_writer.add_expert_count(n_experts)
             logger.info(f"gguf: expert count = {n_experts}")
         if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token", "top_k_experts"], optional=True)) is not None:
@@ -1291,6 +1291,8 @@ class TextModel(ModelBase):
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+            elif score_func == "sqrtsoftplus":
+                self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SQRTSOFTPLUS)
             else:
                 raise ValueError(f"Unsupported expert score gating function value: {score_func}")
             logger.info(f"gguf: expert score gating function = {score_func}")
@@ -2600,6 +2602,17 @@ class LazyTorchTensor(gguf.LazyBase):
         return cls._wrap_fn(func)(*args, **kwargs)
 
 
+if hasattr(torch, "float8_e8m0fnu"):
+    _torch_float8_e8m0 = torch.float8_e8m0fnu
+    LazyTorchTensor._dtype_map[_torch_float8_e8m0] = np.uint8
+    LazyTorchTensor._dtype_byteswap_map[_torch_float8_e8m0] = np.uint8
+    LazyTorchTensor._dtype_str_map["F8_E8M0"] = _torch_float8_e8m0
+else:
+    # Older torch builds do not expose F8_E8M0. Keep the raw bytes so callers
+    # that know the format can decode them explicitly.
+    LazyTorchTensor._dtype_str_map["F8_E8M0"] = torch.uint8
+
+
 def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> str:
     # TODO @ngxson : this won't work correctly if the model has both audio & vision encoders
     # maybe we should fallback to text model's arch in that case, since not many models have both
diff --git a/conversion/deepseek.py b/conversion/deepseek.py
index 4c93fb66d..ea6ae23d5 100644
--- a/conversion/deepseek.py
+++ b/conversion/deepseek.py
@@ -1,15 +1,18 @@
 from __future__ import annotations
 
+import json
 import re
+from pathlib import Path
 
 from typing import Any, Callable, Iterable, TYPE_CHECKING
 
+import numpy as np
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import MmprojModel, ModelBase, TextModel, gguf, logger
+from .base import LazyTorchTensor, MmprojModel, ModelBase, TextModel, gguf, logger
 
 from .qwen import QwenModel
 
@@ -467,3 +470,307 @@ class DeepseekV32Model(DeepseekV2Model):
         self.gguf_writer.add_indexer_head_count(self.hparams["index_n_heads"])
         self.gguf_writer.add_indexer_key_length(self.hparams["index_head_dim"])
         self.gguf_writer.add_indexer_top_k(self.hparams["index_topk"])
+
+
+@ModelBase.register("DeepseekV4ForCausalLM")
+class DeepseekV4Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK4
+    _skipped_mtp_tensors = 0
+
+    def __init__(self, *args, **kwargs):
+        type(self)._skipped_mtp_tensors = 0
+        super().__init__(*args, **kwargs)
+
+        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
+            raw_hparams = json.load(f)
+        for key, value in raw_hparams.items():
+            self.hparams.setdefault(key, value)
+
+        self.block_count = self.hparams["num_hidden_layers"]
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        self._dsv4_fp8_dequantized: set[str] = set()
+        self._dsv4_bf16_tensors: set[str] = set()
+        self._dsv4_f32_tensors: set[str] = set()
+        self._dsv4_mxfp4_generated = False
+        self._collect_source_dtypes()
+
+        if type(self)._skipped_mtp_tensors:
+            logger.info("Skipping %d DeepSeek-V4 MTP tensor(s) for conversion v0", type(self)._skipped_mtp_tensors)
+
+        # add a default chat template; if the model has a built-in template, it will be overridden later
+        template_path = Path(__file__).parent.parent / "models" / "templates" / "deepseek-ai-DeepSeek-V4.jinja"
+        if template_path.is_file():
+            with open(template_path, "r", encoding="utf-8") as f:
+                self.gguf_writer.add_chat_template(f.read())
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, _ = item
+        if name.startswith("mtp."):
+            cls._skipped_mtp_tensors += 1
+            return None
+        return super().filter_tensors(item)
+
+    @staticmethod
+    def _float8_dtypes() -> tuple[torch.dtype, ...]:
+        return tuple(
+            dtype for dtype in (
+                getattr(torch, "float8_e4m3fn", None),
+                getattr(torch, "float8_e5m2", None),
+            ) if dtype is not None
+        )
+
+    @staticmethod
+    def _e8m0_to_float(scale: Tensor) -> Tensor:
+        torch_float8_e8m0 = getattr(torch, "float8_e8m0fnu", None)
+        if torch_float8_e8m0 is not None and scale.dtype == torch_float8_e8m0:
+            return scale.float()
+
+        bits = scale.view(torch.uint8).float()
+        return torch.exp2(bits - 127.0)
+
+    def _collect_source_dtypes(self) -> None:
+        for name, gen in self.model_tensors.items():
+            dtype = gen().dtype
+            if dtype == torch.bfloat16:
+                self._dsv4_bf16_tensors.add(name)
+            elif dtype == torch.float32:
+                self._dsv4_f32_tensors.add(name)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
+        self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+        self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
+        self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
+        self.gguf_writer.add_swiglu_clamp_exp([hparams["swiglu_limit"]] * self.block_count)
+        self.gguf_writer.add_swiglu_clamp_shexp([hparams["swiglu_limit"]] * self.block_count)
+
+        self.gguf_writer.add_indexer_head_count(hparams["index_n_heads"])
+        self.gguf_writer.add_indexer_key_length(hparams["index_head_dim"])
+        self.gguf_writer.add_indexer_top_k(hparams["index_topk"])
+
+        self.gguf_writer.add_attention_output_group_count(hparams["o_groups"])
+        self.gguf_writer.add_attention_output_lora_rank(hparams["o_lora_rank"])
+        self.gguf_writer.add_attention_compress_ratios(hparams["compress_ratios"])
+        self.gguf_writer.add_attention_compress_rope_freq_base(hparams["compress_rope_theta"])
+        self.gguf_writer.add_hyper_connection_count(hparams["hc_mult"])
+        self.gguf_writer.add_hyper_connection_sinkhorn_iterations(hparams["hc_sinkhorn_iters"])
+        self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
+        self.gguf_writer.add_hash_layer_count(hparams["num_hash_layers"])
+
+    def dequant_model(self):
+        fp8_dtypes = self._float8_dtypes()
+        tensors_to_remove: list[str] = []
+
+        def dequant_fp8_weight(weight: Tensor, scale: Tensor) -> Tensor:
+            out_features, in_features = weight.shape
+            scale_f = self._e8m0_to_float(scale)
+            scale_f = scale_f.repeat_interleave(128, 0)[:out_features]
+            scale_f = scale_f.repeat_interleave(128, 1)[:, :in_features]
+            return weight.float() * scale_f
+
+        for name in list(self.model_tensors.keys()):
+            if not name.endswith(".scale"):
+                continue
+            weight_name = name.removesuffix(".scale") + ".weight"
+            if weight_name not in self.model_tensors:
+                continue
+
+            weight = self.model_tensors[weight_name]
+            scale = self.model_tensors[name]
+            if weight().dtype not in fp8_dtypes:
+                continue
+
+            self.model_tensors[weight_name] = lambda w=weight, s=scale: dequant_fp8_weight(w(), s())
+            self._dsv4_fp8_dequantized.add(weight_name)
+            tensors_to_remove.append(name)
+
+        for name in tensors_to_remove:
+            del self.model_tensors[name]
+
+    @staticmethod
+    def _pack_mxfp4_blocks(weight: Tensor, scale: Tensor) -> np.ndarray:
+        packed = weight.contiguous().view(torch.uint8)
+        scale_u8 = scale.contiguous().view(torch.uint8)
+
+        out_features, packed_cols = packed.shape
+        logical_cols = packed_cols * 2
+        if logical_cols % 32 != 0:
+            raise ValueError(f"MXFP4 source row has {logical_cols} values, expected a multiple of 32")
+
+        n_blocks = logical_cols // 32
+        if tuple(scale_u8.shape) != (out_features, n_blocks):
+            raise ValueError(f"MXFP4 scale shape {tuple(scale_u8.shape)} does not match {(out_features, n_blocks)}")
+
+        src = packed.reshape(out_features, n_blocks, 16)
+        low = src & 0x0F
+        high = (src >> 4) & 0x0F
+
+        # The safetensors bytes store adjacent values as low/high nibbles.
+        # ggml MXFP4 blocks store values 0..15 in low nibbles and 16..31 in high nibbles.
+        vals = torch.stack((low, high), dim=-1).reshape(out_features, n_blocks, 32)
+        qs = vals[:, :, :16] | (vals[:, :, 16:] << 4)
+        raw = torch.cat((scale_u8.unsqueeze(-1), qs.to(torch.uint8)), dim=-1)
+        return raw.reshape(out_features, n_blocks * 17).cpu().numpy()
+
+    def _write_mxfp4_expert_tensor(self, bid: int, proj: str, tensor_key: gguf.MODEL_TENSOR) -> list[str]:
+        n_experts = self.hparams["n_routed_experts"]
+        data: np.ndarray | None = None
+        consumed: list[str] = []
+
+        for eid in range(n_experts):
+            weight_name = f"layers.{bid}.ffn.experts.{eid}.{proj}.weight"
+            scale_name = f"layers.{bid}.ffn.experts.{eid}.{proj}.scale"
+            if weight_name not in self.model_tensors or scale_name not in self.model_tensors:
+                raise KeyError(f"Missing routed expert tensors for {weight_name}")
+
+            weight = LazyTorchTensor.to_eager(self.model_tensors[weight_name]())
+            scale = LazyTorchTensor.to_eager(self.model_tensors[scale_name]())
+            packed = self._pack_mxfp4_blocks(weight, scale)
+            if data is None:
+                data = np.empty((n_experts, *packed.shape), dtype=packed.dtype)
+            data[eid] = packed
+            consumed.extend((weight_name, scale_name))
+
+        assert data is not None
+        new_name = self.format_tensor_name(tensor_key, bid)
+        shape = gguf.quant_shape_from_byte_shape(data.shape, gguf.GGMLQuantizationType.MXFP4)
+        logger.info(f"{new_name}: repacked routed experts to MXFP4, shape = {{{', '.join(str(n) for n in reversed(shape))}}}")
+        self.gguf_writer.add_tensor(new_name, data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+        return consumed
+
+    def _write_hash_routing_tensors(self) -> list[str]:
+        consumed: list[str] = []
+
+        for bid in range(self.hparams["num_hash_layers"]):
+            name = f"layers.{bid}.ffn.gate.tid2eid"
+            if name not in self.model_tensors:
+                raise KeyError(f"Missing hash routing tensor {name}")
+
+            data_torch = LazyTorchTensor.to_eager(self.model_tensors[name]())
+            data = data_torch.to(torch.int32).cpu().numpy()
+            new_name = self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_TID2EID, bid, ".weight")
+            logger.info(f"{new_name}: converted hash routing table to I32, shape = {{{', '.join(str(n) for n in reversed(data.shape))}}}")
+            self.gguf_writer.add_tensor(new_name, data)
+            consumed.append(name)
+
+        return consumed
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        if self._dsv4_mxfp4_generated:
+            return ()
+
+        consumed: list[str] = self._write_hash_routing_tensors()
+        for bid in range(self.block_count):
+            consumed.extend(self._write_mxfp4_expert_tensor(bid, "w1", gguf.MODEL_TENSOR.FFN_GATE_EXP))
+            consumed.extend(self._write_mxfp4_expert_tensor(bid, "w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP))
+            consumed.extend(self._write_mxfp4_expert_tensor(bid, "w3", gguf.MODEL_TENSOR.FFN_UP_EXP))
+
+        for name in consumed:
+            del self.model_tensors[name]
+
+        self._dsv4_mxfp4_generated = True
+        return ()
+
+    def _format_dsv4_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> str:
+        return self.format_tensor_name(key, bid, suffix)
+
+    def _map_dsv4_tensor_name(self, name: str, bid: int | None) -> tuple[gguf.MODEL_TENSOR, str]:
+        root_map: dict[str, tuple[gguf.MODEL_TENSOR, str]] = {
+            "embed.weight": (gguf.MODEL_TENSOR.TOKEN_EMBD, ".weight"),
+            "norm.weight": (gguf.MODEL_TENSOR.OUTPUT_NORM, ".weight"),
+            "head.weight": (gguf.MODEL_TENSOR.OUTPUT, ".weight"),
+            "hc_head_fn": (gguf.MODEL_TENSOR.HC_HEAD_FN, ".weight"),
+            "hc_head_base": (gguf.MODEL_TENSOR.HC_HEAD_BASE, ".weight"),
+            "hc_head_scale": (gguf.MODEL_TENSOR.HC_HEAD_SCALE, ".weight"),
+        }
+        if name in root_map:
+            return root_map[name]
+
+        match = re.match(r"layers\.(\d+)\.(.+)$", name)
+        if match is None:
+            raise ValueError(f"Unsupported DeepSeek-V4 tensor {name!r}")
+
+        layer = int(match.group(1))
+        if bid != layer:
+            raise ValueError(f"Tensor {name!r} parsed bid {bid} but layer name has {layer}")
+
+        layer_map: dict[str, tuple[gguf.MODEL_TENSOR, str]] = {
+            "hc_attn_fn": (gguf.MODEL_TENSOR.HC_ATTN_FN, ".weight"),
+            "hc_attn_base": (gguf.MODEL_TENSOR.HC_ATTN_BASE, ".weight"),
+            "hc_attn_scale": (gguf.MODEL_TENSOR.HC_ATTN_SCALE, ".weight"),
+            "hc_ffn_fn": (gguf.MODEL_TENSOR.HC_FFN_FN, ".weight"),
+            "hc_ffn_base": (gguf.MODEL_TENSOR.HC_FFN_BASE, ".weight"),
+            "hc_ffn_scale": (gguf.MODEL_TENSOR.HC_FFN_SCALE, ".weight"),
+            "attn.attn_sink": (gguf.MODEL_TENSOR.ATTN_SINKS, ".weight"),
+            "attn.wq_a.weight": (gguf.MODEL_TENSOR.ATTN_Q_A, ".weight"),
+            "attn.wq_b.weight": (gguf.MODEL_TENSOR.ATTN_Q_B, ".weight"),
+            "attn.q_norm.weight": (gguf.MODEL_TENSOR.ATTN_Q_A_NORM, ".weight"),
+            "attn.wkv.weight": (gguf.MODEL_TENSOR.ATTN_KV, ".weight"),
+            "attn.kv_norm.weight": (gguf.MODEL_TENSOR.ATTN_KV_NORM, ".weight"),
+            "attn.wo_a.weight": (gguf.MODEL_TENSOR.ATTN_OUT_A, ".weight"),
+            "attn.wo_b.weight": (gguf.MODEL_TENSOR.ATTN_OUT_B, ".weight"),
+            "attn.compressor.ape": (gguf.MODEL_TENSOR.ATTN_COMPRESSOR_APE, ".weight"),
+            "attn.compressor.wkv.weight": (gguf.MODEL_TENSOR.ATTN_COMPRESSOR_WKV, ".weight"),
+            "attn.compressor.wgate.weight": (gguf.MODEL_TENSOR.ATTN_COMPRESSOR_WGATE, ".weight"),
+            "attn.compressor.norm.weight": (gguf.MODEL_TENSOR.ATTN_COMPRESSOR_NORM, ".weight"),
+            "attn.indexer.wq_b.weight": (gguf.MODEL_TENSOR.INDEXER_ATTN_Q_B, ".weight"),
+            "attn.indexer.weights_proj.weight": (gguf.MODEL_TENSOR.INDEXER_PROJ, ".weight"),
+            "attn.indexer.compressor.ape": (gguf.MODEL_TENSOR.INDEXER_COMPRESSOR_APE, ".weight"),
+            "attn.indexer.compressor.wkv.weight": (gguf.MODEL_TENSOR.INDEXER_COMPRESSOR_WKV, ".weight"),
+            "attn.indexer.compressor.wgate.weight": (gguf.MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE, ".weight"),
+            "attn.indexer.compressor.norm.weight": (gguf.MODEL_TENSOR.INDEXER_COMPRESSOR_NORM, ".weight"),
+            "attn_norm.weight": (gguf.MODEL_TENSOR.ATTN_NORM, ".weight"),
+            "ffn_norm.weight": (gguf.MODEL_TENSOR.FFN_NORM, ".weight"),
+            "ffn.gate.weight": (gguf.MODEL_TENSOR.FFN_GATE_INP, ".weight"),
+            "ffn.gate.bias": (gguf.MODEL_TENSOR.FFN_EXP_PROBS_B, ".bias"),
+            "ffn.gate.tid2eid": (gguf.MODEL_TENSOR.FFN_GATE_TID2EID, ".weight"),
+            "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
+            "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
+            "ffn.shared_experts.w3.weight": (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"),
+        }
+
+        tensor_name = match.group(2)
+        if tensor_name in layer_map:
+            return layer_map[tensor_name]
+
+        if re.match(r"ffn\.experts\.\d+\.w[123]\.(weight|scale)$", tensor_name):
+            return gguf.MODEL_TENSOR.FFN_GATE_EXP, ".weight"
+
+        raise ValueError(f"Unsupported DeepSeek-V4 tensor {name!r}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if re.match(r"layers\.\d+\.ffn\.experts\.\d+\.w[123]\.(weight|scale)$", name):
+            return []
+
+        tensor_key, suffix = self._map_dsv4_tensor_name(name, bid)
+        if tensor_key == gguf.MODEL_TENSOR.FFN_GATE_TID2EID:
+            return []
+
+        return [(self._format_dsv4_tensor_name(tensor_key, bid, suffix), data_torch)]
+
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        del new_name, bid  # unused
+
+        if name in self._dsv4_fp8_dequantized and n_dims >= 2:
+            return gguf.GGMLQuantizationType.Q8_0
+        if name in self._dsv4_f32_tensors:
+            return gguf.GGMLQuantizationType.F32
+        if name in self._dsv4_bf16_tensors and n_dims >= 2:
+            return gguf.GGMLQuantizationType.BF16
+
+        return False
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        self._is_mxfp4 = True
+        self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 52e9e54de..cd4cdef89 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -145,6 +145,7 @@ class Keys:
         TOKEN_SHIFT_COUNT                 = "{arch}.token_shift_count"
         INTERLEAVE_MOE_LAYER_STEP         = "{arch}.interleave_moe_layer_step"
         FULL_ATTENTION_INTERVAL           = "{arch}.full_attention_interval"
+        HASH_LAYER_COUNT                  = "{arch}.hash_layer_count"
         ACTIVATION_SPARSITY_SCALE         = "{arch}.activation_sparsity_scale"
         ALTUP_ACTIVE_IDX                  = "{arch}.altup.active_idx"
         ALTUP_NUM_INPUTS                  = "{arch}.altup.num_inputs"
@@ -180,8 +181,12 @@ class Keys:
         REL_BUCKETS_COUNT            = "{arch}.attention.relative_buckets_count"
         SLIDING_WINDOW               = "{arch}.attention.sliding_window"
         SCALE                        = "{arch}.attention.scale"
+        OUTPUT_GROUP_COUNT           = "{arch}.attention.output_group_count"
+        OUTPUT_LORA_RANK             = "{arch}.attention.output_lora_rank"
         OUTPUT_SCALE                 = "{arch}.attention.output_scale"
         VALUE_SCALE                  = "{arch}.attention.value_scale"
+        COMPRESS_RATIOS              = "{arch}.attention.compress_ratios"
+        COMPRESS_ROPE_FREQ_BASE      = "{arch}.attention.compress_rope_freq_base"
         TEMPERATURE_LENGTH           = "{arch}.attention.temperature_length"
         KEY_LENGTH_MLA               = "{arch}.attention.key_length_mla"
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
@@ -196,6 +201,11 @@ class Keys:
             KEY_LENGTH = "{arch}.attention.indexer.key_length"
             TOP_K      = "{arch}.attention.indexer.top_k"
 
+    class HyperConnection:
+        COUNT                = "{arch}.hyper_connection.count"
+        SINKHORN_ITERATIONS  = "{arch}.hyper_connection.sinkhorn_iterations"
+        EPSILON              = "{arch}.hyper_connection.epsilon"
+
     class Rope:
         DIMENSION_COUNT           = "{arch}.rope.dimension_count"
         DIMENSION_COUNT_SWA       = "{arch}.rope.dimension_count_swa"
@@ -470,6 +480,7 @@ class MODEL_ARCH(IntEnum):
     DEEPSEEK2        = auto()
     DEEPSEEK2OCR     = auto()
     DEEPSEEK32       = auto()
+    DEEPSEEK4        = auto()
     CHATGLM          = auto()
     GLM4             = auto()
     GLM4_MOE         = auto()
@@ -555,6 +566,9 @@ class MODEL_TENSOR(IntEnum):
     DENSE_2_OUT          = auto() # embeddinggemma 2_Dense
     DENSE_3_OUT          = auto() # embeddinggemma 3_Dense
     OUTPUT_NORM          = auto()
+    HC_HEAD_FN           = auto()
+    HC_HEAD_BASE         = auto()
+    HC_HEAD_SCALE        = auto()
     ROPE_FREQS           = auto()
     ROPE_FACTORS_LONG    = auto()
     ROPE_FACTORS_SHORT   = auto()
@@ -594,6 +608,7 @@ class MODEL_TENSOR(IntEnum):
     FFN_DOWN_CHEXP       = auto()
     FFN_UP_CHEXP         = auto()
     FFN_EXP_PROBS_B      = auto()
+    FFN_GATE_TID2EID     = auto()
     MOE_LATENT_DOWN      = auto() # nemotron 3 super
     MOE_LATENT_UP        = auto() # nemotron 3 super
     ATTN_Q_NORM          = auto()
@@ -681,6 +696,20 @@ class MODEL_TENSOR(IntEnum):
     ATTN_V_B             = auto()
     ATTN_Q_A_NORM        = auto()
     ATTN_KV_A_NORM       = auto()
+    ATTN_KV              = auto()
+    ATTN_KV_NORM         = auto()
+    ATTN_OUT_A           = auto()
+    ATTN_OUT_B           = auto()
+    HC_ATTN_FN           = auto()
+    HC_ATTN_BASE         = auto()
+    HC_ATTN_SCALE        = auto()
+    HC_FFN_FN            = auto()
+    HC_FFN_BASE          = auto()
+    HC_FFN_SCALE         = auto()
+    ATTN_COMPRESSOR_WKV  = auto()
+    ATTN_COMPRESSOR_WGATE = auto()
+    ATTN_COMPRESSOR_APE  = auto()
+    ATTN_COMPRESSOR_NORM = auto()
     FFN_SUB_NORM         = auto()
     ATTN_SUB_NORM        = auto()
     DEC_ATTN_NORM        = auto()
@@ -742,6 +771,10 @@ class MODEL_TENSOR(IntEnum):
     INDEXER_PROJ         = auto()
     INDEXER_ATTN_K       = auto()
     INDEXER_ATTN_Q_B     = auto()
+    INDEXER_COMPRESSOR_WKV = auto()
+    INDEXER_COMPRESSOR_WGATE = auto()
+    INDEXER_COMPRESSOR_APE = auto()
+    INDEXER_COMPRESSOR_NORM = auto()
     # vision
     V_MMPROJ             = auto()
     V_MMPROJ_FC          = auto()
@@ -1027,6 +1060,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.DEEPSEEK2:        "deepseek2",
     MODEL_ARCH.DEEPSEEK2OCR:     "deepseek2-ocr",
     MODEL_ARCH.DEEPSEEK32:       "deepseek32",
+    MODEL_ARCH.DEEPSEEK4:        "deepseek4",
     MODEL_ARCH.CHATGLM:          "chatglm",
     MODEL_ARCH.GLM4:             "glm4",
     MODEL_ARCH.GLM4_MOE:         "glm4moe",
@@ -1111,6 +1145,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.OUTPUT:                    "output",
     MODEL_TENSOR.DENSE_2_OUT:               "dense_2", # embeddinggemma 2_Dense
     MODEL_TENSOR.DENSE_3_OUT:               "dense_3", # embeddinggemma 2_Dense
+    MODEL_TENSOR.HC_HEAD_FN:                "output_hc_fn",
+    MODEL_TENSOR.HC_HEAD_BASE:              "output_hc_base",
+    MODEL_TENSOR.HC_HEAD_SCALE:             "output_hc_scale",
     MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
     MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
     MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
@@ -1152,6 +1189,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.FFN_UP_EXP:                "blk.{bid}.ffn_up_exps",
     MODEL_TENSOR.FFN_GATE_UP_EXP:           "blk.{bid}.ffn_gate_up_exps",
     MODEL_TENSOR.FFN_EXP_PROBS_B:           "blk.{bid}.exp_probs_b",
+    MODEL_TENSOR.FFN_GATE_TID2EID:          "blk.{bid}.ffn_gate_tid2eid",
     MODEL_TENSOR.MOE_LATENT_DOWN:           "blk.{bid}.ffn_latent_down",      # nemotron 3 super
     MODEL_TENSOR.MOE_LATENT_UP:             "blk.{bid}.ffn_latent_up",        # nemotron 3 super
     MODEL_TENSOR.LAYER_OUT_NORM:            "blk.{bid}.layer_output_norm",
@@ -1237,6 +1275,20 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_V_B:                  "blk.{bid}.attn_v_b",
     MODEL_TENSOR.ATTN_Q_A_NORM:             "blk.{bid}.attn_q_a_norm",
     MODEL_TENSOR.ATTN_KV_A_NORM:            "blk.{bid}.attn_kv_a_norm",
+    MODEL_TENSOR.ATTN_KV:                   "blk.{bid}.attn_kv",
+    MODEL_TENSOR.ATTN_KV_NORM:              "blk.{bid}.attn_kv_a_norm",
+    MODEL_TENSOR.ATTN_OUT_A:                "blk.{bid}.attn_output_a",
+    MODEL_TENSOR.ATTN_OUT_B:                "blk.{bid}.attn_output_b",
+    MODEL_TENSOR.HC_ATTN_FN:                "blk.{bid}.hc_attn_fn",
+    MODEL_TENSOR.HC_ATTN_BASE:              "blk.{bid}.hc_attn_base",
+    MODEL_TENSOR.HC_ATTN_SCALE:             "blk.{bid}.hc_attn_scale",
+    MODEL_TENSOR.HC_FFN_FN:                 "blk.{bid}.hc_ffn_fn",
+    MODEL_TENSOR.HC_FFN_BASE:               "blk.{bid}.hc_ffn_base",
+    MODEL_TENSOR.HC_FFN_SCALE:              "blk.{bid}.hc_ffn_scale",
+    MODEL_TENSOR.ATTN_COMPRESSOR_WKV:       "blk.{bid}.attn_compressor_kv",
+    MODEL_TENSOR.ATTN_COMPRESSOR_WGATE:     "blk.{bid}.attn_compressor_gate",
+    MODEL_TENSOR.ATTN_COMPRESSOR_APE:       "blk.{bid}.attn_compressor_ape",
+    MODEL_TENSOR.ATTN_COMPRESSOR_NORM:      "blk.{bid}.attn_compressor_norm",
     MODEL_TENSOR.ATTN_SUB_NORM:             "blk.{bid}.attn_sub_norm",
     MODEL_TENSOR.FFN_SUB_NORM:              "blk.{bid}.ffn_sub_norm",
     MODEL_TENSOR.DEC_ATTN_NORM:             "dec.blk.{bid}.attn_norm",
@@ -1298,6 +1350,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.INDEXER_PROJ:              "blk.{bid}.indexer.proj",
     MODEL_TENSOR.INDEXER_ATTN_K:            "blk.{bid}.indexer.attn_k",
     MODEL_TENSOR.INDEXER_ATTN_Q_B:          "blk.{bid}.indexer.attn_q_b",
+    MODEL_TENSOR.INDEXER_COMPRESSOR_WKV:    "blk.{bid}.indexer_compressor_kv",
+    MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE:  "blk.{bid}.indexer_compressor_gate",
+    MODEL_TENSOR.INDEXER_COMPRESSOR_APE:    "blk.{bid}.indexer_compressor_ape",
+    MODEL_TENSOR.INDEXER_COMPRESSOR_NORM:   "blk.{bid}.indexer_compressor_norm",
     # vision
     MODEL_TENSOR.V_MMPROJ:                  "mm.{bid}",
     MODEL_TENSOR.V_MMPROJ_FC:               "mm.model.fc",
@@ -3138,6 +3194,49 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
         MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
     ],
+    MODEL_ARCH.DEEPSEEK4: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.HC_HEAD_FN,
+        MODEL_TENSOR.HC_HEAD_BASE,
+        MODEL_TENSOR.HC_HEAD_SCALE,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_SINKS,
+        MODEL_TENSOR.ATTN_Q_A,
+        MODEL_TENSOR.ATTN_Q_B,
+        MODEL_TENSOR.ATTN_Q_A_NORM,
+        MODEL_TENSOR.ATTN_KV,
+        MODEL_TENSOR.ATTN_KV_NORM,
+        MODEL_TENSOR.ATTN_OUT_A,
+        MODEL_TENSOR.ATTN_OUT_B,
+        MODEL_TENSOR.HC_ATTN_FN,
+        MODEL_TENSOR.HC_ATTN_BASE,
+        MODEL_TENSOR.HC_ATTN_SCALE,
+        MODEL_TENSOR.HC_FFN_FN,
+        MODEL_TENSOR.HC_FFN_BASE,
+        MODEL_TENSOR.HC_FFN_SCALE,
+        MODEL_TENSOR.ATTN_COMPRESSOR_WKV,
+        MODEL_TENSOR.ATTN_COMPRESSOR_WGATE,
+        MODEL_TENSOR.ATTN_COMPRESSOR_APE,
+        MODEL_TENSOR.ATTN_COMPRESSOR_NORM,
+        MODEL_TENSOR.INDEXER_PROJ,
+        MODEL_TENSOR.INDEXER_ATTN_Q_B,
+        MODEL_TENSOR.INDEXER_COMPRESSOR_WKV,
+        MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE,
+        MODEL_TENSOR.INDEXER_COMPRESSOR_APE,
+        MODEL_TENSOR.INDEXER_COMPRESSOR_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_TID2EID,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     MODEL_ARCH.ERNIE4_5_MOE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -4437,8 +4536,9 @@ class GGMLQuantizationType(IntEnum):
 
 
 class ExpertGatingFuncType(IntEnum):
-    SOFTMAX  = 1
-    SIGMOID  = 2
+    SOFTMAX       = 1
+    SIGMOID       = 2
+    SQRTSOFTPLUS  = 4
 
 
 # TODO: add GGMLFileType from ggml_ftype in ggml.h
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 610555f5e..1e277f068 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -715,6 +715,9 @@ class GGUFWriter:
     def add_full_attention_interval(self, interval: int) -> None:
         self.add_uint32(Keys.LLM.FULL_ATTENTION_INTERVAL.format(arch=self.arch), interval)
 
+    def add_hash_layer_count(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.HASH_LAYER_COUNT.format(arch=self.arch), count)
+
     def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
         if isinstance(length, int):
             self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
@@ -952,6 +955,27 @@ class GGUFWriter:
     def add_norm_before_residual(self, value: bool) -> None:
         self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value)
 
+    def add_attention_output_group_count(self, count: int) -> None:
+        self.add_uint32(Keys.Attention.OUTPUT_GROUP_COUNT.format(arch=self.arch), count)
+
+    def add_attention_output_lora_rank(self, length: int) -> None:
+        self.add_uint32(Keys.Attention.OUTPUT_LORA_RANK.format(arch=self.arch), length)
+
+    def add_attention_compress_ratios(self, values: Sequence[int]) -> None:
+        self.add_array(Keys.Attention.COMPRESS_RATIOS.format(arch=self.arch), values)
+
+    def add_attention_compress_rope_freq_base(self, value: float) -> None:
+        self.add_float32(Keys.Attention.COMPRESS_ROPE_FREQ_BASE.format(arch=self.arch), value)
+
+    def add_hyper_connection_count(self, count: int) -> None:
+        self.add_uint32(Keys.HyperConnection.COUNT.format(arch=self.arch), count)
+
+    def add_hyper_connection_sinkhorn_iterations(self, count: int) -> None:
+        self.add_uint32(Keys.HyperConnection.SINKHORN_ITERATIONS.format(arch=self.arch), count)
+
+    def add_hyper_connection_epsilon(self, value: float) -> None:
+        self.add_float32(Keys.HyperConnection.EPSILON.format(arch=self.arch), value)
+
     def add_attention_scale(self, value: float) -> None:
         self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value)
 
diff --git a/models/templates/deepseek-ai-DeepSeek-V4.jinja b/models/templates/deepseek-ai-DeepSeek-V4.jinja
new file mode 100644
index 000000000..f19f787b1
--- /dev/null
+++ b/models/templates/deepseek-ai-DeepSeek-V4.jinja
@@ -0,0 +1,112 @@
+{%- if not add_generation_prompt is defined -%}
+  {%- set add_generation_prompt = false -%}
+{%- endif -%}
+{%- if not thinking is defined -%}
+  {%- if enable_thinking is defined -%}
+    {%- set thinking = enable_thinking -%}
+  {%- else -%}
+    {%- set thinking = false -%}
+  {%- endif -%}
+{%- endif -%}
+{%- set dsml_token = '｜DSML｜' -%}
+{%- set thinking_start_token = '<think>' -%}
+{%- set thinking_end_token = '</think>' -%}
+{%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</' + dsml_token + 'parameter>\n...\n</' + dsml_token + 'invoke>\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n</' + dsml_token + 'invoke>\n</' + dsml_token + 'tool_calls>\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%}
+{%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%}
+{%- set ns = namespace(system_prompt='', is_first_sp=true) -%}
+{%- for message in messages -%}
+  {%- if message['role'] == 'system' -%}
+    {%- if ns.is_first_sp -%}
+      {%- set ns.system_prompt = ns.system_prompt + (message['content'] or '') -%}
+      {%- set ns.is_first_sp = false -%}
+    {%- else -%}
+      {%- set ns.system_prompt = ns.system_prompt + '\n\n' + (message['content'] or '') -%}
+    {%- endif -%}
+  {%- endif -%}
+{%- endfor -%}
+{%- if tools is defined and tools -%}
+  {%- set ts = namespace(schemas='') -%}
+  {%- for tool in tools -%}
+    {%- if tool['type'] == 'function' -%}
+      {%- set ts.schemas = ts.schemas + (tool['function'] | tojson) + '\n' -%}
+    {%- endif -%}
+  {%- endfor -%}
+  {%- if ns.system_prompt -%}
+    {%- set ns.system_prompt = ns.system_prompt + '\n\n' + tools_header + ts.schemas + tools_footer -%}
+  {%- else -%}
+    {%- set ns.system_prompt = tools_header + ts.schemas + tools_footer -%}
+  {%- endif -%}
+{%- endif -%}
+{{- bos_token -}}
+{{- ns.system_prompt -}}
+{%- set last_user_idx = namespace(value=-1) -%}
+{%- for message in messages -%}
+  {%- if message['role'] == 'user' or message['role'] == 'developer' or message['role'] == 'tool' -%}
+    {%- set last_user_idx.value = loop.index0 -%}
+  {%- endif -%}
+{%- endfor -%}
+{%- set state = namespace(in_user=false) -%}
+{%- for message in messages -%}
+  {%- if message['role'] == 'user' or message['role'] == 'developer' -%}
+    {%- if state.in_user -%}
+      {{- '\n\n' -}}
+    {%- else -%}
+      {{- '<｜User｜>' -}}
+      {%- set state.in_user = true -%}
+    {%- endif -%}
+    {{- message['content'] or '' -}}
+  {%- elif message['role'] == 'tool' -%}
+    {%- if state.in_user -%}
+      {{- '\n\n' -}}
+    {%- else -%}
+      {{- '<｜User｜>' -}}
+      {%- set state.in_user = true -%}
+    {%- endif -%}
+    {{- '<tool_result>' + (message['content'] or '') + '</tool_result>' -}}
+  {%- elif message['role'] == 'assistant' -%}
+    {%- set state.in_user = false -%}
+    {{- '<｜Assistant｜>' -}}
+    {%- set is_after_last_user = loop.index0 > last_user_idx.value -%}
+    {%- if is_after_last_user and thinking -%}
+      {{- thinking_start_token -}}
+      {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%}
+        {{- message['reasoning_content'] -}}
+      {%- endif -%}
+      {{- thinking_end_token -}}
+    {%- else -%}
+      {{- thinking_end_token -}}
+    {%- endif -%}
+    {%- if message['content'] is defined and message['content'] -%}
+      {{- message['content'] -}}
+    {%- endif -%}
+    {%- if message['tool_calls'] -%}
+      {{- '\n\n<' + dsml_token + 'tool_calls>\n' -}}
+      {%- for tool in message['tool_calls'] -%}
+        {%- set func = tool['function'] -%}
+        {{- '<' + dsml_token + 'invoke name="' + func['name'] + '">\n' -}}
+        {%- set args = func['arguments'] -%}
+        {%- if args is string -%}
+          {%- set args = args | from_json -%}
+        {%- endif -%}
+        {%- for key, val in args.items() -%}
+          {%- if val is string -%}
+            {{- '<' + dsml_token + 'parameter name="' + key + '" string="true">' + val + '</' + dsml_token + 'parameter>\n' -}}
+          {%- else -%}
+            {{- '<' + dsml_token + 'parameter name="' + key + '" string="false">' + (val | tojson) + '</' + dsml_token + 'parameter>\n' -}}
+          {%- endif -%}
+        {%- endfor -%}
+        {{- '</' + dsml_token + 'invoke>\n' -}}
+      {%- endfor -%}
+      {{- '</' + dsml_token + 'tool_calls>' -}}
+    {%- endif -%}
+    {{- '<｜end▁of▁sentence｜>' -}}
+  {%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+  {{- '<｜Assistant｜>' -}}
+  {%- if thinking -%}
+    {{- thinking_start_token -}}
+  {%- else -%}
+    {{- thinking_end_token -}}
+  {%- endif -%}
+{%- endif -%}
\ No newline at end of file
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index d15ccfd99..320784c3a 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -25,6 +25,7 @@ add_library(llama
             llama-kv-cache.cpp
             llama-kv-cache-iswa.cpp
             llama-kv-cache-dsa.cpp
+            llama-kv-cache-dsv4.cpp
             llama-memory.cpp
             llama-memory-hybrid.cpp
             llama-memory-hybrid-iswa.cpp
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index d80915ffd..b890e66fc 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -77,6 +77,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
     { LLM_ARCH_DEEPSEEK2OCR,     "deepseek2-ocr"    },
     { LLM_ARCH_DEEPSEEK32,       "deepseek32"       },
+    { LLM_ARCH_DEEPSEEK4,        "deepseek4"        },
     { LLM_ARCH_CHATGLM,          "chatglm"          },
     { LLM_ARCH_GLM4,             "glm4"             },
     { LLM_ARCH_GLM4_MOE,         "glm4moe"          },
@@ -250,9 +251,19 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,           "%s.attention.indexer.head_count"           },
     { LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,           "%s.attention.indexer.key_length"           },
     { LLM_KV_ATTENTION_INDEXER_TOP_K,                "%s.attention.indexer.top_k"                },
+    { LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,           "%s.attention.output_group_count"           },
+    { LLM_KV_ATTENTION_OUTPUT_LORA_RANK,             "%s.attention.output_lora_rank"             },
+    { LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE,      "%s.attention.compress_rope_freq_base"      },
+    { LLM_KV_ATTENTION_COMPRESS_RATIOS,              "%s.attention.compress_ratios"              },
     { LLM_KV_ATTENTION_SHARED_KV_LAYERS,             "%s.attention.shared_kv_layers"             },
     { LLM_KV_ATTENTION_RECURRENT_LAYERS,             "%s.attention.recurrent_layers"             },
 
+    { LLM_KV_HYPER_CONNECTION_COUNT,                 "%s.hyper_connection.count"                 },
+    { LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS,   "%s.hyper_connection.sinkhorn_iterations"   },
+    { LLM_KV_HYPER_CONNECTION_EPSILON,               "%s.hyper_connection.epsilon"               },
+
+    { LLM_KV_HASH_LAYER_COUNT,                       "%s.hash_layer_count"                       },
+
     { LLM_KV_ROPE_DIMENSION_COUNT,           "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_DIMENSION_COUNT_SWA,       "%s.rope.dimension_count_swa"             },
     { LLM_KV_ROPE_DIMENSION_SECTIONS,        "%s.rope.dimension_sections"              },
@@ -440,6 +451,23 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_ATTN_Q_B,                               "blk.%d.attn_q_b" },
     { LLM_TENSOR_ATTN_KV_A_MQA,                          "blk.%d.attn_kv_a_mqa" },
     { LLM_TENSOR_ATTN_KV_B,                              "blk.%d.attn_kv_b" },
+    { LLM_TENSOR_ATTN_KV,                                "blk.%d.attn_kv" },
+    { LLM_TENSOR_ATTN_KV_NORM,                           "blk.%d.attn_kv_a_norm" },
+    { LLM_TENSOR_ATTN_OUT_A,                             "blk.%d.attn_output_a" },
+    { LLM_TENSOR_ATTN_OUT_B,                             "blk.%d.attn_output_b" },
+    { LLM_TENSOR_HC_HEAD_FN,                             "output_hc_fn" },
+    { LLM_TENSOR_HC_HEAD_BASE,                           "output_hc_base" },
+    { LLM_TENSOR_HC_HEAD_SCALE,                          "output_hc_scale" },
+    { LLM_TENSOR_HC_ATTN_FN,                             "blk.%d.hc_attn_fn" },
+    { LLM_TENSOR_HC_ATTN_BASE,                           "blk.%d.hc_attn_base" },
+    { LLM_TENSOR_HC_ATTN_SCALE,                          "blk.%d.hc_attn_scale" },
+    { LLM_TENSOR_HC_FFN_FN,                              "blk.%d.hc_ffn_fn" },
+    { LLM_TENSOR_HC_FFN_BASE,                            "blk.%d.hc_ffn_base" },
+    { LLM_TENSOR_HC_FFN_SCALE,                           "blk.%d.hc_ffn_scale" },
+    { LLM_TENSOR_ATTN_COMPRESSOR_WKV,                    "blk.%d.attn_compressor_kv" },
+    { LLM_TENSOR_ATTN_COMPRESSOR_WGATE,                  "blk.%d.attn_compressor_gate" },
+    { LLM_TENSOR_ATTN_COMPRESSOR_APE,                    "blk.%d.attn_compressor_ape" },
+    { LLM_TENSOR_ATTN_COMPRESSOR_NORM,                   "blk.%d.attn_compressor_norm" },
     { LLM_TENSOR_PER_LAYER_TOKEN_EMBD,                   "per_layer_token_embd" },
     { LLM_TENSOR_PER_LAYER_MODEL_PROJ,                   "per_layer_model_proj" },
     { LLM_TENSOR_PER_LAYER_PROJ_NORM,                    "per_layer_proj_norm" },
@@ -566,6 +594,11 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_INDEXER_PROJ,                           "blk.%d.indexer.proj" },
     { LLM_TENSOR_INDEXER_ATTN_K,                         "blk.%d.indexer.attn_k" },
     { LLM_TENSOR_INDEXER_ATTN_Q_B,                       "blk.%d.indexer.attn_q_b" },
+    { LLM_TENSOR_INDEXER_COMPRESSOR_WKV,                 "blk.%d.indexer_compressor_kv" },
+    { LLM_TENSOR_INDEXER_COMPRESSOR_WGATE,               "blk.%d.indexer_compressor_gate" },
+    { LLM_TENSOR_INDEXER_COMPRESSOR_APE,                 "blk.%d.indexer_compressor_ape" },
+    { LLM_TENSOR_INDEXER_COMPRESSOR_NORM,                "blk.%d.indexer_compressor_norm" },
+    { LLM_TENSOR_FFN_GATE_TID2EID,                       "blk.%d.ffn_gate_tid2eid" },
     { LLM_TENSOR_MASKED_EMBD_CENTROIDS,                  "masked_embd_centroids" },
     { LLM_TENSOR_MASKED_EMBD_ORDERING,                   "masked_embd_ordering" },
     { LLM_TENSOR_FC,                                     "fc" },
@@ -616,6 +649,23 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_ATTN_Q_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_KV_A_MQA,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_KV_NORM,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_OUT_A,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_OUT_B,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_HC_HEAD_FN,                 {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_HC_HEAD_BASE,               {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_ADD}},
+    {LLM_TENSOR_HC_HEAD_SCALE,              {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL}},
+    {LLM_TENSOR_HC_ATTN_FN,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_HC_ATTN_BASE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_HC_ATTN_SCALE,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_HC_FFN_FN,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_HC_FFN_BASE,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_HC_FFN_SCALE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_ATTN_COMPRESSOR_WKV,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_COMPRESSOR_WGATE,      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_COMPRESSOR_APE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_ATTN_COMPRESSOR_NORM,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
     {LLM_TENSOR_ATTN_K_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_V_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_SINKS,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SCALE}},
@@ -779,6 +829,11 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_INDEXER_PROJ,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_INDEXER_ATTN_K,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_INDEXER_ATTN_Q_B,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_INDEXER_COMPRESSOR_WKV,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_INDEXER_COMPRESSOR_WGATE,   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_INDEXER_COMPRESSOR_APE,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_INDEXER_COMPRESSOR_NORM,    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
+    {LLM_TENSOR_FFN_GATE_TID2EID,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
     {LLM_TENSOR_NEXTN_PROJ_PRE,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_NEXTN_PROJ_POST,            {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL_MAT}},
     // NextN/MTP tensors are stored per-block (blk.%d.nextn.*) even though only the
@@ -933,6 +988,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_OLMOE:
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK32:
+        case LLM_ARCH_DEEPSEEK4:
         case LLM_ARCH_GLM_DSA:
         case LLM_ARCH_BITNET:
         case LLM_ARCH_T5:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 946518d5f..a4f5091e7 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -82,6 +82,7 @@ enum llm_arch {
     LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_DEEPSEEK2OCR,
     LLM_ARCH_DEEPSEEK32,
+    LLM_ARCH_DEEPSEEK4,
     LLM_ARCH_CHATGLM,
     LLM_ARCH_GLM4,
     LLM_ARCH_GLM4_MOE,
@@ -255,9 +256,19 @@ enum llm_kv {
     LLM_KV_ATTENTION_INDEXER_HEAD_COUNT,
     LLM_KV_ATTENTION_INDEXER_KEY_LENGTH,
     LLM_KV_ATTENTION_INDEXER_TOP_K,
+    LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,
+    LLM_KV_ATTENTION_OUTPUT_LORA_RANK,
+    LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE,
+    LLM_KV_ATTENTION_COMPRESS_RATIOS,
     LLM_KV_ATTENTION_SHARED_KV_LAYERS,
     LLM_KV_ATTENTION_RECURRENT_LAYERS,
 
+    LLM_KV_HYPER_CONNECTION_COUNT,
+    LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS,
+    LLM_KV_HYPER_CONNECTION_EPSILON,
+
+    LLM_KV_HASH_LAYER_COUNT,
+
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_COUNT_SWA,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
@@ -501,10 +512,27 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_Q_B,
     LLM_TENSOR_ATTN_KV_A_MQA,
     LLM_TENSOR_ATTN_KV_B,
+    LLM_TENSOR_ATTN_KV,
+    LLM_TENSOR_ATTN_KV_NORM,
+    LLM_TENSOR_ATTN_OUT_A,
+    LLM_TENSOR_ATTN_OUT_B,
     LLM_TENSOR_ATTN_K_B,
     LLM_TENSOR_ATTN_V_B,
     LLM_TENSOR_ATTN_Q_A_NORM,
     LLM_TENSOR_ATTN_KV_A_NORM,
+    LLM_TENSOR_HC_HEAD_FN,
+    LLM_TENSOR_HC_HEAD_BASE,
+    LLM_TENSOR_HC_HEAD_SCALE,
+    LLM_TENSOR_HC_ATTN_FN,
+    LLM_TENSOR_HC_ATTN_BASE,
+    LLM_TENSOR_HC_ATTN_SCALE,
+    LLM_TENSOR_HC_FFN_FN,
+    LLM_TENSOR_HC_FFN_BASE,
+    LLM_TENSOR_HC_FFN_SCALE,
+    LLM_TENSOR_ATTN_COMPRESSOR_WKV,
+    LLM_TENSOR_ATTN_COMPRESSOR_WGATE,
+    LLM_TENSOR_ATTN_COMPRESSOR_APE,
+    LLM_TENSOR_ATTN_COMPRESSOR_NORM,
     LLM_TENSOR_ATTN_SUB_NORM,
     LLM_TENSOR_FFN_SUB_NORM,
     LLM_TENSOR_DEC_ATTN_NORM,
@@ -566,6 +594,11 @@ enum llm_tensor {
     LLM_TENSOR_INDEXER_PROJ,
     LLM_TENSOR_INDEXER_ATTN_K,
     LLM_TENSOR_INDEXER_ATTN_Q_B,
+    LLM_TENSOR_INDEXER_COMPRESSOR_WKV,
+    LLM_TENSOR_INDEXER_COMPRESSOR_WGATE,
+    LLM_TENSOR_INDEXER_COMPRESSOR_APE,
+    LLM_TENSOR_INDEXER_COMPRESSOR_NORM,
+    LLM_TENSOR_FFN_GATE_TID2EID,
     LLM_TENSOR_NEXTN_PROJ_PRE,
     LLM_TENSOR_NEXTN_PROJ_POST,
     LLM_TENSOR_NEXTN_EH_PROJ,
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 029141e2a..0465430df 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -2321,7 +2321,11 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
-    if (model.arch == LLM_ARCH_QWEN3NEXT || model.arch == LLM_ARCH_KIMI_LINEAR || model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE) {
+    if (model.arch == LLM_ARCH_QWEN3NEXT ||
+        model.arch == LLM_ARCH_KIMI_LINEAR ||
+        model.arch == LLM_ARCH_QWEN35 ||
+        model.arch == LLM_ARCH_QWEN35MOE ||
+        model.arch == LLM_ARCH_DEEPSEEK4) {
         return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     }
     uint32_t res = std::max<uint32_t>(1024u, 8u*model.n_tensors());
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 3ded70bc0..4c86e43c1 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -8,6 +8,7 @@
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
 #include "llama-kv-cache-dsa.h"
+#include "llama-kv-cache-dsv4.h"
 #include "llama-memory-hybrid.h"
 #include "llama-memory-hybrid-iswa.h"
 #include "llama-memory-recurrent.h"
@@ -17,6 +18,7 @@
 #include <cstring>
 #include <numeric>
 #include <sstream>
+#include <string>
 #include <unordered_set>
 
 // dedup helpers
@@ -568,7 +570,9 @@ void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
     // base tensors may not be allocated if there are no non-SWA attention layers
     if (self_k_idxs && self_k_idxs->buffer) {
         mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
-        mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
+        if (self_v_idxs) {
+            mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
+        }
     }
 
     // the kq mask guards on its own buffer: shared cells leave idxs unbacked while the mask stays live
@@ -579,7 +583,9 @@ void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
     // swa tensors may not be allocated if there are no SWA attention layers
     if (self_k_idxs_swa && self_k_idxs_swa->buffer) {
         mctx->get_swa()->set_input_k_idxs(self_k_idxs_swa, ubatch);
-        mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
+        if (self_v_idxs_swa) {
+            mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
+        }
     }
 
     if (self_kq_mask_swa && self_kq_mask_swa->buffer) {
@@ -633,6 +639,283 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
     return res;
 }
 
+static void dsv4_set_i64(ggml_tensor * dst, const std::vector<int64_t> & src) {
+    if (!dst || !dst->buffer) {
+        return;
+    }
+
+    GGML_ASSERT(dst->ne[0] == (int64_t) src.size());
+    ggml_backend_tensor_set(dst, src.data(), 0, src.size()*ggml_element_size(dst));
+}
+
+static void dsv4_set_i32(ggml_tensor * dst, const std::vector<int32_t> & src) {
+    if (!dst || !dst->buffer) {
+        return;
+    }
+
+    GGML_ASSERT(dst->ne[0] == (int64_t) src.size());
+    ggml_backend_tensor_set(dst, src.data(), 0, src.size()*ggml_element_size(dst));
+}
+
+static void dsv4_set_kq_mask(
+        ggml_tensor * dst,
+        const llama_kv_cache_dsv4_context::comp_plan & plan,
+        uint32_t n_tokens,
+        int64_t n_stream) {
+    if (!dst || !dst->buffer) {
+        return;
+    }
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(n_stream > 0);
+    GGML_ASSERT(n_tokens%n_stream == 0);
+    GGML_ASSERT(dst->ne[0] == plan.n_kv);
+    GGML_ASSERT(dst->ne[1] == (int64_t) n_tokens/n_stream);
+    GGML_ASSERT(dst->ne[2] == 1);
+    GGML_ASSERT(dst->ne[3] == n_stream);
+    GGML_ASSERT((int64_t) plan.n_visible.size() == (int64_t) n_tokens);
+    GGML_ASSERT(ggml_backend_buffer_is_host(dst->buffer));
+
+    float * data = (float *) dst->data;
+
+    for (int64_t i = 0; i < (int64_t) n_tokens; ++i) {
+        const int32_t n_visible = plan.n_visible[i];
+
+        for (int64_t j = 0; j < dst->ne[0]; ++j) {
+            data[i*dst->ne[0] + j] = j < n_visible ? 0.0f : -INFINITY;
+        }
+    }
+}
+
+static ggml_tensor * dsv4_build_raw_kq_mask(
+        ggml_context * ctx,
+        const llama_kv_cache_dsv4_raw_context * mctx,
+        const llama_ubatch & ubatch,
+        const llama_cparams & cparams,
+        int64_t n_stream) {
+    const auto n_kv     = mctx->get_n_kv();
+    const auto n_tokens = ubatch.n_tokens;
+
+    GGML_ASSERT(n_stream > 0);
+    GGML_ASSERT(n_tokens%n_stream == 0);
+
+    const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || n_stream == 1);
+    const auto type = use_fattn ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    ggml_tensor * res = ggml_new_tensor_4d(ctx, type, n_kv, n_tokens/n_stream, 1, n_stream);
+    ggml_set_input(res);
+    ggml_set_name(res, "attn_inp_kq_mask");
+
+    return res;
+}
+
+static bool dsv4_can_reuse_raw_kq_mask(
+        ggml_tensor * kq_mask,
+        const llama_kv_cache_dsv4_raw_context * mctx,
+        const llama_ubatch & ubatch,
+        int64_t n_stream) {
+    const auto n_kv     = mctx->get_n_kv();
+    const auto n_tokens = ubatch.n_tokens;
+
+    GGML_ASSERT(n_stream > 0);
+
+    bool res = true;
+
+    res &= (kq_mask->ne[0] == n_kv);
+    res &= (kq_mask->ne[1] == n_tokens/n_stream);
+    res &= (kq_mask->ne[2] == 1);
+    res &= (kq_mask->ne[3] == n_stream);
+
+    return res;
+}
+
+static std::string dsv4_plan_positions(const std::vector<int32_t> & values) {
+    std::ostringstream ss;
+    ss << "[";
+    for (size_t i = 0; i < values.size(); ++i) {
+        if (i > 0) {
+            ss << ", ";
+        }
+        ss << values[i];
+    }
+    ss << "]";
+    return ss.str();
+}
+
+static bool dsv4_compress_debug() {
+    static const bool debug = []() {
+        const char * env = getenv("LLAMA_DSV4_COMPRESS_DEBUG");
+        return env && atoi(env) > 0;
+    }();
+
+    return debug;
+}
+
+static void dsv4_set_comp_inputs(
+        const llm_graph_input_dsv4::comp_input & inp,
+        const llama_kv_cache_dsv4_context::comp_plan & plan,
+        const char * name,
+        bool debug,
+        uint32_t n_tokens,
+        int64_t n_stream) {
+    dsv4_set_i32(inp.state_pos, plan.state_pos);
+    dsv4_set_i32(inp.state_persist_src_idxs, plan.state_persist_src_idxs);
+    dsv4_set_i32(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs);
+    dsv4_set_i32(inp.state_read_idxs, plan.state_read_idxs);
+    dsv4_set_i64(inp.state_write_idxs, plan.state_write_idxs);
+    dsv4_set_i32(inp.state_write_pos, plan.state_write_pos);
+    dsv4_set_kq_mask(inp.kq_mask, plan, n_tokens, n_stream);
+
+    if (debug || dsv4_compress_debug()) {
+        LLAMA_LOG_INFO("%s: %s n_tokens=%u, n_stream=%d, state_persist_dst=%s, state_write_pos=%s\n",
+                __func__, name, n_tokens, (int) n_stream,
+                dsv4_plan_positions(plan.state_persist_dst_idxs).c_str(),
+                dsv4_plan_positions(plan.state_write_pos).c_str());
+    }
+}
+
+static bool dsv4_can_reuse_tensor_1d(ggml_tensor * t, int64_t ne0) {
+    return (t == nullptr && ne0 == 0) || (t != nullptr && t->ne[0] == ne0);
+}
+
+static bool dsv4_can_reuse_kq_mask(
+        ggml_tensor * t,
+        const llama_kv_cache_dsv4_context::comp_plan & plan,
+        uint32_t n_tokens,
+        int64_t n_stream) {
+    if (plan.n_kv == 0) {
+        return t == nullptr;
+    }
+
+    GGML_ASSERT(n_stream > 0);
+
+    return t != nullptr &&
+           t->ne[0] == plan.n_kv &&
+           t->ne[1] == (int64_t) n_tokens/n_stream &&
+           t->ne[2] == 1 &&
+           t->ne[3] == n_stream;
+}
+
+static bool dsv4_can_reuse_comp_input(
+        const llm_graph_input_dsv4::comp_input & inp,
+        const llama_kv_cache_dsv4_context::comp_plan & plan,
+        uint32_t n_tokens,
+        int64_t n_stream) {
+    bool res = true;
+    res &= dsv4_can_reuse_tensor_1d(inp.state_pos, plan.state_pos.size());
+    res &= dsv4_can_reuse_tensor_1d(inp.state_persist_src_idxs, plan.state_persist_src_idxs.size());
+    res &= dsv4_can_reuse_tensor_1d(inp.state_persist_dst_idxs, plan.state_persist_dst_idxs.size());
+    res &= dsv4_can_reuse_tensor_1d(inp.state_read_idxs, plan.state_read_idxs.size());
+    res &= dsv4_can_reuse_tensor_1d(inp.state_write_idxs, plan.state_write_idxs.size());
+    res &= dsv4_can_reuse_tensor_1d(inp.state_write_pos, plan.state_write_pos.size());
+    res &= dsv4_can_reuse_kq_mask(inp.kq_mask, plan, n_tokens, n_stream);
+
+    return res;
+}
+
+static ggml_tensor * dsv4_build_input_1d(
+        ggml_context * ctx,
+        ggml_type type,
+        int64_t ne0,
+        const std::string & name) {
+    if (ne0 == 0) {
+        return nullptr;
+    }
+
+    ggml_tensor * res = ggml_new_tensor_1d(ctx, type, ne0);
+    ggml_set_input(res);
+    ggml_set_name(res, name.c_str());
+
+    return res;
+}
+
+static void dsv4_build_comp_inputs(
+        ggml_context * ctx,
+        llm_graph_input_dsv4::comp_input & inp,
+        const llama_kv_cache_dsv4_context::comp_plan & plan,
+        const char * name,
+        int64_t n_stream) {
+    inp.state_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_pos.size(), std::string("dsv4_") + name + "_state_pos");
+    inp.state_persist_src_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_src_idxs.size(), std::string("dsv4_") + name + "_state_persist_src_idxs");
+    inp.state_persist_dst_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_persist_dst_idxs.size(), std::string("dsv4_") + name + "_state_persist_dst_idxs");
+    inp.state_read_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_read_idxs.size(), std::string("dsv4_") + name + "_state_read_idxs");
+    inp.state_write_idxs = dsv4_build_input_1d(ctx, GGML_TYPE_I64, plan.state_write_idxs.size(), std::string("dsv4_") + name + "_state_write_idxs");
+    inp.state_write_pos = dsv4_build_input_1d(ctx, GGML_TYPE_I32, plan.state_write_pos.size(), std::string("dsv4_") + name + "_state_write_pos");
+
+    if (plan.n_kv > 0) {
+        const int64_t n_tokens = (int64_t) plan.n_visible.size();
+
+        GGML_ASSERT(n_stream > 0);
+        GGML_ASSERT(n_tokens%n_stream == 0);
+
+        inp.kq_mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, plan.n_kv, n_tokens/n_stream, 1, n_stream);
+        ggml_set_input(inp.kq_mask);
+        ggml_set_name(inp.kq_mask, (std::string("dsv4_") + name + "_kq_mask").c_str());
+    }
+}
+
+void llm_graph_input_dsv4_raw::set_input(const llama_ubatch * ubatch) {
+    if (self_k_idxs && self_k_idxs->buffer) {
+        mctx->set_input_k_idxs(self_k_idxs);
+    }
+
+    if (self_kq_mask && self_kq_mask->buffer) {
+        mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+    }
+
+    if (self_k_rot) {
+        mctx->set_input_k_rot(self_k_rot);
+    }
+}
+
+void llm_graph_input_dsv4::set_input(const llama_ubatch * ubatch) {
+    const auto & plan_csa = mctx->get_csa_plan(*ubatch);
+    const auto & plan_hca = mctx->get_hca_plan(*ubatch);
+    const auto & plan_lid = mctx->get_lid_plan(*ubatch);
+    const int64_t n_stream = plan_csa.n_stream;
+
+    inp_raw->mctx = mctx->get_raw();
+    inp_raw->set_input(ubatch);
+
+    dsv4_set_comp_inputs(inp_csa, plan_csa, "csa", debug > 0, ubatch->n_tokens, n_stream);
+    dsv4_set_comp_inputs(inp_hca, plan_hca, "hca", debug > 0, ubatch->n_tokens, n_stream);
+    dsv4_set_comp_inputs(inp_lid, plan_lid, "lid", debug > 0, ubatch->n_tokens, n_stream);
+
+    if (inp_lid.k_rot && inp_lid.k_rot->buffer) {
+        mctx->get_lid()->set_input_k_rot(inp_lid.k_rot);
+    }
+}
+
+bool llm_graph_input_dsv4::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_dsv4_context *>(params.mctx);
+
+    this->mctx = mctx;
+    inp_raw->mctx = mctx->get_raw();
+
+    bool res = true;
+
+    const auto & plan_csa = mctx->get_csa_plan(params.ubatch);
+    const auto & plan_hca = mctx->get_hca_plan(params.ubatch);
+    const auto & plan_lid = mctx->get_lid_plan(params.ubatch);
+    const int64_t n_stream = plan_csa.n_stream;
+
+    const auto * raw_ctx = mctx->get_raw();
+    inp_raw->mctx = raw_ctx;
+
+    if (inp_raw->self_k_idxs && inp_raw->self_k_idxs->buffer) {
+        res &= inp_raw->self_k_idxs->ne[0] == raw_ctx->get_n_write();
+    }
+    if (inp_raw->self_kq_mask && inp_raw->self_kq_mask->buffer) {
+        res &= dsv4_can_reuse_raw_kq_mask(inp_raw->self_kq_mask, raw_ctx, params.ubatch, n_stream);
+    }
+
+    res &= dsv4_can_reuse_comp_input(inp_csa, plan_csa, params.ubatch.n_tokens, n_stream);
+    res &= dsv4_can_reuse_comp_input(inp_hca, plan_hca, params.ubatch.n_tokens, n_stream);
+    res &= dsv4_can_reuse_comp_input(inp_lid, plan_lid, params.ubatch.n_tokens, n_stream);
+
+    return res;
+}
+
 void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
     GGML_ASSERT(cross_kq_mask);
 
@@ -1351,20 +1634,24 @@ ggml_tensor * llm_graph_context::build_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate && type_gate == LLM_FFN_PAR) {
-                // Step35: HF clamps gate (after SiLU) and up before multiplication
-                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                if (il >= 0) {
                     const float limit = hparams.swiglu_clamp_shexp[il];
                     constexpr float eps = 1e-6f;
                     if (limit > eps) {
-                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
-                        cb(gate_act, "ffn_silu", il);
-                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
-                        cb(gate_act, "ffn_silu_clamped", il);
-
                         tmp = ggml_clamp(ctx0, tmp, -limit, limit);
                         cb(tmp, "ffn_up_clamped", il);
 
-                        cur = ggml_mul(ctx0, gate_act, tmp);
+                        if (arch == LLM_ARCH_DEEPSEEK4) {
+                            cur = ggml_clamp(ctx0, cur, -INFINITY, limit);
+                            cb(cur, "ffn_gate_clamped", il);
+                            cur = ggml_swiglu_split(ctx0, cur, tmp);
+                        } else {
+                            ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                            cb(gate_act, "ffn_silu", il);
+                            gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                            cb(gate_act, "ffn_silu_clamped", il);
+                            cur = ggml_mul(ctx0, gate_act, tmp);
+                        }
                         cb(cur, "ffn_swiglu_limited", il);
                         type_gate = LLM_FFN_SEQ;
                         break;
@@ -1474,7 +1761,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
          ggml_tensor * gate_up_exps,
          ggml_tensor * up_exps_s,
          ggml_tensor * gate_exps_s,
-         ggml_tensor * down_exps_s) const {
+         ggml_tensor * down_exps_s,
+         ggml_tensor * selected_experts_in) const {
     return build_moe_ffn(
         cur,
         gate_inp,  /* gate_inp_b  */ nullptr,
@@ -1494,7 +1782,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
         /* gate_up_exps_b */ nullptr,
         up_exps_s,
         gate_exps_s,
-        down_exps_s
+        down_exps_s,
+        selected_experts_in
     );
 }
 
@@ -1521,7 +1810,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
          ggml_tensor * gate_up_exps_b,
          ggml_tensor * up_exps_s,
          ggml_tensor * gate_exps_s,
-         ggml_tensor * down_exps_s) const {
+         ggml_tensor * down_exps_s,
+         ggml_tensor * selected_experts_in) const {
     const int64_t n_embd   = cur->ne[0];
     const int64_t n_tokens = cur->ne[1];
     const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
@@ -1530,6 +1820,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
     if (probs_in == nullptr) {
         logits = build_lora_mm(gate_inp, cur); // [n_expert, n_tokens]
+        if (gating_op == LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) {
+            ggml_mul_mat_set_prec(logits, GGML_PREC_F32);
+        }
         cb(logits, "ffn_moe_logits", il);
     } else {
         logits = probs_in;
@@ -1554,6 +1847,10 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
             {
                 probs = logits; // [n_expert, n_tokens]
             } break;
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS:
+            {
+                probs = ggml_sqrt(ctx0, ggml_softplus(ctx0, logits)); // [n_expert, n_tokens]
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -1604,8 +1901,11 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     }
 
     // select experts
-    ggml_tensor * selected_experts = ggml_argsort_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
-    cb(selected_experts->src[0], "ffn_moe_argsort", il);
+    ggml_tensor * selected_experts = selected_experts_in;
+    if (selected_experts == nullptr) {
+        selected_experts = ggml_argsort_top_k(ctx0, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
+        cb(selected_experts->src[0], "ffn_moe_argsort", il);
+    }
     cb(selected_experts, "ffn_moe_topk", il);
 
     if (arch == LLM_ARCH_GROVEMOE && n_expert != hparams.n_expert) {
@@ -1718,20 +2018,24 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
     switch (type_op) {
         case LLM_FFN_SILU:
             if (gate_exps) {
-                // Step35: per-layer clamp for routed experts
-                if (arch == LLM_ARCH_STEP35 && il >= 0) {
+                if (il >= 0) {
                     const float limit = hparams.swiglu_clamp_exp[il];
                     constexpr float eps = 1e-6f;
                     if (limit > eps) {
-                        ggml_tensor * gate_act = ggml_silu(ctx0, cur);
-                        cb(gate_act, "ffn_moe_silu", il);
-                        gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
-                        cb(gate_act, "ffn_moe_silu_clamped", il);
-
                         up = ggml_clamp(ctx0, up, -limit, limit);
                         cb(up, "ffn_moe_up_clamped", il);
 
-                        cur = ggml_mul(ctx0, gate_act, up);
+                        if (arch == LLM_ARCH_DEEPSEEK4) {
+                            cur = ggml_clamp(ctx0, cur, -INFINITY, limit);
+                            cb(cur, "ffn_moe_gate_clamped", il);
+                            cur = ggml_swiglu_split(ctx0, cur, up);
+                        } else {
+                            ggml_tensor * gate_act = ggml_silu(ctx0, cur);
+                            cb(gate_act, "ffn_moe_silu", il);
+                            gate_act = ggml_clamp(ctx0, gate_act, -INFINITY, limit);
+                            cb(gate_act, "ffn_moe_silu_clamped", il);
+                            cur = ggml_mul(ctx0, gate_act, up);
+                        }
                         cb(cur, "ffn_moe_swiglu_limited", il);
                         break;
                     }
@@ -2760,6 +3064,31 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
     return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
 }
 
+llm_graph_input_dsv4 * llm_graph_context::build_inp_dsv4() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_dsv4_context *>(mctx);
+    const auto * raw_ctx  = mctx_cur->get_raw();
+
+    auto inp_raw = std::make_unique<llm_graph_input_dsv4_raw>(cparams, raw_ctx);
+
+    const int64_t n_stream = mctx_cur->get_csa_plan(ubatch).n_stream;
+
+    GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "DSV4 expects SWA raw cache");
+
+    inp_raw->self_k_idxs = raw_ctx->build_input_k_idxs(ctx0, ubatch);
+    inp_raw->self_kq_mask = dsv4_build_raw_kq_mask(ctx0, raw_ctx, ubatch, cparams, n_stream);
+    inp_raw->self_kq_mask_cnv = inp_raw->self_kq_mask;
+
+    inp_raw->self_k_rot = raw_ctx->build_input_k_rot(ctx0);
+    auto inp = std::make_unique<llm_graph_input_dsv4>(cparams, std::move(inp_raw), mctx_cur);
+
+    dsv4_build_comp_inputs(ctx0, inp->inp_csa, mctx_cur->get_csa_plan(ubatch), "csa", n_stream);
+    dsv4_build_comp_inputs(ctx0, inp->inp_hca, mctx_cur->get_hca_plan(ubatch), "hca", n_stream);
+    dsv4_build_comp_inputs(ctx0, inp->inp_lid, mctx_cur->get_lid_plan(ubatch), "lid", n_stream);
+    inp->inp_lid.k_rot = mctx_cur->get_lid()->build_input_k_rot(ctx0);
+
+    return (llm_graph_input_dsv4 *) res->add_input(std::move(inp));
+}
+
 ggml_tensor * llm_graph_context::build_rs(
         ggml_tensor * s,
         ggml_tensor * state_copy_main,
diff --git a/src/llama-graph.h b/src/llama-graph.h
index a6e8c3985..4b5b75c63 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -23,6 +23,8 @@ struct llama_memory_context_i;
 
 class llama_kv_cache_context;
 class llama_kv_cache_dsa_context;
+class llama_kv_cache_dsv4_raw_context;
+class llama_kv_cache_dsv4_context;
 class llama_kv_cache_iswa_context;
 class llama_memory_recurrent_context;
 class llama_memory_hybrid_context;
@@ -459,6 +461,79 @@ public:
     const llama_kv_cache_iswa_context * mctx;
 };
 
+// DSV4 raw graph inputs are SWA-only, but their mask may be stream-shaped
+// so raw K can be concatenated with DSV4 compressed K in one attention op.
+class llm_graph_input_dsv4_raw {
+public:
+    llm_graph_input_dsv4_raw(
+            const llama_cparams & cparams,
+            const llama_kv_cache_dsv4_raw_context * mctx) :
+        cparams(cparams),
+        mctx(mctx) {
+    }
+
+    void set_input(const llama_ubatch * ubatch);
+
+    ggml_tensor * get_k_idxs() const { return self_k_idxs; }
+    ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
+
+    ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
+
+    ggml_tensor * self_kq_mask     = nullptr; // F32/F16 [n_kv, n_batch/n_stream, 1, n_stream]
+    ggml_tensor * self_kq_mask_cnv = nullptr; //         [n_kv, n_batch/n_stream, 1, n_stream]
+
+    ggml_tensor * self_k_rot = nullptr;
+
+    const llama_cparams cparams;
+
+    const llama_kv_cache_dsv4_raw_context * mctx;
+};
+
+class llm_graph_input_dsv4 : public llm_graph_input_i {
+public:
+    struct comp_input {
+        ggml_tensor * state_pos        = nullptr; // I32 [n_state]
+        ggml_tensor * state_persist_src_idxs = nullptr; // I32 [n_state_persist]
+        ggml_tensor * state_persist_dst_idxs = nullptr; // I32 [n_state_persist]
+        ggml_tensor * state_read_idxs  = nullptr; // I32 [ratio*n_state_write]
+        ggml_tensor * state_write_idxs = nullptr; // I64 [n_state_write]
+        ggml_tensor * state_write_pos  = nullptr; // I32 [n_state_write]
+
+        ggml_tensor * kq_mask    = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
+
+        ggml_tensor * k_rot      = nullptr;
+    };
+
+    llm_graph_input_dsv4(
+            const llama_cparams & cparams,
+            std::unique_ptr<llm_graph_input_dsv4_raw> inp_raw,
+            const llama_kv_cache_dsv4_context * mctx) :
+        inp_raw(std::move(inp_raw)),
+        cparams(cparams),
+        mctx(mctx) {
+    }
+    ~llm_graph_input_dsv4() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    bool can_reuse(const llm_graph_params & params) override;
+
+    llm_graph_input_dsv4_raw * get_raw() const { return inp_raw.get(); }
+    const comp_input & get_csa() const { return inp_csa; }
+    const comp_input & get_hca() const { return inp_hca; }
+    const comp_input & get_lid() const { return inp_lid; }
+
+    std::unique_ptr<llm_graph_input_dsv4_raw> inp_raw;
+
+    comp_input inp_csa;
+    comp_input inp_hca;
+    comp_input inp_lid;
+
+    const llama_cparams cparams;
+
+    const llama_kv_cache_dsv4_context * mctx;
+};
+
 class llm_graph_input_attn_cross : public llm_graph_input_i {
 public:
     llm_graph_input_attn_cross(const llama_cross * cross) : cross(cross) {}
@@ -920,7 +995,8 @@ struct llm_graph_context {
              ggml_tensor * gate_up_exps = nullptr,
              ggml_tensor * up_exps_s = nullptr,
              ggml_tensor * gate_exps_s = nullptr,
-             ggml_tensor * down_exps_s = nullptr) const;
+             ggml_tensor * down_exps_s = nullptr,
+             ggml_tensor * selected_experts_in = nullptr) const;
 
     ggml_tensor * build_moe_ffn(
              ggml_tensor * cur,
@@ -945,7 +1021,8 @@ struct llm_graph_context {
              ggml_tensor * gate_up_exps_b = nullptr,
              ggml_tensor * up_exps_s = nullptr,
              ggml_tensor * gate_exps_s = nullptr,
-             ggml_tensor * down_exps_s = nullptr) const;
+             ggml_tensor * down_exps_s = nullptr,
+             ggml_tensor * selected_experts_in = nullptr) const;
 
     //
     // inputs
@@ -1045,6 +1122,8 @@ struct llm_graph_context {
 
     llm_graph_input_attn_kv_iswa * build_attn_inp_kv_iswa() const;
 
+    llm_graph_input_dsv4 * build_inp_dsv4() const;
+
     // note: if k_cur or v_cur are not provided, they will not be stored in the memory
     ggml_tensor * build_attn(
             llm_graph_input_attn_kv_iswa * inp,
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 2eadeb214..8be5f28f3 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -14,6 +14,7 @@ enum llama_expert_gating_func_type {
     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX        = 1,
     LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID        = 2,
     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT = 3, // applied to the router weights instead of the logits
+    LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS  = 4,
 };
 
 enum llama_swa_type {
@@ -226,6 +227,16 @@ struct llama_hparams {
     uint32_t indexer_head_size = 0;
     uint32_t indexer_top_k     = 0;
 
+    // DeepSeek-V4
+    uint32_t dsv4_o_group_count        = 0;
+    uint32_t dsv4_o_lora_rank          = 0;
+    uint32_t dsv4_hc_mult              = 0;
+    uint32_t dsv4_hc_sinkhorn_iters    = 0;
+    uint32_t dsv4_hash_layer_count     = 0;
+    float    dsv4_compress_rope_base   = 0.0f;
+    float    dsv4_hc_eps               = 0.0f;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> dsv4_compress_ratios;
+
     // qwen3vl deepstack
     // When parsed from GGUF, this implies the first N layers consume the first
     // N deepstack embeddings. Use deepstack_mapping_arr if you need a more
diff --git a/src/llama-kv-cache-dsv4.cpp b/src/llama-kv-cache-dsv4.cpp
new file mode 100644
index 000000000..3a698d719
--- /dev/null
+++ b/src/llama-kv-cache-dsv4.cpp
@@ -0,0 +1,1807 @@
+#include "llama-kv-cache-dsv4.h"
+
+#include "ggml-backend.h"
+#include "llama-impl.h"
+#include "llama-batch.h"
+#include "llama-io.h"
+#include "llama-model.h"
+
+#include <algorithm>
+#include <cassert>
+#include <climits>
+#include <cstdlib>
+#include <cstring>
+#include <map>
+#include <sstream>
+#include <stdexcept>
+
+static constexpr uint32_t DSV4_CSA_RATIO = 4;
+static constexpr uint32_t DSV4_HCA_RATIO = 128;
+
+static constexpr uint32_t DSV4_STATE_MAGIC         = 0x34565344; // DSV4
+static constexpr uint32_t DSV4_STATE_VERSION       = 1;
+static constexpr uint32_t DSV4_STATE_MODE_FULL     = 0;
+static constexpr uint32_t DSV4_STATE_MODE_PARTIAL  = 1;
+static constexpr uint32_t DSV4_K_CACHE_STATE_VER   = 1;
+static constexpr uint32_t DSV4_COMP_STATE_VER      = 1;
+
+static uint32_t dsv4_comp_size(uint32_t kv_size, uint32_t ratio) {
+    return std::max<uint32_t>(1, (kv_size + ratio - 1)/ratio);
+}
+
+static int64_t dsv4_stream_offset(uint32_t n_stream, llama_seq_id seq_id, uint32_t size) {
+    if (n_stream <= 1) {
+        return 0;
+    }
+    if (seq_id < 0 || (uint32_t) seq_id >= n_stream) {
+        throw std::runtime_error("DSV4 sequence id out of stream range");
+    }
+
+    return (int64_t) seq_id*size;
+}
+
+static bool dsv4_ubatch_has_coupled(const llama_ubatch & ubatch) {
+    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+        if (ubatch.n_seq_id[i] > 1) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+static bool dsv4_token_has_seq(const llama_ubatch & ubatch, uint32_t i, llama_seq_id seq_id) {
+    for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) {
+        if (ubatch.seq_id[i][s] == seq_id) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+static llama_ubatch dsv4_build_raw_write_ubatch(const llama_ubatch & ubatch) {
+    if (!dsv4_ubatch_has_coupled(ubatch)) {
+        return ubatch;
+    }
+    if (ubatch.embd) {
+        throw std::runtime_error("DSV4 coupled embedding ubatches are not supported");
+    }
+
+    std::vector<uint32_t> counts(ubatch.n_seqs_unq, 0);
+    uint32_t n_tokens = 0;
+    for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+        const llama_seq_id seq_id = ubatch.seq_id_unq[s];
+        for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+            if (dsv4_token_has_seq(ubatch, i, seq_id)) {
+                ++counts[s];
+                ++n_tokens;
+            }
+        }
+    }
+
+    if (n_tokens == 0) {
+        return ubatch;
+    }
+
+    const uint32_t n_seq_tokens = counts[0];
+    for (uint32_t s = 1; s < counts.size(); ++s) {
+        if (counts[s] != n_seq_tokens) {
+            throw std::runtime_error("DSV4 coupled raw writes require equal sequence lengths");
+        }
+    }
+
+    auto data = std::make_shared<llama_ubatch::data_t>();
+    data->pos.resize((size_t) n_tokens*ubatch.n_pos);
+    data->n_seq_id.reserve(n_tokens);
+    data->seq_id.reserve(n_tokens);
+    data->seq_id_data.reserve(n_tokens);
+    data->seq_id_unq.assign(ubatch.seq_id_unq, ubatch.seq_id_unq + ubatch.n_seqs_unq);
+    data->seq_idx.assign(LLAMA_MAX_SEQ, -1);
+    data->output.assign(n_tokens, 0);
+    if (ubatch.token) {
+        data->token.reserve(n_tokens);
+    }
+
+    for (uint32_t s = 0; s < data->seq_id_unq.size(); ++s) {
+        data->seq_idx[data->seq_id_unq[s]] = s;
+    }
+
+    for (uint32_t s = 0; s < ubatch.n_seqs_unq; ++s) {
+        const llama_seq_id seq_id = ubatch.seq_id_unq[s];
+        for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+            if (!dsv4_token_has_seq(ubatch, i, seq_id)) {
+                continue;
+            }
+
+            const uint32_t dst = data->n_seq_id.size();
+            if (ubatch.token) {
+                data->token.push_back(ubatch.token[i]);
+            }
+            for (uint32_t p = 0; p < ubatch.n_pos; ++p) {
+                data->pos[(size_t) p*n_tokens + dst] = ubatch.pos[(size_t) p*ubatch.n_tokens + i];
+            }
+            data->n_seq_id.push_back(1);
+            data->seq_id_data.push_back(seq_id);
+        }
+    }
+
+    for (uint32_t i = 0; i < n_tokens; ++i) {
+        data->seq_id.push_back(&data->seq_id_data[i]);
+    }
+
+    llama_ubatch res {
+        /*.b_equal_seqs =*/ true,
+        /*.n_tokens     =*/ n_tokens,
+        /*.n_seq_tokens =*/ n_seq_tokens,
+        /*.n_seqs       =*/ ubatch.n_seqs_unq,
+        /*.n_seqs_unq   =*/ ubatch.n_seqs_unq,
+        /*.n_pos        =*/ ubatch.n_pos,
+        /*.token        =*/ data->token.empty() ? nullptr : data->token.data(),
+        /*.embd         =*/ nullptr,
+        /*.pos          =*/ data->pos.data(),
+        /*.n_seq_id     =*/ data->n_seq_id.data(),
+        /*.seq_id       =*/ data->seq_id.data(),
+        /*.seq_id_unq   =*/ data->seq_id_unq.data(),
+        /*.seq_idx      =*/ data->seq_idx.data(),
+        /*.output       =*/ data->output.data(),
+        /*.data         =*/ data,
+    };
+
+    return res;
+}
+
+static std::vector<llama_ubatch> dsv4_build_raw_write_ubatches(const std::vector<llama_ubatch> & ubatches) {
+    std::vector<llama_ubatch> res;
+    res.reserve(ubatches.size());
+    for (const llama_ubatch & ubatch : ubatches) {
+        res.push_back(dsv4_build_raw_write_ubatch(ubatch));
+    }
+    return res;
+}
+
+static bool dsv4_batch_has_coupled(const llama_batch & batch) {
+    if (!batch.n_seq_id) {
+        return false;
+    }
+
+    for (int32_t i = 0; i < batch.n_tokens; ++i) {
+        if (batch.n_seq_id[i] > 1) {
+            return true;
+        }
+    }
+
+    return false;
+}
+
+static int64_t dsv4_comp_graph_n_stream(const llama_ubatch & ubatch, uint32_t n_stream) {
+    // Coupled sequence sets must stay in one graph stream because their
+    // compressed state is shared. Independent per-seq state can fan out.
+    if (n_stream <= 1 || ubatch.n_seqs_unq <= 1 || dsv4_ubatch_has_coupled(ubatch)) {
+        return 1;
+    }
+
+    return ubatch.n_seqs_unq;
+}
+
+static void dsv4_state_src_stream_range(
+        uint32_t       n_stream,
+        llama_seq_id   seq_id,
+        uint32_t     & s0,
+        uint32_t     & ns) {
+    if (seq_id >= 0 && n_stream > 1) {
+        if ((uint32_t) seq_id >= n_stream) {
+            throw std::runtime_error("DSV4 state sequence id out of stream range");
+        }
+
+        s0 = (uint32_t) seq_id;
+        ns = 1;
+        return;
+    }
+
+    s0 = 0;
+    ns = seq_id >= 0 ? 1 : n_stream;
+}
+
+static void dsv4_state_dst_stream_range(
+        uint32_t       n_stream,
+        llama_seq_id   seq_id,
+        uint32_t       ns,
+        uint32_t     & s0) {
+    if (seq_id >= 0) {
+        if (ns != 1) {
+            throw std::runtime_error("DSV4 sequence state stream count mismatch");
+        }
+        if (n_stream > 1 && (uint32_t) seq_id >= n_stream) {
+            throw std::runtime_error("DSV4 state sequence id out of stream range");
+        }
+
+        s0 = n_stream > 1 ? (uint32_t) seq_id : 0;
+        return;
+    }
+
+    if (ns != n_stream) {
+        throw std::runtime_error("DSV4 full state stream count mismatch");
+    }
+
+    s0 = 0;
+}
+
+static void dsv4_state_write_tensor_streams(
+        llama_io_write_i & io,
+        ggml_tensor      * tensor,
+        uint32_t           n_rows,
+        uint32_t           s0,
+        uint32_t           ns) {
+    const int32_t  type_i   = (int32_t) tensor->type;
+    const uint64_t ne0      = tensor->ne[0];
+    const uint64_t rows     = n_rows;
+    const uint64_t row_size = ggml_row_size(tensor->type, tensor->ne[0]);
+
+    io.write(&type_i,   sizeof(type_i));
+    io.write(&ne0,      sizeof(ne0));
+    io.write(&rows,     sizeof(rows));
+    io.write(&row_size, sizeof(row_size));
+
+    const size_t offset = (size_t) s0*n_rows*row_size;
+    const size_t size   = (size_t) ns*n_rows*row_size;
+
+    io.write_tensor(tensor, offset, size);
+}
+
+static void dsv4_state_read_tensor_streams(
+        llama_io_read_i & io,
+        ggml_tensor     * tensor,
+        uint32_t          n_rows,
+        uint32_t          s0,
+        uint32_t          ns) {
+    int32_t  type_i_ref;
+    uint64_t ne0_ref;
+    uint64_t rows_ref;
+    uint64_t row_size_ref;
+
+    io.read(&type_i_ref,   sizeof(type_i_ref));
+    io.read(&ne0_ref,      sizeof(ne0_ref));
+    io.read(&rows_ref,     sizeof(rows_ref));
+    io.read(&row_size_ref, sizeof(row_size_ref));
+
+    const int32_t  type_i   = (int32_t) tensor->type;
+    const uint64_t ne0      = tensor->ne[0];
+    const uint64_t rows     = n_rows;
+    const uint64_t row_size = ggml_row_size(tensor->type, tensor->ne[0]);
+
+    if (type_i != type_i_ref || ne0 != ne0_ref || rows != rows_ref || row_size != row_size_ref) {
+        throw std::runtime_error("DSV4 state tensor metadata mismatch");
+    }
+
+    const size_t offset = (size_t) s0*n_rows*row_size;
+    const size_t size   = (size_t) ns*n_rows*row_size;
+
+    io.read_tensor(tensor, offset, size);
+}
+
+static void dsv4_state_write_k_cache(
+        llama_io_write_i    & io,
+        const llama_kv_cache * kv,
+        llama_seq_id          seq_id,
+        llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
+    uint32_t s0;
+    uint32_t ns;
+    dsv4_state_src_stream_range(kv->get_n_stream(), seq_id, s0, ns);
+
+    const uint32_t version = DSV4_K_CACHE_STATE_VER;
+    const uint32_t kv_size = kv->get_size();
+    const auto layer_ids = kv->get_layer_ids();
+    const uint32_t n_layer = layer_ids.size();
+
+    io.write(&version, sizeof(version));
+    io.write(&kv_size, sizeof(kv_size));
+    io.write(&ns,      sizeof(ns));
+    io.write(&n_layer, sizeof(n_layer));
+
+    for (uint32_t il : layer_ids) {
+        io.write(&il, sizeof(il));
+        dsv4_state_write_tensor_streams(io, kv->get_k_storage(il), kv_size, s0, ns);
+    }
+}
+
+static void dsv4_state_read_k_cache(
+        llama_io_read_i  & io,
+        llama_kv_cache   * kv,
+        llama_seq_id       seq_id,
+        llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
+    uint32_t version;
+    uint32_t kv_size_ref;
+    uint32_t ns;
+    uint32_t n_layer_ref;
+
+    io.read(&version,     sizeof(version));
+    io.read(&kv_size_ref, sizeof(kv_size_ref));
+    io.read(&ns,          sizeof(ns));
+    io.read(&n_layer_ref, sizeof(n_layer_ref));
+
+    if (version != DSV4_K_CACHE_STATE_VER) {
+        throw std::runtime_error("DSV4 K-cache state version mismatch");
+    }
+    if (kv_size_ref != kv->get_size()) {
+        throw std::runtime_error("DSV4 K-cache state size mismatch");
+    }
+
+    uint32_t s0;
+    dsv4_state_dst_stream_range(kv->get_n_stream(), seq_id, ns, s0);
+
+    const auto layer_ids = kv->get_layer_ids();
+    if (n_layer_ref != layer_ids.size()) {
+        throw std::runtime_error("DSV4 K-cache layer count mismatch");
+    }
+
+    for (uint32_t il : layer_ids) {
+        uint32_t il_ref;
+        io.read(&il_ref, sizeof(il_ref));
+        if (il_ref != il) {
+            throw std::runtime_error("DSV4 K-cache layer id mismatch");
+        }
+
+        dsv4_state_read_tensor_streams(io, kv->get_k_storage(il), kv->get_size(), s0, ns);
+    }
+}
+
+static std::string dsv4_plan_positions(const std::vector<int32_t> & values) {
+    std::ostringstream ss;
+    ss << "[";
+    for (size_t i = 0; i < values.size(); ++i) {
+        if (i > 0) {
+            ss << ", ";
+        }
+        ss << values[i];
+    }
+    ss << "]";
+    return ss.str();
+}
+
+static llama_kv_cache_dsv4_context::comp_plan dsv4_build_comp_plan(
+        const llama_ubatch & ubatch,
+        uint32_t ratio,
+        bool overlap,
+        uint32_t state_size,
+        uint32_t kv_size,
+        uint32_t n_stream) {
+    llama_kv_cache_dsv4_context::comp_plan plan;
+    plan.n_visible.resize(ubatch.n_tokens);
+    plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream);
+
+    // n_stream is the persistent cache/state layout; plan.n_stream is the
+    // graph view for this ubatch and can be a subset of those streams.
+    if (n_stream <= 1 && ubatch.n_seqs_unq > 1) {
+        throw std::runtime_error("DSV4 single compressed stream cannot serve multiple sequences");
+    }
+
+    const int64_t state_rows = (int64_t) state_size*n_stream;
+
+    struct persist_row {
+        int32_t dst;
+        int32_t src;
+        llama_pos pos;
+    };
+
+    std::vector<persist_row> persist_rows;
+
+    // For the overlap compressor, build_overlap_compressed_kv_from_state() consumes
+    // state_read_idxs as two contiguous halves: the first ratio*n_blocks entries are
+    // the "previous-window" gather indices for every block, followed by the
+    // "current-window" indices for every block. Collect them separately here and
+    // append cur after prev once the loop has visited all completed blocks
+    std::vector<int32_t> overlap_prev_reads;
+    std::vector<int32_t> overlap_cur_reads;
+
+    std::map<std::pair<llama_seq_id, llama_pos>, int64_t> curr_token_idx_map;
+
+    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+        for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) {
+            curr_token_idx_map[std::make_pair(ubatch.seq_id[i][s], ubatch.pos[i])] = i;
+        }
+    }
+
+    const auto state_source_idx = [&](llama_seq_id seq_id, llama_pos pos) -> int32_t {
+        if (pos < 0) {
+            // The overlap compressor needs a zero/-inf source for the first
+            // block's previous half. The graph appends that row after the
+            // current-ubatch scratch rows.
+            return (int32_t) (state_rows + ubatch.n_tokens);
+        }
+
+        const auto key = std::make_pair(seq_id, pos);
+        if (curr_token_idx_map.find(key) != curr_token_idx_map.end()) {
+            return (int32_t) (state_rows + curr_token_idx_map.at(key));
+        }
+
+        const int64_t stream_off = dsv4_stream_offset(n_stream, seq_id, state_size);
+        return (int32_t) (stream_off + pos%state_size);
+    };
+
+    for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+        const llama_pos pos = ubatch.pos[i];
+
+        if (pos < 0) {
+            continue;
+        }
+
+        plan.state_pos.push_back((int32_t) (pos%ratio));
+
+        const int64_t n_visible = (int64_t) (pos + 1)/ratio;
+        plan.n_visible[i] = (int32_t) n_visible;
+        plan.n_kv = std::max(plan.n_kv, n_visible);
+
+        for (int32_t s = 0; s < ubatch.n_seq_id[i]; ++s) {
+            const llama_seq_id seq_id = ubatch.seq_id[i][s];
+            const int64_t stream_off = dsv4_stream_offset(n_stream, seq_id, state_size);
+            const int32_t state_idx = (int32_t) (stream_off + pos%state_size);
+
+            const auto it = std::find_if(persist_rows.begin(), persist_rows.end(),
+                    [state_idx](const persist_row & row) {
+                        return row.dst == state_idx;
+                    });
+            if (it == persist_rows.end()) {
+                persist_rows.push_back({ state_idx, (int32_t) i, pos });
+            } else if (pos > it->pos) {
+                it->src = (int32_t) i;
+                it->pos = pos;
+            }
+
+            if ((pos + 1) % ratio != 0) {
+                continue;
+            }
+
+            const llama_pos source_start = pos + 1 - ratio;
+            const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size);
+
+            plan.state_write_idxs.push_back(cache_off + pos/ratio);
+            plan.state_write_pos.push_back((int32_t) source_start);
+
+            if (overlap) {
+                const llama_pos prev_start = source_start - ratio;
+
+                for (uint32_t j = 0; j < ratio; ++j) {
+                    overlap_prev_reads.push_back(state_source_idx(seq_id, prev_start + j));
+                }
+                for (uint32_t j = 0; j < ratio; ++j) {
+                    overlap_cur_reads.push_back(state_source_idx(seq_id, source_start + j));
+                }
+            } else {
+                for (uint32_t j = 0; j < ratio; ++j) {
+                    plan.state_read_idxs.push_back(state_source_idx(seq_id, source_start + j));
+                }
+            }
+        }
+    }
+
+    if (ratio == DSV4_CSA_RATIO && plan.state_write_idxs.empty() && !plan.state_pos.empty()) {
+        // Non-boundary CSA steps still need a write op so their graph matches
+        // boundary steps. Use a padded scratch row that is masked from attention.
+        assert(kv_size > 0);
+
+        uint32_t i = 0;
+        while (i < ubatch.n_tokens && ubatch.pos[i] < 0) {
+            ++i;
+        }
+        assert(i < ubatch.n_tokens);
+
+        const llama_pos    pos    = ubatch.pos[i];
+        const llama_seq_id seq_id = ubatch.seq_id[i][0];
+        const int64_t cache_off = dsv4_stream_offset(n_stream, seq_id, kv_size);
+        const int32_t source_idx = state_source_idx(seq_id, pos);
+
+        plan.state_write_idxs.push_back(cache_off + kv_size - 1);
+        plan.state_write_pos .push_back(0);
+
+        if (overlap) {
+            for (uint32_t j = 0; j < ratio; ++j) {
+                overlap_prev_reads.push_back(source_idx);
+                overlap_cur_reads .push_back(source_idx);
+            }
+        } else {
+            for (uint32_t j = 0; j < ratio; ++j) {
+                plan.state_read_idxs.push_back(source_idx);
+            }
+        }
+    }
+
+    if (overlap) {
+        // [ all blocks' prev-window indices | all blocks' cur-window indices ]
+        plan.state_read_idxs.reserve(overlap_prev_reads.size() + overlap_cur_reads.size());
+        plan.state_read_idxs.insert(plan.state_read_idxs.end(),
+                overlap_prev_reads.begin(), overlap_prev_reads.end());
+        plan.state_read_idxs.insert(plan.state_read_idxs.end(),
+                overlap_cur_reads.begin(), overlap_cur_reads.end());
+    }
+
+    plan.n_kv = GGML_PAD(plan.n_kv, 256u);
+
+    std::sort(persist_rows.begin(), persist_rows.end(),
+            [](const persist_row & a, const persist_row & b) {
+                return a.dst < b.dst;
+            });
+
+    for (const persist_row & row : persist_rows) {
+        plan.state_persist_src_idxs.push_back(row.src);
+        plan.state_persist_dst_idxs.push_back(row.dst);
+    }
+
+    static const bool debug = []() {
+        const char * env = getenv("LLAMA_DSV4_COMPRESS_DEBUG");
+        return env && atoi(env) > 0;
+    }();
+
+    if (debug) {
+        LLAMA_LOG_INFO("%s: ratio=%u, n_tokens=%u, state_persist_dst=%s, state_write_pos=%s\n",
+                __func__, ratio, ubatch.n_tokens,
+                dsv4_plan_positions(plan.state_persist_dst_idxs).c_str(),
+                dsv4_plan_positions(plan.state_write_pos).c_str());
+    }
+
+    return plan;
+}
+
+static std::vector<llama_kv_cache_dsv4_context::comp_plan> dsv4_build_comp_plans(
+        const std::vector<llama_ubatch> & ubatches,
+        uint32_t ratio,
+        bool overlap,
+        uint32_t state_size,
+        uint32_t kv_size,
+        uint32_t n_stream) {
+    std::vector<llama_kv_cache_dsv4_context::comp_plan> plans;
+    plans.reserve(ubatches.size());
+
+    for (const llama_ubatch & ubatch : ubatches) {
+        plans.push_back(dsv4_build_comp_plan(ubatch, ratio, overlap, state_size, kv_size, n_stream));
+    }
+
+    return plans;
+}
+
+static llama_kv_cache::slot_info_vec_t dsv4_build_comp_sinfos(
+        const std::vector<llama_ubatch> & ubatches,
+        uint32_t n_stream) {
+    llama_kv_cache::slot_info_vec_t sinfos;
+    sinfos.reserve(ubatches.size());
+
+    for (const llama_ubatch & ubatch : ubatches) {
+        if (n_stream <= 1 && ubatch.n_seqs_unq > 1) {
+            throw std::runtime_error("DSV4 single compressed stream cannot serve multiple sequences");
+        }
+
+        const uint32_t ns = (uint32_t) dsv4_comp_graph_n_stream(ubatch, n_stream);
+        llama_kv_cache::slot_info sinfo;
+        sinfo.s0 = n_stream > 1 ? LLAMA_MAX_SEQ : 0;
+        sinfo.s1 = 0;
+        sinfo.resize(ns);
+
+        for (uint32_t s = 0; s < ns; ++s) {
+            const llama_seq_id seq_id = n_stream > 1 ? ubatch.seq_id_unq[s] : 0;
+            const uint32_t strm = (uint32_t) dsv4_stream_offset(n_stream, seq_id, 1);
+
+            sinfo.s0 = std::min(sinfo.s0, strm);
+            sinfo.s1 = std::max(sinfo.s1, strm);
+            sinfo.strm[s] = strm;
+            sinfo.idxs[s].resize(1, 0);
+        }
+
+        if (n_stream > 1 && sinfo.s1 - sinfo.s0 + 1 != ns) {
+            throw std::runtime_error("DSV4 compressed streams are not contiguous in ubatch");
+        }
+
+        sinfos.push_back(std::move(sinfo));
+    }
+
+    return sinfos;
+}
+
+static llama_kv_cache::slot_info_vec_t dsv4_build_raw_read_sinfos(
+        const llama_kv_cache::slot_info_vec_t & sinfos_write,
+        const std::vector<llama_ubatch> & ubatches) {
+    llama_kv_cache::slot_info_vec_t sinfos;
+    sinfos.reserve(ubatches.size());
+
+    for (size_t i = 0; i < ubatches.size(); ++i) {
+        const llama_ubatch & ubatch = ubatches[i];
+        const auto & sinfo_write = sinfos_write[i];
+
+        if (!dsv4_ubatch_has_coupled(ubatch)) {
+            sinfos.push_back(sinfo_write);
+            continue;
+        }
+
+        const llama_seq_id seq_id = ubatch.seq_id[0][0];
+        uint32_t i_stream = 0;
+        for (; i_stream < sinfo_write.n_stream(); ++i_stream) {
+            if (sinfo_write.strm[i_stream] == seq_id) {
+                break;
+            }
+        }
+        if (i_stream == sinfo_write.n_stream()) {
+            throw std::runtime_error("DSV4 raw write stream not found for coupled read");
+        }
+
+        llama_kv_cache::slot_info sinfo;
+        sinfo.s0 = sinfo_write.strm[i_stream];
+        sinfo.s1 = sinfo_write.strm[i_stream];
+        sinfo.resize(1);
+        sinfo.strm[0] = sinfo_write.strm[i_stream];
+        sinfo.idxs[0] = sinfo_write.idxs[i_stream];
+        sinfos.push_back(std::move(sinfo));
+    }
+
+    return sinfos;
+}
+
+static llama_kv_cache_dsv4_context::comp_plan dsv4_build_reserve_comp_plan(
+        const llama_ubatch & ubatch,
+        uint32_t ratio,
+        bool overlap,
+        uint32_t state_size,
+        uint32_t kv_size,
+        uint32_t n_stream) {
+    llama_kv_cache_dsv4_context::comp_plan plan;
+    plan.n_visible.resize(ubatch.n_tokens);
+    plan.n_stream = dsv4_comp_graph_n_stream(ubatch, n_stream);
+    plan.n_kv = kv_size;
+
+    if (ubatch.n_tokens == 0) {
+        return plan;
+    }
+
+    const uint32_t n_seqs       = std::max<uint32_t>(1, ubatch.n_seqs);
+    const uint32_t n_seq_tokens = std::max<uint32_t>(1, ubatch.n_seq_tokens);
+    const uint64_t n_blocks_u64 = (uint64_t) n_seqs*((n_seq_tokens + ratio - 1)/ratio);
+    const size_t n_blocks = (size_t) std::max<uint64_t>(1, n_blocks_u64);
+    GGML_ASSERT((uint64_t) n_blocks == std::max<uint64_t>(1, n_blocks_u64));
+
+    const uint64_t state_rows = (uint64_t) state_size*n_stream;
+    const size_t n_persist = (size_t) std::min<uint64_t>(ubatch.n_tokens, state_rows);
+
+    plan.state_pos .resize(ubatch.n_tokens);
+    plan.state_persist_src_idxs.resize(n_persist);
+    plan.state_persist_dst_idxs.resize(n_persist);
+    plan.state_read_idxs .resize((overlap ? 2u : 1u)*ratio*n_blocks);
+    plan.state_write_idxs.resize(n_blocks);
+    plan.state_write_pos .resize(n_blocks);
+
+    return plan;
+}
+
+static void dsv4_make_k_only(llama_hparams & hparams) {
+    // llama_kv_cache uses hparams.is_mla() to allocate K-only storage.
+    hparams.n_embd_head_k_mla_impl = hparams.n_embd_head_k();
+    hparams.n_embd_head_v_mla_impl = hparams.n_embd_head_k();
+}
+
+//
+// llama_dsv4_comp_state
+//
+
+llama_dsv4_comp_state::llama_dsv4_comp_state(
+        const llama_model & model,
+                bool        offload,
+                bool        unified,
+            uint32_t        n_seq_max,
+            uint32_t        ratio,
+            uint32_t        state_size,
+            uint32_t        n_embd_state,
+        const char    * name,
+        const llama_memory_i::layer_filter_cb & filter) :
+    ratio(ratio),
+    state_size(state_size),
+    n_embd_state(n_embd_state),
+    n_stream(unified ? 1 : n_seq_max) {
+    const llama_hparams & hparams = model.hparams;
+
+    struct ggml_backend_buft_comparator {
+        bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const {
+            return strcmp(ggml_backend_buft_name(lhs), ggml_backend_buft_name(rhs)) < 0;
+        }
+    };
+
+    std::map<ggml_backend_buffer_type_t, ggml_context_ptr, ggml_backend_buft_comparator> ctx_map;
+
+    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+        auto it = ctx_map.find(buft);
+        if (it == ctx_map.end()) {
+            ggml_init_params params = {
+                /*.mem_size   =*/ size_t(2u*hparams.n_layer()*ggml_tensor_overhead()),
+                /*.mem_buffer =*/ NULL,
+                /*.no_alloc   =*/ true,
+            };
+
+            ggml_context * ctx = ggml_init(params);
+            if (!ctx) {
+                return nullptr;
+            }
+
+            ctx_map.emplace(buft, ctx);
+
+            return ctx;
+        }
+
+        return it->second.get();
+    };
+
+    for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
+        if (filter && !filter(il)) {
+            continue;
+        }
+
+        const char * dev_name = "CPU";
+
+        ggml_backend_buffer_type_t buft = ggml_backend_cpu_buffer_type();
+
+        if (offload) {
+            auto * dev = model.dev_layer(il);
+            buft = ggml_backend_dev_buffer_type(dev);
+
+            dev_name = ggml_backend_dev_name(dev);
+        }
+
+        LLAMA_LOG_DEBUG("%s: layer %3d: dev = %s\n", __func__, il, dev_name);
+
+        ggml_context * ctx = ctx_for_buft(buft);
+        if (!ctx) {
+            throw std::runtime_error("failed to create ggml context for DSV4 compressor state");
+        }
+
+        ggml_tensor * kv    = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream);
+        ggml_tensor * score = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_state, state_size, n_stream);
+
+        ggml_format_name(kv,    "dsv4_%s_state_kv_l%d",    name, il);
+        ggml_format_name(score, "dsv4_%s_state_score_l%d", name, il);
+
+        map_layer_ids[il] = layers.size();
+
+        layers.push_back({ il, kv, score });
+    }
+
+    for (auto & [buft, ctx] : ctx_map) {
+        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
+        if (!buf) {
+            throw std::runtime_error("failed to allocate buffer for DSV4 compressor state");
+        }
+
+        ggml_backend_buffer_clear(buf, 0);
+
+        LLAMA_LOG_INFO("%s: %10s DSV4 %s state buffer size = %8.2f MiB\n",
+                __func__, ggml_backend_buffer_name(buf), name, ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+
+        ctxs_bufs.emplace_back(std::move(ctx), buf);
+    }
+
+    LLAMA_LOG_INFO("%s: %s ratio = %u, state = %u x %u, streams = %u, layers = %zu, size = %7.2f MiB\n",
+            __func__, name, ratio, state_size, n_embd_state, n_stream, layers.size(), total_size()/1024.0/1024.0);
+}
+
+void llama_dsv4_comp_state::clear(bool data) {
+    if (!data) {
+        return;
+    }
+
+    for (auto & [_, buf] : ctxs_bufs) {
+        ggml_backend_buffer_clear(buf.get(), 0);
+    }
+}
+
+uint32_t llama_dsv4_comp_state::get_ratio() const {
+    return ratio;
+}
+
+uint32_t llama_dsv4_comp_state::get_state_size() const {
+    return state_size;
+}
+
+uint32_t llama_dsv4_comp_state::get_n_stream() const {
+    return n_stream;
+}
+
+std::map<ggml_backend_buffer_type_t, size_t> llama_dsv4_comp_state::memory_breakdown() const {
+    std::map<ggml_backend_buffer_type_t, size_t> ret;
+    for (const auto & [_, buf] : ctxs_bufs) {
+        ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get());
+        ret[buft] += ggml_backend_buffer_get_size(buf.get());
+    }
+    return ret;
+}
+
+void llama_dsv4_comp_state::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    GGML_UNUSED(flags);
+
+    uint32_t s0;
+    uint32_t ns;
+    dsv4_state_src_stream_range(n_stream, seq_id, s0, ns);
+
+    const uint32_t version      = DSV4_COMP_STATE_VER;
+    const uint32_t n_layer      = layers.size();
+
+    io.write(&version,      sizeof(version));
+    io.write(&ratio,        sizeof(ratio));
+    io.write(&state_size,   sizeof(state_size));
+    io.write(&n_embd_state, sizeof(n_embd_state));
+    io.write(&ns,           sizeof(ns));
+    io.write(&n_layer,      sizeof(n_layer));
+
+    for (const auto & layer : layers) {
+        io.write(&layer.il, sizeof(layer.il));
+
+        dsv4_state_write_tensor_streams(io, layer.kv,    state_size, s0, ns);
+        dsv4_state_write_tensor_streams(io, layer.score, state_size, s0, ns);
+    }
+}
+
+void llama_dsv4_comp_state::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    GGML_UNUSED(flags);
+
+    uint32_t version;
+    uint32_t ratio_ref;
+    uint32_t state_size_ref;
+    uint32_t n_embd_state_ref;
+    uint32_t ns;
+    uint32_t n_layer_ref;
+
+    io.read(&version,          sizeof(version));
+    io.read(&ratio_ref,        sizeof(ratio_ref));
+    io.read(&state_size_ref,   sizeof(state_size_ref));
+    io.read(&n_embd_state_ref, sizeof(n_embd_state_ref));
+    io.read(&ns,               sizeof(ns));
+    io.read(&n_layer_ref,      sizeof(n_layer_ref));
+
+    if (version != DSV4_COMP_STATE_VER) {
+        throw std::runtime_error("DSV4 compressor state version mismatch");
+    }
+    if (ratio_ref != ratio || state_size_ref != state_size || n_embd_state_ref != n_embd_state) {
+        throw std::runtime_error("DSV4 compressor state metadata mismatch");
+    }
+    if (n_layer_ref != layers.size()) {
+        throw std::runtime_error("DSV4 compressor state layer count mismatch");
+    }
+
+    uint32_t s0;
+    dsv4_state_dst_stream_range(n_stream, seq_id, ns, s0);
+
+    for (const auto & layer : layers) {
+        uint32_t il_ref;
+        io.read(&il_ref, sizeof(il_ref));
+        if (il_ref != layer.il) {
+            throw std::runtime_error("DSV4 compressor state layer id mismatch");
+        }
+
+        dsv4_state_read_tensor_streams(io, layer.kv,    state_size, s0, ns);
+        dsv4_state_read_tensor_streams(io, layer.score, state_size, s0, ns);
+    }
+}
+
+ggml_tensor * llama_dsv4_comp_state::get_kv(ggml_context * ctx, int32_t il) const {
+    const int32_t ids = map_layer_ids.at(il);
+
+    ggml_tensor * state = layers[ids].kv;
+
+    return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]);
+}
+
+ggml_tensor * llama_dsv4_comp_state::get_score(ggml_context * ctx, int32_t il) const {
+    const int32_t ids = map_layer_ids.at(il);
+
+    ggml_tensor * state = layers[ids].score;
+
+    return ggml_reshape_2d(ctx, state, state->ne[0], state->ne[1]*state->ne[2]);
+}
+
+ggml_tensor * llama_dsv4_comp_state::cpy_kv(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const {
+    return ggml_set_rows(ctx, get_kv(ctx, il), cur, idxs);
+}
+
+ggml_tensor * llama_dsv4_comp_state::cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const {
+    return ggml_set_rows(ctx, get_score(ctx, il), cur, idxs);
+}
+
+size_t llama_dsv4_comp_state::total_size() const {
+    size_t size = 0;
+
+    for (const auto & [_, buf] : ctxs_bufs) {
+        size += ggml_backend_buffer_get_size(buf.get());
+    }
+
+    return size;
+}
+
+//
+// llama_kv_cache_dsv4
+//
+
+llama_kv_cache_dsv4::llama_kv_cache_dsv4(
+        const llama_model & model,
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   offload,
+                     bool   swa_full,
+                     bool   unified,
+                 uint32_t   kv_size,
+                 uint32_t   n_seq_max,
+                 uint32_t   n_ubatch,
+                 uint32_t   n_pad,
+    const layer_filter_cb & filter,
+    const  layer_reuse_cb & reuse) :
+    hparams_raw(model.hparams),
+    hparams_csa(model.hparams),
+    hparams_hca(model.hparams),
+    hparams_lid(model.hparams),
+    n_seq_max(n_seq_max) {
+
+    const layer_filter_cb filter_raw = [&](int32_t il) {
+        if (filter && !filter(il)) {
+            return false;
+        }
+
+        return true;
+    };
+
+    GGML_UNUSED(unified);
+
+    // Keep DSV4 KV/state streams per sequence even when public KV mode is unified.
+    const bool unified_raw = false;
+
+    LLAMA_LOG_INFO("%s: creating DSV4 raw KV cache\n", __func__);
+
+    dsv4_make_k_only(hparams_raw);
+
+    kv_raw = std::make_unique<llama_kv_cache_iswa>(
+            model, hparams_raw, type_k, type_v,
+            v_trans, offload, swa_full, unified_raw, kv_size, n_seq_max, n_ubatch, n_pad,
+            nullptr, filter_raw, reuse, nullptr);
+
+    dsv4_make_k_only(hparams_csa);
+    dsv4_make_k_only(hparams_hca);
+
+    std::fill(hparams_lid.n_head_kv_arr.begin(), hparams_lid.n_head_kv_arr.end(), 1);
+    hparams_lid.n_embd_head_k_full = model.hparams.indexer_head_size;
+    hparams_lid.n_embd_head_v_full = model.hparams.indexer_head_size;
+    hparams_lid.n_embd_head_k_swa  = model.hparams.indexer_head_size;
+    hparams_lid.n_embd_head_v_swa  = model.hparams.indexer_head_size;
+    hparams_lid.rope_type          = LLAMA_ROPE_TYPE_NEOX;
+    dsv4_make_k_only(hparams_lid);
+
+    const layer_filter_cb filter_csa = [&](int32_t il) {
+        if (filter && !filter(il)) {
+            return false;
+        }
+
+        return model.hparams.dsv4_compress_ratios[il] == DSV4_CSA_RATIO;
+    };
+
+    const layer_filter_cb filter_hca = [&](int32_t il) {
+        if (filter && !filter(il)) {
+            return false;
+        }
+
+        return model.hparams.dsv4_compress_ratios[il] == DSV4_HCA_RATIO;
+    };
+
+    const bool unified_compressed = false;
+
+    LLAMA_LOG_INFO("%s: creating DSV4 CSA compressed KV cache, size = %u cells\n",
+            __func__, dsv4_comp_size(kv_size, DSV4_CSA_RATIO));
+
+    kv_csa = std::make_unique<llama_kv_cache>(
+            model, hparams_csa, type_k, type_v,
+            v_trans, offload, unified_compressed, GGML_PAD(dsv4_comp_size(kv_size, DSV4_CSA_RATIO), 256u), n_seq_max, n_pad,
+            0, LLAMA_SWA_TYPE_NONE, nullptr, filter_csa, nullptr, nullptr);
+
+    LLAMA_LOG_INFO("%s: creating DSV4 HCA compressed KV cache, size = %u cells\n",
+            __func__, dsv4_comp_size(kv_size, DSV4_HCA_RATIO));
+
+    kv_hca = std::make_unique<llama_kv_cache>(
+            model, hparams_hca, type_k, type_v,
+            v_trans, offload, unified_compressed, GGML_PAD(dsv4_comp_size(kv_size, DSV4_HCA_RATIO), 256u), n_seq_max, n_pad,
+            0, LLAMA_SWA_TYPE_NONE, nullptr, filter_hca, nullptr, nullptr);
+
+    LLAMA_LOG_INFO("%s: creating DSV4 lightning-indexer KV cache, size = %u cells\n",
+            __func__, dsv4_comp_size(kv_size, DSV4_CSA_RATIO));
+
+    kv_lid = std::make_unique<llama_kv_cache>(
+            model, hparams_lid, type_k, type_v,
+            v_trans, offload, unified_compressed, GGML_PAD(dsv4_comp_size(kv_size, DSV4_CSA_RATIO), 256u), n_seq_max, n_pad,
+            0, LLAMA_SWA_TYPE_NONE, nullptr, filter_csa, nullptr, nullptr);
+
+    LLAMA_LOG_INFO("%s: creating DSV4 CSA compressor state\n", __func__);
+
+    csa_state = std::make_unique<llama_dsv4_comp_state>(
+            model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO,
+            2*model.hparams.n_embd_head_k(), "csa", filter_csa);
+
+    LLAMA_LOG_INFO("%s: creating DSV4 HCA compressor state\n", __func__);
+
+    hca_state = std::make_unique<llama_dsv4_comp_state>(
+            model, offload, unified_compressed, n_seq_max, DSV4_HCA_RATIO, DSV4_HCA_RATIO,
+            model.hparams.n_embd_head_k(), "hca", filter_hca);
+
+    LLAMA_LOG_INFO("%s: creating DSV4 lightning-indexer compressor state\n", __func__);
+
+    lid_state = std::make_unique<llama_dsv4_comp_state>(
+            model, offload, unified_compressed, n_seq_max, DSV4_CSA_RATIO, 2*DSV4_CSA_RATIO,
+            2*model.hparams.indexer_head_size, "lid", filter_csa);
+
+    // DSV4 attention reads compressed-K / compressor-state rows that the current
+    // graph does not necessarily overwrite; uninitialized buffer contents would
+    // otherwise leak in (instance-specific garbage) and corrupt recall. Zero all
+    // compressed buffers up front so reads of un-written rows are deterministic.
+    clear_compressed(true);
+}
+
+llama_memory_context_ptr llama_kv_cache_dsv4::init_batch(
+            llama_batch_allocr & balloc,
+            uint32_t n_ubatch,
+            bool embd_all) {
+    GGML_UNUSED(embd_all);
+
+    const bool raw_per_seq  = kv_raw->get_base()->get_n_stream() != 1;
+    const bool comp_per_seq = csa_state->get_n_stream() > 1;
+    const bool has_coupled = dsv4_batch_has_coupled(balloc.get_batch());
+
+    const auto make_context = [&](std::vector<llama_ubatch> ubatches) -> llama_memory_context_ptr {
+        auto ubatches_raw = dsv4_build_raw_write_ubatches(ubatches);
+
+        auto sinfos_raw_base_write = kv_raw->get_base()->prepare(ubatches_raw);
+        if (sinfos_raw_base_write.empty()) {
+            return nullptr;
+        }
+
+        auto sinfos_raw_swa_write = kv_raw->get_swa()->prepare(ubatches_raw);
+        if (sinfos_raw_swa_write.empty()) {
+            return nullptr;
+        }
+
+        auto sinfos_raw_swa_read = dsv4_build_raw_read_sinfos(sinfos_raw_swa_write, ubatches);
+
+        return std::make_unique<llama_kv_cache_dsv4_context>(
+                this,
+                std::move(sinfos_raw_base_write),
+                std::move(sinfos_raw_swa_write),
+                std::move(sinfos_raw_swa_read),
+                std::move(ubatches),
+                std::move(ubatches_raw));
+    };
+
+    // Match llama_kv_cache_iswa splitting when DSV4 compressed state does not
+    // require per-sequence graph layout.
+    do {
+        if (raw_per_seq || comp_per_seq) {
+            break;
+        }
+
+        balloc.split_reset();
+
+        std::vector<llama_ubatch> ubatches;
+        while (true) {
+            auto ubatch = balloc.split_simple(n_ubatch);
+            if (ubatch.n_tokens == 0) {
+                break;
+            }
+            ubatches.push_back(std::move(ubatch)); // NOLINT
+        }
+
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            break;
+        }
+
+        if (auto ctx = make_context(std::move(ubatches))) {
+            return ctx;
+        }
+    } while (false);
+
+    // When raw or compressed state is per-sequence, independent sequences can
+    // share an equal-length ubatch. Coupled sequence sets still serialize until
+    // DSV4 has explicit shared-state handling for compressed streams.
+    do {
+        balloc.split_reset();
+
+        std::vector<llama_ubatch> ubatches;
+        while (true) {
+            llama_ubatch ubatch;
+            if (has_coupled) {
+                ubatch = balloc.split_seq(n_ubatch);
+            } else {
+                ubatch = balloc.split_equal(n_ubatch, raw_per_seq || comp_per_seq);
+            }
+
+            if (ubatch.n_tokens == 0) {
+                break;
+            }
+            ubatches.push_back(std::move(ubatch)); // NOLINT
+        }
+
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            break;
+        }
+
+        if (auto ctx = make_context(std::move(ubatches))) {
+            return ctx;
+        }
+    } while (false);
+
+    return std::make_unique<llama_kv_cache_dsv4_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+}
+
+llama_memory_context_ptr llama_kv_cache_dsv4::init_full() {
+    return std::make_unique<llama_kv_cache_dsv4_context>(this);
+}
+
+llama_memory_context_ptr llama_kv_cache_dsv4::init_update(llama_context * lctx, bool optimize) {
+    return std::make_unique<llama_kv_cache_dsv4_context>(this, lctx, optimize);
+}
+
+bool llama_kv_cache_dsv4::get_can_shift() const {
+    // Compressed row metadata uses block-derived positions. Keep shifting
+    // disabled until DSV4 compressed-cache shift semantics are wired.
+    return false;
+}
+
+void llama_kv_cache_dsv4::clear(bool data) {
+    kv_raw->clear(data);
+    clear_compressed(true); // DSV4 compressed buffers must never expose stale/uninit rows
+}
+
+bool llama_kv_cache_dsv4::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    if (p1 >= 0) {
+        return false;
+    }
+
+    if (p0 > 0) {
+        // DSV4 compressed cache rows are derived from running compressor state,
+        // so arbitrary rollback is not reconstructible from the raw cache alone.
+        // Allow the common prompt-cache cleanup no-op: remove [end, infinity).
+        if (seq_id >= 0 && p0 > kv_raw->seq_pos_max(seq_id)) {
+            return true;
+        }
+
+        return false;
+    }
+
+    const bool res = kv_raw->seq_rm(seq_id, p0, p1);
+
+    if (res) {
+        clear_compressed(true);
+    }
+
+    return res;
+}
+
+void llama_kv_cache_dsv4::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    kv_raw->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+    clear_compressed(true);
+}
+
+void llama_kv_cache_dsv4::seq_keep(llama_seq_id seq_id) {
+    kv_raw->seq_keep(seq_id);
+    clear_compressed(true);
+}
+
+void llama_kv_cache_dsv4::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    kv_raw->seq_add(seq_id, p0, p1, shift);
+    clear_compressed(true);
+}
+
+void llama_kv_cache_dsv4::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+    kv_raw->seq_div(seq_id, p0, p1, d);
+    clear_compressed(true);
+}
+
+llama_pos llama_kv_cache_dsv4::seq_pos_min(llama_seq_id seq_id) const {
+    if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
+        return -1;
+    }
+
+    // The raw SWA cache may contain a wider window, but the compressed DSV4
+    // state cannot be rolled back within that window. Report only the current
+    // boundary so server-context uses checkpoints for rollback.
+    return kv_raw->seq_pos_max(seq_id);
+}
+
+llama_pos llama_kv_cache_dsv4::seq_pos_max(llama_seq_id seq_id) const {
+    if (seq_id < 0 || (uint32_t) seq_id >= n_seq_max) {
+        return -1;
+    }
+
+    return kv_raw->seq_pos_max(seq_id);
+}
+
+std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache_dsv4::memory_breakdown() const {
+    std::map<ggml_backend_buffer_type_t, size_t> mb = kv_raw->memory_breakdown();
+    for (const auto & buft_size : kv_csa->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    for (const auto & buft_size : kv_hca->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    for (const auto & buft_size : kv_lid->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    for (const auto & buft_size : csa_state->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    for (const auto & buft_size : hca_state->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    for (const auto & buft_size : lid_state->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    return mb;
+}
+
+void llama_kv_cache_dsv4::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    const bool partial_only = flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY;
+
+    const uint32_t magic   = DSV4_STATE_MAGIC;
+    const uint32_t version = DSV4_STATE_VERSION;
+    const uint32_t mode    = partial_only ? DSV4_STATE_MODE_PARTIAL : DSV4_STATE_MODE_FULL;
+
+    io.write(&magic,   sizeof(magic));
+    io.write(&version, sizeof(version));
+    io.write(&mode,    sizeof(mode));
+
+    kv_raw->state_write(io, seq_id, flags);
+
+    if (!partial_only) {
+        dsv4_state_write_k_cache(io, kv_csa.get(), seq_id, flags);
+        dsv4_state_write_k_cache(io, kv_hca.get(), seq_id, flags);
+        dsv4_state_write_k_cache(io, kv_lid.get(), seq_id, flags);
+    }
+
+    csa_state->state_write(io, seq_id, flags);
+    hca_state->state_write(io, seq_id, flags);
+    lid_state->state_write(io, seq_id, flags);
+}
+
+void llama_kv_cache_dsv4::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    uint32_t magic;
+    uint32_t version;
+    uint32_t mode = DSV4_STATE_MODE_FULL;
+
+    io.read(&magic,   sizeof(magic));
+    io.read(&version, sizeof(version));
+
+    if (magic != DSV4_STATE_MAGIC) {
+        throw std::runtime_error("DSV4 state magic mismatch");
+    }
+    if (version != DSV4_STATE_VERSION) {
+        throw std::runtime_error("DSV4 state version mismatch");
+    }
+
+    io.read(&mode, sizeof(mode));
+    if (mode != DSV4_STATE_MODE_FULL && mode != DSV4_STATE_MODE_PARTIAL) {
+        throw std::runtime_error("DSV4 state mode mismatch");
+    }
+
+    const bool partial_only = mode == DSV4_STATE_MODE_PARTIAL;
+    if (partial_only != !!(flags & LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY)) {
+        throw std::runtime_error("DSV4 state flags mismatch");
+    }
+
+    kv_raw->state_read(io, seq_id, flags);
+
+    if (!partial_only) {
+        dsv4_state_read_k_cache(io, kv_csa.get(), seq_id, flags);
+        dsv4_state_read_k_cache(io, kv_hca.get(), seq_id, flags);
+        dsv4_state_read_k_cache(io, kv_lid.get(), seq_id, flags);
+    }
+
+    csa_state->state_read(io, seq_id, flags);
+    hca_state->state_read(io, seq_id, flags);
+    lid_state->state_read(io, seq_id, flags);
+
+}
+
+llama_kv_cache_iswa * llama_kv_cache_dsv4::get_raw() const {
+    return kv_raw.get();
+}
+
+llama_kv_cache * llama_kv_cache_dsv4::get_csa() const {
+    return kv_csa.get();
+}
+
+llama_kv_cache * llama_kv_cache_dsv4::get_hca() const {
+    return kv_hca.get();
+}
+
+llama_kv_cache * llama_kv_cache_dsv4::get_lid() const {
+    return kv_lid.get();
+}
+
+llama_dsv4_comp_state * llama_kv_cache_dsv4::get_csa_state() const {
+    return csa_state.get();
+}
+
+llama_dsv4_comp_state * llama_kv_cache_dsv4::get_hca_state() const {
+    return hca_state.get();
+}
+
+llama_dsv4_comp_state * llama_kv_cache_dsv4::get_lid_state() const {
+    return lid_state.get();
+}
+
+void llama_kv_cache_dsv4::clear_compressed(bool data) {
+    kv_csa->clear(data);
+    kv_hca->clear(data);
+    kv_lid->clear(data);
+    csa_state->clear(data);
+    hca_state->clear(data);
+    lid_state->clear(data);
+}
+
+//
+// llama_kv_cache_dsv4_raw_context
+//
+
+static llama_kv_cache::slot_info dsv4_build_full_sinfo(const llama_kv_cache * kv) {
+    const uint32_t n_stream = kv->get_n_stream();
+
+    llama_kv_cache::slot_info sinfo;
+    sinfo.s0 = 0;
+    sinfo.s1 = n_stream - 1;
+    sinfo.resize(n_stream);
+    for (uint32_t s = 0; s < n_stream; ++s) {
+        sinfo.strm[s] = s;
+        sinfo.idxs[s].resize(1, 0);
+    }
+
+    return sinfo;
+}
+
+llama_kv_cache_dsv4_raw_context::llama_kv_cache_dsv4_raw_context(llama_kv_cache_iswa * kv) :
+    kv_swa(kv->get_swa()),
+    ctx_base_mem(nullptr),
+    ctx_swa_mem(nullptr),
+    n_kv(kv_swa->get_size()),
+    status(LLAMA_MEMORY_STATUS_SUCCESS) {
+    sinfos_read.push_back(dsv4_build_full_sinfo(kv_swa));
+    sinfos_write = sinfos_read;
+}
+
+llama_kv_cache_dsv4_raw_context::llama_kv_cache_dsv4_raw_context(
+        llama_kv_cache_iswa * kv,
+        llama_context * lctx,
+        bool optimize) :
+    kv_swa(kv->get_swa()),
+    ctx_base_mem(kv->get_base()->init_update(lctx, optimize)),
+    ctx_swa_mem(kv->get_swa()->init_update(lctx, optimize)),
+    n_kv(kv_swa->get_size()),
+    status(llama_memory_status_combine(ctx_base_mem->get_status(), ctx_swa_mem->get_status())) {
+}
+
+llama_kv_cache_dsv4_raw_context::llama_kv_cache_dsv4_raw_context(
+        llama_kv_cache_iswa * kv,
+        slot_info_vec_t sinfos_base_write,
+        slot_info_vec_t sinfos_swa_write,
+        slot_info_vec_t sinfos_swa_read,
+        std::vector<llama_ubatch> ubatches,
+        std::vector<llama_ubatch> ubatches_write) :
+    kv_swa(kv->get_swa()),
+    sinfos_write(std::move(sinfos_swa_write)),
+    sinfos_read(std::move(sinfos_swa_read)),
+    ubatches(std::move(ubatches)),
+    ubatches_write(std::move(ubatches_write)),
+    ctx_base_mem(std::make_unique<llama_kv_cache_context>(
+                kv->get_base(), std::move(sinfos_base_write), this->ubatches_write)),
+    ctx_swa_mem(nullptr),
+    n_kv(kv_swa->get_size()),
+    status(LLAMA_MEMORY_STATUS_SUCCESS) {
+}
+
+bool llama_kv_cache_dsv4_raw_context::next() {
+    if (ubatches.empty()) {
+        return true;
+    }
+
+    if (ctx_base_mem) {
+        ctx_base_mem->next();
+    }
+
+    if (++i_next >= ubatches.size()) {
+        return false;
+    }
+
+    return true;
+}
+
+bool llama_kv_cache_dsv4_raw_context::apply() {
+    bool res = true;
+
+    if (ctx_base_mem) {
+        res = res & ctx_base_mem->apply();
+    }
+    if (ctx_swa_mem) {
+        res = res & ctx_swa_mem->apply();
+    }
+    if (!ubatches_write.empty()) {
+        kv_swa->apply_ubatch(sinfos_write[i_next], ubatches_write[i_next]);
+        n_kv = kv_swa->get_n_kv(sinfos_read[i_next]);
+    }
+
+    return res;
+}
+
+llama_memory_status llama_kv_cache_dsv4_raw_context::get_status() const {
+    return status;
+}
+
+const llama_ubatch & llama_kv_cache_dsv4_raw_context::get_ubatch() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ubatches[i_next];
+}
+
+uint32_t llama_kv_cache_dsv4_raw_context::get_n_kv() const {
+    return n_kv;
+}
+
+uint32_t llama_kv_cache_dsv4_raw_context::get_n_write() const {
+    if (ubatches_write.empty()) {
+        return 0;
+    }
+
+    return ubatches_write[i_next].n_tokens;
+}
+
+ggml_tensor * llama_kv_cache_dsv4_raw_context::get_k(ggml_context * ctx, int32_t il) const {
+    return kv_swa->get_k(ctx, il, n_kv, sinfos_read[i_next]);
+}
+
+ggml_tensor * llama_kv_cache_dsv4_raw_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
+    const auto & sinfo = sinfos_write[i_next];
+
+    if (k_cur->ne[2] == k_idxs->ne[0]) {
+        return kv_swa->cpy_k(ctx, k_cur, k_idxs, il, sinfo);
+    }
+
+    // k_idxs may be expanded to one block per stream while k_cur is only
+    // the token block. Keep zero deps on all copies so each write executes.
+    const int64_t n_fanout = (int64_t) sinfo.size()*sinfo.n_stream();
+
+    GGML_ASSERT(sinfo.n_stream() > 1);
+    GGML_ASSERT(k_cur->ne[2] == (int64_t) sinfo.size());
+    GGML_ASSERT(k_idxs->ne[0] == n_fanout);
+
+    ggml_tensor * res = nullptr;
+    for (uint32_t s = 0; s < sinfo.n_stream(); ++s) {
+        ggml_tensor * k_idxs_s = ggml_view_1d(ctx, k_idxs, sinfo.size(), s*sinfo.size()*ggml_element_size(k_idxs));
+        ggml_tensor * cur = kv_swa->cpy_k(ctx, k_cur, k_idxs_s, il, sinfo);
+        if (res == nullptr) {
+            res = cur;
+        } else {
+            res = ggml_add(ctx, res, ggml_sub(ctx, cur, cur));
+        }
+    }
+
+    return res;
+}
+
+ggml_tensor * llama_kv_cache_dsv4_raw_context::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
+    const uint32_t n_tokens = ubatches_write.empty() ? ubatch.n_tokens : ubatches_write[i_next].n_tokens;
+
+    ggml_tensor * k_idxs = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, n_tokens);
+    ggml_set_input(k_idxs);
+
+    return k_idxs;
+}
+
+ggml_tensor * llama_kv_cache_dsv4_raw_context::build_input_k_rot(ggml_context * ctx) const {
+    return kv_swa->build_input_k_rot(ctx);
+}
+
+void llama_kv_cache_dsv4_raw_context::set_input_k_idxs(ggml_tensor * dst) const {
+    kv_swa->set_input_k_idxs(dst, &ubatches_write[i_next], sinfos_write[i_next]);
+}
+
+void llama_kv_cache_dsv4_raw_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
+    kv_swa->set_input_kq_mask(dst, ubatch, causal_attn);
+}
+
+void llama_kv_cache_dsv4_raw_context::set_input_k_rot(ggml_tensor * dst) const {
+    kv_swa->set_input_k_rot(dst);
+}
+
+//
+// llama_kv_cache_dsv4_comp_context
+//
+
+llama_kv_cache_dsv4_comp_context::llama_kv_cache_dsv4_comp_context(llama_kv_cache * kv) : kv(kv), n_kv(kv->get_size()) {
+    const uint32_t n_stream = kv->get_n_stream();
+
+    sinfos.resize(1);
+    sinfos[0].s0 = 0;
+    sinfos[0].s1 = n_stream - 1;
+    sinfos[0].idxs.resize(n_stream);
+    for (uint32_t s = 0; s < n_stream; ++s) {
+        sinfos[0].strm.push_back(s);
+        sinfos[0].idxs[s].resize(1, 0);
+    }
+}
+
+llama_kv_cache_dsv4_comp_context::llama_kv_cache_dsv4_comp_context(
+        llama_kv_cache * kv,
+        slot_info_vec_t sinfos,
+        std::vector<llama_ubatch> ubatches) :
+    kv(kv),
+    sinfos(std::move(sinfos)),
+    ubatches(std::move(ubatches)),
+    n_kv(kv->get_size()) {
+}
+
+bool llama_kv_cache_dsv4_comp_context::next() {
+    if (ubatches.empty()) {
+        return true;
+    }
+
+    if (++i_cur >= ubatches.size()) {
+        return false;
+    }
+
+    return true;
+}
+
+uint32_t llama_kv_cache_dsv4_comp_context::get_n_kv() const {
+    return n_kv;
+}
+
+ggml_tensor * llama_kv_cache_dsv4_comp_context::get_k(ggml_context * ctx, int32_t il) const {
+    return kv->get_k(ctx, il, n_kv, sinfos[i_cur]);
+}
+
+ggml_tensor * llama_kv_cache_dsv4_comp_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const {
+    return kv->cpy_k(ctx, k_cur, k_idxs, il, sinfos[i_cur]);
+}
+
+ggml_tensor * llama_kv_cache_dsv4_comp_context::build_input_k_rot(ggml_context * ctx) const {
+    return kv->build_input_k_rot(ctx);
+}
+
+void llama_kv_cache_dsv4_comp_context::set_input_k_rot(ggml_tensor * dst) const {
+    kv->set_input_k_rot(dst);
+}
+
+//
+// llama_kv_cache_dsv4_context
+//
+
+llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(llama_memory_status status) : status(status) {}
+
+llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
+        llama_kv_cache_dsv4 * kv) :
+    ctx_raw(std::make_unique<llama_kv_cache_dsv4_raw_context>(kv->get_raw())),
+    ctx_csa_mem(kv->get_csa()->init_full()),
+    ctx_hca_mem(kv->get_hca()->init_full()),
+    ctx_lid_mem(kv->get_lid()->init_full()),
+    ctx_csa(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_csa())),
+    ctx_hca(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_hca())),
+    ctx_lid(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_lid())),
+    csa_state(kv->get_csa_state()),
+    hca_state(kv->get_hca_state()),
+    lid_state(kv->get_lid_state()),
+    reserve_plans(true),
+    status(llama_memory_status_combine(
+                llama_memory_status_combine(ctx_raw->get_status(), ctx_csa_mem->get_status()),
+                llama_memory_status_combine(ctx_hca_mem->get_status(), ctx_lid_mem->get_status()))) {
+}
+
+llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
+        llama_kv_cache_dsv4 * kv,
+        llama_context * lctx,
+        bool optimize) :
+    ctx_raw(std::make_unique<llama_kv_cache_dsv4_raw_context>(kv->get_raw(), lctx, optimize)),
+    ctx_csa_mem(kv->get_csa()->init_update(lctx, optimize)),
+    ctx_hca_mem(kv->get_hca()->init_update(lctx, optimize)),
+    ctx_lid_mem(kv->get_lid()->init_update(lctx, optimize)),
+    ctx_csa(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_csa())),
+    ctx_hca(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_hca())),
+    ctx_lid(std::make_unique<llama_kv_cache_dsv4_comp_context>(kv->get_lid())),
+    csa_state(kv->get_csa_state()),
+    hca_state(kv->get_hca_state()),
+    lid_state(kv->get_lid_state()),
+    status(llama_memory_status_combine(
+                llama_memory_status_combine(ctx_raw->get_status(), ctx_csa_mem->get_status()),
+                llama_memory_status_combine(ctx_hca_mem->get_status(), ctx_lid_mem->get_status()))) {
+}
+
+llama_kv_cache_dsv4_context::llama_kv_cache_dsv4_context(
+        llama_kv_cache_dsv4 * kv,
+        slot_info_vec_t sinfos_raw_base_write,
+        slot_info_vec_t sinfos_raw_swa_write,
+        slot_info_vec_t sinfos_raw_swa_read,
+        std::vector<llama_ubatch> ubatches,
+        std::vector<llama_ubatch> ubatches_raw) :
+    ubatches(std::move(ubatches)),
+    plans_csa(dsv4_build_comp_plans(this->ubatches, DSV4_CSA_RATIO, true,
+                kv->get_csa_state()->get_state_size(), kv->get_csa()->get_size(), kv->get_csa_state()->get_n_stream())),
+    plans_hca(dsv4_build_comp_plans(this->ubatches, DSV4_HCA_RATIO, false,
+                kv->get_hca_state()->get_state_size(), kv->get_hca()->get_size(), kv->get_hca_state()->get_n_stream())),
+    plans_lid(plans_csa),
+    ctx_raw(std::make_unique<llama_kv_cache_dsv4_raw_context>(
+                kv->get_raw(),
+                std::move(sinfos_raw_base_write),
+                std::move(sinfos_raw_swa_write),
+                std::move(sinfos_raw_swa_read),
+                this->ubatches,
+                std::move(ubatches_raw))),
+    ctx_csa_mem(nullptr),
+    ctx_hca_mem(nullptr),
+    ctx_lid_mem(nullptr),
+    ctx_csa(std::make_unique<llama_kv_cache_dsv4_comp_context>(
+                kv->get_csa(),
+                dsv4_build_comp_sinfos(this->ubatches, kv->get_csa()->get_n_stream()),
+                this->ubatches)),
+    ctx_hca(std::make_unique<llama_kv_cache_dsv4_comp_context>(
+                kv->get_hca(),
+                dsv4_build_comp_sinfos(this->ubatches, kv->get_hca()->get_n_stream()),
+                this->ubatches)),
+    ctx_lid(std::make_unique<llama_kv_cache_dsv4_comp_context>(
+                kv->get_lid(),
+                dsv4_build_comp_sinfos(this->ubatches, kv->get_lid()->get_n_stream()),
+                this->ubatches)),
+    csa_state(kv->get_csa_state()),
+    hca_state(kv->get_hca_state()),
+    lid_state(kv->get_lid_state()),
+    status(ctx_raw->get_status()) {
+}
+
+llama_kv_cache_dsv4_context::~llama_kv_cache_dsv4_context() = default;
+
+bool llama_kv_cache_dsv4_context::next() {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    ctx_raw->next();
+    ctx_csa->next();
+    ctx_hca->next();
+    ctx_lid->next();
+
+    if (++i_next >= ubatches.size()) {
+        return false;
+    }
+
+    return true;
+}
+
+bool llama_kv_cache_dsv4_context::apply() {
+    assert(!llama_memory_status_is_fail(status));
+
+    bool res = true;
+
+    res = res & ctx_raw->apply();
+
+    return res;
+}
+
+llama_memory_status llama_kv_cache_dsv4_context::get_status() const {
+    return status;
+}
+
+const llama_ubatch & llama_kv_cache_dsv4_context::get_ubatch() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ubatches[i_next];
+}
+
+const llama_kv_cache_dsv4_raw_context * llama_kv_cache_dsv4_context::get_raw() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ctx_raw.get();
+}
+
+const llama_kv_cache_dsv4_comp_context * llama_kv_cache_dsv4_context::get_csa() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ctx_csa.get();
+}
+
+const llama_kv_cache_dsv4_comp_context * llama_kv_cache_dsv4_context::get_hca() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ctx_hca.get();
+}
+
+const llama_kv_cache_dsv4_comp_context * llama_kv_cache_dsv4_context::get_lid() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return ctx_lid.get();
+}
+
+const llama_dsv4_comp_state * llama_kv_cache_dsv4_context::get_csa_state() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return csa_state;
+}
+
+const llama_dsv4_comp_state * llama_kv_cache_dsv4_context::get_hca_state() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return hca_state;
+}
+
+const llama_dsv4_comp_state * llama_kv_cache_dsv4_context::get_lid_state() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    return lid_state;
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_csa_plan() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    static const comp_plan empty;
+    if (plans_csa.empty()) {
+        return empty;
+    }
+
+    return plans_csa[i_next];
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_hca_plan() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    static const comp_plan empty;
+    if (plans_hca.empty()) {
+        return empty;
+    }
+
+    return plans_hca[i_next];
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_lid_plan() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    static const comp_plan empty;
+    if (plans_lid.empty()) {
+        return empty;
+    }
+
+    return plans_lid[i_next];
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_csa_plan(const llama_ubatch & ubatch) const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    if (!reserve_plans) {
+        return get_csa_plan();
+    }
+
+    reserve_plan_csa = dsv4_build_reserve_comp_plan(
+            ubatch, DSV4_CSA_RATIO, true,
+            csa_state->get_state_size(), get_csa()->get_n_kv(), csa_state->get_n_stream());
+
+    return reserve_plan_csa;
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_hca_plan(const llama_ubatch & ubatch) const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    if (!reserve_plans) {
+        return get_hca_plan();
+    }
+
+    reserve_plan_hca = dsv4_build_reserve_comp_plan(
+            ubatch, DSV4_HCA_RATIO, false,
+            hca_state->get_state_size(), get_hca()->get_n_kv(), hca_state->get_n_stream());
+
+    return reserve_plan_hca;
+}
+
+const llama_kv_cache_dsv4_context::comp_plan & llama_kv_cache_dsv4_context::get_lid_plan(const llama_ubatch & ubatch) const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    if (!reserve_plans) {
+        return get_lid_plan();
+    }
+
+    reserve_plan_lid = dsv4_build_reserve_comp_plan(
+            ubatch, DSV4_CSA_RATIO, true,
+            lid_state->get_state_size(), get_lid()->get_n_kv(), lid_state->get_n_stream());
+
+    return reserve_plan_lid;
+}
diff --git a/src/llama-kv-cache-dsv4.h b/src/llama-kv-cache-dsv4.h
new file mode 100644
index 000000000..772b428cd
--- /dev/null
+++ b/src/llama-kv-cache-dsv4.h
@@ -0,0 +1,362 @@
+#pragma once
+
+#include "llama-kv-cache.h"
+#include "llama-kv-cache-iswa.h"
+
+#include <map>
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+class llama_dsv4_comp_state {
+public:
+    llama_dsv4_comp_state(
+            const llama_model & model,
+            bool            offload,
+            bool            unified,
+            uint32_t        n_seq_max,
+            uint32_t        ratio,
+            uint32_t        state_size,
+            uint32_t        n_embd_state,
+            const char    * name,
+        const llama_memory_i::layer_filter_cb & filter);
+
+    void clear(bool data);
+
+    uint32_t get_ratio()    const;
+    uint32_t get_state_size() const;
+    uint32_t get_n_stream() const;
+
+    std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const;
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id, llama_state_seq_flags flags);
+
+    ggml_tensor * get_kv   (ggml_context * ctx, int32_t il) const;
+    ggml_tensor * get_score(ggml_context * ctx, int32_t il) const;
+
+    ggml_tensor * cpy_kv   (ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const;
+    ggml_tensor * cpy_score(ggml_context * ctx, ggml_tensor * cur, ggml_tensor * idxs, int32_t il) const;
+
+private:
+    struct layer {
+        uint32_t il;
+
+        ggml_tensor * kv;
+        ggml_tensor * score;
+    };
+
+    const uint32_t ratio;
+    const uint32_t state_size;
+    const uint32_t n_embd_state;
+    const uint32_t n_stream;
+
+    std::vector<std::pair<ggml_context_ptr, ggml_backend_buffer_ptr>> ctxs_bufs;
+
+    std::vector<layer> layers;
+
+    std::unordered_map<int32_t, int32_t> map_layer_ids;
+
+    size_t total_size() const;
+};
+
+//
+// llama_kv_cache_dsv4
+//
+
+// DSV4 uses a normal raw/SWA token cache plus compressed K-only block caches.
+// The compressed caches are storage only; DSV4-specific visibility and block
+// planning are handled by llama_kv_cache_dsv4_context / llm_graph_input_dsv4.
+
+class llama_kv_cache_dsv4 : public llama_memory_i {
+public:
+    llama_kv_cache_dsv4(
+            const llama_model & model,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                         bool   v_trans,
+                         bool   offload,
+                         bool   swa_full,
+                         bool   unified,
+                     uint32_t   kv_size,
+                     uint32_t   n_seq_max,
+                     uint32_t   n_ubatch,
+                     uint32_t   n_pad,
+        const layer_filter_cb & filter,
+        const  layer_reuse_cb & reuse);
+
+    ~llama_kv_cache_dsv4() = default;
+
+    //
+    // llama_memory_i
+    //
+
+    llama_memory_context_ptr init_batch(
+            llama_batch_allocr & balloc,
+            uint32_t n_ubatch,
+            bool embd_all) override;
+
+    llama_memory_context_ptr init_full() override;
+
+    llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
+
+    bool get_can_shift() const override;
+
+    void clear(bool data) override;
+
+    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
+    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
+    void seq_keep(llama_seq_id seq_id)                                                          override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
+    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
+
+    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+    llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+    std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;
+
+    //
+    // llama_kv_cache_dsv4 specific API
+    //
+
+    llama_kv_cache_iswa * get_raw() const;
+    llama_kv_cache      * get_csa() const;
+    llama_kv_cache      * get_hca() const;
+    llama_kv_cache      * get_lid() const;
+    llama_dsv4_comp_state * get_csa_state() const;
+    llama_dsv4_comp_state * get_hca_state() const;
+    llama_dsv4_comp_state * get_lid_state() const;
+
+private:
+    llama_hparams hparams_raw;
+    llama_hparams hparams_csa;
+    llama_hparams hparams_hca;
+    llama_hparams hparams_lid;
+
+    const uint32_t n_seq_max;
+
+    std::unique_ptr<llama_kv_cache_iswa> kv_raw;
+    std::unique_ptr<llama_kv_cache>      kv_csa;
+    std::unique_ptr<llama_kv_cache>      kv_hca;
+    std::unique_ptr<llama_kv_cache>      kv_lid;
+    std::unique_ptr<llama_dsv4_comp_state> csa_state;
+    std::unique_ptr<llama_dsv4_comp_state> hca_state;
+    std::unique_ptr<llama_dsv4_comp_state> lid_state;
+
+    void clear_compressed(bool data);
+};
+
+// DSV4 raw attention only uses the SWA half of kv_raw. The base half is kept
+// for generic ISWA bookkeeping, but it has no DSV4 layers to expose here.
+class llama_kv_cache_dsv4_raw_context : public llama_memory_context_i {
+public:
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
+
+    llama_kv_cache_dsv4_raw_context(llama_kv_cache_iswa * kv);
+
+    llama_kv_cache_dsv4_raw_context(
+            llama_kv_cache_iswa * kv,
+            llama_context * lctx,
+            bool optimize);
+
+    llama_kv_cache_dsv4_raw_context(
+            llama_kv_cache_iswa * kv,
+            slot_info_vec_t sinfos_base_write,
+            slot_info_vec_t sinfos_swa_write,
+            slot_info_vec_t sinfos_swa_read,
+            std::vector<llama_ubatch> ubatches,
+            std::vector<llama_ubatch> ubatches_write);
+
+    bool next() override;
+    bool apply() override;
+
+    llama_memory_status get_status() const override;
+    const llama_ubatch & get_ubatch() const override;
+
+    uint32_t get_n_kv() const;
+    uint32_t get_n_write() const;
+
+    ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
+
+    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;
+    ggml_tensor * build_input_k_rot(ggml_context * ctx) const;
+
+    void set_input_k_idxs(ggml_tensor * dst) const;
+    void set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;
+    void set_input_k_rot(ggml_tensor * dst) const;
+
+private:
+    size_t i_next = 0;
+
+    llama_kv_cache * kv_swa = nullptr;
+
+    slot_info_vec_t sinfos_write;
+    slot_info_vec_t sinfos_read;
+    std::vector<llama_ubatch> ubatches;
+    std::vector<llama_ubatch> ubatches_write;
+
+    const llama_memory_context_ptr ctx_base_mem;
+    const llama_memory_context_ptr ctx_swa_mem;
+
+    uint32_t n_kv = 0;
+
+    const llama_memory_status status;
+};
+
+// DSV4 compressed KV rows are graph outputs, not normal token KV writes.
+// Keep a small context that exposes K tensors without generic apply() semantics.
+class llama_kv_cache_dsv4_comp_context {
+public:
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
+
+    llama_kv_cache_dsv4_comp_context(llama_kv_cache * kv);
+
+    llama_kv_cache_dsv4_comp_context(
+            llama_kv_cache * kv,
+            slot_info_vec_t sinfos,
+            std::vector<llama_ubatch> ubatches);
+
+    bool next();
+
+    uint32_t get_n_kv() const;
+
+    ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
+    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;
+
+    ggml_tensor * build_input_k_rot(ggml_context * ctx) const;
+    void set_input_k_rot(ggml_tensor * dst) const;
+
+private:
+    llama_kv_cache * kv;
+
+    size_t i_cur = 0;
+    slot_info_vec_t sinfos;
+    std::vector<llama_ubatch> ubatches;
+
+    uint32_t n_kv;
+};
+
+class llama_kv_cache_dsv4_context : public llama_memory_context_i {
+public:
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
+
+    struct comp_plan {
+        // Per-ubatch recipe for updating compressor state, committing completed
+        // compressed rows, and masking the compressed attention source.
+
+        // APE row ids, i.e. pos % ratio, for the compressor-state updates.
+        std::vector<int32_t> state_pos;
+
+        // Current-ubatch source row ids and unique persistent-state
+        // destination row ids for deterministic ring-state updates.
+        std::vector<int32_t> state_persist_src_idxs;
+        std::vector<int32_t> state_persist_dst_idxs;
+
+        // Flattened source row ids used for state-backed commits. Source rows
+        // index the graph-local [persistent_state | current_ubatch_scratch]
+        // tensor. For overlapped compression the first half is previous rows
+        // and the second half is current rows; a final synthetic zero/-inf row
+        // may be addressed for the first block's previous half.
+        std::vector<int32_t> state_read_idxs;
+
+        // Final compressed-cache row ids written by state-backed commits.
+        // A non-boundary CSA/LID decode step can target a masked scratch row.
+        std::vector<int64_t> state_write_idxs;
+
+        // RoPE positions for state-backed commits.
+        std::vector<int32_t> state_write_pos;
+
+        // Number of completed compressed rows visible for each query token.
+        std::vector<int32_t> n_visible;
+
+        // Number of streams used by the attention graph for this ubatch.
+        int64_t n_stream = 1;
+
+        // Graph-width for compressed rows. This can be larger than n_visible
+        // so masked padding rows do not force a new graph at every CSA block.
+        int64_t n_kv = 0;
+    };
+
+    llama_kv_cache_dsv4_context(llama_memory_status status);
+
+    llama_kv_cache_dsv4_context(
+            llama_kv_cache_dsv4 * kv);
+
+    llama_kv_cache_dsv4_context(
+            llama_kv_cache_dsv4 * kv,
+            llama_context * lctx,
+            bool optimize);
+
+    llama_kv_cache_dsv4_context(
+            llama_kv_cache_dsv4 * kv,
+            slot_info_vec_t sinfos_raw_base_write,
+            slot_info_vec_t sinfos_raw_swa_write,
+            slot_info_vec_t sinfos_raw_swa_read,
+            std::vector<llama_ubatch> ubatches,
+            std::vector<llama_ubatch> ubatches_raw);
+
+    virtual ~llama_kv_cache_dsv4_context();
+
+    //
+    // llama_memory_context_i
+    //
+
+    bool next()  override;
+    bool apply() override;
+
+    llama_memory_status  get_status() const override;
+    const llama_ubatch & get_ubatch() const override;
+
+    //
+    // llama_kv_cache_dsv4_context specific API
+    //
+
+    const llama_kv_cache_dsv4_raw_context * get_raw() const;
+    const llama_kv_cache_dsv4_comp_context * get_csa() const;
+    const llama_kv_cache_dsv4_comp_context * get_hca() const;
+    const llama_kv_cache_dsv4_comp_context * get_lid() const;
+    const llama_dsv4_comp_state       * get_csa_state() const;
+    const llama_dsv4_comp_state       * get_hca_state() const;
+    const llama_dsv4_comp_state       * get_lid_state() const;
+
+    const comp_plan & get_csa_plan() const;
+    const comp_plan & get_hca_plan() const;
+    const comp_plan & get_lid_plan() const;
+
+    const comp_plan & get_csa_plan(const llama_ubatch & ubatch) const;
+    const comp_plan & get_hca_plan(const llama_ubatch & ubatch) const;
+    const comp_plan & get_lid_plan(const llama_ubatch & ubatch) const;
+
+private:
+    size_t i_next = 0;
+
+    std::vector<llama_ubatch> ubatches;
+
+    std::vector<comp_plan> plans_csa;
+    std::vector<comp_plan> plans_hca;
+    std::vector<comp_plan> plans_lid;
+
+    const std::unique_ptr<llama_kv_cache_dsv4_raw_context> ctx_raw;
+    const llama_memory_context_ptr ctx_csa_mem;
+    const llama_memory_context_ptr ctx_hca_mem;
+    const llama_memory_context_ptr ctx_lid_mem;
+
+    const std::unique_ptr<llama_kv_cache_dsv4_comp_context> ctx_csa;
+    const std::unique_ptr<llama_kv_cache_dsv4_comp_context> ctx_hca;
+    const std::unique_ptr<llama_kv_cache_dsv4_comp_context> ctx_lid;
+
+    const llama_dsv4_comp_state * csa_state = nullptr;
+    const llama_dsv4_comp_state * hca_state = nullptr;
+    const llama_dsv4_comp_state * lid_state = nullptr;
+
+    bool reserve_plans = false;
+    mutable comp_plan reserve_plan_csa;
+    mutable comp_plan reserve_plan_hca;
+    mutable comp_plan reserve_plan_lid;
+
+    const llama_memory_status status;
+};
diff --git a/src/llama-kv-cache-iswa.cpp b/src/llama-kv-cache-iswa.cpp
index aa1b1b72e..2fcf238d9 100644
--- a/src/llama-kv-cache-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -26,7 +26,28 @@ llama_kv_cache_iswa::llama_kv_cache_iswa(
            llama_memory_t   mem_other,
     const layer_filter_cb & filter,
     const  layer_reuse_cb & reuse,
-    const  layer_share_cb & share) : hparams(model.hparams), unified(unified) {
+    const  layer_share_cb & share) :
+    llama_kv_cache_iswa(model, model.hparams, type_k, type_v, v_trans, offload, swa_full, unified,
+            kv_size, n_seq_max, n_ubatch, n_pad, mem_other, filter, reuse, share) {
+}
+
+llama_kv_cache_iswa::llama_kv_cache_iswa(
+        const llama_model & model,
+        const llama_hparams & hparams,
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   offload,
+                     bool   swa_full,
+                     bool   unified,
+                 uint32_t   kv_size,
+                 uint32_t   n_seq_max,
+                 uint32_t   n_ubatch,
+                 uint32_t   n_pad,
+           llama_memory_t   mem_other,
+    const layer_filter_cb & filter,
+    const  layer_reuse_cb & reuse,
+    const  layer_share_cb & share) : unified(unified) {
 
     // chain filters
     const layer_filter_cb filter_base = [&](int32_t il) {
diff --git a/src/llama-kv-cache-iswa.h b/src/llama-kv-cache-iswa.h
index dfafc1ef5..7dab6eaa8 100644
--- a/src/llama-kv-cache-iswa.h
+++ b/src/llama-kv-cache-iswa.h
@@ -30,6 +30,24 @@ public:
         const  layer_reuse_cb & reuse,
         const  layer_share_cb & share);
 
+    llama_kv_cache_iswa(
+            const llama_model & model,
+            const llama_hparams & hparams,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                         bool   v_trans,
+                         bool   offload,
+                         bool   swa_full,
+                         bool   unified,
+                     uint32_t   kv_size,
+                     uint32_t   n_seq_max,
+                     uint32_t   n_ubatch,
+                     uint32_t   n_pad,
+               llama_memory_t   mem_other,
+        const layer_filter_cb & filter,
+        const  layer_reuse_cb & reuse,
+        const  layer_share_cb & share);
+
     ~llama_kv_cache_iswa() = default;
 
     //
@@ -73,8 +91,6 @@ public:
     llama_kv_cache * get_swa () const;
 
 private:
-    const llama_hparams & hparams;
-
     const bool unified;
 
     std::unique_ptr<llama_kv_cache> kv_base;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 2802103bd..12bf5c379 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -211,10 +211,12 @@ llama_kv_cache::llama_kv_cache(
             n_embd_head_k_all = -1;
         }
 
-        if (n_embd_head_v_all == 0) {
-            n_embd_head_v_all = (int32_t) hparams.n_embd_head_v(il);
-        } else if (n_embd_head_v_all > 0 && n_embd_head_v_all != (int32_t) hparams.n_embd_head_v(il)) {
-            n_embd_head_v_all = -1;
+        if (!is_mla) {
+            if (n_embd_head_v_all == 0) {
+                n_embd_head_v_all = (int32_t) hparams.n_embd_head_v(il);
+            } else if (n_embd_head_v_all > 0 && n_embd_head_v_all != (int32_t) hparams.n_embd_head_v(il)) {
+                n_embd_head_v_all = -1;
+            }
         }
 
         // [TAG_V_CACHE_VARIABLE]
@@ -336,8 +338,9 @@ llama_kv_cache::llama_kv_cache(
             ggml_is_quantized(type_k) &&
             hparams.n_embd_head_k() % 64 == 0;
 
-        // always create Hadamard rotation tensors for DeepSeek V3.2 DSA lightning indexer
-        if (model.arch == LLM_ARCH_DEEPSEEK32 && hparams.n_embd_head_k_full == hparams.indexer_head_size) {
+        // always create Hadamard rotation tensors for DeepSeek lightning indexers
+        if ((model.arch == LLM_ARCH_DEEPSEEK32 || model.arch == LLM_ARCH_DEEPSEEK4) &&
+                hparams.n_embd_head_k_full == hparams.indexer_head_size) {
             attn_rot_k = true;
         }
 
@@ -1220,6 +1223,23 @@ ggml_type llama_kv_cache::type_v() const {
     return layers[0].v->type;
 }
 
+std::vector<uint32_t> llama_kv_cache::get_layer_ids() const {
+    std::vector<uint32_t> res;
+    res.reserve(layers.size());
+
+    for (const auto & layer : layers) {
+        res.push_back(layer.il);
+    }
+
+    return res;
+}
+
+ggml_tensor * llama_kv_cache::get_k_storage(int32_t il) const {
+    const int32_t ikv = map_layer_ids.at(il);
+
+    return layers[ikv].k;
+}
+
 uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const {
     uint32_t result = 0;
 
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index 3d68f98c1..531d99dbd 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -161,6 +161,9 @@ public:
     ggml_type type_k() const;
     ggml_type type_v() const;
 
+    std::vector<uint32_t> get_layer_ids() const;
+    ggml_tensor * get_k_storage(int32_t il) const;
+
     //
     // graph_build API
     //
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index 474cabdfc..e07b0d231 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -294,6 +294,8 @@ namespace GGUFMeta {
     }
 
     template bool llama_model_loader::get_arr_n(enum llm_kv kid, uint32_t & result, bool required);
+    template std::enable_if<std::is_integral<uint32_t>::value, bool>::type
+    llama_model_loader::get_arr_n<uint32_t>(const std::string & key, uint32_t & result, bool required);
 
     template<typename T>
     bool llama_model_loader::get_arr(const std::string & key, std::vector<T> & result, bool required) {
@@ -395,6 +397,7 @@ namespace GGUFMeta {
     template bool llama_model_loader::get_arr<std::vector<std::string>>(enum llm_kv kid, std::vector<std::string> & result, bool required);
     template bool llama_model_loader::get_arr<std::array<int32_t, 512>>(enum llm_kv kid, std::array<int32_t, 512> & result, bool required);
     template bool llama_model_loader::get_arr<std::vector<int32_t>>(enum llm_kv kid, std::vector<int32_t> & result, bool required);
+    template bool llama_model_loader::get_arr<std::array<uint32_t, LLAMA_MAX_LAYERS>>(enum llm_kv kid, std::array<uint32_t, LLAMA_MAX_LAYERS> & result, bool required);
 
     template<typename T>
     bool llama_model_loader::get_key(const std::string & key, T & result, bool required) {
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index eaf29505c..d58ebac28 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -11,6 +11,7 @@
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
 #include "llama-kv-cache-dsa.h"
+#include "llama-kv-cache-dsv4.h"
 #include "llama-memory-hybrid.h"
 #include "llama-memory-hybrid-iswa.h"
 #include "llama-memory-recurrent.h"
@@ -181,6 +182,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_deepseek2ocr(params);
         case LLM_ARCH_DEEPSEEK32:
             return new llama_model_deepseek32(params);
+        case LLM_ARCH_DEEPSEEK4:
+            return new llama_model_deepseek4(params);
         case LLM_ARCH_GLM_DSA:
             return new llama_model_glm_dsa(params);
         case LLM_ARCH_MISTRAL4:
@@ -817,6 +820,7 @@ static const char * llama_expert_gating_func_name(llama_expert_gating_func_type
     switch (type) {
         case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
         case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
+        case LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS: return "sqrtsoftplus";
         default:                                    return "unknown";
     }
 }
@@ -2156,7 +2160,24 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         }
                     }
 
-                    if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
+                    if (arch == LLM_ARCH_DEEPSEEK4) {
+                        GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE);
+
+                        res = new llama_kv_cache_dsv4(
+                                *this,
+                                params.type_k,
+                                params.type_v,
+                                !cparams.flash_attn,
+                                cparams.offload_kqv,
+                                params.swa_full,
+                                cparams.kv_unified,
+                                cparams.n_ctx_seq,
+                                cparams.n_seq_max,
+                                cparams.n_ubatch,
+                                1,
+                                filter,
+                                reuse);
+                    } else if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
                         GGML_ASSERT(hparams.is_swa_any());
 
                         if (arch == LLM_ARCH_GEMMA4_ASSISTANT) {
@@ -2328,6 +2349,11 @@ int32_t llama_model_n_head_kv(const llama_model * model) {
 }
 
 int32_t llama_model_n_swa(const llama_model * model) {
+    // dsv4 kv-cache has SWA but it cannot be used as a rollback because of
+    // other compression ratios, so we return 0 here
+    if (model->arch == LLM_ARCH_DEEPSEEK4) {
+        return 0;
+    }
     return model->hparams.n_swa;
 }
 
@@ -2409,6 +2435,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_DEEPSEEK2OCR:
         case LLM_ARCH_DEEPSEEK32:
+        case LLM_ARCH_DEEPSEEK4:
         case LLM_ARCH_PLM:
         case LLM_ARCH_CHATGLM:
         case LLM_ARCH_GRANITE:
diff --git a/src/llama-model.h b/src/llama-model.h
index 77d8d3b62..4800d2928 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -255,9 +255,11 @@ struct llama_layer {
     struct ggml_tensor * wq_b      = nullptr;
     struct ggml_tensor * wkv_a_mqa = nullptr;
     struct ggml_tensor * wkv_b     = nullptr;
+    struct ggml_tensor * wkv       = nullptr;
     struct ggml_tensor * wk_b      = nullptr;
     struct ggml_tensor * wv_b      = nullptr;
     struct ggml_tensor * wqkv_b    = nullptr;
+    struct ggml_tensor * wo_a      = nullptr;
     struct ggml_tensor * wo_b      = nullptr;
     struct ggml_tensor * wq_cross  = nullptr;
     struct ggml_tensor * wk_cross  = nullptr;
@@ -333,6 +335,7 @@ struct llama_layer {
     struct ggml_tensor * ffn_up_b   = nullptr; // b3
     struct ggml_tensor * ffn_act    = nullptr;
     struct ggml_tensor * ffn_exp_probs_b = nullptr;
+    struct ggml_tensor * ffn_gate_tid2eid = nullptr;
 
     // mamba proj
     struct ggml_tensor * ssm_in  = nullptr;
@@ -463,6 +466,23 @@ struct llama_layer {
     // openai-moe
     struct ggml_tensor * attn_sinks = nullptr;
 
+    // DeepSeek-V4
+    struct ggml_tensor * attn_kv_norm = nullptr;
+    struct ggml_tensor * hc_attn_fn   = nullptr;
+    struct ggml_tensor * hc_attn_base = nullptr;
+    struct ggml_tensor * hc_attn_scale = nullptr;
+    struct ggml_tensor * hc_ffn_fn    = nullptr;
+    struct ggml_tensor * hc_ffn_base  = nullptr;
+    struct ggml_tensor * hc_ffn_scale = nullptr;
+    struct ggml_tensor * attn_comp_wkv   = nullptr;
+    struct ggml_tensor * attn_comp_wgate = nullptr;
+    struct ggml_tensor * attn_comp_ape   = nullptr;
+    struct ggml_tensor * attn_comp_norm  = nullptr;
+    struct ggml_tensor * indexer_comp_wkv   = nullptr;
+    struct ggml_tensor * indexer_comp_wgate = nullptr;
+    struct ggml_tensor * indexer_comp_ape   = nullptr;
+    struct ggml_tensor * indexer_comp_norm  = nullptr;
+
     // cogvlm
     struct ggml_tensor * visexp_attn_wqkv = nullptr;
     struct ggml_tensor * visexp_attn_wo   = nullptr;
@@ -553,6 +573,11 @@ struct llama_model {
     struct ggml_tensor * nextn_proj_pre  = nullptr;
     struct ggml_tensor * nextn_proj_post = nullptr;
 
+    // DeepSeek-V4
+    struct ggml_tensor * hc_head_fn    = nullptr;
+    struct ggml_tensor * hc_head_base  = nullptr;
+    struct ggml_tensor * hc_head_scale = nullptr;
+
     // classifier
     struct ggml_tensor * cls       = nullptr;
     struct ggml_tensor * cls_b     = nullptr;
diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp
new file mode 100644
index 000000000..759654228
--- /dev/null
+++ b/src/models/deepseek4.cpp
@@ -0,0 +1,1186 @@
+#include "models.h"
+
+#include "llama-kv-cache-dsv4.h"
+
+#include <algorithm>
+#include <cmath>
+#include <stdexcept>
+#include <string>
+
+static float dsv4_rope_attn_factor(float freq_scale, float ext_factor) {
+    if (ext_factor == 0.0f) {
+        return 1.0f;
+    }
+
+    return 1.0f / (1.0f + 0.1f*logf(1.0f/freq_scale));
+}
+
+void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK,       hparams.n_lora_q);
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm);
+    ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP,     hparams.swiglu_clamp_exp,   hparams.n_layer());
+    if (!ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP,   hparams.swiglu_clamp_shexp, hparams.n_layer(), 0)) {
+        hparams.swiglu_clamp_shexp = hparams.swiglu_clamp_exp;
+    }
+
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
+    ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K,      hparams.indexer_top_k);
+
+    ml.get_key(LLM_KV_ATTENTION_OUTPUT_GROUP_COUNT,         hparams.dsv4_o_group_count);
+    ml.get_key(LLM_KV_ATTENTION_OUTPUT_LORA_RANK,           hparams.dsv4_o_lora_rank);
+    ml.get_key(LLM_KV_ATTENTION_COMPRESS_ROPE_FREQ_BASE,    hparams.dsv4_compress_rope_base);
+    ml.get_key(LLM_KV_HYPER_CONNECTION_COUNT,               hparams.dsv4_hc_mult);
+    ml.get_key(LLM_KV_HYPER_CONNECTION_SINKHORN_ITERATIONS, hparams.dsv4_hc_sinkhorn_iters);
+    ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON,             hparams.dsv4_hc_eps);
+    ml.get_key(LLM_KV_HASH_LAYER_COUNT,                     hparams.dsv4_hash_layer_count);
+
+    uint32_t n_compress_ratios = 0;
+    ml.get_arr_n(LLM_KV_ATTENTION_COMPRESS_RATIOS, n_compress_ratios);
+    if (n_compress_ratios < hparams.n_layer()) {
+        throw std::runtime_error("DeepSeek-V4 compress_ratios is shorter than block_count");
+    }
+    ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS, hparams.dsv4_compress_ratios);
+
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
+    if (hparams.expert_gating_func != LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) {
+        throw std::runtime_error("DeepSeek-V4 loader currently expects sqrtsoftplus MoE scoring");
+    }
+    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+    hparams.set_swa_pattern(0);
+
+    switch (hparams.n_layer()) {
+        case 43: type = LLM_TYPE_UNKNOWN; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t q_lora_rank     = hparams.n_lora_q;
+    const int64_t n_ff_exp        = hparams.n_ff_exp;
+    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+    const int64_t n_embd_head = hparams.n_embd_head_k();
+    const int64_t o_groups    = hparams.dsv4_o_group_count;
+    const int64_t o_lora_rank = hparams.dsv4_o_lora_rank;
+    const int64_t hc_mult     = hparams.dsv4_hc_mult;
+    const int64_t hc_dim      = hc_mult * n_embd;
+    const int64_t hc_mix_dim  = (2 + hc_mult) * hc_mult;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+    hc_head_fn    = create_tensor(tn(LLM_TENSOR_HC_HEAD_FN, "weight"),    {hc_dim, hc_mult}, 0);
+    hc_head_base  = create_tensor(tn(LLM_TENSOR_HC_HEAD_BASE, "weight"),  {hc_mult}, 0);
+    hc_head_scale = create_tensor(tn(LLM_TENSOR_HC_HEAD_SCALE, "weight"), {1}, 0);
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
+        layer.attn_sinks    = create_tensor(tn(LLM_TENSOR_ATTN_SINKS,    "weight", i), {n_head}, 0);
+        layer.wq_a          = create_tensor(tn(LLM_TENSOR_ATTN_Q_A,      "weight", i), {n_embd, q_lora_rank}, 0);
+        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
+        layer.wq_b          = create_tensor(tn(LLM_TENSOR_ATTN_Q_B,      "weight", i), {q_lora_rank, n_head * n_embd_head}, 0);
+        layer.wkv           = create_tensor(tn(LLM_TENSOR_ATTN_KV,       "weight", i), {n_embd, n_embd_head}, 0);
+        layer.attn_kv_norm  = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM,  "weight", i), {n_embd_head}, 0);
+        layer.wo_a          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A,    "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, 0);
+        layer.wo_b          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B,    "weight", i), {o_groups * o_lora_rank, n_embd}, 0);
+
+        layer.hc_attn_fn    = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN,    "weight", i), {hc_dim, hc_mix_dim}, 0);
+        layer.hc_attn_base  = create_tensor(tn(LLM_TENSOR_HC_ATTN_BASE,  "weight", i), {hc_mix_dim}, 0);
+        layer.hc_attn_scale = create_tensor(tn(LLM_TENSOR_HC_ATTN_SCALE, "weight", i), {3}, 0);
+        layer.hc_ffn_fn     = create_tensor(tn(LLM_TENSOR_HC_FFN_FN,     "weight", i), {hc_dim, hc_mix_dim}, 0);
+        layer.hc_ffn_base   = create_tensor(tn(LLM_TENSOR_HC_FFN_BASE,   "weight", i), {hc_mix_dim}, 0);
+        layer.hc_ffn_scale  = create_tensor(tn(LLM_TENSOR_HC_FFN_SCALE,  "weight", i), {3}, 0);
+
+        const int64_t ratio = hparams.dsv4_compress_ratios[i];
+        if (ratio != 0) {
+            const int64_t coff = ratio == 4 ? 2 : 1;
+
+            layer.attn_comp_wkv   = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WKV,   "weight", i), {n_embd, coff * n_embd_head}, 0);
+            layer.attn_comp_wgate = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_WGATE, "weight", i), {n_embd, coff * n_embd_head}, 0);
+            layer.attn_comp_ape   = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_APE,   "weight", i), {coff * n_embd_head, ratio}, 0);
+            layer.attn_comp_norm  = create_tensor(tn(LLM_TENSOR_ATTN_COMPRESSOR_NORM,  "weight", i), {n_embd_head}, 0);
+
+            if (ratio == 4) {
+                const int64_t n_embd_indexer = hparams.indexer_head_size;
+
+                layer.indexer_proj     = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ,     "weight", i), {n_embd, hparams.indexer_n_head}, 0);
+                layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * n_embd_indexer}, 0);
+
+                layer.indexer_comp_wkv   = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WKV,   "weight", i), {n_embd, 2 * n_embd_indexer}, 0);
+                layer.indexer_comp_wgate = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_WGATE, "weight", i), {n_embd, 2 * n_embd_indexer}, 0);
+                layer.indexer_comp_ape   = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_APE,   "weight", i), {2 * n_embd_indexer, ratio}, 0);
+                layer.indexer_comp_norm  = create_tensor(tn(LLM_TENSOR_INDEXER_COMPRESSOR_NORM,  "weight", i), {n_embd_indexer}, 0);
+            } else if (ratio != 128) {
+                throw std::runtime_error("DeepSeek-V4 loader only supports compression ratios 0, 4, and 128");
+            }
+        }
+
+        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+        if ((uint32_t) i < hparams.dsv4_hash_layer_count) {
+            layer.ffn_gate_tid2eid = create_tensor(tn(LLM_TENSOR_FFN_GATE_TID2EID, "weight", i), {n_expert_used, n_vocab}, 0);
+        } else {
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+        }
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+
+        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,                     n_ff_exp * n_expert_shared}, 0);
+        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_exp * n_expert_shared, n_embd                    }, 0);
+        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,                     n_ff_exp * n_expert_shared}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_deepseek4::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+static size_t dsv4_elem_offset(const ggml_tensor * t, int64_t i) {
+    return ggml_row_size(t->type, i);
+}
+
+static ggml_tensor * dsv4_view_1d(ggml_context * ctx, ggml_tensor * t, int64_t ne0, int64_t i0) {
+    return ggml_view_1d(ctx, t, ne0, dsv4_elem_offset(t, i0));
+}
+
+static ggml_tensor * dsv4_view_2d(
+        ggml_context * ctx,
+        ggml_tensor  * t,
+        int64_t        ne0,
+        int64_t        ne1,
+        int64_t        i0) {
+    return ggml_view_2d(ctx, t, ne0, ne1, t->nb[1], dsv4_elem_offset(t, i0));
+}
+
+static ggml_tensor * dsv4_append_zero_row(ggml_context * ctx, ggml_tensor * t, bool neg_inf) {
+    ggml_tensor * row = ggml_view_1d(ctx, t, t->ne[0], 0);
+    row = neg_inf ? ggml_scale_bias(ctx, row, 0.0f, -INFINITY) : ggml_scale(ctx, row, 0.0f);
+    row = ggml_reshape_2d(ctx, row, t->ne[0], 1);
+
+    return ggml_concat(ctx, t, row, 1);
+}
+
+static ggml_tensor * dsv4_with_zero_dep(ggml_context * ctx, ggml_tensor * t, ggml_tensor * dep) {
+    if (dep == nullptr) {
+        return t;
+    }
+
+    ggml_tensor * zero = ggml_scale(ctx, ggml_sum(ctx, dep), 0.0f);
+    return ggml_add(ctx, t, zero);
+}
+
+// Raw SWA K is stored once, but compressed K/masks can carry a stream axis.
+// Repeat raw K at graph build time before concatenating raw and compressed K.
+static ggml_tensor * dsv4_repeat_streams(ggml_context * ctx, ggml_tensor * t, int64_t n_stream) {
+    if (t->ne[3] == n_stream) {
+        return t;
+    }
+
+    GGML_ASSERT(t->ne[3] == 1);
+    return ggml_repeat_4d(ctx, t, t->ne[0], t->ne[1], t->ne[2], n_stream);
+}
+
+static ggml_tensor * dsv4_build_kq_zero_bias(
+        ggml_context * ctx,
+        const llama_cparams & cparams,
+        ggml_tensor * kq_mask,
+        int64_t n_head) {
+    if (!cparams.kv_unified || !cparams.flash_attn || kq_mask->ne[3] == 1) {
+        return nullptr;
+    }
+
+    // Keep multi-stream unified DSV4 on the explicit attention path.
+    ggml_tensor * res = ggml_new_tensor_4d(ctx, GGML_TYPE_F32,
+            kq_mask->ne[0], kq_mask->ne[1], n_head, kq_mask->ne[3]);
+    return ggml_fill(ctx, res, 0.0f);
+}
+
+static constexpr int64_t DSV4_CSA_RATIO  = 4;
+static constexpr int64_t DSV4_HCA_RATIO  = 128;
+
+static ggml_tensor * dsv4_hc_affine(
+        ggml_context * ctx,
+        ggml_tensor  * x,
+        ggml_tensor  * scale,
+        ggml_tensor  * base) {
+    x = ggml_mul(ctx, x, scale);
+    x = ggml_add(ctx, x, base);
+    return x;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hc_weighted_sum(
+        ggml_tensor * x,
+        ggml_tensor * weights) const {
+    const int64_t hc = hparams.dsv4_hc_mult;
+    const int64_t nt = x->ne[2];
+
+    ggml_tensor * acc = nullptr;
+    for (int64_t ih = 0; ih < hc; ++ih) {
+        ggml_tensor * xh = ggml_view_2d(ctx0, x, n_embd, nt, x->nb[2], ih*x->nb[1]);
+        ggml_tensor * wh = ggml_view_2d(ctx0, weights, 1, nt, weights->nb[1], ih*weights->nb[0]);
+
+        ggml_tensor * cur = ggml_mul(ctx0, xh, wh);
+        acc = acc ? ggml_add(ctx0, acc, cur) : cur;
+    }
+
+    return acc;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hc_sinkhorn(
+        ggml_tensor * comb,
+        int           il) const {
+    GGML_UNUSED(il);
+
+    // comb is [dst_hc, src_hc, n_tokens]. Sinkhorn follows the reference:
+    // row softmax over dst, one column normalization, then repeated row/column normalization.
+    comb = ggml_soft_max(ctx0, comb);
+
+    ggml_tensor * eps = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
+    eps = ggml_fill(ctx0, eps, hparams.dsv4_hc_eps);
+
+    comb = ggml_add(ctx0, comb, eps);
+
+    auto norm_cols = [&]() {
+        ggml_tensor * comb_src_dst = ggml_cont(ctx0, ggml_permute(ctx0, comb, 1, 0, 2, 3));
+        ggml_tensor * col_sum = ggml_sum_rows(ctx0, comb_src_dst);
+        col_sum = ggml_add(ctx0, col_sum, eps);
+        col_sum = ggml_permute(ctx0, col_sum, 1, 0, 2, 3);
+        comb = ggml_div(ctx0, comb, col_sum);
+    };
+
+    auto norm_rows = [&]() {
+        ggml_tensor * row_sum = ggml_sum_rows(ctx0, comb);
+        row_sum = ggml_add(ctx0, row_sum, eps);
+        comb = ggml_div(ctx0, comb, row_sum);
+    };
+
+    norm_cols();
+    for (uint32_t i = 1; i < hparams.dsv4_hc_sinkhorn_iters; ++i) {
+        norm_rows();
+        norm_cols();
+    }
+
+    return comb;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hc_pre(
+        ggml_tensor * x,
+        ggml_tensor * hc_fn,
+        ggml_tensor * hc_scale,
+        ggml_tensor * hc_base,
+        ggml_tensor ** post,
+        ggml_tensor ** comb,
+        int il) const {
+    const int64_t hc         = hparams.dsv4_hc_mult;
+    const int64_t hc_dim     = hc*n_embd;
+    const int64_t hc_mix_dim = (2 + hc)*hc;
+    const int64_t nt         = x->ne[2];
+
+    GGML_ASSERT(hc == 4);
+    GGML_ASSERT(hc_fn->ne[1] == hc_mix_dim);
+
+    ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc_dim, nt);
+    ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, norm_rms_eps);
+    ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm);
+    cb(mixes, "hc_mixes", il);
+
+    ggml_tensor * scale_pre  = dsv4_view_1d(ctx0, hc_scale, 1, 0);
+    ggml_tensor * scale_post = dsv4_view_1d(ctx0, hc_scale, 1, 1);
+    ggml_tensor * scale_comb = dsv4_view_1d(ctx0, hc_scale, 1, 2);
+
+    ggml_tensor * base_pre  = dsv4_view_1d(ctx0, hc_base, hc, 0);
+    ggml_tensor * base_post = dsv4_view_1d(ctx0, hc_base, hc, hc);
+    ggml_tensor * base_comb = dsv4_view_1d(ctx0, hc_base, hc*hc, 2*hc);
+
+    ggml_tensor * pre = dsv4_view_2d(ctx0, mixes, hc, nt, 0);
+    pre = dsv4_hc_affine(ctx0, pre, scale_pre, base_pre);
+    pre = ggml_sigmoid(ctx0, pre);
+    pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
+    cb(pre, "hc_pre", il);
+
+    *post = dsv4_view_2d(ctx0, mixes, hc, nt, hc);
+    *post = dsv4_hc_affine(ctx0, *post, scale_post, base_post);
+    *post = ggml_sigmoid(ctx0, *post);
+    *post = ggml_scale(ctx0, *post, 2.0f);
+    cb(*post, "hc_post", il);
+
+    *comb = dsv4_view_2d(ctx0, mixes, hc*hc, nt, 2*hc);
+    *comb = dsv4_hc_affine(ctx0, *comb, scale_comb, base_comb);
+    *comb = ggml_reshape_3d(ctx0, *comb, hc, hc, nt);
+    *comb = build_hc_sinkhorn(*comb, il);
+    cb(*comb, "hc_comb", il);
+
+    return build_hc_weighted_sum(x, pre);
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hc_post(
+        ggml_tensor * x,
+        ggml_tensor * residual,
+        ggml_tensor * post,
+        ggml_tensor * comb,
+        int il) const {
+    GGML_UNUSED(il);
+
+    const int64_t hc = hparams.dsv4_hc_mult;
+    const int64_t nt = x->ne[1];
+
+    ggml_tensor * out = nullptr;
+    for (int64_t dst = 0; dst < hc; ++dst) {
+        ggml_tensor * post_dst = ggml_view_2d(ctx0, post, 1, nt, post->nb[1], dst*post->nb[0]);
+        ggml_tensor * cur = ggml_mul(ctx0, x, post_dst);
+
+        for (int64_t src = 0; src < hc; ++src) {
+            ggml_tensor * res_src = ggml_view_2d(ctx0, residual, n_embd, nt, residual->nb[2], src*residual->nb[1]);
+            ggml_tensor * comb_src_dst = ggml_view_2d(ctx0, comb, 1, nt, comb->nb[2], dst*comb->nb[0] + src*comb->nb[1]);
+            cur = ggml_add(ctx0, cur, ggml_mul(ctx0, res_src, comb_src_dst));
+        }
+
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, nt);
+        out = out ? ggml_concat(ctx0, out, cur, 1) : cur;
+    }
+
+    return out;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hc_head(
+        ggml_tensor * x,
+        ggml_tensor * hc_fn,
+        ggml_tensor * hc_scale,
+        ggml_tensor * hc_base) const {
+    const int64_t hc     = hparams.dsv4_hc_mult;
+    const int64_t hc_dim = hc*n_embd;
+    const int64_t nt     = x->ne[2];
+
+    ggml_tensor * flat = ggml_reshape_2d(ctx0, x, hc_dim, nt);
+    ggml_tensor * flat_norm = ggml_rms_norm(ctx0, flat, norm_rms_eps);
+    ggml_tensor * mixes = ggml_mul_mat(ctx0, hc_fn, flat_norm);
+    cb(mixes, "hc_head_mixes", -1);
+
+    ggml_tensor * pre = dsv4_hc_affine(ctx0, mixes, hc_scale, hc_base);
+    pre = ggml_sigmoid(ctx0, pre);
+    pre = ggml_scale_bias(ctx0, pre, 1.0f, hparams.dsv4_hc_eps);
+    cb(pre, "hc_head_pre", -1);
+
+    return build_hc_weighted_sum(x, pre);
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hca_compressed_kv_from_state(
+        ggml_tensor * kv_state,
+        ggml_tensor * score_state,
+        ggml_tensor * state_read_idxs,
+        ggml_tensor * comp_pos,
+        ggml_tensor * norm,
+        int64_t n_embd_head,
+        const char * name,
+        int il) const {
+    const int64_t n_embd_head_rope = hparams.n_rot();
+    const int64_t n_embd_head_nope = n_embd_head - n_embd_head_rope;
+    const int64_t n_blocks         = comp_pos ? comp_pos->ne[0] : 0;
+
+    GGML_ASSERT(n_blocks > 0);
+    GGML_ASSERT(state_read_idxs);
+    GGML_ASSERT(state_read_idxs->ne[0] == DSV4_HCA_RATIO*n_blocks);
+    GGML_ASSERT(n_embd_head >= n_embd_head_rope);
+
+    ggml_tensor * kv = ggml_get_rows(ctx0, kv_state, state_read_idxs);
+    kv = ggml_reshape_3d(ctx0, kv, n_embd_head, DSV4_HCA_RATIO, n_blocks);
+    cb(kv, name, il);
+
+    ggml_tensor * score = ggml_get_rows(ctx0, score_state, state_read_idxs);
+    score = ggml_reshape_3d(ctx0, score, n_embd_head, DSV4_HCA_RATIO, n_blocks);
+    cb(score, name, il);
+
+    ggml_tensor * values = ggml_cont(ctx0, ggml_permute(ctx0, kv, 1, 0, 2, 3));
+    ggml_tensor * scores = ggml_cont(ctx0, ggml_permute(ctx0, score, 1, 0, 2, 3));
+
+    ggml_tensor * weights = ggml_soft_max(ctx0, scores);
+    ggml_tensor * comp = ggml_mul(ctx0, values, weights);
+    comp = ggml_sum_rows(ctx0, comp);
+    comp = ggml_cont(ctx0, ggml_permute(ctx0, comp, 1, 0, 2, 3));
+    cb(comp, name, il);
+
+    comp = build_norm(comp, norm, nullptr, LLM_NORM_RMS, il);
+    cb(comp, name, il);
+
+    ggml_tensor * comp_nope = ggml_view_3d(ctx0, comp, n_embd_head_nope, 1, n_blocks,
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head),
+            0);
+    ggml_tensor * comp_pe = ggml_view_3d(ctx0, comp, n_embd_head_rope, 1, n_blocks,
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head_nope));
+
+    comp_pe = ggml_rope_ext(ctx0, comp_pe, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
+            hparams.dsv4_compress_rope_base, freq_scale, ext_factor,
+            dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
+    cb(comp_pe, name, il);
+
+    comp = ggml_concat(ctx0, comp_nope, comp_pe, 0);
+    cb(comp, name, il);
+
+    return comp;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_overlap_compressed_kv_from_state(
+        ggml_tensor * kv_state,
+        ggml_tensor * score_state,
+        ggml_tensor * state_read_idxs,
+        ggml_tensor * comp_pos,
+        ggml_tensor * norm,
+        int64_t ratio,
+        int64_t n_embd_head,
+        const char * name,
+        int il) const {
+    const int64_t n_embd_head_rope = hparams.n_rot();
+    const int64_t n_embd_head_nope = n_embd_head - n_embd_head_rope;
+    const int64_t n_blocks         = comp_pos ? comp_pos->ne[0] : 0;
+
+    GGML_ASSERT(n_blocks > 0);
+    GGML_ASSERT(state_read_idxs);
+    GGML_ASSERT(state_read_idxs->ne[0] == 2*ratio*n_blocks);
+    GGML_ASSERT(kv_state->ne[0] == 2*n_embd_head);
+    GGML_ASSERT(score_state->ne[0] == 2*n_embd_head);
+    GGML_ASSERT(n_embd_head >= n_embd_head_rope);
+
+    kv_state    = dsv4_append_zero_row(ctx0, kv_state,    false);
+    score_state = dsv4_append_zero_row(ctx0, score_state, true);
+
+    ggml_tensor * prev_idxs = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, 0);
+    ggml_tensor * cur_idxs  = dsv4_view_1d(ctx0, state_read_idxs, ratio*n_blocks, ratio*n_blocks);
+
+    ggml_tensor * kv_prev = ggml_get_rows(ctx0, kv_state, prev_idxs);
+    kv_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_prev, n_embd_head, ratio*n_blocks, kv_prev->nb[1], 0));
+    kv_prev = ggml_reshape_3d(ctx0, kv_prev, n_embd_head, ratio, n_blocks);
+    cb(kv_prev, name, il);
+
+    ggml_tensor * score_prev = ggml_get_rows(ctx0, score_state, prev_idxs);
+    score_prev = ggml_cont(ctx0, ggml_view_2d(ctx0, score_prev, n_embd_head, ratio*n_blocks, score_prev->nb[1], 0));
+    score_prev = ggml_reshape_3d(ctx0, score_prev, n_embd_head, ratio, n_blocks);
+    cb(score_prev, name, il);
+
+    ggml_tensor * kv_cur = ggml_get_rows(ctx0, kv_state, cur_idxs);
+    kv_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, kv_cur, n_embd_head, ratio*n_blocks, kv_cur->nb[1],
+            ggml_row_size(kv_cur->type, n_embd_head)));
+    kv_cur = ggml_reshape_3d(ctx0, kv_cur, n_embd_head, ratio, n_blocks);
+
+    ggml_tensor * score_cur = ggml_get_rows(ctx0, score_state, cur_idxs);
+    score_cur = ggml_cont(ctx0, ggml_view_2d(ctx0, score_cur, n_embd_head, ratio*n_blocks, score_cur->nb[1],
+            ggml_row_size(score_cur->type, n_embd_head)));
+    score_cur = ggml_reshape_3d(ctx0, score_cur, n_embd_head, ratio, n_blocks);
+
+    ggml_tensor * values = ggml_concat(ctx0, kv_prev, kv_cur, 1);
+    ggml_tensor * scores = ggml_concat(ctx0, score_prev, score_cur, 1);
+
+    values = ggml_cont(ctx0, ggml_permute(ctx0, values, 1, 0, 2, 3));
+    scores = ggml_cont(ctx0, ggml_permute(ctx0, scores, 1, 0, 2, 3));
+
+    ggml_tensor * weights = ggml_soft_max(ctx0, scores);
+    ggml_tensor * comp = ggml_mul(ctx0, values, weights);
+    comp = ggml_sum_rows(ctx0, comp);
+    comp = ggml_cont(ctx0, ggml_permute(ctx0, comp, 1, 0, 2, 3));
+    cb(comp, name, il);
+
+    comp = build_norm(comp, norm, nullptr, LLM_NORM_RMS, il);
+    cb(comp, name, il);
+
+    ggml_tensor * comp_nope = ggml_view_3d(ctx0, comp, n_embd_head_nope, 1, n_blocks,
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head),
+            0);
+    ggml_tensor * comp_pe = ggml_view_3d(ctx0, comp, n_embd_head_rope, 1, n_blocks,
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head),
+            ggml_row_size(comp->type, n_embd_head_nope));
+
+    comp_pe = ggml_rope_ext(ctx0, comp_pe, comp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig,
+            hparams.dsv4_compress_rope_base, freq_scale, ext_factor,
+            dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
+    cb(comp_pe, name, il);
+
+    comp = ggml_concat(ctx0, comp_nope, comp_pe, 0);
+    cb(comp, name, il);
+
+    return comp;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_lid_top_k(
+        const llama_model & model,
+        llm_graph_input_dsv4 * inp_dsv4,
+        ggml_tensor * qr,
+        ggml_tensor * cur,
+        ggml_tensor * inp_pos,
+        int il) const {
+    const auto & layer = model.layers[il];
+    const auto & inp_lid = inp_dsv4->get_lid();
+    const int64_t n_embd_indexer_head      = hparams.indexer_head_size;
+    const int64_t n_embd_indexer_head_rope = hparams.n_rot();
+    const int64_t n_embd_indexer_head_nope = n_embd_indexer_head - n_embd_indexer_head_rope;
+    const int64_t n_indexer_head           = hparams.indexer_n_head;
+    const int64_t nt                       = cur->ne[1];
+
+    GGML_ASSERT(inp_lid.kq_mask);
+    GGML_ASSERT(inp_lid.k_rot);
+    GGML_ASSERT(n_embd_indexer_head >= n_embd_indexer_head_rope);
+
+    ggml_tensor * indexer_q = build_lora_mm(layer.indexer_attn_q_b, qr);
+    indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, nt);
+    cb(indexer_q, "lid_q", il);
+
+    ggml_tensor * indexer_q_nope = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_nope, n_indexer_head, nt,
+            ggml_row_size(indexer_q->type, n_embd_indexer_head),
+            ggml_row_size(indexer_q->type, n_embd_indexer_head)*n_indexer_head,
+            0);
+    ggml_tensor * indexer_q_pe = ggml_view_3d(ctx0, indexer_q, n_embd_indexer_head_rope, n_indexer_head, nt,
+            ggml_row_size(indexer_q->type, n_embd_indexer_head),
+            ggml_row_size(indexer_q->type, n_embd_indexer_head)*n_indexer_head,
+            ggml_row_size(indexer_q->type, n_embd_indexer_head_nope));
+
+    indexer_q_pe = ggml_rope_ext(ctx0, indexer_q_pe, inp_pos, nullptr, n_embd_indexer_head_rope,
+            rope_type, n_ctx_orig, hparams.dsv4_compress_rope_base, freq_scale,
+            ext_factor, dsv4_rope_attn_factor(freq_scale, ext_factor), beta_fast, beta_slow);
+    cb(indexer_q_pe, "lid_q_pe", il);
+
+    indexer_q = ggml_concat(ctx0, indexer_q_nope, indexer_q_pe, 0);
+    indexer_q = ggml_mul_mat(ctx0, inp_lid.k_rot, indexer_q);
+    cb(indexer_q, "lid_q_rot", il);
+
+    ggml_tensor * indexer_weights = build_lora_mm(layer.indexer_proj, cur);
+    indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f/sqrtf(float(n_embd_indexer_head*n_indexer_head)));
+    cb(indexer_weights, "lid_weights", il);
+
+    ggml_tensor * indexer_k = inp_dsv4->mctx->get_lid()->get_k(ctx0, il);
+    const int64_t n_lid = inp_lid.kq_mask->ne[0];
+    GGML_ASSERT(n_lid > 0);
+    GGML_ASSERT(n_lid <= indexer_k->ne[2]);
+
+    indexer_k = ggml_view_4d(ctx0, indexer_k,
+            indexer_k->ne[0], indexer_k->ne[1], n_lid, indexer_k->ne[3],
+            indexer_k->nb[1], indexer_k->nb[2], indexer_k->nb[3], 0);
+    cb(indexer_k, "lid_k", il);
+
+    const int64_t n_stream = indexer_k->ne[3];
+    indexer_q = ggml_view_4d(ctx0, indexer_q,
+            indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream,
+            indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
+    indexer_weights = ggml_view_4d(ctx0, indexer_weights,
+            indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream,
+            indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
+
+    indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
+    cb(indexer_q, "lid_q", il);
+    indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
+    cb(indexer_k, "lid_k", il);
+
+    ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
+    cb(indexer_kq, "lid_kq", il);
+
+    indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
+    cb(indexer_kq, "lid_kq", il);
+
+    ggml_tensor * indexer_score = ggml_relu(ctx0, indexer_kq);
+    indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
+    indexer_score = ggml_sum_rows(ctx0, indexer_score);
+    indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
+    cb(indexer_score, "lid_score", il);
+
+    indexer_score = ggml_add(ctx0, indexer_score, inp_lid.kq_mask);
+    cb(indexer_score, "lid_score_masked", il);
+
+    const uint32_t n_top_k = indexer_score->ne[0] < hparams.indexer_top_k ? indexer_score->ne[0] : hparams.indexer_top_k;
+    ggml_tensor * top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
+    cb(top_k, "lid_top_k", il);
+
+    return top_k;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_top_k_mask(
+        ggml_tensor * kq_mask,
+        ggml_tensor * top_k,
+        const char * name,
+        int il) const {
+    GGML_ASSERT(kq_mask);
+    GGML_ASSERT(top_k);
+
+    ggml_tensor * kq_mask_all = ggml_fill(ctx0, kq_mask, -INFINITY);
+    kq_mask_all = ggml_view_4d(ctx0, kq_mask_all, 1, kq_mask_all->ne[0], kq_mask_all->ne[1], kq_mask_all->ne[3],
+            kq_mask_all->nb[0], kq_mask_all->nb[1], kq_mask_all->nb[2], 0);
+
+    ggml_tensor * top_k_3d = ggml_view_4d(ctx0, top_k, top_k->ne[0], top_k->ne[1], top_k->ne[3], 1,
+            top_k->nb[1], top_k->nb[2], top_k->ne[3]*top_k->nb[3], 0);
+
+    ggml_tensor * zeros = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, top_k_3d->ne[0], top_k_3d->ne[1], top_k_3d->ne[2]);
+    zeros = ggml_fill(ctx0, zeros, 0.0f);
+
+    ggml_tensor * kq_mask_top_k = ggml_set_rows(ctx0, kq_mask_all, zeros, top_k_3d);
+    kq_mask_top_k = ggml_view_4d(ctx0, kq_mask_top_k,
+            kq_mask_top_k->ne[1], kq_mask_top_k->ne[2], 1, kq_mask_top_k->ne[3],
+            kq_mask_top_k->nb[2], kq_mask_top_k->nb[3], kq_mask_top_k->nb[3], 0);
+
+    kq_mask_top_k = ggml_add(ctx0, kq_mask_top_k, kq_mask);
+    cb(kq_mask_top_k, name, il);
+
+    return kq_mask_top_k;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_csa_lid_attention(
+        const llama_model & model,
+        llm_graph_input_dsv4 * inp_dsv4,
+        llm_graph_input_dsv4_raw * inp_attn,
+        ggml_tensor * q,
+        ggml_tensor * kv,
+        ggml_tensor * qr,
+        ggml_tensor * cur,
+        ggml_tensor * inp_pos,
+        ggml_tensor * sinks,
+        float kq_scale,
+        int il) const {
+    const auto & inp_csa = inp_dsv4->get_csa();
+    GGML_ASSERT(inp_csa.kq_mask);
+    GGML_ASSERT(inp_attn->self_k_rot == nullptr);
+
+    ggml_tensor * top_k = build_lid_top_k(model, inp_dsv4, qr, cur, inp_pos, il);
+
+    ggml_build_forward_expand(gf, q);
+    ggml_build_forward_expand(gf, kv);
+
+    const llama_kv_cache_dsv4_raw_context * mctx_raw = inp_attn->mctx;
+
+    ggml_build_forward_expand(gf, mctx_raw->cpy_k(ctx0, kv, inp_attn->get_k_idxs(), il));
+
+    ggml_tensor * raw_k = mctx_raw->get_k(ctx0, il);
+    cb(raw_k, "csa_raw_k", il);
+
+    ggml_tensor * csa_k = inp_dsv4->mctx->get_csa()->get_k(ctx0, il);
+    const int64_t n_csa = inp_csa.kq_mask->ne[0];
+    GGML_ASSERT(n_csa > 0);
+    GGML_ASSERT(n_csa <= csa_k->ne[2]);
+
+    csa_k = ggml_view_4d(ctx0, csa_k,
+            csa_k->ne[0], csa_k->ne[1], n_csa, csa_k->ne[3],
+            csa_k->nb[1], csa_k->nb[2], csa_k->nb[3], 0);
+    cb(csa_k, "csa_comp_k", il);
+
+    raw_k = dsv4_repeat_streams(ctx0, raw_k, csa_k->ne[3]);
+
+    ggml_tensor * k_all = ggml_concat(ctx0, raw_k, csa_k, 2);
+    cb(k_all, "csa_k_all", il);
+
+    ggml_tensor * raw_mask = inp_attn->get_kq_mask();
+    ggml_tensor * csa_mask = build_top_k_mask(inp_csa.kq_mask, top_k, "csa_top_k_mask", il);
+    const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || csa_mask->ne[3] == 1);
+    if (use_fattn && csa_mask->type != GGML_TYPE_F16) {
+        csa_mask = ggml_cast(ctx0, csa_mask, GGML_TYPE_F16);
+    }
+    if (raw_mask->type != csa_mask->type) {
+        raw_mask = ggml_cast(ctx0, raw_mask, csa_mask->type);
+    }
+
+    ggml_tensor * kq_mask = ggml_concat(ctx0, raw_mask, csa_mask, 0);
+    cb(kq_mask, "csa_lid_kq_mask", il);
+
+    ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
+    ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    cb(out, "attn_csa_lid", il);
+
+    return out;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_hca_attention(
+        llm_graph_input_dsv4 * inp_dsv4,
+        llm_graph_input_dsv4_raw * inp_attn,
+        ggml_tensor * q,
+        ggml_tensor * kv,
+        ggml_tensor * sinks,
+        float kq_scale,
+        int il) const {
+    const auto & inp_hca = inp_dsv4->get_hca();
+    GGML_ASSERT(inp_hca.kq_mask);
+    GGML_ASSERT(inp_attn->self_k_rot == nullptr);
+
+    ggml_build_forward_expand(gf, q);
+    ggml_build_forward_expand(gf, kv);
+
+    const llama_kv_cache_dsv4_raw_context * mctx_raw = inp_attn->mctx;
+
+    ggml_build_forward_expand(gf, mctx_raw->cpy_k(ctx0, kv, inp_attn->get_k_idxs(), il));
+
+    ggml_tensor * raw_k = mctx_raw->get_k(ctx0, il);
+    cb(raw_k, "hca_raw_k", il);
+
+    ggml_tensor * hca_k = inp_dsv4->mctx->get_hca()->get_k(ctx0, il);
+    const int64_t n_hca = inp_hca.kq_mask->ne[0];
+    GGML_ASSERT(n_hca > 0);
+    GGML_ASSERT(n_hca <= hca_k->ne[2]);
+
+    hca_k = ggml_view_4d(ctx0, hca_k,
+            hca_k->ne[0], hca_k->ne[1], n_hca, hca_k->ne[3],
+            hca_k->nb[1], hca_k->nb[2], hca_k->nb[3], 0);
+    cb(hca_k, "hca_comp_k", il);
+
+    raw_k = dsv4_repeat_streams(ctx0, raw_k, hca_k->ne[3]);
+
+    ggml_tensor * k_all = ggml_concat(ctx0, raw_k, hca_k, 2);
+    cb(k_all, "hca_k_all", il);
+
+    ggml_tensor * raw_mask = inp_attn->get_kq_mask();
+    ggml_tensor * hca_mask = inp_hca.kq_mask;
+    const bool use_fattn = cparams.flash_attn && (!cparams.kv_unified || hca_mask->ne[3] == 1);
+    if (use_fattn && hca_mask->type != GGML_TYPE_F16) {
+        hca_mask = ggml_cast(ctx0, hca_mask, GGML_TYPE_F16);
+    }
+    if (raw_mask->type != hca_mask->type) {
+        raw_mask = ggml_cast(ctx0, raw_mask, hca_mask->type);
+    }
+
+    ggml_tensor * kq_mask = ggml_concat(ctx0, raw_mask, hca_mask, 0);
+    cb(kq_mask, "hca_kq_mask", il);
+
+    ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
+    ggml_tensor * out = build_attn_mha(q, k_all, k_all, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    cb(out, "attn_hca", il);
+
+    return out;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_raw_attention(
+        llm_graph_input_dsv4_raw * inp_attn,
+        ggml_tensor * q,
+        ggml_tensor * kv,
+        ggml_tensor * sinks,
+        float kq_scale,
+        int il) const {
+    GGML_ASSERT(hparams.is_swa(il));
+
+    ggml_tensor * k_rot = inp_attn->self_k_rot;
+
+    if (k_rot) {
+        q  = ggml_mul_mat(ctx0, k_rot, q);
+        kv = ggml_mul_mat(ctx0, k_rot, kv);
+    }
+
+    ggml_build_forward_expand(gf, q);
+    ggml_build_forward_expand(gf, kv);
+
+    const llama_kv_cache_dsv4_raw_context * mctx_cur = inp_attn->mctx;
+
+    ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, kv, inp_attn->get_k_idxs(), il));
+
+    ggml_tensor * kq_mask = inp_attn->get_kq_mask();
+
+    ggml_tensor * k = mctx_cur->get_k(ctx0, il);
+    k = dsv4_repeat_streams(ctx0, k, kq_mask->ne[3]);
+
+    ggml_tensor * kq_b = dsv4_build_kq_zero_bias(ctx0, cparams, kq_mask, q->ne[1]);
+    ggml_tensor * out = build_attn_mha(q, k, k, kq_b, kq_mask, sinks, nullptr, kq_scale, il);
+    cb(out, "attn_raw", il);
+
+    return out;
+}
+
+ggml_tensor * llama_model_deepseek4::graph::build_attention(
+        const llama_model & model,
+        llm_graph_input_dsv4 * inp_dsv4,
+        ggml_tensor * cur,
+        ggml_tensor * inp_pos,
+        int il) const {
+    const auto & layer = model.layers[il];
+    llm_graph_input_dsv4_raw * inp_attn = inp_dsv4->get_raw();
+
+    const int64_t n_embd_head      = hparams.n_embd_head_k();
+    const int64_t n_embd_head_rope = hparams.n_rot();
+    const int64_t n_embd_head_nope = n_embd_head - n_embd_head_rope;
+    const int64_t n_groups         = hparams.dsv4_o_group_count;
+    const int64_t n_heads_group    = n_head / n_groups;
+    const int64_t o_lora_rank      = hparams.dsv4_o_lora_rank;
+    const int64_t o_group_dim      = n_heads_group*n_embd_head;
+    const int64_t nt               = cur->ne[1];
+
+    GGML_ASSERT(n_embd_head == n_embd_head_v);
+    GGML_ASSERT(n_head % n_groups == 0);
+
+    const bool use_compress_rope = hparams.dsv4_compress_ratios[il] != 0;
+    const float freq_base_l      = use_compress_rope ? hparams.dsv4_compress_rope_base : freq_base;
+    const float freq_scale_l     = use_compress_rope ? freq_scale : 1.0f;
+    const float ext_factor_l     = use_compress_rope ? ext_factor : 0.0f;
+    const float attn_factor_l    = dsv4_rope_attn_factor(freq_scale_l, ext_factor_l);
+    const float beta_fast_l      = use_compress_rope ? beta_fast : 0.0f;
+    const float beta_slow_l      = use_compress_rope ? beta_slow : 0.0f;
+    const int32_t n_ctx_orig_l   = use_compress_rope ? n_ctx_orig : 0;
+
+    ggml_tensor * qr = build_lora_mm(layer.wq_a, cur);
+    cb(qr, "qr", il);
+
+    qr = build_norm(qr, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+    cb(qr, "qr_norm", il);
+
+    ggml_tensor * q = build_lora_mm(layer.wq_b, qr);
+    q = ggml_reshape_3d(ctx0, q, n_embd_head, n_head, nt);
+    q = ggml_rms_norm(ctx0, q, norm_rms_eps);
+    cb(q, "q_norm", il);
+
+    ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_nope, n_head, nt,
+            ggml_row_size(q->type, n_embd_head),
+            ggml_row_size(q->type, n_embd_head)*n_head,
+            0);
+    ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_rope, n_head, nt,
+            ggml_row_size(q->type, n_embd_head),
+            ggml_row_size(q->type, n_embd_head)*n_head,
+            ggml_row_size(q->type, n_embd_head_nope));
+    q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+            freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
+    cb(q_pe, "q_pe", il);
+    q = ggml_concat(ctx0, q_nope, q_pe, 0);
+    cb(q, "q", il);
+
+    ggml_tensor * kv = build_lora_mm(layer.wkv, cur);
+    kv = build_norm(kv, layer.attn_kv_norm, nullptr, LLM_NORM_RMS, il);
+    kv = ggml_reshape_3d(ctx0, kv, n_embd_head, 1, nt);
+    cb(kv, "kv_norm", il);
+
+    ggml_tensor * kv_nope = ggml_view_3d(ctx0, kv, n_embd_head_nope, 1, nt,
+            ggml_row_size(kv->type, n_embd_head),
+            ggml_row_size(kv->type, n_embd_head),
+            0);
+    ggml_tensor * kv_pe = ggml_view_3d(ctx0, kv, n_embd_head_rope, 1, nt,
+            ggml_row_size(kv->type, n_embd_head),
+            ggml_row_size(kv->type, n_embd_head),
+            ggml_row_size(kv->type, n_embd_head_nope));
+    kv_pe = ggml_rope_ext(ctx0, kv_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+            freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
+    cb(kv_pe, "kv_pe", il);
+    kv = ggml_concat(ctx0, kv_nope, kv_pe, 0);
+    cb(kv, "kv", il);
+
+    const int64_t ratio = hparams.dsv4_compress_ratios[il];
+
+    ggml_tensor * hca_state_kv    = nullptr;
+    ggml_tensor * hca_state_score = nullptr;
+    if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) {
+        hca_state_kv = build_lora_mm(layer.attn_comp_wkv, cur);
+        cb(hca_state_kv, "hca_state_kv", il);
+
+        hca_state_score = build_lora_mm(layer.attn_comp_wgate, cur);
+        cb(hca_state_score, "hca_state_score", il);
+
+        ggml_tensor * ape = layer.attn_comp_ape;
+
+        ggml_tensor * ape_rows = ggml_get_rows(ctx0, ape, inp_dsv4->get_hca().state_pos);
+        hca_state_score = ggml_add(ctx0, hca_state_score, ape_rows);
+        cb(hca_state_score, "hca_state_score_ape", il);
+
+    }
+
+    if (ratio == DSV4_CSA_RATIO && inp_dsv4->get_csa().state_pos) {
+        ggml_tensor * csa_state_kv = build_lora_mm(layer.attn_comp_wkv, cur);
+        cb(csa_state_kv, "csa_state_kv", il);
+
+        ggml_tensor * csa_state_score = build_lora_mm(layer.attn_comp_wgate, cur);
+        cb(csa_state_score, "csa_state_score", il);
+
+        ggml_tensor * csa_ape = layer.attn_comp_ape;
+
+        ggml_tensor * csa_ape_rows = ggml_get_rows(ctx0, csa_ape, inp_dsv4->get_csa().state_pos);
+        csa_state_score = ggml_add(ctx0, csa_state_score, csa_ape_rows);
+        cb(csa_state_score, "csa_state_score_ape", il);
+
+        GGML_ASSERT(inp_dsv4->get_csa().state_write_idxs);
+
+        ggml_tensor * csa_source_kv = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_csa_state()->get_kv(ctx0, il), csa_state_kv, 1);
+        ggml_tensor * csa_source_score = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_csa_state()->get_score(ctx0, il), csa_state_score, 1);
+
+        ggml_tensor * kv_comp_csa_state = build_overlap_compressed_kv_from_state(
+                csa_source_kv,
+                csa_source_score,
+                inp_dsv4->get_csa().state_read_idxs,
+                inp_dsv4->get_csa().state_write_pos,
+                layer.attn_comp_norm,
+                DSV4_CSA_RATIO,
+                n_embd_head,
+                "csa_state_compress",
+                il);
+
+        ggml_build_forward_expand(gf, inp_dsv4->mctx->get_csa()->cpy_k(ctx0,
+                    kv_comp_csa_state, inp_dsv4->get_csa().state_write_idxs, il));
+
+        csa_state_kv    = dsv4_with_zero_dep(ctx0, csa_state_kv,    kv_comp_csa_state);
+        csa_state_score = dsv4_with_zero_dep(ctx0, csa_state_score, kv_comp_csa_state);
+
+        ggml_tensor * csa_persist_kv = ggml_get_rows(ctx0, csa_state_kv, inp_dsv4->get_csa().state_persist_src_idxs);
+        ggml_tensor * csa_persist_score = ggml_get_rows(ctx0, csa_state_score, inp_dsv4->get_csa().state_persist_src_idxs);
+
+        csa_state_kv = inp_dsv4->mctx->get_csa_state()->cpy_kv(ctx0,
+                csa_persist_kv, inp_dsv4->get_csa().state_persist_dst_idxs, il);
+        csa_state_score = inp_dsv4->mctx->get_csa_state()->cpy_score(ctx0,
+                csa_persist_score, inp_dsv4->get_csa().state_persist_dst_idxs, il);
+
+        ggml_build_forward_expand(gf, csa_state_kv);
+        ggml_build_forward_expand(gf, csa_state_score);
+
+        ggml_tensor * lid_state_kv = build_lora_mm(layer.indexer_comp_wkv, cur);
+        cb(lid_state_kv, "lid_state_kv", il);
+
+        ggml_tensor * lid_state_score = build_lora_mm(layer.indexer_comp_wgate, cur);
+        cb(lid_state_score, "lid_state_score", il);
+
+        ggml_tensor * lid_ape = layer.indexer_comp_ape;
+
+        ggml_tensor * lid_ape_rows = ggml_get_rows(ctx0, lid_ape, inp_dsv4->get_lid().state_pos);
+        lid_state_score = ggml_add(ctx0, lid_state_score, lid_ape_rows);
+        cb(lid_state_score, "lid_state_score_ape", il);
+
+        GGML_ASSERT(inp_dsv4->get_lid().state_write_idxs);
+
+        ggml_tensor * lid_source_kv = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_lid_state()->get_kv(ctx0, il), lid_state_kv, 1);
+        ggml_tensor * lid_source_score = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_lid_state()->get_score(ctx0, il), lid_state_score, 1);
+
+        ggml_tensor * kv_comp_lid_state = build_overlap_compressed_kv_from_state(
+                lid_source_kv,
+                lid_source_score,
+                inp_dsv4->get_lid().state_read_idxs,
+                inp_dsv4->get_lid().state_write_pos,
+                layer.indexer_comp_norm,
+                DSV4_CSA_RATIO,
+                hparams.indexer_head_size,
+                "lid_state_compress",
+                il);
+
+        if (inp_dsv4->get_lid().k_rot) {
+            kv_comp_lid_state = ggml_mul_mat(ctx0, inp_dsv4->get_lid().k_rot, kv_comp_lid_state);
+            cb(kv_comp_lid_state, "lid_state_compress_rot", il);
+        }
+
+        ggml_build_forward_expand(gf, inp_dsv4->mctx->get_lid()->cpy_k(ctx0,
+                    kv_comp_lid_state, inp_dsv4->get_lid().state_write_idxs, il));
+
+        lid_state_kv    = dsv4_with_zero_dep(ctx0, lid_state_kv,    kv_comp_lid_state);
+        lid_state_score = dsv4_with_zero_dep(ctx0, lid_state_score, kv_comp_lid_state);
+
+        ggml_tensor * lid_persist_kv = ggml_get_rows(ctx0, lid_state_kv, inp_dsv4->get_lid().state_persist_src_idxs);
+        ggml_tensor * lid_persist_score = ggml_get_rows(ctx0, lid_state_score, inp_dsv4->get_lid().state_persist_src_idxs);
+
+        lid_state_kv = inp_dsv4->mctx->get_lid_state()->cpy_kv(ctx0,
+                lid_persist_kv, inp_dsv4->get_lid().state_persist_dst_idxs, il);
+        lid_state_score = inp_dsv4->mctx->get_lid_state()->cpy_score(ctx0,
+                lid_persist_score, inp_dsv4->get_lid().state_persist_dst_idxs, il);
+
+        ggml_build_forward_expand(gf, lid_state_kv);
+        ggml_build_forward_expand(gf, lid_state_score);
+    }
+
+    ggml_tensor * hca_state_dep = nullptr;
+    if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_write_idxs) {
+        GGML_ASSERT(hca_state_kv);
+        GGML_ASSERT(hca_state_score);
+
+        ggml_tensor * hca_source_kv = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_hca_state()->get_kv(ctx0, il), hca_state_kv, 1);
+        ggml_tensor * hca_source_score = ggml_concat(ctx0,
+                inp_dsv4->mctx->get_hca_state()->get_score(ctx0, il), hca_state_score, 1);
+
+        ggml_tensor * kv_comp_hca = build_hca_compressed_kv_from_state(
+                hca_source_kv,
+                hca_source_score,
+                inp_dsv4->get_hca().state_read_idxs,
+                inp_dsv4->get_hca().state_write_pos,
+                layer.attn_comp_norm,
+                n_embd_head,
+                "hca_state_compress",
+                il);
+
+        ggml_build_forward_expand(gf, inp_dsv4->mctx->get_hca()->cpy_k(ctx0,
+                    kv_comp_hca, inp_dsv4->get_hca().state_write_idxs, il));
+        hca_state_dep = kv_comp_hca;
+    }
+
+    if (ratio == DSV4_HCA_RATIO && inp_dsv4->get_hca().state_pos) {
+        GGML_ASSERT(hca_state_kv);
+        GGML_ASSERT(hca_state_score);
+
+        hca_state_kv    = dsv4_with_zero_dep(ctx0, hca_state_kv,    hca_state_dep);
+        hca_state_score = dsv4_with_zero_dep(ctx0, hca_state_score, hca_state_dep);
+
+        ggml_tensor * hca_persist_kv = ggml_get_rows(ctx0, hca_state_kv, inp_dsv4->get_hca().state_persist_src_idxs);
+        ggml_tensor * hca_persist_score = ggml_get_rows(ctx0, hca_state_score, inp_dsv4->get_hca().state_persist_src_idxs);
+
+        hca_state_kv = inp_dsv4->mctx->get_hca_state()->cpy_kv(ctx0,
+                hca_persist_kv, inp_dsv4->get_hca().state_persist_dst_idxs, il);
+        hca_state_score = inp_dsv4->mctx->get_hca_state()->cpy_score(ctx0,
+                hca_persist_score, inp_dsv4->get_hca().state_persist_dst_idxs, il);
+
+        ggml_build_forward_expand(gf, hca_state_kv);
+        ggml_build_forward_expand(gf, hca_state_score);
+    }
+
+    ggml_tensor * out = nullptr;
+    if (ratio == DSV4_CSA_RATIO &&
+            inp_dsv4->get_csa().kq_mask &&
+            inp_dsv4->get_lid().kq_mask &&
+            inp_dsv4->get_lid().k_rot &&
+            inp_attn->self_k_rot == nullptr) {
+        out = build_csa_lid_attention(model, inp_dsv4, inp_attn, q, kv, qr, cur, inp_pos, layer.attn_sinks,
+                1.0f/sqrtf(float(n_embd_head)), il);
+    } else if (ratio == DSV4_HCA_RATIO &&
+            inp_dsv4->get_hca().kq_mask &&
+            inp_attn->self_k_rot == nullptr) {
+        out = build_hca_attention(inp_dsv4, inp_attn, q, kv, layer.attn_sinks,
+                1.0f/sqrtf(float(n_embd_head)), il);
+    } else {
+        out = build_raw_attention(inp_attn, q, kv, layer.attn_sinks,
+                1.0f/sqrtf(float(n_embd_head)), il);
+    }
+
+    out = ggml_reshape_3d(ctx0, out, n_embd_head, n_head, nt);
+    ggml_tensor * out_nope = ggml_view_3d(ctx0, out, n_embd_head_nope, n_head, nt,
+            ggml_row_size(out->type, n_embd_head),
+            ggml_row_size(out->type, n_embd_head)*n_head,
+            0);
+    ggml_tensor * out_pe = ggml_view_3d(ctx0, out, n_embd_head_rope, n_head, nt,
+            ggml_row_size(out->type, n_embd_head),
+            ggml_row_size(out->type, n_embd_head)*n_head,
+            ggml_row_size(out->type, n_embd_head_nope));
+    out_pe = ggml_rope_ext_back(ctx0, out_pe, inp_pos, nullptr, n_embd_head_rope, rope_type, n_ctx_orig_l,
+            freq_base_l, freq_scale_l, ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
+    out = ggml_concat(ctx0, out_nope, out_pe, 0);
+    cb(out, "attn_derope", il);
+
+    out = ggml_reshape_3d(ctx0, out, o_group_dim, n_groups, nt);
+    out = ggml_permute(ctx0, out, 0, 2, 1, 3);
+    ggml_tensor * oa = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, layer.wo_a, layer.wo_a->ne[0], o_lora_rank, n_groups), out);
+    cb(oa, "attn_wo_a", il);
+    oa = ggml_permute(ctx0, oa, 0, 2, 1, 3);
+    oa = ggml_cont_2d(ctx0, oa, o_lora_rank*n_groups, nt);
+
+    out = build_lora_mm(layer.wo_b, oa);
+    cb(out, "attn_out", il);
+
+    return out;
+}
+
+llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    ggml_tensor * cur;
+
+    ggml_tensor * inp = build_inp_embd(model.tok_embd);
+    ggml_tensor * inp_pos = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+    llm_graph_input_dsv4 * inp_dsv4 = build_inp_dsv4();
+    llm_graph_input_dsv4_raw * inp_attn = inp_dsv4->get_raw();
+    ggml_build_forward_expand(gf, inp_attn->self_kq_mask);
+
+    const int64_t hc = hparams.dsv4_hc_mult;
+    ggml_tensor * inpL = ggml_reshape_3d(ctx0, inp, n_embd, 1, n_tokens);
+    inpL = ggml_repeat_4d(ctx0, inpL, n_embd, hc, n_tokens, 1);
+    cb(inpL, "hc_init", -1);
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * residual = inpL;
+        ggml_tensor * post = nullptr;
+        ggml_tensor * comb = nullptr;
+
+        cur = build_hc_pre(inpL,
+                model.layers[il].hc_attn_fn,
+                model.layers[il].hc_attn_scale,
+                model.layers[il].hc_attn_base,
+                &post, &comb, il);
+        cb(cur, "hc_attn_pre", il);
+
+        cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        cur = build_attention(model, inp_dsv4, cur, inp_pos, il);
+
+        inpL = build_hc_post(cur, residual, post, comb, il);
+        cb(inpL, "hc_attn_post", il);
+
+        residual = inpL;
+        cur = build_hc_pre(inpL,
+                model.layers[il].hc_ffn_fn,
+                model.layers[il].hc_ffn_scale,
+                model.layers[il].hc_ffn_base,
+                &post, &comb, il);
+        cb(cur, "hc_ffn_pre", il);
+
+        cur = build_norm(cur, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        const auto & layer = model.layers[il];
+        ggml_tensor * selected_experts = nullptr;
+        ggml_tensor * exp_probs_b = layer.ffn_exp_probs_b;
+        if ((uint32_t) il < hparams.dsv4_hash_layer_count) {
+            selected_experts = ggml_get_rows(ctx0, layer.ffn_gate_tid2eid, res->t_inp_tokens);
+            exp_probs_b = nullptr;
+        }
+
+        ggml_tensor * moe_out = build_moe_ffn(cur,
+                layer.ffn_gate_inp,
+                layer.ffn_up_exps,
+                layer.ffn_gate_exps,
+                layer.ffn_down_exps,
+                exp_probs_b,
+                n_expert, hparams.n_expert_used,
+                LLM_FFN_SILU, hparams.expert_weights_norm,
+                hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il,
+                nullptr,
+                nullptr,
+                nullptr,
+                nullptr,
+                nullptr,
+                selected_experts);
+        cb(moe_out, "ffn_moe_out", il);
+
+        ggml_tensor * ffn_shexp = build_ffn(cur,
+                layer.ffn_up_shexp, nullptr, nullptr,
+                layer.ffn_gate_shexp, nullptr, nullptr,
+                layer.ffn_down_shexp, nullptr, nullptr,
+                nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "ffn_shexp", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "ffn_out", il);
+
+        inpL = build_hc_post(cur, residual, post, comb, il);
+        inpL = build_cvec(inpL, il);
+        cb(inpL, "l_out", il);
+    }
+
+    if (inp_out_ids) {
+        ggml_tensor * flat = ggml_reshape_2d(ctx0, inpL, n_embd*hc, n_tokens);
+        flat = ggml_get_rows(ctx0, flat, inp_out_ids);
+        inpL = ggml_reshape_3d(ctx0, flat, n_embd, hc, n_outputs);
+    }
+
+    cur = build_hc_head(inpL, model.hc_head_fn, model.hc_head_scale, model.hc_head_base);
+    cb(cur, "hc_head", -1);
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
index d89ab96d0..7a52e7bc1 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -1085,6 +1085,121 @@ struct llama_model_deepseek32 : public llama_model_base {
 };
 
 
+struct llama_model_deepseek4 : public llama_model_base {
+    llama_model_deepseek4(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        ggml_tensor * build_hc_pre(
+                ggml_tensor * x,
+                ggml_tensor * hc_fn,
+                ggml_tensor * hc_scale,
+                ggml_tensor * hc_base,
+                ggml_tensor ** post,
+                ggml_tensor ** comb,
+                int il) const;
+
+        ggml_tensor * build_hc_post(
+                ggml_tensor * x,
+                ggml_tensor * residual,
+                ggml_tensor * post,
+                ggml_tensor * comb,
+                int il) const;
+
+        ggml_tensor * build_hc_head(
+                ggml_tensor * x,
+                ggml_tensor * hc_fn,
+                ggml_tensor * hc_scale,
+                ggml_tensor * hc_base) const;
+
+        ggml_tensor * build_attention(
+                const llama_model & model,
+                llm_graph_input_dsv4 * inp_dsv4,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                int il) const;
+
+        ggml_tensor * build_hca_compressed_kv_from_state(
+                ggml_tensor * kv_state,
+                ggml_tensor * score_state,
+                ggml_tensor * state_read_idxs,
+                ggml_tensor * comp_pos,
+                ggml_tensor * norm,
+                int64_t n_embd_head,
+                const char * name,
+                int il) const;
+
+        ggml_tensor * build_overlap_compressed_kv_from_state(
+                ggml_tensor * kv_state,
+                ggml_tensor * score_state,
+                ggml_tensor * state_read_idxs,
+                ggml_tensor * comp_pos,
+                ggml_tensor * norm,
+                int64_t ratio,
+                int64_t n_embd_head,
+                const char * name,
+                int il) const;
+
+        ggml_tensor * build_lid_top_k(
+                const llama_model & model,
+                llm_graph_input_dsv4 * inp_dsv4,
+                ggml_tensor * qr,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                int il) const;
+
+        ggml_tensor * build_top_k_mask(
+                ggml_tensor * kq_mask,
+                ggml_tensor * top_k,
+                const char * name,
+                int il) const;
+
+        ggml_tensor * build_csa_lid_attention(
+                const llama_model & model,
+                llm_graph_input_dsv4 * inp_dsv4,
+                llm_graph_input_dsv4_raw * inp_attn,
+                ggml_tensor * q,
+                ggml_tensor * kv,
+                ggml_tensor * qr,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                ggml_tensor * sinks,
+                float kq_scale,
+                int il) const;
+
+        ggml_tensor * build_hca_attention(
+                llm_graph_input_dsv4 * inp_dsv4,
+                llm_graph_input_dsv4_raw * inp_attn,
+                ggml_tensor * q,
+                ggml_tensor * kv,
+                ggml_tensor * sinks,
+                float kq_scale,
+                int il) const;
+
+        ggml_tensor * build_raw_attention(
+                llm_graph_input_dsv4_raw * inp_attn,
+                ggml_tensor * q,
+                ggml_tensor * kv,
+                ggml_tensor * sinks,
+                float kq_scale,
+                int il) const;
+
+        ggml_tensor * build_hc_weighted_sum(
+                ggml_tensor * x,
+                ggml_tensor * weights) const;
+
+        ggml_tensor * build_hc_sinkhorn(
+                ggml_tensor * comb,
+                int il) const;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_deepseek2ocr : public llama_model_base {
     llama_model_deepseek2ocr(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;
diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp
index c781d2903..f39abe773 100644
--- a/tests/test-llama-archs.cpp
+++ b/tests/test-llama-archs.cpp
@@ -412,6 +412,9 @@ static bool arch_supported(const llm_arch arch) {
     if (arch == LLM_ARCH_DEEPSEEK2OCR) {
         return false;
     }
+    if (arch == LLM_ARCH_DEEPSEEK4) {
+        return false;
+    }
 
     // FIXME some models are segfaulting with WebGPU:
 #ifdef GGML_USE_WEBGPU

From 16ef2badf623316301df8b4fa27d69b61ef10dca Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 29 Jun 2026 21:04:09 +0800
Subject: [PATCH 219/292] fix superfluous filename defines

---
 common/jinja/caps.cpp    | 3 +++
 common/jinja/lexer.cpp   | 3 +++
 common/jinja/parser.cpp  | 3 +++
 common/jinja/runtime.cpp | 3 +++
 4 files changed, 12 insertions(+)

diff --git a/common/jinja/caps.cpp b/common/jinja/caps.cpp
index ae378ebd4..f7de8b806 100644
--- a/common/jinja/caps.cpp
+++ b/common/jinja/caps.cpp
@@ -9,6 +9,9 @@
 #include <functional>
 #include <sstream>
 
+#ifdef FILENAME
+#undef FILENAME
+#endif
 #define FILENAME "jinja-caps"
 
 using json = nlohmann::ordered_json;
diff --git a/common/jinja/lexer.cpp b/common/jinja/lexer.cpp
index 598982c2f..63740dff6 100644
--- a/common/jinja/lexer.cpp
+++ b/common/jinja/lexer.cpp
@@ -7,6 +7,9 @@
 #include <string>
 #include <vector>
 
+#ifdef FILENAME
+#undef FILENAME
+#endif
 #define FILENAME "jinja-lexer"
 
 namespace jinja {
diff --git a/common/jinja/parser.cpp b/common/jinja/parser.cpp
index 2b25654a7..e9e6dcb7f 100644
--- a/common/jinja/parser.cpp
+++ b/common/jinja/parser.cpp
@@ -8,6 +8,9 @@
 #include <string>
 #include <vector>
 
+#ifdef FILENAME
+#undef FILENAME
+#endif
 #define FILENAME "jinja-parser"
 
 namespace jinja {
diff --git a/common/jinja/runtime.cpp b/common/jinja/runtime.cpp
index 474129df2..afba0025f 100644
--- a/common/jinja/runtime.cpp
+++ b/common/jinja/runtime.cpp
@@ -8,6 +8,9 @@
 #include <memory>
 #include <cmath>
 
+#ifdef FILENAME
+#undef FILENAME
+#endif
 #define FILENAME "jinja-runtime"
 
 bool g_jinja_debug = false;

From 25a1d63f4346b472e508c6dbd9ab2ed1d81ace2e Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <rortlam@redhat.com>
Date: Mon, 29 Jun 2026 15:24:44 +0200
Subject: [PATCH 220/292] vulkan: use flops instead of weight tensor size for
 submission heuristic (#25005)

* vulkan: extract flops calculation into function

* use flops instead of matmul src0 tensor size for submission threshold

* use unsigned ints
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 97 +++++++++++++++++-----------
 1 file changed, 58 insertions(+), 39 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ea9191873..ac021c7a6 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1907,6 +1907,38 @@ static bool vk_enable_sync_logger = false;
 static uint32_t vk_perf_logger_frequency = 1;
 static std::string vk_pipeline_stats_filter;
 
+static uint64_t ggml_vk_get_node_flops(const ggml_tensor * node) {
+    if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
+        const uint64_t m     = node->ne[0];
+        const uint64_t n     = node->ne[1];
+        const uint64_t k     = node->src[1]->ne[0];
+        const uint64_t batch = node->ne[2] * node->ne[3];
+        return m * n * (k + (k - 1)) * batch;
+    }
+    if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) {
+        const ggml_tensor * knl = node->src[0];
+        const uint64_t Cout  = node->ne[2];
+        const uint64_t size_K = node->src[1]->ne[2] * knl->ne[0] * knl->ne[1];
+        const uint64_t size_N = node->ne[3] * node->ne[0] * node->ne[1];
+        return Cout * size_N * (size_K + (size_K - 1));
+    }
+    if (node->op == GGML_OP_CONV_3D) {
+        const ggml_tensor * knl = node->src[0];
+        const uint64_t OC     = ggml_get_op_params_i32(node, 11);
+        const uint64_t IC     = ggml_get_op_params_i32(node, 9);
+        const uint64_t size_K = IC * knl->ne[0] * knl->ne[1] * knl->ne[2];
+        const uint64_t size_N = node->ne[3] / OC * node->ne[0] * node->ne[1] * node->ne[2];
+        return OC * size_N * (size_K + (size_K - 1));
+    }
+    if (node->op == GGML_OP_FLASH_ATTN_EXT) {
+        const ggml_tensor * q = node->src[0];
+        const ggml_tensor * k = node->src[1];
+        const ggml_tensor * v = node->src[2];
+        return 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3];
+    }
+    return 0;
+}
+
 class vk_perf_logger {
   public:
     void print_timings(bool force = false) {
@@ -1955,7 +1987,7 @@ class vk_perf_logger {
     }
 
     std::string get_node_fusion_name(const ggml_tensor * node, const char *fusion_name, uint64_t *n_flops) {
-        *n_flops = 0;
+        *n_flops = ggml_vk_get_node_flops(node);
         std::string fusion_str;
         if (fusion_name) {
             fusion_str = fusion_name + std::string(" ");
@@ -1982,35 +2014,22 @@ class vk_perf_logger {
             if (batch > 1) {
                 name += " batch=" + std::to_string(batch);
             }
-            name = fusion_str + name;
-            *n_flops = m * n * (k + (k - 1)) * batch;
-            return name;
+            return fusion_str + name;
         }
         if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) {
             std::string   name    = ggml_op_name(node->op);
-            ggml_tensor * knl     = node->src[0];
-            uint64_t      OW      = node->ne[0];
-            uint64_t      OH      = node->ne[1];
-            uint64_t      N       = node->ne[3];
+            const ggml_tensor * knl = node->src[0];
             uint64_t      Cout    = node->ne[2];
-            uint64_t      KW      = knl->ne[0];
-            uint64_t      KH      = knl->ne[1];
-            uint64_t      Cin     = node->src[1]->ne[2];
-            // KxCRS @ CRSxNPQ = KxNPQ -> M=K, K=CRS, N=NPQ
-            uint64_t      size_M  = Cout;
-            uint64_t      size_K  = Cin * KW * KH;
-            uint64_t      size_N  = N * OW * OH;
-            *n_flops = size_M * size_N * (size_K + (size_K - 1));
-            name += " M=Cout=" + std::to_string(size_M) + ", K=Cin*KW*KH=" + std::to_string(size_K) +
+            uint64_t      size_K  = node->src[1]->ne[2] * knl->ne[0] * knl->ne[1];
+            uint64_t      size_N  = node->ne[3] * node->ne[0] * node->ne[1];
+            name += " M=Cout=" + std::to_string(Cout) + ", K=Cin*KW*KH=" + std::to_string(size_K) +
                     ", N=N*OW*OH=" + std::to_string(size_N);
-            name = fusion_str + name;
-            return name;
+            return fusion_str + name;
         }
         if (node->op == GGML_OP_RMS_NORM) {
             std::string   name    = ggml_op_name(node->op);
             name += "(" + std::to_string(node->ne[0]) + "," + std::to_string(node->ne[1]) + "," + std::to_string(node->ne[2]) + "," + std::to_string(node->ne[3]) + ")";
-            name = fusion_str + name;
-            return name;
+            return fusion_str + name;
         }
         if (node->op == GGML_OP_FLASH_ATTN_EXT) {
             const ggml_tensor * dst = node;
@@ -2026,7 +2045,6 @@ class vk_perf_logger {
                 " k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " <<
                 " v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " <<
                 " m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")";
-            *n_flops = 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3];
             return name.str();
         }
         if (node->op == GGML_OP_TOP_K) {
@@ -2090,7 +2108,7 @@ struct ggml_backend_vk_context {
     bool do_add_rms_partials_offset_calculation;
     bool do_add_rms_partials;
 
-    uint64_t last_total_mul_mat_bytes {};
+    uint64_t last_total_flops {UINT64_MAX};
 
     // Cache most recent tensor that was converted into prealloc_y, and what pipeline it used to convert.
     vk_pipeline_struct * prealloc_y_last_pipeline_used {};
@@ -16188,22 +16206,23 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     }
 
     // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
-    // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
-    // (and scaled down based on model size, so smaller models submit earlier).
-    int submitted_nodes = 0;
-    int submit_count = 0;
-    uint64_t mul_mat_bytes = 0;
-    uint64_t total_mul_mat_bytes = 0;
-    uint64_t mul_mat_bytes_per_submit = std::min(uint64_t(100*1000*1000), ctx->last_total_mul_mat_bytes / 40u);
+    // Estimate the amount of compute work using flops, and submit every 200 GFLOP
+    // (and scaled down based on total graph flops, so smaller models submit earlier).
+    // Also submit at least every 100 nodes, in case there are workloads without heavy compute.
+    uint32_t submitted_nodes = 0;
+    uint32_t submit_count = 0;
+    uint64_t batch_flops = 0;
+    uint64_t total_flops = 0;
+    uint64_t flops_per_submit = std::min(uint64_t(200'000'000'000), ctx->last_total_flops / 40u);
     for (int i = 0; i < cgraph->n_nodes; i++) {
         if (first_node_in_batch) {
             submit_node_idx = i;
         }
 
-        if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) {
-            auto bytes = ggml_nbytes(cgraph->nodes[i]->src[0]);
-            mul_mat_bytes += bytes;
-            total_mul_mat_bytes += bytes;
+        {
+            auto node_flops = ggml_vk_get_node_flops(cgraph->nodes[i]);
+            batch_flops += node_flops;
+            total_flops += node_flops;
         }
 
         // op_srcs_fused_elementwise indicates whether an op's srcs all contribute to
@@ -16415,8 +16434,8 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining)
         bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5;
-        bool submit = ((uint32_t)submitted_nodes >= ctx->device->max_nodes_per_submit) ||
-                      (mul_mat_bytes_per_submit != 0 && mul_mat_bytes >= mul_mat_bytes_per_submit) ||
+        bool submit = (submitted_nodes >= ctx->device->max_nodes_per_submit) ||
+                      (flops_per_submit != 0 && batch_flops >= flops_per_submit) ||
                       (i + ctx->num_additional_fused_ops >= last_node) ||
                       (almost_ready && !ctx->almost_ready_fence_pending);
 
@@ -16450,9 +16469,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         if (submit && enqueued) {
             first_node_in_batch = true;
             submitted_nodes = 0;
-            mul_mat_bytes = 0;
+            batch_flops = 0;
             if (submit_count < 3) {
-                mul_mat_bytes_per_submit *= 2;
+                flops_per_submit *= 2;
             }
             submit_count++;
         }
@@ -16461,7 +16480,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ctx->fused_ops_write_mask = 0;
     }
 
-    ctx->last_total_mul_mat_bytes = total_mul_mat_bytes;
+    ctx->last_total_flops = total_flops;
 
     if (vk_perf_logger_enabled) {
         // End the command buffer and submit/wait

From 6f4f53f2b7da54fcdbbecaaa734337c337ad6176 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Mon, 29 Jun 2026 17:37:23 +0200
Subject: [PATCH 221/292] common : dedup preset and cached model entries in
 /v1/models (#25131)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 common/preset.cpp | 33 +++++++++++++++++++++++++++++----
 1 file changed, 29 insertions(+), 4 deletions(-)

diff --git a/common/preset.cpp b/common/preset.cpp
index f0cc1fa1a..4362c0621 100644
--- a/common/preset.cpp
+++ b/common/preset.cpp
@@ -7,6 +7,7 @@
 #include <fstream>
 #include <sstream>
 #include <filesystem>
+#include <regex>
 
 static std::string rm_leading_dashes(const std::string & str) {
     size_t pos = 0;
@@ -16,6 +17,23 @@ static std::string rm_leading_dashes(const std::string & str) {
     return str.substr(pos);
 }
 
+static std::string canonical_tag(const std::string & tag) {
+    static const std::regex re_tag("[-.]([A-Z0-9_]+)$", std::regex::icase);
+    std::smatch m;
+    if (std::regex_search(tag, m, re_tag)) {
+        std::string canon = m[1].str();
+        for (char & c : canon) {
+            c = (char) std::toupper((unsigned char) c);
+        }
+        return canon;
+    }
+    std::string upper = tag;
+    for (char & c : upper) {
+        c = (char) std::toupper((unsigned char) c);
+    }
+    return upper;
+}
+
 std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
     std::vector<std::string> args;
 
@@ -270,11 +288,18 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
 
     for (auto section : ini_data) {
         common_preset preset;
-        if (section.first.empty()) {
-            preset.name = COMMON_PRESET_DEFAULT_NAME;
-        } else {
-            preset.name = section.first;
+        std::string section_name = section.first.empty() ? std::string(COMMON_PRESET_DEFAULT_NAME) : section.first;
+        if (section_name != "*" && section_name != COMMON_PRESET_DEFAULT_NAME) {
+            auto colon_idx = section_name.rfind(':');
+            if (colon_idx != std::string::npos) {
+                std::string tag       = section_name.substr(colon_idx + 1);
+                std::string canon_tag = canonical_tag(tag);
+                if (canon_tag != tag) {
+                    section_name = section_name.substr(0, colon_idx + 1) + canon_tag;
+                }
+            }
         }
+        preset.name = section_name;
         LOG_DBG("loading preset: %s\n", preset.name.c_str());
         for (const auto & [key, value] : section.second) {
             if (key == "version") {

From 1365d11990723e95e957c0a76ee87e2f9b60ea06 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 29 Jun 2026 23:40:43 +0800
Subject: [PATCH 222/292] context shifting now works with images, images are
 now inserted inline instead of as placeholders

---
 gpttype_adapter.cpp | 268 +++++++++++++++++++++++++++++++++++++++++---
 koboldcpp.py        |   9 +-
 model_adapter.cpp   |  10 ++
 3 files changed, 265 insertions(+), 22 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index dd80c8b75..f801f716c 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -20,6 +20,7 @@
 #include <cstdint>
 #include <string>
 #include <cctype>
+#include <cstdlib>
 #include <locale>
 #include <chrono>
 #include <algorithm>
@@ -128,6 +129,7 @@ static llama_context * guidance_ctx = nullptr; //for classifier free guidance, w
 static mtmd_context * mtmd_ctx = nullptr; //for multimodal media
 static std::vector<media_object> media_objects;
 static std::vector<int> last_media_mem; //for storing dummy tokens that will be consumed by mtmd
+static std::vector<int> media_object_token_counts; //per media object dummy token counts for inline placeholders
 static std::string media_composite_image_signature = ""; //for identifying when the media changes, we need to invalidate the cache
 static int current_media_identifier = MEDIA_TOKEN_IDENTIFIER_A;
 static int vision_max_res = 2048;
@@ -2395,6 +2397,201 @@ static bool mtmd_text_chunk_has_invalid_tokens(const mtmd_input_chunk * mtmdchun
     return false;
 }
 
+static bool kcpp_is_media_token(int token)
+{
+    return token <= MEDIA_TOKEN_IDENTIFIER_A && token > MEDIA_TOKEN_IDENTIFIER_A - 4096;
+}
+
+static int kcpp_media_token_for_index(int media_index)
+{
+    return current_media_identifier - (media_index * 2);
+}
+
+static int kcpp_media_index_from_token(int token)
+{
+    if(!kcpp_is_media_token(token))
+    {
+        return -1;
+    }
+    const int diff = current_media_identifier - token;
+    if(diff < 0 || (diff % 2) != 0)
+    {
+        return -1;
+    }
+    return diff / 2;
+}
+
+static bool kcpp_parse_attached_media_placeholder(
+    const std::string & prompt,
+    size_t pos,
+    int image_count,
+    int audio_count,
+    int & media_index,
+    size_t & placeholder_len)
+{
+    const std::string image_prefix = "(Attached Image ";
+    const std::string audio_prefix = "(Attached Audio ";
+    bool is_audio = false;
+    size_t prefix_len = 0;
+
+    if(prompt.compare(pos, image_prefix.size(), image_prefix) == 0)
+    {
+        prefix_len = image_prefix.size();
+    }
+    else if(prompt.compare(pos, audio_prefix.size(), audio_prefix) == 0)
+    {
+        is_audio = true;
+        prefix_len = audio_prefix.size();
+    }
+    else
+    {
+        return false;
+    }
+
+    size_t number_pos = pos + prefix_len;
+    size_t end_pos = number_pos;
+    while(end_pos < prompt.size() && std::isdigit((unsigned char) prompt[end_pos]))
+    {
+        ++end_pos;
+    }
+    if(end_pos == number_pos || end_pos >= prompt.size() || prompt[end_pos] != ')')
+    {
+        return false;
+    }
+
+    int number = std::atoi(prompt.substr(number_pos, end_pos - number_pos).c_str());
+    if(number <= 0)
+    {
+        return false;
+    }
+
+    int candidate = -1;
+    if(is_audio)
+    {
+        if(number <= audio_count)
+        {
+            candidate = image_count + number - 1;
+        }
+        else if(number <= (int) media_objects.size() && media_objects[number - 1].is_audio)
+        {
+            candidate = number - 1;
+        }
+    }
+    else
+    {
+        if(number <= image_count)
+        {
+            candidate = number - 1;
+        }
+        else if(number <= (int) media_objects.size() && !media_objects[number - 1].is_audio)
+        {
+            candidate = number - 1;
+        }
+    }
+
+    if(candidate < 0 || candidate >= (int) media_objects.size())
+    {
+        return false;
+    }
+    media_index = candidate;
+    placeholder_len = end_pos - pos + 1;
+    return true;
+}
+
+static void kcpp_append_media_placeholder_tokens(std::vector<int> & tokens, int media_index)
+{
+    if(media_index < 0 || media_index >= (int) media_object_token_counts.size())
+    {
+        return;
+    }
+    const int token_count = media_object_token_counts[media_index];
+    const int media_token = kcpp_media_token_for_index(media_index);
+    for(int i = 0; i < token_count; ++i)
+    {
+        tokens.push_back(media_token);
+    }
+}
+
+static bool kcpp_tokenize_prompt_with_inline_media(
+    const std::string & prompt,
+    std::vector<int> & output_tokens,
+    FileFormat file_format,
+    bool add_bos,
+    int image_count,
+    int audio_count)
+{
+    output_tokens.clear();
+    bool inserted_media = false;
+    bool emitted_anything = false;
+    size_t text_start = 0;
+
+    auto append_text = [&](size_t start, size_t end)
+    {
+        if(end <= start)
+        {
+            return;
+        }
+        std::vector<int> text_tokens;
+        TokenizeString(prompt.substr(start, end - start), text_tokens, file_format, add_bos && !emitted_anything);
+        output_tokens.insert(output_tokens.end(), text_tokens.begin(), text_tokens.end());
+        emitted_anything = true;
+    };
+
+    for(size_t pos = 0; pos < prompt.size(); ++pos)
+    {
+        int media_index = -1;
+        size_t placeholder_len = 0;
+        if(kcpp_parse_attached_media_placeholder(prompt, pos, image_count, audio_count, media_index, placeholder_len))
+        {
+            append_text(text_start, pos);
+            if(add_bos && !emitted_anything)
+            {
+                std::vector<int> bos;
+                TokenizeString("", bos, file_format, true);
+                output_tokens.insert(output_tokens.end(), bos.begin(), bos.end());
+            }
+            kcpp_append_media_placeholder_tokens(output_tokens, media_index);
+            emitted_anything = true;
+            inserted_media = true;
+            pos += placeholder_len - 1;
+            text_start = pos + 1;
+        }
+    }
+
+    append_text(text_start, prompt.size());
+    if(!inserted_media)
+    {
+        output_tokens.clear();
+    }
+    return inserted_media;
+}
+
+static int kcpp_adjust_media_truncation_start(const std::vector<int> & tokens, int offset)
+{
+    if(offset <= 0 || offset >= (int) tokens.size())
+    {
+        return offset;
+    }
+    const int token = tokens[offset];
+    if(kcpp_is_media_token(token) && tokens[offset - 1] == token)
+    {
+        while(offset < (int) tokens.size() && tokens[offset] == token)
+        {
+            ++offset;
+        }
+    }
+    return offset;
+}
+
+static bool kcpp_media_span_boundary_ok(const std::vector<int> & tokens, int pos)
+{
+    if(pos <= 0 || pos >= (int) tokens.size())
+    {
+        return true;
+    }
+    return !(kcpp_is_media_token(tokens[pos]) && tokens[pos - 1] == tokens[pos]);
+}
+
 //given an old GGUF context and a new context that has some middle portion removed,
 //find and remove the middle portion from the old context from the KV. Does not fast forward after this destructive action
 //returns true if contextshift is doable, executes it if dryrun is false
@@ -2415,6 +2612,11 @@ bool DoContextShifting(llama_context * ctx, llama_context * draft_ctx, std::vect
 
     for (int i = 0; i < current_context_tokens.size(); ++i)
     {
+        if(i >= new_tokens_len)
+        {
+            purgeneeded = false;
+            break;
+        }
         if (current_context_tokens[i] == new_context_tokens[i])
         {
             trimstart += 1;
@@ -2449,6 +2651,14 @@ bool DoContextShifting(llama_context * ctx, llama_context * draft_ctx, std::vect
         int found = ArrFindIndexOf(current_context_tokens,shared);
         if(found>=0 && found > trimstart)
         {
+            if(!kcpp_media_span_boundary_ok(current_context_tokens, trimstart) || !kcpp_media_span_boundary_ok(current_context_tokens, found))
+            {
+                if(debugmode==1 && !is_quiet)
+                {
+                    printf("\n[Context Shifting skipped: refusing to split a multimodal span]");
+                }
+                return false;
+            }
             bool ok = true;
             if(!dryrun)
             {
@@ -4994,6 +5204,7 @@ static void PrepareMediaEmbds(const int nctx, const std::vector<int> & media_int
         int introsize = media_intro.size();
         int outrosize = media_outro.size();
         last_media_mem.clear();
+        media_object_token_counts.clear();
 
         for(int i=0;i<media_objects.size();++i)
         {
@@ -5004,6 +5215,7 @@ static void PrepareMediaEmbds(const int nctx, const std::vector<int> & media_int
                 : kcpp_mtmd_bitmap_init_image_from_buf(media_data_buffer.data(), media_data_buffer.size(), vision_max_res));
             if(!bitmap.ptr)
             {
+                media_object_token_counts.push_back(0);
                 printf("\nError: MTMD media %d failed to load!",i);
                 continue;
             }
@@ -5017,6 +5229,7 @@ static void PrepareMediaEmbds(const int nctx, const std::vector<int> & media_int
             int32_t tokenized = mtmd_tokenize(mtmd_ctx, chunks.ptr.get(), &inp_txt, bitmaps.data(), bitmaps.size());
             if(tokenized != 0)
             {
+                media_object_token_counts.push_back(0);
                 media_composite_image_signature = ""; //force invalidate
                 printf("\nError: MTMD media %d failed to tokenize! (status %d)",i, tokenized);
                 continue;
@@ -5079,18 +5292,21 @@ static void PrepareMediaEmbds(const int nctx, const std::vector<int> & media_int
             }
             if(mediatokensneeded>0 && mediatokensneeded < nctx)
             {
+                media_object_token_counts.push_back(mediatokensneeded);
                 int tokcnt = mediatokensneeded;
                 if(i==0)
                 {
                     tokcnt += introsize + outrosize;
                 }
+                const int media_token = kcpp_media_token_for_index(i);
                 for(int n=0;n<tokcnt;++n)
                 {
-                    last_media_mem.push_back(current_media_identifier);
+                    last_media_mem.push_back(media_token);
                 }
             }
             else
             {
+                media_object_token_counts.push_back(0);
                 media_composite_image_signature = ""; //force invalidate
                 printf("\nWarning: Media excluded - Context size too low or not enough mtmd tokens! (needed %d)\nMedia will be IGNORED! You probably want to relaunch with a larger context size!\n",mediatokensneeded);
             }
@@ -5600,6 +5816,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
     if(media_composite_image_signature=="")
     {
         last_media_mem.clear();
+        media_object_token_counts.clear();
     }
     if(media_data_changed)
     {
@@ -5607,7 +5824,21 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
         media_embds_built = true;
     }
 
-    TokenizeString(kcpp_data->prompt, embd_inp, file_format, add_bos_token);
+    bool media_inserted_inline = false;
+    if(last_media_mem.size()>0)
+    {
+        media_inserted_inline = kcpp_tokenize_prompt_with_inline_media(
+            kcpp_data->prompt,
+            embd_inp,
+            file_format,
+            add_bos_token,
+            inputs.images_len,
+            inputs.audio_len);
+    }
+    if(!media_inserted_inline)
+    {
+        TokenizeString(kcpp_data->prompt, embd_inp, file_format, add_bos_token);
+    }
     if(addedmemory!="")
     {
         TokenizeString(addedmemory, embd_inp_mem, file_format, add_bos_token);
@@ -5620,6 +5851,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
         std::vector<int> bos;
         TokenizeString("", bos, file_format, add_bos_token);
         int offset = embd_inp.size() - nctx + kcpp_data->n_predict;
+        offset = kcpp_adjust_media_truncation_start(embd_inp, offset);
         embd_inp = std::vector<int>(embd_inp.begin() + offset, embd_inp.end());
         //replace bos into front if exists
         if(bos.size()>0 && embd_inp.size()>0)
@@ -5628,7 +5860,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
         }
     }
 
-    if(last_media_mem.size()>0) //stick the media placeholders before the added mem
+    if(last_media_mem.size()>0 && !media_inserted_inline) //stick the media placeholders before the added mem if no inline placeholders were found
     {
         if(last_media_mem.size() + kcpp_data->n_predict + 4 > nctx)
         {
@@ -6757,7 +6989,8 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
             while ((int)embd_inp.size() > input_consumed)
             {
                 int currtoken = embd_inp[input_consumed];
-                if(currtoken==MEDIA_TOKEN_IDENTIFIER_A || currtoken==MEDIA_TOKEN_IDENTIFIER_B) //special media token hit
+                int curr_media_index = kcpp_media_index_from_token(currtoken);
+                if(curr_media_index >= 0) //special media token hit
                 {
                     if(!media_embds_built) //this should never happen! however, handle it anyway
                     {
@@ -6778,7 +7011,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                         int mediatokensevaled = 0;
                         int introsize = media_intro.size();
                         int outrosize = media_outro.size();
-                        while(input_consumed < embd_inp.size() && (embd_inp[input_consumed]==MEDIA_TOKEN_IDENTIFIER_A || embd_inp[input_consumed]==MEDIA_TOKEN_IDENTIFIER_B))
+                        while(input_consumed < embd_inp.size() && embd_inp[input_consumed]==currtoken)
                         {
                             if (!last_n_tokens.empty())
                             {
@@ -6789,10 +7022,15 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                             ++input_consumed;
                             ++mediatokenscounted;
                         }
-                        for(int i=0;i<media_objects.size();++i)
+                        bool include_media_header = false;
+                        if(curr_media_index == 0 && curr_media_index < (int) media_object_token_counts.size())
+                        {
+                            include_media_header = (mediatokenscounted == media_object_token_counts[curr_media_index] + introsize + outrosize);
+                        }
+                        if(curr_media_index < (int) media_objects.size())
                         {
                             //note: no handling for draft_ctx as we don't support vision for it
-                            if(introsize>0 && i==0)
+                            if(include_media_header && introsize>0)
                             {
                                 //added at the start of everything
                                 kcpp_embd_batch batch = kcpp_embd_batch(media_intro, n_past, use_mrope, false);
@@ -6809,10 +7047,10 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                                 mediatokensevaled += introsize;
                             }
 
-                            int start_size = media_objects[i].chunk_start_seq.size();
+                            int start_size = media_objects[curr_media_index].chunk_start_seq.size();
                             if (start_size > 0) {
                                 //add a separator between each image
-                                kcpp_embd_batch batch = kcpp_embd_batch(media_objects[i].chunk_start_seq, n_past, use_mrope, false);
+                                kcpp_embd_batch batch = kcpp_embd_batch(media_objects[curr_media_index].chunk_start_seq, n_past, use_mrope, false);
                                 auto evr = llama_decode(llama_ctx_v4, batch.batch);
                                 if(evr!=0)
                                 {
@@ -6826,12 +7064,12 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                                 mediatokensevaled += start_size;
                             }
 
-                            for(int j=0;j<media_objects[i].mediachunks.size();++j)
+                            for(int j=0;j<media_objects[curr_media_index].mediachunks.size();++j)
                             {
-                                media_chunk chunk = media_objects[i].mediachunks[j];
+                                media_chunk chunk = media_objects[curr_media_index].mediachunks[j];
                                 if(allow_regular_prints)
                                 {
-                                    printf("\rProcessing Media Embedding %d (%d tokens)",(i+1), chunk.clp_image_tokens);
+                                    printf("\rProcessing Media Embedding %d (%d tokens)",(curr_media_index+1), chunk.clp_image_tokens);
                                 }
                                 bool err = kcpp_eval_media(llama_ctx_v4,chunk,kcpp_data->n_batch,&n_past);
                                 mediatokensevaled += chunk.clp_image_tokens;
@@ -6849,10 +7087,10 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                                 }
                             }
 
-                            int end_size = media_objects[i].chunk_end_seq.size();
+                            int end_size = media_objects[curr_media_index].chunk_end_seq.size();
                             if (end_size > 0) {
                                 //add a separator between each image
-                                kcpp_embd_batch batch = kcpp_embd_batch(media_objects[i].chunk_end_seq, n_past, use_mrope, false);
+                                kcpp_embd_batch batch = kcpp_embd_batch(media_objects[curr_media_index].chunk_end_seq, n_past, use_mrope, false);
                                 auto evr = llama_decode(llama_ctx_v4, batch.batch);
                                 if(evr!=0)
                                 {
@@ -6866,7 +7104,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
                                 mediatokensevaled += end_size;
                             }
                         }
-                        if(media_objects.size()>0 && outrosize>0)
+                        if(include_media_header && media_objects.size()>0 && outrosize>0)
                         {
                             //added after all media but before prompt
                             kcpp_embd_batch batch = kcpp_embd_batch(media_outro, n_past, use_mrope, false);
diff --git a/koboldcpp.py b/koboldcpp.py
index 17f43b16c..e015cc2bd 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -3772,22 +3772,17 @@ def format_jinja(messages_orig, tools, chat_template_kwargs=None):
             if isinstance(m.get("content"), list):
                 normalized = []
                 turn_text = ""
-                media_text = ""
                 for item in m["content"]:
                     if item.get("type")=="text":
                         turn_text += item.get("text","")
-                for item in m["content"]:
-                    if item.get("type")=="text":
-                        pass
                     elif item.get("type")=="image_url" or item.get("type")=="image":
-                        media_text += f"\n(Attached Image {mediacount})\n"
+                        turn_text += f"\n(Attached Image {mediacount})\n"
                         mediacount += 1
                     elif item.get("type")=="input_audio":
-                        media_text += f"\n(Attached Audio {mediacount})\n"
+                        turn_text += f"\n(Attached Audio {mediacount})\n"
                         mediacount += 1
                     else:
                         normalized.append(item)
-                turn_text = media_text + turn_text
                 if turn_text:
                     normalized.append({"type": "text","text": turn_text})
                 m["content"] = normalized
diff --git a/model_adapter.cpp b/model_adapter.cpp
index 753104d5a..80dcddab1 100644
--- a/model_adapter.cpp
+++ b/model_adapter.cpp
@@ -486,6 +486,16 @@ bool useSmartContext, const bool requireFullSubset, const int minimum_to_proceed
 
     for (int i = 0; i < cur_ctx_len; ++i)
     {
+        if (i >= embd_inp_len)
+        {
+            if(requireFullSubset)
+            {
+                last_n_tokens.erase(last_n_tokens.end() - n_past, last_n_tokens.end());
+                n_past = 0;
+                fastforwardok = false;
+            }
+            break;
+        }
         if (current_context_tokens[i] == embd_inp[i])
         {
             n_past += 1;

From 86b94708f22478f900b76ca02e316f4f3418faff Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Tue, 30 Jun 2026 02:41:45 +0200
Subject: [PATCH 223/292] Revert "sched : reintroduce less synchronizations
 during split compute (#20793)" (#25138)

---
 ggml/src/ggml-backend.cpp       | 10 +++-------
 ggml/src/ggml-cuda/ggml-cuda.cu | 24 ++++--------------------
 2 files changed, 7 insertions(+), 27 deletions(-)

diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 644130cd1..87615921c 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -1551,8 +1551,6 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         int split_backend_id = split->backend_id;
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
-        ggml_backend_synchronize(split_backend);
-
         // copy the input tensors to the split backend
         for (int input_id = 0; input_id < split->n_inputs; input_id++) {
             ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[input_id]);
@@ -1563,15 +1561,15 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else if (!split_backend->iface.cpy_tensor_async) {
+                } else {
                     ggml_backend_synchronize(split_backend);
                 }
-                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
+                ggml_backend_tensor_copy(input, input_cpy);
             } else {
                 // wait for the split backend to finish using the input before overwriting it
                 if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
                     ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else if (!split_backend->iface.cpy_tensor_async) {
+                } else {
                     ggml_backend_synchronize(split_backend);
                 }
 
@@ -1676,8 +1674,6 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
             }
         }
 
-        ggml_backend_synchronize(split_backend);
-
         if (!sched->callback_eval) {
             enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
             if (ec != GGML_STATUS_SUCCESS) {
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index f3fb32452..cca70592f 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3192,24 +3192,11 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer;
     ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer;
 
-    // Enables async copies from CPU to CUDA, instead of only CUDA-to-CUDA
-    // Excluding this path for HIP and MUSA as a precaution.
-    // According to the summary in https://github.com/ggml-org/llama.cpp/pull/20793#issuecomment-4275794315, this change is not beneficial for hip anyways.
-    // Additionally, there is a lot of anectodal evidence that hip/musa stream behavior might not always 1:1 match CUDA behavior.
-    // e.g. https://github.com/ROCm/rocm-systems/issues/5109
-    // It thus makes sense to exclude this path for HIP and MUSA. This PR was not aimed these backends, the majority of testing happened on CUDA.
-    // This can be revisited in the future if enabling copy_from_host benefits hip/MUSA, and if the PR author can extensively test on these backends.
-#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA)
-    const bool copy_from_host = false;
-#else
-    const bool copy_from_host = ggml_backend_buffer_is_host(buf_src) && ggml_backend_dev_type(backend_src->device) == GGML_BACKEND_DEVICE_TYPE_CPU;
-#endif
-
-    if (!(copy_from_host || ggml_backend_is_cuda(backend_src)) || !ggml_backend_is_cuda(backend_dst)) {
+    if (!ggml_backend_is_cuda(backend_src) || !ggml_backend_is_cuda(backend_dst)) {
         return false;
     }
 
-    if (!(copy_from_host || ggml_backend_buffer_is_cuda(buf_src)) || !ggml_backend_buffer_is_cuda(buf_dst)) {
+    if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
         return false;
     }
 
@@ -3220,17 +3207,14 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
     ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
     ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
 
-    if ((copy_from_host && cuda_ctx_dst->device != buf_ctx_dst->device) ||
-        !copy_from_host && (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device)) {
+    if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
 #endif // NDEBUG
         return false;
     }
 
-    if (copy_from_host) {
-        CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyHostToDevice, cuda_ctx_dst->stream()));
-    } else if (backend_src != backend_dst) {
+    if (backend_src != backend_dst) {
         // copy on src stream
         if (cuda_ctx_src->device == cuda_ctx_dst->device) {
             CUDA_CHECK(cudaMemcpyAsync(dst->data, src->data, ggml_nbytes(dst), cudaMemcpyDeviceToDevice, cuda_ctx_src->stream()));

From 6c5de1cc83537bce5616ed08474f6fe119973a27 Mon Sep 17 00:00:00 2001
From: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
Date: Tue, 30 Jun 2026 17:20:04 +0900
Subject: [PATCH 224/292] ggml-webgpu: add support for NVFP4 (#25143)

---
 .../ggml-webgpu/ggml-webgpu-shader-lib.hpp    |  7 +++
 ggml/src/ggml-webgpu/ggml-webgpu.cpp          |  3 ++
 .../wgsl-shaders/common_decls.tmpl            | 18 +++++++-
 .../ggml-webgpu/wgsl-shaders/get_rows.wgsl    | 21 +++++++++
 .../wgsl-shaders/mul_mat_decls.tmpl           | 43 ++++++++++++++++-
 .../wgsl-shaders/mul_mat_vec_acc.tmpl         | 46 +++++++++++++++++++
 6 files changed, 135 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
index c00a2e9ee..80a16d16d 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
@@ -1563,6 +1563,7 @@ class ggml_webgpu_shader_lib {
                         case GGML_TYPE_IQ1_S:
                         case GGML_TYPE_IQ4_NL:
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             {
                                 // Quantized types using u32 buffers for portability.
                                 defines.push_back("SRC_TYPE=u32");
@@ -1593,6 +1594,8 @@ class ggml_webgpu_shader_lib {
                     } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) ||
                                key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) {
                         defines.push_back("BLOCK_SIZE=32u");
+                    } else if (key.src_type == GGML_TYPE_NVFP4) {
+                        defines.push_back("BLOCK_SIZE=64u");
                     } else if (key.src_type >= GGML_TYPE_Q2_K) {
                         defines.push_back("BLOCK_SIZE=256u");
                     } else {
@@ -1960,6 +1963,7 @@ class ggml_webgpu_shader_lib {
                             defines.push_back(type_upper + "_TABLES");
                             break;
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             defines.push_back(type_upper + "_LUT");
                             break;
                         default:
@@ -2103,6 +2107,7 @@ class ggml_webgpu_shader_lib {
                             defines.push_back(type_upper + "_TABLES");
                             break;
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             defines.push_back(type_upper + "_LUT");
                             break;
                         default:
@@ -2274,6 +2279,7 @@ class ggml_webgpu_shader_lib {
                             defines.push_back(type_upper + "_TABLES");
                             break;
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             defines.push_back(type_upper + "_LUT");
                             break;
                         default:
@@ -2394,6 +2400,7 @@ class ggml_webgpu_shader_lib {
                             defines.push_back(type_upper + "_TABLES");
                             break;
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             defines.push_back(type_upper + "_LUT");
                             break;
                         default:
diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
index f0ec18abd..29025e9ba 100644
--- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp
@@ -4056,6 +4056,7 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) {
         case GGML_TYPE_IQ4_NL:
         case GGML_TYPE_IQ4_XS:
         case GGML_TYPE_MXFP4:
+        case GGML_TYPE_NVFP4:
             return true;
         default:
             return false;
@@ -4156,6 +4157,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                             case GGML_TYPE_IQ4_NL:
                             case GGML_TYPE_IQ4_XS:
                             case GGML_TYPE_MXFP4:
+                            case GGML_TYPE_NVFP4:
                                 supports_op = true;
                                 break;
                             default:
@@ -4196,6 +4198,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                         case GGML_TYPE_IQ4_NL:
                         case GGML_TYPE_IQ4_XS:
                         case GGML_TYPE_MXFP4:
+                        case GGML_TYPE_NVFP4:
                             supports_op = true;
                             break;
                         default:
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
index 758efa17d..6634fbd65 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
@@ -896,9 +896,23 @@ const kvalues_iq4nl = array<i32, 16>(
 
 #endif
 
-#ifdef MXFP4_LUT
+#if defined(MXFP4_LUT) || defined(NVFP4_LUT)
 const kvalues_mxfp4 = array<i32, 16>(
     0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12
 );
-#endif
+#endif // MXFP4_LUT || NVFP4_LUT
 
+#ifdef NVFP4_LUT
+fn ue4m3_to_fp32(u: u32) -> f32 {
+    if (u == 0u || u == 127u) {
+        return 0.0;
+    }
+    let exp = (u >> 3u) & 15u;
+    let man = u & 7u;
+    if (exp == 0u) {
+        return f32(man) * (1.0 / 512.0);
+    }
+    let bits = ((exp + 120u) << 23u) | (man << 20u);
+    return bitcast<f32>(bits);
+}
+#endif // NVFP4_LUT
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
index 78d61a93d..487edb327 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
@@ -672,6 +672,27 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
 }
 #endif
 
+#ifdef NVFP4
+fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+    let block_byte_base = (src_base + offset) * 36;
+    let d_word = load_u32_at_src(block_byte_base);
+    for (var sub: u32 = 0u; sub < 4; sub++) {
+        let d = ue4m3_to_fp32(get_byte(d_word, sub)) * 0.5;
+        for (var j: u32 = 0u; j < 2; j++) {
+            let q_packed = load_u32_at_src(block_byte_base + 4 + sub * 8 + j * 4);
+            for (var k: u32 = 0; k < 4; k++) {
+                let q_byte = get_byte(q_packed, k);
+                let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d;
+                let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d;
+                let dst_offset = dst_base + offset * 64 + sub * 16 + j * 4 + k;
+                dst[dst_offset] = q_lo;
+                dst[dst_offset + 8u] = q_hi;
+            }
+        }
+    }
+}
+#endif
+
 
 @group(0) @binding(0)
 var<storage, read_write> src: array<SRC_TYPE>;
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
index 6a2eb8c82..13996ab51 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
@@ -241,7 +241,7 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
 #endif // INIT_SRC0_SHMEM_Q8_1
 
 #if defined(INIT_SRC0_SHMEM_MXFP4)
-            let block_byte_base = src0_idx * 17u;
+            let block_byte_base = src0_idx * 17u; // BLOCK_SIZE_BYTES = 17u;
             let eu8 = get_byte(load_u32_at_src0_aligned(block_byte_base), block_byte_base & 3u);
             let e = ldexp(1.0, i32(eu8) - 128);
 
@@ -263,6 +263,47 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
 }
 #endif // legacy-quants
 
+#if defined(INIT_SRC0_SHMEM_NVFP4)
+const BLOCK_SIZE = 64u;
+const BLOCK_SIZE_BYTES = 36u;
+const SUB_BLOCK_SIZE = 16u; // elements sharing one UE4M3 scale
+const NQ = 16u;
+const BYTES_PER_THREAD = 8u;
+const BYTES_PER_INNER_LOOP = 4u;
+
+fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) {
+    for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) {
+        let tile_m = i / TILE_K;
+        let tile_k_start = i % TILE_K;
+        let global_m = offset_m + tile_m;
+        let global_k_start = k_outer + tile_k_start;
+
+        if (global_m >= params.m) {
+            break;
+        }
+
+        let block_k  = global_k_start / BLOCK_SIZE;
+        let sub_block      = (global_k_start % BLOCK_SIZE) / SUB_BLOCK_SIZE;
+        let src0_idx = batch_offset + global_m * params.stride_01 + block_k;
+
+        let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+        let d_byte_base     = block_byte_base;
+        let qs_byte_base    = block_byte_base + 4u;
+
+        let d = ue4m3_to_fp32(get_byte(load_u32_at_src0_aligned(d_byte_base), sub_block)) * 0.5;
+
+        for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j++) {
+            let q_packed = load_u32_at_src0_aligned(qs_byte_base + sub_block * 8u + j * 4u);
+            for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) {
+                let q_byte = get_byte(q_packed, k);
+                shmem[i + j * BYTES_PER_INNER_LOOP + k]      = f16(f32(kvalues_mxfp4[q_byte & 0xF]) * d);
+                shmem[i + j * BYTES_PER_INNER_LOOP + k + 8u] = f16(f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d);
+            }
+        }
+    }
+}
+#endif // INIT_SRC0_SHMEM_NVFP4
+
 // k-quants
 #if defined(INIT_SRC0_SHMEM_Q2_K) || defined(INIT_SRC0_SHMEM_Q3_K) || defined(INIT_SRC0_SHMEM_Q4_K) || defined(INIT_SRC0_SHMEM_Q5_K) || defined(INIT_SRC0_SHMEM_Q6_K)
 const BLOCK_SIZE = 256u;
diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
index b0703fe90..8fd0d1907 100644
--- a/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
+++ b/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
@@ -1505,3 +1505,49 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
     return acc;
 }
 #endif
+
+#ifdef MUL_ACC_NVFP4
+#define BLOCK_SIZE 64
+#define BLOCK_SIZE_BYTES 36
+#define THREADS_PER_BLOCK 4
+#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK)
+fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array<array<f32, OUTPUTS_PER_WG>, NUM_COLS> {
+    var acc: array<array<f32, OUTPUTS_PER_WG>, NUM_COLS>;
+
+    let num_blocks = params.k / BLOCK_SIZE;
+    let sub = thread_id % THREADS_PER_BLOCK;
+    for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) {
+        let x_base = src1_idx_base + block * BLOCK_SIZE + sub * ELEMS_PER_THREAD;
+        var x_block: array<array<f32, ELEMS_PER_THREAD>, NUM_COLS>;
+        for (var col = 0u; col < NUM_COLS;col += 1) {
+            for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) {
+                x_block[col][i]     = f32(src1[x_base + col * params.stride_11 + i]);
+                x_block[col][i + 8] = f32(src1[x_base + col * params.stride_11 + i + 8]);
+            }
+        }
+        for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+            let output_row = row_base + row;
+            if (output_row < params.m) {
+                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                let d = ue4m3_to_fp32(get_byte(load_u32_at_src0_aligned(block_byte_base), sub)) * 0.5;
+                let q_w0 = load_u32_at_src0_aligned(block_byte_base + 4u + 8u * sub);
+                let q_w1 = load_u32_at_src0_aligned(block_byte_base + 8u + 8u * sub);
+                for (var col = 0u;col < NUM_COLS;col += 1) {
+                    var row_sum = 0.0;
+                    for (var l = 0u; l < 8u; l++) {
+                        let q_word = select(q_w0, q_w1, l >= 4u);
+                        let q_byte = get_byte(q_word, l % 4u);
+                        let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d;
+                        let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * d;
+                        row_sum += q_lo * x_block[col][l];
+                        row_sum += q_hi * x_block[col][l + 8u];
+                    }
+                    acc[col][row] += row_sum;
+                }
+            }
+        }
+    }
+
+    return acc;
+}
+#endif

From d9df11006f6d3cc34772379bf6897f892874656f Mon Sep 17 00:00:00 2001
From: zduford <z.duford@gmail.com>
Date: Tue, 30 Jun 2026 05:51:38 -0400
Subject: [PATCH 225/292] HIP: use hipBLAS for dense prefill on gfx900, keep
 MMQ for MoE (#24588)

* HIP: keep MMQ for gfx900 MoE and Q8_0, use hipBLAS for dense K-quants

Assisted-by: GitHub Copilot CLI

* HIP: tighten conditional block to be explicitly for gfx900

* HIP: Further simplified gfx900 conditional block

* removed unnecessary comment
---
 ggml/src/ggml-cuda/mmq.cu | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index e1add5e03..6b3b0d064 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -368,5 +368,12 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
         return true;
     }
 
+    // gfx900 (Vega 10) lacks native dp4a, loses to dequant + hipBLAS
+    // for dense matrices; keep MMQ only for MoE, where the
+    // hipBLAS path is much slower.
+    if (cc == GGML_CUDA_CC_VEGA) {
+        return n_experts > 0;
+    }
+
     return (!GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
 }

From f708a5b2caaee0226c0af220e366785699ba41e2 Mon Sep 17 00:00:00 2001
From: Kevin Liu <4396kevinliu@gmail.com>
Date: Tue, 30 Jun 2026 06:27:38 -0400
Subject: [PATCH 226/292] vulkan: roll bk loop in matmul for asahi linux
 (#24663)

* vulkan: roll bk loop in matmul for asahi linux

* vulkan: fix inline comment

* vulkan: revert BK-loop unroll change

* vulkan: edit spirv directly for asahi roll bk loop

* vulkan: remove trailing whitespace at the end of comments
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 95 ++++++++++++++++++++++++++++
 1 file changed, 95 insertions(+)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ac021c7a6..c0ab9f1c6 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -2475,6 +2475,85 @@ static bool ggml_vk_strip_decode_vector(const uint32_t * code, size_t word_count
     return true;
 }
 
+// Remove the loop unrolling hint of the matmul shader's BK loop
+// and replace it with the dont_unroll hint for better performance on
+// hardware like Apple M1/M2.
+// Assumes 1. code comes from mul_mm.comp 2. the K-tile loop has no loop
+// control hint and 3. the BK loop is the last loop nested directly inside
+// the K-tile loop.
+// Returns true when the input was modified; returns false otherwise
+// without touching `out`.
+static bool ggml_vk_roll_bk_loop(const uint32_t * code, size_t word_count, std::vector<uint32_t> & out) {
+    if (word_count < 5) {
+        return false;
+    }
+
+    struct vk_spv_loop {
+        size_t   header;
+        size_t   end;
+        uint32_t control;
+    };
+
+    std::vector<vk_spv_loop> loops;
+
+    // Collect a list of all loops in the module.
+    for (size_t pos = 5; pos < word_count; ) {
+        const uint32_t wc = code[pos] >> spv::WordCountShift;
+        const uint32_t op = code[pos] & spv::OpCodeMask;
+        if (wc == 0 || pos + wc > word_count) {
+            return false;
+        }
+
+        if (op == spv::OpLoopMerge && wc >= 4) { loops.push_back({ pos, 0, code[pos + 3] }); }
+
+        if (op == spv::OpLabel && wc >= 2) {
+            for (auto & l : loops) {
+                if (l.end == 0 && code[l.header + 1] == code[pos + 1]) { l.end = pos; }
+            }
+        }
+
+        pos += wc;
+    }
+
+    auto encloses = [](const vk_spv_loop & a, const vk_spv_loop & b) {
+        return a.header < b.header && b.header < a.end;
+    };
+
+    // Find the BK loop.
+    const vk_spv_loop * bk = nullptr;
+    for (const auto & h : loops) {
+        if (h.control != spv::LoopControlUnrollMask) {
+            continue;
+        }
+        const vk_spv_loop * parent = nullptr;
+        bool has_child = false;
+        for (const auto & g : loops) {
+            if (encloses(g, h) && (!parent || g.header > parent->header)) {
+                parent = &g;
+            }
+            if (encloses(h, g)) {
+                has_child = true;
+            }
+        }
+        // BK loop should be the last loop nested inside the loop with no hint
+        // and have at least one child loop.
+        if (parent &&
+            parent->control == spv::LoopControlMaskNone &&
+            has_child &&
+            (!bk || h.header > bk->header)) {
+            bk = &h;
+        }
+    }
+    if (!bk) {
+        return false;
+    }
+
+    // set DontUnroll instead of Unroll
+    out.assign(code, code + word_count);
+    out[bk->header + 3] = spv::LoopControlDontUnrollMask;
+    return true;
+}
+
 static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, size_t spv_size, const void* spv_data, const std::string entrypoint,
                                          uint32_t parameter_count, std::array<uint32_t, 3> wg_denoms, std::vector<uint32_t> specialization_constants,
                                          bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) {
@@ -2558,6 +2637,22 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
     }
 #endif
 
+#if VK_HEADER_VERSION >= 287
+    // Roll the mul_mm BK loop on Asahi Linux. Skip bf16 and the mul_mmq pipelines.
+    if (device->driver_id == vk::DriverId::eMesaHoneykrisp &&
+        pipeline->name.rfind("matmul", 0) == 0 &&
+        pipeline->name.find("bf16") == std::string::npos &&
+        pipeline->name.find("q8_1") == std::string::npos) {
+        const uint32_t * src   = spirv.empty() ? reinterpret_cast<const uint32_t *>(spv_data) : spirv.data();
+        size_t           src_n = spirv.empty() ? spv_size / sizeof(uint32_t) : spirv.size();
+        std::vector<uint32_t> rolled;
+        if (ggml_vk_roll_bk_loop(src, src_n, rolled)) {
+            spirv = std::move(rolled);
+            shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data());
+        }
+    }
+#endif
+
     pipeline->shader_module = device->device.createShaderModule(shader_module_create_info);
 
     vk::PushConstantRange pcr(

From e495d1e748b4b683559b4beb82b367825ab781a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 30 Jun 2026 14:06:54 +0200
Subject: [PATCH 227/292] CUDA: fix Gemma E4B MTP FlashAttention (#25148)

* CUDA: fix Gemma E4B MTP FlashAttention

* remove unused template declaration
---
 ggml/src/ggml-cuda/fattn-mma-f16.cuh               |  4 ++++
 ggml/src/ggml-cuda/fattn-tile.cuh                  | 14 +++++++++-----
 ggml/src/ggml-cuda/fattn.cu                        | 10 +++++-----
 .../fattn-mma-f16-instance-ncols1_16-ncols2_2.cu   |  1 +
 .../fattn-mma-f16-instance-ncols1_32-ncols2_2.cu   |  1 +
 .../fattn-mma-f16-instance-ncols1_4-ncols2_2.cu    |  1 +
 .../fattn-mma-f16-instance-ncols1_8-ncols2_2.cu    |  1 +
 .../template-instances/generate_cu_files.py        |  2 +-
 8 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 83478a02c..7f4cfd551 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -2003,6 +2003,10 @@ DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112,  64)
 DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128,  64)
 DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256,  64)
 
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  4,  2);
+extern DECL_FATTN_MMA_F16_CASE(512, 512,  8,  2);
+extern DECL_FATTN_MMA_F16_CASE(512, 512, 16,  2);
+extern DECL_FATTN_MMA_F16_CASE(512, 512, 32,  2);
 extern DECL_FATTN_MMA_F16_CASE(512, 512,  2,  4);
 extern DECL_FATTN_MMA_F16_CASE(512, 512,  4,  4);
 extern DECL_FATTN_MMA_F16_CASE(512, 512,  8,  4);
diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh
index 0a099810e..3e07a9f7e 100644
--- a/ggml/src/ggml-cuda/fattn-tile.cuh
+++ b/ggml/src/ggml-cuda/fattn-tile.cuh
@@ -76,6 +76,7 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
 
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  64,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  2,  64, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -144,6 +145,7 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_nv
 
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 16, 256, 2,  32,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  2,  64, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  32,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  32,  64)
@@ -219,6 +221,7 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
 
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 512, 1, 128,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  2,  64, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 2,  64,  64)
@@ -296,6 +299,7 @@ static constexpr __host__ __device__ uint32_t ggml_cuda_fattn_tile_get_config_am
 
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(320, 256, 32, 256, 2, 128,  64)
 
+    GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  2,  64, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  4, 128, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512,  8, 256, 2,  64,  64)
     GGML_CUDA_FATTN_TILE_CONFIG_CASE(512, 512, 16, 256, 4,  64,  64)
@@ -1308,12 +1312,12 @@ static void launch_fattn_tile_switch_ncols2(ggml_backend_cuda_context & ctx, ggm
             return;
         }
 
-        if constexpr (DV <= 256) {
-            if (use_gqa_opt && gqa_ratio % 2 == 0) {
-                launch_fattn_tile_switch_ncols1<DKQ, DV, 2, use_logit_softcap>(ctx, dst);
-                return;
-            }
+        if (use_gqa_opt && gqa_ratio % 2 == 0) {
+            launch_fattn_tile_switch_ncols1<DKQ, DV, 2, use_logit_softcap>(ctx, dst);
+            return;
+        }
 
+        if constexpr (DV <= 256) {
             launch_fattn_tile_switch_ncols1<DKQ, DV, 1, use_logit_softcap>(ctx, dst);
             return;
         }
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index d6c501b1d..c3039211c 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -99,12 +99,12 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con
         return;
     }
 
-    if constexpr (DKQ <= 256) {
-        if (use_gqa_opt && gqa_ratio > 1) {
-            ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
-            return;
-        }
+    if (use_gqa_opt && gqa_ratio > 1) {
+        ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
+        return;
+    }
 
+    if constexpr (DKQ <= 256) {
         ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
     } else {
         GGML_ABORT("fatal error");
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_16-ncols2_2.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_16-ncols2_2.cu
index 2e1883af4..0c1a37fd0 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_16-ncols2_2.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_16-ncols2_2.cu
@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 16, 2);
 DECL_FATTN_MMA_F16_CASE(112, 112, 16, 2);
 DECL_FATTN_MMA_F16_CASE(128, 128, 16, 2);
 DECL_FATTN_MMA_F16_CASE(256, 256, 16, 2);
+DECL_FATTN_MMA_F16_CASE(512, 512, 16, 2);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_32-ncols2_2.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_32-ncols2_2.cu
index 407b6cf4c..d5a4d160a 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_32-ncols2_2.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_32-ncols2_2.cu
@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 32, 2);
 DECL_FATTN_MMA_F16_CASE(112, 112, 32, 2);
 DECL_FATTN_MMA_F16_CASE(128, 128, 32, 2);
 DECL_FATTN_MMA_F16_CASE(256, 256, 32, 2);
+DECL_FATTN_MMA_F16_CASE(512, 512, 32, 2);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_2.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_2.cu
index 5e4668502..d8e7588cf 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_2.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_4-ncols2_2.cu
@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 4, 2);
 DECL_FATTN_MMA_F16_CASE(112, 112, 4, 2);
 DECL_FATTN_MMA_F16_CASE(128, 128, 4, 2);
 DECL_FATTN_MMA_F16_CASE(256, 256, 4, 2);
+DECL_FATTN_MMA_F16_CASE(512, 512, 4, 2);
diff --git a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_2.cu b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_2.cu
index a6e6f093d..e48e28c4e 100644
--- a/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_2.cu
+++ b/ggml/src/ggml-cuda/template-instances/fattn-mma-f16-instance-ncols1_8-ncols2_2.cu
@@ -8,3 +8,4 @@ DECL_FATTN_MMA_F16_CASE(96, 96, 8, 2);
 DECL_FATTN_MMA_F16_CASE(112, 112, 8, 2);
 DECL_FATTN_MMA_F16_CASE(128, 128, 8, 2);
 DECL_FATTN_MMA_F16_CASE(256, 256, 8, 2);
+DECL_FATTN_MMA_F16_CASE(512, 512, 8, 2);
diff --git a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
index af05a9eff..614b1566c 100755
--- a/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
+++ b/ggml/src/ggml-cuda/template-instances/generate_cu_files.py
@@ -92,7 +92,7 @@ for ncols in [8, 16, 32, 64]:
                     continue
                 if head_size_kq == 320 and ncols2 != 32: # Mistral Small 4
                     continue
-                if head_size_kq == 512 and ncols2 not in (4, 8): # Gemma 4
+                if head_size_kq == 512 and ncols2 not in (2, 4, 8): # Gemma 4 (+ MTP)
                     continue
                 if head_size_kq == 576 and ncols2 not in (4, 16, 32): # Deepseek, GLM 4.7 Flash
                     continue

From 931eb37f8cac5a6ca84d5641445d460af2a9d7dd Mon Sep 17 00:00:00 2001
From: Matt Jallo <matt@mattjallo.com>
Date: Tue, 30 Jun 2026 05:16:24 -0700
Subject: [PATCH 228/292] CUDA: fix get_rows_back for tables with more than
 65535 rows (grid-y clamp + stride) (#25103)

---
 ggml/src/ggml-cuda/getrows.cu | 31 +++++++++++++++++--------------
 tests/test-backend-ops.cpp    |  1 +
 2 files changed, 18 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-cuda/getrows.cu b/ggml/src/ggml-cuda/getrows.cu
index eb157b8ba..0e1570709 100644
--- a/ggml/src/ggml-cuda/getrows.cu
+++ b/ggml/src/ggml-cuda/getrows.cu
@@ -78,26 +78,29 @@ static __global__ void k_get_rows_float(
 
 template<typename grad_t, typename dst_t>
 static __global__ void k_get_rows_back_float(
-        const grad_t * __restrict__ grad, const int32_t * __restrict__ rows, dst_t * __restrict__ dst, const int64_t ncols, const int64_t nrows_grad) {
+        const grad_t * __restrict__ grad, const int32_t * __restrict__ rows, dst_t * __restrict__ dst,
+        const int64_t ncols, const int64_t nrows_grad, const int64_t nrows_dst) {
     const int col = blockIdx.x*blockDim.x + threadIdx.x;
 
     if (col >= ncols) {
         return;
     }
 
-    const int dst_row = blockIdx.y*blockDim.y + threadIdx.y;
-
-    float sum = 0.0f;
-
     ggml_cuda_pdl_sync();
-    for (int64_t i = 0; i < nrows_grad; ++i) {
-        if (rows[i] != dst_row) {
-            continue;
-        }
-        sum += grad[i*ncols + col];
-    }
 
-    dst[dst_row*ncols + col] = sum;
+    // grid.y is clamped to the CUDA grid limit, so stride over the destination rows
+    for (int64_t dst_row = blockIdx.y; dst_row < nrows_dst; dst_row += gridDim.y) {
+        float sum = 0.0f;
+
+        for (int64_t i = 0; i < nrows_grad; ++i) {
+            if (rows[i] != dst_row) {
+                continue;
+            }
+            sum += grad[i*ncols + col];
+        }
+
+        dst[dst_row*ncols + col] = sum;
+    }
 }
 
 template<int qk, int qr, dequantize_kernel_t dq, typename dst_t>
@@ -302,7 +305,7 @@ void ggml_cuda_op_get_rows_back(ggml_backend_cuda_context & ctx, ggml_tensor * d
 
     const dim3 block_dims(CUDA_GET_ROWS_BACK_BLOCK_SIZE, 1, 1);
     const int block_num_x = (ne00 + CUDA_GET_ROWS_BACK_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BACK_BLOCK_SIZE;
-    const dim3 block_nums(block_num_x, ne1, 1);
+    const dim3 block_nums(block_num_x, MIN(ne1, (int64_t)UINT16_MAX), 1);
 
-    k_get_rows_back_float<<<block_nums, block_dims, 0, stream>>>(src0_d, src1_d, dst_d, ne00, ne10);
+    k_get_rows_back_float<<<block_nums, block_dims, 0, stream>>>(src0_d, src1_d, dst_d, ne00, ne10, ne1);
 }
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 15b50209c..34811bd50 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7759,6 +7759,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     }
 
     test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_F32, 1, 8, 2, 1, false));
+    test_cases.emplace_back(new test_get_rows_back(GGML_TYPE_F32, 1, 70000, 4, 1, false)); // row count > CUDA grid-y limit (65535)
     for (ggml_type type : all_types) {
         for (bool v : {false, true}) {
             test_cases.emplace_back(new test_get_rows_back(type, 256, 5, 4, 1, v));

From e1c6bf40e8b430f760371a07b88e3e08cebb26dc Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 30 Jun 2026 20:54:10 +0800
Subject: [PATCH 229/292] lcpp ui mic off by default

---
 tools/ui/src/lib/constants/settings-registry.ts | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
index 7bd8aa858..d059c3813 100644
--- a/tools/ui/src/lib/constants/settings-registry.ts
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -261,7 +261,7 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				key: SETTINGS_KEYS.AUTO_MIC_ON_EMPTY,
 				label: 'Show microphone on empty input',
 				help: 'Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.',
-				defaultValue: true,
+				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.DISPLAY,
 				isExperimental: true,

From 748a3139971b6c786a7f55551d410adaf863060a Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 30 Jun 2026 21:26:41 +0800
Subject: [PATCH 230/292] also freeze lcpp ui again, from the 22 june commit

---
 embd_res/lcpp.gz.embd | Bin 2571315 -> 2571319 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/embd_res/lcpp.gz.embd b/embd_res/lcpp.gz.embd
index 6d0df643be60d0c92c7d615fdbf16310450af040..18fbf2481ad7de330781bec3b42dac0612dcad98 100644
GIT binary patch
delta 1842377
zcmV)3K+C_g_?!W^_?!U;ABzYGv%^Dy2ZaX#g$DwK2LrVS1Tb!Y+s18wZ=WLZ`a1iC
zb>r06A9hvGZPVWM+NO2db-TRGCtH-PR+e-UWjjv1&prTzD9TB;-j?pY-n+&Ue}DuC
zf*=4=SmyuUC?k6X1^@W?E=lDlfBeHA9;0DyCzFxWaMKh|UL?hIrjIxj=^ZlRHFk(o
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From 799fcc04a5bbe2823e698b9e524f5a6cd0c594b6 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Tue, 30 Jun 2026 16:16:44 +0200
Subject: [PATCH 231/292] common,server: handle bracketed IPv6 literals in URL
 authority (#25140)

* common,server: handle bracketed IPv6 literals in URL authority

Parse the [host]:port form (RFC 3986) and bracket IPv6 hosts when
formatting a URL authority: listening log, proxy Host header, proxy
log, client rebuild. The per-request remote_addr stays bare.

* common: restore unsupported scheme throw in url parser

Address @ngxson review: keep the explicit reject in port resolution so
the block stays self-contained. Non-http(s) schemes still throw (also
gated at the top of common_http_parse_url).
---
 common/http.h                    | 34 ++++++++++++++++++++++++++------
 tools/server/server-cors-proxy.h |  2 +-
 tools/server/server-http.cpp     |  3 ++-
 tools/server/server-models.cpp   |  4 +++-
 4 files changed, 34 insertions(+), 9 deletions(-)

diff --git a/common/http.h b/common/http.h
index d3daccd6b..e88bc6a5e 100644
--- a/common/http.h
+++ b/common/http.h
@@ -11,6 +11,11 @@ struct common_http_url {
     std::string path;
 };
 
+// bracket an IPv6 literal host for a URL authority (RFC 3986)
+static std::string common_http_format_host(const std::string & host) {
+    return host.find(':') != std::string::npos ? "[" + host + "]" : host;
+}
+
 static common_http_url common_http_parse_url(const std::string & url) {
     common_http_url parts;
     auto scheme_end = url.find("://");
@@ -49,11 +54,28 @@ static common_http_url common_http_parse_url(const std::string & url) {
         parts.path = "/";
     }
 
-    auto colon_pos = parts.host.find(':');
+    // split the authority into host and optional port, a bracketed IPv6 literal keeps its inner colons (RFC 3986)
+    std::string port_str;
+    if (!parts.host.empty() && parts.host.front() == '[') {
+        auto close = parts.host.find(']');
+        if (close == std::string::npos) {
+            throw std::runtime_error("invalid IPv6 URL authority: " + parts.host);
+        }
+        auto after = parts.host.substr(close + 1);
+        if (!after.empty() && after.front() == ':') {
+            port_str = after.substr(1);
+        }
+        parts.host = parts.host.substr(1, close - 1);
+    } else {
+        auto colon_pos = parts.host.find(':');
+        if (colon_pos != std::string::npos) {
+            port_str = parts.host.substr(colon_pos + 1);
+            parts.host = parts.host.substr(0, colon_pos);
+        }
+    }
 
-    if (colon_pos != std::string::npos) {
-        parts.port = std::stoi(parts.host.substr(colon_pos + 1));
-        parts.host = parts.host.substr(0, colon_pos);
+    if (!port_str.empty()) {
+        parts.port = std::stoi(port_str);
     } else if (parts.scheme == "http") {
         parts.port = 80;
     } else if (parts.scheme == "https") {
@@ -83,7 +105,7 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
     }
 #endif
 
-    httplib::Client cli(parts.scheme + "://" + parts.host + ":" + std::to_string(parts.port));
+    httplib::Client cli(parts.scheme + "://" + common_http_format_host(parts.host) + ":" + std::to_string(parts.port));
 
     if (!parts.user.empty()) {
         cli.set_basic_auth(parts.user, parts.password);
@@ -95,5 +117,5 @@ static std::pair<httplib::Client, common_http_url> common_http_client(const std:
 }
 
 static std::string common_http_show_masked_url(const common_http_url & parts) {
-    return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + parts.host + parts.path;
+    return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + common_http_format_host(parts.host) + parts.path;
 }
diff --git a/tools/server/server-cors-proxy.h b/tools/server/server-cors-proxy.h
index 53a6909ed..3600a33ec 100644
--- a/tools/server/server-cors-proxy.h
+++ b/tools/server/server-cors-proxy.h
@@ -39,7 +39,7 @@ static server_http_res_ptr proxy_request(const server_http_req & req, std::strin
         throw std::runtime_error("unsupported URL scheme in target URL: " + parsed_url.scheme);
     }
 
-    SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), parsed_url.host.c_str(), parsed_url.port, parsed_url.path.c_str());
+    SRV_INF("proxying %s request to %s://%s:%i%s\n", method.c_str(), parsed_url.scheme.c_str(), common_http_format_host(parsed_url.host).c_str(), parsed_url.port, parsed_url.path.c_str());
 
     std::map<std::string, std::string> headers;
     const std::string proxy_header_prefix = "x-llama-server-proxy-header-";
diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp
index 21bed64c9..bb88dda21 100644
--- a/tools/server/server-http.cpp
+++ b/tools/server/server-http.cpp
@@ -1,4 +1,5 @@
 #include "common.h"
+#include "http.h"
 #include "server-http.h"
 #include "server-stream.h"
 #include "server-common.h"
@@ -441,7 +442,7 @@ bool server_http_context::start() {
     srv->wait_until_ready();
 
     listening_address = is_sock ? string_format("unix://%s", hostname.c_str())
-                                : string_format("%s://%s:%d", is_ssl ? "https" : "http", hostname.c_str(), port);
+                                : string_format("%s://%s:%d", is_ssl ? "https" : "http", common_http_format_host(hostname).c_str(), port);
     return true;
 }
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 81da00c0e..2ee318f14 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1,4 +1,5 @@
 #include "server-common.h"
+#include "http.h"
 #include "server-models.h"
 #include "server-context.h"
 #include "server-stream.h"
@@ -2263,7 +2264,8 @@ server_http_proxy::server_http_proxy(
             }
             if (lowered == "host") {
                 bool is_default_port = (scheme == "https" && port == 443) || (scheme == "http" && port == 80);
-                req.set_header(key, is_default_port ? host : host + ":" + std::to_string(port));
+                const std::string url_host = common_http_format_host(host);
+                req.set_header(key, is_default_port ? url_host : url_host + ":" + std::to_string(port));
             } else {
                 req.set_header(key, value);
             }

From ca3a77a87bc0eacdfcc6e4ddd27c8a4d2438d0e6 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 30 Jun 2026 22:26:02 +0800
Subject: [PATCH 232/292] testing max nodes per submit

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +-
 koboldcpp.py                         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 1b7542257..ada50f43a 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5908,7 +5908,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         // Submit at least every 100 nodes, in case there are workloads without as much matmul.
         // device->max_nodes_per_submit = 100;
-        device->max_nodes_per_submit = device->uma ? 8 : 64; //kcpp fix for https://github.com/ggml-org/llama.cpp/issues/21724
+        device->max_nodes_per_submit = device->uma ? 4 : 16; //kcpp fix for https://github.com/ggml-org/llama.cpp/issues/21724
         const char* GGML_VK_MAX_NODES_PER_SUBMIT = getenv("GGML_VK_MAX_NODES_PER_SUBMIT");
         if (GGML_VK_MAX_NODES_PER_SUBMIT != nullptr) {
             uint32_t max_nodes_per_submit = std::stoul(GGML_VK_MAX_NODES_PER_SUBMIT);
diff --git a/koboldcpp.py b/koboldcpp.py
index e015cc2bd..7d2fd4c78 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -12225,7 +12225,7 @@ if __name__ == '__main__':
     modelgroup.add_argument("--model","-m", metavar=('[filenames]'), help="Model file to load. Accepts multiple values if they are URLs.", type=str, nargs='+', default=[])
     modelgroup.add_argument("model_param", help="Model file to load (positional)", nargs="?")
     parser.add_argument("--config", metavar=('[filename]'), help="Load settings from a .kcpps file. Other arguments will be ignored", type=str, nargs=1)
-    parser.add_argument("--contextsize","--ctx-size", "-c", help=f"Controls the memory allocated for maximum context size, only change if you need more RAM for big contexts. (default {default_maxctx}).",metavar=('[256 to 262144]'), type=check_range(int,256,262144), default=default_maxctx)
+    parser.add_argument("--contextsize","--ctx-size", "-c", help=f"Controls the memory allocated for maximum context size, only change if you need more RAM for big contexts. (default {default_maxctx}).",metavar=('[256 to 524288]'), type=check_range(int,256,524288), default=default_maxctx)
     parser.add_argument("--gpulayers","--gpu-layers","--n-gpu-layers","-ngl", help="Set number of layers to offload to GPU (when using GPU). Set to -1 to enable autofit (default), set to 0 to disable GPU offload.",metavar=('[GPU layers]'), nargs='?', const=1, type=int, default=-1)
     parser.add_argument("--host", metavar=('[ipaddr]'), help="Host IP to listen on. If this flag is not set, all routable interfaces are accepted.", default="")
     parser.add_argument("--launch", help="Launches a web browser when load is completed.", action='store_true')

From 8d29a18bd249f189768d6a27039e7fd99fe2ba68 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 30 Jun 2026 22:57:41 +0800
Subject: [PATCH 233/292] default 32k ctx

---
 colab.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/colab.ipynb b/colab.ipynb
index e5194a430..b9b11bb9e 100644
--- a/colab.ipynb
+++ b/colab.ipynb
@@ -62,7 +62,7 @@
     "Model = \"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\" #@param [\"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Estopia-GGUF/resolve/main/LLaMA2-13B-Estopia.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q6_K.gguf\",\"https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v4.2.0-GGUF/resolve/main/TheDrummer_Cydonia-24B-v4.2.0-Q4_K_S.gguf\",\"https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q4_K_S.gguf\",\"https://huggingface.co/bartowski/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b-GGUF/resolve/main/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b-Q4_K_S.gguf\",\"https://huggingface.co/LatitudeGames/Harbinger-24B-GGUF/resolve/main/Harbinger-24B-Q4_K_S.gguf\",\"https://huggingface.co/LatitudeGames/Muse-12B-GGUF/resolve/main/Muse-12B-Q4_K_S.gguf\",\"https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF/resolve/main/Qwen3-VL-8B-Instruct-Q6_K.gguf\",\"https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/resolve/main/Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf\",\"https://huggingface.co/ggml-org/gpt-oss-20b-GGUF/resolve/main/gpt-oss-20b-mxfp4.gguf\",\"https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q6_K.gguf\",\"https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF/resolve/main/google_gemma-3-12b-it-Q4_K_S.gguf\",\"https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF/resolve/main/gemma-3n-E4B-it-Q6_K.gguf\",\"https://huggingface.co/unsloth/GLM-4-9B-0414-GGUF/resolve/main/GLM-4-9B-0414-Q6_K.gguf\",\"https://huggingface.co/mradermacher/Fimbulvetr-11B-v2-GGUF/resolve/main/Fimbulvetr-11B-v2.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/resolve/main/mythomax-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/ReMM-SLERP-L2-13B-GGUF/resolve/main/remm-slerp-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/Xwin-LM-13B-v0.2-GGUF/resolve/main/xwin-lm-13b-v0.2.Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/mini-magnum-12b-v1.1-GGUF/resolve/main/mini-magnum-12b-v1.1.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/Stheno-L2-13B-GGUF/resolve/main/stheno-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-Kimiko-v2-13B-GGUF/resolve/main/mythomax-l2-kimiko-v2-13b.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/Rocinante-12B-v1.1-GGUF/resolve/main/Rocinante-12B-v1.1-Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MistRP-Airoboros-7B-GGUF/resolve/main/mistrp-airoboros-7b.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/airoboros-mistral2.2-7B-GGUF/resolve/main/airoboros-mistral2.2-7b.Q4_K_S.gguf\",\"https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF/resolve/main/KobbleTiny-Q4_K.gguf\",\"https://huggingface.co/grimjim/kukulemon-7B-GGUF/resolve/main/kukulemon-7B.Q8_0.gguf\",\"https://huggingface.co/mradermacher/LemonKunoichiWizardV3-GGUF/resolve/main/LemonKunoichiWizardV3.Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Kunoichi-DPO-v2-7B-GGUF-Imatrix/resolve/main/Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf\",\"https://huggingface.co/mradermacher/L3-8B-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Stheno-v3.2.i1-Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix/resolve/main/v2-Llama-3-Lumimaid-8B-v0.1-OAS-Q4_K_M-imat.gguf\",\"https://huggingface.co/bartowski/NeuralDaredevil-8B-abliterated-GGUF/resolve/main/NeuralDaredevil-8B-abliterated-Q4_K_M.gguf\",\"https://huggingface.co/bartowski/L3-8B-Lunaris-v1-GGUF/resolve/main/L3-8B-Lunaris-v1-Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v2-GGUF/resolve/main/TheDrummer_Cydonia-24B-v2-Q4_K_S.gguf\",\"https://huggingface.co/bartowski/PocketDoc_Dans-PersonalityEngine-V1.2.0-24b-GGUF/resolve/main/PocketDoc_Dans-PersonalityEngine-V1.2.0-24b-IQ4_XS.gguf\",\"https://huggingface.co/mradermacher/Tlacuilo-12B-GGUF/resolve/main/Tlacuilo-12B.Q4_K_S.gguf\"] {\"allow-input\":true}\n",
     "MdCommand = \"\" #@markdown <br>\n",
     "Layers = \"Auto\" #@param [\"Auto\",\"999\"]{allow-input: true}\n",
-    "ContextSize = \"4096\" #@param [\"4096\",\"8192\",\"12288\",\"16384\"] {allow-input: true}\n",
+    "ContextSize = \"4096\" #@param [\"4096\",\"8192\",\"12288\",\"16384\",\"24576\",\"32768\"] {allow-input: true}\n",
     "\n",
     "#@markdown <hr>\n",
     "LoadVisionMMProjector = False #@param {type:\"boolean\"}\n",
@@ -130,7 +130,7 @@
     "  if Template == \"Gemma4 E4B Uncensored (General)\":\n",
     "    Customized = True\n",
     "    Model = \"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\"\n",
-    "    CustomCtxSize = \"16384\"\n",
+    "    CustomCtxSize = \"32768\"\n",
     "    CustomMmproj = \"https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/resolve/main/mmproj-BF16.gguf\"\n",
     "  if Template == \"Tiefighter 13B (General)\":\n",
     "    Customized = True\n",

From 0626395511ace211cdbbf5163f55ca49e48fc2b5 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 30 Jun 2026 23:14:47 +0800
Subject: [PATCH 234/292] default 40k ctx colab

---
 colab.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/colab.ipynb b/colab.ipynb
index b9b11bb9e..b8778a411 100644
--- a/colab.ipynb
+++ b/colab.ipynb
@@ -62,7 +62,7 @@
     "Model = \"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\" #@param [\"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Estopia-GGUF/resolve/main/LLaMA2-13B-Estopia.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q6_K.gguf\",\"https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v4.2.0-GGUF/resolve/main/TheDrummer_Cydonia-24B-v4.2.0-Q4_K_S.gguf\",\"https://huggingface.co/mradermacher/Broken-Tutu-24B-GGUF/resolve/main/Broken-Tutu-24B.Q4_K_S.gguf\",\"https://huggingface.co/bartowski/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b-GGUF/resolve/main/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b-Q4_K_S.gguf\",\"https://huggingface.co/LatitudeGames/Harbinger-24B-GGUF/resolve/main/Harbinger-24B-Q4_K_S.gguf\",\"https://huggingface.co/LatitudeGames/Muse-12B-GGUF/resolve/main/Muse-12B-Q4_K_S.gguf\",\"https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF/resolve/main/Qwen3-VL-8B-Instruct-Q6_K.gguf\",\"https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/resolve/main/Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf\",\"https://huggingface.co/ggml-org/gpt-oss-20b-GGUF/resolve/main/gpt-oss-20b-mxfp4.gguf\",\"https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q6_K.gguf\",\"https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF/resolve/main/google_gemma-3-12b-it-Q4_K_S.gguf\",\"https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF/resolve/main/gemma-3n-E4B-it-Q6_K.gguf\",\"https://huggingface.co/unsloth/GLM-4-9B-0414-GGUF/resolve/main/GLM-4-9B-0414-Q6_K.gguf\",\"https://huggingface.co/mradermacher/Fimbulvetr-11B-v2-GGUF/resolve/main/Fimbulvetr-11B-v2.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/resolve/main/mythomax-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/ReMM-SLERP-L2-13B-GGUF/resolve/main/remm-slerp-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/Xwin-LM-13B-v0.2-GGUF/resolve/main/xwin-lm-13b-v0.2.Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/mini-magnum-12b-v1.1-GGUF/resolve/main/mini-magnum-12b-v1.1.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/Stheno-L2-13B-GGUF/resolve/main/stheno-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-Kimiko-v2-13B-GGUF/resolve/main/mythomax-l2-kimiko-v2-13b.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/Rocinante-12B-v1.1-GGUF/resolve/main/Rocinante-12B-v1.1-Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MistRP-Airoboros-7B-GGUF/resolve/main/mistrp-airoboros-7b.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/airoboros-mistral2.2-7B-GGUF/resolve/main/airoboros-mistral2.2-7b.Q4_K_S.gguf\",\"https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF/resolve/main/KobbleTiny-Q4_K.gguf\",\"https://huggingface.co/grimjim/kukulemon-7B-GGUF/resolve/main/kukulemon-7B.Q8_0.gguf\",\"https://huggingface.co/mradermacher/LemonKunoichiWizardV3-GGUF/resolve/main/LemonKunoichiWizardV3.Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Kunoichi-DPO-v2-7B-GGUF-Imatrix/resolve/main/Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf\",\"https://huggingface.co/mradermacher/L3-8B-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Stheno-v3.2.i1-Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix/resolve/main/v2-Llama-3-Lumimaid-8B-v0.1-OAS-Q4_K_M-imat.gguf\",\"https://huggingface.co/bartowski/NeuralDaredevil-8B-abliterated-GGUF/resolve/main/NeuralDaredevil-8B-abliterated-Q4_K_M.gguf\",\"https://huggingface.co/bartowski/L3-8B-Lunaris-v1-GGUF/resolve/main/L3-8B-Lunaris-v1-Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/TheDrummer_Cydonia-24B-v2-GGUF/resolve/main/TheDrummer_Cydonia-24B-v2-Q4_K_S.gguf\",\"https://huggingface.co/bartowski/PocketDoc_Dans-PersonalityEngine-V1.2.0-24b-GGUF/resolve/main/PocketDoc_Dans-PersonalityEngine-V1.2.0-24b-IQ4_XS.gguf\",\"https://huggingface.co/mradermacher/Tlacuilo-12B-GGUF/resolve/main/Tlacuilo-12B.Q4_K_S.gguf\"] {\"allow-input\":true}\n",
     "MdCommand = \"\" #@markdown <br>\n",
     "Layers = \"Auto\" #@param [\"Auto\",\"999\"]{allow-input: true}\n",
-    "ContextSize = \"4096\" #@param [\"4096\",\"8192\",\"12288\",\"16384\",\"24576\",\"32768\"] {allow-input: true}\n",
+    "ContextSize = \"4096\" #@param [\"4096\",\"8192\",\"12288\",\"16384\",\"24576\",\"32768\",\"40960\"] {allow-input: true}\n",
     "\n",
     "#@markdown <hr>\n",
     "LoadVisionMMProjector = False #@param {type:\"boolean\"}\n",
@@ -130,7 +130,7 @@
     "  if Template == \"Gemma4 E4B Uncensored (General)\":\n",
     "    Customized = True\n",
     "    Model = \"https://huggingface.co/HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive/resolve/main/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive-Q5_K_M.gguf\"\n",
-    "    CustomCtxSize = \"32768\"\n",
+    "    CustomCtxSize = \"40960\"\n",
     "    CustomMmproj = \"https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF/resolve/main/mmproj-BF16.gguf\"\n",
     "  if Template == \"Tiefighter 13B (General)\":\n",
     "    Customized = True\n",

From 4f31eedb0ccf546b7e8d6bb243b170f12522f54d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?J=C3=BCrgen=20Schmied?= <github@juergenschmied.de>
Date: Tue, 30 Jun 2026 17:57:14 +0200
Subject: [PATCH 235/292] model : register t_layer_inp for qwen3next (#25141)

* Fix input assignment in layer processing loop

Fix DFLASH for qwen-coder-next

* add line break

Added tensor for attention normalization in Qwen3 model.
---
 src/models/qwen3next.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 97200a440..09b66423d 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -121,6 +121,8 @@ llama_model_qwen3next::graph::graph(const llama_model & model, const llm_graph_p
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);

From 0eca4d490e591d4e93058d07540cf47278a72577 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Tue, 30 Jun 2026 20:47:05 +0200
Subject: [PATCH 236/292] cuda : prevent integer truncation and overflow errors
 when using KQ mask strides in flash_attn_mask_to_KV_max kernel (#24945)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 ggml/src/ggml-cuda/fattn-common.cuh | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index 8dfa51ad1..b76121fef 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -664,7 +664,7 @@ constexpr __device__ dequantize_V_t get_dequantize_V() {
 template <int ncols1>
 __launch_bounds__(FATTN_KQ_STRIDE/2, 1)
 static __global__ void flash_attn_mask_to_KV_max(
-        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int s31, const int s33) {
+        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int64_t s31, const int64_t s33) {
     const int ne31     = gridDim.x;
     const int tid      = threadIdx.x;
     const int sequence = blockIdx.y;
@@ -1089,8 +1089,8 @@ void launch_fattn(
     // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
     //     multiple sequences of possibly different lengths.
     if (mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
-        const int s31 = mask->nb[1] / sizeof(half2);
-        const int s33 = mask->nb[3] / sizeof(half2);
+        const int64_t s31 = mask->nb[1] / sizeof(half2);
+        const int64_t s33 = mask->nb[3] / sizeof(half2);
 
         const dim3 blocks_num_KV_max(ntiles_x, Q->ne[3], 1);
         const dim3 block_dim_KV_max(FATTN_KQ_STRIDE/2, 1, 1);

From fd1a05791da1c06dc8bebdd537aa5212831d7cf6 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Tue, 30 Jun 2026 21:43:20 -0700
Subject: [PATCH 237/292] opencl: initial q1_0 support (#25160)

* opencl: general q1_0 support

* opencl: add Adreno GEMM/GEMV for q1_0
---
 ggml/src/ggml-opencl/CMakeLists.txt           |   5 +
 ggml/src/ggml-opencl/ggml-opencl.cpp          | 615 ++++++++++++++++++
 ggml/src/ggml-opencl/kernels/cvt.cl           |  46 ++
 .../kernels/gemm_noshuffle_q1_0_f32.cl        |  94 +++
 .../kernels/gemv_noshuffle_q1_0_f32.cl        | 121 ++++
 .../kernels/mul_mm_q1_0_f32_l4_lm.cl          | 156 +++++
 .../ggml-opencl/kernels/mul_mv_q1_0_f32.cl    | 141 ++++
 .../kernels/mul_mv_q1_0_f32_flat.cl           | 190 ++++++
 8 files changed, 1368 insertions(+)
 create mode 100644 ggml/src/ggml-opencl/kernels/gemm_noshuffle_q1_0_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mm_q1_0_f32_l4_lm.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32.cl
 create mode 100644 ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32_flat.cl

diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 09efbc566..52f48d122 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -78,6 +78,8 @@ set(GGML_OPENCL_KERNELS
     mul_mv_f16_f32_l4
     mul_mv_f16_f32
     mul_mv_f32_f32
+    mul_mv_q1_0_f32
+    mul_mv_q1_0_f32_flat
     mul_mv_q4_0_f32
     mul_mv_q4_0_f32_v
     mul_mv_q4_0_f32_8x_flat
@@ -128,6 +130,7 @@ set(GGML_OPENCL_KERNELS
     moe_sort_by_expert
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
+    mul_mm_q1_0_f32_l4_lm
     mul_mm_q4_0_f32_l4_lm
     mul_mm_q4_1_f32_l4_lm
     mul_mm_q5_0_f32_l4_lm
@@ -137,6 +140,8 @@ set(GGML_OPENCL_KERNELS
     mul_mm_q4_k_f32_l4_lm
     mul_mm_q5_k_f32_l4_lm
     mul_mm_q6_k_f32_l4_lm
+    gemv_noshuffle_q1_0_f32
+    gemm_noshuffle_q1_0_f32
     gemv_noshuffle_q4_0_f32
     gemv_noshuffle_q4_0_f32_spec
     gemm_noshuffle_q4_0_f32
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 32581901b..215aa5079 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -631,6 +631,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mm_f16_f32_kqv;
     cl_kernel kernel_mul_mm_f16_f32_kq;
     cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
+    cl_kernel kernel_convert_block_q1_0, kernel_restore_block_q1_0;
     cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
     cl_kernel kernel_convert_block_q4_0_trans4_ns, kernel_restore_block_q4_0_trans4_ns;
     cl_kernel kernel_convert_block_q4_1, kernel_restore_block_q4_1;
@@ -670,6 +671,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_convert_block_iq4_nl, kernel_restore_block_iq4_nl;
     cl_kernel kernel_convert_block_iq4_nl_noshuffle;
     cl_kernel kernel_restore_block_iq4_nl_noshuffle;
+    cl_kernel kernel_mul_mv_q1_0_f32, kernel_mul_mv_q1_0_f32_flat;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
     cl_kernel kernel_mul_mv_q4_1_f32;
     cl_kernel kernel_mul_mv_q4_1_f32_flat;
@@ -733,6 +735,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
+    cl_kernel kernel_mul_mm_q1_0_f32_l4_lm;
     cl_kernel kernel_mul_mm_q4_0_f32_l4_lm;
     cl_kernel kernel_mul_mm_q4_1_f32_l4_lm;
     cl_kernel kernel_mul_mm_q5_0_f32_l4_lm;
@@ -890,6 +893,8 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_gemm_noshuffle_q4_1_f32;
     cl_kernel kernel_gemm_noshuffle_q8_0_f32;
     cl_kernel kernel_gemv_noshuffle_q8_0_f32;
+    cl_kernel kernel_gemm_noshuffle_q1_0_f32;
+    cl_kernel kernel_gemv_noshuffle_q1_0_f32;
     cl_kernel kernel_gemv_noshuffle_q4_k_f32;
     cl_kernel kernel_gemm_noshuffle_q4_k_f32;
     cl_kernel kernel_gemv_noshuffle_q6_K_f32;
@@ -1151,6 +1156,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         backend_ctx->program_cvt =
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
 
+        CL_CHECK((backend_ctx->kernel_convert_block_q1_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q1_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_q1_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q1_0", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
@@ -1685,6 +1692,40 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_q1_0_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q1_0_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q1_0_f32.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32 = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // mul_mv_q1_0_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_q1_0_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_q1_0_f32_flat.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_q1_0_f32_flat = clCreateKernel(prog, "kernel_mul_mv_q1_0_f32_flat", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_iq4_nl_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1990,6 +2031,23 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // mul_mm_q1_0_f32_l4_lm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mm_q1_0_f32_l4_lm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mm_q1_0_f32_l4_lm.cl");
+#endif
+        cl_program prog =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mm_q1_0_f32_l4_lm = clCreateKernel(prog, "kernel_mul_mm_q1_0_f32_l4_lm", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mm_iq4_nl_f32_l4_lm
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2939,6 +2997,44 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_noshuffle_q1_0_f32
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "gemm_noshuffle_q1_0_f32.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("gemm_noshuffle_q1_0_f32.cl");
+#endif
+        cl_program prog = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+        CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemm_noshuffle_q1_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
+    // gemv_noshuffle_q1_0_f32
+    {
+        std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable "
+                                       " -DSIMDGROUP_WIDTH=" +
+                                       std::to_string(backend_ctx->adreno_wave_size);
+
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src_CL_gemv_general {
+            #include "gemv_noshuffle_q1_0_f32.cl.h"
+        };
+#else
+        const std::string kernel_src_CL_gemv_general = read_file("gemv_noshuffle_q1_0_f32.cl");
+#endif
+
+        cl_program prog = build_program_from_source(
+            backend_ctx->context, backend_ctx->device, kernel_src_CL_gemv_general.c_str(), CL_gemv_compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_gemv_noshuffle_q1_0_f32 = clCreateKernel(prog, "kernel_gemv_noshuffle_q1_0_f32", &err), err));
+        CL_CHECK(clReleaseProgram(prog));
+        GGML_LOG_CONT(".");
+    }
+
     // gemv_noshuffle_general
     {
         std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
@@ -4829,6 +4925,39 @@ struct ggml_tensor_extra_cl {
     }
 };
 
+struct ggml_tensor_extra_cl_q1_0 {
+    cl_mem q = nullptr;
+    cl_mem q_img = nullptr;
+
+    cl_mem d = nullptr;
+    cl_mem d_img = nullptr;
+
+    size_t size_q = 0;
+    size_t size_d = 0;
+
+    ~ggml_tensor_extra_cl_q1_0() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q));
+            q = nullptr;
+        }
+        if (d != nullptr) {
+            CL_CHECK(clReleaseMemObject(d));
+            d = nullptr;
+        }
+        q_img = nullptr;
+        d_img = nullptr;
+        size_q = 0;
+        size_d = 0;
+    }
+};
+
 // Additional tensor extra structs for quantized tensors.
 // These tensors are loaded from files and should not be allocated in scratch --
 // they should always be allocated from the pool. Hence, they do not have an
@@ -5732,6 +5861,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                 return true;
             } else if (op->src[0]->type == GGML_TYPE_F32) {
                 return op->src[1]->type == GGML_TYPE_F32;
+            } else if (op->src[0]->type == GGML_TYPE_Q1_0) {
+                return op->src[1]->type == GGML_TYPE_F32;
             } else if (op->src[0]->type == GGML_TYPE_Q4_0) {
                 // Non-contig src0 routes through on-device dequant-to-f16.
                 return op->src[1]->type == GGML_TYPE_F32;
@@ -5988,6 +6119,12 @@ struct ggml_backend_opencl_buffer_context {
         for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) {
             delete e;
         }
+        for (ggml_tensor_extra_cl_q1_0 * e : temp_tensor_extras_q1_0) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_q1_0 * e : temp_tensor_extras_q1_0_in_use) {
+            delete e;
+        }
         for (ggml_tensor_extra_cl_iq4_nl * e : temp_tensor_extras_iq4_nl) {
             delete e;
         }
@@ -6029,6 +6166,21 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_q1_0 * ggml_opencl_alloc_temp_tensor_extra_q1_0() {
+        ggml_tensor_extra_cl_q1_0 * extra;
+        if (temp_tensor_extras_q1_0.empty()) {
+            extra = new ggml_tensor_extra_cl_q1_0();
+        } else {
+            extra = temp_tensor_extras_q1_0.back();
+            temp_tensor_extras_q1_0.pop_back();
+        }
+
+        temp_tensor_extras_q1_0_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     ggml_tensor_extra_cl_q4_0 * ggml_opencl_alloc_temp_tensor_extra_q4_0() {
         ggml_tensor_extra_cl_q4_0 * extra;
         if (temp_tensor_extras_q4_0.empty()) {
@@ -6185,6 +6337,11 @@ struct ggml_backend_opencl_buffer_context {
         }
         temp_tensor_extras_in_use.clear();
 
+        for (ggml_tensor_extra_cl_q1_0 * e : temp_tensor_extras_q1_0_in_use) {
+            temp_tensor_extras_q1_0.push_back(e);
+        }
+        temp_tensor_extras_q1_0_in_use.clear();
+
         for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
             temp_tensor_extras_q4_0.push_back(e);
         }
@@ -6246,6 +6403,8 @@ struct ggml_backend_opencl_buffer_context {
     // for reuse.
     std::vector<ggml_tensor_extra_cl *> temp_tensor_extras;
     std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
+    std::vector<ggml_tensor_extra_cl_q1_0 *> temp_tensor_extras_q1_0;
+    std::vector<ggml_tensor_extra_cl_q1_0 *> temp_tensor_extras_q1_0_in_use;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
     std::vector<ggml_tensor_extra_cl_q4_1 *> temp_tensor_extras_q4_1;
@@ -6353,6 +6512,82 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
     cl_command_queue queue = backend_ctx->queue;
 
 #ifdef GGML_OPENCL_SOA_Q
+    if (tensor->type == GGML_TYPE_Q1_0) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_q1_0 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q1_0();
+
+        // q1_0 block = ggml_half d + (QK1_0/8) quant bytes = 2 + 16 = 18 bytes
+        size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(ggml_blck_size(tensor->type)/8);
+        GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, then quants.
+        cl_buffer_region region;
+
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_d;
+        extra->d = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_convert_block_q1_0;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        tensor->extra = extra;
+
+        // q is uint32 (32 sign bits each); d is one half per 128-block.
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (enable_adreno_trans_weight(backend_ctx, tensor)) {
+            int M = tensor->ne[1];   // ne01
+            int K = tensor->ne[0];   // ne00
+
+            GGML_ASSERT(K % 128 == 0);
+            GGML_ASSERT(M % 4 == 0);
+            GGML_ASSERT(tensor->ne[2] == 1);
+            GGML_ASSERT(tensor->ne[3] == 1);
+
+            transpose_2d_as_32b(backend_ctx, extra->q, extra->q, size_q, K/32,  M);
+            transpose_2d_as_16b(backend_ctx, extra->d, extra->d, size_d, K/128, M);
+        } // end transpose
+#endif // GGML_OPENCL_USE_ADRENO_KERNELS
+
+        return;
+    }
     // We separate the quantized bits and scale from block_q4_0 by using an
     // additional kernel, where each thread handles a block. We first read the
     // original weights into a temporary buffer, then create two separate
@@ -7743,6 +7978,63 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
     sync_with_other_backends(backend_ctx);
 
 #ifdef GGML_OPENCL_SOA_Q
+    if (tensor->type == GGML_TYPE_Q1_0) {
+        ggml_tensor_extra_cl_q1_0 * extra = (ggml_tensor_extra_cl_q1_0 *)tensor->extra;
+
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+        if (enable_adreno_trans_weight(backend_ctx, tensor)) {
+            ggml_cl_buffer buf_trans_q;
+            ggml_cl_buffer buf_trans_d;
+            ggml_cl_buffer buf_unpacked;
+
+            int M = tensor->ne[1];
+            int K = tensor->ne[0];
+
+            size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
+            size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(ggml_blck_size(tensor->type)/8);
+
+            buf_trans_q.allocate(backend_ctx->context, size_q);
+            buf_trans_d.allocate(backend_ctx->context, size_d);
+            buf_unpacked.allocate(backend_ctx->context, ggml_nbytes(tensor));
+
+            transpose_2d_as_32b(backend_ctx, extra->q, buf_trans_q.buffer, size_q, M, K/32);
+            transpose_2d_as_16b(backend_ctx, extra->d, buf_trans_d.buffer, size_d, M, K/128);
+
+            cl_kernel kernel = backend_ctx->kernel_restore_block_q1_0;
+            CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d.buffer));
+            CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &buf_unpacked.buffer));
+
+            size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+            size_t local_work_size[] = {1, 1, 1};
+
+            cl_event evt;
+            CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+            CL_CHECK(clWaitForEvents(1, &evt));
+            CL_CHECK(clEnqueueReadBuffer(queue, buf_unpacked.buffer, CL_TRUE, offset, size, data, 0, NULL, NULL));
+            return;
+        }
+#endif
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_restore_block_q1_0;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(queue, data_device, CL_TRUE, offset, size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    }
     // In end-to-end runs, get_tensor is usually used to get back the logits,
     // where we can simply do clEnqueueReadBuffer since they are f32.
     // However, in test-backend-ops, the GPU graph is copied to the CPU backend,
@@ -13437,6 +13729,203 @@ static void ggml_cl_mul_mat_kq_kqv_adreno(ggml_backend_t backend, const ggml_ten
     CL_CHECK(clReleaseMemObject(D_sub_buffer));
 }
 
+static void ggml_cl_mul_mat_q1_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    GGML_ASSERT(src0->type == GGML_TYPE_Q1_0);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+    ggml_tensor_extra_cl_q1_0 * extra0_q1_0 = (ggml_tensor_extra_cl_q1_0 *)src0->extra;
+
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    GGML_ASSERT(src1->view_offs == 0);
+    GGML_ASSERT(dst->view_offs == 0);
+
+    const int  ne00 = src0->ne[0];
+    const int  ne01 = src0->ne[1];
+    const int  ne02 = src0->ne[2];
+
+    const int  ne10 = src1->ne[0];
+    const int  ne12 = src1->ne[2];
+
+    const int  ne0 = dst->ne[0];
+    const int  ne1 = dst->ne[1];
+
+    GGML_ASSERT(ne00 == ne10);
+    GGML_ASSERT((ne00 % 128) == 0);
+    GGML_ASSERT(ne0 == ne01);
+
+    cl_context context = backend_ctx->context;
+    cl_kernel kernel;
+
+    cl_int              err;
+    cl_image_format     img_fmt;
+    cl_image_desc       img_desc;
+    cl_buffer_region    region;
+
+    int M = ne01;
+    int N = ne1;
+    int K = ne00;
+
+    if (ne1 == 1) {
+        cl_mem q_img = nullptr;
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_img = nullptr;
+
+        // image for q (uint32: each texel packs 32 sign bits)
+        img_fmt = { CL_R, CL_UNSIGNED_INT32};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = M * K / 32;
+        img_desc.buffer = extra0_q1_0->q;
+        CL_CHECK((q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // create a sub_buffer for B
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer((extra1->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        kernel = backend_ctx->kernel_gemv_noshuffle_q1_0_f32;
+
+        int r2 = 1;
+        int r3 = 1;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &q_img));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q1_0->d));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &b_img));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &extra1->offset));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &extrad->offset));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne1));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &r2));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
+
+        size_t wavesize = backend_ctx->adreno_wave_size;
+        size_t local_work_size[]  = { wavesize, 4, 1 };
+        size_t global_work_size[] = { CEIL_DIV(M, wavesize)*wavesize, 4, 1 };
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(q_img));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+    } else {
+        cl_mem b_sub_buf = nullptr;
+        cl_mem b_sub_buf_trans = nullptr;
+        cl_mem b_img = nullptr;
+        cl_mem b_img_trans = nullptr;
+
+        // subbuffer for activations
+        region.origin = offset1;
+        region.size = K * N * sizeof(float);
+        CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for activations
+        img_fmt = {CL_RGBA, CL_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * N / 4;
+        img_desc.buffer = b_sub_buf;
+        CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+        // pad N to multiple of 8
+        int extra_elements = N % 8;
+        int padding = 0;
+        if (extra_elements > 0){
+            padding = 8 - extra_elements;
+        }
+
+        // subbuffer for transposed activations
+        region.origin = 0;
+        region.size = K * (N + padding) * sizeof(float)/2;
+        backend_ctx->prealloc_act_trans.allocate(context, region.size);
+        CL_CHECK((b_sub_buf_trans = clCreateSubBuffer(backend_ctx->prealloc_act_trans.buffer, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+        // image for transposed activations
+        img_fmt = {CL_RGBA, CL_HALF_FLOAT};
+        memset(&img_desc, 0, sizeof(img_desc));
+        img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+        img_desc.image_width = K * (N + padding) / 4;
+        img_desc.buffer = b_sub_buf_trans;
+        CL_CHECK((b_img_trans = clCreateImage(context, 0, &img_fmt, &img_desc, NULL, &err), err));
+
+        // transpose activations
+        int height_B = N/4;
+        if (height_B == 0) {
+            height_B = 1;
+        }
+        int width_B = K/4;
+        int padded_height_B = (N + padding)/4;
+
+        kernel = backend_ctx->kernel_transpose_32_16;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &b_img));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(int),    &height_B));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int),    &width_B));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),    &padded_height_B));
+
+        size_t local_work_size_t[2] = { 1, 16 };
+        size_t global_work_size_t[2] = { (size_t)width_B, (size_t)padded_height_B };
+        backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size_t, local_work_size_t, dst);
+
+        // gemm
+        kernel = backend_ctx->kernel_gemm_noshuffle_q1_0_f32;
+        int padded_N = N + padding;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q1_0->q));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q1_0->d));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &b_img_trans));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(int),      &K));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),      &M));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &padded_N));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &N));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &offsetd));
+
+        size_t global_work_size[] = { (size_t)CEIL_DIV(N, 8), (size_t)CEIL_DIV(M, 4), 1 };
+        size_t local_work_size[]  = { 2, 128, 1 };
+
+        backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+        CL_CHECK(clReleaseMemObject(b_img_trans));
+        CL_CHECK(clReleaseMemObject(b_sub_buf_trans));
+        CL_CHECK(clReleaseMemObject(b_img));
+        CL_CHECK(clReleaseMemObject(b_sub_buf));
+    }
+#else
+    GGML_UNUSED(backend);
+    GGML_UNUSED(src0);
+    GGML_UNUSED(src1);
+    GGML_UNUSED(dst);
+#endif
+}
+
 static void ggml_cl_mul_mat_q4_0_f32_adreno(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     GGML_ASSERT(src0);
@@ -15311,6 +15800,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
     // view->extra stays pre-SoA; cast to the SoA struct would SIGSEGV.
     // Follow view_src to reach the real SoA extra.
     const ggml_tensor * soa0_src = src0->view_src != nullptr ? src0->view_src : src0;
+    ggml_tensor_extra_cl_q1_0 * extra0_q1_0 = (ggml_tensor_extra_cl_q1_0 *)src0->extra;
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)soa0_src->extra;
     ggml_tensor_extra_cl_q4_1 * extra0_q4_1 = (ggml_tensor_extra_cl_q4_1 *)soa0_src->extra;
     ggml_tensor_extra_cl_q5_0 * extra0_q5_0 = (ggml_tensor_extra_cl_q5_0 *)soa0_src->extra;
@@ -15374,6 +15864,13 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
         // a limit check, but q4_0 / q4_1 tensors are very unlikely to exceed that
         // limit, so the check is omitted.
 
+        // q1_0 x fp32
+        if (src0t == GGML_TYPE_Q1_0 && src1t == GGML_TYPE_F32 &&
+            enable_adreno_trans_weight(backend_ctx, src0)) {
+                ggml_cl_mul_mat_q1_0_f32_adreno(backend, src0, src1, dst);
+                return;
+        }
+
         // q4_0 x fp32
         if(src0t == GGML_TYPE_Q4_0 && src1t == GGML_TYPE_F32) {
             ggml_cl_mul_mat_q4_0_f32_adreno(backend, src0, src1, dst);
@@ -15577,6 +16074,48 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
                 backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
                 return;
             }
+            case GGML_TYPE_Q1_0: {
+                if (ne11 < 32) {
+                    break;
+                }
+                if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1)) {
+                    break;
+                }
+
+                kernel = backend_ctx->kernel_mul_mm_q1_0_f32_l4_lm;
+                nth0 = 128; // calculated as (BM*BN)/(TM*TN)
+
+                int batch_stride_a = ne00*ne01;
+                int batch_stride_b = ne10*ne11;
+                int batch_stride_d = ne0*ne1;
+
+                CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q1_0->q));
+                CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q1_0->d));
+                CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+                CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+                CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+                CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+                CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+                CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne02));
+                CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),      &ne11));
+                CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+                CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10)); // stride_a
+                CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne10)); // stride_b
+                CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne01)); // stride_d
+                CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &batch_stride_a));
+                CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &batch_stride_b));
+                CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &batch_stride_d));
+                CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+                CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+
+                // 64 is block tile size BM and BN - change here when BM and BN in the kernel are changed.
+                size_t global_work_size[] = {(size_t)(CEIL_DIV(ne01, 64)*nth0), (size_t)(CEIL_DIV(ne11, 64)), (size_t)ne12*ne13};
+                size_t local_work_size[] = {(size_t)nth0, 1, 1};
+
+                backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+                return;
+            }
             case GGML_TYPE_Q4_0: {
                 if (ne11 < 32) {
                     break;
@@ -16165,6 +16704,81 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
             CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
             break;
+        case GGML_TYPE_Q1_0: {
+#ifdef GGML_OPENCL_SOA_Q
+            kernel = backend_ctx->kernel_mul_mv_q1_0_f32_flat;
+
+            // nth0 - subgroup size
+            // nth1 - number of subgroups per workgroup
+            // ndst - number of output values per workgroup = output per subgroup * number of subgroups
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 2;
+                ndst = nth1*4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 2;
+                ndst = nth1*4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0_q1_0->q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_q1_0->d));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+#else
+            kernel = backend_ctx->kernel_mul_mv_q1_0_f32;
+
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 2;
+                ndst = nth1*4;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 2;
+                ndst = nth1*4;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),      &ne01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &r3));
+#endif // GGML_OPENCL_SOA_Q
+            break;
+        }
         case GGML_TYPE_Q4_0:
             // This should have been satisfied.
             GGML_ASSERT(ne11 == ne1);
@@ -16879,6 +17493,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
         src0t == GGML_TYPE_Q5_0 ||
         src0t == GGML_TYPE_Q5_1 ||
         src0t == GGML_TYPE_Q8_0 ||
+        src0t == GGML_TYPE_Q1_0 ||
         src0t == GGML_TYPE_IQ4_NL ||
         src0t == GGML_TYPE_Q2_K) {
         // Each SIMD group produces N_DST values in the result. Assuming each
diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl
index 82a130559..bf0335a69 100644
--- a/ggml/src/ggml-opencl/kernels/cvt.cl
+++ b/ggml/src/ggml-opencl/kernels/cvt.cl
@@ -27,6 +27,8 @@
 #define QR5_1                   2
 #define QK8_0                   32
 #define QR8_0                   1
+#define QK1_0                   128
+#define QR1_0                   1
 #define QK_K                    256
 #define K_SCALE_SIZE            (3 * QK_K / 64)
 #define K_QUANTS_PER_ITERATION  2
@@ -38,6 +40,14 @@ typedef ushort uint16_t;
 typedef int int32_t;
 typedef uint uint32_t;
 
+//------------------------------------------------------------------------------
+// block_q1_0
+//------------------------------------------------------------------------------
+typedef struct {
+    half d;             // delta
+    uchar qs[QK1_0/8];  // 1-bit signs (16 bytes)
+} block_q1_0;
+
 //------------------------------------------------------------------------------
 // block_q4_0
 //------------------------------------------------------------------------------
@@ -159,6 +169,42 @@ kernel void kernel_convert_f16_to_bf16(
     }
 }
 
+//------------------------------------------------------------------------------
+// kernel_convert_block_q1_0
+// Convert block_q1_0 (AOS) to 2 separate arrays (SOA): quant bytes + scales.
+// q1_0 bits are stored in natural order (bit j of byte i -> weight 8*i + j)
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_q1_0(
+    global block_q1_0 * src0,
+    global uchar * dst_q,
+    global half  * dst_d
+) {
+    global block_q1_0 * b = (global block_q1_0 *) src0 + get_global_id(0);
+    global uchar      * q = (global uchar *) dst_q + (QK1_0/8)*get_global_id(0);
+    global half       * d = (global half *) dst_d + get_global_id(0);
+
+    *d = b->d;
+
+    for (int i = 0; i < QK1_0/8; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_q1_0(
+    global uchar * src_q,
+    global half  * src_d,
+    global block_q1_0 * dst
+) {
+    global block_q1_0 * b = (global block_q1_0 *) dst + get_global_id(0);
+    global uchar      * q = (global uchar *) src_q + (QK1_0/8)*get_global_id(0);
+    global half       * d = (global half *) src_d + get_global_id(0);
+
+    b->d = *d;
+    for (int i = 0; i < QK1_0/8; ++i) {
+        b->qs[i] = q[i];
+    }
+}
+
 //------------------------------------------------------------------------------
 // kernel_convert_block_q4_0
 // Convert the block_q4_0 format to 2 separate arrays (AOS -> SOA).
diff --git a/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q1_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q1_0_f32.cl
new file mode 100644
index 000000000..ba46cc75c
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemm_noshuffle_q1_0_f32.cl
@@ -0,0 +1,94 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+// each work-item computes a 4 (rows of A / m) x 8 (cols of B / n) output tile.
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_128
+#endif
+kernel void kernel_gemm_noshuffle_q1_0_f32(
+        global const uint * src0_q,
+        global const half  * src0_d,
+        read_only image1d_buffer_t src1,
+        global float * dst,
+        int k,
+        int m,
+        int n,
+        int n_no_padding,
+        ulong offsetd
+) {
+    int n_4 = n >> 2;
+
+    int gy   = get_global_id(0);
+    int gx   = get_global_id(1);
+    int gx_2 = gx << 2;
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    half8 c0 = 0, c1 = 0, c2 = 0, c3 = 0;
+    half8 B;
+
+    global const uint* wptr = src0_q + gx_2;
+    global const half* sptr = src0_d + gx_2;
+
+    // 32 weights per uint32, 128 weights (one block / one scale) per 4 uint32.
+    for (int i = 0; i < k; i += 32) {
+        uint4 pack4 = vload4(0, wptr + (i / 32)  * m); // 4 rows, 32 K-values each
+        half4 scale = vload4(0, sptr + (i / 128) * m); // 4 rows, one scale per 128
+
+        for (int j = 0; j < 32; ++j) {
+            B.s0123 = read_imageh(src1, gy * 2 + (i + j) * n_4);
+            B.s4567 = read_imageh(src1, gy * 2 + (i + j) * n_4 + 1);
+
+            // sign bit -> +-1 (half arithmetic avoids unsigned underflow)
+            half4 wj = (half4)(
+                2.0h * (half)((pack4.s0 >> j) & 1u) - 1.0h,
+                2.0h * (half)((pack4.s1 >> j) & 1u) - 1.0h,
+                2.0h * (half)((pack4.s2 >> j) & 1u) - 1.0h,
+                2.0h * (half)((pack4.s3 >> j) & 1u) - 1.0h) * scale;
+
+            c0 += B * wj.s0;
+            c1 += B * wj.s1;
+            c2 += B * wj.s2;
+            c3 += B * wj.s3;
+        }
+    }
+
+    int idx = (gy << 3) * m + (gx << 2);
+
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s0, c1.s0, c2.s0, c3.s0), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s1, c1.s1, c2.s1, c3.s1), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s2, c1.s2, c2.s2, c3.s2), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s3, c1.s3, c2.s3, c3.s3), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s4, c1.s4, c2.s4, c3.s4), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s5, c1.s5, c2.s5, c3.s5), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s6, c1.s6, c2.s6, c3.s6), 0, dst + idx);
+        idx += m;
+    }
+    if(idx+3 < m*n_no_padding){
+        vstore4((float4)(c0.s7, c1.s7, c2.s7, c3.s7), 0, dst + idx);
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl
new file mode 100644
index 000000000..e83c5d068
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/gemv_noshuffle_q1_0_f32.cl
@@ -0,0 +1,121 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+
+#ifdef cl_qcom_reqd_sub_group_size
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
+#endif
+
+#define QK1_0 128
+#define N_SIMDGROUP 4
+
+#define dequantizeBlockAccum_q1(total, bits, scale, regB, lb)                                       \
+    total += (2.0f*(float)((bits >>  0) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s0, lb+0); \
+    total += (2.0f*(float)((bits >>  1) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s1, lb+0); \
+    total += (2.0f*(float)((bits >>  2) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s2, lb+0); \
+    total += (2.0f*(float)((bits >>  3) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s3, lb+0); \
+    total += (2.0f*(float)((bits >>  4) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s4, lb+0); \
+    total += (2.0f*(float)((bits >>  5) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s5, lb+0); \
+    total += (2.0f*(float)((bits >>  6) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s6, lb+0); \
+    total += (2.0f*(float)((bits >>  7) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s7, lb+0); \
+    total += (2.0f*(float)((bits >>  8) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s0, lb+1); \
+    total += (2.0f*(float)((bits >>  9) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s1, lb+1); \
+    total += (2.0f*(float)((bits >> 10) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s2, lb+1); \
+    total += (2.0f*(float)((bits >> 11) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s3, lb+1); \
+    total += (2.0f*(float)((bits >> 12) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s4, lb+1); \
+    total += (2.0f*(float)((bits >> 13) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s5, lb+1); \
+    total += (2.0f*(float)((bits >> 14) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s6, lb+1); \
+    total += (2.0f*(float)((bits >> 15) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s7, lb+1); \
+    total += (2.0f*(float)((bits >> 16) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s0, lb+2); \
+    total += (2.0f*(float)((bits >> 17) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s1, lb+2); \
+    total += (2.0f*(float)((bits >> 18) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s2, lb+2); \
+    total += (2.0f*(float)((bits >> 19) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s3, lb+2); \
+    total += (2.0f*(float)((bits >> 20) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s4, lb+2); \
+    total += (2.0f*(float)((bits >> 21) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s5, lb+2); \
+    total += (2.0f*(float)((bits >> 22) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s6, lb+2); \
+    total += (2.0f*(float)((bits >> 23) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s7, lb+2); \
+    total += (2.0f*(float)((bits >> 24) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s0, lb+3); \
+    total += (2.0f*(float)((bits >> 25) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s1, lb+3); \
+    total += (2.0f*(float)((bits >> 26) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s2, lb+3); \
+    total += (2.0f*(float)((bits >> 27) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s3, lb+3); \
+    total += (2.0f*(float)((bits >> 28) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s4, lb+3); \
+    total += (2.0f*(float)((bits >> 29) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s5, lb+3); \
+    total += (2.0f*(float)((bits >> 30) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s6, lb+3); \
+    total += (2.0f*(float)((bits >> 31) & 1u) - 1.0f) * scale * sub_group_broadcast(regB.s7, lb+3);
+
+
+#ifdef ADRENO_GPU
+REQD_SUBGROUP_SIZE_64
+#endif
+__kernel void kernel_gemv_noshuffle_q1_0_f32(
+        read_only  image1d_buffer_t src0_q,
+        global half  * src0_d,
+        read_only  image1d_buffer_t src1,
+        ulong offset1,
+        global float * dst,
+        ulong offsetd,
+        int ne00,
+        int ne01,
+        int ne02,
+        int ne10,
+        int ne12,
+        int ne0,
+        int ne1,
+        int r2,
+        int r3)
+{
+    uint groupId = get_local_id(1);
+    uint gid     = get_global_id(0);
+    ushort slid  = get_sub_group_local_id();
+
+    uint K = ne00;
+    uint M = ne01;
+
+    uint LINE_STRIDE_A  = M;
+    uint BLOCK_STRIDE_A = 4 * M;
+
+    uint4  regA;
+    half   regS;
+    float8 regB;
+
+    float totalSum = 0.0f;
+
+    #pragma unroll 1
+    for (uint kb = groupId; kb < (K / QK1_0); kb += N_SIMDGROUP) {
+        regS = src0_d[gid + kb * LINE_STRIDE_A]; // each fiber loads its row's scale
+
+        // first 16 fibers load 8 B values each -> 128 activations for this block
+        if (slid < 16) {
+            regB.s0123 = read_imagef(src1, (slid * 2 + kb * 32));
+            regB.s4567 = read_imagef(src1, (1 + slid * 2 + kb * 32));
+        }
+
+        // load this row's 4 uint32 (128 sign bits)
+        regA.s0 = read_imageui(src0_q, (gid + kb * BLOCK_STRIDE_A + LINE_STRIDE_A * 0)).x;
+        regA.s1 = read_imageui(src0_q, (gid + kb * BLOCK_STRIDE_A + LINE_STRIDE_A * 1)).x;
+        regA.s2 = read_imageui(src0_q, (gid + kb * BLOCK_STRIDE_A + LINE_STRIDE_A * 2)).x;
+        regA.s3 = read_imageui(src0_q, (gid + kb * BLOCK_STRIDE_A + LINE_STRIDE_A * 3)).x;
+
+        float scale = (float)regS;
+        dequantizeBlockAccum_q1(totalSum, regA.s0, scale, regB, 0);
+        dequantizeBlockAccum_q1(totalSum, regA.s1, scale, regB, 4);
+        dequantizeBlockAccum_q1(totalSum, regA.s2, scale, regB, 8);
+        dequantizeBlockAccum_q1(totalSum, regA.s3, scale, regB, 12);
+    }
+
+    // reduction in local memory, assumes #wave = N_SIMDGROUP = 4
+    local float reduceLM[SIMDGROUP_WIDTH * 3];
+    if (groupId == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = totalSum;
+    if (groupId == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = totalSum;
+    if (groupId == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = totalSum;
+    barrier(CLK_LOCAL_MEM_FENCE);
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 0 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 1 + slid];
+    if (groupId == 0) totalSum += reduceLM[SIMDGROUP_WIDTH * 2 + slid];
+
+    if (groupId == 0) {
+        dst = (global float*)((global char*)dst + offsetd);
+        dst[gid] = totalSum;
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q1_0_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q1_0_f32_l4_lm.cl
new file mode 100644
index 000000000..67710fac4
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_q1_0_f32_l4_lm.cl
@@ -0,0 +1,156 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+// LOAD_VEC_A is 8 because one q1_0 quant byte expands to 8 weights along K.
+#define LOAD_VEC_A 8
+#define LOAD_VEC_B 4
+
+#define BM 64
+#define BN 64
+#define BK 32
+#define TM 4
+#define TN 8
+
+kernel void kernel_mul_mm_q1_0_f32_l4_lm(
+    global uchar  * src0_q,
+    global half   * src0_d,
+    global float4 * src1,
+    ulong offset1,
+    global float  * dst,
+    ulong offsetd,
+
+    int ne00,
+    int ne01,
+    int ne02,
+    int ne11,
+    int ne12,
+
+    int stride_a,
+    int stride_b,
+    int stride_d,
+
+    int batch_stride_a,
+    int batch_stride_b,
+    int batch_stride_d,
+
+    int r2,
+    int r3
+) {
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst  = (global float *)((global char*)dst  + offsetd);
+
+    local float buf_a[BM * BK];
+    local float buf_b[BN * BK];
+
+    const int batch_idx = get_global_id(2);
+
+    const int i13 = batch_idx / ne12;
+    const int i12 = batch_idx % ne12;
+
+    const int i03 = i13 / r3;
+    const int i02 = i12 / r2;
+
+    const int batch_idx_a = i03 * ne02 + i02;
+
+    const int ir = get_group_id(0);
+    const int ic = get_group_id(1);
+
+    const int tid = get_local_id(0);
+    const int th_r  = tid % (BM / TM);
+    const int th_c  = tid / (BM / TM);
+
+    const int loadr_a = get_local_id(0) % (BK / LOAD_VEC_A);
+    const int loadc_a = get_local_id(0) / (BK / LOAD_VEC_A);
+    const int loadr_b = get_local_id(0) % (BK / LOAD_VEC_B);
+    const int loadc_b = get_local_id(0) / (BK / LOAD_VEC_B);
+
+    const int loadstride_a = get_local_size(0) * LOAD_VEC_A / BK;
+    const int loadstride_b = get_local_size(0) * LOAD_VEC_B / BK;
+
+    int pos_a = (batch_idx_a * batch_stride_a + ir * BM * stride_a) / LOAD_VEC_A;
+    int pos_b = (batch_idx   * batch_stride_b + ic * BN * stride_b) / LOAD_VEC_B;
+
+    float sums[TM * TN];
+    float cache_a[TM];
+    float cache_b[TN];
+
+    for (int i = 0; i < TM * TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    for (int block = 0; block < ne00; block += BK) {
+        for (int l = 0; l < BM; l += loadstride_a) {
+            if (ir*BM + loadc_a + l < ne01) {
+                int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+                int ib  = idx / 16; // 16 quant bytes per q1_0 block
+
+                float d = (float)src0_d[ib];
+                uint bits = src0_q[idx];
+
+                // use float to avoid unsigned underflow of (2*0 - 1).
+                buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 0) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 1) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 2) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 3) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 3) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 4) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 4) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 5) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 5) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 6) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 6) & 1) - 1.0f);
+                buf_a[(loadr_a * LOAD_VEC_A + 7) * BM + loadc_a + l] = d * (2.0f*(float)((bits >> 7) & 1) - 1.0f);
+            } else {
+                for (int b = 0; b < LOAD_VEC_A; ++b) {
+                    buf_a[(loadr_a * LOAD_VEC_A + b) * BM + loadc_a + l] = 0.0f;
+                }
+            }
+        }
+
+        for (int l = 0; l < BN; l += loadstride_b) {
+            if (ic*BN + loadc_b + l < ne11) {
+                int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = src1[idx].s2;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = src1[idx].s3;
+            } else {
+                buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 2) * BN + loadc_b + l] = 0.0f;
+                buf_b[(loadr_b * LOAD_VEC_B + 3) * BN + loadc_b + l] = 0.0f;
+            }
+        }
+
+        barrier(CLK_LOCAL_MEM_FENCE);
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (int i = 0; i < BK; i++) {
+            for (int j = 0; j < TM; j++) {
+                cache_a[j] = buf_a[(i) * BM + th_r * TM + j];
+            }
+
+            for (int j = 0; j < TN; j++) {
+                cache_b[j] = buf_b[(i) * BN + th_c * TN + j];
+            }
+
+            for (int cc = 0; cc < TN; cc++) {
+                for (int cr = 0; cr < TM; cr++) {
+                    const int sums_idx = cc*TM + cr;
+                    sums[sums_idx] = mad(cache_a[cr], cache_b[cc], sums[sums_idx]);
+                }
+            }
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+
+    const int dr = ir * BM + th_r * TM;
+    const int dc = ic * BN + th_c * TN;
+
+    const int offsets = batch_idx * batch_stride_d;
+
+    for (int cc = 0; cc < TN; cc++) {
+        for (int cr = 0; cr < TM; cr++) {
+            if (dr + cr < ne01 && dc + cc < ne11) {
+                dst[offsets + (dc + cc) * stride_d + dr + cr] = sums[cc * TM + cr];
+            }
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32.cl
new file mode 100644
index 000000000..8746e2a3a
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32.cl
@@ -0,0 +1,141 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK1_0 128
+typedef struct {
+    half d;
+    uchar qs[QK1_0/8];
+} block_q1_0;
+
+#define NB_Q1_0 16
+
+#ifdef INTEL_GPU
+#define N_R0_Q1_0 4 // number of rows each subgroup works on
+#define N_SG_Q1_0 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_Q1_0 4
+#define N_SG_Q1_0 2
+#define N_SIMDWIDTH 64
+#endif
+
+inline float block_q_1_0_dot_y(global block_q1_0 * qb, float sumy, float yl[NB_Q1_0], short il) {
+    global uchar * qs = qb->qs + il*2;
+    uint b0 = qs[0];
+    uint b1 = qs[1];
+
+    float acc = 0.f;
+    acc += yl[ 0]*(float)((b0 >> 0) & 1) + yl[ 1]*(float)((b0 >> 1) & 1);
+    acc += yl[ 2]*(float)((b0 >> 2) & 1) + yl[ 3]*(float)((b0 >> 3) & 1);
+    acc += yl[ 4]*(float)((b0 >> 4) & 1) + yl[ 5]*(float)((b0 >> 5) & 1);
+    acc += yl[ 6]*(float)((b0 >> 6) & 1) + yl[ 7]*(float)((b0 >> 7) & 1);
+
+    acc += yl[ 8]*(float)((b1 >> 0) & 1) + yl[ 9]*(float)((b1 >> 1) & 1);
+    acc += yl[10]*(float)((b1 >> 2) & 1) + yl[11]*(float)((b1 >> 3) & 1);
+    acc += yl[12]*(float)((b1 >> 4) & 1) + yl[13]*(float)((b1 >> 5) & 1);
+    acc += yl[14]*(float)((b1 >> 6) & 1) + yl[15]*(float)((b1 >> 7) & 1);
+
+    return qb->d * (2.0f*acc - sumy);
+}
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q1_0_f32(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * dst,
+    ulong         offsetd,
+    int           ne00,
+    int           ne01,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb03,
+    int           ne12,
+    ulong         nb11,
+    ulong         nb12,
+    ulong         nb13,
+    int           ne0,
+    int           ne1,
+    int           r2,
+    int           r3
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    int nb = ne00/QK1_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0*N_SG_Q1_0 + get_sub_group_id()) * N_R0_Q1_0;
+
+    uint i12 = im%ne12;
+    uint i13 = im/ne12;
+
+    ulong offset_src1 = r1*nb11 + i12*nb12 + i13*nb13;
+    global float * y  = (global float *) (src1 + offset_src1);
+
+    // pointers to src0 rows
+    global block_q1_0 * ax[N_R0_Q1_0];
+    for (int row = 0; row < N_R0_Q1_0; ++row) {
+        ulong offset_src0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+        ax[row] = (global block_q1_0 *) ((global char *) src0 + offset_src0);
+    }
+
+    float yl[NB_Q1_0];
+    float sumf[N_R0_Q1_0] = { 0.f };
+
+    const short ix = get_sub_group_local_id()/8;
+    const short il = get_sub_group_local_id()%8;
+
+    global float * yb = y + ix*QK1_0 + il*NB_Q1_0;
+
+    // each thread handles NB_Q1_0 quants at a time
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/8) {
+        float sumy = 0.f;
+        for (short i = 0; i < NB_Q1_0; ++i) {
+            yl[i] = yb[i];
+            sumy += yb[i];
+        }
+
+        for (short row = 0; row < N_R0_Q1_0; row++) {
+            sumf[row] += block_q_1_0_dot_y(ax[row] + ib, sumy, yl, il);
+        }
+
+        yb += N_SIMDWIDTH*NB_Q1_0;
+    }
+
+    global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
+
+    for (int row = 0; row < N_R0_Q1_0; ++row) {
+        float tot = sub_group_reduce_add(sumf[row]);
+
+        if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
+            dst_f32[first_row + row] = tot;
+        }
+    }
+}
diff --git a/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32_flat.cl b/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32_flat.cl
new file mode 100644
index 000000000..870e36d86
--- /dev/null
+++ b/ggml/src/ggml-opencl/kernels/mul_mv_q1_0_f32_flat.cl
@@ -0,0 +1,190 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK1_0 128
+#define QK1_0_BYTES (QK1_0/8)              // 16 quant bytes per block
+#define QK1_0_BLK_BYTES (QK1_0_BYTES + 2)  // d + qs in original tensor = 18
+
+#define NB_Q1_0 16 // quants handled per thread (two qs bytes)
+
+#ifdef INTEL_GPU
+#define N_R0_Q1_0 4 // number of rows each subgroup works on
+#define N_SG_Q1_0 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_Q1_0 4
+#define N_SG_Q1_0 2
+#define N_SIMDWIDTH 64
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_q1_0_f32_flat(
+    global char * src0_q,
+    global half * src0_d,
+    global char * src1,
+    ulong         offset1,
+    global char * dst,
+    ulong         offsetd,
+    int           ne00,
+    int           ne01,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb03,
+    int           ne12,
+    ulong         nb11,
+    ulong         nb12,
+    ulong         nb13,
+    int           ne0,
+    int           ne1,
+    int           r2,
+    int           r3
+) {
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    int nb = ne00/QK1_0;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0*N_SG_Q1_0 + get_sub_group_id()) * N_R0_Q1_0;
+
+    uint i12 = im%ne12;
+    uint i13 = im/ne12;
+
+    ulong offset_src1 = r1*nb11 + i12*nb12 + i13*nb13;
+    global float * y  = (global float *) (src1 + offset_src1);
+
+    // pointers to src0 rows (flat: q bytes + scales)
+    uint offset_src0_base = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+
+    global uchar * ax0, * ax1, * ax2, * ax3;
+    global half  * ad0, * ad1, * ad2, * ad3;
+    uint offset_src0;
+
+    offset_src0 = (offset_src0_base + 0*nb01) / QK1_0_BLK_BYTES;
+    ax0 = (global uchar *) ((global char *) src0_q + offset_src0*QK1_0_BYTES);
+    ad0 = (global half  *) ((global char *) src0_d + offset_src0*sizeof(half));
+
+    offset_src0 = (offset_src0_base + 1*nb01) / QK1_0_BLK_BYTES;
+    ax1 = (global uchar *) ((global char *) src0_q + offset_src0*QK1_0_BYTES);
+    ad1 = (global half  *) ((global char *) src0_d + offset_src0*sizeof(half));
+
+    offset_src0 = (offset_src0_base + 2*nb01) / QK1_0_BLK_BYTES;
+    ax2 = (global uchar *) ((global char *) src0_q + offset_src0*QK1_0_BYTES);
+    ad2 = (global half  *) ((global char *) src0_d + offset_src0*sizeof(half));
+
+    offset_src0 = (offset_src0_base + 3*nb01) / QK1_0_BLK_BYTES;
+    ax3 = (global uchar *) ((global char *) src0_q + offset_src0*QK1_0_BYTES);
+    ad3 = (global half  *) ((global char *) src0_d + offset_src0*sizeof(half));
+
+    const short ix = get_sub_group_local_id()/8;
+    const short il = get_sub_group_local_id()%8;
+
+    global float * yb = y + ix*QK1_0 + il*NB_Q1_0;
+
+    float8 yl_lo;
+    float8 yl_hi;
+    float4 sumf = 0.f;
+
+    // each thread handles NB_Q1_0 = 16 quants (two qs bytes) at a time
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/8) {
+        yl_lo = vload8(0, yb);
+        yl_hi = vload8(0, yb + 8);
+        float sumy = yl_lo.s0 + yl_lo.s1 + yl_lo.s2 + yl_lo.s3
+                   + yl_lo.s4 + yl_lo.s5 + yl_lo.s6 + yl_lo.s7
+                   + yl_hi.s0 + yl_hi.s1 + yl_hi.s2 + yl_hi.s3
+                   + yl_hi.s4 + yl_hi.s5 + yl_hi.s6 + yl_hi.s7;
+
+        uint b0, b1;
+        float acc;
+
+        b0 = ax0[ib*QK1_0_BYTES + il*2 + 0];
+        b1 = ax0[ib*QK1_0_BYTES + il*2 + 1];
+        acc  = yl_lo.s0*(float)((b0 >> 0) & 1) + yl_lo.s1*(float)((b0 >> 1) & 1)
+             + yl_lo.s2*(float)((b0 >> 2) & 1) + yl_lo.s3*(float)((b0 >> 3) & 1)
+             + yl_lo.s4*(float)((b0 >> 4) & 1) + yl_lo.s5*(float)((b0 >> 5) & 1)
+             + yl_lo.s6*(float)((b0 >> 6) & 1) + yl_lo.s7*(float)((b0 >> 7) & 1)
+             + yl_hi.s0*(float)((b1 >> 0) & 1) + yl_hi.s1*(float)((b1 >> 1) & 1)
+             + yl_hi.s2*(float)((b1 >> 2) & 1) + yl_hi.s3*(float)((b1 >> 3) & 1)
+             + yl_hi.s4*(float)((b1 >> 4) & 1) + yl_hi.s5*(float)((b1 >> 5) & 1)
+             + yl_hi.s6*(float)((b1 >> 6) & 1) + yl_hi.s7*(float)((b1 >> 7) & 1);
+        sumf.s0 += (float)ad0[ib] * (2.0f*acc - sumy);
+
+        b0 = ax1[ib*QK1_0_BYTES + il*2 + 0];
+        b1 = ax1[ib*QK1_0_BYTES + il*2 + 1];
+        acc  = yl_lo.s0*(float)((b0 >> 0) & 1) + yl_lo.s1*(float)((b0 >> 1) & 1)
+             + yl_lo.s2*(float)((b0 >> 2) & 1) + yl_lo.s3*(float)((b0 >> 3) & 1)
+             + yl_lo.s4*(float)((b0 >> 4) & 1) + yl_lo.s5*(float)((b0 >> 5) & 1)
+             + yl_lo.s6*(float)((b0 >> 6) & 1) + yl_lo.s7*(float)((b0 >> 7) & 1)
+             + yl_hi.s0*(float)((b1 >> 0) & 1) + yl_hi.s1*(float)((b1 >> 1) & 1)
+             + yl_hi.s2*(float)((b1 >> 2) & 1) + yl_hi.s3*(float)((b1 >> 3) & 1)
+             + yl_hi.s4*(float)((b1 >> 4) & 1) + yl_hi.s5*(float)((b1 >> 5) & 1)
+             + yl_hi.s6*(float)((b1 >> 6) & 1) + yl_hi.s7*(float)((b1 >> 7) & 1);
+        sumf.s1 += (float)ad1[ib] * (2.0f*acc - sumy);
+
+        b0 = ax2[ib*QK1_0_BYTES + il*2 + 0];
+        b1 = ax2[ib*QK1_0_BYTES + il*2 + 1];
+        acc  = yl_lo.s0*(float)((b0 >> 0) & 1) + yl_lo.s1*(float)((b0 >> 1) & 1)
+             + yl_lo.s2*(float)((b0 >> 2) & 1) + yl_lo.s3*(float)((b0 >> 3) & 1)
+             + yl_lo.s4*(float)((b0 >> 4) & 1) + yl_lo.s5*(float)((b0 >> 5) & 1)
+             + yl_lo.s6*(float)((b0 >> 6) & 1) + yl_lo.s7*(float)((b0 >> 7) & 1)
+             + yl_hi.s0*(float)((b1 >> 0) & 1) + yl_hi.s1*(float)((b1 >> 1) & 1)
+             + yl_hi.s2*(float)((b1 >> 2) & 1) + yl_hi.s3*(float)((b1 >> 3) & 1)
+             + yl_hi.s4*(float)((b1 >> 4) & 1) + yl_hi.s5*(float)((b1 >> 5) & 1)
+             + yl_hi.s6*(float)((b1 >> 6) & 1) + yl_hi.s7*(float)((b1 >> 7) & 1);
+        sumf.s2 += (float)ad2[ib] * (2.0f*acc - sumy);
+
+        b0 = ax3[ib*QK1_0_BYTES + il*2 + 0];
+        b1 = ax3[ib*QK1_0_BYTES + il*2 + 1];
+        acc  = yl_lo.s0*(float)((b0 >> 0) & 1) + yl_lo.s1*(float)((b0 >> 1) & 1)
+             + yl_lo.s2*(float)((b0 >> 2) & 1) + yl_lo.s3*(float)((b0 >> 3) & 1)
+             + yl_lo.s4*(float)((b0 >> 4) & 1) + yl_lo.s5*(float)((b0 >> 5) & 1)
+             + yl_lo.s6*(float)((b0 >> 6) & 1) + yl_lo.s7*(float)((b0 >> 7) & 1)
+             + yl_hi.s0*(float)((b1 >> 0) & 1) + yl_hi.s1*(float)((b1 >> 1) & 1)
+             + yl_hi.s2*(float)((b1 >> 2) & 1) + yl_hi.s3*(float)((b1 >> 3) & 1)
+             + yl_hi.s4*(float)((b1 >> 4) & 1) + yl_hi.s5*(float)((b1 >> 5) & 1)
+             + yl_hi.s6*(float)((b1 >> 6) & 1) + yl_hi.s7*(float)((b1 >> 7) & 1);
+        sumf.s3 += (float)ad3[ib] * (2.0f*acc - sumy);
+
+        yb += N_SIMDWIDTH*NB_Q1_0;
+    }
+
+    global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
+
+    float4 tot = (float4)(
+        sub_group_reduce_add(sumf.s0),
+        sub_group_reduce_add(sumf.s1),
+        sub_group_reduce_add(sumf.s2),
+        sub_group_reduce_add(sumf.s3)
+    );
+
+    if (get_sub_group_local_id() == 0) {
+        if (first_row + 0 < ne01) dst_f32[first_row + 0] = tot.s0;
+        if (first_row + 1 < ne01) dst_f32[first_row + 1] = tot.s1;
+        if (first_row + 2 < ne01) dst_f32[first_row + 2] = tot.s2;
+        if (first_row + 3 < ne01) dst_f32[first_row + 3] = tot.s3;
+    }
+}

From 7af4279f4579094cbe121cccb3c28357396e55d0 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Wed, 1 Jul 2026 07:32:55 +0200
Subject: [PATCH 238/292] ui: Remove PWA navigate fallback to prevent caching
 API endpoint requests (#25174)

---
 tools/ui/src/lib/constants/pwa.ts | 17 ++++++++++-------
 tools/ui/tests/e2e/pwa.e2e.ts     |  5 ++++-
 tools/ui/tests/unit/pwa.spec.ts   |  6 ++++--
 3 files changed, 18 insertions(+), 10 deletions(-)

diff --git a/tools/ui/src/lib/constants/pwa.ts b/tools/ui/src/lib/constants/pwa.ts
index 80505fabd..b9171d4bb 100644
--- a/tools/ui/src/lib/constants/pwa.ts
+++ b/tools/ui/src/lib/constants/pwa.ts
@@ -288,9 +288,7 @@ export const API_CACHING_PATTERNS = {
 } as const;
 
 // SvelteKit PWA plugin options
-export const PWA_KIT_OPTIONS = {
-	NAVIGATE_FALLBACK: './'
-} as const;
+export const PWA_KIT_OPTIONS = {} as const;
 
 export const APPLE_META_TAGS = {
 	MOBILE_WEB_APP_CAPABLE: { name: 'apple-mobile-web-app-capable', content: 'yes' },
@@ -322,6 +320,14 @@ export const SVELTEKIT_PWA_OPTIONS: SvelteKitPWAOptions = {
 		globIgnores: GLOB_IGNORES,
 		maximumFileSizeToCacheInBytes: CACHE_SETTINGS.MAX_FILE_SIZE_BYTES,
 
+		// Prevent @vite-pwa/sveltekit from auto-adding a NavigationRoute by
+		// setting navigateFallback to empty string. This keeps the service
+		// worker from intercepting direct browser navigation to server API
+		// endpoints (e.g. /slots, /models, /v1/models) which should return
+		// JSON, not the SPA HTML shell. The server's own static-file fallback
+		// handles non-API navigation to index.html for the SPA router.
+		navigateFallback: '',
+
 		// Runtime caching for API calls - use NetworkFirst so APIs are always fresh
 		runtimeCaching: [
 			{
@@ -351,10 +357,7 @@ export const SVELTEKIT_PWA_OPTIONS: SvelteKitPWAOptions = {
 
 	devOptions: {
 		enabled: true,
-		suppressWarnings: true,
-		// Use PWA_KIT_OPTIONS.NAVIGATE_FALLBACK to match production SW behaviour
-		// (navigateFallback defaults to the configured base path, which is '/' for this SPA).
-		navigateFallback: PWA_KIT_OPTIONS.NAVIGATE_FALLBACK
+		suppressWarnings: true
 	},
 
 	// SvelteKit-specific options
diff --git a/tools/ui/tests/e2e/pwa.e2e.ts b/tools/ui/tests/e2e/pwa.e2e.ts
index e21672239..c20eec276 100644
--- a/tools/ui/tests/e2e/pwa.e2e.ts
+++ b/tools/ui/tests/e2e/pwa.e2e.ts
@@ -43,7 +43,10 @@ test.describe('PWA Service Worker', () => {
 		expect(swContent).toMatch(/"_app\/immutable\/assets\/bundle\.[a-zA-Z0-9_-]+\.css"/);
 		expect(swContent).toMatch(/"manifest\.webmanifest"/);
 		expect(swContent).toMatch(/"_app\/version\.json"/);
-		expect(swContent).toMatch(/NavigationRoute/);
+		// NavigationRoute is intentionally absent — server API endpoints
+		// (e.g. /slots, /models) must not be intercepted by the PWA and
+		// should return JSON directly from the server.
+		expect(swContent).not.toMatch(/NavigationRoute/);
 		expect(swContent).toMatch(/api-cache/);
 	});
 
diff --git a/tools/ui/tests/unit/pwa.spec.ts b/tools/ui/tests/unit/pwa.spec.ts
index 0059e246c..17da27986 100644
--- a/tools/ui/tests/unit/pwa.spec.ts
+++ b/tools/ui/tests/unit/pwa.spec.ts
@@ -108,9 +108,11 @@ describe('PWA Build Output', () => {
 			expect(swContent).toMatch(/"manifest\.webmanifest"/);
 		});
 
-		it('has navigation route registered', () => {
+		it('no navigation route — API endpoints bypass PWA', () => {
 			expect(swContent).toBeTruthy();
-			expect(swContent).toMatch(/NavigationRoute/);
+			// NavigationRoute is intentionally absent so direct browser
+			// navigation to server API endpoints returns JSON, not HTML.
+			expect(swContent).not.toMatch(/NavigationRoute/);
 		});
 
 		it('has runtime caching for API routes', () => {

From 9d88e7cedd5054e80cd282f031111e9fa6c9e33d Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Wed, 1 Jul 2026 09:25:18 +0200
Subject: [PATCH 239/292] ui Prevent tool messages from incorrectly appending
 to other conversations (#25177)

* fix: Prevent tool messages from incorrectly appending to other conversations

* ui: prevent agentic loop from poisoning another conv's currNode

* ui: make editedContent a  so background recompute does not wipe in-progress edits

---------

Co-authored-by: Pascal <admin@serveurperso.com>
---
 .../ChatMessage/ChatMessage.svelte            |  2 +-
 tools/ui/src/lib/stores/chat.svelte.ts        | 37 +++++++++++++++----
 2 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
index dadcae0c4..7189ce1c7 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
@@ -43,7 +43,7 @@
 		assistantMessages: number;
 		messageTypes: string[];
 	} | null>(null);
-	let editedContent = $derived(message.content);
+	let editedContent = $state(message.content);
 
 	let rawEditContent = $derived.by(() => {
 		if (message.role !== MessageRole.ASSISTANT) return undefined;
diff --git a/tools/ui/src/lib/stores/chat.svelte.ts b/tools/ui/src/lib/stores/chat.svelte.ts
index 43f9e72c7..6949047b5 100644
--- a/tools/ui/src/lib/stores/chat.svelte.ts
+++ b/tools/ui/src/lib/stores/chat.svelte.ts
@@ -1083,6 +1083,11 @@ class ChatStore {
 		let resolvedModel: string | null = null;
 		let modelPersisted = false;
 		const convId = assistantMessage.convId;
+		// Tracks the last message created in this flow. Used as the parent for the next
+		// turn's assistant message so createAssistantMessage does not have to read
+		// conversationsStore.activeMessages, which may belong to a different conversation
+		// after the user navigates while the loop is still running.
+		let lastCreatedInFlow = currentMessageId;
 		// freeze the POST identity from t0 so a stop cancels with the exact session key,
 		// never a stale or empty model resolved later
 		this.setChatStreaming(convId, streamedContent, currentMessageId, effectiveModel);
@@ -1208,8 +1213,15 @@ class ChatStore {
 				};
 				if (timings) uiUpdate.timings = timings;
 				if (resolvedModel) uiUpdate.model = resolvedModel;
-				conversationsStore.updateMessageAtIndex(idx, uiUpdate);
-				await conversationsStore.updateCurrentNode(currentMessageId);
+				// touch the active ui array and node pointer only when this conversation
+				// is displayed; otherwise persist the node move straight to the db so a
+				// foreign conv's currNode stays untouched
+				if (conversationsStore.activeConversation?.id === convId) {
+					conversationsStore.updateMessageAtIndex(idx, uiUpdate);
+					await conversationsStore.updateCurrentNode(currentMessageId);
+				} else {
+					await DatabaseService.updateCurrentNode(convId, currentMessageId);
+				}
 			},
 			createToolResultMessage: async (
 				toolCallId: string,
@@ -1230,8 +1242,16 @@ class ChatStore {
 					},
 					currentMessageId
 				);
-				conversationsStore.addMessageToActive(msg);
-				await conversationsStore.updateCurrentNode(msg.id);
+				// mirror into the active store and move the node pointer only when this
+				// conversation is displayed; otherwise persist the node move straight to
+				// the db for the owning conv so a foreign conv's currNode stays untouched
+				if (conversationsStore.activeConversation?.id === convId) {
+					conversationsStore.addMessageToActive(msg);
+					await conversationsStore.updateCurrentNode(msg.id);
+				} else {
+					await DatabaseService.updateCurrentNode(convId, msg.id);
+				}
+				lastCreatedInFlow = msg.id;
 				return msg;
 			},
 			createAssistantMessage: async () => {
@@ -1239,8 +1259,6 @@ class ChatStore {
 				streamedContent = '';
 				streamedReasoningContent = '';
 
-				const lastMsg =
-					conversationsStore.activeMessages[conversationsStore.activeMessages.length - 1];
 				const msg = await DatabaseService.createMessageBranch(
 					{
 						convId,
@@ -1252,10 +1270,13 @@ class ChatStore {
 						children: [],
 						model: resolvedModel
 					},
-					lastMsg.id
+					lastCreatedInFlow
 				);
-				conversationsStore.addMessageToActive(msg);
+				if (conversationsStore.activeConversation?.id === convId) {
+					conversationsStore.addMessageToActive(msg);
+				}
 				currentMessageId = msg.id;
+				lastCreatedInFlow = msg.id;
 				return msg;
 			},
 			onFlowComplete: (finalTimings?: ChatMessageTimings) => {

From 6dbc1174b8f46a8e064259985d84c81a472beea4 Mon Sep 17 00:00:00 2001
From: ragz4125 <65285549+ragz4125@users.noreply.github.com>
Date: Wed, 1 Jul 2026 13:01:20 +0530
Subject: [PATCH 240/292] ggml-cpu: add AVX2 optimization for nvfp4 dot product
 and use UE4M3 LUT (#23961)

---
 ggml/src/ggml-common.h              |   5 +-
 ggml/src/ggml-cpu/arch-fallback.h   |   1 -
 ggml/src/ggml-cpu/arch/x86/quants.c | 146 +++++++++++++++++++++++++++-
 ggml/src/ggml-cpu/ggml-cpu.c        |   8 ++
 ggml/src/ggml-cpu/simd-mappings.h   |  11 +++
 5 files changed, 164 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index f05683b44..a51b37dcd 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -1111,11 +1111,12 @@ GGML_TABLE_BEGIN(int8_t, kvalues_iq4nl, 16)
     -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
 GGML_TABLE_END()
 
-// e2m1 values (doubled)
+// e2m1 values (doubled), shared by MXFP4 and NVFP4
 // ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
-GGML_TABLE_BEGIN(int8_t, kvalues_mxfp4, 16)
+GGML_TABLE_BEGIN(int8_t, kvalues_fp4, 16)
     0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12,
 GGML_TABLE_END()
+#define kvalues_mxfp4 kvalues_fp4
 
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h
index 1fc2b4b71..da2ac98a4 100644
--- a/ggml/src/ggml-cpu/arch-fallback.h
+++ b/ggml/src/ggml-cpu/arch-fallback.h
@@ -82,7 +82,6 @@
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // quants.c
-#define ggml_vec_dot_nvfp4_q8_0_generic ggml_vec_dot_nvfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
diff --git a/ggml/src/ggml-cpu/arch/x86/quants.c b/ggml/src/ggml-cpu/arch/x86/quants.c
index 94b19b82b..ea54cfe44 100644
--- a/ggml/src/ggml-cpu/arch/x86/quants.c
+++ b/ggml/src/ggml-cpu/arch/x86/quants.c
@@ -934,7 +934,7 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 
 #if defined __AVX2__
 
-    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_fp4);
     const __m128i m4b  = _mm_set1_epi8(0x0f);
     const __m256i mone = _mm256_set1_epi16(1);
 
@@ -963,7 +963,7 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
 
 #elif defined __AVX__
-    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_fp4);
     const __m128i m4b  = _mm_set1_epi8(0x0f);
 
     __m256 accum = _mm256_setzero_ps();
@@ -993,14 +993,152 @@ void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         int sumi1 = 0;
         int sumi2 = 0;
         for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+            sumi1 += y[ib].qs[j +          0] * kvalues_fp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_fp4[x[ib].qs[j] >>  4];
         }
         sumf += d * (sumi1 + sumi2);
     }
     *s = sumf;
 }
 
+void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_NVFP4 == 0);
+
+    const block_nvfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_NVFP4;
+    int ib = 0;
+    float sumf = 0;
+
+#if defined(__AVX2__)
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_fp4);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum = _mm256_setzero_ps();
+    for(; ib < nb; ib++){
+
+        const __m128i q4bits_01 = _mm_loadu_si128((const __m128i *)(x[ib].qs + 0));
+        const __m128i q4bits_23 = _mm_loadu_si128((const __m128i *)(x[ib].qs + 16));
+
+        const __m256i q8_01 = _mm256_loadu_si256((const __m256i *)y[2*ib + 0].qs);
+        const __m256i q8_23 = _mm256_loadu_si256((const __m256i *)y[2*ib + 1].qs);
+
+        const __m128i q4_01_lo = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_01, m4b));
+        const __m128i q4_01_hi = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_01, 4), m4b));
+        const __m128i q4_23_lo = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_23, m4b));
+        const __m128i q4_23_hi = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_23, 4), m4b));
+
+        //reordering
+        const __m256i q4_01 = MM256_SET_M128I(_mm_unpackhi_epi64(q4_01_lo,q4_01_hi), _mm_unpacklo_epi64(q4_01_lo,q4_01_hi));
+        const __m256i q4_23 = MM256_SET_M128I(_mm_unpackhi_epi64(q4_23_lo,q4_23_hi),_mm_unpacklo_epi64(q4_23_lo,q4_23_hi));
+
+        const __m256i p01 = mul_add_epi8(q4_01,q8_01);
+        const __m256i p_1 = _mm256_madd_epi16(p01, mone);
+
+        const __m256i p23 = mul_add_epi8(q4_23,q8_23);
+        const __m256i p_2 = _mm256_madd_epi16(p23, mone);
+
+        const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d);
+        const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d);
+
+        const float s0 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[0]) * dy0;
+        const float s1 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[1]) * dy0;
+        const float s2 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[2]) * dy1;
+        const float s3 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[3]) * dy1;
+
+        const __m256 scales01 = _mm256_set_m128(_mm_set1_ps(s1), _mm_set1_ps(s0));
+        const __m256 scales23 = _mm256_set_m128(_mm_set1_ps(s3), _mm_set1_ps(s2));
+
+        accum = _mm256_fmadd_ps(scales01, _mm256_cvtepi32_ps(p_1), accum);
+        accum = _mm256_fmadd_ps(scales23, _mm256_cvtepi32_ps(p_2), accum);
+    }
+    sumf = hsum_float_8(accum);
+
+#elif defined(__AVX__)
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_fp4);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+
+    __m256 accum = _mm256_setzero_ps();
+    for(; ib < nb; ib++){
+
+        const __m128i q4bits_01 = _mm_loadu_si128((const __m128i *)(x[ib].qs + 0));
+        const __m128i q4bits_23 = _mm_loadu_si128((const __m128i *)(x[ib].qs + 16));
+
+        const __m128i q8_0 = _mm_loadu_si128((const __m128i *)(y[2*ib + 0].qs + 0));
+        const __m128i q8_1 = _mm_loadu_si128((const __m128i *)(y[2*ib + 0].qs + 16));
+        const __m128i q8_2 = _mm_loadu_si128((const __m128i *)(y[2*ib + 1].qs + 0));
+        const __m128i q8_3 = _mm_loadu_si128((const __m128i *)(y[2*ib + 1].qs + 16));
+
+        const __m128i q4_01_lo = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_01, m4b));
+        const __m128i q4_01_hi = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_01, 4), m4b));
+        const __m128i q4_23_lo = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_23, m4b));
+        const __m128i q4_23_hi = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_23, 4), m4b));
+
+        const __m128i q4_0 = _mm_unpacklo_epi64(q4_01_lo, q4_01_hi);
+        const __m128i q4_1 = _mm_unpackhi_epi64(q4_01_lo, q4_01_hi);
+        const __m128i q4_2 = _mm_unpacklo_epi64(q4_23_lo, q4_23_hi);
+        const __m128i q4_3 = _mm_unpackhi_epi64(q4_23_lo, q4_23_hi);
+
+        const __m128i p0_i32 = mul_sum_i8_pairs(q4_0, q8_0);
+        const __m128i p1_i32 = mul_sum_i8_pairs(q4_1, q8_1);
+        const __m128i p2_i32 = mul_sum_i8_pairs(q4_2, q8_2);
+        const __m128i p3_i32 = mul_sum_i8_pairs(q4_3, q8_3);
+
+        const __m128 p0 = _mm_cvtepi32_ps(p0_i32);
+        const __m128 p1 = _mm_cvtepi32_ps(p1_i32);
+        const __m128 p2 = _mm_cvtepi32_ps(p2_i32);
+        const __m128 p3 = _mm_cvtepi32_ps(p3_i32);
+
+        const __m256 p01 = _mm256_set_m128(p1, p0);
+        const __m256 p23 = _mm256_set_m128(p3, p2);
+
+        const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d);
+        const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d);
+
+        const float s0 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[0]) * dy0;
+        const float s1 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[1]) * dy0;
+        const float s2 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[2]) * dy1;
+        const float s3 = GGML_CPU_UE4M3_TO_FP32(x[ib].d[3]) * dy1;
+
+        const __m256 scales01 = _mm256_set_m128(_mm_set1_ps(s1), _mm_set1_ps(s0));
+        const __m256 scales23 = _mm256_set_m128(_mm_set1_ps(s3), _mm_set1_ps(s2));
+
+        accum = _mm256_add_ps(accum, _mm256_mul_ps(p01, scales01));
+        accum = _mm256_add_ps(accum, _mm256_mul_ps(p23, scales23));
+    }
+    sumf = hsum_float_8(accum);
+
+#endif
+
+    for (;ib < nb; ++ib) {
+        for (int s_idx = 0; s_idx < 4; ++s_idx) {
+            const float d = GGML_CPU_UE4M3_TO_FP32(x[ib].d[s_idx]);
+            const int q8_block = s_idx / 2;
+            const int q8_off   = (s_idx % 2) * QK_NVFP4_SUB;
+            const float dy = GGML_CPU_FP16_TO_FP32(y[2*ib + q8_block].d);
+
+            int sumi_lo = 0, sumi_hi = 0;
+            for (int j = 0; j < QK_NVFP4_SUB/2; ++j) {
+                const uint8_t qv = x[ib].qs[s_idx*(QK_NVFP4_SUB/2) + j];
+                sumi_lo += y[2*ib + q8_block].qs[q8_off + j +               0] * kvalues_fp4[qv & 0xf];
+                sumi_hi += y[2*ib + q8_block].qs[q8_off + j + QK_NVFP4_SUB/2] * kvalues_fp4[qv >>  4];
+            }
+
+            sumf += dy * d * (sumi_lo + sumi_hi);
+        }
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index eb8341c9a..8a0e5c255 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -82,6 +82,9 @@ float ggml_table_f32_f16[1 << 16];
 // precomputed f32 table for e8m0 half (1 KB) (simd-mappings.h)
 float ggml_table_f32_e8m0_half[1 << 8];
 
+// precomputed f32 table for ue4m3 (1 KB) (simd-mappings.h)
+float ggml_table_f32_ue4m3[1 << 8];
+
 #if defined(__ARM_ARCH)
 struct ggml_arm_arch_features_type {
     int sve_cnt;
@@ -3798,6 +3801,11 @@ void ggml_cpu_init(void) {
                 ggml_table_f32_e8m0_half[i] = GGML_E8M0_TO_FP32_HALF(i);
             }
 
+            // initialize UE4M3 table (256 entries)
+            for (int i = 0; i < (1 << 8); ++i) {
+                ggml_table_f32_ue4m3[i] = ggml_ue4m3_to_fp32(i);
+            }
+
             const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
 
             GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0);
diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h
index 62e687201..be50c25c0 100644
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -120,6 +120,10 @@ extern float ggml_table_f32_f16[1 << 16];
 // defined in ggml-cpu.c, initialized in ggml_cpu_init()
 extern float ggml_table_f32_e8m0_half[1 << 8];
 
+// precomputed f32 table for ue4m3 (1 KB)
+// defined in ggml-cpu.c, initialized in ggml_cpu_init()
+extern float ggml_table_f32_ue4m3[1 << 8];
+
 // Use lookup table for E8M0 on x86 (faster than bit manipulation)
 #if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
 #define GGML_CPU_E8M0_TO_FP32_HALF(x) ggml_table_f32_e8m0_half[(uint8_t)(x)]
@@ -127,6 +131,13 @@ extern float ggml_table_f32_e8m0_half[1 << 8];
 #define GGML_CPU_E8M0_TO_FP32_HALF(x) GGML_E8M0_TO_FP32_HALF(x)
 #endif
 
+// Use lookup table for UE4M3 on x86 (faster than bit manipulation)
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+#define GGML_CPU_UE4M3_TO_FP32(x) ggml_table_f32_ue4m3[(uint8_t)(x)]
+#else
+#define GGML_CPU_UE4M3_TO_FP32(x) ggml_ue4m3_to_fp32(x)
+#endif
+
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_CPU_FP16_TO_FP32 and GGML_CPU_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.

From 983dec9a54b28b842d7c07457c94a09d083eb913 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 16:17:13 +0800
Subject: [PATCH 241/292] prevent MTP drafting with batching

---
 gpttype_adapter.cpp | 9 +++++----
 koboldcpp.py        | 2 +-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index f801f716c..edcae6eb6 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -599,8 +599,9 @@ static size_t estimate_draft_autofit_tax_mb(
     if(draft_is_mtp_estimate)
     {
         draft_ctx_params.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+        draft_ctx_params.n_seq_max = base_ctx_params.n_seq_max;
         draft_ctx_params.n_rs_seq = speculative_chunk_amt;
-        draft_ctx_params.n_outputs_max = base_ctx_params.n_seq_max; //match the real MTP draft context (see speculative_decoding_setup) so the autofit tax doesn't over-reserve the draft compute buffer at n_batch*n_vocab (~2GB on large-vocab models like Gemma)
+        draft_ctx_params.n_outputs_max = std::max<uint32_t>(1, base_ctx_params.n_seq_max); //match the real MTP draft context so the autofit tax doesn't over-reserve the draft compute buffer at n_batch*n_vocab (~2GB on large-vocab models like Gemma)
         measure_model_bytes = has_draft_model;
     }
 
@@ -885,7 +886,7 @@ static void mtp_decoding_setup(llama_model * main_model, llama_context * main_ct
     mtp_ctx_params.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
     mtp_ctx_params.ctx_other = main_ctx;
     mtp_ctx_params.n_rs_seq = 0;
-    mtp_ctx_params.n_outputs_max = 1;
+    mtp_ctx_params.n_outputs_max = std::max<uint32_t>(1, mtp_ctx_params.n_seq_max);
 
     printf("\nAttempting to create built-in MTP context from the main model.\n");
     draft_ctx = llama_init_from_model(main_model, mtp_ctx_params);
@@ -4266,7 +4267,7 @@ public:
         std::unique_lock<std::mutex> lock(batch_mutex);
         batch_legacy_waiting++;
         batch_cv.notify_all();
-        batch_cv.wait(lock, [](){ return !batch_has_live_locked(); });
+        batch_cv.wait(lock, [](){ return !batch_legacy_active && !batch_has_live_locked(); });
         batch_legacy_waiting--;
         batch_invalidate_legacy_context_locked();
         batch_legacy_active = true;
@@ -4762,7 +4763,7 @@ static void batch_start_worker_locked()
 
 bool gpttype_batch_generate_enabled()
 {
-    return continuous_batching_slots > 1 && file_format == FileFormat::GGUF_GENERIC && llama_ctx_v4 && kcpp_data;
+    return continuous_batching_slots > 1 && file_format == FileFormat::GGUF_GENERIC && llama_ctx_v4 && kcpp_data && !draft_ctx && !guidance_ctx;
 }
 
 int gpttype_batch_generate_submit(const generation_inputs inputs)
diff --git a/koboldcpp.py b/koboldcpp.py
index 7d2fd4c78..a7dd81df7 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -2351,7 +2351,7 @@ def continuous_batching_python_eligible(genparams, api_format):
     if model_path and not model_path.endswith(".gguf"):
         utfprint("Batching disabled due to file format",2)
         return False
-    if not getattr(args, "noshift", False) or getattr(args, "smartcontext", False) or getattr(args, "draftmodel", "") or getattr(args, "enableguidance", False):
+    if not getattr(args, "noshift", False) or getattr(args, "smartcontext", False) or getattr(args, "draftmodel", "") or getattr(args, "usemtp", False) or getattr(args, "enableguidance", False):
         utfprint("Batching disabled due to loaded settings",2)
         return False
     if genparams.get("negative_prompt") or genparams.get("images") or genparams.get("audio"):

From b820cc8e6f94843d32f92c8ebd7db837dae2bd8b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 1 Jul 2026 10:55:14 +0200
Subject: [PATCH 242/292] CUDA: consistent use of __restrict__ + PDL for FA
 (#25185)

---
 ggml/src/ggml-cuda/fattn-common.cuh | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index b76121fef..e67cc7fdf 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -664,7 +664,10 @@ constexpr __device__ dequantize_V_t get_dequantize_V() {
 template <int ncols1>
 __launch_bounds__(FATTN_KQ_STRIDE/2, 1)
 static __global__ void flash_attn_mask_to_KV_max(
-        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int64_t s31, const int64_t s33) {
+        const half2 * mask_ptr, int * KV_max_ptr, const int ne30, const int64_t s31, const int64_t s33) {
+    const half2 * GGML_CUDA_RESTRICT mask   = mask_ptr;
+    int         * GGML_CUDA_RESTRICT KV_max = KV_max_ptr;
+
     const int ne31     = gridDim.x;
     const int tid      = threadIdx.x;
     const int sequence = blockIdx.y;
@@ -1099,8 +1102,9 @@ void launch_fattn(
         const int iter_k = K->ne[1] / FATTN_KQ_STRIDE;
 
         KV_max.alloc(ne_KV_max);
-        flash_attn_mask_to_KV_max<ncols1><<<blocks_num_KV_max, block_dim_KV_max, 0, main_stream>>>
-            ((const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
+        ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_KV_max, block_dim_KV_max, 0, main_stream);
+        ggml_cuda_kernel_launch(flash_attn_mask_to_KV_max<ncols1>, launch_params,
+            (const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
         CUDA_CHECK(cudaGetLastError());
     }
 

From 849ec89bad0f4a5d8066786fb28ce38d07679bf8 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 18:51:25 +0800
Subject: [PATCH 243/292] restructure some compilation units

---
 CMakeLists.txt              |  5 +-
 Makefile                    | 70 ++++++++++++++-------------
 gpttype_adapter.cpp         | 95 ++++++++++++++++++++++++++++++++++---
 koboldcpp.py                |  2 +-
 src/llama-context.cpp       |  2 +-
 src/llama-grammar.cpp       |  2 +-
 src/llama-grammar.h         |  2 +
 src/llama-kv-cache-iswa.cpp |  4 +-
 src/llama-vocab.cpp         |  2 +-
 src/llama.cpp               |  5 --
 src/models/deepseek4.cpp    |  5 +-
 11 files changed, 139 insertions(+), 55 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6102364f2..34adf0f89 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -570,7 +570,10 @@ target_link_libraries(embeddings_adapter PRIVATE common2 ggml ${LLAMA_EXTRA_LIBS
 set_target_properties(embeddings_adapter PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
 add_library(gpttype_adapter
-            gpttype_adapter.cpp)
+            gpttype_adapter.cpp
+            src/llama.cpp
+            common/chat.cpp
+            src/llama-model.cpp)
 target_include_directories(gpttype_adapter PUBLIC . ./src ./ggml/include ./ggml/src ./ggml/src/ggml-cpu ./include ./otherarch ./otherarch/tools ./vendor/stb ./vendor/nlohmann ./vendor ./otherarch/sdcpp ./otherarch/sdcpp/thirdparty ./tools ./common)
 target_compile_features(gpttype_adapter PUBLIC cxx_std_17) # don't bump
 target_link_libraries(gpttype_adapter PRIVATE common2 ggml ggml_v1 ggml_v2 ggml_v3 ${LLAMA_EXTRA_LIBS})
diff --git a/Makefile b/Makefile
index d0d2f04a7..d09f5d2a4 100644
--- a/Makefile
+++ b/Makefile
@@ -682,7 +682,9 @@ ggml-vulkan-shaders-noext.o: ggml/src/ggml-vulkan-shaders-noext.cpp ggml/include
 	$(CXX) $(CXXFLAGS) $(VKGEN_NOEXT_FORCE) $(VULKAN_FLAGS) -c $< -o $@
 
 # intermediate objects
-llama.o: src/llama.cpp ggml/include/ggml.h ggml/include/ggml-alloc.h ggml/include/ggml-backend.h ggml/include/ggml-cuda.h ggml/include/ggml-metal.h include/llama.h otherarch/llama-util.h
+llama.o: src/llama.cpp ggml/include/ggml.h ggml/include/ggml-alloc.h ggml/include/ggml-backend.h ggml/include/ggml-cuda.h ggml/include/ggml-metal.h include/llama.h otherarch/llama-util.h src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-dsa.cpp src/llama-kv-cache-dsv4.cpp src/llama-kv-cache-iswa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model-saver.cpp src/llama-quant.cpp src/llama-hparams.cpp src/llama-graph.cpp src/llama-io.cpp src/llama-memory.cpp common/fit.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+llama-model.o: src/llama-model.cpp src/llama-model.h src/models/models.h ggml/include/ggml.h include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 common.o: common/common.cpp common/common.h common/log.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
@@ -698,6 +700,8 @@ llama-impl.o: src/llama-impl.cpp src/llama-impl.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
+chat.o: common/chat.cpp common/chat.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
@@ -732,7 +736,7 @@ OBJS_SDTYPE := otherarch/sdcpp/sdtype_adapter.o $(OBJS_SDCOMMON)
 
 LLAMASERVER_SRCS := tools/server/main.cpp tools/server/server.cpp tools/server/server-schema.cpp tools/server/server-chat.cpp tools/server/server-common.cpp tools/server/server-context.cpp tools/server/server-http.cpp tools/server/server-models.cpp tools/server/server-queue.cpp tools/server/server-task.cpp tools/server/server-tools.cpp tools/server/ui.cpp
 COMMON_DOWNLOAD_SRCS := common/download.cpp common/hf-cache.cpp vendor/cpp-httplib/httplib.cpp
-LLAMASERVER_COMMON_SRCS := common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS)
+LLAMASERVER_COMMON_SRCS := common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS)
 LLAMASERVER_CXXFLAGS := -I./tools/mtmd
 
 
@@ -755,7 +759,7 @@ music_default.o: otherarch/acestep/music_adapter.cpp
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 # idiotic "for easier compilation"
-GPTTYPE_ADAPTER = gpttype_adapter.cpp common/chat.cpp otherarch/llama_v2.cpp otherarch/llama_v3.cpp src/llama.cpp src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-iswa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model.cpp src/llama-quant.cpp src/llama-hparams.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
+GPTTYPE_ADAPTER = gpttype_adapter.cpp model_adapter.h otherarch/otherarch.h include/llama.h otherarch/llama_v2.cpp otherarch/llama_v3.cpp otherarch/gptj_v1.cpp otherarch/gptj_v2.cpp otherarch/gptj_v3.cpp otherarch/gpt2_v1.cpp otherarch/gpt2_v2.cpp otherarch/gpt2_v3.cpp otherarch/rwkv_v2.cpp otherarch/rwkv_v3.cpp otherarch/neox_v2.cpp otherarch/neox_v3.cpp otherarch/mpt_v3.cpp
 gpttype_adapter_failsafe.o: $(GPTTYPE_ADAPTER)
 	$(CXX) $(CXXFLAGS) $(FAILSAFE_FLAGS) -c $< -o $@
 gpttype_adapter.o: $(GPTTYPE_ADAPTER)
@@ -775,35 +779,35 @@ clean:
 	rm -vf otherarch/sdcpp/*.o otherarch/sdcpp/*/*.o otherarch/sdcpp/*/*/*.o otherarch/sdcpp/*/*/*/*.o
 
 # useful tools
-main: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+main: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mainvk: tools/completion/completion.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+mainvk: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-ttsmain: tools/tts/tts.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+ttsmain: tools/tts/tts.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-gguf-split: tools/gguf-split/gguf-split.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o build-info.h clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+gguf-split: tools/gguf-split/gguf-split.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o build-info.h clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h mtmd.o mtmd-helper.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h mtmd.o mtmd-helper.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embedding: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+embedding: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/chat.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-llamaservervk: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+llamaservervk: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ggml/src/ggml-vulkan-shaders.cpp: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -904,14 +908,14 @@ else
 endif
 
 #generated libraries
-koboldcpp_default: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_default: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(DEFAULT_BUILD)
 
-koboldcpp_macos_failsafe: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_macos_failsafe: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(DEFAULT_BUILD)
 
 ifdef FAILSAFE_BUILD
-koboldcpp_failsafe: ggml_v4_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FAILSAFE) $(OBJS)
+koboldcpp_failsafe: ggml_v4_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FAILSAFE) $(OBJS)
 	$(FAILSAFE_BUILD)
 else
 koboldcpp_failsafe:
@@ -919,7 +923,7 @@ koboldcpp_failsafe:
 endif
 
 ifdef NOAVX2_BUILD
-koboldcpp_noavx2: ggml_v4_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
+koboldcpp_noavx2: ggml_v4_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
 	$(NOAVX2_BUILD)
 else
 koboldcpp_noavx2:
@@ -927,7 +931,7 @@ koboldcpp_noavx2:
 endif
 
 ifdef CUBLAS_BUILD
-koboldcpp_cublas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(CUBLAS_OBJS) $(OBJS_FULL) $(OBJS)
+koboldcpp_cublas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(CUBLAS_OBJS) $(OBJS_FULL) $(OBJS)
 	$(CUBLAS_BUILD)
 else
 koboldcpp_cublas:
@@ -935,7 +939,7 @@ koboldcpp_cublas:
 endif
 
 ifdef HIPBLAS_BUILD
-koboldcpp_hipblas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(HIP_OBJS) $(OBJS_FULL) $(OBJS)
+koboldcpp_hipblas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(HIP_OBJS) $(OBJS_FULL) $(OBJS)
 	$(HIPBLAS_BUILD)
 else
 koboldcpp_hipblas:
@@ -943,12 +947,12 @@ koboldcpp_hipblas:
 endif
 
 ifdef VULKAN_BUILD
-koboldcpp_vulkan: ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_vulkan: ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter_vulkan.o llama.o chat.o llama-model.o ggml-vulkan.o ggml-vulkan-shaders.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(VULKAN_BUILD)
 ifdef NOAVX2_BUILD
-koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
+koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o llama.o chat.o llama-model.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
 	$(VULKAN_BUILD)
-koboldcpp_vulkan_failsafe: ggml_v4_vulkan_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLER) $(OBJS)
+koboldcpp_vulkan_failsafe: ggml_v4_vulkan_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o llama.o chat.o llama-model.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLER) $(OBJS)
 	$(VULKAN_BUILD)
 else
 koboldcpp_vulkan_noavx2:
@@ -966,17 +970,17 @@ koboldcpp_vulkan_failsafe:
 endif
 
 # tools
-quantize_gguf: tools/quantize/main.cpp tools/quantize/quantize.cpp common/imatrix-loader.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gguf: tools/quantize/main.cpp tools/quantize/quantize.cpp common/imatrix-loader.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gptj: otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gptj: otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gpt2: otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gpt2: otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_neox: otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_neox: otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_mpt: otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_mpt: otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_ace: otherarch/acestep/quantize-acestep.cpp tools/mtmd/clip.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_ace: otherarch/acestep/quantize-acestep.cpp tools/mtmd/clip.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
 
diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index edcae6eb6..83ae65a21 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -21,6 +21,7 @@
 #include <string>
 #include <cctype>
 #include <cstdlib>
+#include <cfloat>
 #include <locale>
 #include <chrono>
 #include <algorithm>
@@ -32,12 +33,8 @@
 #include "utils.h"
 #include "llmutils.h"
 
-//for easier compilation
-//concat source files into one file for compilation purposes
 #include "llama_v2.cpp"
 #include "llama_v3.cpp"
-#include "src/llama.cpp"
-#include "common/chat.cpp"
 #include "gptj_v1.cpp"
 #include "gptj_v2.cpp"
 #include "gptj_v3.cpp"
@@ -49,14 +46,23 @@
 #include "neox_v2.cpp"
 #include "neox_v3.cpp"
 #include "mpt_v3.cpp"
+
 #include "tools/mtmd/mtmd.h"
 #include "tools/mtmd/mtmd-helper.h"
 #include "common/speculative.h"
+#include "common/chat.h"
+#include "common/log.h"
+#include "llama-grammar.h"
 #include "vendor/stb/stb_image.h"
 #include "otherarch/sdcpp/thirdparty/stb_image_resize.h"
 #include "common/common.h"
 #include "common/fit.h"
 #include "ggml-rpc.h"
+#include "llama-impl.h"
+#include "llama-ext.h"
+#include "llama-model.h"
+#include "llama-vocab.h"
+#include "nlohmann/json.hpp"
 
 #if defined(GGML_USE_HIP)
 // for rocblas_initialize()
@@ -186,6 +192,10 @@ static const int smartcache_rnn_lifeboat_percent = 65;
 static const int smartcache_rnn_lifeboat_extra_slot_min_user_slots = 4;
 
 extern bool kcpp_permit_any_repack;
+extern bool kcpp_pipeline_parallelism;
+extern bool OldBPETokenizerMode;
+extern int kcpp_extra_swa_padding;
+extern int kcpp_active_swa_size;
 
 inline int kcpp_cpu_has_blas(void) {
 #if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL)
@@ -2053,6 +2063,77 @@ void sample_temperature(llama_token_data_array * candidates_p, float temp, float
     }
 }
 
+static std::pair<std::vector<uint32_t>, llama_partial_utf8> kcpp_decode_utf8(
+        const std::string & src,
+        llama_partial_utf8 partial_start) {
+    static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
+    const char * pos = src.c_str();
+    std::vector<uint32_t> code_points;
+
+    code_points.reserve(src.size() + 1);
+    uint32_t value    = partial_start.value;
+    int      n_remain = partial_start.n_remain;
+
+    while (*pos != 0 && n_remain > 0) {
+        uint8_t next_byte = static_cast<uint8_t>(*pos);
+        if ((next_byte >> 6) != 2) {
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
+        }
+        value = (value << 6) + (next_byte & 0x3F);
+        ++pos;
+        --n_remain;
+    }
+
+    if (partial_start.n_remain > 0 && n_remain == 0) {
+        code_points.push_back(value);
+    }
+
+    while (*pos != 0) {
+        uint8_t first_byte = static_cast<uint8_t>(*pos);
+        uint8_t highbits   = first_byte >> 4;
+        n_remain = lookup[highbits] - 1;
+
+        if (n_remain < 0) {
+            code_points.clear();
+            code_points.push_back(0);
+            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
+        }
+
+        uint8_t mask = (1 << (7 - n_remain)) - 1;
+        value = first_byte & mask;
+
+        ++pos;
+        while (*pos != 0 && n_remain > 0) {
+            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
+            ++pos;
+            --n_remain;
+        }
+        if (n_remain == 0) {
+            code_points.push_back(value);
+        }
+    }
+    code_points.push_back(0);
+
+    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
+}
+
+static llama_grammar_candidates kcpp_llama_grammar_reject_candidates(
+        const llama_grammar_rules      & rules,
+        const llama_grammar_stacks     & stacks,
+        const llama_grammar_candidates & candidates) {
+    if (stacks.empty()) {
+        return {};
+    }
+
+    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
+    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
+        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
+    }
+
+    return rejects;
+}
+
 void sample_grammar(FileFormat file_format, int32_t n_vocab, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
 
     const int64_t t_start_sample_us = ggml_time_us();
@@ -2085,12 +2166,12 @@ void sample_grammar(FileFormat file_format, int32_t n_vocab, llama_token_data_ar
         } else if (piece.empty() || piece[0] == 0) {
             rejects[i] = true;
         } else {
-            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
+            candidates_decoded.push_back(kcpp_decode_utf8(piece, grammar->partial_utf8));
             candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
         }
     }
 
-    for (auto reject: llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar)) {
+    for (auto reject: kcpp_llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar)) {
         rejects[reject.index] = true;
     }
 
@@ -2324,7 +2405,7 @@ static void grammar_accept_token(FileFormat file_format, int32_t n_vocab, struct
     const std::string piece = FileFormatTokenizeID(token,file_format);
 
     // Note terminating 0 in decoded string
-    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
+    const auto   decoded     = kcpp_decode_utf8(piece, grammar->partial_utf8);
     const auto & code_points = decoded.first;
     for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
         llama_grammar_accept(grammar, *it);
diff --git a/koboldcpp.py b/koboldcpp.py
index a7dd81df7..2d314e743 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -9164,7 +9164,7 @@ def show_gui():
     makecheckbox(admin_tab, "Enable Model Administration", admin_var, 1, 0, command=toggleadmin,tooltiptxt="Enable a admin server, allowing you to remotely relaunch and swap models and configs.")
     makelabelentry(admin_tab, "Admin Password:" , admin_password_var, 3, 150,padx=(120),singleline=True,tooltip="Require a password to access admin functions. You are strongly advised to use one for publically accessible instances!")
     makefileentry(admin_tab, "Config Directory (Required):", "Select directory containing .gguf or .kcpps files to relaunch from", admin_dir_var, 5, width=280, dialog_type=2, tooltiptxt="Specify a directory to look for .kcpps configs in, which can be used to swap models.")
-    makefileentry(admin_tab, "Base config .kcpps (For reloading):", "", baseconfig_var, 7, width=280, dialog_type=0, tooltiptxt="Specify a base .kcpps config to apply, if no custom base config is selected during a model swap.")
+    makefileentry(admin_tab, "Base config .kcpps (Optional, for reloading):", "", baseconfig_var, 7, width=280, dialog_type=0, tooltiptxt="Specify a base .kcpps config to apply, if no custom base config is selected during a model swap.")
     makelabelentry(admin_tab, "Auto Unload Timeout:" , admin_unload_timeout_var, 17, 70,padx=(150),singleline=True,tooltip="Set an idle timeout in seconds after which KoboldCpp will automatically unload the current model.")
     makecheckbox(admin_tab, "SingleInstance Mode", singleinstance_var, 19, 0,tooltiptxt="Allows this server to be shut down by another KoboldCpp instance with singleinstance starting on the same port.")
     router_mode_box = makecheckbox(admin_tab, "Router Mode", router_mode_var, 21, 0, command=togglerouter, tooltiptxt="Router mode uses a reverse proxy router, allowing you to easily hotswap models and configs within a single request. Requires admin mode.")
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index fdb725282..c73a3c9fd 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -23,7 +23,7 @@
 //
 
 //kcpp: use a global flag to toggle pipeline parallelism to avoid messing with ctx params
-static bool kcpp_pipeline_parallelism = false;
+bool kcpp_pipeline_parallelism = false;
 
 static llm_graph_type ctx_type_to_graph_type(llama_context_type ctx_type) {
     switch (ctx_type) {
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index f24a99410..a56f2c5b6 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -36,7 +36,7 @@ using candidates_memos = std::unordered_map<size_t, llama_grammar_candidates>;
 using stack_memos = std::unordered_map<size_t, candidates_memos>;
 static stack_memos memo_cache;
 
-static void llama_grammar_reset_memos() {
+void llama_grammar_reset_memos() {
     memo_cache.clear();
 }
 
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index b5a0e588e..bfef68860 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -78,6 +78,8 @@ const llama_grammar_rules  & llama_grammar_get_rules (const struct llama_grammar
 // positions
 void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr);
 
+void llama_grammar_reset_memos();
+
 std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
         const llama_grammar_rules      & rules,
         const llama_grammar_stack      & stack,
diff --git a/src/llama-kv-cache-iswa.cpp b/src/llama-kv-cache-iswa.cpp
index d8065892e..547533cfd 100644
--- a/src/llama-kv-cache-iswa.cpp
+++ b/src/llama-kv-cache-iswa.cpp
@@ -11,8 +11,8 @@
 // llama_kv_cache_iswa
 //
 //kcpp: use a global flag to adjust swa padding
-static int kcpp_extra_swa_padding = 0;
-static int kcpp_active_swa_size = 0;
+int kcpp_extra_swa_padding = 0;
+int kcpp_active_swa_size = 0;
 
 llama_kv_cache_iswa::llama_kv_cache_iswa(
         const llama_model & model,
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index c6eb2ed2c..156346414 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -27,7 +27,7 @@
 // helpers
 //
 
-static bool OldBPETokenizerMode = false;
+bool OldBPETokenizerMode = false;
 
 struct naive_trie {
     naive_trie() : has_value(false), value(0) {
diff --git a/src/llama.cpp b/src/llama.cpp
index d8bfc255e..07bb5205e 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -19,7 +19,6 @@
 #include "llama-memory-recurrent.cpp"
 #include "llama-model-loader.cpp"
 #include "llama-model-saver.cpp"
-#include "llama-model.cpp"
 #include "llama-quant.cpp"
 #include "llama-hparams.cpp"
 #include "llama-graph.cpp"
@@ -47,10 +46,6 @@
 #include <iostream>
 #include <vector>
 
-#ifdef GGML_USE_CUDA
-#  include "ggml-cuda.h"
-#endif
-
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
diff --git a/src/models/deepseek4.cpp b/src/models/deepseek4.cpp
index c4702571a..759654228 100644
--- a/src/models/deepseek4.cpp
+++ b/src/models/deepseek4.cpp
@@ -210,9 +210,8 @@ static ggml_tensor * dsv4_build_kq_zero_bias(
     return ggml_fill(ctx, res, 0.0f);
 }
 
-//kcpp: avoid redefine conflict
-// static constexpr int64_t DSV4_CSA_RATIO  = 4;
-// static constexpr int64_t DSV4_HCA_RATIO  = 128;
+static constexpr int64_t DSV4_CSA_RATIO  = 4;
+static constexpr int64_t DSV4_HCA_RATIO  = 128;
 
 static ggml_tensor * dsv4_hc_affine(
         ggml_context * ctx,

From 4790b912c7335fa9c0bfaa03aa9f4e91c203a8b9 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 19:42:38 +0800
Subject: [PATCH 244/292] ollama embeddings endpoint

---
 koboldcpp.py | 33 +++++++++++++++++++--------------
 1 file changed, 19 insertions(+), 14 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 2d314e743..acf091730 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -4842,7 +4842,7 @@ class KcppProxyHandler(http.server.BaseHTTPRequestHandler):
         is_chat_completions_path = (clean_path.endswith('/v1/chat/completions') or clean_path=='/chat/completions')
 
         #any requests to the following endpoints is capable of waking the server
-        wake_requests = ["/api/extra/generate/stream","/api/extra/tokencount","/api/v1/generate","/sdapi/v1/interrogate","/v1/completions","/v1/chat/completions","/v1/responses","/completions","/chat/completions","/responses","/api/extra/transcribe","/v1/audio/transcriptions","/api/extra/tts","/v1/audio/speech","/api/extra/embeddings","/v1/embeddings","/api/extra/music/prepare","/api/extra/music/generate","/sdapi/v1/txt2img","/sdapi/v1/img2img","/sdapi/v1/upscale"]
+        wake_requests = ["/api/extra/generate/stream","/api/extra/tokencount","/api/v1/generate","/sdapi/v1/interrogate","/v1/completions","/v1/chat/completions","/v1/responses","/completions","/chat/completions","/responses","/api/extra/transcribe","/v1/audio/transcriptions","/api/extra/tts","/v1/audio/speech","/api/extra/embeddings","/v1/embeddings","/api/embed","/api/extra/music/prepare","/api/extra/music/generate","/sdapi/v1/txt2img","/sdapi/v1/img2img","/sdapi/v1/upscale"]
         is_wake_request = clean_path in wake_requests
 
         autoswapEnabled = global_memory["autoswapmode"] is not None and global_memory["autoswapmode"]
@@ -4894,7 +4894,7 @@ class KcppProxyHandler(http.server.BaseHTTPRequestHandler):
                 textReqs = ["/api/extra/generate/stream","/api/extra/tokencount","/api/v1/generate","/sdapi/v1/interrogate","/v1/completions","/v1/chat/completions","/v1/responses","/completions","/chat/completions","/responses"]
                 sttReqs = ["/api/extra/transcribe","/v1/audio/transcriptions"]
                 ttsReqs = ["/api/extra/tts", "/v1/audio/speech"]
-                embedReqs = ["/api/extra/embeddings", "/v1/embeddings"]
+                embedReqs = ["/api/extra/embeddings", "/v1/embeddings", "/api/embed"]
                 musicReqs = ["/api/extra/music/prepare","/api/extra/music/generate"]
                 imageReqs = ["/sdapi/v1/txt2img", "/sdapi/v1/img2img", "/sdapi/v1/upscale"] # "/sdapi/v1/sd-models", "/sdapi/v1/options", "/sdapi/v1/samplers"
 
@@ -6905,6 +6905,7 @@ Change Mode<br>
             is_transcribe = False
             is_tts = False
             is_embeddings = False
+            is_ollama_embeddings = False
             is_music_codes = False
             is_music_audio = False
             response_body = None
@@ -7009,8 +7010,9 @@ Change Mode<br>
                 is_transcribe = True
             elif clean_path.endswith('/api/extra/tts') or clean_path.endswith('/v1/audio/speech') or clean_path=="/audio/speech" or clean_path.endswith('/tts_to_audio'):
                 is_tts = True
-            elif clean_path.endswith('/api/extra/embeddings') or clean_path.endswith('/v1/embeddings'):
+            elif clean_path.endswith('/api/extra/embeddings') or clean_path.endswith('/v1/embeddings') or clean_path=="/api/embed":
                 is_embeddings = True
+                is_ollama_embeddings = (clean_path=="/api/embed")
             elif clean_path.endswith('/api/extra/music/prepare'):
                 is_music_codes = True
             elif clean_path.endswith('/api/extra/music/generate'):
@@ -7470,17 +7472,20 @@ Change Mode<br>
                         if autoswapmode and embedName is not None:
                             modelNameToReturn = embedName
                         gendat = embeddings_generate(genparams)
-                        outdatas = []
-                        odidx = 0
-                        for od in gendat["data"]:
-                            if genparams.get("encoding_format", "")=="base64":
-                                binary_data = struct.pack('<' + 'f' * len(od), *od)
-                                b64_string = base64.b64encode(binary_data).decode('utf-8')
-                                outdatas.append({"object":"embedding","index":odidx,"embedding":b64_string})
-                            else:
-                                outdatas.append({"object":"embedding","index":odidx,"embedding":od})
-                            odidx += 1
-                        genresp = (json.dumps({"object":"list","data":outdatas,"model":modelNameToReturn,"usage":{"prompt_tokens":gendat["count"],"total_tokens":gendat["count"]}}).encode())
+                        if is_ollama_embeddings:
+                            genresp = (json.dumps({"model":modelNameToReturn,"embeddings":gendat["data"],"total_duration":1,"load_duration":1,"prompt_eval_count":gendat["count"]}).encode())
+                        else:
+                            outdatas = []
+                            odidx = 0
+                            for od in gendat["data"]:
+                                if genparams.get("encoding_format", "")=="base64":
+                                    binary_data = struct.pack('<' + 'f' * len(od), *od)
+                                    b64_string = base64.b64encode(binary_data).decode('utf-8')
+                                    outdatas.append({"object":"embedding","index":odidx,"embedding":b64_string})
+                                else:
+                                    outdatas.append({"object":"embedding","index":odidx,"embedding":od})
+                                odidx += 1
+                            genresp = (json.dumps({"object":"list","data":outdatas,"model":modelNameToReturn,"usage":{"prompt_tokens":gendat["count"],"total_tokens":gendat["count"]}}).encode())
                         self.send_response(200)
                         self.send_header('content-length', str(len(genresp)))
                         self.end_headers(content_type='application/json')

From e2de771b2abfd11d23699b2f7c3f0924741ef98a Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 20:30:25 +0800
Subject: [PATCH 245/292] fixed a missing header

---
 gpttype_adapter.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index 83ae65a21..c3edcdb3a 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -33,6 +33,10 @@
 #include "utils.h"
 #include "llmutils.h"
 
+#ifdef GGML_USE_CUDA
+#  include "ggml-cuda.h"
+#endif
+
 #include "llama_v2.cpp"
 #include "llama_v3.cpp"
 #include "gptj_v1.cpp"

From f76b5a9e318a08285f3b1d64c5b2de5ede4df4bf Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 21:37:51 +0800
Subject: [PATCH 246/292] ollama streaming

---
 koboldcpp.py | 73 +++++++++++++++++++++++++++++++---------------------
 1 file changed, 43 insertions(+), 30 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index acf091730..3ffb175ce 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -5360,6 +5360,10 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
         self.wfile.write(f'data: {data}\n\n'.encode())
         self.wfile.flush()
 
+    async def send_ollama_stream_event(self, data):
+        self.wfile.write(f'{data}\n'.encode())
+        self.wfile.flush()
+
     async def handle_sse_stream(self, genparams, api_format):
         global friendlymodelname, currfinishreason, thinkformats, tool_call_pairs, cached_chat_template
         global autoswapmode, textName, sttName, ttsName, embedName, musicName, imageName, mmprojName
@@ -5594,6 +5598,13 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                                 elif api_format == 3:  # non chat completions
                                     event_str = json.dumps({"id":cmpl_id,"object":"text_completion","created":int(time.time()),"model":modelNameToReturn,"choices":[{"index":0,"finish_reason":None,"text":tokenStr}]})
                                     await self.send_oai_sse_event(event_str)
+                                elif api_format == 6 or api_format == 7:
+                                    created_at = str(datetime.now(timezone.utc).isoformat())
+                                    if api_format == 6:
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"response":tokenStr,"done":False})
+                                    else:
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":tokenStr},"done":False})
+                                    await self.send_ollama_stream_event(event_str)
                                 elif api_format == 9:
                                     if anthropic_first_loop:
                                         await self.send_anthropic_sse_event("message_start", json.dumps({"type":"message_start","message":{"type":"message","id":f"msg_A{req_id_suffix}","role":"assistant","model":modelNameToReturn,"usage":{"input_tokens":prompttokens,"output_tokens":0}}}))
@@ -5637,6 +5648,27 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                                     await self.send_oai_sse_event(addonstr)
                                 event_str = json.dumps({"id":cmpl_id,"object":"text_completion","created":int(time.time()),"model":modelNameToReturn,"choices":[{"index":0,"finish_reason":currfinishreason,"text":tokenStr}]})
                                 await self.send_oai_sse_event(event_str)
+                            elif api_format == 6 or api_format == 7: # Ollama newline-delimited JSON streaming
+                                created_at = str(datetime.now(timezone.utc).isoformat())
+                                if tokenStr:
+                                    if api_format == 6:
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"response":tokenStr,"done":False})
+                                    else:
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":tokenStr},"done":False})
+                                    await self.send_ollama_stream_event(event_str)
+                                if streamDone:
+                                    prompttokens = batch_final_result.prompt_tokens if using_batch_stream else handle.get_last_input_count()
+                                    completion_tokens = current_token
+                                    final_chunk = {"model":modelNameToReturn,"created_at":created_at,"done":True,"done_reason":currfinishreason,"total_duration":1,"load_duration":1,"prompt_eval_count":prompttokens,"prompt_eval_duration":1,"eval_count":completion_tokens,"eval_duration":1}
+                                    if api_format == 6:
+                                        finalraw = handle.batch_generate_pending_output(batch_request_id) if using_batch_stream else handle.get_pending_output()
+                                        finaltxt = finalraw.decode("UTF-8", "ignore")
+                                        oldprompt = genparams.get('ollamabodyprompt', "")
+                                        final_chunk["response"] = ""
+                                        final_chunk["context"] = tokenize_ids(oldprompt+finaltxt,False)
+                                    else:
+                                        final_chunk["message"] = {"role":"assistant","content":""}
+                                    await self.send_ollama_stream_event(json.dumps(final_chunk))
                             elif api_format == 8: #oai-responses
                                 resp_id = f"resp-A{genparams.get('oai_uniqueid', 1)}"
                                 item_id = f"msg_0{genparams.get('oai_uniqueid', 1)}"
@@ -7111,6 +7143,8 @@ Change Mode<br>
                     # Check if streaming chat completions, if so, set stream mode to true
                     if (api_format == 4 or api_format == 3 or api_format == 8 or api_format == 9) and "stream" in genparams and genparams["stream"]:
                         sse_stream_flag = True
+                    if (api_format == 6 or api_format == 7) and genparams.get('stream', True):
+                        sse_stream_flag = True
                     if continuous_batching_python_eligible(genparams, api_format):
                         genparams['_batch_expected'] = True
                         modelbusy.release()
@@ -7125,35 +7159,7 @@ Change Mode<br>
                         if autoswapmode and textName is not None:
                             modelNameToReturn = textName
                         # Headers are already sent when streaming
-                        if (api_format == 6 or api_format == 7) and genparams.get('stream', True):
-                            #ollama fake streaming
-                            self.send_response(200)
-                            self.send_header("X-Accel-Buffering", "no")
-                            self.send_header("cache-control", "no-cache")
-                            self.send_header("connection", "keep-alive")
-                            self.end_headers(content_type='text/event-stream')
-                            if api_format == 6:
-                                bodytxt = gendat.get("response","") # extract and erase the AI response from the sync payload.
-                                gendat["response"] = ""
-                                pl = {"model":modelNameToReturn,"created_at":str(datetime.now(timezone.utc).isoformat()),"response":bodytxt,"done":False}
-                                self.wfile.write(f'{json.dumps(pl)}\n'.encode())
-                                self.wfile.flush()
-                                time.sleep(0.05) #short delay
-                                self.wfile.write(f'{json.dumps(gendat)}\n'.encode()) # note: gendat already contains done=true and empty response
-                                self.wfile.flush()
-                                time.sleep(0.05) #short delay
-                            else:
-                                bodytxt = gendat.get("message",{}).get("content","") # extract and erase the AI response from the sync payload.
-                                gendat["message"] = {"role":"assistant","content":""}
-                                pl = {"model":modelNameToReturn,"created_at":str(datetime.now(timezone.utc).isoformat()),"message":{"role":"assistant","content":bodytxt},"done":False}
-                                self.wfile.write(f'{json.dumps(pl)}\n'.encode())
-                                self.wfile.flush()
-                                time.sleep(0.05) #short delay
-                                self.wfile.write(f'{json.dumps(gendat)}\n'.encode()) # note: gendat already contains done=true and empty response
-                                self.wfile.flush()
-                                time.sleep(0.05) #short delay
-                            self.close_connection = True
-                        elif not sse_stream_flag:
+                        if not sse_stream_flag:
                             self.send_response(200)
                             genresp = (json.dumps(gendat).encode())
                             self.send_header('content-length', str(len(genresp)))
@@ -7550,9 +7556,16 @@ Change Mode<br>
         self.end_headers(content_type='text/html')
 
     def end_headers(self, content_type=None):
-        self.send_header('access-control-allow-origin', '*')
+        origin = self.headers.get('Origin')
+        if origin:
+            self.send_header('access-control-allow-origin', origin)
+            self.send_header('access-control-allow-credentials', 'true')
+            self.send_header('vary', 'Origin')
+        else:
+            self.send_header('access-control-allow-origin', '*')
         self.send_header('access-control-allow-methods', '*')
         self.send_header('access-control-allow-headers', '*, Accept, Content-Type, Content-Length, Cache-Control, Accept-Encoding, X-CSRF-Token, Client-Agent, X-Fields, Content-Type, Authorization, X-Requested-With, X-HTTP-Method-Override, apikey, genkey')
+        self.send_header('access-control-allow-private-network', 'true')
         self.send_header("cache-control", "no-store")
         if content_type is not None:
             self.send_header('content-type', content_type)

From 13e673863b36993d9e80c0a9ee3c1e02f8b9ace4 Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Wed, 1 Jul 2026 06:59:19 -0700
Subject: [PATCH 247/292] hexagon: flash attention rework (optimizations,
 accuracy improvements, etc) (#25085)

* hex-mm: fold mm quant tasks into the main matmul threads

* hex-mm: minor formatting fixes

* hex-mm: cleanup is_quant checks in dma dispatch

* hex-mm: fix dst-spad alignment

* hex-mm: move fp kernels in the hvx-mm-kernels header

* hex-mm: fuse with ADD

* hex-fa: factor out ukernels into separate headers and unify the rest

* hex-fa: move kernel-params compute into the host

* hex-fa: refactor vtcm alloc for consistency

* hex-fa: add support for FA_SELECT

* hex-fa: update tracing insrumentation to cover all functions

* hex-fa: update hvx fallback thresholds to recover t/g regressions

* hex-fa: update tracing instrumentation

* hex-fa: improved tracing with additional events

* hex-fa: optimize mask processing (fastdiv, etc)

* hex-fa: improve mask dma caching

* hmx-fa: change loop order to maximize mask cache hits

* hex-fa: remove over instrumentation

* hex-fa: breakdown QKV prep trace events

* hmx-fa: further mask proc optimizations

* hex-fa: mask broadcast is the common case, optimize for that

* hex-fa: use aligned loads where possible

* hex-fa: update loops to use uint32_t indices

* hmx-fa: fold vtcm init into q prep task

* hex-fa: update rest of the hmx funcs to use uint32_t

* hmx-fa: fold build_d into the main softmax loop

* hmx-fa: start kv dmas earlier

* hmx-fa: start mask dma a bit earlier

* hex-fa: precompute rows per task to avoid divs

* hmx-fa: specialize fa_o_store for f16 and f32

* hmx-fa: prelim support for Sinks

* hmx-fa: keep softmax accumulators in fp32

* hex-fa: add tanh_f16 and exp2_f16 and use that in FA

* hex-fa: use fp16 math in the hvx kernel

* hex-fa: avoid expensive float -> __fp16 cast for slopes and softcap

* hex-fa: replace most vec_exp_f32 with vec_exp2_f16

* hmx-fa: vectorize sinks update

* hex-fa: minor formatting

* hmx-fa: fold softcap loop into the tile load

* hmx-fa: use vectoralias to populate sinks

* hex-fa: remove redudant check

* hex-fa: fix vtcm size compute to use fp32 for accumulators

* hex-mm: fix trailing spaces

* hmx-fa: dont use -inf to init mask to avoid conversion overflows

* hex-fa: no need to explicitly guard -inf in the f16->f32 converter now

* hmx-fa: cleanup fa sinks handling

* hex-mm: fixed src2 stride handling when mm is fused with add

* hex-fa: make lto happy
---
 ggml/src/ggml-hexagon/CMakeLists.txt          |    1 -
 ggml/src/ggml-hexagon/ggml-hexagon.cpp        |  247 +-
 ggml/src/ggml-hexagon/htp-opnode.h            |   14 +-
 ggml/src/ggml-hexagon/htp/CMakeLists.txt      |    9 +-
 ggml/src/ggml-hexagon/htp/flash-attn-ops.c    | 2171 ++++++++++++++---
 ggml/src/ggml-hexagon/htp/flash-attn-ops.h    |  253 ++
 ggml/src/ggml-hexagon/htp/hex-dma.h           |   30 +-
 ggml/src/ggml-hexagon/htp/hmx-fa-kernels.h    |   96 +
 .../src/ggml-hexagon/htp/hmx-flash-attn-ops.c | 1838 --------------
 .../ggml-hexagon/htp/hmx-mm-kernels-tiled.h   |   45 +-
 ggml/src/ggml-hexagon/htp/hmx-utils.h         |   70 +-
 ggml/src/ggml-hexagon/htp/htp-ops.h           |    6 +
 ggml/src/ggml-hexagon/htp/hvx-base.h          |   13 +-
 ggml/src/ggml-hexagon/htp/hvx-exp.h           |   39 +
 ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h    |  232 ++
 .../ggml-hexagon/htp/hvx-mm-kernels-flat.h    |  537 +++-
 .../ggml-hexagon/htp/hvx-mm-kernels-tiled.h   |  110 +-
 ggml/src/ggml-hexagon/htp/hvx-sigmoid.h       |   39 +
 ggml/src/ggml-hexagon/htp/main.c              |    1 +
 ggml/src/ggml-hexagon/htp/matmul-ops.c        |  982 +++-----
 ggml/src/ggml-hexagon/htp/matmul-ops.h        |   51 +-
 scripts/snapdragon/adb/run-completion.sh      |    5 +-
 scripts/snapdragon/adb/run-tool.sh            |    5 +-
 scripts/snapdragon/ggml-hexagon-profile.py    |    6 +
 24 files changed, 3777 insertions(+), 3023 deletions(-)
 create mode 100644 ggml/src/ggml-hexagon/htp/flash-attn-ops.h
 create mode 100644 ggml/src/ggml-hexagon/htp/hmx-fa-kernels.h
 delete mode 100644 ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
 create mode 100644 ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h

diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt
index c6e49a71d..845b922bf 100644
--- a/ggml/src/ggml-hexagon/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/CMakeLists.txt
@@ -23,7 +23,6 @@ include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
 include(ExternalProject)
 
 option(GGML_HEXAGON_HTP_DEBUG  "ggml-hexagon: enable HTP debug output" OFF)
-option(GGML_HEXAGON_FA_EXP2_HF "ggml-hexagon: use FP16 exp2 polynomial in FA softmax instead of F32 exp round-trip" OFF)
 set(GGML_HEXAGON_HTP_CERT  "$ENV{HEXAGON_HTP_CERT}" CACHE PATH "ggml-hexagon: enable HTP library signing using certificate")
 
 add_library(htp_iface OBJECT
diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 3d41c47b6..4f9dafed0 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -43,6 +43,7 @@
 #include "htp-opnode.h"
 #include "htp-ops.h"
 #include "htp/matmul-ops.h"
+#include "htp/flash-attn-ops.h"
 #include "htp_iface.h"
 #include "htp-drv.h"
 
@@ -62,6 +63,7 @@ static int    opt_profile = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
 static int    opt_hostbuf = 1; // hostbuf ON by default
 
 static int    opt_mm_select = 3; // 3 = HMX -> Tiled -> Flat -> CPU, 2 = Tiled -> Flat -> CPU, 1 = Flat -> CPU
+static int    opt_fa_select = 2; // 2 = HMX -> HVX -> CPU, 1 = HVX -> CPU, 0 = CPU (unsupported)
 
 // Default PMU events, if profiling with PMU (mode=2) is enabled
 // See https://docs.qualcomm.com/doc/80-N2040-60/topic/pmu-events.html
@@ -125,6 +127,11 @@ static const char * htp_event_name(uint16_t id) {
         case HTP_TRACE_EVT_HVX_W_DEQUANT:  return "HVX_W_DEQUANT";
         case HTP_TRACE_EVT_HVX_W_PREP:     return "HVX_W_PREP";
         case HTP_TRACE_EVT_HVX_O_PROC:     return "HVX_O_PROC";
+        case HTP_TRACE_EVT_HVX_FA_QK:      return "HVX_QK_FA";
+        case HTP_TRACE_EVT_HVX_FA_SFM:     return "HVX_SFM_FA";
+        case HTP_TRACE_EVT_HVX_FA_Q_PREP:  return "HVX_Q_PREP";
+        case HTP_TRACE_EVT_HVX_FA_K_PREP:  return "HVX_K_PREP";
+        case HTP_TRACE_EVT_HVX_FA_V_PREP:  return "HVX_V_PREP";
         case HTP_TRACE_EVT_HMX_COMP:       return "HMX_COMP";
         default:                           return "UNKNOWN";
     }
@@ -1879,6 +1886,162 @@ ggml_hexagon_session::~ggml_hexagon_session() noexcept(true) {
 
 // ** backend interface
 
+static bool ggml_hexagon_flash_attn_is_hmx_eligible(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * q,
+    const struct ggml_tensor * k,
+    const struct ggml_tensor * v,
+    const struct ggml_tensor * sinks
+) {
+    if (sess->n_hmx == 0) {
+        return false;
+    }
+
+    if (opt_fa_select < 2) {
+        return false;
+    }
+
+    if (k->type != GGML_TYPE_F16 || v->type != GGML_TYPE_F16) {
+        return false;
+    }
+
+    const uint32_t DK = q->ne[0];
+    const uint32_t DV = v->ne[0];
+
+    if (DK % 64 != 0 || DV % 64 != 0) {
+        return false;
+    }
+
+    // Fall back to HVX for small token counts if head dimension is small (DK <= 128)
+    const uint32_t neq1 = q->ne[1];
+    if (DK <= 128 && neq1 < 5) {
+        return false;
+    }
+
+    return true;
+}
+
+static bool ggml_hexagon_precompute_flash_attn_params(
+    const struct ggml_hexagon_session * sess,
+    const struct ggml_tensor * op,
+    struct htp_fa_kernel_params * kparams
+) {
+    if (opt_fa_select < 1) {
+        return false;
+    }
+
+    memset(kparams, 0, sizeof(*kparams));
+
+    const struct ggml_tensor * q    = op->src[0];
+    const struct ggml_tensor * k    = op->src[1];
+    const struct ggml_tensor * v    = op->src[2];
+    const struct ggml_tensor * mask = op->src[3];
+    const struct ggml_tensor * dst  = op;
+
+    const uint32_t neq0 = q->ne[0];  // head_dim (DK)
+    const uint32_t neq1 = q->ne[1];  // n_tokens
+    const uint32_t neq2 = q->ne[2];  // n_heads
+
+    const uint32_t nek1 = k->ne[1];  // kv_len
+
+    const uint32_t nev0 = v->ne[0];  // head_dim (DV)
+
+    const uint32_t DK = neq0;
+    const uint32_t DV = nev0;
+
+    const uint32_t n_kv_heads = k->ne[2];
+    const uint32_t G          = neq2 / n_kv_heads;
+
+    float scale         = 1.0f;
+    float max_bias      = 0.0f;
+    float logit_softcap = 0.0f;
+    memcpy(&scale,         &op->op_params[0], sizeof(float));
+    memcpy(&max_bias,      &op->op_params[1], sizeof(float));
+    memcpy(&logit_softcap, &op->op_params[2], sizeof(float));
+
+    if (logit_softcap != 0.0f) {
+        scale /= logit_softcap;
+    }
+
+    kparams->scale = scale;
+    kparams->max_bias = max_bias;
+    kparams->logit_softcap = logit_softcap;
+
+    kparams->is_q_fp32 = (q->type == GGML_TYPE_F32) ? 1 : 0;
+    kparams->is_dst_fp32 = (dst->type == GGML_TYPE_F32) ? 1 : 0;
+    kparams->G = G;
+
+    const uint32_t n_head = q->ne[2];
+    kparams->n_head_log2 = 1u << (uint32_t) std::floor(std::log2(n_head));
+    kparams->m0 = std::pow(2.0f, -(max_bias) / kparams->n_head_log2);
+    kparams->m1 = std::pow(2.0f, -(max_bias / 2.0f) / kparams->n_head_log2);
+
+    // Check HMX eligibility
+    const struct ggml_tensor * sinks = op->src[4];
+    if (ggml_hexagon_flash_attn_is_hmx_eligible(sess, q, k, v, sinks)) {
+        size_t Br = 0, Bc = 0;
+        int ret = hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, sess->vtcm_size, sess->n_threads);
+        if (ret == 0) {
+            kparams->kernel_type = HTP_FA_KERNEL_HMX;
+            kparams->Br = Br;
+            kparams->Bc = Bc;
+            kparams->n_kv_blocks = (nek1 + Bc - 1) / Bc;
+            kparams->n_threads = (kparams->n_kv_blocks >= 3 && sess->n_threads >= 2) ? sess->n_threads : 1;
+
+            kparams->u.hmx.g_br = hex_align_up(G * Br, 32);
+            kparams->u.hmx.pipeline = (kparams->n_kv_blocks >= 3 && sess->n_threads >= 2) ? 1 : 0;
+            kparams->vtcm_size = hmx_fa_compute_vtcm_usage(G, DK, DV, Br, Bc, kparams->n_threads, kparams->u.hmx.pipeline != 0);
+
+            const size_t row_vec_bytes = hex_align_up(Bc * sizeof(uint16_t), 256);
+            kparams->u.hmx.row_buf_stride = row_vec_bytes / 128; // HVX vector is 128 bytes
+
+            const size_t m_line_bytes = hex_align_up(Bc * sizeof(uint16_t), 128);
+            kparams->u.hmx.mask_buf_row_stride = m_line_bytes / sizeof(uint16_t);
+            kparams->u.hmx.mask_broadcast = (mask != nullptr && mask->ne[2] == 1) ? 1 : 0;
+            kparams->u.hmx.div_G = init_fastdiv_values(G);
+            if (mask) {
+                kparams->src3_div2 = init_fastdiv_values(mask->ne[2]);
+                kparams->src3_div3 = init_fastdiv_values(mask->ne[3]);
+            }
+
+            kparams->qrows = 0;
+            kparams->qrows_per_thread = 0;
+            return true;
+        }
+    }
+
+    // Fallback to HVX
+    kparams->kernel_type = HTP_FA_KERNEL_HVX;
+    kparams->Br = 1;
+    kparams->Bc = 64; // FLASH_ATTN_BLOCK_SIZE
+    kparams->n_kv_blocks = (k->ne[1] + 64 - 1) / 64;
+    kparams->n_threads = sess->n_threads;
+
+    const size_t size_q_row_padded = hex_round_up(q->ne[0] * (kparams->is_q_fp32 ? 4 : 2), 128);
+    const size_t size_k_row_padded = hex_round_up(k->ne[0] * 2, 128);
+    const size_t size_v_row_padded = hex_round_up(v->ne[0] * 2, 128);
+
+    kparams->vtcm_size = hvx_fa_compute_vtcm_usage(DK, DV, kparams->is_q_fp32 != 0, mask != nullptr, sess->n_threads);
+
+    kparams->u.hvx.size_q_row_padded = size_q_row_padded;
+    kparams->u.hvx.size_k_row_padded = size_k_row_padded;
+    kparams->u.hvx.size_v_row_padded = size_v_row_padded;
+    kparams->u.hvx.src0_div21 = init_fastdiv_values(q->ne[2] * q->ne[1]);
+    kparams->u.hvx.src0_div1 = init_fastdiv_values(q->ne[1]);
+    kparams->u.hvx.broadcast_rk2 = init_fastdiv_values(q->ne[2]/k->ne[2]);
+    kparams->u.hvx.broadcast_rk3 = init_fastdiv_values(q->ne[3]/k->ne[3]);
+    kparams->u.hvx.broadcast_rv2 = init_fastdiv_values(q->ne[2]/v->ne[2]);
+    kparams->u.hvx.broadcast_rv3 = init_fastdiv_values(q->ne[3]/v->ne[3]);
+    if (mask) {
+        kparams->src3_div2 = init_fastdiv_values(mask->ne[2]);
+        kparams->src3_div3 = init_fastdiv_values(mask->ne[3]);
+    }
+
+    kparams->qrows = q->ne[1] * q->ne[2] * q->ne[3];
+    kparams->qrows_per_thread = (kparams->qrows + sess->n_threads - 1) / sess->n_threads;
+
+    return true;
+}
 
 static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
     const struct ggml_tensor * src0 = op->src[0];
@@ -1912,6 +2075,17 @@ static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_sess
         return false;
     }
 
+    struct htp_fa_kernel_params kparams;
+    if (!ggml_hexagon_precompute_flash_attn_params(sess, op, &kparams)) {
+        return false;
+    }
+
+    if ((size_t) kparams.vtcm_size > sess->vtcm_size) {
+        HEX_VERBOSE("ggml-hex: skip flash_attn_ext because VTCM needed (%d) > budget (%zu)\n",
+                    kparams.vtcm_size, sess->vtcm_size);
+        return false;
+    }
+
     return true;
 }
 
@@ -2211,14 +2385,14 @@ static void ggml_hexagon_precompute_hvx_mm_params(
             kparams->kernel_type   = (src1_nrows < (int) sess->n_threads) ? HTP_MM_KERNEL_HVX_QUANT_BLOCK : HTP_MM_KERNEL_HVX_QUANT_ROW;
             kparams->src1_row_size = (wtype == GGML_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
 
-            size_t vtcm_src0_size = 0, vtcm_src1_size = 0;
+            size_t vtcm_src0_size = 0, vtcm_src1_size = 0, vtcm_dst_size = 0;
             uint32_t max_prefetch = (src1_nrows > HTP_MM_HMX_MIN_NROWS) ? 2 : 16;
             uint32_t best_n_prefetch = 2;
             size_t total_size = 0;
             for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
                 total_size = htp_mm_hvx_id_get_vtcm_sizes(
                     wtype, ne10, src1_nrows, sess->n_threads, src0->nb[1], d,
-                    &vtcm_src0_size, &vtcm_src1_size
+                    &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
                 );
                 if (total_size <= vtcm_budget) {
                     best_n_prefetch = d;
@@ -2228,14 +2402,14 @@ static void ggml_hexagon_precompute_hvx_mm_params(
             if (best_n_prefetch == 2 && total_size > vtcm_budget) {
                 total_size = htp_mm_hvx_id_get_vtcm_sizes(
                     wtype, ne10, src1_nrows, sess->n_threads, src0->nb[1], 2,
-                    &vtcm_src0_size, &vtcm_src1_size
+                    &vtcm_src0_size, &vtcm_src1_size, &vtcm_dst_size
                 );
             }
             kparams->n_prefetch = best_n_prefetch;
             kparams->vtcm_size      = total_size;
             kparams->vtcm_src0_size = vtcm_src0_size;
             kparams->vtcm_src1_size = vtcm_src1_size;
-            kparams->vtcm_dst_size  = 0;
+            kparams->vtcm_dst_size  = vtcm_dst_size;
         } else {
             bool try_tiled = (k_align && opt_mm_select >= 2);
             if (try_tiled) {
@@ -2441,11 +2615,12 @@ static void ggml_hexagon_precompute_fused_qkv_params(
     size_t src3_sz_per_thread = 0;
     uint32_t best_n_prefetch = 16;
 
+    size_t quant_scratch_size = hex_round_up(ne10 * sizeof(float), QK_Q8_0_TILED * sizeof(float)) * sess->n_threads;
+
     if (is_repack) {
         uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
         uint32_t n_k_tiles = hex_round_up(ne10, 32) / 32;
         uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
-        size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
         size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
         size_t src1_sz = src1_sz_per_thread;
 
@@ -2453,13 +2628,10 @@ static void ggml_hexagon_precompute_fused_qkv_params(
         best_n_prefetch = 2;
         for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
             size_t repacked_vtcm_size = hex_round_up(d * tile_row_size, 128);
-            if (repacked_vtcm_size < src1_row_size_padded) {
-                repacked_vtcm_size = src1_row_size_padded;
-            }
             size_t src0_sz = repacked_vtcm_size * sess->n_threads;
             size_t src2_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
             size_t src3_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
-            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz;
+            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz + quant_scratch_size;
 
             if (tiled_vtcm_size <= sess->vtcm_size) {
                 best_n_prefetch = d;
@@ -2471,9 +2643,6 @@ static void ggml_hexagon_precompute_fused_qkv_params(
         }
         if (best_n_prefetch == 2 && src0_sz_per_thread == 0) {
             size_t repacked_vtcm_size = hex_round_up(2 * tile_row_size, 128);
-            if (repacked_vtcm_size < src1_row_size_padded) {
-                repacked_vtcm_size = src1_row_size_padded;
-            }
             src0_sz_per_thread = repacked_vtcm_size;
             src2_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
             src3_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
@@ -2492,7 +2661,7 @@ static void ggml_hexagon_precompute_fused_qkv_params(
     size_t src2_sz = src2_sz_per_thread * sess->n_threads;
     size_t src3_sz = src3_sz_per_thread * sess->n_threads;
 
-    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz;
+    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + src3_sz + quant_scratch_size;
     bool try_tiled = (opt_mm_select >= 2);
     if (try_tiled && tiled_vtcm_size <= sess->vtcm_size) {
         kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW;
@@ -2500,6 +2669,7 @@ static void ggml_hexagon_precompute_fused_qkv_params(
         kparams->vtcm_src1_size = src1_sz;
         kparams->vtcm_src2_size = src2_sz;
         kparams->vtcm_src3_size = src3_sz;
+        kparams->vtcm_dst_size  = quant_scratch_size;
         kparams->vtcm_size      = tiled_vtcm_size;
         kparams->n_prefetch     = best_n_prefetch;
     } else {
@@ -2510,7 +2680,8 @@ static void ggml_hexagon_precompute_fused_qkv_params(
         kparams->vtcm_src1_size = flat_src1_sz;
         kparams->vtcm_src2_size = src2_sz;
         kparams->vtcm_src3_size = src3_sz;
-        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz + src3_sz;
+        kparams->vtcm_dst_size  = quant_scratch_size;
+        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz + src3_sz + quant_scratch_size;
         kparams->n_prefetch     = best_n_prefetch;
     }
 }
@@ -2536,11 +2707,12 @@ static void ggml_hexagon_precompute_fused_ffn_params(
     size_t src2_sz_per_thread = 0;
     uint32_t best_n_prefetch = 16;
 
+    size_t quant_scratch_size = hex_round_up(ne10 * sizeof(float), QK_Q8_0_TILED * sizeof(float)) * sess->n_threads;
+
     if (is_repack) {
         uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
         uint32_t n_k_tiles = hex_round_up(ne10, 32) / 32;
         uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
-        size_t src1_row_size_padded = hex_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
         size_t src1_sz_per_thread = hex_round_up(src1_row_size * src1_nrows, 128);
         size_t src1_sz = src1_sz_per_thread;
 
@@ -2548,12 +2720,9 @@ static void ggml_hexagon_precompute_fused_ffn_params(
         best_n_prefetch = 2;
         for (uint32_t d = max_prefetch; d >= 2; d /= 2) {
             size_t repacked_vtcm_size = hex_round_up(d * tile_row_size, 128);
-            if (repacked_vtcm_size < src1_row_size_padded) {
-                repacked_vtcm_size = src1_row_size_padded;
-            }
             size_t src0_sz = repacked_vtcm_size * sess->n_threads;
             size_t src2_sz = hex_round_up(d * tile_row_size, 128) * sess->n_threads;
-            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz;
+            size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + quant_scratch_size;
 
             if (tiled_vtcm_size <= sess->vtcm_size) {
                 best_n_prefetch = d;
@@ -2564,9 +2733,6 @@ static void ggml_hexagon_precompute_fused_ffn_params(
         }
         if (best_n_prefetch == 2 && src0_sz_per_thread == 0) {
             size_t repacked_vtcm_size = hex_round_up(2 * tile_row_size, 128);
-            if (repacked_vtcm_size < src1_row_size_padded) {
-                repacked_vtcm_size = src1_row_size_padded;
-            }
             src0_sz_per_thread = repacked_vtcm_size;
             src2_sz_per_thread = hex_round_up(2 * tile_row_size, 128);
         }
@@ -2582,13 +2748,14 @@ static void ggml_hexagon_precompute_fused_ffn_params(
     size_t src1_sz = src1_sz_per_thread;
     size_t src2_sz = src2_sz_per_thread * sess->n_threads;
 
-    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz;
+    size_t tiled_vtcm_size = src0_sz + src1_sz + src2_sz + quant_scratch_size;
     bool try_tiled = (opt_mm_select >= 2);
     if (try_tiled && tiled_vtcm_size <= sess->vtcm_size) {
         kparams->kernel_type = HTP_MM_KERNEL_HVX_QUANT_ROW;
         kparams->vtcm_src0_size = src0_sz;
         kparams->vtcm_src1_size = src1_sz;
         kparams->vtcm_src2_size = src2_sz;
+        kparams->vtcm_dst_size  = quant_scratch_size;
         kparams->vtcm_size      = tiled_vtcm_size;
         kparams->n_prefetch     = best_n_prefetch;
     } else {
@@ -2598,7 +2765,8 @@ static void ggml_hexagon_precompute_fused_ffn_params(
         kparams->vtcm_src0_size = src0_sz;
         kparams->vtcm_src1_size = flat_src1_sz;
         kparams->vtcm_src2_size = src2_sz;
-        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz;
+        kparams->vtcm_dst_size  = quant_scratch_size;
+        kparams->vtcm_size      = src0_sz + flat_src1_sz + src2_sz + quant_scratch_size;
         kparams->n_prefetch     = best_n_prefetch;
     }
 }
@@ -3243,7 +3411,7 @@ static inline bool op_is_compute(ggml_tensor *node)
     return !ggml_op_is_empty(node->op) && !ggml_is_empty(node) && (node->flags & GGML_TENSOR_FLAG_COMPUTE);
 }
 
-static bool is_hmx_eligible(const ggml_tensor * t) {
+static bool mm_is_hmx_eligible(const ggml_tensor * t) {
     if (opt_nhmx == 0) { return false; }
 
     const ggml_tensor * src0 = t->src[0];
@@ -3262,7 +3430,7 @@ static bool is_hmx_eligible(const ggml_tensor * t) {
 static bool is_mergeable_mul_mat(const ggml_tensor * t) {
     if (!t || t->op != GGML_OP_MUL_MAT)   return false;
     if (t->src[1]->type != GGML_TYPE_F32) return false;
-    return ggml_is_quantized(t->src[0]->type) && !is_hmx_eligible(t);
+    return ggml_is_quantized(t->src[0]->type) && !mm_is_hmx_eligible(t);
 }
 
 static bool is_mergeable_mul_mat_pair(const ggml_tensor * n1, const ggml_tensor * n2) {
@@ -3357,6 +3525,26 @@ static bool try_fuse_node(const ggml_hexagon_session * sess, const ggml_cgraph *
         }
     }
 
+    if (n->op == GGML_OP_MUL_MAT && next_node) {
+        if (next_node->op == GGML_OP_ADD && op_is_compute(next_node) && ggml_can_fuse(graph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) {
+            if (next_node->src[0] == n || next_node->src[1] == n) {
+                struct htp_mm_kernel_params kparams;
+                ggml_hexagon_precompute_matmul_params(sess, n->src[0], n->src[1], next_node, &kparams);
+                if ((size_t)kparams.vtcm_size <= sess->vtcm_size) {
+                    htp_opnode node(n, {}, HTP_OP_MUL_MAT_ADD);
+                    node.add_fused(next_node);
+                    memcpy(node.kernel_params, &kparams, sizeof(kparams));
+                    nodes.push_back(std::move(node));
+                    i += 1;
+                    return true;
+                } else {
+                    HEX_VERBOSE("ggml-hex: skip MUL_MAT_ADD fusion because VTCM needed (%d) > budget (%zu)\n",
+                                kparams.vtcm_size, sess->vtcm_size);
+                }
+            }
+        }
+    }
+
     return false;
 }
 
@@ -3393,6 +3581,11 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                     node.node->src[0], node.node->src[1], node.node,
                     (struct htp_mm_kernel_params *)node.kernel_params
                 );
+            } else if (node.opcode == HTP_OP_FLASH_ATTN_EXT) {
+                ggml_hexagon_precompute_flash_attn_params(sess,
+                    node.node,
+                    (struct htp_fa_kernel_params *)node.kernel_params
+                );
             }
             computed_nodes.push_back(std::move(node));
         }
@@ -4079,6 +4272,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     const char * str_use_hmx  = getenv("GGML_HEXAGON_USE_HMX");
     const char * str_nhmx     = getenv("GGML_HEXAGON_NHMX");
     const char * str_mm_select = getenv("GGML_HEXAGON_MM_SELECT");
+    const char * str_fa_select = getenv("GGML_HEXAGON_FA_SELECT");
     const char * str_ndev     = getenv("GGML_HEXAGON_NDEV");
     const char * str_arch     = getenv("GGML_HEXAGON_ARCH");
     const char * str_vmem     = getenv("GGML_HEXAGON_VMEM");
@@ -4120,6 +4314,7 @@ static void ggml_hexagon_init(ggml_backend_reg * reg) {
     opt_nhvx      = str_nhvx     ? strtoul(str_nhvx, NULL, 0)             : opt_nhvx;
     opt_nhmx      = str_nhmx     ? atoi(str_nhmx)                         : (str_use_hmx ? atoi(str_use_hmx) : opt_nhmx);
     opt_mm_select = str_mm_select ? atoi(str_mm_select)                   : opt_mm_select;
+    opt_fa_select = str_fa_select ? atoi(str_fa_select)                   : opt_fa_select;
     opt_ndev      = str_ndev     ? strtoul(str_ndev, NULL, 0)             : opt_ndev;
     opt_hostbuf   = str_hostbuf  ? atoi(str_hostbuf)                      : opt_hostbuf;
     opt_mbuf      = str_mbuf     ? strtoul(str_mbuf, NULL, 0) * MiB       : opt_mbuf;
diff --git a/ggml/src/ggml-hexagon/htp-opnode.h b/ggml/src/ggml-hexagon/htp-opnode.h
index 6fe23b0d6..19a2504c7 100644
--- a/ggml/src/ggml-hexagon/htp-opnode.h
+++ b/ggml/src/ggml-hexagon/htp-opnode.h
@@ -11,6 +11,7 @@
 #include <stdio.h>
 #include "htp-ops.h"
 #include "htp/matmul-ops.h"
+#include "htp/flash-attn-ops.h"
 
 struct htp_opnode {
     ggml_tensor * node = nullptr;
@@ -335,7 +336,8 @@ struct htp_opformat {
     }
     void format_kernel_params(char * str, size_t max_size, const htp_opnode & node) {
         if (node.opcode == HTP_OP_MUL_MAT || node.opcode == HTP_OP_MUL_MAT_ID ||
-            node.opcode == HTP_OP_MUL_MAT_QKV || node.opcode == HTP_OP_MUL_MAT_FFN) {
+            node.opcode == HTP_OP_MUL_MAT_QKV || node.opcode == HTP_OP_MUL_MAT_FFN ||
+            node.opcode == HTP_OP_MUL_MAT_ADD) {
             const auto * kparams = (const struct htp_mm_kernel_params *) node.kernel_params;
             const char * path = "unknown";
             int32_t type = kparams->kernel_type;
@@ -350,6 +352,16 @@ struct htp_opformat {
                 path = "hvx-flat";
             }
             snprintf(str, max_size, "%s vtcm %d", path, (int) kparams->vtcm_size);
+        } else if (node.opcode == HTP_OP_FLASH_ATTN_EXT) {
+            const auto * kparams = (const struct htp_fa_kernel_params *) node.kernel_params;
+            const char * path = "unknown";
+            int32_t type = kparams->kernel_type;
+            if (type == HTP_FA_KERNEL_HMX) {
+                path = kparams->u.hmx.pipeline ? "hmx-pipe" : "hmx-seq";
+            } else if (type == HTP_FA_KERNEL_HVX) {
+                path = "hvx";
+            }
+            snprintf(str, max_size, "%s vtcm %d", path, (int) kparams->vtcm_size);
         } else {
             snprintf(str, max_size, "----");
         }
diff --git a/ggml/src/ggml-hexagon/htp/CMakeLists.txt b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
index c48a5b86e..9f9471f69 100644
--- a/ggml/src/ggml-hexagon/htp/CMakeLists.txt
+++ b/ggml/src/ggml-hexagon/htp/CMakeLists.txt
@@ -20,9 +20,6 @@ add_library(${HTP_LIB} SHARED
     worker-pool.c
     hex-dma.c
     hmx-queue.c
-    flash-attn-ops.c
-    hmx-flash-attn-ops.c
-    matmul-ops.c
     binary-ops.c
     unary-ops.c
     sum-rows-ops.c
@@ -42,16 +39,14 @@ add_library(${HTP_LIB} SHARED
     solve-tri-ops.c
     gated-delta-net-ops.c
     pad-ops.c
+    matmul-ops.c
+    flash-attn-ops.c
 )
 
 target_compile_definitions(${HTP_LIB} PRIVATE
     $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
     $<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,FARF_HIGH=1,>)
 
-if (GGML_HEXAGON_FA_EXP2_HF)
-    message(STATUS "ggml-htp: HMX_FA_USE_EXP2_HF=1 (use FP16 exp2 polynomial in FA softmax)")
-    target_compile_definitions(${HTP_LIB} PRIVATE HMX_FA_USE_EXP2_HF=1)
-endif()
 
 build_idl(htp_iface.idl ${HTP_LIB})
 
diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
index 65f7844ae..a1d15fc7a 100644
--- a/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
+++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.c
@@ -3,229 +3,40 @@
 #pragma clang diagnostic ignored "-Wunused-but-set-variable"
 
 #include <assert.h>
+#include <HAP_compute_res.h>
 #include <HAP_farf.h>
 #include <HAP_perf.h>
 #include <math.h>
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
 #include <string.h>
 
 #include "hex-dma.h"
+#include "hex-fastdiv.h"
+#include "hex-profile.h"
+#include "hmx-queue.h"
+#include "hmx-utils.h"
 #include "hvx-utils.h"
 #include "hvx-dump.h"
+#include "hvx-copy.h"
+#include "hvx-reduce.h"
 #include "hvx-flash-attn.h"
+#include "vtcm-utils.h"
+#include "worker-pool.h"
 
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
 #include "htp-ops.h"
-#include "htp-ops.h"
 
-int hmx_flash_attn_ext(struct htp_ops_context * octx);
+#include "flash-attn-ops.h"
+#include "hvx-fa-kernels.h"
+#include "hmx-fa-kernels.h"
 
 // Must be multiple of 32
 #define FLASH_ATTN_BLOCK_SIZE (32 * 2)
 
-#if __HVX_ARCH__ < 79
-#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
-#define HVX_OP_SUB_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b))
-#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
-#else
-#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
-#define HVX_OP_SUB_F32(a, b) Q6_Vsf_vsub_VsfVsf(a, b)
-#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
-#endif
-
-// This is a bit of a hack because the compiler is strugling to properly inline
-// the default hvx_vec_f32_to_f16 with output into the local array.
-static __attribute__((noinline)) void hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
-{
-    *(HVX_Vector *) ptr = hvx_vec_f32_to_f16(v0, v1);
-}
-
-// Dot product of two F16 vectors, accumulating to float
-static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict x, const void * restrict y, unsigned int n, float s) {
-    const HVX_Vector * restrict vx = (const HVX_Vector * restrict) x; // fp16
-    const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16
-
-    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16; // leftover elements
-
-    HVX_VectorPair rsum_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
-
-    uint32_t i = 0;
-
-    #pragma unroll(4)
-    for (i = 0; i < nvec; i++) {
-        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, vx[i], vy[i]);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]);
-        HVX_Vector x_hf = Q6_V_vand_QV(bmask, vx[i]);
-
-        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf);
-    }
-
-    HVX_Vector rsum = HVX_OP_ADD_F32(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p));
-    rsum = HVX_OP_MUL_F32(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum));
-    hvx_vec_store_u(r, 4, rsum);
-}
-
-static inline HVX_Vector hvx_dot_f16_f16_aa_rx4(const void * restrict y,
-                                                const uint8_t * restrict x,
-                                                const size_t stride_x,
-                                                const size_t nvec,
-                                                const size_t nloe) {
-    const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x;                   // fp16
-    const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) (x + stride_x);      // fp16
-    const HVX_Vector * restrict vx2 = (const HVX_Vector * restrict) (x + stride_x * 2);  // fp16
-    const HVX_Vector * restrict vx3 = (const HVX_Vector * restrict) (x + stride_x * 3);  // fp16
-    const HVX_Vector * restrict vy  = (const HVX_Vector * restrict) y;                   // fp16
-
-    HVX_VectorPair rsum0_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
-    HVX_VectorPair rsum1_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
-    HVX_VectorPair rsum2_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
-    HVX_VectorPair rsum3_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
-
-    uint32_t i = 0;
-
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector y_hf  = vy[i];
-        HVX_Vector x0_hf = vx0[i];
-        HVX_Vector x1_hf = vx1[i];
-        HVX_Vector x2_hf = vx2[i];
-        HVX_Vector x3_hf = vx3[i];
-
-        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
-        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
-        rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf);
-        rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf);
-    }
-
-    if (nloe) {
-        // Load x (fp16) and zero-out unused elements
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        HVX_Vector     y_hf  = Q6_V_vand_QV(bmask, vy[i]);
-        HVX_Vector     x0_hf = Q6_V_vand_QV(bmask, vx0[i]);
-        HVX_Vector     x1_hf = Q6_V_vand_QV(bmask, vx1[i]);
-        HVX_Vector     x2_hf = Q6_V_vand_QV(bmask, vx2[i]);
-        HVX_Vector     x3_hf = Q6_V_vand_QV(bmask, vx3[i]);
-
-        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
-        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
-        rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf);
-        rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf);
-    }
-
-    HVX_Vector rsum0 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p));
-    HVX_Vector rsum1 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p));
-    HVX_Vector rsum2 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum2_p), Q6_V_hi_W(rsum2_p));
-    HVX_Vector rsum3 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum3_p), Q6_V_hi_W(rsum3_p));
-
-    HVX_Vector_x4 rsum0123 = { .v = { rsum0, rsum1, rsum2, rsum3 } };
-    return hvx_vec_reduce_sum_f32x4(rsum0123);
-}
-
-static inline HVX_Vector hvx_dot_f16_f16_aa_rx32(const void * restrict y,
-                                                 const uint8_t * restrict x,
-                                                 const size_t stride_x,
-                                                 const size_t n,
-                                                 float        s) {
-
-    const size_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    const size_t nloe = n % VLEN_FP16; // leftover elements
-
-    HVX_Vector   sums = Q6_V_vzero();
-    const size_t stride_x_4 = stride_x * 4;
-    for (uint32_t j = 0; j < VLEN_FP32; j += 4) {
-        HVX_Vector     sums_x4 = hvx_dot_f16_f16_aa_rx4(y, x, stride_x, nvec, nloe);
-        HVX_VectorPred pred    = Q6_Q_vsetq_R(j * SIZEOF_FP32);
-        sums                   = Q6_V_vmux_QVV(pred, sums, sums_x4);
-        x += stride_x_4;
-    }
-
-    return HVX_OP_MUL_F32(hvx_vec_splat_f32(s), sums);
-}
-
-// MAD: y (F32) += x (F16) * s (F16)
-static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, const __fp16 * restrict s, int n) {
-    const HVX_Vector * restrict vx0 = (const HVX_Vector *) x;
-
-    HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y;
-    HVX_Vector * restrict vy = (HVX_Vector *) y;
-
-    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16; // leftover elements
-
-    HVX_Vector S0 = hvx_vec_splat_f16(*s);
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; ++i) {
-        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0);
-    }
-
-    if (nloe) {
-        HVX_VectorPair xy_p = vy_p[i];
-        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0);
-
-        HVX_Vector xy = Q6_V_lo_W(xy_p);
-        i = 2 * i;  // index for vy
-
-        if (nloe >= VLEN_FP32) {
-            vy[i] = xy;
-            nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p);
-        }
-
-        if (nloe) {
-            hvx_vec_store_a(&vy[i], nloe * 4, xy);
-        }
-    }
-}
-
-// MAD: y (F32) += x0 (F16) * s0 (F16) + x1 (F16) * s1 (F16)
-static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, const void * restrict x0, const void * restrict x1,
-                                          const __fp16 * restrict s0, const __fp16 * restrict s1, int n) {
-    const HVX_Vector * restrict vx0 = (const HVX_Vector *) x0;
-    const HVX_Vector * restrict vx1 = (const HVX_Vector *) x1;
-
-    HVX_VectorPair * restrict vy_p  = (HVX_VectorPair *) y;
-    HVX_Vector * restrict vy        = (HVX_Vector *) y;
-
-    uint32_t nvec = n / VLEN_FP16;  // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16;  // leftover elements
-
-    HVX_Vector S0 = hvx_vec_splat_f16(*s0);
-    HVX_Vector S1 = hvx_vec_splat_f16(*s1);
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; ++i) {
-        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0);
-        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx1[i]), S1);
-    }
-
-    if (nloe) {
-        HVX_VectorPair xy_p = vy_p[i];
-        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0);
-        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx1[i]), S1);
-
-        HVX_Vector xy = Q6_V_lo_W(xy_p);
-        i = 2 * i;  // index for vy
-
-        if (nloe >= VLEN_FP32) {
-            vy[i] = xy;
-            nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p);
-        }
-
-        if (nloe) {
-            hvx_vec_store_a(&vy[i], nloe * 4, xy);
-        }
-    }
-}
-
 struct htp_fa_context {
     const struct htp_ops_context * octx;
 
@@ -242,12 +53,12 @@ struct htp_fa_context {
 
     float scale;
     float max_bias;
-    float logit_softcap;
+    __fp16 logit_softcap;
 
     uint32_t n_head_log2;
     float m0;
     float m1;
-    float slopes[512];
+    __fp16 slopes[512];
 
     uint32_t n_blocks;
 
@@ -264,28 +75,76 @@ struct htp_fa_context {
 
     bool is_q_fp32;
 
+    size_t size_q_block;
+    size_t size_vkq_acc;
+
+    uint8_t * spad_q;
+    uint8_t * spad_k;
+    uint8_t * spad_v;
+    uint8_t * spad_m;
+    uint8_t * spad_a;
+
     uint64_t t_start;
 };
 
-static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const int n, HVX_Vector vs) {
-    assert((size_t) dst % 128 == 0);
-    assert((size_t) src % 128 == 0);
+struct hmx_fa_context {
+    const struct htp_ops_context * octx;
+    const struct htp_tensor *      sinks;  // attention sinks (src[4]), NULL if absent
+    bool         pipeline;  // true when n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2
+    uint32_t     n_threads;
 
-    const HVX_Vector * restrict vsrc = (const HVX_Vector * restrict) src;
-    HVX_Vector * restrict vdst       = (HVX_Vector * restrict) dst;
+    // Op parameters
+    __fp16       scale;
+    float        max_bias;
+    __fp16       logit_softcap;
+    uint32_t     n_head_log2;
+    float        m0, m1;
 
-    const uint32_t nvec = n / VLEN_FP32;
-    const uint32_t nloe = n % VLEN_FP32;
+    // Dimensions
+    uint32_t     DK, DV;
+    uint32_t     n_kv;        // kv_len
+    uint32_t     n_kv_heads;  // number of KV heads
+    uint32_t     n_heads;     // number of Q heads
+    uint32_t     G;           // GQA factor = n_heads / n_kv_heads
+    struct fastdiv_values div_G;
+    struct fastdiv_values src3_div2;
+    struct fastdiv_values src3_div3;
+    uint32_t     n_kv_blocks;
+    uint32_t     neq1;        // Q token count
 
-    uint32_t i = 0;
-    #pragma unroll(4)
-    for (; i < nvec; ++i) {
-        vdst[i] = HVX_OP_MUL_F32(vsrc[i], vs);
-    }
-    if (nloe) {
-        hvx_vec_store_a(&vdst[i], nloe * sizeof(float), HVX_OP_MUL_F32(vsrc[i], vs));
-    }
-}
+    // Types
+    bool         is_q_fp32;
+    bool         is_dst_fp32;
+
+    // Dynamic block sizes
+    uint32_t     Br;    // Q tokens per block (before GQA expansion)
+    uint32_t     Bc;
+    uint32_t     g_br;  // hex_align_up(G * Br, 32) - actual tile row dim
+
+    // VTCM buffers (allocated by vtcm_seq_alloc)
+    __fp16 *     vtcm_q_tiles;         // Q tile format [g_br, D]
+    __fp16 *     vtcm_o_tiles[2];      // O ping-pong [g_br, D]
+    __fp16 *     vtcm_k_fp16[2];       // K DMA double-buffer [Bc, D]
+    __fp16 *     vtcm_v_fp16[2];       // V DMA double-buffer [Bc, D]
+    __fp16 *     vtcm_k_tiles;         // K tiles (transposed)
+    __fp16 *     vtcm_v_tiles[2];      // V tiles (column-major, double-buffered)
+    __fp16 *     vtcm_s_tiles;         // S = QK^T [g_br, Bc]
+    __fp16 *     vtcm_p_tiles;         // P = softmax(S) [g_br, Bc]
+    __fp16 *     vtcm_d_tiles;         // Diagonal rescale [g_br, g_br]
+    HVX_Vector * vtcm_m_vec;           // Row max [g_br]
+    HVX_Vector * vtcm_l_vec;           // Row sum [g_br]
+    HVX_Vector * vtcm_s_rowmax;        // Softmax intermediate [g_br]
+    HVX_Vector * vtcm_p_rowsum;        // Softmax intermediate [g_br]
+    HVX_Vector * vtcm_row_bufs;        // Per-thread softmax row scratch [n_threads][2][Bc/64]
+    uint8_t *    vtcm_hmx_scales_id;   // HMX output scales (identity)
+    uint8_t *    vtcm_hmx_scales_qk;   // HMX output scales (qk_scale)
+    __fp16 *     vtcm_mask_buf;        // VTCM mask buffer [Br * m_line], DMA'd per KV block
+    __fp16 *     vtcm_slopes;          // ALiBi slopes [g_br]
+    size_t       row_buf_stride;       // HVX vectors per row buffer (Bc/64)
+    size_t       mask_buf_row_stride;  // elements (__fp16) per row in mask buffer
+    bool         mask_broadcast;       // true when mask->ne[2] == 1 (head-independent, single 2D DMA)
+    dma_cache    m_cache;
+};
 
 static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_fa_context * factx = (struct htp_fa_context *) data;
@@ -341,7 +200,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
     if (ir0 >= ir1) return;
 
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
 
     dma_queue * dma = octx->ctx->dma[ith];
 
@@ -353,16 +211,14 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
     const size_t size_v_row = DV * sizeof(__fp16);
 
     // Scratchpad buffers for Q, K, V, Mask, and VKQ32 accumulator
-    uint8_t * spad_q = octx->src0_spad.data + octx->src0_spad.size_per_thread * ith;
-    uint8_t * spad_k = octx->src1_spad.data + octx->src1_spad.size_per_thread * ith;
-    uint8_t * spad_v = octx->src2_spad.data + octx->src2_spad.size_per_thread * ith;
-    uint8_t * spad_m = octx->src3_spad.data + octx->src3_spad.size_per_thread * ith;
-    uint8_t * spad_a = octx->dst_spad.data  + octx->dst_spad.size_per_thread  * ith;
-
-    const HVX_Vector logit_cap = hvx_vec_splat_f32(factx->logit_softcap);
+    uint8_t * spad_q = factx->spad_q + factx->size_q_block * ith;
+    uint8_t * spad_k = factx->spad_k + factx->size_k_block * 2 * ith;
+    uint8_t * spad_v = factx->spad_v + factx->size_v_block * 2 * ith;
+    uint8_t * spad_m = factx->spad_m + (mask ? factx->size_m_block * HVX_FA_DMA_CACHE_SIZE : 0) * ith;
+    uint8_t * spad_a = factx->spad_a + factx->size_vkq_acc * ith;
 
     dma_cache m_cache;
-    dma_cache_init(&m_cache, spad_m, factx->size_m_block, DMA_CACHE_MAX_SIZE);
+    dma_cache_init(&m_cache, spad_m, factx->size_m_block, HVX_FA_DMA_CACHE_SIZE);
 
     for (uint32_t ir = ir0; ir < ir1; ++ir) {
         const uint32_t iq3 = fastdiv(ir, &factx->src0_div21);
@@ -379,9 +235,6 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
         const uint8_t * q_row_ptr = (const uint8_t *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3);
         dma_queue_push(dma, dma_make_ptr(spad_q, q_row_ptr), factx->size_q_row_padded, nbq1, size_q_row, 1);
 
-        // FARF(HIGH, "fa %u: prefetch Q: ir %u iq1 %u iq2 %u iq3 %u q_row_ptr %p size %u : usec %u", ith, ir, iq1, iq2, iq3, q_row_ptr, size_q_row,
-        //                 (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start));
-
         const __fp16 * mp_base = NULL;
         if (mask) {
             const uint32_t im2 = fastmodulo(iq2, mask->ne[2], &factx->src3_div2);
@@ -410,18 +263,13 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
                 // Mask is 1D contiguous for this row
                 dma_cache_push(dma, &m_cache, m_src, current_block_size * 2, current_block_size * 2, current_block_size * 2, 1);
             }
-
-            // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
-            //             ith, ir, ib, iq1, iq2, iq3,
-            //             size_k_row, size_v_row, current_block_size,
-            //             (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start));
         }
 
         const uint32_t h = iq2; // head index
-        const float slope = factx->slopes[h];
+        const __fp16 slope = factx->slopes[h];
 
         HVX_Vector S_vec = hvx_vec_splat_f32(0.0f);
-        HVX_Vector M_vec = hvx_vec_splat_f32(-INFINITY);
+        HVX_Vector M_vec = hvx_vec_splat_f32(HTP_FA_M_INITIAL_VAL);
 
         // Clear accumulator
         hvx_splat_f32_a(spad_a, 0, DV);
@@ -433,6 +281,7 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
         }
 
         const HVX_Vector slope_vec = hvx_vec_splat_f16(slope);
+        const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff);
         for (uint32_t ib = 0; ib < factx->n_blocks; ++ib) {
             const uint32_t ic_start = ib * FLASH_ATTN_BLOCK_SIZE;
             const uint32_t current_block_size = MIN(FLASH_ATTN_BLOCK_SIZE, nek1 - ic_start);
@@ -442,113 +291,101 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
             uint8_t * v_base = dma_queue_pop(dma).dst; // V
             __fp16  * m_base = mask ? dma_queue_pop(dma).dst : NULL; // M
 
-            // FARF(HIGH, "fa %u: process: ir %u ib %u : iq1 %u iq2 %u iq3 %u q_ptr_vtcm %p : usec %u",
-            //              ith, ir, ib, iq1, iq2, iq3, q_ptr_vtcm,
-            //             (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start));
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_QK, ir);
 
             // Inner loop processing the block from VTCM
-            uint32_t ic = 0;
-
-            // Process in sub-blocks of 32 (VLEN_FP32)
-            HVX_Vector sb_scores[FLASH_ATTN_BLOCK_SIZE / VLEN_FP32];
-            HVX_Vector v_max = hvx_vec_splat_f32(-INFINITY);
-            for (uint32_t iv = 0; ic < current_block_size; ic += VLEN_FP32, ++iv) {
-                // 1. Compute scores
-                HVX_Vector scores = hvx_dot_f16_f16_aa_rx32(q_ptr_vtcm, k_base + ic * factx->size_k_row_padded, factx->size_k_row_padded, DK, factx->scale);
-
-                // 2. Softcap
-                if (factx->logit_softcap != 0.0f) {
-                    scores = hvx_vec_tanh_f32(scores);
-                    scores = HVX_OP_MUL_F32(scores, logit_cap);
-                }
-
-                // 3. Mask
-                if (mask) {
-                    const __fp16 * mp = m_base + ic;
-                    HVX_Vector m_vals_f16 = *(const HVX_UVector *) mp;
-
-                    // Multiplying -INFINITY (0xFC00) by a slope in VhfVhf instructions can incorrectly produce NaN on v79.
-                    // Clamp -INFINITY to the max negative fp16 finite value (-65504.0f).
-                    HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00);
-                    HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF);
-                    HVX_VectorPred is_inf = Q6_Q_vcmp_eq_VhVh(m_vals_f16, vinf);
-                    m_vals_f16 = Q6_V_vmux_QVV(is_inf, vmin, m_vals_f16);
-
-                    #if __HVX_ARCH__ >= 79
-                        HVX_VectorPair m_vals_f32_pair = Q6_Wsf_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
-                        HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
-                        scores = Q6_Vsf_vadd_VsfVsf(add_val, scores);
-                    #else
-                        HVX_VectorPair m_vals_f32_pair = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(m_vals_f16), slope_vec);
-                        HVX_Vector add_val = Q6_V_lo_W(m_vals_f32_pair);
-                        scores = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(add_val, scores));
-                    #endif
-                }
-
-                // Mask out invalid lanes for leftover handling
-                uint32_t valid_lanes = current_block_size - ic;
-                if (valid_lanes < VLEN_FP32) {
-                    HVX_VectorPred valid_pred = Q6_Q_vsetq_R(valid_lanes * 4); // 4 bytes per fp32 lane
-                    scores = Q6_V_vmux_QVV(valid_pred, scores, hvx_vec_splat_f32(-INFINITY));
-                }
-
-                sb_scores[iv] = scores;
-                v_max = hvx_vec_reduce_max2_f32(scores, v_max); // All lanes have block max
+            // 1. Compute scores (64 elements FP16)
+            HVX_Vector scores_f16 = Q6_V_vzero();
+            if (current_block_size > 0) {
+                HVX_Vector scores0 = hvx_dot_f16_f16_aa_rx32(q_ptr_vtcm, k_base, factx->size_k_row_padded, DK, factx->scale);
+                HVX_Vector scores1 = (current_block_size > 32) ? hvx_dot_f16_f16_aa_rx32(q_ptr_vtcm, k_base + 32 * factx->size_k_row_padded, factx->size_k_row_padded, DK, factx->scale) : Q6_V_vzero();
+                scores_f16 = hvx_vec_f32_to_f16(scores0, scores1);
             }
 
+            // 2. Softcap (in FP16)
+            if (factx->logit_softcap != 0.0f) {
+                const HVX_Vector v_cap = hvx_vec_splat_f16(factx->logit_softcap);
+                scores_f16 = hvx_vec_tanh_f16(scores_f16);
+                scores_f16 = hvx_vec_mul_f16_f16(scores_f16, v_cap);
+            }
+
+            HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(current_block_size * sizeof(__fp16));
+
+            // 3. Mask (in FP16)
+            if (mask) {
+                HVX_Vector m_vals_f16 = *(const HVX_UVector *) m_base;
+                HVX_Vector vinf = Q6_Vh_vsplat_R(0xFC00);
+                HVX_Vector vmin = Q6_Vh_vsplat_R(0xFBFF);
+                HVX_VectorPred is_inf = Q6_Q_vcmp_eq_VhVh(m_vals_f16, vinf);
+                m_vals_f16 = Q6_V_vmux_QVV(is_inf, vmin, m_vals_f16);
+
+                HVX_Vector m_scaled = hvx_vec_mul_f16_f16(m_vals_f16, slope_vec);
+                scores_f16 = Q6_V_vmux_QVV(q_tail_keep, hvx_vec_add_f16_f16(scores_f16, m_scaled), v_neg_inf);
+            } else {
+                scores_f16 = Q6_V_vmux_QVV(q_tail_keep, scores_f16, v_neg_inf);
+            }
+
+            // Compute block max in FP16
+            HVX_Vector v_max_f16 = hvx_vec_reduce_max_f16(scores_f16);
+            HVX_Vector v_max     = Q6_V_lo_W(hvx_vec_f16_to_f32(v_max_f16)); // splat block max in FP32
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_QK, ir);
+
+            htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir);
             {
+                const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F);
+
                 // 4. Online Softmax Update
                 HVX_Vector M_new_vec = Q6_Vsf_vmax_VsfVsf(v_max, M_vec);
                 HVX_Vector diff_vec  = HVX_OP_SUB_F32(M_vec, M_new_vec);
-                HVX_Vector ms_vec    = hvx_vec_exp_f32(diff_vec);
+
+                HVX_Vector diff_f16   = hvx_vec_f32_to_f16(diff_vec, diff_vec);
+                HVX_Vector diff_base2 = hvx_vec_mul_f16_f16(diff_f16, v_log2e);
+                HVX_Vector ms_f16     = hvx_vec_exp2_f16(diff_base2);
+                HVX_Vector ms_vec     = Q6_V_lo_W(hvx_vec_f16_to_f32(ms_f16));
+
                 M_vec = M_new_vec;
 
                 hvx_scale_vec_f32_aa((uint8_t *) VKQ32, (const uint8_t *) VKQ32, DV, ms_vec);
 
-                HVX_Vector p_sum_vec = hvx_vec_splat_f32(0.0f);
-                for (uint32_t ic2 = 0, iv = 0; ic2 < current_block_size; ic2 += VLEN_FP32, ++iv) {
-                    HVX_Vector scores = sb_scores[iv];
-                    HVX_Vector scores_shifted = HVX_OP_SUB_F32(scores, M_vec);
-                    HVX_Vector P = hvx_vec_exp_f32(scores_shifted);
+                // Compute P = exp2((S - M) * log2(e)) in FP16
+                HVX_Vector v_m_vec_f16 = hvx_vec_f32_to_f16(M_vec, M_vec);
+                HVX_Vector v_s_minus_m = Q6_Vqf16_vsub_VhfVhf(scores_f16, v_m_vec_f16);
 
-                    p_sum_vec = HVX_OP_ADD_F32(p_sum_vec, P);
+                HVX_Vector v_s_minus_m_base2 = hvx_vec_mul_f16_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m), v_log2e);
 
-                    // 5. Accumulate V
-                    __fp16 __attribute__((aligned(VLEN))) p_arr[VLEN_FP16];
-                    hvx_vec_f32_to_f16_a(p_arr, P, hvx_vec_splat_f32(0));
+                HVX_Vector P = hvx_vec_exp2_f16(v_s_minus_m_base2);
+                P = Q6_V_vmux_QVV(q_tail_keep, P, Q6_V_vzero());
 
-                    float __attribute__((aligned(128))) P_arr[VLEN_FP32];
-                    hvx_vec_store_a(P_arr, 128, P);
+                // Convert P to FP32 to update the running sum S_vec
+                HVX_VectorPair P_pair = hvx_vec_f16_to_f32(P);
+                HVX_Vector P0 = Q6_V_lo_W(P_pair);
+                HVX_Vector P1 = Q6_V_hi_W(P_pair);
+                HVX_Vector p_sum_vec = hvx_vec_reduce_sum_f32(HVX_OP_ADD_F32(P0, P1));
 
-                    for (uint32_t j = 0; j < VLEN_FP32; j += 2) {
-                        const uint32_t cur_ic = ic2 + j;
-                        if (cur_ic >= current_block_size) {
-                            break;
-                        }
-
-                        if (cur_ic + 1 == current_block_size) {
-                            // Odd leftover, process single row
-                            if (P_arr[j] != 0.0f) {
-                                const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded;
-                                hvx_mad_f32_f16_aa(VKQ32, v_ptr, (p_arr + j), DV);
-                            }
-                            break;
-                        }
-
-                        // Avoid NaN * 0.0 = NaN for uninitialized V cache rows.
-                        // Check the f32 values to safely avoid strict aliasing violations.
-                        if (P_arr[j] == 0.0f && P_arr[j + 1] == 0.0f) {
-                            continue;
-                        }
-
-                        const uint8_t * v_ptr = v_base + cur_ic * factx->size_v_row_padded;
-                        hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV);
-                    }
-                }
-
-                p_sum_vec = hvx_vec_reduce_sum_f32(p_sum_vec);
                 S_vec = HVX_OP_ADD_F32(HVX_OP_MUL_F32(S_vec, ms_vec), p_sum_vec);
+
+                // 5. Accumulate V (F16 * F16 -> F32 accumulator)
+                __fp16 __attribute__((aligned(128))) p_arr[VLEN_FP16];
+                hvx_vec_store_a(p_arr, 128, P);
+
+                for (uint32_t j = 0; j < current_block_size; j += 2) {
+                    if (j + 1 == current_block_size) {
+                        if (p_arr[j] != 0.0f) {
+                            const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded;
+                            hvx_mad_f32_f16_aa(VKQ32, v_ptr, (p_arr + j), DV);
+                        }
+                        break;
+                    }
+
+                    if (p_arr[j] == 0.0f && p_arr[j + 1] == 0.0f) {
+                        continue;
+                    }
+
+                    const uint8_t * v_ptr = v_base + j * factx->size_v_row_padded;
+                    hvx_mad_f32_f16_aa_rx2(VKQ32, v_ptr, v_ptr + factx->size_v_row_padded, (p_arr + j), (p_arr + j + 1), DV);
+                }
             }
+            htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_SFM, ir);
 
             // Issue DMA for next+1 block (if exists)
             if (ib + 2 < factx->n_blocks) {
@@ -569,14 +406,10 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
                     const uint8_t * m_src = (const uint8_t *) (mp_base + next_ic_start);
                     dma_cache_push(dma, &m_cache, m_src, next_block_size * 2, next_block_size * 2, next_block_size * 2, 1);
                 }
-
-                // FARF(HIGH, "fa %u: prefetch KVM: ir %u ib %u : iq1 %u iq2 %u iq3 %u : size_k_row %u size_v_row %u bs %u: usec %u",
-                //         ith, ir, next_ib, iq1, iq2, iq3,
-                //         size_k_row, size_v_row, next_block_size,
-                //         (unsigned)HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - factx->t_start));
             }
         }
 
+        htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, ir);
         // sinks
         float M = hvx_vec_get_f32(M_vec);
         float S = hvx_vec_get_f32(S_vec);
@@ -605,9 +438,9 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
 
         // Store result
         // dst indices
-        const int i1 = iq1;
-        const int i2 = iq2;
-        const int i3 = iq3;
+        const uint32_t i1 = iq1;
+        const uint32_t i2 = iq2;
+        const uint32_t i3 = iq3;
 
         // dst is permuted: [DV, n_heads, n_tokens, n_seq]
         // head stride is nb[1], token stride is nb[2], batch stride is nb[3]
@@ -618,8 +451,1563 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
         } else if (dst->type == HTP_TYPE_F16) {
             hvx_copy_f16_f32_ua(dst_ptr, (uint8_t *) VKQ32, DV);
         }
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, ir);
     }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
+}
+
+// ============================================================================
+// HMX Phase args and thread logic
+// ============================================================================
+
+typedef struct {
+    struct hmx_fa_context * factx;
+    uint32_t                kv_rows;
+    size_t                  src_stride;
+    size_t                  buf_idx;
+    uint32_t                kv_start;
+    uint32_t                rows_per_t;
+} fa_k_int_args_t;
+
+static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) {
+    fa_k_int_args_t *       args  = (fa_k_int_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const uint32_t total_rows = args->kv_rows;
+    const uint32_t rows_per_t = args->rows_per_t;
+    const uint32_t start      = i * rows_per_t;
+    const uint32_t end        = (uint32_t) hex_smin(start + rows_per_t, total_rows);
+
+    if (start >= total_rows) {
+        return;
+    }
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start));
+    hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, factx->vtcm_k_fp16[args->buf_idx], total_rows, factx->DK,
+                             args->src_stride, start, end);
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_K_PREP, (uint16_t) (args->kv_start + start));
+}
+
+static void fa_phase_k_interleave(struct hmx_fa_context * factx, uint32_t kv_rows, size_t src_stride, size_t buf_idx, uint32_t kv_start) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    uint32_t n = 1;
+    if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) {
+        n = factx->n_threads;
+    }
+    uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2);
+    fa_k_int_args_t args = { factx, kv_rows, src_stride, buf_idx, kv_start, rows_per_t };
+    if (n > 1) {
+        worker_pool_run_func(wp, fa_k_interleave_thread, &args, n);
+    } else {
+        fa_k_interleave_thread(1, 0, &args);
+    }
+}
+
+typedef struct {
+    struct hmx_fa_context * factx;
+    uint32_t                kv_rows;
+    size_t                  src_stride;
+    size_t                  buf_idx;
+    size_t                  n_col_tiles;
+    uint32_t                kv_start;
+    uint32_t                rows_per_t;
+} fa_v_int_args_t;
+
+static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data) {
+    fa_v_int_args_t *       args  = (fa_v_int_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const uint32_t total_rows = args->kv_rows;
+    const uint32_t rows_per_t = args->rows_per_t;
+    const uint32_t start      = i * rows_per_t;
+    const uint32_t end        = (uint32_t) hex_smin(start + rows_per_t, total_rows);
+
+    if (start >= total_rows) {
+        return;
+    }
+
+    __fp16 * v_tiles_dest = factx->pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_V_PREP, (uint16_t) (args->kv_start + start));
+    hmx_interleave_cols_to_tiles(v_tiles_dest, factx->vtcm_v_fp16[args->buf_idx], total_rows, factx->DV,
+                             args->src_stride, (uint32_t) args->n_col_tiles, start, end);
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_V_PREP, (uint16_t) (args->kv_start + start));
+}
+
+static void fa_phase_v_interleave(struct hmx_fa_context * factx,
+                                  uint32_t                kv_rows,
+                                  size_t                  src_stride,
+                                  size_t                  buf_idx,
+                                  size_t                  n_col_tiles,
+                                  uint32_t                kv_start) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    uint32_t n = 1;
+    if (factx->n_threads > 1 && kv_rows >= factx->n_threads * 2) {
+        n = factx->n_threads;
+    }
+    uint32_t rows_per_t = hex_align_up(hmx_ceil_div(kv_rows, n), 2);
+    fa_v_int_args_t args = { factx, kv_rows, src_stride, buf_idx, n_col_tiles, kv_start, rows_per_t };
+    if (n > 1) {
+        worker_pool_run_func(wp, fa_v_interleave_thread, &args, n);
+    } else {
+        fa_v_interleave_thread(1, 0, &args);
+    }
+}
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    const struct htp_tensor * q;
+    uint32_t                  q_start;
+    uint32_t                  kv_head;
+    uint32_t                  ib3;
+    size_t                    n_rows_g;
+    size_t                    rows_per_t;
+} fa_q_load_args_t;
+
+static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
+    fa_q_load_args_t *      args  = (fa_q_load_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g = args->n_rows_g;
+    const size_t G        = factx->G;
+    const size_t DK       = factx->DK;
+
+    // Partition the padded Q rows (g_br) across threads.
+    // Keep start/end even so r and r+1 are always in the same thread's range.
+    const size_t rows_per_t = args->rows_per_t;
+    const size_t start      = (size_t) i * rows_per_t;
+    const size_t end        = hex_smin(start + rows_per_t, factx->g_br);
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_Q_PREP, (uint16_t) (args->q_start * G + start));
+
+    // Parallel initialization of per-block state
+    {
+        const uint32_t g_br = factx->g_br;
+        const uint32_t DV   = factx->DV;
+
+        const size_t col_vec_bytes = hex_align_up(g_br * sizeof(float), 256);
+        const size_t d_tile_bytes  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);
+        const size_t o_tile_bytes  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);
+
+        // Initialize vtcm_l_vec & vtcm_m_vec
+        const size_t l_bytes_per_t = hex_align_up(col_vec_bytes / n, 128);
+        const size_t l_start       = i * l_bytes_per_t;
+        const size_t l_end         = hex_smin(l_start + l_bytes_per_t, col_vec_bytes);
+
+        const size_t m_bytes_per_t = hex_align_up(col_vec_bytes / n, 128);
+        const size_t m_start       = i * m_bytes_per_t;
+        const size_t m_end         = hex_smin(m_start + m_bytes_per_t, col_vec_bytes);
+
+        if (factx->sinks) {
+            const float * sinks_data = (const float *) (uintptr_t) factx->sinks->data;
+            float *       m_vec      = (float *) factx->vtcm_m_vec;
+            const size_t  r_start    = l_start / sizeof(float);
+            const size_t  r_end      = l_end / sizeof(float);
+            const float   scale_factor = EXP_LOG2E_F;
+
+            const HVX_Vector v_scale = hvx_vec_splat_f32(scale_factor);
+
+            for (size_t r = r_start; r < r_end; r += 32) {
+                HVX_VectorAlias local_m;
+                for (size_t j = 0; j < 32; ++j) {
+                    size_t curr_r = r + j;
+                    if (curr_r < n_rows_g) {
+                        const size_t h_idx = fastmodulo(curr_r, G, &factx->div_G);
+                        const size_t head  = args->kv_head * G + h_idx;
+                        local_m.fp32[j] = sinks_data[head];
+                    } else {
+                        local_m.fp32[j] = HTP_FA_M_INITIAL_VAL;
+                    }
+                }
+                HVX_Vector v_scaled = HVX_OP_MUL_F32(local_m.v, v_scale);
+                *(HVX_Vector *) (m_vec + r) = v_scaled;
+            }
+            if (l_start < col_vec_bytes) {
+                hvx_splat_u8_a((char *) factx->vtcm_l_vec + l_start, 0, l_end - l_start);
+            }
+        } else {
+            if (l_start < col_vec_bytes) {
+                hvx_splat_u8_a((char *) factx->vtcm_l_vec + l_start, 0, l_end - l_start);
+            }
+            if (m_start < col_vec_bytes) {
+                hvx_splat_f32_a((char *) factx->vtcm_m_vec + m_start, HTP_FA_M_INITIAL_VAL, (m_end - m_start) / sizeof(float));
+            }
+        }
+
+        // Initialize vtcm_d_tiles to 0
+        const size_t d_bytes_per_t = hex_align_up(d_tile_bytes / n, 128);
+        const size_t d_start       = i * d_bytes_per_t;
+        const size_t d_end         = hex_smin(d_start + d_bytes_per_t, d_tile_bytes);
+        if (d_start < d_tile_bytes) {
+            hvx_splat_u8_a((char *) factx->vtcm_d_tiles + d_start, 0, d_end - d_start);
+        }
+
+        // Initialize vtcm_o_tiles[0] to 0
+        __fp16 * o_tile_prev       = factx->vtcm_o_tiles[0];
+        const size_t o_bytes_per_t = hex_align_up(o_tile_bytes / n, 128);
+        const size_t o_start       = i * o_bytes_per_t;
+        const size_t o_end         = hex_smin(o_start + o_bytes_per_t, o_tile_bytes);
+        if (o_start < o_tile_bytes) {
+            hvx_splat_u8_a((char *) o_tile_prev + o_start, 0, o_end - o_start);
+        }
+    }
+
+    if (start >= factx->g_br) {
+        htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_Q_PREP, (uint16_t) (args->q_start * G + start));
+        return;
+    }
+
+    const struct htp_tensor * q       = args->q;
+    const uint32_t            q_start = args->q_start;
+    const uint32_t            kv_head = args->kv_head;
+    const uint32_t            ib3     = args->ib3;
+
+    for (size_t r = start; r < end; r += 2) {
+        const size_t q_idx0 = fastdiv(r + 0, &factx->div_G);
+        const size_t h_idx0 = fastmodulo(r + 0, G, &factx->div_G);
+        const size_t q_idx1 = fastdiv(r + 1, &factx->div_G);
+        const size_t h_idx1 = fastmodulo(r + 1, G, &factx->div_G);
+
+        const uint8_t * q_ptr0 = (r + 0 < n_rows_g) ? ((const uint8_t *) q->data + (q_start + q_idx0) * q->nb[1] +
+                                                      (kv_head * G + h_idx0) * q->nb[2] + ib3 * q->nb[3]) :
+                                                      NULL;
+        const uint8_t * q_ptr1 = (r + 1 < n_rows_g) ? ((const uint8_t *) q->data + (q_start + q_idx1) * q->nb[1] +
+                                                      (kv_head * G + h_idx1) * q->nb[2] + ib3 * q->nb[3]) :
+                                                      NULL;
+
+        size_t   r0       = r / HMX_FP16_TILE_N_ROWS;
+        size_t   r1       = r % HMX_FP16_TILE_N_ROWS;
+        __fp16 * out_base = factx->vtcm_q_tiles + r0 * HMX_FP16_TILE_N_ROWS * DK;
+
+        if (factx->is_q_fp32) {
+            const HVX_Vector * pv_in0 = q_ptr0 ? (const HVX_Vector *) q_ptr0 : NULL;
+            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
+
+            for (uint32_t d = 0; d < DK / 32; ++d) {
+                HVX_Vector v0   = pv_in0 ? pv_in0[d] : Q6_V_vzero();
+                HVX_Vector v1   = pv_in1 ? pv_in1[d] : Q6_V_vzero();
+                HVX_Vector v_hf = hvx_vec_f32_to_f16_shuff(v0, v1);
+
+                HVX_Vector * out_tile = (HVX_Vector *) (out_base + d * HMX_FP16_TILE_N_ELMS);
+                out_tile[r1 / 2]      = v_hf;
+            }
+        } else {
+            const HVX_Vector * pv_in0 = q_ptr0 ? (const HVX_Vector *) q_ptr0 : NULL;
+            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
+
+            for (uint32_t d = 0; d < DK / 64; ++d) {
+                HVX_Vector     v0 = pv_in0 ? pv_in0[d] : Q6_V_vzero();
+                HVX_Vector     v1 = pv_in1 ? pv_in1[d] : Q6_V_vzero();
+                HVX_VectorPair vp = Q6_W_vshuff_VVR(v1, v0, -2);
+
+                __fp16 *     out_dual_tile = out_base + d * HMX_FP16_TILE_N_ELMS * 2;
+                HVX_Vector * pv_out0       = ((HVX_Vector *) out_dual_tile) + r1 / 2;
+                HVX_Vector * pv_out1       = pv_out0 + 16;
+
+                *pv_out0 = Q6_V_lo_W(vp);
+                *pv_out1 = Q6_V_hi_W(vp);
+            }
+        }
+    }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_Q_PREP, (uint16_t) (args->q_start * G + start));
+}
+
+static void fa_phase_q_load(struct hmx_fa_context *   factx,
+                            const struct htp_tensor * q,
+                            uint32_t                  q_start,
+                            uint32_t                  kv_head,
+                            uint32_t                  ib3,
+                            size_t                    n_rows_g) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    uint32_t n = 1;
+    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
+        n = factx->n_threads;
+    }
+    size_t rows_per_t = hex_align_up(hmx_ceil_div(factx->g_br, n), 2);
+    fa_q_load_args_t args = { factx, q, q_start, kv_head, ib3, n_rows_g, rows_per_t };
+    if (n > 1) {
+        worker_pool_run_func(wp, fa_q_load_thread, &args, n);
+    } else {
+        fa_q_load_thread(1, 0, &args);
+    }
+}
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    const struct htp_tensor * dst;
+    const __fp16 *            o_tile_src;
+    uint32_t                  q_start;
+    uint32_t                  kv_head;
+    uint32_t                  ib3;
+    size_t                    n_rows_g;
+    size_t                    rows_per_t;
+} fa_o_store_args_t;
+
+static void fa_o_store_thread_f32(unsigned int n, unsigned int i, void * data) {
+    fa_o_store_args_t *     args  = (fa_o_store_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g = args->n_rows_g;
+    const size_t G        = factx->G;
+    const size_t DV       = factx->DV;
+
+    const size_t rows_per_t = args->rows_per_t;
+    const size_t start      = (size_t) i * rows_per_t;
+    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
+
+    if (start >= n_rows_g) {
+        return;
+    }
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start));
+
+    const struct htp_tensor * dst        = args->dst;
+    const __fp16 *            o_tile_src = args->o_tile_src;
+    const uint32_t            q_start    = args->q_start;
+    const uint32_t            kv_head    = args->kv_head;
+    const uint32_t            ib3        = args->ib3;
+
+    for (size_t r = start; r < end; ++r) {
+        const size_t q_idx = fastdiv(r, &factx->div_G);
+        const size_t h_idx = fastmodulo(r, G, &factx->div_G);
+
+        float * out = (float *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] +
+                                 (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]);
+
+        size_t         r0            = r / HMX_FP16_TILE_N_ROWS;
+        size_t         r1            = r % HMX_FP16_TILE_N_ROWS;
+        const __fp16 * tile_row_base = o_tile_src + r0 * HMX_FP16_TILE_N_ROWS * DV;
+
+        for (uint32_t d = 0; d < DV / 32; ++d) {
+            const HVX_Vector * in_tile = (const HVX_Vector *) (tile_row_base + d * HMX_FP16_TILE_N_ELMS);
+            HVX_VectorPair     vp      = hvx_vec_f16_to_f32_shuff(in_tile[r1 / 2]);
+            if (r1 % 2 == 0) {
+                *(HVX_UVector *) (out + d * 32) = Q6_V_lo_W(vp);
+            } else {
+                *(HVX_UVector *) (out + d * 32) = Q6_V_hi_W(vp);
+            }
+        }
+    }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start));
+}
+
+static void fa_o_store_thread_f16(unsigned int n, unsigned int i, void * data) {
+    fa_o_store_args_t *     args  = (fa_o_store_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g = args->n_rows_g;
+    const size_t G        = factx->G;
+    const size_t DV       = factx->DV;
+
+    const size_t rows_per_t = args->rows_per_t;
+    const size_t start      = (size_t) i * rows_per_t;
+    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
+
+    if (start >= n_rows_g) {
+        return;
+    }
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start));
+
+    const struct htp_tensor * dst        = args->dst;
+    const __fp16 *            o_tile_src = args->o_tile_src;
+    const uint32_t            q_start    = args->q_start;
+    const uint32_t            kv_head    = args->kv_head;
+    const uint32_t            ib3        = args->ib3;
+
+    for (size_t r = start; r < end; ++r) {
+        const size_t q_idx = fastdiv(r, &factx->div_G);
+        const size_t h_idx = fastmodulo(r, G, &factx->div_G);
+
+        __fp16 * out = (__fp16 *) ((uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] +
+                                   (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3]);
+
+        size_t         r0            = r / HMX_FP16_TILE_N_ROWS;
+        size_t         r1            = r % HMX_FP16_TILE_N_ROWS;
+        const __fp16 * tile_row_base = o_tile_src + r0 * HMX_FP16_TILE_N_ROWS * DV;
+
+        for (uint32_t d = 0; d < DV / 64; ++d) {
+            const __fp16 *     in_dual_tile = tile_row_base + d * HMX_FP16_TILE_N_ELMS * 2;
+            const HVX_Vector * pv_in0       = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
+            const HVX_Vector * pv_in1       = pv_in0 + 16;
+            HVX_VectorPair     vp           = Q6_W_vdeal_VVR(*pv_in1, *pv_in0, -2);
+            if (r1 % 2 == 0) {
+                *(HVX_UVector *) (out + d * 64) = Q6_V_lo_W(vp);
+            } else {
+                *(HVX_UVector *) (out + d * 64) = Q6_V_hi_W(vp);
+            }
+        }
+    }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) (args->q_start * G + start));
+}
+
+static void fa_phase_o_store(struct hmx_fa_context *   factx,
+                             const struct htp_tensor * dst,
+                             const __fp16 *            o_tile_src,
+                             uint32_t                  q_start,
+                             uint32_t                  kv_head,
+                             uint32_t                  ib3,
+                             size_t                    n_rows_g) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    uint32_t n = 1;
+    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
+        n = factx->n_threads;
+    }
+    size_t rows_per_t = hmx_ceil_div(n_rows_g, n);
+    fa_o_store_args_t args = { factx, dst, o_tile_src, q_start, kv_head, ib3, n_rows_g, rows_per_t };
+    worker_callback_t store_fn = factx->is_dst_fp32 ? fa_o_store_thread_f32 : fa_o_store_thread_f16;
+    if (n > 1) {
+        worker_pool_run_func(wp, store_fn, &args, n);
+    } else {
+        store_fn(1, 0, &args);
+    }
+}
+
+typedef struct {
+    struct hmx_fa_context *   factx;
+    size_t                    kv_rows;
+    size_t                    n_rows_g;
+    size_t                    n_col_tiles;
+    size_t                    n_tiles_per_bc;
+    size_t                    n_row_tiles;
+    size_t                    n_row_tiles_g_br;
+    uint32_t                  Bc;
+    uint32_t                  G;
+    uint32_t                  kv_head;
+    uint32_t                  kv_start;
+    uint32_t                  q_start;
+    uint32_t                  ib3;
+    bool                      has_alibi;  // true when max_bias != 0 (need slope * mask + add)
+    __fp16 *                  slopes;
+    const struct htp_tensor * mask;
+    const __fp16 *            mask_vtcm;             // VTCM mask buffer base (NULL = DDR fallback)
+    size_t                    mask_vtcm_row_stride;  // elements (__fp16) per row in VTCM mask buffer
+    struct fastdiv_values     thread_div;
+} fa_softmax_args_t;
+
+static inline void fa_softmax_impl(
+    unsigned int n, unsigned int i, void * data,
+    const bool has_mask,
+    const bool mask_broadcast,
+    const bool is_g1,
+    const bool has_alibi,
+    const bool has_softcap
+) {
+    fa_softmax_args_t *     args  = (fa_softmax_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const size_t n_rows_g       = args->n_rows_g;
+    const size_t kv_rows        = args->kv_rows;
+    const size_t Bc             = args->Bc;
+    const size_t G              = args->G;
+    const size_t n_tiles_per_bc = args->n_tiles_per_bc;
+    const size_t n_row_vec_cnt  = hmx_ceil_div(n_rows_g, 64);
+    const uint32_t im3          = has_mask ? fastmodulo(args->ib3, args->mask->ne[3], &factx->src3_div3) : 0;
+
+    size_t vec_start = 0;
+    size_t vec_end   = n_row_vec_cnt;
+    if (n > 1) {
+        const size_t vecs_per_t = fastdiv(n_row_vec_cnt + n - 1, &args->thread_div);
+        vec_start = i * vecs_per_t;
+        vec_end   = hex_smin(vec_start + vecs_per_t, n_row_vec_cnt);
+    }
+
+    if (vec_start >= n_row_vec_cnt) {
+        return;
+    }
+
+    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
+    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_FA_SFM, (uint16_t) (args->q_start * G + vec_start * 64));
+
+    // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride
+    const size_t row_buf_stride = factx->row_buf_stride;
+    HVX_Vector * my_row_buf0    = factx->vtcm_row_bufs + i * 2 * row_buf_stride;
+    HVX_Vector * my_row_buf1    = my_row_buf0 + row_buf_stride;
+
+    const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff);
+
+    for (size_t r_vec_idx = vec_start; r_vec_idx < vec_end; ++r_vec_idx) {
+        HVX_Vector rowmax_acc_v = v_neg_inf;
+        HVX_Vector rowsum_acc_v = Q6_V_vzero();
+        HVX_Vector m_prev_v0    = factx->vtcm_m_vec[r_vec_idx * 2 + 0];
+        HVX_Vector m_prev_v1    = factx->vtcm_m_vec[r_vec_idx * 2 + 1];
+
+        HVX_Vector v_slopes = Q6_V_vzero();
+        if (has_alibi) {
+            v_slopes = hvx_vmem(args->slopes + r_vec_idx * 64);
+        }
+
+        for (uint32_t r_vec_off = 0; r_vec_off < 64; r_vec_off += 2) {
+            uint32_t r = r_vec_idx * 64 + r_vec_off;
+            if (r >= hex_align_up(n_rows_g, 2)) {
+                break;
+            }
+
+            uint32_t r0 = r / HMX_FP16_TILE_N_ROWS;
+            uint32_t r1 = r % HMX_FP16_TILE_N_ROWS;
+
+            const __fp16 * s_ld_base = factx->vtcm_s_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
+            __fp16 *       p_st_base = factx->vtcm_p_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
+
+            // Decode 2 rows from S tiles into per-thread row buffers
+            if (has_softcap) {
+                const HVX_Vector v_cap = hvx_vec_splat_f16(factx->logit_softcap);
+                for (size_t c = 0; c < kv_rows; c += 64) {
+                    size_t             ci           = c / 64;
+                    const __fp16 *     in_dual_tile = s_ld_base + ci * HMX_FP16_TILE_N_ELMS * 2;
+                    const HVX_Vector * pv_s_in0     = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
+                    const HVX_Vector * pv_s_in1     = pv_s_in0 + 16;
+
+                    HVX_VectorPair vp_s_dual_row = Q6_W_vdeal_VVR(*pv_s_in1, *pv_s_in0, -2);
+                    HVX_Vector     v_s_row0      = Q6_V_lo_W(vp_s_dual_row);
+                    HVX_Vector     v_s_row1      = Q6_V_hi_W(vp_s_dual_row);
+
+                    HVX_Vector t0   = hvx_vec_tanh_f16(v_s_row0);
+                    my_row_buf0[ci] = hvx_vec_mul_f16_f16(t0, v_cap);
+
+                    HVX_Vector t1   = hvx_vec_tanh_f16(v_s_row1);
+                    my_row_buf1[ci] = hvx_vec_mul_f16_f16(t1, v_cap);
+                }
+            } else {
+                for (size_t c = 0; c < kv_rows; c += 64) {
+                    size_t             ci           = c / 64;
+                    const __fp16 *     in_dual_tile = s_ld_base + ci * HMX_FP16_TILE_N_ELMS * 2;
+                    const HVX_Vector * pv_s_in0     = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
+                    const HVX_Vector * pv_s_in1     = pv_s_in0 + 16;
+
+                    HVX_VectorPair vp_s_dual_row = Q6_W_vdeal_VVR(*pv_s_in1, *pv_s_in0, -2);
+                    my_row_buf0[ci]              = Q6_V_lo_W(vp_s_dual_row);
+                    my_row_buf1[ci]              = Q6_V_hi_W(vp_s_dual_row);
+                }
+            }
+
+            // Apply mask & compute rowmax(S)
+            HVX_Vector v_slope0 = Q6_V_vzero();
+            HVX_Vector v_slope1 = Q6_V_vzero();
+            if (has_alibi) {
+                v_slope0 = hvx_vec_repl_f16(Q6_V_vror_VR(v_slopes, r_vec_off * 2));
+                v_slope1 = (r + 1 < n_rows_g) ? hvx_vec_repl_f16(Q6_V_vror_VR(v_slopes, (r_vec_off + 1) * 2)) : Q6_V_vzero();
+            }
+
+            const HVX_Vector v_threshold = Q6_Vh_vsplat_R(0xcc00);  // fp16 -16.0
+
+            HVX_Vector v_s_rowmax0 = v_neg_inf;
+            HVX_Vector v_s_rowmax1 = v_neg_inf;
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                size_t         ci          = c / 64;
+                const size_t   ne          = hex_smin(kv_rows - c, 64);
+                HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16));
+
+                if (has_mask) {
+                    HVX_Vector v_mask0, v_mask1;
+
+                    if (mask_broadcast) {
+                        if (is_g1) {
+                            const size_t qi0 = r + 0;
+                            v_mask0 = *(const HVX_Vector *) (args->mask_vtcm + qi0 * args->mask_vtcm_row_stride + c);
+                            v_mask1 = v_neg_inf;
+                            if (r + 1 < n_rows_g) {
+                                const size_t qi1 = r + 1;
+                                v_mask1 = *(const HVX_Vector *) (args->mask_vtcm + qi1 * args->mask_vtcm_row_stride + c);
+                            }
+                        } else {
+                            const size_t qi0 = fastdiv(r + 0, &factx->div_G);
+                            v_mask0 = *(const HVX_Vector *) (args->mask_vtcm + qi0 * args->mask_vtcm_row_stride + c);
+                            v_mask1 = v_neg_inf;
+                            if (r + 1 < n_rows_g) {
+                                const size_t qi1 = fastdiv(r + 1, &factx->div_G);
+                                if (qi1 == qi0) {
+                                    v_mask1 = v_mask0;
+                                } else {
+                                    v_mask1 = *(const HVX_Vector *) (args->mask_vtcm + qi1 * args->mask_vtcm_row_stride + c);
+                                }
+                            }
+                        }
+                    } else {
+                        // Head-dependent mask: pre-interleaved per row r.
+                        const size_t r0 = r + 0;
+                        v_mask0 = *(const HVX_Vector *) (args->mask_vtcm + r0 * args->mask_vtcm_row_stride + c);
+                        v_mask1 = v_neg_inf;
+                        if (r + 1 < n_rows_g) {
+                            const size_t r1 = r + 1;
+                            v_mask1 = *(const HVX_Vector *) (args->mask_vtcm + r1 * args->mask_vtcm_row_stride + c);
+                        }
+                    }
+
+                    // Threshold: mask values below -16.0 are treated as -inf (causal mask).
+                    HVX_VectorPred q_keep0 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask0, v_threshold), q_tail_keep);
+                    HVX_VectorPred q_keep1 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask1, v_threshold), q_tail_keep);
+
+                    // Scale mask values by log2(e) for base-2 calculations
+                    const HVX_Vector v_log2e = hvx_vec_splat_f16(EXP_LOG2E_F);
+                    HVX_Vector v_mask0_scaled = hvx_vec_mul_f16_f16(v_mask0, v_log2e);
+                    HVX_Vector v_mask1_scaled = hvx_vec_mul_f16_f16(v_mask1, v_log2e);
+
+                    if (has_alibi) {
+                        HVX_Vector v_sm0 = hvx_vec_mul_f16_f16(v_mask0_scaled, v_slope0);
+                        HVX_Vector v_sm1 = hvx_vec_mul_f16_f16(v_mask1_scaled, v_slope1);
+                        my_row_buf0[ci]  = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_sm0), v_neg_inf);
+                        my_row_buf1[ci]  = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_sm1), v_neg_inf);
+                    } else {
+                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_mask0_scaled), v_neg_inf);
+                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_mask1_scaled), v_neg_inf);
+                    }
+                } else {
+                    if (ne < 64) {
+                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf0[ci], v_neg_inf);
+                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf1[ci], v_neg_inf);
+                    }
+                }
+
+                v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]);
+                v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]);
+            }
+
+            v_s_rowmax0 = hvx_vec_reduce_max_f16(v_s_rowmax0);
+            v_s_rowmax1 = hvx_vec_reduce_max_f16(v_s_rowmax1);
+
+            // Splat m_prev[r], m_prev[r+1] from the float per-row accumulators and convert to fp16 vectors
+            HVX_Vector v_m_prev0, v_m_prev1;
+            if (r_vec_off < 32) {
+                HVX_Vector v0 = hvx_vec_repl_f32(Q6_V_vror_VR(m_prev_v0, r_vec_off * 4));
+                v_m_prev0 = hvx_vec_f32_to_f16(v0, v0);
+                if (r + 1 < n_rows_g) {
+                    HVX_Vector v1 = hvx_vec_repl_f32(Q6_V_vror_VR(m_prev_v0, (r_vec_off + 1) * 4));
+                    v_m_prev1 = hvx_vec_f32_to_f16(v1, v1);
+                } else {
+                    v_m_prev1 = Q6_V_vzero();
+                }
+            } else {
+                HVX_Vector v0 = hvx_vec_repl_f32(Q6_V_vror_VR(m_prev_v1, (r_vec_off - 32) * 4));
+                v_m_prev0 = hvx_vec_f32_to_f16(v0, v0);
+                if (r + 1 < n_rows_g) {
+                    HVX_Vector v1 = hvx_vec_repl_f32(Q6_V_vror_VR(m_prev_v1, (r_vec_off + 1 - 32) * 4));
+                    v_m_prev1 = hvx_vec_f32_to_f16(v1, v1);
+                } else {
+                    v_m_prev1 = Q6_V_vzero();
+                }
+            }
+
+            HVX_Vector v_dup_m0 = Q6_Vhf_vmax_VhfVhf(v_m_prev0, v_s_rowmax0);
+            HVX_Vector v_dup_m1 = Q6_Vhf_vmax_VhfVhf(v_m_prev1, v_s_rowmax1);
+
+            // Insert row r, r+1 rowmax into rowmax_acc_v
+            {
+                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
+                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
+                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
+                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
+                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
+                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane0, v_dup_m0, rowmax_acc_v);
+                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane1, v_dup_m1, rowmax_acc_v);
+            }
+
+            // Compute P = exp(S - m_new)
+            const HVX_Vector v_zero      = Q6_V_vzero();
+            HVX_Vector       v_p_rowsum0 = v_zero;
+            HVX_Vector       v_p_rowsum1 = v_zero;
+
+            for (size_t c = 0; c < kv_rows; c += 64) {
+                size_t     ci           = c / 64;
+                HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0);
+                HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1);
+
+                HVX_Vector v_p_row0_hf  = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m0));
+                HVX_Vector v_p_row1_hf  = hvx_vec_exp2_f16(Q6_Vhf_equals_Vqf16(v_s_minus_m1));
+                __fp16 *     out_dual_tile = p_st_base + (c / 64) * HMX_FP16_TILE_N_ELMS * 2;
+                HVX_Vector * pv_p_out0     = ((HVX_Vector *) out_dual_tile) + r1 / 2;
+                HVX_Vector * pv_p_out1     = pv_p_out0 + 16;
+
+                HVX_VectorPair vp_p_dual = Q6_W_vshuff_VVR(v_p_row1_hf, v_p_row0_hf, -2);
+                *pv_p_out0               = Q6_V_lo_W(vp_p_dual);
+                *pv_p_out1               = Q6_V_hi_W(vp_p_dual);
+
+                HVX_VectorPair vp_p0 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf);
+                HVX_VectorPair vp_p1 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf);
+
+                v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0), Q6_V_hi_W(vp_p0)));
+                v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1), Q6_V_hi_W(vp_p1)));
+            }
+
+            HVX_Vector rowsum0_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum0));
+            HVX_Vector rowsum1_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum1));
+            {
+                HVX_Vector rv0_v = hvx_vec_f32_to_f16(rowsum0_sf, rowsum0_sf);
+                HVX_Vector rv1_v = hvx_vec_f32_to_f16(rowsum1_sf, rowsum1_sf);
+
+                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
+                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
+                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
+                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
+                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
+                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane0, rv0_v, rowsum_acc_v);
+                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane1, rv1_v, rowsum_acc_v);
+            }
+        }
+
+        // Inline fa_ml_update_and_build_d for this vector (lock-free and in parallel)
+        HVX_VectorPair rowmax_acc_pair = hvx_vec_f16_to_f32(rowmax_acc_v);
+        HVX_Vector     v_rowmax_acc_f32_0 = Q6_V_lo_W(rowmax_acc_pair);
+        HVX_Vector     v_rowmax_acc_f32_1 = Q6_V_hi_W(rowmax_acc_pair);
+
+        HVX_Vector v_m_curr0 = Q6_Vsf_vmax_VsfVsf(m_prev_v0, v_rowmax_acc_f32_0);
+        HVX_Vector v_m_curr1 = Q6_Vsf_vmax_VsfVsf(m_prev_v1, v_rowmax_acc_f32_1);
+
+        HVX_Vector v_m_diff0 = HVX_OP_SUB_F32(m_prev_v0, v_m_curr0);
+        HVX_Vector v_m_diff1 = HVX_OP_SUB_F32(m_prev_v1, v_m_curr1);
+
+        HVX_Vector v_m_diff_f16 = hvx_vec_f32_to_f16(v_m_diff0, v_m_diff1);
+        HVX_Vector exp_m_diff_f16 = hvx_vec_exp2_f16(v_m_diff_f16);
+
+        HVX_VectorPair exp_m_diff_pair = hvx_vec_f16_to_f32(exp_m_diff_f16);
+        HVX_Vector exp_m_diff0 = Q6_V_lo_W(exp_m_diff_pair);
+        HVX_Vector exp_m_diff1 = Q6_V_hi_W(exp_m_diff_pair);
+
+        HVX_VectorPair rowsum_acc_pair = hvx_vec_f16_to_f32(rowsum_acc_v);
+        HVX_Vector     v_rowsum_acc_f32_0 = Q6_V_lo_W(rowsum_acc_pair);
+        HVX_Vector     v_rowsum_acc_f32_1 = Q6_V_hi_W(rowsum_acc_pair);
+
+        HVX_Vector v_l_curr0;
+        HVX_Vector v_l_curr1;
+        if (args->kv_start == 0 && factx->sinks != NULL) {
+            // First KV block with sinks: m_prev holds the seeded sink value (not -inf),
+            // so exp_m_diff = exp2(sink - m_curr) is the sink's contribution to the
+            // denominator. l_prev is 0 here, so add exp_m_diff directly instead of
+            // multiplying the (uninitialized) l_prev term.
+            v_l_curr0 = HVX_OP_ADD_F32(exp_m_diff0, v_rowsum_acc_f32_0);
+            v_l_curr1 = HVX_OP_ADD_F32(exp_m_diff1, v_rowsum_acc_f32_1);
+        } else {
+            HVX_Vector l_prev_v0 = factx->vtcm_l_vec[r_vec_idx * 2 + 0];
+            HVX_Vector l_prev_v1 = factx->vtcm_l_vec[r_vec_idx * 2 + 1];
+            v_l_curr0 = HVX_OP_ADD_F32(HVX_OP_MUL_F32(l_prev_v0, exp_m_diff0), v_rowsum_acc_f32_0);
+            v_l_curr1 = HVX_OP_ADD_F32(HVX_OP_MUL_F32(l_prev_v1, exp_m_diff1), v_rowsum_acc_f32_1);
+        }
+
+        factx->vtcm_m_vec[r_vec_idx * 2 + 0] = v_m_curr0;
+        factx->vtcm_m_vec[r_vec_idx * 2 + 1] = v_m_curr1;
+        factx->vtcm_l_vec[r_vec_idx * 2 + 0] = v_l_curr0;
+        factx->vtcm_l_vec[r_vec_idx * 2 + 1] = v_l_curr1;
+
+        // Build diagonal tile D = diag(exp(m_diff))
+        const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
+        const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
+        HVX_Vector           v_exp_m_diff = exp_m_diff_f16;
+
+        size_t t0 = r_vec_idx * 2;
+        if (t0 < args->n_row_tiles) {
+            const HVX_Vector v_content = v_exp_m_diff;
+            __fp16 *         out_base  = factx->vtcm_d_tiles + t0 * (args->n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
+        }
+
+        size_t t1 = r_vec_idx * 2 + 1;
+        if (t1 < args->n_row_tiles) {
+            const HVX_Vector v_content = Q6_V_vror_VR(v_exp_m_diff, 64);
+            __fp16 *         out_base  = factx->vtcm_d_tiles + t1 * (args->n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
+        }
+    }
+    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_FA_SFM, (uint16_t) (args->q_start * G + vec_start * 64));
+}
+
+static void fa_softmax_thread_nomask(unsigned int n, unsigned int i, void * data) {
+    fa_softmax_impl(n, i, data,
+                    /*has_mask=*/false,
+                    /*mask_broadcast=*/false,
+                    /*is_g1=*/false,
+                    /*has_alibi=*/false,
+                    /*has_softcap=*/false);
+}
+
+static void fa_softmax_thread_mask_broadcast_g1(unsigned int n, unsigned int i, void * data) {
+    fa_softmax_impl(n, i, data,
+                    /*has_mask=*/true,
+                    /*mask_broadcast=*/true,
+                    /*is_g1=*/true,
+                    /*has_alibi=*/false,
+                    /*has_softcap=*/false);
+}
+
+static void fa_softmax_thread_mask_broadcast_gn(unsigned int n, unsigned int i, void * data) {
+    fa_softmax_impl(n, i, data,
+                    /*has_mask=*/true,
+                    /*mask_broadcast=*/true,
+                    /*is_g1=*/false,
+                    /*has_alibi=*/false,
+                    /*has_softcap=*/false);
+}
+
+static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
+    fa_softmax_args_t *     args  = (fa_softmax_args_t *) data;
+    struct hmx_fa_context * factx = args->factx;
+
+    const bool has_mask       = (args->mask != NULL);
+    const bool mask_broadcast = factx->mask_broadcast;
+    const bool is_g1          = (args->G == 1);
+    const bool has_alibi      = args->has_alibi;
+    const bool has_softcap    = (factx->logit_softcap != 0.0f);
+
+    fa_softmax_impl(n, i, data, has_mask, mask_broadcast, is_g1, has_alibi, has_softcap);
+}
+
+static __attribute__((noinline)) void fa_build_d_diag_inv_l(struct hmx_fa_context * factx,
+                                                            size_t                  n_row_tiles,
+                                                            size_t                  n_row_tiles_g_br) {
+    const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
+    const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
+    const HVX_Vector     one       = hvx_vec_splat_f32(1.0f);
+
+    HVX_Vector v_content = Q6_V_vzero();
+    for (size_t i = 0; i < n_row_tiles; ++i) {
+        if ((i % 2) == 0) {
+            HVX_Vector inv_lo = HVX_OP_MUL_F32(one, hvx_vec_inverse_f32(factx->vtcm_l_vec[i]));
+            HVX_Vector inv_hi = (i + 1 < n_row_tiles) ? HVX_OP_MUL_F32(one, hvx_vec_inverse_f32(factx->vtcm_l_vec[i + 1])) : Q6_V_vzero();
+            v_content = hvx_vec_f32_to_f16(inv_lo, inv_hi);
+        } else {
+            v_content = Q6_V_vror_VR(v_content, 64);
+        }
+
+        __fp16 * out_base = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+        Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
+    }
+}
+
+static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx,
+                                         fa_softmax_args_t *     sargs,
+                                         size_t                  n_row_tiles,
+                                         size_t                  n_row_tiles_g_br) {
+    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
+    const size_t n_row_vec_cnt = hmx_ceil_div(sargs->n_rows_g, 64);
+
+    worker_callback_t softmax_fn = fa_softmax_thread;
+    if (sargs->mask == NULL && factx->logit_softcap == 0.0f && !sargs->has_alibi) {
+        softmax_fn = fa_softmax_thread_nomask;
+    } else if (sargs->mask != NULL && factx->mask_broadcast && factx->logit_softcap == 0.0f && !sargs->has_alibi) {
+        if (sargs->G == 1) {
+            softmax_fn = fa_softmax_thread_mask_broadcast_g1;
+        } else {
+            softmax_fn = fa_softmax_thread_mask_broadcast_gn;
+        }
+    }
+
+    if (factx->n_threads > 1 && n_row_vec_cnt >= 2) {
+        uint32_t n_use = (uint32_t) hex_smin((size_t) factx->n_threads, n_row_vec_cnt);
+        sargs->thread_div = init_fastdiv_values(n_use);
+        worker_pool_run_func(wp, softmax_fn, sargs, n_use);
+    } else {
+        softmax_fn(1, 0, sargs);
+    }
+}
+
+// ============================================================================
+// HMX job structs and worker functions
+// ============================================================================
+
+typedef struct {
+    const __fp16 * q_tiles;
+    const __fp16 * k_tiles;
+    __fp16 *       s_tiles;
+    size_t         n_row_tiles;
+    size_t         n_col_tiles;
+    size_t         n_dot_tiles;  // DK / 32
+    size_t         n_tiles_per_bc;
+    uint8_t *      hmx_scales;
+} hmx_fa_qk_job_t;
+
+static void hmx_fa_qk_dot_worker(void * data) {
+    hmx_fa_qk_job_t * job            = (hmx_fa_qk_job_t *) data;
+    const size_t      n_row_tiles    = job->n_row_tiles;
+    const size_t      n_col_tiles    = job->n_col_tiles;
+    const size_t      n_dot_tiles    = job->n_dot_tiles;
+    const size_t      n_tiles_per_bc = job->n_tiles_per_bc;
+    const __fp16 * restrict q_tiles  = job->q_tiles;
+    const __fp16 * restrict k_tiles  = job->k_tiles;
+    __fp16 * restrict s_tiles        = job->s_tiles;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < n_col_tiles; ++c) {
+            const __fp16 * row_tiles = q_tiles + r * HMX_FP16_TILE_N_ROWS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
+            const __fp16 * col_tiles = k_tiles + c * HMX_FP16_TILE_N_COLS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
+            __fp16 *       out_tile  = s_tiles + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
+
+            hmx_fa_qk_dot_tile(row_tiles, col_tiles, out_tile, n_dot_tiles);
+        }
+    }
+}
+
+typedef struct {
+    __fp16 *       o_curr;
+    const __fp16 * o_prev;
+    const __fp16 * p_tiles;
+    const __fp16 * v_tiles;
+    const __fp16 * d_tiles;
+    uint8_t *      hmx_scales;
+    size_t         n_row_tiles;
+    size_t         n_col_tiles;
+    size_t         n_row_tiles_g_br;
+    size_t         n_tiles_per_bc;
+    size_t         DV;
+} hmx_fa_o_update_job_t;
+
+static void hmx_fa_o_update_worker(void * data) {
+    hmx_fa_o_update_job_t * job              = (hmx_fa_o_update_job_t *) data;
+    const size_t            n_row_tiles      = job->n_row_tiles;
+    const size_t            n_col_tiles      = job->n_col_tiles;
+    const size_t            n_row_tiles_g_br = job->n_row_tiles_g_br;
+    const size_t            n_tiles_per_bc   = job->n_tiles_per_bc;
+    const size_t            DV_tiles         = job->DV / 32;
+    const __fp16 * restrict d_tiles          = job->d_tiles;
+    const __fp16 * restrict p_tiles          = job->p_tiles;
+    const __fp16 * restrict v_tiles          = job->v_tiles;
+    const __fp16 * restrict o_prev           = job->o_prev;
+    __fp16 * restrict o_curr                 = job->o_curr;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(DV_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < DV_tiles; ++c) {
+            // D[r,r] @ O_prev[r,c] — only the diagonal tile
+            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * p_tile_in = p_tiles + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * v_tile_in = v_tiles + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+            __fp16 * o_tile_out = o_curr + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+
+            hmx_fa_o_update_tile(d_diag, o_rc, p_tile_in, v_tile_in, o_tile_out, n_col_tiles);
+        }
+    }
+}
+
+typedef struct {
+    __fp16 *       o_curr;   // output (row-major tile layout)
+    const __fp16 * o_prev;   // input (column-major tile layout)
+    const __fp16 * d_tiles;  // diag(1/l) tiles
+    uint8_t *      hmx_scales;
+    size_t         n_row_tiles;
+    size_t         n_row_tiles_g_br;
+    size_t         DV;
+} hmx_fa_o_norm_job_t;
+
+static void hmx_fa_o_norm_worker(void * data) {
+    hmx_fa_o_norm_job_t * job              = (hmx_fa_o_norm_job_t *) data;
+    const size_t          n_row_tiles      = job->n_row_tiles;
+    const size_t          n_row_tiles_g_br = job->n_row_tiles_g_br;
+    const size_t          DV_tiles         = job->DV / 32;
+    const __fp16 * restrict d_tiles        = job->d_tiles;
+    const __fp16 * restrict o_prev         = job->o_prev;
+    __fp16 * restrict o_curr               = job->o_curr;
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(DV_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *) job->hmx_scales);
+    for (size_t r = 0; r < n_row_tiles; ++r) {
+        for (size_t c = 0; c < DV_tiles; ++c) {
+            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+            __fp16 *       o_out  = o_curr + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
+
+            hmx_fa_o_norm_tile(d_diag, o_rc, o_out);
+        }
+    }
+}
+
+// Populate per-GQA-row ALiBi slopes for a given KV head.
+static __attribute__((noinline)) void fa_compute_slopes(
+                              const struct hmx_fa_context * factx,
+                              uint32_t                      kv_head,
+                              size_t                        n_rows_g) {
+    __fp16 * slopes = factx->vtcm_slopes;
+    if (factx->max_bias == 0.0f) {
+        hvx_splat_f16_a(slopes, 1.0f, n_rows_g);
+        return;
+    }
+
+    const uint32_t G           = factx->G;
+    const uint32_t n_head_log2 = factx->n_head_log2;
+    const float    m0          = factx->m0;
+    const float    m1          = factx->m1;
+
+    __fp16 temp_slopes[512] __attribute__((aligned(128)));
+    if (G <= 32) {
+        // Fast path: Compute G unique slope values in vector registers
+        HVX_Vector v_val = hvx_alibi_slopes(kv_head, G, n_head_log2, m0, m1);
+
+        __fp16 temp_slopes_aligned[64] __attribute__((aligned(128)));
+        hvx_vmem(temp_slopes_aligned) = hvx_vec_f32_to_f16(v_val, Q6_V_vzero());
+
+        for (uint32_t i = 0; i < G; ++i) {
+            temp_slopes[i] = temp_slopes_aligned[i];
+        }
+    } else {
+        // Fallback path: G > 32 (rare configurations)
+        for (uint32_t i = 0; i < G; ++i) {
+            temp_slopes[i] = (__fp16)alibi_slope(kv_head * G + i, n_head_log2, m0, m1);
+        }
+    }
+
+    // Allocate stack buffer to avoid scalar writes to VTCM (which generates L2 misses)
+    __fp16 local_slopes[n_rows_g] __attribute__((aligned(128)));
+    for (size_t r = 0; r < n_rows_g; ++r) {
+        local_slopes[r] = temp_slopes[fastmodulo(r, G, &factx->div_G)];
+    }
+
+    // Copy to VTCM slopes using HVX block copy (both are aligned to 128 bytes)
+    hvx_copy_f16_aa((uint8_t *)slopes, (const uint8_t *)local_slopes, n_rows_g);
+}
+
+static void fa_push_mask_dma_gqa(
+    dma_queue *               dma,
+    const struct htp_tensor * mask,
+    uint32_t                  q_start,
+    uint32_t                  im3,
+    uint32_t                  kv_start,
+    uint32_t                  kv_head,
+    uint32_t                  G,
+    uint32_t                  m_line_bytes,
+    uint32_t                  kv_rows,
+    uint32_t                  n_q_rows,
+    struct hmx_fa_context *   factx
+) {
+    for (uint32_t g = 0; g < G; ++g) {
+        const uint32_t h_idx = kv_head * G + g;
+        const uint32_t im2 = fastmodulo(h_idx, mask->ne[2], &factx->src3_div2);
+        const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] +
+                                 im2 * mask->nb[2] + im3 * mask->nb[3] + kv_start * sizeof(__fp16);
+        uint8_t * ms_dst = (uint8_t *) factx->vtcm_mask_buf + g * m_line_bytes;
+        dma_queue_push(dma, dma_make_ptr(ms_dst, ms_src), G * m_line_bytes, mask->nb[1], kv_rows * sizeof(__fp16), n_q_rows);
+    }
+}
+
+static void fa_pop_mask_dma_gqa(dma_queue * dma, uint32_t G) {
+    for (uint32_t g = 0; g < G; ++g) {
+        dma_queue_pop(dma);
+    }
+}
+
+// ============================================================================
+// Core HMX flash attention algorithm (GQA-merged)
+// ============================================================================
+
+int hmx_flash_attn_ext(struct htp_ops_context * octx) {
+    struct htp_thread_trace * tr_hvx = octx->ctx ? &octx->ctx->trace[0] : NULL;
+    struct htp_thread_trace * tr_hmx = octx->ctx ? &octx->ctx->trace[HTP_MAX_NTHREADS] : NULL;
+    const struct htp_tensor * q    = octx->src[0];
+    const struct htp_tensor * k    = octx->src[1];
+    const struct htp_tensor * v    = octx->src[2];
+    const struct htp_tensor * mask = (octx->src[3] && octx->src[3]->data) ? octx->src[3] : NULL;
+    const struct htp_tensor * dst  = octx->dst;
+
+    struct htp_context * const ctx = octx->ctx;
+
+    if (!ctx->hmx_enabled) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    // Dimensions
+    const uint32_t neq0 = q->ne[0];  // head_dim (DK)
+    const uint32_t neq1 = q->ne[1];  // n_tokens
+    const uint32_t neq2 = q->ne[2];  // n_heads
+    const uint32_t neq3 = q->ne[3];  // n_seqs
+
+    const uint32_t nek0 = k->ne[0];  // head_dim
+    const uint32_t nek1 = k->ne[1];  // kv_len
+
+    const uint32_t nev0 = v->ne[0];  // head_dim (DV)
+
+    const uint32_t DK = neq0;
+    const uint32_t DV = nev0;
+
+    // HMX requires head_dim to be multiple of 32
+    if (DK % 32 != 0 || DV % 32 != 0) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    const struct htp_fa_kernel_params * kparams = (const struct htp_fa_kernel_params *) octx->kernel_params;
+    const uint32_t n_kv_heads = k->ne[2];
+
+    // ======== Build context ========
+    struct hmx_fa_context factx;
+    memset(&factx, 0, sizeof(factx));
+    factx.octx           = octx;
+    factx.sinks          = octx->src[4];  // NULL if this op has no attention sinks
+    factx.n_threads      = kparams->n_threads;
+    factx.DK             = DK;
+    factx.DV             = DV;
+    factx.n_kv           = nek1;
+    factx.n_kv_heads     = n_kv_heads;
+    factx.n_heads        = neq2;
+    factx.G              = kparams->G;
+    factx.div_G          = kparams->u.hmx.div_G;
+    factx.neq1           = neq1;
+    factx.Br             = kparams->Br;
+    factx.Bc             = kparams->Bc;
+    factx.g_br           = kparams->u.hmx.g_br;
+    factx.n_kv_blocks    = kparams->n_kv_blocks;
+    factx.is_q_fp32      = (kparams->is_q_fp32 != 0);
+    factx.is_dst_fp32    = (kparams->is_dst_fp32 != 0);
+    factx.pipeline       = (kparams->u.hmx.pipeline != 0);
+    factx.mask_broadcast = (kparams->u.hmx.mask_broadcast != 0);
+    if (mask) {
+        factx.src3_div2  = kparams->src3_div2;
+        factx.src3_div3  = kparams->src3_div3;
+    }
+
+    if (kparams->logit_softcap == 0.0f) {
+        factx.scale = (__fp16) (kparams->scale * EXP_LOG2E_F);  // log2(e)
+    } else {
+        factx.scale = (__fp16) kparams->scale;
+    }
+    factx.max_bias      = kparams->max_bias;
+    factx.logit_softcap = (__fp16) (kparams->logit_softcap * EXP_LOG2E_F);
+
+    factx.n_head_log2 = kparams->n_head_log2;
+    factx.m0          = kparams->m0;
+    factx.m1          = kparams->m1;
+
+    const uint32_t Br = factx.Br;
+    const uint32_t Bc = factx.Bc;
+    const uint32_t g_br = factx.g_br;
+    const bool pipeline = factx.pipeline;
+    const uint32_t n_threads = factx.n_threads;
+    const uint32_t G = factx.G;
+
+    // ======== VTCM allocation (GQA-aware) ========
+    const size_t size_k_row        = DK * sizeof(__fp16);
+    const size_t size_v_row        = DV * sizeof(__fp16);
+    const size_t size_k_row_padded = hex_round_up(size_k_row, 128);
+    const size_t size_v_row_padded = hex_round_up(size_v_row, 128);
+
+    const size_t q_tile_bytes  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);
+    const size_t o_tile_bytes  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);
+    const size_t k_dma_bytes   = hex_align_up(Bc * size_k_row_padded, 4096);
+    const size_t v_dma_bytes   = hex_align_up(Bc * size_v_row_padded, 4096);
+    const size_t k_tile_bytes  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);
+    const size_t v_tile_bytes  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);
+    const size_t s_tile_bytes  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);
+    const size_t d_tile_bytes  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);
+    const size_t col_vec_bytes = hex_align_up(g_br * sizeof(float), 256);
+    const size_t row_vec_bytes = hex_align_up(Bc * sizeof(__fp16), 256);
+    const size_t m_line_bytes  = hex_align_up(Bc * sizeof(__fp16), 128);
+    const size_t m_buf_bytes   = hex_align_up(Br * m_line_bytes, 4096) * HMX_FA_DMA_CACHE_SIZE;
+    const size_t slopes_bytes  = hex_align_up(g_br * sizeof(__fp16), 128);
+
+    uint8_t * vtcm_cur = ctx->vtcm_base;
+
+    factx.vtcm_q_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, q_tile_bytes);
+    factx.vtcm_o_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
+    factx.vtcm_o_tiles[1]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
+    factx.vtcm_k_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
+    factx.vtcm_k_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
+    factx.vtcm_v_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
+    factx.vtcm_v_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
+    factx.vtcm_k_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_tile_bytes);
+    factx.vtcm_v_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    if (pipeline) {
+        factx.vtcm_v_tiles[1] = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
+    } else {
+        factx.vtcm_v_tiles[1] = NULL;
+    }
+    factx.vtcm_s_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
+    factx.vtcm_p_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
+    factx.vtcm_d_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, d_tile_bytes);
+    factx.vtcm_m_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_l_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_s_rowmax       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_p_rowsum       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
+    factx.vtcm_row_bufs       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, row_vec_bytes * 2 * n_threads);
+    factx.row_buf_stride      = row_vec_bytes / sizeof(HVX_Vector);
+    factx.vtcm_hmx_scales_id  = vtcm_seq_alloc(&vtcm_cur, 256);
+    factx.vtcm_hmx_scales_qk  = vtcm_seq_alloc(&vtcm_cur, 256);
+    factx.vtcm_mask_buf       = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, m_buf_bytes);
+    factx.mask_buf_row_stride = m_line_bytes / sizeof(__fp16);
+    factx.vtcm_slopes         = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, slopes_bytes);
+
+    dma_cache_init(&factx.m_cache, (uint8_t *) factx.vtcm_mask_buf, hex_align_up(Br * m_line_bytes, 4096), HMX_FA_DMA_CACHE_SIZE);
+
+    if ((size_t) (vtcm_cur - ctx->vtcm_base) > ctx->vtcm_size) {
+        return HTP_STATUS_VTCM_TOO_SMALL;
+    }
+
+    // ======== Initialize HMX output scales ========
+    hmx_init_column_scales(factx.vtcm_hmx_scales_id, Q6_V_vsplat_R(0x3c00)); // 1.0
+    hmx_init_column_scales(factx.vtcm_hmx_scales_qk, hvx_vec_splat_f16(factx.scale));
+
+    // ======== Skip compute if profiling ========
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
+        return HTP_STATUS_OK;
+    }
+
+    // ======== DMA setup ========
+    dma_queue * const dma = ctx->dma[0];
+
+    const size_t n_row_tiles_g_br = g_br / HMX_FP16_TILE_N_ROWS;
+    const size_t n_tiles_per_bc   = Bc / HMX_FP16_TILE_N_COLS;
+
+    const size_t qo_element_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16);
+
+    // ======== HMX lock strategy ========
+    if (!factx.pipeline) {
+        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
+    }
+
+    // ======== Reusable job descriptors for pipeline ========
+    hmx_fa_qk_job_t       qk_job;
+    hmx_fa_o_update_job_t ou_job;
+    hmx_fa_o_norm_job_t   on_job;
+
+    // ======== Main loop ========
+    for (uint32_t ib3 = 0; ib3 < neq3; ++ib3) {
+        const uint32_t im3 = mask ? fastmodulo(ib3, mask->ne[3], &factx.src3_div3) : 0;
+        for (uint32_t q_start = 0; q_start < neq1; q_start += Br) {
+            const uint32_t n_q_rows    = hex_smin(Br, neq1 - q_start);
+            const size_t   n_rows_g    = n_q_rows * G;
+            const size_t   g_br_actual = hex_align_up(n_rows_g, HMX_FP16_TILE_N_ROWS);
+            const size_t   n_row_tiles = g_br_actual / HMX_FP16_TILE_N_ROWS;
+
+            for (uint32_t kv_head = 0; kv_head < n_kv_heads; ++kv_head) {
+                const uint32_t ik2 = kv_head;
+                const uint32_t ik3 = ib3 / (neq3 / k->ne[3]);
+                const uint32_t iv2 = kv_head;
+                const uint32_t iv3 = ib3 / (neq3 / v->ne[3]);
+
+                // Prefetch first KV block
+                if (factx.n_kv_blocks > 0) {
+                    const uint32_t kv_rows0 = hex_smin(Bc, nek1);
+
+                    const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3];
+                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1],
+                                   size_k_row, kv_rows0);
+
+                    const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3];
+                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1],
+                                   size_v_row, kv_rows0);
+                }
+
+                // ---- Load Q block & Initialize per-block state ----
+                fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g);
+
+                __fp16 * o_tile_prev = factx.vtcm_o_tiles[0];
+                __fp16 * o_tile_curr = factx.vtcm_o_tiles[1];
+
+                // ---- KV block loop with DMA double-buffering ----
+                size_t buf_idx = 0;
+
+                htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_A_PREP, (uint16_t) q_start);
+                fa_compute_slopes(&factx, kv_head, n_rows_g);
+                htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_A_PREP, (uint16_t) q_start);
+
+                const size_t k_src_stride = size_k_row_padded / sizeof(__fp16);
+                const size_t v_src_stride = size_v_row_padded / sizeof(__fp16);
+
+                if (factx.pipeline) {
+                    // ==================================================================
+                    // Pipeline path
+                    // ==================================================================
+                    struct hmx_queue * hmx_q = ctx->hmx_queue;
+
+                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
+                        const uint32_t kv_start    = kv_blk * Bc;
+                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
+                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
+
+                        // Push mask DMA
+                        if (mask) {
+                            if (__builtin_expect(factx.mask_broadcast, true)) {
+                                const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + kv_start * sizeof(__fp16);
+                                dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows * sizeof(__fp16), n_q_rows);
+                            } else {
+                                fa_push_mask_dma_gqa(dma, mask, q_start, im3, kv_start, kv_head, G, m_line_bytes, kv_rows, n_q_rows, &factx);
+                            }
+                        }
+
+                        // Wait for current KV DMA
+                        dma_queue_pop(dma);  // K
+                        dma_queue_pop(dma);  // V
+
+                        // ---- Phase 1: K_int ----
+                        if (kv_blk > 0) {
+                            ou_job.o_curr           = o_tile_curr;
+                            ou_job.o_prev           = o_tile_prev;
+                            ou_job.p_tiles          = factx.vtcm_p_tiles;
+                            ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
+                            ou_job.d_tiles          = factx.vtcm_d_tiles;
+                            ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                            ou_job.n_row_tiles      = n_row_tiles;
+                            ou_job.n_col_tiles      = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS);
+                            ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                            ou_job.n_tiles_per_bc   = n_tiles_per_bc;
+                            ou_job.DV               = DV;
+                            hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
+                        }
+                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx, kv_start);
+
+                        // ---- Phase 2: qk_dot ----
+                        qk_job.q_tiles        = factx.vtcm_q_tiles;
+                        qk_job.k_tiles        = factx.vtcm_k_tiles;
+                        qk_job.s_tiles        = factx.vtcm_s_tiles;
+                        qk_job.n_row_tiles    = n_row_tiles;
+                        qk_job.n_col_tiles    = n_col_tiles;
+                        qk_job.n_dot_tiles    = DK / 32;
+                        qk_job.n_tiles_per_bc = n_tiles_per_bc;
+                        qk_job.hmx_scales     = factx.vtcm_hmx_scales_qk;
+                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job));
+
+                        if (kv_blk + 1 < factx.n_kv_blocks) {
+                            const uint32_t  prefetch_start = (kv_blk + 1) * Bc;
+                            const uint32_t  prefetch_rows  = hex_smin(Bc, nek1 - prefetch_start);
+                            const size_t    prefetch_buf   = 1 - buf_idx;
+                            const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3];
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows);
+                            const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3];
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows);
+                        }
+                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc, kv_start);
+
+                        if (kv_blk > 0) {
+                            hmx_queue_pop(hmx_q);
+                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                        }
+
+                        hmx_queue_pop(hmx_q);
+
+                        // ---- Phase 3: softmax + build_D ----
+                        __fp16 * current_mask_vtcm = NULL;
+                        if (mask) {
+                            if (__builtin_expect(factx.mask_broadcast, true)) {
+                                current_mask_vtcm = (__fp16 *) dma_queue_pop(dma).dst;
+                            } else {
+                                fa_pop_mask_dma_gqa(dma, G);
+                                current_mask_vtcm = factx.vtcm_mask_buf;
+                            }
+                        }
+
+                        fa_softmax_args_t sargs;
+                        memset(&sargs, 0, sizeof(sargs));
+                        sargs.factx                = &factx;
+                        sargs.kv_rows              = kv_rows;
+                        sargs.n_rows_g             = n_rows_g;
+                        sargs.n_col_tiles          = n_col_tiles;
+                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
+                        sargs.n_row_tiles          = n_row_tiles;
+                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
+                        sargs.Bc                   = Bc;
+                        sargs.G                    = G;
+                        sargs.kv_head              = kv_head;
+                        sargs.kv_start             = kv_start;
+                        sargs.q_start              = q_start;
+                        sargs.ib3                  = ib3;
+                        sargs.has_alibi            = (factx.max_bias != 0.0f);
+                        sargs.mask                 = mask;
+                        sargs.mask_vtcm            = current_mask_vtcm;
+                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
+                        sargs.slopes               = factx.vtcm_slopes;
+                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
+
+                        buf_idx = 1 - buf_idx;
+                    }
+
+                    // Epilogue
+                    if (factx.n_kv_blocks > 0) {
+                        const uint32_t last_blk = factx.n_kv_blocks - 1;
+                        const size_t last_cols  = hmx_ceil_div(hex_smin(Bc, nek1 - last_blk * Bc), HMX_FP16_TILE_N_COLS);
+                        ou_job.o_curr           = o_tile_curr;
+                        ou_job.o_prev           = o_tile_prev;
+                        ou_job.p_tiles          = factx.vtcm_p_tiles;
+                        ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
+                        ou_job.d_tiles          = factx.vtcm_d_tiles;
+                        ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                        ou_job.n_row_tiles      = n_row_tiles;
+                        ou_job.n_col_tiles      = last_cols;
+                        ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                        ou_job.n_tiles_per_bc   = n_tiles_per_bc;
+                        ou_job.DV               = DV;
+                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
+                        hmx_queue_pop(hmx_q);
+
+                        hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                    }
+
+                } else {
+                    // ==================================================================
+                    // Fallback path
+                    // ==================================================================
+                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
+                        const uint32_t kv_start    = kv_blk * Bc;
+                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
+                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
+                        dma_queue_pop(dma);  // K
+                        dma_queue_pop(dma);  // V
+
+                        if (mask) {
+                            if (__builtin_expect(factx.mask_broadcast, true)) {
+                                const uint8_t * ms_src = (const uint8_t *) mask->data + q_start * mask->nb[1] + im3 * mask->nb[3] + kv_start * sizeof(__fp16);
+                                dma_cache_push(dma, &factx.m_cache, ms_src, m_line_bytes, mask->nb[1], kv_rows * sizeof(__fp16), n_q_rows);
+                            } else {
+                                fa_push_mask_dma_gqa(dma, mask, q_start, im3, kv_start, kv_head, G, m_line_bytes, kv_rows, n_q_rows, &factx);
+                            }
+                        }
+                        if (kv_blk + 1 < factx.n_kv_blocks) {
+                            const uint32_t  prefetch_start = (kv_blk + 1) * Bc;
+                            const uint32_t  prefetch_rows  = hex_smin(Bc, nek1 - prefetch_start);
+                            const size_t    prefetch_buf   = 1 - buf_idx;
+                            const uint8_t * k_prefetch_src = (const uint8_t *) k->data + prefetch_start * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3];
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[prefetch_buf], k_prefetch_src), size_k_row_padded, k->nb[1], size_k_row, prefetch_rows);
+                            const uint8_t * v_prefetch_src = (const uint8_t *) v->data + prefetch_start * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3];
+                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[prefetch_buf], v_prefetch_src), size_v_row_padded, v->nb[1], size_v_row, prefetch_rows);
+                        }
+                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx, kv_start);
+
+                        {
+                            const size_t n_dot_tiles       = (size_t) (DK / 32);
+                            const __fp16 * restrict q_base = factx.vtcm_q_tiles;
+                            const __fp16 * restrict k_base = factx.vtcm_k_tiles;
+                            __fp16 * restrict s_base       = factx.vtcm_s_tiles;
+                            __builtin_assume(n_row_tiles > 0);
+                            __builtin_assume(n_col_tiles > 0);
+                            __builtin_assume(n_dot_tiles > 0);
+
+                            htp_trace_event_start(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_qk);
+                            for (size_t r = 0; r < n_row_tiles; ++r) {
+                                for (size_t c = 0; c < n_col_tiles; ++c) {
+                                    const __fp16 * row_tiles = q_base + r * HMX_FP16_TILE_N_ROWS * DK;
+                                    const __fp16 * col_tiles = k_base + c * HMX_FP16_TILE_N_COLS * DK;
+                                    __fp16 *       out_tile  = s_base + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
+
+                                    hmx_fa_qk_dot_tile(row_tiles, col_tiles, out_tile, n_dot_tiles);
+                                }
+                            }
+                            htp_trace_event_stop(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                        }
+
+                        // ---- Phase 3: softmax + build_D ----
+                        __fp16 * current_mask_vtcm = NULL;
+                        if (mask) {
+                            if (__builtin_expect(factx.mask_broadcast, true)) {
+                                current_mask_vtcm = (__fp16 *) dma_queue_pop(dma).dst;
+                            } else {
+                                fa_pop_mask_dma_gqa(dma, G);
+                                current_mask_vtcm = factx.vtcm_mask_buf;
+                            }
+                        }
+
+                        fa_softmax_args_t sargs;
+                        memset(&sargs, 0, sizeof(sargs));
+                        sargs.factx                = &factx;
+                        sargs.kv_rows              = kv_rows;
+                        sargs.n_rows_g             = n_rows_g;
+                        sargs.n_col_tiles          = n_col_tiles;
+                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
+                        sargs.n_row_tiles          = n_row_tiles;
+                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
+                        sargs.Bc                   = Bc;
+                        sargs.G                    = G;
+                        sargs.kv_head              = kv_head;
+                        sargs.kv_start             = kv_start;
+                        sargs.q_start              = q_start;
+                        sargs.ib3                  = ib3;
+                        sargs.has_alibi            = (factx.max_bias != 0.0f);
+                        sargs.mask                 = mask;
+                        sargs.mask_vtcm            = current_mask_vtcm;
+                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
+                        sargs.slopes               = factx.vtcm_slopes;
+                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
+                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc, kv_start);
+
+                        {
+                            const size_t DV_tiles           = (size_t) (DV / 32);
+                            const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
+                            const __fp16 * restrict p_base  = factx.vtcm_p_tiles;
+                            const __fp16 * restrict v_base  = factx.vtcm_v_tiles[0];
+                            const __fp16 * restrict op_base = o_tile_prev;
+                            __fp16 * restrict oc_base       = o_tile_curr;
+                            __builtin_assume(n_row_tiles > 0);
+                            __builtin_assume(n_col_tiles > 0);
+                            __builtin_assume(DV_tiles > 0);
+
+                            htp_trace_event_start(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
+                            for (size_t r = 0; r < n_row_tiles; ++r) {
+                                for (size_t c = 0; c < DV_tiles; ++c) {
+                                    const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+                                    const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+                                    const __fp16 * p_tile_in = p_base + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+                                    const __fp16 * v_tile_in = v_base + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
+                                    __fp16 * o_tile_out = oc_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+
+                                    hmx_fa_o_update_tile(d_diag, o_rc, p_tile_in, v_tile_in, o_tile_out, n_col_tiles);
+                                }
+                            }
+                            htp_trace_event_stop(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
+                        }
+
+                        buf_idx = 1 - buf_idx;
+                    }
+                }
+
+                // ---- Final normalization ----
+                {
+                    htp_trace_event_start(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start);
+                    fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br);
+                    htp_trace_event_stop(tr_hvx, HTP_TRACE_EVT_HVX_O_PROC, (uint16_t) q_start);
+
+                    if (factx.pipeline) {
+                        on_job.o_curr           = o_tile_curr;
+                        on_job.o_prev           = o_tile_prev;
+                        on_job.d_tiles          = factx.vtcm_d_tiles;
+                        on_job.hmx_scales       = factx.vtcm_hmx_scales_id;
+                        on_job.n_row_tiles      = n_row_tiles;
+                        on_job.n_row_tiles_g_br = n_row_tiles_g_br;
+                        on_job.DV               = DV;
+                        hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_fa_o_norm_worker, &on_job));
+                        hmx_queue_pop(ctx->hmx_queue);
+                    } else {
+                        const size_t DV_tiles           = (size_t) (DV / 32);
+                        const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
+                        const __fp16 * restrict op_base = o_tile_prev;
+                        __fp16 * restrict oc_base       = o_tile_curr;
+                        __builtin_assume(n_row_tiles > 0);
+                        __builtin_assume(DV_tiles > 0);
+
+                        htp_trace_event_start(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                        Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
+                        for (size_t r = 0; r < n_row_tiles; ++r) {
+                            for (size_t c = 0; c < DV_tiles; ++c) {
+                                const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
+                                const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
+                                __fp16 *       o_out  = oc_base + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
+
+                                hmx_fa_o_norm_tile(d_diag, o_rc, o_out);
+                            }
+                        }
+                        htp_trace_event_stop(tr_hmx, HTP_TRACE_EVT_HMX_COMP, (uint16_t) q_start);
+                    }
+                }
+
+                // ---- Store O block ----
+                fa_phase_o_store(&factx, dst, o_tile_curr, q_start, kv_head, ib3, n_rows_g);
+            }
+        }
+    }
+
+    if (factx.pipeline) {
+        hmx_queue_suspend(ctx->hmx_queue);
+    } else {
+        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+    }
+
+    return HTP_STATUS_OK;
 }
 
 int op_flash_attn_ext(struct htp_ops_context * octx) {
@@ -634,13 +2022,14 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    // HMX path: head_dim multiple of 64, F16 KV, and no sinks
-    if (k->type == HTP_TYPE_F16 && v->type == HTP_TYPE_F16 && k->ne[0] % 64 == 0 && v->ne[0] % 64 == 0 && octx->src[4] == NULL) {
-        int ret = hmx_flash_attn_ext(octx);
-        if (ret == HTP_STATUS_OK) {
-            return ret;
-        }
-        // VTCM too small or other failure -> fall through to HVX path
+    const struct htp_fa_kernel_params * kparams = (const struct htp_fa_kernel_params *) octx->kernel_params;
+
+    if (kparams->kernel_type == HTP_FA_KERNEL_UNSUPPORTED) {
+        return HTP_STATUS_NO_SUPPORT;
+    }
+
+    if (kparams->kernel_type == HTP_FA_KERNEL_HMX) {
+        return hmx_flash_attn_ext(octx);
     }
 
     struct htp_fa_context factx;
@@ -648,94 +2037,68 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
 
     factx.t_start = HAP_perf_get_qtimer_count();
 
-    factx.src0_div21 = init_fastdiv_values(q->ne[2] * q->ne[1]);
-    factx.src0_div1  = init_fastdiv_values(q->ne[1]);
+    factx.src0_div21 = kparams->u.hvx.src0_div21;
+    factx.src0_div1  = kparams->u.hvx.src0_div1;
 
-    factx.broadcast_rk2 = init_fastdiv_values(q->ne[2]/k->ne[2]);
-    factx.broadcast_rk3 = init_fastdiv_values(q->ne[3]/k->ne[3]);
-    factx.broadcast_rv2 = init_fastdiv_values(q->ne[2]/v->ne[2]);
-    factx.broadcast_rv3 = init_fastdiv_values(q->ne[3]/v->ne[3]);
+    factx.broadcast_rk2 = kparams->u.hvx.broadcast_rk2;
+    factx.broadcast_rk3 = kparams->u.hvx.broadcast_rk3;
+    factx.broadcast_rv2 = kparams->u.hvx.broadcast_rv2;
+    factx.broadcast_rv3 = kparams->u.hvx.broadcast_rv3;
 
     if (mask) {
-        factx.src3_div2 = init_fastdiv_values(mask->ne[2]);
-        factx.src3_div3 = init_fastdiv_values(mask->ne[3]);
+        factx.src3_div2 = kparams->src3_div2;
+        factx.src3_div3 = kparams->src3_div3;
     }
 
-    factx.is_q_fp32 = (q->type == HTP_TYPE_F32);
-    factx.size_q_row_padded = hex_round_up(q->ne[0] * (factx.is_q_fp32 ? 4 : 2), 128);
-    factx.size_k_row_padded = hex_round_up(k->ne[0] * sizeof(__fp16), 128);
-    factx.size_v_row_padded = hex_round_up(v->ne[0] * sizeof(__fp16), 128);
+    factx.is_q_fp32 = (kparams->is_q_fp32 != 0);
+    factx.size_q_row_padded = kparams->u.hvx.size_q_row_padded;
+    factx.size_k_row_padded = kparams->u.hvx.size_k_row_padded;
+    factx.size_v_row_padded = kparams->u.hvx.size_v_row_padded;
 
     size_t size_q_block = factx.size_q_row_padded * 1; // single row for now
     factx.size_k_block = factx.size_k_row_padded * FLASH_ATTN_BLOCK_SIZE;
     factx.size_v_block = factx.size_v_row_padded * FLASH_ATTN_BLOCK_SIZE;
     factx.size_m_block = hex_round_up(FLASH_ATTN_BLOCK_SIZE * sizeof(__fp16), 128);
 
-    factx.n_blocks = (k->ne[1] + FLASH_ATTN_BLOCK_SIZE - 1) / FLASH_ATTN_BLOCK_SIZE;
+    factx.n_blocks = kparams->n_kv_blocks;
 
-    float scale         = 1.0f;
-    float max_bias      = 0.0f;
-    float logit_softcap = 0.0f;
+    factx.scale = kparams->scale;
+    factx.max_bias = kparams->max_bias;
+    factx.logit_softcap = (__fp16) kparams->logit_softcap;
 
-    memcpy(&scale,         (float *) octx->op_params + 0, sizeof(float));
-    memcpy(&max_bias,      (float *) octx->op_params + 1, sizeof(float));
-    memcpy(&logit_softcap, (float *) octx->op_params + 2, sizeof(float));
-
-    if (logit_softcap != 0.0f) {
-        scale /= logit_softcap;
-    }
-
-    factx.scale = scale;
-    factx.max_bias = max_bias;
-    factx.logit_softcap = logit_softcap;
-
-    uint32_t n_head = q->ne[2];
-    factx.n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
-    factx.m0 = powf(2.0f, -(max_bias       ) / factx.n_head_log2);
-    factx.m1 = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2);
+    factx.n_head_log2 = kparams->n_head_log2;
+    factx.m0          = kparams->m0;
+    factx.m1          = kparams->m1;
 
+    const uint32_t n_head = q->ne[2];
     if (n_head > 512) {
         return HTP_STATUS_NO_SUPPORT;
     }
     for (uint32_t h = 0; h < n_head; ++h) {
-        factx.slopes[h] = (max_bias > 0.0f) ? alibi_slope(h, factx.n_head_log2, factx.m0, factx.m1) : 1.0f;
+        factx.slopes[h] = (__fp16) ((kparams->max_bias > 0.0f) ? alibi_slope(h, factx.n_head_log2, factx.m0, factx.m1) : 1.0f);
     }
 
     // total rows in q
-    const uint32_t neq0 = q->ne[0];
-    const uint32_t neq1 = q->ne[1];
-    const uint32_t neq2 = q->ne[2];
-    const uint32_t neq3 = q->ne[3];
-
-    factx.qrows = neq1*neq2*neq3;
-    factx.qrows_per_thread = (factx.qrows + octx->n_threads - 1) / octx->n_threads;
+    factx.qrows = kparams->qrows;
+    factx.qrows_per_thread = kparams->qrows_per_thread;
 
     size_t size_vkq_acc = hex_round_up(v->ne[0] * sizeof(float), 128); // VKQ32
 
-    octx->src0_spad.size_per_thread = size_q_block * 1;
-    octx->src1_spad.size_per_thread = factx.size_k_block * 2;
-    octx->src2_spad.size_per_thread = factx.size_v_block * 2;
-    octx->src3_spad.size_per_thread = mask ? factx.size_m_block * DMA_CACHE_MAX_SIZE : 0;
-    octx->dst_spad.size_per_thread  = size_vkq_acc;
+    factx.size_q_block = size_q_block;
+    factx.size_vkq_acc = size_vkq_acc;
 
-    octx->src0_spad.size = octx->src0_spad.size_per_thread * octx->n_threads;
-    octx->src1_spad.size = octx->src1_spad.size_per_thread * octx->n_threads;
-    octx->src2_spad.size = octx->src2_spad.size_per_thread * octx->n_threads;
-    octx->src3_spad.size = octx->src3_spad.size_per_thread * octx->n_threads;
-    octx->dst_spad.size  = octx->dst_spad.size_per_thread  * octx->n_threads;
+    uint8_t * vtcm_cur = octx->ctx->vtcm_base;
 
-    size_t total_spad = octx->src0_spad.size + octx->src1_spad.size + octx->src2_spad.size + octx->src3_spad.size + octx->dst_spad.size;
+    factx.spad_q = vtcm_seq_alloc(&vtcm_cur, size_q_block * octx->n_threads);
+    factx.spad_k = vtcm_seq_alloc(&vtcm_cur, factx.size_k_block * 2 * octx->n_threads);
+    factx.spad_v = vtcm_seq_alloc(&vtcm_cur, factx.size_v_block * 2 * octx->n_threads);
+    factx.spad_m = vtcm_seq_alloc(&vtcm_cur, (mask ? factx.size_m_block * HVX_FA_DMA_CACHE_SIZE : 0) * octx->n_threads);
+    factx.spad_a = vtcm_seq_alloc(&vtcm_cur, size_vkq_acc * octx->n_threads);
 
-    if (octx->ctx->vtcm_size < total_spad) {
+    if ((size_t) (vtcm_cur - octx->ctx->vtcm_base) > octx->ctx->vtcm_size) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    octx->src0_spad.data = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
-    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; octx->src1_spad.src = NULL;
-    octx->src2_spad.data = octx->src1_spad.data + octx->src1_spad.size; octx->src2_spad.src = NULL;
-    octx->src3_spad.data = octx->src2_spad.data + octx->src2_spad.size; octx->src3_spad.src = NULL;
-    octx->dst_spad.data  = octx->src3_spad.data + octx->src3_spad.size; octx->dst_spad.src  = NULL;
-
     if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
         worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads);
     }
diff --git a/ggml/src/ggml-hexagon/htp/flash-attn-ops.h b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h
new file mode 100644
index 000000000..fbae247e0
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/flash-attn-ops.h
@@ -0,0 +1,253 @@
+#ifndef HTP_FLASH_ATTN_OPS_H
+#define HTP_FLASH_ATTN_OPS_H
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdbool.h>
+
+#include "hex-fastdiv.h"
+#include "hex-common.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Tile constants (mirrored from hmx-utils.h for use on host side if needed)
+#define HMX_FP16_TILE_N_ROWS   32
+#define HMX_FP16_TILE_N_COLS   32
+#define HMX_FP16_TILE_N_ELMS   1024
+#define HMX_FP16_TILE_SIZE     2048
+#define HVX_FA_DMA_CACHE_SIZE  128
+#define HMX_FA_DMA_CACHE_SIZE  4
+
+#define HTP_FA_M_INITIAL_VAL  -10000.0f
+
+enum htp_fa_kernel_type {
+    HTP_FA_KERNEL_UNSUPPORTED = 0,
+    HTP_FA_KERNEL_HVX,
+    HTP_FA_KERNEL_HMX
+};
+
+struct htp_fa_kernel_params {
+    uint8_t  kernel_type;        // enum htp_fa_kernel_type
+    uint8_t  is_q_fp32;          // 1 = Q type is F32, 0 = F16
+    uint8_t  is_dst_fp32;        // 1 = dst type is F32, 0 = F16
+    uint8_t  n_threads;          // Number of threads to run
+
+    // Common parameters
+    uint16_t Br;
+    uint16_t Bc;
+    uint16_t n_kv_blocks;        // also HVX's n_blocks
+    uint16_t G;                  // GQA factor (n_heads / n_kv_heads)
+
+    float    scale;
+    float    max_bias;
+    float    logit_softcap;
+    uint32_t vtcm_size;
+
+    uint32_t qrows;
+    uint32_t qrows_per_thread;
+    float    m0;
+    float    m1;
+    uint32_t n_head_log2;
+
+    struct fastdiv_values src3_div2;
+    struct fastdiv_values src3_div3;
+
+    union {
+        struct {
+            uint32_t g_br;
+            uint32_t row_buf_stride;
+            uint32_t mask_buf_row_stride;
+            int32_t  mask_broadcast;
+            int32_t  pipeline;
+            struct fastdiv_values div_G;
+        } hmx;
+        struct {
+            uint32_t size_q_row_padded;
+            uint32_t size_k_row_padded;
+            uint32_t size_v_row_padded;
+            struct fastdiv_values src0_div21;
+            struct fastdiv_values src0_div1;
+            struct fastdiv_values broadcast_rk2;
+            struct fastdiv_values broadcast_rk3;
+            struct fastdiv_values broadcast_rv2;
+            struct fastdiv_values broadcast_rv3;
+        } hvx;
+    } u;
+};
+
+#if defined(__cplusplus)
+static_assert(sizeof(struct htp_fa_kernel_params) <= 128, "htp_fa_kernel_params is too large for kernel_params blob");
+#endif
+
+// Exact VTCM usage for a given (gqa_factor, DK, DV, Br, Bc) configuration.
+// g_br = hex_align_up(gqa_factor * Br, 32) replaces Br for all Q/O/S/P/D dimensions.
+// Layout: Q + O_ping + O_pong + K_dma*2 + V_dma*2 + K_tile + V_tile + S + P + D + vectors + scales
+// Mask is DMA'd into a VTCM buffer (Br rows per KV block) to avoid DDR reads in softmax.
+static inline size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline) {
+    const size_t g_br         = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS);
+    const size_t q_tile_size  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);    // Q:  [g_br, DK]
+    const size_t o_tile_size  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);    // O:  [g_br, DV] x2 ping-pong
+    const size_t k_dma_size   = hex_align_up(Bc * hex_round_up(DK * sizeof(__fp16), 128), 4096);      // K DMA: [Bc, DK] x2 double-buf
+    const size_t v_dma_size   = hex_align_up(Bc * hex_round_up(DV * sizeof(__fp16), 128), 4096);      // V DMA: [Bc, DV] x2 double-buf
+    const size_t k_tile_size  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);      // K tiles: [Bc, DK] interleaved
+    const size_t v_tile_size  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);      // V tiles: [Bc, DV] interleaved
+    const size_t s_tile_size  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);    // S/P:[g_br, Bc]
+    const size_t d_tile_size  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);  // D:  [g_br, g_br]
+    const size_t col_vec_size = hex_align_up(g_br * sizeof(float), 256);          // m, l, etc.
+    const size_t row_vec_size = hex_align_up(Bc * sizeof(__fp16), 256);
+    const size_t m_line_size  = hex_align_up(Bc * sizeof(__fp16), 128);
+    const size_t m_buf_size   = hex_align_up(Br * m_line_size, 4096) * HMX_FA_DMA_CACHE_SIZE;
+    const size_t slopes_size  = hex_align_up(g_br * sizeof(__fp16), 128);
+
+    return   q_tile_size * 1               // Q tiles
+           + o_tile_size * 2               // O ping-pong
+           + k_dma_size  * 2               // K DMA x2
+           + v_dma_size  * 2               // V DMA x2
+           + k_tile_size * 1               // K tiles
+           + v_tile_size * (pipeline ? 2 : 1) // V tiles (double-buffered if pipelining)
+           + s_tile_size * 2               // S + P
+           + d_tile_size * 1               // D (diagonal matrix)
+           + col_vec_size * 4              // m_vec, l_vec, s_rowmax, p_rowsum
+           + row_vec_size * 2 * n_threads  // per-thread softmax row scratch
+           + m_buf_size * 1                // mask VTCM buffer [Br rows]
+           + slopes_size                   // Slopes
+           + 256 * 2;                      // HMX scales (id + qk)
+}
+
+#define FA_HVX_BLOCK_SIZE 64
+
+static inline size_t hvx_fa_compute_vtcm_usage(size_t DK, size_t DV, bool is_q_fp32, bool has_mask, size_t n_threads) {
+    const size_t size_q_row_padded = hex_round_up(DK * (is_q_fp32 ? 4 : 2), 128);
+    const size_t size_k_row_padded = hex_round_up(DK * sizeof(__fp16), 128);
+    const size_t size_v_row_padded = hex_round_up(DV * sizeof(__fp16), 128);
+
+    const size_t size_q_block = size_q_row_padded * 1;
+    const size_t size_k_block = size_k_row_padded * FA_HVX_BLOCK_SIZE;
+    const size_t size_v_block = size_v_row_padded * FA_HVX_BLOCK_SIZE;
+    const size_t size_m_block = hex_round_up(FA_HVX_BLOCK_SIZE * sizeof(__fp16), 128);
+    const size_t size_vkq_acc = hex_round_up(DV * sizeof(float), 128);
+
+    const size_t size_per_thread = size_q_block * 1
+                                 + size_k_block * 2
+                                 + size_v_block * 2
+                                 + (has_mask ? size_m_block * HVX_FA_DMA_CACHE_SIZE : 0)
+                                 + size_vkq_acc;
+
+    return size_per_thread * n_threads;
+}
+
+#define FA_MIN_KV_BLOCKS 3
+
+// Cost-based (Br, Bc) search for flash attention with pipeline constraint.
+static inline int hmx_fa_find_chunk_size(size_t * Br_out,
+                                  size_t * Bc_out,
+                                  size_t   gqa_factor,
+                                  size_t   DK,
+                                  size_t   DV,
+                                  size_t   qo_len,
+                                  size_t   kv_len,
+                                  size_t   vtcm_budget,
+                                  size_t   n_threads) {
+    const size_t T       = HMX_FP16_TILE_N_ROWS;  // 32
+    const size_t br_unit = hmx_ceil_div(T, gqa_factor);
+    const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2;  // 64
+    const size_t fp16    = sizeof(__fp16);
+    const bool   can_pipeline = (kv_len >= FA_MIN_KV_BLOCKS * bc_unit && n_threads >= 2);
+
+    // Approximate per-unit VTCM costs (without per-buffer alignment padding).
+    const size_t per_gbr  = (DK + 2 * DV) * fp16 + 4 * sizeof(float);  // Q + O*2 + 4 col vectors
+    const size_t per_gbr2 = fp16;                             // D diagonal matrix
+    const size_t per_bc =
+        3 * DK * fp16 + (can_pipeline ? 4 : 3) * DV * fp16 + 2 * n_threads * fp16;          // K/V DMA x2 + tiles + row bufs
+    const size_t per_gbr_bc = 2 * fp16;                       // S + P
+
+    const size_t overhead = 256 * 2 + 13 * 4096;
+
+    if (vtcm_budget <= overhead) {
+        return -1;
+    }
+    const size_t usable = vtcm_budget - overhead;
+
+    // Br_max: largest Br aligned to br_unit that does not exceed qo_len.
+    const size_t Br_max = qo_len >= br_unit ? hex_align_down(qo_len, br_unit) : br_unit;
+
+    // Pipeline constraint: cap Bc so n_kv_blocks >= FA_MIN_KV_BLOCKS.
+    // Only relax when kv_len is too short to form enough blocks.
+    const size_t Bc_limit     = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) :
+                                               (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit);
+    // Cost coefficients calibrated from profiling
+    const size_t c_q_fixed    = 1400;  // per-Q-block: q_load + epilogue o_update + o_norm + o_store
+    const size_t c_iter_fixed = 200;   // per-KV-iter: HMX queue push/pop + DMA pop + barriers
+
+    size_t best_cost = SIZE_MAX, best_mn = 0;
+    size_t best_Br = 0, best_Bc = 0;
+
+    for (size_t Br = Br_max; Br >= br_unit; Br -= br_unit) {
+        const size_t g_br = hex_align_up(gqa_factor * Br, T);
+
+        // g_br-dependent VTCM cost: g_br * per_gbr + g_br*g_br * per_gbr2
+        const size_t gbr_cost = g_br * per_gbr + g_br * g_br * per_gbr2;
+        if (gbr_cost >= usable) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        // Analytically solve for max Bc:
+        //   remain >= Bc * (per_bc + g_br * per_gbr_bc + Br * fp16 * HMX_FA_DMA_CACHE_SIZE)
+        // The Br * fp16 term accounts for the VTCM mask buffer [Br * Bc].
+        const size_t remain   = usable - gbr_cost;
+        const size_t bc_denom = per_bc + g_br * per_gbr_bc + Br * fp16 * HMX_FA_DMA_CACHE_SIZE;
+        size_t       Bc       = hex_smin(hex_align_down(remain / bc_denom, bc_unit), Bc_limit);
+        if (Bc < bc_unit) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        // Exact VTCM verification (alignment padding may push over budget)
+        while (Bc >= bc_unit && hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline) > vtcm_budget) {
+            Bc -= bc_unit;
+        }
+        if (Bc < bc_unit) {
+            if (Br == br_unit) {
+                break;
+            }
+            continue;
+        }
+
+        const size_t q_blocks  = (qo_len + Br - 1) / Br;
+        const size_t kv_blocks = (kv_len + Bc - 1) / Bc;
+        const size_t cost      = q_blocks * (c_q_fixed + kv_blocks * c_iter_fixed);
+        const size_t mn        = Br * Bc;
+
+        if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
+            best_cost = cost;
+            best_mn   = mn;
+            best_Br   = Br;
+            best_Bc   = Bc;
+        }
+
+        if (Br == br_unit) {
+            break;
+        }
+    }
+
+    if (best_Br == 0) {
+        return -1;
+    }
+
+    *Br_out = best_Br;
+    *Bc_out = best_Bc;
+    return 0;
+}
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* HTP_FLASH_ATTN_OPS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hex-dma.h b/ggml/src/ggml-hexagon/htp/hex-dma.h
index 8031a5679..98fcc9fda 100644
--- a/ggml/src/ggml-hexagon/htp/hex-dma.h
+++ b/ggml/src/ggml-hexagon/htp/hex-dma.h
@@ -138,27 +138,28 @@ static inline bool dma_queue_push_single_1d(dma_queue * q, dma_ptr dptr, size_t
     }
 
     dma_descriptor_1d * desc = (dma_descriptor_1d *) &q->desc[q->push_idx];
-    desc->next       = NULL;
-    desc->desc_size  = 0; // 1D mode
-    desc->src_bypass = dma_src_l2_bypass_on;
-    desc->dst_bypass = dma_dst_l2_bypass_on;
-    desc->order      = 0;
-    desc->done       = 0;
-    desc->src        = (void *) dptr.src;
-    desc->dst        = (void *) dptr.dst;
-    desc->size       = size;
+    desc->src  = (void *) dptr.src;
+    desc->dst  = (void *) dptr.dst;
+    desc->size = size;
 
     q->dptr[q->push_idx] = dptr;
 
     if (size) {
+        desc->next       = NULL;
+        desc->desc_size  = 0; // 1D mode
+        desc->src_bypass = dma_src_l2_bypass_on;
+        desc->dst_bypass = dma_dst_l2_bypass_on;
+        desc->order      = 0;
+        desc->done       = 0;
+
         htp_trace_event_start(q->trace, HTP_TRACE_EVT_DMA, q->push_idx);
         dmlink(q->tail, desc);
         q->tail = (dma_descriptor_2d *) desc;
     } else {
-        desc->done = 1;
+        desc->desc_size = 0;
+        desc->done      = 1;
     }
 
-    // FARF(ERROR, "dma-push: i %u row-size %u nrows %d dst %p src %p\n", q->push_idx, row_size, nrows, dptr.dst, dptr.src);
     q->push_idx = (q->push_idx + 1) & q->idx_mask;
     return true;
 }
@@ -320,7 +321,7 @@ static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, dma_ptr dptr, size_
     return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows);
 }
 
-#define DMA_CACHE_MAX_SIZE 64U
+#define DMA_CACHE_MAX_SIZE 256U
 
 typedef struct {
     uint8_t *base;
@@ -352,20 +353,19 @@ static inline bool dma_cache_push(dma_queue *q, dma_cache *c, const uint8_t * sr
         if (c->src[i] == (uint32_t) src) {
             c->age[i] = 0;
             dst = c->base + (i * c->line_size); nrows = 0; // dummy dma
-            // FARF(ERROR, "dma-cache: found %p", src);
         } else {
             c->age[i]++;
             if (c->age[i] > o_age) { o_age = c->age[i]; o_idx = i; }
         }
     }
     if (!dst) {
-        // FARF(ERROR, "dma-cache: replacing #%u : age %u %p -> %p", o_idx, c->age[o_idx], (void *) c->src[o_idx], src);
         c->age[o_idx] = 0;
         c->src[o_idx] = (uint32_t) src;
         dst = c->base + o_idx * c->line_size; // normal nrows dma
+        return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows);
     }
 
-    return dma_queue_push(q, dma_make_ptr(dst, src), dst_stride, src_stride, row_size, nrows);
+    return dma_queue_push_single_1d(q, dma_make_ptr(dst, src), 0);
 }
 
 #ifdef __cplusplus
diff --git a/ggml/src/ggml-hexagon/htp/hmx-fa-kernels.h b/ggml/src/ggml-hexagon/htp/hmx-fa-kernels.h
new file mode 100644
index 000000000..1ab286e1c
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hmx-fa-kernels.h
@@ -0,0 +1,96 @@
+#ifndef HMX_FA_KERNELS_H
+#define HMX_FA_KERNELS_H
+
+#include <stdint.h>
+#include <stddef.h>
+#include <stdbool.h>
+#include "hvx-utils.h"
+#include "hmx-utils.h"
+
+// HMX-specific parameters, offsets and inner kernels for Flash Attention
+
+// Scatter offsets for diagonal tile: entry[2i] = i*136, entry[2i+1] = i*136+6
+// 136 = 4 * 32 + 8 = byte offset to diagonal in a 32x32 fp16 interleaved tile
+static const int16_t d_tile_scatter_offsets[64] __attribute__((aligned(128))) = {
+    0 * 136,  0 * 136 + 6,
+    1 * 136,  1 * 136 + 6,
+    2 * 136,  2 * 136 + 6,
+    3 * 136,  3 * 136 + 6,
+    4 * 136,  4 * 136 + 6,
+    5 * 136,  5 * 136 + 6,
+    6 * 136,  6 * 136 + 6,
+    7 * 136,  7 * 136 + 6,
+    8 * 136,  8 * 136 + 6,
+    9 * 136,  9 * 136 + 6,
+    10 * 136, 10 * 136 + 6,
+    11 * 136, 11 * 136 + 6,
+    12 * 136, 12 * 136 + 6,
+    13 * 136, 13 * 136 + 6,
+    14 * 136, 14 * 136 + 6,
+    15 * 136, 15 * 136 + 6,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+    0,        0,
+};
+// Inner HMX tile computation kernels
+
+static inline void hmx_fa_qk_dot_tile(
+    const __fp16 * row_tiles,
+    const __fp16 * col_tiles,
+    __fp16 *       out_tile,
+    size_t         n_dot_tiles
+) {
+    for (size_t k = 0; k < n_dot_tiles; ++k) {
+        Q6_activation_hf_mxmem_RR((unsigned int) row_tiles, 2047);
+        Q6_weight_hf_mxmem_RR((unsigned int) col_tiles, 2047);
+        row_tiles += HMX_FP16_TILE_N_ELMS;
+        col_tiles += HMX_FP16_TILE_N_ELMS;
+    }
+    Q6_mxmem_AR_after_hf(out_tile, 0);
+}
+
+static inline void hmx_fa_o_update_tile(
+    const __fp16 * d_diag,
+    const __fp16 * o_rc,
+    const __fp16 * p_tile_in,
+    const __fp16 * v_tile_in,
+    __fp16 *       o_tile_out,
+    size_t         n_col_tiles
+) {
+    Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+    Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+
+    for (size_t k = 0; k < n_col_tiles; ++k) {
+        Q6_activation_hf_mxmem_RR((unsigned int) p_tile_in, 2047);
+        Q6_weight_hf_mxmem_RR((unsigned int) v_tile_in, 2047);
+        p_tile_in += HMX_FP16_TILE_N_ELMS;
+        v_tile_in += HMX_FP16_TILE_N_ELMS;
+    }
+
+    Q6_mxmem_AR_after_hf(o_tile_out, 0);
+}
+
+static inline void hmx_fa_o_norm_tile(
+    const __fp16 * d_diag,
+    const __fp16 * o_rc,
+    __fp16 *       o_out
+) {
+    Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
+    Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
+    Q6_mxmem_AR_after_hf(o_out, 0);
+}
+
+#endif /* HMX_FA_KERNELS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c b/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
deleted file mode 100644
index 996fd5975..000000000
--- a/ggml/src/ggml-hexagon/htp/hmx-flash-attn-ops.c
+++ /dev/null
@@ -1,1838 +0,0 @@
-// HMX-accelerated Flash Attention for prefill (neq1 >= 32).
-// Ported from htp-ops-lib/src/dsp/ops/flash_attn.c, adapted to the htp/ codebase.
-
-#pragma clang diagnostic ignored "-Wunused-variable"
-#pragma clang diagnostic ignored "-Wunused-function"
-#pragma clang diagnostic ignored "-Wunused-but-set-variable"
-
-#include <assert.h>
-#include <HAP_compute_res.h>
-#include <HAP_farf.h>
-#include <math.h>
-#include <stdbool.h>
-#include <stddef.h>
-#include <stdint.h>
-#include <string.h>
-
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-#include "hex-dma.h"
-#include "hex-fastdiv.h"
-#include "hex-profile.h"
-#include "hmx-queue.h"
-#include "hmx-utils.h"
-#include "htp-ctx.h"
-#include "htp-ops.h"
-#include "hvx-dump.h"
-#include "hvx-copy.h"
-#include "hvx-reduce.h"
-#include "hvx-utils.h"
-#include "hvx-flash-attn.h"
-#include "vtcm-utils.h"
-#include "worker-pool.h"
-
-// ============================================================================
-// Constants
-// ============================================================================
-
-// Tile constants from hmx-utils.h
-// HMX_FP16_TILE_N_ROWS  = 32
-// HMX_FP16_TILE_N_COLS  = 32
-// HMX_FP16_TILE_N_ELMS  = 1024
-// HMX_FP16_TILE_SIZE    = 2048
-
-// ============================================================================
-// Dynamic block size computation (GQA-aware)
-// ============================================================================
-
-// Exact VTCM usage for a given (gqa_factor, DK, DV, Br, Bc) configuration.
-// g_br = hex_align_up(gqa_factor * Br, 32) replaces Br for all Q/O/S/P/D dimensions.
-// Layout: Q + O_ping + O_pong + K_dma*2 + V_dma*2 + K_tile + V_tile + S + P + D + vectors + scales
-// Mask is DMA'd into a VTCM buffer (Br rows per KV block) to avoid DDR reads in softmax.
-static size_t hmx_fa_compute_vtcm_usage(size_t gqa_factor, size_t DK, size_t DV, size_t Br, size_t Bc, size_t n_threads, bool pipeline) {
-    const size_t g_br         = hex_align_up(gqa_factor * Br, HMX_FP16_TILE_N_ROWS);
-    const size_t q_tile_size  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);    // Q:  [g_br, DK]
-    const size_t o_tile_size  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);    // O:  [g_br, DV] x2 ping-pong
-    const size_t k_dma_size   = hex_align_up(Bc * hex_round_up(DK * sizeof(__fp16), 128), 4096);      // K DMA: [Bc, DK] x2 double-buf
-    const size_t v_dma_size   = hex_align_up(Bc * hex_round_up(DV * sizeof(__fp16), 128), 4096);      // V DMA: [Bc, DV] x2 double-buf
-    const size_t k_tile_size  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);      // K tiles: [Bc, DK] interleaved
-    const size_t v_tile_size  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);      // V tiles: [Bc, DV] interleaved
-    const size_t s_tile_size  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);    // S/P:[g_br, Bc]
-    const size_t d_tile_size  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);  // D:  [g_br, g_br]
-    const size_t col_vec_size = hex_align_up(g_br * sizeof(__fp16), 256);          // m, l, etc.
-    const size_t row_vec_size = hex_align_up(Bc * sizeof(__fp16), 256);
-    const size_t m_line_size  = hex_align_up(Bc * sizeof(__fp16), 128);
-    const size_t m_buf_size   = hex_align_up(Br * m_line_size, 4096);
-    const size_t slopes_size  = hex_align_up(g_br * sizeof(__fp16), 128);
-
-    return   q_tile_size * 1               // Q tiles
-           + o_tile_size * 2               // O ping-pong
-           + k_dma_size  * 2               // K DMA x2
-           + v_dma_size  * 2               // V DMA x2
-           + k_tile_size * 1               // K tiles
-           + v_tile_size * (pipeline ? 2 : 1) // V tiles (double-buffered if pipelining)
-           + s_tile_size * 2               // S + P
-           + d_tile_size * 1               // D (diagonal matrix)
-           + col_vec_size * 4              // m_vec, l_vec, s_rowmax, p_rowsum
-           + row_vec_size * 2 * n_threads  // per-thread softmax row scratch
-           + m_buf_size * 1                // mask VTCM buffer [Br rows]
-           + slopes_size                   // Slopes
-           + 256 * 2;                      // HMX scales (id + qk)
-}
-
-// ============================================================================
-// FP16 exp2 polynomial (ported from htp-ops-lib/include/dsp/hvx_math.h)
-// ============================================================================
-// 5th-order Horner polynomial for exp2(x) in qf16/hf16 domain.  Input must be
-// ≤ 0 (safe softmax invariant — overflow handling omitted).  ~18 ALU ops per
-// 64 fp16 lanes, fully parallel across HVX threads (no scatter/gather engine).
-// Replaces the F32 round-trip (qf16→f32→exp→f32→f16, ~44 ops for 2×32 lanes).
-static inline HVX_Vector hvx_exp2_hf(HVX_Vector x_v) {
-    const HVX_Vector zero_v    = Q6_V_vzero();
-    const HVX_Vector half_hf_v = Q6_Vh_vsplat_R(0x3800);  // fp16 0.5
-
-    // k = round_toward_neg_inf(x);  f = (float)k;  frac = x - f
-    HVX_Vector x_minus_half = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vsub_VhfVhf(x_v, half_hf_v));
-    HVX_Vector k_v          = Q6_Vh_equals_Vhf(x_minus_half);  // truncate to int16
-    HVX_Vector f_v          = Q6_Vhf_equals_Vh(k_v);           // back to fp16
-
-    HVX_Vector x_qf16 = Q6_Vqf16_vsub_VhfVhf(x_v, f_v);        // fractional part in qf16
-
-    // Horner: y = ((((E5*x + E4)*x + E3)*x + E2)*x + E1)*x + E0
-    HVX_Vector y = Q6_Vqf16_vmpy_Vqf16Vqf16(Q6_Vh_vsplat_R(0x5082), x_qf16); // E5*x
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x157d));        // + E4
-    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x20ed));        // + E3
-    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x2b1b));        // + E2
-    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x33b0));        // + E1
-    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x398c));        // + E0
-    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);                      // y = y * x
-    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x3c00));        // + 1.0
-
-    // Combine polynomial (mantissa) with integer part (exponent): result = y * 2^k
-    y                          = Q6_Vhf_equals_Vqf16(y);
-    HVX_Vector y_exp           = Q6_Vuh_vlsr_VuhR(Q6_Vh_vasl_VhR(y, 1), 11);
-    y_exp                      = Q6_Vh_vadd_VhVh(k_v, y_exp);
-    HVX_VectorPred q_underflow = Q6_Q_vcmp_gt_VhVh(zero_v, y_exp);
-    y                          = Q6_Vh_vaslacc_VhVhR(y, k_v, 10);
-    return Q6_V_vmux_QVV(q_underflow, zero_v, y);
-}
-
-#define FA_MIN_KV_BLOCKS 3
-
-// Cost-based (Br, Bc) search for flash attention with pipeline constraint.
-//
-// VTCM model (same as before):
-//   overhead + g_br * per_gbr + g_br² * per_gbr2 + Bc * per_bc + g_br * Bc * per_gbr_bc
-//
-// Cost model (minimization objective):
-//   Q * (c_q_fixed + K * c_iter_fixed),  where Q = ceil(qo/Br), K = ceil(kv/Bc)
-static int hmx_fa_find_chunk_size(size_t * Br_out,
-                                  size_t * Bc_out,
-                                  size_t   gqa_factor,
-                                  size_t   DK,
-                                  size_t   DV,
-                                  size_t   qo_len,
-                                  size_t   kv_len,
-                                  size_t   vtcm_budget,
-                                  size_t   n_threads) {
-    const size_t T       = HMX_FP16_TILE_N_ROWS;  // 32
-    const size_t br_unit = hmx_ceil_div(T, gqa_factor);
-    // Bc must be a multiple of 64 so that n_tiles_per_bc is even.  The softmax
-    // P-tile write uses a dual-tile pattern (vshuff + two stores 16 slots apart)
-    // that would race across r0 blocks if the last dual-tile is half-occupied.
-    // See .cursor/todos/hmx-flash-attn-bc-search-space.md for the perf trade-off.
-    const size_t bc_unit = HMX_FP16_TILE_N_COLS * 2;  // 64
-    const size_t fp16    = sizeof(__fp16);
-    const bool   can_pipeline = (kv_len >= FA_MIN_KV_BLOCKS * bc_unit && n_threads >= 2);
-
-    // Approximate per-unit VTCM costs (without per-buffer alignment padding).
-    const size_t per_gbr  = (DK + 2 * DV) * fp16 + 4 * fp16;  // Q + O×2 + 4 col vectors
-    const size_t per_gbr2 = fp16;                             // D diagonal matrix
-    const size_t per_bc =
-        3 * DK * fp16 + (can_pipeline ? 4 : 3) * DV * fp16 + 2 * n_threads * fp16;          // K/V DMA x2 + tiles + row bufs
-    const size_t per_gbr_bc = 2 * fp16;                       // S + P
-
-    const size_t overhead = 256 * 2 + 13 * 4096;
-
-    if (vtcm_budget <= overhead) {
-        return -1;
-    }
-    const size_t usable = vtcm_budget - overhead;
-
-    // Br_max: largest Br aligned to br_unit that does not exceed qo_len.
-    const size_t Br_max = qo_len >= br_unit ? hex_align_down(qo_len, br_unit) : br_unit;
-
-    // Pipeline constraint: cap Bc so n_kv_blocks >= FA_MIN_KV_BLOCKS.
-    // Only relax when kv_len is too short to form enough blocks.
-    const size_t Bc_limit     = can_pipeline ? hex_align_down(kv_len / FA_MIN_KV_BLOCKS, bc_unit) :
-                                               (kv_len >= bc_unit ? hex_align_down(kv_len, bc_unit) : bc_unit);
-    // Cost coefficients calibrated from profiling
-    const size_t c_q_fixed    = 1400;  // per-Q-block: q_load + epilogue o_update + o_norm + o_store
-    const size_t c_iter_fixed = 200;   // per-KV-iter: HMX queue push/pop + DMA pop + barriers
-
-    size_t best_cost = SIZE_MAX, best_mn = 0;
-    size_t best_Br = 0, best_Bc = 0;
-
-    for (size_t Br = Br_max; Br >= br_unit; Br -= br_unit) {
-        const size_t g_br = hex_align_up(gqa_factor * Br, T);
-
-        // g_br-dependent VTCM cost: g_br * per_gbr + g_br² * per_gbr2
-        const size_t gbr_cost = g_br * per_gbr + g_br * g_br * per_gbr2;
-        if (gbr_cost >= usable) {
-            if (Br == br_unit) {
-                break;
-            }
-            continue;
-        }
-
-        // Analytically solve for max Bc:
-        //   remain >= Bc * (per_bc + g_br * per_gbr_bc + Br * fp16_mask)
-        // The Br * fp16 term accounts for the VTCM mask buffer [Br × Bc].
-        const size_t remain   = usable - gbr_cost;
-        const size_t bc_denom = per_bc + g_br * per_gbr_bc + Br * fp16;
-        size_t       Bc       = hex_smin(hex_align_down(remain / bc_denom, bc_unit), Bc_limit);
-        if (Bc < bc_unit) {
-            if (Br == br_unit) {
-                break;
-            }
-            continue;
-        }
-
-        // Exact VTCM verification (alignment padding may push over budget)
-        while (Bc >= bc_unit && hmx_fa_compute_vtcm_usage(gqa_factor, DK, DV, Br, Bc, n_threads, can_pipeline) > vtcm_budget) {
-            Bc -= bc_unit;
-        }
-        if (Bc < bc_unit) {
-            if (Br == br_unit) {
-                break;
-            }
-            continue;
-        }
-
-        const size_t q_blocks  = (qo_len + Br - 1) / Br;
-        const size_t kv_blocks = (kv_len + Bc - 1) / Bc;
-        const size_t cost      = q_blocks * (c_q_fixed + kv_blocks * c_iter_fixed);
-        const size_t mn        = Br * Bc;
-
-        if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
-            best_cost = cost;
-            best_mn   = mn;
-            best_Br   = Br;
-            best_Bc   = Bc;
-        }
-
-        if (Br == br_unit) {
-            break;
-        }
-    }
-
-    if (best_Br == 0) {
-        return -1;
-    }
-
-    *Br_out = best_Br;
-    *Bc_out = best_Bc;
-    return 0;
-}
-
-// ============================================================================
-// Tile interleave / extract helpers
-// ============================================================================
-
-// transpose scatter offsets moved to hmx-utils.h as hmx_transpose_scatter_offsets
-
-// Scatter offsets for diagonal tile: entry[2i] = i*136, entry[2i+1] = i*136+6
-// 136 = 4 * 32 + 8 = byte offset to diagonal in a 32x32 fp16 interleaved tile
-static const int16_t d_tile_scatter_offsets[64] __attribute__((aligned(128))) = {
-    0 * 136,  0 * 136 + 6,
-    1 * 136,  1 * 136 + 6,
-    2 * 136,  2 * 136 + 6,
-    3 * 136,  3 * 136 + 6,
-    4 * 136,  4 * 136 + 6,
-    5 * 136,  5 * 136 + 6,
-    6 * 136,  6 * 136 + 6,
-    7 * 136,  7 * 136 + 6,
-    8 * 136,  8 * 136 + 6,
-    9 * 136,  9 * 136 + 6,
-    10 * 136, 10 * 136 + 6,
-    11 * 136, 11 * 136 + 6,
-    12 * 136, 12 * 136 + 6,
-    13 * 136, 13 * 136 + 6,
-    14 * 136, 14 * 136 + 6,
-    15 * 136, 15 * 136 + 6,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-    0,        0,
-};
-
-// hmx_interleave_rows_to_tiles and hmx_interleave_cols_to_tiles are in hmx-utils.h
-
-// ============================================================================
-// HMX Flash Attention context (GQA-merged)
-// ============================================================================
-
-struct hmx_fa_context {
-    const struct htp_ops_context * octx;
-    bool         pipeline;  // true when n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads >= 2
-    uint32_t     n_threads;
-
-    // Op parameters
-    float        scale;
-    float        max_bias;
-    float        logit_softcap;
-    uint32_t     n_head_log2;
-    float        m0, m1;
-
-    // Dimensions
-    uint32_t     DK, DV;
-    uint32_t     n_kv;        // kv_len
-    uint32_t     n_kv_heads;  // number of KV heads
-    uint32_t     n_heads;     // number of Q heads
-    uint32_t     G;           // GQA factor = n_heads / n_kv_heads
-    struct fastdiv_values div_G;
-    uint32_t     n_kv_blocks;
-    uint32_t     neq1;        // Q token count
-
-    // Types
-    bool         is_q_fp32;
-    bool         is_dst_fp32;
-
-    // Dynamic block sizes
-    uint32_t     Br;    // Q tokens per block (before GQA expansion)
-    uint32_t     Bc;
-    uint32_t     g_br;  // hex_align_up(G * Br, 32) - actual tile row dim
-
-    // VTCM buffers (allocated by vtcm_seq_alloc)
-    __fp16 *     vtcm_q_tiles;         // Q tile format [g_br, D]
-    __fp16 *     vtcm_o_tiles[2];      // O ping-pong [g_br, D]
-    __fp16 *     vtcm_k_fp16[2];       // K DMA double-buffer [Bc, D]
-    __fp16 *     vtcm_v_fp16[2];       // V DMA double-buffer [Bc, D]
-    __fp16 *     vtcm_k_tiles;         // K tiles (transposed)
-    __fp16 *     vtcm_v_tiles[2];      // V tiles (column-major, double-buffered)
-    __fp16 *     vtcm_s_tiles;         // S = QK^T [g_br, Bc]
-    __fp16 *     vtcm_p_tiles;         // P = softmax(S) [g_br, Bc]
-    __fp16 *     vtcm_d_tiles;         // Diagonal rescale [g_br, g_br]
-    HVX_Vector * vtcm_m_vec;           // Row max [g_br]
-    HVX_Vector * vtcm_l_vec;           // Row sum [g_br]
-    HVX_Vector * vtcm_s_rowmax;        // Softmax intermediate [g_br]
-    HVX_Vector * vtcm_p_rowsum;        // Softmax intermediate [g_br]
-    HVX_Vector * vtcm_row_bufs;        // Per-thread softmax row scratch [n_threads][2][Bc/64]
-    uint8_t *    vtcm_hmx_scales_id;   // HMX output scales (identity)
-    uint8_t *    vtcm_hmx_scales_qk;   // HMX output scales (qk_scale)
-    __fp16 *     vtcm_mask_buf;        // VTCM mask buffer [Br × m_line], DMA'd per KV block
-    __fp16 *     vtcm_slopes;          // ALiBi slopes [g_br]
-    size_t       row_buf_stride;       // HVX vectors per row buffer (Bc/64)
-    size_t       mask_buf_row_stride;  // elements (__fp16) per row in mask buffer
-    bool         mask_broadcast;       // true when mask->ne[2] == 1 (head-independent, single 2D DMA)
-};
-
-// ============================================================================
-// Multi-thread K interleave phase
-// ============================================================================
-
-typedef struct {
-    struct hmx_fa_context * factx;
-    int                     kv_rows;
-    size_t                  src_stride;
-    size_t                  buf_idx;
-} fa_k_int_args_t;
-
-static void fa_k_interleave_thread(unsigned int n, unsigned int i, void * data) {
-    fa_k_int_args_t *       args  = (fa_k_int_args_t *) data;
-    struct hmx_fa_context * factx = args->factx;
-
-    const int total_rows = args->kv_rows;
-    const int rows_per_t = hex_align_up(hmx_ceil_div(total_rows, n), 2);  // ensure even (row pairs)
-    const int start      = i * rows_per_t;
-    const int end        = hex_smin(start + rows_per_t, total_rows);
-
-    if (start >= total_rows) {
-        return;
-    }
-
-    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
-    hmx_interleave_rows_to_tiles(factx->vtcm_k_tiles, factx->vtcm_k_fp16[args->buf_idx], total_rows, (int) factx->DK,
-                             (int) args->src_stride, start, end);
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
-}
-
-static void fa_phase_k_interleave(struct hmx_fa_context * factx, int kv_rows, size_t src_stride, size_t buf_idx) {
-    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
-    fa_k_int_args_t args = { factx, kv_rows, src_stride, buf_idx };
-    if (factx->n_threads > 1 && kv_rows >= (int) (factx->n_threads * 2)) {
-        worker_pool_run_func(wp, fa_k_interleave_thread, &args, factx->n_threads);
-    } else {
-        fa_k_interleave_thread(1, 0, &args);
-    }
-}
-
-// ============================================================================
-// Multi-thread V interleave phase
-// ============================================================================
-
-typedef struct {
-    struct hmx_fa_context * factx;
-    int                     kv_rows;
-    size_t                  src_stride;
-    size_t                  buf_idx;
-    size_t                  n_col_tiles;
-} fa_v_int_args_t;
-
-static void fa_v_interleave_thread(unsigned int n, unsigned int i, void * data) {
-    fa_v_int_args_t *       args  = (fa_v_int_args_t *) data;
-    struct hmx_fa_context * factx = args->factx;
-
-    const int total_rows = args->kv_rows;
-    const int rows_per_t = hex_align_up(hmx_ceil_div(total_rows, n), 2);
-    const int start      = i * rows_per_t;
-    const int end        = hex_smin(start + rows_per_t, total_rows);
-
-    if (start >= total_rows) {
-        return;
-    }
-
-    __fp16 * v_tiles_dest = factx->pipeline ? factx->vtcm_v_tiles[args->buf_idx] : factx->vtcm_v_tiles[0];
-
-    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
-    hmx_interleave_cols_to_tiles(v_tiles_dest, factx->vtcm_v_fp16[args->buf_idx], total_rows, (int) factx->DV,
-                             (int) args->src_stride, (int) args->n_col_tiles, start, end);
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
-}
-
-static void fa_phase_v_interleave(struct hmx_fa_context * factx,
-                                  int                     kv_rows,
-                                  size_t                  src_stride,
-                                  size_t                  buf_idx,
-                                  size_t                  n_col_tiles) {
-    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
-    fa_v_int_args_t args = { factx, kv_rows, src_stride, buf_idx, n_col_tiles };
-    if (factx->n_threads > 1 && kv_rows >= (int) (factx->n_threads * 2)) {
-        worker_pool_run_func(wp, fa_v_interleave_thread, &args, factx->n_threads);
-    } else {
-        fa_v_interleave_thread(1, 0, &args);
-    }
-}
-
-// ============================================================================
-// Multi-thread Q load phase: read Q[G × neq1, DK] from DDR, convert F32→F16
-// (or deal F16 pairs), and write interleaved into vtcm_q_tiles.
-// Each thread owns a disjoint range of row pairs; writes target distinct tile
-// slots (r0 selects tile row, r1 selects intra-tile slot), so there is no
-// write conflict.  Padding fill (when n_rows_g < g_br) is done single-threaded
-// by the caller before dispatching.
-// ============================================================================
-
-typedef struct {
-    struct hmx_fa_context *   factx;
-    const struct htp_tensor * q;
-    uint32_t                  q_start;
-    uint32_t                  kv_head;
-    uint32_t                  ib3;
-    size_t                    n_rows_g;
-} fa_q_load_args_t;
-
-static void fa_q_load_thread(unsigned int n, unsigned int i, void * data) {
-    fa_q_load_args_t *      args  = (fa_q_load_args_t *) data;
-    struct hmx_fa_context * factx = args->factx;
-
-    const size_t n_rows_g = args->n_rows_g;
-    const size_t G        = factx->G;
-    const size_t DK       = factx->DK;
-
-    // Partition row pairs across threads.  Keep each thread's start even so r/r+1
-    // are always in the same thread's range.
-    const size_t rows_per_t = hex_align_up(hmx_ceil_div(n_rows_g, n), 2);
-    const size_t start      = (size_t) i * rows_per_t;
-    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
-
-    if (start >= n_rows_g) {
-        return;
-    }
-
-    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
-
-    const struct htp_tensor * q       = args->q;
-    const uint32_t            q_start = args->q_start;
-    const uint32_t            kv_head = args->kv_head;
-    const uint32_t            ib3     = args->ib3;
-
-    for (size_t r = start; r < end; r += 2) {
-        const bool next_row_valid = (r + 1) < n_rows_g;
-
-        const size_t q_idx0 = fastdiv(r + 0, &factx->div_G);
-        const size_t h_idx0 = fastmodulo(r + 0, G, &factx->div_G);
-        const size_t q_idx1 = fastdiv(r + 1, &factx->div_G);
-        const size_t h_idx1 = fastmodulo(r + 1, G, &factx->div_G);
-
-        const uint8_t * q_ptr0 = (const uint8_t *) q->data + (q_start + q_idx0) * q->nb[1] +
-                                                  (kv_head * G + h_idx0) * q->nb[2] + ib3 * q->nb[3];
-        const uint8_t * q_ptr1 = next_row_valid ? ((const uint8_t *) q->data + (q_start + q_idx1) * q->nb[1] +
-                                                  (kv_head * G + h_idx1) * q->nb[2] + ib3 * q->nb[3]) :
-                                                  NULL;
-
-        size_t   r0       = r / HMX_FP16_TILE_N_ROWS;
-        size_t   r1       = r % HMX_FP16_TILE_N_ROWS;
-        __fp16 * out_base = factx->vtcm_q_tiles + r0 * HMX_FP16_TILE_N_ROWS * DK;
-
-        if (factx->is_q_fp32) {
-            const HVX_Vector * pv_in0 = (const HVX_Vector *) q_ptr0;
-            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
-
-            for (uint32_t d = 0; d < DK / 32; ++d) {
-                HVX_Vector v0   = pv_in0[d];
-                HVX_Vector v1   = pv_in1 ? pv_in1[d] : Q6_V_vzero();
-                HVX_Vector v_hf = hvx_vec_f32_to_f16_shuff(v0, v1);
-
-                HVX_Vector * out_tile = (HVX_Vector *) (out_base + d * HMX_FP16_TILE_N_ELMS);
-                out_tile[r1 / 2]      = v_hf;
-            }
-        } else {
-            const HVX_Vector * pv_in0 = (const HVX_Vector *) q_ptr0;
-            const HVX_Vector * pv_in1 = q_ptr1 ? (const HVX_Vector *) q_ptr1 : NULL;
-
-            for (uint32_t d = 0; d < DK / 64; ++d) {
-                HVX_Vector     v0 = pv_in0[d];
-                HVX_Vector     v1 = pv_in1 ? pv_in1[d] : Q6_V_vzero();
-                HVX_VectorPair vp = Q6_W_vshuff_VVR(v1, v0, -2);
-
-                __fp16 *     out_dual_tile = out_base + d * HMX_FP16_TILE_N_ELMS * 2;
-                HVX_Vector * pv_out0       = ((HVX_Vector *) out_dual_tile) + r1 / 2;
-                HVX_Vector * pv_out1       = pv_out0 + 16;
-
-                *pv_out0 = Q6_V_lo_W(vp);
-                *pv_out1 = Q6_V_hi_W(vp);
-            }
-        }
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
-}
-
-static void fa_phase_q_load(struct hmx_fa_context *   factx,
-                            const struct htp_tensor * q,
-                            uint32_t                  q_start,
-                            uint32_t                  kv_head,
-                            uint32_t                  ib3,
-                            size_t                    n_rows_g) {
-    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
-    fa_q_load_args_t args = { factx, q, q_start, kv_head, ib3, n_rows_g };
-    // Require >= 2 row pairs per thread so partitioning is worthwhile.
-    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
-        worker_pool_run_func(wp, fa_q_load_thread, &args, factx->n_threads);
-    } else {
-        fa_q_load_thread(1, 0, &args);
-    }
-}
-
-// ============================================================================
-// Multi-thread O store phase: read O tiles from VTCM, convert F16->F32 (or
-// deal F16 pairs), and write to strided DDR dst tensor.  Each thread owns a
-// disjoint row range; writes target distinct dst rows (different q_idx/h_idx
-// pairs produced by r/G and r%G), so there is no write conflict.
-// ============================================================================
-
-typedef struct {
-    struct hmx_fa_context *   factx;
-    const struct htp_tensor * dst;
-    const __fp16 *            o_tile_src;
-    uint32_t                  q_start;
-    uint32_t                  kv_head;
-    uint32_t                  ib3;
-    size_t                    n_rows_g;
-} fa_o_store_args_t;
-
-static void fa_o_store_thread(unsigned int n, unsigned int i, void * data) {
-    fa_o_store_args_t *     args  = (fa_o_store_args_t *) data;
-    struct hmx_fa_context * factx = args->factx;
-
-    const size_t n_rows_g = args->n_rows_g;
-    const size_t G        = factx->G;
-    const size_t DV       = factx->DV;
-
-    const size_t rows_per_t = hmx_ceil_div(n_rows_g, n);
-    const size_t start      = (size_t) i * rows_per_t;
-    const size_t end        = hex_smin(start + rows_per_t, n_rows_g);
-
-    if (start >= n_rows_g) {
-        return;
-    }
-
-    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, start);
-
-    const struct htp_tensor * dst        = args->dst;
-    const __fp16 *            o_tile_src = args->o_tile_src;
-    const uint32_t            q_start    = args->q_start;
-    const uint32_t            kv_head    = args->kv_head;
-    const uint32_t            ib3        = args->ib3;
-
-    for (size_t r = start; r < end; ++r) {
-        const size_t q_idx = fastdiv(r, &factx->div_G);
-        const size_t h_idx = fastmodulo(r, G, &factx->div_G);
-
-        // FIX(dst-indexing): ggml_flash_attn_ext() creates dst as permute(0,2,1,3) ->
-        // [DV, n_heads, n_tokens, n_seq], so head stride is nb[1] and token stride is nb[2].
-        uint8_t * dst_row = (uint8_t *) dst->data + (kv_head * G + h_idx) * dst->nb[1] +
-                            (q_start + q_idx) * dst->nb[2] + ib3 * dst->nb[3];
-
-        size_t         r0            = r / HMX_FP16_TILE_N_ROWS;
-        size_t         r1            = r % HMX_FP16_TILE_N_ROWS;
-        const __fp16 * tile_row_base = o_tile_src + r0 * HMX_FP16_TILE_N_ROWS * DV;
-
-        if (factx->is_dst_fp32) {
-            float * out = (float *) dst_row;
-            for (uint32_t d = 0; d < DV / 32; ++d) {
-                const HVX_Vector * in_tile = (const HVX_Vector *) (tile_row_base + d * HMX_FP16_TILE_N_ELMS);
-                HVX_VectorPair     vp      = hvx_vec_f16_to_f32_shuff(in_tile[r1 / 2]);
-                if (r1 % 2 == 0) {
-                    *(HVX_UVector *) (out + d * 32) = Q6_V_lo_W(vp);
-                } else {
-                    *(HVX_UVector *) (out + d * 32) = Q6_V_hi_W(vp);
-                }
-            }
-        } else {
-            __fp16 * out = (__fp16 *) dst_row;
-            for (uint32_t d = 0; d < DV / 64; ++d) {
-                const __fp16 *     in_dual_tile = tile_row_base + d * HMX_FP16_TILE_N_ELMS * 2;
-                const HVX_Vector * pv_in0       = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
-                const HVX_Vector * pv_in1       = pv_in0 + 16;
-                HVX_VectorPair     vp           = Q6_W_vdeal_VVR(*pv_in1, *pv_in0, -2);
-                if (r1 % 2 == 0) {
-                    *(HVX_UVector *) (out + d * 64) = Q6_V_lo_W(vp);
-                } else {
-                    *(HVX_UVector *) (out + d * 64) = Q6_V_hi_W(vp);
-                }
-            }
-        }
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, start);
-}
-
-static void fa_phase_o_store(struct hmx_fa_context *   factx,
-                             const struct htp_tensor * dst,
-                             const __fp16 *            o_tile_src,
-                             uint32_t                  q_start,
-                             uint32_t                  kv_head,
-                             uint32_t                  ib3,
-                             size_t                    n_rows_g) {
-    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
-    fa_o_store_args_t args = { factx, dst, o_tile_src, q_start, kv_head, ib3, n_rows_g };
-    if (factx->n_threads > 1 && n_rows_g >= (size_t) (factx->n_threads * 2)) {
-        worker_pool_run_func(wp, fa_o_store_thread, &args, factx->n_threads);
-    } else {
-        fa_o_store_thread(1, 0, &args);
-    }
-}
-
-// ============================================================================
-// Multi-thread softmax phase + serial m/l update + build_D
-// ============================================================================
-
-typedef struct {
-    struct hmx_fa_context *   factx;
-    size_t                    kv_rows;
-    size_t                    n_rows_g;
-    size_t                    n_col_tiles;
-    size_t                    n_tiles_per_bc;
-    size_t                    n_row_tiles;
-    size_t                    n_row_tiles_g_br;
-    uint32_t                  Bc;
-    uint32_t                  G;
-    uint32_t                  kv_head;
-    uint32_t                  kv_start;
-    uint32_t                  q_start;
-    uint32_t                  ib3;
-    bool                      has_alibi;  // true when max_bias != 0 (need slope * mask + add)
-
-    // ALiBi per-head slopes (indexed by GQA-merged row: slope[r] for r in [0, n_rows_g))
-    // slope[r] = 1.0 when max_bias == 0 (no ALiBi)
-    // Pointer into hmx_fa_context.vtcm_slopes (sized to g_br)
-    __fp16 *                  slopes;
-
-    // Mask info (preloaded before softmax)
-    const struct htp_tensor * mask;
-    const __fp16 *            mask_vtcm;             // VTCM mask buffer base (NULL = DDR fallback)
-    size_t                    mask_vtcm_row_stride;  // elements (__fp16) per row in VTCM mask buffer
-} fa_softmax_args_t;
-
-static void fa_softmax_thread(unsigned int n, unsigned int i, void * data) {
-    fa_softmax_args_t *     args  = (fa_softmax_args_t *) data;
-    struct hmx_fa_context * factx = args->factx;
-
-    const size_t n_rows_g       = args->n_rows_g;
-    const size_t kv_rows        = args->kv_rows;
-    const size_t Bc             = args->Bc;
-    const size_t G              = args->G;
-    const size_t n_tiles_per_bc = args->n_tiles_per_bc;
-    const size_t n_row_vec_cnt  = hmx_ceil_div(n_rows_g, 64);
-
-    // Partition r_vec_idx across threads
-    const size_t vecs_per_t = hmx_ceil_div(n_row_vec_cnt, n);
-    const size_t vec_start  = i * vecs_per_t;
-    const size_t vec_end    = hex_smin(vec_start + vecs_per_t, n_row_vec_cnt);
-
-    if (vec_start >= n_row_vec_cnt) {
-        return;
-    }
-
-    struct htp_thread_trace * tr = factx->octx->ctx ? &factx->octx->ctx->trace[i] : NULL;
-    htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, vec_start);
-
-    // Per-thread row scratch: thread i uses bufs at offset i * 2 * stride
-    const size_t row_buf_stride = factx->row_buf_stride;
-    HVX_Vector * my_row_buf0    = factx->vtcm_row_bufs + i * 2 * row_buf_stride;
-    HVX_Vector * my_row_buf1    = my_row_buf0 + row_buf_stride;
-
-    const HVX_Vector v_neg_inf = Q6_Vh_vsplat_R(0xfbff);
-
-    // Per-row accumulators: each fp16 lane in a 64-lane vector holds one row's scalar.
-    // CONTRACT: lane bits must be IEEE fp16 (hf), never qf16 — qf16 uses a different
-    // bit layout, so a later hf-domain read would silently produce wrong values.
-    // Convert first via Q6_Vhf_equals_Vqf16(). For reference: vtcm_m_vec/vtcm_s_rowmax
-    // are hf; vtcm_l_vec is qf16 — don't mix them up.
-
-    for (size_t r_vec_idx = vec_start; r_vec_idx < vec_end; ++r_vec_idx) {
-        HVX_Vector rowmax_acc_v = v_neg_inf;
-        HVX_Vector rowsum_acc_v = Q6_V_vzero();
-        HVX_Vector m_prev_v     = factx->vtcm_m_vec[r_vec_idx];
-
-        for (int r_vec_off = 0; r_vec_off < 64; r_vec_off += 2) {
-            int r = r_vec_idx * 64 + r_vec_off;
-            if (r >= (int) hex_align_up(n_rows_g, 2)) {
-                break;
-            }
-
-            int r0 = r / HMX_FP16_TILE_N_ROWS;
-            int r1 = r % HMX_FP16_TILE_N_ROWS;
-
-            const __fp16 * s_ld_base = factx->vtcm_s_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
-            __fp16 *       p_st_base = factx->vtcm_p_tiles + r0 * HMX_FP16_TILE_N_ROWS * Bc;
-
-            // Decode 2 rows from S tiles into per-thread row buffers
-            HVX_Vector * pv_row_buf0 = my_row_buf0;
-            HVX_Vector * pv_row_buf1 = my_row_buf1;
-            for (size_t c = 0; c < kv_rows; c += 64) {
-                const __fp16 *     in_dual_tile = s_ld_base + (c / 64) * HMX_FP16_TILE_N_ELMS * 2;
-                const HVX_Vector * pv_s_in0     = ((const HVX_Vector *) in_dual_tile) + r1 / 2;
-                const HVX_Vector * pv_s_in1     = pv_s_in0 + 16;
-
-                HVX_VectorPair vp_s_dual_row = Q6_W_vdeal_VVR(*pv_s_in1, *pv_s_in0, -2);
-                *pv_row_buf0++               = Q6_V_lo_W(vp_s_dual_row);
-                *pv_row_buf1++               = Q6_V_hi_W(vp_s_dual_row);
-            }
-
-            // Apply softcap if enabled (in F32 precision)
-            if (factx->logit_softcap != 0.0f) {
-                // When EXP2_HF is on, fold log2(e) into v_cap so the output lands in
-                // log2(e)-scaled space for the downstream exp2.  log2(e) is kept OUT
-                // of qk_scale in this configuration (see scale setup) so tanh sees
-                // the physical QK/(√d·c) argument.
-                float cap = factx->logit_softcap;
-#ifdef HMX_FA_USE_EXP2_HF
-                cap *= 1.44269504f;  // log2(e)
-#endif
-                const HVX_Vector v_cap = hvx_vec_splat_f32(cap);
-                for (size_t c = 0; c < kv_rows; c += 64) {
-                    size_t ci = c / 64;
-
-                    HVX_VectorPair r0_f32 = hvx_vec_f16_to_f32(my_row_buf0[ci]);
-                    HVX_Vector     t0_lo  = hvx_vec_tanh_f32(Q6_V_lo_W(r0_f32));
-                    HVX_Vector     t0_hi  = hvx_vec_tanh_f32(Q6_V_hi_W(r0_f32));
-                    t0_lo                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t0_lo, v_cap));
-                    t0_hi                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t0_hi, v_cap));
-                    my_row_buf0[ci]       = hvx_vec_f32_to_f16(t0_lo, t0_hi);
-
-                    HVX_VectorPair r1_f32 = hvx_vec_f16_to_f32(my_row_buf1[ci]);
-                    HVX_Vector     t1_lo  = hvx_vec_tanh_f32(Q6_V_lo_W(r1_f32));
-                    HVX_Vector     t1_hi  = hvx_vec_tanh_f32(Q6_V_hi_W(r1_f32));
-                    t1_lo                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t1_lo, v_cap));
-                    t1_hi                 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(t1_hi, v_cap));
-                    my_row_buf1[ci]       = hvx_vec_f32_to_f16(t1_lo, t1_hi);
-                }
-            }
-
-            // Apply mask & compute rowmax(S)
-            //
-            // Optimizations over baseline:
-            //   A. No-ALiBi fast path: when max_bias==0 (slope≡1.0), skip the
-            //      slope multiplication — still add mask (additive bias) but
-            //      avoid the mul_f16_f16.  Saves 2 ops/dual-row vs ALiBi path.
-            //   B. GQA mask row dedup: G consecutive Q rows share one mask row
-            //      (qi = r / G).  Reuse mask vector when qi is unchanged between
-            //      row0 and row1 (saves ~75% of VTCM loads for G=4).
-
-            // ALiBi slopes — only needed when has_alibi (scheme A)
-            HVX_Vector v_slope0, v_slope1;
-            if (args->has_alibi) {
-                HVX_Vector v_s = hvx_vmemu(args->slopes + r);
-                v_slope0 = hvx_vec_repl_f16(v_s);
-                v_slope1 = (r + 1 < (int) n_rows_g) ? hvx_vec_repl_f16(Q6_V_vror_VR(v_s, 2)) : Q6_V_vzero();
-            }
-
-            const HVX_Vector v_threshold = Q6_Vh_vsplat_R(0xcc00);  // fp16 -16.0 (hoisted outside for-c)
-
-            HVX_Vector v_s_rowmax0 = v_neg_inf;
-            HVX_Vector v_s_rowmax1 = v_neg_inf;
-            for (size_t c = 0; c < kv_rows; c += 64) {
-                size_t         ci          = c / 64;
-                const size_t   ne          = hex_smin(kv_rows - c, 64);
-                HVX_VectorPred q_tail_keep = Q6_Q_vsetq2_R(ne * sizeof(__fp16));
-
-                if (args->mask) {
-                    HVX_Vector v_mask0, v_mask1;
-
-                    if (args->mask_vtcm) {
-                        // Read mask from VTCM buffer (DMA'd per KV block).
-                        // GQA dedup (scheme B): skip load when qi unchanged.
-                        const size_t qi0 = fastdiv(r + 0, &factx->div_G);
-                        v_mask0 = *(const HVX_UVector *) (args->mask_vtcm + qi0 * args->mask_vtcm_row_stride + c);
-                        v_mask1 = v_neg_inf;
-                        if (r + 1 < (int) n_rows_g) {
-                            const size_t qi1 = fastdiv(r + 1, &factx->div_G);
-                            if (qi1 == qi0) {
-                                v_mask1 = v_mask0;  // scheme B: reuse — same mask row
-                            } else {
-                                v_mask1 = *(const HVX_UVector *) (args->mask_vtcm + qi1 * args->mask_vtcm_row_stride + c);
-                            }
-                        }
-                    } else {
-                        // Fallback: read mask directly from DDR (when mask->ne[2] > 1).
-                        const struct htp_tensor * mask   = args->mask;
-                        const size_t              q_idx0 = args->q_start + fastdiv(r + 0, &factx->div_G);
-                        const size_t              h_idx0 = args->kv_head * G + fastmodulo(r + 0, G, &factx->div_G);
-                        const uint32_t            im2_0  = h_idx0 % mask->ne[2];
-                        const uint32_t            im3_0  = args->ib3 % mask->ne[3];
-
-                        const __fp16 * m0_ptr = (const __fp16 *) ((const uint8_t *) mask->data + q_idx0 * mask->nb[1] +
-                                                        im2_0 * mask->nb[2] + im3_0 * mask->nb[3]) + args->kv_start + c;
-                        v_mask0 = *(const HVX_UVector *) m0_ptr;
-                        v_mask1 = v_neg_inf;
-
-                        if (r + 1 < (int) n_rows_g) {
-                            const size_t q_idx1 = args->q_start + fastdiv(r + 1, &factx->div_G);
-                            if (q_idx1 == q_idx0) {
-                                // scheme B: same mask row in DDR path
-                                v_mask1 = v_mask0;
-                            } else {
-                                const size_t   h_idx1 = args->kv_head * G + fastmodulo(r + 1, G, &factx->div_G);
-                                const uint32_t im2_1  = h_idx1 % mask->ne[2];
-                                const uint32_t im3_1  = args->ib3 % mask->ne[3];
-                                const __fp16 * m1_ptr = (const __fp16 *) ((const uint8_t *) mask->data + q_idx1 * mask->nb[1] +
-                                                                im2_1 * mask->nb[2] + im3_1 * mask->nb[3]) + args->kv_start + c;
-                                v_mask1 = *(const HVX_UVector *) m1_ptr;
-                            }
-                        }
-                    }
-
-                    // Threshold: mask values below -16.0 are treated as -inf (causal mask).
-                    HVX_VectorPred q_keep0 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask0, v_threshold), q_tail_keep);
-                    HVX_VectorPred q_keep1 = Q6_Q_and_QQ(Q6_Q_vcmp_gt_VhfVhf(v_mask1, v_threshold), q_tail_keep);
-
-                    if (args->has_alibi) {
-                        // ALiBi path: S += slope * mask (full mul + add)
-                        HVX_Vector v_sm0 = hvx_vec_mul_f16_f16(v_mask0, v_slope0);
-                        HVX_Vector v_sm1 = hvx_vec_mul_f16_f16(v_mask1, v_slope1);
-                        my_row_buf0[ci]  = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_sm0), v_neg_inf);
-                        my_row_buf1[ci]  = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_sm1), v_neg_inf);
-                    } else {
-                        // No-ALiBi fast path (scheme A): slope≡1.0, skip the mul
-                        // but still add mask (additive positional bias).  vmux
-                        // clamps mask < -16 to -inf as a numerical safeguard.
-                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_keep0, hvx_vec_add_f16_f16(my_row_buf0[ci], v_mask0), v_neg_inf);
-                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_keep1, hvx_vec_add_f16_f16(my_row_buf1[ci], v_mask1), v_neg_inf);
-                    }
-                } else {
-                    if (ne < 64) {
-                        my_row_buf0[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf0[ci], v_neg_inf);
-                        my_row_buf1[ci] = Q6_V_vmux_QVV(q_tail_keep, my_row_buf1[ci], v_neg_inf);
-                    }
-                }
-
-                v_s_rowmax0 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax0, my_row_buf0[ci]);
-                v_s_rowmax1 = Q6_Vhf_vmax_VhfVhf(v_s_rowmax1, my_row_buf1[ci]);
-            }
-
-            v_s_rowmax0 = hvx_vec_reduce_max_f16(v_s_rowmax0);
-            v_s_rowmax1 = hvx_vec_reduce_max_f16(v_s_rowmax1);
-
-            // Splat m_prev[r], m_prev[r+1] from the per-row accumulator.
-            // vror brings the target lane to lane 0, then vdelta replicates it
-            // across all lanes — stays in the vector domain (no store/reload).
-            HVX_Vector v_m_prev0 = hvx_vec_repl_f16(Q6_V_vror_VR(m_prev_v, r_vec_off * 2));
-            HVX_Vector v_m_prev1 = hvx_vec_repl_f16(Q6_V_vror_VR(m_prev_v, (r_vec_off + 1) * 2));
-
-            // HVX max — both operands are splats, so result is splat of m_new.
-            HVX_Vector v_dup_m0 = Q6_Vhf_vmax_VhfVhf(v_m_prev0, v_s_rowmax0);
-            HVX_Vector v_dup_m1 = Q6_Vhf_vmax_VhfVhf(v_m_prev1, v_s_rowmax1);
-
-            // Insert row r, r+1 rowmax into rowmax_acc_v via 2-byte-wide vmux.
-            // Byte ranges: lane0 = [r_vec_off*2 .. r_vec_off*2+1], lane1 shifted by 2.
-            // vsetq2 handles the n=128 corner case when r_vec_off reaches 62.
-            {
-                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
-                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
-                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
-                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
-                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
-                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane0, v_dup_m0, rowmax_acc_v);
-                rowmax_acc_v           = Q6_V_vmux_QVV(p_lane1, v_dup_m1, rowmax_acc_v);
-            }
-
-            // Compute P = exp(S - m_new), using HVX exp
-            const HVX_Vector v_zero      = Q6_V_vzero();
-            HVX_Vector       v_p_rowsum0 = v_zero;
-            HVX_Vector       v_p_rowsum1 = v_zero;
-
-#ifdef HMX_FA_USE_EXP2_HF
-            // FP16 exp2 polynomial path (matches htp-ops-lib flash_attn.c):
-            // P = exp2(S - m_new)
-            for (size_t c = 0; c < kv_rows; c += 64) {
-                size_t     ci           = c / 64;
-                HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0);
-                HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1);
-
-                HVX_Vector v_p_row0_hf  = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_s_minus_m0));
-                HVX_Vector v_p_row1_hf  = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_s_minus_m1));
-#else
-            // F32 exp path: qf16 → f32 → exp → f32 → f16.  Higher precision,
-            for (size_t c = 0; c < kv_rows; c += 64) {
-                size_t     ci           = c / 64;
-                HVX_Vector v_s_minus_m0 = Q6_Vqf16_vsub_VhfVhf(my_row_buf0[ci], v_dup_m0);
-                HVX_Vector v_s_minus_m1 = Q6_Vqf16_vsub_VhfVhf(my_row_buf1[ci], v_dup_m1);
-
-                HVX_VectorPair vp0         = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_s_minus_m0));
-                HVX_Vector     p0_lo       = hvx_vec_exp_f32(Q6_V_lo_W(vp0));
-                HVX_Vector     p0_hi       = hvx_vec_exp_f32(Q6_V_hi_W(vp0));
-                HVX_Vector     v_p_row0_hf = hvx_vec_f32_to_f16_shuff(p0_lo, p0_hi);
-
-                HVX_VectorPair vp1         = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_s_minus_m1));
-                HVX_Vector     p1_lo       = hvx_vec_exp_f32(Q6_V_lo_W(vp1));
-                HVX_Vector     p1_hi       = hvx_vec_exp_f32(Q6_V_hi_W(vp1));
-                HVX_Vector     v_p_row1_hf = hvx_vec_f32_to_f16_shuff(p1_lo, p1_hi);
-#endif
-                // Write P to tile format.  Dual-tile pattern assumes Bc is a
-                // multiple of 64 (enforced by bc_unit=64 in hmx_fa_find_chunk_size),
-                // so both tile halves are always in the current r0 block.
-                __fp16 *     out_dual_tile = p_st_base + (c / 64) * HMX_FP16_TILE_N_ELMS * 2;
-                HVX_Vector * pv_p_out0     = ((HVX_Vector *) out_dual_tile) + r1 / 2;
-                HVX_Vector * pv_p_out1     = pv_p_out0 + 16;
-
-                HVX_VectorPair vp_p_dual = Q6_W_vshuff_VVR(v_p_row1_hf, v_p_row0_hf, -2);
-                *pv_p_out0               = Q6_V_lo_W(vp_p_dual);
-                *pv_p_out1               = Q6_V_hi_W(vp_p_dual);
-
-                HVX_VectorPair vp_p0 = hvx_vec_f16_to_f32_shuff(v_p_row0_hf);
-                HVX_VectorPair vp_p1 = hvx_vec_f16_to_f32_shuff(v_p_row1_hf);
-
-                v_p_rowsum0 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum0, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p0), Q6_V_hi_W(vp_p0)));
-                v_p_rowsum1 = Q6_Vqf32_vadd_Vqf32Vqf32(v_p_rowsum1, Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(vp_p1), Q6_V_hi_W(vp_p1)));
-            }
-
-            HVX_Vector rowsum0_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum0));
-            HVX_Vector rowsum1_sf = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(v_p_rowsum1));
-            {
-                // Both inputs are f32 splats, so the f32->f16 output is an fp16 splat.
-                HVX_Vector rv0_v = hvx_vec_f32_to_f16(rowsum0_sf, rowsum0_sf);
-                HVX_Vector rv1_v = hvx_vec_f32_to_f16(rowsum1_sf, rowsum1_sf);
-
-                HVX_VectorPred p_start = Q6_Q_vsetq_R(r_vec_off * 2);
-                HVX_VectorPred p_mid   = Q6_Q_vsetq_R((r_vec_off + 1) * 2);
-                HVX_VectorPred p_end   = Q6_Q_vsetq2_R((r_vec_off + 2) * 2);
-                HVX_VectorPred p_lane0 = Q6_Q_and_QQn(p_mid, p_start);
-                HVX_VectorPred p_lane1 = Q6_Q_and_QQn(p_end, p_mid);
-                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane0, rv0_v, rowsum_acc_v);
-                rowsum_acc_v           = Q6_V_vmux_QVV(p_lane1, rv1_v, rowsum_acc_v);
-            }
-        }
-
-        factx->vtcm_s_rowmax[r_vec_idx] = rowmax_acc_v;
-        factx->vtcm_p_rowsum[r_vec_idx] = rowsum_acc_v;
-    }
-    htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, vec_start);
-}
-
-// Serial m/l update + build_D.  Must run after softmax barrier (s_rowmax written by all threads).
-//
-// noinline: function boundary acts as a hard compiler barrier so the (size_t)addr scatter
-// intrinsics inside cannot be hoisted past the call site.  Mirrors the structural protection
-// matmul gets for free via worker_pool function-pointer dispatch.  Without this, the compiler
-// can reorder the scatter past the subsequent hmx_queue_push and the HMX-queue worker thread
-// reads stale VTCM (PPL → ~vocab-size).
-static __attribute__((noinline)) void fa_ml_update_and_build_d(struct hmx_fa_context * factx,
-                                                               size_t                  n_rows_g,
-                                                               size_t                  n_row_tiles,
-                                                               size_t                  n_row_tiles_g_br) {
-    // Reuse s_rowmax buffer for exp(m_diff) — safe because softmax is fully complete
-    HVX_Vector * const mvec_exp_m_diff = factx->vtcm_s_rowmax;
-
-    const size_t n_row_vec_cnt = hmx_ceil_div(n_rows_g, 64);
-    for (size_t i = 0; i < n_row_vec_cnt; ++i) {
-        HVX_Vector v_m_prev = factx->vtcm_m_vec[i];
-        HVX_Vector v_m_curr = Q6_Vhf_vmax_VhfVhf(v_m_prev, factx->vtcm_s_rowmax[i]);
-        HVX_Vector v_m_diff = Q6_Vqf16_vsub_VhfVhf(v_m_prev, v_m_curr);
-
-#ifdef HMX_FA_USE_EXP2_HF
-        // Base-2 path: must match P = exp2(S - m_new) in fa_softmax_thread.
-        HVX_Vector v_exp_m_diff      = hvx_exp2_hf(Q6_Vhf_equals_Vqf16(v_m_diff));
-#else
-        HVX_VectorPair vp_diff       = hvx_vec_f16_to_f32_shuff(Q6_Vhf_equals_Vqf16(v_m_diff));
-        HVX_Vector     exp_lo        = hvx_vec_exp_f32(Q6_V_lo_W(vp_diff));
-        HVX_Vector     exp_hi        = hvx_vec_exp_f32(Q6_V_hi_W(vp_diff));
-        HVX_Vector     v_exp_m_diff  = hvx_vec_f32_to_f16_shuff(exp_lo, exp_hi);
-#endif
-
-        HVX_Vector v_l_curr = Q6_Vqf16_vmpy_Vqf16Vhf(factx->vtcm_l_vec[i], v_exp_m_diff);
-        v_l_curr            = Q6_Vqf16_vadd_Vqf16Vhf(v_l_curr, factx->vtcm_p_rowsum[i]);
-
-        factx->vtcm_m_vec[i] = v_m_curr;
-        factx->vtcm_l_vec[i] = v_l_curr;
-        mvec_exp_m_diff[i]   = v_exp_m_diff;
-    }
-
-    // Build diagonal tile D = diag(exp(m_diff))
-    const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
-    const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
-    for (size_t i = 0; i < n_row_tiles; ++i) {
-        const HVX_Vector v_content = Q6_V_vror_VR(mvec_exp_m_diff[i / 2], (i % 2) * 64);
-        __fp16 *         out_base  = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-        Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
-        // Compiler barrier — Q6_vscatter takes (size_t)addr; without this the
-        // compiler may not recognize the volatile read below as aliasing and
-        // could reorder it before the scatter, defeating the HW drain.
-        __asm__ __volatile__("" ::: "memory");
-        // Per-tile drain: scatter regions are disjoint (stride > tile size),
-        // so a single drain at tile 0 does NOT retire later tiles' entries.
-        (void) *(volatile HVX_Vector *) out_base;
-    }
-}
-
-// Build D = diag(1/l) tile for the final O = D @ O normalization.
-//
-// noinline: same rationale as fa_ml_update_and_build_d — keeps Q6_vscatter from
-// being hoisted past the subsequent hmx_queue_push at the o_norm call site.
-static __attribute__((noinline)) void fa_build_d_diag_inv_l(struct hmx_fa_context * factx,
-                                                            size_t                  n_row_tiles,
-                                                            size_t                  n_row_tiles_g_br) {
-    const HVX_Vector     v_offsets = *(const HVX_Vector *) d_tile_scatter_offsets;
-    const HVX_VectorPred q_32_mask = Q6_Q_vsetq_R(32 * sizeof(__fp16));
-    const HVX_Vector     one       = hvx_vec_splat_f32(1.0f);
-
-    HVX_Vector v_content = Q6_V_vzero();
-    for (size_t i = 0; i < n_row_tiles; ++i) {
-        if ((i % 2) == 0) {
-            HVX_Vector     v_l_hf = Q6_Vhf_equals_Vqf16(factx->vtcm_l_vec[i / 2]);
-            HVX_VectorPair vp_l   = hvx_vec_f16_to_f32_shuff(v_l_hf);
-            HVX_Vector     inv_lo = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(one, hvx_vec_inverse_f32(Q6_V_lo_W(vp_l))));
-            HVX_Vector     inv_hi = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(one, hvx_vec_inverse_f32(Q6_V_hi_W(vp_l))));
-            v_content = hvx_vec_f32_to_f16_shuff(inv_lo, inv_hi);
-        } else {
-            v_content = Q6_V_vror_VR(v_content, 64);
-        }
-
-        __fp16 * out_base = factx->vtcm_d_tiles + i * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-        Q6_vscatter_QRMVhV(q_32_mask, (size_t) out_base, HMX_FP16_TILE_SIZE - 1, v_offsets, v_content);
-        // Compiler barrier — see fa_ml_update_and_build_d for rationale.
-        __asm__ __volatile__("" ::: "memory");
-        (void) *(volatile HVX_Vector *) out_base;
-    }
-}
-
-// Combined: multi-thread softmax -> barrier -> serial m/l update + build_D
-static void fa_phase_softmax_and_build_d(struct hmx_fa_context * factx,
-                                         fa_softmax_args_t *     sargs,
-                                         size_t                  n_row_tiles,
-                                         size_t                  n_row_tiles_g_br) {
-    worker_pool_context_t wp = factx->octx->ctx->worker_pool;
-    const size_t n_row_vec_cnt = hmx_ceil_div(sargs->n_rows_g, 64);
-
-    if (factx->n_threads > 1 && n_row_vec_cnt >= 2) {
-        uint32_t n_use = (uint32_t) hex_smin((size_t) factx->n_threads, n_row_vec_cnt);
-        worker_pool_run_func(wp, fa_softmax_thread, sargs, n_use);
-    } else {
-        fa_softmax_thread(1, 0, sargs);
-    }
-    // barrier implicit in worker_pool_run_func return
-
-    fa_ml_update_and_build_d(factx, sargs->n_rows_g, n_row_tiles, n_row_tiles_g_br);
-}
-
-// ============================================================================
-// HMX job structs and worker functions
-// ============================================================================
-
-typedef struct {
-    const __fp16 * q_tiles;
-    const __fp16 * k_tiles;
-    __fp16 *       s_tiles;
-    size_t         n_row_tiles;
-    size_t         n_col_tiles;
-    size_t         n_dot_tiles;  // DK / 32
-    size_t         n_tiles_per_bc;
-    uint8_t *      hmx_scales;
-} hmx_fa_qk_job_t;
-
-static void hmx_fa_qk_dot_worker(void * data) {
-    hmx_fa_qk_job_t * job            = (hmx_fa_qk_job_t *) data;
-    const size_t      n_row_tiles    = job->n_row_tiles;
-    const size_t      n_col_tiles    = job->n_col_tiles;
-    const size_t      n_dot_tiles    = job->n_dot_tiles;
-    const size_t      n_tiles_per_bc = job->n_tiles_per_bc;
-    const __fp16 * restrict q_tiles  = job->q_tiles;
-    const __fp16 * restrict k_tiles  = job->k_tiles;
-    __fp16 * restrict s_tiles        = job->s_tiles;
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(n_col_tiles > 0);
-    __builtin_assume(n_dot_tiles > 0);
-
-    Q6_bias_mxmem2_A((void *) job->hmx_scales);
-    for (size_t r = 0; r < n_row_tiles; ++r) {
-        for (size_t c = 0; c < n_col_tiles; ++c) {
-            const __fp16 * row_tiles = q_tiles + r * HMX_FP16_TILE_N_ROWS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
-            const __fp16 * col_tiles = k_tiles + c * HMX_FP16_TILE_N_COLS * n_dot_tiles * HMX_FP16_TILE_N_COLS;
-            __fp16 *       out_tile  = s_tiles + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
-
-            for (size_t k = 0; k < n_dot_tiles; ++k) {
-                Q6_activation_hf_mxmem_RR((unsigned int) row_tiles, 2047);
-                Q6_weight_hf_mxmem_RR((unsigned int) col_tiles, 2047);
-                row_tiles += HMX_FP16_TILE_N_ELMS;
-                col_tiles += HMX_FP16_TILE_N_ELMS;
-            }
-            Q6_mxmem_AR_after_hf(out_tile, 0);
-        }
-    }
-}
-
-typedef struct {
-    __fp16 *       o_curr;
-    const __fp16 * o_prev;
-    const __fp16 * p_tiles;
-    const __fp16 * v_tiles;
-    const __fp16 * d_tiles;
-    uint8_t *      hmx_scales;
-    size_t         n_row_tiles;
-    size_t         n_col_tiles;
-    size_t         n_row_tiles_g_br;
-    size_t         n_tiles_per_bc;
-    size_t         DV;
-} hmx_fa_o_update_job_t;
-
-static void hmx_fa_o_update_worker(void * data) {
-    hmx_fa_o_update_job_t * job              = (hmx_fa_o_update_job_t *) data;
-    const size_t            n_row_tiles      = job->n_row_tiles;
-    const size_t            n_col_tiles      = job->n_col_tiles;
-    const size_t            n_row_tiles_g_br = job->n_row_tiles_g_br;
-    const size_t            n_tiles_per_bc   = job->n_tiles_per_bc;
-    const size_t            DV_tiles         = job->DV / 32;
-    const __fp16 * restrict d_tiles          = job->d_tiles;
-    const __fp16 * restrict p_tiles          = job->p_tiles;
-    const __fp16 * restrict v_tiles          = job->v_tiles;
-    const __fp16 * restrict o_prev           = job->o_prev;
-    __fp16 * restrict o_curr                 = job->o_curr;
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(n_col_tiles > 0);
-    __builtin_assume(DV_tiles > 0);
-
-    Q6_bias_mxmem2_A((void *) job->hmx_scales);
-    for (size_t r = 0; r < n_row_tiles; ++r) {
-        for (size_t c = 0; c < DV_tiles; ++c) {
-            // D[r,r] @ O_prev[r,c] — only the diagonal tile
-            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-            Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
-            Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
-
-            // P @ V (accumulate on same accumulator)
-            const __fp16 * p_tile_in = p_tiles + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
-            const __fp16 * v_tile_in = v_tiles + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
-            for (size_t k = 0; k < n_col_tiles; ++k) {
-                Q6_activation_hf_mxmem_RR((unsigned int) p_tile_in, 2047);
-                Q6_weight_hf_mxmem_RR((unsigned int) v_tile_in, 2047);
-                p_tile_in += HMX_FP16_TILE_N_ELMS;
-                v_tile_in += HMX_FP16_TILE_N_ELMS;
-            }
-
-            __fp16 * o_tile_out = o_curr + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-            Q6_mxmem_AR_after_hf(o_tile_out, 0);
-        }
-    }
-}
-
-typedef struct {
-    __fp16 *       o_curr;   // output (row-major tile layout)
-    const __fp16 * o_prev;   // input (column-major tile layout)
-    const __fp16 * d_tiles;  // diag(1/l) tiles
-    uint8_t *      hmx_scales;
-    size_t         n_row_tiles;
-    size_t         n_row_tiles_g_br;
-    size_t         DV;
-} hmx_fa_o_norm_job_t;
-
-static void hmx_fa_o_norm_worker(void * data) {
-    hmx_fa_o_norm_job_t * job              = (hmx_fa_o_norm_job_t *) data;
-    const size_t          n_row_tiles      = job->n_row_tiles;
-    const size_t          n_row_tiles_g_br = job->n_row_tiles_g_br;
-    const size_t          DV_tiles         = job->DV / 32;
-    const __fp16 * restrict d_tiles        = job->d_tiles;
-    const __fp16 * restrict o_prev         = job->o_prev;
-    __fp16 * restrict o_curr               = job->o_curr;
-    __builtin_assume(n_row_tiles > 0);
-    __builtin_assume(DV_tiles > 0);
-
-    Q6_bias_mxmem2_A((void *) job->hmx_scales);
-    for (size_t r = 0; r < n_row_tiles; ++r) {
-        for (size_t c = 0; c < DV_tiles; ++c) {
-            const __fp16 * d_diag = d_tiles + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-            const __fp16 * o_rc   = o_prev + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-            __fp16 *       o_out  = o_curr + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
-
-            Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
-            Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
-            Q6_mxmem_AR_after_hf(o_out, 0);
-        }
-    }
-}
-
-// Populate per-GQA-row ALiBi slopes for a given KV head.
-// Row r in the GQA-merged block maps to Q head h = kv_head * G + r % G.
-// slope(h) = m0^(h+1) when h < n_head_log2, else m1^(2*(h-n_head_log2)+1).
-// When max_bias == 0, all slopes are 1.0 (no ALiBi).
-static __attribute__((noinline)) void fa_compute_slopes(
-                              const struct hmx_fa_context * factx,
-                              uint32_t                      kv_head,
-                              size_t                        n_rows_g) {
-    __fp16 * slopes = factx->vtcm_slopes;
-    if (factx->max_bias == 0.0f) {
-        hvx_splat_f16_a(slopes, 1.0f, n_rows_g);
-        return;
-    }
-
-    const uint32_t G           = factx->G;
-    const uint32_t n_head_log2 = factx->n_head_log2;
-    const float    m0          = factx->m0;
-    const float    m1          = factx->m1;
-
-    __fp16 temp_slopes[512] __attribute__((aligned(128)));
-    if (G <= 32) {
-        // Fast path: Compute G unique slope values in vector registers
-        HVX_Vector v_val = hvx_alibi_slopes(kv_head, G, n_head_log2, m0, m1);
-
-        __fp16 temp_slopes_aligned[64] __attribute__((aligned(128)));
-        hvx_vmem(temp_slopes_aligned) = hvx_vec_f32_to_f16(v_val, Q6_V_vzero());
-
-        for (uint32_t i = 0; i < G; ++i) {
-            temp_slopes[i] = temp_slopes_aligned[i];
-        }
-    } else {
-        // Fallback path: G > 32 (rare configurations)
-        for (uint32_t i = 0; i < G; ++i) {
-            temp_slopes[i] = (__fp16)alibi_slope(kv_head * G + i, n_head_log2, m0, m1);
-        }
-    }
-
-    // Allocate stack buffer to avoid scalar writes to VTCM (which generates L2 misses)
-    __fp16 local_slopes[n_rows_g] __attribute__((aligned(128)));
-    for (size_t r = 0; r < n_rows_g; ++r) {
-        local_slopes[r] = temp_slopes[fastmodulo(r, G, &factx->div_G)];
-    }
-
-    // Copy to VTCM slopes using HVX block copy (both are aligned to 128 bytes)
-    hvx_copy_f16_aa((uint8_t *)slopes, (const uint8_t *)local_slopes, n_rows_g);
-}
-
-// ============================================================================
-// Core HMX flash attention algorithm (GQA-merged)
-// ============================================================================
-
-int hmx_flash_attn_ext(struct htp_ops_context * octx) {
-    struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[HTP_MAX_NTHREADS] : NULL;
-    const struct htp_tensor * q    = octx->src[0];
-    const struct htp_tensor * k    = octx->src[1];
-    const struct htp_tensor * v    = octx->src[2];
-    const struct htp_tensor * mask = (octx->src[3] && octx->src[3]->data) ? octx->src[3] : NULL;
-    const struct htp_tensor * dst  = octx->dst;
-
-    struct htp_context * const ctx = octx->ctx;
-
-    if (!ctx->hmx_enabled) {
-        return HTP_STATUS_NO_SUPPORT;
-    }
-
-    // Dimensions
-    const uint32_t neq0 = q->ne[0];  // head_dim (DK)
-    const uint32_t neq1 = q->ne[1];  // n_tokens
-    const uint32_t neq2 = q->ne[2];  // n_heads
-    const uint32_t neq3 = q->ne[3];  // n_seqs
-
-    const uint32_t nek0 = k->ne[0];  // head_dim
-    const uint32_t nek1 = k->ne[1];  // kv_len
-
-    const uint32_t nev0 = v->ne[0];  // head_dim (DV)
-
-    const uint32_t DK = neq0;
-    const uint32_t DV = nev0;
-
-    // HMX requires head_dim to be multiple of 32
-    if (DK % 32 != 0 || DV % 32 != 0) {
-        return HTP_STATUS_NO_SUPPORT;
-    }
-
-    // GQA factor
-    const uint32_t n_kv_heads = k->ne[2];
-    const uint32_t G          = neq2 / n_kv_heads;
-
-    // Thread count for multi-thread HVX phases
-    const uint32_t n_threads_init = octx->n_threads;
-
-    // Compute dynamic block sizes (GQA-aware, accounting for per-thread row bufs)
-    size_t       Br, Bc;
-    const size_t vtcm_budget = ctx->vtcm_size;
-    if (hmx_fa_find_chunk_size(&Br, &Bc, G, DK, DV, neq1, nek1, vtcm_budget, n_threads_init) != 0) {
-        return HTP_STATUS_VTCM_TOO_SMALL;
-    }
-
-    const size_t g_br = hex_align_up(G * Br, HMX_FP16_TILE_N_ROWS);
-
-    const uint32_t n_kv_blocks  = (nek1 + Bc - 1) / Bc;
-    const bool     pipeline = (n_kv_blocks >= FA_MIN_KV_BLOCKS && n_threads_init >= 2);
-
-    // Bypass thread pool dispatch for small prompts/non-pipelined prefill by setting n_threads = 1
-    const uint32_t n_threads = pipeline ? n_threads_init : 1;
-
-    FARF(HIGH, "hmx-fa: neq1=%u nek1=%u DK=%u DV=%u G=%u Br=%zu Bc=%zu g_br=%zu n_kv_blocks=%u pipeline=%d vtcm=%zu",
-         neq1, nek1, DK, DV, G, Br, Bc, g_br, n_kv_blocks, pipeline, vtcm_budget);
-
-    // ======== Build context ========
-    struct hmx_fa_context factx;
-    memset(&factx, 0, sizeof(factx));
-    factx.octx           = octx;
-    factx.n_threads      = n_threads;
-    factx.DK             = DK;
-    factx.DV             = DV;
-    factx.n_kv           = nek1;
-    factx.n_kv_heads     = n_kv_heads;
-    factx.n_heads        = neq2;
-    factx.G              = G;
-    factx.div_G          = init_fastdiv_values(G);
-    factx.neq1           = neq1;
-    factx.Br             = (uint32_t) Br;
-    factx.Bc             = (uint32_t) Bc;
-    factx.g_br           = (uint32_t) g_br;
-    factx.n_kv_blocks    = n_kv_blocks;
-    factx.is_q_fp32      = (q->type == HTP_TYPE_F32);
-    factx.is_dst_fp32    = (dst->type == HTP_TYPE_F32);
-    factx.pipeline   = pipeline;
-    factx.mask_broadcast = (mask != NULL && mask->ne[2] == 1);
-
-    // Extract op parameters (mutable during softcap adjustment, then stored as const in factx)
-    float scale = 1.0f, max_bias = 0.0f, logit_softcap = 0.0f;
-    memcpy(&scale, (float *) octx->op_params + 0, sizeof(float));
-    memcpy(&max_bias, (float *) octx->op_params + 1, sizeof(float));
-    memcpy(&logit_softcap, (float *) octx->op_params + 2, sizeof(float));
-
-    if (logit_softcap != 0.0f) {
-        scale /= logit_softcap;
-    }
-
-#ifdef HMX_FA_USE_EXP2_HF
-    // Pre-bake log2(e) into qk_scale so HMX-produced S tiles are in log2(e)-scaled
-    // space.  Then exp2(S - m) in the softmax equals base-e exp((S - m) / log2(e)),
-    // preserving ggml's base-e softmax semantics.  Matches htp-ops-lib flash_attn.c.
-    //
-    // When softcap is active we cannot pre-bake log2(e) here — it would land inside
-    // the tanh argument and shift the softcap knee from x≈c to x≈c/log2(e), giving
-    // numerically wrong softcapped values.  Instead fold log2(e) into the post-tanh
-    // multiplier (see softcap block: v_cap absorbs log2(e)).
-    if (logit_softcap == 0.0f) {
-        scale *= 1.44269504f;  // log2(e)
-    }
-#endif
-
-    factx.scale         = scale;
-    factx.max_bias      = max_bias;
-    factx.logit_softcap = logit_softcap;
-
-    factx.n_head_log2 = 1u << (uint32_t) floor(log2(neq2));
-    factx.m0          = powf(2.0f, -(max_bias) / factx.n_head_log2);
-    factx.m1          = powf(2.0f, -(max_bias / 2.0f) / factx.n_head_log2);
-
-    // ======== VTCM allocation (GQA-aware) ========
-    const size_t size_k_row        = DK * sizeof(__fp16);
-    const size_t size_v_row        = DV * sizeof(__fp16);
-    const size_t size_k_row_padded = hex_round_up(size_k_row, 128);
-    const size_t size_v_row_padded = hex_round_up(size_v_row, 128);
-
-    const size_t q_tile_bytes  = hex_align_up(g_br * DK * sizeof(__fp16), 4096);
-    const size_t o_tile_bytes  = hex_align_up(g_br * DV * sizeof(__fp16), 4096);
-    const size_t k_dma_bytes   = hex_align_up(Bc * size_k_row_padded, 4096);
-    const size_t v_dma_bytes   = hex_align_up(Bc * size_v_row_padded, 4096);
-    const size_t k_tile_bytes  = hex_align_up(Bc * DK * sizeof(__fp16), 4096);
-    const size_t v_tile_bytes  = hex_align_up(Bc * DV * sizeof(__fp16), 4096);
-    const size_t s_tile_bytes  = hex_align_up(g_br * Bc * sizeof(__fp16), 4096);
-    const size_t d_tile_bytes  = hex_align_up(g_br * g_br * sizeof(__fp16), 4096);
-    const size_t col_vec_bytes = hex_align_up(g_br * sizeof(__fp16), 256);
-    const size_t row_vec_bytes = hex_align_up(Bc * sizeof(__fp16), 256);
-    const size_t m_line_bytes  = hex_align_up(Bc * sizeof(__fp16), 128);
-    const size_t m_buf_bytes   = hex_align_up(Br * m_line_bytes, 4096);
-    const size_t slopes_bytes  = hex_align_up(g_br * sizeof(__fp16), 128);
-
-    uint8_t * vtcm_cur = ctx->vtcm_base;
-
-    factx.vtcm_q_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, q_tile_bytes);
-    factx.vtcm_o_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
-    factx.vtcm_o_tiles[1]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, o_tile_bytes);
-    factx.vtcm_k_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
-    factx.vtcm_k_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_dma_bytes);
-    factx.vtcm_v_fp16[0]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
-    factx.vtcm_v_fp16[1]      = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_dma_bytes);
-    factx.vtcm_k_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, k_tile_bytes);
-    factx.vtcm_v_tiles[0]     = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
-    if (pipeline) {
-        factx.vtcm_v_tiles[1] = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, v_tile_bytes);
-    } else {
-        factx.vtcm_v_tiles[1] = NULL;
-    }
-    factx.vtcm_s_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
-    factx.vtcm_p_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, s_tile_bytes);
-    factx.vtcm_d_tiles        = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, d_tile_bytes);
-    factx.vtcm_m_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
-    factx.vtcm_l_vec          = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
-    factx.vtcm_s_rowmax       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
-    factx.vtcm_p_rowsum       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, col_vec_bytes);
-    factx.vtcm_row_bufs       = (HVX_Vector *) vtcm_seq_alloc(&vtcm_cur, row_vec_bytes * 2 * n_threads);
-    factx.row_buf_stride      = row_vec_bytes / sizeof(HVX_Vector);
-    factx.vtcm_hmx_scales_id  = vtcm_seq_alloc(&vtcm_cur, 256);
-    factx.vtcm_hmx_scales_qk  = vtcm_seq_alloc(&vtcm_cur, 256);
-    factx.vtcm_mask_buf       = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, m_buf_bytes);
-    factx.mask_buf_row_stride = m_line_bytes / sizeof(__fp16);
-    factx.vtcm_slopes         = (__fp16 *) vtcm_seq_alloc(&vtcm_cur, slopes_bytes);
-
-    if ((size_t) (vtcm_cur - ctx->vtcm_base) > ctx->vtcm_size) {
-        return HTP_STATUS_VTCM_TOO_SMALL;
-    }
-
-    // ======== Initialize HMX output scales ========
-    // Identity scale (1.0) for O updates and normalization
-    hmx_init_column_scales(factx.vtcm_hmx_scales_id, Q6_V_vsplat_R(0x3c00)); // 1.0
-
-    // QK scale embedded in HMX output
-    hmx_init_column_scales(factx.vtcm_hmx_scales_qk, hvx_vec_splat_f16(factx.scale));
-
-    // ======== Skip compute if profiling ========
-    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
-        return HTP_STATUS_OK;
-    }
-
-
-    // ======== DMA setup ========
-    dma_queue * const dma = ctx->dma[0];
-
-    // Padded row sizes for DMA (defined in outer scope)
-
-    const size_t n_row_tiles_g_br = g_br / HMX_FP16_TILE_N_ROWS;
-    const size_t n_tiles_per_bc   = Bc / HMX_FP16_TILE_N_COLS;
-
-    // Q/O element size for Q load and O store
-    const size_t qo_element_size = factx.is_q_fp32 ? sizeof(float) : sizeof(__fp16);
-
-    // ======== HMX lock strategy ========
-    // Pipeline: queue thread auto-acquires HMX lock on first push; released by suspend.
-    // Fallback: main thread holds the lock (original behavior).
-    if (!factx.pipeline) {
-        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
-    }
-
-    // ======== Reusable job descriptors for pipeline ========
-    hmx_fa_qk_job_t       qk_job;
-    hmx_fa_o_update_job_t ou_job;
-    hmx_fa_o_norm_job_t   on_job;
-
-    // ======== Main loop: per batch, per KV head, per Q block ========
-    for (uint32_t ib3 = 0; ib3 < neq3; ++ib3) {
-        for (uint32_t kv_head = 0; kv_head < n_kv_heads; ++kv_head) {
-            const uint32_t ik2 = kv_head;
-            const uint32_t ik3 = ib3 / (neq3 / k->ne[3]);
-            const uint32_t iv2 = kv_head;
-            const uint32_t iv3 = ib3 / (neq3 / v->ne[3]);
-
-            for (uint32_t q_start = 0; q_start < neq1; q_start += Br) {
-                const uint32_t n_q_rows    = hex_smin(Br, neq1 - q_start);
-                const size_t   n_rows_g    = n_q_rows * G;
-                const size_t   g_br_actual = hex_align_up(n_rows_g, HMX_FP16_TILE_N_ROWS);
-                const size_t   n_row_tiles = g_br_actual / HMX_FP16_TILE_N_ROWS;
-
-                // ---- Load Q block [g_br, D] -> tiles, interleaving G heads ----
-                if (n_rows_g < g_br) {
-                    hvx_splat_u8_a(factx.vtcm_q_tiles, 0, q_tile_bytes);
-                }
-                fa_phase_q_load(&factx, q, q_start, kv_head, ib3, n_rows_g);
-
-                // ---- Initialize per-block state ----
-                hvx_splat_u8_a(factx.vtcm_l_vec,   0,      col_vec_bytes);
-                hvx_splat_u8_a(factx.vtcm_d_tiles, 0,      d_tile_bytes);
-                hvx_splat_u16_a(factx.vtcm_m_vec,  0xfbff, col_vec_bytes/2);
-
-                __fp16 * o_tile_prev = factx.vtcm_o_tiles[0];
-                __fp16 * o_tile_curr = factx.vtcm_o_tiles[1];
-                hvx_splat_u8_a(o_tile_prev, 0, o_tile_bytes);
-
-                // ---- KV block loop with DMA double-buffering ----
-                size_t buf_idx = 0;
-
-                fa_compute_slopes(&factx, kv_head, n_rows_g);
-
-                // Prefetch first KV block
-                if (factx.n_kv_blocks > 0) {
-                    const uint32_t kv_rows0 = hex_smin(Bc, nek1);
-
-                    const uint8_t * k_src = (const uint8_t *) k->data + ik2 * k->nb[2] + ik3 * k->nb[3];
-                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[0], k_src), size_k_row_padded, k->nb[1],
-                                   size_k_row, kv_rows0);
-
-                    const uint8_t * v_src = (const uint8_t *) v->data + iv2 * v->nb[2] + iv3 * v->nb[3];
-                    dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[0], v_src), size_v_row_padded, v->nb[1],
-                                   size_v_row, kv_rows0);
-                }
-
-                // Mask DMA: single 2D transfer of n_q_rows unique mask rows into VTCM buffer.
-                // Only when mask is head-broadcast (ne[2]==1); otherwise softmax reads DDR directly.
-                #define MASK_DMA_PUSH(kv_start_val, kv_rows_val, has_mask_dma_var)                                             \
-                    do {                                                                                                       \
-                        has_mask_dma_var = false;                                                                              \
-                        if (mask && factx.mask_broadcast) {                                                                    \
-                            const uint32_t  _im3 = ib3 % mask->ne[3];                                                          \
-                            const uint8_t * _ms  = (const uint8_t *) mask->data + q_start * mask->nb[1] + _im3 * mask->nb[3] + \
-                                                  (kv_start_val) * sizeof(__fp16);                                             \
-                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_mask_buf, _ms), m_line_bytes, mask->nb[1],             \
-                                           (kv_rows_val) * sizeof(__fp16), n_q_rows);                                          \
-                            has_mask_dma_var = true;                                                                           \
-                        }                                                                                                      \
-                    } while (0)
-
-                #define MASK_DMA_POP(has_mask_dma_var) \
-                    do {                               \
-                        if (has_mask_dma_var) {        \
-                            dma_queue_pop(dma);        \
-                        }                              \
-                    } while (0)
-
-                #define DMA_PREFETCH_KV(blk_val)                                                                                          \
-                    do {                                                                                                                  \
-                        if ((blk_val) < factx.n_kv_blocks) {                                                                              \
-                            const uint32_t  _ns = (blk_val) * Bc;                                                                         \
-                            const uint32_t  _nr = hex_smin(Bc, nek1 - _ns);                                                               \
-                            size_t          _nb = 1 - buf_idx;                                                                            \
-                            const uint8_t * _ks = (const uint8_t *) k->data + _ns * k->nb[1] + ik2 * k->nb[2] + ik3 * k->nb[3];           \
-                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_k_fp16[_nb], _ks), size_k_row_padded, k->nb[1], size_k_row, _nr); \
-                            const uint8_t * _vs = (const uint8_t *) v->data + _ns * v->nb[1] + iv2 * v->nb[2] + iv3 * v->nb[3];           \
-                            dma_queue_push(dma, dma_make_ptr(factx.vtcm_v_fp16[_nb], _vs), size_v_row_padded, v->nb[1], size_v_row, _nr); \
-                        }                                                                                                                 \
-                    } while (0)
-
-                const size_t k_src_stride = size_k_row_padded / sizeof(__fp16);
-                const size_t v_src_stride = size_v_row_padded / sizeof(__fp16);
-
-                if (factx.pipeline) {
-                    // ==================================================================
-                    // Pipeline path: HVX phases ‖ HMX queue worker
-                    // ==================================================================
-                    struct hmx_queue * hmx_q = ctx->hmx_queue;
-
-                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
-                        const uint32_t kv_start    = kv_blk * Bc;
-                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
-                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
-
-                        // Wait for current KV DMA
-                        dma_queue_pop(dma);  // K
-                        dma_queue_pop(dma);  // V
-
-                        // Push mask DMA for this block (single 2D DMA when broadcast)
-                        bool has_mask_dma = false;
-                        MASK_DMA_PUSH(kv_start, kv_rows, has_mask_dma);
-
-                        // ---- Phase 1: K_int(blk) ‖ O_update(blk-1) ----
-                        if (kv_blk > 0) {
-                            // Submit O_update for previous block (HMX worker)
-                            ou_job.o_curr           = o_tile_curr;
-                            ou_job.o_prev           = o_tile_prev;
-                            ou_job.p_tiles          = factx.vtcm_p_tiles;
-                            ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
-                            ou_job.d_tiles          = factx.vtcm_d_tiles;
-                            ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
-                            ou_job.n_row_tiles      = n_row_tiles;
-                            ou_job.n_col_tiles      = hmx_ceil_div(hex_smin(Bc, nek1 - (kv_blk - 1) * Bc), HMX_FP16_TILE_N_COLS);
-                            ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
-                            ou_job.n_tiles_per_bc   = n_tiles_per_bc;
-                            ou_job.DV               = DV;
-                            hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
-                        }
-                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
-
-                        // ---- Phase 2: qk_dot(blk) on HMX ‖ V_int(blk) + DMA prefetch on HVX ----
-                        qk_job.q_tiles        = factx.vtcm_q_tiles;
-                        qk_job.k_tiles        = factx.vtcm_k_tiles;
-                        qk_job.s_tiles        = factx.vtcm_s_tiles;
-                        qk_job.n_row_tiles    = n_row_tiles;
-                        qk_job.n_col_tiles    = n_col_tiles;
-                        qk_job.n_dot_tiles    = DK / 32;
-                        qk_job.n_tiles_per_bc = n_tiles_per_bc;
-                        qk_job.hmx_scales     = factx.vtcm_hmx_scales_qk;
-                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_qk_dot_worker, &qk_job));
-
-                        // DMA push next block (non-blocking, before worker_pool)
-                        DMA_PREFETCH_KV(kv_blk + 1);
-                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
-
-                        // Pop and swap previous block's output update (deferred HMX pop)
-                        if (kv_blk > 0) {
-                            hmx_queue_pop(hmx_q);
-                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
-                        }
-
-                        // Pop current block's dot product job
-                        hmx_queue_pop(hmx_q);
-
-                        // ---- Phase 3: softmax(blk) + build_D(blk) | HMX idle ----
-                        // Pop mask DMA before softmax (ensures VTCM buffer is ready)
-                        MASK_DMA_POP(has_mask_dma);
-
-                        fa_softmax_args_t sargs;
-                        memset(&sargs, 0, sizeof(sargs));
-                        sargs.factx                = &factx;
-                        sargs.kv_rows              = kv_rows;
-                        sargs.n_rows_g             = n_rows_g;
-                        sargs.n_col_tiles          = n_col_tiles;
-                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
-                        sargs.n_row_tiles          = n_row_tiles;
-                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
-                        sargs.Bc                   = Bc;
-                        sargs.G                    = G;
-                        sargs.kv_head              = kv_head;
-                        sargs.kv_start             = kv_start;
-                        sargs.q_start              = q_start;
-                        sargs.ib3                  = ib3;
-                        sargs.has_alibi            = (factx.max_bias != 0.0f);
-                        sargs.mask                 = mask;
-                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
-                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
-                        sargs.slopes               = factx.vtcm_slopes;
-                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
-
-                        buf_idx = 1 - buf_idx;
-                    }  // end KV block loop (pipeline)
-
-                    // Epilogue: O_update for last block
-                    if (factx.n_kv_blocks > 0) {
-                        const uint32_t last_blk = factx.n_kv_blocks - 1;
-                        const size_t last_cols  = hmx_ceil_div(hex_smin(Bc, nek1 - last_blk * Bc), HMX_FP16_TILE_N_COLS);
-                        ou_job.o_curr           = o_tile_curr;
-                        ou_job.o_prev           = o_tile_prev;
-                        ou_job.p_tiles          = factx.vtcm_p_tiles;
-                        ou_job.v_tiles          = factx.vtcm_v_tiles[1 - buf_idx];
-                        ou_job.d_tiles          = factx.vtcm_d_tiles;
-                        ou_job.hmx_scales       = factx.vtcm_hmx_scales_id;
-                        ou_job.n_row_tiles      = n_row_tiles;
-                        ou_job.n_col_tiles      = last_cols;
-                        ou_job.n_row_tiles_g_br = n_row_tiles_g_br;
-                        ou_job.n_tiles_per_bc   = n_tiles_per_bc;
-                        ou_job.DV               = DV;
-                        hmx_queue_push(hmx_q, hmx_queue_make_desc(hmx_fa_o_update_worker, &ou_job));
-                        hmx_queue_pop(hmx_q);
-
-                        hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
-                    }
-
-                } else {
-                    // ==================================================================
-                    // Fallback path: sequential with multi-thread HVX phases
-                    // Main thread holds HMX lock, runs HMX inline.
-                    // ==================================================================
-
-                    for (uint32_t kv_blk = 0; kv_blk < factx.n_kv_blocks; ++kv_blk) {
-                        const uint32_t kv_start    = kv_blk * Bc;
-                        const uint32_t kv_rows     = hex_smin(Bc, nek1 - kv_start);
-                        const size_t   n_col_tiles = hmx_ceil_div(kv_rows, HMX_FP16_TILE_N_COLS);
-                        dma_queue_pop(dma);  // K
-                        dma_queue_pop(dma);  // V
-
-                        bool has_mask_dma = false;
-                        MASK_DMA_PUSH(kv_start, kv_rows, has_mask_dma);
-                        DMA_PREFETCH_KV(kv_blk + 1);
-                        fa_phase_k_interleave(&factx, kv_rows, k_src_stride, buf_idx);
-
-                        {
-                            const size_t n_dot_tiles       = (size_t) (DK / 32);
-                            const __fp16 * restrict q_base = factx.vtcm_q_tiles;
-                            const __fp16 * restrict k_base = factx.vtcm_k_tiles;
-                            __fp16 * restrict s_base       = factx.vtcm_s_tiles;
-                            __builtin_assume(n_row_tiles > 0);
-                            __builtin_assume(n_col_tiles > 0);
-                            __builtin_assume(n_dot_tiles > 0);
-
-                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_qk);
-                            for (size_t r = 0; r < n_row_tiles; ++r) {
-                                for (size_t c = 0; c < n_col_tiles; ++c) {
-                                    const __fp16 * row_tiles = q_base + r * HMX_FP16_TILE_N_ROWS * DK;
-                                    const __fp16 * col_tiles = k_base + c * HMX_FP16_TILE_N_COLS * DK;
-                                    __fp16 *       out_tile  = s_base + (r * n_tiles_per_bc + c) * HMX_FP16_TILE_N_ELMS;
-                                    for (size_t k = 0; k < n_dot_tiles; ++k) {
-                                        Q6_activation_hf_mxmem_RR((unsigned int) row_tiles, 2047);
-                                        Q6_weight_hf_mxmem_RR((unsigned int) col_tiles, 2047);
-                                        row_tiles += HMX_FP16_TILE_N_ELMS;
-                                        col_tiles += HMX_FP16_TILE_N_ELMS;
-                                    }
-                                    Q6_mxmem_AR_after_hf(out_tile, 0);
-                                }
-                            }
-                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                        }
-
-                        // Pop mask DMA
-                        MASK_DMA_POP(has_mask_dma);
-
-                        // Softmax + build_D (multi-thread HVX + serial m/l update)
-                        fa_softmax_args_t sargs;
-                        memset(&sargs, 0, sizeof(sargs));
-                        sargs.factx                = &factx;
-                        sargs.kv_rows              = kv_rows;
-                        sargs.n_rows_g             = n_rows_g;
-                        sargs.n_col_tiles          = n_col_tiles;
-                        sargs.n_tiles_per_bc       = n_tiles_per_bc;
-                        sargs.n_row_tiles          = n_row_tiles;
-                        sargs.n_row_tiles_g_br     = n_row_tiles_g_br;
-                        sargs.Bc                   = Bc;
-                        sargs.G                    = G;
-                        sargs.kv_head              = kv_head;
-                        sargs.kv_start             = kv_start;
-                        sargs.q_start              = q_start;
-                        sargs.ib3                  = ib3;
-                        sargs.has_alibi            = (factx.max_bias != 0.0f);
-                        sargs.mask                 = mask;
-                        sargs.mask_vtcm            = has_mask_dma ? (const __fp16 *) factx.vtcm_mask_buf : NULL;
-                        sargs.mask_vtcm_row_stride = factx.mask_buf_row_stride;
-                        sargs.slopes               = factx.vtcm_slopes;
-                        fa_phase_softmax_and_build_d(&factx, &sargs, n_row_tiles, n_row_tiles_g_br);
-                        fa_phase_v_interleave(&factx, kv_rows, v_src_stride, buf_idx, n_tiles_per_bc);
-
-                        {
-                            const size_t DV_tiles           = (size_t) (DV / 32);
-                            const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
-                            const __fp16 * restrict p_base  = factx.vtcm_p_tiles;
-                            const __fp16 * restrict v_base  = factx.vtcm_v_tiles[0];
-                            const __fp16 * restrict op_base = o_tile_prev;
-                            __fp16 * restrict oc_base       = o_tile_curr;
-                            __builtin_assume(n_row_tiles > 0);
-                            __builtin_assume(n_col_tiles > 0);
-                            __builtin_assume(DV_tiles > 0);
-
-                            htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                            Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
-                            for (size_t r = 0; r < n_row_tiles; ++r) {
-                                for (size_t c = 0; c < DV_tiles; ++c) {
-                                    const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-                                    const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-                                    Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
-                                    Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
-
-                                    const __fp16 * p_tile_in = p_base + (r * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
-                                    const __fp16 * v_tile_in = v_base + (c * n_tiles_per_bc) * HMX_FP16_TILE_N_ELMS;
-                                    for (size_t k = 0; k < n_col_tiles; ++k) {
-                                        Q6_activation_hf_mxmem_RR((unsigned int) p_tile_in, 2047);
-                                        Q6_weight_hf_mxmem_RR((unsigned int) v_tile_in, 2047);
-                                        p_tile_in += HMX_FP16_TILE_N_ELMS;
-                                        v_tile_in += HMX_FP16_TILE_N_ELMS;
-                                    }
-
-                                    __fp16 * o_tile_out = oc_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-                                    Q6_mxmem_AR_after_hf(o_tile_out, 0);
-                                }
-                            }
-                            htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                            hex_swap_ptr((void **) &o_tile_curr, (void **) &o_tile_prev);
-                        }
-
-                        buf_idx = 1 - buf_idx;
-                    }  // end KV block loop (fallback)
-                }
-
-                // ---- Final normalization: O = diag(1/l) @ O ----
-                {
-                    fa_build_d_diag_inv_l(&factx, n_row_tiles, n_row_tiles_g_br);
-
-                    // HMX: O_final = diag(1/l) @ O_prev
-                    if (factx.pipeline) {
-                        on_job.o_curr           = o_tile_curr;
-                        on_job.o_prev           = o_tile_prev;
-                        on_job.d_tiles          = factx.vtcm_d_tiles;
-                        on_job.hmx_scales       = factx.vtcm_hmx_scales_id;
-                        on_job.n_row_tiles      = n_row_tiles;
-                        on_job.n_row_tiles_g_br = n_row_tiles_g_br;
-                        on_job.DV               = DV;
-                        hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_fa_o_norm_worker, &on_job));
-                        hmx_queue_pop(ctx->hmx_queue);
-                    } else {
-                        const size_t DV_tiles           = (size_t) (DV / 32);
-                        const __fp16 * restrict d_base  = factx.vtcm_d_tiles;
-                        const __fp16 * restrict op_base = o_tile_prev;
-                        __fp16 * restrict oc_base       = o_tile_curr;
-                        __builtin_assume(n_row_tiles > 0);
-                        __builtin_assume(DV_tiles > 0);
-
-                        htp_trace_event_start(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                        Q6_bias_mxmem2_A((void *) factx.vtcm_hmx_scales_id);
-                        for (size_t r = 0; r < n_row_tiles; ++r) {
-                            for (size_t c = 0; c < DV_tiles; ++c) {
-                                const __fp16 * d_diag = d_base  + r * (n_row_tiles_g_br + 1) * HMX_FP16_TILE_N_ELMS;
-                                const __fp16 * o_rc   = op_base + (c * n_row_tiles_g_br + r) * HMX_FP16_TILE_N_ELMS;
-                                __fp16 *       o_out  = oc_base + (r * DV_tiles + c) * HMX_FP16_TILE_N_ELMS;
-
-                                Q6_activation_hf_mxmem_RR((unsigned int) d_diag, 2047);
-                                Q6_weight_hf_mxmem_RR((unsigned int) o_rc, 2047);
-                                Q6_mxmem_AR_after_hf(o_out, 0);
-                            }
-                        }
-                        htp_trace_event_stop(tr, HTP_TRACE_EVT_HMX_COMP, HTP_MAX_NTHREADS);
-                    }
-                }
-
-                // ---- Store O block ----
-                fa_phase_o_store(&factx, dst, o_tile_curr, q_start, kv_head, ib3, n_rows_g);
-
-#undef MASK_DMA_PUSH
-#undef MASK_DMA_POP
-#undef DMA_PREFETCH_KV
-
-            }  // end Q block loop
-        }  // end KV head loop
-    }  // end batch loop
-
-    if (factx.pipeline) {
-        hmx_queue_suspend(ctx->hmx_queue);
-    } else {
-        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
-    }
-
-
-
-    return HTP_STATUS_OK;
-}
diff --git a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
index b7fba22a8..4a0ca7885 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
+++ b/ggml/src/ggml-hexagon/htp/hmx-mm-kernels-tiled.h
@@ -712,7 +712,17 @@ static inline void hmx_matmul_job_init(hmx_matmul_job_t * job,
 
 // output : fp16 -> f32p
 
-static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16 *restrict vtcm_src, uint32_t start_row, uint32_t n_rows, uint32_t n_cols, uint32_t dst_stride, uint32_t dst_cols) {
+static void transfer_output_chunk_fp16_to_fp32(
+    float *restrict dst,
+    const float *restrict src2,
+    const __fp16 *restrict vtcm_src,
+    uint32_t start_row,
+    uint32_t n_rows,
+    uint32_t n_cols,
+    uint32_t dst_stride,
+    uint32_t src2_stride,
+    uint32_t dst_cols
+) {
     assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0);
     const size_t tile_row_stride = (n_cols / HTP_MM_HMX_TILE_N_COLS) * HTP_MM_HMX_TILE_N_ELMS;
 
@@ -727,6 +737,7 @@ static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16
         const size_t r1 = (r_idx0 % HTP_MM_HMX_TILE_N_ROWS) / 2;  // index of the row pair within the tile
         const __fp16 *row_base = vtcm_src + r0 * tile_row_stride;
         float *output_row_base = dst + r * dst_stride;  // global memory row base for row r (and r+1)
+        const float *src2_row_base = src2 ? (src2 + r * src2_stride) : NULL;
 
         #pragma unroll(4)
         for (size_t c = 0; c < limit_c_aligned; c += HTP_MM_HMX_TILE_N_COLS) {
@@ -738,9 +749,20 @@ static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16
             HVX_Vector *pv_out0 = (HVX_Vector *) (output_row_base + c + 0);
             HVX_Vector *pv_out1 = (HVX_Vector *) (output_row_base + c + dst_stride);
 
-            *pv_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            HVX_Vector v_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            if (src2_row_base) {
+                HVX_Vector v_src2_0 = hvx_vmemu(src2_row_base + c + 0);
+                v_out0 = hvx_vec_add_f32_f32(v_out0, v_src2_0);
+            }
+            *pv_out0 = v_out0;
+
             if (r + 1 < n_rows) {
-                *pv_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+                HVX_Vector v_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+                if (src2_row_base) {
+                    HVX_Vector v_src2_1 = hvx_vmemu(src2_row_base + c + src2_stride);
+                    v_out1 = hvx_vec_add_f32_f32(v_out1, v_src2_1);
+                }
+                *pv_out1 = v_out1;
             }
         }
 
@@ -752,9 +774,20 @@ static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16
             HVX_Vector v = ((const HVX_Vector *) tile)[r1];
             HVX_VectorPair vp = Q6_Wqf32_vmpy_VhfVhf(v, one);
 
-            hvx_vec_store_u(output_row_base + c, valid_c * sizeof(float), Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp)));
+            HVX_Vector v_out0 = Q6_Vsf_equals_Vqf32(Q6_V_lo_W(vp));
+            if (src2_row_base) {
+                HVX_Vector v_src2_0 = hvx_vmemu(src2_row_base + c + 0);
+                v_out0 = hvx_vec_add_f32_f32(v_out0, v_src2_0);
+            }
+            hvx_vec_store_u(output_row_base + c, valid_c * sizeof(float), v_out0);
+
             if (r + 1 < n_rows) {
-                hvx_vec_store_u(output_row_base + c + dst_stride, valid_c * sizeof(float), Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp)));
+                HVX_Vector v_out1 = Q6_Vsf_equals_Vqf32(Q6_V_hi_W(vp));
+                if (src2_row_base) {
+                    HVX_Vector v_src2_1 = hvx_vmemu(src2_row_base + c + src2_stride);
+                    v_out1 = hvx_vec_add_f32_f32(v_out1, v_src2_1);
+                }
+                hvx_vec_store_u(output_row_base + c + dst_stride, valid_c * sizeof(float), v_out1);
             }
         }
     }
@@ -763,11 +796,13 @@ static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16
 typedef struct {
     const __fp16  *vtcm_src;
     float         *dst;
+    const float   *src2;
     uint32_t       n_tasks;
     uint32_t       n_tot_chunks;
     uint32_t       n_chunks_per_task;
     uint32_t       n_cols;
     uint32_t       dst_stride;  // DDR row stride
+    uint32_t       src2_stride; // DDR row stride for residual
     uint32_t       dst_cols;    // Actual output columns
     struct htp_thread_trace * traces;
 } output_transfer_task_state_t;
diff --git a/ggml/src/ggml-hexagon/htp/hmx-utils.h b/ggml/src/ggml-hexagon/htp/hmx-utils.h
index f448ee337..561d26944 100644
--- a/ggml/src/ggml-hexagon/htp/hmx-utils.h
+++ b/ggml/src/ggml-hexagon/htp/hmx-utils.h
@@ -42,14 +42,14 @@ static const int32_t hmx_transpose_scatter_offsets[32] __attribute__((aligned(VL
 // Full range: start_row=0, end_row=n_cols.
 static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
                                             const __fp16 * restrict vtcm_src,
-                                            int n_cols,
-                                            int k,
-                                            int src_stride,
-                                            int start_row,
-                                            int end_row) {
+                                            uint32_t n_cols,
+                                            uint32_t k,
+                                            size_t src_stride,
+                                            uint32_t start_row,
+                                            uint32_t end_row) {
     assert(k % HMX_FP16_TILE_N_COLS == 0);
 
-    const int            n_k_tiles     = k / HMX_FP16_TILE_N_COLS;
+    const uint32_t       n_k_tiles     = k / HMX_FP16_TILE_N_COLS;
     const HVX_Vector     v_scat_base   = hvx_vmem(hmx_transpose_scatter_offsets);
     const HVX_Vector     v_scat_step   = Q6_V_vsplat_R(4);
     const HVX_VectorPred q_mask64      = Q6_Q_vsetq_R(64);
@@ -65,14 +65,14 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
 
     if (pair_scatter) {
         // Step c by 64 fp16 (two K-tiles per scatter), advance dst by 2 tiles per iter.
-        const int    c_step      = 2 * HMX_FP16_TILE_N_COLS;
-        const size_t c_byte_step = (size_t) c_step * sizeof(__fp16);
-        const size_t dst_step    = 2 * (size_t) HMX_FP16_TILE_N_ELMS;
-        const int    n_c_iters   = k / c_step;
+        const uint32_t c_step      = 2 * HMX_FP16_TILE_N_COLS;
+        const size_t   c_byte_step = (size_t) c_step * sizeof(__fp16);
+        const size_t   dst_step    = 2 * (size_t) HMX_FP16_TILE_N_ELMS;
+        const uint32_t n_c_iters   = k / c_step;
 
-        for (int r = start_row; r < end_row; r += 2) {
-            const int        ct             = r / HMX_FP16_TILE_N_ROWS;
-            const int        local_r        = r % HMX_FP16_TILE_N_ROWS;
+        for (uint32_t r = start_row; r < end_row; r += 2) {
+            const uint32_t   ct             = r / HMX_FP16_TILE_N_ROWS;
+            const uint32_t   local_r        = r % HMX_FP16_TILE_N_ROWS;
             const bool       next_row_valid = (r + 1) < end_row && (r + 1) < n_cols;
             const HVX_Vector v_off0         = Q6_Vw_vadd_VwVw(v_scat_base, Q6_V_vsplat_R(local_r * 4));
             const HVX_Vector v_off1         = Q6_Vw_vadd_VwVw(v_off0, v_scat_step);
@@ -86,7 +86,7 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
             assert(c_byte_step % 128 == 0);
 
             if (p1) {
-                for (int i = 0; i < n_c_iters; ++i) {
+                for (uint32_t i = 0; i < n_c_iters; ++i) {
                     HVX_Vector v0 = hvx_vmem(p0); p0 += c_byte_step;
                     HVX_Vector v1 = hvx_vmem(p1); p1 += c_byte_step;
                     Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off0, v0);
@@ -95,7 +95,7 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
                 }
             } else {
                 const HVX_Vector vzero = Q6_V_vzero();
-                for (int i = 0; i < n_c_iters; ++i) {
+                for (uint32_t i = 0; i < n_c_iters; ++i) {
                     HVX_Vector v0 = hvx_vmem(p0); p0 += c_byte_step;
                     Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off0, v0);
                     Q6_vscatter_RMVwV((size_t) tile_base, pair_region, v_off1, vzero);
@@ -105,14 +105,14 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
         }
     } else {
         // Fallback: scatter one K-tile per call (region 2047, masked).
-        const int    c_step      = HMX_FP16_TILE_N_COLS;
-        const size_t c_byte_step = (size_t) c_step * sizeof(__fp16);
-        const size_t dst_step    = (size_t) HMX_FP16_TILE_N_ELMS;
-        const int    n_c_iters   = k / c_step;
+        const uint32_t c_step      = HMX_FP16_TILE_N_COLS;
+        const size_t   c_byte_step = (size_t) c_step * sizeof(__fp16);
+        const size_t   dst_step    = (size_t) HMX_FP16_TILE_N_ELMS;
+        const uint32_t n_c_iters   = k / c_step;
 
-        for (int r = start_row; r < end_row; r += 2) {
-            const int        ct             = r / HMX_FP16_TILE_N_ROWS;
-            const int        local_r        = r % HMX_FP16_TILE_N_ROWS;
+        for (uint32_t r = start_row; r < end_row; r += 2) {
+            const uint32_t   ct             = r / HMX_FP16_TILE_N_ROWS;
+            const uint32_t   local_r        = r % HMX_FP16_TILE_N_ROWS;
             const bool       next_row_valid = (r + 1) < end_row && (r + 1) < n_cols;
             const HVX_Vector v_off0         = Q6_Vw_vadd_VwVw(v_scat_base, Q6_V_vsplat_R(local_r * 4));
             const HVX_Vector v_off1         = Q6_Vw_vadd_VwVw(v_off0, v_scat_step);
@@ -122,7 +122,7 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
             const uint8_t * p1 = next_row_valid ? (const uint8_t *) (vtcm_src + (r + 1) * src_stride) : NULL;
 
             if (p1) {
-                for (int i = 0; i < n_c_iters; ++i) {
+                for (uint32_t i = 0; i < n_c_iters; ++i) {
                     HVX_Vector v0 = hvx_vmemu(p0); p0 += c_byte_step;
                     HVX_Vector v1 = hvx_vmemu(p1); p1 += c_byte_step;
                     Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off0, v0);
@@ -131,7 +131,7 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
                 }
             } else {
                 const HVX_Vector vzero = Q6_V_vzero();
-                for (int i = 0; i < n_c_iters; ++i) {
+                for (uint32_t i = 0; i < n_c_iters; ++i) {
                     HVX_Vector v0 = hvx_vmemu(p0); p0 += c_byte_step;
                     Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off0, v0);
                     Q6_vscatter_QRMVwV(q_mask64, (size_t) tile_base, single_region, v_off1, vzero);
@@ -148,24 +148,24 @@ static inline void hmx_interleave_rows_to_tiles(__fp16 * restrict vtcm_dst,
 // Full range: start_row=0, end_row=n_rows.
 static inline void hmx_interleave_cols_to_tiles(__fp16 * restrict tiles_out,
                                             const __fp16 * restrict src,
-                                            int n_rows,
-                                            int head_dim,
-                                            int src_stride,
-                                            int n_row_tiles,
-                                            int start_row,
-                                            int end_row) {
+                                            uint32_t n_rows,
+                                            uint32_t head_dim,
+                                            size_t src_stride,
+                                            uint32_t n_row_tiles,
+                                            uint32_t start_row,
+                                            uint32_t end_row) {
     __builtin_assume(head_dim > 0);
     const size_t tile_stride_elms = (size_t) n_row_tiles * HMX_FP16_TILE_N_ELMS;
 
-    for (int r = start_row; r < end_row; r += 2) {
+    for (uint32_t r = start_row; r < end_row; r += 2) {
         const bool next_row_valid = (r + 1) < end_row && (r + 1) < n_rows;
 
         const HVX_Vector * pv_in0 = (const HVX_Vector *) (src + r * src_stride);
         const HVX_Vector * pv_in1 = next_row_valid ? (const HVX_Vector *) (src + (r + 1) * src_stride) : NULL;
 
         // Row-pair invariants hoisted out of the c loop.
-        const int r0      = r / HMX_FP16_TILE_N_ROWS;
-        const int r1_half = (r % HMX_FP16_TILE_N_ROWS) / 2;
+        const uint32_t r0      = r / HMX_FP16_TILE_N_ROWS;
+        const uint32_t r1_half = (r % HMX_FP16_TILE_N_ROWS) / 2;
 
         // tb0 starts at tile (c0=0, r0); tb1 at the adjacent dim-tile (c0=1, r0).
         // Each c step (+= 64) advances both by 2 dim-tiles worth of fp16.
@@ -174,7 +174,7 @@ static inline void hmx_interleave_cols_to_tiles(__fp16 * restrict tiles_out,
         const size_t tb_step = 2 * tile_stride_elms;
 
         if (pv_in1) {
-            for (int c = 0; c < head_dim; c += 64) {
+            for (uint32_t c = 0; c < head_dim; c += 64) {
                 HVX_Vector     v0             = *pv_in0++;
                 HVX_Vector     v1             = *pv_in1++;
                 HVX_VectorPair vp             = Q6_W_vshuff_VVR(v1, v0, -2);
@@ -185,7 +185,7 @@ static inline void hmx_interleave_cols_to_tiles(__fp16 * restrict tiles_out,
             }
         } else {
             const HVX_Vector vzero = Q6_V_vzero();
-            for (int c = 0; c < head_dim; c += 64) {
+            for (uint32_t c = 0; c < head_dim; c += 64) {
                 HVX_Vector     v0             = *pv_in0++;
                 HVX_VectorPair vp             = Q6_W_vshuff_VVR(vzero, v0, -2);
                 ((HVX_Vector *) tb0)[r1_half] = Q6_V_lo_W(vp);
diff --git a/ggml/src/ggml-hexagon/htp/htp-ops.h b/ggml/src/ggml-hexagon/htp/htp-ops.h
index d04090135..c9d0b3539 100644
--- a/ggml/src/ggml-hexagon/htp/htp-ops.h
+++ b/ggml/src/ggml-hexagon/htp/htp-ops.h
@@ -60,6 +60,7 @@ enum htp_op_code {
     HTP_OP_MUL_MAT_ID,
     HTP_OP_MUL_MAT_QKV,
     HTP_OP_MUL_MAT_FFN,
+    HTP_OP_MUL_MAT_ADD,
     HTP_OP_RMS_NORM,
     HTP_OP_RMS_NORM_MUL,
     HTP_OP_UNARY_SILU,
@@ -175,6 +176,11 @@ enum htp_trace_event_id {
     HTP_TRACE_EVT_HVX_W_DEQUANT       = 23,
     HTP_TRACE_EVT_HVX_W_PREP          = 24,
     HTP_TRACE_EVT_HVX_O_PROC          = 25,
+    HTP_TRACE_EVT_HVX_FA_QK           = 26,
+    HTP_TRACE_EVT_HVX_FA_SFM          = 27,
+    HTP_TRACE_EVT_HVX_FA_Q_PREP       = 28,
+    HTP_TRACE_EVT_HVX_FA_K_PREP       = 29,
+    HTP_TRACE_EVT_HVX_FA_V_PREP       = 30,
 
     HTP_TRACE_EVT_HMX_COMP            = 40,
 };
diff --git a/ggml/src/ggml-hexagon/htp/hvx-base.h b/ggml/src/ggml-hexagon/htp/hvx-base.h
index 493b26c6e..d7575b9ff 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-base.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-base.h
@@ -134,16 +134,7 @@ static inline HVX_Vector hvx_vec_f32_to_f16_shuff(HVX_Vector v0, HVX_Vector v1)
 }
 
 static inline HVX_Vector hvx_vec_f32_to_f16(HVX_Vector v0, HVX_Vector v1) {
-    HVX_Vector v = Q6_Vh_vdeal_Vh(hvx_vec_f32_to_f16_shuff(v0, v1));
-
-#if __HVX_ARCH__ < 79
-    // replace NaNs with -INF, older arches produce NaNs for (-INF + 0.0)
-    const HVX_Vector neg_inf = hvx_vec_splat_f16(-INFINITY);
-    HVX_VectorPred nan = hvx_vec_is_nan_f16(v);
-    v = Q6_V_vmux_QVV(nan, neg_inf, v);
-#endif
-
-    return v;
+    return Q6_Vh_vdeal_Vh(hvx_vec_f32_to_f16_shuff(v0, v1));
 }
 
 #if __HVX_ARCH__ >= 79
@@ -170,8 +161,6 @@ static inline HVX_VectorPair hvx_vec_f16_to_f32(HVX_Vector v) {
 }
 #endif
 
-
-
 static inline HVX_Vector hvx_vec_i16_from_hf_rnd_sat(HVX_Vector vin) {
     // This looks complicated.
     // Ideally should just be Q6_Vh_equals_Vhf(vin)
diff --git a/ggml/src/ggml-hexagon/htp/hvx-exp.h b/ggml/src/ggml-hexagon/htp/hvx-exp.h
index e71ec4909..bcd3d2d32 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-exp.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-exp.h
@@ -16,6 +16,7 @@
 #define EXP_COEFF_0 (0x3F000000)  // 0.5         = 1/(2!)
 #define EXP_LOGN2   (0x3F317218)  // ln(2)   = 0.6931471805
 #define EXP_LOG2E   (0x3FB8AA3B)  // log2(e) = 1/ln(2) = 1.4426950408
+#define EXP_LOG2E_F 1.44269504f
 #define EXP_ONE     (0x3f800000)  // 1.0
 #define EXP_RANGE_R (0x42B17218)  // ln(FLT_MAX) approx = 88.7228
 #define EXP_RANGE_L (0xC2B00000)  // -88.0 (approx log(FLT_MIN))
@@ -213,4 +214,42 @@ static inline void hvx_exp_f32(uint8_t * restrict dst, const uint8_t * restrict
     }
 }
 
+static inline HVX_Vector hvx_vec_exp2_f16(HVX_Vector x_v) {
+    const HVX_Vector zero_v    = Q6_V_vzero();
+    const HVX_Vector half_hf_v = Q6_Vh_vsplat_R(0x3800);  // fp16 0.5
+
+    // Clamp input to prevent integer underflow in FP16-to-INT16 conversion
+    const HVX_Vector v_clamp_min = hvx_vec_splat_f16(-24.0f);
+    x_v = Q6_Vhf_vmax_VhfVhf(v_clamp_min, x_v);
+
+    // k = round_toward_neg_inf(x);  f = (float)k;  frac = x - f
+    HVX_Vector x_minus_half = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vsub_VhfVhf(x_v, half_hf_v));
+    HVX_Vector k_v          = Q6_Vh_equals_Vhf(x_minus_half);  // truncate to int16
+    HVX_Vector f_v          = Q6_Vhf_equals_Vh(k_v);           // back to fp16
+
+    HVX_Vector x_qf16 = Q6_Vqf16_vsub_VhfVhf(x_v, f_v);        // fractional part in qf16
+
+    // Horner: y = ((((E5*x + E4)*x + E3)*x + E2)*x + E1)*x + E0
+    HVX_Vector y = Q6_Vqf16_vmpy_Vqf16Vqf16(Q6_Vh_vsplat_R(0x5082), x_qf16); // E5*x
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x157d));        // + E4
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x20ed));        // + E3
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x2b1b));        // + E2
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x33b0));        // + E1
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x398c));        // + E0
+    y            = Q6_Vqf16_vmpy_Vqf16Vqf16(y, x_qf16);                      // y = y * x
+    y            = Q6_Vqf16_vadd_Vqf16Vhf(y, Q6_Vh_vsplat_R(0x3c00));        // + 1.0
+
+    // Combine polynomial (mantissa) with integer part (exponent): result = y * 2^k
+    y                          = Q6_Vhf_equals_Vqf16(y);
+    HVX_Vector y_exp           = Q6_Vuh_vlsr_VuhR(Q6_Vh_vasl_VhR(y, 1), 11);
+    y_exp                      = Q6_Vh_vadd_VhVh(k_v, y_exp);
+    HVX_VectorPred q_underflow = Q6_Q_vcmp_gt_VhVh(zero_v, y_exp);
+    y                          = Q6_Vh_vaslacc_VhVhR(y, k_v, 10);
+    return Q6_V_vmux_QVV(q_underflow, zero_v, y);
+}
+
 #endif /* HVX_EXP_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h
new file mode 100644
index 000000000..c05bd0b85
--- /dev/null
+++ b/ggml/src/ggml-hexagon/htp/hvx-fa-kernels.h
@@ -0,0 +1,232 @@
+#ifndef HVX_FA_KERNELS_H
+#define HVX_FA_KERNELS_H
+
+#include <assert.h>
+#include <math.h>
+#include "hvx-utils.h"
+
+// Little inner kernels for HVX
+
+#if __HVX_ARCH__ < 79
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
+#define HVX_OP_SUB_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a, b))
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
+#define HVX_OP_SUB_F32(a, b) Q6_Vsf_vsub_VsfVsf(a, b)
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
+// This is a bit of a hack because the compiler is struggling to properly inline
+// the default hvx_vec_f32_to_f16 with output into the local array.
+static __attribute__((unused)) __attribute__((noinline)) void hvx_vec_f32_to_f16_a(void *ptr, HVX_Vector v0, HVX_Vector v1)
+{
+    *(HVX_Vector *) ptr = hvx_vec_f32_to_f16(v0, v1);
+}
+
+// Dot product of two F16 vectors, accumulating to float
+static inline void hvx_dot_f16_f16_aa(float * restrict r, const void * restrict x, const void * restrict y, unsigned int n, float s) {
+    const HVX_Vector * restrict vx = (const HVX_Vector * restrict) x; // fp16
+    const HVX_Vector * restrict vy = (const HVX_Vector * restrict) y; // fp16
+
+    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16; // leftover elements
+
+    HVX_VectorPair rsum_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
+
+    uint32_t i = 0;
+
+    #pragma unroll(4)
+    for (i = 0; i < nvec; i++) {
+        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, vx[i], vy[i]);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        HVX_Vector y_hf = Q6_V_vand_QV(bmask, vy[i]);
+        HVX_Vector x_hf = Q6_V_vand_QV(bmask, vx[i]);
+
+        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf);
+    }
+
+    HVX_Vector rsum = HVX_OP_ADD_F32(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p));
+    rsum = HVX_OP_MUL_F32(hvx_vec_splat_f32(s), hvx_vec_reduce_sum_f32(rsum));
+    hvx_vec_store_u(r, 4, rsum);
+}
+
+static inline HVX_Vector hvx_dot_f16_f16_aa_rx4(const void * restrict y,
+                                                const uint8_t * restrict x,
+                                                const size_t stride_x,
+                                                const size_t nvec,
+                                                const size_t nloe) {
+    const HVX_Vector * restrict vx0 = (const HVX_Vector * restrict) x;                   // fp16
+    const HVX_Vector * restrict vx1 = (const HVX_Vector * restrict) (x + stride_x);      // fp16
+    const HVX_Vector * restrict vx2 = (const HVX_Vector * restrict) (x + stride_x * 2);  // fp16
+    const HVX_Vector * restrict vx3 = (const HVX_Vector * restrict) (x + stride_x * 3);  // fp16
+    const HVX_Vector * restrict vy  = (const HVX_Vector * restrict) y;                   // fp16
+
+    HVX_VectorPair rsum0_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
+    HVX_VectorPair rsum1_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
+    HVX_VectorPair rsum2_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
+    HVX_VectorPair rsum3_p = Q6_W_vcombine_VV(Q6_V_vsplat_R(0), Q6_V_vsplat_R(0));
+
+    uint32_t i = 0;
+
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector y_hf  = vy[i];
+        HVX_Vector x0_hf = vx0[i];
+        HVX_Vector x1_hf = vx1[i];
+        HVX_Vector x2_hf = vx2[i];
+        HVX_Vector x3_hf = vx3[i];
+
+        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
+        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
+        rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf);
+        rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf);
+    }
+
+    if (nloe) {
+        // Load x (fp16) and zero-out unused elements
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        HVX_Vector     y_hf  = Q6_V_vand_QV(bmask, vy[i]);
+        HVX_Vector     x0_hf = Q6_V_vand_QV(bmask, vx0[i]);
+        HVX_Vector     x1_hf = Q6_V_vand_QV(bmask, vx1[i]);
+        HVX_Vector     x2_hf = Q6_V_vand_QV(bmask, vx2[i]);
+        HVX_Vector     x3_hf = Q6_V_vand_QV(bmask, vx3[i]);
+
+        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
+        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
+        rsum2_p = hvx_vec_mpyacc_f32_f16(rsum2_p, x2_hf, y_hf);
+        rsum3_p = hvx_vec_mpyacc_f32_f16(rsum3_p, x3_hf, y_hf);
+    }
+
+    HVX_Vector rsum0 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p));
+    HVX_Vector rsum1 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p));
+    HVX_Vector rsum2 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum2_p), Q6_V_hi_W(rsum2_p));
+    HVX_Vector rsum3 = HVX_OP_ADD_F32(Q6_V_lo_W(rsum3_p), Q6_V_hi_W(rsum3_p));
+
+    HVX_Vector_x4 rsum0123 = { .v = { rsum0, rsum1, rsum2, rsum3 } };
+    return hvx_vec_reduce_sum_f32x4(rsum0123);
+}
+
+static inline HVX_Vector hvx_dot_f16_f16_aa_rx32(const void * restrict y,
+                                                 const uint8_t * restrict x,
+                                                 const size_t stride_x,
+                                                 const size_t n,
+                                                 float        s) {
+
+    const size_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    const size_t nloe = n % VLEN_FP16; // leftover elements
+
+    HVX_Vector   sums = Q6_V_vzero();
+    const size_t stride_x_4 = stride_x * 4;
+    for (uint32_t j = 0; j < VLEN_FP32; j += 4) {
+        HVX_Vector     sums_x4 = hvx_dot_f16_f16_aa_rx4(y, x, stride_x, nvec, nloe);
+        HVX_VectorPred pred    = Q6_Q_vsetq_R(j * SIZEOF_FP32);
+        sums                   = Q6_V_vmux_QVV(pred, sums, sums_x4);
+        x += stride_x_4;
+    }
+
+    return HVX_OP_MUL_F32(hvx_vec_splat_f32(s), sums);
+}
+
+// MAD: y (F32) += x (F16) * s (F16)
+static inline void hvx_mad_f32_f16_aa(float * restrict y, const void * restrict x, const __fp16 * restrict s, uint32_t n) {
+    const HVX_Vector * restrict vx0 = (const HVX_Vector *) x;
+
+    HVX_VectorPair * restrict vy_p = (HVX_VectorPair *) y;
+    HVX_Vector * restrict vy = (HVX_Vector *) y;
+
+    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16; // leftover elements
+
+    HVX_Vector S0 = hvx_vec_splat_f16(*s);
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; ++i) {
+        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0);
+    }
+
+    if (nloe) {
+        HVX_VectorPair xy_p = vy_p[i];
+        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0);
+
+        HVX_Vector xy = Q6_V_lo_W(xy_p);
+        i = 2 * i;  // index for vy
+
+        if (nloe >= VLEN_FP32) {
+            vy[i] = xy;
+            nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p);
+        }
+
+        if (nloe) {
+            hvx_vec_store_a(&vy[i], nloe * 4, xy);
+        }
+    }
+}
+
+// MAD: y (F32) += x0 (F16) * s0 (F16) + x1 (F16) * s1 (F16)
+static inline void hvx_mad_f32_f16_aa_rx2(float * restrict y, const void * restrict x0, const void * restrict x1,
+                                          const __fp16 * restrict s0, const __fp16 * restrict s1, uint32_t n) {
+    const HVX_Vector * restrict vx0 = (const HVX_Vector *) x0;
+    const HVX_Vector * restrict vx1 = (const HVX_Vector *) x1;
+
+    HVX_VectorPair * restrict vy_p  = (HVX_VectorPair *) y;
+    HVX_Vector * restrict vy        = (HVX_Vector *) y;
+
+    uint32_t nvec = n / VLEN_FP16;  // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16;  // leftover elements
+
+    HVX_Vector S0 = hvx_vec_splat_f16(*s0);
+    HVX_Vector S1 = hvx_vec_splat_f16(*s1);
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; ++i) {
+        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx0[i]), S0);
+        vy_p[i] = hvx_vec_mpyacc_f32_f16(vy_p[i], Q6_Vh_vshuff_Vh(vx1[i]), S1);
+    }
+
+    if (nloe) {
+        HVX_VectorPair xy_p = vy_p[i];
+        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx0[i]), S0);
+        xy_p = hvx_vec_mpyacc_f32_f16(xy_p, Q6_Vh_vshuff_Vh(vx1[i]), S1);
+
+        HVX_Vector xy = Q6_V_lo_W(xy_p);
+        i = 2 * i;  // index for vy
+
+        if (nloe >= VLEN_FP32) {
+            vy[i] = xy;
+            nloe -= VLEN_FP32; ++i; xy = Q6_V_hi_W(xy_p);
+        }
+
+        if (nloe) {
+            hvx_vec_store_a(&vy[i], nloe * 4, xy);
+        }
+    }
+}
+
+static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t * restrict src, const uint32_t n, HVX_Vector vs) {
+    assert((size_t) dst % 128 == 0);
+    assert((size_t) src % 128 == 0);
+
+    const HVX_Vector * restrict vsrc = (const HVX_Vector * restrict) src;
+    HVX_Vector * restrict vdst       = (HVX_Vector * restrict) dst;
+
+    const uint32_t nvec = n / VLEN_FP32;
+    const uint32_t nloe = n % VLEN_FP32;
+
+    uint32_t i = 0;
+    #pragma unroll(4)
+    for (; i < nvec; ++i) {
+        vdst[i] = HVX_OP_MUL_F32(vsrc[i], vs);
+    }
+    if (nloe) {
+        hvx_vec_store_a(&vdst[i], nloe * sizeof(float), HVX_OP_MUL_F32(vsrc[i], vs));
+    }
+}
+
+#endif /* HVX_FA_KERNELS_H */
diff --git a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
index 52351b103..328a83118 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-flat.h
@@ -256,7 +256,7 @@ static inline void quantize_f16_f16_flat_kernel(
 
 // Dot kernels that consume flat (non-tiled) activations
 
-static void flat_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void flat_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -312,10 +312,14 @@ static void flat_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const v
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void flat_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void flat_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -397,11 +401,19 @@ static void flat_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void flat_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void flat_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -464,10 +476,14 @@ static void flat_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const v
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void flat_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void flat_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -561,11 +577,19 @@ static void flat_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void flat_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void flat_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -620,10 +644,14 @@ static void flat_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const v
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void flat_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void flat_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -704,11 +732,19 @@ static void flat_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void flat_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void flat_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -765,10 +801,14 @@ static void flat_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void flat_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void flat_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -851,11 +891,19 @@ static void flat_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void flat_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void flat_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -921,10 +969,14 @@ static void flat_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const
 
     v_sum_float = hvx_vec_mul_f32_f32(v_sum_float, hvx_vec_splat_f32(0.5f));
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void flat_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void flat_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -1019,6 +1071,441 @@ static void flat_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float
     v_sum_float_c0 = hvx_vec_mul_f32_f32(v_sum_float_c0, hvx_vec_splat_f32(0.5f));
     v_sum_float_c1 = hvx_vec_mul_f32_f32(v_sum_float_c1, hvx_vec_splat_f32(0.5f));
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
+
+#if __HVX_ARCH__ < 79
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
+#else
+#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
+#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
+#endif
+
+static inline void vec_dot_f32_f32_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const HVX_Vector * restrict x = (const HVX_Vector *) vx;
+    const HVX_Vector * restrict y = (const HVX_Vector *) vy;
+
+    uint32_t nvec = n / VLEN_FP32; // num full fp32 hvx vectors
+    uint32_t nloe = n % VLEN_FP32; // leftover elements
+
+    HVX_Vector rsum = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(4)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector prod = HVX_OP_MUL_F32(x[i], y[i]);
+        rsum = HVX_OP_ADD_F32(rsum, prod);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector x_sf = Q6_V_vand_QV(bmask, x[i]);
+        HVX_Vector y_sf = Q6_V_vand_QV(bmask, y[i]);
+        HVX_Vector prod = HVX_OP_MUL_F32(x_sf, y_sf);
+        rsum = HVX_OP_ADD_F32(rsum, prod);
+    }
+
+    *s = hvx_vec_get_f32(hvx_vec_reduce_sum_f32(rsum));
+}
+
+static inline void vec_dot_f32_f32_aa_2x1(const uint32_t n, float * restrict s0,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y  = (const HVX_Vector *) vy0;
+
+    uint32_t nvec = n / VLEN_FP32;
+    uint32_t nloe = n % VLEN_FP32;
+
+    HVX_Vector rsum0 = Q6_V_vzero();
+    HVX_Vector rsum1 = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector y_sf = y[i];
+        HVX_Vector prod0 = HVX_OP_MUL_F32(x0[i], y_sf);
+        HVX_Vector prod1 = HVX_OP_MUL_F32(x1[i], y_sf);
+        rsum0 = HVX_OP_ADD_F32(rsum0, prod0);
+        rsum1 = HVX_OP_ADD_F32(rsum1, prod1);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        HVX_Vector y_sf  = Q6_V_vand_QV(bmask, y[i]);
+        HVX_Vector x0_sf = Q6_V_vand_QV(bmask, x0[i]);
+        HVX_Vector x1_sf = Q6_V_vand_QV(bmask, x1[i]);
+        HVX_Vector prod0 = HVX_OP_MUL_F32(x0_sf, y_sf);
+        HVX_Vector prod1 = HVX_OP_MUL_F32(x1_sf, y_sf);
+        rsum0 = HVX_OP_ADD_F32(rsum0, prod0);
+        rsum1 = HVX_OP_ADD_F32(rsum1, prod1);
+    }
+
+    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(rsum0, rsum1);
+    hvx_vec_store_u(s0, 8, rsum);
+}
+
+static inline void vec_dot_f32_f32_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0, const void * restrict vy1) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y0 = (const HVX_Vector *) vy0;
+    const HVX_Vector * restrict y1 = (const HVX_Vector *) vy1;
+
+    uint32_t nvec = n / VLEN_FP32;
+    uint32_t nloe = n % VLEN_FP32;
+
+    HVX_Vector r0_c0_sum = Q6_V_vzero();
+    HVX_Vector r0_c1_sum = Q6_V_vzero();
+    HVX_Vector r1_c0_sum = Q6_V_vzero();
+    HVX_Vector r1_c1_sum = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector r0_sf = x0[i];
+        HVX_Vector r1_sf = x1[i];
+        HVX_Vector c0_sf = y0[i];
+        HVX_Vector c1_sf = y1[i];
+
+        r0_c0_sum = HVX_OP_ADD_F32(r0_c0_sum, HVX_OP_MUL_F32(r0_sf, c0_sf));
+        r0_c1_sum = HVX_OP_ADD_F32(r0_c1_sum, HVX_OP_MUL_F32(r0_sf, c1_sf));
+        r1_c0_sum = HVX_OP_ADD_F32(r1_c0_sum, HVX_OP_MUL_F32(r1_sf, c0_sf));
+        r1_c1_sum = HVX_OP_ADD_F32(r1_c1_sum, HVX_OP_MUL_F32(r1_sf, c1_sf));
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+
+        HVX_Vector r0_sf = Q6_V_vand_QV(bmask, x0[i]);
+        HVX_Vector r1_sf = Q6_V_vand_QV(bmask, x1[i]);
+        HVX_Vector c0_sf = Q6_V_vand_QV(bmask, y0[i]);
+        HVX_Vector c1_sf = Q6_V_vand_QV(bmask, y1[i]);
+
+        r0_c0_sum = HVX_OP_ADD_F32(r0_c0_sum, HVX_OP_MUL_F32(r0_sf, c0_sf));
+        r0_c1_sum = HVX_OP_ADD_F32(r0_c1_sum, HVX_OP_MUL_F32(r0_sf, c1_sf));
+        r1_c0_sum = HVX_OP_ADD_F32(r1_c0_sum, HVX_OP_MUL_F32(r1_sf, c0_sf));
+        r1_c1_sum = HVX_OP_ADD_F32(r1_c1_sum, HVX_OP_MUL_F32(r1_sf, c1_sf));
+    }
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(s0, 8, r0_r1_c0_sum);
+    hvx_vec_store_u(s1, 8, r0_r1_c1_sum);
+}
+
+static inline void vec_dot_f32_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
+    const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
+    const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
+
+    uint32_t nvec = n / VLEN_FP32; // num full fp32 hvx vectors
+    uint32_t nloe = n % VLEN_FP32; // leftover elements
+
+    HVX_Vector       rsum = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector x_sf = vx[i];
+        HVX_Vector y_sf = vy[i];
+
+        rsum = HVX_OP_ADD_F32(rsum, HVX_OP_MUL_F32(x_sf, y_sf));
+    }
+
+    if (nloe) {
+        HVX_Vector x_sf = vx[i];
+        HVX_Vector y_sf = vy[i];
+
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
+        x_sf = Q6_V_vand_QV(bmask, x_sf);
+        y_sf = Q6_V_vand_QV(bmask, y_sf);
+
+        rsum = HVX_OP_ADD_F32(rsum, HVX_OP_MUL_F32(x_sf, y_sf));
+    }
+
+    rsum = hvx_vec_reduce_sum_f32(rsum);
+    hvx_vec_store_u(&s[0], 4, rsum);
+}
+
+#undef HVX_OP_ADD_F32
+#undef HVX_OP_MUL_F32
+
+static inline void vec_dot_f16_f16_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const HVX_Vector * restrict x = (const HVX_Vector *) vx;
+    const HVX_Vector * restrict y = (const HVX_Vector *) vy;
+
+    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16; // leftover elements
+
+    HVX_VectorPair rsum_p = Q6_W_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(4)
+    for (i = 0; i < nvec; i++) {
+        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x[i], y[i]);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        HVX_Vector x_hf = Q6_V_vand_QV(bmask, x[i]);
+        HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]);
+        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf);
+    }
+
+    HVX_Vector rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p)));
+    hvx_vec_store_u(s, 4, hvx_vec_reduce_sum_f32(rsum));
+}
+
+static inline void vec_dot_f16_f16_aa_2x1(const uint32_t n, float * restrict s0,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y  = (const HVX_Vector *) vy0;
+
+    uint32_t nvec = n / VLEN_FP16;
+    uint32_t nloe = n % VLEN_FP16;
+
+    HVX_VectorPair rsum0_p = Q6_W_vzero();
+    HVX_VectorPair rsum1_p = Q6_W_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector y_hf = y[i];
+        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0[i], y_hf);
+        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1[i], y_hf);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        HVX_Vector y_hf  = Q6_V_vand_QV(bmask, y[i]);
+        HVX_Vector x0_hf = Q6_V_vand_QV(bmask, x0[i]);
+        HVX_Vector x1_hf = Q6_V_vand_QV(bmask, x1[i]);
+        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
+        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
+    }
+
+    HVX_Vector rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p)));
+    HVX_Vector rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p)));
+    HVX_Vector rsum  = hvx_vec_reduce_sum_f32x2(rsum0, rsum1);
+    hvx_vec_store_u(s0, 8, rsum);
+}
+
+static inline void vec_dot_f16_f16_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
+                                const void * restrict vx0, const void * restrict vx1,
+                                const void * restrict vy0, const void * restrict vy1) {
+    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
+    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
+    const HVX_Vector * restrict y0 = (const HVX_Vector *) vy0;
+    const HVX_Vector * restrict y1 = (const HVX_Vector *) vy1;
+
+    uint32_t nvec = n / VLEN_FP16;
+    uint32_t nloe = n % VLEN_FP16;
+
+    // Row sums (sf) - 4 accumulators for 2x2 tile
+    HVX_VectorPair r0_c0_sum_p = Q6_W_vzero();
+    HVX_VectorPair r0_c1_sum_p = Q6_W_vzero();
+    HVX_VectorPair r1_c0_sum_p = Q6_W_vzero();
+    HVX_VectorPair r1_c1_sum_p = Q6_W_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        HVX_Vector r0_hf = x0[i];
+        HVX_Vector r1_hf = x1[i];
+        HVX_Vector c0_hf = y0[i];
+        HVX_Vector c1_hf = y1[i];
+
+        // Compute 4 dot products: r0xc0, r0xc1, r1xc0, r1xc1
+        r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf);
+        r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf);
+        r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf);
+        r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf);
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+
+        HVX_Vector r0_hf = Q6_V_vand_QV(bmask, x0[i]);
+        HVX_Vector r1_hf = Q6_V_vand_QV(bmask, x1[i]);
+        HVX_Vector c0_hf = Q6_V_vand_QV(bmask, y0[i]);
+        HVX_Vector c1_hf = Q6_V_vand_QV(bmask, y1[i]);
+
+        r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf);
+        r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf);
+        r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf);
+        r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf);
+    }
+
+    HVX_Vector r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c0_sum_p), Q6_V_hi_W(r0_c0_sum_p)));
+    HVX_Vector r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c1_sum_p), Q6_V_hi_W(r0_c1_sum_p)));
+    HVX_Vector r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c0_sum_p), Q6_V_hi_W(r1_c0_sum_p)));
+    HVX_Vector r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c1_sum_p), Q6_V_hi_W(r1_c1_sum_p)));
+
+    // Reduce and store results
+    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
+    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
+
+    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
+    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
+}
+
+static inline void vec_dot_f16_f16_uu_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
+    const HVX_UVector * restrict x = (const HVX_UVector *) vx;
+    const HVX_UVector * restrict y = (const HVX_UVector *) vy;
+
+    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16; // leftover elements
+
+    HVX_Vector rsum = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(4)
+    for (i = 0; i < nvec; i++) {
+        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x[i], y[i]);
+        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
+    }
+
+    if (nloe) {
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        HVX_Vector x_hf = Q6_V_vand_QV(bmask, x[i]);
+        HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]);
+
+        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
+        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
+    }
+
+    rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum));
+    hvx_vec_store_u(&s[0], 4, rsum);
+}
+
+static inline void vec_dot_f16_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
+    const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
+    const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
+
+    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
+    uint32_t nloe = n % VLEN_FP16; // leftover elements
+
+    const HVX_Vector zero = Q6_V_vzero();
+
+    HVX_Vector       rsum = Q6_V_vzero();
+
+    uint32_t i = 0;
+
+    #pragma unroll(2)
+    for (i = 0; i < nvec; i++) {
+        // Load y (fp32) and convert into fp16
+        HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero);  // 32 elements
+        HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero);  // 32 elements
+        HVX_Vector y_hf  = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf)));
+
+        // Load x (fp16)
+        HVX_Vector x_hf  = vx[i];
+
+        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
+
+        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
+    }
+
+    if (nloe) {
+        // Load y (fp32) and convert into fp16
+        HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero);  // 32 elements
+        HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero);  // 32 elements
+        HVX_Vector y_hf  = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf)));
+
+        // Load x (fp16)
+        HVX_Vector x_hf  = vx[i];
+
+        // Zero-out unused elements
+        // Note that we need to clear both x and y because they may contain NANs
+        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
+        x_hf = Q6_V_vand_QV(bmask, x_hf);
+        y_hf = Q6_V_vand_QV(bmask, y_hf);
+
+        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
+
+        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
+    }
+
+    // Convert into fp32 and reduce
+    rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum));
+    hvx_vec_store_u(&s[0], 4, rsum);
+}
+
+static inline void hvx_tensor_add_f32_grid(
+    const struct htp_tensor * restrict dst,
+    const struct htp_tensor * restrict src2,
+    uint32_t start_row,
+    uint32_t end_row,
+    uint32_t start_col,
+    uint32_t end_col,
+    const struct fastdiv_values * div_ne11_12,
+    const struct fastdiv_values * div_ne11
+) {
+    if (start_row >= end_row || start_col >= end_col) return;
+    const uint32_t nb1 = dst->nb[1]; // row stride in bytes
+
+    const uint32_t ne11 = dst->ne[1];
+    const uint32_t ne12 = dst->ne[2];
+    const uint32_t ne11_12 = ne11 * ne12;
+
+    const bool is_broadcast1 = (src2->ne[1] == 1);
+    const bool is_broadcast2 = (src2->ne[2] == 1);
+    const bool is_broadcast3 = (src2->ne[3] == 1);
+
+    for (uint32_t r = start_row; r < end_row; r++) {
+        float * dst_row = (float *) ((uint8_t *) dst->data + r * nb1);
+
+        uint32_t i13 = fastdiv(r, div_ne11_12);
+        uint32_t i12 = fastdiv(r - i13 * ne11_12, div_ne11);
+        uint32_t i11 = r - i13 * ne11_12 - i12 * ne11;
+
+        uint32_t i23 = is_broadcast3 ? 0 : i13;
+        uint32_t i22 = is_broadcast2 ? 0 : i12;
+        uint32_t i21 = is_broadcast1 ? 0 : i11;
+
+        const float * src2_row = (const float *) ((const uint8_t *) src2->data +
+                                  i21 * src2->nb[1] + i22 * src2->nb[2] + i23 * src2->nb[3]);
+
+        float * dst_ptr = &dst_row[start_col];
+        const float * src2_ptr = &src2_row[start_col];
+        int remaining = end_col - start_col;
+        while (remaining >= 32) {
+            HVX_Vector v_out = hvx_vmemu(dst_ptr);
+            HVX_Vector v_z   = hvx_vmemu(src2_ptr);
+            hvx_vmemu(dst_ptr) = hvx_vec_add_f32_f32(v_out, v_z);
+            dst_ptr += 32;
+            src2_ptr += 32;
+            remaining -= 32;
+        }
+        if (remaining > 0) {
+            HVX_Vector v_out = hvx_vmemu(dst_ptr);
+            HVX_Vector v_z   = hvx_vmemu(src2_ptr);
+            hvx_vec_store_u(dst_ptr, remaining * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z));
+        }
+    }
+}
+
diff --git a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
index bcb0b8f9e..40b65aa3b 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-mm-kernels-tiled.h
@@ -378,7 +378,7 @@ static inline HVX_VectorPair accum_q8_0_32x2(
     return Q6_W_vcombine_VV(v_sum1, v_sum0);
 }
 
-static void tiled_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void tiled_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -401,10 +401,14 @@ static void tiled_vec_dot_q4_0_32x1(const uint32_t n, float * restrict s, const
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void tiled_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void tiled_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -484,11 +488,19 @@ static void tiled_vec_dot_q4_0_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void tiled_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void tiled_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -519,10 +531,14 @@ static void tiled_vec_dot_q4_1_32x1(const uint32_t n, float * restrict s, const
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void tiled_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void tiled_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -637,11 +653,19 @@ static void tiled_vec_dot_q4_1_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void tiled_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void tiled_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -663,10 +687,14 @@ static void tiled_vec_dot_q8_0_32x1(const uint32_t n, float * restrict s, const
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void tiled_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void tiled_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -745,11 +773,19 @@ static void tiled_vec_dot_q8_0_32x2(const uint32_t n, float * restrict s0, float
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void tiled_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void tiled_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -773,10 +809,14 @@ static void tiled_vec_dot_iq4nl_32x1(const uint32_t n, float * restrict s, const
         v_sum_float = hvx_vec_add_f32_f32(v_sum_float, v_sum_scaled);
     }
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void tiled_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void tiled_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -857,11 +897,19 @@ static void tiled_vec_dot_iq4nl_32x2(const uint32_t n, float * restrict s0, floa
         v_sum_float_c1 = hvx_vec_add_f32_f32(v_sum_float_c1, v_sum_scaled_c1);
     }
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
-static void tiled_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows) {
+static void tiled_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy, uint32_t valid_rows, const float * restrict sz) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y_q = vy;
 
@@ -896,10 +944,14 @@ static void tiled_vec_dot_mxfp4_32x1(const uint32_t n, float * restrict s, const
 
     v_sum_float = hvx_vec_mul_f32_f32(v_sum_float, hvx_vec_splat_f32(0.5f));
 
-    hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    if (sz) {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float, hvx_vmemu(sz)));
+    } else {
+        hvx_vec_store_u(s, valid_rows * sizeof(float), v_sum_float);
+    }
 }
 
-static void tiled_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows) {
+static void tiled_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, float * restrict s1, const void * restrict vx, const void * restrict vy0, const void * restrict vy1, uint32_t valid_rows, const float * restrict sz0, const float * restrict sz1) {
     const uint8_t * restrict tile_ptr = vx;
     const uint8_t * restrict y0_q = vy0;
     const uint8_t * restrict y1_q = vy1;
@@ -1013,8 +1065,16 @@ static void tiled_vec_dot_mxfp4_32x2(const uint32_t n, float * restrict s0, floa
     v_sum_float_c0 = hvx_vec_mul_f32_f32(v_sum_float_c0, hvx_vec_splat_f32(0.5f));
     v_sum_float_c1 = hvx_vec_mul_f32_f32(v_sum_float_c1, hvx_vec_splat_f32(0.5f));
 
-    hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
-    hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    if (sz0) {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c0, hvx_vmemu(sz0)));
+    } else {
+        hvx_vec_store_u(s0, valid_rows * sizeof(float), v_sum_float_c0);
+    }
+    if (sz1) {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), hvx_vec_add_f32_f32(v_sum_float_c1, hvx_vmemu(sz1)));
+    } else {
+        hvx_vec_store_u(s1, valid_rows * sizeof(float), v_sum_float_c1);
+    }
 }
 
 static inline void quantize_f32_q8_0_tiled_kernel(
diff --git a/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h b/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
index 37f3e7b6f..dd66dd84c 100644
--- a/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
+++ b/ggml/src/ggml-hexagon/htp/hvx-sigmoid.h
@@ -3,6 +3,7 @@
 
 #include "hvx-base.h"
 #include "hvx-inverse.h"
+#include "hvx-exp.h"
 
 #define FAST_SIGMOID_LOG2F (0x3fb8aa3b)  // 1.442695022
 #define FAST_SIGMOID_C1    (0x3d009076)  // 0.03138777
@@ -139,4 +140,42 @@ static inline void hvx_tanh_f32_aa(uint8_t * restrict dst, const uint8_t * restr
     hvx_tanh_loop_body(HVX_Vector, HVX_Vector, hvx_vec_store_a);
 }
 
+static inline HVX_Vector hvx_vec_fast_sigmoid_f16(HVX_Vector x_v) {
+    const HVX_Vector v_one       = hvx_vec_splat_f16(1.0f);
+    const HVX_Vector v_neg_log2e = hvx_vec_splat_f16(-EXP_LOG2E_F);
+    const HVX_Vector em_mask     = Q6_Vh_vsplat_R(0x7FFF);
+
+    // Compute absolute value of x_v
+    HVX_Vector abs_x = Q6_V_vand_VV(x_v, em_mask);
+
+    // Compute u = -abs_x * log2(e) <= 0.
+    HVX_Vector u = hvx_vec_mul_f16_f16(abs_x, v_neg_log2e);
+
+    // Clamp input to prevent underflow in exp2
+    const HVX_Vector v_clamp_min = hvx_vec_splat_f16(-24.0f);
+    u = Q6_Vhf_vmax_VhfVhf(v_clamp_min, u);
+
+    HVX_Vector exp_val = hvx_vec_exp2_f16(u);
+    HVX_Vector denom   = hvx_vec_add_f16_f16(v_one, exp_val);
+    HVX_Vector sig_abs = hvx_vec_inverse_f16(denom);
+
+    // check if x_v < 0 (using integer comparison on absolute value)
+    HVX_VectorPred is_neg = Q6_Q_vcmp_gt_VhVh(abs_x, x_v);
+
+    // If x_v < 0, return 1.0f - sig_abs
+    HVX_Vector sig_neg = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vsub_VhfVhf(v_one, sig_abs));
+    return Q6_V_vmux_QVV(is_neg, sig_neg, sig_abs);
+}
+
+static inline HVX_Vector hvx_vec_tanh_f16(HVX_Vector x) {
+    // tanh(x) = 2 * sigmoid(2x) - 1
+    const HVX_Vector v_two = hvx_vec_splat_f16(2.0f);
+
+    HVX_Vector x2 = hvx_vec_mul_f16_f16(x, v_two);
+    HVX_Vector sig2x = hvx_vec_fast_sigmoid_f16(x2);
+
+    const HVX_Vector v_neg_one = hvx_vec_splat_f16(-1.0f);
+    return hvx_vec_add_f16_f16(hvx_vec_mul_f16_f16(sig2x, v_two), v_neg_one);
+}
+
 #endif /* HVX_SIGMOID_H */
diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c
index d76512ea4..684625e42 100644
--- a/ggml/src/ggml-hexagon/htp/main.c
+++ b/ggml/src/ggml-hexagon/htp/main.c
@@ -575,6 +575,7 @@ static inline void profile_stop(uint32_t mode, struct profile_data * d) {
 static int execute_op(struct htp_ops_context * octx) {
     switch (octx->op) {
         case HTP_OP_MUL_MAT:
+        case HTP_OP_MUL_MAT_ADD:
             return op_matmul(octx);
 
         case HTP_OP_MUL_MAT_ID:
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index 81a0ffbeb..79c292dc2 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -9,6 +9,7 @@
 
 #include <math.h>
 #include <string.h>
+#include <stdatomic.h>
 
 #include "hex-dma.h"
 #include "hvx-utils.h"
@@ -21,8 +22,20 @@
 #include "matmul-ops.h"
 #include "vtcm-utils.h"
 
+static void hvx_tensor_add_f32_grid(
+    const struct htp_tensor * restrict dst,
+    const struct htp_tensor * restrict src2,
+    uint32_t start_row,
+    uint32_t end_row,
+    uint32_t start_col,
+    uint32_t end_col,
+    const struct fastdiv_values * div_ne11_12,
+    const struct fastdiv_values * div_ne11
+);
+
 typedef struct {
     float        *dst;
+    const float  *src2;
     const float  *activation;
     const __fp16 *weight;
     int           m;
@@ -31,6 +44,7 @@ typedef struct {
     int           act_stride;
     int           weight_stride;
     int           dst_stride;
+    uint32_t      src2_stride;
     int           ne02;
     int           ne03;
     int           ne12;
@@ -41,6 +55,8 @@ typedef struct {
     size_t        src1_nb3;
     size_t        dst_nb2;
     size_t        dst_nb3;
+    size_t        src2_nb2;
+    size_t        src2_nb3;
 } hmx_mm_f16_f32_batched_params_t;
 
 struct htp_mm_context {
@@ -61,11 +77,11 @@ struct htp_mm_context {
 
     void (*vec_dot_32x1)(const uint32_t n, float * restrict s,
          const void * restrict vx,
-         const void * restrict vy, uint32_t valid_rows);
+         const void * restrict vy, uint32_t valid_rows,
+         const float * restrict sz);
 
     // Precomputed values
     uint32_t src0_nrows_per_thread;
-    uint32_t src1_nrows_per_thread;
 
     struct fastdiv_values mm_div_ne12_ne1;
     struct fastdiv_values mm_div_ne1;
@@ -73,11 +89,16 @@ struct htp_mm_context {
     struct fastdiv_values mm_div_r3;
     struct fastdiv_values mm_div_ne11;
 
+    // Per thread quant tasks
     // Precomputed block-parallel quantization values
-    uint32_t quant_ib_first[MAX_NUM_WORKERS];
-    uint32_t quant_ib_last[MAX_NUM_WORKERS];
-    uint32_t quant_r[MAX_NUM_WORKERS];
-    uint32_t quant_c[MAX_NUM_WORKERS];
+    worker_callback_t quant_task_func;
+    uint32_t          quant_ib_first[MAX_NUM_WORKERS];
+    uint32_t          quant_ib_last[MAX_NUM_WORKERS];
+    uint32_t          quant_r[MAX_NUM_WORKERS];
+    uint32_t          quant_c[MAX_NUM_WORKERS];
+    uint32_t          n_quant_tasks;
+    uint32_t          n_quant_rows_per_thread;
+    atomic_uint       quant_barrier;
 
     // Fields for scattered mapping & HMX support in MUL_MAT_ID
     const uint32_t * matrix_row_counts;
@@ -133,382 +154,7 @@ static const uint8_t __attribute__((aligned(VLEN))) kvalues_mxfp4_lut[] = {
     0,    0, 0,    0, 0,    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  0, 0, 0, 0,    0, 0,    0, 0,    0,
 };
 
-#if __HVX_ARCH__ < 79
-#define HVX_OP_ADD_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a, b))
-#define HVX_OP_MUL_F32(a, b) Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a, b))
-#else
-#define HVX_OP_ADD_F32(a, b) Q6_Vsf_vadd_VsfVsf(a, b)
-#define HVX_OP_MUL_F32(a, b) Q6_Vsf_vmpy_VsfVsf(a, b)
-#endif
 
-static void vec_dot_f32_f32_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const HVX_Vector * restrict x = (const HVX_Vector *) vx;
-    const HVX_Vector * restrict y = (const HVX_Vector *) vy;
-
-    uint32_t nvec = n / VLEN_FP32; // num full fp32 hvx vectors
-    uint32_t nloe = n % VLEN_FP32; // leftover elements
-
-    HVX_Vector rsum = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(4)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector prod = HVX_OP_MUL_F32(x[i], y[i]);
-        rsum = HVX_OP_ADD_F32(rsum, prod);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
-        HVX_Vector x_sf = Q6_V_vand_QV(bmask, x[i]);
-        HVX_Vector y_sf = Q6_V_vand_QV(bmask, y[i]);
-        HVX_Vector prod = HVX_OP_MUL_F32(x_sf, y_sf);
-        rsum = HVX_OP_ADD_F32(rsum, prod);
-    }
-
-    *s = hvx_vec_get_f32(hvx_vec_reduce_sum_f32(rsum));
-}
-
-static void vec_dot_f32_f32_aa_2x1(const uint32_t n, float * restrict s0,
-                                const void * restrict vx0, const void * restrict vx1,
-                                const void * restrict vy0) {
-    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
-    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
-    const HVX_Vector * restrict y  = (const HVX_Vector *) vy0;
-
-    uint32_t nvec = n / VLEN_FP32;
-    uint32_t nloe = n % VLEN_FP32;
-
-    HVX_Vector rsum0 = Q6_V_vzero();
-    HVX_Vector rsum1 = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector y_sf = y[i];
-        HVX_Vector prod0 = HVX_OP_MUL_F32(x0[i], y_sf);
-        HVX_Vector prod1 = HVX_OP_MUL_F32(x1[i], y_sf);
-        rsum0 = HVX_OP_ADD_F32(rsum0, prod0);
-        rsum1 = HVX_OP_ADD_F32(rsum1, prod1);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
-        HVX_Vector y_sf  = Q6_V_vand_QV(bmask, y[i]);
-        HVX_Vector x0_sf = Q6_V_vand_QV(bmask, x0[i]);
-        HVX_Vector x1_sf = Q6_V_vand_QV(bmask, x1[i]);
-        HVX_Vector prod0 = HVX_OP_MUL_F32(x0_sf, y_sf);
-        HVX_Vector prod1 = HVX_OP_MUL_F32(x1_sf, y_sf);
-        rsum0 = HVX_OP_ADD_F32(rsum0, prod0);
-        rsum1 = HVX_OP_ADD_F32(rsum1, prod1);
-    }
-
-    HVX_Vector rsum = hvx_vec_reduce_sum_f32x2(rsum0, rsum1);
-    HVX_VectorAlias va;
-    va.v = rsum;
-    s0[0] = va.fp32[0];
-    s0[1] = va.fp32[1];
-}
-
-static void vec_dot_f32_f32_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
-                                const void * restrict vx0, const void * restrict vx1,
-                                const void * restrict vy0, const void * restrict vy1) {
-    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
-    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
-    const HVX_Vector * restrict y0 = (const HVX_Vector *) vy0;
-    const HVX_Vector * restrict y1 = (const HVX_Vector *) vy1;
-
-    uint32_t nvec = n / VLEN_FP32;
-    uint32_t nloe = n % VLEN_FP32;
-
-    HVX_Vector r0_c0_sum = Q6_V_vzero();
-    HVX_Vector r0_c1_sum = Q6_V_vzero();
-    HVX_Vector r1_c0_sum = Q6_V_vzero();
-    HVX_Vector r1_c1_sum = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector r0_sf = x0[i];
-        HVX_Vector r1_sf = x1[i];
-        HVX_Vector c0_sf = y0[i];
-        HVX_Vector c1_sf = y1[i];
-
-        r0_c0_sum = HVX_OP_ADD_F32(r0_c0_sum, HVX_OP_MUL_F32(r0_sf, c0_sf));
-        r0_c1_sum = HVX_OP_ADD_F32(r0_c1_sum, HVX_OP_MUL_F32(r0_sf, c1_sf));
-        r1_c0_sum = HVX_OP_ADD_F32(r1_c0_sum, HVX_OP_MUL_F32(r1_sf, c0_sf));
-        r1_c1_sum = HVX_OP_ADD_F32(r1_c1_sum, HVX_OP_MUL_F32(r1_sf, c1_sf));
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
-
-        HVX_Vector r0_sf = Q6_V_vand_QV(bmask, x0[i]);
-        HVX_Vector r1_sf = Q6_V_vand_QV(bmask, x1[i]);
-        HVX_Vector c0_sf = Q6_V_vand_QV(bmask, y0[i]);
-        HVX_Vector c1_sf = Q6_V_vand_QV(bmask, y1[i]);
-
-        r0_c0_sum = HVX_OP_ADD_F32(r0_c0_sum, HVX_OP_MUL_F32(r0_sf, c0_sf));
-        r0_c1_sum = HVX_OP_ADD_F32(r0_c1_sum, HVX_OP_MUL_F32(r0_sf, c1_sf));
-        r1_c0_sum = HVX_OP_ADD_F32(r1_c0_sum, HVX_OP_MUL_F32(r1_sf, c0_sf));
-        r1_c1_sum = HVX_OP_ADD_F32(r1_c1_sum, HVX_OP_MUL_F32(r1_sf, c1_sf));
-    }
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    HVX_VectorAlias va0, va1;
-    va0.v = r0_r1_c0_sum;
-    va1.v = r0_r1_c1_sum;
-    s0[0] = va0.fp32[0];
-    s0[1] = va0.fp32[1];
-    s1[0] = va1.fp32[0];
-    s1[1] = va1.fp32[1];
-}
-
-static void vec_dot_f32_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
-    const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
-    const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
-
-    uint32_t nvec = n / VLEN_FP32; // num full fp32 hvx vectors
-    uint32_t nloe = n % VLEN_FP32; // leftover elements
-
-    HVX_Vector       rsum = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector x_sf = vx[i];
-        HVX_Vector y_sf = vy[i];
-
-        rsum = HVX_OP_ADD_F32(rsum, HVX_OP_MUL_F32(x_sf, y_sf));
-    }
-
-    if (nloe) {
-        HVX_Vector x_sf = vx[i];
-        HVX_Vector y_sf = vy[i];
-
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 4);
-        x_sf = Q6_V_vand_QV(bmask, x_sf);
-        y_sf = Q6_V_vand_QV(bmask, y_sf);
-
-        rsum = HVX_OP_ADD_F32(rsum, HVX_OP_MUL_F32(x_sf, y_sf));
-    }
-
-    rsum = hvx_vec_reduce_sum_f32(rsum);
-    hvx_vec_store_u(&s[0], 4, rsum);
-}
-
-static void vec_dot_f16_f16_aa_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const HVX_Vector * restrict x = (const HVX_Vector *) vx;
-    const HVX_Vector * restrict y = (const HVX_Vector *) vy;
-
-    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16; // leftover elements
-
-    HVX_VectorPair rsum_p = Q6_W_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(4)
-    for (i = 0; i < nvec; i++) {
-        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x[i], y[i]);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        HVX_Vector x_hf = Q6_V_vand_QV(bmask, x[i]);
-        HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]);
-        rsum_p = hvx_vec_mpyacc_f32_f16(rsum_p, x_hf, y_hf);
-    }
-
-    HVX_Vector rsum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum_p), Q6_V_hi_W(rsum_p)));
-    hvx_vec_store_u(s, 4, hvx_vec_reduce_sum_f32(rsum));
-}
-
-static void vec_dot_f16_f16_aa_2x1(const uint32_t n, float * restrict s0,
-                                const void * restrict vx0, const void * restrict vx1,
-                                const void * restrict vy0) {
-    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
-    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
-    const HVX_Vector * restrict y  = (const HVX_Vector *) vy0;
-
-    uint32_t nvec = n / VLEN_FP16;
-    uint32_t nloe = n % VLEN_FP16;
-
-    HVX_VectorPair rsum0_p = Q6_W_vzero();
-    HVX_VectorPair rsum1_p = Q6_W_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector y_hf = y[i];
-        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0[i], y_hf);
-        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1[i], y_hf);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        HVX_Vector y_hf  = Q6_V_vand_QV(bmask, y[i]);
-        HVX_Vector x0_hf = Q6_V_vand_QV(bmask, x0[i]);
-        HVX_Vector x1_hf = Q6_V_vand_QV(bmask, x1[i]);
-        rsum0_p = hvx_vec_mpyacc_f32_f16(rsum0_p, x0_hf, y_hf);
-        rsum1_p = hvx_vec_mpyacc_f32_f16(rsum1_p, x1_hf, y_hf);
-    }
-
-    HVX_Vector rsum0 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum0_p), Q6_V_hi_W(rsum0_p)));
-    HVX_Vector rsum1 = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(rsum1_p), Q6_V_hi_W(rsum1_p)));
-    HVX_Vector rsum  = hvx_vec_reduce_sum_f32x2(rsum0, rsum1);
-    hvx_vec_store_u(s0, 8, rsum);
-}
-
-static void vec_dot_f16_f16_aa_2x2(const uint32_t n, float * restrict s0, float * restrict s1,
-                                const void * restrict vx0, const void * restrict vx1,
-                                const void * restrict vy0, const void * restrict vy1) {
-    const HVX_Vector * restrict x0 = (const HVX_Vector *) vx0;
-    const HVX_Vector * restrict x1 = (const HVX_Vector *) vx1;
-    const HVX_Vector * restrict y0 = (const HVX_Vector *) vy0;
-    const HVX_Vector * restrict y1 = (const HVX_Vector *) vy1;
-
-    uint32_t nvec = n / VLEN_FP16;
-    uint32_t nloe = n % VLEN_FP16;
-
-    // Row sums (sf) - 4 accumulators for 2×2 tile
-    HVX_VectorPair r0_c0_sum_p = Q6_W_vzero();
-    HVX_VectorPair r0_c1_sum_p = Q6_W_vzero();
-    HVX_VectorPair r1_c0_sum_p = Q6_W_vzero();
-    HVX_VectorPair r1_c1_sum_p = Q6_W_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        HVX_Vector r0_hf = x0[i];
-        HVX_Vector r1_hf = x1[i];
-        HVX_Vector c0_hf = y0[i];
-        HVX_Vector c1_hf = y1[i];
-
-        // Compute 4 dot products: r0×c0, r0×c1, r1×c0, r1×c1
-        r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf);
-        r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf);
-        r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf);
-        r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf);
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-
-        HVX_Vector r0_hf = Q6_V_vand_QV(bmask, x0[i]);
-        HVX_Vector r1_hf = Q6_V_vand_QV(bmask, x1[i]);
-        HVX_Vector c0_hf = Q6_V_vand_QV(bmask, y0[i]);
-        HVX_Vector c1_hf = Q6_V_vand_QV(bmask, y1[i]);
-
-        r0_c0_sum_p = hvx_vec_mpyacc_f32_f16(r0_c0_sum_p, r0_hf, c0_hf);
-        r0_c1_sum_p = hvx_vec_mpyacc_f32_f16(r0_c1_sum_p, r0_hf, c1_hf);
-        r1_c0_sum_p = hvx_vec_mpyacc_f32_f16(r1_c0_sum_p, r1_hf, c0_hf);
-        r1_c1_sum_p = hvx_vec_mpyacc_f32_f16(r1_c1_sum_p, r1_hf, c1_hf);
-    }
-
-    HVX_Vector r0_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c0_sum_p), Q6_V_hi_W(r0_c0_sum_p)));
-    HVX_Vector r0_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r0_c1_sum_p), Q6_V_hi_W(r0_c1_sum_p)));
-    HVX_Vector r1_c0_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c0_sum_p), Q6_V_hi_W(r1_c0_sum_p)));
-    HVX_Vector r1_c1_sum = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(Q6_V_lo_W(r1_c1_sum_p), Q6_V_hi_W(r1_c1_sum_p)));
-
-    // Reduce and store results
-    HVX_Vector r0_r1_c0_sum = hvx_vec_reduce_sum_f32x2(r0_c0_sum, r1_c0_sum);
-    HVX_Vector r0_r1_c1_sum = hvx_vec_reduce_sum_f32x2(r0_c1_sum, r1_c1_sum);
-
-    hvx_vec_store_u(&s0[0], 8, r0_r1_c0_sum);  // row0,col0 row1,col0
-    hvx_vec_store_u(&s1[0], 8, r0_r1_c1_sum);  // row0,col1 row1,col1
-}
-
-static void vec_dot_f16_f16_uu_1x1(const uint32_t n, float * restrict s, const void * restrict vx, const void * restrict vy) {
-    const HVX_UVector * restrict x = (const HVX_UVector *) vx;
-    const HVX_UVector * restrict y = (const HVX_UVector *) vy;
-
-    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16; // leftover elements
-
-    HVX_Vector rsum = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(4)
-    for (i = 0; i < nvec; i++) {
-        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x[i], y[i]);
-        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
-    }
-
-    if (nloe) {
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        HVX_Vector x_hf = Q6_V_vand_QV(bmask, x[i]);
-        HVX_Vector y_hf = Q6_V_vand_QV(bmask, y[i]);
-
-        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
-        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
-    }
-
-    rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum));
-    hvx_vec_store_u(&s[0], 4, rsum);
-}
-
-static void vec_dot_f16_f32_uu_1x1(const uint32_t n, float * restrict s, const void * restrict x, const void * restrict y) {
-    const HVX_UVector * restrict vx = (const HVX_UVector * restrict) x;
-    const HVX_UVector * restrict vy = (const HVX_UVector * restrict) y;
-
-    uint32_t nvec = n / VLEN_FP16; // num full fp16 hvx vectors
-    uint32_t nloe = n % VLEN_FP16; // leftover elements
-
-    const HVX_Vector zero = Q6_V_vzero();
-
-    HVX_Vector       rsum = Q6_V_vzero();
-
-    uint32_t i = 0;
-
-    #pragma unroll(2)
-    for (i = 0; i < nvec; i++) {
-        // Load y (fp32) and convert into fp16
-        HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero);  // 32 elements
-        HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero);  // 32 elements
-        HVX_Vector y_hf  = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf)));
-
-        // Load x (fp16)
-        HVX_Vector x_hf  = vx[i];
-
-        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
-
-        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
-    }
-
-    if (nloe) {
-        // Load y (fp32) and convert into fp16
-        HVX_Vector y0_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+0], zero);  // 32 elements
-        HVX_Vector y1_qf = Q6_Vqf32_vsub_VsfVsf(vy[i*2+1], zero);  // 32 elements
-        HVX_Vector y_hf  = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(y1_qf, y0_qf)));
-
-        // Load x (fp16)
-        HVX_Vector x_hf  = vx[i];
-
-        // Zero-out unused elements
-        // Note that we need to clear both x and y because they may contain NANs
-        HVX_VectorPred bmask = Q6_Q_vsetq_R(nloe * 2);
-        x_hf = Q6_V_vand_QV(bmask, x_hf);
-        y_hf = Q6_V_vand_QV(bmask, y_hf);
-
-        HVX_VectorPair xy_qf = Q6_Wqf32_vmpy_VhfVhf(x_hf, y_hf);
-
-        rsum = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_lo_W(xy_qf),  Q6_V_hi_W(xy_qf)));
-    }
-
-    // Convert into fp32 and reduce
-    rsum = hvx_vec_reduce_sum_f32(Q6_Vsf_equals_Vqf32(rsum));
-    hvx_vec_store_u(&s[0], 4, rsum);
-}
 
 #define htp_matmul_tensors_preamble                                 \
     const struct htp_tensor * restrict src0 = octx->src[0];         \
@@ -526,10 +172,10 @@ static void vec_dot_f16_f32_uu_1x1(const uint32_t n, float * restrict s, const v
     const uint32_t ne12 = src1->ne[2];                              \
     const uint32_t ne13 = src1->ne[3];                              \
                                                                     \
-    const uint32_t ne20 = src2->ne[0];                              \
-    const uint32_t ne21 = src2->ne[1];                              \
-    const uint32_t ne22 = src2->ne[2];                              \
-    const uint32_t ne23 = src2->ne[3];                              \
+    const uint32_t ne20 = src2 ? src2->ne[0] : 0;                   \
+    const uint32_t ne21 = src2 ? src2->ne[1] : 0;                   \
+    const uint32_t ne22 = src2 ? src2->ne[2] : 0;                   \
+    const uint32_t ne23 = src2 ? src2->ne[3] : 0;                   \
                                                                     \
     const uint32_t ne0 = dst->ne[0];                                \
     const uint32_t ne1 = dst->ne[1];                                \
@@ -558,6 +204,18 @@ static void vec_dot_f16_f32_uu_1x1(const uint32_t n, float * restrict s, const v
     uint32_t src0_nrows_per_thread = mmctx->src0_nrows_per_thread;  \
     htp_matmul_tensors_preamble;
 
+static inline void hvx_mm_run_quant_task(struct htp_mm_context * mmctx, unsigned int ith) {
+    if (mmctx->quant_task_func) {
+        if (ith < mmctx->n_quant_tasks) {
+            mmctx->quant_task_func(mmctx->n_quant_tasks, ith, mmctx);
+            atomic_fetch_sub(&mmctx->quant_barrier, 1);
+        }
+        while (atomic_load(&mmctx->quant_barrier) > 0) {
+            // spin
+        }
+    }
+}
+
 // *** matmul with support for 4d tensors and full broadcasting
 
 static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) {
@@ -631,6 +289,9 @@ static void hvx_mm_4d(unsigned int nth, unsigned int ith, void * data) {
     }
 
     htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0_start);
+    if (src2) {
+        hvx_tensor_add_f32_grid(dst, src2, ir1_start, ir1_end, ir0_start, ir0_end, &mmctx->mm_div_ne12_ne1, &mmctx->mm_div_ne1);
+    }
 }
 
 #include "hmx-mm-kernels-tiled.h"
@@ -648,10 +309,6 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;                                                                 \
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);                                     \
                                                                                                                                   \
-    if (src0_start_row >= src0_end_row) {                                                                                         \
-        return;                                                                                                                   \
-    }                                                                                                                             \
-                                                                                                                                  \
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
                                                                                                                                   \
     const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
@@ -661,6 +318,7 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     const size_t dst_row_size  = nb1;                                                                                             \
     const size_t src1_row_size = nb11;                                                                                            \
     const size_t src1_stride = mmctx->vtcm_src1_stride;                                                                           \
+    const size_t src2_stride = src2 ? ((src2->ne[1] == 1) ? 0 : src2->nb[1]) : 0;                                                 \
                                                                                                                                   \
     uint8_t * restrict vtcm_dst_ptr  = mmctx->vtcm_dst  + mmctx->vtcm_dst_size_per_thread  * ith;                                 \
     uint8_t * restrict vtcm_src0_ptr = mmctx->vtcm_src0 + mmctx->vtcm_src0_size_per_thread * ith;                                 \
@@ -680,9 +338,17 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
                                                                                                                                   \
     uint32_t push_ct = ct_start;                                                                                                  \
-    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
-                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    if (src0_start_row < src0_end_row) {                                                                                          \
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+        }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    hvx_mm_run_quant_task(mmctx, ith);                                                                                            \
+                                                                                                                                  \
+    if (src0_start_row >= src0_end_row) {                                                                                         \
+        return;                                                                                                                   \
     }                                                                                                                             \
                                                                                                                                   \
     for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
@@ -702,7 +368,15 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
             float * dst_ptr0 = &dst_row0[ct * 32];                                                                                \
             float * dst_ptr1 = &dst_row1[ct * 32];                                                                                \
                                                                                                                                   \
-            DOT_2X2(ne10, dst_ptr0, dst_ptr1, w_tile, src1_col0, src1_col1, valid_rows);                                          \
+            const float * src2_ptr0 = NULL;                                                                                       \
+            const float * src2_ptr1 = NULL;                                                                                       \
+            if (src2) {                                                                                                           \
+                const float * restrict src2_row0 = (const float *) ((const uint8_t *) src2->data + ((ir1+0) * src2_stride));      \
+                const float * restrict src2_row1 = (const float *) ((const uint8_t *) src2->data + ((ir1+1) * src2_stride));      \
+                src2_ptr0 = &src2_row0[ct * 32];                                                                                  \
+                src2_ptr1 = &src2_row1[ct * 32];                                                                                  \
+            }                                                                                                                     \
+            DOT_2X2(ne10, dst_ptr0, dst_ptr1, w_tile, src1_col0, src1_col1, valid_rows, src2_ptr0, src2_ptr1);                    \
         }                                                                                                                         \
                                                                                                                                   \
         for (; ir1 < src1_nrows; ++ir1) {                                                                                         \
@@ -710,7 +384,12 @@ static void hvx_mm_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
             float * restrict dst_row          = (float *) (dst->data + (ir1 * dst_row_size));                                     \
             float * dst_ptr = &dst_row[ct * 32];                                                                                  \
                                                                                                                                   \
-            DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows);                                                                 \
+            const float * src2_ptr = NULL;                                                                                        \
+            if (src2) {                                                                                                           \
+                const float * restrict src2_row = (const float *) ((const uint8_t *) src2->data + (ir1 * src2_stride));           \
+                src2_ptr = &src2_row[ct * 32];                                                                                    \
+            }                                                                                                                     \
+            DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows, src2_ptr);                                                       \
         }                                                                                                                         \
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
                                                                                                                                   \
@@ -731,10 +410,6 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;                                                                 \
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);                                     \
                                                                                                                                   \
-    if (src0_start_row >= src0_end_row) {                                                                                         \
-        return;                                                                                                                   \
-    }                                                                                                                             \
-                                                                                                                                  \
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;                                                     \
                                                                                                                                   \
     const struct htp_mm_kernel_params * kparams = (const struct htp_mm_kernel_params *) octx->kernel_params;                      \
@@ -752,6 +427,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     float * tmp = (float *) vtcm_dst_ptr;                                                                                         \
                                                                                                                                   \
     const uint8_t * restrict src0_row = (const uint8_t *) src0->data;                                                             \
+                                                                                                                                  \
     const uint8_t * restrict src1_col = (const uint8_t *) src1_data;                                                              \
     float * restrict dst_col          = (float *) dst->data;                                                                      \
                                                                                                                                   \
@@ -767,9 +443,17 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
     uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
                                                                                                                                   \
     uint32_t push_ct = ct_start;                                                                                                  \
-    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
-                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    if (src0_start_row < src0_end_row) {                                                                                          \
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+        }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    hvx_mm_run_quant_task(mmctx, ith);                                                                                            \
+                                                                                                                                  \
+    if (src0_start_row >= src0_end_row) {                                                                                         \
+        return;                                                                                                                   \
     }                                                                                                                             \
                                                                                                                                   \
     for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
@@ -780,7 +464,7 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
         valid_rows = MIN(32, MAX(0, valid_rows));                                                                                 \
                                                                                                                                   \
         htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                    \
-        DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows);                                                                     \
+        DOT_2X1(ne10, dst_ptr, w_tile, src1_col, valid_rows, NULL);                                                               \
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
                                                                                                                                   \
         if (push_ct < ct_end) {                                                                                                   \
@@ -792,7 +476,24 @@ static void hvx_mv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith, void
                                                                                                                                   \
     int copy_cnt = (int)MIN(src0_end_row, ne0) - (int)src0_start_row;                                                             \
     if (copy_cnt > 0) {                                                                                                           \
-        hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt);                                         \
+        if (src2) {                                                                                                               \
+            float * dst_ptr = &dst_col[src0_start_row];                                                                           \
+            const float * src2_ptr = (const float *) src2->data + src0_start_row;                                                 \
+            float * tmp_ptr = tmp;                                                                                                \
+            int remaining = copy_cnt;                                                                                             \
+            while (remaining > 0) {                                                                                               \
+                int n = MIN(remaining, 32);                                                                                       \
+                HVX_Vector v_out = hvx_vmemu(tmp_ptr);                                                                            \
+                HVX_Vector v_z   = hvx_vmemu(src2_ptr);                                                                           \
+                hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z));                                     \
+                dst_ptr += n;                                                                                                     \
+                src2_ptr += n;                                                                                                    \
+                tmp_ptr += n;                                                                                                     \
+                remaining -= n;                                                                                                   \
+            }                                                                                                                     \
+        } else {                                                                                                                  \
+            hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt);                                     \
+        }                                                                                                                         \
     }                                                                                                                             \
 }
 
@@ -850,17 +551,22 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
     const uint32_t start_row_kv = src0_nrows_per_thread_kv * ith;                                                                 \
     const uint32_t end_row_kv   = MIN(start_row_kv + src0_nrows_per_thread_kv, src0_nrows_kv);                                    \
                                                                                                                                   \
-    if (start_row_kv < end_row_kv) {                                                                                              \
-        uint32_t ct_start_kv = start_row_kv / 32;                                                                                 \
-        uint32_t ct_end_kv   = (end_row_kv + 31) / 32;                                                                            \
+    uint32_t ct_start_kv = start_row_kv / 32;                                                                                     \
+    uint32_t ct_end_kv   = (end_row_kv + 31) / 32;                                                                                \
                                                                                                                                   \
-        uint32_t push_ct = ct_start_kv;                                                                                           \
+    uint32_t push_ct = ct_start_kv;                                                                                               \
+    if (start_row_kv < end_row_kv) {                                                                                              \
         for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end_kv; d++, push_ct++) {                                             \
             dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                            \
                            src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
             dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + d * tile_row_transfer_size_aligned,                            \
                            src2_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
         }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    hvx_mm_run_quant_task(mmctx, ith);                                                                                            \
+                                                                                                                                  \
+    if (start_row_kv < end_row_kv) {                                                                                              \
                                                                                                                                   \
         for (uint32_t ct = ct_start_kv; ct < ct_end_kv; ct++) {                                                                   \
             const uint8_t * w_tile_k = dma_queue_pop(dma_queue).dst;                                                              \
@@ -885,8 +591,8 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
                 float * dst_ptr0_v = &dst_row0_v[ct * 32];                                                                        \
                 float * dst_ptr1_v = &dst_row1_v[ct * 32];                                                                        \
                                                                                                                                   \
-                DOT_2X2(ne10, dst_ptr0_k, dst_ptr1_k, w_tile_k, src1_col0, src1_col1, valid_rows);                                \
-                DOT_2X2(ne10, dst_ptr0_v, dst_ptr1_v, w_tile_v, src1_col0, src1_col1, valid_rows);                                \
+                DOT_2X2(ne10, dst_ptr0_k, dst_ptr1_k, w_tile_k, src1_col0, src1_col1, valid_rows, NULL, NULL);                    \
+                DOT_2X2(ne10, dst_ptr0_v, dst_ptr1_v, w_tile_v, src1_col0, src1_col1, valid_rows, NULL, NULL);                    \
             }                                                                                                                     \
                                                                                                                                   \
             for (; ir1 < src1_nrows; ++ir1) {                                                                                     \
@@ -898,8 +604,8 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
                 float * restrict dst_row_v = (float *) (dst_v->data + (ir1 * dst_k_row_size));                                    \
                 float * dst_ptr_v = &dst_row_v[ct * 32];                                                                          \
                                                                                                                                   \
-                DOT_2X1(ne10, dst_ptr_k, w_tile_k, src1_col, valid_rows);                                                         \
-                DOT_2X1(ne10, dst_ptr_v, w_tile_v, src1_col, valid_rows);                                                         \
+                DOT_2X1(ne10, dst_ptr_k, w_tile_k, src1_col, valid_rows, NULL);                                                   \
+                DOT_2X1(ne10, dst_ptr_v, w_tile_v, src1_col, valid_rows, NULL);                                                   \
             }                                                                                                                     \
             htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ith);                                                                \
                                                                                                                                   \
@@ -948,7 +654,7 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
                 float * dst_ptr0_q = &dst_row0_q[ct * 32];                                                                        \
                 float * dst_ptr1_q = &dst_row1_q[ct * 32];                                                                        \
                                                                                                                                   \
-                DOT_2X2(ne10, dst_ptr0_q, dst_ptr1_q, w_tile_q, src1_col0, src1_col1, valid_rows);                                \
+                DOT_2X2(ne10, dst_ptr0_q, dst_ptr1_q, w_tile_q, src1_col0, src1_col1, valid_rows, NULL, NULL);                    \
             }                                                                                                                     \
                                                                                                                                   \
             for (; ir1 < src1_nrows; ++ir1) {                                                                                     \
@@ -957,7 +663,7 @@ static void hvx_mm_qkv_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
                 float * restrict dst_row_q = (float *) (dst_q->data + (ir1 * dst_q_row_size));                                    \
                 float * dst_ptr_q = &dst_row_q[ct * 32];                                                                          \
                                                                                                                                   \
-                DOT_2X1(ne10, dst_ptr_q, w_tile_q, src1_col, valid_rows);                                                         \
+                DOT_2X1(ne10, dst_ptr_q, w_tile_q, src1_col, valid_rows, NULL);                                                   \
             }                                                                                                                     \
             htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                 \
                                                                                                                                   \
@@ -1019,11 +725,19 @@ static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
     uint32_t ct_end   = (src0_end_row + 31) / 32;                                                                                 \
                                                                                                                                   \
     uint32_t push_ct = ct_start;                                                                                                  \
-    for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                    \
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                                \
-                       src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + d * tile_row_transfer_size_aligned,                                \
-                       src2_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);              \
+    if (src0_start_row < src0_end_row) {                                                                                          \
+        for (uint32_t d = 0; d < n_prefetch && push_ct < ct_end; d++, push_ct++) {                                                \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src0_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + d * tile_row_transfer_size_aligned,                            \
+                           src2_row + push_ct * tile_row_stride), aligned_tile_size, tile_size, tile_size, n_k_tiles_a);          \
+        }                                                                                                                         \
+    }                                                                                                                             \
+                                                                                                                                  \
+    hvx_mm_run_quant_task(mmctx, ith);                                                                                            \
+                                                                                                                                  \
+    if (src0_start_row >= src0_end_row) {                                                                                         \
+        return;                                                                                                                   \
     }                                                                                                                             \
                                                                                                                                   \
     for (uint32_t ct = ct_start; ct < ct_end; ct++) {                                                                             \
@@ -1049,8 +763,8 @@ static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
             float * dst_ptr0_up = &dst_row0_up[ct * 32];                                                                          \
             float * dst_ptr1_up = &dst_row1_up[ct * 32];                                                                          \
                                                                                                                                   \
-            DOT_2X2(ne10, dst_ptr0_gate, dst_ptr1_gate, w_tile_gate, src1_col0, src1_col1, valid_rows);                           \
-            DOT_2X2(ne10, dst_ptr0_up, dst_ptr1_up, w_tile_up, src1_col0, src1_col1, valid_rows);                                 \
+            DOT_2X2(ne10, dst_ptr0_gate, dst_ptr1_gate, w_tile_gate, src1_col0, src1_col1, valid_rows, NULL, NULL);               \
+            DOT_2X2(ne10, dst_ptr0_up, dst_ptr1_up, w_tile_up, src1_col0, src1_col1, valid_rows, NULL, NULL);                     \
         }                                                                                                                         \
                                                                                                                                   \
         for (; ir1 < src1_nrows; ++ir1) {                                                                                         \
@@ -1062,8 +776,8 @@ static void hvx_mm_ffn_2d_repacked_##SUFFIX(unsigned int nth, unsigned int ith,
             float * restrict dst_row_up = (float *) (dst_up->data + (ir1 * dst_row_size));                                        \
             float * dst_ptr_up = &dst_row_up[ct * 32];                                                                            \
                                                                                                                                   \
-            DOT_2X1(ne10, dst_ptr_gate, w_tile_gate, src1_col, valid_rows);                                                       \
-            DOT_2X1(ne10, dst_ptr_up, w_tile_up, src1_col, valid_rows);                                                           \
+            DOT_2X1(ne10, dst_ptr_gate, w_tile_gate, src1_col, valid_rows, NULL);                                                 \
+            DOT_2X1(ne10, dst_ptr_up, w_tile_up, src1_col, valid_rows, NULL);                                                     \
         }                                                                                                                         \
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);                                                                     \
                                                                                                                                   \
@@ -1099,7 +813,7 @@ static void name(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t ne2 = src->ne[2];                                                                       \
     const uint32_t ne3 = src->ne[3];                                                                       \
     const uint32_t nrows = ne1 * ne2 * ne3;                                                                \
-    const uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;                                        \
+    const uint32_t nrows_per_thread = mmctx->n_quant_rows_per_thread;                                      \
                                                                                                            \
     const uint32_t ir_first = nrows_per_thread * ith;                                                      \
     if (ir_first >= nrows) {                                                                               \
@@ -1115,7 +829,7 @@ static void name(unsigned int nth, unsigned int ith, void * data) {
     const size_t dst_row_size = (dst_row_size_expr);                                                       \
     const uint8_t * restrict src_data = (const uint8_t *) src->data + (src_row_size * ir_first);           \
     uint8_t * restrict dst_data = (uint8_t *) dst + (dst_row_size * ir_first);                             \
-    uint8_t * restrict tmp_data = (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith); \
+    uint8_t * restrict tmp_data = (uint8_t *) mmctx->vtcm_dst + (mmctx->vtcm_dst_size_per_thread * ith);   \
     kernel_fn(src_data, dst_data, tmp_data, ne0, ir_last - ir_first, src_row_size, dst_row_size);          \
                                                                                                            \
     htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_A_QUANT, ir_first);                                         \
@@ -1140,7 +854,7 @@ static void quantize_f32_q8_0_tiled_block(unsigned int nth, unsigned int ith, vo
     quantize_f32_q8_0_tiled_block_kernel(
         (const float *) src->data,
         mmctx->vtcm_src1,
-        (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith),
+        (uint8_t *) mmctx->vtcm_dst + (mmctx->vtcm_dst_size_per_thread * ith),
         src->ne[0],
         mmctx->quant_ib_first[ith],
         mmctx->quant_ib_last[ith],
@@ -1164,7 +878,7 @@ static void quantize_f32_q8_1_tiled_block(unsigned int nth, unsigned int ith, vo
     quantize_f32_q8_1_tiled_block_kernel(
         (const float *) src->data,
         mmctx->vtcm_src1,
-        (uint8_t *) mmctx->vtcm_src0 + (mmctx->vtcm_src0_size_per_thread * ith),
+        (uint8_t *) mmctx->vtcm_dst + (mmctx->vtcm_dst_size_per_thread * ith),
         src->ne[0],
         mmctx->quant_ib_first[ith],
         mmctx->quant_ib_last[ith],
@@ -1230,11 +944,6 @@ static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
     const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
-    // no work for this thread
-    if (src0_start_row >= src0_end_row) {
-        return;
-    }
-
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const size_t dst_row_size  = nb1;
@@ -1252,14 +961,21 @@ static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint8_t * restrict src0_row = (const uint8_t *) src0->data;
 
     // Prefill vtcm with src0 rows
-    #pragma unroll(4)
-    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-        const int is0 = (ir0 - src0_start_row);
-        if (is0 >= (int)n_prefetch) {
-            break;
+    if (src0_start_row < src0_end_row) {
+        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+            const int is0 = (ir0 - src0_start_row);
+            if (is0 >= (int)n_prefetch) {
+                break;
+            }
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
         }
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, src0_row_size, 2);
+    }
+
+    hvx_mm_run_quant_task(mmctx, ith);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
     }
 
     // Process src0 rows
@@ -1311,6 +1027,9 @@ static void hvx_mm_2d(unsigned int nth, unsigned int ith, void * data) {
         }
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
     }
+    if (src2) {
+        hvx_tensor_add_f32_grid(dst, src2, 0, src1_nrows, src0_start_row, src0_end_row, &kparams->div_ne12_ne1, &kparams->div_ne1);
+    }
 }
 
 static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) {
@@ -1321,11 +1040,6 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
 
-    // no work for this thread
-    if (src0_start_row >= src0_end_row) {
-        return;
-    }
-
     struct htp_thread_trace * tr = octx->ctx ? &octx->ctx->trace[ith] : NULL;
 
     const size_t dst_row_size  = nb1;
@@ -1354,14 +1068,21 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t prefetch_mask = n_prefetch - 1;
 
     // Prefill vtcm with 2x src0 rows
-    #pragma unroll(2)
-    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-        const uint32_t is0 = (ir0 - src0_start_row);
-        if (is0 >= n_prefetch) {
-            break;
+    if (src0_start_row < src0_end_row) {
+        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+            const uint32_t is0 = (ir0 - src0_start_row);
+            if (is0 >= n_prefetch) {
+                break;
+            }
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
         }
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, src0_row_size, 2);
+    }
+
+    hvx_mm_run_quant_task(mmctx, ith);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
     }
 
     // Process src0 rows
@@ -1392,7 +1113,25 @@ static void hvx_mv_2d(unsigned int nth, unsigned int ith, void * data) {
         htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ir0);
     }
 
-    hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, src0_end_row - src0_start_row);
+    int copy_cnt = src0_end_row - src0_start_row;
+    if (src2) {
+        float * dst_ptr = &dst_col[src0_start_row];
+        const float * src2_ptr = (const float *) src2->data + src0_start_row;
+        float * tmp_ptr = tmp;
+        int remaining = copy_cnt;
+        while (remaining > 0) {
+            int n = MIN(remaining, 32);
+            HVX_Vector v_out = hvx_vmemu(tmp_ptr);
+            HVX_Vector v_z   = hvx_vmemu(src2_ptr);
+            hvx_vec_store_u(dst_ptr, n * sizeof(float), hvx_vec_add_f32_f32(v_out, v_z));
+            dst_ptr += n;
+            src2_ptr += n;
+            tmp_ptr += n;
+            remaining -= n;
+        }
+    } else {
+        hvx_copy_f32_ua((uint8_t *) &dst_col[src0_start_row], (uint8_t *) tmp, copy_cnt);
+    }
 }
 
 #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id) * ids->ne[0] * ids->ne[1] + (i1)]
@@ -1410,7 +1149,8 @@ static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
 
-    // no work for this thread
+    hvx_mm_run_quant_task(mmctx, ith);
+
     if (src0_start_row >= src0_end_row) {
         return;
     }
@@ -1476,7 +1216,7 @@ static void hvx_mm_id(unsigned int nth, unsigned int ith, void * data) {
                 const uint8_t * restrict src1_col = (const uint8_t *) (src1_data + (ir1 + rm2 * ne11 + 0) * src1_stride);
                 float * restrict dst_row = (float *) (dst->data + (rm1 * nb1 + rm2 * nb2 + 0));
 
-                mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows);
+                mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows, NULL);
             }
             htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);
 
@@ -1498,7 +1238,8 @@ static void hvx_mv_id(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_start_row  = src0_nrows_per_thread * ith;
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
 
-    // no work for this thread
+    hvx_mm_run_quant_task(mmctx, ith);
+
     if (src0_start_row >= src0_end_row) {
         return;
     }
@@ -1555,7 +1296,7 @@ static void hvx_mv_id(unsigned int nth, unsigned int ith, void * data) {
             valid_rows = MIN(32, MAX(0, valid_rows));
 
             htp_trace_event_start(tr, HTP_TRACE_EVT_HVX_COMP, ct);
-            mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows);
+            mmctx->vec_dot_32x1(ne10, &dst_row[ct * 32], w_tile, src1_col, valid_rows, NULL);
             htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_COMP, ct);
 
             if (push_ct < ct_end) {
@@ -1625,9 +1366,9 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
     const size_t src0_row_size_padded = hex_round_up(src0_row_size, 128);
     size_t       src1_row_size_padded;
 
-    worker_callback_t quant_job_func;
+    worker_callback_t quant_task_func;
     worker_callback_t matmul_job_func;
-    uint32_t n_quant_jobs = 1;
+    uint32_t n_quant_tasks = 1;
     if (src1_nrows > 1) {
         if (is_repacked) {
             switch (src0->type) {
@@ -1660,7 +1401,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
 
     switch (kparams->kernel_type) {
         case HTP_MM_KERNEL_HVX_F16_F16_VTCM:
-            quant_job_func         = (src1->type == HTP_TYPE_F32) ? quantize_f32_f16_flat : quantize_f16_f16_flat;
+            quant_task_func        = (src1->type == HTP_TYPE_F32) ? quantize_f32_f16_flat : quantize_f16_f16_flat;
             mmctx->type            = "f16-f16";
             mmctx->vec_dot_1x1     = vec_dot_f16_f16_aa_1x1;
             mmctx->vec_dot_2x1     = vec_dot_f16_f16_aa_2x1;
@@ -1677,7 +1418,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
             mmctx->mm_div_r2       = kparams->div_r2;
             mmctx->mm_div_r3       = kparams->div_r3;
             need_quant             = false;
-            quant_job_func         = NULL;
+            quant_task_func        = NULL;
             src1_row_size          = nb11;
             break;
 
@@ -1691,11 +1432,11 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
             mmctx->mm_div_r3       = kparams->div_r3;
             src1_row_size          = nb11;
             need_quant             = false;
-            quant_job_func         = NULL;
+            quant_task_func        = NULL;
             break;
 
         case HTP_MM_KERNEL_HVX_F32_F32_VTCM:
-            quant_job_func         = quantize_f32_f32_flat;
+            quant_task_func        = quantize_f32_f32_flat;
             mmctx->type            = "f32-f32";
             mmctx->vec_dot_1x1     = vec_dot_f32_f32_aa_1x1;
             mmctx->vec_dot_2x1     = vec_dot_f32_f32_aa_2x1;
@@ -1704,7 +1445,7 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
             break;
 
         case HTP_MM_KERNEL_HVX_F32_F32_DDR:
-            quant_job_func         = NULL;
+            quant_task_func        = NULL;
             mmctx->type            = "f32-f32";
             mmctx->vec_dot_1x1     = vec_dot_f32_f32_uu_1x1;
             mmctx->mm_div_ne12_ne1 = kparams->div_ne12_ne1;
@@ -1717,8 +1458,8 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
             break;
 
         case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
-            n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-            quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+            n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+            quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
             src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
 
             if (src1_nrows > 1) {
@@ -1755,19 +1496,19 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
             const uint32_t total_nb = src1_nrows * nb;
 
             if (src1_nrows < octx->n_threads) {
-                n_quant_jobs = MIN(total_nb, octx->n_threads);
-                quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
-                for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
-                    uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
-                    uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+                n_quant_tasks = MIN(total_nb, octx->n_threads);
+                quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+                for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
+                    uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
+                    uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
                     mmctx->quant_ib_first[ith] = ib_first;
                     mmctx->quant_ib_last[ith]  = ib_last;
                     mmctx->quant_r[ith]        = ib_first / nb;
                     mmctx->quant_c[ith]        = ib_first % nb;
                 }
             } else {
-                n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-                quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+                n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+                quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
             }
             src1_row_size = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
             break;
@@ -1831,8 +1572,13 @@ static int hvx_mm_matmul(struct htp_ops_context * octx) {
         return HTP_STATUS_OK;
 
     if (need_quant) {
-        mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-        worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+        mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks;
+        mmctx->quant_task_func = quant_task_func;
+        mmctx->n_quant_tasks = n_quant_tasks;
+        atomic_init(&mmctx->quant_barrier, n_quant_tasks);
+    } else {
+        mmctx->quant_task_func = NULL;
+        mmctx->n_quant_tasks = 0;
     }
 
     const uint32_t n_matmul_jobs = octx->n_threads;
@@ -1870,10 +1616,6 @@ static void hvx_mm_qkv_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
     const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
-    if (src0_start_row >= src0_end_row) {
-        return;
-    }
-
     const size_t dst_k_row_size  = dst_k->nb[1]; // K and V share output width
     const size_t dst_q_row_size  = dst_q->nb[1]; // Q may be wider (GQA)
     const size_t src0_row_size = src0->nb[1];
@@ -1902,17 +1644,25 @@ static void hvx_mm_qkv_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint8_t * restrict src3_row = (const uint8_t *) src3->data;
 
     // Prefill spad with src0, src2, src3 rows
-    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-        const int is0 = (ir0 - src0_start_row);
-        if (is0 >= (int)n_prefetch) {
-            break;
+    if (src0_start_row < src0_end_row) {
+        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+            const int is0 = (ir0 - src0_start_row);
+            if (is0 >= (int)n_prefetch) {
+                break;
+            }
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                           src2_stride, src2_row_size, src2_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + is0 * src3_stride, src3_row + ir0 * src3_row_size),
+                           src3_stride, src3_row_size, src3_row_size, 2);
         }
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, src0_row_size, 2);
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
-                       src2_stride, src2_row_size, src2_row_size, 2);
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src3_ptr + is0 * src3_stride, src3_row + ir0 * src3_row_size),
-                       src3_stride, src3_row_size, src3_row_size, 2);
+    }
+
+    hvx_mm_run_quant_task(mmctx, ith);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
     }
 
     // Process rows
@@ -2024,10 +1774,6 @@ static void hvx_mm_ffn_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint32_t src0_end_row    = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
     const uint32_t src0_end_row_x2 = src0_start_row + ((src0_end_row - src0_start_row) & ~1U);
 
-    if (src0_start_row >= src0_end_row) {
-        return;
-    }
-
     const size_t dst_row_size  = dst_gate->nb[1];
     const size_t src0_row_size = src0->nb[1];
     const size_t src2_row_size = src2->nb[1];
@@ -2051,15 +1797,23 @@ static void hvx_mm_ffn_2d(unsigned int nth, unsigned int ith, void * data) {
     const uint8_t * restrict src2_row = (const uint8_t *) src2->data;
 
     // Prefill spad with src0, src2 rows
-    for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
-        const int is0 = (ir0 - src0_start_row);
-        if (is0 >= (int)n_prefetch) {
-            break;
+    if (src0_start_row < src0_end_row) {
+        for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) {
+            const int is0 = (ir0 - src0_start_row);
+            if (is0 >= (int)n_prefetch) {
+                break;
+            }
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
+                           src0_stride, src0_row_size, src0_row_size, 2);
+            dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
+                           src2_stride, src2_row_size, src2_row_size, 2);
         }
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src0_ptr + is0 * src0_stride, src0_row + ir0 * src0_row_size),
-                       src0_stride, src0_row_size, src0_row_size, 2);
-        dma_queue_push(dma_queue, dma_make_ptr(vtcm_src2_ptr + is0 * src2_stride, src2_row + ir0 * src2_row_size),
-                       src2_stride, src2_row_size, src2_row_size, 2);
+    }
+
+    hvx_mm_run_quant_task(mmctx, ith);
+
+    if (src0_start_row >= src0_end_row) {
+        return;
     }
 
     // Process rows
@@ -2187,7 +1941,8 @@ static void transfer_output_chunk_worker_fn(unsigned int n, unsigned int i, void
         size_t chunk_size = hex_smin(st->n_tot_chunks - chunk_idx, st->n_chunks_per_task);
 
         float        *dst      = st->dst      + chunk_idx * st->dst_stride;
-        transfer_output_chunk_fp16_to_fp32(dst, st->vtcm_src, chunk_idx, chunk_size, st->n_cols, st->dst_stride, st->dst_cols);
+        const float  *src2     = st->src2     ? (st->src2     + chunk_idx * st->src2_stride) : NULL;
+        transfer_output_chunk_fp16_to_fp32(dst, src2, st->vtcm_src, chunk_idx, chunk_size, st->n_cols, st->dst_stride, st->src2_stride, st->dst_cols);
     }
 
     htp_trace_event_stop(tr, HTP_TRACE_EVT_HVX_O_PROC, start_chunk_idx);
@@ -2316,8 +2071,8 @@ static void dequantize_tiled_weight_chunk_to_fp16_tiles(
     }
 }
 
-static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const __fp16 *vtcm_src,
-                                              int n_rows, int n_cols, int dst_stride, int dst_cols, int n_threads) {
+static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst, const float *src2, const __fp16 *vtcm_src,
+                                              int n_rows, int n_cols, int dst_stride, uint32_t src2_stride, int dst_cols, int n_threads) {
     assert(n_cols % HTP_MM_HMX_TILE_N_COLS == 0);
 
     if (n_rows <= 0) return;
@@ -2333,9 +2088,11 @@ static void transfer_output_chunk_threaded(struct htp_context *ctx, float *dst,
     state.n_tot_chunks      = n_tot_chunks;
     state.n_chunks_per_task = n_chunks_per_task;
     state.dst               = dst;
+    state.src2              = src2;
     state.vtcm_src          = vtcm_src;
     state.n_cols            = n_cols;
     state.dst_stride        = dst_stride;
+    state.src2_stride       = src2_stride;
     state.dst_cols          = dst_cols;
     state.traces            = ctx->trace;
 
@@ -2384,6 +2141,7 @@ static void transfer_activation_chunk_threaded(
 
 static int hmx_mm_2d_f32(struct htp_context *ctx,
                                   float *restrict dst,
+                                  const float *restrict src2,
                                   const float *activation,
                                   const uint8_t *weight,
                                   int m, int k, int n,
@@ -2392,6 +2150,7 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
                                   int weight_type,
                                   int k_valid,
                                   int dst_stride,
+                                  uint32_t src2_stride,
                                   int dst_cols,
                                   int m_chunk,
                                   int n_chunk,
@@ -2425,10 +2184,14 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
     const int n_k_tiles = k / HTP_MM_HMX_TILE_N_COLS;
     const struct fastdiv_values n_k_tiles_div = init_fastdiv_values(n_k_tiles);
 
-    const bool is_quant     = (weight_type != HTP_TYPE_F16 && weight_type != HTP_TYPE_F32);
+    const bool is_quant       = (weight_type != HTP_TYPE_F16 && weight_type != HTP_TYPE_F32);
     const size_t vec_dot_size = k * sizeof(__fp16);
     const size_t vtcm_budget  = ctx->vtcm_size;
 
+    const uint32_t dma_dst_stride  = is_quant ? aligned_tile_size : row_stride;
+    const uint32_t dma_src_stride  = is_quant ? tile_size : weight_stride;
+    const uint32_t dma_width_bytes = is_quant ? tile_size : row_stride;
+
     size_t m_chunk_n_rows = m_chunk;
     size_t n_chunk_n_cols = n_chunk;
     size_t vtcm_used      = vtcm_size;
@@ -2458,8 +2221,9 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
             vtcm_weight_raw[0] = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size);
         }
     }
-    __fp16  *vtcm_f16_act = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
-    float   *vtcm_f32_act    = (float *) vtcm_seq_alloc(&vtcm_ptr, act_f32_size);
+
+    __fp16  *vtcm_f16_act    = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
+    float   *vtcm_f32_act    = (float *)  vtcm_seq_alloc(&vtcm_ptr, act_f32_size);
     __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
     void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
     void    *vtcm_scratch1   = scratch1_size ? vtcm_seq_alloc(&vtcm_ptr, scratch1_size) : NULL;
@@ -2494,19 +2258,15 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
 
             // Prologue: push A0 and optionally A1 (if n_chunk_cnt > 1)
             const size_t n_cols_A0 = hex_smin(n - 0 * n_chunk_n_cols, n_chunk_n_cols);
-            if (is_quant) {
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight), aligned_tile_size, tile_size, tile_size, (n_cols_A0 / 32) * n_k_tiles);
-            } else {
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight), row_stride, weight_stride, row_stride, n_cols_A0);
-            }
+            const uint32_t height_A0 = is_quant ? (n_cols_A0 / 32) * n_k_tiles : n_cols_A0;
+            dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight),
+                           dma_dst_stride, dma_src_stride, dma_width_bytes, height_A0);
 
             if (1 < n_chunk_cnt) {
                 const size_t n_cols_A1 = hex_smin(n - 1 * n_chunk_n_cols, n_chunk_n_cols);
-                if (is_quant) {
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[1], weight + n_chunk_n_cols * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols_A1 / 32) * n_k_tiles);
-                } else {
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[1], weight + n_chunk_n_cols * weight_stride), row_stride, weight_stride, row_stride, n_cols_A1);
-                }
+                const uint32_t height_A1 = is_quant ? (n_cols_A1 / 32) * n_k_tiles : n_cols_A1;
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[1], weight + n_chunk_n_cols * weight_stride),
+                               dma_dst_stride, dma_src_stride, dma_width_bytes, height_A1);
             }
 
             // pop A0 -> dequantize A0 -> submit C0
@@ -2543,11 +2303,9 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
 
                 // 2. push A_{i+2} (if i+2 < n_chunk_cnt)
                 if (i + 2 < n_chunk_cnt) {
-                    if (is_quant) {
-                        dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[(i + 2) % 2], weight + nc_p2 * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols_p2 / 32) * n_k_tiles);
-                    } else {
-                        dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[(i + 2) % 2], weight + nc_p2 * weight_stride), row_stride, weight_stride, row_stride, n_cols_p2);
-                    }
+                    const uint32_t height_p2 = is_quant ? (n_cols_p2 / 32) * n_k_tiles : n_cols_p2;
+                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[(i + 2) % 2], weight + nc_p2 * weight_stride),
+                                   dma_dst_stride, dma_src_stride, dma_width_bytes, height_p2);
                 }
 
                 // 3. submit C_{i+1} (if i+1 < n_chunk_cnt)
@@ -2562,9 +2320,10 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
                 // 4. wait C_i and store D_i (multi-thread HVX, parallel with C_{i+1})
                 hmx_queue_pop(ctx->hmx_queue);
                 float *output_chunk = dst + (mr * dst_stride + nc);
+                const float *src2_chunk = src2 ? (src2 + mr * src2_stride + nc) : NULL;
                 int chunk_dst_cols = dst_cols - (int)nc;
                 if (chunk_dst_cols > 0) {
-                    transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output_bufs[i % 2], n_rows, n_cols, dst_stride, chunk_dst_cols, n_threads);
+                    transfer_output_chunk_threaded(ctx, output_chunk, src2_chunk, vtcm_output_bufs[i % 2], n_rows, n_cols, dst_stride, src2_stride, chunk_dst_cols, n_threads);
                 }
             }
         }
@@ -2583,11 +2342,9 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
                 const size_t n_col_tiles = hmx_ceil_div(n_cols, HTP_MM_HMX_TILE_N_COLS);
 
                 // A: Weight DMA (Synchronous)
-                if (is_quant) {
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight + nc * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols / 32) * n_k_tiles);
-                } else {
-                    dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight + nc * weight_stride), row_stride, weight_stride, row_stride, n_cols);
-                }
+                const uint32_t height = is_quant ? (n_cols / 32) * n_k_tiles : n_cols;
+                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight_raw[0], weight + nc * weight_stride),
+                               dma_dst_stride, dma_src_stride, dma_width_bytes, height);
                 dma_queue_pop(ctx->dma[0]);
 
                 // B: Weight Dequantize (Threaded)
@@ -2601,9 +2358,10 @@ static int hmx_mm_2d_f32(struct htp_context *ctx,
 
                 // D: Output Store
                 float *output_chunk = dst + (mr * dst_stride + nc);
+                const float *src2_chunk = src2 ? (src2 + mr * src2_stride + nc) : NULL;
                 int chunk_dst_cols = dst_cols - (int)nc;
                 if (chunk_dst_cols > 0) {
-                    transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output, n_rows, n_cols, dst_stride, chunk_dst_cols, n_threads);
+                    transfer_output_chunk_threaded(ctx, output_chunk, src2_chunk, vtcm_output, n_rows, n_cols, dst_stride, src2_stride, chunk_dst_cols, n_threads);
                 }
             }
         }
@@ -2644,6 +2402,13 @@ static inline float *hmx_mm_dst_batch_ptr(const hmx_mm_f16_f32_batched_params_t
                       (size_t) dst_b3 * params->dst_nb3);
 }
 
+static inline const float *hmx_mm_src2_batch_ptr(const hmx_mm_f16_f32_batched_params_t *params,
+                                               int src2_b2, int src2_b3) {
+    return params->src2 ? (const float *) ((const uint8_t *) params->src2 +
+                      (size_t) src2_b2 * params->src2_nb2 +
+                      (size_t) src2_b3 * params->src2_nb3) : NULL;
+}
+
 static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx,
                                                         const hmx_mm_f16_f32_batched_params_t *params,
                                                         int m_chunk, int n_chunk, int pipeline, int n_threads, int act_threads, int vtcm_size) {
@@ -2651,11 +2416,12 @@ static int hmx_mm_f16_f32_batched_simple(struct htp_context *ctx,
     for (int b3 = 0; b3 < params->ne13 && ret == 0; ++b3) {
         for (int b2 = 0; b2 < params->ne12 && ret == 0; ++b2) {
             ret = hmx_mm_2d_f32(ctx, hmx_mm_dst_batch_ptr(params, b2, b3),
+                                           hmx_mm_src2_batch_ptr(params, b2, b3),
                                            hmx_mm_activation_batch_ptr(params, b2, b3),
                                            (const uint8_t *)hmx_mm_weight_batch_ptr(params, b2, b3),
                                            params->m, params->k, params->n,
                                            params->act_stride, params->weight_stride * (int)sizeof(__fp16),
-                                           HTP_TYPE_F16, params->k, params->n, params->n,
+                                           HTP_TYPE_F16, params->k, params->dst_stride, params->src2_stride, params->n,
                                            m_chunk, n_chunk, pipeline, n_threads, act_threads,
                                            0, 0, vtcm_size);
         }
@@ -2740,15 +2506,9 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_
                 for (int g = 0; g < group_size; ++g) {
                     const float *activation_chunk = hmx_mm_activation_batch_ptr(params, b2_base + g, b3) + mr * params->act_stride;
                     __fp16 *vtcm_act_g = vtcm_f16_act + (size_t) g * act_head_stride;
-                    if (use_dma_activation) {
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                               activation_chunk, (int) n_rows,
-                                                               params->k, params->act_stride, act_threads, params->k, vtcm_f32_act);
-                    } else {
-                        transfer_activation_chunk_threaded(ctx, vtcm_act_g,
-                                                               activation_chunk, (int) n_rows,
-                                                               params->k, params->act_stride, act_threads, params->k, NULL);
-                    }
+                    transfer_activation_chunk_threaded(ctx, vtcm_act_g,
+                                                           activation_chunk, (int) n_rows,
+                                                           params->k, params->act_stride, act_threads, params->k, vtcm_f32_act);
                 }
 
                 void *buf_curr = vtcm_scratch0;
@@ -2793,9 +2553,10 @@ static int hmx_mm_f16_f32_batched(struct htp_context *ctx, const hmx_mm_f16_f32_
 
                         {
                             float *output = hmx_mm_dst_batch_ptr(params, b2_base + g, b3) + mr * params->dst_stride + nc;
+                            const float *src2_chunk = params->src2 ? (hmx_mm_src2_batch_ptr(params, b2_base + g, b3) + mr * params->src2_stride + nc) : NULL;
                             int chunk_dst_cols = params->n - (int)nc;
                             if (chunk_dst_cols > 0) {
-                                transfer_output_chunk_threaded(ctx, output, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride, chunk_dst_cols, ctx->n_threads);
+                                transfer_output_chunk_threaded(ctx, output, src2_chunk, vtcm_output, (int) n_rows, (int) n_cols, params->dst_stride, params->src2_stride, chunk_dst_cols, ctx->n_threads);
                             }
                         }
                     }
@@ -2956,6 +2717,10 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx,
     int tile_size = htp_mm_get_weight_tile_size(weight_type);
     int aligned_tile_size = htp_mm_get_weight_aligned_tile_size(weight_type);
 
+    const uint32_t dma_dst_stride  = is_quant ? aligned_tile_size : row_stride;
+    const uint32_t dma_src_stride  = is_quant ? tile_size : weight_stride;
+    const uint32_t dma_width_bytes = is_quant ? tile_size : row_stride;
+
     const size_t qweight_row_stride = is_quant ? (size_t)(n_k_tiles * aligned_tile_size) / 32 : 0;
     const size_t weight_row_stride = is_quant ? qweight_row_stride : row_stride;
 
@@ -2978,12 +2743,12 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx,
 
     size_t scratch0_size = hex_align_up(n_chunk_n_cols * vec_dot_size, HTP_MM_HMX_TILE_SIZE);
 
-    uint8_t *vtcm_ptr        = (uint8_t *) ctx->vtcm_base;
-    __fp16  *vtcm_weight     = weight_area_size ? (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size) : NULL;
-    __fp16  *vtcm_f16_act = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
-    __fp16  *vtcm_output     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
-    void    *vtcm_scratch0   = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
-    __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
+    uint8_t *vtcm_ptr      = (uint8_t *) ctx->vtcm_base;
+    __fp16  *vtcm_weight   = weight_area_size ? (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, weight_area_size) : NULL;
+    __fp16  *vtcm_f16_act  = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, act_area_size);
+    __fp16  *vtcm_output   = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, output_area_size);
+    void    *vtcm_scratch0 = vtcm_seq_alloc(&vtcm_ptr, scratch0_size);
+    __fp16  *vtcm_scales   = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
 
     vtcm_used = vtcm_ptr - (uint8_t *) ctx->vtcm_base;
     if (vtcm_used > vtcm_budget) {
@@ -3007,11 +2772,9 @@ static int hmx_mm_id_2d_f32(struct htp_context *ctx,
             const size_t n_cols = hex_smin((size_t) n - nc, n_chunk_n_cols);
             const size_t n_col_tiles = hmx_ceil_div(n_cols, HTP_MM_HMX_TILE_N_COLS);
 
-            if (is_quant) {
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, weight + nc * weight_stride), aligned_tile_size, tile_size, tile_size, (n_cols / 32) * n_k_tiles);
-            } else {
-                dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, weight + nc * weight_stride), row_stride, weight_stride, row_stride, n_cols);
-            }
+            const uint32_t height = is_quant ? (n_cols / 32) * n_k_tiles : n_cols;
+            dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_weight, weight + nc * weight_stride),
+                           dma_dst_stride, dma_src_stride, dma_width_bytes, height);
             dma_queue_pop(ctx->dma[0]);
 
             dequantize_tiled_weight_chunk_to_fp16_tiles(
@@ -3051,11 +2814,23 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k
         return HTP_STATUS_OK;
     }
 
+    const float * src2_ptr = NULL;
+    uint32_t src2_stride = 0;
+    size_t src2_nb2 = 0;
+    size_t src2_nb3 = 0;
+    if (src2) {
+        src2_ptr = (const float *) src2->data;
+        src2_stride = (src2->ne[1] == 1) ? 0 : (uint32_t) (src2->nb[1] / sizeof(float));
+        src2_nb2 = (src2->ne[2] == 1) ? 0 : src2->nb[2];
+        src2_nb3 = (src2->ne[3] == 1) ? 0 : src2->nb[3];
+    }
+
     int ret = -1;
     const int n_threads = MIN(kparams->n_threads, (int) octx->n_threads);
     if (kparams->kernel_type == HTP_MM_KERNEL_HMX_F16_BATCHED) {
         hmx_mm_f16_f32_batched_params_t batch_params = {
             .dst             = (float *) dst->data,
+            .src2            = src2_ptr,
             .activation      = (float *) src1->data,
             .weight          = (const __fp16 *) src0->data,
             .m               = m_total,
@@ -3064,6 +2839,7 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k
             .act_stride      = act_stride,
             .weight_stride   = wgt_stride,
             .dst_stride      = (int) (dst->nb[1] / sizeof(float)),
+            .src2_stride     = src2_stride,
             .ne02            = ne02,
             .ne03            = ne03,
             .ne12            = ne12,
@@ -3074,6 +2850,8 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k
             .src1_nb3        = src1->nb[3],
             .dst_nb2         = dst->nb[2],
             .dst_nb3         = dst->nb[3],
+            .src2_nb2        = src2_nb2,
+            .src2_nb3        = src2_nb3,
         };
         ret = hmx_mm_f16_f32_batched(octx->ctx, &batch_params,
                                      kparams->m_chunk, kparams->n_chunk,
@@ -3082,9 +2860,9 @@ static int hmx_mm_op_matmul(struct htp_ops_context * octx, const struct htp_mm_k
                                      kparams->vtcm_size);
     } else {
         ret = hmx_mm_2d_f32(
-            octx->ctx, (float*) dst->data, (float*) src1->data, (const uint8_t *) src0->data,
+            octx->ctx, (float*) dst->data, src2_ptr, (float*) src1->data, (const uint8_t *) src0->data,
             m_total, k, n, act_stride, (int) src0->nb[1], (int) src0->type, (int) src1->ne[0],
-            (int)(dst->nb[1] / sizeof(float)), (int)dst->ne[0],
+            (int)(dst->nb[1] / sizeof(float)), src2_stride, (int)dst->ne[0],
             kparams->m_chunk, kparams->n_chunk, kparams->pipeline, n_threads,
             kparams->n_act_threads,
             kparams->tile_size, kparams->aligned_tile_size, kparams->vtcm_size
@@ -3145,7 +2923,7 @@ static int hmx_mm_op_matmul_id(
     return HTP_STATUS_OK;
 }
 
-static int hvx_mm_op_matmul_id(
+static int hvx_mm_matmul_id(
     struct htp_ops_context * octx,
     struct htp_mm_context * mmctx,
     size_t src0_row_size_padded,
@@ -3163,22 +2941,22 @@ static int hvx_mm_op_matmul_id(
     const uint32_t nb = (ne10 + qk - 1) / qk;
     const uint32_t total_nb = src1_nrows * nb;
 
-    worker_callback_t quant_job_func;
-    uint32_t n_quant_jobs = 1;
+    worker_callback_t quant_task_func;
+    uint32_t n_quant_tasks = 1;
     if (src1_nrows < octx->n_threads) {
-        n_quant_jobs = MIN(total_nb, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
-        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
-            uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
-            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+        n_quant_tasks = MIN(total_nb, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
+            uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
             mmctx->quant_ib_first[ith] = ib_first;
             mmctx->quant_ib_last[ith]  = ib_last;
             mmctx->quant_r[ith]        = ib_first / nb;
             mmctx->quant_c[ith]        = ib_first % nb;
         }
     } else {
-        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
     }
     size_t src1_row_size  = (src0->type == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
 
@@ -3188,13 +2966,13 @@ static int hvx_mm_op_matmul_id(
     size_t src0_sz = kparams->vtcm_src0_size;
     size_t src1_sz = kparams->vtcm_src1_size;
     size_t src2_sz = 0; // mapping lives in DDR
-    size_t dst_sz  = 0; // ID kernels scatter straight to DDR
+    size_t dst_sz  = kparams->vtcm_dst_size;
     size_t vtcm_size = kparams->vtcm_size;
 
     size_t src0_sz_per_thread = src0_sz / octx->n_threads;
     size_t src1_sz_per_thread = src1_sz;
     size_t src2_sz_per_thread = 0;
-    size_t dst_sz_per_thread  = 0;
+    size_t dst_sz_per_thread  = dst_sz / octx->n_threads;
 
     FARF(HIGH, "matmul-id-%s : src0-spad-size %zu src1-spad-size %zu src2-spad-size %zu dst-spad-size %zu (%zu)\n", mmctx->type,
          src0_sz, src1_sz, src2_sz, dst_sz, vtcm_size);
@@ -3230,8 +3008,10 @@ static int hvx_mm_op_matmul_id(
     mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
     mmctx->vtcm_dst_size_per_thread  = dst_sz_per_thread;
 
-    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+    mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks;
+    mmctx->quant_task_func = quant_task_func;
+    mmctx->n_quant_tasks = n_quant_tasks;
+    atomic_init(&mmctx->quant_barrier, n_quant_tasks);
 
     const uint32_t n_matmul_jobs = octx->n_threads;
     worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
@@ -3259,7 +3039,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
     const uint32_t src0_nrows = ne01;  // per expert
     const uint32_t src1_nrows = ne11 * ne12 * ne13;
 
-    worker_callback_t quant_job_func;
+    worker_callback_t quant_task_func;
     worker_callback_t matmul_id_job_func = src1_nrows > 1 ? hvx_mm_id : hvx_mv_id;
 
     // Compute src0_nrows_per_thread
@@ -3329,7 +3109,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
         return hmx_mm_op_matmul_id(octx, mmctx, matrix_row_counts, matrix_rows, mapping_buf, must_free_mapping);
     }
 
-    return hvx_mm_op_matmul_id(octx, mmctx, src0_row_size_padded, src1_nrows, matmul_id_job_func, mapping_buf, must_free_mapping);
+    return hvx_mm_matmul_id(octx, mmctx, src0_row_size_padded, src1_nrows, matmul_id_job_func, mapping_buf, must_free_mapping);
 }
 
 int op_matmul_qkv(struct htp_ops_context * octx) {
@@ -3373,25 +3153,25 @@ int op_matmul_qkv(struct htp_ops_context * octx) {
     const uint32_t nb = (src1->ne[0] + qk - 1) / qk;
     const uint32_t total_nb = src1_nrows * nb;
 
-    worker_callback_t quant_job_func;
-    uint32_t n_quant_jobs = 1;
+    worker_callback_t quant_task_func;
+    uint32_t n_quant_tasks = 1;
     if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
-        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
     } else if (src1_nrows < octx->n_threads) {
-        n_quant_jobs = MIN(total_nb, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
-        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
-            uint32_t ib_first = (total_nb * ith) / n_quant_jobs;
-            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+        n_quant_tasks = MIN(total_nb, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
+            uint32_t ib_first = (total_nb * ith) / n_quant_tasks;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
             mmctx->quant_ib_first[ith] = ib_first;
             mmctx->quant_ib_last[ith]  = ib_last;
             mmctx->quant_r[ith]        = ib_first / nb;
             mmctx->quant_c[ith]        = ib_first % nb;
         }
     } else {
-        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
     }
 
     size_t src1_row_size;
@@ -3406,12 +3186,14 @@ int op_matmul_qkv(struct htp_ops_context * octx) {
     size_t src1_sz = kparams->vtcm_src1_size;
     size_t src2_sz = kparams->vtcm_src2_size;
     size_t src3_sz = kparams->vtcm_src3_size;
+    size_t dst_sz  = kparams->vtcm_dst_size;
     size_t vtcm_size = kparams->vtcm_size;
 
     size_t src0_sz_per_thread = src0_sz / octx->n_threads;
     size_t src1_sz_per_thread = src1_sz;
     size_t src2_sz_per_thread = src2_sz / octx->n_threads;
     size_t src3_sz_per_thread = src3_sz / octx->n_threads;
+    size_t dst_sz_per_thread  = dst_sz / octx->n_threads;
 
     if (octx->ctx->vtcm_size < vtcm_size) {
         FARF(ERROR, "matmul-qkv: current VTCM reservation %zu is too small, needed %zu\n",
@@ -3424,11 +3206,13 @@ int op_matmul_qkv(struct htp_ops_context * octx) {
     mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
     mmctx->vtcm_src2 = vtcm_seq_alloc(&vtcm_ptr, src2_sz);
     mmctx->vtcm_src3 = vtcm_seq_alloc(&vtcm_ptr, src3_sz);
+    mmctx->vtcm_dst  = vtcm_seq_alloc(&vtcm_ptr, dst_sz);
 
     octx->src1_spad.src  = NULL;
     octx->src0_spad.src  = NULL;
     octx->src2_spad.src  = NULL;
     octx->src3_spad.src  = NULL;
+    octx->dst_spad.src   = NULL;
 
     mmctx->vtcm_src0_stride = is_repacked ? 0 : src0_row_size_padded;
     mmctx->vtcm_src2_stride = is_repacked ? 0 : src0_row_size_padded;
@@ -3439,13 +3223,15 @@ int op_matmul_qkv(struct htp_ops_context * octx) {
     mmctx->vtcm_src1_size_per_thread = src1_sz_per_thread;
     mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
     mmctx->vtcm_src3_size_per_thread = src3_sz_per_thread;
+    mmctx->vtcm_dst_size_per_thread  = dst_sz_per_thread;
 
     if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
         return HTP_STATUS_OK;
 
-    // Run quantization once
-    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+    mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks;
+    mmctx->quant_task_func = quant_task_func;
+    mmctx->n_quant_tasks = n_quant_tasks;
+    atomic_init(&mmctx->quant_barrier, n_quant_tasks);
 
     // Run fused matmul
     const uint32_t n_matmul_jobs = octx->n_threads;
@@ -3517,25 +3303,25 @@ int op_matmul_ffn(struct htp_ops_context * octx) {
     const uint32_t nb = (src1->ne[0] + qk - 1) / qk;
     const uint32_t total_nb = src1_nrows * nb;
 
-    worker_callback_t quant_job_func;
-    uint32_t n_quant_jobs = 1;
+    worker_callback_t quant_task_func;
+    uint32_t n_quant_tasks = 1;
     if (kparams->kernel_type == HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT) {
-        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
+        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_flat : quantize_f32_q8_0_flat;
     } else if (src1_nrows < octx->n_threads) {
-        n_quant_jobs = MIN(total_nb, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
-        for (uint32_t ith = 0; ith < n_quant_jobs; ++ith) {
-            uint32_t ib_first = (total_nb * (ith + 0)) / n_quant_jobs;
-            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_jobs;
+        n_quant_tasks = MIN(total_nb, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled_block : quantize_f32_q8_0_tiled_block;
+        for (uint32_t ith = 0; ith < n_quant_tasks; ++ith) {
+            uint32_t ib_first = (total_nb * (ith + 0)) / n_quant_tasks;
+            uint32_t ib_last  = (total_nb * (ith + 1)) / n_quant_tasks;
             mmctx->quant_ib_first[ith] = ib_first;
             mmctx->quant_ib_last[ith]  = ib_last;
             mmctx->quant_r[ith]        = ib_first / nb;
             mmctx->quant_c[ith]        = ib_first % nb;
         }
     } else {
-        n_quant_jobs = MIN(src1_nrows, octx->n_threads);
-        quant_job_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
+        n_quant_tasks = MIN(src1_nrows, octx->n_threads);
+        quant_task_func = (src0->type == HTP_TYPE_Q4_1) ? quantize_f32_q8_1_tiled : quantize_f32_q8_0_tiled;
     }
 
     size_t src1_row_size;
@@ -3549,11 +3335,13 @@ int op_matmul_ffn(struct htp_ops_context * octx) {
     size_t src0_sz = kparams->vtcm_src0_size;
     size_t src1_sz = kparams->vtcm_src1_size;
     size_t src2_sz = kparams->vtcm_src2_size;
+    size_t dst_sz  = kparams->vtcm_dst_size;
     size_t vtcm_size = kparams->vtcm_size;
 
     size_t src0_sz_per_thread = src0_sz / octx->n_threads;
     size_t src1_sz_per_thread = src1_sz;
     size_t src2_sz_per_thread = src2_sz / octx->n_threads;
+    size_t dst_sz_per_thread  = dst_sz / octx->n_threads;
 
     if (octx->ctx->vtcm_size < vtcm_size) {
         FARF(ERROR, "matmul-ffn: current VTCM reservation %zu is too small, needed %zu\n", octx->ctx->vtcm_size, vtcm_size);
@@ -3564,10 +3352,12 @@ int op_matmul_ffn(struct htp_ops_context * octx) {
     mmctx->vtcm_src1 = vtcm_seq_alloc(&vtcm_ptr, src1_sz);
     mmctx->vtcm_src0 = vtcm_seq_alloc(&vtcm_ptr, src0_sz);
     mmctx->vtcm_src2 = vtcm_seq_alloc(&vtcm_ptr, src2_sz);
+    mmctx->vtcm_dst  = vtcm_seq_alloc(&vtcm_ptr, dst_sz);
 
     octx->src1_spad.src  = NULL;
     octx->src0_spad.src  = NULL;
     octx->src2_spad.src  = NULL;
+    octx->dst_spad.src   = NULL;
 
     mmctx->vtcm_src0_stride = is_repacked ? 0 : src0_row_size_padded;
     mmctx->vtcm_src2_stride = is_repacked ? 0 : src0_row_size_padded;
@@ -3576,13 +3366,15 @@ int op_matmul_ffn(struct htp_ops_context * octx) {
     mmctx->vtcm_src0_size_per_thread = src0_sz_per_thread;
     mmctx->vtcm_src1_size_per_thread = src1_sz_per_thread;
     mmctx->vtcm_src2_size_per_thread = src2_sz_per_thread;
+    mmctx->vtcm_dst_size_per_thread  = dst_sz_per_thread;
 
     if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
         return HTP_STATUS_OK;
 
-    // Run quantization once
-    mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
-    worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
+    mmctx->n_quant_rows_per_thread = (src1_nrows + n_quant_tasks - 1) / n_quant_tasks;
+    mmctx->quant_task_func = quant_task_func;
+    mmctx->n_quant_tasks = n_quant_tasks;
+    atomic_init(&mmctx->quant_barrier, n_quant_tasks);
 
     // Run fused matmul
     const uint32_t n_matmul_jobs = octx->n_threads;
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.h b/ggml/src/ggml-hexagon/htp/matmul-ops.h
index a94d5430d..96369825a 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.h
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.h
@@ -392,56 +392,49 @@ static inline size_t htp_mm_hvx_get_vtcm_sizes(
         case HTP_MM_KERNEL_HVX_QUANT_ROW: {
             size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_tiled_row_size(ne10) : htp_mm_q8_0_tiled_row_size(ne10);
 
-            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
             vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
             vtcm_src1_size = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
 
-            // src0 spad is also used in dynamic quantizer to store padded src1 rows
-            size_t src1_row_size_padded = htp_mm_round_up(q_src1_row_size, QK_Q8_0_TILED * sizeof(float));
-            if (vtcm_src0_size < src1_row_size_padded) {
-                vtcm_src0_size = src1_row_size_padded;
-            }
-
             vtcm_src0_size = vtcm_src0_size * n_threads;
-            vtcm_dst_size  = vtcm_dst_size * n_threads;
 
             if (is_repack) {
                 uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
                 uint32_t n_k_tiles = ne10 / 32;
                 uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
                 size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
-                if (repacked_vtcm_size < src1_row_size_padded) {
-                    repacked_vtcm_size = src1_row_size_padded;
-                }
                 vtcm_src0_size = repacked_vtcm_size * n_threads;
             }
+
+            size_t quant_scratch_size_per_thread = htp_mm_round_up(ne10 * sizeof(float), QK_Q8_0_TILED * sizeof(float));
+            size_t dst_size_per_thread = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
+            if (dst_size_per_thread < quant_scratch_size_per_thread) {
+                dst_size_per_thread = quant_scratch_size_per_thread;
+            }
+            vtcm_dst_size = dst_size_per_thread * n_threads;
             break;
         }
         case HTP_MM_KERNEL_HVX_QUANT_ROW_FLAT: {
             size_t q_src1_row_size = (wtype == HTP_TYPE_Q4_1) ? htp_mm_q8_1_flat_row_size(ne10) : htp_mm_q8_0_flat_row_size(ne10);
 
-            vtcm_dst_size  = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
             vtcm_src0_size = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
             vtcm_src1_size = htp_mm_round_up(q_src1_row_size * src1_nrows, 256);
 
-            size_t src1_row_size_padded = htp_mm_round_up(q_src1_row_size, 256);
-            if (vtcm_src0_size < src1_row_size_padded) {
-                vtcm_src0_size = src1_row_size_padded;
-            }
-
             vtcm_src0_size = vtcm_src0_size * n_threads;
-            vtcm_dst_size  = vtcm_dst_size * n_threads;
 
             if (is_repack) {
                 uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
                 uint32_t n_k_tiles = ne10 / 32;
                 uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
                 size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
-                if (repacked_vtcm_size < src1_row_size_padded) {
-                    repacked_vtcm_size = src1_row_size_padded;
-                }
                 vtcm_src0_size = repacked_vtcm_size * n_threads;
             }
+
+            size_t quant_scratch_size_per_thread = htp_mm_round_up(ne10 * sizeof(float), QK_Q8_0_TILED * sizeof(float));
+            size_t dst_size_per_thread = dst_nrows > 0 ? htp_mm_round_up(dst_row_size, 128) : 0;
+            if (dst_size_per_thread < quant_scratch_size_per_thread) {
+                dst_size_per_thread = quant_scratch_size_per_thread;
+            }
+            vtcm_dst_size = dst_size_per_thread * n_threads;
             break;
         }
         default:
@@ -463,7 +456,8 @@ static inline size_t htp_mm_hvx_id_get_vtcm_sizes(
     size_t src0_row_size,    // nb01
     uint32_t n_prefetch,
     size_t * vtcm_src0_size_out,
-    size_t * vtcm_src1_size_out
+    size_t * vtcm_src1_size_out,
+    size_t * vtcm_dst_size_out
 ) {
     const bool is_repack = (wtype == HTP_TYPE_Q4_0 || wtype == HTP_TYPE_Q4_1 ||
                             wtype == HTP_TYPE_Q8_0 || wtype == HTP_TYPE_IQ4_NL ||
@@ -476,29 +470,22 @@ static inline size_t htp_mm_hvx_id_get_vtcm_sizes(
     size_t src0_sz_per_thread = htp_mm_round_up(n_prefetch * src0_row_size_padded, 256);
     size_t src1_sz            = htp_mm_round_up(src1_row_size * src1_nrows, 256);
 
-    // src0 spad also holds temporary transposed src1 columns during dynamic quantization.
-    const size_t src1_row_size_padded = htp_mm_round_up(src1_row_size, QK_Q8_0_TILED * sizeof(float));
-    if (src0_sz_per_thread < src1_row_size_padded) {
-        src0_sz_per_thread = src1_row_size_padded;
-    }
-
     if (is_repack) {
         const uint32_t aligned_tile_size = htp_mm_get_weight_aligned_tile_size(wtype);
         const uint32_t n_k_tiles    = ne10 / 32;
         const uint32_t tile_row_size = n_k_tiles * aligned_tile_size;
         size_t repacked_vtcm_size = htp_mm_round_up(n_prefetch * tile_row_size, 256);
-        if (repacked_vtcm_size < src1_row_size_padded) {
-            repacked_vtcm_size = src1_row_size_padded;
-        }
         src0_sz_per_thread = repacked_vtcm_size;
     }
 
     const size_t vtcm_src0_size = src0_sz_per_thread * n_threads;
+    const size_t vtcm_dst_size  = htp_mm_round_up(ne10 * sizeof(float), QK_Q8_0_TILED * sizeof(float)) * n_threads;
 
     *vtcm_src0_size_out = vtcm_src0_size;
     *vtcm_src1_size_out = src1_sz;
+    *vtcm_dst_size_out  = vtcm_dst_size;
 
-    return vtcm_src0_size + src1_sz;
+    return vtcm_src0_size + src1_sz + vtcm_dst_size;
 }
 
 #ifdef __cplusplus
diff --git a/scripts/snapdragon/adb/run-completion.sh b/scripts/snapdragon/adb/run-completion.sh
index f7622eb52..2130b9a74 100755
--- a/scripts/snapdragon/adb/run-completion.sh
+++ b/scripts/snapdragon/adb/run-completion.sh
@@ -69,13 +69,16 @@ mbuf=
 mmsel=
 [ "$MM" != "" ] && mmsel="GGML_HEXAGON_MM_SELECT=$MM"
 
+fasel=
+[ "$FA" != "" ] && fasel="GGML_HEXAGON_FA_SELECT=$FA"
+
 set -x
 
 adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $opfuse $vmem $mbuf $mmsel \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $opfuse $vmem $mbuf $mmsel $fasel \
       ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                  \
          --ctx-size 8192 --ubatch-size 1024 -fa on                        \
diff --git a/scripts/snapdragon/adb/run-tool.sh b/scripts/snapdragon/adb/run-tool.sh
index f6332391b..8bf7ba3a5 100755
--- a/scripts/snapdragon/adb/run-tool.sh
+++ b/scripts/snapdragon/adb/run-tool.sh
@@ -57,6 +57,9 @@ opfuse=
 mmsel=
 [ "$MM" != "" ] && mmsel="GGML_HEXAGON_MM_SELECT=$MM"
 
+fasel=
+[ "$FA" != "" ] && fasel="GGML_HEXAGON_FA_SELECT=$FA"
+
 set -x
 
 tool=$1; shift
@@ -65,5 +68,5 @@ adb $adbserial $adbhost shell " \
   cd $basedir; ulimit -c unlimited;        \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opfuse $mmsel ./$branch/bin/$tool $@ \
+    $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opfuse $mmsel $fasel ./$branch/bin/$tool $@ \
 "
diff --git a/scripts/snapdragon/ggml-hexagon-profile.py b/scripts/snapdragon/ggml-hexagon-profile.py
index c53ad7779..0f9240ddc 100755
--- a/scripts/snapdragon/ggml-hexagon-profile.py
+++ b/scripts/snapdragon/ggml-hexagon-profile.py
@@ -230,6 +230,12 @@ def print_ascii_timeline(op_name, dims, types, usec, cycles, events, evt_val=Non
             char = 'Q'
         elif norm_evt == 'A-PREP':
             char = 'A'
+        elif norm_evt == 'Q-PREP':
+            char = 'q'
+        elif norm_evt == 'K-PREP':
+            char = 'k'
+        elif norm_evt == 'V-PREP':
+            char = 'v'
         elif norm_evt == 'W-DEQUANT':
             char = 'D'
         elif norm_evt == 'O-PROC':

From 0bc2936f064fefe911c713bb67d796e04eaf433a Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 22:19:28 +0800
Subject: [PATCH 248/292] ollama tool calling

---
 koboldcpp.py | 85 +++++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 74 insertions(+), 11 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 3ffb175ce..3bf88281f 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -3877,6 +3877,29 @@ def normalize_tool_call_resp(obj): # Normalize various tool call formats to Open
 
     return obj
 
+def convert_tool_calls_to_ollama(tool_calls):
+    ollama_tool_calls = []
+    for idx, tool_call in enumerate(tool_calls or []):
+        try:
+            func = tool_call.get("function", {})
+            args = func.get("arguments", {})
+            if isinstance(args, str):
+                try:
+                    args = json.loads(args)
+                except Exception:
+                    args = {}
+            ollama_tool_calls.append({
+                "type": "function",
+                "function": {
+                    "index": idx,
+                    "name": func.get("name", ""),
+                    "arguments": args
+                }
+            })
+        except Exception:
+            pass
+    return ollama_tool_calls
+
 # Used to parse json for openai tool calls
 def extract_json_from_string(input_string, check_strict=False):
     parsed_json = None
@@ -5223,7 +5246,7 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
 
         #tool calls resolution
         tool_calls = []
-        if api_format == 4 or api_format == 2 or api_format == 8 or api_format == 9:
+        if api_format == 4 or api_format == 2 or api_format == 7 or api_format == 8 or api_format == 9:
             using_openai_tools = genparams.get('using_openai_tools', False)
             if using_openai_tools:
                 # first, let llama.cpp's chat parser handle known template-specific tool formats
@@ -5273,7 +5296,11 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
             tokarr = tokenize_ids(oldprompt+recvtxt,False)
             res = {"model": modelNameToReturn,"created_at": str(datetime.now(timezone.utc).isoformat()),"response":recvtxt,"done": True,"done_reason":currfinishreason,"context": tokarr,"total_duration": 1,"load_duration": 1,"prompt_eval_count": prompttokens,"prompt_eval_duration": 1,"eval_count": comptokens,"eval_duration": 1}
         elif api_format == 7:
-            res = {"model": modelNameToReturn,"created_at": str(datetime.now(timezone.utc).isoformat()),"message":{"role":"assistant","content":recvtxt},"done": True,"done_reason":currfinishreason,"total_duration": 1,"load_duration": 1,"prompt_eval_count": prompttokens,"prompt_eval_duration": 1,"eval_count": comptokens,"eval_duration": 1}
+            ccmsg = {"role":"assistant","content":recvtxt or ""}
+            ollama_tool_calls = convert_tool_calls_to_ollama(tool_calls)
+            if ollama_tool_calls:
+                ccmsg["tool_calls"] = ollama_tool_calls
+            res = {"model": modelNameToReturn,"created_at": str(datetime.now(timezone.utc).isoformat()),"message":ccmsg,"done": True,"done_reason":currfinishreason,"total_duration": 1,"load_duration": 1,"prompt_eval_count": prompttokens,"prompt_eval_duration": 1,"eval_count": comptokens,"eval_duration": 1}
         elif api_format == 8: #oai-responses
             resp_id = f"resp-A{genparams.get('oai_uniqueid', 1)}"
             output_item_id = f"msg_0{genparams.get('oai_uniqueid', 1)}"
@@ -5380,7 +5407,8 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
         self.send_header("X-Accel-Buffering", "no")
         self.send_header("cache-control", "no-cache")
         self.send_header("connection", "keep-alive")
-        self.end_headers(content_type='text/event-stream')
+        stream_content_type = 'application/x-ndjson' if api_format == 6 or api_format == 7 else 'text/event-stream'
+        self.end_headers(content_type=stream_content_type)
 
         # if tools, do not send anything else - OAI tool calls will be handled with fakestreaming!
         # only exception is if we know the exact toolcall tag to segment!
@@ -5392,7 +5420,7 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                 tool_segment_tag = start
                 break
         jinjatools = (args.jinja and args.jinja_tools)
-        if (api_format == 4 or api_format == 9) and using_openai_tools:
+        if (api_format == 4 or api_format == 7 or api_format == 9) and using_openai_tools:
             if not jinjatools or not tool_segment_tag:
                 genparams['sync_toolcall_stream_ineligible'] = True
                 return
@@ -5486,9 +5514,22 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
 
                         sync_potential_toolcall_splitmatch = ""
                         if tokenStr!="" or streamDone:
+                            if (api_format == 4 or api_format == 7 or api_format == 9) and using_openai_tools and tool_segment_tag and not streamDone and not genparams.get("sync_toolcall_potential_triggered", False) and tool_segment_tag not in tokenStr:
+                                tail = ""
+                                for n in range(1, len(tool_segment_tag)):
+                                    prefix = tool_segment_tag[:n]
+                                    if tokenStr.endswith(prefix) and len(prefix) > len(tail):
+                                        tail = prefix
+                                if tail:
+                                    tokenReserve += tail
+                                    tokenStr = tokenStr[:-len(tail)]
+                                    if tokenStr == "":
+                                        await asyncio.sleep(async_sleep_short)
+                                        continue
+
                             # Tool boundary detection for tool-capable chat completions.
                             # if triggered, stop real streaming, and let the buffered fakestreaming take over
-                            if (api_format == 4 or api_format == 9) and using_openai_tools:
+                            if (api_format == 4 or api_format == 7 or api_format == 9) and using_openai_tools:
                                 tokenStr = tokenReserve + tokenStr
                                 tokenReserve = ""
                                 if tool_segment_tag in tokenStr:
@@ -5600,11 +5641,14 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                                     await self.send_oai_sse_event(event_str)
                                 elif api_format == 6 or api_format == 7:
                                     created_at = str(datetime.now(timezone.utc).isoformat())
+                                    ollama_content = ""
                                     if api_format == 6:
                                         event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"response":tokenStr,"done":False})
                                     else:
-                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":tokenStr},"done":False})
-                                    await self.send_ollama_stream_event(event_str)
+                                        ollama_content = delta.get("content", tokenStr) if delta else tokenStr
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":ollama_content},"done":False})
+                                    if api_format == 6 or ollama_content:
+                                        await self.send_ollama_stream_event(event_str)
                                 elif api_format == 9:
                                     if anthropic_first_loop:
                                         await self.send_anthropic_sse_event("message_start", json.dumps({"type":"message_start","message":{"type":"message","id":f"msg_A{req_id_suffix}","role":"assistant","model":modelNameToReturn,"usage":{"input_tokens":prompttokens,"output_tokens":0}}}))
@@ -5650,11 +5694,12 @@ class KcppServerRequestHandler(http.server.SimpleHTTPRequestHandler):
                                 await self.send_oai_sse_event(event_str)
                             elif api_format == 6 or api_format == 7: # Ollama newline-delimited JSON streaming
                                 created_at = str(datetime.now(timezone.utc).isoformat())
-                                if tokenStr:
+                                ollama_content = delta.get("content", tokenStr) if api_format == 7 and delta else tokenStr
+                                if tokenStr and (api_format == 6 or ollama_content):
                                     if api_format == 6:
                                         event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"response":tokenStr,"done":False})
                                     else:
-                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":tokenStr},"done":False})
+                                        event_str = json.dumps({"model":modelNameToReturn,"created_at":created_at,"message":{"role":"assistant","content":ollama_content},"done":False})
                                     await self.send_ollama_stream_event(event_str)
                                 if streamDone:
                                     prompttokens = batch_final_result.prompt_tokens if using_batch_stream else handle.get_last_input_count()
@@ -7165,7 +7210,7 @@ Change Mode<br>
                             self.send_header('content-length', str(len(genresp)))
                             self.end_headers(content_type='application/json')
                             self.wfile.write(genresp)
-                        elif (api_format == 4 or api_format == 9) and genparams.get('using_openai_tools', False): #special case, fake streaming for openai tool calls
+                        elif (api_format == 4 or api_format == 7 or api_format == 9) and genparams.get('using_openai_tools', False): #special case, fake streaming for tool calls
                             # we only send content_text and reasoning_text if tools aren't used. they contain the balance of the output after sync_toolcall_potential_triggered was triggered
                             content_text = genparams.get('sync_toolcall_extra_content', "") #populated by the sse call, we don't use gendat['choices'][0]['message'].get('content', None)
                             reasoning_text = genparams.get('sync_toolcall_extra_reasoning_content', "")
@@ -7175,6 +7220,8 @@ Change Mode<br>
                                     toolsdata_res = gendat['choices'][0]['message']['tool_calls']
                                     if toolsdata_res and len(toolsdata_res)>0:
                                         toolsdata_res[0]["index"] = 0 # need to add an index for OWUI
+                                elif api_format == 7:
+                                    toolsdata_res = gendat.get("message", {}).get("tool_calls", [])
                                 elif api_format == 9:
                                     # gendat["content"] is a list of Anthropic content blocks; pull out the tool_use ones and reformat to OAI shape for the shared emission code
                                     for block in gendat.get("content", []):
@@ -7190,7 +7237,23 @@ Change Mode<br>
                             except Exception:
                                 toolsdata_res = []
 
-                            if api_format == 9: # Anthropic fake-stream for tool calls
+                            if api_format == 7: # Ollama fake-stream for tool calls
+                                created_at = str(datetime.now(timezone.utc).isoformat())
+                                if not content_text and genparams.get('sync_toolcall_stream_ineligible', False):
+                                    content_text = gendat.get("message", {}).get("content", "")
+                                if content_text or toolsdata_res:
+                                    chunk_msg = {"role":"assistant","content":"" if toolsdata_res else (content_text or "")}
+                                    if toolsdata_res:
+                                        chunk_msg["tool_calls"] = toolsdata_res
+                                    chunk = {"model":modelNameToReturn,"created_at":created_at,"message":chunk_msg,"done":False}
+                                    self.wfile.write(f'{json.dumps(chunk)}\n'.encode())
+                                    self.wfile.flush()
+                                final_msg = {"role":"assistant","content":""}
+                                final_chunk = {"model":modelNameToReturn,"created_at":created_at,"message":final_msg,"done":True,"done_reason":gendat.get("done_reason", currfinishreason),"total_duration":gendat.get("total_duration", 1),"load_duration":gendat.get("load_duration", 1),"prompt_eval_count":gendat.get("prompt_eval_count", handle.get_last_input_count()),"prompt_eval_duration":gendat.get("prompt_eval_duration", 1),"eval_count":gendat.get("eval_count", handle.get_last_token_count()),"eval_duration":gendat.get("eval_duration", 1)}
+                                self.wfile.write(f'{json.dumps(final_chunk)}\n'.encode())
+                                self.wfile.flush()
+
+                            elif api_format == 9: # Anthropic fake-stream for tool calls
                                 req_id_suffix = genparams.get('oai_uniqueid', 1)
                                 start_msg = {"type": "message", "id": f"msg_A{req_id_suffix}", "role": "assistant", "model": modelNameToReturn, "usage": {"input_tokens": 0, "output_tokens": 0}}
                                 self.wfile.write(f'event: message_start\ndata: {json.dumps({"type":"message_start","message":start_msg})}\n\n'.encode())

From 44c966a764fb809047ee2eb8c80cc9b19149c911 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 1 Jul 2026 22:31:04 +0800
Subject: [PATCH 249/292] ollama show model name to return

---
 koboldcpp.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 3bf88281f..9a06da35b 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -6325,7 +6325,7 @@ Change Mode<br>
             modelNameToReturn = friendlymodelname
             if autoswapmode and textName is not None:
                 modelNameToReturn = textName
-            response_body = (json.dumps({"models":[{"name":"koboldcpp","model":f"{modelNameToReturn}:latest","modified_at":"2024-07-19T15:26:55.6122841+08:00","expires_at": "2055-06-04T19:06:25.5433636+08:00","size":394998579,"size_vram":394998579,"digest":"b5dc5e784f2a3ee1582373093acf69a2f4e2ac1710b253a001712b86a61f88bb","details":{"parent_model":"","format":"gguf","family":"koboldcpp","families":["koboldcpp"],"parameter_size":"128M","quantization_level":"Q4_0"}},{"name":"koboldcpp","model":modelNameToReturn,"modified_at":"2025-01-01T01:00:00.0000000+00:00","expires_at": "2069-01-01T01:00:00.0000000+00:00","size":394998579,"size_vram":394998579,"digest":"b5dc5e784f2a3ee1582373093acf69a2f4e2ac1710b253a001712b86a61f88bb","details":{"parent_model":"","format":"gguf","family":"koboldcpp","families":["koboldcpp"],"parameter_size":"128M","quantization_level":"Q4_0"}}]}).encode())
+            response_body = (json.dumps({"models":[{"name":f"{modelNameToReturn}","model":f"{modelNameToReturn}:latest","modified_at":"2024-07-19T15:26:55.6122841+08:00","expires_at": "2055-06-04T19:06:25.5433636+08:00","size":394998579,"size_vram":394998579,"digest":"b5dc5e784f2a3ee1582373093acf69a2f4e2ac1710b253a001712b86a61f88bb","details":{"parent_model":"","format":"gguf","family":"koboldcpp","families":["koboldcpp"],"parameter_size":"128M","quantization_level":"Q4_0"}},{"name":modelNameToReturn,"model":modelNameToReturn,"modified_at":"2025-01-01T01:00:00.0000000+00:00","expires_at": "2069-01-01T01:00:00.0000000+00:00","size":394998579,"size_vram":394998579,"digest":"b5dc5e784f2a3ee1582373093acf69a2f4e2ac1710b253a001712b86a61f88bb","details":{"parent_model":"","format":"gguf","family":"koboldcpp","families":["koboldcpp"],"parameter_size":"128M","quantization_level":"Q4_0"}}]}).encode())
         elif clean_path.endswith('/api/version'): #ollama compatible, NOT the kcpp version
             response_body = (json.dumps({"version":"0.7.0"}).encode())
         elif clean_path=='/ping':

From a6647b1a32f2ee456abff98ee2db15ed6e957c74 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Wed, 1 Jul 2026 18:33:00 +0200
Subject: [PATCH 250/292] common : use hf primary split as model path (#25194)

Fixes #25181
---
 common/arg.cpp | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 0fc94e553..fdf58614b 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -496,13 +496,15 @@ void common_models_handler_apply(common_models_handler & handler, common_params
     }
 
     // handle hf_plan tasks
-    auto add_tasks = [&opts, &tasks](const hf_cache::hf_files & model_files, common_params_model & model) {
+    auto add_tasks = [&opts, &tasks](const hf_cache::hf_files  & model_files,
+                                    const hf_cache::hf_file    & primary,
+                                    common_params_model        & model) {
         for (size_t i = 0; i < model_files.size(); ++i) {
             auto & model_file = model_files[i];
-            bool is_first = (i == 0);
-            tasks.emplace_back(model_file, opts, [&, is_first]() {
-                if (is_first) {
-                    // only use first part as model path
+            bool is_primary = (model_file.path == primary.path);
+            tasks.emplace_back(model_file, opts, [&, is_primary]() {
+                if (is_primary) {
+                    // the primary file is the first split (00001-of), use it as model path
                     model.path = hf_cache::finalize_file(model_file);
                 } else {
                     hf_cache::finalize_file(model_file);
@@ -511,7 +513,7 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         }
     };
     if (!plan.model_files.empty()) {
-        add_tasks(plan.model_files, params.model);
+        add_tasks(plan.model_files, plan.primary, params.model);
     }
     if (!plan.mmproj.local_path.empty()) {
         tasks.emplace_back(plan.mmproj, opts, [&]() {
@@ -539,12 +541,12 @@ void common_models_handler_apply(common_models_handler & handler, common_params
 
     // handle plan_spec (e.g. --spec-draft-hf)
     if (!plan_spec.model_files.empty()) {
-        add_tasks(plan_spec.model_files, params.speculative.draft.mparams);
+        add_tasks(plan_spec.model_files, plan_spec.primary, params.speculative.draft.mparams);
     }
 
     // handle vocoder plan (e.g. --hf-repo-v)
     if (!plan_voc.model_files.empty()) {
-        add_tasks(plan_voc.model_files, params.vocoder.model);
+        add_tasks(plan_voc.model_files, plan_voc.primary, params.vocoder.model);
     }
 
     // run all tasks in parallel

From 4fc4ec5541b243957ae5099edb67372f8f3b550e Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Wed, 1 Jul 2026 10:29:22 -0700
Subject: [PATCH 251/292] opencl: allow loading precompiled binary kernels from
 library (#23042)

* opencl: allow loading binary kernel

* opencl: add libdl.h

* ggml-backend-dl is in ggml, which depends backend libs, thus
  ggml-opencl cannot depend on ggml-backend-dl
* add libdl.h to break cyclic dep

* opencl: allow loading bin kernel lib

* opencl: load `gemm_moe_mxfp4_f32_ns` from kernel lib if available

* opencl: load q8_0 gemm from kernel lib

* opencl: load q4_0 moe gemm from kernel lib

* opencl: load q4_1 moe gemm from kernel lib

* opencl: load q4_k moe gemm from kernel lib

* opencl: always declare `get_adreno_bin_kernel_func_t`

* opencl: rephrase message

* opencl: fix for rebase

* opencl: update doc
---
 docs/backend/OPENCL.md               |  90 ++++----
 ggml/src/ggml-opencl/CMakeLists.txt  |   5 +
 ggml/src/ggml-opencl/ggml-opencl.cpp | 321 ++++++++++++++++++++++++++-
 ggml/src/ggml-opencl/libdl.h         |  79 +++++++
 4 files changed, 448 insertions(+), 47 deletions(-)
 create mode 100644 ggml/src/ggml-opencl/libdl.h

diff --git a/docs/backend/OPENCL.md b/docs/backend/OPENCL.md
index 044ac606b..1bce56cd8 100644
--- a/docs/backend/OPENCL.md
+++ b/docs/backend/OPENCL.md
@@ -1,16 +1,26 @@
 # llama.cpp for OpenCL
 
-- [Background](#background)
-- [OS](#os)
-- [Hardware](#hardware)
-- [DataType Supports](#datatype-supports)
-- [Model Preparation](#model-preparation)
-- [CMake Options](#cmake-options)
-- [Android](#android)
-- [Windows 11 Arm64](#windows-11-arm64)
-- [Linux](#Linux)
-- [Known Issue](#known-issues)
-- [TODO](#todo)
+- [llama.cpp for OpenCL](#llamacpp-for-opencl)
+  - [Background](#background)
+    - [Llama.cpp + OpenCL](#llamacpp--opencl)
+  - [OS](#os)
+  - [Hardware](#hardware)
+    - [Adreno GPU](#adreno-gpu)
+  - [DataType Supports](#datatype-supports)
+  - [Model Preparation](#model-preparation)
+  - [Binary Kernel Library](#binary-kernel-library)
+  - [CMake Options](#cmake-options)
+  - [Android](#android)
+    - [I. Setup Environment](#i-setup-environment)
+    - [II. Build llama.cpp](#ii-build-llamacpp)
+  - [Windows 11 Arm64](#windows-11-arm64)
+    - [I. Setup Environment](#i-setup-environment-1)
+    - [II. Build llama.cpp](#ii-build-llamacpp-1)
+  - [Linux](#linux)
+    - [I. Setup Environment](#i-setup-environment-2)
+    - [II. Build llama.cpp](#ii-build-llamacpp-2)
+  - [Known Issues](#known-issues)
+  - [TODO](#todo)
 
 ## Background
 
@@ -34,11 +44,13 @@ The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adren
 
 **Verified devices**
 
-| Adreno GPU                           | Status  |
-|:------------------------------------:|:-------:|
-| Adreno 750 (Snapdragon 8 Gen 3)      | Support |
-| Adreno 830 (Snapdragon 8 Elite)      | Support |
-| Adreno X85 (Snapdragon X Elite)      | Support |
+| Adreno GPU                            | Status  |
+|:-------------------------------------:|:-------:|
+| Adreno 750 (Snapdragon 8 Gen 3)       | Support |
+| Adreno 830 (Snapdragon 8 Elite)       | Support |
+| Adreno 840 (Snapdragon 8 Elite Gen 5) | Support |
+| Adreno X1-85 (Snapdragon X Elite)     | Support |
+| Adreno X2-90 (Snapdragon X2 Elite)    | Support |
 
 > A6x GPUs with a recent driver and compiler are supported; they are usually found in IoT platforms.
 However, A6x GPUs in phones are likely not supported due to the outdated driver and compiler.
@@ -47,42 +59,43 @@ However, A6x GPUs in phones are likely not supported due to the outdated driver
 
 | DataType               | Status                     |
 |:----------------------:|:--------------------------:|
+| Q1_0                   | Support                    |
 | Q4_0                   | Support                    |
-| Q6_K                   | Support, but not optimized |
+| Q4_1                   | Support                    |
+| Q5_0                   | Support                    |
+| Q5_1                   | Support                    |
 | Q8_0                   | Support                    |
+| Q4_K                   | Support                    |
+| Q5_K                   | Support                    |
+| Q6_K                   | Support                    |
 | MXFP4                  | Support                    |
+| IQ4_NL                 | Support                    |
 
 ## Model Preparation
 
-You can refer to the general [llama-quantize tool](/tools/quantize/README.md) for steps to convert a model in Hugging Face safetensor format to GGUF with quantization.
+Since common quantizations are supported now, it is recommanded to download GGUF models directly from Huggingface.
 
-Currently we support `Q4_0` quantization and have optimized for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize` (i.e., make all weights in `Q4_0`). For example,
+## Binary Kernel Library
 
-```sh
-./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0
-```
+A prebuilt binary kernel library has been introduced for Adreno GPUs.
+It currently targets X2 GPUs (X2-90, X2-85 and X2-45) found in Snapdragon X2 SoC.
+The library currently contains kernels for MUL_MAT_ID with Q4_0, Q4_1, Q4_K, MXFP4.
+The library must be manually downloaded from https://softwarecenter.qualcomm.com/catalog/item/Adreno_Kernel_Library_GGML.
 
-Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization.
+To allow using the kernel library, add `-DGGML_OPENCL_USE_ADRENO_BIN_KERNELS=ON` when configuring with CMake.
+Then, extract `adreno-opencl-kernels.dll` from the zip file downloaded from the above URL and put it alongside the executables.
+If kernels compatible with the current GPU are found in the library, they will be loaded and used.
 
-### `MXFP4` MoE Models
-
-OpenAI gpt-oss models are MoE models in `MXFP4`. The quantized model will be in `MXFP4_MOE`, a mixture of `MXFP4` and `Q8_0`.
-For this quantization, there is no need to specify `--pure`.
-For gpt-oss-20b model, you can directly [download](https://huggingface.co/ggml-org/gpt-oss-20b-GGUF) the quantized GGUF file in `MXFP4_MOE` from Hugging Face.
-
-Although it is possible to quantize gpt-oss-20b model in pure `Q4_0` (all weights in `Q4_0`), it is not recommended since `MXFP4` has been optimized for MoE while `Q4_0` is not. In addition, accuracy should degrade with such pure `Q4_0` quantization.
-Hence, using the default `MXFP4_MOE` quantization (see the link above) is recommended for this model.
-
-> Note that the `Q4_0` model found [here](https://huggingface.co/unsloth/gpt-oss-20b-GGUF/blob/main/gpt-oss-20b-Q4_0.gguf) is a mixture of `Q4_0`, `Q8_0` and `MXFP4` and gives better performance than `MXFP4_MOE` quantization.
 
 ## CMake Options
 
 The OpenCL backend has the following CMake options that control the behavior of the backend.
 
-| CMake options                     | Default value  | Description                               |
-|:---------------------------------:|:--------------:|:------------------------------------------|
-| `GGML_OPENCL_EMBED_KERNELS`       | `ON`           | Embed OpenCL kernels into the executable. |
-| `GGML_OPENCL_USE_ADRENO_KERNELS`  | `ON`           | Use kernels optimized for Adreno.         |
+| CMake options                        | Default value  | Description                               |
+|:------------------------------------:|:--------------:|:------------------------------------------|
+| `GGML_OPENCL_EMBED_KERNELS`          | `ON`           | Embed OpenCL kernels into the executable. |
+| `GGML_OPENCL_USE_ADRENO_KERNELS`     | `ON`           | Use kernels optimized for Adreno.         |
+| `GGML_OPENCL_USE_ADRENO_BIN_KERNELS` | `OFF`          | Allow using binary kernel lib for Adreno. |
 
 ## Android
 
@@ -277,6 +290,5 @@ ninja
 
 ## TODO
 
-- Optimization for Q6_K
-- Support and optimization for Q4_K
 - Improve flash attention
+- Improve OpenCL C kernels performance
diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt
index 52f48d122..9ec3268b7 100644
--- a/ggml/src/ggml-opencl/CMakeLists.txt
+++ b/ggml/src/ggml-opencl/CMakeLists.txt
@@ -31,6 +31,11 @@ if (GGML_OPENCL_EMBED_KERNELS)
     target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/autogenerated")
 endif ()
 
+if (GGML_OPENCL_USE_ADRENO_BIN_KERNELS)
+    message(STATUS "OpenCL will use precompiled binary kernels for Adreno (improved performance on some platforms)")
+    add_compile_definitions(GGML_OPENCL_USE_ADRENO_BIN_KERNELS)
+endif ()
+
 function(ggml_opencl_add_kernel KNAME)
     set(KERN_HDR ${CMAKE_CURRENT_BINARY_DIR}/autogenerated/${KNAME}.cl.h)
     set(KERN_SRC ${CMAKE_CURRENT_SOURCE_DIR}/kernels/${KNAME}.cl)
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 215aa5079..10f9cc22d 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -13,6 +13,22 @@
 #include "ggml-backend-impl.h"
 #include "ggml.h"
 
+#ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+#include "libdl.h"
+#ifdef _WIN32
+#define KERNEL_LIB_NAME "adreno-opencl-kernels.dll"
+#else
+#define KERNEL_LIB_NAME "libadreno-opencl-kernels.so"
+#endif // _WIN32
+#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+
+typedef const void * (*get_adreno_bin_kernel_func_t)(
+    const char * name,
+    const char * gpu_name,
+    const char * compiler_ver,
+    size_t     * out_size
+);
+
 #include <CL/cl.h>
 
 #include <inttypes.h>
@@ -476,6 +492,8 @@ struct ggml_backend_opencl_context {
 
     bool adreno_has_large_buffer;
     bool adreno_use_large_buffer;
+    bool adreno_use_bin_kernels;
+    get_adreno_bin_kernel_func_t get_adreno_bin_kernel_func = nullptr;
     ggml_cl_compiler_version adreno_cl_compiler_version;
 
     std::string kernel_compile_opts;  // cached for lazy-compiled kernels.
@@ -718,15 +736,15 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_gated_delta_net_f32[4][2][2] = {};
 
     cl_kernel kernel_timestep_embedding;
-    cl_kernel kernel_gemv_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns;
-    cl_kernel kernel_gemv_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns;
+    cl_kernel kernel_gemv_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns, kernel_gemm_moe_q4_0_f32_ns_bin;
+    cl_kernel kernel_gemv_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns, kernel_gemm_moe_q4_1_f32_ns_bin;
     cl_kernel kernel_gemv_moe_q5_0_f32_ns, kernel_gemm_moe_q5_0_f32_ns;
     cl_kernel kernel_gemv_moe_q5_1_f32_ns, kernel_gemm_moe_q5_1_f32_ns;
-    cl_kernel kernel_gemv_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns;
+    cl_kernel kernel_gemv_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns, kernel_gemm_moe_q4_k_f32_ns_bin;
     cl_kernel kernel_gemv_moe_q5_k_f32_ns, kernel_gemm_moe_q5_k_f32_ns;
     cl_kernel kernel_gemv_moe_q6_k_f32_ns, kernel_gemm_moe_q6_k_f32_ns;
     cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32;
-    cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns;
+    cl_kernel kernel_gemv_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns, kernel_gemm_moe_mxfp4_f32_ns_bin;
     cl_kernel kernel_moe_reorder_b;
     cl_kernel kernel_moe_histogram, kernel_moe_scan, kernel_moe_fill, kernel_moe_scatter;
     cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
@@ -870,6 +888,20 @@ struct ggml_backend_opencl_context {
 #endif
     }
 
+    const void * get_adreno_bin_kernel(const std::string &kernel_name, size_t *bin_size) const {
+        if (!get_adreno_bin_kernel_func) {
+            return nullptr;
+        }
+
+        size_t sz;
+        const void * kernel_bin = get_adreno_bin_kernel_func(
+            kernel_name.c_str(), device_name.c_str(), driver_version.c_str(), &sz);
+        if (bin_size) {
+            *bin_size = sz;
+        }
+        return kernel_bin;
+    }
+
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     // Transpose kernels
     cl_program program_transpose;
@@ -891,7 +923,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_gemv_noshuffle_q4_0_f32_32000_1_4096;
     cl_kernel kernel_gemv_noshuffle_q4_1_f32;
     cl_kernel kernel_gemm_noshuffle_q4_1_f32;
-    cl_kernel kernel_gemm_noshuffle_q8_0_f32;
+    cl_kernel kernel_gemm_noshuffle_q8_0_f32, kernel_gemm_noshuffle_q8_0_f32_bin;
     cl_kernel kernel_gemv_noshuffle_q8_0_f32;
     cl_kernel kernel_gemm_noshuffle_q1_0_f32;
     cl_kernel kernel_gemv_noshuffle_q1_0_f32;
@@ -988,6 +1020,32 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
     return build_program_from_source_ex(ctx, dev, program_buffer, compile_opts, /*fatal=*/true);
 }
 
+static cl_program build_program_from_binary(cl_context ctx, cl_device_id dev, const char* program_buffer, const std::string &compile_opts, size_t bin_size = 0) {
+    cl_program p;
+    char *program_log;
+    size_t log_size;
+    int err;
+
+    p = clCreateProgramWithBinary(ctx, 1, &dev, &bin_size, (const unsigned char**)&program_buffer, NULL, &err);
+    if(err < 0) {
+        GGML_LOG_ERROR("OpenCL error creating program from binary");
+        exit(1);
+    }
+
+    err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
+    if(err < 0) {
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
+        program_log = (char*) malloc(log_size + 1);
+        program_log[log_size] = '\0';
+        clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL);
+        GGML_LOG_ERROR("ggml_opencl: kernel compile error:\n\n%s\n", program_log);
+        free(program_log);
+        exit(1);
+    }
+
+    return p;
+}
+
 static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) {
     // compiler options for general kernels
     auto opencl_c_std =
@@ -1014,6 +1072,17 @@ static void load_cl_kernels_argsort(ggml_backend_opencl_context *backend_ctx) {
     }
 }
 
+static bool use_adreno_bin_kernels(ggml_backend_opencl_context * backend_ctx) {
+#ifndef GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+    return false;
+#else
+    if (backend_ctx->gpu_family != GPU_FAMILY::ADRENO) {
+        return false;
+    }
+    return backend_ctx->adreno_use_bin_kernels;
+#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+}
+
 static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
     if (backend_ctx->kernels_loaded) {
         return;
@@ -3323,6 +3392,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_noshuffle_q8_0_f32_bin
+    {
+        size_t bin_size = 0;
+        backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin = nullptr;
+
+        if (use_adreno_bin_kernels(backend_ctx)) {
+            const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_noshuffle_q8_0_f32_ila", &bin_size);
+            if (kernel_bin && bin_size > 0) {
+                cl_program prog =
+                    build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, compile_opts, bin_size);
+
+                CL_CHECK((backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin = clCreateKernel(prog, "kernel_gemm_noshuffle_q8_0_f32_ila", &err), err));
+                CL_CHECK(clReleaseProgram(prog));
+                GGML_LOG_CONT(".");
+            }
+        }
+    }
+
     // gemv_noshuffle_general_q8_0_f32
     {
         std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
@@ -3424,6 +3511,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_moe_q4_1_f32_ns_bin
+    {
+        size_t bin_size = 0;
+        backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin = nullptr;
+
+        if (use_adreno_bin_kernels(backend_ctx)) {
+            const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_1_f32_ns_ila", &bin_size);
+            if (kernel_bin && bin_size > 0) {
+                cl_program prog =
+                    build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size);
+
+                CL_CHECK((backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_1_f32_ns_ila", &err), err));
+                CL_CHECK(clReleaseProgram(prog));
+                GGML_LOG_CONT(".");
+            }
+        }
+    }
+
     // gemv_moe_mxfp4_f32
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3490,6 +3595,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_moe_q4_0_f32_ns_bin
+    {
+        size_t bin_size = 0;
+        backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin = nullptr;
+
+        if (use_adreno_bin_kernels(backend_ctx)) {
+            const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_0_f32_ns_ila", &bin_size);
+            if (kernel_bin && bin_size > 0) {
+                cl_program prog =
+                    build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size);
+
+                CL_CHECK((backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_0_f32_ns_ila", &err), err));
+                CL_CHECK(clReleaseProgram(prog));
+                GGML_LOG_CONT(".");
+            }
+        }
+    }
+
     // gemv_moe_q5_0_f32_ns
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3592,6 +3715,24 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         GGML_LOG_CONT(".");
     }
 
+    // gemm_moe_q4_k_f32_ns_bin
+    {
+        size_t bin_size = 0;
+        backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin = nullptr;
+
+        if (use_adreno_bin_kernels(backend_ctx)) {
+            const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_q4_k_f32_ns_ila", &bin_size);
+            if (kernel_bin && bin_size > 0) {
+                cl_program prog =
+                    build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size);
+
+                CL_CHECK((backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_q4_k_f32_ns_ila", &err), err));
+                CL_CHECK(clReleaseProgram(prog));
+                GGML_LOG_CONT(".");
+            }
+        }
+    }
+
     // gemv_moe_q5_k_f32_ns
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -3689,9 +3830,27 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx) {
         cl_program prog =
             build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts);
 
-        CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err));
-        CL_CHECK(clReleaseProgram(prog));
-        GGML_LOG_CONT(".");
+            CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns", &err), err));
+            CL_CHECK(clReleaseProgram(prog));
+            GGML_LOG_CONT(".");
+    }
+
+    // gemm_moe_mxfp4_f32_ns_bin
+    {
+        size_t bin_size = 0;
+        backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin = nullptr;
+
+        if (use_adreno_bin_kernels(backend_ctx)) {
+            const char * kernel_bin = (const char *)backend_ctx->get_adreno_bin_kernel("gemm_moe_mxfp4_f32_ns_ila", &bin_size);
+            if (kernel_bin && bin_size > 0) {
+                cl_program prog =
+                    build_program_from_binary(backend_ctx->context, backend_ctx->device, kernel_bin, CL_moe_compile_opts, bin_size);
+
+                CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin = clCreateKernel(prog, "kernel_gemm_moe_mxfp4_f32_ns_ila", &err), err));
+                CL_CHECK(clReleaseProgram(prog));
+                GGML_LOG_CONT(".");
+            }
+        }
     }
 
     // moe_reorder_b
@@ -4770,6 +4929,27 @@ static ggml_backend_opencl_context * ggml_cl_init(ggml_backend_dev_t dev) {
     backend_ctx->adreno_use_large_buffer = getenv("GGML_OPENCL_ADRENO_USE_LARGE_BUFFER") != nullptr &&
                                            backend_ctx->gpu_family == GPU_FAMILY::ADRENO;
 
+#ifdef GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+    // try loading adreno binary kernels if enabled
+    // if fails to load, builtin kernels will be used
+    {
+        dl_handle * kernel_lib_handle = dl_load_library(KERNEL_LIB_NAME);
+        backend_ctx->adreno_use_bin_kernels = false;
+
+        if (kernel_lib_handle) {
+            backend_ctx->get_adreno_bin_kernel_func = (get_adreno_bin_kernel_func_t)dl_get_sym(kernel_lib_handle, "get_adreno_kernels");
+            if (backend_ctx->get_adreno_bin_kernel_func) {
+                GGML_LOG_INFO("ggml_opencl: loaded bin kernel library %s\n", KERNEL_LIB_NAME);
+                backend_ctx->adreno_use_bin_kernels = true;
+            } else {
+                GGML_LOG_INFO("ggml_opencl: bin kernel library %s is invalid, will use builtin kernels\n", KERNEL_LIB_NAME);
+            }
+        } else {
+            GGML_LOG_INFO("ggml_opencl: failed to load %s, will use builtin kernels\n", KERNEL_LIB_NAME);
+        }
+    }
+#endif // GGML_OPENCL_USE_ADRENO_BIN_KERNELS
+
     cl_int err;
 
     // A local ref of cl_context for convenience
@@ -14972,6 +15152,99 @@ static void ggml_cl_mul_mat_q8_0_f32_adreno(ggml_backend_t backend, const ggml_t
         CL_CHECK(clReleaseMemObject(b_img));
         CL_CHECK(clReleaseMemObject(b_sub_buf));
     } else {
+        // use bin kernel if available
+        if (backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin) {
+            int K_pad = K;
+
+            cl_mem b_sub_buf = nullptr;
+            cl_mem d_sub_buf = nullptr;
+
+            cl_mem a_img = nullptr;
+            cl_mem s_img = nullptr;
+            cl_mem b_img = nullptr;
+            cl_mem d_img = nullptr;
+
+            // subbuffer for activations
+            region.origin = offset1;
+            region.size = K_pad * N * sizeof(float);
+            CL_CHECK((b_sub_buf = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+            // Create subbuffer and image1d_buffer for dst
+            region.origin = (extrad->offset); // + dst->view_offs;
+            region.size = M * N * sizeof(float);
+            CL_CHECK((d_sub_buf = clCreateSubBuffer((extrad->data_device), 0, CL_BUFFER_CREATE_TYPE_REGION, &region, &err), err));
+
+            // create an image for A
+            img_fmt = { CL_R, CL_FLOAT};
+            memset(&img_desc, 0, sizeof(img_desc));
+            img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+            img_desc.image_width = M * K / 4;    // Divide by 4 for char -> float
+            img_desc.buffer = extra0_q8_0->q;
+            CL_CHECK((a_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+            // create an image for Scale
+            img_fmt = { CL_R, CL_HALF_FLOAT};
+            memset(&img_desc, 0, sizeof(img_desc));
+            img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+            img_desc.image_width = M * K / 32;    // Block size is 32
+            img_desc.buffer = extra0_q8_0->d;
+            CL_CHECK((s_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+            // create an image for B from sub_buffer
+            img_fmt = {CL_R, CL_FLOAT};
+            memset(&img_desc, 0, sizeof(img_desc));
+            img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+            img_desc.image_width = K_pad * N;
+            img_desc.buffer = b_sub_buf;
+            CL_CHECK((b_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+            // img for d
+            img_fmt = {CL_R, CL_FLOAT};
+            memset(&img_desc, 0, sizeof(img_desc));
+            img_desc.image_type = CL_MEM_OBJECT_IMAGE1D_BUFFER;
+            img_desc.image_width = M * N;
+            img_desc.buffer = d_sub_buf;
+            CL_CHECK((d_img = clCreateImage(context, CL_MEM_WRITE_ONLY, &img_fmt, &img_desc, NULL, &err), err));
+
+            // gemm
+            kernel = backend_ctx->kernel_gemm_noshuffle_q8_0_f32_bin;
+
+            bool layoutA_Mfirst = true;
+            bool layoutS_Mfirst = true;
+            bool layoutB_Nfirst = false;
+            bool layoutC_Mfirst = true;
+
+            cl_uint lineStrideMatrixAinBytes = layoutA_Mfirst ? M * 4 : K;                // int8
+            cl_uint lineStrideMatrixSinBytes = layoutS_Mfirst ? M * 2 : (K / 32) * 2;     // fp16
+            cl_uint lineStrideMatrixBinBytes = layoutB_Nfirst ? N * 4 : K_pad * 4;        // fp32
+            cl_uint lineStrideMatrixCinBytes = layoutC_Mfirst ? M * 4 : N * 4;            // fp32
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem), &a_img));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem), &s_img));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem), &b_img));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(int),    &extra1->offset));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem), &d_img));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(int),    &extrad->offset));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),    &K));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(int),    &lineStrideMatrixAinBytes));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),    &lineStrideMatrixSinBytes));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(int),    &lineStrideMatrixBinBytes));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),    &lineStrideMatrixCinBytes));
+
+            size_t global_work_size[] = { 64, (size_t)CEIL_DIV(M, 64), (size_t)CEIL_DIV(N, 64)};
+            size_t local_work_size[]  = { 64, 2, 2 };
+
+            backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+
+            CL_CHECK(clReleaseMemObject(b_sub_buf));
+            CL_CHECK(clReleaseMemObject(d_sub_buf));
+            CL_CHECK(clReleaseMemObject(a_img));
+            CL_CHECK(clReleaseMemObject(s_img));
+            CL_CHECK(clReleaseMemObject(b_img));
+            CL_CHECK(clReleaseMemObject(d_img));
+            return;
+        }
+
         cl_mem b_sub_buf = nullptr;
         cl_mem b_sub_buf_trans = nullptr;
         cl_mem b_img = nullptr;
@@ -17825,6 +18098,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
                 } else { // for gemm
                     kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns;
+                    if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) {
+                        kernel = backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin;
+                    }
 
                     // Reorder router if called from test-backend-ops or when new router is generated.
                     // Otherwise reuse the reordered result from previous mul_mat_id call.
@@ -17870,6 +18146,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                     cl_image_desc image_desc_buf_src1;
                     image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
                     image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    if (backend_ctx->kernel_gemm_moe_q4_0_f32_ns_bin) {
+                        // bin kernel uses slightly different image format
+                        image_format_buf_src1 = {CL_R, CL_FLOAT};
+                        image_desc_buf_src1.image_width = static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size);
+                    }
                     image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
                     CL_CHECK(status);
 
@@ -18042,6 +18323,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
                 } else { // for gemm
                     kernel = backend_ctx->kernel_gemm_moe_q4_1_f32_ns;
+                    if (backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin) {
+                        kernel = backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin;
+                    }
 
                     // Reorder router if called from test-backend-ops or when new router is generated.
                     // Otherwise reuse the reordered result from previous mul_mat_id call.
@@ -18087,6 +18371,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                     cl_image_desc image_desc_buf_src1;
                     image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
                     image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    if (backend_ctx->kernel_gemm_moe_q4_1_f32_ns_bin) {
+                        // bin kernel uses slightly different image format
+                        image_format_buf_src1 = {CL_R, CL_FLOAT};
+                        image_desc_buf_src1.image_width = static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size);
+                    }
                     image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
                     CL_CHECK(status);
 
@@ -18648,6 +18937,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
                 } else { // for gemm
                     kernel = backend_ctx->kernel_gemm_moe_q4_k_f32_ns;
+                    if (backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin) {
+                        kernel = backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin;
+                    }
 
                     // Reorder router if called from test-backend-ops or when new router is generated.
                     // Otherwise reuse the reordered result from previous mul_mat_id call.
@@ -18689,6 +18981,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                     CL_CHECK(status);
                     cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
                     cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    if (backend_ctx->kernel_gemm_moe_q4_k_f32_ns_bin) {
+                        // bin kernel uses slightly different image format
+                        image_format_buf_src1 = {CL_R, CL_FLOAT};
+                        image_desc_buf_src1.image_width = static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size);
+                    }
                     image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
                     CL_CHECK(status);
 
@@ -19172,6 +19469,9 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
 
                 } else { // for gemm
                     kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32_ns;
+                    if (backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin) {
+                        kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin;
+                    }
 
                     // Reorder router if called from test-backend-ops or when new router is generated.
                     // Otherwise reuse the reordered result from previous mul_mat_id call.
@@ -19218,6 +19518,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
                     cl_image_desc image_desc_buf_src1;
                     image_format_buf_src1 = {CL_RGBA, CL_FLOAT};
                     image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size / 4), 0,0,0,0,0,0,0, {buf_src1_reordered}};
+                    if (backend_ctx->kernel_gemm_moe_mxfp4_f32_ns_bin) {
+                        // bin kernel uses slightly different image format
+                        image_format_buf_src1 = {CL_R, CL_FLOAT};
+                        image_desc_buf_src1.image_width = static_cast<size_t>(ne00 * max_post_router_tile * n_tile_size);
+                    }
                     image_src1_reordered = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status);
                     CL_CHECK(status);
 
diff --git a/ggml/src/ggml-opencl/libdl.h b/ggml/src/ggml-opencl/libdl.h
new file mode 100644
index 000000000..8ca5016f0
--- /dev/null
+++ b/ggml/src/ggml-opencl/libdl.h
@@ -0,0 +1,79 @@
+#pragma once
+
+#ifdef _WIN32
+#   define WIN32_LEAN_AND_MEAN
+#   ifndef NOMINMAX
+#       define NOMINMAX
+#   endif
+#   include <windows.h>
+#   include <winevt.h>
+#else
+#    include <dlfcn.h>
+#    include <unistd.h>
+#endif
+#include <filesystem>
+
+namespace fs = std::filesystem;
+
+#ifdef _WIN32
+
+using dl_handle = std::remove_pointer_t<HMODULE>;
+
+struct dl_handle_deleter {
+    void operator()(HMODULE handle) {
+        FreeLibrary(handle);
+    }
+};
+
+static inline dl_handle * dl_load_library(const fs::path & path) {
+    // suppress error dialogs for missing DLLs
+    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
+    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
+
+    HMODULE handle = LoadLibraryW(path.wstring().c_str());
+
+    SetErrorMode(old_mode);
+
+    return handle;
+}
+
+static inline void * dl_get_sym(dl_handle * handle, const char * name) {
+    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
+    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
+
+    void * p = (void *) GetProcAddress(handle, name);
+
+    SetErrorMode(old_mode);
+
+    return p;
+}
+
+static inline const char * dl_error() {
+    return "";
+}
+
+#else
+
+using dl_handle = void;
+
+struct dl_handle_deleter {
+    void operator()(void * handle) {
+        dlclose(handle);
+    }
+};
+
+static inline dl_handle * dl_load_library(const fs::path & path) {
+    dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL);
+    return handle;
+}
+
+static inline void * dl_get_sym(dl_handle * handle, const char * name) {
+    return dlsym(handle, name);
+}
+
+static inline const char * dl_error() {
+    const char *rslt = dlerror();
+    return rslt != nullptr ? rslt : "";
+}
+
+#endif

From a4aa06315309e605539bde3131dd36d27d72fc2c Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Thu, 2 Jul 2026 22:30:21 +0800
Subject: [PATCH 252/292] vision fix

---
 gpttype_adapter.cpp | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp
index c3edcdb3a..2abd5a8fe 100644
--- a/gpttype_adapter.cpp
+++ b/gpttype_adapter.cpp
@@ -5645,11 +5645,11 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
     std::vector<int> media_intro; //added before media list
     std::vector<int> media_outro; //added before media list
     std::string intro = "\nAttached Media:\n";
-    if(mtmd_ctx && kcpp_mtmd_is_gemma4uv(mtmd_ctx)) //ugly fix for gemma4uv vision coherency
-    {
-        intro = "\n<|channel><channel|>" + intro;
-    }
-    TokenizeString(intro, media_intro, file_format, add_bos_token);
+    // if(mtmd_ctx && kcpp_mtmd_is_gemma4uv(mtmd_ctx)) //ugly fix for gemma4uv vision coherency
+    // {
+    //     intro = "\n<|channel><channel|>" + intro;
+    // }
+    TokenizeString(intro, media_intro, file_format, false);
 
     //clear previous run media memory, just-in-time free
     for(int i=0;i<media_objects.size();++i)

From fdb1db877c526ec90f668eca1b858da5dba85560 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Thu, 2 Jul 2026 17:26:47 +0200
Subject: [PATCH 253/292] llama : add llama_model_ftype_name() (#25134)
MIME-Version: 1.0
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* llama : add llama_model_ftype_name()

Expose the model file type (quantization) name, e.g. "Q8_0" or
"Q4_K - Medium", through a new public C API. The returned pointer is
valid for the lifetime of the model and nullptr when the model is
invalid or the file type is unknown.

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* Export enum

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* s/llama_model_ftype_name/llama_ftype_name/

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* Move "(guessed)" to the front in llama_ftype_name

Prepend the "(guessed)" label instead of appending it. This allows removing
the non-thread-safe static std::string, making the function allocation-free.

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* Add LLAMA_FTYPE_PREFIX

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

* Dont check for model

Signed-off-by: Adrien Gallouët <angt@huggingface.co>

---------

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 include/llama.h                 |  6 +++
 src/llama-model-loader.cpp      | 90 +++++++++++++++++----------------
 src/llama-model.cpp             | 12 +++++
 src/llama-model.h               |  2 +
 tools/cli/cli.cpp               |  3 ++
 tools/server/server-context.cpp |  4 ++
 tools/server/server-context.h   |  1 +
 7 files changed, 74 insertions(+), 44 deletions(-)

diff --git a/include/llama.h b/include/llama.h
index f723c9f60..b89ab758b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -159,6 +159,9 @@ extern "C" {
         LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
 
+    // Get the model file type (quantization) as a string, e.g. "Q8_0" or "Q4_K - Medium"
+    LLAMA_API const char * llama_ftype_name(enum llama_ftype ftype);
+
     enum llama_rope_scaling_type {
         LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
         LLAMA_ROPE_SCALING_TYPE_NONE        = 0,
@@ -606,6 +609,9 @@ extern "C" {
     // Get a string describing the model type
     LLAMA_API int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
 
+    // Get the model file type (quantization), e.g. LLAMA_FTYPE_MOSTLY_Q8_0
+    LLAMA_API enum llama_ftype llama_model_ftype(const struct llama_model * model);
+
     // Returns the total size of all the tensors in the model in bytes
     LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
 
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index e07b0d231..55554735d 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -27,52 +27,54 @@ const char * llama_file_version_name(llama_fver version) {
     return "unknown";
 }
 
-static std::string llama_model_ftype_name(llama_ftype ftype) {
-    if (ftype & LLAMA_FTYPE_GUESSED) {
-        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
-    }
+#define LLAMA_FTYPE_PREFIX "(guessed) "
 
-    switch (ftype) {
-        case LLAMA_FTYPE_ALL_F32:         return "all F32";
-        case LLAMA_FTYPE_MOSTLY_F16:      return "F16";
-        case LLAMA_FTYPE_MOSTLY_BF16:     return "BF16";
-        case LLAMA_FTYPE_MOSTLY_Q1_0:     return "Q1_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_0:     return "Q4_0";
-        case LLAMA_FTYPE_MOSTLY_Q4_1:     return "Q4_1";
-        case LLAMA_FTYPE_MOSTLY_Q5_0:     return "Q5_0";
-        case LLAMA_FTYPE_MOSTLY_Q5_1:     return "Q5_1";
-        case LLAMA_FTYPE_MOSTLY_Q8_0:     return "Q8_0";
-        case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return "MXFP4 MoE";
-        case LLAMA_FTYPE_MOSTLY_NVFP4:    return "NVFP4";
-        case LLAMA_FTYPE_MOSTLY_Q2_K:     return "Q2_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q2_K_S:   return "Q2_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_S:   return "Q3_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_M:   return "Q3_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q3_K_L:   return "Q3_K - Large";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_S:   return "Q4_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q4_K_M:   return "Q4_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_S:   return "Q5_K - Small";
-        case LLAMA_FTYPE_MOSTLY_Q5_K_M:   return "Q5_K - Medium";
-        case LLAMA_FTYPE_MOSTLY_Q6_K:     return "Q6_K";
-        case LLAMA_FTYPE_MOSTLY_TQ1_0:    return "TQ1_0 - 1.69 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_TQ2_0:    return "TQ2_0 - 2.06 bpw ternary";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:  return "IQ2_XXS - 2.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_XS:   return "IQ2_XS - 2.3125 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_S:    return "IQ2_S - 2.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ2_M:    return "IQ2_M - 2.7 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XS:   return "IQ3_XS - 3.3 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:  return "IQ3_XXS - 3.0625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_S:    return "IQ1_S - 1.5625 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ1_M:    return "IQ1_M - 1.75 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_NL:   return "IQ4_NL - 4.5 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ4_XS:   return "IQ4_XS - 4.25 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_S:    return "IQ3_S - 3.4375 bpw";
-        case LLAMA_FTYPE_MOSTLY_IQ3_M:    return "IQ3_S mix - 3.66 bpw";
-
-        default: return "unknown, may not work";
+const char * llama_ftype_name(llama_ftype ftype) {
+    static constexpr size_t guessed_prefix_len = sizeof(LLAMA_FTYPE_PREFIX) - 1;
+    const char * name;
+    switch ((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) {
+        case LLAMA_FTYPE_ALL_F32:          name = LLAMA_FTYPE_PREFIX "all F32"; break;
+        case LLAMA_FTYPE_MOSTLY_F16:       name = LLAMA_FTYPE_PREFIX "F16"; break;
+        case LLAMA_FTYPE_MOSTLY_BF16:      name = LLAMA_FTYPE_PREFIX "BF16"; break;
+        case LLAMA_FTYPE_MOSTLY_Q1_0:      name = LLAMA_FTYPE_PREFIX "Q1_0"; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_0:      name = LLAMA_FTYPE_PREFIX "Q4_0"; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_1:      name = LLAMA_FTYPE_PREFIX "Q4_1"; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_0:      name = LLAMA_FTYPE_PREFIX "Q5_0"; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_1:      name = LLAMA_FTYPE_PREFIX "Q5_1"; break;
+        case LLAMA_FTYPE_MOSTLY_Q8_0:      name = LLAMA_FTYPE_PREFIX "Q8_0"; break;
+        case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: name = LLAMA_FTYPE_PREFIX "MXFP4 MoE"; break;
+        case LLAMA_FTYPE_MOSTLY_NVFP4:     name = LLAMA_FTYPE_PREFIX "NVFP4"; break;
+        case LLAMA_FTYPE_MOSTLY_Q2_K:      name = LLAMA_FTYPE_PREFIX "Q2_K - Medium"; break;
+        case LLAMA_FTYPE_MOSTLY_Q2_K_S:    name = LLAMA_FTYPE_PREFIX "Q2_K - Small"; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_S:    name = LLAMA_FTYPE_PREFIX "Q3_K - Small"; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_M:    name = LLAMA_FTYPE_PREFIX "Q3_K - Medium"; break;
+        case LLAMA_FTYPE_MOSTLY_Q3_K_L:    name = LLAMA_FTYPE_PREFIX "Q3_K - Large"; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_S:    name = LLAMA_FTYPE_PREFIX "Q4_K - Small"; break;
+        case LLAMA_FTYPE_MOSTLY_Q4_K_M:    name = LLAMA_FTYPE_PREFIX "Q4_K - Medium"; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_S:    name = LLAMA_FTYPE_PREFIX "Q5_K - Small"; break;
+        case LLAMA_FTYPE_MOSTLY_Q5_K_M:    name = LLAMA_FTYPE_PREFIX "Q5_K - Medium"; break;
+        case LLAMA_FTYPE_MOSTLY_Q6_K:      name = LLAMA_FTYPE_PREFIX "Q6_K"; break;
+        case LLAMA_FTYPE_MOSTLY_TQ1_0:     name = LLAMA_FTYPE_PREFIX "TQ1_0 - 1.69 bpw ternary"; break;
+        case LLAMA_FTYPE_MOSTLY_TQ2_0:     name = LLAMA_FTYPE_PREFIX "TQ2_0 - 2.06 bpw ternary"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XXS:   name = LLAMA_FTYPE_PREFIX "IQ2_XXS - 2.0625 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_XS:    name = LLAMA_FTYPE_PREFIX "IQ2_XS - 2.3125 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_S:     name = LLAMA_FTYPE_PREFIX "IQ2_S - 2.5 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ2_M:     name = LLAMA_FTYPE_PREFIX "IQ2_M - 2.7 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XS:    name = LLAMA_FTYPE_PREFIX "IQ3_XS - 3.3 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_XXS:   name = LLAMA_FTYPE_PREFIX "IQ3_XXS - 3.0625 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_S:     name = LLAMA_FTYPE_PREFIX "IQ1_S - 1.5625 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ1_M:     name = LLAMA_FTYPE_PREFIX "IQ1_M - 1.75 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_NL:    name = LLAMA_FTYPE_PREFIX "IQ4_NL - 4.5 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ4_XS:    name = LLAMA_FTYPE_PREFIX "IQ4_XS - 4.25 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_S:     name = LLAMA_FTYPE_PREFIX "IQ3_S - 3.4375 bpw"; break;
+        case LLAMA_FTYPE_MOSTLY_IQ3_M:     name = LLAMA_FTYPE_PREFIX "IQ3_S mix - 3.66 bpw"; break;
+        default:                           name = LLAMA_FTYPE_PREFIX "unknown, may not work"; break;
     }
+    return (ftype & LLAMA_FTYPE_GUESSED) ? name : name + guessed_prefix_len;
 }
 
+#undef LLAMA_FTYPE_PREFIX
+
 // return a list of splits for a given path
 // for example, given "<name>-00002-of-00004.gguf", returns list of all 4 splits
 static std::vector<std::string> llama_get_list_splits(const std::string & path, const int idx, const int n_split) {
@@ -1693,12 +1695,12 @@ bool llama_model_loader::load_all_data(
 }
 
 std::string llama_model_loader::ftype_name() const {
-    return llama_model_ftype_name(ftype);
+    return llama_ftype_name(ftype);
 }
 
 void llama_model_loader::print_info() const {
     LLAMA_LOG_INFO("%s: file format = %s\n", __func__, llama_file_version_name(fver));
-    LLAMA_LOG_INFO("%s: file type   = %s\n", __func__, llama_model_ftype_name(ftype).c_str());
+    LLAMA_LOG_INFO("%s: file type   = %s\n", __func__, llama_ftype_name(ftype));
     if (n_bytes < GiB) {
         LLAMA_LOG_INFO("%s: file size   = %.2f MiB (%.2f BPW) \n", __func__, n_bytes/1024.0/1024.0,        n_bytes*8.0/n_elements);
     } else {
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index d58ebac28..e07f6e986 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -987,6 +987,8 @@ struct llama_model::impl {
 
     std::string desc_str;
 
+    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
+
     // model memory mapped files
     llama_mmaps mappings;
 
@@ -1200,6 +1202,8 @@ void llama_model_base::load_hparams(llama_model_loader & ml) {
 
     pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
 
+    pimpl->ftype = ml.ftype;
+
     if (hparams.f_max_alibi_bias > 0.0f) {
         hparams.use_alibi = true;
     }
@@ -1646,6 +1650,10 @@ std::string llama_model::desc() const {
     return pimpl->desc_str;
 }
 
+llama_ftype llama_model::ftype() const {
+    return pimpl->ftype;
+}
+
 size_t llama_model::size() const {
     return pimpl->n_bytes;
 }
@@ -2616,6 +2624,10 @@ int32_t llama_model_desc(const llama_model * model, char * buf, size_t buf_size)
     return snprintf(buf, buf_size, "%s", model->desc().c_str());
 }
 
+llama_ftype llama_model_ftype(const llama_model * model) {
+    return model->ftype();
+}
+
 uint64_t llama_model_size(const llama_model * model) {
     return model->size();
 }
diff --git a/src/llama-model.h b/src/llama-model.h
index 4800d2928..45b054ced 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -637,6 +637,8 @@ struct llama_model {
 
     std::string desc() const;
 
+    llama_ftype ftype() const;
+
     size_t size() const; // file size
     size_t n_tensors() const;
     size_t n_devices() const;
diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp
index 8b7b58693..d974a4019 100644
--- a/tools/cli/cli.cpp
+++ b/tools/cli/cli.cpp
@@ -448,6 +448,9 @@ int llama_cli(int argc, char ** argv) {
     console::log("%s\n", LLAMA_ASCII_LOGO);
     console::log("build      : %s\n", inf.build_info.c_str());
     console::log("model      : %s\n", inf.model_name.c_str());
+    if (!inf.model_ftype.empty()) {
+        console::log("ftype      : %s\n", inf.model_ftype.c_str());
+    }
     console::log("modalities : %s\n", modalities.c_str());
     if (!params.system_prompt.empty()) {
         console::log("using custom system prompt\n");
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 39aa20b32..20e93258f 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -3989,6 +3989,8 @@ server_context_meta server_context::get_meta() const {
     auto bos_token_str = bos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx_tgt, bos_id, true) : "";
     auto eos_token_str = eos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx_tgt, eos_id, true) : "";
 
+    const char * ftype_name = llama_ftype_name(llama_model_ftype(impl->model_tgt));
+
     return server_context_meta {
         /* build_info             */ std::string(llama_build_info()),
         /* model_name             */ impl->model_name,
@@ -4023,6 +4025,7 @@ server_context_meta server_context::get_meta() const {
         /* model_n_embd_inp       */ llama_model_n_embd(impl->model_tgt),
         /* model_n_params         */ llama_model_n_params(impl->model_tgt),
         /* model_size             */ llama_model_size(impl->model_tgt),
+        /* model_ftype            */ ftype_name,
     };
 }
 
@@ -5118,6 +5121,7 @@ json server_routes::get_model_info() const {
             {"n_embd",      meta->model_n_embd_inp},
             {"n_params",    meta->model_n_params},
             {"size",        meta->model_size},
+            {"ftype",       meta->model_ftype},
         }},
     };
 }
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index 952f825f7..f9ab1132b 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -50,6 +50,7 @@ struct server_context_meta {
     int32_t model_n_embd_inp;
     uint64_t model_n_params;
     uint64_t model_size;
+    std::string model_ftype;
 };
 
 enum server_state {

From 2ad093af75d1905d933e5005024b38d770f482ba Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 3 Jul 2026 15:27:30 +0800
Subject: [PATCH 254/292] Revert "CUDA: consistent use of __restrict__ + PDL
 for FA (#25185)"

This reverts commit b820cc8e6f94843d32f92c8ebd7db837dae2bd8b.
---
 ggml/src/ggml-cuda/fattn-common.cuh | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index e67cc7fdf..b76121fef 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -664,10 +664,7 @@ constexpr __device__ dequantize_V_t get_dequantize_V() {
 template <int ncols1>
 __launch_bounds__(FATTN_KQ_STRIDE/2, 1)
 static __global__ void flash_attn_mask_to_KV_max(
-        const half2 * mask_ptr, int * KV_max_ptr, const int ne30, const int64_t s31, const int64_t s33) {
-    const half2 * GGML_CUDA_RESTRICT mask   = mask_ptr;
-    int         * GGML_CUDA_RESTRICT KV_max = KV_max_ptr;
-
+        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int64_t s31, const int64_t s33) {
     const int ne31     = gridDim.x;
     const int tid      = threadIdx.x;
     const int sequence = blockIdx.y;
@@ -1102,9 +1099,8 @@ void launch_fattn(
         const int iter_k = K->ne[1] / FATTN_KQ_STRIDE;
 
         KV_max.alloc(ne_KV_max);
-        ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(blocks_num_KV_max, block_dim_KV_max, 0, main_stream);
-        ggml_cuda_kernel_launch(flash_attn_mask_to_KV_max<ncols1>, launch_params,
-            (const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
+        flash_attn_mask_to_KV_max<ncols1><<<blocks_num_KV_max, block_dim_KV_max, 0, main_stream>>>
+            ((const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
         CUDA_CHECK(cudaGetLastError());
     }
 

From 6482a596e1d391539b08b8c40cd555e4fbd372b2 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Fri, 3 Jul 2026 04:28:51 -0300
Subject: [PATCH 255/292] sd: sync with master-746-2574f59 (#2291)

* sd: clean up SD_USE_ defines

* sd: sync with master-721-8caa3f9+5 (9956436)

* sd: simplify taehv selection

* sd: sync with master-731-9f855c9

* sd: sync with master-737-3b6c9ca

* sd: sync with master-741-484baa4

* sd: sync with master-743-3590aa8

* sd: sync with master-746-2574f59

* sd: fix ggml_ext_pad_ext call
---
 Makefile                                      |  24 +-
 otherarch/sdcpp/examples/cli/main.cpp         |  85 ++-
 otherarch/sdcpp/examples/common/common.cpp    |  31 +-
 otherarch/sdcpp/examples/common/common.h      |   1 +
 otherarch/sdcpp/include/stable-diffusion.h    |  39 +-
 otherarch/sdcpp/sdtype_adapter.cpp            | 199 +++---
 .../sdcpp/src/conditioning/conditioner.hpp    | 111 +++-
 otherarch/sdcpp/src/convert.cpp               |  85 ++-
 otherarch/sdcpp/src/core/ggml_extend.hpp      |  50 +-
 .../sdcpp/src/core/ggml_extend_backend.cpp    | 144 ++++-
 .../sdcpp/src/core/ggml_extend_backend.h      |   5 +
 otherarch/sdcpp/src/core/util.cpp             |  16 +
 otherarch/sdcpp/src/core/util.h               |   3 +
 otherarch/sdcpp/src/model.h                   |  30 +-
 otherarch/sdcpp/src/model/common/rope.hpp     | 144 +++--
 otherarch/sdcpp/src/model/diffusion/anima.hpp |   9 +-
 .../sdcpp/src/model/diffusion/control.hpp     |   2 +-
 otherarch/sdcpp/src/model/diffusion/dit.hpp   |  24 +-
 .../sdcpp/src/model/diffusion/ernie_image.hpp |   4 +-
 otherarch/sdcpp/src/model/diffusion/flux.hpp  |  48 +-
 .../sdcpp/src/model/diffusion/hidream_o1.hpp  |   2 +-
 .../sdcpp/src/model/diffusion/minit2i.hpp     | 611 ++++++++++++++++++
 otherarch/sdcpp/src/model/diffusion/model.hpp |  12 +-
 .../sdcpp/src/model/diffusion/qwen_image.hpp  | 120 +++-
 .../sdcpp/src/model/diffusion/sefi_image.hpp  |  91 +++
 .../sdcpp/src/model/diffusion/z_image.hpp     |  12 +-
 otherarch/sdcpp/src/model/te/clip.hpp         |   2 +-
 otherarch/sdcpp/src/model/te/llm.hpp          |   4 +-
 otherarch/sdcpp/src/model/te/t5.hpp           |  59 +-
 otherarch/sdcpp/src/model/upscaler/esrgan.hpp |   2 +-
 .../model/upscaler/ltx_latent_upscaler.hpp    |   2 +-
 .../sdcpp/src/model/vae/auto_encoder_kl.hpp   |   7 +-
 .../sdcpp/src/model/vae/ltx_audio_vae.hpp     |   6 +-
 otherarch/sdcpp/src/model/vae/tae.hpp         |  17 +-
 otherarch/sdcpp/src/model/vae/vae.hpp         |   4 +-
 otherarch/sdcpp/src/model/vae/wan_vae.hpp     | 265 ++++++--
 otherarch/sdcpp/src/model_loader.cpp          |  24 +-
 otherarch/sdcpp/src/name_conversion.cpp       |  76 ++-
 otherarch/sdcpp/src/runtime/denoiser.hpp      | 458 ++++++++++++-
 otherarch/sdcpp/src/runtime/imatrix.cpp       | 308 +++++++++
 otherarch/sdcpp/src/runtime/imatrix.h         |  45 ++
 otherarch/sdcpp/src/stable-diffusion.cpp      | 277 ++++++--
 otherarch/sdcpp/src/upscaler.cpp              |  38 +-
 43 files changed, 3009 insertions(+), 487 deletions(-)
 create mode 100644 otherarch/sdcpp/src/model/diffusion/minit2i.hpp
 create mode 100644 otherarch/sdcpp/src/model/diffusion/sefi_image.hpp
 create mode 100644 otherarch/sdcpp/src/runtime/imatrix.cpp
 create mode 100644 otherarch/sdcpp/src/runtime/imatrix.h

diff --git a/Makefile b/Makefile
index d09f5d2a4..ae2351e5b 100644
--- a/Makefile
+++ b/Makefile
@@ -101,9 +101,9 @@ NONECFLAGS =
 LLAMA_USE_BUNDLED_GLSLC := 1
 
 FAILSAFE_FLAGS = -DUSE_FAILSAFE
-VULKAN_FLAGS = -DGGML_USE_VULKAN -DSD_USE_VULKAN
+VULKAN_FLAGS = -DGGML_USE_VULKAN
 ifdef LLAMA_CUBLAS
-CUBLAS_FLAGS = -DGGML_USE_CUDA -DSD_USE_CUDA
+CUBLAS_FLAGS = -DGGML_USE_CUDA
 else
 CUBLAS_FLAGS =
 endif
@@ -215,7 +215,7 @@ OBJS_CUDA_TEMP_INST += \
     ggml/src/ggml-cuda/template-instances/fattn-vec-instance-bf16-bf16.o
 
 ifdef LLAMA_CUBLAS
-CUBLAS_FLAGS = -DGGML_USE_CUDA -DSD_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
+CUBLAS_FLAGS = -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
 CUBLASLD_FLAGS = -lcuda -lcublas -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L$(CUDA_PATH)/lib64/stubs -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/local/cuda/targets/sbsa-linux/lib -L/usr/lib/wsl/lib
 CUBLAS_OBJS = ggml-cuda.o ggml_v3-cuda.o ggml_v2-cuda.o ggml_v2-cuda-legacy.o
 CUBLAS_OBJS += $(patsubst %.cu,%.o,$(filter-out ggml/src/ggml-cuda/ggml-cuda.cu, $(wildcard ggml/src/ggml-cuda/*.cu)))
@@ -315,7 +315,7 @@ HIPFLAGS   += -DGGML_HIP_NO_ROCWMMA_FATTN
 endif
 endif
 
-HIPFLAGS   += -DGGML_USE_HIP -DGGML_HIP_NO_VMM -DGGML_USE_CUDA -DSD_USE_CUDA $(shell $(ROCM_PATH)/bin/hipconfig -C)
+HIPFLAGS   += -DGGML_USE_HIP -DGGML_HIP_NO_VMM -DGGML_USE_CUDA $(shell $(ROCM_PATH)/bin/hipconfig -C)
 HIPLDFLAGS    += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
 HIPLDFLAGS    += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
 HIPLDFLAGS    += -lhipblas -lamdhip64 -lrocblas
@@ -339,8 +339,8 @@ endif # LLAMA_HIPBLAS
 
 
 ifdef LLAMA_METAL
-CFLAGS   += -DGGML_USE_METAL -DGGML_METAL_NDEBUG -DSD_USE_METAL
-CXXFLAGS += -DGGML_USE_METAL -DSD_USE_METAL
+CFLAGS   += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
+CXXFLAGS += -DGGML_USE_METAL
 LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
 OBJS     += ggml-metal.o ggml-metal-device.o ggml-metal-device-m.o ggml-metal-context-m.o ggml-metal-common.o ggml-metal-ops.o
 
@@ -703,7 +703,7 @@ budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 chat.o: common/chat.cpp common/chat.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
+SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/minit2i.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/sefi_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/imatrix.cpp src/runtime/imatrix.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
 SDCPP_MAIN_BASENAMES := examples/cli/image_metadata.cpp examples/cli/image_metadata.h examples/cli/main.cpp examples/cli/msf_gif.h examples/common/common.cpp examples/common/common.h examples/common/log.cpp examples/common/log.h examples/common/media_io.cpp examples/common/media_io.h examples/common/resource_owners.hpp src/tokenizers/vocab/clip_merges.hpp src/tokenizers/vocab/gemma2_merges.hpp src/tokenizers/vocab/gemma2_vocab.hpp src/tokenizers/vocab/gemma_merges.hpp src/tokenizers/vocab/gemma_vocab.hpp src/tokenizers/vocab/gpt_oss_merges.hpp src/tokenizers/vocab/gpt_oss_vocab.hpp src/tokenizers/vocab/mistral_merges.hpp src/tokenizers/vocab/mistral_vocab.hpp src/tokenizers/vocab/qwen_merges.hpp src/tokenizers/vocab/t5.hpp src/tokenizers/vocab/umt5.hpp src/tokenizers/vocab/vocab.cpp src/convert.cpp src/version.cpp
 
@@ -782,9 +782,9 @@ clean:
 main: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 mainvk: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
-	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
-	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
@@ -798,17 +798,17 @@ mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/ar
 embedding: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
-	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
-	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 llamaservervk: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
-	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) -DGGML_USE_VULKAN -DSD_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ggml/src/ggml-vulkan-shaders.cpp: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
 ifdef VULKAN_BUILD
diff --git a/otherarch/sdcpp/examples/cli/main.cpp b/otherarch/sdcpp/examples/cli/main.cpp
index 84e9e7853..7892d5213 100644
--- a/otherarch/sdcpp/examples/cli/main.cpp
+++ b/otherarch/sdcpp/examples/cli/main.cpp
@@ -1,6 +1,7 @@
 #include <stdio.h>
 #include <string.h>
 #include <time.h>
+#include <algorithm>
 #include <cctype>
 #include <filesystem>
 #include <functional>
@@ -53,6 +54,9 @@ struct SDCliParams {
     bool metadata_brief      = false;
     bool metadata_all        = false;
 
+    std::string imatrix_out;
+    std::vector<std::string> imatrix_in;
+
     bool normal_exit = false;
 
     ArgOptions get_options() {
@@ -79,6 +83,11 @@ struct SDCliParams {
              "path to write preview image to (default: ./preview.png). Multi-frame previews support .avi, .webm, and animated .webp",
              0,
              &preview_path},
+            {"",
+             "--imat-out",
+             "compute the imatrix for this run and save it to the provided path",
+             0,
+             &imatrix_out},
         };
 
         options.int_options = {
@@ -179,6 +188,14 @@ struct SDCliParams {
             return -1;
         };
 
+        auto on_imatrix_in_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            imatrix_in.push_back(argv[index]);
+            return 1;
+        };
+
         options.manual_options = {
             {"-M",
              "--mode",
@@ -192,6 +209,10 @@ struct SDCliParams {
              "--help",
              "show this help message and exit",
              on_help_arg},
+            {"",
+             "--imat-in",
+             "load an imatrix file for quantization or continued collection; can be specified multiple times",
+             on_imatrix_in_arg},
         };
 
         return options;
@@ -253,6 +274,7 @@ struct SDCliParams {
             << "  preview_fps: " << preview_fps << ",\n"
             << "  taesd_preview: " << (taesd_preview ? "true" : "false") << ",\n"
             << "  preview_noisy: " << (preview_noisy ? "true" : "false") << ",\n"
+            << "  imatrix_out: \"" << imatrix_out << "\",\n"
             << "  metadata_raw: " << (metadata_raw ? "true" : "false") << ",\n"
             << "  metadata_brief: " << (metadata_brief ? "true" : "false") << ",\n"
             << "  metadata_all: " << (metadata_all ? "true" : "false") << "\n"
@@ -459,7 +481,8 @@ bool save_results(const SDCliParams& cli_params,
         if (!img.data)
             return false;
 
-        const int64_t metadata_seed = cli_params.mode == VID_GEN ? gen_params.seed : gen_params.seed + idx;
+        int images_per_batch        = gen_params.batch_count > 0 ? std::max(1, num_results / gen_params.batch_count) : 1;
+        const int64_t metadata_seed = cli_params.mode == VID_GEN ? gen_params.seed : gen_params.seed + idx / images_per_batch;
         std::string params          = gen_params.embed_image_metadata
                                           ? get_image_params(ctx_params, gen_params, metadata_seed, cli_params.mode)
                                           : "";
@@ -605,13 +628,32 @@ int main(int argc, const char* argv[]) {
     LOG_DEBUG("%s", ctx_params.to_string().c_str());
     LOG_DEBUG("%s", gen_params.to_string().c_str());
 
+    if (!cli_params.imatrix_out.empty()) {
+        if (fs::exists(cli_params.imatrix_out) &&
+            std::find(cli_params.imatrix_in.begin(), cli_params.imatrix_in.end(), cli_params.imatrix_out) == cli_params.imatrix_in.end()) {
+            LOG_WARN("imatrix file '%s' already exists and will be overwritten", cli_params.imatrix_out.c_str());
+        }
+        enable_imatrix_collection();
+    }
+
+    for (const auto& in_file : cli_params.imatrix_in) {
+        LOG_INFO("loading imatrix from '%s'", in_file.c_str());
+        if (!load_imatrix(in_file.c_str())) {
+            LOG_WARN("failed to load imatrix from '%s'", in_file.c_str());
+        }
+    }
+
     if (cli_params.mode == CONVERT) {
-        bool success = convert(ctx_params.model_path.c_str(),
-                               ctx_params.vae_path.c_str(),
-                               cli_params.output_path.c_str(),
-                               ctx_params.wtype,
-                               ctx_params.tensor_type_rules.c_str(),
-                               cli_params.convert_name);
+        bool success = convert_with_components(ctx_params.model_path.c_str(),
+                                               ctx_params.clip_l_path.c_str(),
+                                               ctx_params.clip_g_path.c_str(),
+                                               ctx_params.t5xxl_path.c_str(),
+                                               ctx_params.diffusion_model_path.c_str(),
+                                               ctx_params.vae_path.c_str(),
+                                               cli_params.output_path.c_str(),
+                                               ctx_params.wtype,
+                                               ctx_params.tensor_type_rules.c_str(),
+                                               cli_params.convert_name);
         if (!success) {
             LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                       ctx_params.model_path.c_str(),
@@ -766,8 +808,12 @@ int main(int argc, const char* argv[]) {
         if (cli_params.mode == IMG_GEN) {
             sd_img_gen_params_t img_gen_params = gen_params.to_sd_img_gen_params_t();
 
-            num_results = gen_params.batch_count;
-            results.adopt(generate_image(sd_ctx.get(), &img_gen_params), num_results);
+            sd_image_t* generated_images = nullptr;
+            if (!generate_image(sd_ctx.get(), &img_gen_params, &generated_images, &num_results)) {
+                generated_images = nullptr;
+                num_results      = 0;
+            }
+            results.adopt(generated_images, num_results);
         } else if (cli_params.mode == VID_GEN) {
             sd_vid_gen_params_t vid_gen_params = gen_params.to_sd_vid_gen_params_t();
             sd_image_t* generated_video        = nullptr;
@@ -802,12 +848,22 @@ int main(int argc, const char* argv[]) {
                 SDImageOwner current_image(results[i]);
                 results[i] = {0, 0, 0, nullptr};
                 for (int u = 0; u < gen_params.upscale_repeats; ++u) {
-                    SDImageOwner upscaled_image(upscale(upscaler_ctx.get(), current_image.get(), upscale_factor));
-                    if (upscaled_image.get().data == nullptr) {
+                    sd_image_t* upscaled_images = nullptr;
+                    int upscaled_count          = 0;
+                    bool upscale_ok             = upscale(upscaler_ctx.get(),
+                                                          current_image.get(),
+                                                          upscale_factor,
+                                                          &upscaled_images,
+                                                          &upscaled_count);
+                    if (!upscale_ok || upscaled_count <= 0 || upscaled_images[0].data == nullptr) {
+                        free_sd_images(upscaled_images, upscaled_count);
                         LOG_ERROR("upscale failed");
                         break;
                     }
-                    current_image = std::move(upscaled_image);
+                    sd_image_t upscaled_image = upscaled_images[0];
+                    upscaled_images[0]        = {0, 0, 0, nullptr};
+                    free_sd_images(upscaled_images, upscaled_count);
+                    current_image.reset(upscaled_image);
                 }
                 results[i] = current_image.release();  // Set the final upscaled image as the result
             }
@@ -819,6 +875,11 @@ int main(int argc, const char* argv[]) {
         return 1;
     }
 
+    if (!cli_params.imatrix_out.empty()) {
+        LOG_INFO("saving imatrix to '%s'", cli_params.imatrix_out.c_str());
+        save_imatrix(cli_params.imatrix_out.c_str());
+    }
+
     free_sd_audio(generated_audio);
 
     return 0;
diff --git a/otherarch/sdcpp/examples/common/common.cpp b/otherarch/sdcpp/examples/common/common.cpp
index 744005af9..ac1cf32f6 100644
--- a/otherarch/sdcpp/examples/common/common.cpp
+++ b/otherarch/sdcpp/examples/common/common.cpp
@@ -653,7 +653,7 @@ ArgOptions SDContextParams::get_options() {
          on_sampler_rng_arg},
         {"",
          "--prediction",
-         "prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]",
+         "prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, sefi_flow]",
          on_prediction_arg},
         {"",
          "--lora-apply-mode",
@@ -710,7 +710,18 @@ bool SDContextParams::resolve(SDMode mode) {
 }
 
 bool SDContextParams::validate(SDMode mode) {
-    if (mode != UPSCALE && mode != METADATA && model_path.length() == 0 && diffusion_model_path.length() == 0) {
+    if (mode == CONVERT) {
+        const bool has_convert_input = model_path.length() != 0 ||
+                                       clip_l_path.length() != 0 ||
+                                       clip_g_path.length() != 0 ||
+                                       t5xxl_path.length() != 0 ||
+                                       diffusion_model_path.length() != 0 ||
+                                       vae_path.length() != 0;
+        if (!has_convert_input) {
+            LOG_ERROR("error: convert mode needs at least one model input path\n");
+            return false;
+        }
+    } else if (mode != UPSCALE && mode != METADATA && model_path.length() == 0 && diffusion_model_path.length() == 0) {
         LOG_ERROR("error: the following arguments are required: model_path/diffusion_model\n");
         return false;
     }
@@ -960,7 +971,7 @@ ArgOptions SDGenerationParams::get_options() {
          &hires_upscaler},
         {"",
          "--extra-sample-args",
-         "extra sampler/scheduler/guidance args, key=value list. CFG supports guidance_schedule; APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma;; logit_normal supports mu, std, logsnr_min, logsnr_max, resolution_aware",
+         "extra sampler/scheduler/guidance args, key=value list. CFG supports guidance_schedule; APG supports apg_eta, apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end; flux supports base_shift, max_shift; ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma;; logit_normal supports mu, std, logsnr_min, logsnr_max, resolution_aware",
          (int)',',
          &extra_sample_args},
         {"",
@@ -996,6 +1007,10 @@ ArgOptions SDGenerationParams::get_options() {
          "--batch-count",
          "batch count",
          &batch_count},
+        {"",
+         "--qwen-image-layers",
+         "number of Qwen Image Layered layers; latent/output count is layers + 1 (default: 3)",
+         &qwen_image_layers},
         {"",
          "--video-frames",
          "video frames (default: 1)",
@@ -1475,7 +1490,7 @@ ArgOptions SDGenerationParams::get_options() {
          on_high_noise_sample_method_arg},
         {"",
          "--scheduler",
-         "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2, logit_normal], default: model-specific",
+         "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2, logit_normal, flux2, flux, beta], alias: normal=discrete, default: model-specific",
          on_scheduler_arg},
         {"",
          "--sigmas",
@@ -1816,6 +1831,7 @@ bool SDGenerationParams::from_json_str(
     load_if_exists("width", width);
     load_if_exists("height", height);
     load_if_exists("batch_count", batch_count);
+    load_if_exists("qwen_image_layers", qwen_image_layers);
     load_if_exists("video_frames", video_frames);
     load_if_exists("fps", fps);
     load_if_exists("upscale_repeats", upscale_repeats);
@@ -2240,6 +2256,11 @@ bool SDGenerationParams::validate(SDMode mode) {
         return false;
     }
 
+    if (qwen_image_layers < 0) {
+        LOG_ERROR("error: qwen_image_layers must be non-negative");
+        return false;
+    }
+
     if (sample_params.sample_steps <= 0) {
         LOG_ERROR("error: the sample_steps must be greater than 0\n");
         return false;
@@ -2406,6 +2427,7 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
     params.strength              = strength;
     params.seed                  = seed;
     params.batch_count           = batch_count;
+    params.qwen_image_layers     = qwen_image_layers;
     params.control_image         = control_image.get();
     params.control_strength      = control_strength;
     params.pm_params             = pm_params;
@@ -2531,6 +2553,7 @@ std::string SDGenerationParams::to_string() const {
         << "  width: " << width << ",\n"
         << "  height: " << height << ",\n"
         << "  batch_count: " << batch_count << ",\n"
+        << "  qwen_image_layers: " << qwen_image_layers << ",\n"
         << "  init_image_path: \"" << init_image_path << "\",\n"
         << "  end_image_path: \"" << end_image_path << "\",\n"
         << "  mask_image_path: \"" << mask_image_path << "\",\n"
diff --git a/otherarch/sdcpp/examples/common/common.h b/otherarch/sdcpp/examples/common/common.h
index e7c25015b..941fa3317 100644
--- a/otherarch/sdcpp/examples/common/common.h
+++ b/otherarch/sdcpp/examples/common/common.h
@@ -197,6 +197,7 @@ struct SDGenerationParams {
     int width                  = -1;
     int height                 = -1;
     int batch_count            = 1;
+    int qwen_image_layers      = 3;
     int64_t seed               = 42;
     float strength             = 0.75f;
     float control_strength     = 0.9f;
diff --git a/otherarch/sdcpp/include/stable-diffusion.h b/otherarch/sdcpp/include/stable-diffusion.h
index 7058852cc..d5bace723 100644
--- a/otherarch/sdcpp/include/stable-diffusion.h
+++ b/otherarch/sdcpp/include/stable-diffusion.h
@@ -71,6 +71,9 @@ enum scheduler_t {
     BONG_TANGENT_SCHEDULER,
     LTX2_SCHEDULER,
     LOGIT_NORMAL_SCHEDULER,
+    FLUX2_SCHEDULER,
+    FLUX_SCHEDULER,
+    BETA_SCHEDULER,
     SCHEDULER_COUNT
 };
 
@@ -80,7 +83,8 @@ enum prediction_t {
     EDM_V_PRED,
     FLOW_PRED,
     FLUX_FLOW_PRED,
-    FLUX2_FLOW_PRED,
+    SEFI_FLOW_PRED,
+    MINIT2I_FLOW_PRED,
     PREDICTION_COUNT
 };
 
@@ -376,6 +380,7 @@ typedef struct {
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
     sd_hires_params_t hires;
+    int qwen_image_layers;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -405,14 +410,17 @@ typedef struct {
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
+struct ggml_tensor;
 
 typedef void (*sd_log_cb_t)(enum sd_log_level_t level, const char* text, void* data);
 typedef void (*sd_progress_cb_t)(int step, int steps, float time, void* data);
 typedef void (*sd_preview_cb_t)(int step, int frame_count, sd_image_t* frames, bool is_noisy, void* data);
+typedef bool (*sd_graph_eval_callback_t)(struct ggml_tensor* t, bool ask, void* user_data);
 
 SD_API void sd_set_log_callback(sd_log_cb_t sd_log_cb, void* data);
 SD_API void sd_set_progress_callback(sd_progress_cb_t cb, void* data);
 SD_API void sd_set_preview_callback(sd_preview_cb_t cb, enum preview_t mode, int interval, bool denoised, bool noisy, void* data);
+SD_API void sd_set_backend_eval_callback(sd_graph_eval_callback_t cb, void* data);
 SD_API int32_t sd_get_num_physical_cores();
 SD_API const char* sd_get_system_info();
 SD_API bool sd_ctx_supports_image_generation(const sd_ctx_t* sd_ctx);
@@ -453,7 +461,10 @@ SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sa
 
 SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
-SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);
+SD_API bool generate_image(sd_ctx_t* sd_ctx,
+                           const sd_img_gen_params_t* sd_img_gen_params,
+                           sd_image_t** images_out,
+                           int* num_images_out);
 
 enum sd_cancel_mode_t {
     // Stop the current generation as soon as possible.
@@ -483,9 +494,11 @@ SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
                                         const char* params_backend);
 SD_API void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx);
 
-SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx,
-                          sd_image_t input_image,
-                          uint32_t upscale_factor);
+SD_API bool upscale(upscaler_ctx_t* upscaler_ctx,
+                    sd_image_t input_image,
+                    uint32_t upscale_factor,
+                    sd_image_t** images_out,
+                    int* num_images_out);
 
 SD_API int get_upscale_factor(upscaler_ctx_t* upscaler_ctx);
 
@@ -496,6 +509,17 @@ SD_API bool convert(const char* input_path,
                     const char* tensor_type_rules,
                     bool convert_name);
 
+SD_API bool convert_with_components(const char* model_path,
+                                    const char* clip_l_path,
+                                    const char* clip_g_path,
+                                    const char* t5xxl_path,
+                                    const char* diffusion_model_path,
+                                    const char* vae_path,
+                                    const char* output_path,
+                                    enum sd_type_t output_type,
+                                    const char* tensor_type_rules,
+                                    bool convert_name);
+
 SD_API bool preprocess_canny(sd_image_t image,
                              float high_threshold,
                              float low_threshold,
@@ -503,6 +527,11 @@ SD_API bool preprocess_canny(sd_image_t image,
                              float strong,
                              bool inverse);
 
+SD_API bool load_imatrix(const char* imatrix_path);
+SD_API void save_imatrix(const char* imatrix_path);
+SD_API void enable_imatrix_collection(void);
+SD_API void disable_imatrix_collection(void);
+
 SD_API const char* sd_commit(void);
 SD_API const char* sd_version(void);
 
diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 5dff78031..73c0b73f7 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -977,6 +977,21 @@ bool supports_reference_images(kcpp_sd::model_info info)
     return supported;
 }
 
+static std::string upscale_image_to_png_base64(upscaler_ctx_t* upscaler_ctx, const sd_image_t& input_image, int upscale_factor = 2, const std::string& meta_image_info = "")
+{
+    std::string gen_data;
+    sd_image_t* upscaled = nullptr;
+    int upscaled_count = 0;
+    if (upscale(upscaler_ctx, input_image, upscale_factor, &upscaled, &upscaled_count)) {
+        gen_data = raw_image_to_png_base64(*upscaled, meta_image_info);
+        free_sd_images(upscaled, upscaled_count);
+    } else {
+        printf("Upscaling failed!\n");
+        gen_data = raw_image_to_png_base64(input_image, meta_image_info);
+    }
+    return gen_data;
+}
+
 sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
 {
     if(sd_ctx == nullptr || sd_params == nullptr)
@@ -984,6 +999,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         return sd_generation.error("Warning: KCPP image generation not initialized!");
     }
     sd_image_t * results = nullptr;
+    int generated_num_results = 0;
 
     std::string img2img_data = std::string(inputs.init_images);
     std::string img2img_mask = std::string(inputs.mask);
@@ -1309,7 +1325,6 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
     //the below params are only used in video models. May move into standalone object in future
     int vid_req_frames = inputs.vid_req_frames;
     int video_output_type = inputs.video_output_type;
-    int generated_num_results = 1;
     int vid_fps = inputs.vid_fps;
     remove_limits = inputs.remove_limits;
 
@@ -1399,7 +1414,10 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
 
         fflush(stdout);
 
-        results = generate_image(sd_ctx, &params);
+        if (!generate_image(sd_ctx, &params, &results, &generated_num_results)) {
+            results = nullptr;
+            generated_num_results = 0;
+        }
 
     } else {
 
@@ -1466,7 +1484,10 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         if (is_passthrough) {
             printf("No generation triggered, passthrough mode.\n");
         } else {
-            results = generate_image(sd_ctx, &params);
+            if (!generate_image(sd_ctx, &params, &results, &generated_num_results)) {
+                results = nullptr;
+                generated_num_results = 0;
+            }
         }
     }
 
@@ -1511,111 +1532,102 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         jsoninfo["all_seeds"] = nlohmann::json::array();
         jsoninfo["version"] = "KoboldCpp";
     }
-    sd_image_t upscaled_image;
-    upscaled_image.data = nullptr;
+    sd_image_t* upscaled_image = nullptr;
     std::string gen_data;
     std::string gen_data2;
     std::string final_frame_data;
 
     if (is_passthrough)
     {
-        //either return original image or upscale if needed
-        sd_image_t *result_image = &input_image;
         if(inputs.upscale && upscaler_ctx != nullptr)
         {
             printf("Upscaling original image (passthrough)...\n");
-            upscaled_image = upscale(upscaler_ctx, input_image, 2);
-            result_image = &upscaled_image;
+            gen_data = upscale_image_to_png_base64(upscaler_ctx, input_image, 2);
         }
-        gen_data = raw_image_to_png_base64(*result_image);
+        else {
+            gen_data = raw_image_to_png_base64(input_image);
+        }
+    }
+    else if (isanim)
+    {
+        //if multiframe, make a video
+        if (generated_num_results > 0 && results && results->data)
+        {
+            if(!sd_is_quiet && sddebugmode==1)
+            {
+                printf("\nSaving video buffer, VIDEO_OUTPUT_TYPE=%d...",video_output_type);
+            }
+            uint8_t * out_data = nullptr;
+            uint8_t * out_data2 = nullptr;
+            size_t out_len = 0;
+            size_t out_len2 = 0;
+            int status = 0;
+            int status2 = 0;
+
+            if(video_output_type==0 || video_output_type==2)
+            {
+                status = create_gif_buf_from_sd_images_msf(results, generated_num_results, vid_fps, &out_data,&out_len);
+            }
+            if(video_output_type==1 || video_output_type==2)
+            {
+                status2 = create_mjpg_avi_membuf_from_sd_images(results, generated_num_results, vid_fps, 40, &out_data2,&out_len2, generated_audio);
+            }
+
+            if(generated_num_results>1)
+            {
+                sd_image_t *final_frame_image = &results[generated_num_results-1];
+                final_frame_data = raw_image_to_png_base64(*final_frame_image);
+            }
+
+            if(!sd_is_quiet && sddebugmode==1)
+            {
+                printf("Video Output Sizes: GIF=%zu AVI=%zu\n",out_len,out_len2);
+                if(status==0 && status2==0)
+                {
+                    printf("Video(s) Saved (Len %zu)!\n",out_len);
+                } else {
+                    printf("Save Failed!\n");
+                }
+            }
+            if(status==0 && out_len>0)
+            {
+                gen_data = kcpp_base64_encode(out_data, out_len);
+                free(out_data);
+            }
+            if (status2 == 0 && out_len2 > 0) {
+                if (gen_data == "") {
+                    gen_data = kcpp_base64_encode(out_data2, out_len2);
+                } else {
+                    gen_data2 = kcpp_base64_encode(out_data2, out_len2);
+                }
+                free(out_data2);
+            }
+        }
+        free_sd_images(results, generated_num_results);
     }
     else
     {
-        for (int i = 0; i < params.batch_count; i++)
+        for (int i = 0; i < generated_num_results; i++)
         {
-            if (results[i].data == NULL) {
+            sd_image_t& result_image = results[i];
+            if (result_image.data == NULL) {
                 continue;
             }
-
-            //if multiframe, make a video
-            if(isanim)
+            std::string meta_image_info = get_image_params(params, lora_meta, i);
+            if(inputs.upscale && upscaler_ctx != nullptr)
             {
-                if(!sd_is_quiet && sddebugmode==1)
-                {
-                    printf("\nSaving video buffer, VIDEO_OUTPUT_TYPE=%d...",video_output_type);
-                }
-                uint8_t * out_data = nullptr;
-                uint8_t * out_data2 = nullptr;
-                size_t out_len = 0;
-                size_t out_len2 = 0;
-                int status = 0;
-                int status2 = 0;
-
-                if(video_output_type==0 || video_output_type==2)
-                {
-                    status = create_gif_buf_from_sd_images_msf(results, generated_num_results, vid_fps, &out_data,&out_len);
-                }
-                if(video_output_type==1 || video_output_type==2)
-                {
-                    status2 = create_mjpg_avi_membuf_from_sd_images(results, generated_num_results, vid_fps, 40, &out_data2,&out_len2, generated_audio);
-                }
-
-                if(generated_num_results>1)
-                {
-                    sd_image_t *final_frame_image = &results[generated_num_results-1];
-                    final_frame_data = raw_image_to_png_base64(*final_frame_image);
-                }
-
-                if(!sd_is_quiet && sddebugmode==1)
-                {
-                    printf("Video Output Sizes: GIF=%zu AVI=%zu\n",out_len,out_len2);
-                    if(status==0 && status2==0)
-                    {
-                        printf("Video(s) Saved (Len %zu)!\n",out_len);
-                    } else {
-                        printf("Save Failed!\n");
-                    }
-                }
-                if(status==0 && out_len>0)
-                {
-                    gen_data = kcpp_base64_encode(out_data, out_len);
-                    free(out_data);
-                }
-                if (status2 == 0 && out_len2 > 0) {
-                    if (gen_data == "") {
-                        gen_data = kcpp_base64_encode(out_data2, out_len2);
-                    } else {
-                        gen_data2 = kcpp_base64_encode(out_data2, out_len2);
-                    }
-                    free(out_data2);
-                }
+                printf("Upscaling output image...\n");
+                gen_data = upscale_image_to_png_base64(upscaler_ctx, result_image, 2, meta_image_info);
+            } else {
+                gen_data = raw_image_to_png_base64(result_image, meta_image_info);
             }
-            else
-            {
-                sd_image_t *result_image = &results[i];
-                if(inputs.upscale && upscaler_ctx != nullptr)
-                {
-                    printf("Upscaling output image...\n");
-                    upscaled_image = upscale(upscaler_ctx, results[i], 2);
-                    result_image = &upscaled_image;
-                }
-                std::string meta_image_info = get_image_params(params, lora_meta, i);
-                gen_data = raw_image_to_png_base64(*result_image, meta_image_info);
-                jsoninfo["infotexts"][i] = meta_image_info;
-                jsoninfo["all_seeds"][i] = params.seed + i;
-                jsoninfo["all_prompts"][i] = params.prompt;
-                jsoninfo["all_negative_prompts"][i] = params.negative_prompt;
-            }
-
-            free(results[i].data);
-            results[i].data = NULL;
+            jsoninfo["infotexts"][i] = meta_image_info;
+            jsoninfo["all_seeds"][i] = params.seed + i;
+            jsoninfo["all_prompts"][i] = params.prompt;
+            jsoninfo["all_negative_prompts"][i] = params.negative_prompt;
         }
-    }
-
-    if(upscaled_image.data)
-    {
-        free(upscaled_image.data);
-        upscaled_image.data = nullptr;
+        free_sd_images(results, generated_num_results);
+        results = nullptr;
     }
 
     if (generated_audio) {
@@ -1627,8 +1639,6 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         input_audio.data = nullptr;
     }
 
-    free(results);
-
     total_img_gens += 1;
     if(!sd_is_quiet)
     {
@@ -1662,9 +1672,7 @@ sd_generation_outputs sdtype_upscale(const sd_upscale_inputs inputs)
     }
     upscale_src_buffer = load_image_from_b64(rawb64,nx,ny);
     sd_image_t source_img;
-    sd_image_t upscaled_image;
     source_img.data = nullptr;
-    upscaled_image.data = nullptr;
     std::string result;
     if(upscale_src_buffer)
     {
@@ -1673,10 +1681,7 @@ sd_generation_outputs sdtype_upscale(const sd_upscale_inputs inputs)
         source_img.channel = 3;
         source_img.data = upscale_src_buffer;
 
-        upscaled_image = upscale(upscaler_ctx, source_img, inputs.upscaling_resize);
-        result = raw_image_to_png_base64(upscaled_image);
-        free(upscaled_image.data);
-
+        result = upscale_image_to_png_base64(upscaler_ctx, source_img, inputs.upscaling_resize);
     }
 
     if (result == "") {
diff --git a/otherarch/sdcpp/src/conditioning/conditioner.hpp b/otherarch/sdcpp/src/conditioning/conditioner.hpp
index e037fe76b..d63303a82 100644
--- a/otherarch/sdcpp/src/conditioning/conditioner.hpp
+++ b/otherarch/sdcpp/src/conditioning/conditioner.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
+#ifndef __SD_CONDITIONING_CONDITIONER_HPP__
 #define __SD_CONDITIONING_CONDITIONER_HPP__
 
 #include <cmath>
@@ -1378,6 +1378,101 @@ struct T5CLIPEmbedder : public Conditioner {
     }
 };
 
+struct MiniT2IConditioner : public Conditioner {
+    T5UniGramTokenizer tokenizer;
+    std::shared_ptr<T5Runner> t5;
+    size_t prompt_length = 256;
+
+    MiniT2IConditioner(ggml_backend_t backend,
+                       const String2TensorStorage& tensor_storage_map      = {},
+                       std::shared_ptr<RunnerWeightManager> weight_manager = nullptr) {
+        bool use_t5 = false;
+        for (const auto& pair : tensor_storage_map) {
+            if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
+                use_t5 = true;
+                break;
+            }
+        }
+        if (!use_t5) {
+            LOG_WARN("IMPORTANT NOTICE: No MiniT2I T5 text encoder provided, cannot process prompts!");
+            return;
+        }
+        t5 = std::make_shared<T5Runner>(backend, tensor_storage_map, "text_encoders.t5xxl.transformer", false, weight_manager);
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
+    }
+
+    void set_max_graph_vram_bytes(size_t max_vram_bytes) override {
+        if (t5) {
+            t5->set_max_graph_vram_bytes(max_vram_bytes);
+        }
+    }
+
+    void set_stream_layers_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_stream_layers_enabled(enabled);
+        }
+    }
+
+    void set_flash_attention_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_flash_attention_enabled(enabled);
+        }
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        if (t5) {
+            t5->set_weight_adapter(adapter);
+        }
+    }
+
+    void runner_done() override {
+        if (t5) {
+            t5->runner_done();
+        }
+    }
+
+    SDCondition get_learned_condition(int n_threads,
+                                      const ConditionerParams& conditioner_params) override {
+        SDCondition result;
+        if (!t5) {
+            result.c_crossattn = sd::Tensor<float>::zeros({1024, static_cast<int64_t>(prompt_length)});
+            result.c_vector    = sd::Tensor<float>::zeros({static_cast<int64_t>(prompt_length)});
+            return result;
+        }
+
+        std::vector<int> tokens = tokenizer.encode(conditioner_params.text);
+        if (tokens.size() > prompt_length) {
+            tokens.resize(prompt_length);
+        }
+        std::vector<float> mask(tokens.size(), 1.0f);
+        while (tokens.size() < prompt_length) {
+            tokens.push_back(tokenizer.PAD_TOKEN_ID);
+            mask.push_back(0.0f);
+        }
+
+        sd::Tensor<int32_t> input_ids({static_cast<int64_t>(tokens.size())}, tokens);
+        std::vector<float> t5_mask(mask.size(), 0.0f);
+        for (size_t i = 0; i < mask.size(); ++i) {
+            t5_mask[i] = mask[i] > 0.0f ? 0.0f : -HUGE_VALF;
+        }
+        sd::Tensor<float> hidden_states = t5->compute(n_threads,
+                                                      input_ids,
+                                                      sd::Tensor<float>::from_vector(t5_mask),
+                                                      false,
+                                                      true,
+                                                      true);
+        GGML_ASSERT(!hidden_states.empty());
+        result.c_crossattn = std::move(hidden_states);
+        result.c_vector    = sd::Tensor<float>::from_vector(mask);
+        return result;
+    }
+};
+
 struct AnimaConditioner : public Conditioner {
     std::shared_ptr<BPETokenizer> qwen_tokenizer;
     T5UniGramTokenizer t5_tokenizer;
@@ -1518,7 +1613,7 @@ struct LLMEmbedder : public Conditioner {
             arch = LLM::LLMArch::GPT_OSS_20B;
         } else if (sd_version_is_pid(version)) {
             arch = LLM::LLMArch::GEMMA2_2B;
-        } else if (sd_version_is_ideogram4(version) || sd_version_is_boogu_image(version) || sd_version_is_krea2(version)) {
+        } else if (sd_version_is_ideogram4(version) || sd_version_is_boogu_image(version) || sd_version_is_sefi_image(version) || sd_version_is_krea2(version)) {
             arch = LLM::LLMArch::QWEN3_VL;
         } else if (sd_version_is_z_image(version) || version == VERSION_OVIS_IMAGE || version == VERSION_FLUX2_KLEIN) {
             arch = LLM::LLMArch::QWEN3;
@@ -1997,6 +2092,18 @@ struct LLMEmbedder : public Conditioner {
             prompt_attn_range.second = static_cast<int>(prompt.size());
 
             prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
+        } else if (sd_version_is_sefi_image(version)) {
+            prompt_template_encode_start_idx = 0;
+            min_length                       = 1024;
+            out_layers                       = {9, 18, 27};
+
+            prompt = "<|im_start|>user\n";
+
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+
+            prompt += "<|im_end|>\n<|im_start|>assistant\n";
         } else if (version == VERSION_OVIS_IMAGE) {
             prompt_template_encode_start_idx = 28;
             min_length                       = prompt_template_encode_start_idx + 256;
diff --git a/otherarch/sdcpp/src/convert.cpp b/otherarch/sdcpp/src/convert.cpp
index 27d377ec0..0b7fe2cfb 100644
--- a/otherarch/sdcpp/src/convert.cpp
+++ b/otherarch/sdcpp/src/convert.cpp
@@ -76,29 +76,22 @@ static bool load_tensors_for_export(ModelLoader& model_loader,
     return success;
 }
 
-bool convert(const char* input_path,
-             const char* vae_path,
-             const char* output_path,
-             sd_type_t output_type,
-             const char* tensor_type_rules,
-             bool convert_name) {
-    ModelLoader model_loader;
-
-    if (!model_loader.init_from_file(input_path)) {
-        LOG_ERROR("init model loader from file failed: '%s'", input_path);
+static bool init_convert_path(ModelLoader& model_loader, const char* path, const char* prefix, bool& loaded_any) {
+    if (path == nullptr || strlen(path) == 0) {
+        return true;
+    }
+    if (!model_loader.init_from_file(path, prefix)) {
+        LOG_ERROR("init model loader from file failed: '%s'", path);
         return false;
     }
+    loaded_any = true;
+    return true;
+}
 
-    if (vae_path != nullptr && strlen(vae_path) > 0) {
-        if (!model_loader.init_from_file(vae_path, "vae.")) {
-            LOG_ERROR("init model loader from file failed: '%s'", vae_path);
-            return false;
-        }
-    }
-    if (convert_name) {
-        model_loader.convert_tensors_name();
-    }
-
+static bool export_loaded_model(ModelLoader& model_loader,
+                                const char* output_path,
+                                sd_type_t output_type,
+                                const char* tensor_type_rules) {
     ggml_type type             = sd_type_to_ggml_type(output_type);
     bool output_is_safetensors = ends_with(output_path, ".safetensors");
     TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
@@ -136,3 +129,55 @@ bool convert(const char* input_path,
     ggml_free(ggml_ctx);
     return success;
 }
+
+bool convert_with_components(const char* model_path,
+                             const char* clip_l_path,
+                             const char* clip_g_path,
+                             const char* t5xxl_path,
+                             const char* diffusion_model_path,
+                             const char* vae_path,
+                             const char* output_path,
+                             sd_type_t output_type,
+                             const char* tensor_type_rules,
+                             bool convert_name) {
+    ModelLoader model_loader;
+    bool loaded_any = false;
+
+    if (!init_convert_path(model_loader, model_path, "", loaded_any) ||
+        !init_convert_path(model_loader, clip_l_path, "text_encoders.clip_l.transformer.", loaded_any) ||
+        !init_convert_path(model_loader, clip_g_path, "text_encoders.clip_g.transformer.", loaded_any) ||
+        !init_convert_path(model_loader, t5xxl_path, "text_encoders.t5xxl.transformer.", loaded_any) ||
+        !init_convert_path(model_loader, diffusion_model_path, "model.diffusion_model.", loaded_any) ||
+        !init_convert_path(model_loader, vae_path, "vae.", loaded_any)) {
+        return false;
+    }
+
+    if (!loaded_any) {
+        LOG_ERROR("no input model path provided for convert");
+        return false;
+    }
+
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
+
+    return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules);
+}
+
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
+    return convert_with_components(input_path,
+                                   nullptr,
+                                   nullptr,
+                                   nullptr,
+                                   nullptr,
+                                   vae_path,
+                                   output_path,
+                                   output_type,
+                                   tensor_type_rules,
+                                   convert_name);
+}
diff --git a/otherarch/sdcpp/src/core/ggml_extend.hpp b/otherarch/sdcpp/src/core/ggml_extend.hpp
index 9883103e2..48a7ad740 100644
--- a/otherarch/sdcpp/src/core/ggml_extend.hpp
+++ b/otherarch/sdcpp/src/core/ggml_extend.hpp
@@ -391,7 +391,7 @@ __STATIC_INLINE__ uint8_t* ggml_tensor_to_sd_image(ggml_tensor* input, uint8_t*
     int64_t width    = input->ne[0];
     int64_t height   = input->ne[1];
     int64_t channels = input->ne[2];
-    GGML_ASSERT(channels == 3 && input->type == GGML_TYPE_F32);
+    GGML_ASSERT(input->type == GGML_TYPE_F32);
     if (image_data == nullptr) {
         image_data = (uint8_t*)malloc(width * height * channels);
     }
@@ -1038,6 +1038,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_linear(ggml_context* ctx,
 }
 
 __STATIC_INLINE__ ggml_tensor* ggml_ext_pad_ext(ggml_context* ctx,
+                                                ggml_backend_t backend,
                                                 ggml_tensor* x,
                                                 int lp0,
                                                 int rp0,
@@ -1063,7 +1064,17 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_pad_ext(ggml_context* ctx,
     }
 
     if (lp0 != 0 || rp0 != 0 || lp1 != 0 || rp1 != 0 || lp2 != 0 || rp2 != 0 || lp3 != 0 || rp3 != 0) {
-        x = ggml_pad_ext(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+        ggml_tensor* padded = ggml_pad_ext(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+        if (backend == nullptr || ggml_backend_supports_op(backend, padded)) {
+            x = padded;
+        } else {
+            // Some backends (e.g. Metal) only implement right-padding for
+            // GGML_OP_PAD (see #850): pad right by lp+rp instead, then roll
+            // the padding around to the left. shift < ne always holds because
+            // ne grew by lp+rp.
+            x = ggml_pad_ext(ctx, x, 0, lp0 + rp0, 0, lp1 + rp1, 0, lp2 + rp2, 0, lp3 + rp3);
+            x = ggml_roll(ctx, x, lp0, lp1, lp2, lp3);
+        }
     }
     return x;
 }
@@ -1076,7 +1087,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_pad(ggml_context* ctx,
                                             int p3          = 0,
                                             bool circular_x = false,
                                             bool circular_y = false) {
-    return ggml_ext_pad_ext(ctx, x, 0, p0, 0, p1, 0, p2, 0, p3, circular_x, circular_y);
+    return ggml_ext_pad_ext(ctx, nullptr, x, 0, p0, 0, p1, 0, p2, 0, p3, circular_x, circular_y);
 }
 
 // w: [OC，IC, KH, KW]
@@ -1105,7 +1116,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_conv_2d(ggml_context* ctx,
     }
 
     if ((p0 != 0 || p1 != 0) && (circular_x || circular_y)) {
-        x  = ggml_ext_pad_ext(ctx, x, p0, p0, p1, p1, 0, 0, 0, 0, circular_x, circular_y);
+        x  = ggml_ext_pad_ext(ctx, nullptr, x, p0, p0, p1, p1, 0, 0, 0, 0, circular_x, circular_y);
         p0 = 0;
         p1 = 0;
     }
@@ -1130,6 +1141,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_conv_2d(ggml_context* ctx,
 // b: [OC,]
 // result: [N*OC, OD, OH, OW]
 __STATIC_INLINE__ ggml_tensor* ggml_ext_conv_3d(ggml_context* ctx,
+                                                ggml_backend_t backend,
                                                 ggml_tensor* x,
                                                 ggml_tensor* w,
                                                 ggml_tensor* b,
@@ -1159,7 +1171,21 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_conv_3d(ggml_context* ctx,
         x          = ggml_cont(ctx, ggml_permute(ctx, x, 0, 1, 3, 2));
         x          = ggml_reshape_4d(ctx, x, im2col->ne[1], im2col->ne[2], OD, OC * N);
     } else {
-        x = ggml_conv_3d(ctx, w, x, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2);
+        // ggml_conv_3d decomposes into GGML_OP_IM2COL_3D, which some backends
+        // (e.g. Metal, see #850) do not implement. Fall back to
+        // GGML_OP_CONV_3D on those backends.
+        bool im2col_3d_supported = true;
+        if (backend != nullptr) {
+            ggml_tensor* im2col = ggml_im2col_3d(ctx, w, x, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, w->type);
+            im2col_3d_supported = ggml_backend_supports_op(backend, im2col);
+        }
+        if (im2col_3d_supported) {
+            x = ggml_conv_3d(ctx, w, x, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2);
+        } else {
+            int64_t OC = w->ne[3] / IC;
+            int64_t N  = x->ne[3] / IC;
+            x          = ggml_conv_3d_direct(ctx, w, x, s0, s1, s2, p0, p1, p2, d0, d1, d2, (int)IC, (int)N, (int)OC);
+        }
     }
 
     if (b != nullptr) {
@@ -1362,6 +1388,9 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
         }
 
         auto out = ggml_flash_attn_ext(ctx, q_in, k_in, v_in, mask_in, scale / kv_scale, 0, 0);
+        if (!ggml_backend_supports_op(backend, out)) {
+            return nullptr;
+        }
         ggml_flash_attn_ext_set_prec(out, GGML_PREC_F32);
         if (kv_scale != 1.0f) {
             out = ggml_ext_scale(ctx, out, 1.0f / kv_scale);
@@ -1379,9 +1408,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
 
         if (can_use_flash_attn) {
             kqv = build_kqv(q, k, v, mask);
-            if (!ggml_backend_supports_op(backend, kqv)) {
-                kqv = nullptr;
-            } else {
+            if (kqv != nullptr) {
                 kqv = ggml_view_4d(ctx,
                                    kqv,
                                    d_head,
@@ -2473,7 +2500,10 @@ protected:
             sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
         }
 
-        ggml_status status = ggml_backend_graph_compute(runtime_backend, gf);
+        ggml_status status = sd_backend_graph_compute_with_eval_callback(runtime_backend,
+                                                                         gf,
+                                                                         sd_get_backend_eval_callback(),
+                                                                         sd_get_backend_eval_callback_data());
         if (status != GGML_STATUS_SUCCESS) {
             LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
             return std::nullopt;
@@ -3365,7 +3395,7 @@ public:
                 b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, b, prefix + "bias");
             }
         }
-        return ggml_ext_conv_3d(ctx->ggml_ctx, x, w, b, in_channels,
+        return ggml_ext_conv_3d(ctx->ggml_ctx, ctx->backend, x, w, b, in_channels,
                                 std::get<2>(stride), std::get<1>(stride), std::get<0>(stride),
                                 std::get<2>(padding), std::get<1>(padding), std::get<0>(padding),
                                 std::get<2>(dilation), std::get<1>(dilation), std::get<0>(dilation),
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
index f3e2cceba..f29bdb696 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
@@ -9,6 +9,7 @@
 #include <vector>
 
 #include "core/util.h"
+#include "ggml/src/ggml-impl.h"
 #include "stable-diffusion.h"
 
 static std::string trim_copy(const std::string& value) {
@@ -110,7 +111,67 @@ static std::string resolve_first_device_by_type(enum ggml_backend_dev_type type)
     if (dev == nullptr) {
         return "";
     }
-    return ggml_backend_dev_name(dev);
+    const char* dev_name = ggml_backend_dev_name(dev);
+    if (dev_name != nullptr && dev_name[0] != '\0') {
+        return dev_name;
+    }
+    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+    const char* reg_name   = reg != nullptr ? ggml_backend_reg_name(reg) : nullptr;
+    return reg_name != nullptr ? reg_name : "";
+}
+
+static ggml_backend_dev_t resolve_first_device_by_registry_name(const std::string& name) {
+    std::string lower = lower_copy(trim_copy(name));
+    if (lower == "metal") {
+        lower = "mtl";
+    }
+    if (lower.empty()) {
+        return nullptr;
+    }
+
+    const size_t device_count = ggml_backend_dev_count();
+    for (size_t i = 0; i < device_count; ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+        if (reg == nullptr) {
+            continue;
+        }
+        const char* reg_name = ggml_backend_reg_name(reg);
+        if (reg_name != nullptr && lower_copy(reg_name) == lower) {
+            return dev;
+        }
+    }
+    return nullptr;
+}
+
+static ggml_backend_dev_t resolve_device_by_name(const std::string& name) {
+    const std::string lower = lower_copy(trim_copy(name));
+    if (lower.empty()) {
+        return nullptr;
+    }
+
+    const size_t device_count = ggml_backend_dev_count();
+    for (size_t i = 0; i < device_count; ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        const char* dev_name   = ggml_backend_dev_name(dev);
+        if (dev_name != nullptr && lower_copy(dev_name) == lower) {
+            return dev;
+        }
+    }
+    return nullptr;
+}
+
+static std::string backend_device_name(ggml_backend_dev_t dev) {
+    if (dev == nullptr) {
+        return "";
+    }
+    const char* name = ggml_backend_dev_name(dev);
+    if (name != nullptr && name[0] != '\0') {
+        return name;
+    }
+    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+    const char* reg_name   = reg != nullptr ? ggml_backend_reg_name(reg) : nullptr;
+    return reg_name != nullptr ? reg_name : "";
 }
 
 static ggml_backend_buffer_t ggml_backend_tensor_buffer(const struct ggml_tensor* tensor) {
@@ -296,6 +357,10 @@ std::string sd_backend_resolve_name(const std::string& name) {
         return resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
     }
 
+    if (ggml_backend_dev_t dev = resolve_first_device_by_registry_name(requested)) {
+        return backend_device_name(dev);
+    }
+
     const size_t device_count = ggml_backend_dev_count();
     for (size_t i = 0; i < device_count; ++i) {
         ggml_backend_dev_t dev = ggml_backend_dev_get(i);
@@ -328,7 +393,20 @@ static ggml_backend_t init_named_backend(const std::string& name) {
         return ggml_backend_init_best();
     }
 
+    if (ggml_backend_dev_t dev = resolve_device_by_name(name)) {
+        return ggml_backend_dev_init(dev, nullptr);
+    }
+    if (ggml_backend_dev_t dev = resolve_first_device_by_registry_name(name)) {
+        return ggml_backend_dev_init(dev, nullptr);
+    }
+
     std::string resolved = sd_backend_resolve_name(name);
+    if (ggml_backend_dev_t dev = resolve_device_by_name(resolved)) {
+        return ggml_backend_dev_init(dev, nullptr);
+    }
+    if (ggml_backend_dev_t dev = resolve_first_device_by_registry_name(resolved)) {
+        return ggml_backend_dev_init(dev, nullptr);
+    }
     if (resolved.empty()) {
         return nullptr;
     }
@@ -364,6 +442,68 @@ bool sd_backend_cpu_set_n_threads(ggml_backend_t backend, int n_threads) {
     return false;
 }
 
+static ggml_cgraph sd_ggml_graph_view(ggml_cgraph* cgraph0, int i0, int i1) {
+    ggml_cgraph cgraph = {
+        /*.size             =*/0,
+        /*.n_nodes          =*/i1 - i0,
+        /*.n_leafs          =*/0,
+        /*.nodes            =*/cgraph0->nodes + i0,
+        /*.grads            =*/nullptr,
+        /*.grad_accs        =*/nullptr,
+        /*.leafs            =*/nullptr,
+        /*.use_counts       =*/cgraph0->use_counts,
+        /*.visited_hash_set =*/cgraph0->visited_hash_set,
+        /*.order            =*/cgraph0->order,
+        /*.uid              =*/0,
+    };
+    return cgraph;
+}
+
+ggml_status sd_backend_graph_compute_with_eval_callback(ggml_backend_t backend,
+                                                        ggml_cgraph* gf,
+                                                        sd_graph_eval_callback_t callback_eval,
+                                                        void* callback_eval_user_data) {
+    if (callback_eval == nullptr) {
+        return ggml_backend_graph_compute(backend, gf);
+    }
+
+    ggml_status status = GGML_STATUS_SUCCESS;
+    const int n_nodes  = ggml_graph_n_nodes(gf);
+    bool stopped       = false;
+
+    for (int j0 = 0; j0 < n_nodes; ++j0) {
+        ggml_tensor* t = ggml_graph_node(gf, j0);
+        bool need      = callback_eval(t, true, callback_eval_user_data);
+        int j1         = j0;
+
+        while (!need && j1 < n_nodes - 1) {
+            t    = ggml_graph_node(gf, ++j1);
+            need = callback_eval(t, true, callback_eval_user_data);
+        }
+
+        ggml_cgraph gv = sd_ggml_graph_view(gf, j0, j1 + 1);
+        status         = ggml_backend_graph_compute_async(backend, &gv);
+        if (status != GGML_STATUS_SUCCESS) {
+            break;
+        }
+
+        ggml_backend_synchronize(backend);
+
+        if (need && !callback_eval(t, false, callback_eval_user_data)) {
+            stopped = true;
+            break;
+        }
+
+        j0 = j1;
+    }
+
+    ggml_backend_synchronize(backend);
+    if (stopped && status == GGML_STATUS_SUCCESS) {
+        status = GGML_STATUS_ABORTED;
+    }
+    return status;
+}
+
 const char* sd_get_system_info() {
     static std::string cache_info = []() -> std::string {
         ggml_backend_load_all_once();
@@ -599,7 +739,7 @@ bool SDBackendManager::validate(std::string* error) const {
             }
             return false;
         }
-        if (!sd_backend_resolve_name(name).empty()) {
+        if (!sd_backend_resolve_name(name).empty() || resolve_first_device_by_registry_name(name) != nullptr) {
             return true;
         }
         if (error != nullptr) {
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.h b/otherarch/sdcpp/src/core/ggml_extend_backend.h
index 9aecf97c0..19b71d432 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.h
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.h
@@ -9,6 +9,7 @@
 
 #include "ggml-backend.h"
 #include "ggml.h"
+#include "stable-diffusion.h"
 
 enum class SDBackendModule {
     DIFFUSION,
@@ -71,6 +72,10 @@ bool sd_backend_is(ggml_backend_t backend, const std::string& name);
 bool sd_backend_is_cpu(ggml_backend_t backend);
 ggml_backend_t sd_backend_cpu_init();
 bool sd_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+ggml_status sd_backend_graph_compute_with_eval_callback(ggml_backend_t backend,
+                                                        ggml_cgraph* gf,
+                                                        sd_graph_eval_callback_t callback_eval,
+                                                        void* callback_eval_user_data);
 std::string sd_backend_resolve_name(const std::string& name);
 const char* sd_backend_module_name(SDBackendModule module);
 void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
diff --git a/otherarch/sdcpp/src/core/util.cpp b/otherarch/sdcpp/src/core/util.cpp
index 05c308d9b..cf1460cb9 100644
--- a/otherarch/sdcpp/src/core/util.cpp
+++ b/otherarch/sdcpp/src/core/util.cpp
@@ -360,6 +360,9 @@ int sd_preview_interval              = 1;
 bool sd_preview_denoised             = true;
 bool sd_preview_noisy                = false;
 
+static sd_graph_eval_callback_t sd_backend_eval_cb = nullptr;
+static void* sd_backend_eval_cb_data               = nullptr;
+
 std::u32string utf8_to_utf32(const std::string& utf8_str) {
     std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
     return converter.from_bytes(utf8_str);
@@ -662,6 +665,11 @@ void sd_set_preview_callback(sd_preview_cb_t cb, preview_t mode, int interval, b
     sd_preview_noisy    = noisy;
 }
 
+void sd_set_backend_eval_callback(sd_graph_eval_callback_t cb, void* data) {
+    sd_backend_eval_cb      = cb;
+    sd_backend_eval_cb_data = data;
+}
+
 sd_preview_cb_t sd_get_preview_callback() {
     return sd_preview_cb;
 }
@@ -682,6 +690,14 @@ bool sd_should_preview_noisy() {
     return sd_preview_noisy;
 }
 
+sd_graph_eval_callback_t sd_get_backend_eval_callback() {
+    return sd_backend_eval_cb;
+}
+
+void* sd_get_backend_eval_callback_data() {
+    return sd_backend_eval_cb_data;
+}
+
 sd_progress_cb_t sd_get_progress_callback() {
     return sd_progress_cb;
 }
diff --git a/otherarch/sdcpp/src/core/util.h b/otherarch/sdcpp/src/core/util.h
index ec4c45595..07968cbcd 100644
--- a/otherarch/sdcpp/src/core/util.h
+++ b/otherarch/sdcpp/src/core/util.h
@@ -101,6 +101,9 @@ int sd_get_preview_interval();
 bool sd_should_preview_denoised();
 bool sd_should_preview_noisy();
 
+sd_graph_eval_callback_t sd_get_backend_eval_callback();
+void* sd_get_backend_eval_callback_data();
+
 // test if the backend is a specific one, e.g. "CUDA", "ROCm", "Vulkan" etc.
 bool sd_backend_is(ggml_backend_t backend, const std::string& name);
 
diff --git a/otherarch/sdcpp/src/model.h b/otherarch/sdcpp/src/model.h
index cce309138..2de82006a 100644
--- a/otherarch/sdcpp/src/model.h
+++ b/otherarch/sdcpp/src/model.h
@@ -36,6 +36,7 @@ enum SDVersion {
     VERSION_WAN2_2_I2V,
     VERSION_WAN2_2_TI2V,
     VERSION_QWEN_IMAGE,
+    VERSION_QWEN_IMAGE_LAYERED,
     VERSION_ANIMA,
     VERSION_FLUX2,
     VERSION_FLUX2_KLEIN,
@@ -46,9 +47,11 @@ enum SDVersion {
     VERSION_OVIS_IMAGE,
     VERSION_ERNIE_IMAGE,
     VERSION_LENS,
+    VERSION_MINIT2I,
     VERSION_LONGCAT,
     VERSION_PID,
     VERSION_IDEOGRAM4,
+    VERSION_SEFI_IMAGE,
     VERSION_KREA2,
     VERSION_ESRGAN,
     VERSION_COUNT,
@@ -125,7 +128,7 @@ static inline bool sd_version_is_wan(SDVersion version) {
 }
 
 static inline bool sd_version_is_qwen_image(SDVersion version) {
-    if (version == VERSION_QWEN_IMAGE) {
+    if (version == VERSION_QWEN_IMAGE || version == VERSION_QWEN_IMAGE_LAYERED) {
         return true;
     }
     return false;
@@ -173,6 +176,13 @@ static inline bool sd_version_is_lens(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_minit2i(SDVersion version) {
+    if (version == VERSION_MINIT2I) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_pid(SDVersion version) {
     if (version == VERSION_PID) {
         return true;
@@ -187,6 +197,13 @@ static inline bool sd_version_is_ideogram4(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_sefi_image(SDVersion version) {
+    if (version == VERSION_SEFI_IMAGE) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_krea2(SDVersion version) {
     if (version == VERSION_KREA2) {
         return true;
@@ -202,7 +219,14 @@ static inline bool sd_version_uses_flux_vae(SDVersion version) {
 }
 
 static inline bool sd_version_uses_flux2_vae(SDVersion version) {
-    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version)) {
+    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version) || sd_version_is_sefi_image(version)) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_uses_wan_vae(SDVersion version) {
+    if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_krea2(version) || sd_version_is_anima(version)) {
         return true;
     }
     return false;
@@ -232,9 +256,11 @@ static inline bool sd_version_is_dit(SDVersion version) {
         sd_version_is_boogu_image(version) ||
         sd_version_is_ernie_image(version) ||
         sd_version_is_lens(version) ||
+        sd_version_is_minit2i(version) ||
         sd_version_is_longcat(version) ||
         sd_version_is_pid(version) ||
         sd_version_is_ideogram4(version) ||
+        sd_version_is_sefi_image(version) ||
         sd_version_is_krea2(version)) {
         return true;
     }
diff --git a/otherarch/sdcpp/src/model/common/rope.hpp b/otherarch/sdcpp/src/model/common/rope.hpp
index 2e21ef7c2..d6cbb2e6a 100644
--- a/otherarch/sdcpp/src/model/common/rope.hpp
+++ b/otherarch/sdcpp/src/model/common/rope.hpp
@@ -12,6 +12,16 @@ namespace Rope {
         ErnieImage,
     };
 
+    enum class RefIndexMode {
+        FIXED,
+        INCREASE,
+        DECREASE,
+    };
+
+    __STATIC_INLINE__ RefIndexMode ref_index_mode_from_bool(bool increase_ref_index) {
+        return increase_ref_index ? RefIndexMode::INCREASE : RefIndexMode::FIXED;
+    }
+
     template <class T>
     __STATIC_INLINE__ std::vector<T> linspace(T start, T end, int num) {
         std::vector<T> result(num);
@@ -346,7 +356,7 @@ namespace Rope {
                                                                    int axes_dim_num,
                                                                    int start_index,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
-                                                                   bool increase_ref_index,
+                                                                   RefIndexMode ref_index_mode,
                                                                    float ref_index_scale,
                                                                    bool scale_rope,
                                                                    int base_offset = 0) {
@@ -357,13 +367,15 @@ namespace Rope {
         for (ggml_tensor* ref : ref_latents) {
             int h_offset = 0;
             int w_offset = 0;
-            if (!increase_ref_index) {
+            if (ref_index_mode == RefIndexMode::FIXED) {
                 if (ref->ne[1] + curr_h_offset > ref->ne[0] + curr_w_offset) {
                     w_offset = curr_w_offset;
                 } else {
                     h_offset = curr_h_offset;
                 }
                 scale_rope = false;
+            } else if (ref_index_mode == RefIndexMode::DECREASE) {
+                index--;
             }
 
             auto ref_ids = gen_flux_img_ids(static_cast<int>(ref->ne[1]),
@@ -377,7 +389,7 @@ namespace Rope {
                                             scale_rope);
             ids          = concat_ids(ids, ref_ids, bs);
 
-            if (increase_ref_index) {
+            if (ref_index_mode == RefIndexMode::INCREASE) {
                 index++;
             }
 
@@ -395,7 +407,7 @@ namespace Rope {
                                                                    int context_len,
                                                                    std::set<int> txt_arange_dims,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
-                                                                   bool increase_ref_index,
+                                                                   RefIndexMode ref_index_mode,
                                                                    float ref_index_scale,
                                                                    bool is_longcat) {
         int x_index = is_longcat ? 1 : 0;
@@ -406,7 +418,7 @@ namespace Rope {
 
         auto ids = concat_ids(txt_ids, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, x_index + 1, ref_latents, increase_ref_index, ref_index_scale, false, offset);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, x_index + 1, ref_latents, ref_index_mode, ref_index_scale, false, offset);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -420,7 +432,7 @@ namespace Rope {
                                                      int context_len,
                                                      std::set<int> txt_arange_dims,
                                                      const std::vector<ggml_tensor*>& ref_latents,
-                                                     bool increase_ref_index,
+                                                     RefIndexMode ref_index_mode,
                                                      float ref_index_scale,
                                                      int theta,
                                                      bool circular_h,
@@ -435,7 +447,7 @@ namespace Rope {
                                                            context_len,
                                                            txt_arange_dims,
                                                            ref_latents,
-                                                           increase_ref_index,
+                                                           ref_index_mode,
                                                            ref_index_scale,
                                                            is_longcat);
         std::vector<std::vector<int>> wrap_dims;
@@ -481,17 +493,64 @@ namespace Rope {
         return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
     }
 
-    __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
+                                                                  int h,
+                                                                  int w,
+                                                                  int pt,
+                                                                  int ph,
+                                                                  int pw,
+                                                                  int bs,
+                                                                  int t_offset    = 0,
+                                                                  int h_offset    = 0,
+                                                                  int w_offset    = 0,
+                                                                  bool scale_rope = false) {
+        int t_len = (t + (pt / 2)) / pt;
+        int h_len = (h + (ph / 2)) / ph;
+        int w_len = (w + (pw / 2)) / pw;
+
+        std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));
+
+        if (scale_rope) {
+            h_offset -= h_len / 2;
+            w_offset -= w_len / 2;
+        }
+
+        std::vector<float> t_ids = linspace<float>(1.f * t_offset, 1.f * t_len - 1 + t_offset, t_len);
+        std::vector<float> h_ids = linspace<float>(1.f * h_offset, 1.f * h_len - 1 + h_offset, h_len);
+        std::vector<float> w_ids = linspace<float>(1.f * w_offset, 1.f * w_len - 1 + w_offset, w_len);
+
+        for (int i = 0; i < t_len; ++i) {
+            for (int j = 0; j < h_len; ++j) {
+                for (int k = 0; k < w_len; ++k) {
+                    int idx         = i * h_len * w_len + j * w_len + k;
+                    vid_ids[idx][0] = t_ids[i];
+                    vid_ids[idx][1] = h_ids[j];
+                    vid_ids[idx][2] = w_ids[k];
+                }
+            }
+        }
+
+        std::vector<std::vector<float>> vid_ids_repeated(bs * vid_ids.size(), std::vector<float>(3));
+        for (int i = 0; i < bs; ++i) {
+            for (int j = 0; j < vid_ids.size(); ++j) {
+                vid_ids_repeated[i * vid_ids.size() + j] = vid_ids[j];
+            }
+        }
+        return vid_ids_repeated;
+    }
+
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int t,
+                                                                         int h,
                                                                          int w,
                                                                          int patch_size,
                                                                          int bs,
                                                                          int context_len,
                                                                          const std::vector<ggml_tensor*>& ref_latents,
-                                                                         bool increase_ref_index) {
+                                                                         RefIndexMode ref_index_mode) {
         int h_len        = (h + (patch_size / 2)) / patch_size;
         int w_len        = (w + (patch_size / 2)) / patch_size;
-        int txt_id_start = std::max(h_len, w_len);
-        auto txt_ids     = linspace<float>(1.f * txt_id_start, 1.f * context_len + txt_id_start, context_len);
+        int txt_id_start = std::max(h_len, w_len) / 2;
+        auto txt_ids     = linspace<float>(1.f * txt_id_start, 1.f * txt_id_start + context_len - 1, context_len);
         std::vector<std::vector<float>> txt_ids_repeated(bs * context_len, std::vector<float>(3));
         for (int i = 0; i < bs; ++i) {
             for (int j = 0; j < txt_ids.size(); ++j) {
@@ -499,28 +558,30 @@ namespace Rope {
             }
         }
         int axes_dim_num = 3;
-        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num, 0, 0, 0, true);
+        auto img_ids     = gen_vid_ids(t, h, w, 1, patch_size, patch_size, bs, 0, 0, 0, true);
         auto ids         = concat_ids(txt_ids_repeated, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, 1, ref_latents, increase_ref_index, 1.f, true);
-            ids           = concat_ids(ids, refs_ids, bs);
+            int ref_start_index = ref_index_mode == RefIndexMode::DECREASE ? 0 : 1;
+            auto refs_ids       = gen_refs_ids(patch_size, bs, axes_dim_num, ref_start_index, ref_latents, ref_index_mode, 1.f, true);
+            ids                 = concat_ids(ids, refs_ids, bs);
         }
         return ids;
     }
 
     // Generate qwen_image positional embeddings
-    __STATIC_INLINE__ std::vector<float> gen_qwen_image_pe(int h,
+    __STATIC_INLINE__ std::vector<float> gen_qwen_image_pe(int t,
+                                                           int h,
                                                            int w,
                                                            int patch_size,
                                                            int bs,
                                                            int context_len,
                                                            const std::vector<ggml_tensor*>& ref_latents,
-                                                           bool increase_ref_index,
+                                                           RefIndexMode ref_index_mode,
                                                            int theta,
                                                            bool circular_h,
                                                            bool circular_w,
                                                            const std::vector<int>& axes_dim) {
-        std::vector<std::vector<float>> ids = gen_qwen_image_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
+        std::vector<std::vector<float>> ids = gen_qwen_image_ids(t, h, w, patch_size, bs, context_len, ref_latents, ref_index_mode);
         std::vector<std::vector<int>> wrap_dims;
         // This logic simply stores the (pad and patch_adjusted) sizes of images so we can make sure rope correctly tiles
         if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
@@ -533,7 +594,7 @@ namespace Rope {
                 // Track per-token wrap lengths for the row/column axes so only spatial tokens become periodic.
                 wrap_dims.assign(axes_dim.size(), std::vector<int>(total_tokens / bs, 0));
                 size_t cursor           = context_len;  // ignore text tokens
-                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                const size_t img_tokens = static_cast<size_t>(t) * static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
                 for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
                     if (circular_h) {
                         wrap_dims[1][cursor + token_i] = h_len;
@@ -684,46 +745,6 @@ namespace Rope {
         return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims, EmbedNDLayout::ErnieImage);
     }
 
-    __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
-                                                                  int h,
-                                                                  int w,
-                                                                  int pt,
-                                                                  int ph,
-                                                                  int pw,
-                                                                  int bs,
-                                                                  int t_offset = 0,
-                                                                  int h_offset = 0,
-                                                                  int w_offset = 0) {
-        int t_len = (t + (pt / 2)) / pt;
-        int h_len = (h + (ph / 2)) / ph;
-        int w_len = (w + (pw / 2)) / pw;
-
-        std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));
-
-        std::vector<float> t_ids = linspace<float>(1.f * t_offset, 1.f * t_len - 1 + t_offset, t_len);
-        std::vector<float> h_ids = linspace<float>(1.f * h_offset, 1.f * h_len - 1 + h_offset, h_len);
-        std::vector<float> w_ids = linspace<float>(1.f * w_offset, 1.f * w_len - 1 + w_offset, w_len);
-
-        for (int i = 0; i < t_len; ++i) {
-            for (int j = 0; j < h_len; ++j) {
-                for (int k = 0; k < w_len; ++k) {
-                    int idx         = i * h_len * w_len + j * w_len + k;
-                    vid_ids[idx][0] = t_ids[i];
-                    vid_ids[idx][1] = h_ids[j];
-                    vid_ids[idx][2] = w_ids[k];
-                }
-            }
-        }
-
-        std::vector<std::vector<float>> vid_ids_repeated(bs * vid_ids.size(), std::vector<float>(3));
-        for (int i = 0; i < bs; ++i) {
-            for (int j = 0; j < vid_ids.size(); ++j) {
-                vid_ids_repeated[i * vid_ids.size() + j] = vid_ids[j];
-            }
-        }
-        return vid_ids_repeated;
-    }
-
     // Generate wan positional embeddings
     __STATIC_INLINE__ std::vector<float> gen_wan_pe(int t,
                                                     int h,
@@ -785,7 +806,8 @@ namespace Rope {
                                                                       int context_len,
                                                                       int seq_multi_of,
                                                                       const std::vector<ggml_tensor*>& ref_latents,
-                                                                      bool increase_ref_index) {
+                                                                      RefIndexMode ref_index_mode) {
+        SD_UNUSED(ref_index_mode);
         int padded_context_len = context_len + bound_mod(context_len, seq_multi_of);
         auto txt_ids           = std::vector<std::vector<float>>(bs * padded_context_len, std::vector<float>(3, 0.0f));
         for (int i = 0; i < bs * padded_context_len; i++) {
@@ -816,12 +838,12 @@ namespace Rope {
                                                         int context_len,
                                                         int seq_multi_of,
                                                         const std::vector<ggml_tensor*>& ref_latents,
-                                                        bool increase_ref_index,
+                                                        RefIndexMode ref_index_mode,
                                                         int theta,
                                                         bool circular_h,
                                                         bool circular_w,
                                                         const std::vector<int>& axes_dim) {
-        std::vector<std::vector<float>> ids = gen_z_image_ids(h, w, patch_size, bs, context_len, seq_multi_of, ref_latents, increase_ref_index);
+        std::vector<std::vector<float>> ids = gen_z_image_ids(h, w, patch_size, bs, context_len, seq_multi_of, ref_latents, ref_index_mode);
         std::vector<std::vector<int>> wrap_dims;
         if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
             int pad_h = (patch_size - (h % patch_size)) % patch_size;
diff --git a/otherarch/sdcpp/src/model/diffusion/anima.hpp b/otherarch/sdcpp/src/model/diffusion/anima.hpp
index 504904d41..1c1ec2ed2 100644
--- a/otherarch/sdcpp/src/model/diffusion/anima.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/anima.hpp
@@ -227,7 +227,6 @@ namespace Anima {
             k4 = k_norm->forward(ctx, k4);
 
             ggml_tensor* attn_out = nullptr;
-            float scale           = (sd_backend_is(ctx->backend, "Vulkan") && ctx->flash_attn_enabled) ? 1.0f / 32.0f : 1.0f;
             if (pe_q != nullptr || pe_k != nullptr) {
                 if (pe_q == nullptr) {
                     pe_q = pe_k;
@@ -245,8 +244,7 @@ namespace Anima {
                                                      num_heads,
                                                      nullptr,
                                                      true,
-                                                     ctx->flash_attn_enabled,
-                                                     scale);
+                                                     ctx->flash_attn_enabled);
             } else {
                 auto q_flat = ggml_reshape_3d(ctx->ggml_ctx, q4, head_dim * num_heads, L_q, N);
                 auto k_flat = ggml_reshape_3d(ctx->ggml_ctx, k4, head_dim * num_heads, L_k, N);
@@ -258,8 +256,7 @@ namespace Anima {
                                                      num_heads,
                                                      nullptr,
                                                      false,
-                                                     ctx->flash_attn_enabled,
-                                                     scale);
+                                                     ctx->flash_attn_enabled);
             }
 
             return out_proj->forward(ctx, attn_out);
@@ -615,7 +612,7 @@ namespace Anima {
                                           0,
                                           {},
                                           empty_ref_latents,
-                                          false,
+                                          Rope::RefIndexMode::FIXED,
                                           1.0f,
                                           false);
 
diff --git a/otherarch/sdcpp/src/model/diffusion/control.hpp b/otherarch/sdcpp/src/model/diffusion/control.hpp
index 57e3616f2..eeb8f5109 100644
--- a/otherarch/sdcpp/src/model/diffusion/control.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/control.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_DIFFUSION_CONTROL_HPP__
+#ifndef __SD_MODEL_DIFFUSION_CONTROL_HPP__
 #define __SD_MODEL_DIFFUSION_CONTROL_HPP__
 
 #include "model/common/block.hpp"
diff --git a/otherarch/sdcpp/src/model/diffusion/dit.hpp b/otherarch/sdcpp/src/model/diffusion/dit.hpp
index c18518564..a88e7546d 100644
--- a/otherarch/sdcpp/src/model/diffusion/dit.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/dit.hpp
@@ -135,23 +135,23 @@ namespace DiT {
         return x;
     }
 
-    inline ggml_tensor* unpatchify(ggml_context* ctx,
-                                   ggml_tensor* x,
-                                   int64_t t_len,
-                                   int64_t h_len,
-                                   int64_t w_len,
-                                   int pt,
-                                   int ph,
-                                   int pw) {
-        // x: [N, t_len*h_len*w_len, pt*ph*pw*C]
+    inline ggml_tensor* unpatchify_3d(ggml_context* ctx,
+                                      ggml_tensor* x,
+                                      int64_t t_len,
+                                      int64_t h_len,
+                                      int64_t w_len,
+                                      int pt,
+                                      int ph,
+                                      int pw) {
+        // x: [N, t_len*h_len*w_len, C*pt*ph*pw]
         // return: [N*C, t_len*pt, h_len*ph, w_len*pw]
-        int64_t N = x->ne[3];
+        int64_t N = x->ne[2];
         int64_t C = x->ne[0] / pt / ph / pw;
 
         GGML_ASSERT(C * pt * ph * pw == x->ne[0]);
 
-        x = ggml_reshape_4d(ctx, x, C, pw * ph * pt, w_len * h_len * t_len, N);  // [N, t_len*h_len*w_len, pt*ph*pw, C]
-        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 1, 2, 0, 3));      // [N, C, t_len*h_len*w_len, pt*ph*pw]
+        x = ggml_reshape_4d(ctx, x, pw * ph * pt, C, w_len * h_len * t_len, N);  // [N, t_len*h_len*w_len, C, pt*ph*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));      // [N, C, t_len*h_len*w_len, pt*ph*pw]
         x = ggml_reshape_4d(ctx, x, pw, ph * pt, w_len, h_len * t_len * C * N);  // [N*C*t_len*h_len, w_len, pt*ph, pw]
         x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));      // [N*C*t_len*h_len, pt*ph, w_len, pw]
         x = ggml_reshape_4d(ctx, x, pw * w_len, ph, pt, h_len * t_len * C * N);  // [N*C*t_len*h_len, pt, ph, w_len*pw]
diff --git a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
index 0427b3b38..12fcada59 100644
--- a/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/ernie_image.hpp
@@ -162,8 +162,6 @@ namespace ErnieImage {
             int64_t S = x->ne[1];
             int64_t N = x->ne[2];
 
-            float scale = (sd_backend_is(ctx->backend, "Vulkan") && ctx->flash_attn_enabled) ? 1.0f / 32.0f : 1.0f;
-
             auto q = to_q->forward(ctx, x);
             auto k = to_k->forward(ctx, x);
             auto v = to_v->forward(ctx, x);
@@ -184,7 +182,7 @@ namespace ErnieImage {
             k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 0, 2, 1, 3));  // [N, heads, S, head_dim]
             k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]);
 
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, ctx->flash_attn_enabled, scale);  // [N, S, hidden_size]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, ctx->flash_attn_enabled);  // [N, S, hidden_size]
             x = to_out_0->forward(ctx, x);
             return x;
         }
diff --git a/otherarch/sdcpp/src/model/diffusion/flux.hpp b/otherarch/sdcpp/src/model/diffusion/flux.hpp
index b5e6c63bf..e3cd6ba7c 100644
--- a/otherarch/sdcpp/src/model/diffusion/flux.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/flux.hpp
@@ -8,6 +8,7 @@
 #include "model/common/rope.hpp"
 #include "model/diffusion/dit.hpp"
 #include "model/diffusion/model.hpp"
+#include "model/diffusion/sefi_image.hpp"
 #include "model_loader.h"
 
 #define FLUX_GRAPH_SIZE 10240
@@ -26,6 +27,9 @@ namespace Flux {
     struct FluxConfig {
         SDVersion version         = VERSION_FLUX;
         bool is_chroma            = false;
+        bool is_sefi              = false;
+        int64_t semantic_channels = 0;
+        float sefi_delta_t        = 0.1f;
         int patch_size            = 2;
         int64_t in_channels       = 64;
         int64_t out_channels      = 64;
@@ -88,6 +92,21 @@ namespace Flux {
                 config.share_modulation = true;
                 config.ref_index_scale  = 10.f;
                 config.use_mlp_silu_act = true;
+            } else if (sd_version_is_sefi_image(version)) {
+                config.is_sefi           = true;
+                config.semantic_channels = 16;
+                config.in_channels       = 128 + config.semantic_channels;
+                config.patch_size        = 1;
+                config.out_channels      = 128 + config.semantic_channels;
+                config.mlp_ratio         = 3.f;
+                config.theta             = 2000;
+                config.axes_dim          = {32, 32, 32, 32};
+                config.vec_in_dim        = 0;
+                config.qkv_bias          = false;
+                config.disable_bias      = true;
+                config.share_modulation  = true;
+                config.ref_index_scale   = 10.f;
+                config.use_mlp_silu_act  = true;
             } else if (sd_version_is_longcat(version)) {
                 config.context_in_dim = 3584;
                 config.vec_in_dim     = 0;
@@ -723,8 +742,8 @@ namespace Flux {
 
                 auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, 2 * hidden_size]
                 auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, 2, 0);
-                shift      = m_vec[0];  // [N, hidden_size]
-                scale      = m_vec[1];  // [N, hidden_size]
+                shift      = m_vec[0];
+                scale      = m_vec[1];
             }
 
             x = Flux::modulate(ctx->ggml_ctx, norm_final->forward(ctx, x), shift, scale);
@@ -902,6 +921,8 @@ namespace Flux {
             }
             if (config.is_chroma) {
                 blocks["distilled_guidance_layer"] = std::make_shared<ChromaApproximator>(config.in_dim, config.hidden_size);
+            } else if (config.is_sefi) {
+                blocks["dual_time_embed"] = std::make_shared<SefiImage::SefiDualTimestepEmbeddings>(256, config.hidden_size);
             } else {
                 blocks["time_in"] = std::make_shared<MLPEmbedder>(256, config.hidden_size, !config.disable_bias);
                 if (config.vec_in_dim > 0) {
@@ -1027,6 +1048,11 @@ namespace Flux {
                 if (y != nullptr) {
                     txt_img_mask = ggml_pad(ctx->ggml_ctx, y, static_cast<int>(img->ne[1]), 0, 0, 0);
                 }
+            } else if (config.is_sefi) {
+                auto dual_time_embed = std::dynamic_pointer_cast<SefiImage::SefiDualTimestepEmbeddings>(blocks["dual_time_embed"]);
+                auto timestep_sem    = ggml_view_1d(ctx->ggml_ctx, timesteps, 1, 0);
+                auto timestep_tex    = ggml_view_1d(ctx->ggml_ctx, timesteps, 1, ggml_element_size(timesteps));
+                vec                  = dual_time_embed->forward(ctx, timestep_sem, timestep_tex);
             } else {
                 auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
                 vec          = time_in->forward(ctx, ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1000.f));
@@ -1459,7 +1485,7 @@ namespace Flux {
                                  const sd::Tensor<float>& y_tensor                        = {},
                                  const sd::Tensor<float>& guidance_tensor                 = {},
                                  const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
-                                 bool increase_ref_index                                  = false,
+                                 Rope::RefIndexMode ref_index_mode                        = Rope::RefIndexMode::FIXED,
                                  std::vector<int> skip_layers                             = {},
                                  const sd::Tensor<float>& pulid_id_tensor                 = {},
                                  float pulid_id_weight                                    = 1.0f) {
@@ -1500,9 +1526,9 @@ namespace Flux {
                 set_backend_tensor_data(mod_index_arange, mod_index_arange_vec.data());
             }
             std::set<int> txt_arange_dims;
-            if (sd_version_is_flux2(version)) {
-                txt_arange_dims    = {3};
-                increase_ref_index = true;
+            if (sd_version_is_flux2(version) || sd_version_is_sefi_image(version)) {
+                txt_arange_dims = {3};
+                ref_index_mode  = Rope::RefIndexMode::INCREASE;
             } else if (version == VERSION_OVIS_IMAGE) {
                 txt_arange_dims = {1, 2};
             }
@@ -1513,7 +1539,7 @@ namespace Flux {
                                             static_cast<int>(context->ne[1]),
                                             txt_arange_dims,
                                             ref_latents,
-                                            increase_ref_index,
+                                            ref_index_mode,
                                             config.ref_index_scale,
                                             config.theta,
                                             circular_y_enabled,
@@ -1573,7 +1599,7 @@ namespace Flux {
                                   const sd::Tensor<float>& y                        = {},
                                   const sd::Tensor<float>& guidance                 = {},
                                   const std::vector<sd::Tensor<float>>& ref_latents = {},
-                                  bool increase_ref_index                           = false,
+                                  Rope::RefIndexMode ref_index_mode                 = Rope::RefIndexMode::FIXED,
                                   std::vector<int> skip_layers                      = std::vector<int>(),
                                   const sd::Tensor<float>& pulid_id                 = {},
                                   float pulid_id_weight                             = 1.0f) {
@@ -1584,7 +1610,7 @@ namespace Flux {
             // guidance: [N, ]
             // pulid_id: empty (no injection) or [N, num_id_tokens=32, kv_dim=2048]
             auto get_graph = [&]() -> ggml_cgraph* {
-                return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, increase_ref_index, skip_layers, pulid_id, pulid_id_weight);
+                return build_graph(x, timesteps, context, c_concat, y, guidance, ref_latents, ref_index_mode, skip_layers, pulid_id, pulid_id_weight);
             };
 
             auto result = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
@@ -1606,7 +1632,7 @@ namespace Flux {
                            tensor_or_empty(diffusion_params.y),
                            tensor_or_empty(extra->guidance),
                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                           diffusion_params.increase_ref_index,
+                           diffusion_params.ref_index_mode,
                            extra->skip_layers ? *extra->skip_layers : empty_skip_layers,
                            tensor_or_empty(extra->pulid_id),
                            extra->pulid_id_weight);
@@ -1657,7 +1683,7 @@ namespace Flux {
                                        {},
                                        guidance,
                                        {},
-                                       false);
+                                       Rope::RefIndexMode::FIXED);
                 int64_t t1   = ggml_time_ms();
 
                 GGML_ASSERT(!out_opt.empty());
diff --git a/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp b/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
index 559f61bcf..9d3df0391 100644
--- a/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/hidream_o1.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
+#ifndef __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
 #define __SD_MODEL_DIFFUSION_HIDREAM_O1_HPP__
 
 #include <algorithm>
diff --git a/otherarch/sdcpp/src/model/diffusion/minit2i.hpp b/otherarch/sdcpp/src/model/diffusion/minit2i.hpp
new file mode 100644
index 000000000..284661054
--- /dev/null
+++ b/otherarch/sdcpp/src/model/diffusion/minit2i.hpp
@@ -0,0 +1,611 @@
+#ifndef __SD_MODEL_DIFFUSION_MINIT2I_HPP__
+#define __SD_MODEL_DIFFUSION_MINIT2I_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstdlib>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "core/ggml_extend.hpp"
+#include "model/common/rope.hpp"
+#include "model/diffusion/dit.hpp"
+#include "model/diffusion/model.hpp"
+#include "model_loader.h"
+
+namespace MiniT2I {
+    constexpr int MINIT2I_GRAPH_SIZE = 196608;
+
+    struct MiniT2IConfig {
+        int64_t image_size         = 512;
+        int64_t patch_size         = 16;
+        int64_t in_channels        = 3;
+        int64_t txt_input_size     = 1024;
+        int64_t hidden_size        = 768;
+        int64_t txt_hidden_size    = 768;
+        int64_t cond_vec_size      = 768;
+        int64_t depth_double       = 17;
+        int64_t txt_preamble_depth = 2;
+        int64_t num_heads          = 12;
+        int64_t head_dim           = 64;
+        float mlp_ratio            = 2.6667f;
+        int64_t pca_channels       = 128;
+        int64_t prompt_length      = 256;
+        int64_t n_T                = 100;
+        float cfg_interval_start   = 0.0f;
+        float cfg_interval_end     = 1.0f;
+
+        static MiniT2IConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            MiniT2IConfig config;
+            config.depth_double       = 0;
+            config.txt_preamble_depth = 0;
+
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "img_embedder.proj1.weight") && tensor_storage.n_dims == 4) {
+                    config.patch_size   = tensor_storage.ne[0];
+                    config.in_channels  = tensor_storage.ne[2];
+                    config.pca_channels = tensor_storage.ne[3];
+                } else if (ends_with(name, "img_embedder.proj2.weight") && tensor_storage.n_dims == 4) {
+                    config.pca_channels = tensor_storage.ne[2];
+                    config.hidden_size  = tensor_storage.ne[3];
+                } else if (ends_with(name, "txt_embedder.weight") && tensor_storage.n_dims == 2) {
+                    config.txt_input_size  = tensor_storage.ne[0];
+                    config.txt_hidden_size = tensor_storage.ne[1];
+                } else if (ends_with(name, "pooled_embedder.weight") && tensor_storage.n_dims == 2) {
+                    config.cond_vec_size = tensor_storage.ne[1];
+                } else if (ends_with(name, "double_blocks.0.img_qkv.weight") && tensor_storage.n_dims == 2) {
+                    int64_t inner3     = tensor_storage.ne[1];
+                    int64_t inner      = inner3 / 3;
+                    config.hidden_size = tensor_storage.ne[0];
+                    if (config.hidden_size == 768) {
+                        config.num_heads = 12;
+                        config.head_dim  = 64;
+                    } else if (config.hidden_size == 1248) {
+                        config.num_heads = 24;
+                        config.head_dim  = 52;
+                    } else if (inner > 0) {
+                        config.head_dim  = 64;
+                        config.num_heads = std::max<int64_t>(1, inner / config.head_dim);
+                    }
+                } else if (ends_with(name, "final_layer.linear.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area = config.patch_size * config.patch_size;
+                    config.hidden_size = tensor_storage.ne[0];
+                    config.in_channels = patch_area > 0 ? tensor_storage.ne[1] / patch_area : config.in_channels;
+                } else if (ends_with(name, "mask_token") && tensor_storage.n_dims >= 2) {
+                    config.prompt_length = tensor_storage.ne[1];
+                }
+
+                size_t pos = name.find("double_blocks.");
+                if (pos != std::string::npos) {
+                    auto items = split_string(name.substr(pos), '.');
+                    if (items.size() > 1) {
+                        int64_t idx         = atoi(items[1].c_str());
+                        config.depth_double = std::max<int64_t>(config.depth_double, idx + 1);
+                    }
+                }
+                pos = name.find("txt_preamble_blocks.");
+                if (pos != std::string::npos) {
+                    auto items = split_string(name.substr(pos), '.');
+                    if (items.size() > 1) {
+                        int64_t idx               = atoi(items[1].c_str());
+                        config.txt_preamble_depth = std::max<int64_t>(config.txt_preamble_depth, idx + 1);
+                    }
+                }
+            }
+
+            if (config.depth_double <= 0) {
+                config.depth_double = config.hidden_size == 1248 ? 23 : 17;
+            }
+            if (config.txt_preamble_depth <= 0) {
+                config.txt_preamble_depth = 2;
+            }
+            if (config.head_dim <= 0 || config.num_heads <= 0) {
+                config.head_dim  = config.hidden_size == 1248 ? 52 : 64;
+                config.num_heads = config.hidden_size / config.head_dim;
+            }
+            LOG_DEBUG("minit2i: hidden_size=%" PRId64 ", txt_hidden_size=%" PRId64 ", heads=%" PRId64 ", head_dim=%" PRId64 ", double_blocks=%" PRId64 ", txt_blocks=%" PRId64 ", patch=%" PRId64 ", in_channels=%" PRId64,
+                      config.hidden_size,
+                      config.txt_hidden_size,
+                      config.num_heads,
+                      config.head_dim,
+                      config.depth_double,
+                      config.txt_preamble_depth,
+                      config.patch_size,
+                      config.in_channels);
+            return config;
+        }
+    };
+
+    inline std::vector<float> make_2d_sincos_pos_embed(int grid_size, int dim) {
+        GGML_ASSERT(dim % 4 == 0);
+        int half_dim = dim / 2;
+        int quarter  = half_dim / 2;
+        std::vector<float> out(static_cast<size_t>(grid_size) * grid_size * dim);
+        std::vector<float> omega(quarter);
+        for (int i = 0; i < quarter; ++i) {
+            omega[i] = 1.0f / std::pow(10000.0f, static_cast<float>(i) / static_cast<float>(quarter));
+        }
+        for (int y = 0; y < grid_size; ++y) {
+            for (int x = 0; x < grid_size; ++x) {
+                size_t base = static_cast<size_t>(y * grid_size + x) * dim;
+                for (int i = 0; i < quarter; ++i) {
+                    float ay                           = y * omega[i];
+                    float ax                           = x * omega[i];
+                    out[base + i]                      = std::sin(ax);
+                    out[base + quarter + i]            = std::cos(ax);
+                    out[base + half_dim + i]           = std::sin(ay);
+                    out[base + half_dim + quarter + i] = std::cos(ay);
+                }
+            }
+        }
+        return out;
+    }
+
+    inline std::vector<float> make_text_rope(int length, int head_dim) {
+        return Rope::flatten(Rope::rope(Rope::linspace(0.f, static_cast<float>(length - 1), length), head_dim, 10000.f));
+    }
+
+    inline std::vector<float> make_vision_rope(int side, int head_dim) {
+        GGML_ASSERT(head_dim % 4 == 0);
+        int dim     = head_dim / 2;
+        int quarter = dim / 2;
+        int length  = side * side;
+        std::vector<float> out(static_cast<size_t>(length) * (head_dim / 2) * 4);
+        std::vector<float> freqs(quarter);
+        for (int i = 0; i < quarter; ++i) {
+            freqs[i] = 1.0f / std::pow(10000.0f, static_cast<float>(2 * i) / static_cast<float>(dim));
+        }
+        for (int y = 0; y < side; ++y) {
+            for (int x = 0; x < side; ++x) {
+                int pos     = y * side + x;
+                size_t base = static_cast<size_t>(pos) * (head_dim / 2) * 4;
+                for (int i = 0; i < quarter; ++i) {
+                    float ay        = y * freqs[i];
+                    float ax        = x * freqs[i];
+                    float angles[2] = {ay, ax};
+                    for (int axis = 0; axis < 2; ++axis) {
+                        int j                 = axis * quarter + i;
+                        out[base + 4 * j]     = std::cos(angles[axis]);
+                        out[base + 4 * j + 1] = -std::sin(angles[axis]);
+                        out[base + 4 * j + 2] = std::sin(angles[axis]);
+                        out[base + 4 * j + 3] = std::cos(angles[axis]);
+                    }
+                }
+            }
+        }
+        return out;
+    }
+
+    struct SwiGLUMlp : public GGMLBlock {
+        SwiGLUMlp(int64_t in_features, int64_t hidden_features) {
+            int64_t hidden_dim = ((hidden_features + 7) / 8) * 8;
+            blocks["w1"]       = std::make_shared<Linear>(in_features, hidden_dim, false);
+            blocks["w3"]       = std::make_shared<Linear>(in_features, hidden_dim, false);
+            blocks["w2"]       = std::make_shared<Linear>(hidden_dim, in_features, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto w1   = std::dynamic_pointer_cast<Linear>(blocks["w1"]);
+            auto w3   = std::dynamic_pointer_cast<Linear>(blocks["w3"]);
+            auto w2   = std::dynamic_pointer_cast<Linear>(blocks["w2"]);
+            auto gate = ggml_silu(ctx->ggml_ctx, w1->forward(ctx, x));
+            auto up   = w3->forward(ctx, x);
+            return w2->forward(ctx, ggml_mul(ctx->ggml_ctx, gate, up));
+        }
+    };
+
+    struct BottleneckPatchEmbed : public GGMLBlock {
+        int64_t patch_size;
+
+        BottleneckPatchEmbed(int64_t patch_size, int64_t in_channels, int64_t pca_channels, int64_t hidden_size)
+            : patch_size(patch_size) {
+            blocks["proj1"] = std::make_shared<Conv2d>(in_channels,
+                                                       pca_channels,
+                                                       std::pair<int, int>{static_cast<int>(patch_size), static_cast<int>(patch_size)},
+                                                       std::pair<int, int>{static_cast<int>(patch_size), static_cast<int>(patch_size)},
+                                                       std::pair<int, int>{0, 0},
+                                                       std::pair<int, int>{1, 1},
+                                                       false);
+            blocks["proj2"] = std::make_shared<Conv2d>(pca_channels,
+                                                       hidden_size,
+                                                       std::pair<int, int>{1, 1},
+                                                       std::pair<int, int>{1, 1},
+                                                       std::pair<int, int>{0, 0},
+                                                       std::pair<int, int>{1, 1},
+                                                       true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto proj1 = std::dynamic_pointer_cast<Conv2d>(blocks["proj1"]);
+            auto proj2 = std::dynamic_pointer_cast<Conv2d>(blocks["proj2"]);
+            x          = proj1->forward(ctx, x);
+            x          = proj2->forward(ctx, x);
+            x          = ggml_reshape_3d(ctx->ggml_ctx, x, x->ne[0] * x->ne[1], x->ne[2], x->ne[3]);
+            x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));
+            return x;
+        }
+    };
+
+    struct TimestepEmbedder : public GGMLBlock {
+        int frequency_embedding_size;
+
+        TimestepEmbedder(int64_t hidden_size, int frequency_embedding_size = 256)
+            : frequency_embedding_size(frequency_embedding_size) {
+            blocks["mlp.0"] = std::make_shared<Linear>(frequency_embedding_size, hidden_size, true, true);
+            blocks["mlp.2"] = std::make_shared<Linear>(hidden_size, hidden_size, true, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* t) {
+            auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
+            auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
+            auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, t, frequency_embedding_size, 10000, 1.0f);
+            t_emb      = mlp_0->forward(ctx, t_emb);
+            t_emb      = ggml_silu_inplace(ctx->ggml_ctx, t_emb);
+            return mlp_2->forward(ctx, t_emb);
+        }
+    };
+
+    inline std::vector<ggml_tensor*> split_qkv(ggml_context* ctx, ggml_tensor* qkv, int64_t num_heads, int64_t head_dim) {
+        int64_t N = qkv->ne[2];
+        int64_t L = qkv->ne[1];
+        auto q    = ggml_view_4d(ctx, qkv, head_dim, num_heads, L, N,
+                                 qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], 0);
+        auto k    = ggml_view_4d(ctx, qkv, head_dim, num_heads, L, N,
+                                 qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], qkv->nb[0] * head_dim * num_heads);
+        auto v    = ggml_view_4d(ctx, qkv, head_dim, num_heads, L, N,
+                                 qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], qkv->nb[0] * head_dim * num_heads * 2);
+        return {q, k, v};
+    }
+
+    struct PlainTextTransformerBlock : public GGMLBlock {
+        int64_t num_heads;
+        int64_t head_dim;
+
+        PlainTextTransformerBlock(int64_t hidden_size, int64_t num_heads, int64_t head_dim, float mlp_ratio)
+            : num_heads(num_heads), head_dim(head_dim) {
+            int64_t inner_dim   = num_heads * head_dim;
+            blocks["norm1"]     = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            blocks["norm2"]     = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            blocks["qkv"]       = std::make_shared<Linear>(hidden_size, inner_dim * 3, true);
+            blocks["attn_proj"] = std::make_shared<Linear>(inner_dim, hidden_size, true);
+            blocks["mlp"]       = std::make_shared<SwiGLUMlp>(hidden_size, static_cast<int64_t>(hidden_size * mlp_ratio));
+            blocks["q_norm"]    = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks["k_norm"]    = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* txt, ggml_tensor* pe) {
+            auto norm1     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm1"]);
+            auto norm2     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm2"]);
+            auto qkv_proj  = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
+            auto attn_proj = std::dynamic_pointer_cast<Linear>(blocks["attn_proj"]);
+            auto mlp       = std::dynamic_pointer_cast<SwiGLUMlp>(blocks["mlp"]);
+            auto q_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
+            auto k_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"]);
+
+            auto qkv = split_qkv(ctx->ggml_ctx, qkv_proj->forward(ctx, norm1->forward(ctx, txt)), num_heads, head_dim);
+            auto q   = q_norm->forward(ctx, qkv[0]);
+            auto k   = k_norm->forward(ctx, qkv[1]);
+            auto v   = qkv[2];
+            auto out = Rope::attention(ctx, q, k, v, pe, nullptr, 1.0f, false);
+            txt      = ggml_add(ctx->ggml_ctx, txt, attn_proj->forward(ctx, out));
+            txt      = ggml_add(ctx->ggml_ctx, txt, mlp->forward(ctx, norm2->forward(ctx, txt)));
+            return txt;
+        }
+    };
+
+    struct DoubleStreamDiTBlock : public GGMLBlock {
+        int64_t num_heads;
+        int64_t head_dim;
+
+        DoubleStreamDiTBlock(int64_t hidden_size, int64_t txt_hidden_size, int64_t num_heads, int64_t head_dim, float mlp_ratio)
+            : num_heads(num_heads), head_dim(head_dim) {
+            int64_t inner_dim       = num_heads * head_dim;
+            blocks["img_norm1"]     = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            blocks["img_norm2"]     = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            blocks["txt_norm1"]     = std::make_shared<RMSNorm>(txt_hidden_size, 1e-6f);
+            blocks["txt_norm2"]     = std::make_shared<RMSNorm>(txt_hidden_size, 1e-6f);
+            blocks["img_qkv"]       = std::make_shared<Linear>(hidden_size, inner_dim * 3, true);
+            blocks["txt_qkv"]       = std::make_shared<Linear>(txt_hidden_size, inner_dim * 3, true);
+            blocks["q_norm"]        = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks["k_norm"]        = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks["img_attn_proj"] = std::make_shared<Linear>(inner_dim, hidden_size, true);
+            blocks["txt_attn_proj"] = std::make_shared<Linear>(inner_dim, txt_hidden_size, true);
+            blocks["img_mlp"]       = std::make_shared<SwiGLUMlp>(hidden_size, static_cast<int64_t>(hidden_size * mlp_ratio));
+            blocks["txt_mlp"]       = std::make_shared<SwiGLUMlp>(txt_hidden_size, static_cast<int64_t>(txt_hidden_size * mlp_ratio));
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* pe) {
+            auto img_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["img_norm1"]);
+            auto img_norm2 = std::dynamic_pointer_cast<RMSNorm>(blocks["img_norm2"]);
+            auto txt_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm1"]);
+            auto txt_norm2 = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm2"]);
+            auto img_qkv_p = std::dynamic_pointer_cast<Linear>(blocks["img_qkv"]);
+            auto txt_qkv_p = std::dynamic_pointer_cast<Linear>(blocks["txt_qkv"]);
+            auto q_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
+            auto k_norm    = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"]);
+            auto img_proj  = std::dynamic_pointer_cast<Linear>(blocks["img_attn_proj"]);
+            auto txt_proj  = std::dynamic_pointer_cast<Linear>(blocks["txt_attn_proj"]);
+            auto img_mlp   = std::dynamic_pointer_cast<SwiGLUMlp>(blocks["img_mlp"]);
+            auto txt_mlp   = std::dynamic_pointer_cast<SwiGLUMlp>(blocks["txt_mlp"]);
+
+            int64_t li = img->ne[1];
+            int64_t lt = txt->ne[1];
+
+            auto img_qkv = split_qkv(ctx->ggml_ctx, img_qkv_p->forward(ctx, img_norm1->forward(ctx, img)), num_heads, head_dim);
+            auto txt_qkv = split_qkv(ctx->ggml_ctx, txt_qkv_p->forward(ctx, txt_norm1->forward(ctx, txt)), num_heads, head_dim);
+
+            auto q = ggml_concat(ctx->ggml_ctx, q_norm->forward(ctx, txt_qkv[0]), q_norm->forward(ctx, img_qkv[0]), 2);
+            auto k = ggml_concat(ctx->ggml_ctx, k_norm->forward(ctx, txt_qkv[1]), k_norm->forward(ctx, img_qkv[1]), 2);
+            auto v = ggml_concat(ctx->ggml_ctx, txt_qkv[2], img_qkv[2], 2);
+
+            auto out     = Rope::attention(ctx, q, k, v, pe, nullptr, 1.0f, false);
+            auto out_txt = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, lt);
+            auto out_img = ggml_ext_slice(ctx->ggml_ctx, out, 1, lt, lt + li);
+
+            img = ggml_add(ctx->ggml_ctx, img, img_proj->forward(ctx, out_img));
+            txt = ggml_add(ctx->ggml_ctx, txt, txt_proj->forward(ctx, out_txt));
+            img = ggml_add(ctx->ggml_ctx, img, img_mlp->forward(ctx, img_norm2->forward(ctx, img)));
+            txt = ggml_add(ctx->ggml_ctx, txt, txt_mlp->forward(ctx, txt_norm2->forward(ctx, txt)));
+            return {img, txt};
+        }
+    };
+
+    struct FinalLayer : public GGMLBlock {
+        FinalLayer(int64_t hidden_size, int64_t patch_size, int64_t out_channels) {
+            blocks["norm_final"] = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            blocks["linear"]     = std::make_shared<Linear>(hidden_size, patch_size * patch_size * out_channels, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto norm_final = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_final"]);
+            auto linear     = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            return linear->forward(ctx, norm_final->forward(ctx, x));
+        }
+    };
+
+    struct MMJiT : public GGMLBlock {
+        MiniT2IConfig config;
+
+        MMJiT(const MiniT2IConfig& config)
+            : config(config) {
+            blocks["img_embedder"]    = std::make_shared<BottleneckPatchEmbed>(config.patch_size, config.in_channels, config.pca_channels, config.hidden_size);
+            blocks["txt_embedder"]    = std::make_shared<Linear>(config.txt_input_size, config.txt_hidden_size, false);
+            blocks["t_embedder"]      = std::make_shared<TimestepEmbedder>(config.cond_vec_size);
+            blocks["pooled_embedder"] = std::make_shared<Linear>(config.txt_input_size, config.cond_vec_size, false);
+            for (int64_t i = 0; i < config.txt_preamble_depth; ++i) {
+                blocks["txt_preamble_blocks." + std::to_string(i)] = std::make_shared<PlainTextTransformerBlock>(config.txt_hidden_size, config.num_heads, config.head_dim, config.mlp_ratio);
+            }
+            for (int64_t i = 0; i < config.depth_double; ++i) {
+                blocks["double_blocks." + std::to_string(i)] = std::make_shared<DoubleStreamDiTBlock>(config.hidden_size, config.txt_hidden_size, config.num_heads, config.head_dim, config.mlp_ratio);
+            }
+            blocks["final_layer"] = std::make_shared<FinalLayer>(config.hidden_size, config.patch_size, config.in_channels);
+        }
+
+        void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+            GGMLBlock::init_params(ctx, tensor_storage_map, prefix);
+            enum ggml_type wtype = get_type(prefix + "mask_token", tensor_storage_map, GGML_TYPE_F32);
+            params["mask_token"] = ggml_new_tensor_3d(ctx, wtype, config.txt_input_size, 1, 1);
+        }
+
+        ggml_tensor* apply_text_mask(GGMLRunnerContext* ctx, ggml_tensor* context, ggml_tensor* mask) {
+            if (mask == nullptr) {
+                return context;
+            }
+            mask            = ggml_reshape_3d(ctx->ggml_ctx, mask, 1, mask->ne[0], mask->ne[1]);
+            mask            = ggml_repeat(ctx->ggml_ctx, mask, context);
+            auto keep       = ggml_mul(ctx->ggml_ctx, context, mask);
+            auto inv        = ggml_sub(ctx->ggml_ctx, ggml_ext_ones_like(ctx->ggml_ctx, mask), mask);
+            auto mask_token = ggml_repeat(ctx->ggml_ctx, params["mask_token"], context);
+            return ggml_add(ctx->ggml_ctx, keep, ggml_mul(ctx->ggml_ctx, mask_token, inv));
+        }
+
+        ggml_tensor* pool_context(GGMLRunnerContext* ctx, ggml_tensor* context) {
+            int64_t dim = context->ne[0];
+            int64_t len = context->ne[1];
+            int64_t N   = context->ne[2];
+            auto x      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, context, 1, 0, 2, 3));
+            x           = ggml_reshape_3d(ctx->ggml_ctx, x, len, dim, N);
+            x           = ggml_mean(ctx->ggml_ctx, x);
+            x           = ggml_reshape_2d(ctx->ggml_ctx, x, dim, N);
+            return x;
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* img,
+                             ggml_tensor* context,
+                             ggml_tensor* mask,
+                             ggml_tensor* pos_embed,
+                             ggml_tensor* txt_pe,
+                             ggml_tensor* joint_pe) {
+            auto img_embedder = std::dynamic_pointer_cast<BottleneckPatchEmbed>(blocks["img_embedder"]);
+            auto txt_embedder = std::dynamic_pointer_cast<Linear>(blocks["txt_embedder"]);
+            auto final_layer  = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]);
+
+            int64_t W  = img->ne[0];
+            int64_t H  = img->ne[1];
+            int64_t hp = H / config.patch_size;
+            int64_t wp = W / config.patch_size;
+
+            context = apply_text_mask(ctx, context, mask);
+            auto x  = img_embedder->forward(ctx, img);
+            x       = ggml_add(ctx->ggml_ctx, x, pos_embed);
+
+            auto txt = txt_embedder->forward(ctx, context);
+            for (int64_t i = 0; i < config.txt_preamble_depth; ++i) {
+                auto block = std::dynamic_pointer_cast<PlainTextTransformerBlock>(blocks["txt_preamble_blocks." + std::to_string(i)]);
+                txt        = block->forward(ctx, txt, txt_pe);
+                sd::ggml_graph_cut::mark_graph_cut(txt, "minit2i.txt_preamble_blocks." + std::to_string(i), "txt");
+            }
+            for (int64_t i = 0; i < config.depth_double; ++i) {
+                auto block = std::dynamic_pointer_cast<DoubleStreamDiTBlock>(blocks["double_blocks." + std::to_string(i)]);
+                auto out   = block->forward(ctx, x, txt, joint_pe);
+                x          = out.first;
+                txt        = out.second;
+                sd::ggml_graph_cut::mark_graph_cut(x, "minit2i.double_blocks." + std::to_string(i), "x");
+                sd::ggml_graph_cut::mark_graph_cut(txt, "minit2i.double_blocks." + std::to_string(i), "txt");
+            }
+            auto combined = ggml_concat(ctx->ggml_ctx, txt, x, 1);
+            auto out      = final_layer->forward(ctx, combined);
+            auto img_out  = ggml_ext_slice(ctx->ggml_ctx, out, 1, txt->ne[1], txt->ne[1] + x->ne[1]);
+            return DiT::unpatchify(ctx->ggml_ctx, img_out, hp, wp, static_cast<int>(config.patch_size), static_cast<int>(config.patch_size), false);
+        }
+    };
+
+    struct MiniT2IRunner : public DiffusionModelRunner {
+        MiniT2IConfig config;
+        MMJiT model;
+        ggml_context* position_cache_ctx            = nullptr;
+        ggml_backend_buffer_t position_cache_buffer = nullptr;
+        ggml_tensor* cached_pos_embed               = nullptr;
+        ggml_tensor* cached_txt_pe                  = nullptr;
+        ggml_tensor* cached_joint_pe                = nullptr;
+        int64_t cached_img_side                     = -1;
+        int64_t cached_txt_len                      = -1;
+        int64_t cached_hidden_size                  = -1;
+        int64_t cached_head_dim                     = -1;
+
+        MiniT2IRunner(ggml_backend_t backend,
+                      const String2TensorStorage& tensor_storage_map      = {},
+                      const std::string prefix                            = "",
+                      std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+            : DiffusionModelRunner(backend, prefix, weight_manager),
+              config(MiniT2IConfig::detect_from_weights(tensor_storage_map, this->prefix)),
+              model(config) {
+            model.init(params_ctx, tensor_storage_map, this->prefix);
+        }
+
+        ~MiniT2IRunner() override {
+            free_position_cache();
+        }
+
+        std::string get_desc() override {
+            return "MiniT2I";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            model.get_param_tensors(tensors, prefix);
+        }
+
+        void free_position_cache() {
+            if (position_cache_buffer != nullptr) {
+                ggml_backend_buffer_free(position_cache_buffer);
+                position_cache_buffer = nullptr;
+            }
+            if (position_cache_ctx != nullptr) {
+                ggml_free(position_cache_ctx);
+                position_cache_ctx = nullptr;
+            }
+            cached_pos_embed   = nullptr;
+            cached_txt_pe      = nullptr;
+            cached_joint_pe    = nullptr;
+            cached_img_side    = -1;
+            cached_txt_len     = -1;
+            cached_hidden_size = -1;
+            cached_head_dim    = -1;
+        }
+
+        void ensure_position_cache(int64_t img_side, int64_t txt_len) {
+            if (cached_img_side == img_side &&
+                cached_txt_len == txt_len &&
+                cached_hidden_size == config.hidden_size &&
+                cached_head_dim == config.head_dim &&
+                cached_pos_embed != nullptr &&
+                cached_txt_pe != nullptr &&
+                cached_joint_pe != nullptr) {
+                return;
+            }
+
+            free_position_cache();
+
+            auto pos_embed_vec = make_2d_sincos_pos_embed(static_cast<int>(img_side), static_cast<int>(config.hidden_size));
+            auto txt_pe_vec    = make_text_rope(static_cast<int>(txt_len), static_cast<int>(config.head_dim));
+            auto img_pe_vec    = make_vision_rope(static_cast<int>(img_side), static_cast<int>(config.head_dim));
+            auto joint_pe_vec  = txt_pe_vec;
+            joint_pe_vec.insert(joint_pe_vec.end(), img_pe_vec.begin(), img_pe_vec.end());
+
+            ggml_init_params params;
+            params.mem_size    = static_cast<size_t>(3 * ggml_tensor_overhead());
+            params.mem_buffer  = nullptr;
+            params.no_alloc    = true;
+            position_cache_ctx = ggml_init(params);
+            GGML_ASSERT(position_cache_ctx != nullptr);
+
+            cached_pos_embed = ggml_new_tensor_3d(position_cache_ctx, GGML_TYPE_F32, config.hidden_size, img_side * img_side, 1);
+            ggml_set_name(cached_pos_embed, "minit2i.pos_embed");
+            cached_txt_pe = ggml_new_tensor_4d(position_cache_ctx, GGML_TYPE_F32, 2, 2, config.head_dim / 2, txt_len);
+            ggml_set_name(cached_txt_pe, "minit2i.txt_pe");
+            cached_joint_pe = ggml_new_tensor_4d(position_cache_ctx, GGML_TYPE_F32, 2, 2, config.head_dim / 2, txt_len + img_side * img_side);
+            ggml_set_name(cached_joint_pe, "minit2i.joint_pe");
+
+            position_cache_buffer = ggml_backend_alloc_ctx_tensors(position_cache_ctx, runtime_backend);
+            GGML_ASSERT(position_cache_buffer != nullptr);
+            ggml_backend_buffer_set_usage(position_cache_buffer, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+            ggml_backend_tensor_set(cached_pos_embed, pos_embed_vec.data(), 0, ggml_nbytes(cached_pos_embed));
+            ggml_backend_tensor_set(cached_txt_pe, txt_pe_vec.data(), 0, ggml_nbytes(cached_txt_pe));
+            ggml_backend_tensor_set(cached_joint_pe, joint_pe_vec.data(), 0, ggml_nbytes(cached_joint_pe));
+            ggml_backend_synchronize(runtime_backend);
+
+            cached_img_side    = img_side;
+            cached_txt_len     = txt_len;
+            cached_hidden_size = config.hidden_size;
+            cached_head_dim    = config.head_dim;
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 const sd::Tensor<float>& mask_tensor) {
+            ggml_cgraph* gf      = new_graph_custom(MINIT2I_GRAPH_SIZE);
+            ggml_tensor* x       = make_input(x_tensor);
+            ggml_tensor* context = make_input(context_tensor);
+            ggml_tensor* mask    = make_input(mask_tensor);
+            SD_UNUSED(timesteps_tensor);
+
+            int64_t W        = x->ne[0];
+            int64_t H        = x->ne[1];
+            int64_t img_side = H / config.patch_size;
+            int64_t txt_len  = context->ne[1];
+            ensure_position_cache(img_side, txt_len);
+
+            auto runner_ctx = get_context();
+            auto out        = model.forward(&runner_ctx, x, context, mask, cached_pos_embed, cached_txt_pe, cached_joint_pe);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  const sd::Tensor<float>& mask) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context, mask);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            GGML_ASSERT(diffusion_params.context != nullptr);
+            const auto* extra = diffusion_extra_as<MiniT2IDiffusionExtra>(diffusion_params);
+            GGML_ASSERT(extra->mask != nullptr);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           *diffusion_params.context,
+                           *extra->mask);
+        }
+    };
+}  // namespace MiniT2I
+
+#endif  // __SD_MODEL_DIFFUSION_MINIT2I_HPP__
diff --git a/otherarch/sdcpp/src/model/diffusion/model.hpp b/otherarch/sdcpp/src/model/diffusion/model.hpp
index 67f0fee02..8ef00023e 100644
--- a/otherarch/sdcpp/src/model/diffusion/model.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/model.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_DIFFUSION_MODEL_HPP__
+#ifndef __SD_MODEL_DIFFUSION_MODEL_HPP__
 #define __SD_MODEL_DIFFUSION_MODEL_HPP__
 
 #include <string>
@@ -7,6 +7,7 @@
 
 #include "core/ggml_extend.hpp"
 #include "core/tensor_ggml.hpp"
+#include "model/common/rope.hpp"
 #include "model_manager.h"
 
 struct UNetDiffusionExtra {
@@ -52,6 +53,10 @@ struct LTXAVDiffusionExtra {
     const sd::Tensor<float>* video_positions = nullptr;
 };
 
+struct MiniT2IDiffusionExtra {
+    const sd::Tensor<float>* mask = nullptr;
+};
+
 using DiffusionExtraParams = std::variant<std::monostate,
                                           UNetDiffusionExtra,
                                           SkipLayerDiffusionExtra,
@@ -59,7 +64,8 @@ using DiffusionExtraParams = std::variant<std::monostate,
                                           AnimaDiffusionExtra,
                                           WanDiffusionExtra,
                                           HiDreamO1DiffusionExtra,
-                                          LTXAVDiffusionExtra>;
+                                          LTXAVDiffusionExtra,
+                                          MiniT2IDiffusionExtra>;
 
 struct DiffusionParams {
     const sd::Tensor<float>* x                        = nullptr;
@@ -68,7 +74,7 @@ struct DiffusionParams {
     const sd::Tensor<float>* c_concat                 = nullptr;
     const sd::Tensor<float>* y                        = nullptr;
     const std::vector<sd::Tensor<float>>* ref_latents = nullptr;
-    bool increase_ref_index                           = false;
+    Rope::RefIndexMode ref_index_mode                 = Rope::RefIndexMode::FIXED;
     DiffusionExtraParams extra                        = std::monostate{};
 };
 
diff --git a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
index aecd8bcef..5dabbb176 100644
--- a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
@@ -4,6 +4,7 @@
 #include <memory>
 
 #include "model/common/block.hpp"
+#include "model/diffusion/dit.hpp"
 #include "model/diffusion/flux.hpp"
 #include "model/diffusion/model.hpp"
 #include "model_loader.h"
@@ -23,6 +24,7 @@ namespace Qwen {
         std::vector<int> axes_dim   = {16, 56, 56};
         int axes_dim_sum            = 128;
         bool zero_cond_t            = false;
+        bool use_additional_t_cond  = false;
 
         static QwenImageConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
             QwenImageConfig config;
@@ -88,19 +90,33 @@ namespace Qwen {
     };
 
     struct QwenTimestepProjEmbeddings : public GGMLBlock {
+    protected:
+        bool use_additional_t_cond = false;
+
     public:
-        QwenTimestepProjEmbeddings(int64_t embedding_dim) {
+        QwenTimestepProjEmbeddings(int64_t embedding_dim, bool use_additional_t_cond = false)
+            : use_additional_t_cond(use_additional_t_cond) {
             blocks["timestep_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedding(256, embedding_dim));
+            if (use_additional_t_cond) {
+                blocks["addition_t_embedding"] = std::shared_ptr<GGMLBlock>(new Embedding(2, embedding_dim));
+            }
         }
 
         ggml_tensor* forward(GGMLRunnerContext* ctx,
-                             ggml_tensor* timesteps) {
+                             ggml_tensor* timesteps,
+                             ggml_tensor* addition_t_cond = nullptr) {
             // timesteps: [N,]
             // return: [N, embedding_dim]
             auto timestep_embedder = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["timestep_embedder"]);
 
             auto timesteps_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1.f);
             auto timesteps_emb  = timestep_embedder->forward(ctx, timesteps_proj);
+            if (use_additional_t_cond) {
+                GGML_ASSERT(addition_t_cond != nullptr);
+                auto addition_t_embedding = std::dynamic_pointer_cast<Embedding>(blocks["addition_t_embedding"]);
+                auto addition_t_emb       = addition_t_embedding->forward(ctx, addition_t_cond);
+                timesteps_emb             = ggml_add(ctx->ggml_ctx, timesteps_emb, addition_t_emb);
+            }
             return timesteps_emb;
         }
     };
@@ -402,7 +418,7 @@ namespace Qwen {
         QwenImageModel(QwenImageConfig config)
             : config(config) {
             int64_t inner_dim         = config.num_attention_heads * config.attention_head_dim;
-            blocks["time_text_embed"] = std::shared_ptr<GGMLBlock>(new QwenTimestepProjEmbeddings(inner_dim));
+            blocks["time_text_embed"] = std::shared_ptr<GGMLBlock>(new QwenTimestepProjEmbeddings(inner_dim, config.use_additional_t_cond));
             blocks["txt_norm"]        = std::shared_ptr<GGMLBlock>(new RMSNorm(config.joint_attention_dim, 1e-6f));
             blocks["img_in"]          = std::shared_ptr<GGMLBlock>(new Linear(config.in_channels, inner_dim));
             blocks["txt_in"]          = std::shared_ptr<GGMLBlock>(new Linear(config.joint_attention_dim, inner_dim));
@@ -424,6 +440,7 @@ namespace Qwen {
         ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
                                   ggml_tensor* x,
                                   ggml_tensor* timestep,
+                                  ggml_tensor* addition_t_cond,
                                   ggml_tensor* context,
                                   ggml_tensor* pe,
                                   ggml_tensor* modulate_index = nullptr) {
@@ -434,9 +451,9 @@ namespace Qwen {
             auto norm_out        = std::dynamic_pointer_cast<AdaLayerNormContinuous>(blocks["norm_out"]);
             auto proj_out        = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
 
-            auto t_emb = time_text_embed->forward(ctx, timestep);
+            auto t_emb = time_text_embed->forward(ctx, timestep, addition_t_cond);
             if (config.zero_cond_t) {
-                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros_like(ctx->ggml_ctx, timestep));
+                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros_like(ctx->ggml_ctx, timestep), addition_t_cond);
                 t_emb        = ggml_concat(ctx->ggml_ctx, t_emb, t_emb_0, 1);
             }
             auto img = img_in->forward(ctx, x);
@@ -469,33 +486,50 @@ namespace Qwen {
         ggml_tensor* forward(GGMLRunnerContext* ctx,
                              ggml_tensor* x,
                              ggml_tensor* timestep,
+                             ggml_tensor* addition_t_cond,
                              ggml_tensor* context,
                              ggml_tensor* pe,
                              std::vector<ggml_tensor*> ref_latents = {},
                              ggml_tensor* modulate_index           = nullptr) {
             // Forward pass of DiT.
-            // x: [N, C, H, W]
+            // x: [N, C, H, W] or [N*C, T, H, W]
             // timestep: [N,]
             // context: [N, L, D]
             // pe: [L, d_head/2, 2, 2]
-            // return: [N, C, H, W]
+            // return: [N, C, H, W] or [N*C, T, H, W]
 
-            int64_t W = x->ne[0];
-            int64_t H = x->ne[1];
-            int64_t C = x->ne[2];
-            int64_t N = x->ne[3];
+            int64_t W          = x->ne[0];
+            int64_t H          = x->ne[1];
+            int64_t T          = 1;
+            int64_t N          = addition_t_cond != nullptr ? addition_t_cond->ne[0] : x->ne[3];
+            bool has_time_axis = false;
+            if (x->ne[3] != 1) {
+                T             = x->ne[2];
+                has_time_axis = true;
+            }
 
-            auto img           = DiT::pad_and_patchify(ctx, x, config.patch_size, config.patch_size);
+            auto patchify_input = [&](ggml_tensor* input) -> ggml_tensor* {
+                input = DiT::pad_to_patch_size(ctx, input, config.patch_size, config.patch_size);
+                if (!has_time_axis) {
+                    return DiT::patchify(ctx->ggml_ctx, input, config.patch_size, config.patch_size);
+                }
+                if (input->ne[3] == 1) {
+                    input = ggml_reshape_4d(ctx->ggml_ctx, input, input->ne[0], input->ne[1], 1, input->ne[2]);
+                }
+                return DiT::patchify(ctx->ggml_ctx, input, 1, config.patch_size, config.patch_size, N);
+            };
+
+            auto img           = patchify_input(x);
             int64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = DiT::pad_and_patchify(ctx, ref, config.patch_size, config.patch_size);
+                    ref = patchify_input(ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
 
-            auto out = forward_orig(ctx, img, timestep, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]
+            auto out = forward_orig(ctx, img, timestep, addition_t_cond, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]
 
             if (out->ne[1] > img_tokens) {
                 out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
@@ -503,7 +537,17 @@ namespace Qwen {
                 out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
             }
 
-            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, config.patch_size, config.patch_size);  // [N, C, H, W]
+            if (has_time_axis) {
+                int pad_h = (config.patch_size - H % config.patch_size) % config.patch_size;
+                int pad_w = (config.patch_size - W % config.patch_size) % config.patch_size;
+                int h_len = static_cast<int>((H + pad_h) / config.patch_size);
+                int w_len = static_cast<int>((W + pad_w) / config.patch_size);
+                out       = DiT::unpatchify_3d(ctx->ggml_ctx, out, T, h_len, w_len, 1, config.patch_size, config.patch_size);
+                out       = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N*C, T, H, W + pad_w]
+                out       = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N*C, T, H, W]
+            } else {
+                out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, config.patch_size, config.patch_size);  // [N, C, H, W]
+            }
 
             return out;
         }
@@ -515,6 +559,7 @@ namespace Qwen {
         QwenImageModel qwen_image;
         std::vector<float> pe_vec;
         std::vector<float> modulate_index_vec;
+        std::vector<int32_t> additional_t_cond_vec;
         SDVersion version;
 
         QwenImageRunner(ggml_backend_t backend,
@@ -524,9 +569,13 @@ namespace Qwen {
                         bool zero_cond_t                                    = false,
                         std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
             : DiffusionModelRunner(backend, prefix, weight_manager),
-              config(QwenImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
+              config(QwenImageConfig::detect_from_weights(tensor_storage_map, prefix)),
+              version(version) {
             config.zero_cond_t = config.zero_cond_t || zero_cond_t;
-            qwen_image         = QwenImageModel(config);
+            if (version == VERSION_QWEN_IMAGE_LAYERED) {
+                config.use_additional_t_cond = true;
+            }
+            qwen_image = QwenImageModel(config);
             qwen_image.init(params_ctx, tensor_storage_map, prefix);
         }
 
@@ -542,11 +591,11 @@ namespace Qwen {
                                  const sd::Tensor<float>& timesteps_tensor,
                                  const sd::Tensor<float>& context_tensor,
                                  const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
-                                 bool increase_ref_index                                  = false) {
+                                 Rope::RefIndexMode ref_index_mode                        = Rope::RefIndexMode::INCREASE) {
             ggml_cgraph* gf        = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
             ggml_tensor* x         = make_input(x_tensor);
             ggml_tensor* timesteps = make_input(timesteps_tensor);
-            GGML_ASSERT(x->ne[3] == 1);
+            GGML_ASSERT(x->ne[3] == 1 || x_tensor.dim() == 5);
             GGML_ASSERT(!context_tensor.empty());
             ggml_tensor* context = make_input(context_tensor);
             std::vector<ggml_tensor*> ref_latents;
@@ -555,13 +604,29 @@ namespace Qwen {
                 ref_latents.push_back(make_input(ref_latent_tensor));
             }
 
-            pe_vec      = Rope::gen_qwen_image_pe(static_cast<int>(x->ne[1]),
+            int batch_size = static_cast<int>(x->ne[3]);
+            int time_len   = 1;
+            if (x_tensor.dim() == 5) {
+                time_len   = static_cast<int>(x_tensor.shape()[2]);
+                batch_size = static_cast<int>(x_tensor.shape()[4]);
+            }
+
+            ggml_tensor* addition_t_cond = nullptr;
+            if (version == VERSION_QWEN_IMAGE_LAYERED) {
+                additional_t_cond_vec.assign(static_cast<size_t>(batch_size), 0);
+                addition_t_cond = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, batch_size);
+                set_backend_tensor_data(addition_t_cond, additional_t_cond_vec.data());
+                ref_index_mode = Rope::RefIndexMode::DECREASE;
+            }
+
+            pe_vec      = Rope::gen_qwen_image_pe(time_len,
+                                                  static_cast<int>(x->ne[1]),
                                                   static_cast<int>(x->ne[0]),
                                                   config.patch_size,
-                                                  static_cast<int>(x->ne[3]),
+                                                  batch_size,
                                                   static_cast<int>(context->ne[1]),
                                                   ref_latents,
-                                                  increase_ref_index,
+                                                  ref_index_mode,
                                                   config.theta,
                                                   circular_y_enabled,
                                                   circular_x_enabled,
@@ -604,6 +669,7 @@ namespace Qwen {
             ggml_tensor* out = qwen_image.forward(&runner_ctx,
                                                   x,
                                                   timesteps,
+                                                  addition_t_cond,
                                                   context,
                                                   pe,
                                                   ref_latents,
@@ -619,12 +685,12 @@ namespace Qwen {
                                   const sd::Tensor<float>& timesteps,
                                   const sd::Tensor<float>& context,
                                   const std::vector<sd::Tensor<float>>& ref_latents = {},
-                                  bool increase_ref_index                           = false) {
-            // x: [N, in_channels, h, w]
+                                  Rope::RefIndexMode ref_index_mode                 = Rope::RefIndexMode::INCREASE) {
+            // x: [N, C, H, W] or [N*C, T, H, W]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
             auto get_graph = [&]() -> ggml_cgraph* {
-                return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
+                return build_graph(x, timesteps, context, ref_latents, ref_index_mode);
             };
 
             return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
@@ -640,7 +706,7 @@ namespace Qwen {
                            *diffusion_params.timesteps,
                            tensor_or_empty(diffusion_params.context),
                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                           diffusion_params.increase_ref_index);
+                           diffusion_params.ref_index_mode);
         }
 
         void test() {
@@ -674,7 +740,7 @@ namespace Qwen {
                                        timesteps,
                                        context,
                                        {},
-                                       false);
+                                       Rope::RefIndexMode::FIXED);
                 int64_t t1   = ggml_time_ms();
 
                 GGML_ASSERT(!out_opt.empty());
diff --git a/otherarch/sdcpp/src/model/diffusion/sefi_image.hpp b/otherarch/sdcpp/src/model/diffusion/sefi_image.hpp
new file mode 100644
index 000000000..271919882
--- /dev/null
+++ b/otherarch/sdcpp/src/model/diffusion/sefi_image.hpp
@@ -0,0 +1,91 @@
+#ifndef __SD_MODEL_DIFFUSION_SEFI_IMAGE_HPP__
+#define __SD_MODEL_DIFFUSION_SEFI_IMAGE_HPP__
+
+#include <memory>
+
+#include "model/common/block.hpp"
+
+namespace SefiImage {
+    struct SefiImageConfig {
+        int64_t semantic_channels        = 16;
+        int64_t texture_latent_channels  = 32;
+        int64_t timestep_guidance_in_dim = 256;
+        int64_t hidden_size              = 3072;
+        float timestep_shift_alpha       = 0.3f;
+        float delta_t                    = 0.1f;
+
+        int64_t packed_texture_channels(int patch_size) const {
+            return texture_latent_channels * patch_size * patch_size;
+        }
+
+        int64_t packed_input_channels(int patch_size) const {
+            return semantic_channels + packed_texture_channels(patch_size);
+        }
+
+        static SefiImageConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                                   const std::string& prefix) {
+            SefiImageConfig config;
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "dual_time_embed.semantic_embedder.linear_1.weight") && tensor_storage.n_dims == 2) {
+                    config.timestep_guidance_in_dim = tensor_storage.ne[0];
+                    config.hidden_size              = tensor_storage.ne[1] * 2;
+                }
+            }
+            LOG_DEBUG("sefi_image: semantic_channels = %" PRId64 ", texture_latent_channels = %" PRId64 ", hidden_size = %" PRId64,
+                      config.semantic_channels,
+                      config.texture_latent_channels,
+                      config.hidden_size);
+            return config;
+        }
+    };
+
+    struct SefiTimestepEmbedding : public GGMLBlock {
+    public:
+        SefiTimestepEmbedding(int64_t in_channels, int64_t time_embed_dim) {
+            blocks["linear_1"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, time_embed_dim, false));
+            blocks["linear_2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim, false));
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* sample) {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["linear_1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["linear_2"]);
+
+            sample = linear_1->forward(ctx, sample);
+            sample = ggml_silu_inplace(ctx->ggml_ctx, sample);
+            sample = linear_2->forward(ctx, sample);
+            return sample;
+        }
+    };
+
+    struct SefiDualTimestepEmbeddings : public GGMLBlock {
+    public:
+        SefiDualTimestepEmbeddings(int64_t in_channels, int64_t embedding_dim) {
+            GGML_ASSERT(embedding_dim % 2 == 0);
+            int64_t half_dim            = embedding_dim / 2;
+            blocks["semantic_embedder"] = std::make_shared<SefiTimestepEmbedding>(in_channels, half_dim);
+            blocks["texture_embedder"]  = std::make_shared<SefiTimestepEmbedding>(in_channels, half_dim);
+            timestep_guidance_in_dim    = in_channels;
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* timestep_sem,
+                             ggml_tensor* timestep_tex) {
+            auto semantic_embedder = std::dynamic_pointer_cast<SefiTimestepEmbedding>(blocks["semantic_embedder"]);
+            auto texture_embedder  = std::dynamic_pointer_cast<SefiTimestepEmbedding>(blocks["texture_embedder"]);
+
+            auto sem_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_sem, (int)timestep_guidance_in_dim, 10000, 1.f);
+            auto tex_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep_tex, (int)timestep_guidance_in_dim, 10000, 1.f);
+            auto sem_emb  = semantic_embedder->forward(ctx, sem_proj);
+            auto tex_emb  = texture_embedder->forward(ctx, tex_proj);
+            return ggml_concat(ctx->ggml_ctx, sem_emb, tex_emb, 0);
+        }
+
+    private:
+        int64_t timestep_guidance_in_dim = 256;
+    };
+}  // namespace SefiImage
+
+#endif  // __SD_MODEL_DIFFUSION_SEFI_IMAGE_HPP__
diff --git a/otherarch/sdcpp/src/model/diffusion/z_image.hpp b/otherarch/sdcpp/src/model/diffusion/z_image.hpp
index d23c28563..362192f19 100644
--- a/otherarch/sdcpp/src/model/diffusion/z_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/z_image.hpp
@@ -575,7 +575,7 @@ namespace ZImage {
                                  const sd::Tensor<float>& timesteps_tensor,
                                  const sd::Tensor<float>& context_tensor,
                                  const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
-                                 bool increase_ref_index                                  = false) {
+                                 Rope::RefIndexMode ref_index_mode                        = Rope::RefIndexMode::FIXED) {
             ggml_cgraph* gf        = new_graph_custom(Z_IMAGE_GRAPH_SIZE);
             ggml_tensor* x         = make_input(x_tensor);
             ggml_tensor* timesteps = make_input(timesteps_tensor);
@@ -595,7 +595,7 @@ namespace ZImage {
                                                static_cast<int>(context->ne[1]),
                                                SEQ_MULTI_OF,
                                                ref_latents,
-                                               increase_ref_index,
+                                               ref_index_mode,
                                                config.theta,
                                                circular_y_enabled,
                                                circular_x_enabled,
@@ -626,12 +626,12 @@ namespace ZImage {
                                   const sd::Tensor<float>& timesteps,
                                   const sd::Tensor<float>& context,
                                   const std::vector<sd::Tensor<float>>& ref_latents = {},
-                                  bool increase_ref_index                           = false) {
+                                  Rope::RefIndexMode ref_index_mode                 = Rope::RefIndexMode::FIXED) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
             auto get_graph = [&]() -> ggml_cgraph* {
-                return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
+                return build_graph(x, timesteps, context, ref_latents, ref_index_mode);
             };
 
             return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false, false, false), x.dim());
@@ -647,7 +647,7 @@ namespace ZImage {
                            *diffusion_params.timesteps,
                            tensor_or_empty(diffusion_params.context),
                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                           diffusion_params.increase_ref_index);
+                           diffusion_params.ref_index_mode);
         }
 
         void test() {
@@ -681,7 +681,7 @@ namespace ZImage {
                                        timesteps,
                                        context,
                                        {},
-                                       false);
+                                       Rope::RefIndexMode::FIXED);
                 int64_t t1   = ggml_time_ms();
 
                 GGML_ASSERT(!out_opt.empty());
diff --git a/otherarch/sdcpp/src/model/te/clip.hpp b/otherarch/sdcpp/src/model/te/clip.hpp
index 6dc8a947b..2fde3de79 100644
--- a/otherarch/sdcpp/src/model/te/clip.hpp
+++ b/otherarch/sdcpp/src/model/te/clip.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_CLIP_HPP__
+#ifndef __SD_MODEL_TE_CLIP_HPP__
 #define __SD_MODEL_TE_CLIP_HPP__
 
 #include "core/ggml_extend.hpp"
diff --git a/otherarch/sdcpp/src/model/te/llm.hpp b/otherarch/sdcpp/src/model/te/llm.hpp
index 12daf5637..3c5e9bb54 100644
--- a/otherarch/sdcpp/src/model/te/llm.hpp
+++ b/otherarch/sdcpp/src/model/te/llm.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_LLM_HPP__
+#ifndef __SD_MODEL_TE_LLM_HPP__
 #define __SD_MODEL_TE_LLM_HPP__
 
 #include <algorithm>
@@ -250,7 +250,7 @@ namespace LLM {
                     config.intermediate_size = tensor_storage.ne[1];
                 }
             }
-            if (arch == LLMArch::QWEN3 && config.num_layers == 28) {
+            if ((arch == LLMArch::QWEN3 || arch == LLMArch::QWEN3_VL) && config.num_layers == 28) {
                 config.num_heads = 16;
             }
             if (detected_vision_layers > 0) {
diff --git a/otherarch/sdcpp/src/model/te/t5.hpp b/otherarch/sdcpp/src/model/te/t5.hpp
index 23da08222..6d2326f94 100644
--- a/otherarch/sdcpp/src/model/te/t5.hpp
+++ b/otherarch/sdcpp/src/model/te/t5.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_TE_T5_HPP__
+#ifndef __SD_MODEL_TE_T5_HPP__
 #define __SD_MODEL_TE_T5_HPP__
 
 #include <cfloat>
@@ -26,13 +26,66 @@ struct T5Config {
     static T5Config detect_from_weights(const String2TensorStorage& tensor_storage_map,
                                         const std::string& prefix,
                                         bool is_umt5 = false) {
-        (void)tensor_storage_map;
-        (void)prefix;
         T5Config config;
         if (is_umt5) {
             config.vocab_size         = 256384;
             config.relative_attention = false;
         }
+        auto find_tensor = [&](const std::string& suffix) -> const TensorStorage* {
+            auto it = tensor_storage_map.find(prefix + "." + suffix);
+            if (it != tensor_storage_map.end()) {
+                return &it->second;
+            }
+            it = tensor_storage_map.find(prefix + suffix);
+            if (it != tensor_storage_map.end()) {
+                return &it->second;
+            }
+            return nullptr;
+        };
+
+        if (const TensorStorage* shared = find_tensor("shared.weight")) {
+            if (shared->n_dims == 2) {
+                config.vocab_size = shared->ne[1];
+                config.model_dim  = shared->ne[0];
+            }
+        }
+        if (const TensorStorage* q = find_tensor("encoder.block.0.layer.0.SelfAttention.q.weight")) {
+            if (q->n_dims == 2) {
+                config.model_dim  = q->ne[0];
+                int64_t inner_dim = q->ne[1];
+                // Flan-T5/T5 uses d_kv=64 for common sizes.
+                if (inner_dim % 64 == 0) {
+                    config.num_heads = inner_dim / 64;
+                }
+            }
+        }
+        if (const TensorStorage* wi = find_tensor("encoder.block.0.layer.1.DenseReluDense.wi_0.weight")) {
+            if (wi->n_dims == 2) {
+                config.model_dim = wi->ne[0];
+                config.ff_dim    = wi->ne[1];
+            }
+        }
+        int64_t detected_layers = 0;
+        for (const auto& [name, _] : tensor_storage_map) {
+            std::string base = prefix;
+            if (!base.empty() && base.back() != '.') {
+                base += ".";
+            }
+            std::string layer_prefix = base + "encoder.block.";
+            if (!starts_with(name, layer_prefix)) {
+                continue;
+            }
+            size_t pos = layer_prefix.size();
+            size_t dot = name.find('.', pos);
+            if (dot == std::string::npos) {
+                continue;
+            }
+            int64_t layer   = atoi(name.substr(pos, dot - pos).c_str());
+            detected_layers = std::max(detected_layers, layer + 1);
+        }
+        if (detected_layers > 0) {
+            config.num_layers = detected_layers;
+        }
         return config;
     }
 };
diff --git a/otherarch/sdcpp/src/model/upscaler/esrgan.hpp b/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
index 4afbab07d..21c977124 100644
--- a/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
+++ b/otherarch/sdcpp/src/model/upscaler/esrgan.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_UPSCALER_ESRGAN_HPP__
+#ifndef __SD_MODEL_UPSCALER_ESRGAN_HPP__
 #define __SD_MODEL_UPSCALER_ESRGAN_HPP__
 
 #include <algorithm>
diff --git a/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp b/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
index 5343ad036..b70e16136 100644
--- a/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
+++ b/otherarch/sdcpp/src/model/upscaler/ltx_latent_upscaler.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
+#ifndef __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
 #define __SD_MODEL_UPSCALER_LTX_LATENT_UPSCALER_HPP__
 
 #include <algorithm>
diff --git a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
index e41f5fd46..604347d77 100644
--- a/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
+++ b/otherarch/sdcpp/src/model/vae/auto_encoder_kl.hpp
@@ -816,12 +816,13 @@ struct AutoEncoderKL : public VAE {
     }
 
     sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        auto latents_ = sd_version_is_sefi_image(version) ? sd::ops::slice(latents, 2, 16, 144) : latents;
         if (sd_version_uses_flux2_vae(version)) {
             int channel_dim                = 2;
-            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents, channel_dim);
-            return (latents * std_tensor) / scale_factor + mean_tensor;
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents_, channel_dim);
+            return (latents_ * std_tensor) / scale_factor + mean_tensor;
         }
-        return (latents / scale_factor) + shift_factor;
+        return (latents_ / scale_factor) + shift_factor;
     }
 
     sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
diff --git a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
index 49847445d..9912a6136 100644
--- a/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/ltx_audio_vae.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
+#ifndef __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 #define __SD_MODEL_VAE_LTX_AUDIO_VAE_HPP__
 
 #include <algorithm>
@@ -327,7 +327,7 @@ namespace LTXV {
 
         auto x = ggml_reshape_3d(ctx, waveform, time, 1, channels * batch);
         if (left_pad > 0) {
-            x = ggml_pad_ext(ctx, x, static_cast<int>(left_pad), 0, 0, 0, 0, 0, 0, 0);
+            x = ggml_ext_pad_ext(ctx, runner_ctx->backend, x, static_cast<int>(left_pad), 0, 0, 0, 0, 0, 0, 0);
         }
 
         auto frames = ggml_conv_1d(ctx, forward_basis, x, hop_length, 0, 1);
@@ -564,6 +564,7 @@ namespace LTXV {
             int pad_h = kernel_size.first - 1;
             int pad_w = kernel_size.second - 1;
             x         = ggml_ext_pad_ext(ctx->ggml_ctx,
+                                         ctx->backend,
                                          x,
                                          pad_w / 2,
                                          pad_w - pad_w / 2,
@@ -603,6 +604,7 @@ namespace LTXV {
         ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
             x         = ggml_ext_pad_ext(ctx->ggml_ctx,
+                                         ctx->backend,
                                          x,
                                          0,
                                          1,
diff --git a/otherarch/sdcpp/src/model/vae/tae.hpp b/otherarch/sdcpp/src/model/vae/tae.hpp
index 7c6e1d35c..558c0544c 100644
--- a/otherarch/sdcpp/src/model/vae/tae.hpp
+++ b/otherarch/sdcpp/src/model/vae/tae.hpp
@@ -408,7 +408,7 @@ public:
             h = conv->forward(ctx, h);
             for (int j = 0; j < num_blocks; j++) {
                 auto block = std::dynamic_pointer_cast<MemBlock>(blocks[std::to_string(index++)]);
-                auto mem   = ggml_pad_ext(ctx->ggml_ctx, h, 0, 0, 0, 0, 0, 0, 1, 0);
+                auto mem   = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, h, 0, 0, 0, 0, 0, 0, 1, 0);
                 mem        = ggml_view_4d(ctx->ggml_ctx, mem, h->ne[0], h->ne[1], h->ne[2], h->ne[3], h->nb[1], h->nb[2], h->nb[3], 0);
                 h          = block->forward(ctx, h, mem);
             }
@@ -479,7 +479,7 @@ public:
         int index = 3;
         for (int i = 0; i < num_layers; i++) {
             for (int j = 0; j < num_blocks; j++) {
-                auto mem = ggml_pad_ext(ctx->ggml_ctx, h, 0, 0, 0, 0, 0, 0, 1, 0);
+                auto mem = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, h, 0, 0, 0, 0, 0, 0, 1, 0);
                 mem      = ggml_view_4d(ctx->ggml_ctx, mem, h->ne[0], h->ne[1], h->ne[2], h->ne[3], h->nb[1], h->nb[2], h->nb[3], 0);
                 if (is_wide) {
                     auto block = std::dynamic_pointer_cast<WideMemBlock>(blocks[std::to_string(index++)]);
@@ -548,7 +548,7 @@ public:
         }
         auto result = decoder->forward(ctx, z);
         if (sd_version_is_wan(version) || sd_version_is_ltxav(version)) {
-            // (W, H, C, T) -> (W, H, T, C)
+            // (W, H, T, C) -> (W, H, C, T)
             result = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, result, 0, 1, 3, 2));
         }
         return result;
@@ -556,8 +556,10 @@ public:
 
     ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
         auto encoder = std::dynamic_pointer_cast<TinyVideoEncoder>(blocks["encoder"]);
-        // (W, H, T, C) -> (W, H, C, T)
-        x                  = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        if (sd_version_is_wan(version) || sd_version_is_ltxav(version)) {
+            // (W, H, T, C) -> (W, H, C, T)
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        }
         int64_t num_frames = x->ne[3];
         if (num_frames % encoder->t_downscale) {
             // pad to multiple of encoder->t_downscale at the end
@@ -567,7 +569,10 @@ public:
             }
         }
         x = encoder->forward(ctx, x);
-        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        if (sd_version_is_wan(version) || sd_version_is_ltxav(version)) {
+            // (W, H, C, T) -> (W, H, T, C)
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        }
         return x;
     }
 };
diff --git a/otherarch/sdcpp/src/model/vae/vae.hpp b/otherarch/sdcpp/src/model/vae/vae.hpp
index af091bb57..8b8c46ded 100644
--- a/otherarch/sdcpp/src/model/vae/vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/vae.hpp
@@ -1,4 +1,4 @@
-#ifndef __SD_MODEL_VAE_VAE_HPP__
+#ifndef __SD_MODEL_VAE_VAE_HPP__
 #define __SD_MODEL_VAE_VAE_HPP__
 
 #include "core/tensor_ggml.hpp"
@@ -78,7 +78,7 @@ public:
             scale_factor = 16;
         } else if (sd_version_uses_flux2_vae(version)) {
             scale_factor = 16;
-        } else if (version == VERSION_CHROMA_RADIANCE || version == VERSION_HIDREAM_O1) {
+        } else if (version == VERSION_CHROMA_RADIANCE || version == VERSION_HIDREAM_O1 || sd_version_is_minit2i(version)) {
             scale_factor = 1;
         }
         return scale_factor;
diff --git a/otherarch/sdcpp/src/model/vae/wan_vae.hpp b/otherarch/sdcpp/src/model/vae/wan_vae.hpp
index 8a845c7ca..949562096 100644
--- a/otherarch/sdcpp/src/model/vae/wan_vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/wan_vae.hpp
@@ -72,8 +72,8 @@ namespace WAN {
                 lp2 -= (int)cache_x->ne[2];
             }
 
-            x = ggml_ext_pad_ext(ctx->ggml_ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-            return ggml_ext_conv_3d(ctx->ggml_ctx, x, w, b, in_channels,
+            x = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
+            return ggml_ext_conv_3d(ctx->ggml_ctx, ctx->backend, x, w, b, in_channels,
                                     std::get<2>(stride), std::get<1>(stride), std::get<0>(stride),
                                     0, 0, 0,
                                     std::get<2>(dilation), std::get<1>(dilation), std::get<0>(dilation));
@@ -113,6 +113,24 @@ namespace WAN {
         }
     };
 
+    class Conv2dBut3d : public Conv2d {
+    public:
+        using Conv2d::Conv2d;
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            ggml_tensor* x_swapped = ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2);
+            x_swapped              = ggml_cont(ctx->ggml_ctx, x_swapped);
+
+            ggml_tensor* out = Conv2d::forward(ctx, x_swapped);
+
+            ggml_tensor* out_swapped = ggml_permute(ctx->ggml_ctx, out, 0, 1, 3, 2);
+
+            out_swapped = ggml_cont(ctx->ggml_ctx, out_swapped);
+
+            return out_swapped;
+        }
+    };
+
     class Resample : public GGMLBlock {
     protected:
         int64_t dim;
@@ -177,7 +195,7 @@ namespace WAN {
                                                   2);
                         }
                         if (chunk_idx == 1 && cache_x->ne[2] < 2) {  // Rep
-                            cache_x = ggml_pad_ext(ctx->ggml_ctx, cache_x, 0, 0, 0, 0, (int)cache_x->ne[2], 0, 0, 0);
+                            cache_x = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, cache_x, 0, 0, 0, 0, (int)cache_x->ne[2], 0, 0, 0);
                             // aka cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device),cache_x],dim=2)
                         }
                         if (chunk_idx == 1) {
@@ -265,7 +283,7 @@ namespace WAN {
 
             int pad_t = (factor_t - T % factor_t) % factor_t;
 
-            x = ggml_pad_ext(ctx->ggml_ctx, x, 0, 0, 0, 0, pad_t, 0, 0, 0);
+            x = ggml_ext_pad_ext(ctx->ggml_ctx, ctx->backend, x, 0, 0, 0, 0, pad_t, 0, 0, 0);
             T = x->ne[2];
 
             x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, factor_t, T / factor_t, C);                                                  // [C, T/factor_t, factor_t, H*W]
@@ -338,19 +356,32 @@ namespace WAN {
     protected:
         int64_t in_dim;
         int64_t out_dim;
+        bool is_2D;
 
     public:
-        ResidualBlock(int64_t in_dim, int64_t out_dim)
-            : in_dim(in_dim), out_dim(out_dim) {
+        ResidualBlock(int64_t in_dim, int64_t out_dim, bool is_2D = false)
+            : in_dim(in_dim), out_dim(out_dim), is_2D(is_2D) {
             blocks["residual.0"] = std::shared_ptr<GGMLBlock>(new RMS_norm(in_dim));
             // residual.1 is nn.SiLU()
-            blocks["residual.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(in_dim, out_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["residual.2"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(in_dim, out_dim, {3, 3}, {1, 1}, {1, 1}));
+            } else {
+                blocks["residual.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(in_dim, out_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            }
             blocks["residual.3"] = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
             // residual.4 is nn.SiLU()
             // residual.5 is nn.Dropout()
-            blocks["residual.6"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, out_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["residual.6"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(out_dim, out_dim, {3, 3}, {1, 1}, {1, 1}));
+            } else {
+                blocks["residual.6"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, out_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            }
             if (in_dim != out_dim) {
-                blocks["shortcut"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(in_dim, out_dim, {1, 1, 1}));
+                if (is_2D) {
+                    blocks["shortcut"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(in_dim, out_dim, {1, 1}));
+                } else {
+                    blocks["shortcut"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(in_dim, out_dim, {1, 1, 1}));
+                }
             }
         }
 
@@ -363,9 +394,15 @@ namespace WAN {
             GGML_ASSERT(b == 1);
             ggml_tensor* h = x;
             if (in_dim != out_dim) {
-                auto shortcut = std::dynamic_pointer_cast<CausalConv3d>(blocks["shortcut"]);
+                if (is_2D) {
+                    auto shortcut = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["shortcut"]);
 
-                h = shortcut->forward(ctx, x);
+                    h = shortcut->forward(ctx, x);
+                } else {
+                    auto shortcut = std::dynamic_pointer_cast<CausalConv3d>(blocks["shortcut"]);
+
+                    h = shortcut->forward(ctx, x);
+                }
             }
 
             for (int i = 0; i < 7; i++) {
@@ -385,8 +422,13 @@ namespace WAN {
                                                   cache_x,
                                                   2);
                         }
+                        if (is_2D) {
+                            auto layer = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["residual." + std::to_string(i)]);
 
-                        x               = layer->forward(ctx, x, feat_cache[idx]);
+                            x = layer->forward(ctx, x);
+                        } else {
+                            x = layer->forward(ctx, x, feat_cache[idx]);
+                        }
                         feat_cache[idx] = cache_x;
                         feat_idx += 1;
                     }
@@ -412,13 +454,14 @@ namespace WAN {
                            int64_t out_dim,
                            int mult,
                            bool temperal_downsample = false,
-                           bool down_flag           = false)
+                           bool down_flag           = false,
+                           bool is_2D               = false)
             : mult(mult), down_flag(down_flag) {
             blocks["avg_shortcut"] = std::shared_ptr<GGMLBlock>(new AvgDown3D(in_dim, out_dim, temperal_downsample ? 2 : 1, down_flag ? 2 : 1));
 
             int i = 0;
             for (; i < mult; i++) {
-                blocks["downsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim));
+                blocks["downsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim, is_2D));
                 in_dim                                     = out_dim;
             }
             if (down_flag) {
@@ -472,7 +515,8 @@ namespace WAN {
                          int64_t out_dim,
                          int mult,
                          bool temperal_upsample = false,
-                         bool up_flag           = false)
+                         bool up_flag           = false,
+                         bool is_2D             = false)
             : mult(mult), up_flag(up_flag) {
             if (up_flag) {
                 blocks["avg_shortcut"] = std::shared_ptr<GGMLBlock>(new DupUp3D(in_dim, out_dim, temperal_upsample ? 2 : 1, up_flag ? 2 : 1));
@@ -480,7 +524,7 @@ namespace WAN {
 
             int i = 0;
             for (; i < mult; i++) {
-                blocks["upsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim));
+                blocks["upsamples." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim, is_2D));
                 in_dim                                   = out_dim;
             }
             if (up_flag) {
@@ -587,35 +631,41 @@ namespace WAN {
     class Encoder3d : public GGMLBlock {
     protected:
         bool wan2_2;
+        int64_t in_channels;
         int64_t dim;
         int64_t z_dim;
         std::vector<int> dim_mult;
         int num_res_blocks;
         std::vector<bool> temperal_downsample;
+        bool is_2D = false;
 
     public:
         Encoder3d(int64_t dim                           = 128,
                   int64_t z_dim                         = 4,
+                  int64_t in_channels                   = 3,
                   std::vector<int> dim_mult             = {1, 2, 4, 4},
                   int num_res_blocks                    = 2,
                   std::vector<bool> temperal_downsample = {false, true, true},
-                  bool wan2_2                           = false)
-            : dim(dim),
+                  bool wan2_2                           = false,
+                  bool is_2D                            = false)
+            : in_channels(in_channels),
+              dim(dim),
               z_dim(z_dim),
               dim_mult(dim_mult),
               num_res_blocks(num_res_blocks),
               temperal_downsample(temperal_downsample),
-              wan2_2(wan2_2) {
+              wan2_2(wan2_2),
+              is_2D(is_2D) {
             // attn_scales is always []
             std::vector<int64_t> dims = {dim};
             for (int u : dim_mult) {
                 dims.push_back(dim * u);
             }
 
-            if (wan2_2) {
-                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(12, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(in_channels, dims[0], {3, 3}, {1, 1}, {1, 1}));
             } else {
-                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(3, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(in_channels, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
             }
 
             int index = 0;
@@ -630,12 +680,13 @@ namespace WAN {
                                                                                          out_dim,
                                                                                          num_res_blocks,
                                                                                          t_down_flag,
-                                                                                         i != dim_mult.size() - 1));
+                                                                                         i != dim_mult.size() - 1,
+                                                                                         is_2D));
 
                     blocks["downsamples." + std::to_string(index++)] = block;
                 } else {
                     for (int j = 0; j < num_res_blocks; j++) {
-                        auto block                                       = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim));
+                        auto block                                       = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim, is_2D));
                         blocks["downsamples." + std::to_string(index++)] = block;
                         in_dim                                           = out_dim;
                     }
@@ -648,13 +699,17 @@ namespace WAN {
                 }
             }
 
-            blocks["middle.0"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(out_dim, out_dim));
+            blocks["middle.0"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(out_dim, out_dim, is_2D));
             blocks["middle.1"] = std::shared_ptr<GGMLBlock>(new AttentionBlock(out_dim));
-            blocks["middle.2"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(out_dim, out_dim));
+            blocks["middle.2"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(out_dim, out_dim, is_2D));
 
             blocks["head.0"] = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
             // head.1 is nn.SiLU()
-            blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, z_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(out_dim, z_dim, {3, 3}, {1, 1}, {1, 1}));
+            } else {
+                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, z_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            }
         }
 
         ggml_tensor* forward(GGMLRunnerContext* ctx,
@@ -673,7 +728,10 @@ namespace WAN {
             auto head_2   = std::dynamic_pointer_cast<CausalConv3d>(blocks["head.2"]);
 
             // conv1
-            if (feat_cache.size() > 0) {
+            if (is_2D) {
+                auto conv1 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv1"]);
+                x          = conv1->forward(ctx, x);
+            } else if (feat_cache.size() > 0) {
                 int idx      = feat_idx;
                 auto cache_x = ggml_ext_slice(ctx->ggml_ctx, x, 2, -CACHE_T, x->ne[2]);
                 if (cache_x->ne[2] < 2 && feat_cache[idx] != nullptr) {
@@ -728,7 +786,10 @@ namespace WAN {
             // head
             x = head_0->forward(ctx, x);
             x = ggml_silu(ctx->ggml_ctx, x);
-            if (feat_cache.size() > 0) {
+            if (is_2D) {
+                auto head_2 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["head.2"]);
+                x           = head_2->forward(ctx, x);
+            } else if (feat_cache.size() > 0) {
                 int idx      = feat_idx;
                 auto cache_x = ggml_ext_slice(ctx->ggml_ctx, x, 2, -CACHE_T, x->ne[2]);
                 if (cache_x->ne[2] < 2 && feat_cache[idx] != nullptr) {
@@ -753,25 +814,31 @@ namespace WAN {
     class Decoder3d : public GGMLBlock {
     protected:
         bool wan2_2;
+        int64_t out_channels;
         int64_t dim;
         int64_t z_dim;
         std::vector<int> dim_mult;
         int num_res_blocks;
         std::vector<bool> temperal_upsample;
+        bool is_2D = false;
 
     public:
         Decoder3d(int64_t dim                         = 128,
                   int64_t z_dim                       = 4,
+                  int64_t out_channels                = 3,
                   std::vector<int> dim_mult           = {1, 2, 4, 4},
                   int num_res_blocks                  = 2,
                   std::vector<bool> temperal_upsample = {true, true, false},
-                  bool wan2_2                         = false)
-            : dim(dim),
+                  bool wan2_2                         = false,
+                  bool is_2D                          = false)
+            : out_channels(out_channels),
+              dim(dim),
               z_dim(z_dim),
               dim_mult(dim_mult),
               num_res_blocks(num_res_blocks),
               temperal_upsample(temperal_upsample),
-              wan2_2(wan2_2) {
+              wan2_2(wan2_2),
+              is_2D(is_2D) {
             // attn_scales is always []
             std::vector<int64_t> dims = {dim_mult[dim_mult.size() - 1] * dim};
             for (int i = static_cast<int>(dim_mult.size()) - 1; i >= 0; i--) {
@@ -779,12 +846,16 @@ namespace WAN {
             }
 
             // init block
-            blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(z_dim, dims[0], {3, 3}, {1, 1}, {1, 1}));
+            } else {
+                blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, dims[0], {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            }
 
             // middle blocks
-            blocks["middle.0"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(dims[0], dims[0]));
+            blocks["middle.0"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(dims[0], dims[0], is_2D));
             blocks["middle.1"] = std::shared_ptr<GGMLBlock>(new AttentionBlock(dims[0]));
-            blocks["middle.2"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(dims[0], dims[0]));
+            blocks["middle.2"] = std::shared_ptr<GGMLBlock>(new ResidualBlock(dims[0], dims[0], is_2D));
 
             // upsample blocks
             int index = 0;
@@ -799,7 +870,8 @@ namespace WAN {
                                                                                      out_dim,
                                                                                      num_res_blocks + 1,
                                                                                      t_up_flag,
-                                                                                     i != dim_mult.size() - 1));
+                                                                                     i != dim_mult.size() - 1,
+                                                                                     is_2D));
 
                     blocks["upsamples." + std::to_string(index++)] = block;
                 } else {
@@ -807,7 +879,7 @@ namespace WAN {
                         in_dim = in_dim / 2;
                     }
                     for (int j = 0; j < num_res_blocks + 1; j++) {
-                        auto block                                     = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim));
+                        auto block                                     = std::shared_ptr<GGMLBlock>(new ResidualBlock(in_dim, out_dim, is_2D));
                         blocks["upsamples." + std::to_string(index++)] = block;
                         in_dim                                         = out_dim;
                     }
@@ -821,13 +893,14 @@ namespace WAN {
             }
 
             // output blocks
-            blocks["head.0"] = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
+            blocks["head.0"]  = std::shared_ptr<GGMLBlock>(new RMS_norm(out_dim));
+            int64_t final_dim = out_channels;
             // head.1 is nn.SiLU()
-            if (wan2_2) {
-                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, 12, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            if (is_2D) {
+                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(out_dim, final_dim, {3, 3}, {1, 1}, {1, 1}));
 
             } else {
-                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, 3, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+                blocks["head.2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(out_dim, final_dim, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
             }
         }
 
@@ -847,7 +920,10 @@ namespace WAN {
             auto head_2   = std::dynamic_pointer_cast<CausalConv3d>(blocks["head.2"]);
 
             // conv1
-            if (feat_cache.size() > 0) {
+            if (is_2D) {
+                auto conv1 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv1"]);
+                x          = conv1->forward(ctx, x);
+            } else if (feat_cache.size() > 0) {
                 int idx      = feat_idx;
                 auto cache_x = ggml_ext_slice(ctx->ggml_ctx, x, 2, -CACHE_T, x->ne[2]);
                 if (cache_x->ne[2] < 2 && feat_cache[idx] != nullptr) {
@@ -902,7 +978,10 @@ namespace WAN {
             // head
             x = head_0->forward(ctx, x);
             x = ggml_silu(ctx->ggml_ctx, x);
-            if (feat_cache.size() > 0) {
+            if (is_2D) {
+                auto head_2 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["head.2"]);
+                x           = head_2->forward(ctx, x);
+            } else if (feat_cache.size() > 0) {
                 int idx      = feat_idx;
                 auto cache_x = ggml_ext_slice(ctx->ggml_ctx, x, 2, -CACHE_T, x->ne[2]);
                 if (cache_x->ne[2] < 2 && feat_cache[idx] != nullptr) {
@@ -928,6 +1007,8 @@ namespace WAN {
     public:
         bool wan2_2                           = false;
         bool decode_only                      = true;
+        int64_t input_channels                = 3;
+        int patch_size                        = 1;
         int64_t dim                           = 96;
         int64_t dec_dim                       = 96;
         int64_t z_dim                         = 16;
@@ -935,6 +1016,7 @@ namespace WAN {
         int num_res_blocks                    = 2;
         std::vector<bool> temperal_upsample   = {true, true, false};
         std::vector<bool> temperal_downsample = {false, true, true};
+        bool is_2D                            = false;
 
         int _conv_num = 33;
         int _conv_idx = 0;
@@ -951,23 +1033,43 @@ namespace WAN {
         }
 
     public:
-        WanVAE(bool decode_only = true, bool wan2_2 = false)
-            : decode_only(decode_only), wan2_2(wan2_2) {
+        WanVAE(bool decode_only = true, SDVersion version = VERSION_WAN2, bool is_2D = false)
+            : decode_only(decode_only),
+              wan2_2(version == VERSION_WAN2_2_TI2V),
+              is_2D(is_2D) {
             // attn_scales is always []
             if (wan2_2) {
-                dim     = 160;
-                dec_dim = 256;
-                z_dim   = 48;
+                dim            = 160;
+                dec_dim        = 256;
+                z_dim          = 48;
+                input_channels = 12;
+                patch_size     = 2;
 
                 _conv_num     = 34;
                 _enc_conv_num = 26;
+            } else if (version == VERSION_QWEN_IMAGE_LAYERED) {
+                input_channels = 4;
             }
+
+            if (is_2D) {
+                temperal_upsample   = {false, false, false};
+                temperal_downsample = {false, false, false};
+            }
+
             if (!decode_only) {
-                blocks["encoder"] = std::shared_ptr<GGMLBlock>(new Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks, temperal_downsample, wan2_2));
-                blocks["conv1"]   = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim * 2, z_dim * 2, {1, 1, 1}));
+                blocks["encoder"] = std::shared_ptr<GGMLBlock>(new Encoder3d(dim, z_dim * 2, input_channels, dim_mult, num_res_blocks, temperal_downsample, wan2_2, is_2D));
+                if (is_2D) {
+                    blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(z_dim * 2, z_dim * 2, {1, 1}));
+                } else {
+                    blocks["conv1"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim * 2, z_dim * 2, {1, 1, 1}));
+                }
+            }
+            blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder3d(dec_dim, z_dim, input_channels, dim_mult, num_res_blocks, temperal_upsample, wan2_2, is_2D));
+            if (is_2D) {
+                blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2dBut3d(z_dim, z_dim, {1, 1}));
+            } else {
+                blocks["conv2"] = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, z_dim, {1, 1, 1}));
             }
-            blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder3d(dec_dim, z_dim, dim_mult, num_res_blocks, temperal_upsample, wan2_2));
-            blocks["conv2"]   = std::shared_ptr<GGMLBlock>(new CausalConv3d(z_dim, z_dim, {1, 1, 1}));
         }
 
         static ggml_tensor* patchify(ggml_context* ctx,
@@ -1030,11 +1132,13 @@ namespace WAN {
             GGML_ASSERT(b == 1);
             GGML_ASSERT(decode_only == false);
 
+            if (x->ne[2] > 1 && is_2D) {
+                LOG_WARN("Using 2D VAE to encode video, expect poor results");
+            }
+
             clear_cache();
 
-            if (wan2_2) {
-                x = patchify(ctx->ggml_ctx, x, 2, b);
-            }
+            x = patchify(ctx->ggml_ctx, x, patch_size, b);
             // sd::ggml_graph_cut::mark_graph_cut(x, "wan_vae.encode.prelude", "x");
 
             auto encoder = std::dynamic_pointer_cast<Encoder3d>(blocks["encoder"]);
@@ -1049,12 +1153,18 @@ namespace WAN {
                     auto in = ggml_ext_slice(ctx->ggml_ctx, x, 2, 0, 1);  // [b*c, 1, h, w]
                     out     = encoder->forward(ctx, in, b, _enc_feat_map, _enc_conv_idx, i);
                 } else {
-                    auto in   = ggml_ext_slice(ctx->ggml_ctx, x, 2, 1 + 4 * (i - 1), 1 + 4 * i);  // [b*c, 4, h, w]
+                    // if is_2D, drop 3 out of 4 frames
+                    auto in   = ggml_ext_slice(ctx->ggml_ctx, x, 2, 1 + 4 * (i - 1), (is_2D ? 1 - 3 : 1) + 4 * i);  // [b*c, 4, h, w]
                     auto out_ = encoder->forward(ctx, in, b, _enc_feat_map, _enc_conv_idx, i);
                     out       = ggml_concat(ctx->ggml_ctx, out, out_, 2);
                 }
             }
-            out     = conv1->forward(ctx, out);
+            if (is_2D) {
+                auto conv1 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv1"]);
+                out        = conv1->forward(ctx, out);
+            } else {
+                out = conv1->forward(ctx, out);
+            }
             auto mu = ggml_ext_chunk(ctx->ggml_ctx, out, 2, 3)[0];
             // sd::ggml_graph_cut::mark_graph_cut(mu, "wan_vae.encode.final", "mu");
             clear_cache();
@@ -1067,13 +1177,23 @@ namespace WAN {
             // z: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
 
+            if (z->ne[2] > 1 && is_2D) {
+                LOG_WARN("Using 2D VAE to decode video, expect poor results");
+            }
+
             clear_cache();
 
             auto decoder = std::dynamic_pointer_cast<Decoder3d>(blocks["decoder"]);
             auto conv2   = std::dynamic_pointer_cast<CausalConv3d>(blocks["conv2"]);
 
             int64_t iter_ = z->ne[2];
-            auto x        = conv2->forward(ctx, z);
+            auto x        = z;
+            if (is_2D) {
+                auto conv2 = std::dynamic_pointer_cast<Conv2dBut3d>(blocks["conv2"]);
+                x          = conv2->forward(ctx, z);
+            } else {
+                x = conv2->forward(ctx, z);
+            }
             // sd::ggml_graph_cut::mark_graph_cut(x, "wan_vae.decode.prelude", "x");
             ggml_tensor* out;
             for (int i = 0; i < iter_; i++) {
@@ -1085,11 +1205,15 @@ namespace WAN {
                     auto in   = ggml_ext_slice(ctx->ggml_ctx, x, 2, i, i + 1);  // [b*c, 1, h, w]
                     auto out_ = decoder->forward(ctx, in, b, _feat_map, _conv_idx, i);
                     out       = ggml_concat(ctx->ggml_ctx, out, out_, 2);
+                    if (is_2D) {
+                        // repeat frames to avoid mismatch
+                        for (int j = 0; j < 4 - 1; j++) {
+                            out = ggml_concat(ctx->ggml_ctx, out, out_, 2);
+                        }
+                    }
                 }
             }
-            if (wan2_2) {
-                out = unpatchify(ctx->ggml_ctx, out, 2, b);
-            }
+            out = unpatchify(ctx->ggml_ctx, out, patch_size, b);
             // sd::ggml_graph_cut::mark_graph_cut(out, "wan_vae.decode.final", "out");
             clear_cache();
             return out;
@@ -1110,9 +1234,7 @@ namespace WAN {
             auto in   = ggml_ext_slice(ctx->ggml_ctx, x, 2, i, i + 1);  // [b*c, 1, h, w]
             _conv_idx = 0;
             auto out  = decoder->forward(ctx, in, b, _feat_map, _conv_idx, i);
-            if (wan2_2) {
-                out = unpatchify(ctx->ggml_ctx, out, 2, b);
-            }
+            out       = unpatchify(ctx->ggml_ctx, out, patch_size, b);
             // sd::ggml_graph_cut::mark_graph_cut(out, "wan_vae.decode_partial.final", "out");
             return out;
         }
@@ -1129,7 +1251,20 @@ namespace WAN {
                      bool decode_only                                    = false,
                      SDVersion version                                   = VERSION_WAN2,
                      std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
-            : VAE(version, backend, prefix, weight_manager), decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V) {
+            : VAE(version, backend, prefix, weight_manager), decode_only(decode_only) {
+            bool is_2D = false;
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (ends_with(name, "decoder.conv1.weight")) {
+                    if (tensor_storage.ne[2] > 3) {
+                        is_2D = true;
+                    }
+                    break;
+                }
+            }
+            if (is_2D) {
+                LOG_DEBUG("USING 2D VAE");
+            }
+            ae = WanVAE(decode_only, version, is_2D);
             ae.init(params_ctx, tensor_storage_map, prefix);
         }
 
diff --git a/otherarch/sdcpp/src/model_loader.cpp b/otherarch/sdcpp/src/model_loader.cpp
index 788663103..fda1f3e82 100644
--- a/otherarch/sdcpp/src/model_loader.cpp
+++ b/otherarch/sdcpp/src/model_loader.cpp
@@ -20,6 +20,7 @@
 #include "model_io/torch_legacy_io.h"
 #include "model_io/torch_zip_io.h"
 #include "model_loader.h"
+#include "runtime/imatrix.h"
 #include "stable-diffusion.h"
 
 #include "core/ggml_extend_backend.h"
@@ -66,7 +67,6 @@ const char* unused_tensors[] = {
     // "v_pred", // Used to detect SDXL vpred models
     "text_encoders.llm.output.weight",
     "text_encoders.llm.lm_head.",
-    "first_stage_model.bn.",
 };
 
 bool is_unused_tensor(const std::string& name) {
@@ -171,7 +171,8 @@ void convert_tensor(void* src,
                     void* dst,
                     ggml_type dst_type,
                     int nrows,
-                    int n_per_row) {
+                    int n_per_row,
+                    std::vector<float> imatrix = {}) {
     int n = nrows * n_per_row;
     if (src_type == dst_type) {
         size_t nbytes = n * ggml_type_size(src_type) / ggml_blck_size(src_type);
@@ -180,7 +181,7 @@ void convert_tensor(void* src,
         if (dst_type == GGML_TYPE_F16) {
             ggml_fp32_to_fp16_row((float*)src, (ggml_fp16_t*)dst, n);
         } else {
-            std::vector<float> imatrix(n_per_row, 1.0f);  // dummy importance matrix
+            imatrix.resize(n_per_row, 1.0f);
             const float* im = imatrix.data();
             ggml_quantize_chunk(dst_type, (float*)src, dst, 0, nrows, n_per_row, im);
         }
@@ -210,7 +211,7 @@ void convert_tensor(void* src,
         if (dst_type == GGML_TYPE_F16) {
             ggml_fp32_to_fp16_row((float*)src_data_f32, (ggml_fp16_t*)dst, n);
         } else {
-            std::vector<float> imatrix(n_per_row, 1.0f);  // dummy importance matrix
+            imatrix.resize(n_per_row, 1.0f);
             const float* im = imatrix.data();
             ggml_quantize_chunk(dst_type, (float*)src_data_f32, dst, 0, nrows, n_per_row, im);
         }
@@ -499,7 +500,13 @@ SDVersion ModelLoader::get_sd_version() {
             tensor_storage_map.find("model.diffusion_model.transformer_blocks.0.img_mlp.w1.weight") != tensor_storage_map.end()) {
             return VERSION_LENS;
         }
+        if (tensor_storage.name.find("net.img_embedder.proj1.weight") != std::string::npos) {
+            return VERSION_MINIT2I;
+        }
         if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
+            if (tensor_storage_map.find("model.diffusion_model.time_text_embed.addition_t_embedding.weight") != tensor_storage_map.end()) {
+                return VERSION_QWEN_IMAGE_LAYERED;
+            }
             return VERSION_QWEN_IMAGE;
         }
         if (tensor_storage.name.find("llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
@@ -508,6 +515,9 @@ SDVersion ModelLoader::get_sd_version() {
         if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
             is_flux2 = true;
         }
+        if (tensor_storage.name.find("dual_time_embed.semantic_embedder.linear_1.weight") != std::string::npos) {
+            return VERSION_SEFI_IMAGE;
+        }
         if (tensor_storage.name.find("single_blocks.47.linear1.weight") != std::string::npos) {
             has_single_block_47 = true;
         }
@@ -996,6 +1006,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
     size_t total_tensors_processed = 0;
     const int64_t t_start          = start_time;
     int last_n_threads             = 1;
+    SDVersion imatrix_version      = (version_ == VERSION_COUNT) ? get_sd_version() : version_;
 
     for (size_t file_index = 0; file_index < file_data.size(); ++file_index) {
         auto& fdata                  = file_data[file_index];
@@ -1180,12 +1191,15 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                             failed = true;
                             return;
                         }
+                        std::string processed_name = convert_tensor_name(tensor_storage.name, imatrix_version);
+                        std::vector<float> imatrix = get_imatrix_collector().get_values(processed_name);
                         convert_tensor((void*)target_buf,
                                        tensor_storage.type,
                                        convert_buf,
                                        dst_tensor->type,
                                        (int)tensor_storage.nelements() / (int)tensor_storage.ne[0],
-                                       (int)tensor_storage.ne[0]);
+                                       (int)tensor_storage.ne[0],
+                                       std::move(imatrix));
                     } else {
                         convert_buf = read_buf;
                     }
diff --git a/otherarch/sdcpp/src/name_conversion.cpp b/otherarch/sdcpp/src/name_conversion.cpp
index ccc8347b7..64b7c681d 100644
--- a/otherarch/sdcpp/src/name_conversion.cpp
+++ b/otherarch/sdcpp/src/name_conversion.cpp
@@ -743,7 +743,7 @@ std::string convert_diffusion_model_name(std::string name, std::string prefix, S
         name = convert_diffusers_unet_to_original_sdxl(name);
     } else if (sd_version_is_sd3(version)) {
         name = convert_diffusers_dit_to_original_sd3(name);
-    } else if (sd_version_is_flux(version) || sd_version_is_flux2(version) || sd_version_is_longcat(version)) {
+    } else if (sd_version_is_flux(version) || sd_version_is_flux2(version) || sd_version_is_longcat(version) || sd_version_is_sefi_image(version)) {
         name = convert_diffusers_dit_to_original_flux(name);
     } else if (sd_version_is_z_image(version)) {
         name = convert_diffusers_dit_to_original_lumina2(name);
@@ -850,7 +850,77 @@ std::string convert_diffusers_vae_to_original_sd1(std::string name) {
     return result;
 }
 
-std::string convert_first_stage_model_name(std::string name, std::string prefix) {
+std::string convert_diffusers_to_original_wan_vae(std::string name) {
+    static const std::vector<std::pair<std::string, std::string>> prefix_map = {
+        {"quant_conv.", "conv1."},
+        {"post_quant_conv.", "conv2."},
+
+        {"decoder.up_blocks.0.resnets.0.", "decoder.upsamples.0.residual."},
+        {"decoder.up_blocks.0.resnets.1.", "decoder.upsamples.1.residual."},
+        {"decoder.up_blocks.0.resnets.2.", "decoder.upsamples.2.residual."},
+        {"decoder.up_blocks.0.upsamplers.0.", "decoder.upsamples.3."},
+
+        {"decoder.up_blocks.1.resnets.0.conv_shortcut.", "decoder.upsamples.4.shortcut."},
+        {"decoder.up_blocks.1.resnets.0.", "decoder.upsamples.4.residual."},
+        {"decoder.up_blocks.1.resnets.1.", "decoder.upsamples.5.residual."},
+        {"decoder.up_blocks.1.resnets.2.", "decoder.upsamples.6.residual."},
+        {"decoder.up_blocks.1.upsamplers.0.", "decoder.upsamples.7."},
+        {"decoder.up_blocks.2.resnets.0.", "decoder.upsamples.8.residual."},
+        {"decoder.up_blocks.2.resnets.1.", "decoder.upsamples.9.residual."},
+        {"decoder.up_blocks.2.resnets.2.", "decoder.upsamples.10.residual."},
+        {"decoder.up_blocks.2.upsamplers.0.", "decoder.upsamples.11."},
+        {"decoder.up_blocks.3.resnets.0.", "decoder.upsamples.12.residual."},
+        {"decoder.up_blocks.3.resnets.1.", "decoder.upsamples.13.residual."},
+        {"decoder.up_blocks.3.resnets.2.", "decoder.upsamples.14.residual."},
+
+        {"encoder.down_blocks.0.", "encoder.downsamples.0.residual."},
+        {"encoder.down_blocks.1.", "encoder.downsamples.1.residual."},
+        {"encoder.down_blocks.2.", "encoder.downsamples.2."},
+        {"encoder.down_blocks.3.conv_shortcut.", "encoder.downsamples.3.shortcut."},
+        {"encoder.down_blocks.3.", "encoder.downsamples.3.residual."},
+        {"encoder.down_blocks.4.", "encoder.downsamples.4.residual."},
+        {"encoder.down_blocks.5.", "encoder.downsamples.5."},
+        {"encoder.down_blocks.6.conv_shortcut.", "encoder.downsamples.6.shortcut."},
+        {"encoder.down_blocks.6.", "encoder.downsamples.6.residual."},
+        {"encoder.down_blocks.7.", "encoder.downsamples.7.residual."},
+        {"encoder.down_blocks.8.", "encoder.downsamples.8."},
+        {"encoder.down_blocks.9.", "encoder.downsamples.9.residual."},
+        {"encoder.down_blocks.10.", "encoder.downsamples.10.residual."},
+    };
+
+    static const std::vector<std::pair<std::string, std::string>> shared_name_map = {
+        {".conv_in.", ".conv1."},
+        {".norm_out.", ".head.0."},
+        {".conv_out.", ".head.2."},
+
+        {".mid_block.attentions.0.", ".middle.1."},
+        {".mid_block.resnets.0.", ".middle.0.residual."},
+        {".mid_block.resnets.1.", ".middle.2.residual."},
+    };
+
+    static const std::vector<std::pair<std::string, std::string>> resnet_name_map = {
+        {".norm1.", ".0."},
+        {".conv1.", ".2."},
+        {".norm2.", ".3."},
+        {".conv2.", ".6."},
+    };
+
+    replace_with_name_map(name, shared_name_map);
+    replace_with_prefix_map(name, prefix_map);
+
+    // Only apply the ResNet-specific renaming if the tensor belongs to a ResNet block.
+    // This prevents generic ".conv1." or ".conv2." matching on top-level encoder/decoder convolutions.
+    if (name.find(".residual.") != std::string::npos) {
+        replace_with_name_map(name, resnet_name_map);
+    }
+
+    return name;
+}
+
+std::string convert_first_stage_model_name(std::string name, std::string prefix, SDVersion version) {
+    if (sd_version_uses_wan_vae(version)) {
+        return convert_diffusers_to_original_wan_vae(name);
+    }
     static std::unordered_map<std::string, std::string> vae_name_map = {
         {"decoder.post_quant_conv.", "post_quant_conv."},
         {"encoder.quant_conv.", "quant_conv."},
@@ -1239,7 +1309,7 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
     {
         for (const auto& prefix : first_stage_model_prefix_vec) {
             if (starts_with(name, prefix)) {
-                name = convert_first_stage_model_name(name.substr(prefix.size()), prefix);
+                name = convert_first_stage_model_name(name.substr(prefix.size()), prefix, version);
                 if (version == VERSION_SDXS_512_DS || version == VERSION_SDXS_09) {
                     name = "tae." + name;
                 } else {
diff --git a/otherarch/sdcpp/src/runtime/denoiser.hpp b/otherarch/sdcpp/src/runtime/denoiser.hpp
index 28b29ef27..812eebe61 100644
--- a/otherarch/sdcpp/src/runtime/denoiser.hpp
+++ b/otherarch/sdcpp/src/runtime/denoiser.hpp
@@ -302,6 +302,137 @@ struct KarrasScheduler : SigmaScheduler {
     }
 };
 
+struct BetaScheduler : SigmaScheduler {
+    static constexpr double alpha = 0.6;
+    static constexpr double beta  = 0.6;
+
+    static double log_beta(double a, double b) {
+        return std::lgamma(a) + std::lgamma(b) - std::lgamma(a + b);
+    }
+
+    static double incbeta(double x, double a, double b) {
+        if (x <= 0.0) {
+            return 0.0;
+        }
+        if (x >= 1.0) {
+            return 1.0;
+        }
+
+        // Continued fraction approximation using Lentz's method.
+        const int max_iter   = 200;
+        const double epsilon = 3.0e-7;
+        const double tiny    = 1e-30;
+
+        const double qab = a + b;
+        const double qap = a + 1.0;
+        const double qam = a - 1.0;
+
+        double c = 1.0;
+        double d = 1.0 - qab * x / qap;
+        if (std::abs(d) < tiny) {
+            d = tiny;
+        }
+        d        = 1.0 / d;
+        double h = d;
+
+        for (int m = 1; m <= max_iter; m++) {
+            const int m2 = 2 * m;
+
+            double aa = m * (b - m) * x / ((qam + m2) * (a + m2));
+            d         = 1.0 + aa * d;
+            if (std::abs(d) < tiny) {
+                d = tiny;
+            }
+            c = 1.0 + aa / c;
+            if (std::abs(c) < tiny) {
+                c = tiny;
+            }
+            d = 1.0 / d;
+            h *= d * c;
+
+            aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2));
+            d  = 1.0 + aa * d;
+            if (std::abs(d) < tiny) {
+                d = tiny;
+            }
+            c = 1.0 + aa / c;
+            if (std::abs(c) < tiny) {
+                c = tiny;
+            }
+            d                = 1.0 / d;
+            const double del = d * c;
+            h *= del;
+
+            if (std::abs(del - 1.0) < epsilon) {
+                break;
+            }
+        }
+
+        return std::exp(a * std::log(x) + b * std::log(1.0 - x) - log_beta(a, b)) / a * h;
+    }
+
+    static double beta_cdf(double x, double a, double b) {
+        if (x == 0.0) {
+            return 0.0;
+        }
+        if (x == 1.0) {
+            return 1.0;
+        }
+        if (x < (a + 1.0) / (a + b + 2.0)) {
+            return incbeta(x, a, b);
+        }
+        return 1.0 - incbeta(1.0 - x, b, a);
+    }
+
+    static double beta_ppf(double u, double a, double b, int max_iter = 30) {
+        double x = 0.5;
+        for (int i = 0; i < max_iter; i++) {
+            const double f = beta_cdf(x, a, b) - u;
+            if (std::abs(f) < 1e-10) {
+                break;
+            }
+            const double df = std::exp((a - 1.0) * std::log(x) + (b - 1.0) * std::log(1.0 - x) - log_beta(a, b));
+            x -= f / df;
+            if (x <= 0.0) {
+                x = 1e-10;
+            }
+            if (x >= 1.0) {
+                x = 1.0 - 1e-10;
+            }
+        }
+        return x;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> result;
+        result.reserve(n + 1);
+
+        const int t_max = TIMESTEPS - 1;
+        if (n == 0) {
+            return result;
+        } else if (n == 1) {
+            result.push_back(t_to_sigma(static_cast<float>(t_max)));
+            result.push_back(0.f);
+            return result;
+        }
+
+        int last_t = -1;
+        for (uint32_t i = 0; i < n; i++) {
+            const double u      = 1.0 - static_cast<double>(i) / static_cast<double>(n);
+            const double t_cont = beta_ppf(u, alpha, beta) * t_max;
+            const int t         = static_cast<int>(std::lround(t_cont));
+
+            if (t != last_t) {
+                result.push_back(t_to_sigma(static_cast<float>(t)));
+                last_t = t;
+            }
+        }
+
+        result.push_back(0.f);
+        return result;
+    }
+};
+
 struct SimpleScheduler : SigmaScheduler {
     std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> result_sigmas;
@@ -559,6 +690,122 @@ struct LTX2Scheduler : SigmaScheduler {
     }
 };
 
+inline float flux_time_shift(float mu, float sigma, float t) {
+    return ::expf(mu) / (::expf(mu) + ::powf((1.0f / t - 1.0f), sigma));
+}
+
+// https://github.com/black-forest-labs/flux/blob/main/src/flux/sampling.py#L289
+struct FluxScheduler : SigmaScheduler {
+    int image_seq_len = 0;
+    float base_shift  = 0.5f;
+    float max_shift   = 1.15f;
+
+    explicit FluxScheduler(int image_seq_len, const char* extra_sample_args = nullptr)
+        : image_seq_len(image_seq_len) {
+        parse_extra_sample_args(extra_sample_args);
+    }
+
+    void parse_extra_sample_args(const char* extra_sample_args) {
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "flux scheduler arg")) {
+            if (key == "base_shift") {
+                if (!parse_strict_float(value, base_shift)) {
+                    LOG_WARN("ignoring invalid flux scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "max_shift") {
+                if (!parse_strict_float(value, max_shift)) {
+                    LOG_WARN("ignoring invalid flux scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+    }
+
+    float compute_mu() const {
+        constexpr float base_shift_anchor = 256.0f;
+        constexpr float max_shift_anchor  = 4096.0f;
+        float m                           = (max_shift - base_shift) / (max_shift_anchor - base_shift_anchor);
+        float b                           = base_shift - m * base_shift_anchor;
+        return static_cast<float>(image_seq_len) * m + b;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> sigmas;
+        sigmas.reserve(n + 1);
+
+        float mu = compute_mu();
+        LOG_DEBUG("Flux scheduler: image_seq_len=%d, steps=%u, mu=%.3f", image_seq_len, n, mu);
+
+        if (n == 0) {
+            sigmas.push_back(1.0f);
+            return sigmas;
+        }
+
+        for (uint32_t i = 0; i <= n; ++i) {
+            float t = 1.0f - static_cast<float>(i) / static_cast<float>(n);
+            if (t <= 0.0f) {
+                sigmas.push_back(0.0f);
+            } else {
+                sigmas.push_back(flux_time_shift(mu, 1.0f, t));
+            }
+        }
+
+        sigmas[n] = 0.0f;
+        return sigmas;
+    }
+};
+
+// https://github.com/black-forest-labs/flux2/blob/main/src/flux2/sampling.py#L244
+struct Flux2Scheduler : SigmaScheduler {
+    int image_seq_len = 0;
+
+    explicit Flux2Scheduler(int image_seq_len)
+        : image_seq_len(image_seq_len) {}
+
+    static float compute_empirical_mu(int image_seq_len, uint32_t num_steps) {
+        const float a1 = 8.73809524e-05f;
+        const float b1 = 1.89833333f;
+        const float a2 = 0.00016927f;
+        const float b2 = 0.45666666f;
+
+        if (image_seq_len > 4300) {
+            return a2 * image_seq_len + b2;
+        }
+
+        float m_200 = a2 * image_seq_len + b2;
+        float m_10  = a1 * image_seq_len + b1;
+
+        float a = (m_200 - m_10) / 190.0f;
+        float b = m_200 - 200.0f * a;
+        return a * num_steps + b;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> sigmas;
+        sigmas.reserve(n + 1);
+
+        float mu = compute_empirical_mu(image_seq_len, n);
+        LOG_DEBUG("Flux2 scheduler: image_seq_len=%d, steps=%u, mu=%.3f", image_seq_len, n, mu);
+
+        if (n == 0) {
+            sigmas.push_back(1.0f);
+            return sigmas;
+        }
+
+        for (uint32_t i = 0; i <= n; ++i) {
+            float t = 1.0f - static_cast<float>(i) / static_cast<float>(n);
+            if (t <= 0.0f) {
+                sigmas.push_back(0.0f);
+            } else if (t >= 1.0f) {
+                sigmas.push_back(1.0f);
+            } else {
+                sigmas.push_back(flux_time_shift(mu, 1.0f, t));
+            }
+        }
+
+        sigmas[n] = 0.0f;
+        return sigmas;
+    }
+};
+
 /*
  * Logit-Normal Scheduler
  * Based on: https://github.com/ideogram-oss/ideogram4/blob/main/src/ideogram4/scheduler.py
@@ -602,7 +849,7 @@ struct LogitNormalScheduler : SigmaScheduler {
             }
         }
         if (image_seq_len > 0 && resolution_aware) {
-            mean += 0.5 * std::log(static_cast<float>(image_seq_len) / static_cast<float>(known_seq_len));
+            mean += 0.5f * std::log(static_cast<float>(image_seq_len) / static_cast<float>(known_seq_len));
         }
     }
 
@@ -616,7 +863,6 @@ struct LogitNormalScheduler : SigmaScheduler {
         one_minus_t_min = sigmoid(0.5f * logsnr_max);
         // t_max = 1.0f / (1.0f + std::exp(0.5f * logsnr_min));
         one_minus_t_max = sigmoid(0.5f * logsnr_min);
-
     }
 
     LogitNormalScheduler(int image_seq_len = 0, const char* extra_sample_args = nullptr) {
@@ -736,7 +982,7 @@ struct LogitNormalScheduler : SigmaScheduler {
             float t = static_cast<float>(i) / static_cast<float>(n);
 
             // ndtri(1-t) == -ndtri(t)
-            float z = -ndtri(t);
+            float z = static_cast<float>(-ndtri(t));
 
             float y = mean + std * z;
 
@@ -780,6 +1026,10 @@ struct Denoiser {
                 LOG_INFO("get_sigmas with Karras scheduler");
                 scheduler = std::make_shared<KarrasScheduler>();
                 break;
+            case BETA_SCHEDULER:
+                LOG_INFO("get_sigmas with Beta scheduler");
+                scheduler = std::make_shared<BetaScheduler>();
+                break;
             case EXPONENTIAL_SCHEDULER:
                 LOG_INFO("get_sigmas exponential scheduler");
                 scheduler = std::make_shared<ExponentialScheduler>();
@@ -825,6 +1075,16 @@ struct Denoiser {
                 scheduler = std::make_shared<LogitNormalScheduler>(image_seq_len, extra_sample_args);
                 break;
             }
+            case FLUX2_SCHEDULER: {
+                LOG_INFO("get_sigmas with Flux2 scheduler");
+                scheduler = std::make_shared<Flux2Scheduler>(image_seq_len);
+                break;
+            }
+            case FLUX_SCHEDULER: {
+                LOG_INFO("get_sigmas with Flux scheduler");
+                scheduler = std::make_shared<FluxScheduler>(image_seq_len, extra_sample_args);
+                break;
+            }
             default:
                 LOG_INFO("get_sigmas with discrete scheduler (default)");
                 scheduler = std::make_shared<DiscreteScheduler>();
@@ -989,10 +1249,6 @@ struct DiscreteFlowDenoiser : public Denoiser {
     }
 };
 
-inline float flux_time_shift(float mu, float sigma, float t) {
-    return ::expf(mu) / (::expf(mu) + ::powf((1.0f / t - 1.0f), sigma));
-}
-
 struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
     FluxFlowDenoiser() = default;
 
@@ -1006,35 +1262,141 @@ struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
     }
 };
 
-struct Flux2FlowDenoiser : public FluxFlowDenoiser {
-    Flux2FlowDenoiser() = default;
+struct SefiFlowDenoiser;
 
-    float compute_empirical_mu(uint32_t n, int image_seq_len) {
-        const float a1 = 8.73809524e-05f;
-        const float b1 = 1.89833333f;
-        const float a2 = 0.00016927f;
-        const float b2 = 0.45666666f;
+struct SefiFlowDenoiser : public FluxFlowDenoiser {
+    static constexpr int kNumTrainTimesteps = 1000;
+    static constexpr int kSemChannels       = 16;
+    static constexpr int kTotalChannels     = 144;
 
-        if (image_seq_len > 4300) {
-            float mu = a2 * image_seq_len + b2;
-            return mu;
+    float delta_t              = 0.1f;
+    float timestep_shift_alpha = 1.0f;
+
+    std::vector<float> sem_sigmas;
+    std::vector<float> tex_sigmas;
+    std::vector<float> sem_timesteps;
+    std::vector<float> tex_timesteps;
+
+    SefiFlowDenoiser() = default;
+
+    static float apply_alpha_shift(float u_unit, float alpha) {
+        if (alpha == 1.0f) {
+            return u_unit;
+        }
+        float denom = 1.0f + (alpha - 1.0f) * u_unit;
+        return (alpha * u_unit) / denom;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n,
+                                  int image_seq_len,
+                                  scheduler_t scheduler_type,
+                                  SDVersion version,
+                                  const char* extra_sample_args = nullptr) override {
+        sem_sigmas.clear();
+        tex_sigmas.clear();
+        sem_timesteps.clear();
+        tex_timesteps.clear();
+
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "sefi scheduler arg")) {
+            if (key == "sefi_alpha") {
+                if (!parse_strict_float(value, timestep_shift_alpha)) {
+                    LOG_WARN("ignoring invalid sefi scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "sefi_delta_t") {
+                if (!parse_strict_float(value, delta_t)) {
+                    LOG_WARN("ignoring invalid sefi scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
         }
 
-        float m_200 = a2 * image_seq_len + b2;
-        float m_10  = a1 * image_seq_len + b1;
+        for (uint32_t i = 0; i <= n; ++i) {
+            float u_base    = static_cast<float>(i) / static_cast<float>(n);
+            float u_shifted = apply_alpha_shift(u_base, timestep_shift_alpha);
+            float u_sem_raw = u_shifted * (1.0f + delta_t);
 
-        float a  = (m_200 - m_10) / 190.0f;
-        float b  = m_200 - 200.0f * a;
-        float mu = a * n + b;
+            float u_sem = std::min(u_sem_raw, 1.0f);
+            float u_tex = std::max(0.0f, std::min(u_sem_raw - delta_t, 1.0f));
 
-        return mu;
+            int idx_sem = std::min(kNumTrainTimesteps - 1,
+                                   std::max(0, static_cast<int>(u_sem * (kNumTrainTimesteps - 1))));
+            int idx_tex = std::min(kNumTrainTimesteps - 1,
+                                   std::max(0, static_cast<int>(u_tex * (kNumTrainTimesteps - 1))));
+
+            float t_sem     = static_cast<float>(kNumTrainTimesteps - idx_sem);
+            float t_tex     = static_cast<float>(kNumTrainTimesteps - idx_tex);
+            float sigma_sem = t_sem / static_cast<float>(kNumTrainTimesteps);
+            float sigma_tex = t_tex / static_cast<float>(kNumTrainTimesteps);
+
+            sem_timesteps.push_back(t_sem);
+            tex_timesteps.push_back(t_tex);
+            sem_sigmas.push_back(sigma_sem);
+            tex_sigmas.push_back(sigma_tex);
+        }
+        LOG_DEBUG("SefiFlowDenoiser: built %u-step dual schedule (alpha=%.2f delta_t=%.2f)",
+                  n, timestep_shift_alpha, delta_t);
+        return tex_sigmas;
+    }
+};
+
+// MiniT2I predicts x0 directly and integrates a linear flow ODE:
+//   x_{t+dt} = x_t + (x0 - x_t)/(1 - t) * dt,  t in [0, 1), x0 = start = noise * 2.
+// Mapping sigma = 1 - t makes the generic Euler update
+//   x += (x - denoised)/sigma * (sigma_next - sigma)
+// exactly reproduce that step when denoised == x0. To make the generic
+// `denoised = pred * c_out + x * c_skip` yield x0 from the model's raw x0
+// prediction we use c_skip = 0, c_out = 1, c_in = 1. Sigmas run linearly 1 -> 0.
+struct MiniT2IFlowDenoiser : public Denoiser {
+    float sigma_min() override {
+        return 0.0f;
+    }
+
+    float sigma_max() override {
+        return 1.0f;
+    }
+
+    float sigma_to_t(float sigma) override {
+        return 1.0f - sigma;
+    }
+
+    float t_to_sigma(float t) override {
+        return 1.0f - t;
+    }
+
+    std::vector<float> get_scalings(float sigma) override {
+        SD_UNUSED(sigma);
+        float c_skip = 0.0f;
+        float c_out  = 1.0f;
+        float c_in   = 1.0f;
+        return {c_skip, c_out, c_in};
+    }
+
+    sd::Tensor<float> noise_scaling(float sigma,
+                                    const sd::Tensor<float>& noise,
+                                    const sd::Tensor<float>& latent) override {
+        SD_UNUSED(sigma);
+        SD_UNUSED(latent);
+        // Sampling starts from x0_init = noise * 2 (see MiniT2I reference).
+        return noise * 2.0f;
+    }
+
+    sd::Tensor<float> inverse_noise_scaling(float sigma, const sd::Tensor<float>& latent) override {
+        SD_UNUSED(sigma);
+        return latent;
     }
 
     std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) override {
-        float mu = compute_empirical_mu(n, image_seq_len);
-        LOG_DEBUG("Flux2FlowDenoiser: set shift to %.3f", mu);
-        set_shift(mu);
-        return Denoiser::get_sigmas(n, image_seq_len, scheduler_type, version, extra_sample_args);
+        SD_UNUSED(image_seq_len);
+        SD_UNUSED(scheduler_type);
+        SD_UNUSED(version);
+        SD_UNUSED(extra_sample_args);
+        // Uniform t schedule 0 -> 1 => sigma 1 -> 0, matching the reference loop.
+        std::vector<float> sigmas;
+        sigmas.reserve(n + 1);
+        for (uint32_t i = 0; i < n; ++i) {
+            sigmas.push_back(1.0f - static_cast<float>(i) / static_cast<float>(n));
+        }
+        sigmas.push_back(0.0f);
+        return sigmas;
     }
 };
 
@@ -1141,6 +1503,40 @@ static sd::Tensor<float> sample_euler_ancestral(denoise_cb_t model,
     return x;
 }
 
+static sd::Tensor<float> sample_sefi_euler(SefiFlowDenoiser* sefi,
+                                           denoise_cb_t model,
+                                           sd::Tensor<float> x) {
+    const std::vector<float>& sigma_tex_vec = sefi->tex_sigmas;
+    const std::vector<float>& sigma_sem_vec = sefi->sem_sigmas;
+    int steps                               = static_cast<int>(sigma_tex_vec.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        float sigma_tex_cur  = sigma_tex_vec[i];
+        float sigma_tex_next = sigma_tex_vec[i + 1];
+        float sigma_sem_cur  = sigma_sem_vec[i];
+        float sigma_sem_next = sigma_sem_vec[i + 1];
+        if (sigma_tex_cur <= 1e-9f) {
+            continue;
+        }
+        auto denoised_opt = model(x, sigma_tex_cur, i + 1);
+        if (denoised_opt.pred.empty()) {
+            return {};
+        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+        sd::Tensor<float> velocity = (x - denoised) / sigma_tex_cur;
+
+        auto x_sem      = sd::ops::slice(x, 2, 0, SefiFlowDenoiser::kSemChannels);
+        auto x_tex      = sd::ops::slice(x, 2, SefiFlowDenoiser::kSemChannels, SefiFlowDenoiser::kTotalChannels);
+        auto vel_sem    = sd::ops::slice(velocity, 2, 0, SefiFlowDenoiser::kSemChannels);
+        auto vel_tex    = sd::ops::slice(velocity, 2, SefiFlowDenoiser::kSemChannels, SefiFlowDenoiser::kTotalChannels);
+        auto x_sem_next = x_sem + vel_sem * (sigma_sem_next - sigma_sem_cur);
+        auto x_tex_next = x_tex + vel_tex * (sigma_tex_next - sigma_tex_cur);
+
+        sd::ops::slice_assign(&x, 2, 0, SefiFlowDenoiser::kSemChannels, x_sem_next);
+        sd::ops::slice_assign(&x, 2, SefiFlowDenoiser::kSemChannels, SefiFlowDenoiser::kTotalChannels, x_tex_next);
+    }
+    return x;
+}
+
 static sd::Tensor<float> sample_euler(denoise_cb_t model,
                                       sd::Tensor<float> x,
                                       const std::vector<float>& sigmas) {
@@ -2056,7 +2452,13 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
                                             std::shared_ptr<RNG> rng,
                                             float eta,
                                             bool is_flow_denoiser,
-                                            const char* extra_sample_args) {
+                                            const char* extra_sample_args,
+                                            std::shared_ptr<Denoiser> denoiser_for_dispatch = nullptr) {
+    if (denoiser_for_dispatch) {
+        if (auto sefi = std::dynamic_pointer_cast<SefiFlowDenoiser>(denoiser_for_dispatch)) {
+            return sample_sefi_euler(sefi.get(), model, std::move(x));
+        }
+    }
     SamplerExtraArgs extra_args = parse_key_value_args(extra_sample_args, "extra sample arg");
     switch (method) {
         case EULER_A_SAMPLE_METHOD:
diff --git a/otherarch/sdcpp/src/runtime/imatrix.cpp b/otherarch/sdcpp/src/runtime/imatrix.cpp
new file mode 100644
index 000000000..313eadc68
--- /dev/null
+++ b/otherarch/sdcpp/src/runtime/imatrix.cpp
@@ -0,0 +1,308 @@
+#include "runtime/imatrix.h"
+
+/* Adapted from llama.cpp (credits: Kawrakow). */
+
+#include "core/util.h"
+#include "ggml-backend.h"
+#include "ggml.h"
+#include "stable-diffusion.h"
+
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+
+static IMatrixCollector imatrix_collector;
+
+IMatrixCollector& get_imatrix_collector() {
+    return imatrix_collector;
+}
+
+// remove any prefix and suffixes from the name
+// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
+static std::string filter_tensor_name(const char* name) {
+    std::string wname;
+    const char* p = strchr(name, '#');
+    if (p != NULL) {
+        p             = p + 1;
+        const char* q = strchr(p, '#');
+        if (q != NULL) {
+            wname = std::string(p, q - p);
+        } else {
+            wname = p;
+        }
+    } else {
+        wname = name;
+    }
+    return wname;
+}
+
+bool IMatrixCollector::collect_imatrix(struct ggml_tensor* t, bool ask, void* user_data) {
+    GGML_UNUSED(user_data);
+    if (t == nullptr) {
+        return false;
+    }
+    if (t->op != GGML_OP_MUL_MAT && t->op != GGML_OP_MUL_MAT_ID) {
+        return false;
+    }
+
+    const struct ggml_tensor* src0 = t->src[0];
+    const struct ggml_tensor* src1 = t->src[1];
+    if (src0 == nullptr || src1 == nullptr) {
+        return false;
+    }
+    std::string wname = filter_tensor_name(src0->name);
+
+    // when ask is true, the scheduler wants to know if we are interested in data from this tensor
+    // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
+    if (ask) {
+        if (t->op == GGML_OP_MUL_MAT_ID) {
+            return true;  // collect all indirect matrix multiplications
+        }
+        // why are small batches ignored (<16 tokens)?
+        // if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
+        if (!(wname.substr(0, 6) == "model." || wname.substr(0, 17) == "cond_stage_model." || wname.substr(0, 14) == "text_encoders.")) {
+            return false;
+        }
+        return true;
+    }
+    std::lock_guard<std::mutex> lock(mutex_);
+
+    // copy the data from the GPU memory if needed
+    const bool is_host = src1->buffer == NULL || ggml_backend_buffer_is_host(src1->buffer);
+
+    if (!is_host) {
+        src1_data_.resize(ggml_nelements(src1));
+        ggml_backend_tensor_get(src1, src1_data_.data(), 0, ggml_nbytes(src1));
+    }
+
+    const float* data = is_host ? (const float*)src1->data : src1_data_.data();
+
+    // this has been adapted to the new format of storing merged experts in a single 3d tensor
+    // ref: https://github.com/ggml-org/llama.cpp/pull/6387
+    if (t->op == GGML_OP_MUL_MAT_ID) {
+        //   ids  -> [n_experts_used, n_tokens]
+        //   src1 -> [cols, n_expert_used, n_tokens]
+        const ggml_tensor* ids = t->src[2];
+        const int n_as         = static_cast<int>(src0->ne[2]);
+        const int n_ids        = static_cast<int>(ids->ne[0]);
+
+        // the top-k selected expert ids are stored in the ids tensor
+        // for simplicity, always copy ids to host, because it is small
+        // take into account that ids is not contiguous!
+
+        GGML_ASSERT(ids->ne[1] == src1->ne[2]);
+
+        ids_.resize(ggml_nbytes(ids));
+        ggml_backend_tensor_get(ids, ids_.data(), 0, ggml_nbytes(ids));
+
+        auto& e = stats_[wname];
+
+        ++e.ncall;
+
+        if (e.values.empty()) {
+            e.values.resize(src1->ne[0] * n_as, 0);
+            e.counts.resize(src1->ne[0] * n_as, 0);
+        } else if (e.values.size() != (size_t)src1->ne[0] * n_as) {
+            LOG_ERROR("inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0] * n_as);
+            exit(1);  // GGML_ABORT("fatal error");
+        }
+        // loop over all possible experts, regardless if they are used or not in the batch
+        for (int ex = 0; ex < n_as; ++ex) {
+            size_t e_start = ex * src1->ne[0];
+
+            for (int idx = 0; idx < n_ids; ++idx) {
+                for (int row = 0; row < (int)src1->ne[2]; ++row) {
+                    const int excur = *(const int32_t*)(ids_.data() + row * ids->nb[1] + idx * ids->nb[0]);
+
+                    GGML_ASSERT(excur >= 0 && excur < n_as);  // sanity check
+
+                    if (excur != ex)
+                        continue;
+
+                    const int64_t i11 = idx % src1->ne[1];
+                    const int64_t i12 = row;
+                    const float* x    = (const float*)((const char*)data + i11 * src1->nb[1] + i12 * src1->nb[2]);
+
+                    for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                        e.values[e_start + j] += x[j] * x[j];
+                        e.counts[e_start + j]++;
+                        if (!std::isfinite(e.values[e_start + j])) {
+                            LOG_ERROR("%f detected in %s\n", e.values[e_start + j], wname.c_str());
+                            exit(1);
+                        }
+                    }
+                }
+            }
+        }
+    } else {
+        auto& e = stats_[wname];
+        if (e.values.empty()) {
+            e.values.resize(src1->ne[0], 0);
+            e.counts.resize(src1->ne[0], 0);
+        } else if (e.values.size() != (size_t)src1->ne[0]) {
+            LOG_WARN("inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
+            exit(1);  // GGML_ABORT("fatal error");
+        }
+
+        ++e.ncall;
+        for (int row = 0; row < (int)src1->ne[1]; ++row) {
+            const float* x = data + row * src1->ne[0];
+            for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                if (std::isfinite(x[j])) {
+                    e.values[j] += x[j] * x[j];
+                    e.counts[j]++;
+                    if (!std::isfinite(e.values[j])) {
+                        LOG_WARN("%f detected in %s\n", e.values[j], wname.c_str());
+                        exit(1);
+                    }
+                } else {
+                    // Likely something from an attention mask?
+                }
+            }
+        }
+    }
+    return true;
+}
+
+bool load_imatrix(const char* imatrix_path) {
+    return imatrix_collector.load_imatrix(imatrix_path);
+}
+
+void save_imatrix(const char* imatrix_path) {
+    imatrix_collector.save_imatrix(imatrix_path);
+}
+
+static bool collect_imatrix(struct ggml_tensor* t, bool ask, void* user_data) {
+    return imatrix_collector.collect_imatrix(t, ask, user_data);
+}
+
+void enable_imatrix_collection() {
+    sd_set_backend_eval_callback(collect_imatrix, nullptr);
+}
+
+void disable_imatrix_collection() {
+    sd_set_backend_eval_callback(nullptr, nullptr);
+}
+
+void IMatrixCollector::save_imatrix(std::string fname, int ncall) const {
+    if (ncall > 0) {
+        fname += ".at_";
+        fname += std::to_string(ncall);
+    }
+    // avoid writing imatrix entries that do not have full data
+    // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
+
+    int n_entries = 0;
+    std::vector<std::string> to_store;
+
+    for (const auto& kv : stats_) {
+        const int n_all = static_cast<int>(kv.second.counts.size());
+
+        if (n_all == 0) {
+            continue;
+        }
+
+        int n_zeros = 0;
+        for (const int c : kv.second.counts) {
+            if (c == 0) {
+                n_zeros++;
+            }
+        }
+
+        if (n_zeros == n_all) {
+            LOG_WARN("entry '%40s' has no data - skipping\n", kv.first.c_str());
+            continue;
+        }
+
+        if (n_zeros > 0) {
+            LOG_WARN("entry '%40s' has partial data (%.2f%%) - skipping\n", kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
+            continue;
+        }
+
+        n_entries++;
+        to_store.push_back(kv.first);
+    }
+
+    if (to_store.size() < stats_.size()) {
+        LOG_WARN("storing only %zu out of %zu entries\n", to_store.size(), stats_.size());
+    }
+
+    std::ofstream out(fname, std::ios::binary);
+    out.write((const char*)&n_entries, sizeof(n_entries));
+    for (const auto& name : to_store) {
+        const auto& stat = stats_.at(name);
+        int len          = static_cast<int>(name.size());
+        out.write((const char*)&len, sizeof(len));
+        out.write(name.c_str(), len);
+        out.write((const char*)&stat.ncall, sizeof(stat.ncall));
+        int nval = static_cast<int>(stat.values.size());
+        out.write((const char*)&nval, sizeof(nval));
+        if (nval > 0) {
+            std::vector<float> tmp(nval);
+            for (int i = 0; i < nval; i++) {
+                tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
+            }
+            out.write((const char*)tmp.data(), nval * sizeof(float));
+        }
+    }
+
+    // Write the number of call the matrix was computed with
+    out.write((const char*)&last_call_, sizeof(last_call_));
+}
+
+bool IMatrixCollector::load_imatrix(const char* fname) {
+    std::ifstream in(fname, std::ios::binary);
+    if (!in) {
+        LOG_ERROR("failed to open %s\n", fname);
+        return false;
+    }
+    int n_entries;
+    in.read((char*)&n_entries, sizeof(n_entries));
+    if (in.fail() || n_entries < 1) {
+        LOG_ERROR("no data in file %s\n", fname);
+        return false;
+    }
+    for (int i = 0; i < n_entries; ++i) {
+        int len;
+        in.read((char*)&len, sizeof(len));
+        std::vector<char> name_as_vec(len + 1);
+        in.read((char*)name_as_vec.data(), len);
+        if (in.fail()) {
+            LOG_ERROR("failed reading name for entry %d from %s\n", i + 1, fname);
+            return false;
+        }
+        name_as_vec[len] = 0;
+        std::string name{name_as_vec.data()};
+        auto& e = stats_[std::move(name)];
+        int ncall;
+        in.read((char*)&ncall, sizeof(ncall));
+        int nval;
+        in.read((char*)&nval, sizeof(nval));
+        if (in.fail() || nval < 1) {
+            LOG_ERROR("failed reading number of values for entry %d\n", i);
+            stats_ = {};
+            return false;
+        }
+
+        if (e.values.empty()) {
+            e.values.resize(nval, 0);
+            e.counts.resize(nval, 0);
+        }
+
+        std::vector<float> tmp(nval);
+        in.read((char*)tmp.data(), nval * sizeof(float));
+        if (in.fail()) {
+            LOG_ERROR("failed reading data for entry %d\n", i);
+            stats_ = {};
+            return false;
+        }
+
+        // Recreate the state as expected by save_imatrix(), and correct for weighted sum.
+        for (int i = 0; i < nval; i++) {
+            e.values[i] += tmp[i];
+            e.counts[i] += ncall;
+        }
+        e.ncall += ncall;
+    }
+    return true;
+}
diff --git a/otherarch/sdcpp/src/runtime/imatrix.h b/otherarch/sdcpp/src/runtime/imatrix.h
new file mode 100644
index 000000000..341c18c8b
--- /dev/null
+++ b/otherarch/sdcpp/src/runtime/imatrix.h
@@ -0,0 +1,45 @@
+#ifndef __SD_RUNTIME_IMATRIX_H__
+#define __SD_RUNTIME_IMATRIX_H__
+
+#include <fstream>
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+/* Adapted from llama.cpp (credits: Kawrakow). */
+
+struct ggml_tensor;
+
+struct IMatrixStats {
+    std::vector<float> values{};
+    std::vector<int> counts{};
+    int ncall = 0;
+};
+
+class IMatrixCollector {
+private:
+    std::unordered_map<std::string, IMatrixStats> stats_ = {};
+    std::mutex mutex_;
+    int last_call_ = 0;
+    std::vector<float> src1_data_;
+    std::vector<char> ids_;  // the expert ids from ggml_mul_mat_id
+
+public:
+    IMatrixCollector() = default;
+    bool collect_imatrix(struct ggml_tensor* t, bool ask, void* user_data);
+    void save_imatrix(std::string fname, int ncall = -1) const;
+    bool load_imatrix(const char* fname);
+    std::vector<float> get_values(const std::string& key) const {
+        auto it = stats_.find(key);
+        if (it != stats_.end()) {
+            return it->second.values;
+        } else {
+            return {};
+        }
+    }
+};
+
+IMatrixCollector& get_imatrix_collector();
+
+#endif  // __SD_RUNTIME_IMATRIX_H__
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index e951e4143..a0ee643b3 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -31,6 +31,7 @@
 #include "model/diffusion/krea2.hpp"
 #include "model/diffusion/lens.hpp"
 #include "model/diffusion/ltxv.hpp"
+#include "model/diffusion/minit2i.hpp"
 #include "model/diffusion/mmdit.hpp"
 #include "model/diffusion/model.hpp"
 #include "model/diffusion/pid.hpp"
@@ -85,6 +86,7 @@ const char* model_version_to_str[] = {
     "Wan 2.2 I2V",
     "Wan 2.2 TI2V",
     "Qwen Image",
+    "Qwen Image Layered",
     "Anima",
     "Flux.2",
     "Flux.2 klein",
@@ -95,9 +97,11 @@ const char* model_version_to_str[] = {
     "Ovis Image",
     "Ernie Image",
     "Lens",
+    "MiniT2I",
     "Longcat-Image",
     "PiD",
     "Ideogram 4",
+    "SeFi-Image",
     "Krea2",
     "ESRGAN",
 };
@@ -497,7 +501,8 @@ public:
         bool is_ideogram = sd_version_is_ideogram4(tempver);
         bool is_boogu = sd_version_is_boogu_image(tempver);
         bool is_krea2 = sd_version_is_krea2(tempver);
-        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx || is_ideogram || is_boogu || is_krea2);
+        bool is_sefi = sd_version_is_sefi_image(tempver);
+        bool conditioner_is_llm = (is_qwenimg || iszimg || isflux2 || is_ovis || is_anima || is_ernie || is_longcat || is_lens || is_ltx || is_ideogram || is_boogu || is_krea2 || is_sefi);
         bool has_llm_vision = (is_qwenimg || is_longcat || is_boogu);
 
         //kcpp qol fallback: if a llm was loaded as t5 by mistake
@@ -603,7 +608,7 @@ public:
             {
                 to_replace = "taesd_f2.embd";
             }
-            else if(is_wan21||is_qwenimg||is_anima||is_krea2)
+            else if(sd_version_uses_wan_vae(tempver))
             {
                 to_replace = "taesd_w21.embd";
             }
@@ -939,7 +944,7 @@ public:
                                                                      version,
                                                                      sd_ctx_params->chroma_use_dit_mask,
                                                                      model_manager);
-            } else if (sd_version_is_flux2(version)) {
+            } else if (sd_version_is_flux2(version) || sd_version_is_sefi_image(version)) {
                 bool is_chroma   = false;
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
@@ -999,13 +1004,14 @@ public:
                     }
                 }
             } else if (sd_version_is_qwen_image(version)) {
-                cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
+                bool enable_vision = version != VERSION_QWEN_IMAGE_LAYERED;
+                cond_stage_model   = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
                                                                  version,
                                                                  "",
-                                                                 true,
+                                                                 enable_vision,
                                                                  model_manager);
-                diffusion_model  = std::make_shared<Qwen::QwenImageRunner>(backend_for(SDBackendModule::DIFFUSION),
+                diffusion_model    = std::make_shared<Qwen::QwenImageRunner>(backend_for(SDBackendModule::DIFFUSION),
                                                                           tensor_storage_map,
                                                                           "model.diffusion_model",
                                                                           version,
@@ -1032,6 +1038,14 @@ public:
                                                                                tensor_storage_map,
                                                                                "model",
                                                                                model_manager);
+            } else if (sd_version_is_minit2i(version)) {
+                cond_stage_model = std::make_shared<MiniT2IConditioner>(backend_for(SDBackendModule::TE),
+                                                                        tensor_storage_map,
+                                                                        model_manager);
+                diffusion_model  = std::make_shared<MiniT2I::MiniT2IRunner>(backend_for(SDBackendModule::DIFFUSION),
+                                                                           tensor_storage_map,
+                                                                           "model.diffusion_model.model.net",
+                                                                           model_manager);
             } else if (sd_version_is_anima(version)) {
                 cond_stage_model = std::make_shared<AnimaConditioner>(backend_for(SDBackendModule::TE),
                                                                       tensor_storage_map,
@@ -1140,11 +1154,7 @@ public:
             }
 
             auto create_tae = [&](bool decode_only) -> std::shared_ptr<VAE> {
-                if (sd_version_is_wan(version) ||
-                    sd_version_is_qwen_image(version) ||
-                    sd_version_is_krea2(version) ||
-                    sd_version_is_anima(version) ||
-                    sd_version_is_ltxav(version)) {
+                if (sd_version_uses_wan_vae(version) || sd_version_is_ltxav(version)) {
                     return std::make_shared<TinyVideoAutoEncoder>(backend_for(SDBackendModule::VAE),
                                                                   tensor_storage_map,
                                                                   "decoder",
@@ -1181,10 +1191,7 @@ public:
                                                          false,
                                                          version,
                                                          model_manager);
-                } else if (sd_version_is_wan(version) ||
-                           sd_version_is_qwen_image(version) ||
-                           sd_version_is_krea2(version) ||
-                           sd_version_is_anima(version)) {
+                } else if (sd_version_uses_wan_vae(version)) {
                     return std::make_shared<WAN::WanVAERunner>(backend_for(SDBackendModule::VAE),
                                                                tensor_storage_map,
                                                                "first_stage_model",
@@ -1212,7 +1219,7 @@ public:
                 }
             };
 
-            if (version == VERSION_CHROMA_RADIANCE || version == VERSION_HIDREAM_O1) {
+            if (version == VERSION_CHROMA_RADIANCE || version == VERSION_HIDREAM_O1 || sd_version_is_minit2i(version)) {
                 LOG_INFO("using FakeVAE");
                 first_stage_model = std::make_shared<FakeVAE>(version,
                                                               backend_for(SDBackendModule::VAE),
@@ -1528,6 +1535,7 @@ public:
                         default_flow_shift = 3.f;
                     }
                 } else if (sd_version_is_flux(version) ||
+                           sd_version_is_flux2(version) ||
                            sd_version_is_longcat(version) ||
                            sd_version_is_lens(version) ||
                            sd_version_is_ltxav(version) ||
@@ -1550,8 +1558,10 @@ public:
                     } else if (sd_version_is_krea2(version)) {
                         default_flow_shift = 1.15f;
                     }
-                } else if (sd_version_is_flux2(version)) {
-                    pred_type = FLUX2_FLOW_PRED;
+                } else if (sd_version_is_sefi_image(version)) {
+                    pred_type = SEFI_FLOW_PRED;
+                } else if (sd_version_is_minit2i(version)) {
+                    pred_type = MINIT2I_FLOW_PRED;
                 } else {
                     pred_type = EPS_PRED;
                 }
@@ -1584,9 +1594,14 @@ public:
                     denoiser = std::make_shared<FluxFlowDenoiser>();
                     break;
                 }
-                case FLUX2_FLOW_PRED: {
-                    LOG_INFO("running in Flux2 FLOW mode");
-                    denoiser = std::make_shared<Flux2FlowDenoiser>();
+                case SEFI_FLOW_PRED: {
+                    LOG_INFO("running in SeFi-Image dual-time FLOW mode");
+                    denoiser = std::make_shared<SefiFlowDenoiser>();
+                    break;
+                }
+                case MINIT2I_FLOW_PRED: {
+                    LOG_INFO("running in MiniT2I FLOW mode");
+                    denoiser = std::make_shared<MiniT2IFlowDenoiser>();
                     break;
                 }
                 default: {
@@ -1913,7 +1928,16 @@ public:
 
     std::vector<float> process_timesteps(const std::vector<float>& timesteps,
                                          const sd::Tensor<float>& init_latent,
-                                         const sd::Tensor<float>& denoise_mask) {
+                                         const sd::Tensor<float>& denoise_mask,
+                                         int step) {
+        if (auto sefi_denoiser = std::dynamic_pointer_cast<SefiFlowDenoiser>(denoiser)) {
+            int sched_idx = step > 0 ? step - 1 : 0;
+            if (sched_idx >= static_cast<int>(sefi_denoiser->tex_timesteps.size())) {
+                sched_idx = static_cast<int>(sefi_denoiser->tex_timesteps.size()) - 1;
+            }
+            return {sefi_denoiser->sem_timesteps[sched_idx],
+                    sefi_denoiser->tex_timesteps[sched_idx]};
+        }
         if (diffusion_model->get_desc() == "Wan2.2-TI2V-5B") {
             int64_t frame_count = init_latent.shape()[2];
             auto new_timesteps  = std::vector<float>(static_cast<size_t>(frame_count), timesteps[0]);
@@ -2009,7 +2033,7 @@ public:
                 } else if (sd_version_uses_flux_vae(version)) {
                     latent_rgb_proj = flux_latent_rgb_proj;
                     latent_rgb_bias = flux_latent_rgb_bias;
-                } else if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version) || sd_version_is_krea2(version)) {
+                } else if (sd_version_uses_wan_vae(version)) {
                     latent_rgb_proj = wan_21_latent_rgb_proj;
                     latent_rgb_bias = wan_21_latent_rgb_bias;
                 } else {
@@ -2295,12 +2319,13 @@ public:
         }
 
         int64_t last_progress_us     = ggml_time_us();
-        sd::Tensor<float> x_t        = !noise.empty()
-                                           ? denoiser->noise_scaling(sigmas[0], noise, init_latent)
-                                           : init_latent;
-        sd::Tensor<float> denoised   = x_t;
         SamplePreviewContext preview = prepare_sample_preview_context();
 
+        sd::Tensor<float> x_t      = !noise.empty()
+                                         ? denoiser->noise_scaling(sigmas[0], noise, init_latent)
+                                         : init_latent;
+        sd::Tensor<float> denoised = x_t;
+
         auto denoise = [&](const sd::Tensor<float>& x, float sigma, int step) -> sd::guidance::GuiderOutput {
             if (get_cancel_flag() == SD_CANCEL_ALL) {
                 LOG_DEBUG("cancelling generation");
@@ -2325,7 +2350,7 @@ public:
                 timesteps_vec          = process_ltxav_video_timesteps(base_timesteps_vec, init_latent, denoise_mask);
                 audio_timesteps_tensor = sd::Tensor<float>({static_cast<int64_t>(base_timesteps_vec.size())}, base_timesteps_vec);
             } else {
-                timesteps_vec = process_timesteps(timesteps_vec, init_latent, denoise_mask);
+                timesteps_vec = process_timesteps(timesteps_vec, init_latent, denoise_mask, step);
             }
             const std::vector<float>& scaling_timesteps_vec = (sd_version_is_ltxav(version) && !denoise_mask.empty())
                                                                   ? base_timesteps_vec
@@ -2363,9 +2388,9 @@ public:
             sd_sample::SampleStepCacheDispatcher step_cache(cache_runtime, step, sigma);
             std::vector<sd::Tensor<float>> controls;
             DiffusionParams diffusion_params;
-            diffusion_params.x                  = &noised_input;
-            diffusion_params.timesteps          = &timesteps_tensor;
-            diffusion_params.increase_ref_index = increase_ref_index;
+            diffusion_params.x              = &noised_input;
+            diffusion_params.timesteps      = &timesteps_tensor;
+            diffusion_params.ref_index_mode = Rope::ref_index_mode_from_bool(increase_ref_index);
             sd::guidance::GuidanceInput step_guidance_input;
             step_guidance_input.step          = step;
             step_guidance_input.schedule_size = sigmas.size();
@@ -2395,7 +2420,7 @@ public:
                     diffusion_params.extra = UNetDiffusionExtra{-1, &controls, control_strength};
                 } else if (sd_version_is_sd3(version)) {
                     diffusion_params.extra = SkipLayerDiffusionExtra{local_skip_layers};
-                } else if (sd_version_is_flux(version) || sd_version_is_flux2(version) || sd_version_is_longcat(version)) {
+                } else if (sd_version_is_flux(version) || sd_version_is_flux2(version) || sd_version_is_longcat(version) || sd_version_is_sefi_image(version)) {
                     diffusion_params.extra = FluxDiffusionExtra{&guidance_tensor,
                                                                 local_skip_layers};
                 } else if (sd_version_is_anima(version)) {
@@ -2418,6 +2443,9 @@ public:
                         audio_length,
                         frame_rate,
                         video_positions.empty() ? nullptr : &video_positions};
+                } else if (sd_version_is_minit2i(version)) {
+                    diffusion_params.extra = MiniT2IDiffusionExtra{
+                        condition.c_vector.empty() ? nullptr : &condition.c_vector};
                 } else {
                     diffusion_params.extra = std::monostate{};
                 }
@@ -2539,7 +2567,7 @@ public:
             return output;
         };
 
-        auto x0_opt = sample_k_diffusion(method, denoise, x_t, sigmas, sampler_rng, eta, is_flow_denoiser, extra_sample_args);
+        auto x0_opt = sample_k_diffusion(method, denoise, x_t, sigmas, sampler_rng, eta, is_flow_denoiser, extra_sample_args, denoiser);
         if (x0_opt.empty()) {
             LOG_ERROR("Diffusion model sampling failed");
             if (control_net) {
@@ -2598,8 +2626,12 @@ public:
                 latent_channel = 3;
             } else if (version == VERSION_CHROMA_RADIANCE) {
                 latent_channel = 3;
+            } else if (sd_version_is_minit2i(version)) {
+                latent_channel = 3;
             } else if (sd_version_is_pid(version)) {
                 latent_channel = 3;
+            } else if (sd_version_is_sefi_image(version)) {
+                latent_channel = 144;
             } else if (sd_version_uses_flux2_vae(version)) {
                 latent_channel = 128;
             } else {
@@ -2609,6 +2641,10 @@ public:
         return latent_channel;
     }
 
+    int get_image_channels() const {
+        return version == VERSION_QWEN_IMAGE_LAYERED ? 4 : 3;
+    }
+
     int get_image_seq_len(int h, int w) {
         int vae_scale_factor = get_vae_scale_factor();
         return (h / vae_scale_factor) * (w / vae_scale_factor);
@@ -2677,7 +2713,7 @@ public:
     }
 
     sd::Tensor<float> decode_first_stage(const sd::Tensor<float>& x, bool decode_video = false) {
-        if (sd_version_is_pid(version)) {
+        if (sd_version_is_pid(version) || sd_version_is_minit2i(version)) {
             return sd::ops::clamp((x + 1.f) * 0.5f, 0.0f, 1.0f);
         }
         auto latents = first_stage_model->diffusion_to_vae_latents(x);
@@ -2838,6 +2874,9 @@ const char* scheduler_to_str[] = {
     "bong_tangent",
     "ltx2",
     "logit_normal",
+    "flux2",
+    "flux",
+    "beta",
 };
 
 const char* sd_scheduler_name(enum scheduler_t scheduler) {
@@ -2848,6 +2887,9 @@ const char* sd_scheduler_name(enum scheduler_t scheduler) {
 }
 
 enum scheduler_t str_to_scheduler(const char* str) {
+    if (!strcmp(str, "normal")) {
+        return DISCRETE_SCHEDULER;
+    }
     for (int i = 0; i < SCHEDULER_COUNT; i++) {
         if (!strcmp(str, scheduler_to_str[i])) {
             return (enum scheduler_t)i;
@@ -2862,7 +2904,8 @@ const char* prediction_to_str[] = {
     "edm_v",
     "sd3_flow",
     "flux_flow",
-    "flux2_flow",
+    "sefi_flow",
+    "minit2i_flow",
 };
 
 const char* sd_prediction_name(enum prediction_t prediction) {
@@ -3206,6 +3249,7 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->seed              = -1;
     sd_img_gen_params->batch_count       = 1;
     sd_img_gen_params->control_strength  = 0.9f;
+    sd_img_gen_params->qwen_image_layers = 3;
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
     sd_img_gen_params->pulid_params      = {nullptr, 1.0f};
     sd_img_gen_params->vae_tiling_params = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
@@ -3232,6 +3276,7 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "seed: %" PRId64
              "\n"
              "batch_count: %d\n"
+             "qwen_image_layers: %d\n"
              "ref_images_count: %d\n"
              "auto_resize_ref_image: %s\n"
              "increase_ref_index: %s\n"
@@ -3248,6 +3293,7 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              sd_img_gen_params->strength,
              sd_img_gen_params->seed,
              sd_img_gen_params->batch_count,
+             sd_img_gen_params->qwen_image_layers,
              sd_img_gen_params->ref_images_count,
              BOOL_STR(sd_img_gen_params->auto_resize_ref_image),
              BOOL_STR(sd_img_gen_params->increase_ref_index),
@@ -3510,9 +3556,13 @@ enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_me
         return LCM_SCHEDULER;
     } else if (sample_method == DDIM_TRAILING_SAMPLE_METHOD) {
         return SIMPLE_SCHEDULER;
+    } else if (sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_flux(sd_ctx->sd->version)) {
+        return FLUX_SCHEDULER;
+    } else if (sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_flux2(sd_ctx->sd->version)) {
+        return FLUX2_SCHEDULER;
     } else if (sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_ltxav(sd_ctx->sd->version)) {
         return LTX2_SCHEDULER;
-    } else if(sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_ideogram4(sd_ctx->sd->version)) {
+    } else if (sd_ctx != nullptr && sd_ctx->sd != nullptr && sd_version_is_ideogram4(sd_ctx->sd->version)) {
         return LOGIT_NORMAL_SCHEDULER;
     }
     return DISCRETE_SCHEDULER;
@@ -3583,6 +3633,7 @@ struct GenerationRequest {
     bool has_ref_images                      = false;
     const sd_cache_params_t* cache_params    = nullptr;
     int batch_count                          = 1;
+    int qwen_image_layers                    = 3;
     int shifted_timestep                     = 0;
     float strength                           = 1.f;
     float control_strength                   = 0.f;
@@ -3608,6 +3659,7 @@ struct GenerationRequest {
         diffusion_model_down_factor = sd_ctx->sd->get_diffusion_model_down_factor();
         seed                        = sd_img_gen_params->seed;
         batch_count                 = sd_img_gen_params->batch_count;
+        qwen_image_layers           = std::max(0, sd_img_gen_params->qwen_image_layers);
         clip_skip                   = sd_img_gen_params->clip_skip;
         shifted_timestep            = sd_img_gen_params->sample_params.shifted_timestep;
         strength                    = sd_img_gen_params->strength;
@@ -4313,6 +4365,34 @@ public:
     ImageVaeAxesGuard& operator=(const ImageVaeAxesGuard&) = delete;
 };
 
+static sd::Tensor<float> ensure_image_tensor_channels(sd::Tensor<float> image, int channels) {
+    if (image.empty()) {
+        return image;
+    }
+    GGML_ASSERT(image.dim() == 4);
+    int64_t current_channels = image.shape()[2];
+    if (current_channels == channels) {
+        return image;
+    }
+    if (channels == 4) {
+        sd::Tensor<float> alpha = sd::full<float>({image.shape()[0], image.shape()[1], 1, image.shape()[3]}, 1.f);
+        if (current_channels == 3) {
+            return sd::ops::concat(image, alpha, 2);
+        }
+        if (current_channels == 1) {
+            sd::Tensor<float> rgb = sd::ops::concat(image, image, 2);
+            rgb                   = sd::ops::concat(rgb, image, 2);
+            return sd::ops::concat(rgb, alpha, 2);
+        }
+    }
+    if (channels == 3 && current_channels >= 3) {
+        return sd::ops::slice(image, 2, 0, 3);
+    }
+    GGML_ABORT("cannot convert image tensor from %lld to %d channels",
+               (long long)current_channels,
+               channels);
+}
+
 static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd_ctx_t* sd_ctx,
                                                                               const sd_img_gen_params_t* sd_img_gen_params,
                                                                               GenerationRequest* request,
@@ -4322,6 +4402,7 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
     sd::Tensor<float> init_image_tensor;
     sd::Tensor<float> control_image_tensor;
     sd::Tensor<float> mask_image_tensor;
+    int image_channels = sd_ctx->sd->get_image_channels();
 
     if (sd_img_gen_params->init_image.data != nullptr) {
         LOG_INFO("IMG2IMG");
@@ -4338,7 +4419,8 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
             plan->sample_steps = static_cast<int>(plan->sigmas.size() - 1);
         }
 
-        init_image_tensor = sd_image_to_tensor(sd_img_gen_params->init_image, request->width, request->height);
+        init_image_tensor = ensure_image_tensor_channels(sd_image_to_tensor(sd_img_gen_params->init_image, request->width, request->height),
+                                                         image_channels);
     }
 
     if (sd_img_gen_params->mask_image.data != nullptr) {
@@ -4370,7 +4452,11 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
     sd::Tensor<float> init_latent;
     sd::Tensor<float> control_latent;
     if (init_image_tensor.empty()) {
-        init_latent = sd_ctx->sd->generate_init_latent(request->width, request->height);
+        if (sd_ctx->sd->version == VERSION_QWEN_IMAGE_LAYERED) {
+            init_latent = sd_ctx->sd->generate_init_latent(request->width, request->height, request->qwen_image_layers + 1, true);
+        } else {
+            init_latent = sd_ctx->sd->generate_init_latent(request->width, request->height);
+        }
     } else {
         init_latent = sd_ctx->sd->encode_first_stage(init_image_tensor);
         if (init_latent.empty()) {
@@ -4389,12 +4475,13 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
 
     std::vector<sd::Tensor<float>> ref_images;
     for (int i = 0; i < sd_img_gen_params->ref_images_count; i++) {
-        ref_images.push_back(sd_image_to_tensor(sd_img_gen_params->ref_images[i]));
+        ref_images.push_back(ensure_image_tensor_channels(sd_image_to_tensor(sd_img_gen_params->ref_images[i]),
+                                                          image_channels));
     }
 
     if (ref_images.empty() && sd_version_is_unet_edit(sd_ctx->sd->version)) {
         LOG_WARN("This model needs at least one reference image; using an empty reference");
-        ref_images.push_back(sd::zeros<float>({request->width, request->height, 3, 1}));
+        ref_images.push_back(sd::zeros<float>({request->width, request->height, image_channels, 1}));
         request->guidance.img_cfg = request->guidance.txt_cfg;
         request->use_img_uncond   = false;
     }
@@ -4420,7 +4507,10 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
             vae_width  = round(vae_width / factor) * factor;
 
             auto resized_ref_img = sd::ops::interpolate(ref_images[i],
-                                                        {static_cast<int>(vae_width), static_cast<int>(vae_height), 3, 1});
+                                                        {static_cast<int>(vae_width),
+                                                         static_cast<int>(vae_height),
+                                                         ref_images[i].shape()[2],
+                                                         ref_images[i].shape()[3]});
 
             LOG_DEBUG("resize vae ref image %d from %" PRId64 "x%" PRId64 " to %" PRId64 "x%" PRId64,
                       static_cast<int>(i),
@@ -4565,6 +4655,11 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
     if (request->use_uncond || request->use_high_noise_uncond) {
         if (sd_version_is_ideogram4(sd_ctx->sd->version)) {
             uncond.c_vector = sd::Tensor<float>::from_vector({1.0f});
+        } else if (sd_version_is_minit2i(sd_ctx->sd->version)) {
+            // MiniT2I derives the unconditional signal from the same T5 hidden
+            // states with a zeroed prompt mask, so no extra text encode is needed.
+            uncond.c_crossattn = cond.c_crossattn;
+            uncond.c_vector    = sd::Tensor<float>::zeros_like(cond.c_vector);
         } else {
             bool zero_out_masked = false;
             if (sd_version_is_sdxl(sd_ctx->sd->version) &&
@@ -4620,7 +4715,8 @@ static std::optional<ImageGenerationEmbeds> prepare_image_generation_embeds(sd_c
 
 static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
                                         const GenerationRequest& request,
-                                        const std::vector<sd::Tensor<float>>& final_latents) {
+                                        const std::vector<sd::Tensor<float>>& final_latents,
+                                        int* num_images_out) {
     if (final_latents.empty()) {
         LOG_ERROR("no latent images to decode");
         return nullptr;
@@ -4644,13 +4740,41 @@ static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
             cancelled = true;
             break;
         }
-        int64_t t1              = ggml_time_ms();
-        sd::Tensor<float> image = sd_ctx->sd->decode_first_stage(final_latents[i]);
-        if (image.empty()) {
-            LOG_ERROR("decode_first_stage failed for latent %" PRId64, i + 1);
-            return nullptr;
+        int64_t t1 = ggml_time_ms();
+        if (sd_ctx->sd->version == VERSION_QWEN_IMAGE_LAYERED) {
+            int qwen_image_latent_layers = request.qwen_image_layers + 1;
+            if (final_latents[i].dim() < 5 || final_latents[i].shape()[2] < qwen_image_latent_layers) {
+                LOG_ERROR("qwen image layered expected at least %d latent layers, got shape dim=%d",
+                          qwen_image_latent_layers,
+                          final_latents[i].dim());
+                return nullptr;
+            }
+            for (int layer_index = 0; layer_index < qwen_image_latent_layers; layer_index++) {
+                if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
+                    LOG_ERROR("cancelling latent decodings");
+                    cancelled = true;
+                    break;
+                }
+                sd::Tensor<float> layer_latent = sd::ops::slice(final_latents[i], 2, layer_index, layer_index + 1);
+                layer_latent.squeeze_(2);
+                sd::Tensor<float> image = sd_ctx->sd->decode_first_stage(layer_latent);
+                if (image.empty()) {
+                    LOG_ERROR("decode_first_stage failed for latent %zu layer %d", i + 1, layer_index + 1);
+                    return nullptr;
+                }
+                decoded_images.push_back(std::move(image));
+            }
+            if (cancelled) {
+                break;
+            }
+        } else {
+            sd::Tensor<float> image = sd_ctx->sd->decode_first_stage(final_latents[i]);
+            if (image.empty()) {
+                LOG_ERROR("decode_first_stage failed for latent %" PRId64, i + 1);
+                return nullptr;
+            }
+            decoded_images.push_back(std::move(image));
         }
-        decoded_images.push_back(std::move(image));
         int64_t t2 = ggml_time_ms();
         LOG_INFO("latent %zu decoded, taking %.2fs", i + 1, (t2 - t1) * 1.0f / 1000);
     }
@@ -4662,11 +4786,14 @@ static sd_image_t* decode_image_outputs(sd_ctx_t* sd_ctx,
         return nullptr;
     }
 
-    sd_image_t* result_images = (sd_image_t*)calloc(request.batch_count, sizeof(sd_image_t));
+    int image_count           = static_cast<int>(decoded_images.size());
+    sd_image_t* result_images = (sd_image_t*)calloc(image_count, sizeof(sd_image_t));
     if (result_images == nullptr) {
         return nullptr;
     }
-    memset(result_images, 0, request.batch_count * sizeof(sd_image_t));
+    if (num_images_out != nullptr) {
+        *num_images_out = image_count;
+    }
 
     for (size_t i = 0; i < decoded_images.size(); i++) {
         result_images[i] = tensor_to_sd_image(decoded_images[i]);
@@ -4859,9 +4986,18 @@ static std::vector<float> make_hires_sigma_schedule(sd_ctx_t* sd_ctx,
                               sigmas.end());
 }
 
-SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params) {
+SD_API bool generate_image(sd_ctx_t* sd_ctx,
+                           const sd_img_gen_params_t* sd_img_gen_params,
+                           sd_image_t** images_out,
+                           int* num_images_out) {
+    if (images_out != nullptr) {
+        *images_out = nullptr;
+    }
+    if (num_images_out != nullptr) {
+        *num_images_out = 0;
+    }
     if (sd_ctx == nullptr || sd_img_gen_params == nullptr) {
-        return nullptr;
+        return false;
     }
 
     sd_ctx->sd->reset_cancel_flag();
@@ -4884,7 +5020,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                         &request,
                                                         &plan);
     if (!latents_opt.has_value()) {
-        return nullptr;
+        return false;
     }
     ImageGenerationLatents latents = std::move(*latents_opt);
 
@@ -4894,7 +5030,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                       &plan,
                                                       &latents);
     if (!embeds_opt.has_value()) {
-        return nullptr;
+        return false;
     }
     ImageGenerationEmbeds embeds = std::move(*embeds_opt);
 
@@ -4904,7 +5040,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         sd_cancel_mode_t cancel = sd_ctx->sd->get_cancel_flag();
         if (cancel == SD_CANCEL_ALL) {
             LOG_ERROR("cancelling generation");
-            return nullptr;
+            return false;
         }
         if (cancel == SD_CANCEL_NEW_LATENTS) {
             LOG_INFO("cancelling new latent generation, returning %zu/%d completed latents",
@@ -4956,7 +5092,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                   b + 1,
                   request.batch_count,
                   (sampling_end - sampling_start) * 1.0f / 1000);
-        return nullptr;
+        return false;
     }
     int64_t denoise_end = ggml_time_ms();
     LOG_INFO("generating %zu latent images completed, taking %.2fs",
@@ -4964,13 +5100,13 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
              (denoise_end - denoise_start) * 1.0f / 1000);
     if (final_latents.empty()) {
         LOG_ERROR("no latent images generated");
-        return nullptr;
+        return false;
     }
 
     if (request.hires.enabled && request.hires.target_width > 0) {
         if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
             LOG_ERROR("cancelling generation before hires fix");
-            return nullptr;
+            return false;
         }
         LOG_INFO("hires fix: upscaling to %dx%d", request.hires.target_width, request.hires.target_height);
 
@@ -4978,7 +5114,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         if (request.hires.upscaler == SD_HIRES_UPSCALER_MODEL) {
             if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
                 LOG_ERROR("cancelling generation before hires model load");
-                return nullptr;
+                return false;
             }
             LOG_INFO("hires fix: loading model upscaler from '%s'", request.hires.model_path);
             hires_upscaler                    = std::make_unique<UpscalerGGML>(sd_ctx->sd->n_threads,
@@ -4991,7 +5127,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
             if (!hires_upscaler->load_from_file(request.hires.model_path,
                                                 sd_ctx->sd->n_threads)) {
                 LOG_ERROR("load hires model upscaler failed");
-                return nullptr;
+                return false;
             }
         }
 
@@ -5015,7 +5151,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         for (int b = 0; b < (int)final_latents.size(); b++) {
             if (sd_ctx->sd->get_cancel_flag() == SD_CANCEL_ALL) {
                 LOG_ERROR("cancelling generation during hires fix");
-                return nullptr;
+                return false;
             }
             int64_t cur_seed = request.seed + b;
             sd_ctx->sd->rng->manual_seed(cur_seed);
@@ -5026,7 +5162,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                                                               request,
                                                               hires_upscaler.get());
             if (upscaled.empty()) {
-                return nullptr;
+                return false;
             }
 
             sd::Tensor<float> noise = sd::randn_like<float>(upscaled, sd_ctx->sd->rng);
@@ -5080,7 +5216,7 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
                       b + 1,
                       (int)final_latents.size(),
                       (hires_sample_end - hires_sample_start) * 1.0f / 1000);
-            return nullptr;
+            return false;
         }
         int64_t hires_denoise_end = ggml_time_ms();
         LOG_INFO("hires fix completed, taking %.2fs", (hires_denoise_end - hires_denoise_start) * 1.0f / 1000);
@@ -5088,16 +5224,25 @@ SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* s
         final_latents = std::move(hires_final_latents);
     }
 
-    auto result = decode_image_outputs(sd_ctx, request, final_latents);
+    int num_images = 0;
+    auto result    = decode_image_outputs(sd_ctx, request, final_latents, &num_images);
     if (result == nullptr) {
-        return nullptr;
+        return false;
     }
 
     sd_ctx->sd->lora_stat();
 
     int64_t t1 = ggml_time_ms();
     LOG_INFO("generate_image completed in %.2fs", (t1 - t0) * 1.0f / 1000);
-    return result;
+    if (num_images_out != nullptr) {
+        *num_images_out = num_images;
+    }
+    if (images_out != nullptr) {
+        *images_out = result;
+    } else {
+        free_sd_images(result, num_images);
+    }
+    return true;
 }
 
 static std::optional<ImageGenerationLatents> prepare_video_generation_latents(sd_ctx_t* sd_ctx,
diff --git a/otherarch/sdcpp/src/upscaler.cpp b/otherarch/sdcpp/src/upscaler.cpp
index d02366ecb..88a8a6336 100644
--- a/otherarch/sdcpp/src/upscaler.cpp
+++ b/otherarch/sdcpp/src/upscaler.cpp
@@ -4,6 +4,7 @@
 #include "model_loader.h"
 #include "stable-diffusion.h"
 
+#include <cstdlib>
 #include <utility>
 
 UpscalerGGML::UpscalerGGML(int n_threads,
@@ -198,8 +199,41 @@ upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
     return upscaler_ctx;
 }
 
-sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor) {
-    return upscaler_ctx->upscaler->upscale(input_image, upscale_factor);
+bool upscale(upscaler_ctx_t* upscaler_ctx,
+             sd_image_t input_image,
+             uint32_t upscale_factor,
+             sd_image_t** images_out,
+             int* num_images_out) {
+    if (images_out != nullptr) {
+        *images_out = nullptr;
+    }
+    if (num_images_out != nullptr) {
+        *num_images_out = 0;
+    }
+    if (upscaler_ctx == nullptr || upscaler_ctx->upscaler == nullptr) {
+        return false;
+    }
+
+    sd_image_t* result_images = (sd_image_t*)calloc(1, sizeof(sd_image_t));
+    if (result_images == nullptr) {
+        return false;
+    }
+
+    result_images[0] = upscaler_ctx->upscaler->upscale(input_image, upscale_factor);
+    if (result_images[0].data == nullptr) {
+        free(result_images);
+        return false;
+    }
+
+    if (num_images_out != nullptr) {
+        *num_images_out = 1;
+    }
+    if (images_out != nullptr) {
+        *images_out = result_images;
+    } else {
+        free_sd_images(result_images, 1);
+    }
+    return true;
 }
 
 int get_upscale_factor(upscaler_ctx_t* upscaler_ctx) {

From c8ae9a750c6c89dc928503a16175b7b3c2d224e4 Mon Sep 17 00:00:00 2001
From: "Alessandro de Oliveira Faria (A.K.A.CABELO)" <cabelo@opensuse.org>
Date: Fri, 3 Jul 2026 05:26:54 -0300
Subject: [PATCH 256/292] vendor : update cpp-httplib to 0.49.0 (#25218)

---
 scripts/sync_vendor.py         |   2 +-
 vendor/cpp-httplib/httplib.cpp | 162 ++++++++++++++++++++++++++-------
 vendor/cpp-httplib/httplib.h   |  68 +++++++++++---
 3 files changed, 184 insertions(+), 48 deletions(-)

diff --git a/scripts/sync_vendor.py b/scripts/sync_vendor.py
index f913b0c7d..f66e78d63 100755
--- a/scripts/sync_vendor.py
+++ b/scripts/sync_vendor.py
@@ -5,7 +5,7 @@ import os
 import sys
 import subprocess
 
-HTTPLIB_VERSION = "refs/tags/v0.48.0"
+HTTPLIB_VERSION = "refs/tags/v0.49.0"
 
 vendor = {
     "https://github.com/nlohmann/json/releases/latest/download/json.hpp":     "vendor/nlohmann/json.hpp",
diff --git a/vendor/cpp-httplib/httplib.cpp b/vendor/cpp-httplib/httplib.cpp
index 1ac4fa4ba..d65b7921b 100644
--- a/vendor/cpp-httplib/httplib.cpp
+++ b/vendor/cpp-httplib/httplib.cpp
@@ -478,7 +478,7 @@ bool set_socket_opt_time(socket_t sock, int level, int optname,
 }
 
 bool is_hex(char c, int &v) {
-  if (isdigit(static_cast<unsigned char>(c))) {
+  if (is_ascii_digit(c)) {
     v = c - '0';
     return true;
   } else if ('A' <= c && c <= 'F') {
@@ -695,7 +695,11 @@ std::string base64_encode(const std::string &in) {
   std::string out;
   out.reserve(in.size());
 
-  auto val = 0;
+  // Unsigned: the accumulator is never masked, so with a signed int the
+  // `val << 8` below overflows once enough bytes are folded in (undefined
+  // behaviour before C++20). Only the low bits are ever emitted, so the
+  // wrap-around of an unsigned accumulator does not affect the output.
+  uint32_t val = 0;
   auto valb = -6;
 
   for (auto c : in) {
@@ -3887,8 +3891,7 @@ bool parse_range_header(const std::string &s, Ranges &ranges) {
 bool parse_range_header(const std::string &s, Ranges &ranges) try {
 #endif
   auto is_valid = [](const std::string &str) {
-    return std::all_of(str.cbegin(), str.cend(),
-                       [](unsigned char c) { return std::isdigit(c); });
+    return std::all_of(str.cbegin(), str.cend(), is_ascii_digit);
   };
 
   if (s.size() > 7 && s.compare(0, 6, "bytes=") == 0) {
@@ -4336,7 +4339,7 @@ bool is_multipart_boundary_chars_valid(const std::string &boundary) {
   auto valid = true;
   for (size_t i = 0; i < boundary.size(); i++) {
     auto c = boundary[i];
-    if (!std::isalnum(static_cast<unsigned char>(c)) && c != '-' && c != '_') {
+    if (!is_ascii_alnum(c) && c != '-' && c != '_') {
       valid = false;
       break;
     }
@@ -4344,18 +4347,47 @@ bool is_multipart_boundary_chars_valid(const std::string &boundary) {
   return valid;
 }
 
+// Escape a multipart field name/filename following the WHATWG HTML standard
+// ("escape a multipart form-data name"), which is what browsers send:
+// '"' -> %22, CR -> %0D, LF -> %0A
+// With escape_quote = false, only CR and LF are escaped; this is for header
+// values outside a quoted-string (e.g. Content-Type), where '"' is legal.
+std::string escape_multipart_field(const std::string &s,
+                                          bool escape_quote = true) {
+  std::string result;
+  result.reserve(s.size());
+  for (auto c : s) {
+    switch (c) {
+    case '"':
+      if (escape_quote) {
+        result += "%22";
+      } else {
+        result += c;
+      }
+      break;
+    case '\r': result += "%0D"; break;
+    case '\n': result += "%0A"; break;
+    default: result += c; break;
+    }
+  }
+  return result;
+}
+
 template <typename T>
 std::string
 serialize_multipart_formdata_item_begin(const T &item,
                                         const std::string &boundary) {
   std::string body = "--" + boundary + "\r\n";
-  body += "Content-Disposition: form-data; name=\"" + item.name + "\"";
+  body += "Content-Disposition: form-data; name=\"" +
+          escape_multipart_field(item.name) + "\"";
   if (!item.filename.empty()) {
-    body += "; filename=\"" + item.filename + "\"";
+    body += "; filename=\"" + escape_multipart_field(item.filename) + "\"";
   }
   body += "\r\n";
   if (!item.content_type.empty()) {
-    body += "Content-Type: " + item.content_type + "\r\n";
+    body +=
+        "Content-Type: " + escape_multipart_field(item.content_type, false) +
+        "\r\n";
   }
   body += "\r\n";
 
@@ -4821,10 +4853,9 @@ private:
 namespace fields {
 
 bool is_token_char(char c) {
-  return std::isalnum(static_cast<unsigned char>(c)) || c == '!' || c == '#' ||
-         c == '$' || c == '%' || c == '&' || c == '\'' || c == '*' ||
-         c == '+' || c == '-' || c == '.' || c == '^' || c == '_' || c == '`' ||
-         c == '|' || c == '~';
+  return is_ascii_alnum(c) || c == '!' || c == '#' || c == '$' || c == '%' ||
+         c == '&' || c == '\'' || c == '*' || c == '+' || c == '-' ||
+         c == '.' || c == '^' || c == '_' || c == '`' || c == '|' || c == '~';
 }
 
 bool is_token(const std::string &s) {
@@ -4873,7 +4904,8 @@ bool is_field_value(const std::string &s) { return is_field_content(s); }
 } // namespace fields
 
 bool perform_websocket_handshake(Stream &strm, const std::string &host,
-                                        int port, const std::string &path,
+                                        int port, bool is_ssl,
+                                        const std::string &path,
                                         const Headers &headers,
                                         std::string &selected_subprotocol) {
   // Validate path and host
@@ -4899,7 +4931,7 @@ bool perform_websocket_handshake(Stream &strm, const std::string &host,
 
   // Build upgrade request
   std::string req_str = "GET " + path + " HTTP/1.1\r\n";
-  req_str += "Host: " + host + ":" + std::to_string(port) + "\r\n";
+  req_str += "Host: " + make_host_and_port_string(host, port, is_ssl) + "\r\n";
   req_str += "Upgrade: websocket\r\n";
   req_str += "Connection: Upgrade\r\n";
   req_str += "Sec-WebSocket-Key: " + client_key + "\r\n";
@@ -5599,9 +5631,8 @@ std::string encode_uri_component(const std::string &value) {
   escaped << std::hex;
 
   for (auto c : value) {
-    if (std::isalnum(static_cast<uint8_t>(c)) || c == '-' || c == '_' ||
-        c == '.' || c == '!' || c == '~' || c == '*' || c == '\'' || c == '(' ||
-        c == ')') {
+    if (detail::is_ascii_alnum(c) || c == '-' || c == '_' || c == '.' ||
+        c == '!' || c == '~' || c == '*' || c == '\'' || c == '(' || c == ')') {
       escaped << c;
     } else {
       escaped << std::uppercase;
@@ -5620,10 +5651,10 @@ std::string encode_uri(const std::string &value) {
   escaped << std::hex;
 
   for (auto c : value) {
-    if (std::isalnum(static_cast<uint8_t>(c)) || c == '-' || c == '_' ||
-        c == '.' || c == '!' || c == '~' || c == '*' || c == '\'' || c == '(' ||
-        c == ')' || c == ';' || c == '/' || c == '?' || c == ':' || c == '@' ||
-        c == '&' || c == '=' || c == '+' || c == '$' || c == ',' || c == '#') {
+    if (detail::is_ascii_alnum(c) || c == '-' || c == '_' || c == '.' ||
+        c == '!' || c == '~' || c == '*' || c == '\'' || c == '(' || c == ')' ||
+        c == ';' || c == '/' || c == '?' || c == ':' || c == '@' || c == '&' ||
+        c == '=' || c == '+' || c == '$' || c == ',' || c == '#') {
       escaped << c;
     } else {
       escaped << std::uppercase;
@@ -5684,7 +5715,8 @@ std::string encode_path_component(const std::string &component) {
     auto c = static_cast<unsigned char>(component[i]);
 
     // Unreserved characters per RFC 3986: ALPHA / DIGIT / "-" / "." / "_" / "~"
-    if (std::isalnum(c) || c == '-' || c == '.' || c == '_' || c == '~') {
+    if (detail::is_ascii_alnum(static_cast<char>(c)) || c == '-' || c == '.' ||
+        c == '_' || c == '~') {
       result += static_cast<char>(c);
     }
     // Path-safe sub-delimiters: "!" / "$" / "&" / "'" / "(" / ")" / "*" / "+" /
@@ -5757,7 +5789,8 @@ std::string encode_query_component(const std::string &component,
     auto c = static_cast<unsigned char>(component[i]);
 
     // Unreserved characters per RFC 3986
-    if (std::isalnum(c) || c == '-' || c == '.' || c == '_' || c == '~') {
+    if (detail::is_ascii_alnum(static_cast<char>(c)) || c == '-' || c == '.' ||
+        c == '_' || c == '~') {
       result += static_cast<char>(c);
     }
     // Space handling
@@ -6010,6 +6043,48 @@ size_t MultipartFormData::get_file_count(const std::string &key) const {
   return static_cast<size_t>(std::distance(r.first, r.second));
 }
 
+// Multipart FormData writer implementation
+bool is_valid_multipart_boundary(const std::string &boundary) {
+  return detail::is_multipart_boundary_chars_valid(boundary);
+}
+
+MultipartFormDataWriter::MultipartFormDataWriter()
+    : boundary_(detail::make_multipart_data_boundary()) {}
+
+MultipartFormDataWriter::MultipartFormDataWriter(std::string boundary)
+    : boundary_(std::move(boundary)) {}
+
+const std::string &MultipartFormDataWriter::boundary() const {
+  return boundary_;
+}
+
+std::string MultipartFormDataWriter::content_type() const {
+  return detail::serialize_multipart_formdata_get_content_type(boundary_);
+}
+
+std::string
+MultipartFormDataWriter::serialize(const UploadFormDataItems &items) const {
+  return detail::serialize_multipart_formdata(items, boundary_);
+}
+
+size_t MultipartFormDataWriter::content_length(
+    const UploadFormDataItems &items) const {
+  return detail::get_multipart_content_length(items, boundary_);
+}
+
+std::string
+MultipartFormDataWriter::item_begin(const UploadFormData &item) const {
+  return detail::serialize_multipart_formdata_item_begin(item, boundary_);
+}
+
+std::string MultipartFormDataWriter::item_end() {
+  return detail::serialize_multipart_formdata_item_end();
+}
+
+std::string MultipartFormDataWriter::finish() const {
+  return detail::serialize_multipart_formdata_finish(boundary_);
+}
+
 // Response implementation
 size_t Response::get_header_value_u64(const std::string &key, size_t def,
                                              size_t id) const {
@@ -6229,8 +6304,10 @@ ssize_t detail::BodyReader::read(char *buf, size_t len) {
 }
 
 // ThreadPool implementation
-ThreadPool::ThreadPool(size_t n, size_t max_n, size_t mqr)
-    : base_thread_count_(n), max_queued_requests_(mqr), idle_thread_count_(0),
+ThreadPool::ThreadPool(size_t n, size_t max_n, size_t mqr,
+                              time_t idle_timeout_sec)
+    : base_thread_count_(n), max_queued_requests_(mqr),
+      idle_timeout_sec_(idle_timeout_sec), idle_thread_count_(0),
       shutdown_(false) {
 #ifndef CPPHTTPLIB_NO_EXCEPTIONS
   if (max_n != 0 && max_n < n) {
@@ -6340,9 +6417,9 @@ void ThreadPool::worker(bool is_dynamic) {
       idle_thread_count_++;
 
       if (is_dynamic) {
-        auto has_work = cond_.wait_for(
-            lock, std::chrono::seconds(CPPHTTPLIB_THREAD_POOL_IDLE_TIMEOUT),
-            [&] { return !jobs_.empty() || shutdown_; });
+        auto has_work =
+            cond_.wait_for(lock, std::chrono::seconds(idle_timeout_sec_),
+                           [&] { return !jobs_.empty() || shutdown_; });
         if (!has_work) {
           // Timed out with no work - exit this dynamic thread
           idle_thread_count_--;
@@ -9687,9 +9764,18 @@ bool ClientImpl::write_request(Stream &strm, Request &req,
 
     if (!query_part.empty()) {
       // Normalize the query string (decode then re-encode) while preserving
-      // the original parameter order.
-      auto normalized = detail::normalize_query_string(query_part);
-      if (!normalized.empty()) { path_with_query += '?' + normalized; }
+      // the original parameter order. When path encoding is disabled the
+      // caller has supplied an already-encoded target and expects the exact
+      // bytes to be sent on the wire, so skip normalization for the query
+      // too. Normalizing here would decode-then-re-encode the query and
+      // corrupt pre-encoded binary payloads (e.g. turning `%20` into `+`,
+      // which a strict RFC 3986 server decodes back as `+`, not a space).
+      if (path_encode_) {
+        auto normalized = detail::normalize_query_string(query_part);
+        if (!normalized.empty()) { path_with_query += '?' + normalized; }
+      } else {
+        path_with_query += '?' + query_part;
+      }
 
       // Still populate req.params for handlers/users who read them.
       detail::parse_query_text(query_part, req.params);
@@ -12518,7 +12604,7 @@ bool is_ipv4_address(const std::string &str) {
   for (char c : str) {
     if (c == '.') {
       dots++;
-    } else if (!isdigit(static_cast<unsigned char>(c))) {
+    } else if (!detail::is_ascii_digit(c)) {
       return false;
     }
   }
@@ -12535,7 +12621,7 @@ bool parse_ipv4(const std::string &str, unsigned char *out) {
     }
     int val = 0;
     int digits = 0;
-    while (*p >= '0' && *p <= '9') {
+    while (detail::is_ascii_digit(*p)) {
       val = val * 10 + (*p - '0');
       if (val > 255) { return false; }
       p++;
@@ -16487,9 +16573,15 @@ bool WebSocketClient::connect() {
     return false;
   }
 
+#ifdef CPPHTTPLIB_SSL_ENABLED
+  auto is_ssl = is_ssl_;
+#else
+  auto is_ssl = false;
+#endif
+
   std::string selected_subprotocol;
-  if (!detail::perform_websocket_handshake(*strm, host_, port_, path_, headers_,
-                                           selected_subprotocol)) {
+  if (!detail::perform_websocket_handshake(*strm, host_, port_, is_ssl, path_,
+                                           headers_, selected_subprotocol)) {
     shutdown_and_close();
     return false;
   }
diff --git a/vendor/cpp-httplib/httplib.h b/vendor/cpp-httplib/httplib.h
index bfdbfc1da..e7ef56370 100644
--- a/vendor/cpp-httplib/httplib.h
+++ b/vendor/cpp-httplib/httplib.h
@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.48.0"
-#define CPPHTTPLIB_VERSION_NUM "0x003000"
+#define CPPHTTPLIB_VERSION "0.49.0"
+#define CPPHTTPLIB_VERSION_NUM "0x003100"
 
 #ifdef _WIN32
 #if defined(_WIN32_WINNT) && _WIN32_WINNT < 0x0A00
@@ -309,7 +309,6 @@ using socket_t = int;
 #include <array>
 #include <atomic>
 #include <cassert>
-#include <cctype>
 #include <chrono>
 #include <climits>
 #include <condition_variable>
@@ -540,6 +539,21 @@ make_unique(std::size_t n) {
   return std::unique_ptr<T>(new RT[n]);
 }
 
+// Locale-independent ASCII character classification. The <cctype>
+// counterparts (std::isalnum, std::isdigit, ...) consult the global C locale,
+// so e.g. std::isalnum(0xC5) can return true once an embedder calls
+// setlocale(). HTTP grammars are defined over ASCII, so raw bytes must be
+// classified without regard to the locale.
+inline bool is_ascii_digit(char c) { return '0' <= c && c <= '9'; }
+
+inline bool is_ascii_alpha(char c) {
+  return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z');
+}
+
+inline bool is_ascii_alnum(char c) {
+  return is_ascii_digit(c) || is_ascii_alpha(c);
+}
+
 namespace case_ignore {
 
 inline unsigned char to_lower(int c) {
@@ -661,7 +675,7 @@ inline from_chars_result<T> from_chars(const char *first, const char *last,
   for (; p != last; ++p) {
     char c = *p;
     int digit = -1;
-    if ('0' <= c && c <= '9') {
+    if (is_ascii_digit(c)) {
       digit = c - '0';
     } else if ('a' <= c && c <= 'z') {
       digit = c - 'a' + 10;
@@ -733,14 +747,14 @@ inline from_chars_result<double> from_chars(const char *first, const char *last,
     return false;
   };
 
-  for (; p != last && '0' <= *p && *p <= '9'; ++p) {
+  for (; p != last && is_ascii_digit(*p); ++p) {
     seen_digit = true;
     accumulate(*p);
   }
 
   if (p != last && *p == '.') {
     ++p;
-    for (; p != last && '0' <= *p && *p <= '9'; ++p) {
+    for (; p != last && is_ascii_digit(*p); ++p) {
       seen_digit = true;
       if (frac_digits < max_frac_digits && accumulate(*p)) { ++frac_digits; }
     }
@@ -803,8 +817,8 @@ inline bool parse_url(const std::string &url, UrlComponents &uc) {
       // IPv6 host must be [a-fA-F0-9:]+ only
       if (uc.host.empty()) { return false; }
       for (auto c : uc.host) {
-        if (!((c >= 'a' && c <= 'f') || (c >= 'A' && c <= 'F') ||
-              (c >= '0' && c <= '9') || c == ':')) {
+        if (!(is_ascii_digit(c) || (c >= 'a' && c <= 'f') ||
+              (c >= 'A' && c <= 'F') || c == ':')) {
           return false;
         }
       }
@@ -1541,7 +1555,9 @@ public:
 
 class ThreadPool final : public TaskQueue {
 public:
-  explicit ThreadPool(size_t n, size_t max_n = 0, size_t mqr = 0);
+  explicit ThreadPool(
+      size_t n, size_t max_n = 0, size_t mqr = 0,
+      time_t idle_timeout_sec = CPPHTTPLIB_THREAD_POOL_IDLE_TIMEOUT);
   ThreadPool(const ThreadPool &) = delete;
   ~ThreadPool() override = default;
 
@@ -1556,6 +1572,7 @@ private:
   size_t base_thread_count_;
   size_t max_thread_count_;
   size_t max_queued_requests_;
+  time_t idle_timeout_sec_;
   size_t idle_thread_count_;
 
   bool shutdown_;
@@ -1680,6 +1697,35 @@ make_multipart_content_provider(const UploadFormDataItems &items,
 
 } // namespace detail
 
+bool is_valid_multipart_boundary(const std::string &boundary);
+
+// Serializer for multipart/form-data request bodies. The boundary is owned
+// by the writer so that per-part framing and the final terminator always
+// agree. Field names and filenames are escaped following the WHATWG HTML
+// standard ('"' -> %22, CR -> %0D, LF -> %0A); CR and LF are also escaped
+// in content types.
+class MultipartFormDataWriter {
+public:
+  MultipartFormDataWriter();
+  // precondition: is_valid_multipart_boundary(boundary)
+  explicit MultipartFormDataWriter(std::string boundary);
+
+  const std::string &boundary() const;
+  std::string content_type() const;
+
+  // In-memory items -> whole body (known length)
+  std::string serialize(const UploadFormDataItems &items) const;
+  size_t content_length(const UploadFormDataItems &items) const;
+
+  // Per-part framing for streaming via a content provider
+  std::string item_begin(const UploadFormData &item) const;
+  static std::string item_end();
+  std::string finish() const;
+
+private:
+  std::string boundary_;
+};
+
 class Server {
 public:
   using Handler = std::function<void(const Request &, Response &)>;
@@ -2897,9 +2943,7 @@ template <size_t N> inline constexpr size_t str_len(const char (&)[N]) {
 }
 
 inline bool is_numeric(const std::string &str) {
-  return !str.empty() &&
-         std::all_of(str.cbegin(), str.cend(),
-                     [](unsigned char c) { return std::isdigit(c); });
+  return !str.empty() && std::all_of(str.cbegin(), str.cend(), is_ascii_digit);
 }
 
 inline size_t get_header_value_u64(const Headers &headers,

From 5a460dea9f961cdb508d58a6e7b0f9e259b4c19f Mon Sep 17 00:00:00 2001
From: Gaurav Garg <gaugarg@nvidia.com>
Date: Fri, 3 Jul 2026 14:36:29 +0530
Subject: [PATCH 257/292] Remove redundant CUDA copies after gated_delta_net.
 (#23940)

* Remove redundant CUDA copies after gated_delta_net.

Currently, GDN writes recurrent state snapshots into its output tail, then the graph immediately copies those snapshots into ssm_states_all. With MTP draft length 3, target decode uses K=4, so that becomes 4 extra ggml_cuda_cpy calls.

The change detects that gated_delta_net -> view -> cpy pattern and makes the CUDA GDN kernel write the state snapshot(s) directly into the recurrent cache, skipping the intermediate tail writes and copy kernels when safe.

* Address review comments
---
 ggml/src/ggml-cuda/gated_delta_net.cu  | 65 +++++++++++--------
 ggml/src/ggml-cuda/gated_delta_net.cuh | 10 +++
 ggml/src/ggml-cuda/ggml-cuda.cu        | 87 +++++++++++++++++++++++++-
 3 files changed, 135 insertions(+), 27 deletions(-)

diff --git a/ggml/src/ggml-cuda/gated_delta_net.cu b/ggml/src/ggml-cuda/gated_delta_net.cu
index a547360eb..1b431a724 100644
--- a/ggml/src/ggml-cuda/gated_delta_net.cu
+++ b/ggml/src/ggml-cuda/gated_delta_net.cu
@@ -10,6 +10,7 @@ gated_delta_net_cuda(const float * q,
                                      const float * beta,
                                      const float * curr_state,
                                      float *       dst,
+                                     float *       state,
                                      int64_t       H,
                                      int64_t       n_tokens,
                                      int64_t       n_seqs,
@@ -25,6 +26,7 @@ gated_delta_net_cuda(const float * q,
                                      const uint3   neqk1_magic,
                                      const uint3   rq3_magic,
                                      float         scale,
+                                     int64_t       state_slot_stride,
                                      int           K) {
     const uint32_t h_idx    = blockIdx.x;
     const uint32_t sequence = blockIdx.y;
@@ -35,9 +37,7 @@ gated_delta_net_cuda(const float * q,
     const uint32_t iq1 = fastmodulo(h_idx, neqk1_magic);
     const uint32_t iq3 = fastdiv(sequence, rq3_magic);
 
-    const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs;
     float *       attn_data        = dst;
-    float *       state            = dst + attn_score_elems;
 
     // input state holds s0 only: [S_v, S_v, H, n_seqs] — seq stride is D = H * S_v * S_v.
     // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
@@ -145,10 +145,9 @@ gated_delta_net_cuda(const float * q,
         if constexpr (keep_rs_t) {
             // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
             // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
-            const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
             const int target_slot = (int) n_tokens - 1 - t;
             if (target_slot >= 0 && target_slot < K) {
-                float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
+                float * curr_state = state + target_slot * state_slot_stride;
 #pragma unroll
                 for (int r = 0; r < rows_per_lane; r++) {
                     const int i = r * warp_size + lane;
@@ -171,13 +170,13 @@ template <bool KDA, bool keep_rs_t>
 static void launch_gated_delta_net(
         const float * q_d, const float * k_d, const float * v_d,
         const float * g_d, const float * b_d, const float * s_d,
-        float * dst_d,
+        float * dst_d, float * state_d,
         int64_t S_v,   int64_t H, int64_t n_tokens, int64_t n_seqs,
         int64_t sq1,   int64_t sq2, int64_t sq3,
         int64_t sv1,   int64_t sv2, int64_t sv3,
         int64_t sb1,   int64_t sb2, int64_t sb3,
         int64_t neqk1, int64_t rq3,
-        float scale, int K, cudaStream_t stream) {
+        float scale, int64_t state_slot_stride, int K, cudaStream_t stream) {
     //TODO: Add chunked kernel for even faster pre-fill
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const int num_warps = 4;
@@ -187,34 +186,32 @@ static void launch_gated_delta_net(
     const uint3 neqk1_magic = init_fastdiv_values(neqk1);
     const uint3 rq3_magic   = init_fastdiv_values(rq3);
 
-    int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-
     const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, stream);
     switch (S_v) {
         case 16:
             ggml_cuda_kernel_launch(gated_delta_net_cuda<16, KDA, keep_rs_t>, launch_params,
-                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K);
             break;
         case 32:
             ggml_cuda_kernel_launch(gated_delta_net_cuda<32, KDA, keep_rs_t>, launch_params,
-                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K);
             break;
         case 64: {
             ggml_cuda_kernel_launch(gated_delta_net_cuda<64, KDA, keep_rs_t>, launch_params,
-                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K);
             break;
         }
         case 128: {
             ggml_cuda_kernel_launch(gated_delta_net_cuda<128, KDA, keep_rs_t>, launch_params,
-                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H,
+                q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H,
                 n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K);
+                sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K);
             break;
         }
         default:
@@ -223,7 +220,8 @@ static void launch_gated_delta_net(
     }
 }
 
-void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+static void ggml_cuda_op_gated_delta_net_impl(
+        ggml_backend_cuda_context & ctx, ggml_tensor * dst, const ggml_cuda_gated_delta_net_fused_cache * cache) {
     ggml_tensor * src_q     = dst->src[0];
     ggml_tensor * src_k     = dst->src[1];
     ggml_tensor * src_v     = dst->src[2];
@@ -288,25 +286,42 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor *
     const int K = ggml_get_op_params_i32(dst, 0);
     const bool keep_rs = K > 1;
 
+    // recurrent state -> gdn_out tail (after attention scores), or the cache when fusing
+    float * state_d           = dst_d + S_v * H * n_tokens * n_seqs;
+    int64_t state_slot_stride = S_v * S_v * H * n_seqs;
+    if (cache != nullptr) {
+        state_d           = cache->data;
+        state_slot_stride = cache->slot_stride;
+    }
+
     if (kda) {
         if (keep_rs) {
-            launch_gated_delta_net<true, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            launch_gated_delta_net<true, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d,
                 S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+                sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream);
         } else {
-            launch_gated_delta_net<true, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            launch_gated_delta_net<true, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d,
                 S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+                sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream);
         }
     } else {
         if (keep_rs) {
-            launch_gated_delta_net<false, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            launch_gated_delta_net<false, true>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d,
                 S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+                sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream);
         } else {
-            launch_gated_delta_net<false, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d,
+            launch_gated_delta_net<false, false>(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d,
                 S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3,
-                sb1, sb2, sb3, neqk1, rq3, scale, K, stream);
+                sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream);
         }
     }
 }
+
+void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_gated_delta_net_impl(ctx, dst, nullptr);
+}
+
+void ggml_cuda_op_gated_delta_net_fused_cache(
+        ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_cuda_gated_delta_net_fused_cache cache) {
+    ggml_cuda_op_gated_delta_net_impl(ctx, dst, &cache);
+}
diff --git a/ggml/src/ggml-cuda/gated_delta_net.cuh b/ggml/src/ggml-cuda/gated_delta_net.cuh
index 7375e81c0..f9bf43706 100644
--- a/ggml/src/ggml-cuda/gated_delta_net.cuh
+++ b/ggml/src/ggml-cuda/gated_delta_net.cuh
@@ -1,4 +1,14 @@
 #include "common.cuh"
 #include "ggml.h"
 
+// fused-kernel recurrent-state output; strides in elements (per-seq stride is always D, set in-kernel)
+struct ggml_cuda_gated_delta_net_fused_cache {
+    float * data;        // rollback slot 0
+    int64_t slot_stride; // between rollback slots (0 when K==1)
+};
+
 void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+// same op, but writes the snapshot(s) into the cache instead of dst (see ggml_cuda_try_gdn_cache_fusion)
+void ggml_cuda_op_gated_delta_net_fused_cache(ggml_backend_cuda_context & ctx, ggml_tensor * dst,
+                                              ggml_cuda_gated_delta_net_fused_cache cache);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index cca70592f..78d2218e5 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3251,6 +3251,11 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
     GGML_UNUSED(backend);
 }
 
+static bool ggml_cuda_is_view_or_noop(const ggml_tensor * t) {
+    return ggml_is_empty(t) || t->op == GGML_OP_RESHAPE || t->op == GGML_OP_TRANSPOSE ||
+           t->op == GGML_OP_VIEW || t->op == GGML_OP_PERMUTE || t->op == GGML_OP_NONE;
+}
+
 #ifdef USE_CUDA_GRAPH
 static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
 
@@ -3260,7 +3265,7 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
 
-        if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+        if (ggml_cuda_is_view_or_noop(node)) {
             continue;
         }
 
@@ -3403,6 +3408,70 @@ static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope,
     return true;
 }
 
+// match gated_delta_net + the strided cpy that scatters its state snapshots into the cache
+// (slot i -> rollback group i, slot 0 newest), so the kernel can write them and skip the cpy.
+static int ggml_cuda_try_gdn_cache_fusion(
+        const ggml_cgraph * cgraph, int node_idx, ggml_cuda_gated_delta_net_fused_cache & fused_state_cpy) {
+    const ggml_tensor * gdn = cgraph->nodes[node_idx];
+    // the kernel skips the snapshot tail, so the gdn output must not be a graph output
+    if (gdn->op != GGML_OP_GATED_DELTA_NET || gdn->type != GGML_TYPE_F32 ||
+        (gdn->flags & GGML_TENSOR_FLAG_OUTPUT)) {
+        return 0;
+    }
+
+    const ggml_tensor * src_v     = gdn->src[2];
+    const int64_t       S_v       = src_v->ne[0];
+    const int64_t       H         = src_v->ne[1];
+    const int64_t       n_tokens  = src_v->ne[2];
+    const int64_t       n_seqs    = src_v->ne[3];
+    const int64_t       D         = S_v * S_v * H;
+    const int64_t       K         = ggml_get_op_params_i32(gdn, 0); // snapshot slot count
+    const int64_t       n_written = std::min<int64_t>(n_tokens, K); // newest n_written slots are written
+
+    // snapshot tail starts right after the attention scores
+    const size_t tail_off = ggml_row_size(GGML_TYPE_F32, S_v * H * n_tokens * n_seqs);
+
+    // snapshot cpy is the first real node after the gdn (skip views/no-ops)
+    const ggml_tensor * cpy  = nullptr;
+    int                 skip = 0;
+    for (int j = node_idx + 1; j < cgraph->n_nodes && cpy == nullptr; ++j) {
+        const ggml_tensor * n = cgraph->nodes[j];
+        if (ggml_cuda_is_view_or_noop(n)) {
+            continue;
+        }
+        if (n->op != GGML_OP_CPY || (n->flags & GGML_TENSOR_FLAG_OUTPUT)) {
+            return 0;
+        }
+        cpy  = n;
+        skip = j - node_idx;
+    }
+    if (cpy == nullptr) {
+        return 0;
+    }
+
+    const ggml_tensor * src = cpy->src[0]; // view of the gdn snapshot tail
+    const ggml_tensor * dst = cpy->src[1]; // cache view the kernel writes to
+
+    // src must be this gdn's snapshot tail (contiguous, at the tail offset)
+    if (src->op != GGML_OP_VIEW || src->view_src != gdn || src->view_offs != tail_off ||
+        !ggml_is_contiguous(src)) {
+        return 0;
+    }
+
+    // dst is the [D, n_seqs, n_written] cache view; require nb[1] == D (the per-seq stride the kernel
+    // assumes). ggml_cpy pins src to the same element count.
+    const std::array<int64_t, GGML_MAX_DIMS> expected_ne = { D, n_seqs, n_written, 1 };
+    if (dst->op != GGML_OP_VIEW || dst->type != GGML_TYPE_F32 || dst->data == nullptr ||
+        !std::equal(expected_ne.begin(), expected_ne.end(), dst->ne) ||
+        dst->nb[0] != ggml_type_size(GGML_TYPE_F32) || dst->nb[1] != (size_t) ggml_row_size(GGML_TYPE_F32, D)) {
+        return 0;
+    }
+
+    fused_state_cpy.data        = (float *) dst->data; // rollback group 0 (newest)
+    fused_state_cpy.slot_stride = K > 1 ? (int64_t) (dst->nb[2] / sizeof(float)) : 0;
+    return skip;
+}
+
 static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) {
     args.sigmoid         = false;
     args.softmax         = false;
@@ -3844,6 +3913,20 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
 
     ggml_tensor * node = cgraph->nodes[i];
 
+    // gated_delta_net -> cpy: scatter recurrent-state snapshots into the cache
+    if (node->op == GGML_OP_GATED_DELTA_NET) {
+        ggml_cuda_gated_delta_net_fused_cache fused_state_cpy;
+        const int nodes_to_skip = ggml_cuda_try_gdn_cache_fusion(cgraph, i, fused_state_cpy);
+        if (nodes_to_skip > 0) {
+#ifdef GGML_CUDA_DEBUG
+            GGML_LOG_INFO("%s: fused gated_delta_net snapshot copies for %s (skipped %d nodes)\n",
+                          __func__, node->name, nodes_to_skip);
+#endif
+            ggml_cuda_op_gated_delta_net_fused_cache(*cuda_ctx, node, fused_state_cpy);
+            return nodes_to_skip;
+        }
+    }
+
     //topk-moe
     if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX ||
             cgraph->nodes[i]->op == GGML_OP_ARGSORT) {
@@ -4372,7 +4455,7 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
 #endif
                 prev_i = i;
 
-                if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
+                if (ggml_cuda_is_view_or_noop(node)) {
                     continue;
                 }
 

From fd38fec594f3a7fb32ad611d06617e3bd08db049 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 3 Jul 2026 17:32:48 +0800
Subject: [PATCH 258/292] save lora info when generating image

---
 embd_res/kcpp_sdui.embd            | 28 ++++++++++++++--------------
 otherarch/sdcpp/sdtype_adapter.cpp |  1 +
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/embd_res/kcpp_sdui.embd b/embd_res/kcpp_sdui.embd
index 50807c23b..c398b1c59 100644
--- a/embd_res/kcpp_sdui.embd
+++ b/embd_res/kcpp_sdui.embd
@@ -5,18 +5,18 @@
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z(),be(v,{ref:"popper",visible:e.showPicker,"show-arrow":!1,"fallback-placements":["bottom","top","right","left"],offset:0,"gpu-acceleration":!1,"popper-class":[e.ns.be("picker","panel"),e.ns.b("dropdown"),e.popperClass],"stop-popper-mouse-event":!1,effect:"light",trigger:"click",transition:`${e.ns.namespace.value}-zoom-in-top`,persistent:""},{content:he(()=>[Et((z(),se("div",null,[ne("div",{class:de(e.ns.be("dropdown","main-wrapper"))},[le(g,{ref:"hue",class:"hue-slider",color:e.color,vertical:""},null,8,["color"]),le(o,{ref:"svPanel",color:e.color},null,8,["color"])],2),e.showAlpha?(z(),be(a,{key:0,ref:"alpha",color:e.color},null,8,["color"])):ye("v-if",!0),e.predefine?(z(),be(u,{key:1,ref:"predefine",color:e.color,colors:e.predefine},null,8,["color","colors"])):ye("v-if",!0),ne("div",{class:de(e.ns.be("dropdown","btns"))},[ne("span",{class:de(e.ns.be("dropdown","value"))},[le(d,{modelValue:e.customInput,"onUpdate:modelValue":t[0]||(t[0]=y=>e.customInput=y),"validate-event":!1,size:"small",onKeyup:Dt(e.handleConfirm,["enter"]),onBlur:e.handleConfirm},null,8,["modelValue","onKeyup","onBlur"])],2),le(c,{class:de(e.ns.be("dropdown","link-btn")),text:"",size:"small",onClick:e.clear},{default:he(()=>[He(Oe(e.t("el.colorpicker.clear")),1)]),_:1},8,["class","onClick"]),le(c,{plain:"",size:"small",class:de(e.ns.be("dropdown","btn")),onClick:e.confirmValue},{default:he(()=>[He(Oe(e.t("el.colorpicker.confirm")),1)]),_:1},8,["class","onClick"])],2)])),[[m,e.hide]])]),default:he(()=>[ne("div",{id:e.buttonId,class:de([e.ns.b("picker"),e.ns.is("disabled",e.colorDisabled),e.ns.bm("picker",e.colorSize)]),role:"button","aria-label":e.buttonAriaLabel,"aria-labelledby":e.buttonAriaLabelledby,"aria-description":e.t("el.colorpicker.description",{color:e.modelValue||""}),tabindex:e.tabindex,onKeydown:t[2]||(t[2]=Dt((...y)=>e.handleTrigger&&e.handleTrigger(...y),["enter"]))},[e.colorDisabled?(z(),se("div",{key:0,class:de(e.ns.be("picker","mask"))},null,2)):ye("v-if",!0),ne("div",{class:de(e.ns.be("picker","trigger")),onClick:t[1]||(t[1]=(...y)=>e.handleTrigger&&e.handleTrigger(...y))},[ne("span",{class:de([e.ns.be("picker","color"),e.ns.is("alpha",e.showAlpha)])},[ne("span",{class:de(e.ns.be("picker","color-inner")),style:ze({backgroundColor:e.displayedColor})},[Et(le(s,{class:de([e.ns.be("picker","icon"),e.ns.is("icon-arrow-down")])},{default:he(()=>[le(f)]),_:1},8,["class"]),[[Ht,e.modelValue||e.showPanelColor]]),!e.modelValue&&!e.showPanelColor?(z(),be(s,{key:0,class:de([e.ns.be("picker","empty"),e.ns.is("icon-close")])},{default:he(()=>[le(h)]),_:1},8,["class"])):ye("v-if",!0)],6)],2)],2)],42,xB)]),_:1},8,["visible","popper-class","transition"])}var zu=Re(SB,[["render",TB],["__file","/home/runner/work/element-plus/element-plus/packages/components/color-picker/src/index.vue"]]);zu.install=e=>{e.component(zu.name,zu)};const kB=zu,EB=kB,Yd={},OB=Be({a11y:{type:Boolean,default:!0},locale:{type:Pe(Object)},size:Hi,button:{type:Pe(Object)},experimentalFeatures:{type:Pe(Object)},keyboardNavigation:{type:Boolean,default:!0},message:{type:Pe(Object)},zIndex:Number,namespace:{type:String,default:"el"}});Ee({name:"ElConfigProvider",props:OB,setup(e,{slots:t}){xe(()=>e.message,r=>{Object.assign(Yd,r!=null?r:{})},{immediate:!0,deep:!0});const n=NM(e);return()=>Te(t,"default",{config:n==null?void 0:n.value})}});const AB=Be({mask:{type:Boolean,default:!0},customMaskEvent:{type:Boolean,default:!1},overlayClass:{type:Pe([String,Array,Object])},zIndex:{type:Pe([String,Number])}}),PB={click:e=>e instanceof MouseEvent};var IB=Ee({name:"ElOverlay",props:AB,emits:PB,setup(e,{slots:t,emit:n}){const 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 (?:`+r+":){6}(?:"+n+"|:"+r+`|:)|                             // 1:2:3:4:5:6::    1:2:3:4:5:6::8   1:2:3:4:5:6::8  1:2:3:4:5:6::1.2.3.4
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M=R=>{c.value[R.index]=R},U=R=>{delete c.value[R.index]};vt(`subMenu:${r.uid}`,{addSubMenu:M,removeSubMenu:U,handleMouseleave:ue,mouseInChild:s,level:u.level+1})}return n({opened:x}),nt(()=>{a.addSubMenu(L),u.addSubMenu(L)}),Yt(()=>{u.removeSubMenu(L),a.removeSubMenu(L)}),()=>{var M;const U=[(M=t.title)==null?void 0:M.call(t),wt(Le,{class:o.e("icon-arrow"),style:{transform:x.value?e.expandCloseIcon&&e.expandOpenIcon||e.collapseCloseIcon&&e.collapseOpenIcon&&a.props.collapse?"none":"rotateZ(180deg)":"none"}},{default:()=>Ge(y.value)?wt(r.appContext.components[y.value]):wt(y.value)})],R=dw(a.props,u.level+1),P=a.isMenuPopup?wt(zn,{ref:v,visible:x.value,effect:"light",pure:!0,offset:e.popperOffset,showArrow:!1,persistent:!0,popperClass:e.popperClass,placement:m.value,teleported:_.value,fallbackPlacements:S.value,transition:w.value,gpuAcceleration:!1},{content:()=>{var Q;return wt("div",{class:[g.m(N.value),g.m("popup-container"),e.popperClass],onMouseenter:j=>J(j,100),onMouseleave:()=>ue(!0),onFocus:j=>J(j,100)},[wt("ul",{class:[g.b(),g.m("popup"),g.m(`popup-${m.value}`)],style:R.value},[(Q=t.default)==null?void 0:Q.call(t)])])},default:()=>wt("div",{class:o.e("title"),style:[F.value,{backgroundColor:E.value}],onClick:re},U)}):wt(je,{},[wt("div",{class:o.e("title"),style:[F.value,{backgroundColor:E.value}],ref:h,onClick:re},U),wt(rB,{},{default:()=>{var Q;return Et(wt("ul",{role:"menu",class:[g.b(),g.m("inline")],style:R.value},[(Q=t.default)==null?void 0:Q.call(t)]),[[Ht,x.value]])}})]);return wt("li",{class:[o.b(),o.is("active",T.value),o.is("opened",x.value),o.is("disabled",e.disabled)],role:"menuitem",ariaHaspopup:!0,ariaExpanded:x.value,onMouseenter:J,onMouseleave:()=>ue(!0),onFocus:J},[P])}}});const iN=Be({mode:{type:String,values:["horizontal","vertical"],default:"vertical"},defaultActive:{type:String,default:""},defaultOpeneds:{type:Pe(Array),default:()=>an([])},uniqueOpened:Boolean,router:Boolean,menuTrigger:{type:String,values:["hover","click"],default:"hover"},collapse:Boolean,backgroundColor:String,textColor:String,activeTextColor:String,collapseTransition:{type:Boolean,default:!0},ellipsis:{type:Boolean,default:!0}}),Df=e=>Array.isArray(e)&&e.every(t=>Ge(t)),aN={close:(e,t)=>Ge(e)&&Df(t),open:(e,t)=>Ge(e)&&Df(t),select:(e,t,n,r)=>Ge(e)&&Df(t)&&ot(n)&&(r===void 0||r instanceof Promise)};var oN=Ee({name:"ElMenu",props:iN,emits:aN,setup(e,{emit:t,slots:n,expose:r}){const i=xt(),l=i.appContext.config.globalProperties.$router,g=oe(),o=Ie("menu"),a=Ie("sub-menu"),u=oe(-1),d=oe(e.defaultOpeneds&&!e.collapse?e.defaultOpeneds.slice(0):[]),c=oe(e.defaultActive),f=oe({}),s=oe({}),h=ee(()=>e.mode==="horizontal"||e.mode==="vertical"&&e.collapse),v=()=>{const D=c.value&&f.value[c.value];if(!D||e.mode==="horizontal"||e.collapse)return;D.indexPath.forEach(L=>{const F=s.value[L];F&&m(L,F.indexPath)})},m=(D,N)=>{d.value.includes(D)||(e.uniqueOpened&&(d.value=d.value.filter(L=>N.includes(L))),d.value.push(D),t("open",D,N))},y=(D,N)=>{const L=d.value.indexOf(D);L!==-1&&d.value.splice(L,1),t("close",D,N)},b=({index:D,indexPath:N})=>{d.value.includes(D)?y(D,N):m(D,N)},_=D=>{(e.mode==="horizontal"||e.collapse)&&(d.value=[]);const{index:N,indexPath:L}=D;if(!(N===void 0||L===void 0))if(e.router&&l){const F=D.route||N,O=l.push(F).then(V=>(V||(c.value=N),V));t("select",N,L,{index:N,indexPath:L,route:F},O)}else c.value=N,t("select",N,L,{index:N,indexPath:L})},w=D=>{const N=f.value,L=N[D]||c.value&&N[c.value]||N[e.defaultActive];L?c.value=L.index:c.value=D},S=()=>{var D,N;if(!g.value)return-1;const L=Array.from((N=(D=g.value)==null?void 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sN=Be({index:{type:Pe([String,null]),default:null},route:{type:Pe([String,Object])},disabled:Boolean}),lN={click:e=>Ge(e.index)&&Array.isArray(e.indexPath)},$f="ElMenuItem",uN=Ee({name:$f,components:{ElTooltip:zn},props:sN,emits:lN,setup(e,{emit:t}){const n=xt(),r=qe("rootMenu"),i=Ie("menu"),l=Ie("menu-item");r||Cn($f,"can not inject root menu");const{parentMenu:g,indexPath:o}=fw(n,In(e,"index")),a=qe(`subMenu:${g.value.uid}`);a||Cn($f,"can not inject sub menu");const u=ee(()=>e.index===r.activeIndex),d=yt({index:e.index,indexPath:o,active:u}),c=()=>{e.disabled||(r.handleMenuItemClick({index:e.index,indexPath:o.value,route:e.route}),t("click",d))};return nt(()=>{a.addSubMenu(d),r.addMenuItem(d)}),Yt(()=>{a.removeSubMenu(d),r.removeMenuItem(d)}),{Effect:BL,parentMenu:g,rootMenu:r,active:u,nsMenu:i,nsMenuItem:l,handleClick:c}}});function cN(e,t,n,r,i,l){const g=gt("el-tooltip");return 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 Permission to use, copy, modify, and/or distribute this software for any
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@@ -51,13 +51,13 @@ INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
 LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
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this._ctx.unique="unique",this.eachKey(t)},e.prototype.eachPrimaryKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.primaryKey,i)})},e.prototype.keys=function(t){var n=this._ctx;n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.key)}).then(function(){return r}).then(t)},e.prototype.primaryKeys=function(t){var n=this._ctx;if(n.dir==="next"&&ro(n,!0)&&n.limit>0)return this._read(function(i){var l=Yu(n,n.table.core.schema);return n.table.core.query({trans:i,values:!1,limit:n.limit,query:{index:l,range:n.range}})}).then(function(i){var l=i.result;return l}).then(t);n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.primaryKey)}).then(function(){return r}).then(t)},e.prototype.uniqueKeys=function(t){return this._ctx.unique="unique",this.keys(t)},e.prototype.firstKey=function(t){return this.limit(1).keys(function(n){return n[0]}).then(t)},e.prototype.lastKey=function(t){return 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this.inAnyRange([[hh,t],[t,this.db._maxKey]],{includeLowers:!1,includeUppers:!1})},e.prototype.noneOf=function(){var t=Zr.apply(fo,arguments);if(t.length===0)return new this.Collection(this);try{t.sort(this._ascending)}catch{return Dn(this,Vr)}var n=t.reduce(function(r,i){return r?r.concat([[r[r.length-1][1],i]]):[[hh,i]]},null);return n.push([t[t.length-1],this.db._maxKey]),this.inAnyRange(n,{includeLowers:!1,includeUppers:!1})},e.prototype.inAnyRange=function(t,n){var r=this,i=this._cmp,l=this._ascending,g=this._descending,o=this._min,a=this._max;if(t.length===0)return io(this);if(!t.every(function(S){return S[0]!==void 0&&S[1]!==void 0&&l(S[0],S[1])<=0}))return Dn(this,"First argument to inAnyRange() must be an Array of two-value Arrays [lower,upper] where upper must not be lower than lower",Je.InvalidArgument);var u=!n||n.includeLowers!==!1,d=n&&n.includeUppers===!0;function c(S,x){for(var T=0,E=S.length;T<E;++T){var 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hs(!Ke.global),++this._reculock,this._reculock===1&&!Ke.global&&(Ke.lockOwnerFor=this),this},e.prototype._unlock=function(){if(hs(!Ke.global),--this._reculock===0)for(Ke.global||(Ke.lockOwnerFor=null);this._blockedFuncs.length>0&&!this._locked();){var t=this._blockedFuncs.shift();try{Ko(t[1],t[0])}catch{}}return this},e.prototype._locked=function(){return this._reculock&&Ke.lockOwnerFor!==this},e.prototype.create=function(t){var n=this;if(!this.mode)return this;var r=this.db.idbdb,i=this.db._state.dbOpenError;if(hs(!this.idbtrans),!t&&!r)switch(i&&i.name){case"DatabaseClosedError":throw new Je.DatabaseClosed(i);case"MissingAPIError":throw new Je.MissingAPI(i.message,i);default:throw new Je.OpenFailed(i)}if(!this.active)throw new Je.TransactionInactive;return hs(this._completion._state===null),t=this.idbtrans=t||(this.db.core?this.db.core.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability}):r.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability})),t.onerror=Ft(function(l){ul(l),n._reject(t.error)}),t.onabort=Ft(function(l){ul(l),n.active&&n._reject(new Je.Abort(t.error)),n.active=!1,n.on("abort").fire(l)}),t.oncomplete=Ft(function(){n.active=!1,n._resolve(),"mutatedParts"in t&&Ki.storagemutated.fire(t.mutatedParts)}),this},e.prototype._promise=function(t,n,r){var i=this;if(t==="readwrite"&&this.mode!=="readwrite")return Kt(new Je.ReadOnly("Transaction is readonly"));if(!this.active)return Kt(new Je.TransactionInactive);if(this._locked())return new $e(function(g,o){i._blockedFuncs.push([function(){i._promise(t,n,r).then(g,o)},Ke])});if(r)return Yi(function(){var g=new $e(function(o,a){i._lock();var u=n(o,a,i);u&&u.then&&u.then(o,a)});return g.finally(function(){return 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h=t2(f,s);h.add.forEach(function(w){rm(r,w[0],w[1].primKey,w[1].indexes)}),h.change.forEach(function(w){if(w.recreate)throw new Je.Upgrade("Not yet support for changing primary key");var S=r.objectStore(w.name);w.add.forEach(function(x){return vh(S,x)}),w.change.forEach(function(x){S.deleteIndex(x.name),vh(S,x)}),w.del.forEach(function(x){return S.deleteIndex(x)})});var v=c._cfg.contentUpgrade;if(v&&c._cfg.version>t){nm(i,r),n._memoizedTables={},a=!0;var m=Mw(s);h.del.forEach(function(w){m[w]=f[w]}),gh(i,[i.Transaction.prototype]),_c(i,[i.Transaction.prototype],Wt(m),m),n.schema=m;var y=Kp(v);y&&Xo();var b,_=$e.follow(function(){if(b=v(n),b&&y){var w=li.bind(null,null);b.then(w,w)}});return b&&typeof b.then=="function"?$e.resolve(b):_.then(function(){return b})}}),l.push(function(f){if(!a||!y7){var s=c._cfg.dbschema;W7(s,f)}gh(i,[i.Transaction.prototype]),_c(i,[i.Transaction.prototype],i._storeNames,i._dbSchema),n.schema=i._dbSchema})});function d(){return 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this.db.Collection(new this.db.WhereClause(this,dn(t)?"["+t.join("+")+"]":t))},e.prototype.reverse=function(){return this.toCollection().reverse()},e.prototype.mapToClass=function(t){this.schema.mappedClass=t;var n=function(r){if(!r)return r;var i=Object.create(t.prototype);for(var l in r)if(Wn(r,l))try{i[l]=r[l]}catch{}return i};return this.schema.readHook&&this.hook.reading.unsubscribe(this.schema.readHook),this.schema.readHook=n,this.hook("reading",n),t},e.prototype.defineClass=function(){function t(n){Sn(this,n)}return this.mapToClass(t)},e.prototype.add=function(t,n){var r=this,i=this.schema.primKey,l=i.auto,g=i.keyPath,o=t;return g&&l&&(o=du(g)(t)),this._trans("readwrite",function(a){return r.core.mutate({trans:a,type:"add",keys:n!=null?[n]:null,values:[o]})}).then(function(a){return a.numFailures?$e.reject(a.failures[0]):a.lastResult}).then(function(a){if(g)try{dr(t,g,a)}catch{}return a})},e.prototype.update=function(t,n){if(typeof t=="object"&&!dn(t)){var 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t.core.mutate({trans:n,type:"deleteRange",range:qw})}).then(function(n){return n.numFailures?$e.reject(n.failures[0]):void 0})},e.prototype.bulkGet=function(t){var n=this;return this._trans("readonly",function(r){return n.core.getMany({keys:t,trans:r}).then(function(i){return i.map(function(l){return n.hook.reading.fire(l)})})})},e.prototype.bulkAdd=function(t,n,r){var i=this,l=Array.isArray(n)?n:void 0;r=r||(l?void 0:n);var g=r?r.allKeys:void 0;return this._trans("readwrite",function(o){var a=i.schema.primKey,u=a.auto,d=a.keyPath;if(d&&l)throw new Je.InvalidArgument("bulkAdd(): keys argument invalid on tables with inbound keys");if(l&&l.length!==t.length)throw new Je.InvalidArgument("Arguments objects and keys must have the same length");var c=t.length,f=d&&u?t.map(du(d)):t;return i.core.mutate({trans:o,type:"add",keys:l,values:f,wantResults:g}).then(function(s){var h=s.numFailures,v=s.results,m=s.lastResult,y=s.failures,b=g?v:m;if(h===0)return b;throw new Ps(i.name+".bulkAdd(): "+h+" 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n.error?n.table._trans(null,Kt.bind(null,n.error)):n.table._trans("readwrite",t,"locked")},e.prototype._addAlgorithm=function(t){var n=this._ctx;n.algorithm=Ra(n.algorithm,t)},e.prototype._iterate=function(t,n){return hu(this._ctx,t,n,this._ctx.table.core)},e.prototype.clone=function(t){var n=Object.create(this.constructor.prototype),r=Object.create(this._ctx);return t&&Sn(r,t),n._ctx=r,n},e.prototype.raw=function(){return this._ctx.valueMapper=null,this},e.prototype.each=function(t){var n=this._ctx;return this._read(function(r){return hu(n,t,r,n.table.core)})},e.prototype.count=function(t){var n=this;return this._read(function(r){var i=n._ctx,l=i.table.core;if(ro(i,!0))return l.count({trans:r,query:{index:Yu(i,l.schema),range:i.range}}).then(function(o){return Math.min(o,i.limit)});var g=0;return hu(i,function(){return++g,!1},r,l).then(function(){return g})}).then(t)},e.prototype.sortBy=function(t,n){var r=t.split(".").reverse(),i=r[0],l=r.length-1;function g(u,d){return 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function(r,i,l){return--n<=0&&i(l),n>=0}},!0),this},e.prototype.until=function(t,n){return Uf(this._ctx,function(r,i,l){return t(r.value)?(i(l),n):!0}),this},e.prototype.first=function(t){return this.limit(1).toArray(function(n){return n[0]}).then(t)},e.prototype.last=function(t){return this.reverse().first(t)},e.prototype.filter=function(t){return Uf(this._ctx,function(n){return t(n.value)}),C7(this._ctx,t),this},e.prototype.and=function(t){return this.filter(t)},e.prototype.or=function(t){return new this.db.WhereClause(this._ctx.table,t,this)},e.prototype.reverse=function(){return this._ctx.dir=this._ctx.dir==="prev"?"next":"prev",this._ondirectionchange&&this._ondirectionchange(this._ctx.dir),this},e.prototype.desc=function(){return this.reverse()},e.prototype.eachKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.key,i)})},e.prototype.eachUniqueKey=function(t){return this._ctx.unique="unique",this.eachKey(t)},e.prototype.eachPrimaryKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.primaryKey,i)})},e.prototype.keys=function(t){var n=this._ctx;n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.key)}).then(function(){return r}).then(t)},e.prototype.primaryKeys=function(t){var n=this._ctx;if(n.dir==="next"&&ro(n,!0)&&n.limit>0)return this._read(function(i){var l=Yu(n,n.table.core.schema);return n.table.core.query({trans:i,values:!1,limit:n.limit,query:{index:l,range:n.range}})}).then(function(i){var l=i.result;return l}).then(t);n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.primaryKey)}).then(function(){return r}).then(t)},e.prototype.uniqueKeys=function(t){return this._ctx.unique="unique",this.keys(t)},e.prototype.firstKey=function(t){return this.limit(1).keys(function(n){return n[0]}).then(t)},e.prototype.lastKey=function(t){return 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N=S[D],L={value:Dl(N),primKey:y[_+D]};l.call(L,L.value,L)!==!1&&(L.value==null?I.push(y[_+D]):!d&&_n(c(N),c(L.value))!==0?(I.push(y[_+D]),x.push(L.value)):(T.push(L.value),d&&E.push(y[_+D])))}var F=ro(r)&&r.limit===1/0&&(typeof t!="function"||t===Wf)&&{index:r.index,range:r.range};return Promise.resolve(x.length>0&&a.mutate({trans:i,type:"add",values:x}).then(function(O){for(var V in O.failures)I.splice(parseInt(V),1);m(x.length,O)})).then(function(){return(T.length>0||F&&typeof t=="object")&&a.mutate({trans:i,type:"put",keys:E,values:T,criteria:F,changeSpec:typeof t!="function"&&t}).then(function(O){return m(T.length,O)})}).then(function(){return(I.length>0||F&&t===Wf)&&a.mutate({trans:i,type:"delete",keys:I,criteria:F}).then(function(O){return m(I.length,O)})}).then(function(){return y.length>_+w&&b(_+f)})})};return b(0).then(function(){if(s.length>0)throw new pc("Error modifying one or more objects",s,h,v);return y.length})})})},e.prototype.delete=function(){var 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this._cmp(t,n)>0||this._cmp(t,n)===0&&(r||i)&&!(r&&i)?io(this):new this.Collection(this,function(){return ki(t,n,!r,!i)})}catch{return Dn(this,Vr)}},e.prototype.equals=function(t){return t==null?Dn(this,Vr):new this.Collection(this,function(){return Zw(t)})},e.prototype.above=function(t){return t==null?Dn(this,Vr):new this.Collection(this,function(){return ki(t,void 0,!0)})},e.prototype.aboveOrEqual=function(t){return t==null?Dn(this,Vr):new this.Collection(this,function(){return ki(t,void 0,!1)})},e.prototype.below=function(t){return t==null?Dn(this,Vr):new this.Collection(this,function(){return ki(void 0,t,!1,!0)})},e.prototype.belowOrEqual=function(t){return t==null?Dn(this,Vr):new this.Collection(this,function(){return ki(void 0,t)})},e.prototype.startsWith=function(t){return typeof t!="string"?Dn(this,Kw):this.between(t,t+ya,!0,!0)},e.prototype.startsWithIgnoreCase=function(t){return t===""?this.startsWith(t):pu(this,function(n,r){return n.indexOf(r[0])===0},[t],ya)},e.prototype.equalsIgnoreCase=function(t){return pu(this,function(n,r){return n===r[0]},[t],"")},e.prototype.anyOfIgnoreCase=function(){var t=Zr.apply(fo,arguments);return t.length===0?io(this):pu(this,function(n,r){return r.indexOf(n)!==-1},t,"")},e.prototype.startsWithAnyOfIgnoreCase=function(){var t=Zr.apply(fo,arguments);return t.length===0?io(this):pu(this,function(n,r){return r.some(function(i){return n.indexOf(i)===0})},t,ya)},e.prototype.anyOf=function(){var t=this,n=Zr.apply(fo,arguments),r=this._cmp;try{n.sort(r)}catch{return Dn(this,Vr)}if(n.length===0)return io(this);var i=new this.Collection(this,function(){return ki(n[0],n[n.length-1])});i._ondirectionchange=function(g){r=g==="next"?t._ascending:t._descending,n.sort(r)};var l=0;return i._addAlgorithm(function(g,o,a){for(var u=g.key;r(u,n[l])>0;)if(++l,l===n.length)return o(a),!1;return r(u,n[l])===0?!0:(o(function(){g.continue(n[l])}),!1)}),i},e.prototype.notEqual=function(t){return this.inAnyRange([[hh,t],[t,this.db._maxKey]],{includeLowers:!1,includeUppers:!1})},e.prototype.noneOf=function(){var t=Zr.apply(fo,arguments);if(t.length===0)return new this.Collection(this);try{t.sort(this._ascending)}catch{return Dn(this,Vr)}var n=t.reduce(function(r,i){return r?r.concat([[r[r.length-1][1],i]]):[[hh,i]]},null);return n.push([t[t.length-1],this.db._maxKey]),this.inAnyRange(n,{includeLowers:!1,includeUppers:!1})},e.prototype.inAnyRange=function(t,n){var r=this,i=this._cmp,l=this._ascending,g=this._descending,o=this._min,a=this._max;if(t.length===0)return io(this);if(!t.every(function(S){return S[0]!==void 0&&S[1]!==void 0&&l(S[0],S[1])<=0}))return Dn(this,"First argument to inAnyRange() must be an Array of two-value Arrays [lower,upper] where upper must not be lower than lower",Je.InvalidArgument);var u=!n||n.includeLowers!==!1,d=n&&n.includeUppers===!0;function c(S,x){for(var T=0,E=S.length;T<E;++T){var I=S[T];if(i(x[0],I[1])<0&&i(x[1],I[0])>0){I[0]=o(I[0],x[0]),I[1]=a(I[1],x[1]);break}}return T===E&&S.push(x),S}var f=l;function s(S,x){return f(S[0],x[0])}var h;try{h=t.reduce(c,[]),h.sort(s)}catch{return Dn(this,Vr)}var v=0,m=d?function(S){return l(S,h[v][1])>0}:function(S){return l(S,h[v][1])>=0},y=u?function(S){return g(S,h[v][0])>0}:function(S){return g(S,h[v][0])>=0};function b(S){return!m(S)&&!y(S)}var _=m,w=new this.Collection(this,function(){return ki(h[0][0],h[h.length-1][1],!u,!d)});return w._ondirectionchange=function(S){S==="next"?(_=m,f=l):(_=y,f=g),h.sort(s)},w._addAlgorithm(function(S,x,T){for(var E=S.key;_(E);)if(++v,v===h.length)return x(T),!1;return b(E)?!0:(r._cmp(E,h[v][1])===0||r._cmp(E,h[v][0])===0||x(function(){f===l?S.continue(h[v][0]):S.continue(h[v][1])}),!1)}),w},e.prototype.startsWithAnyOf=function(){var t=Zr.apply(fo,arguments);return t.every(function(n){return typeof n=="string"})?t.length===0?io(this):this.inAnyRange(t.map(function(n){return[n,n+ya]})):Dn(this,"startsWithAnyOf() only works with strings")},e}();function M7(e){return Nl(Jw.prototype,function(n,r,i){this.db=e,this._ctx={table:n,index:r===":id"?null:r,or:i};var l=e._deps.indexedDB;if(!l)throw new Je.MissingAPI;this._cmp=this._ascending=l.cmp.bind(l),this._descending=function(g,o){return l.cmp(o,g)},this._max=function(g,o){return l.cmp(g,o)>0?g:o},this._min=function(g,o){return l.cmp(g,o)<0?g:o},this._IDBKeyRange=e._deps.IDBKeyRange})}function Cr(e){return Ft(function(t){return ul(t),e(t.target.error),!1})}function ul(e){e.stopPropagation&&e.stopPropagation(),e.preventDefault&&e.preventDefault()}var cl="storagemutated",Mi="x-storagemutated-1",Gi=zl(null,cl),L7=function(){function e(){}return e.prototype._lock=function(){return hs(!Ke.global),++this._reculock,this._reculock===1&&!Ke.global&&(Ke.lockOwnerFor=this),this},e.prototype._unlock=function(){if(hs(!Ke.global),--this._reculock===0)for(Ke.global||(Ke.lockOwnerFor=null);this._blockedFuncs.length>0&&!this._locked();){var t=this._blockedFuncs.shift();try{Ko(t[1],t[0])}catch{}}return this},e.prototype._locked=function(){return this._reculock&&Ke.lockOwnerFor!==this},e.prototype.create=function(t){var n=this;if(!this.mode)return this;var r=this.db.idbdb,i=this.db._state.dbOpenError;if(hs(!this.idbtrans),!t&&!r)switch(i&&i.name){case"DatabaseClosedError":throw new Je.DatabaseClosed(i);case"MissingAPIError":throw new Je.MissingAPI(i.message,i);default:throw new Je.OpenFailed(i)}if(!this.active)throw new Je.TransactionInactive;return hs(this._completion._state===null),t=this.idbtrans=t||(this.db.core?this.db.core.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability}):r.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability})),t.onerror=Ft(function(l){ul(l),n._reject(t.error)}),t.onabort=Ft(function(l){ul(l),n.active&&n._reject(new Je.Abort(t.error)),n.active=!1,n.on("abort").fire(l)}),t.oncomplete=Ft(function(){n.active=!1,n._resolve(),"mutatedParts"in t&&Gi.storagemutated.fire(t.mutatedParts)}),this},e.prototype._promise=function(t,n,r){var i=this;if(t==="readwrite"&&this.mode!=="readwrite")return Kt(new Je.ReadOnly("Transaction is readonly"));if(!this.active)return Kt(new Je.TransactionInactive);if(this._locked())return new $e(function(g,o){i._blockedFuncs.push([function(){i._promise(t,n,r).then(g,o)},Ke])});if(r)return Xi(function(){var g=new $e(function(o,a){i._lock();var u=n(o,a,i);u&&u.then&&u.then(o,a)});return g.finally(function(){return i._unlock()}),g._lib=!0,g});var l=new $e(function(g,o){var a=n(g,o,i);a&&a.then&&a.then(g,o)});return l._lib=!0,l},e.prototype._root=function(){return this.parent?this.parent._root():this},e.prototype.waitFor=function(t){var n=this._root(),r=$e.resolve(t);if(n._waitingFor)n._waitingFor=n._waitingFor.then(function(){return r});else{n._waitingFor=r,n._waitingQueue=[];var i=n.idbtrans.objectStore(n.storeNames[0]);(function g(){for(++n._spinCount;n._waitingQueue.length;)n._waitingQueue.shift()();n._waitingFor&&(i.get(-1/0).onsuccess=g)})()}var l=n._waitingFor;return new $e(function(g,o){r.then(function(a){return n._waitingQueue.push(Ft(g.bind(null,a)))},function(a){return n._waitingQueue.push(Ft(o.bind(null,a)))}).finally(function(){n._waitingFor===l&&(n._waitingFor=null)})})},e.prototype.abort=function(){this.active&&(this.active=!1,this.idbtrans&&this.idbtrans.abort(),this._reject(new Je.Abort))},e.prototype.table=function(t){var n=this._memoizedTables||(this._memoizedTables={});if(Wn(n,t))return n[t];var r=this.schema[t];if(!r)throw new Je.NotFound("Table "+t+" not part of transaction");var i=new this.db.Table(t,r,this);return i.core=this.db.core.table(t),n[t]=i,i},e}();function R7(e){return Nl(L7.prototype,function(n,r,i,l,g){var o=this;this.db=e,this.mode=n,this.storeNames=r,this.schema=i,this.chromeTransactionDurability=l,this.idbtrans=null,this.on=zl(this,"complete","error","abort"),this.parent=g||null,this.active=!0,this._reculock=0,this._blockedFuncs=[],this._resolve=null,this._reject=null,this._waitingFor=null,this._waitingQueue=null,this._spinCount=0,this._completion=new $e(function(a,u){o._resolve=a,o._reject=u}),this._completion.then(function(){o.active=!1,o.on.complete.fire()},function(a){var u=o.active;return o.active=!1,o.on.error.fire(a),o.parent?o.parent._reject(a):u&&o.idbtrans&&o.idbtrans.abort(),Kt(a)})})}function ph(e,t,n,r,i,l,g){return{name:e,keyPath:t,unique:n,multi:r,auto:i,compound:l,src:(n&&!g?"&":"")+(r?"*":"")+(i?"++":"")+Qw(t)}}function Qw(e){return typeof e=="string"?e:e?"["+[].join.call(e,"+")+"]":""}function e2(e,t,n){return{name:e,primKey:t,indexes:n,mappedClass:null,idxByName:Iw(n,function(r){return[r.name,r]})}}function D7(e){return e.length===1?e[0]:e}var fl=function(e){try{return e.only([[]]),fl=function(){return[[]]},[[]]}catch{return fl=function(){return ya},ya}};function mh(e){return e==null?function(){}:typeof e=="string"?$7(e):function(t){return ti(t,e)}}function $7(e){var t=e.split(".");return t.length===1?function(n){return n[e]}:function(n){return ti(n,e)}}function B0(e){return[].slice.call(e)}var B7=0;function Ms(e){return e==null?":id":typeof e=="string"?e:"["+e.join("+")+"]"}function F7(e,t,n){function r(c,f){var s=B0(c.objectStoreNames);return{schema:{name:c.name,tables:s.map(function(h){return f.objectStore(h)}).map(function(h){var v=h.keyPath,m=h.autoIncrement,y=dn(v),b=v==null,_={},w={name:h.name,primaryKey:{name:null,isPrimaryKey:!0,outbound:b,compound:y,keyPath:v,autoIncrement:m,unique:!0,extractKey:mh(v)},indexes:B0(h.indexNames).map(function(S){return h.index(S)}).map(function(S){var x=S.name,T=S.unique,E=S.multiEntry,I=S.keyPath,D=dn(I),N={name:x,compound:D,keyPath:I,unique:T,multiEntry:E,extractKey:mh(I)};return _[Ms(I)]=N,N}),getIndexByKeyPath:function(S){return _[Ms(S)]}};return _[":id"]=w.primaryKey,v!=null&&(_[Ms(v)]=w.primaryKey),w})},hasGetAll:s.length>0&&"getAll"in f.objectStore(s[0])&&!(typeof navigator<"u"&&/Safari/.test(navigator.userAgent)&&!/(Chrome\/|Edge\/)/.test(navigator.userAgent)&&[].concat(navigator.userAgent.match(/Safari\/(\d*)/))[1]<604)}}function i(c){if(c.type===3)return null;if(c.type===4)throw new Error("Cannot convert never type to IDBKeyRange");var f=c.lower,s=c.upper,h=c.lowerOpen,v=c.upperOpen,m=f===void 0?s===void 0?null:t.upperBound(s,!!v):s===void 0?t.lowerBound(f,!!h):t.bound(f,s,!!h,!!v);return m}function l(c){var f=c.name;function s(m){var y=m.trans,b=m.type,_=m.keys,w=m.values,S=m.range;return new Promise(function(x,T){x=Ft(x);var E=y.objectStore(f),I=E.keyPath==null,D=b==="put"||b==="add";if(!D&&b!=="delete"&&b!=="deleteRange")throw new Error("Invalid operation type: "+b);var N=(_||w||{length:1}).length;if(_&&w&&_.length!==w.length)throw new Error("Given keys array must have same length as given values array.");if(N===0)return x({numFailures:0,failures:{},results:[],lastResult:void 0});var L,F=[],O=[],V=0,re=function(P){++V,ul(P)};if(b==="deleteRange"){if(S.type===4)return x({numFailures:V,failures:O,results:[],lastResult:void 0});S.type===3?F.push(L=E.clear()):F.push(L=E.delete(i(S)))}else{var J=D?I?[w,_]:[w,null]:[_,null],ue=J[0],M=J[1];if(D)for(var U=0;U<N;++U)F.push(L=M&&M[U]!==void 0?E[b](ue[U],M[U]):E[b](ue[U])),L.onerror=re;else for(var U=0;U<N;++U)F.push(L=E[b](ue[U])),L.onerror=re}var R=function(P){var Q=P.target.result;F.forEach(function(j,A){return j.error!=null&&(O[A]=j.error)}),x({numFailures:V,failures:O,results:b==="delete"?_:F.map(function(j){return j.result}),lastResult:Q})};L.onerror=function(P){re(P),R(P)},L.onsuccess=R})}function h(m){var y=m.trans,b=m.values,_=m.query,w=m.reverse,S=m.unique;return new Promise(function(x,T){x=Ft(x);var E=_.index,I=_.range,D=y.objectStore(f),N=E.isPrimaryKey?D:D.index(E.name),L=w?S?"prevunique":"prev":S?"nextunique":"next",F=b||!("openKeyCursor"in N)?N.openCursor(i(I),L):N.openKeyCursor(i(I),L);F.onerror=Cr(T),F.onsuccess=Ft(function(O){var V=F.result;if(!V){x(null);return}V.___id=++B7,V.done=!1;var re=V.continue.bind(V),J=V.continuePrimaryKey;J&&(J=J.bind(V));var ue=V.advance.bind(V),M=function(){throw new Error("Cursor not started")},U=function(){throw new Error("Cursor not stopped")};V.trans=y,V.stop=V.continue=V.continuePrimaryKey=V.advance=M,V.fail=Ft(T),V.next=function(){var R=this,P=1;return this.start(function(){return P--?R.continue():R.stop()}).then(function(){return R})},V.start=function(R){var P=new Promise(function(j,A){j=Ft(j),F.onerror=Cr(A),V.fail=A,V.stop=function(q){V.stop=V.continue=V.continuePrimaryKey=V.advance=U,j(q)}}),Q=function(){if(F.result)try{R()}catch(j){V.fail(j)}else V.done=!0,V.start=function(){throw new Error("Cursor behind last entry")},V.stop()};return F.onsuccess=Ft(function(j){F.onsuccess=Q,Q()}),V.continue=re,V.continuePrimaryKey=J,V.advance=ue,Q(),P},x(V)},T)})}function v(m){return function(y){return new Promise(function(b,_){b=Ft(b);var w=y.trans,S=y.values,x=y.limit,T=y.query,E=x===1/0?void 0:x,I=T.index,D=T.range,N=w.objectStore(f),L=I.isPrimaryKey?N:N.index(I.name),F=i(D);if(x===0)return b({result:[]});if(m){var O=S?L.getAll(F,E):L.getAllKeys(F,E);O.onsuccess=function(ue){return b({result:ue.target.result})},O.onerror=Cr(_)}else{var V=0,re=S||!("openKeyCursor"in L)?L.openCursor(F):L.openKeyCursor(F),J=[];re.onsuccess=function(ue){var M=re.result;if(!M)return 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g=r(e,n),o=g.schema,a=g.hasGetAll,u=o.tables.map(function(c){return l(c)}),d={};return u.forEach(function(c){return d[c.name]=c}),{stack:"dbcore",transaction:e.transaction.bind(e),table:function(c){var f=d[c];if(!f)throw new Error("Table '"+c+"' not found");return d[c]},MIN_KEY:-1/0,MAX_KEY:fl(t),schema:o}}function z7(e,t){return t.reduce(function(n,r){var i=r.create;return ut(ut({},n),i(n))},e)}function N7(e,t,n,r){var i=n.IDBKeyRange;n.indexedDB;var l=z7(F7(t,i,r),e.dbcore);return{dbcore:l}}function nm(e,t){var n=e._novip,r=t.db,i=N7(n._middlewares,r,n._deps,t);n.core=i.dbcore,n.tables.forEach(function(l){var g=l.name;n.core.schema.tables.some(function(o){return o.name===g})&&(l.core=n.core.table(g),n[g]instanceof n.Table&&(n[g].core=l.core))})}function _c(e,t,n,r){var i=e._novip;n.forEach(function(l){var g=r[l];t.forEach(function(o){var a=Xp(o,l);(!a||"value"in a&&a.value===void 0)&&(o===i.Transaction.prototype||o instanceof i.Transaction?si(o,l,{get:function(){return 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 https://github.com/nodeca/pako/blob/main/LICENSE
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u,d,c,f,s,h,v=0,m=0,y="data:";if(a.substr(0,y.length)===y)throw new Error("Invalid base64 input, it looks like a data url.");var b,_=3*(a=a.replace(/[^A-Za-z0-9+/=]/g,"")).length/4;if(a.charAt(a.length-1)===o.charAt(64)&&_--,a.charAt(a.length-2)===o.charAt(64)&&_--,_%1!=0)throw new Error("Invalid base64 input, bad content length.");for(b=g.uint8array?new Uint8Array(0|_):new Array(0|_);v<a.length;)u=o.indexOf(a.charAt(v++))<<2|(f=o.indexOf(a.charAt(v++)))>>4,d=(15&f)<<4|(s=o.indexOf(a.charAt(v++)))>>2,c=(3&s)<<6|(h=o.indexOf(a.charAt(v++))),b[m++]=u,s!==64&&(b[m++]=d),h!==64&&(b[m++]=c);return b}},{"./support":30,"./utils":32}],2:[function(n,r,i){var l=n("./external"),g=n("./stream/DataWorker"),o=n("./stream/Crc32Probe"),a=n("./stream/DataLengthProbe");function u(d,c,f,s,h){this.compressedSize=d,this.uncompressedSize=c,this.crc32=f,this.compression=s,this.compressedContent=h}u.prototype={getContentWorker:function(){var d=new g(l.Promise.resolve(this.compressedContent)).pipe(this.compression.uncompressWorker()).pipe(new a("data_length")),c=this;return d.on("end",function(){if(this.streamInfo.data_length!==c.uncompressedSize)throw new Error("Bug : uncompressed data size mismatch")}),d},getCompressedWorker:function(){return new g(l.Promise.resolve(this.compressedContent)).withStreamInfo("compressedSize",this.compressedSize).withStreamInfo("uncompressedSize",this.uncompressedSize).withStreamInfo("crc32",this.crc32).withStreamInfo("compression",this.compression)}},u.createWorkerFrom=function(d,c,f){return d.pipe(new o).pipe(new a("uncompressedSize")).pipe(c.compressWorker(f)).pipe(new a("compressedSize")).withStreamInfo("compression",c)},r.exports=u},{"./external":6,"./stream/Crc32Probe":25,"./stream/DataLengthProbe":26,"./stream/DataWorker":27}],3:[function(n,r,i){var l=n("./stream/GenericWorker");i.STORE={magic:"\0\0",compressWorker:function(){return new l("STORE compression")},uncompressWorker:function(){return new l("STORE decompression")}},i.DEFLATE=n("./flate")},{"./flate":7,"./stream/GenericWorker":28}],4:[function(n,r,i){var l=n("./utils"),g=function(){for(var o,a=[],u=0;u<256;u++){o=u;for(var d=0;d<8;d++)o=1&o?3988292384^o>>>1:o>>>1;a[u]=o}return a}();r.exports=function(o,a){return o!==void 0&&o.length?l.getTypeOf(o)!=="string"?function(u,d,c,f){var s=g,h=f+c;u^=-1;for(var v=f;v<h;v++)u=u>>>8^s[255&(u^d[v])];return-1^u}(0|a,o,o.length,0):function(u,d,c,f){var s=g,h=f+c;u^=-1;for(var v=f;v<h;v++)u=u>>>8^s[255&(u^d.charCodeAt(v))];return-1^u}(0|a,o,o.length,0):0}},{"./utils":32}],5:[function(n,r,i){i.base64=!1,i.binary=!1,i.dir=!1,i.createFolders=!0,i.date=null,i.compression=null,i.compressionOptions=null,i.comment=null,i.unixPermissions=null,i.dosPermissions=null},{}],6:[function(n,r,i){var l=null;l=typeof Promise<"u"?Promise:n("lie"),r.exports={Promise:l}},{lie:37}],7:[function(n,r,i){var l=typeof Uint8Array<"u"&&typeof Uint16Array<"u"&&typeof Uint32Array<"u",g=n("pako"),o=n("./utils"),a=n("./stream/GenericWorker"),u=l?"uint8array":"array";function d(c,f){a.call(this,"FlateWorker/"+c),this._pako=null,this._pakoAction=c,this._pakoOptions=f,this.meta={}}i.magic="\b\0",o.inherits(d,a),d.prototype.processChunk=function(c){this.meta=c.meta,this._pako===null&&this._createPako(),this._pako.push(o.transformTo(u,c.data),!1)},d.prototype.flush=function(){a.prototype.flush.call(this),this._pako===null&&this._createPako(),this._pako.push([],!0)},d.prototype.cleanUp=function(){a.prototype.cleanUp.call(this),this._pako=null},d.prototype._createPako=function(){this._pako=new g[this._pakoAction]({raw:!0,level:this._pakoOptions.level||-1});var c=this;this._pako.onData=function(f){c.push({data:f,meta:c.meta})}},i.compressWorker=function(c){return new d("Deflate",c)},i.uncompressWorker=function(){return new d("Inflate",{})}},{"./stream/GenericWorker":28,"./utils":32,pako:38}],8:[function(n,r,i){function l(s,h){var v,m="";for(v=0;v<h;v++)m+=String.fromCharCode(255&s),s>>>=8;return m}function g(s,h,v,m,y,b){var _,w,S=s.file,x=s.compression,T=b!==u.utf8encode,E=o.transformTo("string",b(S.name)),I=o.transformTo("string",u.utf8encode(S.name)),D=S.comment,N=o.transformTo("string",b(D)),L=o.transformTo("string",u.utf8encode(D)),F=I.length!==S.name.length,O=L.length!==D.length,V="",re="",J="",ue=S.dir,M=S.date,U={crc32:0,compressedSize:0,uncompressedSize:0};h&&!v||(U.crc32=s.crc32,U.compressedSize=s.compressedSize,U.uncompressedSize=s.uncompressedSize);var R=0;h&&(R|=8),T||!F&&!O||(R|=2048);var P=0,Q=0;ue&&(P|=16),y==="UNIX"?(Q=798,P|=function(A,q){var G=A;return A||(G=q?16893:33204),(65535&G)<<16}(S.unixPermissions,ue)):(Q=20,P|=function(A){return 63&(A||0)}(S.dosPermissions)),_=M.getUTCHours(),_<<=6,_|=M.getUTCMinutes(),_<<=5,_|=M.getUTCSeconds()/2,w=M.getUTCFullYear()-1980,w<<=4,w|=M.getUTCMonth()+1,w<<=5,w|=M.getUTCDate(),F&&(re=l(1,1)+l(d(E),4)+I,V+="up"+l(re.length,2)+re),O&&(J=l(1,1)+l(d(N),4)+L,V+="uc"+l(J.length,2)+J);var j="";return j+=`
-\0`,j+=l(R,2),j+=x.magic,j+=l(_,2),j+=l(w,2),j+=l(U.crc32,4),j+=l(U.compressedSize,4),j+=l(U.uncompressedSize,4),j+=l(E.length,2),j+=l(V.length,2),{fileRecord:c.LOCAL_FILE_HEADER+j+E+V,dirRecord:c.CENTRAL_FILE_HEADER+l(Q,2)+j+l(N.length,2)+"\0\0\0\0"+l(P,4)+l(m,4)+E+V+N}}var o=n("../utils"),a=n("../stream/GenericWorker"),u=n("../utf8"),d=n("../crc32"),c=n("../signature");function f(s,h,v,m){a.call(this,"ZipFileWorker"),this.bytesWritten=0,this.zipComment=h,this.zipPlatform=v,this.encodeFileName=m,this.streamFiles=s,this.accumulate=!1,this.contentBuffer=[],this.dirRecords=[],this.currentSourceOffset=0,this.entriesCount=0,this.currentFile=null,this._sources=[]}o.inherits(f,a),f.prototype.push=function(s){var h=s.meta.percent||0,v=this.entriesCount,m=this._sources.length;this.accumulate?this.contentBuffer.push(s):(this.bytesWritten+=s.data.length,a.prototype.push.call(this,{data:s.data,meta:{currentFile:this.currentFile,percent:v?(h+100*(v-m-1))/v:100}}))},f.prototype.openedSource=function(s){this.currentSourceOffset=this.bytesWritten,this.currentFile=s.file.name;var h=this.streamFiles&&!s.file.dir;if(h){var v=g(s,h,!1,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);this.push({data:v.fileRecord,meta:{percent:0}})}else this.accumulate=!0},f.prototype.closedSource=function(s){this.accumulate=!1;var h=this.streamFiles&&!s.file.dir,v=g(s,h,!0,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);if(this.dirRecords.push(v.dirRecord),h)this.push({data:function(m){return c.DATA_DESCRIPTOR+l(m.crc32,4)+l(m.compressedSize,4)+l(m.uncompressedSize,4)}(s),meta:{percent:100}});else for(this.push({data:v.fileRecord,meta:{percent:0}});this.contentBuffer.length;)this.push(this.contentBuffer.shift());this.currentFile=null},f.prototype.flush=function(){for(var s=this.bytesWritten,h=0;h<this.dirRecords.length;h++)this.push({data:this.dirRecords[h],meta:{percent:100}});var v=this.bytesWritten-s,m=function(y,b,_,w,S){var x=o.transformTo("string",S(w));return c.CENTRAL_DIRECTORY_END+"\0\0\0\0"+l(y,2)+l(y,2)+l(b,4)+l(_,4)+l(x.length,2)+x}(this.dirRecords.length,v,s,this.zipComment,this.encodeFileName);this.push({data:m,meta:{percent:100}})},f.prototype.prepareNextSource=function(){this.previous=this._sources.shift(),this.openedSource(this.previous.streamInfo),this.isPaused?this.previous.pause():this.previous.resume()},f.prototype.registerPrevious=function(s){this._sources.push(s);var h=this;return s.on("data",function(v){h.processChunk(v)}),s.on("end",function(){h.closedSource(h.previous.streamInfo),h._sources.length?h.prepareNextSource():h.end()}),s.on("error",function(v){h.error(v)}),this},f.prototype.resume=function(){return!!a.prototype.resume.call(this)&&(!this.previous&&this._sources.length?(this.prepareNextSource(),!0):this.previous||this._sources.length||this.generatedError?void 0:(this.end(),!0))},f.prototype.error=function(s){var h=this._sources;if(!a.prototype.error.call(this,s))return!1;for(var v=0;v<h.length;v++)try{h[v].error(s)}catch{}return!0},f.prototype.lock=function(){a.prototype.lock.call(this);for(var s=this._sources,h=0;h<s.length;h++)s[h].lock()},r.exports=f},{"../crc32":4,"../signature":23,"../stream/GenericWorker":28,"../utf8":31,"../utils":32}],9:[function(n,r,i){var l=n("../compressions"),g=n("./ZipFileWorker");i.generateWorker=function(o,a,u){var d=new g(a.streamFiles,u,a.platform,a.encodeFileName),c=0;try{o.forEach(function(f,s){c++;var h=function(b,_){var w=b||_,S=l[w];if(!S)throw new Error(w+" is not a valid compression method !");return S}(s.options.compression,a.compression),v=s.options.compressionOptions||a.compressionOptions||{},m=s.dir,y=s.date;s._compressWorker(h,v).withStreamInfo("file",{name:f,dir:m,date:y,comment:s.comment||"",unixPermissions:s.unixPermissions,dosPermissions:s.dosPermissions}).pipe(d)}),d.entriesCount=c}catch(f){d.error(f)}return d}},{"../compressions":3,"./ZipFileWorker":8}],10:[function(n,r,i){function l(){if(!(this instanceof l))return new l;if(arguments.length)throw new Error("The constructor with parameters has been removed in JSZip 3.0, please check the upgrade guide.");this.files=Object.create(null),this.comment=null,this.root="",this.clone=function(){var g=new l;for(var o in this)typeof this[o]!="function"&&(g[o]=this[o]);return g}}(l.prototype=n("./object")).loadAsync=n("./load"),l.support=n("./support"),l.defaults=n("./defaults"),l.version="3.10.1",l.loadAsync=function(g,o){return new l().loadAsync(g,o)},l.external=n("./external"),r.exports=l},{"./defaults":5,"./external":6,"./load":11,"./object":15,"./support":30}],11:[function(n,r,i){var l=n("./utils"),g=n("./external"),o=n("./utf8"),a=n("./zipEntries"),u=n("./stream/Crc32Probe"),d=n("./nodejsUtils");function c(f){return new g.Promise(function(s,h){var v=f.decompressed.getContentWorker().pipe(new u);v.on("error",function(m){h(m)}).on("end",function(){v.streamInfo.crc32!==f.decompressed.crc32?h(new Error("Corrupted zip : CRC32 mismatch")):s()}).resume()})}r.exports=function(f,s){var h=this;return s=l.extend(s||{},{base64:!1,checkCRC32:!1,optimizedBinaryString:!1,createFolders:!1,decodeFileName:o.utf8decode}),d.isNode&&d.isStream(f)?g.Promise.reject(new Error("JSZip can't accept a stream when loading a zip file.")):l.prepareContent("the loaded zip file",f,!0,s.optimizedBinaryString,s.base64).then(function(v){var m=new a(s);return m.load(v),m}).then(function(v){var m=[g.Promise.resolve(v)],y=v.files;if(s.checkCRC32)for(var b=0;b<y.length;b++)m.push(c(y[b]));return g.Promise.all(m)}).then(function(v){for(var m=v.shift(),y=m.files,b=0;b<y.length;b++){var _=y[b],w=_.fileNameStr,S=l.resolve(_.fileNameStr);h.file(S,_.decompressed,{binary:!0,optimizedBinaryString:!0,date:_.date,dir:_.dir,comment:_.fileCommentStr.length?_.fileCommentStr:null,unixPermissions:_.unixPermissions,dosPermissions:_.dosPermissions,createFolders:s.createFolders}),_.dir||(h.file(S).unsafeOriginalName=w)}return m.zipComment.length&&(h.comment=m.zipComment),h})}},{"./external":6,"./nodejsUtils":14,"./stream/Crc32Probe":25,"./utf8":31,"./utils":32,"./zipEntries":33}],12:[function(n,r,i){var l=n("../utils"),g=n("../stream/GenericWorker");function o(a,u){g.call(this,"Nodejs stream input adapter for "+a),this._upstreamEnded=!1,this._bindStream(u)}l.inherits(o,g),o.prototype._bindStream=function(a){var u=this;(this._stream=a).pause(),a.on("data",function(d){u.push({data:d,meta:{percent:0}})}).on("error",function(d){u.isPaused?this.generatedError=d:u.error(d)}).on("end",function(){u.isPaused?u._upstreamEnded=!0:u.end()})},o.prototype.pause=function(){return!!g.prototype.pause.call(this)&&(this._stream.pause(),!0)},o.prototype.resume=function(){return!!g.prototype.resume.call(this)&&(this._upstreamEnded?this.end():this._stream.resume(),!0)},r.exports=o},{"../stream/GenericWorker":28,"../utils":32}],13:[function(n,r,i){var l=n("readable-stream").Readable;function g(o,a,u){l.call(this,a),this._helper=o;var d=this;o.on("data",function(c,f){d.push(c)||d._helper.pause(),u&&u(f)}).on("error",function(c){d.emit("error",c)}).on("end",function(){d.push(null)})}n("../utils").inherits(g,l),g.prototype._read=function(){this._helper.resume()},r.exports=g},{"../utils":32,"readable-stream":16}],14:[function(n,r,i){r.exports={isNode:typeof Buffer<"u",newBufferFrom:function(l,g){if(Buffer.from&&Buffer.from!==Uint8Array.from)return Buffer.from(l,g);if(typeof l=="number")throw new Error('The "data" argument must not be a number');return new Buffer(l,g)},allocBuffer:function(l){if(Buffer.alloc)return Buffer.alloc(l);var g=new Buffer(l);return g.fill(0),g},isBuffer:function(l){return Buffer.isBuffer(l)},isStream:function(l){return l&&typeof l.on=="function"&&typeof l.pause=="function"&&typeof l.resume=="function"}}},{}],15:[function(n,r,i){function l(S,x,T){var E,I=o.getTypeOf(x),D=o.extend(T||{},d);D.date=D.date||new Date,D.compression!==null&&(D.compression=D.compression.toUpperCase()),typeof D.unixPermissions=="string"&&(D.unixPermissions=parseInt(D.unixPermissions,8)),D.unixPermissions&&16384&D.unixPermissions&&(D.dir=!0),D.dosPermissions&&16&D.dosPermissions&&(D.dir=!0),D.dir&&(S=y(S)),D.createFolders&&(E=m(S))&&b.call(this,E,!0);var N=I==="string"&&D.binary===!1&&D.base64===!1;T&&T.binary!==void 0||(D.binary=!N),(x instanceof c&&x.uncompressedSize===0||D.dir||!x||x.length===0)&&(D.base64=!1,D.binary=!0,x="",D.compression="STORE",I="string");var L=null;L=x instanceof c||x instanceof a?x:h.isNode&&h.isStream(x)?new v(S,x):o.prepareContent(S,x,D.binary,D.optimizedBinaryString,D.base64);var F=new f(S,L,D);this.files[S]=F}var g=n("./utf8"),o=n("./utils"),a=n("./stream/GenericWorker"),u=n("./stream/StreamHelper"),d=n("./defaults"),c=n("./compressedObject"),f=n("./zipObject"),s=n("./generate"),h=n("./nodejsUtils"),v=n("./nodejs/NodejsStreamInputAdapter"),m=function(S){S.slice(-1)==="/"&&(S=S.substring(0,S.length-1));var x=S.lastIndexOf("/");return 0<x?S.substring(0,x):""},y=function(S){return S.slice(-1)!=="/"&&(S+="/"),S},b=function(S,x){return x=x!==void 0?x:d.createFolders,S=y(S),this.files[S]||l.call(this,S,null,{dir:!0,createFolders:x}),this.files[S]};function _(S){return Object.prototype.toString.call(S)==="[object RegExp]"}var w={load:function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},forEach:function(S){var x,T,E;for(x in this.files)E=this.files[x],(T=x.slice(this.root.length,x.length))&&x.slice(0,this.root.length)===this.root&&S(T,E)},filter:function(S){var x=[];return this.forEach(function(T,E){S(T,E)&&x.push(E)}),x},file:function(S,x,T){if(arguments.length!==1)return S=this.root+S,l.call(this,S,x,T),this;if(_(S)){var E=S;return this.filter(function(D,N){return!N.dir&&E.test(D)})}var I=this.files[this.root+S];return I&&!I.dir?I:null},folder:function(S){if(!S)return this;if(_(S))return this.filter(function(I,D){return D.dir&&S.test(I)});var x=this.root+S,T=b.call(this,x),E=this.clone();return E.root=T.name,E},remove:function(S){S=this.root+S;var x=this.files[S];if(x||(S.slice(-1)!=="/"&&(S+="/"),x=this.files[S]),x&&!x.dir)delete this.files[S];else for(var T=this.filter(function(I,D){return D.name.slice(0,S.length)===S}),E=0;E<T.length;E++)delete this.files[T[E].name];return this},generate:function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},generateInternalStream:function(S){var x,T={};try{if((T=o.extend(S||{},{streamFiles:!1,compression:"STORE",compressionOptions:null,type:"",platform:"DOS",comment:null,mimeType:"application/zip",encodeFileName:g.utf8encode})).type=T.type.toLowerCase(),T.compression=T.compression.toUpperCase(),T.type==="binarystring"&&(T.type="string"),!T.type)throw new Error("No output type specified.");o.checkSupport(T.type),T.platform!=="darwin"&&T.platform!=="freebsd"&&T.platform!=="linux"&&T.platform!=="sunos"||(T.platform="UNIX"),T.platform==="win32"&&(T.platform="DOS");var E=T.comment||this.comment||"";x=s.generateWorker(this,T,E)}catch(I){(x=new a("error")).error(I)}return new u(x,T.type||"string",T.mimeType)},generateAsync:function(S,x){return this.generateInternalStream(S).accumulate(x)},generateNodeStream:function(S,x){return(S=S||{}).type||(S.type="nodebuffer"),this.generateInternalStream(S).toNodejsStream(x)}};r.exports=w},{"./compressedObject":2,"./defaults":5,"./generate":9,"./nodejs/NodejsStreamInputAdapter":12,"./nodejsUtils":14,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31,"./utils":32,"./zipObject":35}],16:[function(n,r,i){r.exports=n("stream")},{stream:void 0}],17:[function(n,r,i){var l=n("./DataReader");function g(o){l.call(this,o);for(var a=0;a<this.data.length;a++)o[a]=255&o[a]}n("../utils").inherits(g,l),g.prototype.byteAt=function(o){return this.data[this.zero+o]},g.prototype.lastIndexOfSignature=function(o){for(var a=o.charCodeAt(0),u=o.charCodeAt(1),d=o.charCodeAt(2),c=o.charCodeAt(3),f=this.length-4;0<=f;--f)if(this.data[f]===a&&this.data[f+1]===u&&this.data[f+2]===d&&this.data[f+3]===c)return f-this.zero;return-1},g.prototype.readAndCheckSignature=function(o){var a=o.charCodeAt(0),u=o.charCodeAt(1),d=o.charCodeAt(2),c=o.charCodeAt(3),f=this.readData(4);return a===f[0]&&u===f[1]&&d===f[2]&&c===f[3]},g.prototype.readData=function(o){if(this.checkOffset(o),o===0)return[];var a=this.data.slice(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./DataReader":18}],18:[function(n,r,i){var l=n("../utils");function g(o){this.data=o,this.length=o.length,this.index=0,this.zero=0}g.prototype={checkOffset:function(o){this.checkIndex(this.index+o)},checkIndex:function(o){if(this.length<this.zero+o||o<0)throw new Error("End of data reached (data length = "+this.length+", asked index = "+o+"). 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u=this;this.dataIsReady=!1,this.index=0,this.max=0,this.data=null,this.type="",this._tickScheduled=!1,a.then(function(d){u.dataIsReady=!0,u.data=d,u.max=d&&d.length||0,u.type=l.getTypeOf(d),u.isPaused||u._tickAndRepeat()},function(d){u.error(d)})}l.inherits(o,g),o.prototype.cleanUp=function(){g.prototype.cleanUp.call(this),this.data=null},o.prototype.resume=function(){return!!g.prototype.resume.call(this)&&(!this._tickScheduled&&this.dataIsReady&&(this._tickScheduled=!0,l.delay(this._tickAndRepeat,[],this)),!0)},o.prototype._tickAndRepeat=function(){this._tickScheduled=!1,this.isPaused||this.isFinished||(this._tick(),this.isFinished||(l.delay(this._tickAndRepeat,[],this),this._tickScheduled=!0))},o.prototype._tick=function(){if(this.isPaused||this.isFinished)return!1;var a=null,u=Math.min(this.max,this.index+16384);if(this.index>=this.max)return 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this._listeners[g].push(o),this},cleanUp:function(){this.streamInfo=this.generatedError=this.extraStreamInfo=null,this._listeners=[]},emit:function(g,o){if(this._listeners[g])for(var a=0;a<this._listeners[g].length;a++)this._listeners[g][a].call(this,o)},pipe:function(g){return g.registerPrevious(this)},registerPrevious:function(g){if(this.isLocked)throw new Error("The stream '"+this+"' has already been used.");this.streamInfo=g.streamInfo,this.mergeStreamInfo(),this.previous=g;var o=this;return g.on("data",function(a){o.processChunk(a)}),g.on("end",function(){o.end()}),g.on("error",function(a){o.error(a)}),this},pause:function(){return!this.isPaused&&!this.isFinished&&(this.isPaused=!0,this.previous&&this.previous.pause(),!0)},resume:function(){if(!this.isPaused||this.isFinished)return!1;var g=this.isPaused=!1;return this.generatedError&&(this.error(this.generatedError),g=!0),this.previous&&this.previous.resume(),!g},flush:function(){},processChunk:function(g){this.push(g)},withStreamInfo:function(g,o){return this.extraStreamInfo[g]=o,this.mergeStreamInfo(),this},mergeStreamInfo:function(){for(var g in this.extraStreamInfo)Object.prototype.hasOwnProperty.call(this.extraStreamInfo,g)&&(this.streamInfo[g]=this.extraStreamInfo[g])},lock:function(){if(this.isLocked)throw new Error("The stream '"+this+"' has already been used.");this.isLocked=!0,this.previous&&this.previous.lock()},toString:function(){var g="Worker "+this.name;return this.previous?this.previous+" -> "+g:g}},r.exports=l},{}],29:[function(n,r,i){var l=n("../utils"),g=n("./ConvertWorker"),o=n("./GenericWorker"),a=n("../base64"),u=n("../support"),d=n("../external"),c=null;if(u.nodestream)try{c=n("../nodejs/NodejsStreamOutputAdapter")}catch{}function f(h,v){return new d.Promise(function(m,y){var 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Is it in a supported JavaScript type (String, Blob, ArrayBuffer, etc) ?"))})}},{"./base64":1,"./external":6,"./nodejsUtils":14,"./support":30,setimmediate:54}],33:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./signature"),a=n("./zipEntry"),u=n("./support");function d(c){this.files=[],this.loadOptions=c}d.prototype={checkSignature:function(c){if(!this.reader.readAndCheckSignature(c)){this.reader.index-=4;var f=this.reader.readString(4);throw new Error("Corrupted zip or bug: unexpected signature ("+g.pretty(f)+", expected "+g.pretty(c)+")")}},isSignature:function(c,f){var s=this.reader.index;this.reader.setIndex(c);var h=this.reader.readString(4)===f;return this.reader.setIndex(s),h},readBlockEndOfCentral:function(){this.diskNumber=this.reader.readInt(2),this.diskWithCentralDirStart=this.reader.readInt(2),this.centralDirRecordsOnThisDisk=this.reader.readInt(2),this.centralDirRecords=this.reader.readInt(2),this.centralDirSize=this.reader.readInt(4),this.centralDirOffset=this.reader.readInt(4),this.zipCommentLength=this.reader.readInt(2);var c=this.reader.readData(this.zipCommentLength),f=u.uint8array?"uint8array":"array",s=g.transformTo(f,c);this.zipComment=this.loadOptions.decodeFileName(s)},readBlockZip64EndOfCentral:function(){this.zip64EndOfCentralSize=this.reader.readInt(8),this.reader.skip(4),this.diskNumber=this.reader.readInt(4),this.diskWithCentralDirStart=this.reader.readInt(4),this.centralDirRecordsOnThisDisk=this.reader.readInt(8),this.centralDirRecords=this.reader.readInt(8),this.centralDirSize=this.reader.readInt(8),this.centralDirOffset=this.reader.readInt(8),this.zip64ExtensibleData={};for(var c,f,s,h=this.zip64EndOfCentralSize-44;0<h;)c=this.reader.readInt(2),f=this.reader.readInt(4),s=this.reader.readData(f),this.zip64ExtensibleData[c]={id:c,length:f,value:s}},readBlockZip64EndOfCentralLocator:function(){if(this.diskWithZip64CentralDirStart=this.reader.readInt(4),this.relativeOffsetEndOfZip64CentralDir=this.reader.readInt(8),this.disksCount=this.reader.readInt(4),1<this.disksCount)throw new Error("Multi-volumes zip are not supported")},readLocalFiles:function(){var c,f;for(c=0;c<this.files.length;c++)f=this.files[c],this.reader.setIndex(f.localHeaderOffset),this.checkSignature(o.LOCAL_FILE_HEADER),f.readLocalPart(this.reader),f.handleUTF8(),f.processAttributes()},readCentralDir:function(){var c;for(this.reader.setIndex(this.centralDirOffset);this.reader.readAndCheckSignature(o.CENTRAL_FILE_HEADER);)(c=new a({zip64:this.zip64},this.loadOptions)).readCentralPart(this.reader),this.files.push(c);if(this.centralDirRecords!==this.files.length&&this.centralDirRecords!==0&&this.files.length===0)throw new Error("Corrupted zip or bug: expected "+this.centralDirRecords+" records in central dir, got "+this.files.length)},readEndOfCentral:function(){var c=this.reader.lastIndexOfSignature(o.CENTRAL_DIRECTORY_END);if(c<0)throw this.isSignature(0,o.LOCAL_FILE_HEADER)?new Error("Corrupted zip: can't find end of central directory"):new Error("Can't find end of central directory : is this a zip file ? If it is, see https://stuk.github.io/jszip/documentation/howto/read_zip.html");this.reader.setIndex(c);var f=c;if(this.checkSignature(o.CENTRAL_DIRECTORY_END),this.readBlockEndOfCentral(),this.diskNumber===g.MAX_VALUE_16BITS||this.diskWithCentralDirStart===g.MAX_VALUE_16BITS||this.centralDirRecordsOnThisDisk===g.MAX_VALUE_16BITS||this.centralDirRecords===g.MAX_VALUE_16BITS||this.centralDirSize===g.MAX_VALUE_32BITS||this.centralDirOffset===g.MAX_VALUE_32BITS){if(this.zip64=!0,(c=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR))<0)throw new Error("Corrupted zip: can't find the ZIP64 end of central directory locator");if(this.reader.setIndex(c),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR),this.readBlockZip64EndOfCentralLocator(),!this.isSignature(this.relativeOffsetEndOfZip64CentralDir,o.ZIP64_CENTRAL_DIRECTORY_END)&&(this.relativeOffsetEndOfZip64CentralDir=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.relativeOffsetEndOfZip64CentralDir<0))throw new Error("Corrupted zip: can't find the ZIP64 end of central directory");this.reader.setIndex(this.relativeOffsetEndOfZip64CentralDir),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.readBlockZip64EndOfCentral()}var s=this.centralDirOffset+this.centralDirSize;this.zip64&&(s+=20,s+=12+this.zip64EndOfCentralSize);var h=f-s;if(0<h)this.isSignature(f,o.CENTRAL_FILE_HEADER)||(this.reader.zero=h);else if(h<0)throw new Error("Corrupted zip: missing "+Math.abs(h)+" bytes.")},prepareReader:function(c){this.reader=l(c)},load:function(c){this.prepareReader(c),this.readEndOfCentral(),this.readCentralDir(),this.readLocalFiles()}},r.exports=d},{"./reader/readerFor":22,"./signature":23,"./support":30,"./utils":32,"./zipEntry":34}],34:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./compressedObject"),a=n("./crc32"),u=n("./utf8"),d=n("./compressions"),c=n("./support");function f(s,h){this.options=s,this.loadOptions=h}f.prototype={isEncrypted:function(){return(1&this.bitFlag)==1},useUTF8:function(){return(2048&this.bitFlag)==2048},readLocalPart:function(s){var h,v;if(s.skip(22),this.fileNameLength=s.readInt(2),v=s.readInt(2),this.fileName=s.readData(this.fileNameLength),s.skip(v),this.compressedSize===-1||this.uncompressedSize===-1)throw new Error("Bug or corrupted zip : didn't get enough information from the central directory (compressedSize === -1 || uncompressedSize === -1)");if((h=function(m){for(var y in d)if(Object.prototype.hasOwnProperty.call(d,y)&&d[y].magic===m)return d[y];return null}(this.compressionMethod))===null)throw new Error("Corrupted zip : compression "+g.pretty(this.compressionMethod)+" unknown (inner file : "+g.transformTo("string",this.fileName)+")");this.decompressed=new o(this.compressedSize,this.uncompressedSize,this.crc32,h,s.readData(this.compressedSize))},readCentralPart:function(s){this.versionMadeBy=s.readInt(2),s.skip(2),this.bitFlag=s.readInt(2),this.compressionMethod=s.readString(2),this.date=s.readDate(),this.crc32=s.readInt(4),this.compressedSize=s.readInt(4),this.uncompressedSize=s.readInt(4);var h=s.readInt(2);if(this.extraFieldsLength=s.readInt(2),this.fileCommentLength=s.readInt(2),this.diskNumberStart=s.readInt(2),this.internalFileAttributes=s.readInt(2),this.externalFileAttributes=s.readInt(4),this.localHeaderOffset=s.readInt(4),this.isEncrypted())throw new Error("Encrypted zip are not supported");s.skip(h),this.readExtraFields(s),this.parseZIP64ExtraField(s),this.fileComment=s.readData(this.fileCommentLength)},processAttributes:function(){this.unixPermissions=null,this.dosPermissions=null;var s=this.versionMadeBy>>8;this.dir=!!(16&this.externalFileAttributes),s==0&&(this.dosPermissions=63&this.externalFileAttributes),s==3&&(this.unixPermissions=this.externalFileAttributes>>16&65535),this.dir||this.fileNameStr.slice(-1)!=="/"||(this.dir=!0)},parseZIP64ExtraField:function(){if(this.extraFields[1]){var s=l(this.extraFields[1].value);this.uncompressedSize===g.MAX_VALUE_32BITS&&(this.uncompressedSize=s.readInt(8)),this.compressedSize===g.MAX_VALUE_32BITS&&(this.compressedSize=s.readInt(8)),this.localHeaderOffset===g.MAX_VALUE_32BITS&&(this.localHeaderOffset=s.readInt(8)),this.diskNumberStart===g.MAX_VALUE_32BITS&&(this.diskNumberStart=s.readInt(4))}},readExtraFields:function(s){var h,v,m,y=s.index+this.extraFieldsLength;for(this.extraFields||(this.extraFields={});s.index+4<y;)h=s.readInt(2),v=s.readInt(2),m=s.readData(v),this.extraFields[h]={id:h,length:v,value:m};s.setIndex(y)},handleUTF8:function(){var s=c.uint8array?"uint8array":"array";if(this.useUTF8())this.fileNameStr=u.utf8decode(this.fileName),this.fileCommentStr=u.utf8decode(this.fileComment);else{var h=this.findExtraFieldUnicodePath();if(h!==null)this.fileNameStr=h;else{var v=g.transformTo(s,this.fileName);this.fileNameStr=this.loadOptions.decodeFileName(v)}var m=this.findExtraFieldUnicodeComment();if(m!==null)this.fileCommentStr=m;else{var y=g.transformTo(s,this.fileComment);this.fileCommentStr=this.loadOptions.decodeFileName(y)}}},findExtraFieldUnicodePath:function(){var s=this.extraFields[28789];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileName)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null},findExtraFieldUnicodeComment:function(){var s=this.extraFields[25461];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileComment)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null}},r.exports=f},{"./compressedObject":2,"./compressions":3,"./crc32":4,"./reader/readerFor":22,"./support":30,"./utf8":31,"./utils":32}],35:[function(n,r,i){function l(h,v,m){this.name=h,this.dir=m.dir,this.date=m.date,this.comment=m.comment,this.unixPermissions=m.unixPermissions,this.dosPermissions=m.dosPermissions,this._data=v,this._dataBinary=m.binary,this.options={compression:m.compression,compressionOptions:m.compressionOptions}}var g=n("./stream/StreamHelper"),o=n("./stream/DataWorker"),a=n("./utf8"),u=n("./compressedObject"),d=n("./stream/GenericWorker");l.prototype={internalStream:function(h){var v=null,m="string";try{if(!h)throw new Error("No output type specified.");var y=(m=h.toLowerCase())==="string"||m==="text";m!=="binarystring"&&m!=="text"||(m="string"),v=this._decompressWorker();var b=!this._dataBinary;b&&!y&&(v=v.pipe(new a.Utf8EncodeWorker)),!b&&y&&(v=v.pipe(new a.Utf8DecodeWorker))}catch(_){(v=new d("error")).error(_)}return new g(v,m,"")},async:function(h,v){return this.internalStream(h).accumulate(v)},nodeStream:function(h,v){return this.internalStream(h||"nodebuffer").toNodejsStream(v)},_compressWorker:function(h,v){if(this._data instanceof u&&this._data.compression.magic===h.magic)return this._data.getCompressedWorker();var m=this._decompressWorker();return this._dataBinary||(m=m.pipe(new a.Utf8EncodeWorker)),u.createWorkerFrom(m,h,v)},_decompressWorker:function(){return this._data instanceof u?this._data.getContentWorker():this._data instanceof d?this._data:new o(this._data)}};for(var c=["asText","asBinary","asNodeBuffer","asUint8Array","asArrayBuffer"],f=function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},s=0;s<c.length;s++)l.prototype[c[s]]=f;r.exports=l},{"./compressedObject":2,"./stream/DataWorker":27,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31}],36:[function(n,r,i){(function(l){var g,o,a=l.MutationObserver||l.WebKitMutationObserver;if(a){var u=0,d=new a(h),c=l.document.createTextNode("");d.observe(c,{characterData:!0}),g=function(){c.data=u=++u%2}}else if(l.setImmediate||l.MessageChannel===void 0)g="document"in l&&"onreadystatechange"in l.document.createElement("script")?function(){var v=l.document.createElement("script");v.onreadystatechange=function(){h(),v.onreadystatechange=null,v.parentNode.removeChild(v),v=null},l.document.documentElement.appendChild(v)}:function(){setTimeout(h,0)};else{var f=new l.MessageChannel;f.port1.onmessage=h,g=function(){f.port2.postMessage(0)}}var s=[];function h(){var v,m;o=!0;for(var y=s.length;y;){for(m=s,s=[],v=-1;++v<y;)m[v]();y=s.length}o=!1}r.exports=function(v){s.push(v)!==1||o||g()}}).call(this,typeof su<"u"?su:typeof self<"u"?self:typeof window<"u"?window:{})},{}],37:[function(n,r,i){var l=n("immediate");function g(){}var o={},a=["REJECTED"],u=["FULFILLED"],d=["PENDING"];function c(y){if(typeof y!="function")throw new TypeError("resolver must be a function");this.state=d,this.queue=[],this.outcome=void 0,y!==g&&v(this,y)}function f(y,b,_){this.promise=y,typeof b=="function"&&(this.onFulfilled=b,this.callFulfilled=this.otherCallFulfilled),typeof _=="function"&&(this.onRejected=_,this.callRejected=this.otherCallRejected)}function s(y,b,_){l(function(){var w;try{w=b(_)}catch(S){return o.reject(y,S)}w===y?o.reject(y,new TypeError("Cannot resolve promise with itself")):o.resolve(y,w)})}function h(y){var b=y&&y.then;if(y&&(typeof y=="object"||typeof y=="function")&&typeof b=="function")return function(){b.apply(y,arguments)}}function v(y,b){var _=!1;function w(T){_||(_=!0,o.reject(y,T))}function S(T){_||(_=!0,o.resolve(y,T))}var x=m(function(){b(S,w)});x.status==="error"&&w(x.value)}function m(y,b){var _={};try{_.value=y(b),_.status="success"}catch(w){_.status="error",_.value=w}return _}(r.exports=c).prototype.finally=function(y){if(typeof y!="function")return this;var b=this.constructor;return this.then(function(_){return b.resolve(y()).then(function(){return _})},function(_){return b.resolve(y()).then(function(){throw _})})},c.prototype.catch=function(y){return this.then(null,y)},c.prototype.then=function(y,b){if(typeof y!="function"&&this.state===u||typeof b!="function"&&this.state===a)return this;var _=new this.constructor(g);return this.state!==d?s(_,this.state===u?y:b,this.outcome):this.queue.push(new f(_,y,b)),_},f.prototype.callFulfilled=function(y){o.resolve(this.promise,y)},f.prototype.otherCallFulfilled=function(y){s(this.promise,this.onFulfilled,y)},f.prototype.callRejected=function(y){o.reject(this.promise,y)},f.prototype.otherCallRejected=function(y){s(this.promise,this.onRejected,y)},o.resolve=function(y,b){var _=m(h,b);if(_.status==="error")return o.reject(y,_.value);var w=_.value;if(w)v(y,w);else{y.state=u,y.outcome=b;for(var S=-1,x=y.queue.length;++S<x;)y.queue[S].callFulfilled(b)}return y},o.reject=function(y,b){y.state=a,y.outcome=b;for(var _=-1,w=y.queue.length;++_<w;)y.queue[_].callRejected(b);return y},c.resolve=function(y){return y instanceof this?y:o.resolve(new this(g),y)},c.reject=function(y){var b=new this(g);return o.reject(b,y)},c.all=function(y){var b=this;if(Object.prototype.toString.call(y)!=="[object Array]")return this.reject(new TypeError("must be an array"));var _=y.length,w=!1;if(!_)return this.resolve([]);for(var S=new Array(_),x=0,T=-1,E=new this(g);++T<_;)I(y[T],T);return E;function I(D,N){b.resolve(D).then(function(L){S[N]=L,++x!==_||w||(w=!0,o.resolve(E,S))},function(L){w||(w=!0,o.reject(E,L))})}},c.race=function(y){var b=this;if(Object.prototype.toString.call(y)!=="[object Array]")return this.reject(new TypeError("must be an array"));var _=y.length,w=!1;if(!_)return this.resolve([]);for(var S=-1,x=new this(g);++S<_;)T=y[S],b.resolve(T).then(function(E){w||(w=!0,o.resolve(x,E))},function(E){w||(w=!0,o.reject(x,E))});var T;return x}},{immediate:36}],38:[function(n,r,i){var l={};(0,n("./lib/utils/common").assign)(l,n("./lib/deflate"),n("./lib/inflate"),n("./lib/zlib/constants")),r.exports=l},{"./lib/deflate":39,"./lib/inflate":40,"./lib/utils/common":41,"./lib/zlib/constants":44}],39:[function(n,r,i){var l=n("./zlib/deflate"),g=n("./utils/common"),o=n("./utils/strings"),a=n("./zlib/messages"),u=n("./zlib/zstream"),d=Object.prototype.toString,c=0,f=-1,s=0,h=8;function v(y){if(!(this instanceof v))return new v(y);this.options=g.assign({level:f,method:h,chunkSize:16384,windowBits:15,memLevel:8,strategy:s,to:""},y||{});var b=this.options;b.raw&&0<b.windowBits?b.windowBits=-b.windowBits:b.gzip&&0<b.windowBits&&b.windowBits<16&&(b.windowBits+=16),this.err=0,this.msg="",this.ended=!1,this.chunks=[],this.strm=new u,this.strm.avail_out=0;var _=l.deflateInit2(this.strm,b.level,b.method,b.windowBits,b.memLevel,b.strategy);if(_!==c)throw new Error(a[_]);if(b.header&&l.deflateSetHeader(this.strm,b.header),b.dictionary){var w;if(w=typeof b.dictionary=="string"?o.string2buf(b.dictionary):d.call(b.dictionary)==="[object ArrayBuffer]"?new Uint8Array(b.dictionary):b.dictionary,(_=l.deflateSetDictionary(this.strm,w))!==c)throw new Error(a[_]);this._dict_set=!0}}function m(y,b){var _=new v(b);if(_.push(y,!0),_.err)throw _.msg||a[_.err];return _.result}v.prototype.push=function(y,b){var _,w,S=this.strm,x=this.options.chunkSize;if(this.ended)return!1;w=b===~~b?b:b===!0?4:0,typeof y=="string"?S.input=o.string2buf(y):d.call(y)==="[object ArrayBuffer]"?S.input=new Uint8Array(y):S.input=y,S.next_in=0,S.avail_in=S.input.length;do{if(S.avail_out===0&&(S.output=new g.Buf8(x),S.next_out=0,S.avail_out=x),(_=l.deflate(S,w))!==1&&_!==c)return this.onEnd(_),!(this.ended=!0);S.avail_out!==0&&(S.avail_in!==0||w!==4&&w!==2)||(this.options.to==="string"?this.onData(o.buf2binstring(g.shrinkBuf(S.output,S.next_out))):this.onData(g.shrinkBuf(S.output,S.next_out)))}while((0<S.avail_in||S.avail_out===0)&&_!==1);return w===4?(_=l.deflateEnd(this.strm),this.onEnd(_),this.ended=!0,_===c):w!==2||(this.onEnd(c),!(S.avail_out=0))},v.prototype.onData=function(y){this.chunks.push(y)},v.prototype.onEnd=function(y){y===c&&(this.options.to==="string"?this.result=this.chunks.join(""):this.result=g.flattenChunks(this.chunks)),this.chunks=[],this.err=y,this.msg=this.strm.msg},i.Deflate=v,i.deflate=m,i.deflateRaw=function(y,b){return(b=b||{}).raw=!0,m(y,b)},i.gzip=function(y,b){return(b=b||{}).gzip=!0,m(y,b)}},{"./utils/common":41,"./utils/strings":42,"./zlib/deflate":46,"./zlib/messages":51,"./zlib/zstream":53}],40:[function(n,r,i){var l=n("./zlib/inflate"),g=n("./utils/common"),o=n("./utils/strings"),a=n("./zlib/constants"),u=n("./zlib/messages"),d=n("./zlib/zstream"),c=n("./zlib/gzheader"),f=Object.prototype.toString;function s(v){if(!(this instanceof s))return new s(v);this.options=g.assign({chunkSize:16384,windowBits:0,to:""},v||{});var 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repeat",O.mode=30;break}for(;A--;)O.lens[O.have++]=k}}if(O.mode===30)break;if(O.lens[256]===0){L.msg="invalid code -- missing end-of-block",O.mode=30;break}if(O.lenbits=9,Y={bits:O.lenbits},X=u(d,O.lens,0,O.nlen,O.lencode,0,O.work,Y),O.lenbits=Y.bits,X){L.msg="invalid literal/lengths set",O.mode=30;break}if(O.distbits=6,O.distcode=O.distdyn,Y={bits:O.distbits},X=u(c,O.lens,O.nlen,O.ndist,O.distcode,0,O.work,Y),O.distbits=Y.bits,X){L.msg="invalid distances set",O.mode=30;break}if(O.mode=20,F===6)break e;case 20:O.mode=21;case 21:if(6<=M&&258<=U){L.next_out=ue,L.avail_out=U,L.next_in=J,L.avail_in=M,O.hold=R,O.bits=P,a(L,j),ue=L.next_out,re=L.output,U=L.avail_out,J=L.next_in,V=L.input,M=L.avail_in,R=O.hold,P=O.bits,O.mode===12&&(O.back=-1);break}for(O.back=0;fe=(B=O.lencode[R&(1<<O.lenbits)-1])>>>16&255,pe=65535&B,!((te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(fe&&(240&fe)==0){for(ve=te,H=fe,W=pe;fe=(B=O.lencode[W+((R&(1<<ve+H)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,O.length=pe,fe===0){O.mode=26;break}if(32&fe){O.back=-1,O.mode=12;break}if(64&fe){L.msg="invalid literal/length code",O.mode=30;break}O.extra=15&fe,O.mode=22;case 22:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.length+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}O.was=O.length,O.mode=23;case 23:for(;fe=(B=O.distcode[R&(1<<O.distbits)-1])>>>16&255,pe=65535&B,!((te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if((240&fe)==0){for(ve=te,H=fe,W=pe;fe=(B=O.distcode[W+((R&(1<<ve+H)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,64&fe){L.msg="invalid distance code",O.mode=30;break}O.offset=pe,O.extra=15&fe,O.mode=24;case 24:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.offset+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}if(O.offset>O.dmax){L.msg="invalid distance too far back",O.mode=30;break}O.mode=25;case 25:if(U===0)break e;if(A=j-U,O.offset>A){if((A=O.offset-A)>O.whave&&O.sane){L.msg="invalid distance too far back",O.mode=30;break}q=A>O.wnext?(A-=O.wnext,O.wsize-A):O.wnext-A,A>O.length&&(A=O.length),G=O.window}else G=re,q=ue-O.offset,A=O.length;for(U<A&&(A=U),U-=A,O.length-=A;re[ue++]=G[q++],--A;);O.length===0&&(O.mode=21);break;case 26:if(U===0)break e;re[ue++]=O.length,U--,O.mode=21;break;case 27:if(O.wrap){for(;P<32;){if(M===0)break e;M--,R|=V[J++]<<P,P+=8}if(j-=U,L.total_out+=j,O.total+=j,j&&(L.adler=O.check=O.flags?o(O.check,re,j,ue-j):g(O.check,re,j,ue-j)),j=U,(O.flags?R:y(R))!==O.check){L.msg="incorrect data check",O.mode=30;break}P=R=0}O.mode=28;case 28:if(O.wrap&&O.flags){for(;P<32;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(R!==(4294967295&O.total)){L.msg="incorrect length check",O.mode=30;break}P=R=0}O.mode=29;case 29:X=1;break e;case 30:X=-3;break e;case 31:return-4;case 32:default:return s}return L.next_out=ue,L.avail_out=U,L.next_in=J,L.avail_in=M,O.hold=R,O.bits=P,(O.wsize||j!==L.avail_out&&O.mode<30&&(O.mode<27||F!==4))&&N(L,L.output,L.next_out,j-L.avail_out)?(O.mode=31,-4):(Q-=L.avail_in,j-=L.avail_out,L.total_in+=Q,L.total_out+=j,O.total+=j,O.wrap&&j&&(L.adler=O.check=O.flags?o(O.check,re,j,L.next_out-j):g(O.check,re,j,L.next_out-j)),L.data_type=O.bits+(O.last?64:0)+(O.mode===12?128:0)+(O.mode===20||O.mode===15?256:0),(Q==0&&j===0||F===4)&&X===f&&(X=-5),X)},i.inflateEnd=function(L){if(!L||!L.state)return s;var F=L.state;return F.window&&(F.window=null),L.state=null,f},i.inflateGetHeader=function(L,F){var O;return L&&L.state?(2&(O=L.state).wrap)==0?s:((O.head=F).done=!1,f):s},i.inflateSetDictionary=function(L,F){var O,V=F.length;return L&&L.state?(O=L.state).wrap!==0&&O.mode!==11?s:O.mode===11&&g(1,F,V,0)!==O.check?-3:N(L,F,V,V)?(O.mode=31,-4):(O.havedict=1,f):s},i.inflateInfo="pako inflate (from Nodeca project)"},{"../utils/common":41,"./adler32":43,"./crc32":45,"./inffast":48,"./inftrees":50}],50:[function(n,r,i){var l=n("../utils/common"),g=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],o=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],a=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],u=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];r.exports=function(d,c,f,s,h,v,m,y){var b,_,w,S,x,T,E,I,D,N=y.bits,L=0,F=0,O=0,V=0,re=0,J=0,ue=0,M=0,U=0,R=0,P=null,Q=0,j=new l.Buf16(16),A=new l.Buf16(16),q=null,G=0;for(L=0;L<=15;L++)j[L]=0;for(F=0;F<s;F++)j[c[f+F]]++;for(re=N,V=15;1<=V&&j[V]===0;V--);if(V<re&&(re=V),V===0)return h[v++]=20971520,h[v++]=20971520,y.bits=1,0;for(O=1;O<V&&j[O]===0;O++);for(re<O&&(re=O),L=M=1;L<=15;L++)if(M<<=1,(M-=j[L])<0)return-1;if(0<M&&(d===0||V!==1))return-1;for(A[1]=0,L=1;L<15;L++)A[L+1]=A[L]+j[L];for(F=0;F<s;F++)c[f+F]!==0&&(m[A[c[f+F]]++]=F);if(T=d===0?(P=q=m,19):d===1?(P=g,Q-=257,q=o,G-=257,256):(P=a,q=u,-1),L=O,x=v,ue=F=R=0,w=-1,S=(U=1<<(J=re))-1,d===1&&852<U||d===2&&592<U)return 1;for(;;){for(E=L-ue,D=m[F]<T?(I=0,m[F]):m[F]>T?(I=q[G+m[F]],P[Q+m[F]]):(I=96,0),b=1<<L-ue,O=_=1<<J;h[x+(R>>ue)+(_-=b)]=E<<24|I<<16|D|0,_!==0;);for(b=1<<L-1;R&b;)b>>=1;if(b!==0?(R&=b-1,R+=b):R=0,F++,--j[L]==0){if(L===V)break;L=c[f+m[F]]}if(re<L&&(R&S)!==w){for(ue===0&&(ue=re),x+=O,M=1<<(J=L-ue);J+ue<V&&!((M-=j[J+ue])<=0);)J++,M<<=1;if(U+=1<<J,d===1&&852<U||d===2&&592<U)return 1;h[w=R&S]=re<<24|J<<16|x-v|0}}return R!==0&&(h[x+R]=L-ue<<24|64<<16|0),y.bits=re,0}},{"../utils/common":41}],51:[function(n,r,i){r.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"}},{}],52:[function(n,r,i){var l=n("../utils/common"),g=0,o=1;function a(B){for(var K=B.length;0<=--K;)B[K]=0}var u=0,d=29,c=256,f=c+1+d,s=30,h=19,v=2*f+1,m=15,y=16,b=7,_=256,w=16,S=17,x=18,T=[0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0],E=[0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13],I=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,7],D=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15],N=new Array(2*(f+2));a(N);var L=new Array(2*s);a(L);var F=new Array(512);a(F);var O=new Array(256);a(O);var V=new Array(d);a(V);var re,J,ue,M=new Array(s);function U(B,K,ce,ie,Z){this.static_tree=B,this.extra_bits=K,this.extra_base=ce,this.elems=ie,this.max_length=Z,this.has_stree=B&&B.length}function R(B,K){this.dyn_tree=B,this.max_code=0,this.stat_desc=K}function P(B){return B<256?F[B]:F[256+(B>>>7)]}function Q(B,K){B.pending_buf[B.pending++]=255&K,B.pending_buf[B.pending++]=K>>>8&255}function j(B,K,ce){B.bi_valid>y-ce?(B.bi_buf|=K<<B.bi_valid&65535,Q(B,B.bi_buf),B.bi_buf=K>>y-B.bi_valid,B.bi_valid+=ce-y):(B.bi_buf|=K<<B.bi_valid&65535,B.bi_valid+=ce)}function A(B,K,ce){j(B,ce[2*K],ce[2*K+1])}function q(B,K){for(var ce=0;ce|=1&B,B>>>=1,ce<<=1,0<--K;);return ce>>>1}function G(B,K,ce){var ie,Z,me=new Array(m+1),ae=0;for(ie=1;ie<=m;ie++)me[ie]=ae=ae+ce[ie-1]<<1;for(Z=0;Z<=K;Z++){var ge=B[2*Z+1];ge!==0&&(B[2*Z]=q(me[ge]++,ge))}}function te(B){var K;for(K=0;K<f;K++)B.dyn_ltree[2*K]=0;for(K=0;K<s;K++)B.dyn_dtree[2*K]=0;for(K=0;K<h;K++)B.bl_tree[2*K]=0;B.dyn_ltree[2*_]=1,B.opt_len=B.static_len=0,B.last_lit=B.matches=0}function fe(B){8<B.bi_valid?Q(B,B.bi_buf):0<B.bi_valid&&(B.pending_buf[B.pending++]=B.bi_buf),B.bi_buf=0,B.bi_valid=0}function pe(B,K,ce,ie){var Z=2*K,me=2*ce;return B[Z]<B[me]||B[Z]===B[me]&&ie[K]<=ie[ce]}function ve(B,K,ce){for(var ie=B.heap[ce],Z=ce<<1;Z<=B.heap_len&&(Z<B.heap_len&&pe(K,B.heap[Z+1],B.heap[Z],B.depth)&&Z++,!pe(K,ie,B.heap[Z],B.depth));)B.heap[ce]=B.heap[Z],ce=Z,Z<<=1;B.heap[ce]=ie}function H(B,K,ce){var ie,Z,me,ae,ge=0;if(B.last_lit!==0)for(;ie=B.pending_buf[B.d_buf+2*ge]<<8|B.pending_buf[B.d_buf+2*ge+1],Z=B.pending_buf[B.l_buf+ge],ge++,ie===0?A(B,Z,K):(A(B,(me=O[Z])+c+1,K),(ae=T[me])!==0&&j(B,Z-=V[me],ae),A(B,me=P(--ie),ce),(ae=E[me])!==0&&j(B,ie-=M[me],ae)),ge<B.last_lit;);A(B,_,K)}function W(B,K){var ce,ie,Z,me=K.dyn_tree,ae=K.stat_desc.static_tree,ge=K.stat_desc.has_stree,we=K.stat_desc.elems,ke=-1;for(B.heap_len=0,B.heap_max=v,ce=0;ce<we;ce++)me[2*ce]!==0?(B.heap[++B.heap_len]=ke=ce,B.depth[ce]=0):me[2*ce+1]=0;for(;B.heap_len<2;)me[2*(Z=B.heap[++B.heap_len]=ke<2?++ke:0)]=1,B.depth[Z]=0,B.opt_len--,ge&&(B.static_len-=ae[2*Z+1]);for(K.max_code=ke,ce=B.heap_len>>1;1<=ce;ce--)ve(B,me,ce);for(Z=we;ce=B.heap[1],B.heap[1]=B.heap[B.heap_len--],ve(B,me,1),ie=B.heap[1],B.heap[--B.heap_max]=ce,B.heap[--B.heap_max]=ie,me[2*Z]=me[2*ce]+me[2*ie],B.depth[Z]=(B.depth[ce]>=B.depth[ie]?B.depth[ce]:B.depth[ie])+1,me[2*ce+1]=me[2*ie+1]=Z,B.heap[1]=Z++,ve(B,me,1),2<=B.heap_len;);B.heap[--B.heap_max]=B.heap[1],function(Oe,Fe){var 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a=o.charCodeAt(0),u=o.charCodeAt(1),d=o.charCodeAt(2),c=o.charCodeAt(3),f=this.readData(4);return a===f[0]&&u===f[1]&&d===f[2]&&c===f[3]},g.prototype.readData=function(o){if(this.checkOffset(o),o===0)return[];var a=this.data.slice(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./DataReader":18}],18:[function(n,r,i){var l=n("../utils");function g(o){this.data=o,this.length=o.length,this.index=0,this.zero=0}g.prototype={checkOffset:function(o){this.checkIndex(this.index+o)},checkIndex:function(o){if(this.length<this.zero+o||o<0)throw new Error("End of data reached (data length = "+this.length+", asked index = "+o+"). 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this._listeners[g].push(o),this},cleanUp:function(){this.streamInfo=this.generatedError=this.extraStreamInfo=null,this._listeners=[]},emit:function(g,o){if(this._listeners[g])for(var a=0;a<this._listeners[g].length;a++)this._listeners[g][a].call(this,o)},pipe:function(g){return g.registerPrevious(this)},registerPrevious:function(g){if(this.isLocked)throw new Error("The stream '"+this+"' has already been used.");this.streamInfo=g.streamInfo,this.mergeStreamInfo(),this.previous=g;var o=this;return g.on("data",function(a){o.processChunk(a)}),g.on("end",function(){o.end()}),g.on("error",function(a){o.error(a)}),this},pause:function(){return!this.isPaused&&!this.isFinished&&(this.isPaused=!0,this.previous&&this.previous.pause(),!0)},resume:function(){if(!this.isPaused||this.isFinished)return!1;var g=this.isPaused=!1;return 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Array(S),b=_=0;_<S;b++)(64512&(m=h.charCodeAt(b)))==55296&&b+1<w&&(64512&(y=h.charCodeAt(b+1)))==56320&&(m=65536+(m-55296<<10)+(y-56320),b++),m<128?v[_++]=m:(m<2048?v[_++]=192|m>>>6:(m<65536?v[_++]=224|m>>>12:(v[_++]=240|m>>>18,v[_++]=128|m>>>12&63),v[_++]=128|m>>>6&63),v[_++]=128|63&m);return v}(s)},i.utf8decode=function(s){return g.nodebuffer?l.transformTo("nodebuffer",s).toString("utf-8"):function(h){var v,m,y,b,_=h.length,w=new Array(2*_);for(v=m=0;v<_;)if((y=h[v++])<128)w[m++]=y;else if(4<(b=u[y]))w[m++]=65533,v+=b-1;else{for(y&=b===2?31:b===3?15:7;1<b&&v<_;)y=y<<6|63&h[v++],b--;1<b?w[m++]=65533:y<65536?w[m++]=y:(y-=65536,w[m++]=55296|y>>10&1023,w[m++]=56320|1023&y)}return w.length!==m&&(w.subarray?w=w.subarray(0,m):w.length=m),l.applyFromCharCode(w)}(s=l.transformTo(g.uint8array?"uint8array":"array",s))},l.inherits(c,a),c.prototype.processChunk=function(s){var h=l.transformTo(g.uint8array?"uint8array":"array",s.data);if(this.leftOver&&this.leftOver.length){if(g.uint8array){var v=h;(h=new Uint8Array(v.length+this.leftOver.length)).set(this.leftOver,0),h.set(v,this.leftOver.length)}else h=this.leftOver.concat(h);this.leftOver=null}var m=function(b,_){var w;for((_=_||b.length)>b.length&&(_=b.length),w=_-1;0<=w&&(192&b[w])==128;)w--;return w<0||w===0?_:w+u[b[w]]>_?w:_}(h),y=h;m!==h.length&&(g.uint8array?(y=h.subarray(0,m),this.leftOver=h.subarray(m,h.length)):(y=h.slice(0,m),this.leftOver=h.slice(m,h.length))),this.push({data:i.utf8decode(y),meta:s.meta})},c.prototype.flush=function(){this.leftOver&&this.leftOver.length&&(this.push({data:i.utf8decode(this.leftOver),meta:{}}),this.leftOver=null)},i.Utf8DecodeWorker=c,l.inherits(f,a),f.prototype.processChunk=function(s){this.push({data:i.utf8encode(s.data),meta:s.meta})},i.Utf8EncodeWorker=f},{"./nodejsUtils":14,"./stream/GenericWorker":28,"./support":30,"./utils":32}],32:[function(n,r,i){var l=n("./support"),g=n("./base64"),o=n("./nodejsUtils"),a=n("./external");function u(v){return v}function d(v,m){for(var y=0;y<v.length;++y)m[y]=255&v.charCodeAt(y);return m}n("setimmediate"),i.newBlob=function(v,m){i.checkSupport("blob");try{return new Blob([v],{type:m})}catch{try{var y=new(self.BlobBuilder||self.WebKitBlobBuilder||self.MozBlobBuilder||self.MSBlobBuilder);return y.append(v),y.getBlob(m)}catch{throw new Error("Bug : can't construct the Blob.")}}};var c={stringifyByChunk:function(v,m,y){var b=[],_=0,w=v.length;if(w<=y)return String.fromCharCode.apply(null,v);for(;_<w;)m==="array"||m==="nodebuffer"?b.push(String.fromCharCode.apply(null,v.slice(_,Math.min(_+y,w)))):b.push(String.fromCharCode.apply(null,v.subarray(_,Math.min(_+y,w)))),_+=y;return b.join("")},stringifyByChar:function(v){for(var m="",y=0;y<v.length;y++)m+=String.fromCharCode(v[y]);return m},applyCanBeUsed:{uint8array:function(){try{return l.uint8array&&String.fromCharCode.apply(null,new Uint8Array(1)).length===1}catch{return!1}}(),nodebuffer:function(){try{return l.nodebuffer&&String.fromCharCode.apply(null,o.allocBuffer(1)).length===1}catch{return!1}}()}};function f(v){var m=65536,y=i.getTypeOf(v),b=!0;if(y==="uint8array"?b=c.applyCanBeUsed.uint8array:y==="nodebuffer"&&(b=c.applyCanBeUsed.nodebuffer),b)for(;1<m;)try{return c.stringifyByChunk(v,y,m)}catch{m=Math.floor(m/2)}return c.stringifyByChar(v)}function s(v,m){for(var y=0;y<v.length;y++)m[y]=v[y];return m}i.applyFromCharCode=f;var h={};h.string={string:u,array:function(v){return d(v,new Array(v.length))},arraybuffer:function(v){return h.string.uint8array(v).buffer},uint8array:function(v){return d(v,new Uint8Array(v.length))},nodebuffer:function(v){return d(v,o.allocBuffer(v.length))}},h.array={string:f,array:u,arraybuffer:function(v){return new Uint8Array(v).buffer},uint8array:function(v){return new Uint8Array(v)},nodebuffer:function(v){return o.newBufferFrom(v)}},h.arraybuffer={string:function(v){return f(new Uint8Array(v))},array:function(v){return s(new Uint8Array(v),new 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Array]"?"array":l.nodebuffer&&o.isBuffer(v)?"nodebuffer":l.uint8array&&v instanceof Uint8Array?"uint8array":l.arraybuffer&&v instanceof ArrayBuffer?"arraybuffer":void 0},i.checkSupport=function(v){if(!l[v.toLowerCase()])throw new Error(v+" is not supported by this platform")},i.MAX_VALUE_16BITS=65535,i.MAX_VALUE_32BITS=-1,i.pretty=function(v){var m,y,b="";for(y=0;y<(v||"").length;y++)b+="\\x"+((m=v.charCodeAt(y))<16?"0":"")+m.toString(16).toUpperCase();return b},i.delay=function(v,m,y){setImmediate(function(){v.apply(y||null,m||[])})},i.inherits=function(v,m){function y(){}y.prototype=m.prototype,v.prototype=new y},i.extend=function(){var v,m,y={};for(v=0;v<arguments.length;v++)for(m in arguments[v])Object.prototype.hasOwnProperty.call(arguments[v],m)&&y[m]===void 0&&(y[m]=arguments[v][m]);return y},i.prepareContent=function(v,m,y,b,_){return a.Promise.resolve(m).then(function(w){return l.blob&&(w instanceof Blob||["[object File]","[object 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Is it in a supported JavaScript type (String, Blob, ArrayBuffer, etc) ?"))})}},{"./base64":1,"./external":6,"./nodejsUtils":14,"./support":30,setimmediate:54}],33:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./signature"),a=n("./zipEntry"),u=n("./support");function d(c){this.files=[],this.loadOptions=c}d.prototype={checkSignature:function(c){if(!this.reader.readAndCheckSignature(c)){this.reader.index-=4;var f=this.reader.readString(4);throw new Error("Corrupted zip or bug: unexpected signature ("+g.pretty(f)+", expected "+g.pretty(c)+")")}},isSignature:function(c,f){var s=this.reader.index;this.reader.setIndex(c);var h=this.reader.readString(4)===f;return this.reader.setIndex(s),h},readBlockEndOfCentral:function(){this.diskNumber=this.reader.readInt(2),this.diskWithCentralDirStart=this.reader.readInt(2),this.centralDirRecordsOnThisDisk=this.reader.readInt(2),this.centralDirRecords=this.reader.readInt(2),this.centralDirSize=this.reader.readInt(4),this.centralDirOffset=this.reader.readInt(4),this.zipCommentLength=this.reader.readInt(2);var c=this.reader.readData(this.zipCommentLength),f=u.uint8array?"uint8array":"array",s=g.transformTo(f,c);this.zipComment=this.loadOptions.decodeFileName(s)},readBlockZip64EndOfCentral:function(){this.zip64EndOfCentralSize=this.reader.readInt(8),this.reader.skip(4),this.diskNumber=this.reader.readInt(4),this.diskWithCentralDirStart=this.reader.readInt(4),this.centralDirRecordsOnThisDisk=this.reader.readInt(8),this.centralDirRecords=this.reader.readInt(8),this.centralDirSize=this.reader.readInt(8),this.centralDirOffset=this.reader.readInt(8),this.zip64ExtensibleData={};for(var c,f,s,h=this.zip64EndOfCentralSize-44;0<h;)c=this.reader.readInt(2),f=this.reader.readInt(4),s=this.reader.readData(f),this.zip64ExtensibleData[c]={id:c,length:f,value:s}},readBlockZip64EndOfCentralLocator:function(){if(this.diskWithZip64CentralDirStart=this.reader.readInt(4),this.relativeOffsetEndOfZip64CentralDir=this.reader.readInt(8),this.disksCount=this.reader.readInt(4),1<this.disksCount)throw new Error("Multi-volumes zip are not supported")},readLocalFiles:function(){var c,f;for(c=0;c<this.files.length;c++)f=this.files[c],this.reader.setIndex(f.localHeaderOffset),this.checkSignature(o.LOCAL_FILE_HEADER),f.readLocalPart(this.reader),f.handleUTF8(),f.processAttributes()},readCentralDir:function(){var c;for(this.reader.setIndex(this.centralDirOffset);this.reader.readAndCheckSignature(o.CENTRAL_FILE_HEADER);)(c=new a({zip64:this.zip64},this.loadOptions)).readCentralPart(this.reader),this.files.push(c);if(this.centralDirRecords!==this.files.length&&this.centralDirRecords!==0&&this.files.length===0)throw new Error("Corrupted zip or bug: expected "+this.centralDirRecords+" records in central dir, got "+this.files.length)},readEndOfCentral:function(){var c=this.reader.lastIndexOfSignature(o.CENTRAL_DIRECTORY_END);if(c<0)throw this.isSignature(0,o.LOCAL_FILE_HEADER)?new Error("Corrupted zip: can't find end of central directory"):new Error("Can't find end of central directory : is this a zip file ? If it is, see https://stuk.github.io/jszip/documentation/howto/read_zip.html");this.reader.setIndex(c);var f=c;if(this.checkSignature(o.CENTRAL_DIRECTORY_END),this.readBlockEndOfCentral(),this.diskNumber===g.MAX_VALUE_16BITS||this.diskWithCentralDirStart===g.MAX_VALUE_16BITS||this.centralDirRecordsOnThisDisk===g.MAX_VALUE_16BITS||this.centralDirRecords===g.MAX_VALUE_16BITS||this.centralDirSize===g.MAX_VALUE_32BITS||this.centralDirOffset===g.MAX_VALUE_32BITS){if(this.zip64=!0,(c=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR))<0)throw new Error("Corrupted zip: can't find the ZIP64 end of central directory locator");if(this.reader.setIndex(c),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR),this.readBlockZip64EndOfCentralLocator(),!this.isSignature(this.relativeOffsetEndOfZip64CentralDir,o.ZIP64_CENTRAL_DIRECTORY_END)&&(this.relativeOffsetEndOfZip64CentralDir=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.relativeOffsetEndOfZip64CentralDir<0))throw new Error("Corrupted zip: can't find the ZIP64 end of central directory");this.reader.setIndex(this.relativeOffsetEndOfZip64CentralDir),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.readBlockZip64EndOfCentral()}var s=this.centralDirOffset+this.centralDirSize;this.zip64&&(s+=20,s+=12+this.zip64EndOfCentralSize);var h=f-s;if(0<h)this.isSignature(f,o.CENTRAL_FILE_HEADER)||(this.reader.zero=h);else if(h<0)throw new Error("Corrupted zip: missing "+Math.abs(h)+" bytes.")},prepareReader:function(c){this.reader=l(c)},load:function(c){this.prepareReader(c),this.readEndOfCentral(),this.readCentralDir(),this.readLocalFiles()}},r.exports=d},{"./reader/readerFor":22,"./signature":23,"./support":30,"./utils":32,"./zipEntry":34}],34:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./compressedObject"),a=n("./crc32"),u=n("./utf8"),d=n("./compressions"),c=n("./support");function f(s,h){this.options=s,this.loadOptions=h}f.prototype={isEncrypted:function(){return(1&this.bitFlag)==1},useUTF8:function(){return(2048&this.bitFlag)==2048},readLocalPart:function(s){var h,v;if(s.skip(22),this.fileNameLength=s.readInt(2),v=s.readInt(2),this.fileName=s.readData(this.fileNameLength),s.skip(v),this.compressedSize===-1||this.uncompressedSize===-1)throw new Error("Bug or corrupted zip : didn't get enough information from the central directory (compressedSize === -1 || uncompressedSize === -1)");if((h=function(m){for(var y in d)if(Object.prototype.hasOwnProperty.call(d,y)&&d[y].magic===m)return d[y];return null}(this.compressionMethod))===null)throw new Error("Corrupted zip : compression "+g.pretty(this.compressionMethod)+" unknown (inner file : "+g.transformTo("string",this.fileName)+")");this.decompressed=new o(this.compressedSize,this.uncompressedSize,this.crc32,h,s.readData(this.compressedSize))},readCentralPart:function(s){this.versionMadeBy=s.readInt(2),s.skip(2),this.bitFlag=s.readInt(2),this.compressionMethod=s.readString(2),this.date=s.readDate(),this.crc32=s.readInt(4),this.compressedSize=s.readInt(4),this.uncompressedSize=s.readInt(4);var h=s.readInt(2);if(this.extraFieldsLength=s.readInt(2),this.fileCommentLength=s.readInt(2),this.diskNumberStart=s.readInt(2),this.internalFileAttributes=s.readInt(2),this.externalFileAttributes=s.readInt(4),this.localHeaderOffset=s.readInt(4),this.isEncrypted())throw new Error("Encrypted zip are not supported");s.skip(h),this.readExtraFields(s),this.parseZIP64ExtraField(s),this.fileComment=s.readData(this.fileCommentLength)},processAttributes:function(){this.unixPermissions=null,this.dosPermissions=null;var s=this.versionMadeBy>>8;this.dir=!!(16&this.externalFileAttributes),s==0&&(this.dosPermissions=63&this.externalFileAttributes),s==3&&(this.unixPermissions=this.externalFileAttributes>>16&65535),this.dir||this.fileNameStr.slice(-1)!=="/"||(this.dir=!0)},parseZIP64ExtraField:function(){if(this.extraFields[1]){var s=l(this.extraFields[1].value);this.uncompressedSize===g.MAX_VALUE_32BITS&&(this.uncompressedSize=s.readInt(8)),this.compressedSize===g.MAX_VALUE_32BITS&&(this.compressedSize=s.readInt(8)),this.localHeaderOffset===g.MAX_VALUE_32BITS&&(this.localHeaderOffset=s.readInt(8)),this.diskNumberStart===g.MAX_VALUE_32BITS&&(this.diskNumberStart=s.readInt(4))}},readExtraFields:function(s){var h,v,m,y=s.index+this.extraFieldsLength;for(this.extraFields||(this.extraFields={});s.index+4<y;)h=s.readInt(2),v=s.readInt(2),m=s.readData(v),this.extraFields[h]={id:h,length:v,value:m};s.setIndex(y)},handleUTF8:function(){var s=c.uint8array?"uint8array":"array";if(this.useUTF8())this.fileNameStr=u.utf8decode(this.fileName),this.fileCommentStr=u.utf8decode(this.fileComment);else{var h=this.findExtraFieldUnicodePath();if(h!==null)this.fileNameStr=h;else{var v=g.transformTo(s,this.fileName);this.fileNameStr=this.loadOptions.decodeFileName(v)}var m=this.findExtraFieldUnicodeComment();if(m!==null)this.fileCommentStr=m;else{var y=g.transformTo(s,this.fileComment);this.fileCommentStr=this.loadOptions.decodeFileName(y)}}},findExtraFieldUnicodePath:function(){var s=this.extraFields[28789];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileName)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null},findExtraFieldUnicodeComment:function(){var s=this.extraFields[25461];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileComment)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null}},r.exports=f},{"./compressedObject":2,"./compressions":3,"./crc32":4,"./reader/readerFor":22,"./support":30,"./utf8":31,"./utils":32}],35:[function(n,r,i){function l(h,v,m){this.name=h,this.dir=m.dir,this.date=m.date,this.comment=m.comment,this.unixPermissions=m.unixPermissions,this.dosPermissions=m.dosPermissions,this._data=v,this._dataBinary=m.binary,this.options={compression:m.compression,compressionOptions:m.compressionOptions}}var g=n("./stream/StreamHelper"),o=n("./stream/DataWorker"),a=n("./utf8"),u=n("./compressedObject"),d=n("./stream/GenericWorker");l.prototype={internalStream:function(h){var v=null,m="string";try{if(!h)throw new Error("No output type specified.");var y=(m=h.toLowerCase())==="string"||m==="text";m!=="binarystring"&&m!=="text"||(m="string"),v=this._decompressWorker();var b=!this._dataBinary;b&&!y&&(v=v.pipe(new a.Utf8EncodeWorker)),!b&&y&&(v=v.pipe(new a.Utf8DecodeWorker))}catch(_){(v=new d("error")).error(_)}return new g(v,m,"")},async:function(h,v){return this.internalStream(h).accumulate(v)},nodeStream:function(h,v){return this.internalStream(h||"nodebuffer").toNodejsStream(v)},_compressWorker:function(h,v){if(this._data instanceof u&&this._data.compression.magic===h.magic)return this._data.getCompressedWorker();var m=this._decompressWorker();return this._dataBinary||(m=m.pipe(new a.Utf8EncodeWorker)),u.createWorkerFrom(m,h,v)},_decompressWorker:function(){return this._data instanceof u?this._data.getContentWorker():this._data instanceof d?this._data:new o(this._data)}};for(var c=["asText","asBinary","asNodeBuffer","asUint8Array","asArrayBuffer"],f=function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},s=0;s<c.length;s++)l.prototype[c[s]]=f;r.exports=l},{"./compressedObject":2,"./stream/DataWorker":27,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31}],36:[function(n,r,i){(function(l){var g,o,a=l.MutationObserver||l.WebKitMutationObserver;if(a){var u=0,d=new a(h),c=l.document.createTextNode("");d.observe(c,{characterData:!0}),g=function(){c.data=u=++u%2}}else if(l.setImmediate||l.MessageChannel===void 0)g="document"in l&&"onreadystatechange"in l.document.createElement("script")?function(){var v=l.document.createElement("script");v.onreadystatechange=function(){h(),v.onreadystatechange=null,v.parentNode.removeChild(v),v=null},l.document.documentElement.appendChild(v)}:function(){setTimeout(h,0)};else{var f=new l.MessageChannel;f.port1.onmessage=h,g=function(){f.port2.postMessage(0)}}var s=[];function h(){var v,m;o=!0;for(var y=s.length;y;){for(m=s,s=[],v=-1;++v<y;)m[v]();y=s.length}o=!1}r.exports=function(v){s.push(v)!==1||o||g()}}).call(this,typeof su<"u"?su:typeof self<"u"?self:typeof window<"u"?window:{})},{}],37:[function(n,r,i){var l=n("immediate");function g(){}var o={},a=["REJECTED"],u=["FULFILLED"],d=["PENDING"];function c(y){if(typeof y!="function")throw new TypeError("resolver must be a function");this.state=d,this.queue=[],this.outcome=void 0,y!==g&&v(this,y)}function f(y,b,_){this.promise=y,typeof b=="function"&&(this.onFulfilled=b,this.callFulfilled=this.otherCallFulfilled),typeof _=="function"&&(this.onRejected=_,this.callRejected=this.otherCallRejected)}function s(y,b,_){l(function(){var w;try{w=b(_)}catch(S){return o.reject(y,S)}w===y?o.reject(y,new TypeError("Cannot resolve promise with itself")):o.resolve(y,w)})}function h(y){var b=y&&y.then;if(y&&(typeof y=="object"||typeof y=="function")&&typeof b=="function")return function(){b.apply(y,arguments)}}function v(y,b){var _=!1;function w(T){_||(_=!0,o.reject(y,T))}function S(T){_||(_=!0,o.resolve(y,T))}var 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13:if(O.last){R>>>=7&P,P-=7&P,O.mode=27;break}for(;P<3;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}switch(O.last=1&R,P-=1,3&(R>>>=1)){case 0:O.mode=14;break;case 1:if(D(O),O.mode=20,F!==6)break;R>>>=2,P-=2;break e;case 2:O.mode=17;break;case 3:L.msg="invalid block type",O.mode=30}R>>>=2,P-=2;break;case 14:for(R>>>=7&P,P-=7&P;P<32;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if((65535&R)!=(R>>>16^65535)){L.msg="invalid stored block lengths",O.mode=30;break}if(O.length=65535&R,P=R=0,O.mode=15,F===6)break e;case 15:O.mode=16;case 16:if(A=O.length){if(M<A&&(A=M),U<A&&(A=U),A===0)break e;l.arraySet(re,V,J,A,ue),M-=A,J+=A,U-=A,ue+=A,O.length-=A;break}O.mode=12;break;case 17:for(;P<14;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(O.nlen=257+(31&R),R>>>=5,P-=5,O.ndist=1+(31&R),R>>>=5,P-=5,O.ncode=4+(15&R),R>>>=4,P-=4,286<O.nlen||30<O.ndist){L.msg="too many length or distance symbols",O.mode=30;break}O.have=0,O.mode=18;case 18:for(;O.have<O.ncode;){for(;P<3;){if(M===0)break 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e;M--,R+=V[J++]<<P,P+=8}if(fe&&(240&fe)==0){for(ve=te,H=fe,W=pe;fe=(B=O.lencode[W+((R&(1<<ve+H)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,O.length=pe,fe===0){O.mode=26;break}if(32&fe){O.back=-1,O.mode=12;break}if(64&fe){L.msg="invalid literal/length code",O.mode=30;break}O.extra=15&fe,O.mode=22;case 22:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.length+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}O.was=O.length,O.mode=23;case 23:for(;fe=(B=O.distcode[R&(1<<O.distbits)-1])>>>16&255,pe=65535&B,!((te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if((240&fe)==0){for(ve=te,H=fe,W=pe;fe=(B=O.distcode[W+((R&(1<<ve+H)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,64&fe){L.msg="invalid distance code",O.mode=30;break}O.offset=pe,O.extra=15&fe,O.mode=24;case 24:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.offset+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}if(O.offset>O.dmax){L.msg="invalid distance too far back",O.mode=30;break}O.mode=25;case 25:if(U===0)break e;if(A=j-U,O.offset>A){if((A=O.offset-A)>O.whave&&O.sane){L.msg="invalid distance too far back",O.mode=30;break}q=A>O.wnext?(A-=O.wnext,O.wsize-A):O.wnext-A,A>O.length&&(A=O.length),G=O.window}else G=re,q=ue-O.offset,A=O.length;for(U<A&&(A=U),U-=A,O.length-=A;re[ue++]=G[q++],--A;);O.length===0&&(O.mode=21);break;case 26:if(U===0)break e;re[ue++]=O.length,U--,O.mode=21;break;case 27:if(O.wrap){for(;P<32;){if(M===0)break e;M--,R|=V[J++]<<P,P+=8}if(j-=U,L.total_out+=j,O.total+=j,j&&(L.adler=O.check=O.flags?o(O.check,re,j,ue-j):g(O.check,re,j,ue-j)),j=U,(O.flags?R:y(R))!==O.check){L.msg="incorrect data check",O.mode=30;break}P=R=0}O.mode=28;case 28:if(O.wrap&&O.flags){for(;P<32;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(R!==(4294967295&O.total)){L.msg="incorrect length check",O.mode=30;break}P=R=0}O.mode=29;case 29:X=1;break e;case 30:X=-3;break e;case 31:return-4;case 32:default:return s}return L.next_out=ue,L.avail_out=U,L.next_in=J,L.avail_in=M,O.hold=R,O.bits=P,(O.wsize||j!==L.avail_out&&O.mode<30&&(O.mode<27||F!==4))&&N(L,L.output,L.next_out,j-L.avail_out)?(O.mode=31,-4):(Q-=L.avail_in,j-=L.avail_out,L.total_in+=Q,L.total_out+=j,O.total+=j,O.wrap&&j&&(L.adler=O.check=O.flags?o(O.check,re,j,L.next_out-j):g(O.check,re,j,L.next_out-j)),L.data_type=O.bits+(O.last?64:0)+(O.mode===12?128:0)+(O.mode===20||O.mode===15?256:0),(Q==0&&j===0||F===4)&&X===f&&(X=-5),X)},i.inflateEnd=function(L){if(!L||!L.state)return s;var F=L.state;return F.window&&(F.window=null),L.state=null,f},i.inflateGetHeader=function(L,F){var O;return L&&L.state?(2&(O=L.state).wrap)==0?s:((O.head=F).done=!1,f):s},i.inflateSetDictionary=function(L,F){var O,V=F.length;return L&&L.state?(O=L.state).wrap!==0&&O.mode!==11?s:O.mode===11&&g(1,F,V,0)!==O.check?-3:N(L,F,V,V)?(O.mode=31,-4):(O.havedict=1,f):s},i.inflateInfo="pako inflate (from Nodeca project)"},{"../utils/common":41,"./adler32":43,"./crc32":45,"./inffast":48,"./inftrees":50}],50:[function(n,r,i){var l=n("../utils/common"),g=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],o=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],a=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],u=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];r.exports=function(d,c,f,s,h,v,m,y){var b,_,w,S,x,T,E,I,D,N=y.bits,L=0,F=0,O=0,V=0,re=0,J=0,ue=0,M=0,U=0,R=0,P=null,Q=0,j=new l.Buf16(16),A=new 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v=t.util.invertTransform(h),m=t.util.multiplyTransformMatrices(v,s.calcOwnMatrix());t.util.applyTransformToObject(s,m)},addTransformToObject:function(s,h){t.util.applyTransformToObject(s,t.util.multiplyTransformMatrices(h,s.calcOwnMatrix()))},applyTransformToObject:function(s,h){var v=t.util.qrDecompose(h),m=new t.Point(v.translateX,v.translateY);s.flipX=!1,s.flipY=!1,s.set("scaleX",v.scaleX),s.set("scaleY",v.scaleY),s.skewX=v.skewX,s.skewY=v.skewY,s.angle=v.angle,s.setPositionByOrigin(m,"center","center")},sizeAfterTransform:function(s,h,v){var m=s/2,y=h/2,b=[{x:-m,y:-y},{x:m,y:-y},{x:-m,y},{x:m,y}],_=t.util.calcDimensionsMatrix(v),w=t.util.makeBoundingBoxFromPoints(b,_);return{x:w.width,y:w.height}},mergeClipPaths:function(s,h){var v=s,m=h;v.inverted&&!m.inverted&&(v=h,m=s),t.util.applyTransformToObject(m,t.util.multiplyTransformMatrices(t.util.invertTransform(v.calcTransformMatrix()),m.calcTransformMatrix()));var y=v.inverted&&m.inverted;return y&&(v.inverted=m.inverted=!1),new 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f[3]=s,this.setSource(f),this}},a.Color.reRGBa=/^rgba?\(\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*(?:\s*,\s*((?:\d*\.?\d+)?)\s*)?\)$/i,a.Color.reHSLa=/^hsla?\(\s*(\d{1,3})\s*,\s*(\d{1,3}\%)\s*,\s*(\d{1,3}\%)\s*(?:\s*,\s*(\d+(?:\.\d+)?)\s*)?\)$/i,a.Color.reHex=/^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i,a.Color.colorNameMap={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",darkgreen:"#006400",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#ADFF2F",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",lightgreen:"#90EE90",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",rebeccapurple:"#663399",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"};function d(c,f,s){return s<0&&(s+=1),s>1&&(s-=1),s<1/6?c+(f-c)*6*s:s<1/2?f:s<2/3?c+(f-c)*(2/3-s)*6:c}a.Color.fromRgb=function(c){return u.fromSource(u.sourceFromRgb(c))},a.Color.sourceFromRgb=function(c){var f=c.match(u.reRGBa);if(f){var s=parseInt(f[1],10)/(/%$/.test(f[1])?100:1)*(/%$/.test(f[1])?255:1),h=parseInt(f[2],10)/(/%$/.test(f[2])?100:1)*(/%$/.test(f[2])?255:1),v=parseInt(f[3],10)/(/%$/.test(f[3])?100:1)*(/%$/.test(f[3])?255:1);return[parseInt(s,10),parseInt(h,10),parseInt(v,10),f[4]?parseFloat(f[4]):1]}},a.Color.fromRgba=u.fromRgb,a.Color.fromHsl=function(c){return u.fromSource(u.sourceFromHsl(c))},a.Color.sourceFromHsl=function(c){var f=c.match(u.reHSLa);if(!!f){var s=(parseFloat(f[1])%360+360)%360/360,h=parseFloat(f[2])/(/%$/.test(f[2])?100:1),v=parseFloat(f[3])/(/%$/.test(f[3])?100:1),m,y,b;if(h===0)m=y=b=v;else{var _=v<=.5?v*(h+1):v+h-v*h,w=v*2-_;m=d(w,_,s+1/3),y=d(w,_,s),b=d(w,_,s-1/3)}return[Math.round(m*255),Math.round(y*255),Math.round(b*255),f[4]?parseFloat(f[4]):1]}},a.Color.fromHsla=u.fromHsl,a.Color.fromHex=function(c){return u.fromSource(u.sourceFromHex(c))},a.Color.sourceFromHex=function(c){if(c.match(u.reHex)){var f=c.slice(c.indexOf("#")+1),s=f.length===3||f.length===4,h=f.length===8||f.length===4,v=s?f.charAt(0)+f.charAt(0):f.substring(0,2),m=s?f.charAt(1)+f.charAt(1):f.substring(2,4),y=s?f.charAt(2)+f.charAt(2):f.substring(4,6),b=h?s?f.charAt(3)+f.charAt(3):f.substring(6,8):"FF";return[parseInt(v,16),parseInt(m,16),parseInt(y,16),parseFloat((parseInt(b,16)/255).toFixed(2))]}},a.Color.fromSource=function(c){var f=new u;return f.setSource(c),f}}(e),function(o){var a=o.fabric||(o.fabric={}),u=["e","se","s","sw","w","nw","n","ne","e"],d=["ns","nesw","ew","nwse"],c={},f="left",s="top",h="right",v="bottom",m="center",y={top:v,bottom:s,left:h,right:f,center:m},b=a.util.radiansToDegrees,_=Math.sign||function(W){return(W>0)-(W<0)||+W};function w(W,k){var X=W.angle+b(Math.atan2(k.y,k.x))+360;return Math.round(X%360/45)}function S(W,k){var X=k.transform.target,Y=X.canvas,$=a.util.object.clone(k);$.target=X,Y&&Y.fire("object:"+W,$),X.fire(W,k)}function x(W,k){var X=k.canvas,Y=X.uniScaleKey,$=W[Y];return X.uniformScaling&&!$||!X.uniformScaling&&$}function T(W){return W.originX===m&&W.originY===m}function E(W,k,X){var Y=W.lockScalingX,$=W.lockScalingY;return!!(Y&&$||!k&&(Y||$)&&X||Y&&k==="x"||$&&k==="y")}function I(W,k,X){var Y="not-allowed",$=x(W,X),B="";if(k.x!==0&&k.y===0?B="x":k.x===0&&k.y!==0&&(B="y"),E(X,B,$))return Y;var K=w(X,k);return u[K]+"-resize"}function D(W,k,X){var Y="not-allowed";if(k.x!==0&&X.lockSkewingY||k.y!==0&&X.lockSkewingX)return Y;var $=w(X,k)%4;return d[$]+"-resize"}function N(W,k,X){return W[X.canvas.altActionKey]?c.skewCursorStyleHandler(W,k,X):c.scaleCursorStyleHandler(W,k,X)}function L(W,k,X){var Y=W[X.canvas.altActionKey];if(k.x===0)return Y?"skewX":"scaleY";if(k.y===0)return Y?"skewY":"scaleX"}function F(W,k,X){return X.lockRotation?"not-allowed":k.cursorStyle}function O(W,k,X,Y){return{e:W,transform:k,pointer:{x:X,y:Y}}}function V(W){return function(k,X,Y,$){var B=X.target,K=B.getCenterPoint(),ce=B.translateToOriginPoint(K,X.originX,X.originY),ie=W(k,X,Y,$);return B.setPositionByOrigin(ce,X.originX,X.originY),ie}}function re(W,k){return function(X,Y,$,B){var K=k(X,Y,$,B);return K&&S(W,O(X,Y,$,B)),K}}function J(W,k,X,Y,$){var B=W.target,K=B.controls[W.corner],ce=B.canvas.getZoom(),ie=B.padding/ce,Z=B.toLocalPoint(new a.Point(Y,$),k,X);return Z.x>=ie&&(Z.x-=ie),Z.x<=-ie&&(Z.x+=ie),Z.y>=ie&&(Z.y-=ie),Z.y<=ie&&(Z.y+=ie),Z.x-=K.offsetX,Z.y-=K.offsetY,Z}function ue(W){return W.flipX!==W.flipY}function M(W,k,X,Y,$){if(W[k]!==0){var B=W._getTransformedDimensions()[Y],K=$/B*W[X];W.set(X,K)}}function U(W,k,X,Y){var $=k.target,B=$._getTransformedDimensions(0,$.skewY),K=J(k,k.originX,k.originY,X,Y),ce=Math.abs(K.x*2)-B.x,ie=$.skewX,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleX,B.y/$.scaleY)),k.originX===f&&k.originY===v&&(Z=-Z),k.originX===h&&k.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().y;$.set("skewX",Z),M($,"skewY","scaleY","y",ae)}return me}function R(W,k,X,Y){var $=k.target,B=$._getTransformedDimensions($.skewX,0),K=J(k,k.originX,k.originY,X,Y),ce=Math.abs(K.y*2)-B.y,ie=$.skewY,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleY,B.x/$.scaleX)),k.originX===f&&k.originY===v&&(Z=-Z),k.originX===h&&k.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().x;$.set("skewY",Z),M($,"skewX","scaleX","x",ae)}return me}function P(W,k,X,Y){var $=k.target,B=$.skewX,K,ce=k.originY;if($.lockSkewingX)return!1;if(B===0){var ie=J(k,m,m,X,Y);ie.x>0?K=f:K=h}else B>0&&(K=ce===s?f:h),B<0&&(K=ce===s?h:f),ue($)&&(K=K===f?h:f);k.originX=K;var Z=re("skewing",V(U));return Z(W,k,X,Y)}function Q(W,k,X,Y){var $=k.target,B=$.skewY,K,ce=k.originX;if($.lockSkewingY)return!1;if(B===0){var ie=J(k,m,m,X,Y);ie.y>0?K=s:K=v}else B>0&&(K=ce===f?s:v),B<0&&(K=ce===f?v:s),ue($)&&(K=K===s?v:s);k.originY=K;var Z=re("skewing",V(R));return Z(W,k,X,Y)}function j(W,k,X,Y){var $=k,B=$.target,K=B.translateToOriginPoint(B.getCenterPoint(),$.originX,$.originY);if(B.lockRotation)return!1;var ce=Math.atan2($.ey-K.y,$.ex-K.x),ie=Math.atan2(Y-K.y,X-K.x),Z=b(ie-ce+$.theta),me=!0;if(B.snapAngle>0){var ae=B.snapAngle,ge=B.snapThreshold||ae,we=Math.ceil(Z/ae)*ae,ke=Math.floor(Z/ae)*ae;Math.abs(Z-ke)<ge?Z=ke:Math.abs(Z-we)<ge&&(Z=we)}return Z<0&&(Z=360+Z),Z%=360,me=B.angle!==Z,B.angle=Z,me}function A(W,k,X,Y,$){$=$||{};var B=k.target,K=B.lockScalingX,ce=B.lockScalingY,ie=$.by,Z,me,ae,ge,we=x(W,B),ke=E(B,ie,we),Oe,Fe,_e=k.gestureScale;if(ke)return!1;if(_e)me=k.scaleX*_e,ae=k.scaleY*_e;else{if(Z=J(k,k.originX,k.originY,X,Y),Oe=ie!=="y"?_(Z.x):1,Fe=ie!=="x"?_(Z.y):1,k.signX||(k.signX=Oe),k.signY||(k.signY=Fe),B.lockScalingFlip&&(k.signX!==Oe||k.signY!==Fe))return!1;if(ge=B._getTransformedDimensions(),we&&!ie){var Se=Math.abs(Z.x)+Math.abs(Z.y),De=k.original,Me=Math.abs(ge.x*De.scaleX/B.scaleX)+Math.abs(ge.y*De.scaleY/B.scaleY),bt=Se/Me;me=De.scaleX*bt,ae=De.scaleY*bt}else me=Math.abs(Z.x*B.scaleX/ge.x),ae=Math.abs(Z.y*B.scaleY/ge.y);T(k)&&(me*=2,ae*=2),k.signX!==Oe&&ie!=="y"&&(k.originX=y[k.originX],me*=-1,k.signX=Oe),k.signY!==Fe&&ie!=="x"&&(k.originY=y[k.originY],ae*=-1,k.signY=Fe)}var _t=B.scaleX,ht=B.scaleY;return ie?(ie==="x"&&B.set("scaleX",me),ie==="y"&&B.set("scaleY",ae)):(!K&&B.set("scaleX",me),!ce&&B.set("scaleY",ae)),_t!==B.scaleX||ht!==B.scaleY}function q(W,k,X,Y){return A(W,k,X,Y)}function 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ce&&$.set("left",B),ie&&$.set("top",K),(ce||ie)&&S("moving",O(W,k,X,Y)),ce||ie}c.scaleCursorStyleHandler=I,c.skewCursorStyleHandler=D,c.scaleSkewCursorStyleHandler=N,c.rotationWithSnapping=re("rotating",V(j)),c.scalingEqually=re("scaling",V(q)),c.scalingX=re("scaling",V(G)),c.scalingY=re("scaling",V(te)),c.scalingYOrSkewingX=fe,c.scalingXOrSkewingY=pe,c.changeWidth=re("resizing",V(ve)),c.skewHandlerX=P,c.skewHandlerY=Q,c.dragHandler=H,c.scaleOrSkewActionName=L,c.rotationStyleHandler=F,c.fireEvent=S,c.wrapWithFixedAnchor=V,c.wrapWithFireEvent=re,c.getLocalPoint=J,a.controlsUtils=c}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.degreesToRadians,d=a.controlsUtils;function c(s,h,v,m,y){m=m||{};var b=this.sizeX||m.cornerSize||y.cornerSize,_=this.sizeY||m.cornerSize||y.cornerSize,w=typeof 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a=o.fabric||(o.fabric={});function u(d){for(var c in d)this[c]=d[c]}a.Control=u,a.Control.prototype={visible:!0,actionName:"scale",angle:0,x:0,y:0,offsetX:0,offsetY:0,sizeX:null,sizeY:null,touchSizeX:null,touchSizeY:null,cursorStyle:"crosshair",withConnection:!1,actionHandler:function(){},mouseDownHandler:function(){},mouseUpHandler:function(){},getActionHandler:function(){return this.actionHandler},getMouseDownHandler:function(){return this.mouseDownHandler},getMouseUpHandler:function(){return this.mouseUpHandler},cursorStyleHandler:function(d,c){return c.cursorStyle},getActionName:function(d,c){return c.actionName},getVisibility:function(d,c){var f=d._controlsVisibility;return f&&typeof f[c]<"u"?f[c]:this.visible},setVisibility:function(d){this.visible=d},positionHandler:function(d,c){var f=a.util.transformPoint({x:this.x*d.x+this.offsetX,y:this.y*d.y+this.offsetY},c);return f},calcCornerCoords:function(d,c,f,s,h){var v,m,y,b,_=h?this.touchSizeX:this.sizeX,w=h?this.touchSizeY:this.sizeY;if(_&&w&&_!==w){var S=Math.atan2(w,_),x=Math.sqrt(_*_+w*w)/2,T=S-a.util.degreesToRadians(d),E=Math.PI/2-S-a.util.degreesToRadians(d);v=x*a.util.cos(T),m=x*a.util.sin(T),y=x*a.util.cos(E),b=x*a.util.sin(E)}else{var I=_&&w?_:c;x=I*.7071067812;var T=a.util.degreesToRadians(45-d);v=y=x*a.util.cos(T),m=b=x*a.util.sin(T)}return{tl:{x:f-b,y:s-y},tr:{x:f+v,y:s-m},bl:{x:f-v,y:s+m},br:{x:f+b,y:s+y}}},render:function(d,c,f,s,h){switch(s=s||{},s.cornerStyle||h.cornerStyle){case"circle":a.controlsUtils.renderCircleControl.call(this,d,c,f,s,h);break;default:a.controlsUtils.renderSquareControl.call(this,d,c,f,s,h)}}}}(e),function(){function o(f,s){var h=f.getAttribute("style"),v=f.getAttribute("offset")||0,m,y,b,_;if(v=parseFloat(v)/(/%$/.test(v)?100:1),v=v<0?0:v>1?1:v,h){var w=h.split(/\s*;\s*/);for(w[w.length-1]===""&&w.pop(),_=w.length;_--;){var 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f[3]=s,this.setSource(f),this}},a.Color.reRGBa=/^rgba?\(\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*(?:\s*,\s*((?:\d*\.?\d+)?)\s*)?\)$/i,a.Color.reHSLa=/^hsla?\(\s*(\d{1,3})\s*,\s*(\d{1,3}\%)\s*,\s*(\d{1,3}\%)\s*(?:\s*,\s*(\d+(?:\.\d+)?)\s*)?\)$/i,a.Color.reHex=/^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i,a.Color.colorNameMap={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",darkgreen:"#006400",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#ADFF2F",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",lightgreen:"#90EE90",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",rebeccapurple:"#663399",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"};function d(c,f,s){return s<0&&(s+=1),s>1&&(s-=1),s<1/6?c+(f-c)*6*s:s<1/2?f:s<2/3?c+(f-c)*(2/3-s)*6:c}a.Color.fromRgb=function(c){return u.fromSource(u.sourceFromRgb(c))},a.Color.sourceFromRgb=function(c){var f=c.match(u.reRGBa);if(f){var s=parseInt(f[1],10)/(/%$/.test(f[1])?100:1)*(/%$/.test(f[1])?255:1),h=parseInt(f[2],10)/(/%$/.test(f[2])?100:1)*(/%$/.test(f[2])?255:1),v=parseInt(f[3],10)/(/%$/.test(f[3])?100:1)*(/%$/.test(f[3])?255:1);return[parseInt(s,10),parseInt(h,10),parseInt(v,10),f[4]?parseFloat(f[4]):1]}},a.Color.fromRgba=u.fromRgb,a.Color.fromHsl=function(c){return u.fromSource(u.sourceFromHsl(c))},a.Color.sourceFromHsl=function(c){var f=c.match(u.reHSLa);if(!!f){var s=(parseFloat(f[1])%360+360)%360/360,h=parseFloat(f[2])/(/%$/.test(f[2])?100:1),v=parseFloat(f[3])/(/%$/.test(f[3])?100:1),m,y,b;if(h===0)m=y=b=v;else{var _=v<=.5?v*(h+1):v+h-v*h,w=v*2-_;m=d(w,_,s+1/3),y=d(w,_,s),b=d(w,_,s-1/3)}return[Math.round(m*255),Math.round(y*255),Math.round(b*255),f[4]?parseFloat(f[4]):1]}},a.Color.fromHsla=u.fromHsl,a.Color.fromHex=function(c){return u.fromSource(u.sourceFromHex(c))},a.Color.sourceFromHex=function(c){if(c.match(u.reHex)){var f=c.slice(c.indexOf("#")+1),s=f.length===3||f.length===4,h=f.length===8||f.length===4,v=s?f.charAt(0)+f.charAt(0):f.substring(0,2),m=s?f.charAt(1)+f.charAt(1):f.substring(2,4),y=s?f.charAt(2)+f.charAt(2):f.substring(4,6),b=h?s?f.charAt(3)+f.charAt(3):f.substring(6,8):"FF";return[parseInt(v,16),parseInt(m,16),parseInt(y,16),parseFloat((parseInt(b,16)/255).toFixed(2))]}},a.Color.fromSource=function(c){var f=new u;return f.setSource(c),f}}(e),function(o){var a=o.fabric||(o.fabric={}),u=["e","se","s","sw","w","nw","n","ne","e"],d=["ns","nesw","ew","nwse"],c={},f="left",s="top",h="right",v="bottom",m="center",y={top:v,bottom:s,left:h,right:f,center:m},b=a.util.radiansToDegrees,_=Math.sign||function(W){return(W>0)-(W<0)||+W};function w(W,k){var X=W.angle+b(Math.atan2(k.y,k.x))+360;return Math.round(X%360/45)}function S(W,k){var X=k.transform.target,Y=X.canvas,$=a.util.object.clone(k);$.target=X,Y&&Y.fire("object:"+W,$),X.fire(W,k)}function x(W,k){var X=k.canvas,Y=X.uniScaleKey,$=W[Y];return X.uniformScaling&&!$||!X.uniformScaling&&$}function T(W){return W.originX===m&&W.originY===m}function E(W,k,X){var Y=W.lockScalingX,$=W.lockScalingY;return!!(Y&&$||!k&&(Y||$)&&X||Y&&k==="x"||$&&k==="y")}function I(W,k,X){var Y="not-allowed",$=x(W,X),B="";if(k.x!==0&&k.y===0?B="x":k.x===0&&k.y!==0&&(B="y"),E(X,B,$))return Y;var K=w(X,k);return u[K]+"-resize"}function D(W,k,X){var Y="not-allowed";if(k.x!==0&&X.lockSkewingY||k.y!==0&&X.lockSkewingX)return Y;var $=w(X,k)%4;return d[$]+"-resize"}function N(W,k,X){return W[X.canvas.altActionKey]?c.skewCursorStyleHandler(W,k,X):c.scaleCursorStyleHandler(W,k,X)}function L(W,k,X){var Y=W[X.canvas.altActionKey];if(k.x===0)return Y?"skewX":"scaleY";if(k.y===0)return Y?"skewY":"scaleX"}function F(W,k,X){return X.lockRotation?"not-allowed":k.cursorStyle}function O(W,k,X,Y){return{e:W,transform:k,pointer:{x:X,y:Y}}}function V(W){return function(k,X,Y,$){var B=X.target,K=B.getCenterPoint(),ce=B.translateToOriginPoint(K,X.originX,X.originY),ie=W(k,X,Y,$);return B.setPositionByOrigin(ce,X.originX,X.originY),ie}}function re(W,k){return function(X,Y,$,B){var K=k(X,Y,$,B);return K&&S(W,O(X,Y,$,B)),K}}function J(W,k,X,Y,$){var B=W.target,K=B.controls[W.corner],ce=B.canvas.getZoom(),ie=B.padding/ce,Z=B.toLocalPoint(new a.Point(Y,$),k,X);return Z.x>=ie&&(Z.x-=ie),Z.x<=-ie&&(Z.x+=ie),Z.y>=ie&&(Z.y-=ie),Z.y<=ie&&(Z.y+=ie),Z.x-=K.offsetX,Z.y-=K.offsetY,Z}function ue(W){return W.flipX!==W.flipY}function M(W,k,X,Y,$){if(W[k]!==0){var B=W._getTransformedDimensions()[Y],K=$/B*W[X];W.set(X,K)}}function U(W,k,X,Y){var $=k.target,B=$._getTransformedDimensions(0,$.skewY),K=J(k,k.originX,k.originY,X,Y),ce=Math.abs(K.x*2)-B.x,ie=$.skewX,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleX,B.y/$.scaleY)),k.originX===f&&k.originY===v&&(Z=-Z),k.originX===h&&k.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().y;$.set("skewX",Z),M($,"skewY","scaleY","y",ae)}return me}function R(W,k,X,Y){var $=k.target,B=$._getTransformedDimensions($.skewX,0),K=J(k,k.originX,k.originY,X,Y),ce=Math.abs(K.y*2)-B.y,ie=$.skewY,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleY,B.x/$.scaleX)),k.originX===f&&k.originY===v&&(Z=-Z),k.originX===h&&k.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().x;$.set("skewY",Z),M($,"skewX","scaleX","x",ae)}return me}function P(W,k,X,Y){var $=k.target,B=$.skewX,K,ce=k.originY;if($.lockSkewingX)return!1;if(B===0){var ie=J(k,m,m,X,Y);ie.x>0?K=f:K=h}else B>0&&(K=ce===s?f:h),B<0&&(K=ce===s?h:f),ue($)&&(K=K===f?h:f);k.originX=K;var Z=re("skewing",V(U));return Z(W,k,X,Y)}function Q(W,k,X,Y){var $=k.target,B=$.skewY,K,ce=k.originX;if($.lockSkewingY)return!1;if(B===0){var ie=J(k,m,m,X,Y);ie.y>0?K=s:K=v}else B>0&&(K=ce===f?s:v),B<0&&(K=ce===f?v:s),ue($)&&(K=K===s?v:s);k.originY=K;var Z=re("skewing",V(R));return Z(W,k,X,Y)}function j(W,k,X,Y){var $=k,B=$.target,K=B.translateToOriginPoint(B.getCenterPoint(),$.originX,$.originY);if(B.lockRotation)return!1;var ce=Math.atan2($.ey-K.y,$.ex-K.x),ie=Math.atan2(Y-K.y,X-K.x),Z=b(ie-ce+$.theta),me=!0;if(B.snapAngle>0){var ae=B.snapAngle,ge=B.snapThreshold||ae,we=Math.ceil(Z/ae)*ae,ke=Math.floor(Z/ae)*ae;Math.abs(Z-ke)<ge?Z=ke:Math.abs(Z-we)<ge&&(Z=we)}return Z<0&&(Z=360+Z),Z%=360,me=B.angle!==Z,B.angle=Z,me}function A(W,k,X,Y,$){$=$||{};var B=k.target,K=B.lockScalingX,ce=B.lockScalingY,ie=$.by,Z,me,ae,ge,we=x(W,B),ke=E(B,ie,we),Ae,Fe,_e=k.gestureScale;if(ke)return!1;if(_e)me=k.scaleX*_e,ae=k.scaleY*_e;else{if(Z=J(k,k.originX,k.originY,X,Y),Ae=ie!=="y"?_(Z.x):1,Fe=ie!=="x"?_(Z.y):1,k.signX||(k.signX=Ae),k.signY||(k.signY=Fe),B.lockScalingFlip&&(k.signX!==Ae||k.signY!==Fe))return!1;if(ge=B._getTransformedDimensions(),we&&!ie){var Se=Math.abs(Z.x)+Math.abs(Z.y),De=k.original,Me=Math.abs(ge.x*De.scaleX/B.scaleX)+Math.abs(ge.y*De.scaleY/B.scaleY),bt=Se/Me;me=De.scaleX*bt,ae=De.scaleY*bt}else me=Math.abs(Z.x*B.scaleX/ge.x),ae=Math.abs(Z.y*B.scaleY/ge.y);T(k)&&(me*=2,ae*=2),k.signX!==Ae&&ie!=="y"&&(k.originX=y[k.originX],me*=-1,k.signX=Ae),k.signY!==Fe&&ie!=="x"&&(k.originY=y[k.originY],ae*=-1,k.signY=Fe)}var _t=B.scaleX,ht=B.scaleY;return ie?(ie==="x"&&B.set("scaleX",me),ie==="y"&&B.set("scaleY",ae)):(!K&&B.set("scaleX",me),!ce&&B.set("scaleY",ae)),_t!==B.scaleX||ht!==B.scaleY}function q(W,k,X,Y){return A(W,k,X,Y)}function 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ce&&$.set("left",B),ie&&$.set("top",K),(ce||ie)&&S("moving",O(W,k,X,Y)),ce||ie}c.scaleCursorStyleHandler=I,c.skewCursorStyleHandler=D,c.scaleSkewCursorStyleHandler=N,c.rotationWithSnapping=re("rotating",V(j)),c.scalingEqually=re("scaling",V(q)),c.scalingX=re("scaling",V(G)),c.scalingY=re("scaling",V(te)),c.scalingYOrSkewingX=fe,c.scalingXOrSkewingY=pe,c.changeWidth=re("resizing",V(ve)),c.skewHandlerX=P,c.skewHandlerY=Q,c.dragHandler=H,c.scaleOrSkewActionName=L,c.rotationStyleHandler=F,c.fireEvent=S,c.wrapWithFixedAnchor=V,c.wrapWithFireEvent=re,c.getLocalPoint=J,a.controlsUtils=c}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.degreesToRadians,d=a.controlsUtils;function c(s,h,v,m,y){m=m||{};var b=this.sizeX||m.cornerSize||y.cornerSize,_=this.sizeY||m.cornerSize||y.cornerSize,w=typeof 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a=o.fabric||(o.fabric={});function u(d){for(var c in d)this[c]=d[c]}a.Control=u,a.Control.prototype={visible:!0,actionName:"scale",angle:0,x:0,y:0,offsetX:0,offsetY:0,sizeX:null,sizeY:null,touchSizeX:null,touchSizeY:null,cursorStyle:"crosshair",withConnection:!1,actionHandler:function(){},mouseDownHandler:function(){},mouseUpHandler:function(){},getActionHandler:function(){return this.actionHandler},getMouseDownHandler:function(){return this.mouseDownHandler},getMouseUpHandler:function(){return this.mouseUpHandler},cursorStyleHandler:function(d,c){return c.cursorStyle},getActionName:function(d,c){return c.actionName},getVisibility:function(d,c){var f=d._controlsVisibility;return f&&typeof f[c]<"u"?f[c]:this.visible},setVisibility:function(d){this.visible=d},positionHandler:function(d,c){var f=a.util.transformPoint({x:this.x*d.x+this.offsetX,y:this.y*d.y+this.offsetY},c);return f},calcCornerCoords:function(d,c,f,s,h){var v,m,y,b,_=h?this.touchSizeX:this.sizeX,w=h?this.touchSizeY:this.sizeY;if(_&&w&&_!==w){var S=Math.atan2(w,_),x=Math.sqrt(_*_+w*w)/2,T=S-a.util.degreesToRadians(d),E=Math.PI/2-S-a.util.degreesToRadians(d);v=x*a.util.cos(T),m=x*a.util.sin(T),y=x*a.util.cos(E),b=x*a.util.sin(E)}else{var I=_&&w?_:c;x=I*.7071067812;var T=a.util.degreesToRadians(45-d);v=y=x*a.util.cos(T),m=b=x*a.util.sin(T)}return{tl:{x:f-b,y:s-y},tr:{x:f+v,y:s-m},bl:{x:f-v,y:s+m},br:{x:f+b,y:s+y}}},render:function(d,c,f,s,h){switch(s=s||{},s.cornerStyle||h.cornerStyle){case"circle":a.controlsUtils.renderCircleControl.call(this,d,c,f,s,h);break;default:a.controlsUtils.renderSquareControl.call(this,d,c,f,s,h)}}}}(e),function(){function o(f,s){var h=f.getAttribute("style"),v=f.getAttribute("offset")||0,m,y,b,_;if(v=parseFloat(v)/(/%$/.test(v)?100:1),v=v<0?0:v>1?1:v,h){var w=h.split(/\s*;\s*/);for(w[w.length-1]===""&&w.pop(),_=w.length;_--;){var S=w[_].split(/\s*:\s*/),x=S[0].trim(),T=S[1].trim();x==="stop-color"?m=T:x==="stop-opacity"&&(b=T)}}return m||(m=f.getAttribute("stop-color")||"rgb(0,0,0)"),b||(b=f.getAttribute("stop-opacity")),m=new t.Color(m),y=m.getAlpha(),b=isNaN(parseFloat(b))?1:parseFloat(b),b*=y*s,{offset:v,color:m.toRgb(),opacity:b}}function a(f){return{x1:f.getAttribute("x1")||0,y1:f.getAttribute("y1")||0,x2:f.getAttribute("x2")||"100%",y2:f.getAttribute("y2")||0}}function u(f){return{x1:f.getAttribute("fx")||f.getAttribute("cx")||"50%",y1:f.getAttribute("fy")||f.getAttribute("cy")||"50%",r1:0,x2:f.getAttribute("cx")||"50%",y2:f.getAttribute("cy")||"50%",r2:f.getAttribute("r")||"50%"}}var d=t.util.object.clone;t.Gradient=t.util.createClass({offsetX:0,offsetY:0,gradientTransform:null,gradientUnits:"pixels",type:"linear",initialize:function(f){f||(f={}),f.coords||(f.coords={});var s,h=this;Object.keys(f).forEach(function(v){h[v]=f[v]}),this.id?this.id+="_"+t.Object.__uid++:this.id=t.Object.__uid++,s={x1:f.coords.x1||0,y1:f.coords.y1||0,x2:f.coords.x2||0,y2:f.coords.y2||0},this.type==="radial"&&(s.r1=f.coords.r1||0,s.r2=f.coords.r2||0),this.coords=s,this.colorStops=f.colorStops.slice()},addColorStop:function(f){for(var s in f){var h=new t.Color(f[s]);this.colorStops.push({offset:parseFloat(s),color:h.toRgb(),opacity:h.getAlpha()})}return this},toObject:function(f){var s={type:this.type,coords:this.coords,colorStops:this.colorStops,offsetX:this.offsetX,offsetY:this.offsetY,gradientUnits:this.gradientUnits,gradientTransform:this.gradientTransform?this.gradientTransform.concat():this.gradientTransform};return t.util.populateWithProperties(this,s,f),s},toSVG:function(f,y){var h=d(this.coords,!0),v,m,y=y||{},b,_,w=d(this.colorStops,!0),S=h.r1>h.r2,x=this.gradientTransform?this.gradientTransform.concat():t.iMatrix.concat(),T=-this.offsetX,E=-this.offsetY,I=!!y.additionalTransform,D=this.gradientUnits==="pixels"?"userSpaceOnUse":"objectBoundingBox";if(w.sort(function(V,re){return V.offset-re.offset}),D==="objectBoundingBox"?(T/=f.width,E/=f.height):(T+=f.width/2,E+=f.height/2),f.type==="path"&&this.gradientUnits!=="percentage"&&(T-=f.pathOffset.x,E-=f.pathOffset.y),x[4]-=T,x[5]-=E,_='id="SVGID_'+this.id+'" gradientUnits="'+D+'"',_+=' gradientTransform="'+(I?y.additionalTransform+" ":"")+t.util.matrixToSVG(x)+'" ',this.type==="linear"?b=["<linearGradient ",_,' x1="',h.x1,'" y1="',h.y1,'" x2="',h.x2,'" y2="',h.y2,`">
 `]:this.type==="radial"&&(b=["<radialGradient ",_,' cx="',S?h.x1:h.x2,'" cy="',S?h.y1:h.y2,'" r="',S?h.r1:h.r2,'" fx="',S?h.x2:h.x1,'" fy="',S?h.y2:h.y1,`">
 `]),this.type==="radial"){if(S)for(w=w.concat(),w.reverse(),v=0,m=w.length;v<m;v++)w[v].offset=1-w[v].offset;var N=Math.min(h.r1,h.r2);if(N>0){var L=Math.max(h.r1,h.r2),F=N/L;for(v=0,m=w.length;v<m;v++)w[v].offset+=F*(1-w[v].offset)}}for(v=0,m=w.length;v<m;v++){var O=w[v];b.push("<stop ",'offset="',O.offset*100+"%",'" style="stop-color:',O.color,typeof O.opacity<"u"?";stop-opacity: "+O.opacity:";",`"/>
 `)}return b.push(this.type==="linear"?`</linearGradient>
@@ -506,9 +506,9 @@ gl_FragColor.rgb *= color.a;
 `?(s++,f[s]=0):f[s]++;f[0]>0&&(this.insertCharStyleObject(c.lineIndex,c.charIndex,f[0],d),d=d&&d.slice(f[0]+1)),s&&this.insertNewlineStyleObject(c.lineIndex,c.charIndex+f[0],s);for(var h=1;h<s;h++)f[h]>0?this.insertCharStyleObject(c.lineIndex+h,0,f[h],d):d&&this.styles[c.lineIndex+h]&&d[0]&&(this.styles[c.lineIndex+h][0]=d[0]),d=d&&d.slice(f[h]+1);f[h]>0&&this.insertCharStyleObject(c.lineIndex+h,0,f[h],d)},setSelectionStartEndWithShift:function(a,u,d){d<=a?(u===a?this._selectionDirection="left":this._selectionDirection==="right"&&(this._selectionDirection="left",this.selectionEnd=a),this.selectionStart=d):d>a&&d<u?this._selectionDirection==="right"?this.selectionEnd=d:this.selectionStart=d:(u===a?this._selectionDirection="right":this._selectionDirection==="left"&&(this._selectionDirection="right",this.selectionStart=u),this.selectionEnd=d)},setSelectionInBoundaries:function(){var a=this.text.length;this.selectionStart>a?this.selectionStart=a:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>a?this.selectionEnd=a:this.selectionEnd<0&&(this.selectionEnd=0)}})}(),t.util.object.extend(t.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(o){if(!!this.canvas){this.__newClickTime=+new Date;var a=o.pointer;this.isTripleClick(a)&&(this.fire("tripleclick",o),this._stopEvent(o.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=a,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(o){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===o.x&&this.__lastPointer.y===o.y},_stopEvent:function(o){o.preventDefault&&o.preventDefault(),o.stopPropagation&&o.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(o){!this.isEditing||this.selectWord(this.getSelectionStartFromPointer(o.e))},tripleClickHandler:function(o){!this.isEditing||this.selectLine(this.getSelectionStartFromPointer(o.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(o.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(o){if(this.__isMousedown=!1,!(!this.editable||this.group||o.transform&&o.transform.actionPerformed||o.e.button&&o.e.button!==1)){if(this.canvas){var a=this.canvas._activeObject;if(a&&a!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(o.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(o){var a=this.getSelectionStartFromPointer(o),u=this.selectionStart,d=this.selectionEnd;o.shiftKey?this.setSelectionStartEndWithShift(u,d,a):(this.selectionStart=a,this.selectionEnd=a),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(o){for(var a=this.getLocalPointer(o),u=0,d=0,c=0,f=0,s=0,h,v,m=0,y=this._textLines.length;m<y&&c<=a.y;m++)c+=this.getHeightOfLine(m)*this.scaleY,s=m,m>0&&(f+=this._textLines[m-1].length+this.missingNewlineOffset(m-1));h=this._getLineLeftOffset(s),d=h*this.scaleX,v=this._textLines[s],this.direction==="rtl"&&(a.x=this.width*this.scaleX-a.x+d);for(var b=0,_=v.length;b<_&&(u=d,d+=this.__charBounds[s][b].kernedWidth*this.scaleX,d<=a.x);b++)f++;return this._getNewSelectionStartFromOffset(a,u,d,f,_)},_getNewSelectionStartFromOffset:function(o,a,u,d,c){var f=o.x-a,s=u-o.x,h=s>f||s<0?0:1,v=d+h;return this.flipX&&(v=c-v),v>this._text.length&&(v=this._text.length),v}}),t.util.object.extend(t.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=t.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var o=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+o.top+"; left: "+o.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; padding\uFF70top: "+o.fontSize+";",this.hiddenTextareaContainer?this.hiddenTextareaContainer.appendChild(this.hiddenTextarea):t.document.body.appendChild(this.hiddenTextarea),t.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),t.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),t.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),t.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(t.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},keysMapRtl:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorLeft",36:"moveCursorRight",37:"moveCursorRight",38:"moveCursorUp",39:"moveCursorLeft",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(o){if(!!this.isEditing){var a=this.direction==="rtl"?this.keysMapRtl:this.keysMap;if(o.keyCode in a)this[a[o.keyCode]](o);else if(o.keyCode in this.ctrlKeysMapDown&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapDown[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),o.keyCode>=33&&o.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(o){if(!this.isEditing||this._copyDone||this.inCompositionMode){this._copyDone=!1;return}if(o.keyCode in this.ctrlKeysMapUp&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapUp[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),this.canvas&&this.canvas.requestRenderAll()},onInput:function(o){var a=this.fromPaste;if(this.fromPaste=!1,o&&o.stopPropagation(),!!this.isEditing){var u=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,d=this._text.length,c=u.length,f,s,h=c-d,v=this.selectionStart,m=this.selectionEnd,y=v!==m,b,_,w;if(this.hiddenTextarea.value===""){this.styles={},this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll());return}var S=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),x=v>S.selectionStart;y?(f=this._text.slice(v,m),h+=m-v):c<d&&(x?f=this._text.slice(m+h,m):f=this._text.slice(v,v-h)),s=u.slice(S.selectionEnd-h,S.selectionEnd),f&&f.length&&(s.length&&(b=this.getSelectionStyles(v,v+1,!1),b=s.map(function(){return b[0]})),y?(_=v,w=m):x?(_=m-f.length,w=m):(_=m,w=m+f.length),this.removeStyleFromTo(_,w)),s.length&&(a&&s.join("")===t.copiedText&&!t.disableStyleCopyPaste&&(b=t.copiedTextStyle),this.insertNewStyleBlock(s,v,b)),this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll())}},onCompositionStart:function(){this.inCompositionMode=!0},onCompositionEnd:function(){this.inCompositionMode=!1},onCompositionUpdate:function(o){this.compositionStart=o.target.selectionStart,this.compositionEnd=o.target.selectionEnd,this.updateTextareaPosition()},copy:function(){this.selectionStart!==this.selectionEnd&&(t.copiedText=this.getSelectedText(),t.disableStyleCopyPaste?t.copiedTextStyle=null:t.copiedTextStyle=this.getSelectionStyles(this.selectionStart,this.selectionEnd,!0),this._copyDone=!0)},paste:function(){this.fromPaste=!0},_getClipboardData:function(o){return o&&o.clipboardData||t.window.clipboardData},_getWidthBeforeCursor:function(o,a){var u=this._getLineLeftOffset(o),d;return a>0&&(d=this.__charBounds[o][a-1],u+=d.left+d.width),u},getDownCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===this._textLines.length-1||o.metaKey||o.keyCode===34)return this._text.length-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c+1,s),v=this._textLines[c].slice(f);return v.length+h+1+this.missingNewlineOffset(c)},_getSelectionForOffset:function(o,a){return o.shiftKey&&this.selectionStart!==this.selectionEnd&&a?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===0||o.metaKey||o.keyCode===33)return-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c-1,s),v=this._textLines[c].slice(0,f),m=this.missingNewlineOffset(c-1);return-this._textLines[c-1].length+h-v.length+(1-m)},_getIndexOnLine:function(o,a){for(var u=this._textLines[o],d=this._getLineLeftOffset(o),c=d,f=0,s,h,v=0,m=u.length;v<m;v++)if(s=this.__charBounds[o][v].width,c+=s,c>a){h=!0;var y=c-s,b=c,_=Math.abs(y-a),w=Math.abs(b-a);f=w<_?v:v-1;break}return h||(f=u.length-1),f},moveCursorDown:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorUpOrDown("Down",o)},moveCursorUp:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorUpOrDown("Up",o)},_moveCursorUpOrDown:function(o,a){var u="get"+o+"CursorOffset",d=this[u](a,this._selectionDirection==="right");a.shiftKey?this.moveCursorWithShift(d):this.moveCursorWithoutShift(d),d!==0&&(this.setSelectionInBoundaries(),this.abortCursorAnimation(),this._currentCursorOpacity=1,this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorWithShift:function(o){var a=this._selectionDirection==="left"?this.selectionStart+o:this.selectionEnd+o;return this.setSelectionStartEndWithShift(this.selectionStart,this.selectionEnd,a),o!==0},moveCursorWithoutShift:function(o){return o<0?(this.selectionStart+=o,this.selectionEnd=this.selectionStart):(this.selectionEnd+=o,this.selectionStart=this.selectionEnd),o!==0},moveCursorLeft:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorLeftOrRight("Left",o)},_move:function(o,a,u){var d;if(o.altKey)d=this["findWordBoundary"+u](this[a]);else if(o.metaKey||o.keyCode===35||o.keyCode===36)d=this["findLineBoundary"+u](this[a]);else return this[a]+=u==="Left"?-1:1,!0;if(typeof d<"u"&&this[a]!==d)return this[a]=d,!0},_moveLeft:function(o,a){return this._move(o,a,"Left")},_moveRight:function(o,a){return this._move(o,a,"Right")},moveCursorLeftWithoutShift:function(o){var a=!0;return this._selectionDirection="left",this.selectionEnd===this.selectionStart&&this.selectionStart!==0&&(a=this._moveLeft(o,"selectionStart")),this.selectionEnd=this.selectionStart,a},moveCursorLeftWithShift:function(o){if(this._selectionDirection==="right"&&this.selectionStart!==this.selectionEnd)return this._moveLeft(o,"selectionEnd");if(this.selectionStart!==0)return this._selectionDirection="left",this._moveLeft(o,"selectionStart")},moveCursorRight:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorLeftOrRight("Right",o)},_moveCursorLeftOrRight:function(o,a){var u="moveCursor"+o+"With";this._currentCursorOpacity=1,a.shiftKey?u+="Shift":u+="outShift",this[u](a)&&(this.abortCursorAnimation(),this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorRightWithShift:function(o){if(this._selectionDirection==="left"&&this.selectionStart!==this.selectionEnd)return this._moveRight(o,"selectionStart");if(this.selectionEnd!==this._text.length)return this._selectionDirection="right",this._moveRight(o,"selectionEnd")},moveCursorRightWithoutShift:function(o){var a=!0;return this._selectionDirection="right",this.selectionStart===this.selectionEnd?(a=this._moveRight(o,"selectionStart"),this.selectionEnd=this.selectionStart):this.selectionStart=this.selectionEnd,a},removeChars:function(o,a){typeof a>"u"&&(a=o+1),this.removeStyleFromTo(o,a),this._text.splice(o,a-o),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()},insertChars:function(o,a,u,d){typeof d>"u"&&(d=u),d>u&&this.removeStyleFromTo(u,d);var c=t.util.string.graphemeSplit(o);this.insertNewStyleBlock(c,u,a),this._text=[].concat(this._text.slice(0,u),c,this._text.slice(d)),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()}}),function(){var o=t.util.toFixed,a=/  +/g;t.util.object.extend(t.Text.prototype,{_toSVG:function(){var u=this._getSVGLeftTopOffsets(),d=this._getSVGTextAndBg(u.textTop,u.textLeft);return this._wrapSVGTextAndBg(d)},toSVG:function(u){return this._createBaseSVGMarkup(this._toSVG(),{reviver:u,noStyle:!0,withShadow:!0})},_getSVGLeftTopOffsets:function(){return{textLeft:-this.width/2,textTop:-this.height/2,lineTop:this.getHeightOfLine(0)}},_wrapSVGTextAndBg:function(u){var d=!0,c=this.getSvgTextDecoration(this);return[u.textBgRects.join(""),'		<text xml:space="preserve" ',this.fontFamily?'font-family="'+this.fontFamily.replace(/"/g,"'")+'" ':"",this.fontSize?'font-size="'+this.fontSize+'" ':"",this.fontStyle?'font-style="'+this.fontStyle+'" ':"",this.fontWeight?'font-weight="'+this.fontWeight+'" ':"",c?'text-decoration="'+c+'" ':"",'style="',this.getSvgStyles(d),'"',this.addPaintOrder()," >",u.textSpans.join(""),`</text>
 `]},_getSVGTextAndBg:function(u,d){var c=[],f=[],s=u,h;this._setSVGBg(f);for(var v=0,m=this._textLines.length;v<m;v++)h=this._getLineLeftOffset(v),(this.textBackgroundColor||this.styleHas("textBackgroundColor",v))&&this._setSVGTextLineBg(f,v,d+h,s),this._setSVGTextLineText(c,v,d+h,s),s+=this.getHeightOfLine(v);return{textSpans:c,textBgRects:f}},_createTextCharSpan:function(u,d,c,f){var s=u!==u.trim()||u.match(a),h=this.getSvgSpanStyles(d,s),v=h?'style="'+h+'"':"",m=d.deltaY,y="",b=t.Object.NUM_FRACTION_DIGITS;return m&&(y=' dy="'+o(m,b)+'" '),['<tspan x="',o(c,b),'" y="',o(f,b),'" ',y,v,">",t.util.string.escapeXml(u),"</tspan>"].join("")},_setSVGTextLineText:function(u,d,c,f){var s=this.getHeightOfLine(d),h=this.textAlign.indexOf("justify")!==-1,v,m,y="",b,_,w=0,S=this._textLines[d],x;f+=s*(1-this._fontSizeFraction)/this.lineHeight;for(var T=0,E=S.length-1;T<=E;T++)x=T===E||this.charSpacing,y+=S[T],b=this.__charBounds[d][T],w===0?(c+=b.kernedWidth-b.width,w+=b.width):w+=b.kernedWidth,h&&!x&&this._reSpaceAndTab.test(S[T])&&(x=!0),x||(v=v||this.getCompleteStyleDeclaration(d,T),m=this.getCompleteStyleDeclaration(d,T+1),x=t.util.hasStyleChanged(v,m,!0)),x&&(_=this._getStyleDeclaration(d,T)||{},u.push(this._createTextCharSpan(y,_,c,f)),y="",v=m,c+=w,w=0)},_pushTextBgRect:function(u,d,c,f,s,h){var v=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(d),' x="',o(c,v),'" y="',o(f,v),'" width="',o(s,v),'" height="',o(h,v),`"></rect>
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c=this._styleMap[u];u=c.line,d=c.offset+d,delete this.styles[u][d]},_getLineStyle:function(u){var d=this._styleMap[u];return!!this.styles[d.line]},_setLineStyle:function(u){var d=this._styleMap[u];this.styles[d.line]={}},_wrapText:function(u,d){var c=[],f;for(this.isWrapping=!0,f=0;f<u.length;f++)c=c.concat(this._wrapLine(u[f],f,d));return this.isWrapping=!1,c},_measureWord:function(u,d,c){var f=0,s,h=!0;c=c||0;for(var v=0,m=u.length;v<m;v++){var y=this._getGraphemeBox(u[v],d,v+c,s,h);f+=y.kernedWidth,s=u[v]}return f},_wrapLine:function(u,d,c,D){var s=0,h=this.splitByGrapheme,v=[],m=[],y=h?a.util.string.graphemeSplit(u):u.split(this._wordJoiners),b="",_=0,w=h?"":" ",S=0,x=0,T=0,E=!0,I=this._getWidthOfCharSpacing(),D=D||0;y.length===0&&y.push([]),c-=D;for(var N=0;N<y.length;N++)b=h?y[N]:a.util.string.graphemeSplit(y[N]),S=this._measureWord(b,d,_),_+=b.length,s+=x+S-I,s>c&&!E?(v.push(m),m=[],s=S,E=!0):s+=I,!E&&!h&&m.push(w),m=m.concat(b),x=h?0:this._measureWord([w],d,_),_++,E=!1,S>T&&(T=S);return N&&v.push(m),T+D>this.dynamicMinWidth&&(this.dynamicMinWidth=T-I+D),v},isEndOfWrapping:function(u){return!this._styleMap[u+1]||this._styleMap[u+1].line!==this._styleMap[u].line},missingNewlineOffset:function(u){return this.splitByGrapheme?this.isEndOfWrapping(u)?1:0:1},_splitTextIntoLines:function(u){for(var d=a.Text.prototype._splitTextIntoLines.call(this,u),c=this._wrapText(d.lines,this.width),f=new Array(c.length),s=0;s<c.length;s++)f[s]=c[s].join("");return d.lines=f,d.graphemeLines=c,d},getMinWidth:function(){return Math.max(this.minWidth,this.dynamicMinWidth)},_removeExtraneousStyles:function(){var u={};for(var d in this._styleMap)this._textLines[d]&&(u[this._styleMap[d].line]=1);for(var d in this.styles)u[d]||delete 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version"},qo={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:UW,_tr_stored_block:Ah,_tr_flush_block:HW,_tr_tally:$i,_tr_align:WW}=FW,{Z_NO_FLUSH:Bi,Z_PARTIAL_FLUSH:YW,Z_FULL_FLUSH:XW,Z_FINISH:Gn,Z_BLOCK:ny,Z_OK:rn,Z_STREAM_END:ry,Z_STREAM_ERROR:Pr,Z_DATA_ERROR:KW,Z_BUF_ERROR:nd,Z_DEFAULT_COMPRESSION:GW,Z_FILTERED:qW,Z_HUFFMAN_ONLY:_u,Z_RLE:ZW,Z_FIXED:JW,Z_DEFAULT_STRATEGY:QW,Z_UNKNOWN:eY,Z_DEFLATED:lf}=qo,tY=9,nY=15,rY=8,iY=29,aY=256,Ph=aY+1+iY,oY=30,sY=19,lY=2*Ph+1,uY=15,lt=3,Mi=258,Ir=Mi+lt+1,cY=32,$o=42,wm=57,Ih=69,Mh=73,Lh=91,Rh=103,wa=113,ms=666,wn=1,Zo=2,Ba=3,Jo=4,fY=3,Ca=(e,t)=>(e.msg=$a[t],t),iy=e=>e*2-(e>4?9:0),Ai=e=>{let t=e.length;for(;--t>=0;)e[t]=0},dY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let hY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,Fi=hY;const $n=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},jn=(e,t)=>{HW(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,$n(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},us=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Dh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=yl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Zt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},j2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Ir?e.strstart-(e.w_size-Ir):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Mi;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Mi-(f-r),r=f-Mi,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Bo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Ir)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),dY(e),r+=t),e.strm.avail_in===0)break;if(n=Dh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=Fi(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=Fi(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Ir&&e.strm.avail_in!==0)},V2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==Gn||t===Bi||r!==i+e.strm.avail_in)))break;g=t===Gn&&r===i+e.strm.avail_in?1:0,Ah(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,$n(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Dh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?Jo:t!==Bi&&t!==Gn&&e.strm.avail_in===0&&e.strstart===e.block_start?Zo:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Dh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===Gn)&&t!==Bi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===Gn&&e.strm.avail_in===0&&r===i?1:0,Ah(e,e.block_start,r,g),e.block_start+=r,$n(e.strm)),g?Ba:wn)},rd=(e,t)=>{let n,r;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n)),e.match_length>=lt)if(r=$i(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+1]);else r=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},ao=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Bi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n),e.match_length<=5&&(e.strategy===qW||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=$i(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=Fi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(jn(e,!1),e.strm.avail_out===0))return wn}else if(e.match_available){if(r=$i(e,0,e.window[e.strstart-1]),r&&jn(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return wn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=$i(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},pY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Mi){if(Bo(e),e.lookahead<=Mi&&t===Bi)return wn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Mi;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Mi-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=$i(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},mY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Bo(e),e.lookahead===0)){if(t===Bi)return wn;break}if(e.match_length=0,n=$i(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===Gn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo};function wr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const gs=[new wr(0,0,0,0,V2),new wr(4,4,8,4,rd),new wr(4,5,16,8,rd),new wr(4,6,32,32,rd),new wr(4,4,16,16,ao),new wr(8,16,32,32,ao),new wr(8,16,128,128,ao),new wr(8,32,128,256,ao),new wr(32,128,258,1024,ao),new wr(32,258,258,4096,ao)],gY=e=>{e.window_size=2*e.w_size,Ai(e.head),e.max_lazy_match=gs[e.level].max_lazy,e.good_match=gs[e.level].good_length,e.nice_match=gs[e.level].nice_length,e.max_chain_length=gs[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function vY(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=lf,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(lY*2),this.dyn_dtree=new Uint16Array((2*oY+1)*2),this.bl_tree=new Uint16Array((2*sY+1)*2),Ai(this.dyn_ltree),Ai(this.dyn_dtree),Ai(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(uY+1),this.heap=new Uint16Array(2*Ph+1),Ai(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(2*Ph+1),Ai(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ul=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.status!==$o&&t.status!==wm&&t.status!==Ih&&t.status!==Mh&&t.status!==Lh&&t.status!==Rh&&t.status!==wa&&t.status!==ms?1:0},U2=e=>{if(Ul(e))return Ca(e,Pr);e.total_in=e.total_out=0,e.data_type=eY;const t=e.state;return t.pending=0,t.pending_out=0,t.wrap<0&&(t.wrap=-t.wrap),t.status=t.wrap===2?wm:t.wrap?$o:wa,e.adler=t.wrap===2?0:1,t.last_flush=-2,UW(t),rn},H2=e=>{const t=U2(e);return t===rn&&gY(e.state),t},yY=(e,t)=>Ul(e)||e.state.wrap!==2?Pr:(e.state.gzhead=t,rn),W2=(e,t,n,r,i,l)=>{if(!e)return Pr;let g=1;if(t===GW&&(t=6),r<0?(g=0,r=-r):r>15&&(g=2,r-=16),i<1||i>tY||n!==lf||r<8||r>15||t<0||t>9||l<0||l>JW||r===8&&g!==1)return Ca(e,Pr);r===8&&(r=9);const o=new vY;return e.state=o,o.strm=e,o.status=$o,o.wrap=g,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=i+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+lt-1)/lt),o.window=new Uint8Array(o.w_size*2),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<i+6,o.pending_buf_size=o.lit_bufsize*4,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=(o.lit_bufsize-1)*3,o.level=t,o.strategy=l,o.method=n,H2(e)},bY=(e,t)=>W2(e,t,lf,nY,rY,QW),_Y=(e,t)=>{if(Ul(e)||t>ny||t<0)return e?Ca(e,Pr):Pr;const n=e.state;if(!e.output||e.avail_in!==0&&!e.input||n.status===ms&&t!==Gn)return Ca(e,e.avail_out===0?nd:Pr);const r=n.last_flush;if(n.last_flush=t,n.pending!==0){if($n(e),e.avail_out===0)return n.last_flush=-1,rn}else if(e.avail_in===0&&iy(t)<=iy(r)&&t!==Gn)return Ca(e,nd);if(n.status===ms&&e.avail_in!==0)return 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D[l++]=f[T++];while(--w);T=l-x,E=D}}}else if(T+=c-w,w<S){S-=w;do D[l++]=f[T++];while(--w);T=l-x,E=D}for(;S>2;)D[l++]=E[T++],D[l++]=E[T++],D[l++]=E[T++],S-=3;S&&(D[l++]=E[T++],S>1&&(D[l++]=E[T++]))}else{T=l-x;do D[l++]=D[T++],D[l++]=D[T++],D[l++]=D[T++],S-=3;while(S>2);S&&(D[l++]=D[T++],S>1&&(D[l++]=D[T++]))}}else if((w&64)===0){_=m[(_&65535)+(s&(1<<w)-1)];continue n}else{t.msg="invalid distance code",N.mode=wu;break e}break}}else if((w&64)===0){_=v[(_&65535)+(s&(1<<w)-1)];continue t}else if(w&32){N.mode=nX;break e}else{t.msg="invalid literal/length code",N.mode=wu;break e}break}}while(r<i&&l<o);S=h>>3,r-=S,h-=S<<3,s&=(1<<h)-1,t.next_in=r,t.next_out=l,t.avail_in=r<i?5+(i-r):5-(r-i),t.avail_out=l<o?257+(o-l):257-(l-o),N.hold=s,N.bits=h};const oo=15,ay=852,oy=592,sy=0,id=1,ly=2,iX=new Uint16Array([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0]),aX=new Uint8Array([16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78]),oX=new Uint16Array([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0]),sX=new Uint8Array([16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64]),lX=(e,t,n,r,i,l,g,o)=>{const a=o.bits;let u=0,d=0,c=0,f=0,s=0,h=0,v=0,m=0,y=0,b=0,_,w,S,x,T,E=null,I;const D=new Uint16Array(oo+1),N=new Uint16Array(oo+1);let L=null,F,O,V;for(u=0;u<=oo;u++)D[u]=0;for(d=0;d<r;d++)D[t[n+d]]++;for(s=a,f=oo;f>=1&&D[f]===0;f--);if(s>f&&(s=f),f===0)return i[l++]=1<<24|64<<16|0,i[l++]=1<<24|64<<16|0,o.bits=1,0;for(c=1;c<f&&D[c]===0;c++);for(s<c&&(s=c),m=1,u=1;u<=oo;u++)if(m<<=1,m-=D[u],m<0)return-1;if(m>0&&(e===sy||f!==1))return-1;for(N[1]=0,u=1;u<oo;u++)N[u+1]=N[u]+D[u];for(d=0;d<r;d++)t[n+d]!==0&&(g[N[t[n+d]]++]=d);if(e===sy?(E=L=g,I=20):e===id?(E=iX,L=aX,I=257):(E=oX,L=sX,I=0),b=0,d=0,u=c,T=l,h=s,v=0,S=-1,y=1<<s,x=y-1,e===id&&y>ay||e===ly&&y>oy)return 1;for(;;){F=u-v,g[d]+1<I?(O=0,V=g[d]):g[d]>=I?(O=L[g[d]-I],V=E[g[d]-I]):(O=32+64,V=0),_=1<<u-v,w=1<<h,c=w;do w-=_,i[T+(b>>v)+w]=F<<24|O<<16|V|0;while(w!==0);for(_=1<<u-1;b&_;)_>>=1;if(_!==0?(b&=_-1,b+=_):b=0,d++,--D[u]===0){if(u===f)break;u=t[n+g[d]]}if(u>s&&(b&x)!==S){for(v===0&&(v=s),T+=c,h=u-v,m=1<<h;h+v<f&&(m-=D[h+v],!(m<=0));)h++,m<<=1;if(y+=1<<h,e===id&&y>ay||e===ly&&y>oy)return 1;S=b&x,i[S]=s<<24|h<<16|T-l|0}}return b!==0&&(i[T+b]=u-v<<24|64<<16|0),o.bits=s,0};var $s=lX;const uX=0,G2=1,q2=2,{Z_FINISH:uy,Z_BLOCK:cX,Z_TREES:Cu,Z_OK:Fa,Z_STREAM_END:fX,Z_NEED_DICT:dX,Z_STREAM_ERROR:tr,Z_DATA_ERROR:Z2,Z_MEM_ERROR:J2,Z_BUF_ERROR:hX,Z_DEFLATED:cy}=qo,cf=16180,fy=16181,dy=16182,hy=16183,py=16184,my=16185,gy=16186,vy=16187,yy=16188,by=16189,Oc=16190,Ur=16191,ad=16192,_y=16193,od=16194,wy=16195,Cy=16196,Sy=16197,xy=16198,Su=16199,xu=16200,Ty=16201,ky=16202,Ey=16203,Oy=16204,Ay=16205,sd=16206,Py=16207,Iy=16208,Bt=16209,Q2=16210,eC=16211,pX=852,mX=592,gX=15,vX=gX,My=e=>(e>>>24&255)+(e>>>8&65280)+((e&65280)<<8)+((e&255)<<24);function yX(){this.strm=null,this.mode=0,this.last=!1,this.wrap=0,this.havedict=!1,this.flags=0,this.dmax=0,this.check=0,this.total=0,this.head=null,this.wbits=0,this.wsize=0,this.whave=0,this.wnext=0,this.window=null,this.hold=0,this.bits=0,this.length=0,this.offset=0,this.extra=0,this.lencode=null,this.distcode=null,this.lenbits=0,this.distbits=0,this.ncode=0,this.nlen=0,this.ndist=0,this.have=0,this.next=null,this.lens=new Uint16Array(320),this.work=new Uint16Array(288),this.lendyn=null,this.distdyn=null,this.sane=0,this.back=0,this.was=0}const Ga=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.mode<cf||t.mode>eC?1:0},tC=e=>{if(Ga(e))return tr;const t=e.state;return e.total_in=e.total_out=t.total=0,e.msg="",t.wrap&&(e.adler=t.wrap&1),t.mode=cf,t.last=0,t.havedict=0,t.flags=-1,t.dmax=32768,t.head=null,t.hold=0,t.bits=0,t.lencode=t.lendyn=new Int32Array(pX),t.distcode=t.distdyn=new Int32Array(mX),t.sane=1,t.back=-1,Fa},nC=e=>{if(Ga(e))return tr;const t=e.state;return t.wsize=0,t.whave=0,t.wnext=0,tC(e)},rC=(e,t)=>{let n;if(Ga(e))return tr;const r=e.state;return t<0?(n=0,t=-t):(n=(t>>4)+5,t<48&&(t&=15)),t&&(t<8||t>15)?tr:(r.window!==null&&r.wbits!==t&&(r.window=null),r.wrap=n,r.wbits=t,nC(e))},iC=(e,t)=>{if(!e)return tr;const n=new yX;e.state=n,n.strm=e,n.window=null,n.mode=cf;const r=rC(e,t);return r!==Fa&&(e.state=null),r},bX=e=>iC(e,vX);let Ly=!0,ld,ud;const _X=e=>{if(Ly){ld=new Int32Array(512),ud=new Int32Array(32);let t=0;for(;t<144;)e.lens[t++]=8;for(;t<256;)e.lens[t++]=9;for(;t<280;)e.lens[t++]=7;for(;t<288;)e.lens[t++]=8;for($s(G2,e.lens,0,288,ld,0,e.work,{bits:9}),t=0;t<32;)e.lens[t++]=5;$s(q2,e.lens,0,32,ud,0,e.work,{bits:5}),Ly=!1}e.lencode=ld,e.lenbits=9,e.distcode=ud,e.distbits=5},aC=(e,t,n,r)=>{let i;const l=e.state;return l.window===null&&(l.wsize=1<<l.wbits,l.wnext=0,l.whave=0,l.window=new Uint8Array(l.wsize)),r>=l.wsize?(l.window.set(t.subarray(n-l.wsize,n),0),l.wnext=0,l.whave=l.wsize):(i=l.wsize-l.wnext,i>r&&(i=r),l.window.set(t.subarray(n-r,n-r+i),l.wnext),r-=i,r?(l.window.set(t.subarray(n-r,n),0),l.wnext=r,l.whave=l.wsize):(l.wnext+=i,l.wnext===l.wsize&&(l.wnext=0),l.whave<l.wsize&&(l.whave+=i))),0},wX=(e,t)=>{let n,r,i,l,g,o,a,u,d,c,f,s,h,v,m=0,y,b,_,w,S,x,T,E;const I=new Uint8Array(4);let D,N;const L=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);if(Ga(e)||!e.output||!e.input&&e.avail_in!==0)return tr;n=e.state,n.mode===Ur&&(n.mode=ad),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,c=o,f=a,E=Fa;e:for(;;)switch(n.mode){case cf:if(n.wrap===0){n.mode=ad;break}for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&2&&u===35615){n.wbits===0&&(n.wbits=15),n.check=0,I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0),u=0,d=0,n.mode=fy;break}if(n.head&&(n.head.done=!1),!(n.wrap&1)||(((u&255)<<8)+(u>>8))%31){e.msg="incorrect header check",n.mode=Bt;break}if((u&15)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(u>>>=4,d-=4,T=(u&15)+8,n.wbits===0&&(n.wbits=T),T>15||T>n.wbits){e.msg="invalid window size",n.mode=Bt;break}n.dmax=1<<n.wbits,n.flags=0,e.adler=n.check=1,n.mode=u&512?by:Ur,u=0,d=0;break;case fy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.flags=u,(n.flags&255)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(n.flags&57344){e.msg="unknown header flags set",n.mode=Bt;break}n.head&&(n.head.text=u>>8&1),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=dy;case dy:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.time=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,I[2]=u>>>16&255,I[3]=u>>>24&255,n.check=Zt(n.check,I,4,0)),u=0,d=0,n.mode=hy;case hy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.xflags=u&255,n.head.os=u>>8),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=py;case py:if(n.flags&1024){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length=u,n.head&&(n.head.extra_len=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0}else n.head&&(n.head.extra=null);n.mode=my;case my:if(n.flags&1024&&(s=n.length,s>o&&(s=o),s&&(n.head&&(T=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new Uint8Array(n.head.extra_len)),n.head.extra.set(r.subarray(l,l+s),T)),n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,n.length-=s),n.length))break e;n.length=0,n.mode=gy;case gy:if(n.flags&2048){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.name+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.name=null);n.length=0,n.mode=vy;case vy:if(n.flags&4096){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.comment+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.comment=null);n.mode=yy;case yy:if(n.flags&512){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.check&65535)){e.msg="header crc mismatch",n.mode=Bt;break}u=0,d=0}n.head&&(n.head.hcrc=n.flags>>9&1,n.head.done=!0),e.adler=n.check=0,n.mode=Ur;break;case by:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}e.adler=n.check=My(u),u=0,d=0,n.mode=Oc;case Oc:if(n.havedict===0)return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,dX;e.adler=n.check=1,n.mode=Ur;case Ur:if(t===cX||t===Cu)break e;case ad:if(n.last){u>>>=d&7,d-=d&7,n.mode=sd;break}for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}switch(n.last=u&1,u>>>=1,d-=1,u&3){case 0:n.mode=_y;break;case 1:if(_X(n),n.mode=Su,t===Cu){u>>>=2,d-=2;break e}break;case 2:n.mode=Cy;break;case 3:e.msg="invalid block type",n.mode=Bt}u>>>=2,d-=2;break;case _y:for(u>>>=d&7,d-=d&7;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((u&65535)!==(u>>>16^65535)){e.msg="invalid stored block lengths",n.mode=Bt;break}if(n.length=u&65535,u=0,d=0,n.mode=od,t===Cu)break e;case od:n.mode=wy;case wy:if(s=n.length,s){if(s>o&&(s=o),s>a&&(s=a),s===0)break e;i.set(r.subarray(l,l+s),g),o-=s,l+=s,a-=s,g+=s,n.length-=s;break}n.mode=Ur;break;case Cy:for(;d<14;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.nlen=(u&31)+257,u>>>=5,d-=5,n.ndist=(u&31)+1,u>>>=5,d-=5,n.ncode=(u&15)+4,u>>>=4,d-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=Bt;break}n.have=0,n.mode=Sy;case Sy:for(;n.have<n.ncode;){for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.lens[L[n.have++]]=u&7,u>>>=3,d-=3}for(;n.have<19;)n.lens[L[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,D={bits:n.lenbits},E=$s(uX,n.lens,0,19,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid code lengths set",n.mode=Bt;break}n.have=0,n.mode=xy;case xy:for(;n.have<n.nlen+n.ndist;){for(;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(_<16)u>>>=y,d-=y,n.lens[n.have++]=_;else{if(_===16){for(N=y+2;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(u>>>=y,d-=y,n.have===0){e.msg="invalid bit length repeat",n.mode=Bt;break}T=n.lens[n.have-1],s=3+(u&3),u>>>=2,d-=2}else if(_===17){for(N=y+3;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=3+(u&7),u>>>=3,d-=3}else{for(N=y+7;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=11+(u&127),u>>>=7,d-=7}if(n.have+s>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=Bt;break}for(;s--;)n.lens[n.have++]=T}}if(n.mode===Bt)break;if(n.lens[256]===0){e.msg="invalid code -- missing end-of-block",n.mode=Bt;break}if(n.lenbits=9,D={bits:n.lenbits},E=$s(G2,n.lens,0,n.nlen,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid literal/lengths set",n.mode=Bt;break}if(n.distbits=6,n.distcode=n.distdyn,D={bits:n.distbits},E=$s(q2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,D),n.distbits=D.bits,E){e.msg="invalid distances set",n.mode=Bt;break}if(n.mode=Su,t===Cu)break e;case Su:n.mode=xu;case xu:if(o>=6&&a>=258){e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,rX(e,f),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,n.mode===Ur&&(n.back=-1);break}for(n.back=0;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(b&&(b&240)===0){for(w=y,S=b,x=_;m=n.lencode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,n.length=_,b===0){n.mode=Ay;break}if(b&32){n.back=-1,n.mode=Ur;break}if(b&64){e.msg="invalid literal/length code",n.mode=Bt;break}n.extra=b&15,n.mode=Ty;case Ty:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=ky;case ky:for(;m=n.distcode[u&(1<<n.distbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break 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Uint8Array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,7]),I2=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),CW=512,Gr=new Array((pl+2)*2);Go(Gr);const Rs=new Array(Co*2);Go(Rs);const ml=new Array(CW);Go(ml);const gl=new Array(bW-yW+1);Go(gl);const _m=new Array(vm);Go(_m);const kc=new Array(Co);Go(kc);function Qf(e,t,n,r,i){this.static_tree=e,this.extra_bits=t,this.extra_base=n,this.elems=r,this.max_length=i,this.has_stree=e&&e.length}let M2,L2,R2;function ed(e,t){this.dyn_tree=e,this.max_code=0,this.stat_desc=t}const D2=e=>e<256?ml[e]:ml[256+(e>>>7)],vl=(e,t)=>{e.pending_buf[e.pending++]=t&255,e.pending_buf[e.pending++]=t>>>8&255},En=(e,t,n)=>{e.bi_valid>Jf-n?(e.bi_buf|=t<<e.bi_valid&65535,vl(e,e.bi_buf),e.bi_buf=t>>Jf-e.bi_valid,e.bi_valid+=n-Jf):(e.bi_buf|=t<<e.bi_valid&65535,e.bi_valid+=n)},Tr=(e,t,n)=>{En(e,n[t*2],n[t*2+1])},$2=(e,t)=>{let n=0;do n|=e&1,e>>>=1,n<<=1;while(--t>0);return n>>>1},SW=e=>{e.bi_valid===16?(vl(e,e.bi_buf),e.bi_buf=0,e.bi_valid=0):e.bi_valid>=8&&(e.pending_buf[e.pending++]=e.bi_buf&255,e.bi_buf>>=8,e.bi_valid-=8)},xW=(e,t)=>{const n=t.dyn_tree,r=t.max_code,i=t.stat_desc.static_tree,l=t.stat_desc.has_stree,g=t.stat_desc.extra_bits,o=t.stat_desc.extra_base,a=t.stat_desc.max_length;let u,d,c,f,s,h,v=0;for(f=0;f<=_a;f++)e.bl_count[f]=0;for(n[e.heap[e.heap_max]*2+1]=0,u=e.heap_max+1;u<E2;u++)d=e.heap[u],f=n[n[d*2+1]*2+1]+1,f>a&&(f=a,v++),n[d*2+1]=f,!(d>r)&&(e.bl_count[f]++,s=0,d>=o&&(s=g[d-o]),h=n[d*2],e.opt_len+=h*(f+s),l&&(e.static_len+=h*(i[d*2+1]+s)));if(v!==0){do{for(f=a-1;e.bl_count[f]===0;)f--;e.bl_count[f]--,e.bl_count[f+1]+=2,e.bl_count[a]--,v-=2}while(v>0);for(f=a;f!==0;f--)for(d=e.bl_count[f];d!==0;)c=e.heap[--u],!(c>r)&&(n[c*2+1]!==f&&(e.opt_len+=(f-n[c*2+1])*n[c*2],n[c*2+1]=f),d--)}},B2=(e,t,n)=>{const r=new Array(_a+1);let i=0,l,g;for(l=1;l<=_a;l++)i=i+n[l-1]<<1,r[l]=i;for(g=0;g<=t;g++){let o=e[g*2+1];o!==0&&(e[g*2]=$2(r[o]++,o))}},TW=()=>{let e,t,n,r,i;const l=new Array(_a+1);for(n=0,r=0;r<vm-1;r++)for(_m[r]=n,e=0;e<1<<Eh[r];e++)gl[n++]=r;for(gl[n-1]=r,i=0,r=0;r<16;r++)for(kc[r]=i,e=0;e<1<<Xu[r];e++)ml[i++]=r;for(i>>=7;r<Co;r++)for(kc[r]=i<<7,e=0;e<1<<Xu[r]-7;e++)ml[256+i++]=r;for(t=0;t<=_a;t++)l[t]=0;for(e=0;e<=143;)Gr[e*2+1]=8,e++,l[8]++;for(;e<=255;)Gr[e*2+1]=9,e++,l[9]++;for(;e<=279;)Gr[e*2+1]=7,e++,l[7]++;for(;e<=287;)Gr[e*2+1]=8,e++,l[8]++;for(B2(Gr,pl+1,l),e=0;e<Co;e++)Rs[e*2+1]=5,Rs[e*2]=$2(e,5);M2=new Qf(Gr,Eh,Vl+1,pl,_a),L2=new Qf(Rs,Xu,0,Co,_a),R2=new Qf(new Array(0),wW,0,ym,_W)},F2=e=>{let t;for(t=0;t<pl;t++)e.dyn_ltree[t*2]=0;for(t=0;t<Co;t++)e.dyn_dtree[t*2]=0;for(t=0;t<ym;t++)e.bl_tree[t*2]=0;e.dyn_ltree[bm*2]=1,e.opt_len=e.static_len=0,e.sym_next=e.matches=0},z2=e=>{e.bi_valid>8?vl(e,e.bi_buf):e.bi_valid>0&&(e.pending_buf[e.pending++]=e.bi_buf),e.bi_buf=0,e.bi_valid=0},Z0=(e,t,n,r)=>{const i=t*2,l=n*2;return e[i]<e[l]||e[i]===e[l]&&r[t]<=r[n]},td=(e,t,n)=>{const r=e.heap[n];let i=n<<1;for(;i<=e.heap_len&&(i<e.heap_len&&Z0(t,e.heap[i+1],e.heap[i],e.depth)&&i++,!Z0(t,r,e.heap[i],e.depth));)e.heap[n]=e.heap[i],n=i,i<<=1;e.heap[n]=r},J0=(e,t,n)=>{let r,i,l=0,g,o;if(e.sym_next!==0)do r=e.pending_buf[e.sym_buf+l++]&255,r+=(e.pending_buf[e.sym_buf+l++]&255)<<8,i=e.pending_buf[e.sym_buf+l++],r===0?Tr(e,i,t):(g=gl[i],Tr(e,g+Vl+1,t),o=Eh[g],o!==0&&(i-=_m[g],En(e,i,o)),r--,g=D2(r),Tr(e,g,n),o=Xu[g],o!==0&&(r-=kc[g],En(e,r,o)));while(l<e.sym_next);Tr(e,bm,t)},Oh=(e,t)=>{const n=t.dyn_tree,r=t.stat_desc.static_tree,i=t.stat_desc.has_stree,l=t.stat_desc.elems;let g,o,a=-1,u;for(e.heap_len=0,e.heap_max=E2,g=0;g<l;g++)n[g*2]!==0?(e.heap[++e.heap_len]=a=g,e.depth[g]=0):n[g*2+1]=0;for(;e.heap_len<2;)u=e.heap[++e.heap_len]=a<2?++a:0,n[u*2]=1,e.depth[u]=0,e.opt_len--,i&&(e.static_len-=r[u*2+1]);for(t.max_code=a,g=e.heap_len>>1;g>=1;g--)td(e,n,g);u=l;do g=e.heap[1],e.heap[1]=e.heap[e.heap_len--],td(e,n,1),o=e.heap[1],e.heap[--e.heap_max]=g,e.heap[--e.heap_max]=o,n[u*2]=n[g*2]+n[o*2],e.depth[u]=(e.depth[g]>=e.depth[o]?e.depth[g]:e.depth[o])+1,n[g*2+1]=n[o*2+1]=u,e.heap[1]=u++,td(e,n,1);while(e.heap_len>=2);e.heap[--e.heap_max]=e.heap[1],xW(e,t),B2(n,a,e.bl_count)},Q0=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),t[(n+1)*2+1]=65535,r=0;r<=n;r++)l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)&&(o<u?e.bl_tree[l*2]+=o:l!==0?(l!==i&&e.bl_tree[l*2]++,e.bl_tree[O2*2]++):o<=10?e.bl_tree[A2*2]++:e.bl_tree[P2*2]++,o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4))},ey=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),r=0;r<=n;r++)if(l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)){if(o<u)do Tr(e,l,e.bl_tree);while(--o!==0);else l!==0?(l!==i&&(Tr(e,l,e.bl_tree),o--),Tr(e,O2,e.bl_tree),En(e,o-3,2)):o<=10?(Tr(e,A2,e.bl_tree),En(e,o-3,3)):(Tr(e,P2,e.bl_tree),En(e,o-11,7));o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4)}},kW=e=>{let t;for(Q0(e,e.dyn_ltree,e.l_desc.max_code),Q0(e,e.dyn_dtree,e.d_desc.max_code),Oh(e,e.bl_desc),t=ym-1;t>=3&&e.bl_tree[I2[t]*2+1]===0;t--);return e.opt_len+=3*(t+1)+5+5+4,t},EW=(e,t,n,r)=>{let i;for(En(e,t-257,5),En(e,n-1,5),En(e,r-4,4),i=0;i<r;i++)En(e,e.bl_tree[I2[i]*2+1],3);ey(e,e.dyn_ltree,t-1),ey(e,e.dyn_dtree,n-1)},OW=e=>{let t=4093624447,n;for(n=0;n<=31;n++,t>>>=1)if(t&1&&e.dyn_ltree[n*2]!==0)return G0;if(e.dyn_ltree[9*2]!==0||e.dyn_ltree[10*2]!==0||e.dyn_ltree[13*2]!==0)return q0;for(n=32;n<Vl;n++)if(e.dyn_ltree[n*2]!==0)return q0;return G0};let ty=!1;const AW=e=>{ty||(TW(),ty=!0),e.l_desc=new ed(e.dyn_ltree,M2),e.d_desc=new ed(e.dyn_dtree,L2),e.bl_desc=new ed(e.bl_tree,R2),e.bi_buf=0,e.bi_valid=0,F2(e)},N2=(e,t,n,r)=>{En(e,(gW<<1)+(r?1:0),3),z2(e),vl(e,n),vl(e,~n),n&&e.pending_buf.set(e.window.subarray(t,t+n),e.pending),e.pending+=n},PW=e=>{En(e,k2<<1,3),Tr(e,bm,Gr),SW(e)},IW=(e,t,n,r)=>{let i,l,g=0;e.level>0?(e.strm.data_type===mW&&(e.strm.data_type=OW(e)),Oh(e,e.l_desc),Oh(e,e.d_desc),g=kW(e),i=e.opt_len+3+7>>>3,l=e.static_len+3+7>>>3,l<=i&&(i=l)):i=l=n+5,n+4<=i&&t!==-1?N2(e,t,n,r):e.strategy===pW||l===i?(En(e,(k2<<1)+(r?1:0),3),J0(e,Gr,Rs)):(En(e,(vW<<1)+(r?1:0),3),EW(e,e.l_desc.max_code+1,e.d_desc.max_code+1,g+1),J0(e,e.dyn_ltree,e.dyn_dtree)),F2(e),r&&z2(e)},MW=(e,t,n)=>(e.pending_buf[e.sym_buf+e.sym_next++]=t,e.pending_buf[e.sym_buf+e.sym_next++]=t>>8,e.pending_buf[e.sym_buf+e.sym_next++]=n,t===0?e.dyn_ltree[n*2]++:(e.matches++,t--,e.dyn_ltree[(gl[n]+Vl+1)*2]++,e.dyn_dtree[D2(t)*2]++),e.sym_next===e.sym_end);var LW=AW,RW=N2,DW=IW,$W=MW,BW=PW,FW={_tr_init:LW,_tr_stored_block:RW,_tr_flush_block:DW,_tr_tally:$W,_tr_align:BW};const zW=(e,t,n,r)=>{let i=e&65535|0,l=e>>>16&65535|0,g=0;for(;n!==0;){g=n>2e3?2e3:n,n-=g;do i=i+t[r++]|0,l=l+i|0;while(--g);i%=65521,l%=65521}return i|l<<16|0};var yl=zW;const NW=()=>{let e,t=[];for(var n=0;n<256;n++){e=n;for(var r=0;r<8;r++)e=e&1?3988292384^e>>>1:e>>>1;t[n]=e}return t},jW=new Uint32Array(NW()),VW=(e,t,n,r)=>{const i=jW,l=r+n;e^=-1;for(let g=r;g<l;g++)e=e>>>8^i[(e^t[g])&255];return e^-1};var Zt=VW,$a={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"},qo={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:UW,_tr_stored_block:Ah,_tr_flush_block:HW,_tr_tally:Bi,_tr_align:WW}=FW,{Z_NO_FLUSH:Fi,Z_PARTIAL_FLUSH:YW,Z_FULL_FLUSH:XW,Z_FINISH:qn,Z_BLOCK:ny,Z_OK:rn,Z_STREAM_END:ry,Z_STREAM_ERROR:Pr,Z_DATA_ERROR:KW,Z_BUF_ERROR:nd,Z_DEFAULT_COMPRESSION:GW,Z_FILTERED:qW,Z_HUFFMAN_ONLY:_u,Z_RLE:ZW,Z_FIXED:JW,Z_DEFAULT_STRATEGY:QW,Z_UNKNOWN:eY,Z_DEFLATED:lf}=qo,tY=9,nY=15,rY=8,iY=29,aY=256,Ph=aY+1+iY,oY=30,sY=19,lY=2*Ph+1,uY=15,lt=3,Li=258,Ir=Li+lt+1,cY=32,$o=42,wm=57,Ih=69,Mh=73,Lh=91,Rh=103,wa=113,ms=666,wn=1,Zo=2,Ba=3,Jo=4,fY=3,Ca=(e,t)=>(e.msg=$a[t],t),iy=e=>e*2-(e>4?9:0),Pi=e=>{let t=e.length;for(;--t>=0;)e[t]=0},dY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let hY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,zi=hY;const $n=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},jn=(e,t)=>{HW(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,$n(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},us=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Dh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=yl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Zt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},j2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Ir?e.strstart-(e.w_size-Ir):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Li;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Li-(f-r),r=f-Li,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Bo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Ir)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),dY(e),r+=t),e.strm.avail_in===0)break;if(n=Dh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=zi(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=zi(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Ir&&e.strm.avail_in!==0)},V2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==qn||t===Fi||r!==i+e.strm.avail_in)))break;g=t===qn&&r===i+e.strm.avail_in?1:0,Ah(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,$n(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Dh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?Jo:t!==Fi&&t!==qn&&e.strm.avail_in===0&&e.strstart===e.block_start?Zo:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Dh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===qn)&&t!==Fi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===qn&&e.strm.avail_in===0&&r===i?1:0,Ah(e,e.block_start,r,g),e.block_start+=r,$n(e.strm)),g?Ba:wn)},rd=(e,t)=>{let n,r;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Fi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n)),e.match_length>=lt)if(r=Bi(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=zi(e,e.ins_h,e.window[e.strstart+1]);else r=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},ao=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Fi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n),e.match_length<=5&&(e.strategy===qW||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=Bi(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(jn(e,!1),e.strm.avail_out===0))return wn}else if(e.match_available){if(r=Bi(e,0,e.window[e.strstart-1]),r&&jn(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return wn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=Bi(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},pY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Li){if(Bo(e),e.lookahead<=Li&&t===Fi)return wn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Li;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Li-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=Bi(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},mY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Bo(e),e.lookahead===0)){if(t===Fi)return wn;break}if(e.match_length=0,n=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo};function wr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const gs=[new wr(0,0,0,0,V2),new wr(4,4,8,4,rd),new wr(4,5,16,8,rd),new wr(4,6,32,32,rd),new wr(4,4,16,16,ao),new wr(8,16,32,32,ao),new wr(8,16,128,128,ao),new wr(8,32,128,256,ao),new wr(32,128,258,1024,ao),new wr(32,258,258,4096,ao)],gY=e=>{e.window_size=2*e.w_size,Pi(e.head),e.max_lazy_match=gs[e.level].max_lazy,e.good_match=gs[e.level].good_length,e.nice_match=gs[e.level].nice_length,e.max_chain_length=gs[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function vY(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=lf,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(lY*2),this.dyn_dtree=new Uint16Array((2*oY+1)*2),this.bl_tree=new Uint16Array((2*sY+1)*2),Pi(this.dyn_ltree),Pi(this.dyn_dtree),Pi(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(uY+1),this.heap=new Uint16Array(2*Ph+1),Pi(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(2*Ph+1),Pi(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ul=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.status!==$o&&t.status!==wm&&t.status!==Ih&&t.status!==Mh&&t.status!==Lh&&t.status!==Rh&&t.status!==wa&&t.status!==ms?1:0},U2=e=>{if(Ul(e))return Ca(e,Pr);e.total_in=e.total_out=0,e.data_type=eY;const t=e.state;return t.pending=0,t.pending_out=0,t.wrap<0&&(t.wrap=-t.wrap),t.status=t.wrap===2?wm:t.wrap?$o:wa,e.adler=t.wrap===2?0:1,t.last_flush=-2,UW(t),rn},H2=e=>{const t=U2(e);return t===rn&&gY(e.state),t},yY=(e,t)=>Ul(e)||e.state.wrap!==2?Pr:(e.state.gzhead=t,rn),W2=(e,t,n,r,i,l)=>{if(!e)return Pr;let g=1;if(t===GW&&(t=6),r<0?(g=0,r=-r):r>15&&(g=2,r-=16),i<1||i>tY||n!==lf||r<8||r>15||t<0||t>9||l<0||l>JW||r===8&&g!==1)return Ca(e,Pr);r===8&&(r=9);const o=new vY;return e.state=o,o.strm=e,o.status=$o,o.wrap=g,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=i+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+lt-1)/lt),o.window=new Uint8Array(o.w_size*2),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<i+6,o.pending_buf_size=o.lit_bufsize*4,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=(o.lit_bufsize-1)*3,o.level=t,o.strategy=l,o.method=n,H2(e)},bY=(e,t)=>W2(e,t,lf,nY,rY,QW),_Y=(e,t)=>{if(Ul(e)||t>ny||t<0)return e?Ca(e,Pr):Pr;const n=e.state;if(!e.output||e.avail_in!==0&&!e.input||n.status===ms&&t!==qn)return Ca(e,e.avail_out===0?nd:Pr);const r=n.last_flush;if(n.last_flush=t,n.pending!==0){if($n(e),e.avail_out===0)return n.last_flush=-1,rn}else if(e.avail_in===0&&iy(t)<=iy(r)&&t!==qn)return Ca(e,nd);if(n.status===ms&&e.avail_in!==0)return Ca(e,nd);if(n.status===$o&&n.wrap===0&&(n.status=wa),n.status===$o){let i=lf+(n.w_bits-8<<4)<<8,l=-1;if(n.strategy>=_u||n.level<2?l=0:n.level<6?l=1:n.level===6?l=2:l=3,i|=l<<6,n.strstart!==0&&(i|=cY),i+=31-i%31,us(n,i),n.strstart!==0&&(us(n,e.adler>>>16),us(n,e.adler&65535)),e.adler=1,n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(n.status===wm){if(e.adler=0,mt(n,31),mt(n,139),mt(n,8),n.gzhead)mt(n,(n.gzhead.text?1:0)+(n.gzhead.hcrc?2:0)+(n.gzhead.extra?4:0)+(n.gzhead.name?8:0)+(n.gzhead.comment?16:0)),mt(n,n.gzhead.time&255),mt(n,n.gzhead.time>>8&255),mt(n,n.gzhead.time>>16&255),mt(n,n.gzhead.time>>24&255),mt(n,n.level===9?2:n.strategy>=_u||n.level<2?4:0),mt(n,n.gzhead.os&255),n.gzhead.extra&&n.gzhead.extra.length&&(mt(n,n.gzhead.extra.length&255),mt(n,n.gzhead.extra.length>>8&255)),n.gzhead.hcrc&&(e.adler=Zt(e.adler,n.pending_buf,n.pending,0)),n.gzindex=0,n.status=Ih;else if(mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,n.level===9?2:n.strategy>=_u||n.level<2?4:0),mt(n,fY),n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(n.status===Ih){if(n.gzhead.extra){let i=n.pending,l=(n.gzhead.extra.length&65535)-n.gzindex;for(;n.pending+l>n.pending_buf_size;){let o=n.pending_buf_size-n.pending;if(n.pending_buf.set(n.gzhead.extra.subarray(n.gzindex,n.gzindex+o),n.pending),n.pending=n.pending_buf_size,n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex+=o,$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0,l-=o}let g=new Uint8Array(n.gzhead.extra);n.pending_buf.set(g.subarray(n.gzindex,n.gzindex+l),n.pending),n.pending+=l,n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Mh}if(n.status===Mh){if(n.gzhead.name){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0}n.gzindex<n.gzhead.name.length?l=n.gzhead.name.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Lh}if(n.status===Lh){if(n.gzhead.comment){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i)),$n(e),n.pending!==0)return n.last_flush=-1,rn;i=0}n.gzindex<n.gzhead.comment.length?l=n.gzhead.comment.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Zt(e.adler,n.pending_buf,n.pending-i,i))}n.status=Rh}if(n.status===Rh){if(n.gzhead.hcrc){if(n.pending+2>n.pending_buf_size&&($n(e),n.pending!==0))return n.last_flush=-1,rn;mt(n,e.adler&255),mt(n,e.adler>>8&255),e.adler=0}if(n.status=wa,$n(e),n.pending!==0)return n.last_flush=-1,rn}if(e.avail_in!==0||n.lookahead!==0||t!==Fi&&n.status!==ms){let i=n.level===0?V2(n,t):n.strategy===_u?mY(n,t):n.strategy===ZW?pY(n,t):gs[n.level].func(n,t);if((i===Ba||i===Jo)&&(n.status=ms),i===wn||i===Ba)return e.avail_out===0&&(n.last_flush=-1),rn;if(i===Zo&&(t===YW?WW(n):t!==ny&&(Ah(n,0,0,!1),t===XW&&(Pi(n.head),n.lookahead===0&&(n.strstart=0,n.block_start=0,n.insert=0))),$n(e),e.avail_out===0))return n.last_flush=-1,rn}return t!==qn?rn:n.wrap<=0?ry:(n.wrap===2?(mt(n,e.adler&255),mt(n,e.adler>>8&255),mt(n,e.adler>>16&255),mt(n,e.adler>>24&255),mt(n,e.total_in&255),mt(n,e.total_in>>8&255),mt(n,e.total_in>>16&255),mt(n,e.total_in>>24&255)):(us(n,e.adler>>>16),us(n,e.adler&65535)),$n(e),n.wrap>0&&(n.wrap=-n.wrap),n.pending!==0?rn:ry)},wY=e=>{if(Ul(e))return Pr;const t=e.state.status;return e.state=null,t===wa?Ca(e,KW):rn},CY=(e,t)=>{let n=t.length;if(Ul(e))return Pr;const r=e.state,i=r.wrap;if(i===2||i===1&&r.status!==$o||r.lookahead)return Pr;if(i===1&&(e.adler=yl(e.adler,t,n,0)),r.wrap=0,n>=r.w_size){i===0&&(Pi(r.head),r.strstart=0,r.block_start=0,r.insert=0);let a=new Uint8Array(r.w_size);a.set(t.subarray(n-r.w_size,n),0),t=a,n=r.w_size}const l=e.avail_in,g=e.next_in,o=e.input;for(e.avail_in=n,e.next_in=0,e.input=t,Bo(r);r.lookahead>=lt;){let a=r.strstart,u=r.lookahead-(lt-1);do r.ins_h=zi(r,r.ins_h,r.window[a+lt-1]),r.prev[a&r.w_mask]=r.head[r.ins_h],r.head[r.ins_h]=a,a++;while(--u);r.strstart=a,r.lookahead=lt-1,Bo(r)}return r.strstart+=r.lookahead,r.block_start=r.strstart,r.insert=r.lookahead,r.lookahead=0,r.match_length=r.prev_length=lt-1,r.match_available=0,e.next_in=g,e.input=o,e.avail_in=l,r.wrap=i,rn};var SY=bY,xY=W2,TY=H2,kY=U2,EY=yY,OY=_Y,AY=wY,PY=CY,IY="pako deflate (from Nodeca project)",Ds={deflateInit:SY,deflateInit2:xY,deflateReset:TY,deflateResetKeep:kY,deflateSetHeader:EY,deflate:OY,deflateEnd:AY,deflateSetDictionary:PY,deflateInfo:IY};const MY=(e,t)=>Object.prototype.hasOwnProperty.call(e,t);var 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nr;n=e.state,n.mode===Ur&&(n.mode=ad),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,c=o,f=a,E=Fa;e:for(;;)switch(n.mode){case cf:if(n.wrap===0){n.mode=ad;break}for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&2&&u===35615){n.wbits===0&&(n.wbits=15),n.check=0,I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0),u=0,d=0,n.mode=fy;break}if(n.head&&(n.head.done=!1),!(n.wrap&1)||(((u&255)<<8)+(u>>8))%31){e.msg="incorrect header check",n.mode=Bt;break}if((u&15)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(u>>>=4,d-=4,T=(u&15)+8,n.wbits===0&&(n.wbits=T),T>15||T>n.wbits){e.msg="invalid window size",n.mode=Bt;break}n.dmax=1<<n.wbits,n.flags=0,e.adler=n.check=1,n.mode=u&512?by:Ur,u=0,d=0;break;case fy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.flags=u,(n.flags&255)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(n.flags&57344){e.msg="unknown header flags set",n.mode=Bt;break}n.head&&(n.head.text=u>>8&1),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=dy;case dy:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.time=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,I[2]=u>>>16&255,I[3]=u>>>24&255,n.check=Zt(n.check,I,4,0)),u=0,d=0,n.mode=hy;case hy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.xflags=u&255,n.head.os=u>>8),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=py;case py:if(n.flags&1024){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length=u,n.head&&(n.head.extra_len=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0}else n.head&&(n.head.extra=null);n.mode=my;case my:if(n.flags&1024&&(s=n.length,s>o&&(s=o),s&&(n.head&&(T=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new 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e;o--,u+=r[l++]<<d,d+=8}e.adler=n.check=My(u),u=0,d=0,n.mode=Oc;case Oc:if(n.havedict===0)return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,dX;e.adler=n.check=1,n.mode=Ur;case Ur:if(t===cX||t===Cu)break e;case ad:if(n.last){u>>>=d&7,d-=d&7,n.mode=sd;break}for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}switch(n.last=u&1,u>>>=1,d-=1,u&3){case 0:n.mode=_y;break;case 1:if(_X(n),n.mode=Su,t===Cu){u>>>=2,d-=2;break e}break;case 2:n.mode=Cy;break;case 3:e.msg="invalid block type",n.mode=Bt}u>>>=2,d-=2;break;case _y:for(u>>>=d&7,d-=d&7;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((u&65535)!==(u>>>16^65535)){e.msg="invalid stored block lengths",n.mode=Bt;break}if(n.length=u&65535,u=0,d=0,n.mode=od,t===Cu)break e;case od:n.mode=wy;case wy:if(s=n.length,s){if(s>o&&(s=o),s>a&&(s=a),s===0)break e;i.set(r.subarray(l,l+s),g),o-=s,l+=s,a-=s,g+=s,n.length-=s;break}n.mode=Ur;break;case Cy:for(;d<14;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.nlen=(u&31)+257,u>>>=5,d-=5,n.ndist=(u&31)+1,u>>>=5,d-=5,n.ncode=(u&15)+4,u>>>=4,d-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=Bt;break}n.have=0,n.mode=Sy;case Sy:for(;n.have<n.ncode;){for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.lens[L[n.have++]]=u&7,u>>>=3,d-=3}for(;n.have<19;)n.lens[L[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,D={bits:n.lenbits},E=$s(uX,n.lens,0,19,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid code lengths set",n.mode=Bt;break}n.have=0,n.mode=xy;case xy:for(;n.have<n.nlen+n.ndist;){for(;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(_<16)u>>>=y,d-=y,n.lens[n.have++]=_;else{if(_===16){for(N=y+2;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(u>>>=y,d-=y,n.have===0){e.msg="invalid bit length repeat",n.mode=Bt;break}T=n.lens[n.have-1],s=3+(u&3),u>>>=2,d-=2}else if(_===17){for(N=y+3;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=3+(u&7),u>>>=3,d-=3}else{for(N=y+7;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=11+(u&127),u>>>=7,d-=7}if(n.have+s>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=Bt;break}for(;s--;)n.lens[n.have++]=T}}if(n.mode===Bt)break;if(n.lens[256]===0){e.msg="invalid code -- missing end-of-block",n.mode=Bt;break}if(n.lenbits=9,D={bits:n.lenbits},E=$s(G2,n.lens,0,n.nlen,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid literal/lengths set",n.mode=Bt;break}if(n.distbits=6,n.distcode=n.distdyn,D={bits:n.distbits},E=$s(q2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,D),n.distbits=D.bits,E){e.msg="invalid distances set",n.mode=Bt;break}if(n.mode=Su,t===Cu)break e;case Su:n.mode=xu;case xu:if(o>=6&&a>=258){e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,rX(e,f),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,n.mode===Ur&&(n.back=-1);break}for(n.back=0;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(b&&(b&240)===0){for(w=y,S=b,x=_;m=n.lencode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,n.length=_,b===0){n.mode=Ay;break}if(b&32){n.back=-1,n.mode=Ur;break}if(b&64){e.msg="invalid literal/length code",n.mode=Bt;break}n.extra=b&15,n.mode=Ty;case Ty:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=ky;case ky:for(;m=n.distcode[u&(1<<n.distbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((b&240)===0){for(w=y,S=b,x=_;m=n.distcode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,b&64){e.msg="invalid distance code",n.mode=Bt;break}n.offset=_,n.extra=b&15,n.mode=Ey;case Ey:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.offset+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=Bt;break}n.mode=Oy;case Oy:if(a===0)break e;if(s=f-a,n.offset>s){if(s=n.offset-s,s>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=Bt;break}s>n.wnext?(s-=n.wnext,h=n.wsize-s):h=n.wnext-s,s>n.length&&(s=n.length),v=n.window}else v=i,h=g-n.offset,s=n.length;s>a&&(s=a),a-=s,n.length-=s;do i[g++]=v[h++];while(--s);n.length===0&&(n.mode=xu);break;case Ay:if(a===0)break e;i[g++]=n.length,a--,n.mode=xu;break;case sd:if(n.wrap){for(;d<32;){if(o===0)break e;o--,u|=r[l++]<<d,d+=8}if(f-=a,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Zt(n.check,i,f,g-f):yl(n.check,i,f,g-f)),f=a,n.wrap&4&&(n.flags?u:My(u))!==n.check){e.msg="incorrect data check",n.mode=Bt;break}u=0,d=0}n.mode=Py;case Py:if(n.wrap&&n.flags){for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.total&4294967295)){e.msg="incorrect length check",n.mode=Bt;break}u=0,d=0}n.mode=Iy;case Iy:E=fX;break e;case Bt:E=Z2;break e;case Q2:return J2;case eC:default:return nr}return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,(n.wsize||f!==e.avail_out&&n.mode<Bt&&(n.mode<sd||t!==uy))&&aC(e,e.output,e.next_out,f-e.avail_out),c-=e.avail_in,f-=e.avail_out,e.total_in+=c,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Zt(n.check,i,f,e.next_out-f):yl(n.check,i,f,e.next_out-f)),e.data_type=n.bits+(n.last?64:0)+(n.mode===Ur?128:0)+(n.mode===Su||n.mode===od?256:0),(c===0&&f===0||t===uy)&&E===Fa&&(E=hX),E},CX=e=>{if(Ga(e))return nr;let t=e.state;return t.window&&(t.window=null),e.state=null,Fa},SX=(e,t)=>{if(Ga(e))return nr;const n=e.state;return(n.wrap&2)===0?nr:(n.head=t,t.done=!1,Fa)},xX=(e,t)=>{const n=t.length;let r,i,l;return Ga(e)||(r=e.state,r.wrap!==0&&r.mode!==Oc)?nr:r.mode===Oc&&(i=1,i=yl(i,t,n,0),i!==r.check)?Z2:(l=aC(e,t,n,n),l?(r.mode=Q2,J2):(r.havedict=1,Fa))};var TX=nC,kX=rC,EX=tC,OX=bX,AX=iC,PX=wX,IX=CX,MX=SX,LX=xX,RX="pako inflate (from Nodeca 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n=qr.inflateInit2(this.strm,t.windowBits);if(n!==wl)throw new Error($a[n]);if(this.header=new $X,qr.inflateGetHeader(this.strm,this.header),t.dictionary&&(typeof t.dictionary=="string"?t.dictionary=_l.string2buf(t.dictionary):oC.call(t.dictionary)==="[object ArrayBuffer]"&&(t.dictionary=new Uint8Array(t.dictionary)),t.raw&&(n=qr.inflateSetDictionary(this.strm,t.dictionary),n!==wl)))throw new Error($a[n])}Wl.prototype.push=function(e,t){const n=this.strm,r=this.options.chunkSize,i=this.options.dictionary;let l,g,o;if(this.ended)return!1;for(t===~~t?g=t:g=t===!0?FX:BX,oC.call(e)==="[object ArrayBuffer]"?n.input=new Uint8Array(e):n.input=e,n.next_in=0,n.avail_in=n.input.length;;){for(n.avail_out===0&&(n.output=new Uint8Array(r),n.next_out=0,n.avail_out=r),l=qr.inflate(n,g),l===fd&&i&&(l=qr.inflateSetDictionary(n,i),l===wl?l=qr.inflate(n,g):l===Ry&&(l=fd));n.avail_in>0&&l===cd&&n.state.wrap>0&&e[n.next_in]!==0;)qr.inflateReset(n),l=qr.inflate(n,g);switch(l){case zX:case Ry:case fd:case NX:return this.onEnd(l),this.ended=!0,!1}if(o=n.avail_out,n.next_out&&(n.avail_out===0||l===cd))if(this.options.to==="string"){let a=_l.utf8border(n.output,n.next_out),u=n.next_out-a,d=_l.buf2string(n.output,a);n.next_out=u,n.avail_out=r-u,u&&n.output.set(n.output.subarray(a,a+u),0),this.onData(d)}else this.onData(n.output.length===n.next_out?n.output:n.output.subarray(0,n.next_out));if(!(l===wl&&o===0)){if(l===cd)return l=qr.inflateEnd(this.strm),this.onEnd(l),this.ended=!0,!0;if(n.avail_in===0)break}}return!0};Wl.prototype.onData=function(e){this.chunks.push(e)};Wl.prototype.onEnd=function(e){e===wl&&(this.options.to==="string"?this.result=this.chunks.join(""):this.result=uf.flattenChunks(this.chunks)),this.chunks=[],this.err=e,this.msg=this.strm.msg};function Sm(e,t){const n=new Wl(t);if(n.push(e),n.err)throw n.msg||$a[n.err];return n.result}function jX(e,t){return t=t||{},t.raw=!0,Sm(e,t)}var 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d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask-size:100% 100%;-webkit-mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;-webkit-mask-size:100% 100%;transform:translateY(-50%);width:12px;height:12px}.el-select-dropdown .el-scrollbar.is-empty .el-select-dropdown__list{padding:0}.el-select-dropdown .el-select-dropdown__item.is-disabled:hover{background-color:unset}.el-select-dropdown .el-select-dropdown__item.is-disabled.selected{color:var(--el-text-color-disabled)}.el-select-dropdown__empty{padding:10px 0;margin:0;text-align:center;color:var(--el-text-color-secondary);font-size:var(--el-select-font-size)}.el-select-dropdown__wrap{max-height:274px}.el-select-dropdown__list{list-style:none;padding:6px 0;margin:0;box-sizing:border-box}.el-select{--el-select-border-color-hover:var(--el-border-color-hover);--el-select-disabled-border:var(--el-disabled-border-color);--el-select-font-size:var(--el-font-size-base);--el-select-close-hover-color:var(--el-text-color-secondary);--el-select-input-color:var(--el-text-color-placeholder);--el-select-multiple-input-color:var(--el-text-color-regular);--el-select-input-focus-border-color:var(--el-color-primary);--el-select-input-font-size:14px}.el-select{display:inline-block;position:relative;vertical-align:middle;line-height:32px}.el-select__popper.el-popper{background:var(--el-bg-color-overlay);border:1px solid var(--el-border-color-light);box-shadow:var(--el-box-shadow-light)}.el-select__popper.el-popper .el-popper__arrow:before{border:1px solid var(--el-border-color-light)}.el-select__popper.el-popper[data-popper-placement^=top] 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25%}.el-col-md-offset-6{margin-left:25%}.el-col-md-pull-6{position:relative;right:25%}.el-col-md-push-6{position:relative;left:25%}.el-col-md-7{display:block;max-width:29.1666666667%;flex:0 0 29.1666666667%}.el-col-md-offset-7{margin-left:29.1666666667%}.el-col-md-pull-7{position:relative;right:29.1666666667%}.el-col-md-push-7{position:relative;left:29.1666666667%}.el-col-md-8{display:block;max-width:33.3333333333%;flex:0 0 33.3333333333%}.el-col-md-offset-8{margin-left:33.3333333333%}.el-col-md-pull-8{position:relative;right:33.3333333333%}.el-col-md-push-8{position:relative;left:33.3333333333%}.el-col-md-9{display:block;max-width:37.5%;flex:0 0 37.5%}.el-col-md-offset-9{margin-left:37.5%}.el-col-md-pull-9{position:relative;right:37.5%}.el-col-md-push-9{position:relative;left:37.5%}.el-col-md-10{display:block;max-width:41.6666666667%;flex:0 0 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diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 73c0b73f7..00c0a6dd7 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -1530,6 +1530,7 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         jsoninfo["all_prompts"] = nlohmann::json::array();
         jsoninfo["all_negative_prompts"] = nlohmann::json::array();
         jsoninfo["all_seeds"] = nlohmann::json::array();
+        jsoninfo["lora_meta"] = lora_meta;
         jsoninfo["version"] = "KoboldCpp";
     }
     sd_image_t* upscaled_image = nullptr;

From 94875285e47516da4e1c71e7bca5ba65b82bbe59 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Fri, 3 Jul 2026 12:16:29 +0200
Subject: [PATCH 259/292] ui: Add MCP Servers Opt-In for first time visitors
 (#25239)

* feat: ui: Add predefined recommended MCP servers to settings

* feat: ui: Add MCP server recommendation dialog with custom server support

* feat: Auto-focus input fields on mount and dynamic addition

* feat: Add header validation to MCP server add and edit forms

* feat: Persist recommended MCP server opt-in selections

* test: Cover MCP configuration with tests

* chore: Format & cleanup

* feat: Centralize MCP server overrides to settings config and improve recommendation UI

* fix: Capture index before mutation to prevent focus drift

* refactor: Extract MCP_CARD_VISIBLE_TOOL_LIMIT to shared constants

* refactor: Support arbitrary authorization header schemes

* refactor: Consolidate MCP recommendations dismissal into existing storage key

* fix: Use case-insensitive comparison for MCP server ID prefix check

* refactor: Centralize MCP server visibility logic and extract recommendations hook

* refactor: Cleanup
---
 .../ChatFormActionAddMcpServersSubmenu.svelte |   2 +-
 .../ChatFormActionAddSheet.svelte             |  10 +-
 .../ChatMessage/ChatMessage.svelte            |   2 +-
 ...tMessageActionCardPermissionRequest.svelte |  40 ++--
 .../app/dialogs/DialogMcpServerAddNew.svelte  |  55 +++---
 .../DialogMcpServerRecommendations.svelte     | 180 +++++++++++++++++
 .../src/lib/components/app/dialogs/index.ts   |   9 +
 .../components/app/forms/KeyValuePairs.svelte |  23 ++-
 .../mcp/McpServerCard/McpServerCard.svelte    |   2 +-
 .../McpServerCard/McpServerCardCompact.svelte | 156 +++++++++++++++
 .../McpServerCardEditForm.svelte              |  49 +++--
 .../components/app/mcp/McpServerForm.svelte   | 184 ++++++++++++++----
 .../app/mcp/McpServerIdentity.svelte          |  14 +-
 tools/ui/src/lib/components/app/mcp/index.ts  |  10 +
 .../app/settings/SettingsMcpServers.svelte    |   2 +-
 tools/ui/src/lib/constants/index.ts           |   1 +
 tools/ui/src/lib/constants/mcp-form.ts        |   2 +
 .../lib/constants/recommended-mcp-servers.ts  |  35 ++++
 tools/ui/src/lib/constants/settings-keys.ts   |   1 +
 .../ui/src/lib/constants/settings-registry.ts |  10 +-
 tools/ui/src/lib/constants/storage.ts         |   6 +-
 .../hooks/use-mcp-recommendations.svelte.ts   |  85 ++++++++
 .../ui/src/lib/services/migration.service.ts  |  60 +++++-
 .../ui/src/lib/stores/conversations.svelte.ts |  22 +--
 tools/ui/src/lib/stores/mcp.svelte.ts         |  40 +++-
 tools/ui/src/lib/types/index.ts               |   2 +
 tools/ui/src/lib/types/mcp.d.ts               |  21 +-
 tools/ui/src/routes/+layout.svelte            |   9 +
 .../components/McpServerFormWrapper.svelte    |  37 ++++
 .../client/mcp-server-form.svelte.test.ts     | 133 +++++++++++++
 tools/ui/tests/unit/headers.test.ts           | 126 ++++++++++++
 .../unit/parse-mcp-server-settings.test.ts    | 144 ++++++++++++++
 .../unit/recommended-mcp-servers.test.ts      |  90 +++++++++
 33 files changed, 1411 insertions(+), 151 deletions(-)
 create mode 100644 tools/ui/src/lib/components/app/dialogs/DialogMcpServerRecommendations.svelte
 create mode 100644 tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardCompact.svelte
 create mode 100644 tools/ui/src/lib/constants/recommended-mcp-servers.ts
 create mode 100644 tools/ui/src/lib/hooks/use-mcp-recommendations.svelte.ts
 create mode 100644 tools/ui/tests/client/components/McpServerFormWrapper.svelte
 create mode 100644 tools/ui/tests/client/mcp-server-form.svelte.test.ts
 create mode 100644 tools/ui/tests/unit/headers.test.ts
 create mode 100644 tools/ui/tests/unit/parse-mcp-server-settings.test.ts
 create mode 100644 tools/ui/tests/unit/recommended-mcp-servers.test.ts

diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte
index dd357d6cd..a75f45f37 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddMcpServersSubmenu.svelte
@@ -18,7 +18,7 @@
 
 	let mcpSearchQuery = $state('');
 	let allMcpServers = $derived(mcpStore.getServersSorted());
-	let mcpServers = $derived(allMcpServers.filter((s) => s.enabled));
+	let mcpServers = $derived(mcpStore.visibleMcpServers);
 	let hasMcpServers = $derived(mcpServers.length > 0);
 	// let hasAnyMcpServers = $derived(allMcpServers.length > 0);
 	let filteredMcpServers = $derived.by(() => {
diff --git a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
index 2b708aae5..b67fb267b 100644
--- a/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionAdd/ChatFormActionAddSheet.svelte
@@ -74,9 +74,7 @@
 	const sheetItemRowClass =
 		'flex w-full items-center justify-between gap-2 rounded-md px-3 py-2 text-left text-sm transition-colors hover:bg-accent';
 
-	function getEnabledMcpServers() {
-		return mcpStore.getServersSorted().filter((s) => s.enabled);
-	}
+	let visibleMcpServers = $derived(mcpStore.visibleMcpServers);
 </script>
 
 <div class="flex items-center gap-1 {className}">
@@ -153,13 +151,13 @@
 						<span class="flex-1">MCP Servers</span>
 
 						<span class="text-xs text-muted-foreground">
-							{getEnabledMcpServers().length} server{getEnabledMcpServers().length !== 1 ? 's' : ''}
+							{visibleMcpServers.length} server{visibleMcpServers.length !== 1 ? 's' : ''}
 						</span>
 					</Collapsible.Trigger>
 
 					<Collapsible.Content>
 						<div class="flex flex-col gap-0.5 pl-4">
-							{#each getEnabledMcpServers() as server (server.id)}
+							{#each visibleMcpServers as server (server.id)}
 								{@const healthState = mcpStore.getHealthCheckState(server.id)}
 								{@const hasError = healthState.status === HealthCheckStatus.ERROR}
 								{@const displayName = mcpStore.getServerLabel(server)}
@@ -202,7 +200,7 @@
 								</button>
 							{/each}
 
-							{#if getEnabledMcpServers().length === 0}
+							{#if visibleMcpServers.length === 0}
 								<div class="px-3 py-2 text-center text-sm text-muted-foreground">
 									No MCP servers configured
 								</div>
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
index 7189ce1c7..dadcae0c4 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessage/ChatMessage.svelte
@@ -43,7 +43,7 @@
 		assistantMessages: number;
 		messageTypes: string[];
 	} | null>(null);
-	let editedContent = $state(message.content);
+	let editedContent = $derived(message.content);
 
 	let rawEditContent = $derived.by(() => {
 		if (message.role !== MessageRole.ASSISTANT) return undefined;
diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte
index e466c84ee..4337bb6a1 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessageActions/ChatMessageActionCard/ChatMessageActionCardPermissionRequest.svelte
@@ -1,8 +1,9 @@
 <script lang="ts">
 	import { ChevronDown, ShieldQuestion } from '@lucide/svelte';
 	import { ChatMessageActionCard } from '$lib/components/app';
-	import { Button } from '$lib/components/ui/button';
+	import { Button, buttonVariants } from '$lib/components/ui/button';
 	import * as ButtonGroup from '$lib/components/ui/button-group';
+	import { cn } from '$lib/components/ui/utils';
 	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
 	import { ToolSource, ToolPermissionDecision } from '$lib/enums';
 	import { TOOL_SERVER_LABELS } from '$lib/constants';
@@ -19,25 +20,17 @@
 
 <ChatMessageActionCard icon={ShieldQuestion}>
 	{#snippet message()}
-		Allow use of
-
-		<span class="font-semibold">{toolName}</span>
-
-		{#if serverLabel}
-			from <span class="font-semibold">{serverLabel}</span>
-		{/if}
-
-		?
+		Allow use of <span class="font-semibold">{toolName}</span>{#if serverLabel}
+			from <span class="font-semibold">{serverLabel}</span>{/if}?
 	{/snippet}
 
 	{#snippet actions()}
 		<DropdownMenu.Root>
-			<ButtonGroup.Root
-				class="overflow-hidden rounded-md bg-foreground text-white shadow-sm dark:bg-secondary dark:text-foreground"
-			>
+			<ButtonGroup.Root class="overflow-hidden rounded-md shadow-sm">
 				<Button
-					class="rounded-none! shadow-none!"
+					variant="secondary"
 					size="sm"
+					class="!rounded-r-none !shadow-none"
 					onclick={() => onDecision(ToolPermissionDecision.ONCE)}
 				>
 					Allow once
@@ -45,10 +38,14 @@
 
 				<ButtonGroup.Separator />
 
-				<DropdownMenu.Trigger>
-					<Button size="sm" class="rounded-none! !ps-2 shadow-none!">
-						<ChevronDown class="h-3.5 w-3.5" />
-					</Button>
+				<DropdownMenu.Trigger
+					class={cn(
+						buttonVariants({ variant: 'secondary', size: 'sm' }),
+						'inline-flex cursor-pointer items-center !rounded-l-none !shadow-none !px-2'
+					)}
+					aria-label="More allow options"
+				>
+					<ChevronDown class="h-3.5 w-3.5" />
 				</DropdownMenu.Trigger>
 			</ButtonGroup.Root>
 
@@ -76,12 +73,7 @@
 			</DropdownMenu.Content>
 		</DropdownMenu.Root>
 
-		<Button
-			variant="destructive"
-			size="sm"
-			class="text-destructive hover:text-destructive"
-			onclick={() => onDecision(ToolPermissionDecision.DENY)}
-		>
+		<Button variant="destructive" size="sm" onclick={() => onDecision(ToolPermissionDecision.DENY)}>
 			Deny
 		</Button>
 	{/snippet}
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte
index 349f7e7fb..ee99e6b5e 100644
--- a/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte
+++ b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerAddNew.svelte
@@ -4,7 +4,7 @@
 	import { McpServerForm } from '$lib/components/app/mcp';
 	import { mcpStore } from '$lib/stores/mcp.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
-	import { uuid } from '$lib/utils';
+	import { parseHeadersToArray, uuid } from '$lib/utils';
 	import { MCP_SERVER_ID_PREFIX } from '$lib/constants';
 
 	interface Props {
@@ -26,6 +26,10 @@
 			return 'Invalid URL format';
 		}
 	});
+	let newServerHeaderPairsValid = $derived(
+		parseHeadersToArray(newServerHeaders).every((p) => p.key.trim() && p.value.trim())
+	);
+	let canSave = $derived(!newServerUrlError && newServerHeaderPairsValid);
 
 	function handleOpenChange(value: boolean) {
 		if (!value) {
@@ -37,7 +41,7 @@
 	}
 
 	function saveNewServer() {
-		if (newServerUrlError) return;
+		if (!canSave) return;
 
 		const newServerId = uuid() ?? `${MCP_SERVER_ID_PREFIX}-${Date.now()}`;
 
@@ -52,6 +56,11 @@
 
 		handleOpenChange(false);
 	}
+
+	function handleSubmit(event: SubmitEvent) {
+		event.preventDefault();
+		saveNewServer();
+	}
 </script>
 
 <Dialog.Root {open} onOpenChange={handleOpenChange}>
@@ -60,29 +69,27 @@
 			<Dialog.Title>Add New Server</Dialog.Title>
 		</Dialog.Header>
 
-		<div class="space-y-4 py-4">
-			<McpServerForm
-				url={newServerUrl}
-				headers={newServerHeaders}
-				onUrlChange={(v) => (newServerUrl = v)}
-				onHeadersChange={(v) => (newServerHeaders = v)}
-				urlError={newServerUrl ? newServerUrlError : null}
-				id="new-server"
-			/>
-		</div>
+		<form onsubmit={handleSubmit} class="contents">
+			<div class="space-y-4 py-4">
+				<McpServerForm
+					url={newServerUrl}
+					headers={newServerHeaders}
+					onUrlChange={(v) => (newServerUrl = v)}
+					onHeadersChange={(v) => (newServerHeaders = v)}
+					urlError={newServerUrl ? newServerUrlError : null}
+					id="new-server"
+				/>
+			</div>
 
-		<Dialog.Footer>
-			<Button variant="secondary" size="sm" onclick={() => handleOpenChange(false)}>Cancel</Button>
+			<Dialog.Footer>
+				<Button variant="secondary" size="sm" onclick={() => handleOpenChange(false)}>
+					Cancel
+				</Button>
 
-			<Button
-				variant="default"
-				size="sm"
-				onclick={saveNewServer}
-				disabled={!!newServerUrlError}
-				aria-label="Save"
-			>
-				Add
-			</Button>
-		</Dialog.Footer>
+				<Button variant="default" size="sm" type="submit" disabled={!canSave} aria-label="Save">
+					Add
+				</Button>
+			</Dialog.Footer>
+		</form>
 	</Dialog.Content>
 </Dialog.Root>
diff --git a/tools/ui/src/lib/components/app/dialogs/DialogMcpServerRecommendations.svelte b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerRecommendations.svelte
new file mode 100644
index 000000000..9b4489b82
--- /dev/null
+++ b/tools/ui/src/lib/components/app/dialogs/DialogMcpServerRecommendations.svelte
@@ -0,0 +1,180 @@
+<script lang="ts">
+	import { Button } from '$lib/components/ui/button';
+	import * as Card from '$lib/components/ui/card';
+	import * as Dialog from '$lib/components/ui/dialog';
+	import { fly } from 'svelte/transition';
+	import { McpServerCardCompact, McpServerForm } from '$lib/components/app/mcp';
+	import { RECOMMENDED_MCP_SERVERS } from '$lib/constants';
+	import { conversationsStore } from '$lib/stores/conversations.svelte';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { uuid } from '$lib/utils';
+	import { MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, MCP_SERVER_ID_PREFIX } from '$lib/constants';
+	import type { MCPServerSettingsEntry } from '$lib/types';
+	import { Plus } from '@lucide/svelte';
+
+	interface Props {
+		open: boolean;
+		onOpenChange?: (open: boolean) => void;
+	}
+
+	let { open = $bindable(), onOpenChange }: Props = $props();
+
+	let selected = $state<Record<string, boolean>>(
+		Object.fromEntries(RECOMMENDED_MCP_SERVERS.map((server) => [server.id, false]))
+	);
+
+	let addedServers = $state<MCPServerSettingsEntry[]>([]);
+
+	let showAddForm = $state(false);
+	let newServerUrl = $state('');
+	let newServerHeaders = $state('');
+	let newServerUrlError = $derived.by(() => {
+		if (!newServerUrl.trim()) return 'URL is required';
+		try {
+			new URL(newServerUrl);
+
+			return null;
+		} catch {
+			return 'Invalid URL format';
+		}
+	});
+
+	function handleOpenChange(value: boolean) {
+		if (!value) {
+			showAddForm = false;
+			newServerUrl = '';
+			newServerHeaders = '';
+			addedServers = [];
+
+			localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
+		}
+		open = value;
+		onOpenChange?.(value);
+	}
+
+	function resetAddForm() {
+		showAddForm = false;
+		newServerUrl = '';
+		newServerHeaders = '';
+	}
+
+	function enableSelected() {
+		localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
+
+		for (const server of RECOMMENDED_MCP_SERVERS) {
+			if (selected[server.id]) {
+				const existing = mcpStore.getServerById(server.id);
+				if (existing) {
+					mcpStore.updateServer(server.id, { enabled: true });
+				} else {
+					mcpStore.addServer({
+						id: server.id,
+						enabled: true,
+						url: server.url,
+						name: server.name
+					});
+				}
+				conversationsStore.setMcpServerOverride(server.id, true);
+			}
+		}
+		handleOpenChange(false);
+	}
+
+	function saveNewServer() {
+		if (newServerUrlError) return;
+
+		const newServerId = uuid() ?? `${MCP_SERVER_ID_PREFIX}-${Date.now()}`;
+
+		localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
+
+		const newServer = mcpStore.addServer({
+			id: newServerId,
+			enabled: true,
+			url: newServerUrl.trim(),
+			headers: newServerHeaders.trim() || undefined
+		});
+
+		conversationsStore.setMcpServerOverride(newServerId, true);
+
+		if (newServer) {
+			addedServers = [...addedServers, newServer];
+		}
+
+		resetAddForm();
+	}
+</script>
+
+<Dialog.Root bind:open onOpenChange={handleOpenChange}>
+	<Dialog.Content class="sm:max-w-lg">
+		<Dialog.Header>
+			<Dialog.Title>Do more with MCP</Dialog.Title>
+			<Dialog.Description>
+				Power-up your experience by adding tools, resources and more capabilities provided by MCP
+				servers.
+			</Dialog.Description>
+		</Dialog.Header>
+
+		<div class="max-h-[60vh] space-y-4 overflow-y-auto py-4" in:fly={{ y: 16, duration: 300 }}>
+			<h3 class="text-sm font-semibold">Quickly get started with</h3>
+
+			{#each RECOMMENDED_MCP_SERVERS as server (server.id)}
+				<McpServerCardCompact
+					{server}
+					enabled={selected[server.id]}
+					onToggle={(enabled) => (selected[server.id] = enabled)}
+				/>
+			{/each}
+
+			{#if addedServers.length > 0}
+				{#each addedServers as server (server.id)}
+					<McpServerCardCompact {server} enabled={true} />
+				{/each}
+			{/if}
+
+			{#if showAddForm}
+				<Card.Root class="gap-3! bg-muted/30 p-4">
+					<McpServerForm
+						url={newServerUrl}
+						headers={newServerHeaders}
+						onUrlChange={(v) => (newServerUrl = v)}
+						onHeadersChange={(v) => (newServerHeaders = v)}
+						urlError={newServerUrl ? newServerUrlError : null}
+						id="recommendation-new-server"
+					/>
+
+					<div class="flex justify-end gap-2 pt-2">
+						<Button variant="secondary" size="sm" onclick={resetAddForm}>Cancel</Button>
+
+						<Button
+							variant="default"
+							size="sm"
+							onclick={saveNewServer}
+							disabled={!!newServerUrlError}
+							aria-label="Save"
+						>
+							Add
+						</Button>
+					</div>
+				</Card.Root>
+			{:else}
+				<Card.Root class="gap-0 border-dashed bg-muted/30 p-0 transition-colors hover:bg-muted/50">
+					<button
+						type="button"
+						class="flex w-full items-center justify-center gap-2 rounded-lg p-6 text-sm text-muted-foreground transition-colors hover:text-foreground"
+						onclick={() => (showAddForm = true)}
+						aria-label="Add your own MCP server"
+					>
+						<Plus class="h-4 w-4" />
+						<span>Add your own server</span>
+					</button>
+				</Card.Root>
+			{/if}
+		</div>
+
+		<Dialog.Footer>
+			<Button variant="secondary" size="sm" onclick={() => handleOpenChange(false)}>Not now</Button>
+
+			<Button variant="default" size="sm" onclick={enableSelected}>Add selected</Button>
+		</Dialog.Footer>
+	</Dialog.Content>
+</Dialog.Root>
diff --git a/tools/ui/src/lib/components/app/dialogs/index.ts b/tools/ui/src/lib/components/app/dialogs/index.ts
index 29136308c..73f22c565 100644
--- a/tools/ui/src/lib/components/app/dialogs/index.ts
+++ b/tools/ui/src/lib/components/app/dialogs/index.ts
@@ -18,6 +18,15 @@
  */
 export { default as DialogMcpServerAddNew } from './DialogMcpServerAddNew.svelte';
 
+/**
+ * **DialogMcpServerRecommendations** - Suggested MCP servers opt-in dialog
+ *
+ * Prompts the user to enable pre-defined recommended MCP servers on first launch.
+ * Shows one switch per suggested server and persists the choice as a per-chat
+ * override so the selected servers become available in conversations.
+ */
+export { default as DialogMcpServerRecommendations } from './DialogMcpServerRecommendations.svelte';
+
 /**
  * **DialogExportSettings** - Settings export dialog with sensitive data warning
  *
diff --git a/tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte b/tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte
index e0bd8d98e..fd6e59a5b 100644
--- a/tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte
+++ b/tools/ui/src/lib/components/app/forms/KeyValuePairs.svelte
@@ -1,4 +1,5 @@
 <script lang="ts">
+	import { tick } from 'svelte';
 	import { Plus, Trash2 } from '@lucide/svelte';
 	import { Input } from '$lib/components/ui/input';
 	import {
@@ -33,8 +34,18 @@
 		sectionLabelOptional = true
 	}: Props = $props();
 
-	function addPair() {
+	// Pre-allocate the ref array so `bind:ref={keyInputRefs[index]}` never reads `undefined`
+	// for in-range indices; the $effect below keeps it in sync when `pairs` grows.
+	// svelte-ignore state_referenced_locally
+	let keyInputRefs: (HTMLInputElement | null)[] = $state(pairs.map(() => null));
+
+	async function addPair() {
+		// Capture the target index before mutating so deletions earlier in the
+		// list can't make keyInputRefs.length drift past the newly-appended row.
+		const newIndex = pairs.length;
 		onPairsChange([...pairs, { key: '', value: '' }]);
+		await tick();
+		keyInputRefs[newIndex]?.focus();
 	}
 
 	function removePair(index: number) {
@@ -76,6 +87,15 @@
 		newPairs[index] = { ...newPairs[index], value: trimmed };
 		onPairsChange(newPairs);
 	}
+
+	// Keep keyInputRefs aligned with pairs length so bind:ref never sees `undefined`.
+	// $effect.pre runs during traversal in tree order, before the {#each} block re-renders,
+	// so newly-appended items always have a defined slot when their binding is set up.
+	$effect.pre(() => {
+		while (keyInputRefs.length < pairs.length) {
+			keyInputRefs.push(null);
+		}
+	});
 </script>
 
 <div class={className}>
@@ -103,6 +123,7 @@
 			{#each pairs as pair, index (index)}
 				<div class="flex items-start gap-2">
 					<Input
+						bind:ref={keyInputRefs[index]}
 						type="text"
 						placeholder={keyPlaceholder}
 						value={pair.key}
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
index 199cb1458..d238d1ffb 100644
--- a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCard.svelte
@@ -163,7 +163,7 @@
 			{/if}
 		</div>
 
-		<div class="flex justify-between gap-4">
+		<div class="mt-auto flex justify-between gap-4">
 			{#if showSkeleton}
 				<Skeleton class="h-3 w-28" />
 			{:else if protocolVersion}
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardCompact.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardCompact.svelte
new file mode 100644
index 000000000..6cb3e18b6
--- /dev/null
+++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardCompact.svelte
@@ -0,0 +1,156 @@
+<script lang="ts">
+	import * as Card from '$lib/components/ui/card';
+	import { Badge } from '$lib/components/ui/badge';
+	import { Skeleton } from '$lib/components/ui/skeleton';
+	import { Switch } from '$lib/components/ui/switch';
+	import * as Tooltip from '$lib/components/ui/tooltip';
+	import { McpServerIdentity } from '$lib/components/app/mcp';
+	import { mcpStore } from '$lib/stores/mcp.svelte';
+	import { HealthCheckStatus } from '$lib/enums';
+	import type { MCPServerDisplayInfo, HealthCheckState, MCPServerSettingsEntry } from '$lib/types';
+	import { onMount } from 'svelte';
+	import { MCP_CARD_VISIBLE_TOOL_LIMIT, NEWLINE } from '$lib/constants';
+
+	interface Props {
+		server: MCPServerDisplayInfo & { description?: string };
+		enabled?: boolean;
+		onToggle?: (enabled: boolean) => void;
+	}
+
+	let { server, enabled = false, onToggle }: Props = $props();
+
+	onMount(() => {
+		const state = mcpStore.getHealthCheckState(server.id);
+
+		if (state.status === HealthCheckStatus.IDLE) {
+			mcpStore.runHealthCheck(server as MCPServerSettingsEntry).catch(() => {});
+		}
+	});
+
+	let healthState = $derived<HealthCheckState>(mcpStore.getHealthCheckState(server.id));
+	let displayName = $derived(mcpStore.getServerLabel(server));
+	let faviconUrl = $derived(mcpStore.getServerFavicon(server.id));
+	let isIdle = $derived(healthState.status === HealthCheckStatus.IDLE);
+	let isHealthChecking = $derived(healthState.status === HealthCheckStatus.CONNECTING);
+	let isError = $derived(healthState.status === HealthCheckStatus.ERROR);
+	let errorMessage = $derived(
+		healthState.status === HealthCheckStatus.ERROR ? healthState.message : undefined
+	);
+	let serverInfo = $derived(
+		healthState.status === HealthCheckStatus.SUCCESS ? healthState.serverInfo : undefined
+	);
+	let tools = $derived(healthState.status === HealthCheckStatus.SUCCESS ? healthState.tools : []);
+	let instructions = $derived(
+		healthState.status === HealthCheckStatus.SUCCESS ? healthState.instructions : undefined
+	);
+	let showSkeleton = $derived(isIdle || isHealthChecking);
+
+	// Curated descriptions get two lines; instructions fallback is one line so the
+	// compact card stays scannable.
+	let description = $derived.by(() => {
+		if (server.description) {
+			return { text: server.description, lines: 2 };
+		}
+		if (!instructions) return null;
+		const firstLine = instructions.split(NEWLINE).find((line: string) => line.trim().length > 0);
+		const trimmed = firstLine?.trim();
+		return trimmed ? { text: trimmed, lines: 1 } : null;
+	});
+
+	let visibleTools = $derived(tools.slice(0, MCP_CARD_VISIBLE_TOOL_LIMIT));
+	let hiddenTools = $derived(tools.slice(MCP_CARD_VISIBLE_TOOL_LIMIT));
+	let hiddenToolCount = $derived(hiddenTools.length);
+
+	function handleToggle(checked: boolean) {
+		onToggle?.(checked);
+	}
+</script>
+
+<Card.Root class="!gap-3 bg-muted/30 p-4">
+	<div class="flex items-start justify-between gap-3">
+		<div class="min-w-0 flex-1">
+			{#if showSkeleton}
+				<span class="flex min-w-0 items-center gap-1.5">
+					<Skeleton class="h-5 w-5 rounded" />
+					<Skeleton class="h-4 w-32" />
+				</span>
+			{:else}
+				<McpServerIdentity
+					{displayName}
+					{faviconUrl}
+					{serverInfo}
+					iconClass="h-5 w-5"
+					iconRounded="rounded"
+					nameClass="font-medium"
+				/>
+			{/if}
+		</div>
+
+		<Switch checked={enabled} disabled={isError || showSkeleton} onCheckedChange={handleToggle} />
+	</div>
+
+	{#if isError && errorMessage}
+		<p class="text-xs text-destructive">{errorMessage}</p>
+	{/if}
+
+	{#if showSkeleton}
+		<div class="space-y-1.5">
+			<Skeleton class="h-3 w-full max-w-md" />
+		</div>
+
+		<div class="flex flex-wrap items-center gap-1.5">
+			<Skeleton class="h-5 w-16 rounded-full" />
+			<Skeleton class="h-5 w-20 rounded-full" />
+			<Skeleton class="h-5 w-24 rounded-full" />
+			<Skeleton class="h-5 w-14 rounded-full" />
+		</div>
+	{:else}
+		{#if description}
+			{#if description.lines === 2}
+				<p class="line-clamp-2 text-xs text-muted-foreground" title={description.text}>
+					{description.text}
+				</p>
+			{:else}
+				<p class="line-clamp-1 truncate text-xs text-muted-foreground" title={description.text}>
+					{description.text}
+				</p>
+			{/if}
+		{/if}
+
+		{#if tools.length > 0}
+			<div class="flex flex-wrap items-center gap-1.5">
+				{#each visibleTools as tool (tool.name)}
+					<Tooltip.Root>
+						<Tooltip.Trigger>
+							<Badge variant="secondary" class="h-5 max-w-40 px-2 text-[11px]">
+								<span class="block min-w-0 flex-1 truncate">{tool.name}</span>
+							</Badge>
+						</Tooltip.Trigger>
+
+						<Tooltip.Content>
+							<p class="max-w-xs text-xs">
+								{tool.description ?? 'No description'}
+							</p>
+						</Tooltip.Content>
+					</Tooltip.Root>
+				{/each}
+
+				{#if hiddenToolCount > 0}
+					<Tooltip.Root>
+						<Tooltip.Trigger>
+							<Badge variant="secondary" class="h-5 px-2 text-[11px] text-muted-foreground">
+								+ {hiddenToolCount} more tools
+							</Badge>
+						</Tooltip.Trigger>
+
+						<Tooltip.Content class="max-w-md">
+							<p class="text-xs">
+								{hiddenTools.map((tool) => tool.name).join(', ')}
+							</p>
+						</Tooltip.Content>
+					</Tooltip.Root>
+				{/if}
+			</div>
+		{/if}
+	{/if}
+</Card.Root>
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
index 6727a9000..8ed4ee8b8 100644
--- a/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerCard/McpServerCardEditForm.svelte
@@ -1,6 +1,7 @@
 <script lang="ts">
 	import { Button } from '$lib/components/ui/button';
 	import { McpServerForm } from '$lib/components/app/mcp';
+	import { parseHeadersToArray } from '$lib/utils';
 
 	interface Props {
 		serverId: string;
@@ -26,13 +27,21 @@
 		}
 	});
 
-	let canSave = $derived(!urlError);
+	let headerPairsValid = $derived(
+		parseHeadersToArray(editHeaders).every((p) => p.key.trim() && p.value.trim())
+	);
+	let canSave = $derived(!urlError && headerPairsValid);
 
 	function handleSave() {
 		if (!canSave) return;
 		onSave(editUrl.trim(), editHeaders.trim(), editUseProxy);
 	}
 
+	function handleSubmit(event: SubmitEvent) {
+		event.preventDefault();
+		handleSave();
+	}
+
 	export function setInitialValues(url: string, headers: string, useProxy: boolean) {
 		editUrl = url;
 		editHeaders = headers;
@@ -40,25 +49,27 @@
 	}
 </script>
 
-<div class="space-y-4">
-	<p class="font-medium">Configure Server</p>
+<form onsubmit={handleSubmit} class="contents">
+	<div class="space-y-4">
+		<p class="font-medium">Configure Server</p>
 
-	<McpServerForm
-		url={editUrl}
-		headers={editHeaders}
-		useProxy={editUseProxy}
-		onUrlChange={(v) => (editUrl = v)}
-		onHeadersChange={(v) => (editHeaders = v)}
-		onUseProxyChange={(v) => (editUseProxy = v)}
-		urlError={editUrl ? urlError : null}
-		id={serverId}
-	/>
+		<McpServerForm
+			url={editUrl}
+			headers={editHeaders}
+			useProxy={editUseProxy}
+			onUrlChange={(v) => (editUrl = v)}
+			onHeadersChange={(v) => (editHeaders = v)}
+			onUseProxyChange={(v) => (editUseProxy = v)}
+			urlError={editUrl ? urlError : null}
+			id={serverId}
+		/>
 
-	<div class="flex items-center justify-end gap-2">
-		<Button variant="secondary" size="sm" onclick={onCancel}>Cancel</Button>
+		<div class="flex items-center justify-end gap-2">
+			<Button variant="secondary" size="sm" onclick={onCancel}>Cancel</Button>
 
-		<Button size="sm" onclick={handleSave} disabled={!canSave}>
-			{serverUrl.trim() ? 'Update' : 'Add'}
-		</Button>
+			<Button size="sm" type="submit" disabled={!canSave}>
+				{serverUrl.trim() ? 'Update' : 'Add'}
+			</Button>
+		</div>
 	</div>
-</div>
+</form>
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
index 79738e30d..7f05d5fef 100644
--- a/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerForm.svelte
@@ -38,14 +38,87 @@
 
 	let headerPairs = $derived<KeyValuePair[]>(parseHeadersToArray(headers));
 
+	const AUTHORIZATION_HEADER = 'Authorization';
+	const BEARER_PREFIX = 'Bearer ';
+
+	// Heuristic: this dedicated UI only owns Authorization headers that already
+	// carry a Bearer scheme. Anything else (e.g. Basic, raw tokens) stays in the
+	// KV section so the user can still edit those values verbatim.
+	const matchesAuthorizationKey = (key: string): boolean =>
+		key.trim().toLowerCase() === AUTHORIZATION_HEADER.toLowerCase();
+
+	const isBearerScheme = (value: string): boolean =>
+		value.trim().toLowerCase().startsWith(BEARER_PREFIX.toLowerCase());
+
+	const ownedByBearerUi = (p: KeyValuePair): boolean =>
+		matchesAuthorizationKey(p.key) && isBearerScheme(p.value);
+
+	let hasAuthorization = $derived(headerPairs.some(ownedByBearerUi));
+
+	let wantsAuthorization = $state(false);
+
+	let showAuthorization = $derived(hasAuthorization || wantsAuthorization);
+
+	let urlInput: HTMLInputElement | null = $state(null);
+	let bearerInput: HTMLInputElement | null = $state(null);
+
+	$effect(() => {
+		urlInput?.focus();
+	});
+
+	$effect(() => {
+		if (wantsAuthorization && bearerInput) {
+			bearerInput.focus();
+		}
+	});
+
+	let bearerToken = $derived.by(() => {
+		const auth = headerPairs.find(ownedByBearerUi);
+		if (!auth) return '';
+		return auth.value.trim().slice(BEARER_PREFIX.length).trim();
+	});
+
+	$effect(() => {
+		if (!headers.trim()) {
+			wantsAuthorization = false;
+		}
+	});
+
 	function updateHeaderPairs(newPairs: KeyValuePair[]) {
 		headerPairs = newPairs;
 		onHeadersChange(serializeHeaders(newPairs));
 	}
+
+	// The dedicated UI owns the Authorization slot end-to-end when the user
+	// engages it: any prior Authorization row (Bearer or otherwise) is replaced
+	// by exactly one { Authorization: "Bearer <token>" } entry. JSON's last-key
+	// behavior would otherwise pick one arbitrarily, so we strip first.
+	function updateBearerToken(token: string) {
+		const filtered = headerPairs.filter((p) => !matchesAuthorizationKey(p.key));
+
+		const trimmed = token.trim();
+
+		if (trimmed) {
+			filtered.push({ key: AUTHORIZATION_HEADER, value: `${BEARER_PREFIX}${trimmed}` });
+		}
+
+		updateHeaderPairs(filtered);
+	}
+
+	function setUseAuthorization(checked: boolean) {
+		wantsAuthorization = checked;
+
+		if (!checked) {
+			// Only drop the entry this UI owns; a non-Bearer Authorization row
+			// authored in the KV section must survive a toggle off untouched.
+			const filtered = headerPairs.filter((p) => !ownedByBearerUi(p));
+			updateHeaderPairs(filtered);
+		}
+	}
 </script>
 
-<div class="grid gap-3">
-	<div>
+<div class="grid gap-2">
+	<div class="mb-4">
 		<label for="server-url-{id}" class="mb-2 block text-xs font-medium">
 			Server URL <span class="text-destructive">*</span>
 		</label>
@@ -57,50 +130,52 @@
 			value={url}
 			oninput={(e) => onUrlChange(e.currentTarget.value)}
 			class={urlError ? 'border-destructive' : ''}
+			bind:ref={urlInput}
 		/>
 
 		{#if urlError}
 			<p class="mt-1.5 text-xs text-destructive">{urlError}</p>
 		{/if}
-
-		{#if !isWebSocket && onUseProxyChange}
-			<label
-				class={[
-					'mt-3 flex items-start gap-2',
-					mcpStore.isProxyAvailable && 'cursor-pointer',
-					!mcpStore.isProxyAvailable && 'opacity-80'
-				]}
-			>
-				<Switch
-					class="mt-1"
-					id="use-proxy-{id}"
-					checked={useProxy}
-					disabled={!mcpStore.isProxyAvailable}
-					onCheckedChange={(checked) => onUseProxyChange?.(checked)}
-				/>
-
-				<span>
-					<span class="text-xs text-muted-foreground">Use llama-server proxy</span>
-
-					<br />
-
-					{#if !mcpStore.isProxyAvailable}
-						<span class="inline-flex gap-0.75 text-xs text-muted-foreground/60"
-							>(Run <pre>llama-server</pre>
-							with
-							<pre>{CLI_FLAGS.MCP_PROXY}</pre>
-							flag)</span
-						>
-					{/if}
-				</span>
-			</label>
-		{/if}
 	</div>
 
+	<label class="flex items-center gap-2 cursor-pointer">
+		<Switch
+			id="use-authorization-{id}"
+			checked={showAuthorization}
+			onCheckedChange={setUseAuthorization}
+		/>
+
+		<span class="text-xs text-muted-foreground">Authorization</span>
+	</label>
+
+	{#if showAuthorization}
+		<div class="relative mt-2">
+			<Input
+				id="bearer-token-{id}"
+				type="password"
+				autocomplete="off"
+				placeholder="Paste token here"
+				value={bearerToken}
+				oninput={(e) => updateBearerToken(e.currentTarget.value)}
+				class="pl-16"
+				bind:ref={bearerInput}
+			/>
+
+			<span
+				class="pointer-events-none absolute inset-y-0 left-3 flex items-center text-sm font-medium text-foreground"
+			>
+				Bearer
+			</span>
+		</div>
+	{/if}
+
 	<KeyValuePairs
-		class="mt-2"
-		pairs={headerPairs}
-		onPairsChange={updateHeaderPairs}
+		class="mt-3"
+		pairs={headerPairs.filter((p) => !ownedByBearerUi(p))}
+		onPairsChange={(pairs) => {
+			const auth = headerPairs.find(ownedByBearerUi);
+			updateHeaderPairs(auth ? [...pairs, auth] : pairs);
+		}}
 		keyPlaceholder="Header name"
 		valuePlaceholder="Value"
 		addButtonLabel="Add"
@@ -108,4 +183,37 @@
 		sectionLabel="Custom Headers"
 		sectionLabelOptional
 	/>
+
+	{#if !isWebSocket && onUseProxyChange}
+		<label
+			class={[
+				'mt-3 flex items-start gap-2',
+				mcpStore.isProxyAvailable && 'cursor-pointer',
+				!mcpStore.isProxyAvailable && 'opacity-80'
+			]}
+		>
+			<Switch
+				class="mt-1"
+				id="use-proxy-{id}"
+				checked={useProxy}
+				disabled={!mcpStore.isProxyAvailable}
+				onCheckedChange={(checked) => onUseProxyChange?.(checked)}
+			/>
+
+			<span>
+				<span class="text-xs text-muted-foreground">Use llama-server proxy</span>
+
+				<br />
+
+				{#if !mcpStore.isProxyAvailable}
+					<span class="inline-flex gap-0.75 text-xs text-muted-foreground/60"
+						>(Run <pre>llama-server</pre>
+						with
+						<pre>{CLI_FLAGS.MCP_PROXY}</pre>
+						flag)</span
+					>
+				{/if}
+			</span>
+		</label>
+	{/if}
 </div>
diff --git a/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte b/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte
index feafc5d81..3f128e02c 100644
--- a/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte
+++ b/tools/ui/src/lib/components/app/mcp/McpServerIdentity.svelte
@@ -1,6 +1,7 @@
 <script lang="ts">
 	import { ExternalLink } from '@lucide/svelte';
 	import { Badge } from '$lib/components/ui/badge';
+	import { McpLogo } from '$lib/components/app/mcp';
 	import { TruncatedText } from '$lib/components/app/misc';
 	import { sanitizeExternalUrl } from '$lib/utils';
 	import type { MCPServerInfo } from '$lib/types';
@@ -34,20 +35,15 @@
 
 <span class="flex min-w-0 items-center gap-1.5">
 	{#if faviconUrl}
-		<img
-			src={faviconUrl}
-			alt=""
-			class={['shrink-0', iconRounded, iconClass]}
-			onerror={(e) => {
-				(e.currentTarget as HTMLImageElement).style.display = 'none';
-			}}
-		/>
+		<img src={faviconUrl} alt="" class={['shrink-0 text-foreground', iconRounded, iconClass]} />
+	{:else}
+		<McpLogo class={['shrink-0 text-foreground', iconRounded, iconClass].join(' ')} />
 	{/if}
 
 	<TruncatedText text={displayName ?? ''} class={nameClass ?? ''} />
 
 	{#if showVersion && serverInfo?.version}
-		<Badge variant="secondary" class="h-4 min-w-0 shrink px-1 text-[10px]">
+		<Badge variant="secondary" class="h-4 max-w-24 min-w-0 shrink px-1 text-[10px]">
 			<TruncatedText text={`v${serverInfo.version}`} />
 		</Badge>
 	{/if}
diff --git a/tools/ui/src/lib/components/app/mcp/index.ts b/tools/ui/src/lib/components/app/mcp/index.ts
index 3d30bb3b4..b142841f4 100644
--- a/tools/ui/src/lib/components/app/mcp/index.ts
+++ b/tools/ui/src/lib/components/app/mcp/index.ts
@@ -180,6 +180,16 @@ export { default as McpServerCardDeleteDialog } from './McpServerCard/McpServerC
 /** Skeleton loading state for server card during health checks. */
 export { default as McpServerCardSkeleton } from './McpServerCardSkeleton.svelte';
 
+/**
+ * **McpServerCardCompact** - Condensed MCP server card
+ *
+ * Compact alternative to McpServerCard tailored for picker-style UIs.
+ * Shows the server identity, status, and a flex-wrapped list of available tools.
+ * Tool names are rendered as badges; hovering a badge shows its description in a tooltip.
+ * Does not show connection logs or server instructions.
+ */
+export { default as McpServerCardCompact } from './McpServerCard/McpServerCardCompact.svelte';
+
 /**
  * **McpServerIdentity** - Server identity display (icon, name, version)
  *
diff --git a/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
index e6f4b7e39..e35cfadbb 100644
--- a/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
+++ b/tools/ui/src/lib/components/app/settings/SettingsMcpServers.svelte
@@ -21,7 +21,7 @@
 
 	let { class: className }: Props = $props();
 
-	let servers = $derived(mcpStore.getServersSorted());
+	let servers = $derived(mcpStore.visibleMcpServers);
 
 	let initialLoadComplete = $state(false);
 	let isAddingServer = $state(false);
diff --git a/tools/ui/src/lib/constants/index.ts b/tools/ui/src/lib/constants/index.ts
index b982a5907..643864ad4 100644
--- a/tools/ui/src/lib/constants/index.ts
+++ b/tools/ui/src/lib/constants/index.ts
@@ -8,6 +8,7 @@ export * from './attachment-labels';
 export * from './database';
 export * from './reasoning-effort';
 export * from './reasoning-effort-tokens';
+export * from './recommended-mcp-servers';
 export * from './storage';
 export * from './attachment-menu';
 export * from './auto-scroll';
diff --git a/tools/ui/src/lib/constants/mcp-form.ts b/tools/ui/src/lib/constants/mcp-form.ts
index 7a1ccffb0..dbbdc6f12 100644
--- a/tools/ui/src/lib/constants/mcp-form.ts
+++ b/tools/ui/src/lib/constants/mcp-form.ts
@@ -1,2 +1,4 @@
 export const MCP_SERVER_URL_PLACEHOLDER = 'https://mcp.example.com/sse';
 export const MIN_AUTOCOMPLETE_INPUT_LENGTH = 1;
+/** Number of tools shown on the compact MCP server card before collapsing to a "+ N more" badge */
+export const MCP_CARD_VISIBLE_TOOL_LIMIT = 4;
diff --git a/tools/ui/src/lib/constants/recommended-mcp-servers.ts b/tools/ui/src/lib/constants/recommended-mcp-servers.ts
new file mode 100644
index 000000000..1fb3ba8c7
--- /dev/null
+++ b/tools/ui/src/lib/constants/recommended-mcp-servers.ts
@@ -0,0 +1,35 @@
+import { DEFAULT_MCP_CONFIG } from './mcp';
+import type { RecommendedMCPServer } from '$lib/types';
+
+/**
+ * Pre-defined recommended MCP servers.
+ *
+ * Servers are enabled by default, but they are not turned on for individual
+ * conversations until the user explicitly enables them (so their tools are
+ * disabled by default).
+ */
+export const RECOMMENDED_MCP_SERVERS: RecommendedMCPServer[] = [
+	{
+		id: 'exa-web-search',
+		name: 'Exa Web Search',
+		description: 'Search the web and retrieve relevant content.',
+		url: 'https://mcp.exa.ai/mcp',
+		enabled: true,
+		requestTimeoutSeconds: DEFAULT_MCP_CONFIG.requestTimeoutSeconds
+	},
+	{
+		id: 'huggingface-mcp',
+		name: 'Hugging Face',
+		description:
+			'Browse models, datasets, spaces and machine learning papers from the Hugging Face hub.',
+		url: 'https://huggingface.co/mcp',
+		enabled: true,
+		requestTimeoutSeconds: DEFAULT_MCP_CONFIG.requestTimeoutSeconds
+	}
+];
+
+export const RECOMMENDED_MCP_SERVER_IDS = new Set(
+	RECOMMENDED_MCP_SERVERS.map((server) => server.id)
+);
+
+export const RECOMMENDED_MCP_SERVERS_OPTIN_DIALOG_DELAY = 1000;
diff --git a/tools/ui/src/lib/constants/settings-keys.ts b/tools/ui/src/lib/constants/settings-keys.ts
index a3046caa9..498a79c73 100644
--- a/tools/ui/src/lib/constants/settings-keys.ts
+++ b/tools/ui/src/lib/constants/settings-keys.ts
@@ -59,6 +59,7 @@ export const SETTINGS_KEYS = {
 	// MCP
 	MCP_SERVERS: 'mcpServers',
 	MCP_REQUEST_TIMEOUT_SECONDS: 'mcpRequestTimeoutSeconds',
+	MCP_DEFAULT_SERVER_OVERRIDES: 'mcpDefaultServerOverrides',
 	AGENTIC_MAX_TURNS: 'agenticMaxTurns',
 	ALWAYS_SHOW_AGENTIC_TURNS: 'alwaysShowAgenticTurns',
 	AGENTIC_MAX_TOOL_PREVIEW_LINES: 'agenticMaxToolPreviewLines',
diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
index 37161c8e3..2047d0b43 100644
--- a/tools/ui/src/lib/constants/settings-registry.ts
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -28,6 +28,7 @@ import McpLogo from '$lib/components/app/mcp/McpLogo.svelte';
 import { SETTINGS_KEYS } from './settings-keys';
 import { ROUTES, SETTINGS_SECTION_SLUGS } from './routes';
 import { TITLE_GENERATION } from './title-generation';
+import { RECOMMENDED_MCP_SERVERS } from './recommended-mcp-servers';
 
 export const SETTINGS_SECTION_TITLES = {
 	GENERAL: 'General',
@@ -774,9 +775,16 @@ const NON_UI_SETTINGS: SettingsEntry[] = [
 		key: SETTINGS_KEYS.MCP_SERVERS,
 		label: 'MCP servers',
 		help: 'Configure MCP servers as a JSON list. Use the form in the MCP Client settings section to edit.',
-		defaultValue: '[]',
+		defaultValue: JSON.stringify(RECOMMENDED_MCP_SERVERS),
 		type: SettingsFieldType.INPUT,
 		sync: { serverKey: SETTINGS_KEYS.MCP_SERVERS, paramType: SyncableParameterType.STRING }
+	},
+	{
+		key: SETTINGS_KEYS.MCP_DEFAULT_SERVER_OVERRIDES,
+		label: 'MCP default server overrides',
+		help: 'Per-server enable/disable defaults inherited by new chats. JSON-serialized list of {serverId, enabled} entries.',
+		defaultValue: '[]',
+		type: SettingsFieldType.INPUT
 	}
 	// {
 	// 	key: SETTINGS_KEYS.PY_INTERPRETER_ENABLED,
diff --git a/tools/ui/src/lib/constants/storage.ts b/tools/ui/src/lib/constants/storage.ts
index 0180a76fb..d0c5f2eff 100644
--- a/tools/ui/src/lib/constants/storage.ts
+++ b/tools/ui/src/lib/constants/storage.ts
@@ -21,9 +21,10 @@ export const DISABLED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.disabledTool
 /** Disabled tools keyed by stable selection identity, no migration from the name based key */
 export const DISABLED_TOOL_KEYS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.disabledToolKeys`;
 export const FAVORITE_MODELS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.favoriteModels`;
-export const MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.mcpDefaultEnabled`;
 export const THINKING_ENABLED_DEFAULT_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.thinkingEnabledDefault`;
 export const REASONING_EFFORT_DEFAULT_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.reasoningEffortDefault`;
+/** Set when user has interacted with the MCP server recommendations dialog (checked servers, added custom server, or dismissed) */
+export const MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.mcpServersSetupDone`;
 export const USER_OVERRIDES_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME}.userOverrides`;
 
 /** Key prefix for per-conversation resumable stream state, conversationId is appended */
@@ -38,8 +39,6 @@ export const DEPRECATED_CONFIG_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED
 export const DEPRECATED_DISABLED_TOOLS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.disabledTools`;
 /** @deprecated Use {@link FAVORITE_MODELS_LOCALSTORAGE_KEY} instead */
 export const DEPRECATED_FAVORITE_MODELS_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.favoriteModels`;
-/** @deprecated Use {@link MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY} instead */
-export const DEPRECATED_MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.mcpDefaultEnabled`;
 /** @deprecated Use {@link USER_OVERRIDES_LOCALSTORAGE_KEY} instead */
 export const DEPRECATED_USER_OVERRIDES_LOCALSTORAGE_KEY = `${STORAGE_APP_NAME_DEPRECATED}.userOverrides`;
 
@@ -52,6 +51,5 @@ export const NEW_TO_DEPRECATED_MAP: Record<string, string> = {
 	[CONFIG_LOCALSTORAGE_KEY]: DEPRECATED_CONFIG_LOCALSTORAGE_KEY,
 	[DISABLED_TOOLS_LOCALSTORAGE_KEY]: DEPRECATED_DISABLED_TOOLS_LOCALSTORAGE_KEY,
 	[FAVORITE_MODELS_LOCALSTORAGE_KEY]: DEPRECATED_FAVORITE_MODELS_LOCALSTORAGE_KEY,
-	[MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY]: DEPRECATED_MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY,
 	[USER_OVERRIDES_LOCALSTORAGE_KEY]: DEPRECATED_USER_OVERRIDES_LOCALSTORAGE_KEY
 };
diff --git a/tools/ui/src/lib/hooks/use-mcp-recommendations.svelte.ts b/tools/ui/src/lib/hooks/use-mcp-recommendations.svelte.ts
new file mode 100644
index 000000000..c8a85fa8f
--- /dev/null
+++ b/tools/ui/src/lib/hooks/use-mcp-recommendations.svelte.ts
@@ -0,0 +1,85 @@
+import { browser } from '$app/environment';
+import {
+	MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY,
+	RECOMMENDED_MCP_SERVER_IDS,
+	RECOMMENDED_MCP_SERVERS_OPTIN_DIALOG_DELAY
+} from '$lib/constants';
+import { mcpStore } from '$lib/stores/mcp.svelte';
+
+/**
+ * First-run opt-in dialog for the recommended MCP servers.
+ *
+ * Owns the dismissed / open / trigger-timeout state and the effect that
+ * schedules the dialog. Reads opt-in status and the configured server list
+ * from `mcpStore`, so callers don't need to recompute on their side.
+ */
+export function useMcpRecommendations() {
+	let dismissed = $state(
+		browser && localStorage.getItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY) === 'true'
+	);
+	let open = $state(false);
+	let checked = $state(false);
+	let triggerTimeout: ReturnType<typeof setTimeout> | null = null;
+
+	function dismiss() {
+		if (browser) {
+			localStorage.setItem(MCP_SERVERS_ADDED_TO_CHAT_LOCALSTORAGE_KEY, 'true');
+		}
+		dismissed = true;
+		open = false;
+		if (triggerTimeout) {
+			clearTimeout(triggerTimeout);
+			triggerTimeout = null;
+		}
+	}
+
+	function handleOpenChange(next: boolean) {
+		open = next;
+		if (!next) dismiss();
+	}
+
+	$effect(() => {
+		if (!browser) return;
+
+		if (open || dismissed) {
+			if (triggerTimeout) {
+				clearTimeout(triggerTimeout);
+				triggerTimeout = null;
+			}
+			return;
+		}
+
+		// Already evaluated once this session; leave any pending trigger alone so
+		// it can still fire later. Setting `checked = true` below re-runs this
+		// effect, and we must not wipe the timeout that was just scheduled.
+		if (checked) return;
+
+		if (mcpStore.optedInRecommendationIds.size > 0) {
+			checked = true;
+			return;
+		}
+
+		const hasRecommendations = mcpStore
+			.getServers()
+			.some((server) => RECOMMENDED_MCP_SERVER_IDS.has(server.id));
+
+		if (hasRecommendations) {
+			triggerTimeout = setTimeout(() => {
+				open = true;
+			}, RECOMMENDED_MCP_SERVERS_OPTIN_DIALOG_DELAY);
+		}
+
+		checked = true;
+	});
+
+	return {
+		get open() {
+			return open;
+		},
+		get dismissed() {
+			return dismissed;
+		},
+		dismiss,
+		handleOpenChange
+	};
+}
diff --git a/tools/ui/src/lib/services/migration.service.ts b/tools/ui/src/lib/services/migration.service.ts
index 20dfa9b19..152b78cd3 100644
--- a/tools/ui/src/lib/services/migration.service.ts
+++ b/tools/ui/src/lib/services/migration.service.ts
@@ -20,6 +20,7 @@
 import Dexie from 'dexie';
 import {
 	STORAGE_APP_NAME,
+	STORAGE_APP_NAME_DEPRECATED,
 	DB_APP_NAME_DEPRECATED,
 	CONFIG_LOCALSTORAGE_KEY,
 	IDXDB_TABLES,
@@ -494,12 +495,69 @@ const customJsonKeyMigration: Migration = {
 	}
 };
 
+const MCP_DEFAULT_ENABLED_MIGRATION_ID = 'mcp-default-enabled-to-config-v1';
+
+const LEGACY_MCP_DEFAULT_ENABLED_KEY = `${STORAGE_APP_NAME}.mcpDefaultEnabled`;
+const DEPRECATED_LEGACY_MCP_DEFAULT_ENABLED_KEY = `${STORAGE_APP_NAME_DEPRECATED}.mcpDefaultEnabled`;
+
+const mcpDefaultEnabledMigration: Migration = {
+	id: MCP_DEFAULT_ENABLED_MIGRATION_ID,
+	description:
+		'Copy mcpDefaultEnabled localStorage key into settings config (preserves legacy keys)',
+
+	async run(): Promise<void> {
+		const raw =
+			localStorage.getItem(LEGACY_MCP_DEFAULT_ENABLED_KEY) ??
+			localStorage.getItem(DEPRECATED_LEGACY_MCP_DEFAULT_ENABLED_KEY);
+
+		// Legacy keys intentionally left in place so a downgrade keeps reading them.
+
+		if (raw === null) {
+			if (import.meta.env.DEV && import.meta.env.VITE_DEBUG)
+				console.log('[Migration] MCP default enabled: no legacy key found, skipping');
+			return;
+		}
+
+		const configRaw = localStorage.getItem(CONFIG_LOCALSTORAGE_KEY);
+		const config = configRaw ? JSON.parse(configRaw) : {};
+
+		// Don't overwrite an existing config entry — current data wins.
+		if (SETTINGS_KEYS.MCP_DEFAULT_SERVER_OVERRIDES in config) {
+			if (import.meta.env.DEV && import.meta.env.VITE_DEBUG)
+				console.log('[Migration] MCP default enabled: config already has overrides, skipping');
+			return;
+		}
+
+		try {
+			const parsed = JSON.parse(raw);
+			if (!Array.isArray(parsed)) return;
+			const valid = parsed.every(
+				(o) =>
+					typeof o === 'object' &&
+					o !== null &&
+					typeof (o as Record<string, unknown>).serverId === 'string' &&
+					typeof (o as Record<string, unknown>).enabled === 'boolean'
+			);
+			if (!valid) return;
+		} catch {
+			return;
+		}
+
+		config[SETTINGS_KEYS.MCP_DEFAULT_SERVER_OVERRIDES] = raw;
+		localStorage.setItem(CONFIG_LOCALSTORAGE_KEY, JSON.stringify(config));
+
+		if (import.meta.env.DEV && import.meta.env.VITE_DEBUG)
+			console.log('[Migration] MCP default enabled: moved legacy key into config');
+	}
+};
+
 const migrations: Migration[] = [
 	localStorageMigration,
 	idxdbMigration,
 	legacyMessageMigration,
 	themeMigration,
-	customJsonKeyMigration
+	customJsonKeyMigration,
+	mcpDefaultEnabledMigration
 ];
 
 export const MigrationService = {
diff --git a/tools/ui/src/lib/stores/conversations.svelte.ts b/tools/ui/src/lib/stores/conversations.svelte.ts
index 235015213..ef8d61309 100644
--- a/tools/ui/src/lib/stores/conversations.svelte.ts
+++ b/tools/ui/src/lib/stores/conversations.svelte.ts
@@ -23,7 +23,7 @@ import { browser } from '$app/environment';
 import { toast } from 'svelte-sonner';
 import { DatabaseService } from '$lib/services/database.service';
 import { MigrationService } from '$lib/services/migration.service';
-import { config } from '$lib/stores/settings.svelte';
+import { config, settingsStore } from '$lib/stores/settings.svelte';
 import { filterByLeafNodeId, findLeafNode, generateConversationTitle } from '$lib/utils';
 import type { McpServerOverride } from '$lib/types/database';
 import { zipSync, unzipSync, strToU8, strFromU8 } from 'fflate';
@@ -46,7 +46,7 @@ import {
 	ISO_TIME_SEPARATOR_REPLACEMENT,
 	NON_ALPHANUMERIC_REGEX,
 	MULTIPLE_UNDERSCORE_REGEX,
-	MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY,
+	SETTINGS_KEYS,
 	THINKING_ENABLED_DEFAULT_LOCALSTORAGE_KEY,
 	REASONING_EFFORT_DEFAULT_LOCALSTORAGE_KEY
 } from '$lib/constants';
@@ -90,12 +90,10 @@ class ConversationsStore {
 	/** Global (non-conversation-specific) reasoning effort default */
 	pendingReasoningEffort = $state<ReasoningEffort>(ConversationsStore.loadReasoningEffortDefault());
 
-	/** Load MCP default overrides from localStorage */
 	private static loadMcpDefaults(): McpServerOverride[] {
-		if (typeof globalThis.localStorage === 'undefined') return [];
+		const raw = config()[SETTINGS_KEYS.MCP_DEFAULT_SERVER_OVERRIDES];
+		if (typeof raw !== 'string' || raw.length === 0) return [];
 		try {
-			const raw = localStorage.getItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY);
-			if (!raw) return [];
 			const parsed = JSON.parse(raw);
 			if (!Array.isArray(parsed)) return [];
 			return parsed.filter(
@@ -106,18 +104,12 @@ class ConversationsStore {
 		}
 	}
 
-	/** Persist MCP default overrides to localStorage */
 	private saveMcpDefaults(): void {
-		if (typeof globalThis.localStorage === 'undefined') return;
 		const plain = this.pendingMcpServerOverrides.map((o) => ({
 			serverId: o.serverId,
 			enabled: o.enabled
 		}));
-		if (plain.length > 0) {
-			localStorage.setItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY, JSON.stringify(plain));
-		} else {
-			localStorage.removeItem(MCP_DEFAULT_ENABLED_LOCALSTORAGE_KEY);
-		}
+		settingsStore.updateConfig(SETTINGS_KEYS.MCP_DEFAULT_SERVER_OVERRIDES, JSON.stringify(plain));
 	}
 
 	/** Load thinking-enabled default from localStorage */
@@ -189,6 +181,10 @@ class ConversationsStore {
 		try {
 			await MigrationService.runAllMigrations();
 
+			// Re-read defaults after migrations: a migration may have populated
+			// the settings config (e.g. moved legacy MCP overrides into it).
+			this.pendingMcpServerOverrides = ConversationsStore.loadMcpDefaults();
+
 			await this.loadConversations();
 			this.isInitialized = true;
 		} catch (error) {
diff --git a/tools/ui/src/lib/stores/mcp.svelte.ts b/tools/ui/src/lib/stores/mcp.svelte.ts
index 671ecf287..37eb56332 100644
--- a/tools/ui/src/lib/stores/mcp.svelte.ts
+++ b/tools/ui/src/lib/stores/mcp.svelte.ts
@@ -20,11 +20,13 @@
  */
 
 import { browser } from '$app/environment';
+import { SvelteSet } from 'svelte/reactivity';
 import { SETTINGS_KEYS } from '$lib/constants';
 import { MCPService } from '$lib/services/mcp.service';
 import { config, settingsStore } from '$lib/stores/settings.svelte';
 import { mcpResourceStore } from '$lib/stores/mcp-resources.svelte';
 import { serverStore } from '$lib/stores/server.svelte';
+import { conversationsStore } from '$lib/stores/conversations.svelte';
 import { mode } from 'mode-watcher';
 import {
 	parseMcpServerSettings,
@@ -48,10 +50,11 @@ import {
 	EXPECTED_THEMED_ICON_PAIR_COUNT,
 	MCP_ALLOWED_ICON_MIME_TYPES,
 	MCP_SERVER_ID_PREFIX,
-	MCP_RECONNECT_INITIAL_DELAY,
 	MCP_RECONNECT_BACKOFF_MULTIPLIER,
+	MCP_RECONNECT_INITIAL_DELAY,
 	MCP_RECONNECT_MAX_DELAY,
-	MCP_RECONNECT_ATTEMPT_TIMEOUT_MS
+	MCP_RECONNECT_ATTEMPT_TIMEOUT_MS,
+	RECOMMENDED_MCP_SERVER_IDS
 } from '$lib/constants';
 import type {
 	MCPToolCall,
@@ -70,6 +73,7 @@ import type {
 	Tool,
 	HealthCheckState,
 	MCPServerSettingsEntry,
+	MCPServerDisplayInfo,
 	MCPServerConfig,
 	MCPResourceIcon,
 	MCPResourceAttachment,
@@ -365,7 +369,7 @@ class MCPStore {
 		return this.connections;
 	}
 
-	getServerLabel(server: MCPServerSettingsEntry): string {
+	getServerLabel(server: MCPServerDisplayInfo): string {
 		const healthState = this.getHealthCheckState(server.id);
 
 		if (healthState?.status === HealthCheckStatus.SUCCESS)
@@ -527,7 +531,7 @@ class MCPStore {
 
 	addServer(
 		serverData: Omit<MCPServerSettingsEntry, 'id' | 'requestTimeoutSeconds'> & { id?: string }
-	): void {
+	): MCPServerSettingsEntry {
 		const servers = this.getServers();
 		const newServer: MCPServerSettingsEntry = {
 			id: serverData.id || (uuid() ?? `server-${Date.now()}`),
@@ -540,6 +544,7 @@ class MCPStore {
 			useProxy: serverData.useProxy
 		};
 		settingsStore.updateConfig(SETTINGS_KEYS.MCP_SERVERS, JSON.stringify([...servers, newServer]));
+		return newServer;
 	}
 
 	updateServer(id: string, updates: Partial<MCPServerSettingsEntry>): void {
@@ -576,6 +581,33 @@ class MCPStore {
 		});
 	}
 
+	/**
+	 * Recommended MCP server IDs the user opted in to via per-chat overrides.
+	 * Single source of truth for "which recommendations has the user accepted",
+	 * shared by the recommendations hook and the visible-servers getter.
+	 */
+	get optedInRecommendationIds(): ReadonlySet<string> {
+		const ids = new SvelteSet<string>();
+		for (const override of conversationsStore.pendingMcpServerOverrides) {
+			if (RECOMMENDED_MCP_SERVER_IDS.has(override.serverId) && override.enabled) {
+				ids.add(override.serverId);
+			}
+		}
+		return ids;
+	}
+
+	/**
+	 * MCP servers selectable in chat-add UIs and the settings page:
+	 * enabled in settings and either non-recommended or explicitly opted in.
+	 */
+	get visibleMcpServers(): MCPServerSettingsEntry[] {
+		const optedIn = this.optedInRecommendationIds;
+		return this.getServersSorted().filter(
+			(server) =>
+				server.enabled && (!RECOMMENDED_MCP_SERVER_IDS.has(server.id) || optedIn.has(server.id))
+		);
+	}
+
 	async ensureInitialized(perChatOverrides?: McpServerOverride[]): Promise<boolean> {
 		if (!browser) {
 			return false;
diff --git a/tools/ui/src/lib/types/index.ts b/tools/ui/src/lib/types/index.ts
index cbe0538be..408ac0cbd 100644
--- a/tools/ui/src/lib/types/index.ts
+++ b/tools/ui/src/lib/types/index.ts
@@ -127,6 +127,8 @@ export type {
 	MCPServerConfig,
 	MCPClientConfig,
 	MCPServerSettingsEntry,
+	MCPServerDisplayInfo,
+	RecommendedMCPServer,
 	MCPToolCall,
 	OpenAIToolDefinition,
 	ServerStatus,
diff --git a/tools/ui/src/lib/types/mcp.d.ts b/tools/ui/src/lib/types/mcp.d.ts
index 2a2926142..bf7fd3285 100644
--- a/tools/ui/src/lib/types/mcp.d.ts
+++ b/tools/ui/src/lib/types/mcp.d.ts
@@ -209,17 +209,32 @@ export type MCPToolCall = {
 	};
 };
 
-export type MCPServerSettingsEntry = {
+/**
+ * Minimum fields needed to display or identify an MCP server.
+ */
+export interface MCPServerDisplayInfo {
 	id: string;
-	enabled: boolean;
+	name?: string;
 	url: string;
+}
+
+export type MCPServerSettingsEntry = MCPServerDisplayInfo & {
+	enabled: boolean;
 	requestTimeoutSeconds: number;
 	headers?: string;
-	name?: string;
 	iconUrl?: string;
 	useProxy?: boolean;
 };
 
+/**
+ * Pre-defined recommended MCP server shown to the user in onboarding/picker UIs.
+ */
+export interface RecommendedMCPServer extends MCPServerDisplayInfo {
+	description: string;
+	enabled: boolean;
+	requestTimeoutSeconds: number;
+}
+
 export interface MCPHostManagerConfig {
 	servers: MCPClientConfig['servers'];
 	clientInfo?: Implementation;
diff --git a/tools/ui/src/routes/+layout.svelte b/tools/ui/src/routes/+layout.svelte
index 38848786e..29b5b1510 100644
--- a/tools/ui/src/routes/+layout.svelte
+++ b/tools/ui/src/routes/+layout.svelte
@@ -8,6 +8,7 @@
 	import { onMount } from 'svelte';
 
 	import { SidebarNavigation, DialogConversationTitleUpdate } from '$lib/components/app';
+	import { DialogMcpServerRecommendations } from '$lib/components/app/dialogs';
 	import { PwaMetaTags, PwaRefreshAlert } from '$lib/components/pwa';
 	import { pwaAssetsHead } from 'virtual:pwa-assets/head';
 
@@ -26,6 +27,7 @@
 	import { FAVICON_PATHS, FAVICON_SELECTORS } from '$lib/constants/pwa';
 	import { useKeyboardShortcuts } from '$lib/hooks/use-keyboard-shortcuts.svelte';
 	import { usePwa } from '$lib/hooks/use-pwa.svelte';
+	import { useMcpRecommendations } from '$lib/hooks/use-mcp-recommendations.svelte';
 	import { conversations } from '$lib/stores/conversations.svelte';
 	import { isMobile } from '$lib/stores/viewport.svelte';
 	import { theme } from '$lib/stores/theme.svelte';
@@ -37,6 +39,8 @@
 	let innerHeight = $state<number | undefined>();
 	let innerWidth = $state(browser ? window.innerWidth : 0);
 
+	const mcpRecommendations = useMcpRecommendations();
+
 	let chatSidebar:
 		| {
 				activateSearchMode?: () => void;
@@ -321,6 +325,11 @@
 		onConfirm={handleTitleUpdateConfirm}
 		onCancel={handleTitleUpdateCancel}
 	/>
+
+	<DialogMcpServerRecommendations
+		open={mcpRecommendations.open}
+		onOpenChange={mcpRecommendations.handleOpenChange}
+	/>
 </Tooltip.Provider>
 
 <!-- PWA update prompt + version -->
diff --git a/tools/ui/tests/client/components/McpServerFormWrapper.svelte b/tools/ui/tests/client/components/McpServerFormWrapper.svelte
new file mode 100644
index 000000000..fe2cc958b
--- /dev/null
+++ b/tools/ui/tests/client/components/McpServerFormWrapper.svelte
@@ -0,0 +1,37 @@
+<script lang="ts">
+	import { untrack } from 'svelte';
+	import McpServerForm from '$lib/components/app/mcp/McpServerForm.svelte';
+
+	interface Props {
+		headers?: string;
+	}
+
+	let { headers = '' }: Props = $props();
+
+	let headersState = $state(untrack(() => headers));
+	let lastCapturedHeaders = $state(untrack(() => headers));
+
+	$effect(() => {
+		if (headers !== lastCapturedHeaders) {
+			headersState = headers;
+			lastCapturedHeaders = headers;
+		}
+	});
+</script>
+
+<!--
+	Drives McpServerForm with a controlled `headers` string and exposes the
+	latest captured value through `data-captured-headers` so the client test
+	can read it back without a custom binding API.
+-->
+<McpServerForm
+	url="https://example.test/mcp"
+	headers={headersState}
+	onUrlChange={() => {}}
+	onHeadersChange={(value) => {
+		headersState = value;
+	}}
+	id="mcp-server-form-test"
+/>
+
+<div data-testid="captured-headers" data-captured-headers={headersState} hidden></div>
diff --git a/tools/ui/tests/client/mcp-server-form.svelte.test.ts b/tools/ui/tests/client/mcp-server-form.svelte.test.ts
new file mode 100644
index 000000000..b4bd89234
--- /dev/null
+++ b/tools/ui/tests/client/mcp-server-form.svelte.test.ts
@@ -0,0 +1,133 @@
+import { describe, expect, it } from 'vitest';
+import { render } from 'vitest-browser-svelte';
+import McpServerFormWrapper from './components/McpServerFormWrapper.svelte';
+
+const AUTHORIZATION_HEADER = 'Authorization';
+const BEARER_PREFIX = 'Bearer ';
+const BEARER_PLACEHOLDER = 'Paste token here';
+
+/**
+ * Client-side tests for the McpServerForm bearer UI.
+ *
+ * The dedicated UI only "owns" Authorization headers that already carry a
+ * Bearer scheme (heuristic check on the value). Other Authorization values
+ * stay in the KV section so the user can still edit them verbatim. Storage
+ * always goes through the same custom-headers slot, so a round-trip via this
+ * UI produces exactly one `Authorization: Bearer <token>` entry.
+ *
+ * Equivalent parser coverage lives in `tests/unit/headers.test.ts`.
+ */
+describe('McpServerForm - Authorization / bearer UI', () => {
+	function bearerInput(screen: Awaited<ReturnType<typeof render>>) {
+		return screen.locator.getByPlaceholder(BEARER_PLACEHOLDER);
+	}
+
+	function capturedHeaders(screen: Awaited<ReturnType<typeof render>>) {
+		return screen.getByTestId('captured-headers');
+	}
+
+	it('mounts with the bearer input hidden when no auth header is present', async () => {
+		const screen = await render(McpServerFormWrapper, { headers: '' });
+
+		await expect.element(screen.getByRole('textbox', { name: /server url/i })).toBeVisible();
+
+		await expect.element(bearerInput(screen)).not.toBeInTheDocument();
+	});
+
+	it('toggling Authorization shows the bearer input', async () => {
+		const screen = await render(McpServerFormWrapper, { headers: '' });
+
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+
+		await expect.element(bearerInput(screen)).toBeVisible();
+	});
+
+	it('typing a token writes the Authorization row with the Bearer prefix prepended', async () => {
+		const screen = await render(McpServerFormWrapper, { headers: '' });
+
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+
+		const token = 'super-secret';
+		await bearerInput(screen).fill(token);
+
+		const expected = JSON.stringify({ [AUTHORIZATION_HEADER]: `${BEARER_PREFIX}${token}` });
+		await expect
+			.element(capturedHeaders(screen))
+			.toHaveAttribute('data-captured-headers', expected);
+	});
+
+	it('pre-existing Bearer header pre-fills the bearer input with the token stripped', async () => {
+		const existing = JSON.stringify({
+			'X-Trace-Id': 'abc',
+			[AUTHORIZATION_HEADER]: `${BEARER_PREFIX}preexisting`
+		});
+
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await expect.element(bearerInput(screen)).toBeVisible();
+		await expect.element(bearerInput(screen)).toHaveValue('preexisting');
+	});
+
+	it('non-Bearer Authorization is ignored by the dedicated UI and stays in the KV section', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: 'Basic czNjcjpwYXNz' });
+
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await expect.element(bearerInput(screen)).not.toBeInTheDocument();
+
+		const headerKeyInput = screen.getByPlaceholder('Header name');
+		await expect.element(headerKeyInput).toBeVisible();
+	});
+
+	it('engaging the token UI replaces a non-Bearer Authorization with the Bearer scheme', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: 'Basic old' });
+
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+		await bearerInput(screen).fill('new');
+
+		const expected = JSON.stringify({ [AUTHORIZATION_HEADER]: `${BEARER_PREFIX}new` });
+		await expect
+			.element(capturedHeaders(screen))
+			.toHaveAttribute('data-captured-headers', expected);
+	});
+
+	it('toggling Authorization off with no token drops the Bearer row but keeps non-Bearer schemes', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: `${BEARER_PREFIX}xyz` });
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+
+		await expect.element(capturedHeaders(screen)).toHaveAttribute('data-captured-headers', '');
+	});
+
+	it('toggling Authorization off when no Bearer row is present leaves headers untouched', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: 'Basic czNjcjpwYXNz' });
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+		await screen.getByRole('switch', { name: /authorization/i }).click();
+
+		await expect
+			.element(capturedHeaders(screen))
+			.toHaveAttribute('data-captured-headers', existing);
+	});
+
+	it('clearing the bearer input drops the Authorization row', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: `${BEARER_PREFIX}xyz` });
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		await bearerInput(screen).fill('');
+
+		await expect.element(capturedHeaders(screen)).toHaveAttribute('data-captured-headers', '');
+	});
+
+	it('does not surface Bearer Authorization in the KV section even when pre-existing', async () => {
+		const existing = JSON.stringify({ [AUTHORIZATION_HEADER]: `${BEARER_PREFIX}xyz` });
+		const screen = await render(McpServerFormWrapper, { headers: existing });
+
+		const headerKeyInput = screen.getByPlaceholder('Header name');
+		await expect.element(headerKeyInput).not.toBeInTheDocument();
+	});
+});
diff --git a/tools/ui/tests/unit/headers.test.ts b/tools/ui/tests/unit/headers.test.ts
new file mode 100644
index 000000000..33619547c
--- /dev/null
+++ b/tools/ui/tests/unit/headers.test.ts
@@ -0,0 +1,126 @@
+import { describe, expect, it } from 'vitest';
+import { parseHeadersToArray, serializeHeaders } from '$lib/utils/headers';
+
+/**
+ * Tests for the header serialization helpers used by the MCP server form
+ * (custom header rows) and the new Authorization/Bearer-token flow.
+ */
+describe('parseHeadersToArray', () => {
+	it('returns an empty array for empty or whitespace-only input', () => {
+		expect(parseHeadersToArray('')).toEqual([]);
+		expect(parseHeadersToArray('   ')).toEqual([]);
+		expect(parseHeadersToArray(undefined as unknown as string)).toEqual([]);
+	});
+
+	it('returns an empty array for invalid JSON input', () => {
+		expect(parseHeadersToArray('{not-json')).toEqual([]);
+		expect(parseHeadersToArray('[]')).toEqual([]);
+		expect(parseHeadersToArray('"plain-string"')).toEqual([]);
+	});
+
+	it('converts an object into ordered key/value pairs', () => {
+		expect(parseHeadersToArray('{"X-Foo":"bar","Authorization":"Bearer abc"}')).toEqual([
+			{ key: 'X-Foo', value: 'bar' },
+			{ key: 'Authorization', value: 'Bearer abc' }
+		]);
+	});
+
+	it('stringifies non-string values', () => {
+		expect(parseHeadersToArray('{"count":"42","flag":"true"}')).toEqual([
+			{ key: 'count', value: '42' },
+			{ key: 'flag', value: 'true' }
+		]);
+	});
+});
+
+describe('serializeHeaders', () => {
+	it('returns an empty string when there are no valid pairs', () => {
+		expect(serializeHeaders([])).toBe('');
+		expect(serializeHeaders([{ key: '', value: 'value' }])).toBe('');
+		expect(serializeHeaders([{ key: '   ', value: 'value' }])).toBe('');
+	});
+
+	it('returns an empty string when every pair has a blank key', () => {
+		expect(
+			serializeHeaders([
+				{ key: '', value: 'drop-me' },
+				{ key: '   ', value: 'drop-me-too' },
+				{ key: '\t', value: 'tab-key' }
+			])
+		).toBe('');
+	});
+
+	it('drops pairs with empty keys but keeps the rest', () => {
+		expect(
+			serializeHeaders([
+				{ key: '', value: 'drop-me' },
+				{ key: 'X-Keep', value: 'ok' }
+			])
+		).toBe('{"X-Keep":"ok"}');
+	});
+
+	it('trims keys before serializing', () => {
+		expect(serializeHeaders([{ key: '  X-Space  ', value: 'ok' }])).toBe('{"X-Space":"ok"}');
+	});
+
+	it('preserves the input order of surviving pairs', () => {
+		const serialized = serializeHeaders([
+			{ key: 'X-C', value: '3' },
+			{ key: 'X-A', value: '1' },
+			{ key: 'X-B', value: '2' }
+		]);
+
+		// Object key order follows insertion order in modern JS engines, so
+		// the serialized JSON writes keys in our input order.
+		expect(JSON.parse(serialized)).toEqual({ 'X-C': '3', 'X-A': '1', 'X-B': '2' });
+	});
+});
+
+describe('parseHeadersToArray / serializeHeaders roundtrip', () => {
+	it('serializes back to an equal header object after a parse', () => {
+		const original = JSON.stringify({
+			'Content-Type': 'application/json',
+			'X-Trace-Id': 'abc-123'
+		});
+
+		const roundtrip = serializeHeaders(parseHeadersToArray(original));
+
+		expect(JSON.parse(roundtrip)).toEqual(JSON.parse(original));
+	});
+
+	it('drops rows whose keys are blank after trimming during serialization', () => {
+		const pairs = parseHeadersToArray('{"X-Keep":"ok","":"drop-me"}');
+
+		// parseHeadersToArray keeps raw key strings (the consumer is expected to
+		// filter blanks, not the parser); serialization must strip them.
+		expect(pairs).toEqual([
+			{ key: 'X-Keep', value: 'ok' },
+			{ key: '', value: 'drop-me' }
+		]);
+		expect(serializeHeaders(pairs)).toBe('{"X-Keep":"ok"}');
+	});
+
+	it('preserves upstream keys untouched (does not lowercase them)', () => {
+		const upperCased = '{"Authorization":"Bearer xyz"}';
+
+		const parsed = parseHeadersToArray(upperCased);
+
+		expect(parsed).toEqual([{ key: 'Authorization', value: 'Bearer xyz' }]);
+	});
+
+	it('bearer-token write survives a re-parse when paired with regular custom headers', () => {
+		// The McpServerForm bearer UI writes {Authorization: `Bearer <token>`}
+		// into the same headers string as the custom KV section. The round
+		// trip below mirrors the exact shape the form produces so a future
+		// refactor of either code path cannot silently change the on-disk key.
+		const pairs = [
+			{ key: 'X-Trace-Id', value: 'abc-123' },
+			{ key: 'Authorization', value: 'Bearer super-secret' }
+		];
+
+		const serialized = serializeHeaders(pairs);
+
+		expect(serialized).toBe('{"X-Trace-Id":"abc-123","Authorization":"Bearer super-secret"}');
+		expect(parseHeadersToArray(serialized)).toEqual(pairs);
+	});
+});
diff --git a/tools/ui/tests/unit/parse-mcp-server-settings.test.ts b/tools/ui/tests/unit/parse-mcp-server-settings.test.ts
new file mode 100644
index 000000000..956c677d5
--- /dev/null
+++ b/tools/ui/tests/unit/parse-mcp-server-settings.test.ts
@@ -0,0 +1,144 @@
+import { describe, expect, it, vi } from 'vitest';
+import { parseMcpServerSettings } from '$lib/utils/mcp';
+import { DEFAULT_MCP_CONFIG, MCP_SERVER_ID_PREFIX } from '$lib/constants/mcp';
+
+/**
+ * Tests for the mcpServers settings parser.
+ *
+ * The branch seeds the MCP servers setting with a default value of
+ * `JSON.stringify(RECOMMENDED_MCP_SERVERS)`, so the parser has to be
+ * resilient to anything that may live in the user's localStorage: malformed
+ * JSON, wrong shapes, missing fields, falsy-but-not-zero numbers, and entry
+ * arrays that have been mutated by the user via the settings form.
+ */
+describe('parseMcpServerSettings', () => {
+	it('returns an empty array for falsy or whitespace-only input', () => {
+		expect(parseMcpServerSettings(null)).toEqual([]);
+		expect(parseMcpServerSettings(undefined)).toEqual([]);
+		expect(parseMcpServerSettings('')).toEqual([]);
+		expect(parseMcpServerSettings('   ')).toEqual([]);
+	});
+
+	it('returns an empty array and logs a warning for invalid JSON strings', () => {
+		const warn = vi.spyOn(console, 'warn').mockImplementation(() => {});
+
+		expect(parseMcpServerSettings('{not-json')).toEqual([]);
+		expect(warn).toHaveBeenCalled();
+
+		warn.mockRestore();
+	});
+
+	it('returns an empty array for valid JSON that is not an array', () => {
+		expect(parseMcpServerSettings('"plain-string"')).toEqual([]);
+		expect(parseMcpServerSettings('{"id":"foo"}')).toEqual([]);
+		expect(parseMcpServerSettings('42')).toEqual([]);
+		expect(parseMcpServerSettings('null')).toEqual([]);
+	});
+
+	it('drops entries with no parseable id and substitutes a stable fallback', () => {
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([{ url: 'https://a.test', enabled: true }, { url: 'https://b.test' }])
+		);
+
+		expect(parsed).toHaveLength(2);
+		expect(parsed[0]?.id).toBe(`${MCP_SERVER_ID_PREFIX}-1`);
+		expect(parsed[1]?.id).toBe(`${MCP_SERVER_ID_PREFIX}-2`);
+	});
+
+	it('reuses the first id when it is present and falls back only for missing ones', () => {
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([
+				{ id: 'custom-1', url: 'https://a.test' },
+				{ url: 'https://b.test' },
+				{ id: 'custom-3', url: 'https://c.test' }
+			])
+		);
+
+		expect(parsed[0]?.id).toBe('custom-1');
+		expect(parsed[1]?.id).toBe(`${MCP_SERVER_ID_PREFIX}-2`);
+		expect(parsed[2]?.id).toBe('custom-3');
+	});
+
+	it('falls back to the configured default requestTimeoutSeconds only for nullish values', () => {
+		const fallback = DEFAULT_MCP_CONFIG.requestTimeoutSeconds;
+
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([
+				{ id: 'a', url: 'https://a.test' },
+				{ id: 'b', url: 'https://b.test', requestTimeoutSeconds: undefined },
+				{ id: 'c', url: 'https://c.test', requestTimeoutSeconds: 0 },
+				{ id: 'd', url: 'https://d.test', requestTimeoutSeconds: 45 }
+			])
+		);
+
+		// The parser uses ?? for timeout fallback, which only triggers on
+		// null/undefined. Explicit 0 is preserved at face value.
+		expect(parsed[0]?.requestTimeoutSeconds).toBe(fallback);
+		expect(parsed[1]?.requestTimeoutSeconds).toBe(fallback);
+		expect(parsed[2]?.requestTimeoutSeconds).toBe(0);
+		expect(parsed[3]?.requestTimeoutSeconds).toBe(45);
+	});
+
+	it('treats whitespace-only headers strings as undefined', () => {
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([
+				{ id: 'a', url: 'https://a.test', headers: '   ' },
+				{ id: 'b', url: 'https://b.test', headers: '{"X-Foo":"bar"}' }
+			])
+		);
+
+		// The parser trims headers and coerces empty/whitespace to undefined.
+		expect(parsed[0]?.headers).toBeUndefined();
+		expect(parsed[1]?.headers).toBe('{"X-Foo":"bar"}');
+	});
+
+	it('defaults coercion for booleans (undefined -> false, true -> true)', () => {
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([
+				{ id: 'a', url: 'https://a.test' },
+				{ id: 'b', url: 'https://b.test', enabled: true },
+				{ id: 'c', url: 'https://c.test', enabled: false },
+				{ id: 'd', url: 'https://d.test', useProxy: true }
+			])
+		);
+
+		expect(parsed[0]?.enabled).toBe(false);
+		expect(parsed[1]?.enabled).toBe(true);
+		expect(parsed[2]?.enabled).toBe(false);
+		expect(parsed[0]?.useProxy).toBe(false);
+		expect(parsed[3]?.useProxy).toBe(true);
+	});
+
+	it('preserves input order when mapping entries', () => {
+		const source = [
+			{ id: 'gamma', url: 'https://c.test' },
+			{ id: 'alpha', url: 'https://a.test' },
+			{ id: 'beta', url: 'https://b.test' }
+		];
+
+		const parsed = parseMcpServerSettings(JSON.stringify(source));
+
+		expect(parsed.map((entry) => entry.id)).toEqual(['gamma', 'alpha', 'beta']);
+	});
+
+	it('passes non-string raw input through the JSON-equality path', () => {
+		const parsed = parseMcpServerSettings([
+			{ id: 'a', url: 'https://a.test' },
+			{ id: 'b', url: 'https://b.test', enabled: true }
+		]);
+
+		expect(parsed).toHaveLength(2);
+		expect(parsed[0]?.id).toBe('a');
+		expect(parsed[1]?.enabled).toBe(true);
+	});
+
+	it('coerces non-string url values to an empty string rather than throwing', () => {
+		const parsed = parseMcpServerSettings(
+			JSON.stringify([{ id: 'a', url: 42 }, { id: 'b' }, { id: 'c', url: 'https://c.test' }])
+		);
+
+		expect(parsed[0]?.url).toBe('');
+		expect(parsed[1]?.url).toBe('');
+		expect(parsed[2]?.url).toBe('https://c.test');
+	});
+});
diff --git a/tools/ui/tests/unit/recommended-mcp-servers.test.ts b/tools/ui/tests/unit/recommended-mcp-servers.test.ts
new file mode 100644
index 000000000..3f6fd8f11
--- /dev/null
+++ b/tools/ui/tests/unit/recommended-mcp-servers.test.ts
@@ -0,0 +1,90 @@
+import { describe, expect, it } from 'vitest';
+import {
+	RECOMMENDED_MCP_SERVER_IDS,
+	RECOMMENDED_MCP_SERVERS
+} from '$lib/constants/recommended-mcp-servers';
+import { parseMcpServerSettings } from '$lib/utils/mcp';
+import { DEFAULT_MCP_CONFIG, MCP_SERVER_ID_PREFIX } from '$lib/constants/mcp';
+
+/**
+ * Tests for the predefined recommended MCP servers.
+ *
+ * These are surfaced to first-time users via
+ * DialogMcpServerRecommendations and used as the default value of the MCP
+ * servers setting, so a regression that breaks the round-trip through the
+ * settings parser would silently break onboarding for new users.
+ */
+describe('RECOMMENDED_MCP_SERVERS', () => {
+	it('lists at least one entry and uses stable, unique ids', () => {
+		expect(RECOMMENDED_MCP_SERVERS.length).toBeGreaterThan(0);
+
+		const ids = RECOMMENDED_MCP_SERVERS.map((server) => server.id);
+		expect(new Set(ids).size).toBe(ids.length);
+
+		for (const id of ids) {
+			expect(id).toMatch(/^[a-z0-9-]+$/);
+			expect(id.toLowerCase()).not.toContain(MCP_SERVER_ID_PREFIX.toLowerCase());
+		}
+	});
+
+	it('requires a name, description and url for every entry', () => {
+		for (const server of RECOMMENDED_MCP_SERVERS) {
+			expect(server.name?.trim().length ?? 0).toBeGreaterThan(0);
+			expect(server.description.trim().length).toBeGreaterThan(0);
+			expect(server.url.trim().length).toBeGreaterThan(0);
+			expect(() => new URL(server.url)).not.toThrow();
+		}
+	});
+});
+
+describe('RECOMMENDED_MCP_SERVER_IDS', () => {
+	it('matches the ids declared in RECOMMENDED_MCP_SERVERS', () => {
+		expect(RECOMMENDED_MCP_SERVER_IDS.size).toBe(RECOMMENDED_MCP_SERVERS.length);
+
+		for (const server of RECOMMENDED_MCP_SERVERS) {
+			expect(RECOMMENDED_MCP_SERVER_IDS.has(server.id)).toBe(true);
+		}
+	});
+});
+
+describe('recommended-mcp-servers default value', () => {
+	it('round-trips cleanly through parseMcpServerSettings', () => {
+		const serialized = JSON.stringify(RECOMMENDED_MCP_SERVERS);
+		const parsed = parseMcpServerSettings(serialized);
+
+		expect(parsed).toHaveLength(RECOMMENDED_MCP_SERVERS.length);
+
+		for (let index = 0; index < RECOMMENDED_MCP_SERVERS.length; index++) {
+			const source = RECOMMENDED_MCP_SERVERS[index];
+			const entry = parsed[index];
+
+			expect(entry).toBeDefined();
+			expect(entry?.id).toBe(source.id);
+			expect(entry?.url).toBe(source.url);
+			expect(entry?.enabled).toBe(source.enabled);
+			expect(entry?.requestTimeoutSeconds).toBe(source.requestTimeoutSeconds);
+			expect(entry?.name).toBe(source.name);
+
+			// Headers and useProxy are not set on recommended servers; the
+			// parser must fall back to the inactive defaults rather than
+			// surfacing undefined-boundary states.
+			expect(entry?.headers).toBeUndefined();
+			expect(entry?.useProxy).toBe(false);
+		}
+	});
+
+	it('uses the global default timeout when one is not specified on an entry', () => {
+		const sourceOnlyRequired = {
+			id: 'roundtrip-only',
+			name: 'Only required fields',
+			url: 'https://example.test/mcp',
+			description: 'Smoke entry for parser roundtrip with default timeout.',
+			enabled: true
+		};
+
+		const parsed = parseMcpServerSettings(JSON.stringify([sourceOnlyRequired]));
+		const entry = parsed[0];
+
+		expect(entry?.requestTimeoutSeconds).toBe(DEFAULT_MCP_CONFIG.requestTimeoutSeconds);
+	});
+});

From b5315e16e0c3d19707d4a3e3a9f727f1141d2282 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Fri, 3 Jul 2026 12:47:04 +0200
Subject: [PATCH 260/292] server + ui: ping silent SSE streams every 1s and
 kick only after 3s so slow prefill never drops healthy connections (#25241)

* server + ui: ping silent SSE streams every 1s and kick only after 3s so slow prefill never drops healthy connections

* server + ui: sse_ping_interval becomes a per-request body field

Address review from ngxson: the global default returns to 30 so API
clients see no behavior change, and the WebUI sends sse_ping_interval: 1
in the request body since it owns the 3s visibility-kick contract and
declares the cadence it needs. Positive values keep the existing > 0
gate, -1 keeps its disabled semantics.

* server: move sse_ping_interval into the request schema

Address review from ngxson: the field is now a typed field_num with
hard limits (-1, INT32_MAX) bound to task_params, seeded from the CLI
default alongside the other inherited parameters. The raw json_value
read and its redundant comment are gone, and schema evaluation brings
type and range validation for free.
---
 tools/server/README.md                    | 2 ++
 tools/server/server-context.cpp           | 9 ++++++---
 tools/server/server-schema.cpp            | 5 +++++
 tools/server/server-task.h                | 2 ++
 tools/ui/src/lib/constants/stream.ts      | 2 +-
 tools/ui/src/lib/services/chat.service.ts | 1 +
 tools/ui/src/lib/types/api.d.ts           | 1 +
 7 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index e88bc5f28..501b66123 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -521,6 +521,8 @@ These words will not be included in the completion, so make sure to add them to
 
 `return_progress`: Include prompt processing progress in `stream` mode. The progress will be contained inside `prompt_progress` with 4 values: `total`, `cache`, `processed`, and `time_ms`. The overall progress is `processed/total`, while the actual timed progress is `(processed-cache)/(total-cache)`. The `time_ms` field contains the elapsed time in milliseconds since prompt processing started. Default: `false`
 
+`sse_ping_interval`: Interval in seconds between SSE comment pings emitted while the stream stays silent, keeping the connection observable during long prompt processing. Overrides the server `--sse-ping-interval` setting for this request, `-1` disables pings. Default: server setting
+
 `post_sampling_probs`: Returns the probabilities of top `n_probs` tokens after applying sampling chain.
 
 `response_fields`: A list of response fields, for example: `"response_fields": ["content", "generation_settings/n_predict"]`. If the specified field is missing, it will simply be omitted from the response without triggering an error. Note that fields with a slash will be unnested; for example, `generation_settings/n_predict` will move the field `n_predict` from the `generation_settings` object to the root of the response and give it a new name.
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 20e93258f..bb3b91ab5 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -4089,6 +4089,8 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
     auto & rd = res->rd;
     auto & params = this->params;
 
+    int32_t sse_ping_interval = params.sse_ping_interval;
+
     try {
         std::vector<server_task> tasks;
 
@@ -4139,6 +4141,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
             task.params.message_spans = task.tokens.find_message_spans(delimiters);
 
             task.id_slot = json_value(data, "id_slot", -1);
+            sse_ping_interval = task.params.sse_ping_interval;
 
             // OAI-compat
             task.params.res_type          = res_type;
@@ -4228,7 +4231,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
         }
         res->status = 200;
         res->content_type = "text/event-stream";
-        res->next = [res_this = res.get(), res_type, &req, &params](std::string & output) -> bool {
+        res->next = [res_this = res.get(), res_type, sse_ping_interval, &req](std::string & output) -> bool {
             static auto format_error = [](task_response_type res_type, const json & res_json) {
                 if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
                     return format_anthropic_sse({
@@ -4277,10 +4280,10 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
                 // receive subsequent results
                 bool timeout = false;
                 int64_t start_time = ggml_time_ms();
-                auto result = rd.next([&timeout, &start_time, &params, &effective_should_stop]() {
+                auto result = rd.next([&timeout, &start_time, sse_ping_interval, &effective_should_stop]() {
                     if (effective_should_stop()) {
                         return true; // should_stop condition met
-                    } else if (params.sse_ping_interval > 0 && ggml_time_ms() - start_time > (int64_t)params.sse_ping_interval * 1000) {
+                    } else if (sse_ping_interval > 0 && ggml_time_ms() - start_time > (int64_t)sse_ping_interval * 1000) {
                         timeout = true;
                         return true; // timeout
                     }
diff --git a/tools/server/server-schema.cpp b/tools/server/server-schema.cpp
index 07a842bd6..5713cc831 100644
--- a/tools/server/server-schema.cpp
+++ b/tools/server/server-schema.cpp
@@ -37,6 +37,10 @@ std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params &
     add((new field_bool("return_progress", params.return_progress))
         ->set_desc("Include prompt processing progress events in stream mode"));
 
+    add((new field_num("sse_ping_interval", params.sse_ping_interval))
+        ->set_hard_limits(-1, INT32_MAX)
+        ->set_desc("Interval in seconds between SSE comment pings emitted while the stream stays silent, -1 disables pings"));
+
     add((new field_num("n_predict", params.n_predict))
         ->set_hard_limits(-1, INT32_MAX)
         ->add_alias("max_completion_tokens")
@@ -504,6 +508,7 @@ task_params eval_llama_cmpl_schema(
     params.n_cache_reuse = params_base.n_cache_reuse;
     params.cache_prompt  = params_base.cache_prompt;
     params.antiprompt    = params_base.antiprompt;
+    params.sse_ping_interval = params_base.sse_ping_interval;
 
     // enabling this will output extra debug information in the HTTP responses from the server
     params.verbose       = params_base.verbosity > 9;
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 293bdf053..49f62d386 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -54,6 +54,8 @@ struct task_params {
     bool return_tokens   = false;
     bool return_progress = false;
 
+    int32_t sse_ping_interval = 30; // seconds between SSE comment pings while the stream stays silent, -1 disables
+
     int32_t n_keep    =  0; // number of tokens to keep from initial prompt
     int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
     int32_t n_predict = -1; // new tokens to predict
diff --git a/tools/ui/src/lib/constants/stream.ts b/tools/ui/src/lib/constants/stream.ts
index 3d042451f..67951ee95 100644
--- a/tools/ui/src/lib/constants/stream.ts
+++ b/tools/ui/src/lib/constants/stream.ts
@@ -1,3 +1,3 @@
 // grace window after a visibilitychange before we kick a reader whose socket likely died
 // while the tab was hidden. covers brief background pauses without thrashing live streams
-export const STREAM_VISIBILITY_KICK_MS = 1000;
+export const STREAM_VISIBILITY_KICK_MS = 3000;
diff --git a/tools/ui/src/lib/services/chat.service.ts b/tools/ui/src/lib/services/chat.service.ts
index 7dfee3773..dbd0a94f1 100644
--- a/tools/ui/src/lib/services/chat.service.ts
+++ b/tools/ui/src/lib/services/chat.service.ts
@@ -255,6 +255,7 @@ export class ChatService {
 			}),
 			stream,
 			return_progress: stream ? true : undefined,
+			sse_ping_interval: stream ? 1 : undefined,
 			tools: tools && tools.length > 0 ? tools : undefined
 		};
 
diff --git a/tools/ui/src/lib/types/api.d.ts b/tools/ui/src/lib/types/api.d.ts
index ec695ac61..5421f8b7f 100644
--- a/tools/ui/src/lib/types/api.d.ts
+++ b/tools/ui/src/lib/types/api.d.ts
@@ -265,6 +265,7 @@ export interface ApiChatCompletionRequest {
 	stream?: boolean;
 	model?: string;
 	return_progress?: boolean;
+	sse_ping_interval?: number;
 	tools?: ApiChatCompletionTool[];
 	// Reasoning parameters
 	reasoning_format?: string;

From 067de937183141f54c681ed684f540706d2c420a Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Fri, 3 Jul 2026 13:14:52 +0200
Subject: [PATCH 261/292] ui: align persisted config with strict server schema
 and enable thinking by default (#25242)

* ui: migrate legacy string-encoded booleans in persisted config

* ui: enable thinking by default

Fresh users and legacy conversations without a persisted thinking
preference now default to enabled. The per-conversation toggle and
the persisted localStorage choice keep taking precedence.

Picks up the enable_thinking default from #24876.
---
 .../ui/src/lib/services/migration.service.ts  | 38 ++++++++++++++++++-
 .../ui/src/lib/stores/conversations.svelte.ts | 11 +++---
 2 files changed, 42 insertions(+), 7 deletions(-)

diff --git a/tools/ui/src/lib/services/migration.service.ts b/tools/ui/src/lib/services/migration.service.ts
index 152b78cd3..d7709bc6b 100644
--- a/tools/ui/src/lib/services/migration.service.ts
+++ b/tools/ui/src/lib/services/migration.service.ts
@@ -551,13 +551,49 @@ const mcpDefaultEnabledMigration: Migration = {
 	}
 };
 
+const CONFIG_TYPES_MIGRATION_ID = 'config-type-normalization-v1';
+
+const configTypesMigration: Migration = {
+	id: CONFIG_TYPES_MIGRATION_ID,
+	description: 'Coerce legacy string-encoded booleans in persisted config to real booleans',
+
+	async run(): Promise<void> {
+		const configRaw = localStorage.getItem(CONFIG_LOCALSTORAGE_KEY);
+		if (configRaw === null) return;
+
+		const config = JSON.parse(configRaw);
+		let changed = false;
+
+		// Pre-schema configs persisted booleans as the strings "true"/"false", which the
+		// strict server schema now rejects. Coerce those back to real booleans. No config
+		// string field holds exactly "true"/"false", so the match is unambiguous.
+		for (const key of Object.keys(config)) {
+			if (config[key] === 'true') {
+				config[key] = true;
+				changed = true;
+			} else if (config[key] === 'false') {
+				config[key] = false;
+				changed = true;
+			}
+		}
+
+		if (changed) {
+			localStorage.setItem(CONFIG_LOCALSTORAGE_KEY, JSON.stringify(config));
+		}
+
+		if (import.meta.env.DEV && import.meta.env.VITE_DEBUG)
+			console.log(`[Migration] Config types: coerced string booleans (changed=${changed})`);
+	}
+};
+
 const migrations: Migration[] = [
 	localStorageMigration,
 	idxdbMigration,
 	legacyMessageMigration,
 	themeMigration,
 	customJsonKeyMigration,
-	mcpDefaultEnabledMigration
+	mcpDefaultEnabledMigration,
+	configTypesMigration
 ];
 
 export const MigrationService = {
diff --git a/tools/ui/src/lib/stores/conversations.svelte.ts b/tools/ui/src/lib/stores/conversations.svelte.ts
index ef8d61309..486202207 100644
--- a/tools/ui/src/lib/stores/conversations.svelte.ts
+++ b/tools/ui/src/lib/stores/conversations.svelte.ts
@@ -114,14 +114,13 @@ class ConversationsStore {
 
 	/** Load thinking-enabled default from localStorage */
 	private static loadThinkingDefaults(): boolean {
-		if (typeof globalThis.localStorage === 'undefined') return false;
+		if (typeof globalThis.localStorage === 'undefined') return true;
 		try {
 			const raw = localStorage.getItem(THINKING_ENABLED_DEFAULT_LOCALSTORAGE_KEY);
-			if (!raw) return false;
-			const parsed = raw === 'true';
-			return typeof parsed === 'boolean' ? parsed : false;
+			if (!raw) return true;
+			return raw === 'true';
 		} catch {
-			return false;
+			return true;
 		}
 	}
 
@@ -333,7 +332,7 @@ class ConversationsStore {
 			}
 
 			this.pendingMcpServerOverrides = [];
-			this.pendingThinkingEnabled = false;
+			this.pendingThinkingEnabled = ConversationsStore.loadThinkingDefaults();
 			this.activeConversation = conversation;
 
 			if (conversation.currNode) {

From 75a48a90559abf65df3f3616a53bb16e5afb9d07 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Fri, 3 Jul 2026 15:36:55 +0200
Subject: [PATCH 262/292] cuda: enable topk-moe fusion for 288 experts (#25267)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cuda: enable topk-moe fusion for 288 experts

The topk-moe fusion only accepted power-of-2 expert counts (or the
special-cased 576), so models with 288 experts (e.g. Step-3.7-Flash)
fell back to the unfused per-layer routing chain: softmax/sigmoid,
argsort, get_rows, sum_rows, div, clamp, scale. At batch size 1 that
is ~330 extra tiny graph nodes per token.

288 is a multiple of the warp size, so the existing kernel already
handles it; this adds the missing template instantiation and accepts
288 in the eligibility check.

Measured on gfx1151 with Step-3.7-Flash IQ4_XS (llama-bench,
-b 4096 -ub 4096 -fa 1 -dio 1 -ctk q8_0 -ctv q8_0; machine idle,
before/after paired so pp4096 stays matched as a load control):

  test            | before         | after
  ----------------+----------------+----------------
  pp4096          | 460.99 ± 0.45  | 462.47 ± 0.34   (unchanged)
  tg128           |  19.10 ± 0.04  |  19.56 ± 0.03   (+2.4%)
  tg128 @ d30000  |  12.68 ± 0.04  |  12.69 ± 0.03   (unchanged)

Prompt processing is unaffected (the fusion only touches decode
routing). The decode gain is ~+2.4% at shallow context and fades with
depth: by 30k tokens each step is attention-bound over the KV cache,
so removing the fixed routing overhead is no longer visible.

Assisted-By: Claude Fable 5 <noreply@anthropic.com>

* Update tests/test-backend-ops.cpp

Co-authored-by: Oliver Simons <osimons@nvidia.com>

* Add comment for case 288 in topk-moe.cu

---------

Co-authored-by: Oliver Simons <osimons@nvidia.com>
---
 ggml/src/ggml-cuda/topk-moe.cu | 8 +++++++-
 tests/test-backend-ops.cpp     | 1 +
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu
index c4253bfa4..c80394e31 100644
--- a/ggml/src/ggml-cuda/topk-moe.cu
+++ b/ggml/src/ggml-cuda/topk-moe.cu
@@ -312,6 +312,10 @@ static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx,
             ggml_cuda_kernel_launch(topk_moe_cuda<256, has_bias>, launch_params,
                 logits, weights, ids, bias, n_rows, n_expert_used, clamp_val, scale_val, config);
             break;
+        case 288: // StepFun 3.7
+            ggml_cuda_kernel_launch(topk_moe_cuda<288, has_bias>, launch_params,
+                logits, weights, ids, bias, n_rows, n_expert_used, clamp_val, scale_val, config);
+            break;
         case 512:
             ggml_cuda_kernel_launch(topk_moe_cuda<512, has_bias>, launch_params,
                 logits, weights, ids, bias, n_rows, n_expert_used, clamp_val, scale_val, config);
@@ -377,8 +381,10 @@ bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op,
                                    const ggml_tensor * weights,
                                    const ggml_tensor * logits,
                                    const ggml_tensor * ids) {
+    // must match an instantiation of launch_topk_moe_cuda: a power of 2 up to 512,
+    // or one of the non-power-of-2 expert counts of supported models
     const int n_expert = ids->nb[1] / ids->nb[0];
-    if (((n_expert & (n_expert - 1)) != 0 || n_expert > 512) && n_expert != 576) {
+    if (((n_expert & (n_expert - 1)) != 0 || n_expert > 512) && n_expert != 288 && n_expert != 576) {
         return false;
     }
 
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 34811bd50..5d1f7d8ad 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -9219,6 +9219,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                     test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w));
                     test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w));
                     test_cases.emplace_back(new test_topk_moe({160, 4, 1, 1}, 160, with_norm, bias_probs, gate, scale_w));
+                    test_cases.emplace_back(new test_topk_moe({288, 22, 1, 1}, 8, with_norm, bias_probs, gate, scale_w)); // Used by StepFun 3.7
                 }
             }
         }

From 152d337fadb93c2a099653c4072d5512c92c5bfd Mon Sep 17 00:00:00 2001
From: Ruixiang Wang <wangruixiang07@outlook.com>
Date: Fri, 3 Jul 2026 15:40:06 +0200
Subject: [PATCH 263/292] spec: support spec-draft-p-min in DFlash (#25246)

* spec: support spec-draft-p-min in DFlash

* dflash: add n_min guard

* dflash: guard both n_min and n_max
---
 common/speculative.cpp | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/common/speculative.cpp b/common/speculative.cpp
index 3951bbed5..5b26597f6 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -955,10 +955,11 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
         LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n);
 
         // DFlash input is [id_last, <mask> * (block_size-1)], so it can draft at most block_size-1 tokens per step
-        if (this->params.n_max > block_size - 1) {
-            LOG_WRN("%s: requested draft size %d exceeds the trained DFlash block size %d -- clamping to %d draft tokens per step\n",
-                    __func__, this->params.n_max, block_size - 1, block_size - 1);
-            this->params.n_max = block_size - 1;
+        if (this->params.n_max > block_size - 1 || this->params.n_min > block_size - 1) {
+            LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained DFlash block size %d -- clamping to %d\n",
+                    __func__, this->params.n_max, this->params.n_min, block_size, block_size - 1);
+            this->params.n_max = std::min(this->params.n_max, block_size - 1);
+            this->params.n_min = std::min(this->params.n_min, block_size - 1);
         }
 
         batch        = llama_batch_init(llama_n_batch(ctx_dft), 0,          n_seq);
@@ -968,7 +969,7 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
         for (auto & s : smpls) {
             common_params_sampling sparams;
             sparams.no_perf  = false;
-            sparams.top_k    = 1;
+            sparams.top_k    = 10;
             sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
             s.reset(common_sampler_init(model_dft, sparams));
         }
@@ -1173,10 +1174,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
 
                 const llama_token id = cur_p->data[0].id;
 
+                if (cur_p->data[0].p < params.p_min) {
+                    break;
+                }
+
                 common_sampler_accept(smpl, id, true);
 
                 result.push_back(id);
             }
+
+            if (result.size() < (size_t) params.n_min) {
+                result.clear();
+            }
         }
     }
 

From f113e02d5ab4c4910709d46e8d81af92ef945289 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Fri, 3 Jul 2026 17:32:48 +0200
Subject: [PATCH 264/292] ui: strip path and weight extension from model id in
 single model mode (#25137)

---
 tools/ui/src/lib/constants/model-id.ts      |  5 +++++
 tools/ui/src/lib/services/models.service.ts | 15 ++++++++++-----
 2 files changed, 15 insertions(+), 5 deletions(-)

diff --git a/tools/ui/src/lib/constants/model-id.ts b/tools/ui/src/lib/constants/model-id.ts
index ee314d167..ab7932240 100644
--- a/tools/ui/src/lib/constants/model-id.ts
+++ b/tools/ui/src/lib/constants/model-id.ts
@@ -37,3 +37,8 @@ export const MODEL_ACTIVATED_PARAMS_RE = /^[Aa]\d+(\.\d+)?[BbMmKkTt]$/;
  * Container format segments to exclude from tags (every model uses these).
  */
 export const MODEL_IGNORED_SEGMENTS = new Set(['GGUF', 'GGML']);
+
+/**
+ * Matches a trailing weight file extension, e.g. `model.gguf` -> `model`.
+ */
+export const MODEL_WEIGHT_EXTENSION_RE = /\.(gguf|ggml)$/i;
diff --git a/tools/ui/src/lib/services/models.service.ts b/tools/ui/src/lib/services/models.service.ts
index 209bd7cab..9574da59e 100644
--- a/tools/ui/src/lib/services/models.service.ts
+++ b/tools/ui/src/lib/services/models.service.ts
@@ -1,5 +1,5 @@
 import { ServerModelStatus } from '$lib/enums';
-import { apiFetch, apiPost } from '$lib/utils';
+import { apiFetch, apiPost, normalizeModelName } from '$lib/utils';
 import type { ParsedModelId } from '$lib/types/models';
 import {
 	MODEL_QUANTIZATION_SEGMENT_RE,
@@ -7,6 +7,7 @@ import {
 	MODEL_PARAMS_RE,
 	MODEL_ACTIVATED_PARAMS_RE,
 	MODEL_IGNORED_SEGMENTS,
+	MODEL_WEIGHT_EXTENSION_RE,
 	MODEL_ID_NOT_FOUND,
 	MODEL_ID_ORG_SEPARATOR,
 	MODEL_ID_SEGMENT_SEPARATOR,
@@ -139,15 +140,19 @@ export class ModelsService {
 			tags: []
 		};
 
+		// strip directory path and weight extension so a bare `-m /path/file.gguf`
+		// parses like a clean repo id; the HF `org/model` form is preserved
+		const source = normalizeModelName(modelId).replace(MODEL_WEIGHT_EXTENSION_RE, '');
+
 		// 1. Extract colon-separated quantization (e.g. `model:Q4_K_M`)
-		const colonIdx = modelId.indexOf(MODEL_ID_QUANTIZATION_SEPARATOR);
+		const colonIdx = source.indexOf(MODEL_ID_QUANTIZATION_SEPARATOR);
 		let modelPath: string;
 
 		if (colonIdx !== MODEL_ID_NOT_FOUND) {
-			result.quantization = modelId.slice(colonIdx + 1) || null;
-			modelPath = modelId.slice(0, colonIdx);
+			result.quantization = source.slice(colonIdx + 1) || null;
+			modelPath = source.slice(0, colonIdx);
 		} else {
-			modelPath = modelId;
+			modelPath = source;
 		}
 
 		// 2. Extract org name (e.g. `org/model` -> org = "org")

From d4cff114c0084f1fbc9b4c62717eca8fb2ae494a Mon Sep 17 00:00:00 2001
From: Nick Towle <ntowle@gmail.com>
Date: Fri, 3 Jul 2026 10:03:51 -0700
Subject: [PATCH 265/292] ui: Improve performance when streaming (#25225)

* ui: Improve performance when streaming

* ui: build sibling info map in branching utils

Moves the node map and sibling map construction from the
.by block into buildSiblingInfoMap() in branching.ts.

The map is built once per structural change and only read
afterwards, so it does not need SvelteMap reactivity. Keeping
the construction in plain TypeScript fixes the
svelte/prefer-svelte-reactivity lint error and groups the
branching logic where it already lives.

---------

Co-authored-by: Pascal <admin@serveurperso.com>
---
 .../app/chat/ChatMessages/ChatMessages.svelte |  18 +--
 tools/ui/src/lib/utils/branching.ts           | 134 +++++-------------
 tools/ui/src/lib/utils/index.ts               |   5 +-
 3 files changed, 50 insertions(+), 107 deletions(-)

diff --git a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
index 2d959cfc2..88efcc4b0 100644
--- a/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
+++ b/tools/ui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
@@ -20,9 +20,9 @@
 		agenticInjectSteeringMessage
 	} from '$lib/stores/agentic.svelte';
 	import {
+		buildSiblingInfoMap,
 		copyToClipboard,
 		formatMessageForClipboard,
-		getMessageSiblings,
 		hasAgenticContent
 	} from '$lib/utils';
 
@@ -169,6 +169,8 @@
 		});
 	});
 
+	let siblingInfoByMessageId = $derived(buildSiblingInfoMap(allConversationMessages));
+
 	let displayMessages = $derived.by(() => {
 		if (!messages.length) {
 			return [];
@@ -223,18 +225,18 @@
 				}
 			}
 
-			const siblingInfo = getMessageSiblings(allConversationMessages, msg.id);
+			const siblingInfo = siblingInfoByMessageId.get(msg.id) ?? {
+				message: msg,
+				siblingIds: [msg.id],
+				currentIndex: 0,
+				totalSiblings: 1
+			};
 
 			result.push({
 				message: msg,
 				toolMessages,
 				isLastAssistantMessage: false,
-				siblingInfo: siblingInfo || {
-					message: msg,
-					siblingIds: [msg.id],
-					currentIndex: 0,
-					totalSiblings: 1
-				}
+				siblingInfo
 			});
 		}
 
diff --git a/tools/ui/src/lib/utils/branching.ts b/tools/ui/src/lib/utils/branching.ts
index 4e117b3c2..c40abbdd6 100644
--- a/tools/ui/src/lib/utils/branching.ts
+++ b/tools/ui/src/lib/utils/branching.ts
@@ -92,18 +92,14 @@ export function filterByLeafNodeId(
  * Finds the leaf node (message with no children) for a given message branch.
  * Traverses down the tree following the last child until reaching a leaf.
  *
- * @param messages - All messages in the conversation
+ * @param nodeMap - Map of messages keyed by ID
  * @param messageId - Starting message ID to find leaf for
  * @returns The leaf node ID, or the original messageId if no children
  */
-export function findLeafNode(messages: readonly DatabaseMessage[], messageId: string): string {
-	const nodeMap = new Map<string, DatabaseMessage>();
-
-	// Build node map for quick lookups
-	for (const msg of messages) {
-		nodeMap.set(msg.id, msg);
-	}
-
+function findLeafNodeInMap(
+	nodeMap: ReadonlyMap<string, DatabaseMessage>,
+	messageId: string
+): string {
 	let currentNode: DatabaseMessage | undefined = nodeMap.get(messageId);
 	while (currentNode && currentNode.children.length > 0) {
 		// Follow the last child (most recent branch)
@@ -114,6 +110,22 @@ export function findLeafNode(messages: readonly DatabaseMessage[], messageId: st
 	return currentNode?.id ?? messageId;
 }
 
+/**
+ * Convenience wrapper around {@link findLeafNodeInMap} for callers that only have
+ * a flat message array.
+ *
+ * Finds the leaf node (message with no children) for a given message branch.
+ * Traverses down the tree following the last child until reaching a leaf.
+ *
+ * @param messages - All messages in the conversation
+ * @param messageId - Starting message ID to find leaf for
+ * @returns The leaf node ID, or the original messageId if no children
+ */
+export function findLeafNode(messages: readonly DatabaseMessage[], messageId: string): string {
+	const nodeMap = new Map(messages.map((msg) => [msg.id, msg] as const));
+	return findLeafNodeInMap(nodeMap, messageId);
+}
+
 /**
  * Finds all descendant messages (children, grandchildren, etc.) of a given message.
  * This is used for cascading deletion to remove all messages in a branch.
@@ -156,21 +168,14 @@ export function findDescendantMessages(
  * Gets sibling information for a message, including all sibling IDs and current position.
  * Siblings are messages that share the same parent.
  *
- * @param messages - All messages in the conversation
+ * @param nodeMap - Map of messages keyed by ID
  * @param messageId - The message to get sibling info for
  * @returns Sibling information including leaf node IDs for navigation
  */
 export function getMessageSiblings(
-	messages: readonly DatabaseMessage[],
+	nodeMap: ReadonlyMap<string, DatabaseMessage>,
 	messageId: string
 ): ChatMessageSiblingInfo | null {
-	const nodeMap = new Map<string, DatabaseMessage>();
-
-	// Build node map for quick lookups
-	for (const msg of messages) {
-		nodeMap.set(msg.id, msg);
-	}
-
 	const message = nodeMap.get(messageId);
 	if (!message) {
 		return null;
@@ -203,7 +208,9 @@ export function getMessageSiblings(
 
 	// Convert sibling message IDs to their corresponding leaf node IDs
 	// This allows navigation between different conversation branches
-	const siblingLeafIds = siblingIds.map((siblingId: string) => findLeafNode(messages, siblingId));
+	const siblingLeafIds = siblingIds.map((siblingId: string) =>
+		findLeafNodeInMap(nodeMap, siblingId)
+	);
 
 	// Find current message's position among siblings
 	const currentIndex = siblingIds.indexOf(messageId);
@@ -217,85 +224,22 @@ export function getMessageSiblings(
 }
 
 /**
- * Creates a display-ready list of messages with sibling information for UI rendering.
- * This is the main function used by chat components to render conversation branches.
+ * Builds sibling information for every message in a conversation.
+ * A single node map is shared across all lookups for O(1) access.
  *
  * @param messages - All messages in the conversation
- * @param leafNodeId - Current leaf node being viewed
- * @returns Array of messages with sibling navigation info
+ * @returns Map of message ID to its sibling information
  */
-export function getMessageDisplayList(
-	messages: readonly DatabaseMessage[],
-	leafNodeId: string
-): ChatMessageSiblingInfo[] {
-	// Get the current conversation path
-	const currentPath = filterByLeafNodeId(messages, leafNodeId, true);
-	const result: ChatMessageSiblingInfo[] = [];
-
-	// Add sibling info for each message in the current path
-	for (const message of currentPath) {
-		if (message.type === 'root') {
-			continue; // Skip root messages in display
-		}
-
-		const siblingInfo = getMessageSiblings(messages, message.id);
-		if (siblingInfo) {
-			result.push(siblingInfo);
+export function buildSiblingInfoMap(
+	messages: readonly DatabaseMessage[]
+): Map<string, ChatMessageSiblingInfo> {
+	const nodeMap = new Map(messages.map((msg) => [msg.id, msg] as const));
+	const siblingMap = new Map<string, ChatMessageSiblingInfo>();
+	for (const msg of messages) {
+		const info = getMessageSiblings(nodeMap, msg.id);
+		if (info) {
+			siblingMap.set(msg.id, info);
 		}
 	}
-
-	return result;
-}
-
-/**
- * Checks if a message has multiple siblings (indicating branching at that point).
- *
- * @param messages - All messages in the conversation
- * @param messageId - The message to check
- * @returns True if the message has siblings
- */
-export function hasMessageSiblings(
-	messages: readonly DatabaseMessage[],
-	messageId: string
-): boolean {
-	const siblingInfo = getMessageSiblings(messages, messageId);
-	return siblingInfo ? siblingInfo.totalSiblings > 1 : false;
-}
-
-/**
- * Gets the next sibling message ID for navigation.
- *
- * @param messages - All messages in the conversation
- * @param messageId - Current message ID
- * @returns Next sibling's leaf node ID, or null if at the end
- */
-export function getNextSibling(
-	messages: readonly DatabaseMessage[],
-	messageId: string
-): string | null {
-	const siblingInfo = getMessageSiblings(messages, messageId);
-	if (!siblingInfo || siblingInfo.currentIndex >= siblingInfo.totalSiblings - 1) {
-		return null;
-	}
-
-	return siblingInfo.siblingIds[siblingInfo.currentIndex + 1];
-}
-
-/**
- * Gets the previous sibling message ID for navigation.
- *
- * @param messages - All messages in the conversation
- * @param messageId - Current message ID
- * @returns Previous sibling's leaf node ID, or null if at the beginning
- */
-export function getPreviousSibling(
-	messages: readonly DatabaseMessage[],
-	messageId: string
-): string | null {
-	const siblingInfo = getMessageSiblings(messages, messageId);
-	if (!siblingInfo || siblingInfo.currentIndex <= 0) {
-		return null;
-	}
-
-	return siblingInfo.siblingIds[siblingInfo.currentIndex - 1];
+	return siblingMap;
 }
diff --git a/tools/ui/src/lib/utils/index.ts b/tools/ui/src/lib/utils/index.ts
index 61b9932d3..8474691ac 100644
--- a/tools/ui/src/lib/utils/index.ts
+++ b/tools/ui/src/lib/utils/index.ts
@@ -26,10 +26,7 @@ export {
 	findLeafNode,
 	findDescendantMessages,
 	getMessageSiblings,
-	getMessageDisplayList,
-	hasMessageSiblings,
-	getNextSibling,
-	getPreviousSibling
+	buildSiblingInfoMap
 } from './branching';
 
 // Code

From 2d973636e292ee6f75fadcf08d29cb33511f509f Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Fri, 3 Jul 2026 23:12:11 +0200
Subject: [PATCH 266/292] chat: trim messages sent to StepFun parser (fixes
 long reasoning loops) (#25238)

* chat: trim messages sent to StepFun parser (fixes long reasoning loops)

* add regression test; remove duplicate template

* chat: trim StepFun content parts before rendering

The StepFun trim workaround ran on the already-rendered messages, where
typed content parts have been concatenated into a single string, so the
per-part whitespace could no longer be reached. Move the trim ahead of
rendering and apply it to content_parts text as well as the string
content and reasoning_content. Adds a content-parts regression test.

Co-Authored-By: Piotr Wilkin <ilintar@gmail.com>
Assisted-By: Claude Fable 5 <noreply@anthropic.com>

---------

Co-authored-by: tarruda <tpadilha84@gmail.com>
---
 common/chat.cpp                               | 28 ++++++-
 .../templates/stepfun-ai-Step-3.5-Flash.jinja | 80 -------------------
 tests/test-chat-auto-parser.cpp               |  1 -
 tests/test-chat.cpp                           | 53 ++++++++++++
 4 files changed, 80 insertions(+), 82 deletions(-)
 delete mode 100644 models/templates/stepfun-ai-Step-3.5-Flash.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index 6da59f4db..22d2ee4a2 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -2378,6 +2378,23 @@ static void func_args_not_string(json & messages) {
     }
 }
 
+// Trim leading/trailing whitespace from message contents before rendering. This
+// has to run on the messages (not on the rendered JSON) because templates with
+// string-only content caps concatenate typed content parts into a single string
+// during rendering, after which the per-part whitespace can no longer be reached.
+// Both the plain string content and the text of typed content parts are trimmed.
+static void trim_all_content(std::vector<common_chat_msg> & messages) {
+    for (auto & message : messages) {
+        message.content           = trim_whitespace(message.content);
+        message.reasoning_content = trim_whitespace(message.reasoning_content);
+        for (auto & part : message.content_parts) {
+            if (part.type == "text") {
+                part.text = trim_whitespace(part.text);
+            }
+        }
+    }
+}
+
 }
 
 // MiniCPM5 format:
@@ -2634,7 +2651,16 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
         params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use : *tmpls->template_default;
     const auto & src             = tmpl.source();
     const auto & caps            = tmpl.original_caps();
-    params.messages              = render_message_to_json(inputs.messages, tmpl.original_caps());
+    std::vector<common_chat_msg>        trimmed_messages;
+    const std::vector<common_chat_msg> * messages_to_render = &inputs.messages;
+    if (src.find("You have access to the following functions in JSONSchema format") != std::string::npos) {
+        // StepFun: trim message contents (including typed content parts) before rendering,
+        // otherwise leftover whitespace drives the model into reasoning loops (issue #24181)
+        trimmed_messages   = inputs.messages;
+        workaround::trim_all_content(trimmed_messages);
+        messages_to_render = &trimmed_messages;
+    }
+    params.messages              = render_message_to_json(*messages_to_render, tmpl.original_caps());
     params.tool_choice           = inputs.tool_choice;
     params.reasoning_format      = inputs.reasoning_format;
     params.enable_thinking       = inputs.enable_thinking;
diff --git a/models/templates/stepfun-ai-Step-3.5-Flash.jinja b/models/templates/stepfun-ai-Step-3.5-Flash.jinja
deleted file mode 100644
index c09ea497d..000000000
--- a/models/templates/stepfun-ai-Step-3.5-Flash.jinja
+++ /dev/null
@@ -1,80 +0,0 @@
-{% macro render_content(content) %}{% if content is none %}{{- '' }}{% elif content is string %}{{- content }}{% elif content is mapping %}{{- content['value'] if 'value' in content else content['text'] }}{% elif content is iterable %}{% for item in content %}{% if item.type == 'text' %}{{- item['value'] if 'value' in item else item['text'] }}{% elif item.type == 'image' %}<im_patch>{% endif %}{% endfor %}{% endif %}{% endmacro %}
-{{bos_token}}{%- if tools %}
-    {{- '<|im_start|>system\n' }}
-    {%- if messages[0].role == 'system' %}
-        {{- render_content(messages[0].content) + '\n\n' }}
-    {%- endif %}
-    {{- "# Tools\n\nYou have access to the following functions in JSONSchema format:\n\n<tools>" }}
-    {%- for tool in tools %}
-        {{- "\n" }}
-        {{- tool | tojson(ensure_ascii=False) }}
-    {%- endfor %}
-    {{- "\n</tools>\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...>\n...\n</function> block must be nested within <tool_call>\n...\n</tool_call> XML tags\n- Required parameters MUST be specified\n</IMPORTANT><|im_end|>\n" }}
-{%- else %}
-    {%- if messages[0].role == 'system' %}
-        {{- '<|im_start|>system\n' + render_content(messages[0].content) + '<|im_end|>\n' }}
-    {%- endif %}
-{%- endif %}
-{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
-{%- for message in messages[::-1] %}
-    {%- set index = (messages|length - 1) - loop.index0 %}
-    {%- if ns.multi_step_tool and message.role == "user" and render_content(message.content) is string and not(render_content(message.content).startswith('<tool_response>') and render_content(message.content).endswith('</tool_response>')) %}
-        {%- set ns.multi_step_tool = false %}
-        {%- set ns.last_query_index = index %}
-    {%- endif %}
-{%- endfor %}
-{%- for message in messages %}
-    {%- set content = render_content(message.content) %}
-    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
-        {%- set role_name = 'observation' if (message.role == "system" and not loop.first and message.name == 'observation') else message.role %}
-        {{- '<|im_start|>' + role_name + '\n' + content + '<|im_end|>' + '\n' }}
-    {%- elif message.role == "assistant" %}
-        {%- if message.reasoning_content is string %}
-            {%- set reasoning_content = render_content(message.reasoning_content) %}
-        {%- else %}
-            {%- if '</think>' in content %}
-                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
-                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
-            {%- else %}
-                {%- set reasoning_content = '' %}
-            {%- endif %}
-        {%- endif %}
-        {%- if loop.index0 > ns.last_query_index %}
-            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n' + content }}
-        {%- else %}
-            {{- '<|im_start|>' + message.role + '\n' + content }}
-        {%- endif %}
-        {%- if message.tool_calls %}
-            {%- for tool_call in message.tool_calls %}
-                {%- if tool_call.function is defined %}
-                    {%- set tool_call = tool_call.function %}
-                {%- endif %}
-                {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
-                {%- if tool_call.arguments is defined %}
-                    {%- set arguments = tool_call.arguments %}
-                    {%- for args_name, args_value in arguments|items %}
-                        {{- '<parameter=' + args_name + '>\n' }}
-                        {%- set args_value = args_value | tojson(ensure_ascii=False) | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
-                        {{- args_value }}
-                        {{- '\n</parameter>\n' }}
-                    {%- endfor %}
-                {%- endif %}
-                {{- '</function>\n</tool_call>' }}
-            {%- endfor %}
-        {%- endif %}
-        {{- '<|im_end|>\n' }}
-    {%- elif message.role == "tool" %}
-        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
-            {{- '<|im_start|>tool_response\n' }}
-        {%- endif %}
-        {{- '<tool_response>' }}
-        {{- content }}
-        {{- '</tool_response>' }}
-        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
-            {{- '<|im_end|>\n' }}
-        {%- endif %}
-    {%- endif %}
-{%- endfor %}
-{%- if add_generation_prompt %}
-    {{- '<|im_start|>assistant\n<think>\n' }}
-{%- endif %}
diff --git a/tests/test-chat-auto-parser.cpp b/tests/test-chat-auto-parser.cpp
index 5cc105753..d15fdd2c0 100644
--- a/tests/test-chat-auto-parser.cpp
+++ b/tests/test-chat-auto-parser.cpp
@@ -1887,7 +1887,6 @@ static void test_role_markers_all_templates(testing & t) {
         { "Qwen-Qwen3-0.6B.jinja",                           "<|im_start|>user",       "<|im_start|>assistant"      },
         { "Qwen-QwQ-32B.jinja",                              "<|im_start|>user",       "<|im_start|>assistant"      },
         { "StepFun3.5-Flash.jinja",                          "<|im_start|>user",       "<|im_start|>assistant"      },
-        { "stepfun-ai-Step-3.5-Flash.jinja",                 "<|im_start|>user",       "<|im_start|>assistant"      },
 
         // DeepSeek family
         { "deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja",  "<｜User｜>",                "<｜Assistant｜>"             },
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 5f71e5da6..e1e0a59e6 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -3155,6 +3155,59 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
                 }
             }
         }
+
+        {
+            // StepFun trimming regression test (see https://github.com/ggml-org/llama.cpp/pull/25238)
+            auto tmpls = read_templates("models/templates/StepFun3.5-Flash.jinja");
+
+            common_chat_msg message_chatbot = simple_assist_msg("Let me check.\n\n", "I am thinking.\n\n");
+
+            {
+                common_chat_templates_inputs inputs;
+                inputs.messages              = { message_chatbot };
+                inputs.add_generation_prompt = true;
+
+                auto params = common_chat_templates_apply(tmpls.get(), inputs);
+
+                if (params.prompt.find("Let me check.\n\n") != std::string::npos) {
+                    throw std::runtime_error("StepFun 3.5: content not trimmed");
+                }
+
+                if (params.prompt.find("I am thinking.\n\n") != std::string::npos) {
+                    throw std::runtime_error("StepFun 3.5: reasoning_content not trimmed");
+                }
+            }
+
+            {
+                // Trimming must also reach typed (text) content parts, not just string content
+                // (see https://github.com/ggml-org/llama.cpp/pull/25238)
+                common_chat_msg message_parts;
+                message_parts.role          = "user";
+                message_parts.content_parts = {
+                    { /* .type = */ "text", /* .text = */ "First part.\n\n" },
+                    { /* .type = */ "media_marker", /* .text = */ "<__media__>" },
+                    { /* .type = */ "text", /* .text = */ "Second part.\n\n" },
+                };
+
+                common_chat_templates_inputs inputs;
+                inputs.messages              = { message_parts };
+                inputs.add_generation_prompt = true;
+
+                auto params = common_chat_templates_apply(tmpls.get(), inputs);
+
+                if (params.prompt.find("First part.\n\n") != std::string::npos ||
+                    params.prompt.find("Second part.\n\n") != std::string::npos) {
+                    throw std::runtime_error("StepFun 3.5: text content parts not trimmed");
+                }
+
+                // the trimmed text itself must still be present
+                if (params.prompt.find("First part.") == std::string::npos ||
+                    params.prompt.find("Second part.") == std::string::npos) {
+                    throw std::runtime_error("StepFun 3.5: text content parts missing after trim");
+                }
+            }
+        }
+
     }
 
     {

From ef2d770117db45b05aa7ecd1b0acca36370c5470 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Sat, 4 Jul 2026 13:37:37 +0200
Subject: [PATCH 267/292] ggml : fix broken CPU concat implementation for
 quantized types (#25247)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml : fix broken CPU concat implementation for quantized types

* tests : concat tests for quantized types

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 ggml/src/ggml-cpu/ops.cpp  | 18 +++++++++++++++---
 tests/test-backend-ops.cpp |  6 ++++++
 2 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 6724686b8..c555831ce 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -1913,7 +1913,11 @@ static void ggml_compute_forward_concat_any(
     GGML_ASSERT(dim >= 0 && dim < 4);
 
     int64_t o[4] = {0, 0, 0, 0};
-    o[dim] = src0->ne[dim];
+    if (dim == 0) {
+        o[dim] = src0->ne[dim]/ggml_blck_size(src0->type);
+    } else {
+        o[dim] = src0->ne[dim];
+    }
 
     const char * x;
 
@@ -1921,8 +1925,8 @@ static void ggml_compute_forward_concat_any(
     for (int i3 = 0; i3 < ne3; i3++) {
         for (int i2 = ith; i2 < ne2; i2 += nth) {
             for (int i1 = 0; i1 < ne1; i1++) {
-                for (int i0 = 0; i0 < ne0; i0++) {
-                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                for (int i0 = 0; i0 < ne0/ggml_blck_size(dst->type); i0++) {
+                    if (i0 < ne00/ggml_blck_size(src0->type) && i1 < ne01 && i2 < ne02 && i3 < ne03) {
                         x = (const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03;
                     } else {
                         x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13;
@@ -2071,6 +2075,14 @@ void ggml_compute_forward_concat(
     ggml_tensor * dst) {
 
     const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    if (ggml_is_quantized(src0->type)) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+        GGML_ASSERT(ggml_is_contiguous(src1));
+        GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0);
+        GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0);
+    }
 
     switch (src0->type) {
         case GGML_TYPE_F16:
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 5d1f7d8ad..c21675ef5 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8918,6 +8918,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
+    for (ggml_type type_a : { GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0 }) {
+        for (int dim : { 0, 1, 2, 3, }) {
+            test_cases.emplace_back(new test_concat(type_a, {128, 12, 13, 14}, dim == 0 ? 256 : 7, dim, 0));
+        }
+    }
+
     for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         for (uint32_t i = 4; i <= 1024*1024; i *= 2) {
             test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {i-1, 1, 1, 1}));

From 665892536dfb1b7532161e3182304bd35c33e768 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Sat, 4 Jul 2026 16:12:27 +0200
Subject: [PATCH 268/292] ui: add sync blocks so display/behavior settings can
 be set via --ui-config-file (#25132)

* ui: add sync blocks so display/behavior settings can be set via --ui-config-file

* ui: remove enable thinking setting
---
 tools/ui/src/lib/constants/settings-keys.ts   |  1 -
 .../ui/src/lib/constants/settings-registry.ts | 56 +++++++++++++------
 2 files changed, 40 insertions(+), 17 deletions(-)

diff --git a/tools/ui/src/lib/constants/settings-keys.ts b/tools/ui/src/lib/constants/settings-keys.ts
index 498a79c73..d4d782705 100644
--- a/tools/ui/src/lib/constants/settings-keys.ts
+++ b/tools/ui/src/lib/constants/settings-keys.ts
@@ -69,7 +69,6 @@ export const SETTINGS_KEYS = {
 	// Developer
 	DISABLE_REASONING_PARSING: 'disableReasoningParsing',
 	EXCLUDE_REASONING_FROM_CONTEXT: 'excludeReasoningFromContext',
-	ENABLE_THINKING: 'enableThinking',
 	SHOW_RAW_OUTPUT_SWITCH: 'showRawOutputSwitch',
 	// PY_INTERPRETER_ENABLED: 'pyInterpreterEnabled',
 	JS_SANDBOX_ENABLED: 'jsSandboxEnabled',
diff --git a/tools/ui/src/lib/constants/settings-registry.ts b/tools/ui/src/lib/constants/settings-registry.ts
index 2047d0b43..b314510ab 100644
--- a/tools/ui/src/lib/constants/settings-registry.ts
+++ b/tools/ui/src/lib/constants/settings-registry.ts
@@ -185,7 +185,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
 				section: SETTINGS_SECTION_SLUGS.GENERAL,
-				isExperimental: true
+				isExperimental: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.TITLE_GENERATION_USE_LLM,
+					paramType: SyncableParameterType.BOOLEAN
+				}
 			},
 			{
 				key: SETTINGS_KEYS.TITLE_GENERATION_PROMPT,
@@ -193,7 +197,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'Optional template for the title generation prompt. Use {{USER}} for the user message and {{ASSISTANT}} for the assistant message.',
 				defaultValue: TITLE_GENERATION.DEFAULT_PROMPT,
 				type: SettingsFieldType.TEXTAREA,
-				section: SETTINGS_SECTION_SLUGS.GENERAL
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.TITLE_GENERATION_PROMPT,
+					paramType: SyncableParameterType.STRING
+				}
 			},
 			{
 				key: SETTINGS_KEYS.MAX_IMAGE_RESOLUTION,
@@ -201,7 +209,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'Images larger than this will be resized before sending to server. Set to 0 to disable.',
 				defaultValue: 0,
 				type: SettingsFieldType.INPUT,
-				section: SETTINGS_SECTION_SLUGS.GENERAL
+				section: SETTINGS_SECTION_SLUGS.GENERAL,
+				sync: {
+					serverKey: SETTINGS_KEYS.MAX_IMAGE_RESOLUTION,
+					paramType: SyncableParameterType.NUMBER
+				}
 			}
 		]
 	},
@@ -385,7 +397,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'Display the current build version in the bottom-right corner of the interface.',
 				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
-				section: SETTINGS_SECTION_SLUGS.DISPLAY
+				section: SETTINGS_SECTION_SLUGS.DISPLAY,
+				sync: {
+					serverKey: SETTINGS_KEYS.SHOW_BUILD_VERSION,
+					paramType: SyncableParameterType.BOOLEAN
+				}
 			}
 		]
 	},
@@ -669,7 +685,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'After each response, re-submit the conversation to pre-fill the server KV cache. Makes the next turn faster since the prompt is already encoded while you read the response.',
 				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
-				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				sync: {
+					serverKey: SETTINGS_KEYS.PRE_ENCODE_CONVERSATION,
+					paramType: SyncableParameterType.BOOLEAN
+				}
 			},
 			{
 				key: SETTINGS_KEYS.DISABLE_REASONING_PARSING,
@@ -677,7 +697,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'Send reasoning_format=none so the server returns thinking tokens inline instead of extracting them into a separate field.',
 				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
-				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				sync: {
+					serverKey: SETTINGS_KEYS.DISABLE_REASONING_PARSING,
+					paramType: SyncableParameterType.BOOLEAN
+				}
 			},
 			{
 				key: SETTINGS_KEYS.EXCLUDE_REASONING_FROM_CONTEXT,
@@ -691,14 +715,6 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 					paramType: SyncableParameterType.BOOLEAN
 				}
 			},
-			{
-				key: SETTINGS_KEYS.ENABLE_THINKING,
-				label: 'Enable thinking',
-				help: 'Enable model thinking/reasoning for each request. When off, the model will skip the thinking phase and go straight to the response.',
-				defaultValue: false,
-				type: SettingsFieldType.CHECKBOX,
-				section: SETTINGS_SECTION_SLUGS.DEVELOPER
-			},
 			{
 				key: SETTINGS_KEYS.SHOW_RAW_OUTPUT_SWITCH,
 				label: 'Enable raw output toggle',
@@ -717,7 +733,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				help: 'Expose a run_javascript tool to the model. Code runs in a Web Worker inside a sandboxed iframe with an opaque origin, isolated from the WebUI and its API, with a hard timeout.',
 				defaultValue: false,
 				type: SettingsFieldType.CHECKBOX,
-				section: SETTINGS_SECTION_SLUGS.DEVELOPER
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				sync: {
+					serverKey: SETTINGS_KEYS.JS_SANDBOX_ENABLED,
+					paramType: SyncableParameterType.BOOLEAN
+				}
 			},
 			{
 				key: SETTINGS_KEYS.CUSTOM_JSON,
@@ -753,7 +773,11 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 				defaultValue: DEFAULT_MCP_CONFIG.requestTimeoutSeconds,
 				type: SettingsFieldType.INPUT,
 				section: SETTINGS_SECTION_SLUGS.MCP,
-				isPositiveInteger: true
+				isPositiveInteger: true,
+				sync: {
+					serverKey: SETTINGS_KEYS.MCP_REQUEST_TIMEOUT_SECONDS,
+					paramType: SyncableParameterType.NUMBER
+				}
 			}
 		]
 	}

From a4107133a634250c8c9d888bc0bc8520dcfd6105 Mon Sep 17 00:00:00 2001
From: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
Date: Sun, 5 Jul 2026 04:37:38 +0800
Subject: [PATCH 269/292] llama : add guard for K/V rotation input when buffer
 is unallocated (#25215)

llm_graph_input_attn_kv::set_input and llm_graph_input_attn_kv_iswa::set_input
call set_input_k_rot / set_input_v_rot whenever the rotation tensor pointer is
non-null, but the tensor's buffer can be unallocated (NULL) when a graph only
stores K/V without attending -- e.g. DFlash speculative decoding's KV-injection
pass. set_input_k_rot then calls ggml_backend_buffer_is_host() on a NULL buffer
and aborts with GGML_ASSERT(buffer).

Guard the four k_rot/v_rot inputs with the same "&& ->buffer" check that the
adjacent kq_mask inputs already use in these two functions. When the buffer is
unallocated there is no data to upload, so skipping is correct.

Fixes #25191

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
---
 src/llama-graph.cpp | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 4c86e43c1..dc41d6690 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -494,11 +494,11 @@ void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
         mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
     }
 
-    if (self_k_rot) {
+    if (self_k_rot && self_k_rot->buffer) {
         mctx->set_input_k_rot(self_k_rot);
     }
 
-    if (self_v_rot) {
+    if (self_v_rot && self_v_rot->buffer) {
         mctx->set_input_v_rot(self_v_rot);
     }
 }
@@ -592,19 +592,19 @@ void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
         mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
     }
 
-    if (self_k_rot) {
+    if (self_k_rot && self_k_rot->buffer) {
         mctx->get_base()->set_input_k_rot(self_k_rot);
     }
 
-    if (self_v_rot) {
+    if (self_v_rot && self_v_rot->buffer) {
         mctx->get_base()->set_input_v_rot(self_v_rot);
     }
 
-    if (self_k_rot_swa) {
+    if (self_k_rot_swa && self_k_rot_swa->buffer) {
         mctx->get_swa()->set_input_k_rot(self_k_rot_swa);
     }
 
-    if (self_v_rot_swa) {
+    if (self_v_rot_swa && self_v_rot_swa->buffer) {
         mctx->get_swa()->set_input_v_rot(self_v_rot_swa);
     }
 }

From 6eb556a5b37a41f82a3d4c30a829ed761aedb31b Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Sun, 5 Jul 2026 11:02:46 +0800
Subject: [PATCH 270/292] fix build

---
 CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 34adf0f89..bfced02f9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -519,6 +519,8 @@ add_library(sdtype_adapter
             otherarch/sdcpp/src/upscaler.cpp
             otherarch/sdcpp/src/runtime/guidance.cpp
             otherarch/sdcpp/src/runtime/guidance.h
+            otherarch/sdcpp/src/runtime/imatrix.cpp
+            otherarch/sdcpp/src/runtime/imatrix.h
             otherarch/sdcpp/src/stable-diffusion.cpp
             otherarch/sdcpp/thirdparty/zip.c
             otherarch/sdcpp/src/model_io/gguf_io.cpp

From 78d2f524682d9fee790a6460c93d018dafeb5229 Mon Sep 17 00:00:00 2001
From: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Date: Sun, 5 Jul 2026 17:26:24 +0200
Subject: [PATCH 271/292] cuda : concat implementation for quantized types
 (#25303)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cuda : concat implementation for quantized types

* chore : apply am17an clever suggestion to shorten the code

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
---
 ggml/src/ggml-cuda/concat.cu    | 52 ++++++++++++++++++++-------------
 ggml/src/ggml-cuda/ggml-cuda.cu | 24 +++++++++++----
 2 files changed, 50 insertions(+), 26 deletions(-)

diff --git a/ggml/src/ggml-cuda/concat.cu b/ggml/src/ggml-cuda/concat.cu
index 8d557092b..276ee64e8 100644
--- a/ggml/src/ggml-cuda/concat.cu
+++ b/ggml/src/ggml-cuda/concat.cu
@@ -152,8 +152,8 @@ static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml
                         src0_d + i3*(src0->nb[3] / sizeof(T)),
                         src1_d + i3*(src1->nb[3] / sizeof(T)),
                         dst_d  + i3*( dst->nb[3] / sizeof(T)),
-                        src0->ne[0], src0->ne[1], src0->ne[2],
-                        dst->ne[0],  dst->ne[1],  dst->ne[2], dim, stream);
+                        ggml_row_size(src0->type, src0->ne[0])/sizeof(T), src0->ne[1], src0->ne[2],
+                        ggml_row_size(dst->type, dst->ne[0])/sizeof(T),  dst->ne[1],  dst->ne[2], dim, stream);
             }
         } else {
             const size_t size0 = ggml_nbytes(src0);
@@ -163,6 +163,8 @@ static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml
             CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream));
         }
     } else {
+        GGML_ASSERT(!ggml_is_quantized(src0->type));
+
         dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
         auto launch_kernel = [&](auto dim) {
             concat_non_cont<T, dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
@@ -204,24 +206,34 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 
     GGML_ASSERT(src0->type == src1->type);
     GGML_ASSERT(dst->type  == src0->type);
-    GGML_ASSERT(!ggml_is_quantized(src0->type));
-    GGML_ASSERT(ggml_blck_size(src0->type) == 1);
 
-    switch (ggml_type_size(src0->type)) {
-        case 1:
-            concat_cuda<uint8_t>(src0, src1, dst, dim, stream);
-            break;
-        case 2:
-            concat_cuda<uint16_t>(src0, src1, dst, dim, stream);
-            break;
-        case 4:
-            concat_cuda<uint32_t>(src0, src1, dst, dim, stream);
-            break;
-        case 8:
-            concat_cuda<uint64_t>(src0, src1, dst, dim, stream);
-            break;
-        default:
-            GGML_ABORT("Unsupported type size: %zu", ggml_type_size(src0->type));
-            break;
+    if (ggml_is_quantized(src0->type)) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+        GGML_ASSERT(ggml_is_contiguous(src1));
+        GGML_ASSERT(src0->ne[0] % ggml_blck_size(src0->type) == 0);
+        GGML_ASSERT(src1->ne[0] % ggml_blck_size(src1->type) == 0);
+
+        // if tensors are contiguous and ne[0] is multiple of the block size we can concat both tensors as byte tensors
+        concat_cuda<uint8_t>(src0, src1, dst, dim, stream);
+    } else {
+        GGML_ASSERT(ggml_blck_size(src0->type) == 1);
+
+        switch (ggml_type_size(src0->type)) {
+            case 1:
+                concat_cuda<uint8_t>(src0, src1, dst, dim, stream);
+                break;
+            case 2:
+                concat_cuda<uint16_t>(src0, src1, dst, dim, stream);
+                break;
+            case 4:
+                concat_cuda<uint32_t>(src0, src1, dst, dim, stream);
+                break;
+            case 8:
+                concat_cuda<uint64_t>(src0, src1, dst, dim, stream);
+                break;
+            default:
+                GGML_ABORT("Unsupported type size: %zu", ggml_type_size(src0->type));
+                break;
+        }
     }
 }
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 78d2218e5..83749f094 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -5387,12 +5387,24 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 ggml_type src1_type = op->src[1]->type;
                 return src0_type == src1_type &&
                        src0_type == op->type &&
-                       !ggml_is_quantized(src0_type) &&
-                       ggml_blck_size(src0_type) == 1 &&
-                       (ggml_type_size(src0_type) == 1 ||
-                        ggml_type_size(src0_type) == 2 ||
-                        ggml_type_size(src0_type) == 4 ||
-                        ggml_type_size(src0_type) == 8);
+                       (
+                           (
+                               ggml_is_quantized(src0_type) &&
+                               ggml_is_contiguous(op->src[0]) &&
+                               ggml_is_contiguous(op->src[1]) &&
+                               op->src[0]->ne[0] % ggml_blck_size(src0_type) == 0 &&
+                               op->src[1]->ne[0] % ggml_blck_size(src0_type) == 0
+                           ) || (
+                               !ggml_is_quantized(src0_type) &&
+                               ggml_blck_size(src0_type) == 1 &&
+                               (
+                                   ggml_type_size(src0_type) == 1 ||
+                                   ggml_type_size(src0_type) == 2 ||
+                                   ggml_type_size(src0_type) == 4 ||
+                                   ggml_type_size(src0_type) == 8
+                               )
+                           )
+                       );
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {

From 7a63fdede1aca8b29e64146f33ae03af6c3ee3cd Mon Sep 17 00:00:00 2001
From: Vexxie <rainandriamusic@gmail.com>
Date: Sun, 5 Jul 2026 18:10:09 +0100
Subject: [PATCH 272/292] ggml: Update VMM Pool allocation ggml-cuda.cu -
 Turing P2P access fix (fixes #24489) (#24491)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update ggml-cuda.cu - Turing P2P access fix.

* Add original code as fallback behaviour when NCCL or P2P is not set/true.

* Update ggml/src/ggml-cuda/ggml-cuda.cu to add comment as per suggestion

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/ggml-cuda.cu | 42 ++++++++++++++++++++++++++++-----
 1 file changed, 36 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 83749f094..cda31bbfb 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -543,12 +543,42 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
             // the memory allocation handle is no longer needed after mapping
             CU_CHECK(cuMemRelease(handle));
 
-            // set access
-            CUmemAccessDesc access = {};
-            access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
-            access.location.id = device;
-            access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
-            CU_CHECK(cuMemSetAccess((CUdeviceptr)((char *)(pool_addr) + pool_size), reserve_size, &access, 1));
+            // VMM Bug fix for P2P access if GGML_CUDA_P2P is set, or if NCCL build
+            bool use_peer_access = getenv("GGML_CUDA_P2P") != nullptr;
+#if defined(GGML_USE_NCCL)
+            use_peer_access = true;
+#endif // defined(GGML_USE_NCCL)
+
+            if (use_peer_access) {
+                // NCCL implicitly enables peer access (cudaDeviceEnablePeerAccess), and
+                // GGML_CUDA_P2P enables it explicitly. Unlike cudaMalloc buffers, VMM
+                // allocations do not become peer-accessible from that alone, so access
+                // must be granted explicitly here.
+                std::vector<CUmemAccessDesc> access_descs;
+                const int device_count = ggml_cuda_info().device_count;
+                for (int id = 0; id < device_count; ++id) {
+                    if (id != device) {
+                        int can_access_peer = 0;
+                        CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, device));
+                        if (!can_access_peer) {
+                            continue;
+                        }
+                    }
+                    CUmemAccessDesc access = {};
+                    access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+                    access.location.id = id;
+                    access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+                    access_descs.push_back(access);
+                }
+                CU_CHECK(cuMemSetAccess(start_ptr, reserve_size, access_descs.data(), access_descs.size()));
+            } else {
+                // set access for non P2P
+                CUmemAccessDesc access = {};
+                access.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
+                access.location.id = device;
+                access.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
+                CU_CHECK(cuMemSetAccess(start_ptr, reserve_size, &access, 1));
+            }
 
             // add to the pool
             pool_size += reserve_size;

From 4b2a0cdee141d7906f661ba573e4b455f684bbc3 Mon Sep 17 00:00:00 2001
From: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
Date: Mon, 6 Jul 2026 01:56:11 +0800
Subject: [PATCH 273/292] ggml : fix tensor-parallel + -ncmoe crash on MoE
 models (#25028)

Tensor parallelism (-sm tensor) combined with -ncmoe (CPU-offloaded MoE
experts) aborts during warm-up on MoE models with
GGML_ASSERT(ggml_is_contiguous(tensor)) in ggml-backend-meta.cpp.

The failing tensor is the MoE router output (ffn_moe_topk): it is mirrored
(GGML_BACKEND_SPLIT_AXIS_MIRRORED, replicated across backends since routing
must be identical) and happens to be a non-contiguous view.
ggml_backend_meta_buffer_{get,set}_tensor asserted contiguity before
consulting the split state, so a mirrored non-contiguous tensor tripped the
assert even though the GGML_BACKEND_SPLIT_AXIS_MIRRORED case right below
already handles it.

Move the split-state lookup above the assert and allow the mirrored case in
both get_tensor and set_tensor.

Diagnosis credit to the reporter (@nathanmp).

Fixes #24886

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
---
 ggml/src/ggml-backend-meta.cpp | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp
index 0a36f0990..7bd329164 100644
--- a/ggml/src/ggml-backend-meta.cpp
+++ b/ggml/src/ggml-backend-meta.cpp
@@ -1245,9 +1245,8 @@ static enum ggml_status ggml_backend_meta_buffer_init_tensor(ggml_backend_buffer
 
 static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
-    GGML_ASSERT(ggml_is_contiguous(tensor));
-
     const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+    GGML_ASSERT(ggml_is_contiguous(tensor) || split_state.axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
 
     if (split_state.n_segments != 1 || split_state.nr[0] != 1) {
         GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
@@ -1360,9 +1359,8 @@ static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, gg
 
 static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
-    GGML_ASSERT(ggml_is_contiguous(tensor));
-
     const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+    GGML_ASSERT(ggml_is_contiguous(tensor) || split_state.axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
 
     if (split_state.n_segments != 1 || split_state.nr[0] != 1) {
         GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);

From 3e5036fbfb1613dc19034844c1989aa645183164 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Sun, 5 Jul 2026 18:38:47 +0000
Subject: [PATCH 274/292] abort if we see a multi buffer (#25276)

---
 ggml/src/ggml-backend-meta.cpp | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ggml/src/ggml-backend-meta.cpp b/ggml/src/ggml-backend-meta.cpp
index 7bd329164..1f29ec867 100644
--- a/ggml/src/ggml-backend-meta.cpp
+++ b/ggml/src/ggml-backend-meta.cpp
@@ -1144,6 +1144,11 @@ static enum ggml_status ggml_backend_meta_buffer_init_tensor_impl(ggml_backend_m
         ggml_context          * simple_ctx = stc.ctxs[j].get();
         ggml_backend_buffer_t   simple_buf = buf_ctx->bufs[j].get();
 
+        if ((simple_buf != nullptr) && ggml_backend_buffer_is_multi_buffer(simple_buf)) {
+            // see https://github.com/ggml-org/llama.cpp/issues/22197
+            GGML_ABORT("multi buffers are not supported by the meta backend");
+        }
+
         if (split_dim >= 0 && split_dim < GGML_MAX_DIMS) {
             // TODO: the following assert fails for llama-parallel even though the results are correct:
             // GGML_ASSERT(ggml_is_contiguously_allocated(tensor));

From 2da668617612d2df773f966e3b0ee22dc2beef7b Mon Sep 17 00:00:00 2001
From: Al G <toasting@gmail.com>
Date: Sun, 5 Jul 2026 19:39:36 +0100
Subject: [PATCH 275/292] Fix stale tensor-split params for draft models
 (#24814)

* meta: fix tensor split metadata for GQA attention

* Tidied the code a bit to match existing style

* Revert "Tidied the code a bit to match existing style"

This reverts commit b90c6c6300091fe09e2350a3d4edcfcf15db8d2e.

* Reverted the ggml-backend-meta asset hack.
---
 src/llama-model.cpp | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e07f6e986..8d68cff45 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1012,9 +1012,17 @@ struct llama_model::impl {
     std::vector<layer_dev> dev_layer;
 
     bool has_tensor_overrides;
+
+    std::vector<float> tensor_split_owned;
 };
 
 llama_model::llama_model(const llama_model_params & params) : params(params), pimpl(std::make_unique<impl>()) {
+    if (params.tensor_split != nullptr) {
+        // llama_model_params stores tensor_split as a borrowed pointer, but the model
+        // may need it later for tensor-parallel KV-cache split metadata.
+        pimpl->tensor_split_owned.assign(params.tensor_split, params.tensor_split + llama_max_devices());
+        this->params.tensor_split = pimpl->tensor_split_owned.data();
+    }
     pimpl->has_tensor_overrides = params.tensor_buft_overrides && params.tensor_buft_overrides[0].pattern;
 }
 

From 72874f559c598b8f89fbb24864868337cf5afb4c Mon Sep 17 00:00:00 2001
From: adavyas <121313528+adavyas@users.noreply.github.com>
Date: Sun, 5 Jul 2026 20:49:06 -0700
Subject: [PATCH 276/292] ggml-cuda: optimize conv_transpose_1d indexing
 (#25310)

---
 ggml/src/ggml-cuda/conv-transpose-1d.cu | 26 +++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-cuda/conv-transpose-1d.cu b/ggml/src/ggml-cuda/conv-transpose-1d.cu
index 8418ba667..ebf2aa804 100644
--- a/ggml/src/ggml-cuda/conv-transpose-1d.cu
+++ b/ggml/src/ggml-cuda/conv-transpose-1d.cu
@@ -11,30 +11,32 @@ static  __global__ void conv_transpose_1d_kernel(
         return;
     }
 
-    int out_index = global_index / dst_ne0;
+    int out_t = global_index % dst_ne0;
+    int out_ch = (global_index / dst_ne0) % dst_ne1;
+    int plane = global_index / (dst_ne0 * dst_ne1);
 
     float accumulator = 0;
 
     for (int c = 0; c < src0_ne2; c++) {
-        int idx = global_index % dst_ne0;
+        int kernel_offset = src0_ne0 * (out_ch + src0_ne1 * c);
+        int input_offset = src1_ne0 * (c + src1_ne1 * plane);
 
-        int kernel_offset = (src0_ne0 * src0_ne1 * c) + (out_index * src0_ne0);
-        int input_offset = src1_ne0 * c;
-
-        for (int i = 0; i < src1_ne0; i++) {
-            if (!(idx >= i*s0 && idx < i*s0 + src0_ne0)) {
+        for (int k = 0; k < src0_ne0; k++) {
+            int input_numer = out_t + p0 - k*d0;
+            if (input_numer < 0 || input_numer % s0 != 0) {
                 continue;
             }
-            int weight_idx = idx - i*s0;
 
-            float kernel_weight = src0[kernel_offset + weight_idx];
-            float input_value =  src1[input_offset+i];
+            int input_t = input_numer / s0;
+            if (input_t >= src1_ne0) {
+                continue;
+            }
 
-            accumulator += kernel_weight * input_value;
+            accumulator += src0[kernel_offset + k] * src1[input_offset + input_t];
         }
     }
     dst[global_index] = accumulator;
-    GGML_UNUSED_VARS(p0, d0, src0_ne3, src1_ne3, dst_ne3, src1_ne1, dst_ne1, src1_ne2, dst_ne2);
+    GGML_UNUSED_VARS(src0_ne3, src1_ne2, src1_ne3, dst_ne2, dst_ne3);
 }
 
 static void conv_transpose_1d_f32_f32_cuda(

From 898b08854d64befbbb7f6e22a5e3052d65e94f4b Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 6 Jul 2026 08:41:39 +0200
Subject: [PATCH 277/292] ui: fake 200 for proxy DELETE req (#25298)

---
 tools/ui/src/lib/services/mcp.service.ts | 24 ++++++++++++++++++++++
 tools/ui/tests/unit/mcp-service.test.ts  | 26 ++++++++++++++++++++++++
 2 files changed, 50 insertions(+)

diff --git a/tools/ui/src/lib/services/mcp.service.ts b/tools/ui/src/lib/services/mcp.service.ts
index 443298917..ae98632a6 100644
--- a/tools/ui/src/lib/services/mcp.service.ts
+++ b/tools/ui/src/lib/services/mcp.service.ts
@@ -314,6 +314,30 @@ export class MCPService {
 					)
 				);
 
+				if (method === 'DELETE' && url.includes(CORS_PROXY_ENDPOINT)) {
+					const response = new Response(null, { status: 200, statusText: 'OK' });
+
+					logIfEnabled(
+						this.createLog(
+							MCPConnectionPhase.INITIALIZING,
+							`HTTP 200 ${method} ${url} (fake response)`,
+							MCPLogLevel.INFO,
+							{
+								response: {
+									url,
+									status: response.status,
+									statusText: response.statusText,
+									durationMs: 0,
+									isFake: true
+								}
+							}
+						)
+					);
+
+					// fake response, bypass real fetch()
+					return response;
+				}
+
 				try {
 					const response = await fetch(input, {
 						...baseInit,
diff --git a/tools/ui/tests/unit/mcp-service.test.ts b/tools/ui/tests/unit/mcp-service.test.ts
index 1f6fdda37..d3af90681 100644
--- a/tools/ui/tests/unit/mcp-service.test.ts
+++ b/tools/ui/tests/unit/mcp-service.test.ts
@@ -154,6 +154,32 @@ describe('MCPService', () => {
 		});
 	});
 
+	it('DELETE request with CORS proxy should return a fake 200 response', async () => {
+		const logs: MCPConnectionLog[] = [];
+		const fetchMock = vi.fn();
+
+		vi.stubGlobal('fetch', fetchMock);
+
+		const config: MCPServerConfig = {
+			url: 'https://example.com/mcp',
+			transport: MCPTransportType.STREAMABLE_HTTP,
+			useProxy: true
+		};
+
+		const controller = createDiagnosticFetch(config, (log) => logs.push(log), {}, true);
+
+		const response = await controller.fetch(
+			'http://localhost:8080/cors-proxy?url=https%3A%2F%2Fexample.com%2Fmcp',
+			{ method: 'DELETE' }
+		);
+
+		expect(fetchMock).not.toHaveBeenCalled();
+		expect(response.status).toBe(200);
+		expect(logs.at(-1)?.details).toMatchObject({
+			response: { status: 200, isFake: true }
+		});
+	});
+
 	it('partially redacts mcp-session-id in diagnostic request and response logs', async () => {
 		const logs: MCPConnectionLog[] = [];
 		const response = new Response('{}', {

From d06ddd35896e2748fc95ccdb46fb260c5af2c029 Mon Sep 17 00:00:00 2001
From: a-huk <56552991+a-huk@users.noreply.github.com>
Date: Mon, 6 Jul 2026 09:02:26 +0200
Subject: [PATCH 278/292] ggml-hip: enable -ffast-math for HIP builds (#23862)

---
 ggml/src/ggml-hip/CMakeLists.txt | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt
index a7d4e0ea2..72850f26d 100644
--- a/ggml/src/ggml-hip/CMakeLists.txt
+++ b/ggml/src/ggml-hip/CMakeLists.txt
@@ -155,3 +155,5 @@ if (GGML_HIP_RCCL)
 endif()
 
 target_link_libraries(ggml-hip PRIVATE ggml-base hip::host roc::rocblas roc::hipblas)
+
+target_compile_options(ggml-hip PRIVATE "$<$<COMPILE_LANGUAGE:HIP>:-ffast-math>")

From 48719618e8321e64d4dddaaa56249671295f8a72 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Mon, 6 Jul 2026 09:53:35 +0200
Subject: [PATCH 279/292] scripts : use HF_TOKEN when downloading UI assets
 (#25280)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 scripts/ui-assets.cmake | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/scripts/ui-assets.cmake b/scripts/ui-assets.cmake
index 349fa9bf8..dc0417ea0 100644
--- a/scripts/ui-assets.cmake
+++ b/scripts/ui-assets.cmake
@@ -186,6 +186,12 @@ function(hf_download version out_var out_resolved)
 
     set(archive "${UI_BINARY_DIR}/dist.tar.gz")
 
+    # Use HF_TOKEN to benefit from higher rate limits
+    set(auth_headers "")
+    if(DEFINED ENV{HF_TOKEN} AND NOT "$ENV{HF_TOKEN}" STREQUAL "")
+        list(APPEND auth_headers "HTTPHEADER" "Authorization: Bearer $ENV{HF_TOKEN}")
+    endif()
+
     set(candidates "")
     if(NOT "${version}" STREQUAL "")
         list(APPEND candidates "${version}")
@@ -198,7 +204,7 @@ function(hf_download version out_var out_resolved)
         message(STATUS "UI: downloading from ${resolved}: ${base}/dist.tar.gz")
 
         file(DOWNLOAD "${base}/dist.tar.gz?download=true" "${archive}"
-            STATUS status TIMEOUT 300
+            STATUS status TIMEOUT 300 ${auth_headers}
         )
         list(GET status 0 rc)
         if(NOT rc EQUAL 0)
@@ -208,7 +214,7 @@ function(hf_download version out_var out_resolved)
         endif()
 
         file(DOWNLOAD "${base}/dist.tar.gz.sha256?download=true" "${archive}.sha256"
-            STATUS status TIMEOUT 30
+            STATUS status TIMEOUT 30 ${auth_headers}
         )
         list(GET status 0 rc)
         if(NOT rc EQUAL 0)

From d80e87850173a674ea2673fd003ac54ac93774c0 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Mon, 6 Jul 2026 10:30:07 +0200
Subject: [PATCH 280/292] ui: restore Ctrl+B sidebar toggle shortcut (#25307)

---
 .../navigation/SidebarNavigation/SidebarNavigation.svelte   | 5 ++++-
 tools/ui/src/lib/enums/keyboard.enums.ts                    | 1 +
 tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts     | 6 ++++++
 3 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
index fe503f53b..a23f4682e 100644
--- a/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
+++ b/tools/ui/src/lib/components/app/navigation/SidebarNavigation/SidebarNavigation.svelte
@@ -27,7 +27,10 @@
 
 	let { onSearchClick = () => {} }: Props = $props();
 
-	const { handleKeydown } = useKeyboardShortcuts({ activateSearchMode: () => onSearchClick() });
+	const { handleKeydown } = useKeyboardShortcuts({
+		activateSearchMode: () => onSearchClick(),
+		toggleSidebar: () => toggleExpandedMode()
+	});
 
 	let isExpandedMode = $state(false);
 	let hoveredTooltip = $state<string | null>(null);
diff --git a/tools/ui/src/lib/enums/keyboard.enums.ts b/tools/ui/src/lib/enums/keyboard.enums.ts
index 46cd4a776..735d3e4b4 100644
--- a/tools/ui/src/lib/enums/keyboard.enums.ts
+++ b/tools/ui/src/lib/enums/keyboard.enums.ts
@@ -9,6 +9,7 @@ export enum KeyboardKey {
 	ARROW_LEFT = 'ArrowLeft',
 	ARROW_RIGHT = 'ArrowRight',
 	TAB = 'Tab',
+	B_LOWER = 'b',
 	D_LOWER = 'd',
 	D_UPPER = 'D',
 	E_UPPER = 'E',
diff --git a/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts b/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts
index 05966a1a1..61df30b79 100644
--- a/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts
+++ b/tools/ui/src/lib/hooks/use-keyboard-shortcuts.svelte.ts
@@ -9,6 +9,7 @@ interface KeyboardShortcutsCallbacks {
 	deleteActiveConversation?: () => void;
 	navigateToPrevConversation?: () => void;
 	navigateToNextConversation?: () => void;
+	toggleSidebar?: () => void;
 }
 
 export function useKeyboardShortcuts(callbacks: KeyboardShortcutsCallbacks) {
@@ -21,6 +22,11 @@ export function useKeyboardShortcuts(callbacks: KeyboardShortcutsCallbacks) {
 			callbacks.onSearchActivated?.();
 		}
 
+		if (isCmdOrCtrl && event.key === KeyboardKey.B_LOWER) {
+			event.preventDefault();
+			callbacks.toggleSidebar?.();
+		}
+
 		if (
 			isCmdOrCtrl &&
 			event.shiftKey &&

From 86961efd5675279a4ec2f3d1a3c7d6e3803c1935 Mon Sep 17 00:00:00 2001
From: hokanosekai <69720899+hokanosekai@users.noreply.github.com>
Date: Mon, 6 Jul 2026 10:35:57 +0200
Subject: [PATCH 281/292] vulkan: fix 32-bit integer overflow in CEIL_DIV
 (#25245)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c0ab9f1c6..3e38c17de 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -129,7 +129,7 @@ typedef struct VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE {
 #endif
 
 #define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1))
-#define CEIL_DIV(M, N) (((M) + (N)-1) / (N))
+#define CEIL_DIV(M, N) (((M) / (N)) + (((M) % (N)) != 0))
 static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 
 #define VK_VENDOR_ID_AMD 0x1002

From 3b4fca11ac1b0ded8fabbfad7d1f386d2d24e852 Mon Sep 17 00:00:00 2001
From: shalinib-ibm <Shalini.Salomi.Bodapati@ibm.com>
Date: Mon, 6 Jul 2026 15:48:17 +0530
Subject: [PATCH 282/292] ggml-cpu: Enable tiled matmul on AIX (#25199)

The matmul_tiled path uses large local stack buffers for A_pack and B_pack. On AIX this can trigger a segmentation fault, so reduce the buffer footprint there to keep the tiled path usable.

 Performance Impact:
    ~ 2x gains in PP_Speed for FP32, Q4_0 and Q8_0 models tested with llama-bench, llama-batched-bench and llama-cli.
    Models used: Llama3.2 3b Instruct F32, qwen 2.5 3b Q4_0 and Q8_0
---
 ggml/src/ggml-cpu/llamafile/sgemm.cpp | 34 ++++++++++++++++-----------
 1 file changed, 20 insertions(+), 14 deletions(-)

diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
index 0b8323e60..5efaaa5b2 100644
--- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp
+++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp
@@ -2321,24 +2321,28 @@ class tinyBLAS_Q0_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
-    #if defined(_AIX) || defined(__BIG_ENDIAN__)
-        mnpack(0, m, 0, n);
-    #else
-        const int64_t mc = 64;
-        const int64_t kc = 64;
+        int64_t mc = 64;
         int64_t nc = 64;
+        int64_t kc = 64;
+        int64_t n_chunk = 64;
+    #if defined(_AIX) || defined(__BIG_ENDIAN__)
+        mc = 32;
+        nc = 32;
+        kc = 32;
+        n_chunk = 32
+    #endif
         int64_t n_aligned = 0;
-        if (n % 64 == 0) {
+        if (n % n_chunk == 0) {
             n_aligned = n;
         } else if (n == 4) {
             n_aligned = 4;
-        } else if (n < 64) {
+        } else if (n < n_chunk) {
             n_aligned = (n / 8) * 8;
         } else {
-            n_aligned = (n / 64) * 64;
+            n_aligned = (n / n_chunk) * n_chunk;
         }
         if (n_aligned > 0) {
-            if (n_aligned % 64 == 0)      nc = 64;
+            if (n_aligned % n_chunk == 0) nc = n_chunk;
             else if (n_aligned == n)      nc = n;
             else if (n_aligned % 32 == 0) nc = 32;
             else if (n_aligned % 24 == 0) nc = 24;
@@ -2354,7 +2358,6 @@ class tinyBLAS_Q0_PPC {
         } else {
             mnpack(0, m, 0, n);
         }
-    #endif
     }
 
   private:
@@ -3195,16 +3198,19 @@ class tinyBLAS_PPC {
     }
 
     void matmul(int64_t m, int64_t n) {
+        int64_t mc = 256;
+        int64_t nc = 256;
+        int64_t kc = 256;
     #if defined(_AIX) || defined(__BIG_ENDIAN__)
-        mnpack(0, m, 0, n);
-    #else
-        int64_t mc = 256; int64_t nc = 256; int64_t kc = 256;
+        mc = 128;
+        nc = 128;
+        kc = 128;
+    #endif
         if (m % mc == 0 && n % nc == 0 && k % kc == 0) {
             matmul_tiled(m, n, mc, nc, kc);
         } else {
             mnpack(0, m, 0, n);
         }
-    #endif
     }
 
   private:

From 0f40d5c5d32ddd660e049d419e0264531e46b8fc Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 6 Jul 2026 18:36:15 +0800
Subject: [PATCH 283/292] updated lite

---
 embd_res/klite.embd | 762 ++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 699 insertions(+), 63 deletions(-)

diff --git a/embd_res/klite.embd b/embd_res/klite.embd
index 6d9f92975..3b3f86b40 100644
--- a/embd_res/klite.embd
+++ b/embd_res/klite.embd
@@ -12,7 +12,7 @@ Current version indicated by LITEVER below.
 -->
 <head>
 	<script id="init-config">
-	const LITEVER = 338;
+	const LITEVER = 339;
 	const urlParams = new URLSearchParams(window.location.search);
 	var localflag = urlParams.get('local'); //this will be replaced automatically in embedded kcpp
 	const STORAGE_PREFIX = (localflag?"e_":"")+"kaihordewebui_";
@@ -1676,6 +1676,50 @@ Current version indicated by LITEVER below.
 		left:unset;
 		right:0px;
 	}
+
+	#groupselectcontainer .menutext {
+		overflow-x: auto;
+		padding-left: 10px;
+		padding-right: 10px;
+	}
+	.groupchatselect-table {
+		width: max-content;
+		min-width: 100%;
+		margin: 8px auto;
+	}
+	.groupchatselect-table th,
+	.groupchatselect-table td {
+		padding: 2px 6px;
+	}
+	.groupchatselect-table td:first-child {
+		max-width: 260px;
+		overflow-wrap: anywhere;
+		text-align: center;
+	}
+	.groupchat-inline-action {
+		max-width: 260px;
+		margin: 0px auto;
+		cursor: pointer;
+		justify-content: center;
+		overflow-wrap: anywhere;
+	}
+	#groupchataestheticcontainer {
+		justify-content: center;
+	}
+	.nspopup.groupchataestheticpopup {
+		width: fit-content;
+		min-width: min(270px, calc(100vw - 32px));
+		max-width: calc(100vw - 32px);
+		margin-top: 0px;
+	}
+	#groupchataestheticcontainer .menutext {
+		text-align: left;
+		padding: 10px 14px 4px 14px;
+	}
+	#groupchataestheticcontainer .settinglabel {
+		min-width: min(250px, calc(100vw - 60px));
+	}
+
 	.popupbg {
 		position: fixed;
 		top: 0;
@@ -2577,7 +2621,17 @@ Current version indicated by LITEVER below.
 	.instruct-settings-input input { width:44px; height:20px; }
 	#code-block-background-colorselector, #code-block-foreground-colorselector { text-align: center; margin: 0px 5px; }
 	#you-text-colorselector, #you-speech-colorselector, #you-action-colorselector, #AI-text-colorselector, #AI-speech-colorselector, #AI-action-colorselector { text-align: center; margin: 0px 5px; }
-	#you-bubble-colorselector, #AI-bubble-colorselector, #you-portrait, #AI-portrait { text-align: center; margin: 0px 10px; border-radius: 1rem; padding: 1px 6px; }
+	#you-bubble-colorselector, #AI-bubble-colorselector, #you-portrait, #AI-portrait, #groupchat-portrait { text-align: center; margin: 0px 10px; border-radius: 1rem; padding: 1px 6px; }
+	#groupchat-portrait { cursor: pointer; }
+	#groupchat_aesthetic_avatar {
+		width: 64px;
+		height: 64px;
+		margin: 0px auto 8px auto;
+		background-size: cover;
+		background-position: center;
+		border: 1px solid var(--theme_color_border);
+		border-radius: 6px;
+	}
 	@media screen and (max-width: 780px) {
 		#aesthetic_text_preview_panel { display: none; }
 	}
@@ -4393,6 +4447,7 @@ Current version indicated by LITEVER below.
 		chat_context_mod: true, //extra injection for chat mode
 		chatname: "User", //name to use in chat
 		chatopponent: defaultchatopponent,
+		groupchat_data: {}, //persistent metadata for groupchat characters. key is a hash of the bot character name
 		instruct_starttag: "{{[INPUT]}}",
 		instruct_endtag: "{{[OUTPUT]}}",
 		instruct_systag: "{{[SYSTEM]}}",
@@ -12901,6 +12956,7 @@ Current version indicated by LITEVER below.
 			document.getElementById("charactercreator").classList.contains("hidden") &&
 			document.getElementById("zoomedimgcontainer").classList.contains("hidden") &&
 			document.getElementById("groupselectcontainer").classList.contains("hidden") &&
+			document.getElementById("groupchataestheticcontainer").classList.contains("hidden") &&
 			document.getElementById("addmediacontainer").classList.contains("hidden") &&
 			document.getElementById("pasteimgcontainer").classList.contains("hidden") &&
 			document.getElementById("webcamcontainer").classList.contains("hidden") &&
@@ -12944,6 +13000,8 @@ Current version indicated by LITEVER below.
 		document.getElementById("charactercreator").classList.add("hidden");
 		document.getElementById("zoomedimgcontainer").classList.add("hidden");
 		document.getElementById("groupselectcontainer").classList.add("hidden");
+		document.getElementById("groupchataestheticcontainer").classList.add("hidden");
+		groupchat_select_snapshot = null;
 		document.getElementById("addmediacontainer").classList.add("hidden");
 		document.getElementById("pasteimgcontainer").classList.add("hidden");
 		document.getElementById("webcamcontainer").classList.add("hidden");
@@ -17220,7 +17278,15 @@ Current version indicated by LITEVER below.
 		numberOfLineBreaks = numberOfLineBreaks>8?8:numberOfLineBreaks;
 		textarea.rows = numberOfLineBreaks+1;
 	}
-
+	function groupchat_reply_order(coarr)
+	{
+		//refactored out to a global function so that usermods can change the reply order strategy
+		if(!coarr || coarr.length==0)
+		{
+			return "";
+		}
+		return coarr[Math.floor(Math.random()*coarr.length)];
+	}
 	function toggle_generate_images_mode(silent=false)
 	{
 		document.getElementById("generate_images_model_container").classList.add("hidden");
@@ -18022,6 +18088,7 @@ Current version indicated by LITEVER below.
 			documentdb_chunksize = 800;
 			documentdb_data = "";
 			newlineaftermemory = true;
+			localsettings.groupchat_data = {};
 		}
 		if(localsettings.inject_randomness_seed>0)
 		{
@@ -18708,7 +18775,7 @@ Current version indicated by LITEVER below.
 				image_db[imgid].len = duration;
 			});
 		}
-
+		return imgid;
 	}
 
 	function clear_paste_window()
@@ -19244,7 +19311,8 @@ Current version indicated by LITEVER below.
 		var reader = new FileReader();
 		reader.onload = function() {
 			var base64Audio = this.result;
-			self_upload_audio(base64Audio, "tts_audio", true);
+			let newimgid = self_upload_audio(base64Audio, "tts_audio", true);
+			image_db[newimgid].imsource = 0; //0=generated,1=uploaded
 		};
 		reader.readAsDataURL(file);
 	}
@@ -20152,9 +20220,10 @@ Current version indicated by LITEVER below.
 				{
 					let coarr = co.split("||$||");
 					coarr = coarr.filter(x=>(x&&x!=""));
+					coarr = coarr.map(x=>sanitize_groupchat_participant_name(x));
+					coarr = coarr.filter(x=>(x&&x!=""));
 					coarr = coarr.filter(x=>(!groupchat_removals.includes(x)));
-					coarr = coarr.map(x=>x.trim());
-					co = coarr[Math.floor(Math.random()*coarr.length)];
+					co = groupchat_reply_order(coarr);
 
 					//we check if a name was recently mentioned in the previous response.
 					//if so, switch to that user
@@ -20282,13 +20351,17 @@ Current version indicated by LITEVER below.
 							}
 							let coarr = localsettings.chatopponent.split("||$||");
 							coarr = coarr.filter(x=>(x&&x!=""));
+							coarr = coarr.map(x=>sanitize_groupchat_participant_name(x));
+							coarr = coarr.filter(x=>(x&&x!=""));
 							coarr = coarr.filter(x=>(!groupchat_removals.includes(x)));
-							coarr = coarr.map(x=>x.trim());
-							let co = coarr[Math.floor(Math.random()*coarr.length)];
-							pending_context_preinjection += co + ":";
-							if(localsettings.inject_jailbreak_instruct || (localsettings.think_injected!=0 && localsettings.start_thinking_tag!=""))
+							let co = groupchat_reply_order(coarr);
+							if(co!="")
 							{
-								pending_context_preinjection += " ";
+								pending_context_preinjection += co + ":";
+								if(localsettings.inject_jailbreak_instruct || (localsettings.think_injected!=0 && localsettings.start_thinking_tag!=""))
+								{
+									pending_context_preinjection += " ";
+								}
 							}
 						}
 						if(localsettings.think_injected==1 && localsettings.start_thinking_tag!="")
@@ -20336,7 +20409,13 @@ Current version indicated by LITEVER below.
 				}
 				appendedsysprompt += "\n";
 			}
-			let truncated_memory = truncate_memory(current_memory, max_mem_len, true);
+			let memory_with_groupchat_context = current_memory;
+			let groupchat_context_memory = get_groupchat_context_memory();
+			if(groupchat_context_memory!="")
+			{
+				memory_with_groupchat_context += (memory_with_groupchat_context.trim()!="" ? "\n" : "") + groupchat_context_memory;
+			}
+			let truncated_memory = truncate_memory(memory_with_groupchat_context, max_mem_len, true);
 			if (truncated_memory != null && truncated_memory != "") {
 				if(newlineaftermemory)
 				{
@@ -27713,41 +27792,472 @@ Current version indicated by LITEVER below.
 		}, null);
 	}
 
+
 	var groupchat_removals = []; //list of names removed from groupchat
+	var groupchat_select_snapshot = null;
+	function sanitize_groupchat_participant_name(name)
+	{
+		name = name == null ? "" : String(name);
+		return replaceAll(name,"||$||"," ").replace(/[\r\n]+/g, " ").replace(/\s+/g, " ").trim();
+	}
+	function clone_groupchat_data(data)
+	{
+		if(!data || typeof data!="object" || Array.isArray(data))
+		{
+			return {};
+		}
+		return JSON.parse(JSON.stringify(data || {}));
+	}
+	function ensure_groupchat_data()
+	{
+		if(!localsettings.groupchat_data || typeof localsettings.groupchat_data!="object" || Array.isArray(localsettings.groupchat_data))
+		{
+			localsettings.groupchat_data = {};
+		}
+		return localsettings.groupchat_data;
+	}
+	function begin_groupchat_select_session()
+	{
+		if(groupchat_select_snapshot==null)
+		{
+			groupchat_select_snapshot = {
+				chatname:localsettings.chatname,
+				chatopponent:localsettings.chatopponent,
+				groupchat_data:clone_groupchat_data(ensure_groupchat_data()),
+				aestheticInstructUISettings:deepCopyAestheticSettings(aestheticInstructUISettings)
+			};
+		}
+	}
+	function restore_groupchat_select_snapshot()
+	{
+		if(groupchat_select_snapshot==null)
+		{
+			return;
+		}
+		localsettings.chatname = groupchat_select_snapshot.chatname;
+		localsettings.chatopponent = groupchat_select_snapshot.chatopponent;
+		localsettings.groupchat_data = clone_groupchat_data(groupchat_select_snapshot.groupchat_data);
+		aestheticInstructUISettings = deepCopyAestheticSettings(groupchat_select_snapshot.aestheticInstructUISettings);
+		sync_groupchat_chatname_input();
+		let chatopponent = document.getElementById("chatopponent");
+		if(chatopponent)
+		{
+			chatopponent.value = replaceAll(localsettings.chatopponent,"||$||","\n");
+			handle_bot_name_onchange();
+		}
+		groupchat_select_snapshot = null;
+	}
+	function groupchat_bot_names()
+	{
+		return localsettings.chatopponent ? localsettings.chatopponent.split("||$||").map(x=>sanitize_groupchat_participant_name(x)).filter(x=>x!="") : [];
+	}
+	function groupchat_default_bot_meta()
+	{
+		return {
+			portrait:"default",
+			portrait_width:aestheticInstructUISettings.portrait_width_AI,
+			portrait_ratio:aestheticInstructUISettings.portrait_ratio_AI,
+			bubbleColor:aestheticInstructUISettings.bubbleColor_AI,
+			text_tcolor:aestheticInstructUISettings.text_tcolor_AI,
+			speech_tcolor:aestheticInstructUISettings.speech_tcolor_AI,
+			action_tcolor:aestheticInstructUISettings.action_tcolor_AI,
+			ctx_memory:""
+		};
+	}
+	function apply_groupchat_ai_style(meta)
+	{
+		meta.portrait_width = aestheticInstructUISettings.portrait_width_AI;
+		meta.portrait_ratio = aestheticInstructUISettings.portrait_ratio_AI;
+		meta.bubbleColor = aestheticInstructUISettings.bubbleColor_AI;
+		meta.text_tcolor = aestheticInstructUISettings.text_tcolor_AI;
+		meta.speech_tcolor = aestheticInstructUISettings.speech_tcolor_AI;
+		meta.action_tcolor = aestheticInstructUISettings.action_tcolor_AI;
+		return meta;
+	}
+	function apply_groupchat_ai_style_to_all()
+	{
+		let groupchat_data = ensure_groupchat_data();
+		for (let key in groupchat_data) {
+			apply_groupchat_ai_style(groupchat_data[key]);
+		}
+	}
+	function groupchat_bot_meta_key(name)
+	{
+		return "gc_" + cyrb_hash(sanitize_groupchat_participant_name(name), 0, 8);
+	}
+	function normalize_groupchat_bot_meta(found)
+	{
+		return Object.assign(groupchat_default_bot_meta(), found || {});
+	}
+	function groupchat_bot_meta_exists(name)
+	{
+		let found = ensure_groupchat_data()[groupchat_bot_meta_key(name)];
+		return (found?true:false);
+	}
+	function get_groupchat_bot_meta(name)
+	{
+		let found = ensure_groupchat_data()[groupchat_bot_meta_key(name)];
+		if(found)
+		{
+			return normalize_groupchat_bot_meta(found);
+		}
+		return groupchat_default_bot_meta();
+	}
+	function set_groupchat_bot_meta(name, meta)
+	{
+		ensure_groupchat_data()[groupchat_bot_meta_key(name)] = normalize_groupchat_bot_meta(meta);
+	}
+	function sync_groupchat_chatname_input()
+	{
+		let chatname = document.getElementById("chatname");
+		if(chatname)
+		{
+			chatname.value = localsettings.chatname;
+		}
+	}
+	function set_groupchat_bot_names(names)
+	{
+		localsettings.chatopponent = names.map(x=>sanitize_groupchat_participant_name(x)).filter(x=>x!="").join("||$||");
+		let chatopponent = document.getElementById("chatopponent");
+		if(chatopponent)
+		{
+			chatopponent.value = replaceAll(localsettings.chatopponent,"||$||","\n");
+			handle_bot_name_onchange();
+		}
+		prune_groupchat_bot_meta();
+	}
+	function prune_groupchat_bot_meta()
+	{
+		let validkeys = groupchat_bot_names().map(x=>groupchat_bot_meta_key(x));
+		let groupchat_data = ensure_groupchat_data();
+		for (let key in groupchat_data) {
+			if (!validkeys.includes(key)) {
+				delete groupchat_data[key];
+			}
+		}
+	}
+	function rename_groupchat_participant(index, newname)
+	{
+		let names = groupchat_bot_names();
+		if(index<0 || index>=names.length)
+		{
+			console.log("rename_groupchat_participant: Invalid bot index.");
+			return;
+		}
+		newname = sanitize_groupchat_participant_name(newname);
+		if(newname=="")
+		{
+			if(names.length<=1)
+			{
+				msgbox("You need to keep at least one AI participant.");
+			}
+			else
+			{
+				names.splice(index, 1);
+				set_groupchat_bot_names(names);
+			}
+			show_groupchat_select();
+			return;
+		}
+		let oldname = names[index];
+		let oldmeta = get_groupchat_bot_meta(oldname);
+		names[index] = newname;
+		set_groupchat_bot_names(names);
+		set_groupchat_bot_meta(newname, oldmeta);
+		prune_groupchat_bot_meta();
+		show_groupchat_select();
+	}
+	function edit_groupchat_participant_name(index, is_self)
+	{
+		if(is_self)
+		{
+			let text = "Edit your chat name.";
+			inputBoxOkCancel(text,"Edit your Chat Name",localsettings.chatname,"[Enter User Name]",()=>{
+				let userinput = getInputBoxValue();
+				userinput = sanitize_groupchat_participant_name(userinput);
+				if(userinput.trim()!="")
+				{
+					localsettings.chatname = userinput.trim();
+					sync_groupchat_chatname_input();
+				}
+				show_groupchat_select();
+			},()=>{},false);
+		}
+		else
+		{
+			let names = groupchat_bot_names();
+			let text ="Edit this AI participant name.\n\nLeave this field blank and press OK to delete this participant. The final AI participant cannot be deleted.";
+			inputBoxOkCancel(text,"Rename or Delete Participant",names[index],"[Enter Participant Name]",()=>{
+				let userinput = getInputBoxValue();
+				rename_groupchat_participant(index, userinput);
+			},()=>{},false);
+		}
+	}
+	function edit_groupchat_context(index)
+	{
+		let names = groupchat_bot_names();
+		if(index<0 || index>=names.length)
+		{
+			return;
+		}
+		let currname = names[index];
+		let curr_bot_data = get_groupchat_bot_meta(currname);
+		inputBox(`Set character context memory for ${currname}.`,"Character Context Memory",curr_bot_data.ctx_memory,"Enter participant context",()=>{
+			let userinput = getInputBoxValue();
+			curr_bot_data.ctx_memory = userinput;
+			set_groupchat_bot_meta(currname, curr_bot_data);
+		},false,true);
+	}
+	function get_groupchat_context_memory()
+	{
+		if(!(localsettings.opmode==3 || (localsettings.opmode==4 && localsettings.inject_chatnames_instruct)))
+		{
+			return "";
+		}
+		let names = groupchat_bot_names();
+		let ctx = [];
+		for(let i=0;i<names.length;++i)
+		{
+			if(groupchat_removals.includes(names[i]))
+			{
+				continue;
+			}
+			let curr_bot_data = get_groupchat_bot_meta(names[i]);
+			if(curr_bot_data.ctx_memory && curr_bot_data.ctx_memory.trim()!="")
+			{
+				ctx.push("[" + names[i] + " Info: " + curr_bot_data.ctx_memory.trim() + "]");
+			}
+		}
+		return ctx.join("\n");
+	}
 	function show_groupchat_select()
 	{
+		begin_groupchat_select_session();
 		document.getElementById("groupselectcontainer").classList.remove("hidden");
+		let selectable_groupchat = (localsettings.opmode==3 || (localsettings.opmode==4 && localsettings.inject_chatnames_instruct));
 		let gs = ``;
-		if(localsettings.opmode==3)
-		{
-			if (localsettings.chatopponent != "" && localsettings.chatopponent.includes("||$||")) {
-				gs = `Selected participants will reply randomly, unless you address them directly by name.`
-				+`<br><br>Unselected participants will not reply in group chats.<br>`
-				+`<table style="width:90%; margin:8px auto;">`;
-				let grouplist = localsettings.chatopponent.split("||$||");
-				for (let i = 0; i < grouplist.length; ++i) {
-					let show = !groupchat_removals.includes(grouplist[i]);
-					gs += `<tr><td width='184px'><span style="vertical-align: middle;">` + grouplist[i] + `</span></td>`
-						+`<td width='24px'><button type="button" class="btn btn-primary widelbtn" id="widel0" onclick="impersonate_message(`+i+`)">+</button></td>`
-						+`<td width='24px'><input type="checkbox" id="groupselectitem_` + i + `" style=" vertical-align: top;" ` + (show ? "checked" : "") + `></td></tr>`;
-				}
-				gs += `</table>`;
-			}
-			else if(localsettings.chatopponent != "")
-			{
-				gs = `You're having a one-on-one chat with <b>`+localsettings.chatopponent+`</b>.<br><br>`
-				+`<a href='#' class='color_blueurl' onclick='hide_popups();display_settings();display_settings_tab(0);'>Turn it into a <b>group chat</b> by <b>adding more AI characters</b> (one per line)</a>.<br><br>`
-				+ `<a href='#' class='color_blueurl' onclick='impersonate_message(0)'>Impersonate `+localsettings.chatopponent+` speaking as them</a>`;
-			}
-		}else{
-			gs = `You're in Instruct Mode.<br><br>`
-				+ `<a href='#' class='color_blueurl' onclick='impersonate_message(0)'>Impersonate the AI Assistant</a>`;
-		}
 
-		gs += `<br><a href='#' class='color_blueurl' onclick='impersonate_user()'>Make the AI write a response as me (for 1 turn)</a>`;
+		if(selectable_groupchat)
+		{
+			gs = `Selected participants will reply randomly, unless you address them directly by name.`
+			+`<br><br>Unselected participants will not reply in group chats.<br>`;
+
+			gs += `<table class="groupchatselect-table">`
+			+`<thead><tr>`
+			+`<th style="text-align:center;">Participant</th>`
+			+`<th style="text-align:center;">Avatar</th>`
+			+`<th style="text-align:center;">Persona</th>`
+			+`<th style="text-align:center;">Reply For</th>`
+			+`<th style="text-align:center;">Reply</th>`
+			+`</tr></thead><tbody>`;
+
+			let grouplist = groupchat_bot_names();
+
+			gs += `<tr><td><div title="Edit participant name" class="ui-settings-inline color_blueurl groupchat-inline-action" onclick="edit_groupchat_participant_name(0,true)">${escape_html(localsettings.chatname)}</div></td>`
+				+`<td style="text-align:center;">${render_groupchat_avatar_cell(0,true)}</td>`
+				+`<td style="text-align:center;"></td>`
+				+`<td style="text-align:center;"><button title="Make the AI write a response as me" type="button" class="btn btn-primary widelbtn" onclick="impersonate_user()">+</button></td>`
+				+`<td style="text-align:center;"><input title="Your own name is always available" type="checkbox" style="vertical-align: top;" checked disabled></td></tr>`;
+
+			for (let i = 0; i < grouplist.length; ++i) {
+				let show = !groupchat_removals.includes(grouplist[i]);
+				let participant_index = i;
+				let participant_name = grouplist[participant_index];
+				let impersonate_button = `<button title="Impersonate Character" type="button" class="btn btn-primary widelbtn" onclick="impersonate_message(${i})">+</button>`;
+				let checkbox = `<input type="checkbox" id="groupselectitem_${participant_index}" style="vertical-align: top;" ${(show ? "checked" : "")}>`;
+				gs += `<tr><td><div title="Edit participant name" class="ui-settings-inline color_blueurl groupchat-inline-action" onclick="edit_groupchat_participant_name(${participant_index},false)">${escape_html(participant_name)}</div></td>`
+					+`<td style="text-align:center;">${render_groupchat_avatar_cell(participant_index,false)}</td>`
+					+`<td style="text-align:center;"><button title="Set participant context" type="button" class="btn btn-primary" style="height:24px;min-width:24px;padding:1px 6px;margin:2px;font-weight:bolder;" onclick="edit_groupchat_context(${participant_index})">+</button></td>`
+					+`<td style="text-align:center;">${impersonate_button}</td>`
+					+`<td style="text-align:center;">${checkbox}</td></tr>`;
+			}
+			gs += `</tbody></table>`;
+			gs += `<br><div class="ui-settings-inline color_blueurl groupchat-inline-action" onclick="add_another_participant(true)">Add AI Participant</div>`;
+		}
+		else if(localsettings.opmode==4 && !localsettings.inject_chatnames_instruct)
+		{
+			gs = `You're in Instruct Mode.<br><br>`
+			+ `<a href='#' class='color_blueurl' onclick='impersonate_message(0)'>Impersonate the AI Assistant</a>`;
+			gs += `<br><a href='#' class='color_blueurl' onclick='impersonate_user()'>Make the AI write a response as me (for 1 turn)</a>`;
+		}
 
 		document.getElementById("groupselectitems").innerHTML = gs;
 	}
+	function get_groupchat_portrait(name, is_self)
+	{
+		let portrait = is_self ? aestheticInstructUISettings.you_portrait:aestheticInstructUISettings.AI_portrait;
+		if(!is_self)
+		{
+			if(groupchat_bot_meta_exists(name))
+			{
+				let data = get_groupchat_bot_meta(name);
+				portrait = data.portrait;
+			}
+		}
+		return portrait;
+	}
+	function render_groupchat_avatar_cell(index, is_self)
+	{
+		let name = is_self ? localsettings.chatname : groupchat_bot_names()[index];
+		let portrait = get_groupchat_portrait(name, is_self);
+		portrait = (!is_self && portrait=="default") ? niko_square : portrait;
+		if(!portrait)
+		{
+			return `<button title="Set Aesthetics" type="button" class="btn btn-primary widelbtn" onclick="open_groupchat_aesthetics(${index},${is_self})">Set</button>`;
+		}
+		return `<button title="Set Aesthetics" type="button" class="btn btn-primary" style="padding:1px;" onclick="open_groupchat_aesthetics(${index},${is_self})"><span style="display:block;width:36px;height:36px;background-image:url(${escape_html(portrait)});background-size:cover;background-position:center;border-radius:4px;"></span></button>`;
+	}
+	var current_groupchat_aesthetic_key = "";
+	var temp_groupchat_aesthetic_data = null;
+	function refresh_groupchat_aesthetics()
+	{
+		let color_to_input_value = function(color)
+		{
+			if(!color)
+			{
+				return "#000000";
+			}
+			color = color.trim();
+			return color.startsWith("#") ? color : rgb_to_hex(color);
+		}
+		let portrait = temp_groupchat_aesthetic_data.portrait;
+		document.getElementById("groupchat_aesthetic_avatar").style.backgroundImage = portrait ? `url(${portrait=="default"?niko_square:portrait})` : "none";
+		document.getElementById("groupchat_portrait_width").value = temp_groupchat_aesthetic_data.portrait_width;
+		document.getElementById("groupchat_portrait_ratio").value = temp_groupchat_aesthetic_data.portrait_ratio;
+		document.getElementById("groupchat_bubbleColor").value = color_to_input_value(temp_groupchat_aesthetic_data.bubbleColor);
+		document.getElementById("groupchat_text_tcolor").value = color_to_input_value(temp_groupchat_aesthetic_data.text_tcolor);
+		document.getElementById("groupchat_speech_tcolor").value = color_to_input_value(temp_groupchat_aesthetic_data.speech_tcolor);
+		document.getElementById("groupchat_action_tcolor").value = color_to_input_value(temp_groupchat_aesthetic_data.action_tcolor);
+	}
+	function read_groupchat_aesthetics()
+	{
+		temp_groupchat_aesthetic_data.portrait_width = Math.max(10, Math.min(250, parseFloat(document.getElementById("groupchat_portrait_width").value) || 64));
+		temp_groupchat_aesthetic_data.portrait_ratio = Math.max(0.01, Math.min(3, parseFloat(document.getElementById("groupchat_portrait_ratio").value) || 1));
+		temp_groupchat_aesthetic_data.bubbleColor = document.getElementById("groupchat_bubbleColor").value;
+		temp_groupchat_aesthetic_data.text_tcolor = document.getElementById("groupchat_text_tcolor").value;
+		temp_groupchat_aesthetic_data.speech_tcolor = document.getElementById("groupchat_speech_tcolor").value;
+		temp_groupchat_aesthetic_data.action_tcolor = document.getElementById("groupchat_action_tcolor").value;
+	}
+	function open_groupchat_aesthetics(index, is_self)
+	{
+		let grouplist = groupchat_bot_names();
+		if(!is_self && (index<0 || index>=grouplist.length))
+		{
+			return;
+		}
+		let currname = is_self?localsettings.chatname:grouplist[index];
+		document.getElementById("groupchataestheticcontainer").classList.remove("hidden");
+		document.getElementById("groupchataesthetictitle").innerText = "Participant Aesthetics - " + currname;
+		let portrait = get_groupchat_portrait(currname,is_self);
+		if(is_self)
+		{
+			current_groupchat_aesthetic_key = "self";
+			temp_groupchat_aesthetic_data = {};
+			temp_groupchat_aesthetic_data.portrait_width = aestheticInstructUISettings.portrait_width_you;
+			temp_groupchat_aesthetic_data.portrait_ratio = aestheticInstructUISettings.portrait_ratio_you;
+			temp_groupchat_aesthetic_data.bubbleColor = aestheticInstructUISettings.bubbleColor_you;
+			temp_groupchat_aesthetic_data.text_tcolor = aestheticInstructUISettings.text_tcolor_you;
+			temp_groupchat_aesthetic_data.speech_tcolor = aestheticInstructUISettings.speech_tcolor_you;
+			temp_groupchat_aesthetic_data.action_tcolor = aestheticInstructUISettings.action_tcolor_you;
+			temp_groupchat_aesthetic_data.portrait = portrait;
+		}
+		else
+		{
+			current_groupchat_aesthetic_key = currname;
+			temp_groupchat_aesthetic_data = get_groupchat_bot_meta(currname);
+			temp_groupchat_aesthetic_data.portrait = portrait;
+		}
+		refresh_groupchat_aesthetics();
+	}
+	function select_groupchat_avatar()
+	{
+		if(!temp_groupchat_aesthetic_data)
+		{
+			return;
+		}
+		let finput = document.getElementById('portraitFileInput');
+		finput.click();
+		finput.onchange = (event) => {
+			if (event.target.files.length > 0 && event.target.files[0]) {
+				const file = event.target.files[0];
+				const reader = new FileReader();
+				reader.onload = function(img) {
+					compressImage(img.target.result, (compressedImageURI, aspectratio) => {
+						temp_groupchat_aesthetic_data.portrait = compressedImageURI;
+						temp_groupchat_aesthetic_data.portrait_ratio = aspectratio.toFixed(2);
+						refresh_groupchat_aesthetics();
+					}, true, AVATAR_PX);
+				}
+				reader.readAsDataURL(file);
+			}
+			finput.value = "";
+		};
+	}
+	function reset_groupchat_avatar()
+	{
+		if(!temp_groupchat_aesthetic_data)
+		{
+			return;
+		}
+		if(current_groupchat_aesthetic_key=="self")
+		{
+			temp_groupchat_aesthetic_data.portrait = null;
+			temp_groupchat_aesthetic_data.portrait_width = aestheticInstructUISettings.portrait_width_you;
+			temp_groupchat_aesthetic_data.portrait_ratio = aestheticInstructUISettings.portrait_ratio_you;
+			temp_groupchat_aesthetic_data.bubbleColor = aestheticInstructUISettings.bubbleColor_you;
+			temp_groupchat_aesthetic_data.text_tcolor = aestheticInstructUISettings.text_tcolor_you;
+			temp_groupchat_aesthetic_data.speech_tcolor = aestheticInstructUISettings.speech_tcolor_you;
+			temp_groupchat_aesthetic_data.action_tcolor = aestheticInstructUISettings.action_tcolor_you;
+		}
+		else
+		{
+			temp_groupchat_aesthetic_data.portrait = "default";
+			apply_groupchat_ai_style(temp_groupchat_aesthetic_data);
+		}
+		refresh_groupchat_aesthetics();
+	}
+	function confirm_groupchat_aesthetics()
+	{
+		if(!temp_groupchat_aesthetic_data || !current_groupchat_aesthetic_key)
+		{
+			return;
+		}
+		read_groupchat_aesthetics();
+		if(current_groupchat_aesthetic_key=="self")
+		{
+			aestheticInstructUISettings.you_portrait = temp_groupchat_aesthetic_data.portrait;
+			aestheticInstructUISettings.portrait_width_you = temp_groupchat_aesthetic_data.portrait_width;
+			aestheticInstructUISettings.portrait_ratio_you = temp_groupchat_aesthetic_data.portrait_ratio;
+			aestheticInstructUISettings.bubbleColor_you = temp_groupchat_aesthetic_data.bubbleColor;
+			aestheticInstructUISettings.text_tcolor_you = temp_groupchat_aesthetic_data.text_tcolor;
+			aestheticInstructUISettings.speech_tcolor_you = temp_groupchat_aesthetic_data.speech_tcolor;
+			aestheticInstructUISettings.action_tcolor_you = temp_groupchat_aesthetic_data.action_tcolor;
+		}
+		else
+		{
+			let curr_bot_data = get_groupchat_bot_meta(current_groupchat_aesthetic_key);
+			curr_bot_data.portrait = temp_groupchat_aesthetic_data.portrait;
+			curr_bot_data.portrait_width = temp_groupchat_aesthetic_data.portrait_width;
+			curr_bot_data.portrait_ratio = temp_groupchat_aesthetic_data.portrait_ratio;
+			curr_bot_data.bubbleColor = temp_groupchat_aesthetic_data.bubbleColor;
+			curr_bot_data.text_tcolor = temp_groupchat_aesthetic_data.text_tcolor;
+			curr_bot_data.speech_tcolor = temp_groupchat_aesthetic_data.speech_tcolor;
+			curr_bot_data.action_tcolor = temp_groupchat_aesthetic_data.action_tcolor;
+			set_groupchat_bot_meta(current_groupchat_aesthetic_key, curr_bot_data);
+		}
+		temp_groupchat_aesthetic_data = null;
+		current_groupchat_aesthetic_key = "";
+		document.getElementById("groupchataestheticcontainer").classList.add("hidden");
+		show_groupchat_select();
+	}
+	function cancel_groupchat_aesthetics()
+	{
+		temp_groupchat_aesthetic_data = null;
+		current_groupchat_aesthetic_key = "";
+		document.getElementById("groupchataestheticcontainer").classList.add("hidden");
+		show_groupchat_select();
+	}
+
 	var is_impersonate_user = false;
 	function impersonate_user()
 	{
@@ -27806,50 +28316,64 @@ Current version indicated by LITEVER below.
 			},false);
 		}
 	}
-	function add_another_participant()
+	function add_another_participant(also_open_groupchat)
 	{
 		inputBox("Turn it into a group chat by adding more AI characters.\n\nInput name of additional character:","Add Another Participant","","[Enter Character Name]", ()=>{
 			let userinput = getInputBoxValue();
-			userinput = userinput.trim();
+			userinput = sanitize_groupchat_participant_name(userinput);
 			if(userinput!="")
 			{
-				if(document.getElementById("chatopponent").value=="")
+				let names = groupchat_bot_names();
+				let chatopponent = document.getElementById("chatopponent");
+				if(chatopponent && !also_open_groupchat)
 				{
-					document.getElementById("chatopponent").value = userinput;
-				}else{
-					document.getElementById("chatopponent").value += "||$||"+userinput;
-					handle_bot_name_onchange();
+					names = chatopponent.value.split("\n").map(x=>sanitize_groupchat_participant_name(x)).filter(x=>x!="");
+				}
+				names.push(userinput);
+				set_groupchat_bot_names(names);
+				if(also_open_groupchat)
+				{
+					show_groupchat_select();
 				}
 			}
 		},false);
 	}
 	function confirm_groupchat_select()
 	{
-		groupchat_removals = [];
+		let new_groupchat_removals = [];
 		if(localsettings.chatopponent!="")
 		{
-			let grouplist = localsettings.chatopponent.split("||$||");
+			let grouplist = groupchat_bot_names();
 			for(let i=0;i<grouplist.length;++i)
 			{
 				let sel = document.getElementById("groupselectitem_"+i);
 				if(sel && !sel.checked)
 				{
-					groupchat_removals.push(grouplist[i]);
+					new_groupchat_removals.push(grouplist[i]);
 				}
 			}
-			hide_popups();
-			if(groupchat_removals.length==grouplist.length)
+			if(new_groupchat_removals.length==grouplist.length)
 			{
 				msgbox("You need to select at least one group chat participant!");
-				groupchat_removals = [];
+				return;
 			}
+			groupchat_removals = new_groupchat_removals;
 		}
 		else
 		{
-			hide_popups();
+			groupchat_removals = [];
 		}
-
-
+		groupchat_select_snapshot = null;
+		hide_popups();
+		refreshAestheticPreview(false);
+		render_gametext();
+	}
+	function cancel_groupchat_select()
+	{
+		restore_groupchat_select_snapshot();
+		hide_popups();
+		refreshAestheticPreview(false);
+		render_gametext();
 	}
 
 	function toggleTopNav() {
@@ -27939,6 +28463,7 @@ Current version indicated by LITEVER below.
 
 	let aestheticInstructUISettings = new AestheticInstructUISettings();
 	let tempAestheticInstructUISettings = null; // These exist to act as backup when customizing, to revert when pressing the 'Cancel' button.
+	let tempAestheticGroupchatData = null;
 
 	function selectAvatarImage(isSelfPortrait)
 	{
@@ -28031,6 +28556,7 @@ Current version indicated by LITEVER below.
 		document.getElementById("reset-all-aesthetic-instruct").addEventListener('click', (e) => {
 			let ns = new AestheticInstructUISettings();
 			aestheticInstructUISettings = deepCopyAestheticSettings(ns);
+			apply_groupchat_ai_style_to_all();
 			refreshAestheticPreview(false);
 		});
 
@@ -28039,6 +28565,7 @@ Current version indicated by LITEVER below.
 
 	function openAestheticUISettingsMenu() {
 		tempAestheticInstructUISettings = deepCopyAestheticSettings(aestheticInstructUISettings);
+		tempAestheticGroupchatData = clone_groupchat_data(ensure_groupchat_data());
 		document.getElementById("aestheticsettingscontainer").classList.remove("hidden");
 		updateAestheticTextPreview();
 	}
@@ -28046,9 +28573,11 @@ Current version indicated by LITEVER below.
 	function hideAestheticUISettingsMenu(confirm) {
 		if (!confirm) {
 			aestheticInstructUISettings = deepCopyAestheticSettings(tempAestheticInstructUISettings);
+			localsettings.groupchat_data = tempAestheticGroupchatData || {};
 			refreshAestheticPreview(false);
 		}
 		tempAestheticInstructUISettings = null;
+		tempAestheticGroupchatData = null;
 		document.getElementById("aestheticsettingscontainer").classList.add("hidden");
 		render_gametext();
 	}
@@ -28069,6 +28598,14 @@ Current version indicated by LITEVER below.
 	}
 
 	function updateDataFromUI() {
+		let previousAIStyle = {
+			portrait_width:aestheticInstructUISettings.portrait_width_AI,
+			portrait_ratio:aestheticInstructUISettings.portrait_ratio_AI,
+			bubbleColor:aestheticInstructUISettings.bubbleColor_AI,
+			text_tcolor:aestheticInstructUISettings.text_tcolor_AI,
+			speech_tcolor:aestheticInstructUISettings.speech_tcolor_AI,
+			action_tcolor:aestheticInstructUISettings.action_tcolor_AI
+		};
 		aestheticInstructUISettings.text_tcolor_you = getColorPickerValueFromElement(`you-text-colorselector`);
 		aestheticInstructUISettings.speech_tcolor_you = getColorPickerValueFromElement(`you-speech-colorselector`);
 		aestheticInstructUISettings.action_tcolor_you = getColorPickerValueFromElement(`you-action-colorselector`);
@@ -28108,6 +28645,15 @@ Current version indicated by LITEVER below.
 		aestheticInstructUISettings.portrait_width_you = cleannum(aestheticInstructUISettings.portrait_width_you, 10, 250);
 		aestheticInstructUISettings.portrait_ratio_you = cleannum(aestheticInstructUISettings.portrait_ratio_you, 0.01, 3).toFixed(2);
 		aestheticInstructUISettings.background_minHeight = cleannum(aestheticInstructUISettings.background_minHeight, 0, 300);
+		if(previousAIStyle.portrait_width != aestheticInstructUISettings.portrait_width_AI
+			|| previousAIStyle.portrait_ratio != aestheticInstructUISettings.portrait_ratio_AI
+			|| previousAIStyle.bubbleColor != aestheticInstructUISettings.bubbleColor_AI
+			|| previousAIStyle.text_tcolor != aestheticInstructUISettings.text_tcolor_AI
+			|| previousAIStyle.speech_tcolor != aestheticInstructUISettings.speech_tcolor_AI
+			|| previousAIStyle.action_tcolor != aestheticInstructUISettings.action_tcolor_AI)
+		{
+			apply_groupchat_ai_style_to_all();
+		}
 
 		function getColorPickerValueFromElement(id) {
 			let element = document.getElementById(id);
@@ -28196,7 +28742,7 @@ Current version indicated by LITEVER below.
 
 	function render_aesthetic_ui(input, isPreview) //class suffix string used to prevent defined styles from leaking into global scope
 	{
-		const avatarImage = function(for_ai) { //todo: this is still bad code, but will keep it for now
+		const avatarImage = function(for_ai, bot_key) { //todo: this is still bad code, but will keep it for now
 			if((for_ai && !as.AI_portrait) || (!for_ai && !as.you_portrait) || as.border_style == 'None')
 			{
 				return ''; //for no portrait
@@ -28213,6 +28759,14 @@ Current version indicated by LITEVER below.
 				width = as.portrait_width_AI;
 				height = (as.border_style == 'Circle' ? as.portrait_width_AI : as.portrait_width_AI / as.portrait_ratio_AI);
 				imgclassname = "AI-portrait-image";
+				if(groupchat_bot_meta_exists(bot_key))
+				{
+					let botdata = get_groupchat_bot_meta(bot_key);
+					width = botdata.portrait_width;
+					height = (as.border_style == 'Circle' ? width : width / botdata.portrait_ratio);
+					let metakey = groupchat_bot_meta_key(bot_key);
+					imgclassname = `aui_participant_${metakey} AI-portrait-image`;
+				}
 			}
 			return `<div class='${imgclassname}${classSuffixStr}${reinvertcolor}' style='width:${width}px; height:${height}px; border-radius: ${radius}'></div>`;
 		}
@@ -28241,6 +28795,40 @@ Current version indicated by LITEVER below.
 		}
 		let as = aestheticInstructUISettings;
 		let classSuffixStr = isPreview ? "prv" : "";
+		let participantStyling = "";
+
+		let groupchat_data = ensure_groupchat_data();
+		for (const key in groupchat_data) {
+			if (groupchat_data.hasOwnProperty(key)) {
+				let currchar = normalize_groupchat_bot_meta(groupchat_data[key]);
+				let participantClass = `aui_participant_${key}`;
+				let portrait = currchar.portrait!="default" ? currchar.portrait : niko_square;
+				participantStyling +=
+				`.${participantClass}{
+					color: ${currchar.text_tcolor};
+					background-color:${currchar.bubbleColor};
+				}
+				.${participantClass}.AI-portrait-image${classSuffixStr}{
+					background:url(${portrait});
+					background-clip: content-box;
+					background-position: 50% 50%;
+					background-size: 100% 100%;
+					background-origin: content-box;
+					background-repeat: no-repeat;
+				}
+				.${participantClass} em, .${participantClass} b :not(code){
+					color: ${currchar.action_tcolor};
+					font-style: italic;
+					font-weight: normal;
+				}
+				.${participantClass} .quotespn {
+					color: ${currchar.speech_tcolor};
+					font-weight: normal;
+				}`;
+				participantStyling += "\n";
+			}
+		}
+
 		let portraitsStyling = // Also, implement portraits as css classes. Now chat entries can reuse them instead of recreating them.
 		`<style>
 			.you-portrait-image${classSuffixStr} {margin: 6px 6px; background:url(${as.you_portrait}); background-clip: content-box; background-position: 50% 50%; background-size: 100% 100%; background-origin: content-box; background-repeat: no-repeat; border:none;}
@@ -28284,6 +28872,7 @@ Current version indicated by LITEVER below.
 				color: ${as.speech_tcolor_AI};
 				font-weight: normal;
 			}
+			${participantStyling}
 			#chat_msg_body blockquote, #aesthetic_text_preview blockquote
 			{
 				font-size: ${as.font_size}px;
@@ -28383,6 +28972,8 @@ Current version indicated by LITEVER below.
 			}
 
 			let namepart = (curr.name!="" && localsettings.show_nametags?`<p class='aui_nametag'>${escape_html(curr.name)}</p>`:"");
+
+			let turnParticipantClass = groupchat_bot_meta_exists(curr.name) ? `aui_participant_${groupchat_bot_meta_key(curr.name)}` : "";
 			let showavatar = true;
 
 			//adventure and story has no names or avatars, also handle unlabelled first turns for chat/instruct
@@ -28404,13 +28995,13 @@ Current version indicated by LITEVER below.
 			newbodystr += `<div style='display:flex; align-items:stretch; flex-direction: row;'>`;
 			if(curr.myturn)
 			{
-				newbodystr += `${(showavatar?avatarImage(false):"")}
-				<div class='aui_myturn_block'`;
+				newbodystr += `${(showavatar?avatarImage(false,null):"")}
+				<div class='aui_myturn_block ${turnParticipantClass}'`;
 			}
 			else
 			{
-				newbodystr += `${(showavatar?avatarImage(true):"")}
-				<div class='aui_aiturn_block'`;
+				newbodystr += `${(showavatar?avatarImage(true,curr.name):"")}
+				<div class='aui_aiturn_block ${turnParticipantClass}'`;
 			}
 			newbodystr += ` style='flex: 1; display:flex; padding: ${as.padding()}; margin: ${as.margin()}; min-height:${as.background_minHeight}px;`
 			+ ` font-size: ${as.font_size}px; flex-direction:column; align-items: ${as.centerHorizontally ? 'center' : 'flex-start'};`
@@ -29676,7 +30267,7 @@ Current version indicated by LITEVER below.
 									</div>
 								</div>
 								<div class="settinglabel" style="width:100%">
-									<button type="button" class="btn btn-primary" style="padding:2px 4px;margin:2px;margin-left:auto;font-size:12px;" onclick="add_another_participant()">Add Another Participant</button>
+									<button type="button" class="btn btn-primary" style="padding:2px 4px;margin:2px;margin-left:auto;font-size:12px;" onclick="add_another_participant(false)">Add Another Participant</button>
 								</div>
 							</div>
 
@@ -32240,7 +32831,7 @@ Current version indicated by LITEVER below.
 
 	<div class="popupcontainer flex hidden" id="groupselectcontainer">
 		<div class="popupbg flex"></div>
-		<div class="nspopup flexsizevsmall">
+		<div class="nspopup flexsizesmall">
 			<div class="popuptitlebar">
 				<div class="popuptitletext">Chat Selectors</div>
 			</div>
@@ -32250,7 +32841,52 @@ Current version indicated by LITEVER below.
 			</div>
 			<div class="popupfooter">
 				<button type="button" class="btn btn-primary" onclick="confirm_groupchat_select()">Ok</button>
-				<button type="button" class="btn btn-primary" onclick="hide_popups()">Cancel</button>
+				<button type="button" class="btn btn-primary" onclick="cancel_groupchat_select()">Cancel</button>
+			</div>
+		</div>
+	</div>
+
+	<div class="popupcontainer flex hidden" id="groupchataestheticcontainer">
+		<div class="popupbg flex"></div>
+		<div class="nspopup groupchataestheticpopup">
+			<div class="popuptitlebar">
+				<div class="popuptitletext" id="groupchataesthetictitle">Participant Aesthetics</div>
+			</div>
+			<div class="menutext">
+				<div id="groupchat_aesthetic_avatar"></div>
+				<div class="ui-settings-inline" style="justify-content:center; margin-bottom:8px;">
+					<div style="margin-right: 20px">Portrait: </div>
+					<div id="groupchat-portrait" onclick="select_groupchat_avatar()">🖼️ Set Portrait</div>
+					<div style="margin-left: 10px;"><a href="#" class="color_blueurl" onclick="reset_groupchat_avatar(); return false;">(Reset Image)</a></div>
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Portrait Size</div>
+					<input class="push-right" title="Portrait Size" type="number" min="10" max="250" id="groupchat_portrait_width" style="width:70px">
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Portrait Ratio</div>
+					<input class="push-right" title="Portrait Ratio" type="number" min="0.01" max="3" step="0.01" id="groupchat_portrait_ratio" style="width:70px">
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Bubble Color</div>
+					<input class="push-right" title="Bubble Color" type="color" id="groupchat_bubbleColor">
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Text Color</div>
+					<input class="push-right" title="Text Color" type="color" id="groupchat_text_tcolor">
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Speech Color</div>
+					<input class="push-right" title="Speech Color" type="color" id="groupchat_speech_tcolor">
+				</div>
+				<div class="settinglabel">
+					<div class="justifyleft">Action Color</div>
+					<input class="push-right" title="Action Color" type="color" id="groupchat_action_tcolor">
+				</div>
+			</div>
+			<div class="popupfooter">
+				<button type="button" class="btn btn-primary" onclick="confirm_groupchat_aesthetics()">OK</button>
+				<button type="button" class="btn btn-primary" onclick="cancel_groupchat_aesthetics()">Cancel</button>
 			</div>
 		</div>
 	</div>

From 20a04b22063020cd0f29b7781f5352d7a6abf786 Mon Sep 17 00:00:00 2001
From: ragz4125 <65285549+ragz4125@users.noreply.github.com>
Date: Mon, 6 Jul 2026 16:36:40 +0530
Subject: [PATCH 284/292] ggml-cpu: use UE4M3 LUT in ARM NVFP4 dot product
 (#25331)

---
 ggml/src/ggml-cpu/arch/arm/quants.c | 8 ++++----
 ggml/src/ggml-cpu/simd-mappings.h   | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cpu/arch/arm/quants.c b/ggml/src/ggml-cpu/arch/arm/quants.c
index fe6213329..445366797 100644
--- a/ggml/src/ggml-cpu/arch/arm/quants.c
+++ b/ggml/src/ggml-cpu/arch/arm/quants.c
@@ -812,10 +812,10 @@ void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d);
         const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d);
         const float32x4_t nvsc = {
-            ggml_ue4m3_to_fp32(x[ib].d[0]),
-            ggml_ue4m3_to_fp32(x[ib].d[1]),
-            ggml_ue4m3_to_fp32(x[ib].d[2]),
-            ggml_ue4m3_to_fp32(x[ib].d[3])
+            GGML_CPU_UE4M3_TO_FP32(x[ib].d[0]),
+            GGML_CPU_UE4M3_TO_FP32(x[ib].d[1]),
+            GGML_CPU_UE4M3_TO_FP32(x[ib].d[2]),
+            GGML_CPU_UE4M3_TO_FP32(x[ib].d[3])
         };
         const float32x4_t scales = vmulq_f32(nvsc, (float32x4_t){dy0, dy0, dy1, dy1});
 
diff --git a/ggml/src/ggml-cpu/simd-mappings.h b/ggml/src/ggml-cpu/simd-mappings.h
index be50c25c0..fca5119e1 100644
--- a/ggml/src/ggml-cpu/simd-mappings.h
+++ b/ggml/src/ggml-cpu/simd-mappings.h
@@ -131,8 +131,8 @@ extern float ggml_table_f32_ue4m3[1 << 8];
 #define GGML_CPU_E8M0_TO_FP32_HALF(x) GGML_E8M0_TO_FP32_HALF(x)
 #endif
 
-// Use lookup table for UE4M3 on x86 (faster than bit manipulation)
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__)
+// Use lookup table for UE4M3 on x86 and ARM (faster than bit manipulation)
+#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__ARM_NEON)
 #define GGML_CPU_UE4M3_TO_FP32(x) ggml_table_f32_ue4m3[(uint8_t)(x)]
 #else
 #define GGML_CPU_UE4M3_TO_FP32(x) ggml_ue4m3_to_fp32(x)

From 6c3f7018a747ec49913aa23eec8c97119cc1b757 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 6 Jul 2026 20:12:11 +0800
Subject: [PATCH 285/292] increase max defaultgenamount limit

---
 koboldcpp.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 4261eeeb4..1c5e3df3b 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -11600,7 +11600,7 @@ def kcpp_main_process(launch_args, g_memory=None, gui_launcher=False):
         global maxctx
         maxctx = args.contextsize
 
-    args.defaultgenamt = max(64, min(args.defaultgenamt, 16384))
+    args.defaultgenamt = max(64, min(args.defaultgenamt, 32768))
     args.defaultgenamt = min(args.defaultgenamt, maxctx / 2)
 
     #this uses the true port instead of the displayport, because we dont want to shut down a router

From bfdf581b8b2a3c8e999227a44dfa3e890f1038bd Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 6 Jul 2026 16:10:04 +0200
Subject: [PATCH 286/292] server: temporary skip model downloading API test
 (#25355)

---
 tools/server/tests/unit/test_router.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/server/tests/unit/test_router.py b/tools/server/tests/unit/test_router.py
index 94165e520..cdd6f5314 100644
--- a/tools/server/tests/unit/test_router.py
+++ b/tools/server/tests/unit/test_router.py
@@ -314,6 +314,7 @@ def _wait_for_sse_event(collected: list, event_type: str, model: str, timeout: i
     return False
 
 
+@pytest.mark.skip(reason="sse_thread sometimes hangs on GH actions, to be investigated")
 def test_router_download_model():
     """Case 1: download a model, verify SSE events and GET /models."""
     global server
@@ -357,6 +358,7 @@ def test_router_download_model():
     assert MODEL_DOWNLOAD_ID in ids, f"{MODEL_DOWNLOAD_ID} not found in /models after download"
 
 
+@pytest.mark.skip(reason="sse_thread sometimes hangs on GH actions, to be investigated")
 def test_router_delete_model():
     """Case 2: delete the downloaded model, verify it disappears from GET /models."""
     global server

From cb295bf59663cd3577389315636772f4060bd1f5 Mon Sep 17 00:00:00 2001
From: Alexey Kopytko <alexey@kopytko.com>
Date: Mon, 6 Jul 2026 23:26:50 +0900
Subject: [PATCH 287/292] CUDA: extend K-type validation to V-types for flash
 attention (#24403)

* CUDA: extend K-type validation to V-types for flash attention

* reorder
---
 ggml/src/ggml-cuda/fattn.cu | 38 +++++++++++++++++++++----------------
 1 file changed, 22 insertions(+), 16 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index c3039211c..00ffacf29 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -337,6 +337,26 @@ enum best_fattn_kernel {
     BEST_FATTN_KERNEL_MMA_F16  = 400,
 };
 
+static bool ggml_cuda_fattn_kv_type_supported(ggml_type type) {
+    switch (type) {
+        case GGML_TYPE_F32:
+        case GGML_TYPE_F16:
+            return true;
+        case GGML_TYPE_Q4_1:
+        case GGML_TYPE_Q5_0:
+        case GGML_TYPE_Q5_1:
+#ifndef GGML_CUDA_FA_ALL_QUANTS
+            return false;
+#endif // GGML_CUDA_FA_ALL_QUANTS
+        case GGML_TYPE_Q4_0:
+        case GGML_TYPE_Q8_0:
+        case GGML_TYPE_BF16:
+            return true;
+        default:
+            return false;
+    }
+}
+
 static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const ggml_tensor * dst) {
 #ifndef FLASH_ATTN_AVAILABLE
     GGML_UNUSED(device); GGML_UNUSED(dst);
@@ -427,22 +447,8 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     }
 #endif // GGML_CUDA_FA_ALL_QUANTS
 
-    switch (K->type) {
-        case GGML_TYPE_F32:
-        case GGML_TYPE_F16:
-            break;
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-#ifndef GGML_CUDA_FA_ALL_QUANTS
-            return BEST_FATTN_KERNEL_NONE;
-#endif // GGML_CUDA_FA_ALL_QUANTS
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_BF16:
-            break;
-        default:
-            return BEST_FATTN_KERNEL_NONE;
+    if (!ggml_cuda_fattn_kv_type_supported(K->type) || !ggml_cuda_fattn_kv_type_supported(V->type)) {
+        return BEST_FATTN_KERNEL_NONE;
     }
 
     if (mask && mask->ne[2] != 1) {

From adb2e96e3ca56074bbdc0f90f4efcb3c3785ae86 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Mon, 6 Jul 2026 23:33:33 +0800
Subject: [PATCH 288/292] allow viewing last generated image with the correct
 genkey

---
 koboldcpp.py | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 1c5e3df3b..b6ba4a3a7 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -97,6 +97,8 @@ musicName = None
 imageName = None
 mmprojName = None
 lastgeneratedcomfyimg = b''
+lastgeneratedcachedimg = b''
+lastgeneratedcachedimgkey = b''
 lastuploadedcomfyimg = b''
 fullsdmodelpath = ""  #if empty, it's not initialized
 password = "" #if empty, no auth key required
@@ -6098,7 +6100,7 @@ Change Mode<br>
     def do_GET(self):
         global embedded_kailite, embedded_kcpp_docs, embedded_kcpp_sdui, embedded_kailite_gz, embedded_kcpp_docs_gz, embedded_kcpp_sdui_gz, embedded_lcpp_ui_gz, embedded_musicui, embedded_musicui_gz
         global last_req_time, start_time, cached_chat_template, cached_sd_info, has_vision_support, has_audio_support, has_whisper, friendlymodelname
-        global savedata_obj, has_multiplayer, multiplayer_turn_major, multiplayer_turn_minor, multiplayer_story_data_compressed, multiplayer_dataformat, multiplayer_lastactive, maxctx, maxhordelen, friendlymodelname, lastuploadedcomfyimg, lastgeneratedcomfyimg, KcppVersion, totalgens, preloaded_story, exitcounter, currentusergenkey, friendlysdmodelname, fullsdmodelpath, password, friendlyembeddingsmodelname, voicelist
+        global savedata_obj, has_multiplayer, multiplayer_turn_major, multiplayer_turn_minor, multiplayer_story_data_compressed, multiplayer_dataformat, multiplayer_lastactive, maxctx, maxhordelen, friendlymodelname, lastuploadedcomfyimg, lastgeneratedcomfyimg, lastgeneratedcachedimg, lastgeneratedcachedimgkey, KcppVersion, totalgens, preloaded_story, exitcounter, currentusergenkey, friendlysdmodelname, fullsdmodelpath, password, friendlyembeddingsmodelname, voicelist
         global autoswapmode, textName, sttName, ttsName, embedName, musicName, imageName, mmprojName
 
         clean_path = self.path.split("?")[0] #for cases where we do not want query params
@@ -6351,6 +6353,15 @@ Change Mode<br>
         elif clean_path=='/view' or clean_path=='/view.png' or clean_path=='/api/view' or clean_path.startswith('/view_image'): #emulate comfyui
             content_type = 'image/png'
             response_body = lastgeneratedcomfyimg
+        elif clean_path.startswith('/sdapi/v1/get_last.png'):
+            parsed_url = urllib.parse.urlparse(self.path)
+            parsed_dict = urllib.parse.parse_qs(parsed_url.query)
+            genkey = parsed_dict.get('genkey', [''])[0]
+            if genkey and genkey==lastgeneratedcachedimgkey and lastgeneratedcachedimg:
+                content_type = 'image/png'
+                response_body = lastgeneratedcachedimg
+            else:
+                response_body = None
         elif clean_path=='/history' or clean_path=='/api/history' or clean_path.startswith('/api/history/') or clean_path.startswith('/history/'): #emulate comfyui
             modelNameToReturn = friendlysdmodelname
             if autoswapmode and imageName is not None:
@@ -6486,7 +6497,7 @@ Change Mode<br>
 
     def do_POST(self):
         global thinkformats
-        global modelbusy, batched_request_runner_count, requestsinqueue, currentusergenkey, totalgens, pendingabortkey, lastuploadedcomfyimg, lastgeneratedcomfyimg, multiplayer_turn_major, multiplayer_turn_minor, multiplayer_story_data_compressed, multiplayer_dataformat, multiplayer_lastactive, net_save_slots, has_vision_support, savestate_limit, mcp_lock
+        global modelbusy, batched_request_runner_count, requestsinqueue, currentusergenkey, totalgens, pendingabortkey, lastuploadedcomfyimg, lastgeneratedcomfyimg, lastgeneratedcachedimg, lastgeneratedcachedimgkey, multiplayer_turn_major, multiplayer_turn_minor, multiplayer_story_data_compressed, multiplayer_dataformat, multiplayer_lastactive, net_save_slots, has_vision_support, savestate_limit, mcp_lock
         global autoswapmode, textName, sttName, ttsName, embedName, musicName, imageName, mmprojName
         contlenstr = self.headers['content-length']
         content_length = 0
@@ -7447,6 +7458,8 @@ Change Mode<br>
                     return
                 elif is_imggen: #image gen
                     try:
+                        lastgeneratedcachedimg = b''
+                        lastgeneratedcachedimgkey = ''
                         if is_comfyui_imggen:
                             lastgeneratedcomfyimg = b''
                             genparams = sd_comfyui_tranform_params(genparams)
@@ -7465,6 +7478,11 @@ Change Mode<br>
                         genfinalframe = gen["final_frame"]
                         geninfo = json.dumps(gen["info"]) # sdapi really expects a stringified JSON
                         genresp = None
+                        if gendat:
+                            lastgeneratedcachedimg = base64.b64decode(gendat)
+                            lastgeneratedcachedimgkey = genparams.get('genkey', '')
+                        else:
+                            lastgeneratedcachedimg = b''
                         if is_comfyui_imggen:
                             if gendat:
                                 lastgeneratedcomfyimg = base64.b64decode(gendat)

From 838f14d6521bf3904e98e5b344e0ef4a08174b08 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Tue, 7 Jul 2026 00:25:00 +0800
Subject: [PATCH 289/292] updated sdui

---
 embd_res/kcpp_sdui.embd | 82 ++++++++++++++++++++---------------------
 1 file changed, 41 insertions(+), 41 deletions(-)

diff --git a/embd_res/kcpp_sdui.embd b/embd_res/kcpp_sdui.embd
index c398b1c59..745fec1d3 100644
--- a/embd_res/kcpp_sdui.embd
+++ b/embd_res/kcpp_sdui.embd
@@ -5,18 +5,18 @@
     <link rel="icon" href="data:image/x-icon;base64,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" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Stable UI for KoboldCpp</title>
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t=Ie("color-svpanel"),n=xt(),r=oe(0),i=oe(0),l=oe("hsl(0, 100%, 50%)"),g=ee(()=>{const u=e.color.get("hue"),d=e.color.get("value");return{hue:u,value:d}});function o(){const u=e.color.get("saturation"),d=e.color.get("value"),c=n.vnode.el,{clientWidth:f,clientHeight:s}=c;i.value=u*f/100,r.value=(100-d)*s/100,l.value=`hsl(${e.color.get("hue")}, 100%, 50%)`}function a(u){const c=n.vnode.el.getBoundingClientRect(),{clientX:f,clientY:s}=Lp(u);let h=f-c.left,v=s-c.top;h=Math.max(0,h),h=Math.min(h,c.width),v=Math.max(0,v),v=Math.min(v,c.height),i.value=h,r.value=v,e.color.set({saturation:h/c.width*100,value:100-v/c.height*100})}return ke(()=>g.value,()=>{o()}),rt(()=>{ll(n.vnode.el,{drag:u=>{a(u)},end:u=>{a(u)}}),o()}),{cursorTop:r,cursorLeft:i,background:l,colorValue:g,handleDrag:a,update:o,ns:t}}}),xB=ne("div",null,null,-1),kB=[xB];function TB(e,t,n,r,i,l){return z(),se("div",{class:de(e.ns.b()),style:ze({backgroundColor:e.background})},[ne("div",{class:de(e.ns.e("white"))},null,2),ne("div",{class:de(e.ns.e("black"))},null,2),ne("div",{class:de(e.ns.e("cursor")),style:ze({top:e.cursorTop+"px",left:e.cursorLeft+"px"})},kB,6)],6)}var EB=Le(SB,[["render",TB],["__file","/home/runner/work/element-plus/element-plus/packages/components/color-picker/src/components/sv-panel.vue"]]);const OB=Ee({name:"ElColorPicker",components:{ElButton:at,ElTooltip:jn,ElInput:Ja,ElIcon:Me,Close:Dr,ArrowDown:Pl,SvPanel:EB,HueSlider:pB,AlphaSlider:fB,Predefine:CB},directives:{ClickOutside:rw},props:{modelValue:String,id:String,showAlpha:Boolean,colorFormat:String,disabled:Boolean,size:{type:String,validator:tf},popperClass:String,label:{type:String,default:void 0},tabindex:{type:[String,Number],default:0},predefine:Array,validateEvent:{type:Boolean,default:!0}},emits:["change","active-change",wt],setup(e,{emit:t}){const{t:n}=gn(),r=Ie("color"),{form:i,formItem:l}=ea(),{inputId:g,isLabeledByFormItem:o}=Dl(e,{formItemContext:l}),a=oe(),u=oe(),d=oe(),c=oe(null);let f=!0;const s=yt(new Ls({enableAlpha:e.showAlpha,format:e.colorFormat||"",value:e.modelValue})),h=oe(!1),v=oe(!1),m=oe(""),y=ee(()=>!e.modelValue&&!v.value?"transparent":k(s,e.showAlpha)),b=or(),_=ee(()=>!!(e.disabled||(i==null?void 0:i.disabled))),w=ee(()=>!e.modelValue&&!v.value?"":s.value),S=ee(()=>o.value?void 0:e.label||n("el.colorpicker.defaultLabel")),x=ee(()=>o.value?l==null?void 0:l.labelId:void 0);ke(()=>e.modelValue,re=>{re?re&&re!==s.value&&(f=!1,s.fromString(re)):v.value=!1}),ke(()=>w.value,re=>{m.value=re,f&&t("active-change",re),f=!0}),ke(()=>s.value,()=>{!e.modelValue&&!v.value&&(v.value=!0)});function k(re,J){if(!(re instanceof Ls))throw new TypeError("color should be 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re,J,ue;(re=a.value)==null||re.update(),(J=u.value)==null||J.update(),(ue=d.value)==null||ue.update()})}),vt(fw,{currentColor:w}),{color:s,colorDisabled:_,colorSize:b,displayedColor:y,showPanelColor:v,showPicker:h,customInput:m,buttonId:g,buttonAriaLabel:S,buttonAriaLabelledby:x,handleConfirm:F,hide:D,handleTrigger:L,clear:V,confirmValue:O,t:n,ns:r,hue:a,svPanel:u,alpha:d,popper:c}}}),AB=["id","aria-label","aria-labelledby","aria-description","tabindex"];function PB(e,t,n,r,i,l){const g=gt("hue-slider"),o=gt("sv-panel"),a=gt("alpha-slider"),u=gt("predefine"),d=gt("el-input"),c=gt("el-button"),f=gt("arrow-down"),s=gt("el-icon"),h=gt("close"),v=gt("el-tooltip"),m=Ob("click-outside");return z(),be(v,{ref:"popper",visible:e.showPicker,"show-arrow":!1,"fallback-placements":["bottom","top","right","left"],offset:0,"gpu-acceleration":!1,"popper-class":[e.ns.be("picker","panel"),e.ns.b("dropdown"),e.popperClass],"stop-popper-mouse-event":!1,effect:"light",trigger:"click",transition:`${e.ns.namespace.value}-zoom-in-top`,persistent:""},{content:he(()=>[Et((z(),se("div",null,[ne("div",{class:de(e.ns.be("dropdown","main-wrapper"))},[le(g,{ref:"hue",class:"hue-slider",color:e.color,vertical:""},null,8,["color"]),le(o,{ref:"svPanel",color:e.color},null,8,["color"])],2),e.showAlpha?(z(),be(a,{key:0,ref:"alpha",color:e.color},null,8,["color"])):ye("v-if",!0),e.predefine?(z(),be(u,{key:1,ref:"predefine",color:e.color,colors:e.predefine},null,8,["color","colors"])):ye("v-if",!0),ne("div",{class:de(e.ns.be("dropdown","btns"))},[ne("span",{class:de(e.ns.be("dropdown","value"))},[le(d,{modelValue:e.customInput,"onUpdate:modelValue":t[0]||(t[0]=y=>e.customInput=y),"validate-event":!1,size:"small",onKeyup:Bt(e.handleConfirm,["enter"]),onBlur:e.handleConfirm},null,8,["modelValue","onKeyup","onBlur"])],2),le(c,{class:de(e.ns.be("dropdown","link-btn")),text:"",size:"small",onClick:e.clear},{default:he(()=>[je(Ae(e.t("el.colorpicker.clear")),1)]),_:1},8,["class","onClick"]),le(c,{plain:"",size:"small",class:de(e.ns.be("dropdown","btn")),onClick:e.confirmValue},{default:he(()=>[je(Ae(e.t("el.colorpicker.confirm")),1)]),_:1},8,["class","onClick"])],2)])),[[m,e.hide]])]),default:he(()=>[ne("div",{id:e.buttonId,class:de([e.ns.b("picker"),e.ns.is("disabled",e.colorDisabled),e.ns.bm("picker",e.colorSize)]),role:"button","aria-label":e.buttonAriaLabel,"aria-labelledby":e.buttonAriaLabelledby,"aria-description":e.t("el.colorpicker.description",{color:e.modelValue||""}),tabindex:e.tabindex,onKeydown:t[2]||(t[2]=Bt((...y)=>e.handleTrigger&&e.handleTrigger(...y),["enter"]))},[e.colorDisabled?(z(),se("div",{key:0,class:de(e.ns.be("picker","mask"))},null,2)):ye("v-if",!0),ne("div",{class:de(e.ns.be("picker","trigger")),onClick:t[1]||(t[1]=(...y)=>e.handleTrigger&&e.handleTrigger(...y))},[ne("span",{class:de([e.ns.be("picker","color"),e.ns.is("alpha",e.showAlpha)])},[ne("span",{class:de(e.ns.be("picker","color-inner")),style:ze({backgroundColor:e.displayedColor})},[Et(le(s,{class:de([e.ns.be("picker","icon"),e.ns.is("icon-arrow-down")])},{default:he(()=>[le(f)]),_:1},8,["class"]),[[Wt,e.modelValue||e.showPanelColor]]),!e.modelValue&&!e.showPanelColor?(z(),be(s,{key:0,class:de([e.ns.be("picker","empty"),e.ns.is("icon-close")])},{default:he(()=>[le(h)]),_:1},8,["class"])):ye("v-if",!0)],6)],2)],2)],42,AB)]),_:1},8,["visible","popper-class","transition"])}var 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M=R=>{c.value[R.index]=R},U=R=>{delete c.value[R.index]};vt(`subMenu:${r.uid}`,{addSubMenu:M,removeSubMenu:U,handleMouseleave:ue,mouseInChild:s,level:u.level+1})}return n({opened:x}),nt(()=>{a.addSubMenu(L),u.addSubMenu(L)}),Yt(()=>{u.removeSubMenu(L),a.removeSubMenu(L)}),()=>{var M;const U=[(M=t.title)==null?void 0:M.call(t),wt(Le,{class:o.e("icon-arrow"),style:{transform:x.value?e.expandCloseIcon&&e.expandOpenIcon||e.collapseCloseIcon&&e.collapseOpenIcon&&a.props.collapse?"none":"rotateZ(180deg)":"none"}},{default:()=>Ge(y.value)?wt(r.appContext.components[y.value]):wt(y.value)})],R=dw(a.props,u.level+1),P=a.isMenuPopup?wt(zn,{ref:v,visible:x.value,effect:"light",pure:!0,offset:e.popperOffset,showArrow:!1,persistent:!0,popperClass:e.popperClass,placement:m.value,teleported:_.value,fallbackPlacements:S.value,transition:w.value,gpuAcceleration:!1},{content:()=>{var Q;return wt("div",{class:[g.m(N.value),g.m("popup-container"),e.popperClass],onMouseenter:j=>J(j,100),onMouseleave:()=>ue(!0),onFocus:j=>J(j,100)},[wt("ul",{class:[g.b(),g.m("popup"),g.m(`popup-${m.value}`)],style:R.value},[(Q=t.default)==null?void 0:Q.call(t)])])},default:()=>wt("div",{class:o.e("title"),style:[F.value,{backgroundColor:E.value}],onClick:re},U)}):wt(je,{},[wt("div",{class:o.e("title"),style:[F.value,{backgroundColor:E.value}],ref:h,onClick:re},U),wt(rB,{},{default:()=>{var Q;return Et(wt("ul",{role:"menu",class:[g.b(),g.m("inline")],style:R.value},[(Q=t.default)==null?void 0:Q.call(t)]),[[Ht,x.value]])}})]);return wt("li",{class:[o.b(),o.is("active",T.value),o.is("opened",x.value),o.is("disabled",e.disabled)],role:"menuitem",ariaHaspopup:!0,ariaExpanded:x.value,onMouseenter:J,onMouseleave:()=>ue(!0),onFocus:J},[P])}}});const iN=Be({mode:{type:String,values:["horizontal","vertical"],default:"vertical"},defaultActive:{type:String,default:""},defaultOpeneds:{type:Pe(Array),default:()=>an([])},uniqueOpened:Boolean,router:Boolean,menuTrigger:{type:String,values:["hover","click"],default:"hover"},collapse:Boolean,backgroundColor:String,textColor:String,activeTextColor:String,collapseTransition:{type:Boolean,default:!0},ellipsis:{type:Boolean,default:!0}}),Df=e=>Array.isArray(e)&&e.every(t=>Ge(t)),aN={close:(e,t)=>Ge(e)&&Df(t),open:(e,t)=>Ge(e)&&Df(t),select:(e,t,n,r)=>Ge(e)&&Df(t)&&ot(n)&&(r===void 0||r instanceof Promise)};var oN=Ee({name:"ElMenu",props:iN,emits:aN,setup(e,{emit:t,slots:n,expose:r}){const i=xt(),l=i.appContext.config.globalProperties.$router,g=oe(),o=Ie("menu"),a=Ie("sub-menu"),u=oe(-1),d=oe(e.defaultOpeneds&&!e.collapse?e.defaultOpeneds.slice(0):[]),c=oe(e.defaultActive),f=oe({}),s=oe({}),h=ee(()=>e.mode==="horizontal"||e.mode==="vertical"&&e.collapse),v=()=>{const D=c.value&&f.value[c.value];if(!D||e.mode==="horizontal"||e.collapse)return;D.indexPath.forEach(L=>{const F=s.value[L];F&&m(L,F.indexPath)})},m=(D,N)=>{d.value.includes(D)||(e.uniqueOpened&&(d.value=d.value.filter(L=>N.includes(L))),d.value.push(D),t("open",D,N))},y=(D,N)=>{const L=d.value.indexOf(D);L!==-1&&d.value.splice(L,1),t("close",D,N)},b=({index:D,indexPath:N})=>{d.value.includes(D)?y(D,N):m(D,N)},_=D=>{(e.mode==="horizontal"||e.collapse)&&(d.value=[]);const{index:N,indexPath:L}=D;if(!(N===void 0||L===void 0))if(e.router&&l){const F=D.route||N,O=l.push(F).then(V=>(V||(c.value=N),V));t("select",N,L,{index:N,indexPath:L,route:F},O)}else c.value=N,t("select",N,L,{index:N,indexPath:L})},w=D=>{const N=f.value,L=N[D]||c.value&&N[c.value]||N[e.defaultActive];L?c.value=L.index:c.value=D},S=()=>{var D,N;if(!g.value)return-1;const L=Array.from((N=(D=g.value)==null?void 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sN=Be({index:{type:Pe([String,null]),default:null},route:{type:Pe([String,Object])},disabled:Boolean}),lN={click:e=>Ge(e.index)&&Array.isArray(e.indexPath)},$f="ElMenuItem",uN=Ee({name:$f,components:{ElTooltip:zn},props:sN,emits:lN,setup(e,{emit:t}){const n=xt(),r=qe("rootMenu"),i=Ie("menu"),l=Ie("menu-item");r||Cn($f,"can not inject root menu");const{parentMenu:g,indexPath:o}=fw(n,In(e,"index")),a=qe(`subMenu:${g.value.uid}`);a||Cn($f,"can not inject sub menu");const u=ee(()=>e.index===r.activeIndex),d=yt({index:e.index,indexPath:o,active:u}),c=()=>{e.disabled||(r.handleMenuItemClick({index:e.index,indexPath:o.value,route:e.route}),t("click",d))};return nt(()=>{a.addSubMenu(d),r.addMenuItem(d)}),Yt(()=>{a.removeSubMenu(d),r.removeMenuItem(d)}),{Effect:BL,parentMenu:g,rootMenu:r,active:u,nsMenu:i,nsMenuItem:l,handleClick:c}}});function cN(e,t,n,r,i,l){const g=gt("el-tooltip");return 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JN=Le(KN,[["render",ZN],["__file","/home/runner/work/element-plus/element-plus/packages/components/select/src/select.vue"]]);const QN=Ee({name:"ElOptionGroup",componentName:"ElOptionGroup",props:{label:String,disabled:{type:Boolean,default:!1}},setup(e){const t=Ie("select"),n=oe(!0),r=xt(),i=oe([]);vt(ww,yt({...nr(e)}));const l=Ke(sf);rt(()=>{i.value=g(r.subTree)});const g=a=>{const u=[];return Array.isArray(a.children)&&a.children.forEach(d=>{var c;d.type&&d.type.name==="ElOption"&&d.component&&d.component.proxy?u.push(d.component.proxy):(c=d.children)!=null&&c.length&&u.push(...g(d))}),u},{groupQueryChange:o}=it(l);return ke(o,()=>{n.value=i.value.some(a=>a.visible===!0)},{flush:"post"}),{visible:n,ns:t}}});function e9(e,t,n,r,i,l){return Et((z(),se("ul",{class:de(e.ns.be("group","wrap"))},[ne("li",{class:de(e.ns.be("group","title"))},Ae(e.label),3),ne("li",null,[ne("ul",{class:de(e.ns.b("group"))},[xe(e.$slots,"default")],2)])],2)),[[Wt,e.visible]])}var 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 Copyright (c) Microsoft Corporation.
 Permission to use, copy, modify, and/or distribute this software for any
 purpose with or without fee is hereby granted.
@@ -51,13 +51,13 @@ INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
 LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
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this._ctx.unique="unique",this.eachKey(t)},e.prototype.eachPrimaryKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.primaryKey,i)})},e.prototype.keys=function(t){var n=this._ctx;n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.key)}).then(function(){return r}).then(t)},e.prototype.primaryKeys=function(t){var n=this._ctx;if(n.dir==="next"&&ro(n,!0)&&n.limit>0)return this._read(function(i){var l=Yu(n,n.table.core.schema);return n.table.core.query({trans:i,values:!1,limit:n.limit,query:{index:l,range:n.range}})}).then(function(i){var l=i.result;return l}).then(t);n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.primaryKey)}).then(function(){return r}).then(t)},e.prototype.uniqueKeys=function(t){return this._ctx.unique="unique",this.keys(t)},e.prototype.firstKey=function(t){return this.limit(1).keys(function(n){return n[0]}).then(t)},e.prototype.lastKey=function(t){return 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this.inAnyRange([[hh,t],[t,this.db._maxKey]],{includeLowers:!1,includeUppers:!1})},e.prototype.noneOf=function(){var t=Zr.apply(fo,arguments);if(t.length===0)return new this.Collection(this);try{t.sort(this._ascending)}catch{return Dn(this,Vr)}var n=t.reduce(function(r,i){return r?r.concat([[r[r.length-1][1],i]]):[[hh,i]]},null);return n.push([t[t.length-1],this.db._maxKey]),this.inAnyRange(n,{includeLowers:!1,includeUppers:!1})},e.prototype.inAnyRange=function(t,n){var r=this,i=this._cmp,l=this._ascending,g=this._descending,o=this._min,a=this._max;if(t.length===0)return io(this);if(!t.every(function(S){return S[0]!==void 0&&S[1]!==void 0&&l(S[0],S[1])<=0}))return Dn(this,"First argument to inAnyRange() must be an Array of two-value Arrays [lower,upper] where upper must not be lower than lower",Je.InvalidArgument);var u=!n||n.includeLowers!==!1,d=n&&n.includeUppers===!0;function c(S,x){for(var T=0,E=S.length;T<E;++T){var I=S[T];if(i(x[0],I[1])<0&&i(x[1],I[0])>0){I[0]=o(I[0],x[0]),I[1]=a(I[1],x[1]);break}}return T===E&&S.push(x),S}var f=l;function s(S,x){return f(S[0],x[0])}var h;try{h=t.reduce(c,[]),h.sort(s)}catch{return Dn(this,Vr)}var v=0,m=d?function(S){return l(S,h[v][1])>0}:function(S){return l(S,h[v][1])>=0},y=u?function(S){return g(S,h[v][0])>0}:function(S){return g(S,h[v][0])>=0};function b(S){return!m(S)&&!y(S)}var _=m,w=new this.Collection(this,function(){return ki(h[0][0],h[h.length-1][1],!u,!d)});return w._ondirectionchange=function(S){S==="next"?(_=m,f=l):(_=y,f=g),h.sort(s)},w._addAlgorithm(function(S,x,T){for(var E=S.key;_(E);)if(++v,v===h.length)return x(T),!1;return b(E)?!0:(r._cmp(E,h[v][1])===0||r._cmp(E,h[v][0])===0||x(function(){f===l?S.continue(h[v][0]):S.continue(h[v][1])}),!1)}),w},e.prototype.startsWithAnyOf=function(){var t=Zr.apply(fo,arguments);return t.every(function(n){return typeof 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hs(!Ke.global),++this._reculock,this._reculock===1&&!Ke.global&&(Ke.lockOwnerFor=this),this},e.prototype._unlock=function(){if(hs(!Ke.global),--this._reculock===0)for(Ke.global||(Ke.lockOwnerFor=null);this._blockedFuncs.length>0&&!this._locked();){var t=this._blockedFuncs.shift();try{Ko(t[1],t[0])}catch{}}return this},e.prototype._locked=function(){return this._reculock&&Ke.lockOwnerFor!==this},e.prototype.create=function(t){var n=this;if(!this.mode)return this;var r=this.db.idbdb,i=this.db._state.dbOpenError;if(hs(!this.idbtrans),!t&&!r)switch(i&&i.name){case"DatabaseClosedError":throw new Je.DatabaseClosed(i);case"MissingAPIError":throw new Je.MissingAPI(i.message,i);default:throw new Je.OpenFailed(i)}if(!this.active)throw new Je.TransactionInactive;return hs(this._completion._state===null),t=this.idbtrans=t||(this.db.core?this.db.core.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability}):r.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability})),t.onerror=Ft(function(l){ul(l),n._reject(t.error)}),t.onabort=Ft(function(l){ul(l),n.active&&n._reject(new Je.Abort(t.error)),n.active=!1,n.on("abort").fire(l)}),t.oncomplete=Ft(function(){n.active=!1,n._resolve(),"mutatedParts"in t&&Gi.storagemutated.fire(t.mutatedParts)}),this},e.prototype._promise=function(t,n,r){var i=this;if(t==="readwrite"&&this.mode!=="readwrite")return Kt(new Je.ReadOnly("Transaction is readonly"));if(!this.active)return Kt(new Je.TransactionInactive);if(this._locked())return new $e(function(g,o){i._blockedFuncs.push([function(){i._promise(t,n,r).then(g,o)},Ke])});if(r)return Xi(function(){var g=new $e(function(o,a){i._lock();var u=n(o,a,i);u&&u.then&&u.then(o,a)});return g.finally(function(){return 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h=t2(f,s);h.add.forEach(function(w){rm(r,w[0],w[1].primKey,w[1].indexes)}),h.change.forEach(function(w){if(w.recreate)throw new Je.Upgrade("Not yet support for changing primary key");var S=r.objectStore(w.name);w.add.forEach(function(x){return vh(S,x)}),w.change.forEach(function(x){S.deleteIndex(x.name),vh(S,x)}),w.del.forEach(function(x){return S.deleteIndex(x)})});var v=c._cfg.contentUpgrade;if(v&&c._cfg.version>t){nm(i,r),n._memoizedTables={},a=!0;var m=Mw(s);h.del.forEach(function(w){m[w]=f[w]}),gh(i,[i.Transaction.prototype]),_c(i,[i.Transaction.prototype],Wt(m),m),n.schema=m;var y=Kp(v);y&&Xo();var b,_=$e.follow(function(){if(b=v(n),b&&y){var w=li.bind(null,null);b.then(w,w)}});return b&&typeof b.then=="function"?$e.resolve(b):_.then(function(){return b})}}),l.push(function(f){if(!a||!y7){var s=c._cfg.dbschema;W7(s,f)}gh(i,[i.Transaction.prototype]),_c(i,[i.Transaction.prototype],i._storeNames,i._dbSchema),n.schema=i._dbSchema})});function d(){return 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function(r,i,l){return--n<=0&&i(l),n>=0}},!0),this},e.prototype.until=function(t,n){return Kf(this._ctx,function(r,i,l){return t(r.value)?(i(l),n):!0}),this},e.prototype.first=function(t){return this.limit(1).toArray(function(n){return n[0]}).then(t)},e.prototype.last=function(t){return this.reverse().first(t)},e.prototype.filter=function(t){return Kf(this._ctx,function(n){return t(n.value)}),R7(this._ctx,t),this},e.prototype.and=function(t){return this.filter(t)},e.prototype.or=function(t){return new this.db.WhereClause(this._ctx.table,t,this)},e.prototype.reverse=function(){return this._ctx.dir=this._ctx.dir==="prev"?"next":"prev",this._ondirectionchange&&this._ondirectionchange(this._ctx.dir),this},e.prototype.desc=function(){return this.reverse()},e.prototype.eachKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.key,i)})},e.prototype.eachUniqueKey=function(t){return this._ctx.unique="unique",this.eachKey(t)},e.prototype.eachPrimaryKey=function(t){var n=this._ctx;return n.keysOnly=!n.isMatch,this.each(function(r,i){t(i.primaryKey,i)})},e.prototype.keys=function(t){var n=this._ctx;n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.key)}).then(function(){return r}).then(t)},e.prototype.primaryKeys=function(t){var n=this._ctx;if(n.dir==="next"&&lo(n,!0)&&n.limit>0)return this._read(function(i){var l=Zu(n,n.table.core.schema);return n.table.core.query({trans:i,values:!1,limit:n.limit,query:{index:l,range:n.range}})}).then(function(i){var l=i.result;return l}).then(t);n.keysOnly=!n.isMatch;var r=[];return this.each(function(i,l){r.push(l.primaryKey)}).then(function(){return r}).then(t)},e.prototype.uniqueKeys=function(t){return this._ctx.unique="unique",this.keys(t)},e.prototype.firstKey=function(t){return this.limit(1).keys(function(n){return n[0]}).then(t)},e.prototype.lastKey=function(t){return this.reverse().firstKey(t)},e.prototype.distinct=function(){var t=this._ctx,n=t.index&&t.table.schema.idxByName[t.index];if(!n||!n.multi)return this;var r={};return Kf(this._ctx,function(i){var l=i.primaryKey.toString(),g=Xn(r,l);return r[l]=!0,!g}),this},e.prototype.modify=function(t){var n=this,r=this._ctx;return this._write(function(i){var l;if(typeof t=="function")l=t;else{var g=Xt(t),o=g.length;l=function(y){for(var b=!1,_=0;_<o;++_){var w=g[_],S=t[w];ii(y,w)!==S&&(mr(y,w,S),b=!0)}return b}}var a=r.table.core,u=a.schema.primaryKey,d=u.outbound,c=u.extractKey,f=n.db._options.modifyChunkSize||200,s=[],h=0,v=[],m=function(y,b){var _=b.failures,w=b.numFailures;h+=y-w;for(var S=0,x=Xt(_);S<x.length;S++){var k=x[S];s.push(_[k])}};return n.clone().primaryKeys().then(function(y){var b=function(_){var w=Math.min(f,y.length-_);return a.getMany({trans:i,keys:y.slice(_,_+w),cache:"immutable"}).then(function(S){for(var x=[],k=[],E=d?[]:null,I=[],D=0;D<w;++D){var N=S[D],L={value:Nl(N),primKey:y[_+D]};l.call(L,L.value,L)!==!1&&(L.value==null?I.push(y[_+D]):!d&&Sn(c(N),c(L.value))!==0?(I.push(y[_+D]),x.push(L.value)):(k.push(L.value),d&&E.push(y[_+D])))}var F=lo(r)&&r.limit===1/0&&(typeof t!="function"||t===qf)&&{index:r.index,range:r.range};return Promise.resolve(x.length>0&&a.mutate({trans:i,type:"add",values:x}).then(function(O){for(var V in O.failures)I.splice(parseInt(V),1);m(x.length,O)})).then(function(){return(k.length>0||F&&typeof t=="object")&&a.mutate({trans:i,type:"put",keys:E,values:k,criteria:F,changeSpec:typeof t!="function"&&t}).then(function(O){return m(k.length,O)})}).then(function(){return(I.length>0||F&&t===qf)&&a.mutate({trans:i,type:"delete",keys:I,criteria:F}).then(function(O){return m(I.length,O)})}).then(function(){return y.length>_+w&&b(_+f)})})};return b(0).then(function(){if(s.length>0)throw new bc("Error modifying one or more objects",s,h,v);return y.length})})})},e.prototype.delete=function(){var t=this._ctx,n=t.range;return lo(t)&&(t.isPrimKey&&!I7||n.type===3)?this._write(function(r){var i=t.table.core.schema.primaryKey,l=n;return t.table.core.count({trans:r,query:{index:i,range:l}}).then(function(g){return t.table.core.mutate({trans:r,type:"deleteRange",range:l}).then(function(o){var a=o.failures;o.lastResult,o.results;var u=o.numFailures;if(u)throw new bc("Could not delete some values",Object.keys(a).map(function(d){return a[d]}),g-u);return g-u})})}):this.modify(qf)},e}(),qf=function(e,t){return t.value=null};function F7(e){return Wl(B7.prototype,function(n,r){this.db=e;var i=t2,l=null;if(r)try{i=r()}catch(u){l=u}var g=n._ctx,o=g.table,a=o.hook.reading.fire;this._ctx={table:o,index:g.index,isPrimKey:!g.index||o.schema.primKey.keyPath&&g.index===o.schema.primKey.name,range:i,keysOnly:!1,dir:"next",unique:"",algorithm:null,filter:null,replayFilter:null,justLimit:!0,isMatch:null,offset:0,limit:1/0,error:l,or:g.or,valueMapper:a!==jl?a:null}})}function z7(e,t){return e<t?-1:e===t?0:1}function N7(e,t){return e>t?-1:e===t?0:1}function Bn(e,t,n){var r=e instanceof r2?new e.Collection(e):e;return r._ctx.error=n?new n(t):new TypeError(t),r}function uo(e){return new e.Collection(e,function(){return n2("")}).limit(0)}function j7(e){return e==="next"?function(t){return t.toUpperCase()}:function(t){return t.toLowerCase()}}function V7(e){return e==="next"?function(t){return t.toLowerCase()}:function(t){return t.toUpperCase()}}function U7(e,t,n,r,i,l){for(var g=Math.min(e.length,r.length),o=-1,a=0;a<g;++a){var u=t[a];if(u!==r[a])return i(e[a],n[a])<0?e.substr(0,a)+n[a]+n.substr(a+1):i(e[a],r[a])<0?e.substr(0,a)+r[a]+n.substr(a+1):o>=0?e.substr(0,o)+t[o]+n.substr(o+1):null;i(e[a],u)<0&&(o=a)}return g<r.length&&l==="next"?e+n.substr(e.length):g<e.length&&l==="prev"?e.substr(0,n.length):o<0?null:e.substr(0,o)+r[o]+n.substr(o+1)}function bu(e,t,n,r){var i,l,g,o,a,u,d,c=n.length;if(!n.every(function(v){return typeof v=="string"}))return Bn(e,Qw);function 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this._cmp(t,n)>0||this._cmp(t,n)===0&&(r||i)&&!(r&&i)?uo(this):new this.Collection(this,function(){return Pi(t,n,!r,!i)})}catch{return Bn(this,Wr)}},e.prototype.equals=function(t){return t==null?Bn(this,Wr):new this.Collection(this,function(){return n2(t)})},e.prototype.above=function(t){return t==null?Bn(this,Wr):new this.Collection(this,function(){return Pi(t,void 0,!0)})},e.prototype.aboveOrEqual=function(t){return t==null?Bn(this,Wr):new this.Collection(this,function(){return Pi(t,void 0,!1)})},e.prototype.below=function(t){return t==null?Bn(this,Wr):new this.Collection(this,function(){return Pi(void 0,t,!1,!0)})},e.prototype.belowOrEqual=function(t){return t==null?Bn(this,Wr):new this.Collection(this,function(){return Pi(void 0,t)})},e.prototype.startsWith=function(t){return typeof t!="string"?Bn(this,Qw):this.between(t,t+Sa,!0,!0)},e.prototype.startsWithIgnoreCase=function(t){return t===""?this.startsWith(t):bu(this,function(n,r){return 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this.inAnyRange([[yh,t],[t,this.db._maxKey]],{includeLowers:!1,includeUppers:!1})},e.prototype.noneOf=function(){var t=ei.apply(vo,arguments);if(t.length===0)return new this.Collection(this);try{t.sort(this._ascending)}catch{return Bn(this,Wr)}var n=t.reduce(function(r,i){return r?r.concat([[r[r.length-1][1],i]]):[[yh,i]]},null);return n.push([t[t.length-1],this.db._maxKey]),this.inAnyRange(n,{includeLowers:!1,includeUppers:!1})},e.prototype.inAnyRange=function(t,n){var r=this,i=this._cmp,l=this._ascending,g=this._descending,o=this._min,a=this._max;if(t.length===0)return uo(this);if(!t.every(function(S){return S[0]!==void 0&&S[1]!==void 0&&l(S[0],S[1])<=0}))return Bn(this,"First argument to inAnyRange() must be an Array of two-value Arrays [lower,upper] where upper must not be lower than lower",Ge.InvalidArgument);var u=!n||n.includeLowers!==!1,d=n&&n.includeUppers===!0;function c(S,x){for(var k=0,E=S.length;k<E;++k){var I=S[k];if(i(x[0],I[1])<0&&i(x[1],I[0])>0){I[0]=o(I[0],x[0]),I[1]=a(I[1],x[1]);break}}return k===E&&S.push(x),S}var f=l;function s(S,x){return f(S[0],x[0])}var h;try{h=t.reduce(c,[]),h.sort(s)}catch{return Bn(this,Wr)}var v=0,m=d?function(S){return l(S,h[v][1])>0}:function(S){return l(S,h[v][1])>=0},y=u?function(S){return g(S,h[v][0])>0}:function(S){return g(S,h[v][0])>=0};function b(S){return!m(S)&&!y(S)}var _=m,w=new this.Collection(this,function(){return Pi(h[0][0],h[h.length-1][1],!u,!d)});return w._ondirectionchange=function(S){S==="next"?(_=m,f=l):(_=y,f=g),h.sort(s)},w._addAlgorithm(function(S,x,k){for(var E=S.key;_(E);)if(++v,v===h.length)return x(k),!1;return b(E)?!0:(r._cmp(E,h[v][1])===0||r._cmp(E,h[v][0])===0||x(function(){f===l?S.continue(h[v][0]):S.continue(h[v][1])}),!1)}),w},e.prototype.startsWithAnyOf=function(){var t=ei.apply(vo,arguments);return t.every(function(n){return typeof 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ys(this._completion._state===null),t=this.idbtrans=t||(this.db.core?this.db.core.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability}):r.transaction(this.storeNames,this.mode,{durability:this.chromeTransactionDurability})),t.onerror=Nt(function(l){pl(l),n._reject(t.error)}),t.onabort=Nt(function(l){pl(l),n.active&&n._reject(new Ge.Abort(t.error)),n.active=!1,n.on("abort").fire(l)}),t.oncomplete=Nt(function(){n.active=!1,n._resolve(),"mutatedParts"in t&&Qi.storagemutated.fire(t.mutatedParts)}),this},e.prototype._promise=function(t,n,r){var i=this;if(t==="readwrite"&&this.mode!=="readwrite")return Gt(new Ge.ReadOnly("Transaction is readonly"));if(!this.active)return Gt(new Ge.TransactionInactive);if(this._locked())return new $e(function(g,o){i._blockedFuncs.push([function(){i._promise(t,n,r).then(g,o)},We])});if(r)return Zi(function(){var g=new $e(function(o,a){i._lock();var u=n(o,a,i);u&&u.then&&u.then(o,a)});return g.finally(function(){return 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n=this._memoizedTables||(this._memoizedTables={});if(Xn(n,t))return n[t];var r=this.schema[t];if(!r)throw new Ge.NotFound("Table "+t+" not part of transaction");var i=new this.db.Table(t,r,this);return i.core=this.db.core.table(t),n[t]=i,i},e}();function Y7(e){return Wl(W7.prototype,function(n,r,i,l,g){var o=this;this.db=e,this.mode=n,this.storeNames=r,this.schema=i,this.chromeTransactionDurability=l,this.idbtrans=null,this.on=Hl(this,"complete","error","abort"),this.parent=g||null,this.active=!0,this._reculock=0,this._blockedFuncs=[],this._resolve=null,this._reject=null,this._waitingFor=null,this._waitingQueue=null,this._spinCount=0,this._completion=new $e(function(a,u){o._resolve=a,o._reject=u}),this._completion.then(function(){o.active=!1,o.on.complete.fire()},function(a){var u=o.active;return o.active=!1,o.on.error.fire(a),o.parent?o.parent._reject(a):u&&o.idbtrans&&o.idbtrans.abort(),Gt(a)})})}function 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e.prototype.subscribe=function(t,n,r){return this._subscribe(!t||typeof t=="function"?{next:t,error:n,complete:r}:t)},e.prototype[xU]=function(){return this},e}();function u2(e,t){return Xt(t).forEach(function(n){var r=e[n]||(e[n]=new Zr);Tc(r,t[n])}),e}function mm(e){return new kU(function(t){var n=Qp(e);function r(s){n&&Jo();var h=function(){return Zi(e,{subscr:s,trans:null})},v=We.trans?Qo(We.transless,h):h();return n&&v.then(fi,fi),v}var i=!1,l={},g={},o={get closed(){return i},unsubscribe:function(){i=!0,Qi.storagemutated.unsubscribe(c)}};t.start&&t.start(o);var a=!1,u=!1;function d(){return Xt(g).some(function(s){return l[s]&&wU(l[s],g[s])})}var c=function(s){u2(l,s),d()&&f()},f=function(){if(!(a||i)){l={};var s={},h=r(s);u||(Qi(ml,c),u=!0),a=!0,Promise.resolve(h).then(function(v){a=!1,!i&&(d()?f():(l={},g=s,t.next&&t.next(v)))},function(v){a=!1,t.error&&t.error(v),o.unsubscribe()})}};return f(),o})}var Eh;try{Eh={indexedDB:Ft.indexedDB||Ft.mozIndexedDB||Ft.webkitIndexedDB||Ft.msIndexedDB,IDBKeyRange:Ft.IDBKeyRange||Ft.webkitIDBKeyRange}}catch{Eh={indexedDB:null,IDBKeyRange:null}}var ya=pm;No(ya,ut(ut({},uf),{delete:function(e){var t=new ya(e,{addons:[]});return t.delete()},exists:function(e){return new ya(e,{addons:[]}).open().then(function(t){return t.close(),!0}).catch("NoSuchDatabaseError",function(){return!1})},getDatabaseNames:function(e){try{return uU(ya.dependencies).then(e)}catch{return Gt(new Ge.MissingAPI)}},defineClass:function(){function e(t){Tn(this,t)}return e},ignoreTransaction:function(e){return We.trans?Qo(We.transless,e):e()},vip:xh,async:function(e){return function(){try{var t=kh(e.apply(this,arguments));return!t||typeof t.then!="function"?$e.resolve(t):t}catch(n){return Gt(n)}}},spawn:function(e,t,n){try{var r=kh(e.apply(n,t||[]));return!r||typeof r.then!="function"?$e.resolve(r):r}catch(i){return Gt(i)}},currentTransaction:{get:function(){return We.trans||null}},waitFor:function(e,t){var n=$e.resolve(typeof e=="function"?ya.ignoreTransaction(e):e).timeout(t||6e4);return We.trans?We.trans.waitFor(n):n},Promise:$e,debug:{get:function(){return Nr},set:function(e){Nw(e,e==="dexie"?function(){return!0}:e2)}},derive:qo,extend:Tn,props:No,override:Rw,Events:Hl,on:Qi,liveQuery:mm,extendObservabilitySet:u2,getByKeyPath:ii,setByKeyPath:mr,delByKeyPath:i7,shallowClone:Bw,deepClone:Nl,getObjectDiff:dm,cmp:Sn,asap:Dw,minKey:yh,addons:[],connections:$s,errnames:tm,dependencies:Eh,semVer:B0,version:B0.split(".").map(function(e){return parseInt(e)}).reduce(function(e,t,n){return e+t/Math.pow(10,n*2)})}));ya.maxKey=gl(ya.dependencies.IDBKeyRange);typeof dispatchEvent<"u"&&typeof addEventListener<"u"&&(Qi(ml,function(e){if(!ti){var t;cf?(t=document.createEvent("CustomEvent"),t.initCustomEvent($i,!0,!0,e)):t=new CustomEvent($i,{detail:e}),ti=!0,dispatchEvent(t),ti=!1}}),addEventListener($i,function(e){var t=e.detail;ti||Ec(t)}));function Ec(e){var t=ti;try{ti=!0,Qi.storagemutated.fire(e)}finally{ti=t}}var ti=!1;if(typeof BroadcastChannel<"u"){var W0=new BroadcastChannel($i);Qi(ml,function(e){ti||W0.postMessage(e)}),W0.onmessage=function(e){e.data&&Ec(e.data)}}else if(typeof self<"u"&&typeof navigator<"u"){Qi(ml,function(e){try{ti||(typeof localStorage<"u"&&localStorage.setItem($i,JSON.stringify({trig:Math.random(),changedParts:e})),typeof self.clients=="object"&&ih([],self.clients.matchAll({includeUncontrolled:!0}),!0).forEach(function(t){return t.postMessage({type:$i,changedParts:e})}))}catch{}}),typeof addEventListener<"u"&&addEventListener("storage",function(e){if(e.key===$i){var t=JSON.parse(e.newValue);t&&Ec(t.changedParts)}});var Y0=self.document&&navigator.serviceWorker;Y0&&Y0.addEventListener("message",TU)}function TU(e){var t=e.data;t&&t.type===$i&&Ec(t.changedParts)}$e.rejectionMapper=h7;Nw(Nr,e2);class EU extends pm{constructor(){super("stableui");Rm(this,"outputs");this.version(.2).stores({outputs:""}),this.version(.3).stores({outputs:null,outputsTemp:"++id"}).upgrade(async n=>{const r=await n.table("outputs").get("outputs");await n.table("outputsTemp").bulkPut(JSON.parse(r))}),this.version(.4).stores({outputsTemp:null,outputs:"++id"}).upgrade(async n=>{const r=await n.table("outputsTemp").toArray();await n.table("outputs").bulkPut(r)}),this.version(.5).stores({outputs:"++id,starred,rated"}).upgrade(async n=>await n.table("outputs").toCollection().modify(r=>{r.starred=Number(r.starred),r.rated=Number(r.rated)}))}}const Vt=new EU;Vt.open().catch(()=>{Ht().raiseError("The image database failed to open! This usually happens when using Firefox in Private Browsing mode. Try disabling Private Browsing or using another browser.",!0)});function wu(e){throw new Error('Could not dynamically require "'+e+'". Please configure the dynamicRequireTargets or/and ignoreDynamicRequires option of @rollup/plugin-commonjs appropriately for this require call to work.')}var c2={exports:{}};/*!
 
 JSZip v3.10.1 - A JavaScript class for generating and reading zip files
 <http://stuartk.com/jszip>
@@ -67,17 +67,17 @@ Dual licenced under the MIT license or GPLv3. See https://raw.github.com/Stuk/js
 
 JSZip uses the library pako released under the MIT license :
 https://github.com/nodeca/pako/blob/main/LICENSE
-*/(function(e,t){(function(n){e.exports=n()})(function(){return function n(r,i,l){function g(u,d){if(!i[u]){if(!r[u]){var c=typeof gu=="function"&&gu;if(!d&&c)return c(u,!0);if(o)return o(u,!0);var f=new Error("Cannot find module '"+u+"'");throw f.code="MODULE_NOT_FOUND",f}var s=i[u]={exports:{}};r[u][0].call(s.exports,function(h){var v=r[u][1][h];return g(v||h)},s,s.exports,n,r,i,l)}return i[u].exports}for(var o=typeof gu=="function"&&gu,a=0;a<l.length;a++)g(l[a]);return g}({1:[function(n,r,i){var l=n("./utils"),g=n("./support"),o="ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=";i.encode=function(a){for(var u,d,c,f,s,h,v,m=[],y=0,b=a.length,_=b,w=l.getTypeOf(a)!=="string";y<a.length;)_=b-y,c=w?(u=a[y++],d=y<b?a[y++]:0,y<b?a[y++]:0):(u=a.charCodeAt(y++),d=y<b?a.charCodeAt(y++):0,y<b?a.charCodeAt(y++):0),f=u>>2,s=(3&u)<<4|d>>4,h=1<_?(15&d)<<2|c>>6:64,v=2<_?63&c:64,m.push(o.charAt(f)+o.charAt(s)+o.charAt(h)+o.charAt(v));return m.join("")},i.decode=function(a){var u,d,c,f,s,h,v=0,m=0,y="data:";if(a.substr(0,y.length)===y)throw new Error("Invalid base64 input, it looks like a data url.");var b,_=3*(a=a.replace(/[^A-Za-z0-9+/=]/g,"")).length/4;if(a.charAt(a.length-1)===o.charAt(64)&&_--,a.charAt(a.length-2)===o.charAt(64)&&_--,_%1!=0)throw new Error("Invalid base64 input, bad content length.");for(b=g.uint8array?new Uint8Array(0|_):new Array(0|_);v<a.length;)u=o.indexOf(a.charAt(v++))<<2|(f=o.indexOf(a.charAt(v++)))>>4,d=(15&f)<<4|(s=o.indexOf(a.charAt(v++)))>>2,c=(3&s)<<6|(h=o.indexOf(a.charAt(v++))),b[m++]=u,s!==64&&(b[m++]=d),h!==64&&(b[m++]=c);return b}},{"./support":30,"./utils":32}],2:[function(n,r,i){var l=n("./external"),g=n("./stream/DataWorker"),o=n("./stream/Crc32Probe"),a=n("./stream/DataLengthProbe");function u(d,c,f,s,h){this.compressedSize=d,this.uncompressedSize=c,this.crc32=f,this.compression=s,this.compressedContent=h}u.prototype={getContentWorker:function(){var d=new g(l.Promise.resolve(this.compressedContent)).pipe(this.compression.uncompressWorker()).pipe(new a("data_length")),c=this;return d.on("end",function(){if(this.streamInfo.data_length!==c.uncompressedSize)throw new Error("Bug : uncompressed data size mismatch")}),d},getCompressedWorker:function(){return new g(l.Promise.resolve(this.compressedContent)).withStreamInfo("compressedSize",this.compressedSize).withStreamInfo("uncompressedSize",this.uncompressedSize).withStreamInfo("crc32",this.crc32).withStreamInfo("compression",this.compression)}},u.createWorkerFrom=function(d,c,f){return d.pipe(new o).pipe(new a("uncompressedSize")).pipe(c.compressWorker(f)).pipe(new a("compressedSize")).withStreamInfo("compression",c)},r.exports=u},{"./external":6,"./stream/Crc32Probe":25,"./stream/DataLengthProbe":26,"./stream/DataWorker":27}],3:[function(n,r,i){var l=n("./stream/GenericWorker");i.STORE={magic:"\0\0",compressWorker:function(){return new l("STORE compression")},uncompressWorker:function(){return new l("STORE decompression")}},i.DEFLATE=n("./flate")},{"./flate":7,"./stream/GenericWorker":28}],4:[function(n,r,i){var l=n("./utils"),g=function(){for(var o,a=[],u=0;u<256;u++){o=u;for(var d=0;d<8;d++)o=1&o?3988292384^o>>>1:o>>>1;a[u]=o}return a}();r.exports=function(o,a){return o!==void 0&&o.length?l.getTypeOf(o)!=="string"?function(u,d,c,f){var s=g,h=f+c;u^=-1;for(var v=f;v<h;v++)u=u>>>8^s[255&(u^d[v])];return-1^u}(0|a,o,o.length,0):function(u,d,c,f){var s=g,h=f+c;u^=-1;for(var v=f;v<h;v++)u=u>>>8^s[255&(u^d.charCodeAt(v))];return-1^u}(0|a,o,o.length,0):0}},{"./utils":32}],5:[function(n,r,i){i.base64=!1,i.binary=!1,i.dir=!1,i.createFolders=!0,i.date=null,i.compression=null,i.compressionOptions=null,i.comment=null,i.unixPermissions=null,i.dosPermissions=null},{}],6:[function(n,r,i){var l=null;l=typeof Promise<"u"?Promise:n("lie"),r.exports={Promise:l}},{lie:37}],7:[function(n,r,i){var l=typeof Uint8Array<"u"&&typeof Uint16Array<"u"&&typeof Uint32Array<"u",g=n("pako"),o=n("./utils"),a=n("./stream/GenericWorker"),u=l?"uint8array":"array";function d(c,f){a.call(this,"FlateWorker/"+c),this._pako=null,this._pakoAction=c,this._pakoOptions=f,this.meta={}}i.magic="\b\0",o.inherits(d,a),d.prototype.processChunk=function(c){this.meta=c.meta,this._pako===null&&this._createPako(),this._pako.push(o.transformTo(u,c.data),!1)},d.prototype.flush=function(){a.prototype.flush.call(this),this._pako===null&&this._createPako(),this._pako.push([],!0)},d.prototype.cleanUp=function(){a.prototype.cleanUp.call(this),this._pako=null},d.prototype._createPako=function(){this._pako=new g[this._pakoAction]({raw:!0,level:this._pakoOptions.level||-1});var c=this;this._pako.onData=function(f){c.push({data:f,meta:c.meta})}},i.compressWorker=function(c){return new d("Deflate",c)},i.uncompressWorker=function(){return new d("Inflate",{})}},{"./stream/GenericWorker":28,"./utils":32,pako:38}],8:[function(n,r,i){function l(s,h){var v,m="";for(v=0;v<h;v++)m+=String.fromCharCode(255&s),s>>>=8;return m}function g(s,h,v,m,y,b){var _,w,S=s.file,x=s.compression,T=b!==u.utf8encode,E=o.transformTo("string",b(S.name)),I=o.transformTo("string",u.utf8encode(S.name)),D=S.comment,N=o.transformTo("string",b(D)),L=o.transformTo("string",u.utf8encode(D)),F=I.length!==S.name.length,O=L.length!==D.length,V="",re="",J="",ue=S.dir,M=S.date,U={crc32:0,compressedSize:0,uncompressedSize:0};h&&!v||(U.crc32=s.crc32,U.compressedSize=s.compressedSize,U.uncompressedSize=s.uncompressedSize);var R=0;h&&(R|=8),T||!F&&!O||(R|=2048);var P=0,Q=0;ue&&(P|=16),y==="UNIX"?(Q=798,P|=function(A,q){var G=A;return A||(G=q?16893:33204),(65535&G)<<16}(S.unixPermissions,ue)):(Q=20,P|=function(A){return 63&(A||0)}(S.dosPermissions)),_=M.getUTCHours(),_<<=6,_|=M.getUTCMinutes(),_<<=5,_|=M.getUTCSeconds()/2,w=M.getUTCFullYear()-1980,w<<=4,w|=M.getUTCMonth()+1,w<<=5,w|=M.getUTCDate(),F&&(re=l(1,1)+l(d(E),4)+I,V+="up"+l(re.length,2)+re),O&&(J=l(1,1)+l(d(N),4)+L,V+="uc"+l(J.length,2)+J);var j="";return j+=`
-\0`,j+=l(R,2),j+=x.magic,j+=l(_,2),j+=l(w,2),j+=l(U.crc32,4),j+=l(U.compressedSize,4),j+=l(U.uncompressedSize,4),j+=l(E.length,2),j+=l(V.length,2),{fileRecord:c.LOCAL_FILE_HEADER+j+E+V,dirRecord:c.CENTRAL_FILE_HEADER+l(Q,2)+j+l(N.length,2)+"\0\0\0\0"+l(P,4)+l(m,4)+E+V+N}}var o=n("../utils"),a=n("../stream/GenericWorker"),u=n("../utf8"),d=n("../crc32"),c=n("../signature");function f(s,h,v,m){a.call(this,"ZipFileWorker"),this.bytesWritten=0,this.zipComment=h,this.zipPlatform=v,this.encodeFileName=m,this.streamFiles=s,this.accumulate=!1,this.contentBuffer=[],this.dirRecords=[],this.currentSourceOffset=0,this.entriesCount=0,this.currentFile=null,this._sources=[]}o.inherits(f,a),f.prototype.push=function(s){var h=s.meta.percent||0,v=this.entriesCount,m=this._sources.length;this.accumulate?this.contentBuffer.push(s):(this.bytesWritten+=s.data.length,a.prototype.push.call(this,{data:s.data,meta:{currentFile:this.currentFile,percent:v?(h+100*(v-m-1))/v:100}}))},f.prototype.openedSource=function(s){this.currentSourceOffset=this.bytesWritten,this.currentFile=s.file.name;var h=this.streamFiles&&!s.file.dir;if(h){var v=g(s,h,!1,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);this.push({data:v.fileRecord,meta:{percent:0}})}else this.accumulate=!0},f.prototype.closedSource=function(s){this.accumulate=!1;var h=this.streamFiles&&!s.file.dir,v=g(s,h,!0,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);if(this.dirRecords.push(v.dirRecord),h)this.push({data:function(m){return c.DATA_DESCRIPTOR+l(m.crc32,4)+l(m.compressedSize,4)+l(m.uncompressedSize,4)}(s),meta:{percent:100}});else for(this.push({data:v.fileRecord,meta:{percent:0}});this.contentBuffer.length;)this.push(this.contentBuffer.shift());this.currentFile=null},f.prototype.flush=function(){for(var s=this.bytesWritten,h=0;h<this.dirRecords.length;h++)this.push({data:this.dirRecords[h],meta:{percent:100}});var v=this.bytesWritten-s,m=function(y,b,_,w,S){var x=o.transformTo("string",S(w));return c.CENTRAL_DIRECTORY_END+"\0\0\0\0"+l(y,2)+l(y,2)+l(b,4)+l(_,4)+l(x.length,2)+x}(this.dirRecords.length,v,s,this.zipComment,this.encodeFileName);this.push({data:m,meta:{percent:100}})},f.prototype.prepareNextSource=function(){this.previous=this._sources.shift(),this.openedSource(this.previous.streamInfo),this.isPaused?this.previous.pause():this.previous.resume()},f.prototype.registerPrevious=function(s){this._sources.push(s);var h=this;return s.on("data",function(v){h.processChunk(v)}),s.on("end",function(){h.closedSource(h.previous.streamInfo),h._sources.length?h.prepareNextSource():h.end()}),s.on("error",function(v){h.error(v)}),this},f.prototype.resume=function(){return!!a.prototype.resume.call(this)&&(!this.previous&&this._sources.length?(this.prepareNextSource(),!0):this.previous||this._sources.length||this.generatedError?void 0:(this.end(),!0))},f.prototype.error=function(s){var h=this._sources;if(!a.prototype.error.call(this,s))return!1;for(var v=0;v<h.length;v++)try{h[v].error(s)}catch{}return!0},f.prototype.lock=function(){a.prototype.lock.call(this);for(var s=this._sources,h=0;h<s.length;h++)s[h].lock()},r.exports=f},{"../crc32":4,"../signature":23,"../stream/GenericWorker":28,"../utf8":31,"../utils":32}],9:[function(n,r,i){var l=n("../compressions"),g=n("./ZipFileWorker");i.generateWorker=function(o,a,u){var d=new g(a.streamFiles,u,a.platform,a.encodeFileName),c=0;try{o.forEach(function(f,s){c++;var h=function(b,_){var w=b||_,S=l[w];if(!S)throw new Error(w+" is not a valid compression method !");return S}(s.options.compression,a.compression),v=s.options.compressionOptions||a.compressionOptions||{},m=s.dir,y=s.date;s._compressWorker(h,v).withStreamInfo("file",{name:f,dir:m,date:y,comment:s.comment||"",unixPermissions:s.unixPermissions,dosPermissions:s.dosPermissions}).pipe(d)}),d.entriesCount=c}catch(f){d.error(f)}return d}},{"../compressions":3,"./ZipFileWorker":8}],10:[function(n,r,i){function l(){if(!(this instanceof l))return new l;if(arguments.length)throw new Error("The constructor with parameters has been removed in JSZip 3.0, please check the upgrade guide.");this.files=Object.create(null),this.comment=null,this.root="",this.clone=function(){var g=new l;for(var o in this)typeof this[o]!="function"&&(g[o]=this[o]);return g}}(l.prototype=n("./object")).loadAsync=n("./load"),l.support=n("./support"),l.defaults=n("./defaults"),l.version="3.10.1",l.loadAsync=function(g,o){return new l().loadAsync(g,o)},l.external=n("./external"),r.exports=l},{"./defaults":5,"./external":6,"./load":11,"./object":15,"./support":30}],11:[function(n,r,i){var l=n("./utils"),g=n("./external"),o=n("./utf8"),a=n("./zipEntries"),u=n("./stream/Crc32Probe"),d=n("./nodejsUtils");function c(f){return new g.Promise(function(s,h){var v=f.decompressed.getContentWorker().pipe(new u);v.on("error",function(m){h(m)}).on("end",function(){v.streamInfo.crc32!==f.decompressed.crc32?h(new Error("Corrupted zip : CRC32 mismatch")):s()}).resume()})}r.exports=function(f,s){var h=this;return s=l.extend(s||{},{base64:!1,checkCRC32:!1,optimizedBinaryString:!1,createFolders:!1,decodeFileName:o.utf8decode}),d.isNode&&d.isStream(f)?g.Promise.reject(new Error("JSZip can't accept a stream when loading a zip file.")):l.prepareContent("the loaded zip file",f,!0,s.optimizedBinaryString,s.base64).then(function(v){var m=new a(s);return m.load(v),m}).then(function(v){var m=[g.Promise.resolve(v)],y=v.files;if(s.checkCRC32)for(var b=0;b<y.length;b++)m.push(c(y[b]));return g.Promise.all(m)}).then(function(v){for(var m=v.shift(),y=m.files,b=0;b<y.length;b++){var _=y[b],w=_.fileNameStr,S=l.resolve(_.fileNameStr);h.file(S,_.decompressed,{binary:!0,optimizedBinaryString:!0,date:_.date,dir:_.dir,comment:_.fileCommentStr.length?_.fileCommentStr:null,unixPermissions:_.unixPermissions,dosPermissions:_.dosPermissions,createFolders:s.createFolders}),_.dir||(h.file(S).unsafeOriginalName=w)}return m.zipComment.length&&(h.comment=m.zipComment),h})}},{"./external":6,"./nodejsUtils":14,"./stream/Crc32Probe":25,"./utf8":31,"./utils":32,"./zipEntries":33}],12:[function(n,r,i){var l=n("../utils"),g=n("../stream/GenericWorker");function o(a,u){g.call(this,"Nodejs stream input adapter for "+a),this._upstreamEnded=!1,this._bindStream(u)}l.inherits(o,g),o.prototype._bindStream=function(a){var u=this;(this._stream=a).pause(),a.on("data",function(d){u.push({data:d,meta:{percent:0}})}).on("error",function(d){u.isPaused?this.generatedError=d:u.error(d)}).on("end",function(){u.isPaused?u._upstreamEnded=!0:u.end()})},o.prototype.pause=function(){return!!g.prototype.pause.call(this)&&(this._stream.pause(),!0)},o.prototype.resume=function(){return!!g.prototype.resume.call(this)&&(this._upstreamEnded?this.end():this._stream.resume(),!0)},r.exports=o},{"../stream/GenericWorker":28,"../utils":32}],13:[function(n,r,i){var l=n("readable-stream").Readable;function g(o,a,u){l.call(this,a),this._helper=o;var d=this;o.on("data",function(c,f){d.push(c)||d._helper.pause(),u&&u(f)}).on("error",function(c){d.emit("error",c)}).on("end",function(){d.push(null)})}n("../utils").inherits(g,l),g.prototype._read=function(){this._helper.resume()},r.exports=g},{"../utils":32,"readable-stream":16}],14:[function(n,r,i){r.exports={isNode:typeof Buffer<"u",newBufferFrom:function(l,g){if(Buffer.from&&Buffer.from!==Uint8Array.from)return Buffer.from(l,g);if(typeof l=="number")throw new Error('The "data" argument must not be a number');return new Buffer(l,g)},allocBuffer:function(l){if(Buffer.alloc)return Buffer.alloc(l);var g=new Buffer(l);return g.fill(0),g},isBuffer:function(l){return Buffer.isBuffer(l)},isStream:function(l){return l&&typeof l.on=="function"&&typeof l.pause=="function"&&typeof l.resume=="function"}}},{}],15:[function(n,r,i){function l(S,x,T){var E,I=o.getTypeOf(x),D=o.extend(T||{},d);D.date=D.date||new Date,D.compression!==null&&(D.compression=D.compression.toUpperCase()),typeof D.unixPermissions=="string"&&(D.unixPermissions=parseInt(D.unixPermissions,8)),D.unixPermissions&&16384&D.unixPermissions&&(D.dir=!0),D.dosPermissions&&16&D.dosPermissions&&(D.dir=!0),D.dir&&(S=y(S)),D.createFolders&&(E=m(S))&&b.call(this,E,!0);var N=I==="string"&&D.binary===!1&&D.base64===!1;T&&T.binary!==void 0||(D.binary=!N),(x instanceof c&&x.uncompressedSize===0||D.dir||!x||x.length===0)&&(D.base64=!1,D.binary=!0,x="",D.compression="STORE",I="string");var L=null;L=x instanceof c||x instanceof a?x:h.isNode&&h.isStream(x)?new v(S,x):o.prepareContent(S,x,D.binary,D.optimizedBinaryString,D.base64);var F=new f(S,L,D);this.files[S]=F}var g=n("./utf8"),o=n("./utils"),a=n("./stream/GenericWorker"),u=n("./stream/StreamHelper"),d=n("./defaults"),c=n("./compressedObject"),f=n("./zipObject"),s=n("./generate"),h=n("./nodejsUtils"),v=n("./nodejs/NodejsStreamInputAdapter"),m=function(S){S.slice(-1)==="/"&&(S=S.substring(0,S.length-1));var x=S.lastIndexOf("/");return 0<x?S.substring(0,x):""},y=function(S){return S.slice(-1)!=="/"&&(S+="/"),S},b=function(S,x){return x=x!==void 0?x:d.createFolders,S=y(S),this.files[S]||l.call(this,S,null,{dir:!0,createFolders:x}),this.files[S]};function _(S){return Object.prototype.toString.call(S)==="[object RegExp]"}var w={load:function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},forEach:function(S){var x,T,E;for(x in this.files)E=this.files[x],(T=x.slice(this.root.length,x.length))&&x.slice(0,this.root.length)===this.root&&S(T,E)},filter:function(S){var x=[];return this.forEach(function(T,E){S(T,E)&&x.push(E)}),x},file:function(S,x,T){if(arguments.length!==1)return S=this.root+S,l.call(this,S,x,T),this;if(_(S)){var E=S;return this.filter(function(D,N){return!N.dir&&E.test(D)})}var I=this.files[this.root+S];return I&&!I.dir?I:null},folder:function(S){if(!S)return this;if(_(S))return this.filter(function(I,D){return D.dir&&S.test(I)});var x=this.root+S,T=b.call(this,x),E=this.clone();return E.root=T.name,E},remove:function(S){S=this.root+S;var x=this.files[S];if(x||(S.slice(-1)!=="/"&&(S+="/"),x=this.files[S]),x&&!x.dir)delete this.files[S];else for(var T=this.filter(function(I,D){return D.name.slice(0,S.length)===S}),E=0;E<T.length;E++)delete this.files[T[E].name];return this},generate:function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},generateInternalStream:function(S){var x,T={};try{if((T=o.extend(S||{},{streamFiles:!1,compression:"STORE",compressionOptions:null,type:"",platform:"DOS",comment:null,mimeType:"application/zip",encodeFileName:g.utf8encode})).type=T.type.toLowerCase(),T.compression=T.compression.toUpperCase(),T.type==="binarystring"&&(T.type="string"),!T.type)throw new Error("No output type specified.");o.checkSupport(T.type),T.platform!=="darwin"&&T.platform!=="freebsd"&&T.platform!=="linux"&&T.platform!=="sunos"||(T.platform="UNIX"),T.platform==="win32"&&(T.platform="DOS");var E=T.comment||this.comment||"";x=s.generateWorker(this,T,E)}catch(I){(x=new a("error")).error(I)}return new u(x,T.type||"string",T.mimeType)},generateAsync:function(S,x){return this.generateInternalStream(S).accumulate(x)},generateNodeStream:function(S,x){return(S=S||{}).type||(S.type="nodebuffer"),this.generateInternalStream(S).toNodejsStream(x)}};r.exports=w},{"./compressedObject":2,"./defaults":5,"./generate":9,"./nodejs/NodejsStreamInputAdapter":12,"./nodejsUtils":14,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31,"./utils":32,"./zipObject":35}],16:[function(n,r,i){r.exports=n("stream")},{stream:void 0}],17:[function(n,r,i){var l=n("./DataReader");function g(o){l.call(this,o);for(var a=0;a<this.data.length;a++)o[a]=255&o[a]}n("../utils").inherits(g,l),g.prototype.byteAt=function(o){return this.data[this.zero+o]},g.prototype.lastIndexOfSignature=function(o){for(var a=o.charCodeAt(0),u=o.charCodeAt(1),d=o.charCodeAt(2),c=o.charCodeAt(3),f=this.length-4;0<=f;--f)if(this.data[f]===a&&this.data[f+1]===u&&this.data[f+2]===d&&this.data[f+3]===c)return f-this.zero;return-1},g.prototype.readAndCheckSignature=function(o){var a=o.charCodeAt(0),u=o.charCodeAt(1),d=o.charCodeAt(2),c=o.charCodeAt(3),f=this.readData(4);return a===f[0]&&u===f[1]&&d===f[2]&&c===f[3]},g.prototype.readData=function(o){if(this.checkOffset(o),o===0)return[];var a=this.data.slice(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./DataReader":18}],18:[function(n,r,i){var l=n("../utils");function g(o){this.data=o,this.length=o.length,this.index=0,this.zero=0}g.prototype={checkOffset:function(o){this.checkIndex(this.index+o)},checkIndex:function(o){if(this.length<this.zero+o||o<0)throw new Error("End of data reached (data length = "+this.length+", asked index = "+o+"). Corrupted zip ?")},setIndex:function(o){this.checkIndex(o),this.index=o},skip:function(o){this.setIndex(this.index+o)},byteAt:function(){},readInt:function(o){var a,u=0;for(this.checkOffset(o),a=this.index+o-1;a>=this.index;a--)u=(u<<8)+this.byteAt(a);return this.index+=o,u},readString:function(o){return l.transformTo("string",this.readData(o))},readData:function(){},lastIndexOfSignature:function(){},readAndCheckSignature:function(){},readDate:function(){var o=this.readInt(4);return new Date(Date.UTC(1980+(o>>25&127),(o>>21&15)-1,o>>16&31,o>>11&31,o>>5&63,(31&o)<<1))}},r.exports=g},{"../utils":32}],19:[function(n,r,i){var l=n("./Uint8ArrayReader");function g(o){l.call(this,o)}n("../utils").inherits(g,l),g.prototype.readData=function(o){this.checkOffset(o);var a=this.data.slice(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./Uint8ArrayReader":21}],20:[function(n,r,i){var l=n("./DataReader");function g(o){l.call(this,o)}n("../utils").inherits(g,l),g.prototype.byteAt=function(o){return this.data.charCodeAt(this.zero+o)},g.prototype.lastIndexOfSignature=function(o){return this.data.lastIndexOf(o)-this.zero},g.prototype.readAndCheckSignature=function(o){return o===this.readData(4)},g.prototype.readData=function(o){this.checkOffset(o);var a=this.data.slice(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./DataReader":18}],21:[function(n,r,i){var l=n("./ArrayReader");function g(o){l.call(this,o)}n("../utils").inherits(g,l),g.prototype.readData=function(o){if(this.checkOffset(o),o===0)return new Uint8Array(0);var a=this.data.subarray(this.zero+this.index,this.zero+this.index+o);return this.index+=o,a},r.exports=g},{"../utils":32,"./ArrayReader":17}],22:[function(n,r,i){var l=n("../utils"),g=n("../support"),o=n("./ArrayReader"),a=n("./StringReader"),u=n("./NodeBufferReader"),d=n("./Uint8ArrayReader");r.exports=function(c){var f=l.getTypeOf(c);return l.checkSupport(f),f!=="string"||g.uint8array?f==="nodebuffer"?new u(c):g.uint8array?new d(l.transformTo("uint8array",c)):new o(l.transformTo("array",c)):new a(c)}},{"../support":30,"../utils":32,"./ArrayReader":17,"./NodeBufferReader":19,"./StringReader":20,"./Uint8ArrayReader":21}],23:[function(n,r,i){i.LOCAL_FILE_HEADER="PK",i.CENTRAL_FILE_HEADER="PK
",i.CENTRAL_DIRECTORY_END="PK",i.ZIP64_CENTRAL_DIRECTORY_LOCATOR="PK\x07",i.ZIP64_CENTRAL_DIRECTORY_END="PK",i.DATA_DESCRIPTOR="PK\x07\b"},{}],24:[function(n,r,i){var l=n("./GenericWorker"),g=n("../utils");function o(a){l.call(this,"ConvertWorker to "+a),this.destType=a}g.inherits(o,l),o.prototype.processChunk=function(a){this.push({data:g.transformTo(this.destType,a.data),meta:a.meta})},r.exports=o},{"../utils":32,"./GenericWorker":28}],25:[function(n,r,i){var l=n("./GenericWorker"),g=n("../crc32");function o(){l.call(this,"Crc32Probe"),this.withStreamInfo("crc32",0)}n("../utils").inherits(o,l),o.prototype.processChunk=function(a){this.streamInfo.crc32=g(a.data,this.streamInfo.crc32||0),this.push(a)},r.exports=o},{"../crc32":4,"../utils":32,"./GenericWorker":28}],26:[function(n,r,i){var l=n("../utils"),g=n("./GenericWorker");function o(a){g.call(this,"DataLengthProbe for "+a),this.propName=a,this.withStreamInfo(a,0)}l.inherits(o,g),o.prototype.processChunk=function(a){if(a){var u=this.streamInfo[this.propName]||0;this.streamInfo[this.propName]=u+a.data.length}g.prototype.processChunk.call(this,a)},r.exports=o},{"../utils":32,"./GenericWorker":28}],27:[function(n,r,i){var l=n("../utils"),g=n("./GenericWorker");function o(a){g.call(this,"DataWorker");var u=this;this.dataIsReady=!1,this.index=0,this.max=0,this.data=null,this.type="",this._tickScheduled=!1,a.then(function(d){u.dataIsReady=!0,u.data=d,u.max=d&&d.length||0,u.type=l.getTypeOf(d),u.isPaused||u._tickAndRepeat()},function(d){u.error(d)})}l.inherits(o,g),o.prototype.cleanUp=function(){g.prototype.cleanUp.call(this),this.data=null},o.prototype.resume=function(){return!!g.prototype.resume.call(this)&&(!this._tickScheduled&&this.dataIsReady&&(this._tickScheduled=!0,l.delay(this._tickAndRepeat,[],this)),!0)},o.prototype._tickAndRepeat=function(){this._tickScheduled=!1,this.isPaused||this.isFinished||(this._tick(),this.isFinished||(l.delay(this._tickAndRepeat,[],this),this._tickScheduled=!0))},o.prototype._tick=function(){if(this.isPaused||this.isFinished)return!1;var a=null,u=Math.min(this.max,this.index+16384);if(this.index>=this.max)return this.end();switch(this.type){case"string":a=this.data.substring(this.index,u);break;case"uint8array":a=this.data.subarray(this.index,u);break;case"array":case"nodebuffer":a=this.data.slice(this.index,u)}return this.index=u,this.push({data:a,meta:{percent:this.max?this.index/this.max*100:0}})},r.exports=o},{"../utils":32,"./GenericWorker":28}],28:[function(n,r,i){function l(g){this.name=g||"default",this.streamInfo={},this.generatedError=null,this.extraStreamInfo={},this.isPaused=!0,this.isFinished=!1,this.isLocked=!1,this._listeners={data:[],end:[],error:[]},this.previous=null}l.prototype={push:function(g){this.emit("data",g)},end:function(){if(this.isFinished)return!1;this.flush();try{this.emit("end"),this.cleanUp(),this.isFinished=!0}catch(g){this.emit("error",g)}return!0},error:function(g){return!this.isFinished&&(this.isPaused?this.generatedError=g:(this.isFinished=!0,this.emit("error",g),this.previous&&this.previous.error(g),this.cleanUp()),!0)},on:function(g,o){return this._listeners[g].push(o),this},cleanUp:function(){this.streamInfo=this.generatedError=this.extraStreamInfo=null,this._listeners=[]},emit:function(g,o){if(this._listeners[g])for(var a=0;a<this._listeners[g].length;a++)this._listeners[g][a].call(this,o)},pipe:function(g){return g.registerPrevious(this)},registerPrevious:function(g){if(this.isLocked)throw new Error("The stream '"+this+"' has already been used.");this.streamInfo=g.streamInfo,this.mergeStreamInfo(),this.previous=g;var o=this;return g.on("data",function(a){o.processChunk(a)}),g.on("end",function(){o.end()}),g.on("error",function(a){o.error(a)}),this},pause:function(){return!this.isPaused&&!this.isFinished&&(this.isPaused=!0,this.previous&&this.previous.pause(),!0)},resume:function(){if(!this.isPaused||this.isFinished)return!1;var g=this.isPaused=!1;return this.generatedError&&(this.error(this.generatedError),g=!0),this.previous&&this.previous.resume(),!g},flush:function(){},processChunk:function(g){this.push(g)},withStreamInfo:function(g,o){return this.extraStreamInfo[g]=o,this.mergeStreamInfo(),this},mergeStreamInfo:function(){for(var g in this.extraStreamInfo)Object.prototype.hasOwnProperty.call(this.extraStreamInfo,g)&&(this.streamInfo[g]=this.extraStreamInfo[g])},lock:function(){if(this.isLocked)throw new Error("The stream '"+this+"' has already been used.");this.isLocked=!0,this.previous&&this.previous.lock()},toString:function(){var g="Worker "+this.name;return this.previous?this.previous+" -> "+g:g}},r.exports=l},{}],29:[function(n,r,i){var l=n("../utils"),g=n("./ConvertWorker"),o=n("./GenericWorker"),a=n("../base64"),u=n("../support"),d=n("../external"),c=null;if(u.nodestream)try{c=n("../nodejs/NodejsStreamOutputAdapter")}catch{}function f(h,v){return new d.Promise(function(m,y){var b=[],_=h._internalType,w=h._outputType,S=h._mimeType;h.on("data",function(x,T){b.push(x),v&&v(T)}).on("error",function(x){b=[],y(x)}).on("end",function(){try{var x=function(T,E,I){switch(T){case"blob":return l.newBlob(l.transformTo("arraybuffer",E),I);case"base64":return a.encode(E);default:return l.transformTo(T,E)}}(w,function(T,E){var I,D=0,N=null,L=0;for(I=0;I<E.length;I++)L+=E[I].length;switch(T){case"string":return E.join("");case"array":return Array.prototype.concat.apply([],E);case"uint8array":for(N=new Uint8Array(L),I=0;I<E.length;I++)N.set(E[I],D),D+=E[I].length;return N;case"nodebuffer":return Buffer.concat(E);default:throw new Error("concat : unsupported type '"+T+"'")}}(_,b),S);m(x)}catch(T){y(T)}b=[]}).resume()})}function s(h,v,m){var y=v;switch(v){case"blob":case"arraybuffer":y="uint8array";break;case"base64":y="string"}try{this._internalType=y,this._outputType=v,this._mimeType=m,l.checkSupport(y),this._worker=h.pipe(new g(y)),h.lock()}catch(b){this._worker=new o("error"),this._worker.error(b)}}s.prototype={accumulate:function(h){return f(this,h)},on:function(h,v){var m=this;return h==="data"?this._worker.on(h,function(y){v.call(m,y.data,y.meta)}):this._worker.on(h,function(){l.delay(v,arguments,m)}),this},resume:function(){return l.delay(this._worker.resume,[],this._worker),this},pause:function(){return this._worker.pause(),this},toNodejsStream:function(h){if(l.checkSupport("nodestream"),this._outputType!=="nodebuffer")throw new Error(this._outputType+" is not supported by this method");return new c(this,{objectMode:this._outputType!=="nodebuffer"},h)}},r.exports=s},{"../base64":1,"../external":6,"../nodejs/NodejsStreamOutputAdapter":13,"../support":30,"../utils":32,"./ConvertWorker":24,"./GenericWorker":28}],30:[function(n,r,i){if(i.base64=!0,i.array=!0,i.string=!0,i.arraybuffer=typeof ArrayBuffer<"u"&&typeof Uint8Array<"u",i.nodebuffer=typeof Buffer<"u",i.uint8array=typeof Uint8Array<"u",typeof ArrayBuffer>"u")i.blob=!1;else{var l=new ArrayBuffer(0);try{i.blob=new Blob([l],{type:"application/zip"}).size===0}catch{try{var g=new(self.BlobBuilder||self.WebKitBlobBuilder||self.MozBlobBuilder||self.MSBlobBuilder);g.append(l),i.blob=g.getBlob("application/zip").size===0}catch{i.blob=!1}}}try{i.nodestream=!!n("readable-stream").Readable}catch{i.nodestream=!1}},{"readable-stream":16}],31:[function(n,r,i){for(var l=n("./utils"),g=n("./support"),o=n("./nodejsUtils"),a=n("./stream/GenericWorker"),u=new Array(256),d=0;d<256;d++)u[d]=252<=d?6:248<=d?5:240<=d?4:224<=d?3:192<=d?2:1;u[254]=u[254]=1;function c(){a.call(this,"utf-8 decode"),this.leftOver=null}function f(){a.call(this,"utf-8 encode")}i.utf8encode=function(s){return g.nodebuffer?o.newBufferFrom(s,"utf-8"):function(h){var v,m,y,b,_,w=h.length,S=0;for(b=0;b<w;b++)(64512&(m=h.charCodeAt(b)))==55296&&b+1<w&&(64512&(y=h.charCodeAt(b+1)))==56320&&(m=65536+(m-55296<<10)+(y-56320),b++),S+=m<128?1:m<2048?2:m<65536?3:4;for(v=g.uint8array?new Uint8Array(S):new Array(S),b=_=0;_<S;b++)(64512&(m=h.charCodeAt(b)))==55296&&b+1<w&&(64512&(y=h.charCodeAt(b+1)))==56320&&(m=65536+(m-55296<<10)+(y-56320),b++),m<128?v[_++]=m:(m<2048?v[_++]=192|m>>>6:(m<65536?v[_++]=224|m>>>12:(v[_++]=240|m>>>18,v[_++]=128|m>>>12&63),v[_++]=128|m>>>6&63),v[_++]=128|63&m);return v}(s)},i.utf8decode=function(s){return g.nodebuffer?l.transformTo("nodebuffer",s).toString("utf-8"):function(h){var v,m,y,b,_=h.length,w=new Array(2*_);for(v=m=0;v<_;)if((y=h[v++])<128)w[m++]=y;else if(4<(b=u[y]))w[m++]=65533,v+=b-1;else{for(y&=b===2?31:b===3?15:7;1<b&&v<_;)y=y<<6|63&h[v++],b--;1<b?w[m++]=65533:y<65536?w[m++]=y:(y-=65536,w[m++]=55296|y>>10&1023,w[m++]=56320|1023&y)}return w.length!==m&&(w.subarray?w=w.subarray(0,m):w.length=m),l.applyFromCharCode(w)}(s=l.transformTo(g.uint8array?"uint8array":"array",s))},l.inherits(c,a),c.prototype.processChunk=function(s){var h=l.transformTo(g.uint8array?"uint8array":"array",s.data);if(this.leftOver&&this.leftOver.length){if(g.uint8array){var v=h;(h=new Uint8Array(v.length+this.leftOver.length)).set(this.leftOver,0),h.set(v,this.leftOver.length)}else h=this.leftOver.concat(h);this.leftOver=null}var m=function(b,_){var w;for((_=_||b.length)>b.length&&(_=b.length),w=_-1;0<=w&&(192&b[w])==128;)w--;return w<0||w===0?_:w+u[b[w]]>_?w:_}(h),y=h;m!==h.length&&(g.uint8array?(y=h.subarray(0,m),this.leftOver=h.subarray(m,h.length)):(y=h.slice(0,m),this.leftOver=h.slice(m,h.length))),this.push({data:i.utf8decode(y),meta:s.meta})},c.prototype.flush=function(){this.leftOver&&this.leftOver.length&&(this.push({data:i.utf8decode(this.leftOver),meta:{}}),this.leftOver=null)},i.Utf8DecodeWorker=c,l.inherits(f,a),f.prototype.processChunk=function(s){this.push({data:i.utf8encode(s.data),meta:s.meta})},i.Utf8EncodeWorker=f},{"./nodejsUtils":14,"./stream/GenericWorker":28,"./support":30,"./utils":32}],32:[function(n,r,i){var l=n("./support"),g=n("./base64"),o=n("./nodejsUtils"),a=n("./external");function u(v){return v}function d(v,m){for(var y=0;y<v.length;++y)m[y]=255&v.charCodeAt(y);return m}n("setimmediate"),i.newBlob=function(v,m){i.checkSupport("blob");try{return new Blob([v],{type:m})}catch{try{var y=new(self.BlobBuilder||self.WebKitBlobBuilder||self.MozBlobBuilder||self.MSBlobBuilder);return y.append(v),y.getBlob(m)}catch{throw new Error("Bug : can't construct the Blob.")}}};var c={stringifyByChunk:function(v,m,y){var b=[],_=0,w=v.length;if(w<=y)return String.fromCharCode.apply(null,v);for(;_<w;)m==="array"||m==="nodebuffer"?b.push(String.fromCharCode.apply(null,v.slice(_,Math.min(_+y,w)))):b.push(String.fromCharCode.apply(null,v.subarray(_,Math.min(_+y,w)))),_+=y;return b.join("")},stringifyByChar:function(v){for(var m="",y=0;y<v.length;y++)m+=String.fromCharCode(v[y]);return m},applyCanBeUsed:{uint8array:function(){try{return l.uint8array&&String.fromCharCode.apply(null,new Uint8Array(1)).length===1}catch{return!1}}(),nodebuffer:function(){try{return l.nodebuffer&&String.fromCharCode.apply(null,o.allocBuffer(1)).length===1}catch{return!1}}()}};function f(v){var m=65536,y=i.getTypeOf(v),b=!0;if(y==="uint8array"?b=c.applyCanBeUsed.uint8array:y==="nodebuffer"&&(b=c.applyCanBeUsed.nodebuffer),b)for(;1<m;)try{return c.stringifyByChunk(v,y,m)}catch{m=Math.floor(m/2)}return c.stringifyByChar(v)}function s(v,m){for(var y=0;y<v.length;y++)m[y]=v[y];return m}i.applyFromCharCode=f;var h={};h.string={string:u,array:function(v){return d(v,new Array(v.length))},arraybuffer:function(v){return h.string.uint8array(v).buffer},uint8array:function(v){return d(v,new Uint8Array(v.length))},nodebuffer:function(v){return d(v,o.allocBuffer(v.length))}},h.array={string:f,array:u,arraybuffer:function(v){return new Uint8Array(v).buffer},uint8array:function(v){return new Uint8Array(v)},nodebuffer:function(v){return o.newBufferFrom(v)}},h.arraybuffer={string:function(v){return f(new Uint8Array(v))},array:function(v){return s(new Uint8Array(v),new Array(v.byteLength))},arraybuffer:u,uint8array:function(v){return new Uint8Array(v)},nodebuffer:function(v){return o.newBufferFrom(new Uint8Array(v))}},h.uint8array={string:f,array:function(v){return s(v,new Array(v.length))},arraybuffer:function(v){return v.buffer},uint8array:u,nodebuffer:function(v){return o.newBufferFrom(v)}},h.nodebuffer={string:f,array:function(v){return s(v,new Array(v.length))},arraybuffer:function(v){return h.nodebuffer.uint8array(v).buffer},uint8array:function(v){return s(v,new Uint8Array(v.length))},nodebuffer:u},i.transformTo=function(v,m){if(m=m||"",!v)return m;i.checkSupport(v);var y=i.getTypeOf(m);return h[y][v](m)},i.resolve=function(v){for(var m=v.split("/"),y=[],b=0;b<m.length;b++){var _=m[b];_==="."||_===""&&b!==0&&b!==m.length-1||(_===".."?y.pop():y.push(_))}return y.join("/")},i.getTypeOf=function(v){return typeof v=="string"?"string":Object.prototype.toString.call(v)==="[object Array]"?"array":l.nodebuffer&&o.isBuffer(v)?"nodebuffer":l.uint8array&&v instanceof Uint8Array?"uint8array":l.arraybuffer&&v instanceof ArrayBuffer?"arraybuffer":void 0},i.checkSupport=function(v){if(!l[v.toLowerCase()])throw new Error(v+" is not supported by this platform")},i.MAX_VALUE_16BITS=65535,i.MAX_VALUE_32BITS=-1,i.pretty=function(v){var m,y,b="";for(y=0;y<(v||"").length;y++)b+="\\x"+((m=v.charCodeAt(y))<16?"0":"")+m.toString(16).toUpperCase();return b},i.delay=function(v,m,y){setImmediate(function(){v.apply(y||null,m||[])})},i.inherits=function(v,m){function y(){}y.prototype=m.prototype,v.prototype=new y},i.extend=function(){var v,m,y={};for(v=0;v<arguments.length;v++)for(m in arguments[v])Object.prototype.hasOwnProperty.call(arguments[v],m)&&y[m]===void 0&&(y[m]=arguments[v][m]);return y},i.prepareContent=function(v,m,y,b,_){return a.Promise.resolve(m).then(function(w){return l.blob&&(w instanceof Blob||["[object File]","[object Blob]"].indexOf(Object.prototype.toString.call(w))!==-1)&&typeof FileReader<"u"?new a.Promise(function(S,x){var T=new FileReader;T.onload=function(E){S(E.target.result)},T.onerror=function(E){x(E.target.error)},T.readAsArrayBuffer(w)}):w}).then(function(w){var S=i.getTypeOf(w);return S?(S==="arraybuffer"?w=i.transformTo("uint8array",w):S==="string"&&(_?w=g.decode(w):y&&b!==!0&&(w=function(x){return d(x,l.uint8array?new Uint8Array(x.length):new Array(x.length))}(w))),w):a.Promise.reject(new Error("Can't read the data of '"+v+"'. Is it in a supported JavaScript type (String, Blob, ArrayBuffer, etc) ?"))})}},{"./base64":1,"./external":6,"./nodejsUtils":14,"./support":30,setimmediate:54}],33:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./signature"),a=n("./zipEntry"),u=n("./support");function d(c){this.files=[],this.loadOptions=c}d.prototype={checkSignature:function(c){if(!this.reader.readAndCheckSignature(c)){this.reader.index-=4;var f=this.reader.readString(4);throw new Error("Corrupted zip or bug: unexpected signature ("+g.pretty(f)+", expected "+g.pretty(c)+")")}},isSignature:function(c,f){var s=this.reader.index;this.reader.setIndex(c);var h=this.reader.readString(4)===f;return this.reader.setIndex(s),h},readBlockEndOfCentral:function(){this.diskNumber=this.reader.readInt(2),this.diskWithCentralDirStart=this.reader.readInt(2),this.centralDirRecordsOnThisDisk=this.reader.readInt(2),this.centralDirRecords=this.reader.readInt(2),this.centralDirSize=this.reader.readInt(4),this.centralDirOffset=this.reader.readInt(4),this.zipCommentLength=this.reader.readInt(2);var c=this.reader.readData(this.zipCommentLength),f=u.uint8array?"uint8array":"array",s=g.transformTo(f,c);this.zipComment=this.loadOptions.decodeFileName(s)},readBlockZip64EndOfCentral:function(){this.zip64EndOfCentralSize=this.reader.readInt(8),this.reader.skip(4),this.diskNumber=this.reader.readInt(4),this.diskWithCentralDirStart=this.reader.readInt(4),this.centralDirRecordsOnThisDisk=this.reader.readInt(8),this.centralDirRecords=this.reader.readInt(8),this.centralDirSize=this.reader.readInt(8),this.centralDirOffset=this.reader.readInt(8),this.zip64ExtensibleData={};for(var c,f,s,h=this.zip64EndOfCentralSize-44;0<h;)c=this.reader.readInt(2),f=this.reader.readInt(4),s=this.reader.readData(f),this.zip64ExtensibleData[c]={id:c,length:f,value:s}},readBlockZip64EndOfCentralLocator:function(){if(this.diskWithZip64CentralDirStart=this.reader.readInt(4),this.relativeOffsetEndOfZip64CentralDir=this.reader.readInt(8),this.disksCount=this.reader.readInt(4),1<this.disksCount)throw new Error("Multi-volumes zip are not supported")},readLocalFiles:function(){var c,f;for(c=0;c<this.files.length;c++)f=this.files[c],this.reader.setIndex(f.localHeaderOffset),this.checkSignature(o.LOCAL_FILE_HEADER),f.readLocalPart(this.reader),f.handleUTF8(),f.processAttributes()},readCentralDir:function(){var c;for(this.reader.setIndex(this.centralDirOffset);this.reader.readAndCheckSignature(o.CENTRAL_FILE_HEADER);)(c=new a({zip64:this.zip64},this.loadOptions)).readCentralPart(this.reader),this.files.push(c);if(this.centralDirRecords!==this.files.length&&this.centralDirRecords!==0&&this.files.length===0)throw new Error("Corrupted zip or bug: expected "+this.centralDirRecords+" records in central dir, got "+this.files.length)},readEndOfCentral:function(){var c=this.reader.lastIndexOfSignature(o.CENTRAL_DIRECTORY_END);if(c<0)throw this.isSignature(0,o.LOCAL_FILE_HEADER)?new Error("Corrupted zip: can't find end of central directory"):new Error("Can't find end of central directory : is this a zip file ? If it is, see https://stuk.github.io/jszip/documentation/howto/read_zip.html");this.reader.setIndex(c);var f=c;if(this.checkSignature(o.CENTRAL_DIRECTORY_END),this.readBlockEndOfCentral(),this.diskNumber===g.MAX_VALUE_16BITS||this.diskWithCentralDirStart===g.MAX_VALUE_16BITS||this.centralDirRecordsOnThisDisk===g.MAX_VALUE_16BITS||this.centralDirRecords===g.MAX_VALUE_16BITS||this.centralDirSize===g.MAX_VALUE_32BITS||this.centralDirOffset===g.MAX_VALUE_32BITS){if(this.zip64=!0,(c=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR))<0)throw new Error("Corrupted zip: can't find the ZIP64 end of central directory locator");if(this.reader.setIndex(c),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR),this.readBlockZip64EndOfCentralLocator(),!this.isSignature(this.relativeOffsetEndOfZip64CentralDir,o.ZIP64_CENTRAL_DIRECTORY_END)&&(this.relativeOffsetEndOfZip64CentralDir=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.relativeOffsetEndOfZip64CentralDir<0))throw new Error("Corrupted zip: can't find the ZIP64 end of central directory");this.reader.setIndex(this.relativeOffsetEndOfZip64CentralDir),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.readBlockZip64EndOfCentral()}var s=this.centralDirOffset+this.centralDirSize;this.zip64&&(s+=20,s+=12+this.zip64EndOfCentralSize);var h=f-s;if(0<h)this.isSignature(f,o.CENTRAL_FILE_HEADER)||(this.reader.zero=h);else if(h<0)throw new Error("Corrupted zip: missing "+Math.abs(h)+" bytes.")},prepareReader:function(c){this.reader=l(c)},load:function(c){this.prepareReader(c),this.readEndOfCentral(),this.readCentralDir(),this.readLocalFiles()}},r.exports=d},{"./reader/readerFor":22,"./signature":23,"./support":30,"./utils":32,"./zipEntry":34}],34:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./compressedObject"),a=n("./crc32"),u=n("./utf8"),d=n("./compressions"),c=n("./support");function f(s,h){this.options=s,this.loadOptions=h}f.prototype={isEncrypted:function(){return(1&this.bitFlag)==1},useUTF8:function(){return(2048&this.bitFlag)==2048},readLocalPart:function(s){var h,v;if(s.skip(22),this.fileNameLength=s.readInt(2),v=s.readInt(2),this.fileName=s.readData(this.fileNameLength),s.skip(v),this.compressedSize===-1||this.uncompressedSize===-1)throw new Error("Bug or corrupted zip : didn't get enough information from the central directory (compressedSize === -1 || uncompressedSize === -1)");if((h=function(m){for(var y in d)if(Object.prototype.hasOwnProperty.call(d,y)&&d[y].magic===m)return d[y];return null}(this.compressionMethod))===null)throw new Error("Corrupted zip : compression "+g.pretty(this.compressionMethod)+" unknown (inner file : "+g.transformTo("string",this.fileName)+")");this.decompressed=new o(this.compressedSize,this.uncompressedSize,this.crc32,h,s.readData(this.compressedSize))},readCentralPart:function(s){this.versionMadeBy=s.readInt(2),s.skip(2),this.bitFlag=s.readInt(2),this.compressionMethod=s.readString(2),this.date=s.readDate(),this.crc32=s.readInt(4),this.compressedSize=s.readInt(4),this.uncompressedSize=s.readInt(4);var h=s.readInt(2);if(this.extraFieldsLength=s.readInt(2),this.fileCommentLength=s.readInt(2),this.diskNumberStart=s.readInt(2),this.internalFileAttributes=s.readInt(2),this.externalFileAttributes=s.readInt(4),this.localHeaderOffset=s.readInt(4),this.isEncrypted())throw new Error("Encrypted zip are not supported");s.skip(h),this.readExtraFields(s),this.parseZIP64ExtraField(s),this.fileComment=s.readData(this.fileCommentLength)},processAttributes:function(){this.unixPermissions=null,this.dosPermissions=null;var s=this.versionMadeBy>>8;this.dir=!!(16&this.externalFileAttributes),s==0&&(this.dosPermissions=63&this.externalFileAttributes),s==3&&(this.unixPermissions=this.externalFileAttributes>>16&65535),this.dir||this.fileNameStr.slice(-1)!=="/"||(this.dir=!0)},parseZIP64ExtraField:function(){if(this.extraFields[1]){var s=l(this.extraFields[1].value);this.uncompressedSize===g.MAX_VALUE_32BITS&&(this.uncompressedSize=s.readInt(8)),this.compressedSize===g.MAX_VALUE_32BITS&&(this.compressedSize=s.readInt(8)),this.localHeaderOffset===g.MAX_VALUE_32BITS&&(this.localHeaderOffset=s.readInt(8)),this.diskNumberStart===g.MAX_VALUE_32BITS&&(this.diskNumberStart=s.readInt(4))}},readExtraFields:function(s){var h,v,m,y=s.index+this.extraFieldsLength;for(this.extraFields||(this.extraFields={});s.index+4<y;)h=s.readInt(2),v=s.readInt(2),m=s.readData(v),this.extraFields[h]={id:h,length:v,value:m};s.setIndex(y)},handleUTF8:function(){var s=c.uint8array?"uint8array":"array";if(this.useUTF8())this.fileNameStr=u.utf8decode(this.fileName),this.fileCommentStr=u.utf8decode(this.fileComment);else{var h=this.findExtraFieldUnicodePath();if(h!==null)this.fileNameStr=h;else{var v=g.transformTo(s,this.fileName);this.fileNameStr=this.loadOptions.decodeFileName(v)}var m=this.findExtraFieldUnicodeComment();if(m!==null)this.fileCommentStr=m;else{var y=g.transformTo(s,this.fileComment);this.fileCommentStr=this.loadOptions.decodeFileName(y)}}},findExtraFieldUnicodePath:function(){var s=this.extraFields[28789];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileName)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null},findExtraFieldUnicodeComment:function(){var s=this.extraFields[25461];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileComment)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null}},r.exports=f},{"./compressedObject":2,"./compressions":3,"./crc32":4,"./reader/readerFor":22,"./support":30,"./utf8":31,"./utils":32}],35:[function(n,r,i){function l(h,v,m){this.name=h,this.dir=m.dir,this.date=m.date,this.comment=m.comment,this.unixPermissions=m.unixPermissions,this.dosPermissions=m.dosPermissions,this._data=v,this._dataBinary=m.binary,this.options={compression:m.compression,compressionOptions:m.compressionOptions}}var g=n("./stream/StreamHelper"),o=n("./stream/DataWorker"),a=n("./utf8"),u=n("./compressedObject"),d=n("./stream/GenericWorker");l.prototype={internalStream:function(h){var v=null,m="string";try{if(!h)throw new Error("No output type specified.");var y=(m=h.toLowerCase())==="string"||m==="text";m!=="binarystring"&&m!=="text"||(m="string"),v=this._decompressWorker();var b=!this._dataBinary;b&&!y&&(v=v.pipe(new a.Utf8EncodeWorker)),!b&&y&&(v=v.pipe(new a.Utf8DecodeWorker))}catch(_){(v=new d("error")).error(_)}return new g(v,m,"")},async:function(h,v){return this.internalStream(h).accumulate(v)},nodeStream:function(h,v){return this.internalStream(h||"nodebuffer").toNodejsStream(v)},_compressWorker:function(h,v){if(this._data instanceof u&&this._data.compression.magic===h.magic)return this._data.getCompressedWorker();var m=this._decompressWorker();return this._dataBinary||(m=m.pipe(new a.Utf8EncodeWorker)),u.createWorkerFrom(m,h,v)},_decompressWorker:function(){return this._data instanceof u?this._data.getContentWorker():this._data instanceof d?this._data:new o(this._data)}};for(var c=["asText","asBinary","asNodeBuffer","asUint8Array","asArrayBuffer"],f=function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},s=0;s<c.length;s++)l.prototype[c[s]]=f;r.exports=l},{"./compressedObject":2,"./stream/DataWorker":27,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31}],36:[function(n,r,i){(function(l){var g,o,a=l.MutationObserver||l.WebKitMutationObserver;if(a){var u=0,d=new a(h),c=l.document.createTextNode("");d.observe(c,{characterData:!0}),g=function(){c.data=u=++u%2}}else if(l.setImmediate||l.MessageChannel===void 0)g="document"in l&&"onreadystatechange"in l.document.createElement("script")?function(){var v=l.document.createElement("script");v.onreadystatechange=function(){h(),v.onreadystatechange=null,v.parentNode.removeChild(v),v=null},l.document.documentElement.appendChild(v)}:function(){setTimeout(h,0)};else{var f=new l.MessageChannel;f.port1.onmessage=h,g=function(){f.port2.postMessage(0)}}var s=[];function h(){var v,m;o=!0;for(var y=s.length;y;){for(m=s,s=[],v=-1;++v<y;)m[v]();y=s.length}o=!1}r.exports=function(v){s.push(v)!==1||o||g()}}).call(this,typeof 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g(l.Promise.resolve(this.compressedContent)).pipe(this.compression.uncompressWorker()).pipe(new a("data_length")),c=this;return d.on("end",function(){if(this.streamInfo.data_length!==c.uncompressedSize)throw new Error("Bug : uncompressed data size mismatch")}),d},getCompressedWorker:function(){return new g(l.Promise.resolve(this.compressedContent)).withStreamInfo("compressedSize",this.compressedSize).withStreamInfo("uncompressedSize",this.uncompressedSize).withStreamInfo("crc32",this.crc32).withStreamInfo("compression",this.compression)}},u.createWorkerFrom=function(d,c,f){return d.pipe(new o).pipe(new a("uncompressedSize")).pipe(c.compressWorker(f)).pipe(new a("compressedSize")).withStreamInfo("compression",c)},r.exports=u},{"./external":6,"./stream/Crc32Probe":25,"./stream/DataLengthProbe":26,"./stream/DataWorker":27}],3:[function(n,r,i){var l=n("./stream/GenericWorker");i.STORE={magic:"\0\0",compressWorker:function(){return new l("STORE 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Uint16Array<"u"&&typeof Uint32Array<"u",g=n("pako"),o=n("./utils"),a=n("./stream/GenericWorker"),u=l?"uint8array":"array";function d(c,f){a.call(this,"FlateWorker/"+c),this._pako=null,this._pakoAction=c,this._pakoOptions=f,this.meta={}}i.magic="\b\0",o.inherits(d,a),d.prototype.processChunk=function(c){this.meta=c.meta,this._pako===null&&this._createPako(),this._pako.push(o.transformTo(u,c.data),!1)},d.prototype.flush=function(){a.prototype.flush.call(this),this._pako===null&&this._createPako(),this._pako.push([],!0)},d.prototype.cleanUp=function(){a.prototype.cleanUp.call(this),this._pako=null},d.prototype._createPako=function(){this._pako=new g[this._pakoAction]({raw:!0,level:this._pakoOptions.level||-1});var c=this;this._pako.onData=function(f){c.push({data:f,meta:c.meta})}},i.compressWorker=function(c){return new d("Deflate",c)},i.uncompressWorker=function(){return new d("Inflate",{})}},{"./stream/GenericWorker":28,"./utils":32,pako:38}],8:[function(n,r,i){function l(s,h){var v,m="";for(v=0;v<h;v++)m+=String.fromCharCode(255&s),s>>>=8;return m}function g(s,h,v,m,y,b){var _,w,S=s.file,x=s.compression,k=b!==u.utf8encode,E=o.transformTo("string",b(S.name)),I=o.transformTo("string",u.utf8encode(S.name)),D=S.comment,N=o.transformTo("string",b(D)),L=o.transformTo("string",u.utf8encode(D)),F=I.length!==S.name.length,O=L.length!==D.length,V="",re="",J="",ue=S.dir,M=S.date,H={crc32:0,compressedSize:0,uncompressedSize:0};h&&!v||(H.crc32=s.crc32,H.compressedSize=s.compressedSize,H.uncompressedSize=s.uncompressedSize);var R=0;h&&(R|=8),k||!F&&!O||(R|=2048);var P=0,Q=0;ue&&(P|=16),y==="UNIX"?(Q=798,P|=function(A,K){var q=A;return A||(q=K?16893:33204),(65535&q)<<16}(S.unixPermissions,ue)):(Q=20,P|=function(A){return 63&(A||0)}(S.dosPermissions)),_=M.getUTCHours(),_<<=6,_|=M.getUTCMinutes(),_<<=5,_|=M.getUTCSeconds()/2,w=M.getUTCFullYear()-1980,w<<=4,w|=M.getUTCMonth()+1,w<<=5,w|=M.getUTCDate(),F&&(re=l(1,1)+l(d(E),4)+I,V+="up"+l(re.length,2)+re),O&&(J=l(1,1)+l(d(N),4)+L,V+="uc"+l(J.length,2)+J);var j="";return j+=`
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h=s.meta.percent||0,v=this.entriesCount,m=this._sources.length;this.accumulate?this.contentBuffer.push(s):(this.bytesWritten+=s.data.length,a.prototype.push.call(this,{data:s.data,meta:{currentFile:this.currentFile,percent:v?(h+100*(v-m-1))/v:100}}))},f.prototype.openedSource=function(s){this.currentSourceOffset=this.bytesWritten,this.currentFile=s.file.name;var h=this.streamFiles&&!s.file.dir;if(h){var v=g(s,h,!1,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);this.push({data:v.fileRecord,meta:{percent:0}})}else this.accumulate=!0},f.prototype.closedSource=function(s){this.accumulate=!1;var h=this.streamFiles&&!s.file.dir,v=g(s,h,!0,this.currentSourceOffset,this.zipPlatform,this.encodeFileName);if(this.dirRecords.push(v.dirRecord),h)this.push({data:function(m){return c.DATA_DESCRIPTOR+l(m.crc32,4)+l(m.compressedSize,4)+l(m.uncompressedSize,4)}(s),meta:{percent:100}});else for(this.push({data:v.fileRecord,meta:{percent:0}});this.contentBuffer.length;)this.push(this.contentBuffer.shift());this.currentFile=null},f.prototype.flush=function(){for(var s=this.bytesWritten,h=0;h<this.dirRecords.length;h++)this.push({data:this.dirRecords[h],meta:{percent:100}});var v=this.bytesWritten-s,m=function(y,b,_,w,S){var x=o.transformTo("string",S(w));return c.CENTRAL_DIRECTORY_END+"\0\0\0\0"+l(y,2)+l(y,2)+l(b,4)+l(_,4)+l(x.length,2)+x}(this.dirRecords.length,v,s,this.zipComment,this.encodeFileName);this.push({data:m,meta:{percent:100}})},f.prototype.prepareNextSource=function(){this.previous=this._sources.shift(),this.openedSource(this.previous.streamInfo),this.isPaused?this.previous.pause():this.previous.resume()},f.prototype.registerPrevious=function(s){this._sources.push(s);var h=this;return s.on("data",function(v){h.processChunk(v)}),s.on("end",function(){h.closedSource(h.previous.streamInfo),h._sources.length?h.prepareNextSource():h.end()}),s.on("error",function(v){h.error(v)}),this},f.prototype.resume=function(){return!!a.prototype.resume.call(this)&&(!this.previous&&this._sources.length?(this.prepareNextSource(),!0):this.previous||this._sources.length||this.generatedError?void 0:(this.end(),!0))},f.prototype.error=function(s){var h=this._sources;if(!a.prototype.error.call(this,s))return!1;for(var v=0;v<h.length;v++)try{h[v].error(s)}catch{}return!0},f.prototype.lock=function(){a.prototype.lock.call(this);for(var s=this._sources,h=0;h<s.length;h++)s[h].lock()},r.exports=f},{"../crc32":4,"../signature":23,"../stream/GenericWorker":28,"../utf8":31,"../utils":32}],9:[function(n,r,i){var l=n("../compressions"),g=n("./ZipFileWorker");i.generateWorker=function(o,a,u){var d=new g(a.streamFiles,u,a.platform,a.encodeFileName),c=0;try{o.forEach(function(f,s){c++;var h=function(b,_){var w=b||_,S=l[w];if(!S)throw new Error(w+" is not a valid compression method !");return S}(s.options.compression,a.compression),v=s.options.compressionOptions||a.compressionOptions||{},m=s.dir,y=s.date;s._compressWorker(h,v).withStreamInfo("file",{name:f,dir:m,date:y,comment:s.comment||"",unixPermissions:s.unixPermissions,dosPermissions:s.dosPermissions}).pipe(d)}),d.entriesCount=c}catch(f){d.error(f)}return d}},{"../compressions":3,"./ZipFileWorker":8}],10:[function(n,r,i){function l(){if(!(this instanceof l))return new l;if(arguments.length)throw new Error("The constructor with parameters has been removed in JSZip 3.0, please check the upgrade guide.");this.files=Object.create(null),this.comment=null,this.root="",this.clone=function(){var g=new l;for(var o in this)typeof this[o]!="function"&&(g[o]=this[o]);return g}}(l.prototype=n("./object")).loadAsync=n("./load"),l.support=n("./support"),l.defaults=n("./defaults"),l.version="3.10.1",l.loadAsync=function(g,o){return new l().loadAsync(g,o)},l.external=n("./external"),r.exports=l},{"./defaults":5,"./external":6,"./load":11,"./object":15,"./support":30}],11:[function(n,r,i){var l=n("./utils"),g=n("./external"),o=n("./utf8"),a=n("./zipEntries"),u=n("./stream/Crc32Probe"),d=n("./nodejsUtils");function c(f){return new g.Promise(function(s,h){var v=f.decompressed.getContentWorker().pipe(new u);v.on("error",function(m){h(m)}).on("end",function(){v.streamInfo.crc32!==f.decompressed.crc32?h(new Error("Corrupted zip : CRC32 mismatch")):s()}).resume()})}r.exports=function(f,s){var h=this;return s=l.extend(s||{},{base64:!1,checkCRC32:!1,optimizedBinaryString:!1,createFolders:!1,decodeFileName:o.utf8decode}),d.isNode&&d.isStream(f)?g.Promise.reject(new Error("JSZip can't accept a stream when loading a zip file.")):l.prepareContent("the loaded zip file",f,!0,s.optimizedBinaryString,s.base64).then(function(v){var m=new a(s);return m.load(v),m}).then(function(v){var m=[g.Promise.resolve(v)],y=v.files;if(s.checkCRC32)for(var b=0;b<y.length;b++)m.push(c(y[b]));return g.Promise.all(m)}).then(function(v){for(var m=v.shift(),y=m.files,b=0;b<y.length;b++){var _=y[b],w=_.fileNameStr,S=l.resolve(_.fileNameStr);h.file(S,_.decompressed,{binary:!0,optimizedBinaryString:!0,date:_.date,dir:_.dir,comment:_.fileCommentStr.length?_.fileCommentStr:null,unixPermissions:_.unixPermissions,dosPermissions:_.dosPermissions,createFolders:s.createFolders}),_.dir||(h.file(S).unsafeOriginalName=w)}return m.zipComment.length&&(h.comment=m.zipComment),h})}},{"./external":6,"./nodejsUtils":14,"./stream/Crc32Probe":25,"./utf8":31,"./utils":32,"./zipEntries":33}],12:[function(n,r,i){var l=n("../utils"),g=n("../stream/GenericWorker");function o(a,u){g.call(this,"Nodejs stream input adapter for "+a),this._upstreamEnded=!1,this._bindStream(u)}l.inherits(o,g),o.prototype._bindStream=function(a){var u=this;(this._stream=a).pause(),a.on("data",function(d){u.push({data:d,meta:{percent:0}})}).on("error",function(d){u.isPaused?this.generatedError=d:u.error(d)}).on("end",function(){u.isPaused?u._upstreamEnded=!0:u.end()})},o.prototype.pause=function(){return!!g.prototype.pause.call(this)&&(this._stream.pause(),!0)},o.prototype.resume=function(){return!!g.prototype.resume.call(this)&&(this._upstreamEnded?this.end():this._stream.resume(),!0)},r.exports=o},{"../stream/GenericWorker":28,"../utils":32}],13:[function(n,r,i){var l=n("readable-stream").Readable;function g(o,a,u){l.call(this,a),this._helper=o;var d=this;o.on("data",function(c,f){d.push(c)||d._helper.pause(),u&&u(f)}).on("error",function(c){d.emit("error",c)}).on("end",function(){d.push(null)})}n("../utils").inherits(g,l),g.prototype._read=function(){this._helper.resume()},r.exports=g},{"../utils":32,"readable-stream":16}],14:[function(n,r,i){r.exports={isNode:typeof 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Array]"?"array":l.nodebuffer&&o.isBuffer(v)?"nodebuffer":l.uint8array&&v instanceof Uint8Array?"uint8array":l.arraybuffer&&v instanceof ArrayBuffer?"arraybuffer":void 0},i.checkSupport=function(v){if(!l[v.toLowerCase()])throw new Error(v+" is not supported by this platform")},i.MAX_VALUE_16BITS=65535,i.MAX_VALUE_32BITS=-1,i.pretty=function(v){var m,y,b="";for(y=0;y<(v||"").length;y++)b+="\\x"+((m=v.charCodeAt(y))<16?"0":"")+m.toString(16).toUpperCase();return b},i.delay=function(v,m,y){setImmediate(function(){v.apply(y||null,m||[])})},i.inherits=function(v,m){function y(){}y.prototype=m.prototype,v.prototype=new y},i.extend=function(){var v,m,y={};for(v=0;v<arguments.length;v++)for(m in arguments[v])Object.prototype.hasOwnProperty.call(arguments[v],m)&&y[m]===void 0&&(y[m]=arguments[v][m]);return y},i.prepareContent=function(v,m,y,b,_){return a.Promise.resolve(m).then(function(w){return l.blob&&(w instanceof Blob||["[object File]","[object 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Is it in a supported JavaScript type (String, Blob, ArrayBuffer, etc) ?"))})}},{"./base64":1,"./external":6,"./nodejsUtils":14,"./support":30,setimmediate:54}],33:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./signature"),a=n("./zipEntry"),u=n("./support");function d(c){this.files=[],this.loadOptions=c}d.prototype={checkSignature:function(c){if(!this.reader.readAndCheckSignature(c)){this.reader.index-=4;var f=this.reader.readString(4);throw new Error("Corrupted zip or bug: unexpected signature ("+g.pretty(f)+", expected "+g.pretty(c)+")")}},isSignature:function(c,f){var s=this.reader.index;this.reader.setIndex(c);var h=this.reader.readString(4)===f;return this.reader.setIndex(s),h},readBlockEndOfCentral:function(){this.diskNumber=this.reader.readInt(2),this.diskWithCentralDirStart=this.reader.readInt(2),this.centralDirRecordsOnThisDisk=this.reader.readInt(2),this.centralDirRecords=this.reader.readInt(2),this.centralDirSize=this.reader.readInt(4),this.centralDirOffset=this.reader.readInt(4),this.zipCommentLength=this.reader.readInt(2);var c=this.reader.readData(this.zipCommentLength),f=u.uint8array?"uint8array":"array",s=g.transformTo(f,c);this.zipComment=this.loadOptions.decodeFileName(s)},readBlockZip64EndOfCentral:function(){this.zip64EndOfCentralSize=this.reader.readInt(8),this.reader.skip(4),this.diskNumber=this.reader.readInt(4),this.diskWithCentralDirStart=this.reader.readInt(4),this.centralDirRecordsOnThisDisk=this.reader.readInt(8),this.centralDirRecords=this.reader.readInt(8),this.centralDirSize=this.reader.readInt(8),this.centralDirOffset=this.reader.readInt(8),this.zip64ExtensibleData={};for(var c,f,s,h=this.zip64EndOfCentralSize-44;0<h;)c=this.reader.readInt(2),f=this.reader.readInt(4),s=this.reader.readData(f),this.zip64ExtensibleData[c]={id:c,length:f,value:s}},readBlockZip64EndOfCentralLocator:function(){if(this.diskWithZip64CentralDirStart=this.reader.readInt(4),this.relativeOffsetEndOfZip64CentralDir=this.reader.readInt(8),this.disksCount=this.reader.readInt(4),1<this.disksCount)throw new Error("Multi-volumes zip are not supported")},readLocalFiles:function(){var c,f;for(c=0;c<this.files.length;c++)f=this.files[c],this.reader.setIndex(f.localHeaderOffset),this.checkSignature(o.LOCAL_FILE_HEADER),f.readLocalPart(this.reader),f.handleUTF8(),f.processAttributes()},readCentralDir:function(){var c;for(this.reader.setIndex(this.centralDirOffset);this.reader.readAndCheckSignature(o.CENTRAL_FILE_HEADER);)(c=new a({zip64:this.zip64},this.loadOptions)).readCentralPart(this.reader),this.files.push(c);if(this.centralDirRecords!==this.files.length&&this.centralDirRecords!==0&&this.files.length===0)throw new Error("Corrupted zip or bug: expected "+this.centralDirRecords+" records in central dir, got "+this.files.length)},readEndOfCentral:function(){var c=this.reader.lastIndexOfSignature(o.CENTRAL_DIRECTORY_END);if(c<0)throw this.isSignature(0,o.LOCAL_FILE_HEADER)?new Error("Corrupted zip: can't find end of central directory"):new Error("Can't find end of central directory : is this a zip file ? If it is, see https://stuk.github.io/jszip/documentation/howto/read_zip.html");this.reader.setIndex(c);var f=c;if(this.checkSignature(o.CENTRAL_DIRECTORY_END),this.readBlockEndOfCentral(),this.diskNumber===g.MAX_VALUE_16BITS||this.diskWithCentralDirStart===g.MAX_VALUE_16BITS||this.centralDirRecordsOnThisDisk===g.MAX_VALUE_16BITS||this.centralDirRecords===g.MAX_VALUE_16BITS||this.centralDirSize===g.MAX_VALUE_32BITS||this.centralDirOffset===g.MAX_VALUE_32BITS){if(this.zip64=!0,(c=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR))<0)throw new Error("Corrupted zip: can't find the ZIP64 end of central directory locator");if(this.reader.setIndex(c),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_LOCATOR),this.readBlockZip64EndOfCentralLocator(),!this.isSignature(this.relativeOffsetEndOfZip64CentralDir,o.ZIP64_CENTRAL_DIRECTORY_END)&&(this.relativeOffsetEndOfZip64CentralDir=this.reader.lastIndexOfSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.relativeOffsetEndOfZip64CentralDir<0))throw new Error("Corrupted zip: can't find the ZIP64 end of central directory");this.reader.setIndex(this.relativeOffsetEndOfZip64CentralDir),this.checkSignature(o.ZIP64_CENTRAL_DIRECTORY_END),this.readBlockZip64EndOfCentral()}var s=this.centralDirOffset+this.centralDirSize;this.zip64&&(s+=20,s+=12+this.zip64EndOfCentralSize);var h=f-s;if(0<h)this.isSignature(f,o.CENTRAL_FILE_HEADER)||(this.reader.zero=h);else if(h<0)throw new Error("Corrupted zip: missing "+Math.abs(h)+" bytes.")},prepareReader:function(c){this.reader=l(c)},load:function(c){this.prepareReader(c),this.readEndOfCentral(),this.readCentralDir(),this.readLocalFiles()}},r.exports=d},{"./reader/readerFor":22,"./signature":23,"./support":30,"./utils":32,"./zipEntry":34}],34:[function(n,r,i){var l=n("./reader/readerFor"),g=n("./utils"),o=n("./compressedObject"),a=n("./crc32"),u=n("./utf8"),d=n("./compressions"),c=n("./support");function f(s,h){this.options=s,this.loadOptions=h}f.prototype={isEncrypted:function(){return(1&this.bitFlag)==1},useUTF8:function(){return(2048&this.bitFlag)==2048},readLocalPart:function(s){var h,v;if(s.skip(22),this.fileNameLength=s.readInt(2),v=s.readInt(2),this.fileName=s.readData(this.fileNameLength),s.skip(v),this.compressedSize===-1||this.uncompressedSize===-1)throw new Error("Bug or corrupted zip : didn't get enough information from the central directory (compressedSize === -1 || uncompressedSize === -1)");if((h=function(m){for(var y in d)if(Object.prototype.hasOwnProperty.call(d,y)&&d[y].magic===m)return d[y];return null}(this.compressionMethod))===null)throw new Error("Corrupted zip : compression "+g.pretty(this.compressionMethod)+" unknown (inner file : "+g.transformTo("string",this.fileName)+")");this.decompressed=new o(this.compressedSize,this.uncompressedSize,this.crc32,h,s.readData(this.compressedSize))},readCentralPart:function(s){this.versionMadeBy=s.readInt(2),s.skip(2),this.bitFlag=s.readInt(2),this.compressionMethod=s.readString(2),this.date=s.readDate(),this.crc32=s.readInt(4),this.compressedSize=s.readInt(4),this.uncompressedSize=s.readInt(4);var h=s.readInt(2);if(this.extraFieldsLength=s.readInt(2),this.fileCommentLength=s.readInt(2),this.diskNumberStart=s.readInt(2),this.internalFileAttributes=s.readInt(2),this.externalFileAttributes=s.readInt(4),this.localHeaderOffset=s.readInt(4),this.isEncrypted())throw new Error("Encrypted zip are not supported");s.skip(h),this.readExtraFields(s),this.parseZIP64ExtraField(s),this.fileComment=s.readData(this.fileCommentLength)},processAttributes:function(){this.unixPermissions=null,this.dosPermissions=null;var s=this.versionMadeBy>>8;this.dir=!!(16&this.externalFileAttributes),s==0&&(this.dosPermissions=63&this.externalFileAttributes),s==3&&(this.unixPermissions=this.externalFileAttributes>>16&65535),this.dir||this.fileNameStr.slice(-1)!=="/"||(this.dir=!0)},parseZIP64ExtraField:function(){if(this.extraFields[1]){var s=l(this.extraFields[1].value);this.uncompressedSize===g.MAX_VALUE_32BITS&&(this.uncompressedSize=s.readInt(8)),this.compressedSize===g.MAX_VALUE_32BITS&&(this.compressedSize=s.readInt(8)),this.localHeaderOffset===g.MAX_VALUE_32BITS&&(this.localHeaderOffset=s.readInt(8)),this.diskNumberStart===g.MAX_VALUE_32BITS&&(this.diskNumberStart=s.readInt(4))}},readExtraFields:function(s){var h,v,m,y=s.index+this.extraFieldsLength;for(this.extraFields||(this.extraFields={});s.index+4<y;)h=s.readInt(2),v=s.readInt(2),m=s.readData(v),this.extraFields[h]={id:h,length:v,value:m};s.setIndex(y)},handleUTF8:function(){var s=c.uint8array?"uint8array":"array";if(this.useUTF8())this.fileNameStr=u.utf8decode(this.fileName),this.fileCommentStr=u.utf8decode(this.fileComment);else{var h=this.findExtraFieldUnicodePath();if(h!==null)this.fileNameStr=h;else{var v=g.transformTo(s,this.fileName);this.fileNameStr=this.loadOptions.decodeFileName(v)}var m=this.findExtraFieldUnicodeComment();if(m!==null)this.fileCommentStr=m;else{var y=g.transformTo(s,this.fileComment);this.fileCommentStr=this.loadOptions.decodeFileName(y)}}},findExtraFieldUnicodePath:function(){var s=this.extraFields[28789];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileName)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null},findExtraFieldUnicodeComment:function(){var s=this.extraFields[25461];if(s){var h=l(s.value);return h.readInt(1)!==1||a(this.fileComment)!==h.readInt(4)?null:u.utf8decode(h.readData(s.length-5))}return null}},r.exports=f},{"./compressedObject":2,"./compressions":3,"./crc32":4,"./reader/readerFor":22,"./support":30,"./utf8":31,"./utils":32}],35:[function(n,r,i){function l(h,v,m){this.name=h,this.dir=m.dir,this.date=m.date,this.comment=m.comment,this.unixPermissions=m.unixPermissions,this.dosPermissions=m.dosPermissions,this._data=v,this._dataBinary=m.binary,this.options={compression:m.compression,compressionOptions:m.compressionOptions}}var g=n("./stream/StreamHelper"),o=n("./stream/DataWorker"),a=n("./utf8"),u=n("./compressedObject"),d=n("./stream/GenericWorker");l.prototype={internalStream:function(h){var v=null,m="string";try{if(!h)throw new Error("No output type specified.");var y=(m=h.toLowerCase())==="string"||m==="text";m!=="binarystring"&&m!=="text"||(m="string"),v=this._decompressWorker();var b=!this._dataBinary;b&&!y&&(v=v.pipe(new a.Utf8EncodeWorker)),!b&&y&&(v=v.pipe(new a.Utf8DecodeWorker))}catch(_){(v=new d("error")).error(_)}return new g(v,m,"")},async:function(h,v){return this.internalStream(h).accumulate(v)},nodeStream:function(h,v){return this.internalStream(h||"nodebuffer").toNodejsStream(v)},_compressWorker:function(h,v){if(this._data instanceof u&&this._data.compression.magic===h.magic)return this._data.getCompressedWorker();var m=this._decompressWorker();return this._dataBinary||(m=m.pipe(new a.Utf8EncodeWorker)),u.createWorkerFrom(m,h,v)},_decompressWorker:function(){return this._data instanceof u?this._data.getContentWorker():this._data instanceof d?this._data:new o(this._data)}};for(var c=["asText","asBinary","asNodeBuffer","asUint8Array","asArrayBuffer"],f=function(){throw new Error("This method has been removed in JSZip 3.0, please check the upgrade guide.")},s=0;s<c.length;s++)l.prototype[c[s]]=f;r.exports=l},{"./compressedObject":2,"./stream/DataWorker":27,"./stream/GenericWorker":28,"./stream/StreamHelper":29,"./utf8":31}],36:[function(n,r,i){(function(l){var g,o,a=l.MutationObserver||l.WebKitMutationObserver;if(a){var u=0,d=new a(h),c=l.document.createTextNode("");d.observe(c,{characterData:!0}),g=function(){c.data=u=++u%2}}else if(l.setImmediate||l.MessageChannel===void 0)g="document"in l&&"onreadystatechange"in l.document.createElement("script")?function(){var v=l.document.createElement("script");v.onreadystatechange=function(){h(),v.onreadystatechange=null,v.parentNode.removeChild(v),v=null},l.document.documentElement.appendChild(v)}:function(){setTimeout(h,0)};else{var f=new l.MessageChannel;f.port1.onmessage=h,g=function(){f.port2.postMessage(0)}}var s=[];function h(){var v,m;o=!0;for(var y=s.length;y;){for(m=s,s=[],v=-1;++v<y;)m[v]();y=s.length}o=!1}r.exports=function(v){s.push(v)!==1||o||g()}}).call(this,typeof du<"u"?du:typeof self<"u"?self:typeof window<"u"?window:{})},{}],37:[function(n,r,i){var l=n("immediate");function g(){}var o={},a=["REJECTED"],u=["FULFILLED"],d=["PENDING"];function c(y){if(typeof y!="function")throw new TypeError("resolver must be a function");this.state=d,this.queue=[],this.outcome=void 0,y!==g&&v(this,y)}function f(y,b,_){this.promise=y,typeof b=="function"&&(this.onFulfilled=b,this.callFulfilled=this.otherCallFulfilled),typeof _=="function"&&(this.onRejected=_,this.callRejected=this.otherCallRejected)}function s(y,b,_){l(function(){var w;try{w=b(_)}catch(S){return o.reject(y,S)}w===y?o.reject(y,new TypeError("Cannot resolve promise with itself")):o.resolve(y,w)})}function h(y){var b=y&&y.then;if(y&&(typeof y=="object"||typeof y=="function")&&typeof b=="function")return function(){b.apply(y,arguments)}}function v(y,b){var _=!1;function w(k){_||(_=!0,o.reject(y,k))}function S(k){_||(_=!0,o.resolve(y,k))}var x=m(function(){b(S,w)});x.status==="error"&&w(x.value)}function m(y,b){var _={};try{_.value=y(b),_.status="success"}catch(w){_.status="error",_.value=w}return _}(r.exports=c).prototype.finally=function(y){if(typeof y!="function")return this;var b=this.constructor;return this.then(function(_){return b.resolve(y()).then(function(){return _})},function(_){return b.resolve(y()).then(function(){throw _})})},c.prototype.catch=function(y){return this.then(null,y)},c.prototype.then=function(y,b){if(typeof y!="function"&&this.state===u||typeof b!="function"&&this.state===a)return this;var _=new this.constructor(g);return this.state!==d?s(_,this.state===u?y:b,this.outcome):this.queue.push(new f(_,y,b)),_},f.prototype.callFulfilled=function(y){o.resolve(this.promise,y)},f.prototype.otherCallFulfilled=function(y){s(this.promise,this.onFulfilled,y)},f.prototype.callRejected=function(y){o.reject(this.promise,y)},f.prototype.otherCallRejected=function(y){s(this.promise,this.onRejected,y)},o.resolve=function(y,b){var _=m(h,b);if(_.status==="error")return o.reject(y,_.value);var w=_.value;if(w)v(y,w);else{y.state=u,y.outcome=b;for(var S=-1,x=y.queue.length;++S<x;)y.queue[S].callFulfilled(b)}return y},o.reject=function(y,b){y.state=a,y.outcome=b;for(var _=-1,w=y.queue.length;++_<w;)y.queue[_].callRejected(b);return y},c.resolve=function(y){return y instanceof this?y:o.resolve(new this(g),y)},c.reject=function(y){var b=new this(g);return o.reject(b,y)},c.all=function(y){var b=this;if(Object.prototype.toString.call(y)!=="[object Array]")return this.reject(new TypeError("must be an array"));var _=y.length,w=!1;if(!_)return this.resolve([]);for(var S=new Array(_),x=0,k=-1,E=new this(g);++k<_;)I(y[k],k);return E;function I(D,N){b.resolve(D).then(function(L){S[N]=L,++x!==_||w||(w=!0,o.resolve(E,S))},function(L){w||(w=!0,o.reject(E,L))})}},c.race=function(y){var b=this;if(Object.prototype.toString.call(y)!=="[object Array]")return this.reject(new TypeError("must be an array"));var _=y.length,w=!1;if(!_)return this.resolve([]);for(var S=-1,x=new this(g);++S<_;)k=y[S],b.resolve(k).then(function(E){w||(w=!0,o.resolve(x,E))},function(E){w||(w=!0,o.reject(x,E))});var k;return x}},{immediate:36}],38:[function(n,r,i){var l={};(0,n("./lib/utils/common").assign)(l,n("./lib/deflate"),n("./lib/inflate"),n("./lib/zlib/constants")),r.exports=l},{"./lib/deflate":39,"./lib/inflate":40,"./lib/utils/common":41,"./lib/zlib/constants":44}],39:[function(n,r,i){var l=n("./zlib/deflate"),g=n("./utils/common"),o=n("./utils/strings"),a=n("./zlib/messages"),u=n("./zlib/zstream"),d=Object.prototype.toString,c=0,f=-1,s=0,h=8;function v(y){if(!(this instanceof v))return new 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repeat",O.mode=30;break}for(;A--;)O.lens[O.have++]=T}}if(O.mode===30)break;if(O.lens[256]===0){L.msg="invalid code -- missing end-of-block",O.mode=30;break}if(O.lenbits=9,W={bits:O.lenbits},Y=u(d,O.lens,0,O.nlen,O.lencode,0,O.work,W),O.lenbits=W.bits,Y){L.msg="invalid literal/lengths set",O.mode=30;break}if(O.distbits=6,O.distcode=O.distdyn,W={bits:O.distbits},Y=u(c,O.lens,O.nlen,O.ndist,O.distcode,0,O.work,W),O.distbits=W.bits,Y){L.msg="invalid distances set",O.mode=30;break}if(O.mode=20,F===6)break e;case 20:O.mode=21;case 21:if(6<=M&&258<=H){L.next_out=ue,L.avail_out=H,L.next_in=J,L.avail_in=M,O.hold=R,O.bits=P,a(L,j),ue=L.next_out,re=L.output,H=L.avail_out,J=L.next_in,V=L.input,M=L.avail_in,R=O.hold,P=O.bits,O.mode===12&&(O.back=-1);break}for(O.back=0;fe=(B=O.lencode[R&(1<<O.lenbits)-1])>>>16&255,pe=65535&B,!((te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(fe&&(240&fe)==0){for(ve=te,U=fe,X=pe;fe=(B=O.lencode[X+((R&(1<<ve+U)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,O.length=pe,fe===0){O.mode=26;break}if(32&fe){O.back=-1,O.mode=12;break}if(64&fe){L.msg="invalid literal/length code",O.mode=30;break}O.extra=15&fe,O.mode=22;case 22:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.length+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}O.was=O.length,O.mode=23;case 23:for(;fe=(B=O.distcode[R&(1<<O.distbits)-1])>>>16&255,pe=65535&B,!((te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if((240&fe)==0){for(ve=te,U=fe,X=pe;fe=(B=O.distcode[X+((R&(1<<ve+U)-1)>>ve)])>>>16&255,pe=65535&B,!(ve+(te=B>>>24)<=P);){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}R>>>=ve,P-=ve,O.back+=ve}if(R>>>=te,P-=te,O.back+=te,64&fe){L.msg="invalid distance code",O.mode=30;break}O.offset=pe,O.extra=15&fe,O.mode=24;case 24:if(O.extra){for($=O.extra;P<$;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}O.offset+=R&(1<<O.extra)-1,R>>>=O.extra,P-=O.extra,O.back+=O.extra}if(O.offset>O.dmax){L.msg="invalid distance too far back",O.mode=30;break}O.mode=25;case 25:if(H===0)break e;if(A=j-H,O.offset>A){if((A=O.offset-A)>O.whave&&O.sane){L.msg="invalid distance too far back",O.mode=30;break}K=A>O.wnext?(A-=O.wnext,O.wsize-A):O.wnext-A,A>O.length&&(A=O.length),q=O.window}else q=re,K=ue-O.offset,A=O.length;for(H<A&&(A=H),H-=A,O.length-=A;re[ue++]=q[K++],--A;);O.length===0&&(O.mode=21);break;case 26:if(H===0)break e;re[ue++]=O.length,H--,O.mode=21;break;case 27:if(O.wrap){for(;P<32;){if(M===0)break e;M--,R|=V[J++]<<P,P+=8}if(j-=H,L.total_out+=j,O.total+=j,j&&(L.adler=O.check=O.flags?o(O.check,re,j,ue-j):g(O.check,re,j,ue-j)),j=H,(O.flags?R:y(R))!==O.check){L.msg="incorrect data check",O.mode=30;break}P=R=0}O.mode=28;case 28:if(O.wrap&&O.flags){for(;P<32;){if(M===0)break e;M--,R+=V[J++]<<P,P+=8}if(R!==(4294967295&O.total)){L.msg="incorrect length check",O.mode=30;break}P=R=0}O.mode=29;case 29:Y=1;break e;case 30:Y=-3;break e;case 31:return-4;case 32:default:return s}return L.next_out=ue,L.avail_out=H,L.next_in=J,L.avail_in=M,O.hold=R,O.bits=P,(O.wsize||j!==L.avail_out&&O.mode<30&&(O.mode<27||F!==4))&&N(L,L.output,L.next_out,j-L.avail_out)?(O.mode=31,-4):(Q-=L.avail_in,j-=L.avail_out,L.total_in+=Q,L.total_out+=j,O.total+=j,O.wrap&&j&&(L.adler=O.check=O.flags?o(O.check,re,j,L.next_out-j):g(O.check,re,j,L.next_out-j)),L.data_type=O.bits+(O.last?64:0)+(O.mode===12?128:0)+(O.mode===20||O.mode===15?256:0),(Q==0&&j===0||F===4)&&Y===f&&(Y=-5),Y)},i.inflateEnd=function(L){if(!L||!L.state)return s;var F=L.state;return F.window&&(F.window=null),L.state=null,f},i.inflateGetHeader=function(L,F){var O;return L&&L.state?(2&(O=L.state).wrap)==0?s:((O.head=F).done=!1,f):s},i.inflateSetDictionary=function(L,F){var O,V=F.length;return L&&L.state?(O=L.state).wrap!==0&&O.mode!==11?s:O.mode===11&&g(1,F,V,0)!==O.check?-3:N(L,F,V,V)?(O.mode=31,-4):(O.havedict=1,f):s},i.inflateInfo="pako inflate (from Nodeca project)"},{"../utils/common":41,"./adler32":43,"./crc32":45,"./inffast":48,"./inftrees":50}],50:[function(n,r,i){var l=n("../utils/common"),g=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0],o=[16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78],a=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0],u=[16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64];r.exports=function(d,c,f,s,h,v,m,y){var b,_,w,S,x,k,E,I,D,N=y.bits,L=0,F=0,O=0,V=0,re=0,J=0,ue=0,M=0,H=0,R=0,P=null,Q=0,j=new l.Buf16(16),A=new l.Buf16(16),K=null,q=0;for(L=0;L<=15;L++)j[L]=0;for(F=0;F<s;F++)j[c[f+F]]++;for(re=N,V=15;1<=V&&j[V]===0;V--);if(V<re&&(re=V),V===0)return h[v++]=20971520,h[v++]=20971520,y.bits=1,0;for(O=1;O<V&&j[O]===0;O++);for(re<O&&(re=O),L=M=1;L<=15;L++)if(M<<=1,(M-=j[L])<0)return-1;if(0<M&&(d===0||V!==1))return-1;for(A[1]=0,L=1;L<15;L++)A[L+1]=A[L]+j[L];for(F=0;F<s;F++)c[f+F]!==0&&(m[A[c[f+F]]++]=F);if(k=d===0?(P=K=m,19):d===1?(P=g,Q-=257,K=o,q-=257,256):(P=a,K=u,-1),L=O,x=v,ue=F=R=0,w=-1,S=(H=1<<(J=re))-1,d===1&&852<H||d===2&&592<H)return 1;for(;;){for(E=L-ue,D=m[F]<k?(I=0,m[F]):m[F]>k?(I=K[q+m[F]],P[Q+m[F]]):(I=96,0),b=1<<L-ue,O=_=1<<J;h[x+(R>>ue)+(_-=b)]=E<<24|I<<16|D|0,_!==0;);for(b=1<<L-1;R&b;)b>>=1;if(b!==0?(R&=b-1,R+=b):R=0,F++,--j[L]==0){if(L===V)break;L=c[f+m[F]]}if(re<L&&(R&S)!==w){for(ue===0&&(ue=re),x+=O,M=1<<(J=L-ue);J+ue<V&&!((M-=j[J+ue])<=0);)J++,M<<=1;if(H+=1<<J,d===1&&852<H||d===2&&592<H)return 1;h[w=R&S]=re<<24|J<<16|x-v|0}}return R!==0&&(h[x+R]=L-ue<<24|64<<16|0),y.bits=re,0}},{"../utils/common":41}],51:[function(n,r,i){r.exports={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"}},{}],52:[function(n,r,i){var l=n("../utils/common"),g=0,o=1;function a(B){for(var G=B.length;0<=--G;)B[G]=0}var u=0,d=29,c=256,f=c+1+d,s=30,h=19,v=2*f+1,m=15,y=16,b=7,_=256,w=16,S=17,x=18,k=[0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0],E=[0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13],I=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,7],D=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15],N=new Array(2*(f+2));a(N);var L=new Array(2*s);a(L);var F=new Array(512);a(F);var O=new Array(256);a(O);var V=new Array(d);a(V);var re,J,ue,M=new Array(s);function H(B,G,ce,ie,Z){this.static_tree=B,this.extra_bits=G,this.extra_base=ce,this.elems=ie,this.max_length=Z,this.has_stree=B&&B.length}function 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this.x-=d.x,this.y-=d.y,this},scalarSubtract:function(d){return new u(this.x-d,this.y-d)},scalarSubtractEquals:function(d){return this.x-=d,this.y-=d,this},multiply:function(d){return new u(this.x*d,this.y*d)},multiplyEquals:function(d){return this.x*=d,this.y*=d,this},divide:function(d){return new u(this.x/d,this.y/d)},divideEquals:function(d){return this.x/=d,this.y/=d,this},eq:function(d){return this.x===d.x&&this.y===d.y},lt:function(d){return this.x<d.x&&this.y<d.y},lte:function(d){return this.x<=d.x&&this.y<=d.y},gt:function(d){return this.x>d.x&&this.y>d.y},gte:function(d){return this.x>=d.x&&this.y>=d.y},lerp:function(d,c){return typeof c>"u"&&(c=.5),c=Math.max(Math.min(1,c),0),new u(this.x+(d.x-this.x)*c,this.y+(d.y-this.y)*c)},distanceFrom:function(d){var c=this.x-d.x,f=this.y-d.y;return Math.sqrt(c*c+f*f)},midPointFrom:function(d){return this.lerp(d)},min:function(d){return new u(Math.min(this.x,d.x),Math.min(this.y,d.y))},max:function(d){return new u(Math.max(this.x,d.x),Math.max(this.y,d.y))},toString:function(){return this.x+","+this.y},setXY:function(d,c){return this.x=d,this.y=c,this},setX:function(d){return this.x=d,this},setY:function(d){return this.y=d,this},setFromPoint:function(d){return this.x=d.x,this.y=d.y,this},swap:function(d){var c=this.x,f=this.y;this.x=d.x,this.y=d.y,d.x=c,d.y=f},clone:function(){return new u(this.x,this.y)}}}(e),function(o){var a=o.fabric||(o.fabric={});if(a.Intersection){a.warn("fabric.Intersection is already defined");return}function u(d){this.status=d,this.points=[]}a.Intersection=u,a.Intersection.prototype={constructor:u,appendPoint:function(d){return this.points.push(d),this},appendPoints:function(d){return this.points=this.points.concat(d),this}},a.Intersection.intersectLineLine=function(d,c,f,s){var h,v=(s.x-f.x)*(d.y-f.y)-(s.y-f.y)*(d.x-f.x),m=(c.x-d.x)*(d.y-f.y)-(c.y-d.y)*(d.x-f.x),y=(s.y-f.y)*(c.x-d.x)-(s.x-f.x)*(c.y-d.y);if(y!==0){var b=v/y,_=m/y;0<=b&&b<=1&&0<=_&&_<=1?(h=new u("Intersection"),h.appendPoint(new a.Point(d.x+b*(c.x-d.x),d.y+b*(c.y-d.y)))):h=new u}else v===0||m===0?h=new u("Coincident"):h=new u("Parallel");return h},a.Intersection.intersectLinePolygon=function(d,c,f){var s=new u,h=f.length,v,m,y,b;for(b=0;b<h;b++)v=f[b],m=f[(b+1)%h],y=u.intersectLineLine(d,c,v,m),s.appendPoints(y.points);return s.points.length>0&&(s.status="Intersection"),s},a.Intersection.intersectPolygonPolygon=function(d,c){var f=new u,s=d.length,h;for(h=0;h<s;h++){var v=d[h],m=d[(h+1)%s],y=u.intersectLinePolygon(v,m,c);f.appendPoints(y.points)}return f.points.length>0&&(f.status="Intersection"),f},a.Intersection.intersectPolygonRectangle=function(d,c,f){var s=c.min(f),h=c.max(f),v=new a.Point(h.x,s.y),m=new a.Point(s.x,h.y),y=u.intersectLinePolygon(s,v,d),b=u.intersectLinePolygon(v,h,d),_=u.intersectLinePolygon(h,m,d),w=u.intersectLinePolygon(m,s,d),S=new u;return S.appendPoints(y.points),S.appendPoints(b.points),S.appendPoints(_.points),S.appendPoints(w.points),S.points.length>0&&(S.status="Intersection"),S}}(e),function(o){var a=o.fabric||(o.fabric={});if(a.Color){a.warn("fabric.Color is already defined.");return}function u(c){c?this._tryParsingColor(c):this.setSource([0,0,0,1])}a.Color=u,a.Color.prototype={_tryParsingColor:function(c){var f;c in u.colorNameMap&&(c=u.colorNameMap[c]),c==="transparent"&&(f=[255,255,255,0]),f||(f=u.sourceFromHex(c)),f||(f=u.sourceFromRgb(c)),f||(f=u.sourceFromHsl(c)),f||(f=[0,0,0,1]),f&&this.setSource(f)},_rgbToHsl:function(c,f,s){c/=255,f/=255,s/=255;var h,v,m,y=a.util.array.max([c,f,s]),b=a.util.array.min([c,f,s]);if(m=(y+b)/2,y===b)h=v=0;else{var _=y-b;switch(v=m>.5?_/(2-y-b):_/(y+b),y){case c:h=(f-s)/_+(f<s?6:0);break;case f:h=(s-c)/_+2;break;case s:h=(c-f)/_+4;break}h/=6}return[Math.round(h*360),Math.round(v*100),Math.round(m*100)]},getSource:function(){return this._source},setSource:function(c){this._source=c},toRgb:function(){var c=this.getSource();return"rgb("+c[0]+","+c[1]+","+c[2]+")"},toRgba:function(){var c=this.getSource();return"rgba("+c[0]+","+c[1]+","+c[2]+","+c[3]+")"},toHsl:function(){var c=this.getSource(),f=this._rgbToHsl(c[0],c[1],c[2]);return"hsl("+f[0]+","+f[1]+"%,"+f[2]+"%)"},toHsla:function(){var c=this.getSource(),f=this._rgbToHsl(c[0],c[1],c[2]);return"hsla("+f[0]+","+f[1]+"%,"+f[2]+"%,"+c[3]+")"},toHex:function(){var c=this.getSource(),f,s,h;return f=c[0].toString(16),f=f.length===1?"0"+f:f,s=c[1].toString(16),s=s.length===1?"0"+s:s,h=c[2].toString(16),h=h.length===1?"0"+h:h,f.toUpperCase()+s.toUpperCase()+h.toUpperCase()},toHexa:function(){var c=this.getSource(),f;return f=Math.round(c[3]*255),f=f.toString(16),f=f.length===1?"0"+f:f,this.toHex()+f.toUpperCase()},getAlpha:function(){return this.getSource()[3]},setAlpha:function(c){var f=this.getSource();return f[3]=c,this.setSource(f),this},toGrayscale:function(){var c=this.getSource(),f=parseInt((c[0]*.3+c[1]*.59+c[2]*.11).toFixed(0),10),s=c[3];return this.setSource([f,f,f,s]),this},toBlackWhite:function(c){var f=this.getSource(),s=(f[0]*.3+f[1]*.59+f[2]*.11).toFixed(0),h=f[3];return c=c||127,s=Number(s)<Number(c)?0:255,this.setSource([s,s,s,h]),this},overlayWith:function(c){c instanceof u||(c=new u(c));var f=[],s=this.getAlpha(),h=.5,v=this.getSource(),m=c.getSource(),y;for(y=0;y<3;y++)f.push(Math.round(v[y]*(1-h)+m[y]*h));return f[3]=s,this.setSource(f),this}},a.Color.reRGBa=/^rgba?\(\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*(?:\s*,\s*((?:\d*\.?\d+)?)\s*)?\)$/i,a.Color.reHSLa=/^hsla?\(\s*(\d{1,3})\s*,\s*(\d{1,3}\%)\s*,\s*(\d{1,3}\%)\s*(?:\s*,\s*(\d+(?:\.\d+)?)\s*)?\)$/i,a.Color.reHex=/^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i,a.Color.colorNameMap={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",darkgreen:"#006400",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#ADFF2F",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",lightgreen:"#90EE90",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",rebeccapurple:"#663399",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"};function d(c,f,s){return s<0&&(s+=1),s>1&&(s-=1),s<1/6?c+(f-c)*6*s:s<1/2?f:s<2/3?c+(f-c)*(2/3-s)*6:c}a.Color.fromRgb=function(c){return u.fromSource(u.sourceFromRgb(c))},a.Color.sourceFromRgb=function(c){var f=c.match(u.reRGBa);if(f){var s=parseInt(f[1],10)/(/%$/.test(f[1])?100:1)*(/%$/.test(f[1])?255:1),h=parseInt(f[2],10)/(/%$/.test(f[2])?100:1)*(/%$/.test(f[2])?255:1),v=parseInt(f[3],10)/(/%$/.test(f[3])?100:1)*(/%$/.test(f[3])?255:1);return[parseInt(s,10),parseInt(h,10),parseInt(v,10),f[4]?parseFloat(f[4]):1]}},a.Color.fromRgba=u.fromRgb,a.Color.fromHsl=function(c){return u.fromSource(u.sourceFromHsl(c))},a.Color.sourceFromHsl=function(c){var f=c.match(u.reHSLa);if(!!f){var s=(parseFloat(f[1])%360+360)%360/360,h=parseFloat(f[2])/(/%$/.test(f[2])?100:1),v=parseFloat(f[3])/(/%$/.test(f[3])?100:1),m,y,b;if(h===0)m=y=b=v;else{var _=v<=.5?v*(h+1):v+h-v*h,w=v*2-_;m=d(w,_,s+1/3),y=d(w,_,s),b=d(w,_,s-1/3)}return[Math.round(m*255),Math.round(y*255),Math.round(b*255),f[4]?parseFloat(f[4]):1]}},a.Color.fromHsla=u.fromHsl,a.Color.fromHex=function(c){return u.fromSource(u.sourceFromHex(c))},a.Color.sourceFromHex=function(c){if(c.match(u.reHex)){var f=c.slice(c.indexOf("#")+1),s=f.length===3||f.length===4,h=f.length===8||f.length===4,v=s?f.charAt(0)+f.charAt(0):f.substring(0,2),m=s?f.charAt(1)+f.charAt(1):f.substring(2,4),y=s?f.charAt(2)+f.charAt(2):f.substring(4,6),b=h?s?f.charAt(3)+f.charAt(3):f.substring(6,8):"FF";return[parseInt(v,16),parseInt(m,16),parseInt(y,16),parseFloat((parseInt(b,16)/255).toFixed(2))]}},a.Color.fromSource=function(c){var f=new u;return f.setSource(c),f}}(e),function(o){var a=o.fabric||(o.fabric={}),u=["e","se","s","sw","w","nw","n","ne","e"],d=["ns","nesw","ew","nwse"],c={},f="left",s="top",h="right",v="bottom",m="center",y={top:v,bottom:s,left:h,right:f,center:m},b=a.util.radiansToDegrees,_=Math.sign||function(W){return(W>0)-(W<0)||+W};function w(W,k){var X=W.angle+b(Math.atan2(k.y,k.x))+360;return Math.round(X%360/45)}function S(W,k){var X=k.transform.target,Y=X.canvas,$=a.util.object.clone(k);$.target=X,Y&&Y.fire("object:"+W,$),X.fire(W,k)}function x(W,k){var X=k.canvas,Y=X.uniScaleKey,$=W[Y];return X.uniformScaling&&!$||!X.uniformScaling&&$}function T(W){return W.originX===m&&W.originY===m}function E(W,k,X){var Y=W.lockScalingX,$=W.lockScalingY;return!!(Y&&$||!k&&(Y||$)&&X||Y&&k==="x"||$&&k==="y")}function I(W,k,X){var Y="not-allowed",$=x(W,X),B="";if(k.x!==0&&k.y===0?B="x":k.x===0&&k.y!==0&&(B="y"),E(X,B,$))return Y;var K=w(X,k);return u[K]+"-resize"}function D(W,k,X){var Y="not-allowed";if(k.x!==0&&X.lockSkewingY||k.y!==0&&X.lockSkewingX)return Y;var $=w(X,k)%4;return d[$]+"-resize"}function N(W,k,X){return W[X.canvas.altActionKey]?c.skewCursorStyleHandler(W,k,X):c.scaleCursorStyleHandler(W,k,X)}function L(W,k,X){var Y=W[X.canvas.altActionKey];if(k.x===0)return Y?"skewX":"scaleY";if(k.y===0)return Y?"skewY":"scaleX"}function F(W,k,X){return X.lockRotation?"not-allowed":k.cursorStyle}function O(W,k,X,Y){return{e:W,transform:k,pointer:{x:X,y:Y}}}function V(W){return function(k,X,Y,$){var B=X.target,K=B.getCenterPoint(),ce=B.translateToOriginPoint(K,X.originX,X.originY),ie=W(k,X,Y,$);return B.setPositionByOrigin(ce,X.originX,X.originY),ie}}function re(W,k){return function(X,Y,$,B){var K=k(X,Y,$,B);return K&&S(W,O(X,Y,$,B)),K}}function J(W,k,X,Y,$){var B=W.target,K=B.controls[W.corner],ce=B.canvas.getZoom(),ie=B.padding/ce,Z=B.toLocalPoint(new a.Point(Y,$),k,X);return Z.x>=ie&&(Z.x-=ie),Z.x<=-ie&&(Z.x+=ie),Z.y>=ie&&(Z.y-=ie),Z.y<=ie&&(Z.y+=ie),Z.x-=K.offsetX,Z.y-=K.offsetY,Z}function ue(W){return W.flipX!==W.flipY}function 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v=t.util.invertTransform(h),m=t.util.multiplyTransformMatrices(v,s.calcOwnMatrix());t.util.applyTransformToObject(s,m)},addTransformToObject:function(s,h){t.util.applyTransformToObject(s,t.util.multiplyTransformMatrices(h,s.calcOwnMatrix()))},applyTransformToObject:function(s,h){var v=t.util.qrDecompose(h),m=new t.Point(v.translateX,v.translateY);s.flipX=!1,s.flipY=!1,s.set("scaleX",v.scaleX),s.set("scaleY",v.scaleY),s.skewX=v.skewX,s.skewY=v.skewY,s.angle=v.angle,s.setPositionByOrigin(m,"center","center")},sizeAfterTransform:function(s,h,v){var m=s/2,y=h/2,b=[{x:-m,y:-y},{x:m,y:-y},{x:-m,y},{x:m,y}],_=t.util.calcDimensionsMatrix(v),w=t.util.makeBoundingBoxFromPoints(b,_);return{x:w.width,y:w.height}},mergeClipPaths:function(s,h){var v=s,m=h;v.inverted&&!m.inverted&&(v=h,m=s),t.util.applyTransformToObject(m,t.util.multiplyTransformMatrices(t.util.invertTransform(v.calcTransformMatrix()),m.calcTransformMatrix()));var y=v.inverted&&m.inverted;return y&&(v.inverted=m.inverted=!1),new 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f[3]=s,this.setSource(f),this}},a.Color.reRGBa=/^rgba?\(\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*,\s*(\d{1,3}(?:\.\d+)?\%?)\s*(?:\s*,\s*((?:\d*\.?\d+)?)\s*)?\)$/i,a.Color.reHSLa=/^hsla?\(\s*(\d{1,3})\s*,\s*(\d{1,3}\%)\s*,\s*(\d{1,3}\%)\s*(?:\s*,\s*(\d+(?:\.\d+)?)\s*)?\)$/i,a.Color.reHex=/^#?([0-9a-f]{8}|[0-9a-f]{6}|[0-9a-f]{4}|[0-9a-f]{3})$/i,a.Color.colorNameMap={aliceblue:"#F0F8FF",antiquewhite:"#FAEBD7",aqua:"#00FFFF",aquamarine:"#7FFFD4",azure:"#F0FFFF",beige:"#F5F5DC",bisque:"#FFE4C4",black:"#000000",blanchedalmond:"#FFEBCD",blue:"#0000FF",blueviolet:"#8A2BE2",brown:"#A52A2A",burlywood:"#DEB887",cadetblue:"#5F9EA0",chartreuse:"#7FFF00",chocolate:"#D2691E",coral:"#FF7F50",cornflowerblue:"#6495ED",cornsilk:"#FFF8DC",crimson:"#DC143C",cyan:"#00FFFF",darkblue:"#00008B",darkcyan:"#008B8B",darkgoldenrod:"#B8860B",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",darkgreen:"#006400",darkkhaki:"#BDB76B",darkmagenta:"#8B008B",darkolivegreen:"#556B2F",darkorange:"#FF8C00",darkorchid:"#9932CC",darkred:"#8B0000",darksalmon:"#E9967A",darkseagreen:"#8FBC8F",darkslateblue:"#483D8B",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",darkturquoise:"#00CED1",darkviolet:"#9400D3",deeppink:"#FF1493",deepskyblue:"#00BFFF",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1E90FF",firebrick:"#B22222",floralwhite:"#FFFAF0",forestgreen:"#228B22",fuchsia:"#FF00FF",gainsboro:"#DCDCDC",ghostwhite:"#F8F8FF",gold:"#FFD700",goldenrod:"#DAA520",gray:"#808080",grey:"#808080",green:"#008000",greenyellow:"#ADFF2F",honeydew:"#F0FFF0",hotpink:"#FF69B4",indianred:"#CD5C5C",indigo:"#4B0082",ivory:"#FFFFF0",khaki:"#F0E68C",lavender:"#E6E6FA",lavenderblush:"#FFF0F5",lawngreen:"#7CFC00",lemonchiffon:"#FFFACD",lightblue:"#ADD8E6",lightcoral:"#F08080",lightcyan:"#E0FFFF",lightgoldenrodyellow:"#FAFAD2",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",lightgreen:"#90EE90",lightpink:"#FFB6C1",lightsalmon:"#FFA07A",lightseagreen:"#20B2AA",lightskyblue:"#87CEFA",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#B0C4DE",lightyellow:"#FFFFE0",lime:"#00FF00",limegreen:"#32CD32",linen:"#FAF0E6",magenta:"#FF00FF",maroon:"#800000",mediumaquamarine:"#66CDAA",mediumblue:"#0000CD",mediumorchid:"#BA55D3",mediumpurple:"#9370DB",mediumseagreen:"#3CB371",mediumslateblue:"#7B68EE",mediumspringgreen:"#00FA9A",mediumturquoise:"#48D1CC",mediumvioletred:"#C71585",midnightblue:"#191970",mintcream:"#F5FFFA",mistyrose:"#FFE4E1",moccasin:"#FFE4B5",navajowhite:"#FFDEAD",navy:"#000080",oldlace:"#FDF5E6",olive:"#808000",olivedrab:"#6B8E23",orange:"#FFA500",orangered:"#FF4500",orchid:"#DA70D6",palegoldenrod:"#EEE8AA",palegreen:"#98FB98",paleturquoise:"#AFEEEE",palevioletred:"#DB7093",papayawhip:"#FFEFD5",peachpuff:"#FFDAB9",peru:"#CD853F",pink:"#FFC0CB",plum:"#DDA0DD",powderblue:"#B0E0E6",purple:"#800080",rebeccapurple:"#663399",red:"#FF0000",rosybrown:"#BC8F8F",royalblue:"#4169E1",saddlebrown:"#8B4513",salmon:"#FA8072",sandybrown:"#F4A460",seagreen:"#2E8B57",seashell:"#FFF5EE",sienna:"#A0522D",silver:"#C0C0C0",skyblue:"#87CEEB",slateblue:"#6A5ACD",slategray:"#708090",slategrey:"#708090",snow:"#FFFAFA",springgreen:"#00FF7F",steelblue:"#4682B4",tan:"#D2B48C",teal:"#008080",thistle:"#D8BFD8",tomato:"#FF6347",turquoise:"#40E0D0",violet:"#EE82EE",wheat:"#F5DEB3",white:"#FFFFFF",whitesmoke:"#F5F5F5",yellow:"#FFFF00",yellowgreen:"#9ACD32"};function d(c,f,s){return s<0&&(s+=1),s>1&&(s-=1),s<1/6?c+(f-c)*6*s:s<1/2?f:s<2/3?c+(f-c)*(2/3-s)*6:c}a.Color.fromRgb=function(c){return u.fromSource(u.sourceFromRgb(c))},a.Color.sourceFromRgb=function(c){var f=c.match(u.reRGBa);if(f){var s=parseInt(f[1],10)/(/%$/.test(f[1])?100:1)*(/%$/.test(f[1])?255:1),h=parseInt(f[2],10)/(/%$/.test(f[2])?100:1)*(/%$/.test(f[2])?255:1),v=parseInt(f[3],10)/(/%$/.test(f[3])?100:1)*(/%$/.test(f[3])?255:1);return[parseInt(s,10),parseInt(h,10),parseInt(v,10),f[4]?parseFloat(f[4]):1]}},a.Color.fromRgba=u.fromRgb,a.Color.fromHsl=function(c){return u.fromSource(u.sourceFromHsl(c))},a.Color.sourceFromHsl=function(c){var f=c.match(u.reHSLa);if(!!f){var s=(parseFloat(f[1])%360+360)%360/360,h=parseFloat(f[2])/(/%$/.test(f[2])?100:1),v=parseFloat(f[3])/(/%$/.test(f[3])?100:1),m,y,b;if(h===0)m=y=b=v;else{var _=v<=.5?v*(h+1):v+h-v*h,w=v*2-_;m=d(w,_,s+1/3),y=d(w,_,s),b=d(w,_,s-1/3)}return[Math.round(m*255),Math.round(y*255),Math.round(b*255),f[4]?parseFloat(f[4]):1]}},a.Color.fromHsla=u.fromHsl,a.Color.fromHex=function(c){return u.fromSource(u.sourceFromHex(c))},a.Color.sourceFromHex=function(c){if(c.match(u.reHex)){var f=c.slice(c.indexOf("#")+1),s=f.length===3||f.length===4,h=f.length===8||f.length===4,v=s?f.charAt(0)+f.charAt(0):f.substring(0,2),m=s?f.charAt(1)+f.charAt(1):f.substring(2,4),y=s?f.charAt(2)+f.charAt(2):f.substring(4,6),b=h?s?f.charAt(3)+f.charAt(3):f.substring(6,8):"FF";return[parseInt(v,16),parseInt(m,16),parseInt(y,16),parseFloat((parseInt(b,16)/255).toFixed(2))]}},a.Color.fromSource=function(c){var f=new u;return f.setSource(c),f}}(e),function(o){var a=o.fabric||(o.fabric={}),u=["e","se","s","sw","w","nw","n","ne","e"],d=["ns","nesw","ew","nwse"],c={},f="left",s="top",h="right",v="bottom",m="center",y={top:v,bottom:s,left:h,right:f,center:m},b=a.util.radiansToDegrees,_=Math.sign||function(X){return(X>0)-(X<0)||+X};function w(X,T){var Y=X.angle+b(Math.atan2(T.y,T.x))+360;return Math.round(Y%360/45)}function S(X,T){var Y=T.transform.target,W=Y.canvas,$=a.util.object.clone(T);$.target=Y,W&&W.fire("object:"+X,$),Y.fire(X,T)}function x(X,T){var Y=T.canvas,W=Y.uniScaleKey,$=X[W];return Y.uniformScaling&&!$||!Y.uniformScaling&&$}function k(X){return X.originX===m&&X.originY===m}function E(X,T,Y){var W=X.lockScalingX,$=X.lockScalingY;return!!(W&&$||!T&&(W||$)&&Y||W&&T==="x"||$&&T==="y")}function I(X,T,Y){var W="not-allowed",$=x(X,Y),B="";if(T.x!==0&&T.y===0?B="x":T.x===0&&T.y!==0&&(B="y"),E(Y,B,$))return W;var G=w(Y,T);return u[G]+"-resize"}function D(X,T,Y){var W="not-allowed";if(T.x!==0&&Y.lockSkewingY||T.y!==0&&Y.lockSkewingX)return W;var $=w(Y,T)%4;return d[$]+"-resize"}function N(X,T,Y){return X[Y.canvas.altActionKey]?c.skewCursorStyleHandler(X,T,Y):c.scaleCursorStyleHandler(X,T,Y)}function L(X,T,Y){var W=X[Y.canvas.altActionKey];if(T.x===0)return W?"skewX":"scaleY";if(T.y===0)return W?"skewY":"scaleX"}function F(X,T,Y){return Y.lockRotation?"not-allowed":T.cursorStyle}function O(X,T,Y,W){return{e:X,transform:T,pointer:{x:Y,y:W}}}function V(X){return function(T,Y,W,$){var B=Y.target,G=B.getCenterPoint(),ce=B.translateToOriginPoint(G,Y.originX,Y.originY),ie=X(T,Y,W,$);return B.setPositionByOrigin(ce,Y.originX,Y.originY),ie}}function re(X,T){return function(Y,W,$,B){var G=T(Y,W,$,B);return G&&S(X,O(Y,W,$,B)),G}}function J(X,T,Y,W,$){var B=X.target,G=B.controls[X.corner],ce=B.canvas.getZoom(),ie=B.padding/ce,Z=B.toLocalPoint(new a.Point(W,$),T,Y);return Z.x>=ie&&(Z.x-=ie),Z.x<=-ie&&(Z.x+=ie),Z.y>=ie&&(Z.y-=ie),Z.y<=ie&&(Z.y+=ie),Z.x-=G.offsetX,Z.y-=G.offsetY,Z}function ue(X){return X.flipX!==X.flipY}function M(X,T,Y,W,$){if(X[T]!==0){var B=X._getTransformedDimensions()[W],G=$/B*X[Y];X.set(Y,G)}}function H(X,T,Y,W){var $=T.target,B=$._getTransformedDimensions(0,$.skewY),G=J(T,T.originX,T.originY,Y,W),ce=Math.abs(G.x*2)-B.x,ie=$.skewX,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleX,B.y/$.scaleY)),T.originX===f&&T.originY===v&&(Z=-Z),T.originX===h&&T.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().y;$.set("skewX",Z),M($,"skewY","scaleY","y",ae)}return me}function R(X,T,Y,W){var $=T.target,B=$._getTransformedDimensions($.skewX,0),G=J(T,T.originX,T.originY,Y,W),ce=Math.abs(G.y*2)-B.y,ie=$.skewY,Z;ce<2?Z=0:(Z=b(Math.atan2(ce/$.scaleY,B.x/$.scaleX)),T.originX===f&&T.originY===v&&(Z=-Z),T.originX===h&&T.originY===s&&(Z=-Z),ue($)&&(Z=-Z));var me=ie!==Z;if(me){var ae=$._getTransformedDimensions().x;$.set("skewY",Z),M($,"skewX","scaleX","x",ae)}return me}function P(X,T,Y,W){var $=T.target,B=$.skewX,G,ce=T.originY;if($.lockSkewingX)return!1;if(B===0){var ie=J(T,m,m,Y,W);ie.x>0?G=f:G=h}else B>0&&(G=ce===s?f:h),B<0&&(G=ce===s?h:f),ue($)&&(G=G===f?h:f);T.originX=G;var Z=re("skewing",V(H));return Z(X,T,Y,W)}function Q(X,T,Y,W){var $=T.target,B=$.skewY,G,ce=T.originX;if($.lockSkewingY)return!1;if(B===0){var ie=J(T,m,m,Y,W);ie.y>0?G=s:G=v}else B>0&&(G=ce===f?s:v),B<0&&(G=ce===f?v:s),ue($)&&(G=G===s?v:s);T.originY=G;var Z=re("skewing",V(R));return Z(X,T,Y,W)}function j(X,T,Y,W){var $=T,B=$.target,G=B.translateToOriginPoint(B.getCenterPoint(),$.originX,$.originY);if(B.lockRotation)return!1;var ce=Math.atan2($.ey-G.y,$.ex-G.x),ie=Math.atan2(W-G.y,Y-G.x),Z=b(ie-ce+$.theta),me=!0;if(B.snapAngle>0){var ae=B.snapAngle,ge=B.snapThreshold||ae,we=Math.ceil(Z/ae)*ae,Te=Math.floor(Z/ae)*ae;Math.abs(Z-Te)<ge?Z=Te:Math.abs(Z-we)<ge&&(Z=we)}return Z<0&&(Z=360+Z),Z%=360,me=B.angle!==Z,B.angle=Z,me}function A(X,T,Y,W,$){$=$||{};var B=T.target,G=B.lockScalingX,ce=B.lockScalingY,ie=$.by,Z,me,ae,ge,we=x(X,B),Te=E(B,ie,we),Oe,Fe,_e=T.gestureScale;if(Te)return!1;if(_e)me=T.scaleX*_e,ae=T.scaleY*_e;else{if(Z=J(T,T.originX,T.originY,Y,W),Oe=ie!=="y"?_(Z.x):1,Fe=ie!=="x"?_(Z.y):1,T.signX||(T.signX=Oe),T.signY||(T.signY=Fe),B.lockScalingFlip&&(T.signX!==Oe||T.signY!==Fe))return!1;if(ge=B._getTransformedDimensions(),we&&!ie){var Se=Math.abs(Z.x)+Math.abs(Z.y),De=T.original,Re=Math.abs(ge.x*De.scaleX/B.scaleX)+Math.abs(ge.y*De.scaleY/B.scaleY),bt=Se/Re;me=De.scaleX*bt,ae=De.scaleY*bt}else me=Math.abs(Z.x*B.scaleX/ge.x),ae=Math.abs(Z.y*B.scaleY/ge.y);k(T)&&(me*=2,ae*=2),T.signX!==Oe&&ie!=="y"&&(T.originX=y[T.originX],me*=-1,T.signX=Oe),T.signY!==Fe&&ie!=="x"&&(T.originY=y[T.originY],ae*=-1,T.signY=Fe)}var pt=B.scaleX,ht=B.scaleY;return ie?(ie==="x"&&B.set("scaleX",me),ie==="y"&&B.set("scaleY",ae)):(!G&&B.set("scaleX",me),!ce&&B.set("scaleY",ae)),pt!==B.scaleX||ht!==B.scaleY}function K(X,T,Y,W){return A(X,T,Y,W)}function q(X,T,Y,W){return A(X,T,Y,W,{by:"x"})}function te(X,T,Y,W){return A(X,T,Y,W,{by:"y"})}function fe(X,T,Y,W){return X[T.target.canvas.altActionKey]?c.skewHandlerX(X,T,Y,W):c.scalingY(X,T,Y,W)}function pe(X,T,Y,W){return X[T.target.canvas.altActionKey]?c.skewHandlerY(X,T,Y,W):c.scalingX(X,T,Y,W)}function ve(X,T,Y,W){var $=T.target,B=J(T,T.originX,T.originY,Y,W),G=$.strokeWidth/($.strokeUniform?$.scaleX:1),ce=k(T)?2:1,ie=$.width,Z=Math.abs(B.x*ce/$.scaleX)-G;return $.set("width",Math.max(Z,0)),ie!==Z}function U(X,T,Y,W){var $=T.target,B=Y-T.offsetX,G=W-T.offsetY,ce=!$.get("lockMovementX")&&$.left!==B,ie=!$.get("lockMovementY")&&$.top!==G;return ce&&$.set("left",B),ie&&$.set("top",G),(ce||ie)&&S("moving",O(X,T,Y,W)),ce||ie}c.scaleCursorStyleHandler=I,c.skewCursorStyleHandler=D,c.scaleSkewCursorStyleHandler=N,c.rotationWithSnapping=re("rotating",V(j)),c.scalingEqually=re("scaling",V(K)),c.scalingX=re("scaling",V(q)),c.scalingY=re("scaling",V(te)),c.scalingYOrSkewingX=fe,c.scalingXOrSkewingY=pe,c.changeWidth=re("resizing",V(ve)),c.skewHandlerX=P,c.skewHandlerY=Q,c.dragHandler=U,c.scaleOrSkewActionName=L,c.rotationStyleHandler=F,c.fireEvent=S,c.wrapWithFixedAnchor=V,c.wrapWithFireEvent=re,c.getLocalPoint=J,a.controlsUtils=c}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.degreesToRadians,d=a.controlsUtils;function c(s,h,v,m,y){m=m||{};var b=this.sizeX||m.cornerSize||y.cornerSize,_=this.sizeY||m.cornerSize||y.cornerSize,w=typeof m.transparentCorners<"u"?m.transparentCorners:y.transparentCorners,S=w?"stroke":"fill",x=!w&&(m.cornerStrokeColor||y.cornerStrokeColor),k=h,E=v,I;s.save(),s.fillStyle=m.cornerColor||y.cornerColor,s.strokeStyle=m.cornerStrokeColor||y.cornerStrokeColor,b>_?(I=b,s.scale(1,_/b),E=v*b/_):_>b?(I=_,s.scale(b/_,1),k=h*_/b):I=b,s.lineWidth=1,s.beginPath(),s.arc(k,E,I/2,0,2*Math.PI,!1),s[S](),x&&s.stroke(),s.restore()}function f(s,h,v,m,y){m=m||{};var b=this.sizeX||m.cornerSize||y.cornerSize,_=this.sizeY||m.cornerSize||y.cornerSize,w=typeof m.transparentCorners<"u"?m.transparentCorners:y.transparentCorners,S=w?"stroke":"fill",x=!w&&(m.cornerStrokeColor||y.cornerStrokeColor),k=b/2,E=_/2;s.save(),s.fillStyle=m.cornerColor||y.cornerColor,s.strokeStyle=m.cornerStrokeColor||y.cornerStrokeColor,s.lineWidth=1,s.translate(h,v),s.rotate(u(y.angle)),s[S+"Rect"](-k,-E,b,_),x&&s.strokeRect(-k,-E,b,_),s.restore()}d.renderCircleControl=c,d.renderSquareControl=f}(e),function(o){var a=o.fabric||(o.fabric={});function u(d){for(var c in d)this[c]=d[c]}a.Control=u,a.Control.prototype={visible:!0,actionName:"scale",angle:0,x:0,y:0,offsetX:0,offsetY:0,sizeX:null,sizeY:null,touchSizeX:null,touchSizeY:null,cursorStyle:"crosshair",withConnection:!1,actionHandler:function(){},mouseDownHandler:function(){},mouseUpHandler:function(){},getActionHandler:function(){return this.actionHandler},getMouseDownHandler:function(){return this.mouseDownHandler},getMouseUpHandler:function(){return this.mouseUpHandler},cursorStyleHandler:function(d,c){return c.cursorStyle},getActionName:function(d,c){return c.actionName},getVisibility:function(d,c){var f=d._controlsVisibility;return f&&typeof f[c]<"u"?f[c]:this.visible},setVisibility:function(d){this.visible=d},positionHandler:function(d,c){var f=a.util.transformPoint({x:this.x*d.x+this.offsetX,y:this.y*d.y+this.offsetY},c);return f},calcCornerCoords:function(d,c,f,s,h){var v,m,y,b,_=h?this.touchSizeX:this.sizeX,w=h?this.touchSizeY:this.sizeY;if(_&&w&&_!==w){var S=Math.atan2(w,_),x=Math.sqrt(_*_+w*w)/2,k=S-a.util.degreesToRadians(d),E=Math.PI/2-S-a.util.degreesToRadians(d);v=x*a.util.cos(k),m=x*a.util.sin(k),y=x*a.util.cos(E),b=x*a.util.sin(E)}else{var I=_&&w?_:c;x=I*.7071067812;var k=a.util.degreesToRadians(45-d);v=y=x*a.util.cos(k),m=b=x*a.util.sin(k)}return{tl:{x:f-b,y:s-y},tr:{x:f+v,y:s-m},bl:{x:f-v,y:s+m},br:{x:f+b,y:s+y}}},render:function(d,c,f,s,h){switch(s=s||{},s.cornerStyle||h.cornerStyle){case"circle":a.controlsUtils.renderCircleControl.call(this,d,c,f,s,h);break;default:a.controlsUtils.renderSquareControl.call(this,d,c,f,s,h)}}}}(e),function(){function o(f,s){var h=f.getAttribute("style"),v=f.getAttribute("offset")||0,m,y,b,_;if(v=parseFloat(v)/(/%$/.test(v)?100:1),v=v<0?0:v>1?1:v,h){var w=h.split(/\s*;\s*/);for(w[w.length-1]===""&&w.pop(),_=w.length;_--;){var S=w[_].split(/\s*:\s*/),x=S[0].trim(),k=S[1].trim();x==="stop-color"?m=k:x==="stop-opacity"&&(b=k)}}return m||(m=f.getAttribute("stop-color")||"rgb(0,0,0)"),b||(b=f.getAttribute("stop-opacity")),m=new t.Color(m),y=m.getAlpha(),b=isNaN(parseFloat(b))?1:parseFloat(b),b*=y*s,{offset:v,color:m.toRgb(),opacity:b}}function a(f){return{x1:f.getAttribute("x1")||0,y1:f.getAttribute("y1")||0,x2:f.getAttribute("x2")||"100%",y2:f.getAttribute("y2")||0}}function u(f){return{x1:f.getAttribute("fx")||f.getAttribute("cx")||"50%",y1:f.getAttribute("fy")||f.getAttribute("cy")||"50%",r1:0,x2:f.getAttribute("cx")||"50%",y2:f.getAttribute("cy")||"50%",r2:f.getAttribute("r")||"50%"}}var d=t.util.object.clone;t.Gradient=t.util.createClass({offsetX:0,offsetY:0,gradientTransform:null,gradientUnits:"pixels",type:"linear",initialize:function(f){f||(f={}),f.coords||(f.coords={});var s,h=this;Object.keys(f).forEach(function(v){h[v]=f[v]}),this.id?this.id+="_"+t.Object.__uid++:this.id=t.Object.__uid++,s={x1:f.coords.x1||0,y1:f.coords.y1||0,x2:f.coords.x2||0,y2:f.coords.y2||0},this.type==="radial"&&(s.r1=f.coords.r1||0,s.r2=f.coords.r2||0),this.coords=s,this.colorStops=f.colorStops.slice()},addColorStop:function(f){for(var s in f){var h=new t.Color(f[s]);this.colorStops.push({offset:parseFloat(s),color:h.toRgb(),opacity:h.getAlpha()})}return this},toObject:function(f){var s={type:this.type,coords:this.coords,colorStops:this.colorStops,offsetX:this.offsetX,offsetY:this.offsetY,gradientUnits:this.gradientUnits,gradientTransform:this.gradientTransform?this.gradientTransform.concat():this.gradientTransform};return t.util.populateWithProperties(this,s,f),s},toSVG:function(f,y){var h=d(this.coords,!0),v,m,y=y||{},b,_,w=d(this.colorStops,!0),S=h.r1>h.r2,x=this.gradientTransform?this.gradientTransform.concat():t.iMatrix.concat(),k=-this.offsetX,E=-this.offsetY,I=!!y.additionalTransform,D=this.gradientUnits==="pixels"?"userSpaceOnUse":"objectBoundingBox";if(w.sort(function(V,re){return V.offset-re.offset}),D==="objectBoundingBox"?(k/=f.width,E/=f.height):(k+=f.width/2,E+=f.height/2),f.type==="path"&&this.gradientUnits!=="percentage"&&(k-=f.pathOffset.x,E-=f.pathOffset.y),x[4]-=k,x[5]-=E,_='id="SVGID_'+this.id+'" gradientUnits="'+D+'"',_+=' gradientTransform="'+(I?y.additionalTransform+" ":"")+t.util.matrixToSVG(x)+'" ',this.type==="linear"?b=["<linearGradient ",_,' x1="',h.x1,'" y1="',h.y1,'" x2="',h.x2,'" y2="',h.y2,`">
 `]:this.type==="radial"&&(b=["<radialGradient ",_,' cx="',S?h.x1:h.x2,'" cy="',S?h.y1:h.y2,'" r="',S?h.r1:h.r2,'" fx="',S?h.x2:h.x1,'" fy="',S?h.y2:h.y1,`">
 `]),this.type==="radial"){if(S)for(w=w.concat(),w.reverse(),v=0,m=w.length;v<m;v++)w[v].offset=1-w[v].offset;var N=Math.min(h.r1,h.r2);if(N>0){var L=Math.max(h.r1,h.r2),F=N/L;for(v=0,m=w.length;v<m;v++)w[v].offset+=F*(1-w[v].offset)}}for(v=0,m=w.length;v<m;v++){var O=w[v];b.push("<stop ",'offset="',O.offset*100+"%",'" style="stop-color:',O.color,typeof O.opacity<"u"?";stop-opacity: "+O.opacity:";",`"/>
 `)}return b.push(this.type==="linear"?`</linearGradient>
 `:`</radialGradient>
-`),b.join("")},toLive:function(f){var s,h=t.util.object.clone(this.coords),v,m;if(!!this.type){for(this.type==="linear"?s=f.createLinearGradient(h.x1,h.y1,h.x2,h.y2):this.type==="radial"&&(s=f.createRadialGradient(h.x1,h.y1,h.r1,h.x2,h.y2,h.r2)),v=0,m=this.colorStops.length;v<m;v++){var y=this.colorStops[v].color,b=this.colorStops[v].opacity,_=this.colorStops[v].offset;typeof b<"u"&&(y=new t.Color(y).setAlpha(b).toRgba()),s.addColorStop(_,y)}return s}}}),t.util.object.extend(t.Gradient,{fromElement:function(f,s,h,v){var m=parseFloat(h)/(/%$/.test(h)?100:1);m=m<0?0:m>1?1:m,isNaN(m)&&(m=1);var y=f.getElementsByTagName("stop"),b,_=f.getAttribute("gradientUnits")==="userSpaceOnUse"?"pixels":"percentage",w=f.getAttribute("gradientTransform")||"",S=[],x,T,E=0,I=0,D;for(f.nodeName==="linearGradient"||f.nodeName==="LINEARGRADIENT"?(b="linear",x=a(f)):(b="radial",x=u(f)),T=y.length;T--;)S.push(o(y[T],m));D=t.parseTransformAttribute(w),c(s,x,v,_),_==="pixels"&&(E=-s.left,I=-s.top);var N=new t.Gradient({id:f.getAttribute("id"),type:b,coords:x,colorStops:S,gradientUnits:_,gradientTransform:D,offsetX:E,offsetY:I});return N}});function c(f,s,h,v){var m,y;Object.keys(s).forEach(function(b){m=s[b],m==="Infinity"?y=1:m==="-Infinity"?y=0:(y=parseFloat(s[b],10),typeof m=="string"&&/^(\d+\.\d+)%|(\d+)%$/.test(m)&&(y*=.01,v==="pixels"&&((b==="x1"||b==="x2"||b==="r2")&&(y*=h.viewBoxWidth||h.width),(b==="y1"||b==="y2")&&(y*=h.viewBoxHeight||h.height)))),s[b]=y})}}(),function(){var o=t.util.toFixed;t.Pattern=t.util.createClass({repeat:"repeat",offsetX:0,offsetY:0,crossOrigin:"",patternTransform:null,initialize:function(a,u){if(a||(a={}),this.id=t.Object.__uid++,this.setOptions(a),!a.source||a.source&&typeof a.source!="string"){u&&u(this);return}else{var d=this;this.source=t.util.createImage(),t.util.loadImage(a.source,function(c,f){d.source=c,u&&u(d,f)},null,this.crossOrigin)}},toObject:function(a){var u=t.Object.NUM_FRACTION_DIGITS,d,c;return typeof this.source.src=="string"?d=this.source.src:typeof this.source=="object"&&this.source.toDataURL&&(d=this.source.toDataURL()),c={type:"pattern",source:d,repeat:this.repeat,crossOrigin:this.crossOrigin,offsetX:o(this.offsetX,u),offsetY:o(this.offsetY,u),patternTransform:this.patternTransform?this.patternTransform.concat():null},t.util.populateWithProperties(this,c,a),c},toSVG:function(a){var u=typeof this.source=="function"?this.source():this.source,d=u.width/a.width,c=u.height/a.height,f=this.offsetX/a.width,s=this.offsetY/a.height,h="";return(this.repeat==="repeat-x"||this.repeat==="no-repeat")&&(c=1,s&&(c+=Math.abs(s))),(this.repeat==="repeat-y"||this.repeat==="no-repeat")&&(d=1,f&&(d+=Math.abs(f))),u.src?h=u.src:u.toDataURL&&(h=u.toDataURL()),'<pattern id="SVGID_'+this.id+'" x="'+f+'" y="'+s+'" width="'+d+'" height="'+c+`">
+`),b.join("")},toLive:function(f){var s,h=t.util.object.clone(this.coords),v,m;if(!!this.type){for(this.type==="linear"?s=f.createLinearGradient(h.x1,h.y1,h.x2,h.y2):this.type==="radial"&&(s=f.createRadialGradient(h.x1,h.y1,h.r1,h.x2,h.y2,h.r2)),v=0,m=this.colorStops.length;v<m;v++){var y=this.colorStops[v].color,b=this.colorStops[v].opacity,_=this.colorStops[v].offset;typeof b<"u"&&(y=new t.Color(y).setAlpha(b).toRgba()),s.addColorStop(_,y)}return s}}}),t.util.object.extend(t.Gradient,{fromElement:function(f,s,h,v){var m=parseFloat(h)/(/%$/.test(h)?100:1);m=m<0?0:m>1?1:m,isNaN(m)&&(m=1);var y=f.getElementsByTagName("stop"),b,_=f.getAttribute("gradientUnits")==="userSpaceOnUse"?"pixels":"percentage",w=f.getAttribute("gradientTransform")||"",S=[],x,k,E=0,I=0,D;for(f.nodeName==="linearGradient"||f.nodeName==="LINEARGRADIENT"?(b="linear",x=a(f)):(b="radial",x=u(f)),k=y.length;k--;)S.push(o(y[k],m));D=t.parseTransformAttribute(w),c(s,x,v,_),_==="pixels"&&(E=-s.left,I=-s.top);var N=new t.Gradient({id:f.getAttribute("id"),type:b,coords:x,colorStops:S,gradientUnits:_,gradientTransform:D,offsetX:E,offsetY:I});return N}});function c(f,s,h,v){var m,y;Object.keys(s).forEach(function(b){m=s[b],m==="Infinity"?y=1:m==="-Infinity"?y=0:(y=parseFloat(s[b],10),typeof m=="string"&&/^(\d+\.\d+)%|(\d+)%$/.test(m)&&(y*=.01,v==="pixels"&&((b==="x1"||b==="x2"||b==="r2")&&(y*=h.viewBoxWidth||h.width),(b==="y1"||b==="y2")&&(y*=h.viewBoxHeight||h.height)))),s[b]=y})}}(),function(){var o=t.util.toFixed;t.Pattern=t.util.createClass({repeat:"repeat",offsetX:0,offsetY:0,crossOrigin:"",patternTransform:null,initialize:function(a,u){if(a||(a={}),this.id=t.Object.__uid++,this.setOptions(a),!a.source||a.source&&typeof a.source!="string"){u&&u(this);return}else{var d=this;this.source=t.util.createImage(),t.util.loadImage(a.source,function(c,f){d.source=c,u&&u(d,f)},null,this.crossOrigin)}},toObject:function(a){var u=t.Object.NUM_FRACTION_DIGITS,d,c;return typeof this.source.src=="string"?d=this.source.src:typeof this.source=="object"&&this.source.toDataURL&&(d=this.source.toDataURL()),c={type:"pattern",source:d,repeat:this.repeat,crossOrigin:this.crossOrigin,offsetX:o(this.offsetX,u),offsetY:o(this.offsetY,u),patternTransform:this.patternTransform?this.patternTransform.concat():null},t.util.populateWithProperties(this,c,a),c},toSVG:function(a){var u=typeof this.source=="function"?this.source():this.source,d=u.width/a.width,c=u.height/a.height,f=this.offsetX/a.width,s=this.offsetY/a.height,h="";return(this.repeat==="repeat-x"||this.repeat==="no-repeat")&&(c=1,s&&(c+=Math.abs(s))),(this.repeat==="repeat-y"||this.repeat==="no-repeat")&&(d=1,f&&(d+=Math.abs(f))),u.src?h=u.src:u.toDataURL&&(h=u.toDataURL()),'<pattern id="SVGID_'+this.id+'" x="'+f+'" y="'+s+'" width="'+d+'" height="'+c+`">
 <image x="0" y="0" width="`+u.width+'" height="'+u.height+'" xlink:href="'+h+`"></image>
 </pattern>
 `},setOptions:function(a){for(var u in a)this[u]=a[u]},toLive:function(a){var u=this.source;return!u||typeof u.src<"u"&&(!u.complete||u.naturalWidth===0||u.naturalHeight===0)?"":a.createPattern(u,this.repeat)}})}(),function(o){var a=o.fabric||(o.fabric={}),u=a.util.toFixed;if(a.Shadow){a.warn("fabric.Shadow is already defined.");return}a.Shadow=a.util.createClass({color:"rgb(0,0,0)",blur:0,offsetX:0,offsetY:0,affectStroke:!1,includeDefaultValues:!0,nonScaling:!1,initialize:function(d){typeof d=="string"&&(d=this._parseShadow(d));for(var c in d)this[c]=d[c];this.id=a.Object.__uid++},_parseShadow:function(d){var c=d.trim(),f=a.Shadow.reOffsetsAndBlur.exec(c)||[],s=c.replace(a.Shadow.reOffsetsAndBlur,"")||"rgb(0,0,0)";return{color:s.trim(),offsetX:parseFloat(f[1],10)||0,offsetY:parseFloat(f[2],10)||0,blur:parseFloat(f[3],10)||0}},toString:function(){return[this.offsetX,this.offsetY,this.blur,this.color].join("px ")},toSVG:function(d){var c=40,f=40,s=a.Object.NUM_FRACTION_DIGITS,h=a.util.rotateVector({x:this.offsetX,y:this.offsetY},a.util.degreesToRadians(-d.angle)),v=20,m=new a.Color(this.color);return d.width&&d.height&&(c=u((Math.abs(h.x)+this.blur)/d.width,s)*100+v,f=u((Math.abs(h.y)+this.blur)/d.height,s)*100+v),d.flipX&&(h.x*=-1),d.flipY&&(h.y*=-1),'<filter id="SVGID_'+this.id+'" y="-'+f+'%" height="'+(100+2*f)+'%" x="-'+c+'%" width="'+(100+2*c)+`%" >
@@ -100,20 +100,20 @@ https://github.com/nodeca/pako/blob/main/LICENSE
 `,this.createSVGFontFacesMarkup(),this.createSVGRefElementsMarkup(),this.createSVGClipPathMarkup(y),`</defs>
 `)},createSVGClipPathMarkup:function(m){var y=this.clipPath;return y?(y.clipPathId="CLIPPATH_"+t.Object.__uid++,'<clipPath id="'+y.clipPathId+`" >
 `+this.clipPath.toClipPathSVG(m.reviver)+`</clipPath>
-`):""},createSVGRefElementsMarkup:function(){var m=this,y=["background","overlay"].map(function(b){var _=m[b+"Color"];if(_&&_.toLive){var w=m[b+"Vpt"],S=m.viewportTransform,x={width:m.width/(w?S[0]:1),height:m.height/(w?S[3]:1)};return _.toSVG(x,{additionalTransform:w?t.util.matrixToSVG(S):""})}});return y.join("")},createSVGFontFacesMarkup:function(){var m="",y={},b,_,w,S,x,T,E,I,D,N=t.fontPaths,L=[];for(this._objects.forEach(function O(V){L.push(V),V._objects&&V._objects.forEach(O)}),I=0,D=L.length;I<D;I++)if(b=L[I],_=b.fontFamily,!(b.type.indexOf("text")===-1||y[_]||!N[_])&&(y[_]=!0,!!b.styles)){w=b.styles;for(x in w){S=w[x];for(E in S)T=S[E],_=T.fontFamily,!y[_]&&N[_]&&(y[_]=!0)}}for(var F in y)m+=[`		@font-face {
+`):""},createSVGRefElementsMarkup:function(){var m=this,y=["background","overlay"].map(function(b){var _=m[b+"Color"];if(_&&_.toLive){var w=m[b+"Vpt"],S=m.viewportTransform,x={width:m.width/(w?S[0]:1),height:m.height/(w?S[3]:1)};return _.toSVG(x,{additionalTransform:w?t.util.matrixToSVG(S):""})}});return y.join("")},createSVGFontFacesMarkup:function(){var m="",y={},b,_,w,S,x,k,E,I,D,N=t.fontPaths,L=[];for(this._objects.forEach(function O(V){L.push(V),V._objects&&V._objects.forEach(O)}),I=0,D=L.length;I<D;I++)if(b=L[I],_=b.fontFamily,!(b.type.indexOf("text")===-1||y[_]||!N[_])&&(y[_]=!0,!!b.styles)){w=b.styles;for(x in w){S=w[x];for(E in S)k=S[E],_=k.fontFamily,!y[_]&&N[_]&&(y[_]=!0)}}for(var F in y)m+=[`		@font-face {
 `,"			font-family: '",F,`';
 `,"			src: url('",N[F],`');
 `,`		}
 `].join("");return m&&(m=['	<style type="text/css">',`<![CDATA[
 `,m,"]]>",`</style>
-`].join("")),m},_setSVGObjects:function(m,y){var b,_,w,S=this._objects;for(_=0,w=S.length;_<w;_++)b=S[_],!b.excludeFromExport&&this._setSVGObject(m,b,y)},_setSVGObject:function(m,y,b){m.push(y.toSVG(b))},_setSVGBgOverlayImage:function(m,y,b){this[y]&&!this[y].excludeFromExport&&this[y].toSVG&&m.push(this[y].toSVG(b))},_setSVGBgOverlayColor:function(m,y){var b=this[y+"Color"],_=this.viewportTransform,w=this.width,S=this.height;if(!!b)if(b.toLive){var x=b.repeat,T=t.util.invertTransform(_),E=this[y+"Vpt"],I=E?t.util.matrixToSVG(T):"";m.push('<rect transform="'+I+" translate(",w/2,",",S/2,')"',' x="',b.offsetX-w/2,'" y="',b.offsetY-S/2,'" ','width="',x==="repeat-y"||x==="no-repeat"?b.source.width:w,'" height="',x==="repeat-x"||x==="no-repeat"?b.source.height:S,'" fill="url(#SVGID_'+b.id+')"',`></rect>
+`].join("")),m},_setSVGObjects:function(m,y){var b,_,w,S=this._objects;for(_=0,w=S.length;_<w;_++)b=S[_],!b.excludeFromExport&&this._setSVGObject(m,b,y)},_setSVGObject:function(m,y,b){m.push(y.toSVG(b))},_setSVGBgOverlayImage:function(m,y,b){this[y]&&!this[y].excludeFromExport&&this[y].toSVG&&m.push(this[y].toSVG(b))},_setSVGBgOverlayColor:function(m,y){var b=this[y+"Color"],_=this.viewportTransform,w=this.width,S=this.height;if(!!b)if(b.toLive){var x=b.repeat,k=t.util.invertTransform(_),E=this[y+"Vpt"],I=E?t.util.matrixToSVG(k):"";m.push('<rect transform="'+I+" translate(",w/2,",",S/2,')"',' x="',b.offsetX-w/2,'" y="',b.offsetY-S/2,'" ','width="',x==="repeat-y"||x==="no-repeat"?b.source.width:w,'" height="',x==="repeat-x"||x==="no-repeat"?b.source.height:S,'" fill="url(#SVGID_'+b.id+')"',`></rect>
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this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewLowerIndex:function(m,y,b){var _,w;if(b)for(_=y,w=y-1;w>=0;--w){var S=m.intersectsWithObject(this._objects[w])||m.isContainedWithinObject(this._objects[w])||this._objects[w].isContainedWithinObject(m);if(S){_=w;break}}else _=y-1;return _},bringForward:function(m,y){if(!m)return this;var b=this._activeObject,_,w,S,x,T,E=0;if(m===b&&m.type==="activeSelection")for(T=b._objects,_=T.length;_--;)w=T[_],S=this._objects.indexOf(w),S<this._objects.length-1-E&&(x=S+1,u(this._objects,w),this._objects.splice(x,0,w)),E++;else S=this._objects.indexOf(m),S!==this._objects.length-1&&(x=this._findNewUpperIndex(m,S,y),u(this._objects,m),this._objects.splice(x,0,m));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewUpperIndex:function(m,y,b){var _,w,S;if(b)for(_=y,w=y+1,S=this._objects.length;w<S;++w){var x=m.intersectsWithObject(this._objects[w])||m.isContainedWithinObject(this._objects[w])||this._objects[w].isContainedWithinObject(m);if(x){_=w;break}}else _=y+1;return _},moveTo:function(m,y){return u(this._objects,m),this._objects.splice(y,0,m),this.renderOnAddRemove&&this.requestRenderAll()},dispose:function(){return this.isRendering&&(t.util.cancelAnimFrame(this.isRendering),this.isRendering=0),this.forEachObject(function(m){m.dispose&&m.dispose()}),this._objects=[],this.backgroundImage&&this.backgroundImage.dispose&&this.backgroundImage.dispose(),this.backgroundImage=null,this.overlayImage&&this.overlayImage.dispose&&this.overlayImage.dispose(),this.overlayImage=null,this._iTextInstances=null,this.contextContainer=null,this.lowerCanvasEl.classList.remove("lower-canvas"),t.util.setStyle(this.lowerCanvasEl,this._originalCanvasStyle),delete this._originalCanvasStyle,this.lowerCanvasEl.setAttribute("width",this.width),this.lowerCanvasEl.setAttribute("height",this.height),t.util.cleanUpJsdomNode(this.lowerCanvasEl),this.lowerCanvasEl=void 0,this},toString:function(){return"#<fabric.Canvas ("+this.complexity()+"): { objects: "+this._objects.length+" }>"}}),o(t.StaticCanvas.prototype,t.Observable),o(t.StaticCanvas.prototype,t.Collection),o(t.StaticCanvas.prototype,t.DataURLExporter),o(t.StaticCanvas,{EMPTY_JSON:'{"objects": [], "background": "white"}',supports:function(m){var y=h();if(!y||!y.getContext)return null;var b=y.getContext("2d");if(!b)return null;switch(m){case"setLineDash":return typeof b.setLineDash<"u";default:return null}}}),t.StaticCanvas.prototype.toJSON=t.StaticCanvas.prototype.toObject,t.isLikelyNode&&(t.StaticCanvas.prototype.createPNGStream=function(){var m=s(this.lowerCanvasEl);return m&&m.createPNGStream()},t.StaticCanvas.prototype.createJPEGStream=function(m){var y=s(this.lowerCanvasEl);return y&&y.createJPEGStream(m)})}(),t.BaseBrush=t.util.createClass({color:"rgb(0, 0, 0)",width:1,shadow:null,strokeLineCap:"round",strokeLineJoin:"round",strokeMiterLimit:10,strokeDashArray:null,limitedToCanvasSize:!1,_setBrushStyles:function(o){o.strokeStyle=this.color,o.lineWidth=this.width,o.lineCap=this.strokeLineCap,o.miterLimit=this.strokeMiterLimit,o.lineJoin=this.strokeLineJoin,o.setLineDash(this.strokeDashArray||[])},_saveAndTransform:function(o){var a=this.canvas.viewportTransform;o.save(),o.transform(a[0],a[1],a[2],a[3],a[4],a[5])},_setShadow:function(){if(!!this.shadow){var o=this.canvas,a=this.shadow,u=o.contextTop,d=o.getZoom();o&&o._isRetinaScaling()&&(d*=t.devicePixelRatio),u.shadowColor=a.color,u.shadowBlur=a.blur*d,u.shadowOffsetX=a.offsetX*d,u.shadowOffsetY=a.offsetY*d}},needsFullRender:function(){var o=new t.Color(this.color);return o.getAlpha()<1||!!this.shadow},_resetShadow:function(){var o=this.canvas.contextTop;o.shadowColor="",o.shadowBlur=o.shadowOffsetX=o.shadowOffsetY=0},_isOutSideCanvas:function(o){return o.x<0||o.x>this.canvas.getWidth()||o.y<0||o.y>this.canvas.getHeight()}}),function(){t.PencilBrush=t.util.createClass(t.BaseBrush,{decimate:.4,drawStraightLine:!1,straightLineKey:"shiftKey",initialize:function(o){this.canvas=o,this._points=[]},needsFullRender:function(){return this.callSuper("needsFullRender")||this._hasStraightLine},_drawSegment:function(o,a,u){var d=a.midPointFrom(u);return o.quadraticCurveTo(a.x,a.y,d.x,d.y),d},onMouseDown:function(o,a){!this.canvas._isMainEvent(a.e)||(this.drawStraightLine=a.e[this.straightLineKey],this._prepareForDrawing(o),this._captureDrawingPath(o),this._render())},onMouseMove:function(o,a){if(!!this.canvas._isMainEvent(a.e)&&(this.drawStraightLine=a.e[this.straightLineKey],!(this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o))&&this._captureDrawingPath(o)&&this._points.length>1))if(this.needsFullRender())this.canvas.clearContext(this.canvas.contextTop),this._render();else{var u=this._points,d=u.length,c=this.canvas.contextTop;this._saveAndTransform(c),this.oldEnd&&(c.beginPath(),c.moveTo(this.oldEnd.x,this.oldEnd.y)),this.oldEnd=this._drawSegment(c,u[d-2],u[d-1],!0),c.stroke(),c.restore()}},onMouseUp:function(o){return this.canvas._isMainEvent(o.e)?(this.drawStraightLine=!1,this.oldEnd=void 0,this._finalizeAndAddPath(),!1):!0},_prepareForDrawing:function(o){var a=new t.Point(o.x,o.y);this._reset(),this._addPoint(a),this.canvas.contextTop.moveTo(a.x,a.y)},_addPoint:function(o){return this._points.length>1&&o.eq(this._points[this._points.length-1])?!1:(this.drawStraightLine&&this._points.length>1&&(this._hasStraightLine=!0,this._points.pop()),this._points.push(o),!0)},_reset:function(){this._points=[],this._setBrushStyles(this.canvas.contextTop),this._setShadow(),this._hasStraightLine=!1},_captureDrawingPath:function(o){var a=new t.Point(o.x,o.y);return this._addPoint(a)},_render:function(o){var a,u,d=this._points[0],c=this._points[1];if(o=o||this.canvas.contextTop,this._saveAndTransform(o),o.beginPath(),this._points.length===2&&d.x===c.x&&d.y===c.y){var f=this.width/1e3;d=new t.Point(d.x,d.y),c=new t.Point(c.x,c.y),d.x-=f,c.x+=f}for(o.moveTo(d.x,d.y),a=1,u=this._points.length;a<u;a++)this._drawSegment(o,d,c),d=this._points[a],c=this._points[a+1];o.lineTo(d.x,d.y),o.stroke(),o.restore()},convertPointsToSVGPath:function(o){var a=this.width/1e3;return t.util.getSmoothPathFromPoints(o,a)},_isEmptySVGPath:function(o){var a=t.util.joinPath(o);return a==="M 0 0 Q 0 0 0 0 L 0 0"},createPath:function(o){var a=new t.Path(o,{fill:null,stroke:this.color,strokeWidth:this.width,strokeLineCap:this.strokeLineCap,strokeMiterLimit:this.strokeMiterLimit,strokeLineJoin:this.strokeLineJoin,strokeDashArray:this.strokeDashArray});return this.shadow&&(this.shadow.affectStroke=!0,a.shadow=new t.Shadow(this.shadow)),a},decimatePoints:function(o,a){if(o.length<=2)return o;var u=this.canvas.getZoom(),d=Math.pow(a/u,2),c,f=o.length-1,s=o[0],h=[s],v;for(c=1;c<f-1;c++)v=Math.pow(s.x-o[c].x,2)+Math.pow(s.y-o[c].y,2),v>=d&&(s=o[c],h.push(s));return h.push(o[f]),h},_finalizeAndAddPath:function(){var o=this.canvas.contextTop;o.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate));var a=this.convertPointsToSVGPath(this._points);if(this._isEmptySVGPath(a)){this.canvas.requestRenderAll();return}var u=this.createPath(a);this.canvas.clearContext(this.canvas.contextTop),this.canvas.fire("before:path:created",{path:u}),this.canvas.add(u),this.canvas.requestRenderAll(),u.setCoords(),this._resetShadow(),this.canvas.fire("path:created",{path:u})}})}(),t.CircleBrush=t.util.createClass(t.BaseBrush,{width:10,initialize:function(o){this.canvas=o,this.points=[]},drawDot:function(o){var a=this.addPoint(o),u=this.canvas.contextTop;this._saveAndTransform(u),this.dot(u,a),u.restore()},dot:function(o,a){o.fillStyle=a.fill,o.beginPath(),o.arc(a.x,a.y,a.radius,0,Math.PI*2,!1),o.closePath(),o.fill()},onMouseDown:function(o){this.points.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.drawDot(o)},_render:function(){var o=this.canvas.contextTop,a,u,d=this.points;for(this._saveAndTransform(o),a=0,u=d.length;a<u;a++)this.dot(o,d[a]);o.restore()},onMouseMove:function(o){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o)||(this.needsFullRender()?(this.canvas.clearContext(this.canvas.contextTop),this.addPoint(o),this._render()):this.drawDot(o))},onMouseUp:function(){var o=this.canvas.renderOnAddRemove,a,u;this.canvas.renderOnAddRemove=!1;var d=[];for(a=0,u=this.points.length;a<u;a++){var c=this.points[a],f=new t.Circle({radius:c.radius,left:c.x,top:c.y,originX:"center",originY:"center",fill:c.fill});this.shadow&&(f.shadow=new t.Shadow(this.shadow)),d.push(f)}var s=new t.Group(d);s.canvas=this.canvas,this.canvas.fire("before:path:created",{path:s}),this.canvas.add(s),this.canvas.fire("path:created",{path:s}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=o,this.canvas.requestRenderAll()},addPoint:function(o){var a=new t.Point(o.x,o.y),u=t.util.getRandomInt(Math.max(0,this.width-20),this.width+20)/2,d=new t.Color(this.color).setAlpha(t.util.getRandomInt(0,100)/100).toRgba();return a.radius=u,a.fill=d,this.points.push(a),a}}),t.SprayBrush=t.util.createClass(t.BaseBrush,{width:10,density:20,dotWidth:1,dotWidthVariance:1,randomOpacity:!1,optimizeOverlapping:!0,initialize:function(o){this.canvas=o,this.sprayChunks=[]},onMouseDown:function(o){this.sprayChunks.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.addSprayChunk(o),this.render(this.sprayChunkPoints)},onMouseMove:function(o){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o)||(this.addSprayChunk(o),this.render(this.sprayChunkPoints))},onMouseUp:function(){var o=this.canvas.renderOnAddRemove;this.canvas.renderOnAddRemove=!1;for(var a=[],u=0,d=this.sprayChunks.length;u<d;u++)for(var c=this.sprayChunks[u],f=0,s=c.length;f<s;f++){var h=new t.Rect({width:c[f].width,height:c[f].width,left:c[f].x+1,top:c[f].y+1,originX:"center",originY:"center",fill:this.color});a.push(h)}this.optimizeOverlapping&&(a=this._getOptimizedRects(a));var v=new t.Group(a);this.shadow&&v.set("shadow",new t.Shadow(this.shadow)),this.canvas.fire("before:path:created",{path:v}),this.canvas.add(v),this.canvas.fire("path:created",{path:v}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=o,this.canvas.requestRenderAll()},_getOptimizedRects:function(o){var a={},u,d,c;for(d=0,c=o.length;d<c;d++)u=o[d].left+""+o[d].top,a[u]||(a[u]=o[d]);var f=[];for(u in a)f.push(a[u]);return f},render:function(o){var a=this.canvas.contextTop,u,d;for(a.fillStyle=this.color,this._saveAndTransform(a),u=0,d=o.length;u<d;u++){var c=o[u];typeof c.opacity<"u"&&(a.globalAlpha=c.opacity),a.fillRect(c.x,c.y,c.width,c.width)}a.restore()},_render:function(){var o=this.canvas.contextTop,a,u;for(o.fillStyle=this.color,this._saveAndTransform(o),a=0,u=this.sprayChunks.length;a<u;a++)this.render(this.sprayChunks[a]);o.restore()},addSprayChunk:function(o){this.sprayChunkPoints=[];var a,u,d,c=this.width/2,f;for(f=0;f<this.density;f++){a=t.util.getRandomInt(o.x-c,o.x+c),u=t.util.getRandomInt(o.y-c,o.y+c),this.dotWidthVariance?d=t.util.getRandomInt(Math.max(1,this.dotWidth-this.dotWidthVariance),this.dotWidth+this.dotWidthVariance):d=this.dotWidth;var s=new t.Point(a,u);s.width=d,this.randomOpacity&&(s.opacity=t.util.getRandomInt(0,100)/100),this.sprayChunkPoints.push(s)}this.sprayChunks.push(this.sprayChunkPoints)}}),t.PatternBrush=t.util.createClass(t.PencilBrush,{getPatternSrc:function(){var o=20,a=5,u=t.util.createCanvasElement(),d=u.getContext("2d");return u.width=u.height=o+a,d.fillStyle=this.color,d.beginPath(),d.arc(o/2,o/2,o/2,0,Math.PI*2,!1),d.closePath(),d.fill(),u},getPatternSrcFunction:function(){return String(this.getPatternSrc).replace("this.color",'"'+this.color+'"')},getPattern:function(o){return o.createPattern(this.source||this.getPatternSrc(),"repeat")},_setBrushStyles:function(o){this.callSuper("_setBrushStyles",o),o.strokeStyle=this.getPattern(o)},createPath:function(o){var a=this.callSuper("createPath",o),u=a._getLeftTopCoords().scalarAdd(a.strokeWidth/2);return a.stroke=new t.Pattern({source:this.source||this.getPatternSrcFunction(),offsetX:-u.x,offsetY:-u.y}),a}}),function(){var o=t.util.getPointer,a=t.util.degreesToRadians,u=t.util.isTouchEvent;t.Canvas=t.util.createClass(t.StaticCanvas,{initialize:function(c,f){f||(f={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(c,f),this._initInteractive(),this._createCacheCanvas()},uniformScaling:!0,uniScaleKey:"shiftKey",centeredScaling:!1,centeredRotation:!1,centeredKey:"altKey",altActionKey:"shiftKey",interactive:!0,selection:!0,selectionKey:"shiftKey",altSelectionKey:null,selectionColor:"rgba(100, 100, 255, 0.3)",selectionDashArray:[],selectionBorderColor:"rgba(255, 255, 255, 0.3)",selectionLineWidth:1,selectionFullyContained:!1,hoverCursor:"move",moveCursor:"move",defaultCursor:"default",freeDrawingCursor:"crosshair",notAllowedCursor:"not-allowed",containerClass:"canvas-container",perPixelTargetFind:!1,targetFindTolerance:0,skipTargetFind:!1,isDrawingMode:!1,preserveObjectStacking:!1,snapAngle:0,snapThreshold:null,stopContextMenu:!1,fireRightClick:!1,fireMiddleClick:!1,targets:[],enablePointerEvents:!1,_hoveredTarget:null,_hoveredTargets:[],_initInteractive:function(){this._currentTransform=null,this._groupSelector=null,this._initWrapperElement(),this._createUpperCanvas(),this._initEventListeners(),this._initRetinaScaling(),this.freeDrawingBrush=t.PencilBrush&&new t.PencilBrush(this),this.calcOffset()},_chooseObjectsToRender:function(){var c=this.getActiveObjects(),f,s,h;if(c.length>0&&!this.preserveObjectStacking){s=[],h=[];for(var v=0,m=this._objects.length;v<m;v++)f=this._objects[v],c.indexOf(f)===-1?s.push(f):h.push(f);c.length>1&&(this._activeObject._objects=h),s.push.apply(s,h)}else s=this._objects;return s},renderAll:function(){this.contextTopDirty&&!this._groupSelector&&!this.isDrawingMode&&(this.clearContext(this.contextTop),this.contextTopDirty=!1),this.hasLostContext&&(this.renderTopLayer(this.contextTop),this.hasLostContext=!1);var c=this.contextContainer;return this.renderCanvas(c,this._chooseObjectsToRender()),this},renderTopLayer:function(c){c.save(),this.isDrawingMode&&this._isCurrentlyDrawing&&(this.freeDrawingBrush&&this.freeDrawingBrush._render(),this.contextTopDirty=!0),this.selection&&this._groupSelector&&(this._drawSelection(c),this.contextTopDirty=!0),c.restore()},renderTop:function(){var c=this.contextTop;return this.clearContext(c),this.renderTopLayer(c),this.fire("after:render"),this},_normalizePointer:function(c,f){var s=c.calcTransformMatrix(),h=t.util.invertTransform(s),v=this.restorePointerVpt(f);return t.util.transformPoint(v,h)},isTargetTransparent:function(c,f,s){if(c.shouldCache()&&c._cacheCanvas&&c!==this._activeObject){var h=this._normalizePointer(c,{x:f,y:s}),v=Math.max(c.cacheTranslationX+h.x*c.zoomX,0),m=Math.max(c.cacheTranslationY+h.y*c.zoomY,0),w=t.util.isTransparent(c._cacheContext,Math.round(v),Math.round(m),this.targetFindTolerance);return w}var y=this.contextCache,b=c.selectionBackgroundColor,_=this.viewportTransform;c.selectionBackgroundColor="",this.clearContext(y),y.save(),y.transform(_[0],_[1],_[2],_[3],_[4],_[5]),c.render(y),y.restore(),c.selectionBackgroundColor=b;var w=t.util.isTransparent(y,f,s,this.targetFindTolerance);return w},_isSelectionKeyPressed:function(c){var f=!1;return Array.isArray(this.selectionKey)?f=!!this.selectionKey.find(function(s){return c[s]===!0}):f=c[this.selectionKey],f},_shouldClearSelection:function(c,f){var s=this.getActiveObjects(),h=this._activeObject;return!f||f&&h&&s.length>1&&s.indexOf(f)===-1&&h!==f&&!this._isSelectionKeyPressed(c)||f&&!f.evented||f&&!f.selectable&&h&&h!==f},_shouldCenterTransform:function(c,f,s){if(!!c){var h;return f==="scale"||f==="scaleX"||f==="scaleY"||f==="resizing"?h=this.centeredScaling||c.centeredScaling:f==="rotate"&&(h=this.centeredRotation||c.centeredRotation),h?!s:s}},_getOriginFromCorner:function(c,f){var s={x:c.originX,y:c.originY};return f==="ml"||f==="tl"||f==="bl"?s.x="right":(f==="mr"||f==="tr"||f==="br")&&(s.x="left"),f==="tl"||f==="mt"||f==="tr"?s.y="bottom":(f==="bl"||f==="mb"||f==="br")&&(s.y="top"),s},_getActionFromCorner:function(c,f,s,h){if(!f||!c)return"drag";var v=h.controls[f];return v.getActionName(s,v,h)},_setupCurrentTransform:function(c,f,s){if(!!f){var h=this.getPointer(c),v=f.__corner,m=f.controls[v],y=s&&v?m.getActionHandler(c,f,m):t.controlsUtils.dragHandler,b=this._getActionFromCorner(s,v,c,f),_=this._getOriginFromCorner(f,v),w=c[this.centeredKey],S={target:f,action:b,actionHandler:y,corner:v,scaleX:f.scaleX,scaleY:f.scaleY,skewX:f.skewX,skewY:f.skewY,offsetX:h.x-f.left,offsetY:h.y-f.top,originX:_.x,originY:_.y,ex:h.x,ey:h.y,lastX:h.x,lastY:h.y,theta:a(f.angle),width:f.width*f.scaleX,shiftKey:c.shiftKey,altKey:w,original:t.util.saveObjectTransform(f)};this._shouldCenterTransform(f,b,w)&&(S.originX="center",S.originY="center"),S.original.originX=_.x,S.original.originY=_.y,this._currentTransform=S,this._beforeTransform(c)}},setCursor:function(c){this.upperCanvasEl.style.cursor=c},_drawSelection:function(c){var f=this._groupSelector,s=new t.Point(f.ex,f.ey),h=t.util.transformPoint(s,this.viewportTransform),v=new t.Point(f.ex+f.left,f.ey+f.top),m=t.util.transformPoint(v,this.viewportTransform),y=Math.min(h.x,m.x),b=Math.min(h.y,m.y),_=Math.max(h.x,m.x),w=Math.max(h.y,m.y),S=this.selectionLineWidth/2;this.selectionColor&&(c.fillStyle=this.selectionColor,c.fillRect(y,b,_-y,w-b)),!(!this.selectionLineWidth||!this.selectionBorderColor)&&(c.lineWidth=this.selectionLineWidth,c.strokeStyle=this.selectionBorderColor,y+=S,b+=S,_-=S,w-=S,t.Object.prototype._setLineDash.call(this,c,this.selectionDashArray),c.strokeRect(y,b,_-y,w-b))},findTarget:function(c,f){if(!this.skipTargetFind){var s=!0,h=this.getPointer(c,s),v=this._activeObject,m=this.getActiveObjects(),y,b,_=u(c),w=m.length>1&&!f||m.length===1;if(this.targets=[],w&&v._findTargetCorner(h,_)||m.length>1&&!f&&v===this._searchPossibleTargets([v],h))return v;if(m.length===1&&v===this._searchPossibleTargets([v],h))if(this.preserveObjectStacking)y=v,b=this.targets,this.targets=[];else return v;var S=this._searchPossibleTargets(this._objects,h);return c[this.altSelectionKey]&&S&&y&&S!==y&&(S=y,this.targets=b),S}},_checkTarget:function(c,f,s){if(f&&f.visible&&f.evented&&f.containsPoint(c))if((this.perPixelTargetFind||f.perPixelTargetFind)&&!f.isEditing){var h=this.isTargetTransparent(f,s.x,s.y);if(!h)return!0}else return!0},_searchPossibleTargets:function(c,f){for(var s,h=c.length,v;h--;){var m=c[h],y=m.group?this._normalizePointer(m.group,f):f;if(this._checkTarget(y,m,f)){s=c[h],s.subTargetCheck&&s instanceof t.Group&&(v=this._searchPossibleTargets(s._objects,f),v&&this.targets.push(v));break}}return s},restorePointerVpt:function(c){return t.util.transformPoint(c,t.util.invertTransform(this.viewportTransform))},getPointer:function(c,f){if(this._absolutePointer&&!f)return this._absolutePointer;if(this._pointer&&f)return this._pointer;var s=o(c),h=this.upperCanvasEl,v=h.getBoundingClientRect(),m=v.width||0,y=v.height||0,b;(!m||!y)&&("top"in v&&"bottom"in v&&(y=Math.abs(v.top-v.bottom)),"right"in v&&"left"in v&&(m=Math.abs(v.right-v.left))),this.calcOffset(),s.x=s.x-this._offset.left,s.y=s.y-this._offset.top,f||(s=this.restorePointerVpt(s));var _=this.getRetinaScaling();return _!==1&&(s.x/=_,s.y/=_),m===0||y===0?b={width:1,height:1}:b={width:h.width/m,height:h.height/y},{x:s.x*b.width,y:s.y*b.height}},_createUpperCanvas:function(){var c=this.lowerCanvasEl.className.replace(/\s*lower-canvas\s*/,""),f=this.lowerCanvasEl,s=this.upperCanvasEl;s?s.className="":(s=this._createCanvasElement(),this.upperCanvasEl=s),t.util.addClass(s,"upper-canvas "+c),this.wrapperEl.appendChild(s),this._copyCanvasStyle(f,s),this._applyCanvasStyle(s),this.contextTop=s.getContext("2d")},getTopContext:function(){return this.contextTop},_createCacheCanvas:function(){this.cacheCanvasEl=this._createCanvasElement(),this.cacheCanvasEl.setAttribute("width",this.width),this.cacheCanvasEl.setAttribute("height",this.height),this.contextCache=this.cacheCanvasEl.getContext("2d")},_initWrapperElement:function(){this.wrapperEl=t.util.wrapElement(this.lowerCanvasEl,"div",{class:this.containerClass}),t.util.setStyle(this.wrapperEl,{width:this.width+"px",height:this.height+"px",position:"relative"}),t.util.makeElementUnselectable(this.wrapperEl)},_applyCanvasStyle:function(c){var f=this.width||c.width,s=this.height||c.height;t.util.setStyle(c,{position:"absolute",width:f+"px",height:s+"px",left:0,top:0,"touch-action":this.allowTouchScrolling?"manipulation":"none","-ms-touch-action":this.allowTouchScrolling?"manipulation":"none"}),c.width=f,c.height=s,t.util.makeElementUnselectable(c)},_copyCanvasStyle:function(c,f){f.style.cssText=c.style.cssText},getSelectionContext:function(){return this.contextTop},getSelectionElement:function(){return this.upperCanvasEl},getActiveObject:function(){return this._activeObject},getActiveObjects:function(){var c=this._activeObject;return c?c.type==="activeSelection"&&c._objects?c._objects.slice(0):[c]:[]},_onObjectRemoved:function(c){c===this._activeObject&&(this.fire("before:selection:cleared",{target:c}),this._discardActiveObject(),this.fire("selection:cleared",{target:c}),c.fire("deselected")),c===this._hoveredTarget&&(this._hoveredTarget=null,this._hoveredTargets=[]),this.callSuper("_onObjectRemoved",c)},_fireSelectionEvents:function(c,f){var s=!1,h=this.getActiveObjects(),v=[],m=[];c.forEach(function(y){h.indexOf(y)===-1&&(s=!0,y.fire("deselected",{e:f,target:y}),m.push(y))}),h.forEach(function(y){c.indexOf(y)===-1&&(s=!0,y.fire("selected",{e:f,target:y}),v.push(y))}),c.length>0&&h.length>0?s&&this.fire("selection:updated",{e:f,selected:v,deselected:m}):h.length>0?this.fire("selection:created",{e:f,selected:v}):c.length>0&&this.fire("selection:cleared",{e:f,deselected:m})},setActiveObject:function(c,f){var s=this.getActiveObjects();return this._setActiveObject(c,f),this._fireSelectionEvents(s,f),this},_setActiveObject:function(c,f){return this._activeObject===c||!this._discardActiveObject(f,c)||c.onSelect({e:f})?!1:(this._activeObject=c,!0)},_discardActiveObject:function(c,f){var s=this._activeObject;if(s){if(s.onDeselect({e:c,object:f}))return!1;this._activeObject=null}return!0},discardActiveObject:function(c){var f=this.getActiveObjects(),s=this.getActiveObject();return f.length&&this.fire("before:selection:cleared",{target:s,e:c}),this._discardActiveObject(c),this._fireSelectionEvents(f,c),this},dispose:function(){var c=this.wrapperEl;return this.removeListeners(),c.removeChild(this.upperCanvasEl),c.removeChild(this.lowerCanvasEl),this.contextCache=null,this.contextTop=null,["upperCanvasEl","cacheCanvasEl"].forEach(function(f){t.util.cleanUpJsdomNode(this[f]),this[f]=void 0}.bind(this)),c.parentNode&&c.parentNode.replaceChild(this.lowerCanvasEl,this.wrapperEl),delete this.wrapperEl,t.StaticCanvas.prototype.dispose.call(this),this},clear:function(){return this.discardActiveObject(),this.clearContext(this.contextTop),this.callSuper("clear")},drawControls:function(c){var f=this._activeObject;f&&f._renderControls(c)},_toObject:function(c,f,s){var h=this._realizeGroupTransformOnObject(c),v=this.callSuper("_toObject",c,f,s);return this._unwindGroupTransformOnObject(c,h),v},_realizeGroupTransformOnObject:function(c){if(c.group&&c.group.type==="activeSelection"&&this._activeObject===c.group){var f=["angle","flipX","flipY","left","scaleX","scaleY","skewX","skewY","top"],s={};return f.forEach(function(h){s[h]=c[h]}),t.util.addTransformToObject(c,this._activeObject.calcOwnMatrix()),s}else return null},_unwindGroupTransformOnObject:function(c,f){f&&c.set(f)},_setSVGObject:function(c,f,s){var h=this._realizeGroupTransformOnObject(f);this.callSuper("_setSVGObject",c,f,s),this._unwindGroupTransformOnObject(f,h)},setViewportTransform:function(c){this.renderOnAddRemove&&this._activeObject&&this._activeObject.isEditing&&this._activeObject.clearContextTop(),t.StaticCanvas.prototype.setViewportTransform.call(this,c)}});for(var d in t.StaticCanvas)d!=="prototype"&&(t.Canvas[d]=t.StaticCanvas[d])}(),function(){var o=t.util.addListener,a=t.util.removeListener,u=3,d=2,c=1,f={passive:!1};function s(h,v){return h.button&&h.button===v-1}t.util.object.extend(t.Canvas.prototype,{mainTouchId:null,_initEventListeners:function(){this.removeListeners(),this._bindEvents(),this.addOrRemove(o,"add")},_getEventPrefix:function(){return this.enablePointerEvents?"pointer":"mouse"},addOrRemove:function(h,v){var m=this.upperCanvasEl,y=this._getEventPrefix();h(t.window,"resize",this._onResize),h(m,y+"down",this._onMouseDown),h(m,y+"move",this._onMouseMove,f),h(m,y+"out",this._onMouseOut),h(m,y+"enter",this._onMouseEnter),h(m,"wheel",this._onMouseWheel),h(m,"contextmenu",this._onContextMenu),h(m,"dblclick",this._onDoubleClick),h(m,"dragover",this._onDragOver),h(m,"dragenter",this._onDragEnter),h(m,"dragleave",this._onDragLeave),h(m,"drop",this._onDrop),this.enablePointerEvents||h(m,"touchstart",this._onTouchStart,f),typeof eventjs<"u"&&v in eventjs&&(eventjs[v](m,"gesture",this._onGesture),eventjs[v](m,"drag",this._onDrag),eventjs[v](m,"orientation",this._onOrientationChange),eventjs[v](m,"shake",this._onShake),eventjs[v](m,"longpress",this._onLongPress))},removeListeners:function(){this.addOrRemove(a,"remove");var h=this._getEventPrefix();a(t.document,h+"up",this._onMouseUp),a(t.document,"touchend",this._onTouchEnd,f),a(t.document,h+"move",this._onMouseMove,f),a(t.document,"touchmove",this._onMouseMove,f)},_bindEvents:function(){this.eventsBound||(this._onMouseDown=this._onMouseDown.bind(this),this._onTouchStart=this._onTouchStart.bind(this),this._onMouseMove=this._onMouseMove.bind(this),this._onMouseUp=this._onMouseUp.bind(this),this._onTouchEnd=this._onTouchEnd.bind(this),this._onResize=this._onResize.bind(this),this._onGesture=this._onGesture.bind(this),this._onDrag=this._onDrag.bind(this),this._onShake=this._onShake.bind(this),this._onLongPress=this._onLongPress.bind(this),this._onOrientationChange=this._onOrientationChange.bind(this),this._onMouseWheel=this._onMouseWheel.bind(this),this._onMouseOut=this._onMouseOut.bind(this),this._onMouseEnter=this._onMouseEnter.bind(this),this._onContextMenu=this._onContextMenu.bind(this),this._onDoubleClick=this._onDoubleClick.bind(this),this._onDragOver=this._onDragOver.bind(this),this._onDragEnter=this._simpleEventHandler.bind(this,"dragenter"),this._onDragLeave=this._simpleEventHandler.bind(this,"dragleave"),this._onDrop=this._onDrop.bind(this),this.eventsBound=!0)},_onGesture:function(h,v){this.__onTransformGesture&&this.__onTransformGesture(h,v)},_onDrag:function(h,v){this.__onDrag&&this.__onDrag(h,v)},_onMouseWheel:function(h){this.__onMouseWheel(h)},_onMouseOut:function(h){var v=this._hoveredTarget;this.fire("mouse:out",{target:v,e:h}),this._hoveredTarget=null,v&&v.fire("mouseout",{e:h});var m=this;this._hoveredTargets.forEach(function(y){m.fire("mouse:out",{target:v,e:h}),y&&v.fire("mouseout",{e:h})}),this._hoveredTargets=[]},_onMouseEnter:function(h){!this._currentTransform&&!this.findTarget(h)&&(this.fire("mouse:over",{target:null,e:h}),this._hoveredTarget=null,this._hoveredTargets=[])},_onOrientationChange:function(h,v){this.__onOrientationChange&&this.__onOrientationChange(h,v)},_onShake:function(h,v){this.__onShake&&this.__onShake(h,v)},_onLongPress:function(h,v){this.__onLongPress&&this.__onLongPress(h,v)},_onDragOver:function(h){h.preventDefault();var v=this._simpleEventHandler("dragover",h);this._fireEnterLeaveEvents(v,h)},_onDrop:function(h){return this._simpleEventHandler("drop:before",h),this._simpleEventHandler("drop",h)},_onContextMenu:function(h){return this.stopContextMenu&&(h.stopPropagation(),h.preventDefault()),!1},_onDoubleClick:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"dblclick"),this._resetTransformEventData(h)},getPointerId:function(h){var v=h.changedTouches;return v?v[0]&&v[0].identifier:this.enablePointerEvents?h.pointerId:-1},_isMainEvent:function(h){return h.isPrimary===!0?!0:h.isPrimary===!1?!1:h.type==="touchend"&&h.touches.length===0?!0:h.changedTouches?h.changedTouches[0].identifier===this.mainTouchId:!0},_onTouchStart:function(h){h.preventDefault(),this.mainTouchId===null&&(this.mainTouchId=this.getPointerId(h)),this.__onMouseDown(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();o(t.document,"touchend",this._onTouchEnd,f),o(t.document,"touchmove",this._onMouseMove,f),a(v,m+"down",this._onMouseDown)},_onMouseDown:function(h){this.__onMouseDown(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();a(v,m+"move",this._onMouseMove,f),o(t.document,m+"up",this._onMouseUp),o(t.document,m+"move",this._onMouseMove,f)},_onTouchEnd:function(h){if(!(h.touches.length>0)){this.__onMouseUp(h),this._resetTransformEventData(),this.mainTouchId=null;var v=this._getEventPrefix();a(t.document,"touchend",this._onTouchEnd,f),a(t.document,"touchmove",this._onMouseMove,f);var m=this;this._willAddMouseDown&&clearTimeout(this._willAddMouseDown),this._willAddMouseDown=setTimeout(function(){o(m.upperCanvasEl,v+"down",m._onMouseDown),m._willAddMouseDown=0},400)}},_onMouseUp:function(h){this.__onMouseUp(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();this._isMainEvent(h)&&(a(t.document,m+"up",this._onMouseUp),a(t.document,m+"move",this._onMouseMove,f),o(v,m+"move",this._onMouseMove,f))},_onMouseMove:function(h){!this.allowTouchScrolling&&h.preventDefault&&h.preventDefault(),this.__onMouseMove(h)},_onResize:function(){this.calcOffset()},_shouldRender:function(h){var v=this._activeObject;return!!v!=!!h||v&&h&&v!==h?!0:(v&&v.isEditing,!1)},__onMouseUp:function(h){var v,m=this._currentTransform,y=this._groupSelector,b=!1,_=!y||y.left===0&&y.top===0;if(this._cacheTransformEventData(h),v=this._target,this._handleEvent(h,"up:before"),s(h,u)){this.fireRightClick&&this._handleEvent(h,"up",u,_);return}if(s(h,d)){this.fireMiddleClick&&this._handleEvent(h,"up",d,_),this._resetTransformEventData();return}if(this.isDrawingMode&&this._isCurrentlyDrawing){this._onMouseUpInDrawingMode(h);return}if(!!this._isMainEvent(h)){if(m&&(this._finalizeCurrentTransform(h),b=m.actionPerformed),!_){var w=v===this._activeObject;this._maybeGroupObjects(h),b||(b=this._shouldRender(v)||!w&&v===this._activeObject)}var S,x;if(v){if(S=v._findTargetCorner(this.getPointer(h,!0),t.util.isTouchEvent(h)),v.selectable&&v!==this._activeObject&&v.activeOn==="up")this.setActiveObject(v,h),b=!0;else{var T=v.controls[S],E=T&&T.getMouseUpHandler(h,v,T);E&&(x=this.getPointer(h),E(h,m,x.x,x.y))}v.isMoving=!1}if(m&&(m.target!==v||m.corner!==S)){var I=m.target&&m.target.controls[m.corner],D=I&&I.getMouseUpHandler(h,v,T);x=x||this.getPointer(h),D&&D(h,m,x.x,x.y)}this._setCursorFromEvent(h,v),this._handleEvent(h,"up",c,_),this._groupSelector=null,this._currentTransform=null,v&&(v.__corner=0),b?this.requestRenderAll():_||this.renderTop()}},_simpleEventHandler:function(h,v){var m=this.findTarget(v),y=this.targets,b={e:v,target:m,subTargets:y};if(this.fire(h,b),m&&m.fire(h,b),!y)return m;for(var _=0;_<y.length;_++)y[_].fire(h,b);return m},_handleEvent:function(h,v,m,y){var b=this._target,_=this.targets||[],w={e:h,target:b,subTargets:_,button:m||c,isClick:y||!1,pointer:this._pointer,absolutePointer:this._absolutePointer,transform:this._currentTransform};v==="up"&&(w.currentTarget=this.findTarget(h),w.currentSubTargets=this.targets),this.fire("mouse:"+v,w),b&&b.fire("mouse"+v,w);for(var S=0;S<_.length;S++)_[S].fire("mouse"+v,w)},_finalizeCurrentTransform:function(h){var v=this._currentTransform,m=v.target,y={e:h,target:m,transform:v,action:v.action};m._scaling&&(m._scaling=!1),m.setCoords(),(v.actionPerformed||this.stateful&&m.hasStateChanged())&&this._fire("modified",y)},_onMouseDownInDrawingMode:function(h){this._isCurrentlyDrawing=!0,this.getActiveObject()&&this.discardActiveObject(h).requestRenderAll();var v=this.getPointer(h);this.freeDrawingBrush.onMouseDown(v,{e:h,pointer:v}),this._handleEvent(h,"down")},_onMouseMoveInDrawingMode:function(h){if(this._isCurrentlyDrawing){var v=this.getPointer(h);this.freeDrawingBrush.onMouseMove(v,{e:h,pointer:v})}this.setCursor(this.freeDrawingCursor),this._handleEvent(h,"move")},_onMouseUpInDrawingMode:function(h){var v=this.getPointer(h);this._isCurrentlyDrawing=this.freeDrawingBrush.onMouseUp({e:h,pointer:v}),this._handleEvent(h,"up")},__onMouseDown:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"down:before");var v=this._target;if(s(h,u)){this.fireRightClick&&this._handleEvent(h,"down",u);return}if(s(h,d)){this.fireMiddleClick&&this._handleEvent(h,"down",d);return}if(this.isDrawingMode){this._onMouseDownInDrawingMode(h);return}if(!!this._isMainEvent(h)&&!this._currentTransform){var m=this._pointer;this._previousPointer=m;var y=this._shouldRender(v),b=this._shouldGroup(h,v);if(this._shouldClearSelection(h,v)?this.discardActiveObject(h):b&&(this._handleGrouping(h,v),v=this._activeObject),this.selection&&(!v||!v.selectable&&!v.isEditing&&v!==this._activeObject)&&(this._groupSelector={ex:this._absolutePointer.x,ey:this._absolutePointer.y,top:0,left:0}),v){var _=v===this._activeObject;v.selectable&&v.activeOn==="down"&&this.setActiveObject(v,h);var w=v._findTargetCorner(this.getPointer(h,!0),t.util.isTouchEvent(h));if(v.__corner=w,v===this._activeObject&&(w||!b)){this._setupCurrentTransform(h,v,_);var S=v.controls[w],m=this.getPointer(h),x=S&&S.getMouseDownHandler(h,v,S);x&&x(h,this._currentTransform,m.x,m.y)}}this._handleEvent(h,"down"),(y||b)&&this.requestRenderAll()}},_resetTransformEventData:function(){this._target=null,this._pointer=null,this._absolutePointer=null},_cacheTransformEventData:function(h){this._resetTransformEventData(),this._pointer=this.getPointer(h,!0),this._absolutePointer=this.restorePointerVpt(this._pointer),this._target=this._currentTransform?this._currentTransform.target:this.findTarget(h)||null},_beforeTransform:function(h){var v=this._currentTransform;this.stateful&&v.target.saveState(),this.fire("before:transform",{e:h,transform:v})},__onMouseMove:function(h){this._handleEvent(h,"move:before"),this._cacheTransformEventData(h);var v,m;if(this.isDrawingMode){this._onMouseMoveInDrawingMode(h);return}if(!!this._isMainEvent(h)){var y=this._groupSelector;y?(m=this._absolutePointer,y.left=m.x-y.ex,y.top=m.y-y.ey,this.renderTop()):this._currentTransform?this._transformObject(h):(v=this.findTarget(h)||null,this._setCursorFromEvent(h,v),this._fireOverOutEvents(v,h)),this._handleEvent(h,"move"),this._resetTransformEventData()}},_fireOverOutEvents:function(h,v){var m=this._hoveredTarget,y=this._hoveredTargets,b=this.targets,_=Math.max(y.length,b.length);this.fireSyntheticInOutEvents(h,v,{oldTarget:m,evtOut:"mouseout",canvasEvtOut:"mouse:out",evtIn:"mouseover",canvasEvtIn:"mouse:over"});for(var w=0;w<_;w++)this.fireSyntheticInOutEvents(b[w],v,{oldTarget:y[w],evtOut:"mouseout",evtIn:"mouseover"});this._hoveredTarget=h,this._hoveredTargets=this.targets.concat()},_fireEnterLeaveEvents:function(h,v){var m=this._draggedoverTarget,y=this._hoveredTargets,b=this.targets,_=Math.max(y.length,b.length);this.fireSyntheticInOutEvents(h,v,{oldTarget:m,evtOut:"dragleave",evtIn:"dragenter"});for(var w=0;w<_;w++)this.fireSyntheticInOutEvents(b[w],v,{oldTarget:y[w],evtOut:"dragleave",evtIn:"dragenter"});this._draggedoverTarget=h},fireSyntheticInOutEvents:function(h,v,m){var y,b,_=m.oldTarget,w,S,x=_!==h,T=m.canvasEvtIn,E=m.canvasEvtOut;x&&(y={e:v,target:h,previousTarget:_},b={e:v,target:_,nextTarget:h}),S=h&&x,w=_&&x,w&&(E&&this.fire(E,b),_.fire(m.evtOut,b)),S&&(T&&this.fire(T,y),h.fire(m.evtIn,y))},__onMouseWheel:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"wheel"),this._resetTransformEventData()},_transformObject:function(h){var v=this.getPointer(h),m=this._currentTransform;m.reset=!1,m.shiftKey=h.shiftKey,m.altKey=h[this.centeredKey],this._performTransformAction(h,m,v),m.actionPerformed&&this.requestRenderAll()},_performTransformAction:function(h,v,m){var y=m.x,b=m.y,_=v.action,w=!1,S=v.actionHandler;S&&(w=S(h,v,y,b)),_==="drag"&&w&&(v.target.isMoving=!0,this.setCursor(v.target.moveCursor||this.moveCursor)),v.actionPerformed=v.actionPerformed||w},_fire:t.controlsUtils.fireEvent,_setCursorFromEvent:function(h,v){if(!v)return this.setCursor(this.defaultCursor),!1;var m=v.hoverCursor||this.hoverCursor,y=this._activeObject&&this._activeObject.type==="activeSelection"?this._activeObject:null,b=(!y||!y.contains(v))&&v._findTargetCorner(this.getPointer(h,!0));b?this.setCursor(this.getCornerCursor(b,v,h)):(v.subTargetCheck&&this.targets.concat().reverse().map(function(_){m=_.hoverCursor||m}),this.setCursor(m))},getCornerCursor:function(h,v,m){var y=v.controls[h];return y.cursorStyleHandler(m,y,v)}})}(),function(){var o=Math.min,a=Math.max;t.util.object.extend(t.Canvas.prototype,{_shouldGroup:function(u,d){var c=this._activeObject;return c&&this._isSelectionKeyPressed(u)&&d&&d.selectable&&this.selection&&(c!==d||c.type==="activeSelection")&&!d.onSelect({e:u})},_handleGrouping:function(u,d){var c=this._activeObject;c.__corner||d===c&&(d=this.findTarget(u,!0),!d||!d.selectable)||(c&&c.type==="activeSelection"?this._updateActiveSelection(d,u):this._createActiveSelection(d,u))},_updateActiveSelection:function(u,d){var c=this._activeObject,f=c._objects.slice(0);c.contains(u)?(c.removeWithUpdate(u),this._hoveredTarget=u,this._hoveredTargets=this.targets.concat(),c.size()===1&&this._setActiveObject(c.item(0),d)):(c.addWithUpdate(u),this._hoveredTarget=c,this._hoveredTargets=this.targets.concat()),this._fireSelectionEvents(f,d)},_createActiveSelection:function(u,d){var c=this.getActiveObjects(),f=this._createGroup(u);this._hoveredTarget=f,this._setActiveObject(f,d),this._fireSelectionEvents(c,d)},_createGroup:function(u){var d=this._objects,c=d.indexOf(this._activeObject)<d.indexOf(u),f=c?[this._activeObject,u]:[u,this._activeObject];return this._activeObject.isEditing&&this._activeObject.exitEditing(),new t.ActiveSelection(f,{canvas:this})},_groupSelectedObjects:function(u){var d=this._collectObjects(u),c;d.length===1?this.setActiveObject(d[0],u):d.length>1&&(c=new t.ActiveSelection(d.reverse(),{canvas:this}),this.setActiveObject(c,u))},_collectObjects:function(u){for(var d=[],c,f=this._groupSelector.ex,s=this._groupSelector.ey,h=f+this._groupSelector.left,v=s+this._groupSelector.top,m=new t.Point(o(f,h),o(s,v)),y=new t.Point(a(f,h),a(s,v)),b=!this.selectionFullyContained,_=f===h&&s===v,w=this._objects.length;w--&&(c=this._objects[w],!(!(!c||!c.selectable||!c.visible)&&(b&&c.intersectsWithRect(m,y,!0)||c.isContainedWithinRect(m,y,!0)||b&&c.containsPoint(m,null,!0)||b&&c.containsPoint(y,null,!0))&&(d.push(c),_))););return d.length>1&&(d=d.filter(function(S){return!S.onSelect({e:u})})),d},_maybeGroupObjects:function(u){this.selection&&this._groupSelector&&this._groupSelectedObjects(u),this.setCursor(this.defaultCursor),this._groupSelector=null}})}(),function(){t.util.object.extend(t.StaticCanvas.prototype,{toDataURL:function(o){o||(o={});var a=o.format||"png",u=o.quality||1,d=(o.multiplier||1)*(o.enableRetinaScaling?this.getRetinaScaling():1),c=this.toCanvasElement(d,o);return t.util.toDataURL(c,a,u)},toCanvasElement:function(o,a){o=o||1,a=a||{};var u=(a.width||this.width)*o,d=(a.height||this.height)*o,c=this.getZoom(),f=this.width,s=this.height,h=c*o,v=this.viewportTransform,m=(v[4]-(a.left||0))*o,y=(v[5]-(a.top||0))*o,b=this.interactive,_=[h,0,0,h,m,y],w=this.enableRetinaScaling,S=t.util.createCanvasElement(),x=this.contextTop;return S.width=u,S.height=d,this.contextTop=null,this.enableRetinaScaling=!1,this.interactive=!1,this.viewportTransform=_,this.width=u,this.height=d,this.calcViewportBoundaries(),this.renderCanvas(S.getContext("2d"),this._objects),this.viewportTransform=v,this.width=f,this.height=s,this.calcViewportBoundaries(),this.interactive=b,this.enableRetinaScaling=w,this.contextTop=x,S}})}(),t.util.object.extend(t.StaticCanvas.prototype,{loadFromJSON:function(o,a,u){if(!!o){var d=typeof o=="string"?JSON.parse(o):t.util.object.clone(o),c=this,f=d.clipPath,s=this.renderOnAddRemove;return this.renderOnAddRemove=!1,delete d.clipPath,this._enlivenObjects(d.objects,function(h){c.clear(),c._setBgOverlay(d,function(){f?c._enlivenObjects([f],function(v){c.clipPath=v[0],c.__setupCanvas.call(c,d,h,s,a)}):c.__setupCanvas.call(c,d,h,s,a)})},u),this}},__setupCanvas:function(o,a,u,d){var c=this;a.forEach(function(f,s){c.insertAt(f,s)}),this.renderOnAddRemove=u,delete o.objects,delete o.backgroundImage,delete o.overlayImage,delete o.background,delete o.overlay,this._setOptions(o),this.renderAll(),d&&d()},_setBgOverlay:function(o,a){var u={backgroundColor:!1,overlayColor:!1,backgroundImage:!1,overlayImage:!1};if(!o.backgroundImage&&!o.overlayImage&&!o.background&&!o.overlay){a&&a();return}var d=function(){u.backgroundImage&&u.overlayImage&&u.backgroundColor&&u.overlayColor&&a&&a()};this.__setBgOverlay("backgroundImage",o.backgroundImage,u,d),this.__setBgOverlay("overlayImage",o.overlayImage,u,d),this.__setBgOverlay("backgroundColor",o.background,u,d),this.__setBgOverlay("overlayColor",o.overlay,u,d)},__setBgOverlay:function(o,a,u,d){var c=this;if(!a){u[o]=!0,d&&d();return}o==="backgroundImage"||o==="overlayImage"?t.util.enlivenObjects([a],function(f){c[o]=f[0],u[o]=!0,d&&d()}):this["set"+t.util.string.capitalize(o,!0)](a,function(){u[o]=!0,d&&d()})},_enlivenObjects:function(o,a,u){if(!o||o.length===0){a&&a([]);return}t.util.enlivenObjects(o,function(d){a&&a(d)},null,u)},_toDataURL:function(o,a){this.clone(function(u){a(u.toDataURL(o))})},_toDataURLWithMultiplier:function(o,a,u){this.clone(function(d){u(d.toDataURLWithMultiplier(o,a))})},clone:function(o,a){var u=JSON.stringify(this.toJSON(a));this.cloneWithoutData(function(d){d.loadFromJSON(u,function(){o&&o(d)})})},cloneWithoutData:function(o){var a=t.util.createCanvasElement();a.width=this.width,a.height=this.height;var u=new t.Canvas(a);this.backgroundImage?(u.setBackgroundImage(this.backgroundImage.src,function(){u.renderAll(),o&&o(u)}),u.backgroundImageOpacity=this.backgroundImageOpacity,u.backgroundImageStretch=this.backgroundImageStretch):o&&o(u)}}),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.util.object.clone,c=a.util.toFixed,f=a.util.string.capitalize,s=a.util.degreesToRadians,h=!a.isLikelyNode,v=2;a.Object||(a.Object=a.util.createClass(a.CommonMethods,{type:"object",originX:"left",originY:"top",top:0,left:0,width:0,height:0,scaleX:1,scaleY:1,flipX:!1,flipY:!1,opacity:1,angle:0,skewX:0,skewY:0,cornerSize:13,touchCornerSize:24,transparentCorners:!0,hoverCursor:null,moveCursor:null,padding:0,borderColor:"rgb(178,204,255)",borderDashArray:null,cornerColor:"rgb(178,204,255)",cornerStrokeColor:null,cornerStyle:"rect",cornerDashArray:null,centeredScaling:!1,centeredRotation:!0,fill:"rgb(0,0,0)",fillRule:"nonzero",globalCompositeOperation:"source-over",backgroundColor:"",selectionBackgroundColor:"",stroke:null,strokeWidth:1,strokeDashArray:null,strokeDashOffset:0,strokeLineCap:"butt",strokeLineJoin:"miter",strokeMiterLimit:4,shadow:null,borderOpacityWhenMoving:.4,borderScaleFactor:1,minScaleLimit:0,selectable:!0,evented:!0,visible:!0,hasControls:!0,hasBorders:!0,perPixelTargetFind:!1,includeDefaultValues:!0,lockMovementX:!1,lockMovementY:!1,lockRotation:!1,lockScalingX:!1,lockScalingY:!1,lockSkewingX:!1,lockSkewingY:!1,lockScalingFlip:!1,excludeFromExport:!1,objectCaching:h,statefullCache:!1,noScaleCache:!0,strokeUniform:!1,dirty:!0,__corner:0,paintFirst:"fill",activeOn:"down",stateProperties:"top left width height scaleX scaleY flipX flipY originX originY transformMatrix stroke strokeWidth strokeDashArray strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit angle opacity fill globalCompositeOperation shadow visible backgroundColor skewX skewY fillRule paintFirst clipPath strokeUniform".split(" "),cacheProperties:"fill stroke strokeWidth strokeDashArray width height paintFirst strokeUniform strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit backgroundColor clipPath".split(" "),colorProperties:"fill stroke backgroundColor".split(" "),clipPath:void 0,inverted:!1,absolutePositioned:!1,initialize:function(m){m&&this.setOptions(m)},_createCacheCanvas:function(){this._cacheProperties={},this._cacheCanvas=a.util.createCanvasElement(),this._cacheContext=this._cacheCanvas.getContext("2d"),this._updateCacheCanvas(),this.dirty=!0},_limitCacheSize:function(m){var y=a.perfLimitSizeTotal,b=m.width,_=m.height,w=a.maxCacheSideLimit,S=a.minCacheSideLimit;if(b<=w&&_<=w&&b*_<=y)return b<S&&(m.width=S),_<S&&(m.height=S),m;var x=b/_,T=a.util.limitDimsByArea(x,y),E=a.util.capValue,I=E(S,T.x,w),D=E(S,T.y,w);return b>I&&(m.zoomX/=b/I,m.width=I,m.capped=!0),_>D&&(m.zoomY/=_/D,m.height=D,m.capped=!0),m},_getCacheCanvasDimensions:function(){var m=this.getTotalObjectScaling(),y=this._getTransformedDimensions(0,0),b=y.x*m.scaleX/this.scaleX,_=y.y*m.scaleY/this.scaleY;return{width:b+v,height:_+v,zoomX:m.scaleX,zoomY:m.scaleY,x:b,y:_}},_updateCacheCanvas:function(){var m=this.canvas;if(this.noScaleCache&&m&&m._currentTransform){var y=m._currentTransform.target,b=m._currentTransform.action;if(this===y&&b.slice&&b.slice(0,5)==="scale")return!1}var _=this._cacheCanvas,w=this._limitCacheSize(this._getCacheCanvasDimensions()),S=a.minCacheSideLimit,x=w.width,T=w.height,E,I,D=w.zoomX,N=w.zoomY,L=x!==this.cacheWidth||T!==this.cacheHeight,F=this.zoomX!==D||this.zoomY!==N,O=L||F,V=0,re=0,J=!1;if(L){var ue=this._cacheCanvas.width,M=this._cacheCanvas.height,U=x>ue||T>M,R=(x<ue*.9||T<M*.9)&&ue>S&&M>S;J=U||R,U&&!w.capped&&(x>S||T>S)&&(V=x*.1,re=T*.1)}return this instanceof a.Text&&this.path&&(O=!0,J=!0,V+=this.getHeightOfLine(0)*this.zoomX,re+=this.getHeightOfLine(0)*this.zoomY),O?(J?(_.width=Math.ceil(x+V),_.height=Math.ceil(T+re)):(this._cacheContext.setTransform(1,0,0,1,0,0),this._cacheContext.clearRect(0,0,_.width,_.height)),E=w.x/2,I=w.y/2,this.cacheTranslationX=Math.round(_.width/2-E)+E,this.cacheTranslationY=Math.round(_.height/2-I)+I,this.cacheWidth=x,this.cacheHeight=T,this._cacheContext.translate(this.cacheTranslationX,this.cacheTranslationY),this._cacheContext.scale(D,N),this.zoomX=D,this.zoomY=N,!0):!1},setOptions:function(m){this._setOptions(m),this._initGradient(m.fill,"fill"),this._initGradient(m.stroke,"stroke"),this._initPattern(m.fill,"fill"),this._initPattern(m.stroke,"stroke")},transform:function(m){var y=this.group&&!this.group._transformDone||this.group&&this.canvas&&m===this.canvas.contextTop,b=this.calcTransformMatrix(!y);m.transform(b[0],b[1],b[2],b[3],b[4],b[5])},toObject:function(m){var y=a.Object.NUM_FRACTION_DIGITS,b={type:this.type,version:a.version,originX:this.originX,originY:this.originY,left:c(this.left,y),top:c(this.top,y),width:c(this.width,y),height:c(this.height,y),fill:this.fill&&this.fill.toObject?this.fill.toObject():this.fill,stroke:this.stroke&&this.stroke.toObject?this.stroke.toObject():this.stroke,strokeWidth:c(this.strokeWidth,y),strokeDashArray:this.strokeDashArray?this.strokeDashArray.concat():this.strokeDashArray,strokeLineCap:this.strokeLineCap,strokeDashOffset:this.strokeDashOffset,strokeLineJoin:this.strokeLineJoin,strokeUniform:this.strokeUniform,strokeMiterLimit:c(this.strokeMiterLimit,y),scaleX:c(this.scaleX,y),scaleY:c(this.scaleY,y),angle:c(this.angle,y),flipX:this.flipX,flipY:this.flipY,opacity:c(this.opacity,y),shadow:this.shadow&&this.shadow.toObject?this.shadow.toObject():this.shadow,visible:this.visible,backgroundColor:this.backgroundColor,fillRule:this.fillRule,paintFirst:this.paintFirst,globalCompositeOperation:this.globalCompositeOperation,skewX:c(this.skewX,y),skewY:c(this.skewY,y)};return this.clipPath&&!this.clipPath.excludeFromExport&&(b.clipPath=this.clipPath.toObject(m),b.clipPath.inverted=this.clipPath.inverted,b.clipPath.absolutePositioned=this.clipPath.absolutePositioned),a.util.populateWithProperties(this,b,m),this.includeDefaultValues||(b=this._removeDefaultValues(b)),b},toDatalessObject:function(m){return this.toObject(m)},_removeDefaultValues:function(m){var y=a.util.getKlass(m.type).prototype,b=y.stateProperties;return b.forEach(function(_){_==="left"||_==="top"||(m[_]===y[_]&&delete m[_],Array.isArray(m[_])&&Array.isArray(y[_])&&m[_].length===0&&y[_].length===0&&delete m[_])}),m},toString:function(){return"#<fabric."+f(this.type)+">"},getObjectScaling:function(){if(!this.group)return{scaleX:this.scaleX,scaleY:this.scaleY};var m=a.util.qrDecompose(this.calcTransformMatrix());return{scaleX:Math.abs(m.scaleX),scaleY:Math.abs(m.scaleY)}},getTotalObjectScaling:function(){var m=this.getObjectScaling(),y=m.scaleX,b=m.scaleY;if(this.canvas){var _=this.canvas.getZoom(),w=this.canvas.getRetinaScaling();y*=_*w,b*=_*w}return{scaleX:y,scaleY:b}},getObjectOpacity:function(){var m=this.opacity;return this.group&&(m*=this.group.getObjectOpacity()),m},_set:function(m,y){var b=m==="scaleX"||m==="scaleY",_=this[m]!==y,w=!1;return b&&(y=this._constrainScale(y)),m==="scaleX"&&y<0?(this.flipX=!this.flipX,y*=-1):m==="scaleY"&&y<0?(this.flipY=!this.flipY,y*=-1):m==="shadow"&&y&&!(y instanceof a.Shadow)?y=new a.Shadow(y):m==="dirty"&&this.group&&this.group.set("dirty",y),this[m]=y,_&&(w=this.group&&this.group.isOnACache(),this.cacheProperties.indexOf(m)>-1?(this.dirty=!0,w&&this.group.set("dirty",!0)):w&&this.stateProperties.indexOf(m)>-1&&this.group.set("dirty",!0)),this},setOnGroup:function(){},getViewportTransform:function(){return this.canvas&&this.canvas.viewportTransform?this.canvas.viewportTransform:a.iMatrix.concat()},isNotVisible:function(){return this.opacity===0||!this.width&&!this.height&&this.strokeWidth===0||!this.visible},render:function(m){this.isNotVisible()||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(m.save(),this._setupCompositeOperation(m),this.drawSelectionBackground(m),this.transform(m),this._setOpacity(m),this._setShadow(m,this),this.shouldCache()?(this.renderCache(),this.drawCacheOnCanvas(m)):(this._removeCacheCanvas(),this.dirty=!1,this.drawObject(m),this.objectCaching&&this.statefullCache&&this.saveState({propertySet:"cacheProperties"})),m.restore())},renderCache:function(m){m=m||{},(!this._cacheCanvas||!this._cacheContext)&&this._createCacheCanvas(),this.isCacheDirty()&&(this.statefullCache&&this.saveState({propertySet:"cacheProperties"}),this.drawObject(this._cacheContext,m.forClipping),this.dirty=!1)},_removeCacheCanvas:function(){this._cacheCanvas=null,this._cacheContext=null,this.cacheWidth=0,this.cacheHeight=0},hasStroke:function(){return this.stroke&&this.stroke!=="transparent"&&this.strokeWidth!==0},hasFill:function(){return this.fill&&this.fill!=="transparent"},needsItsOwnCache:function(){return!!(this.paintFirst==="stroke"&&this.hasFill()&&this.hasStroke()&&typeof this.shadow=="object"||this.clipPath)},shouldCache:function(){return this.ownCaching=this.needsItsOwnCache()||this.objectCaching&&(!this.group||!this.group.isOnACache()),this.ownCaching},willDrawShadow:function(){return!!this.shadow&&(this.shadow.offsetX!==0||this.shadow.offsetY!==0)},drawClipPathOnCache:function(m,y){if(m.save(),y.inverted?m.globalCompositeOperation="destination-out":m.globalCompositeOperation="destination-in",y.absolutePositioned){var b=a.util.invertTransform(this.calcTransformMatrix());m.transform(b[0],b[1],b[2],b[3],b[4],b[5])}y.transform(m),m.scale(1/y.zoomX,1/y.zoomY),m.drawImage(y._cacheCanvas,-y.cacheTranslationX,-y.cacheTranslationY),m.restore()},drawObject:function(m,y){var b=this.fill,_=this.stroke;y?(this.fill="black",this.stroke="",this._setClippingProperties(m)):this._renderBackground(m),this._render(m),this._drawClipPath(m,this.clipPath),this.fill=b,this.stroke=_},_drawClipPath:function(m,y){!y||(y.canvas=this.canvas,y.shouldCache(),y._transformDone=!0,y.renderCache({forClipping:!0}),this.drawClipPathOnCache(m,y))},drawCacheOnCanvas:function(m){m.scale(1/this.zoomX,1/this.zoomY),m.drawImage(this._cacheCanvas,-this.cacheTranslationX,-this.cacheTranslationY)},isCacheDirty:function(m){if(this.isNotVisible())return!1;if(this._cacheCanvas&&this._cacheContext&&!m&&this._updateCacheCanvas())return!0;if(this.dirty||this.clipPath&&this.clipPath.absolutePositioned||this.statefullCache&&this.hasStateChanged("cacheProperties")){if(this._cacheCanvas&&this._cacheContext&&!m){var y=this.cacheWidth/this.zoomX,b=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-y/2,-b/2,y,b)}return!0}return!1},_renderBackground:function(m){if(!!this.backgroundColor){var y=this._getNonTransformedDimensions();m.fillStyle=this.backgroundColor,m.fillRect(-y.x/2,-y.y/2,y.x,y.y),this._removeShadow(m)}},_setOpacity:function(m){this.group&&!this.group._transformDone?m.globalAlpha=this.getObjectOpacity():m.globalAlpha*=this.opacity},_setStrokeStyles:function(m,y){var b=y.stroke;b&&(m.lineWidth=y.strokeWidth,m.lineCap=y.strokeLineCap,m.lineDashOffset=y.strokeDashOffset,m.lineJoin=y.strokeLineJoin,m.miterLimit=y.strokeMiterLimit,b.toLive?b.gradientUnits==="percentage"||b.gradientTransform||b.patternTransform?this._applyPatternForTransformedGradient(m,b):(m.strokeStyle=b.toLive(m,this),this._applyPatternGradientTransform(m,b)):m.strokeStyle=y.stroke)},_setFillStyles:function(m,y){var b=y.fill;b&&(b.toLive?(m.fillStyle=b.toLive(m,this),this._applyPatternGradientTransform(m,y.fill)):m.fillStyle=b)},_setClippingProperties:function(m){m.globalAlpha=1,m.strokeStyle="transparent",m.fillStyle="#000000"},_setLineDash:function(m,y){!y||y.length===0||(1&y.length&&y.push.apply(y,y),m.setLineDash(y))},_renderControls:function(m,y){var b=this.getViewportTransform(),_=this.calcTransformMatrix(),w,S,x;y=y||{},S=typeof y.hasBorders<"u"?y.hasBorders:this.hasBorders,x=typeof y.hasControls<"u"?y.hasControls:this.hasControls,_=a.util.multiplyTransformMatrices(b,_),w=a.util.qrDecompose(_),m.save(),m.translate(w.translateX,w.translateY),m.lineWidth=1*this.borderScaleFactor,this.group||(m.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1),this.flipX&&(w.angle-=180),m.rotate(s(this.group?w.angle:this.angle)),y.forActiveSelection||this.group?S&&this.drawBordersInGroup(m,w,y):S&&this.drawBorders(m,y),x&&this.drawControls(m,y),m.restore()},_setShadow:function(m){if(!!this.shadow){var y=this.shadow,b=this.canvas,_,w=b&&b.viewportTransform[0]||1,S=b&&b.viewportTransform[3]||1;y.nonScaling?_={scaleX:1,scaleY:1}:_=this.getObjectScaling(),b&&b._isRetinaScaling()&&(w*=a.devicePixelRatio,S*=a.devicePixelRatio),m.shadowColor=y.color,m.shadowBlur=y.blur*a.browserShadowBlurConstant*(w+S)*(_.scaleX+_.scaleY)/4,m.shadowOffsetX=y.offsetX*w*_.scaleX,m.shadowOffsetY=y.offsetY*S*_.scaleY}},_removeShadow:function(m){!this.shadow||(m.shadowColor="",m.shadowBlur=m.shadowOffsetX=m.shadowOffsetY=0)},_applyPatternGradientTransform:function(m,y){if(!y||!y.toLive)return{offsetX:0,offsetY:0};var b=y.gradientTransform||y.patternTransform,_=-this.width/2+y.offsetX||0,w=-this.height/2+y.offsetY||0;return y.gradientUnits==="percentage"?m.transform(this.width,0,0,this.height,_,w):m.transform(1,0,0,1,_,w),b&&m.transform(b[0],b[1],b[2],b[3],b[4],b[5]),{offsetX:_,offsetY:w}},_renderPaintInOrder:function(m){this.paintFirst==="stroke"?(this._renderStroke(m),this._renderFill(m)):(this._renderFill(m),this._renderStroke(m))},_render:function(){},_renderFill:function(m){!this.fill||(m.save(),this._setFillStyles(m,this),this.fillRule==="evenodd"?m.fill("evenodd"):m.fill(),m.restore())},_renderStroke:function(m){if(!(!this.stroke||this.strokeWidth===0)){if(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(m),m.save(),this.strokeUniform&&this.group){var y=this.getObjectScaling();m.scale(1/y.scaleX,1/y.scaleY)}else this.strokeUniform&&m.scale(1/this.scaleX,1/this.scaleY);this._setLineDash(m,this.strokeDashArray),this._setStrokeStyles(m,this),m.stroke(),m.restore()}},_applyPatternForTransformedGradient:function(m,y){var b=this._limitCacheSize(this._getCacheCanvasDimensions()),_=a.util.createCanvasElement(),w,S=this.canvas.getRetinaScaling(),x=b.x/this.scaleX/S,T=b.y/this.scaleY/S;_.width=x,_.height=T,w=_.getContext("2d"),w.beginPath(),w.moveTo(0,0),w.lineTo(x,0),w.lineTo(x,T),w.lineTo(0,T),w.closePath(),w.translate(x/2,T/2),w.scale(b.zoomX/this.scaleX/S,b.zoomY/this.scaleY/S),this._applyPatternGradientTransform(w,y),w.fillStyle=y.toLive(m),w.fill(),m.translate(-this.width/2-this.strokeWidth/2,-this.height/2-this.strokeWidth/2),m.scale(S*this.scaleX/b.zoomX,S*this.scaleY/b.zoomY),m.strokeStyle=w.createPattern(_,"no-repeat")},_findCenterFromElement:function(){return{x:this.left+this.width/2,y:this.top+this.height/2}},_assignTransformMatrixProps:function(){if(this.transformMatrix){var m=a.util.qrDecompose(this.transformMatrix);this.flipX=!1,this.flipY=!1,this.set("scaleX",m.scaleX),this.set("scaleY",m.scaleY),this.angle=m.angle,this.skewX=m.skewX,this.skewY=0}},_removeTransformMatrix:function(m){var y=this._findCenterFromElement();this.transformMatrix&&(this._assignTransformMatrixProps(),y=a.util.transformPoint(y,this.transformMatrix)),this.transformMatrix=null,m&&(this.scaleX*=m.scaleX,this.scaleY*=m.scaleY,this.cropX=m.cropX,this.cropY=m.cropY,y.x+=m.offsetLeft,y.y+=m.offsetTop,this.width=m.width,this.height=m.height),this.setPositionByOrigin(y,"center","center")},clone:function(m,y){var b=this.toObject(y);this.constructor.fromObject?this.constructor.fromObject(b,m):a.Object._fromObject("Object",b,m)},cloneAsImage:function(m,y){var b=this.toCanvasElement(y);return m&&m(new a.Image(b)),this},toCanvasElement:function(m){m||(m={});var y=a.util,b=y.saveObjectTransform(this),_=this.group,w=this.shadow,S=Math.abs,x=(m.multiplier||1)*(m.enableRetinaScaling?a.devicePixelRatio:1);delete this.group,m.withoutTransform&&y.resetObjectTransform(this),m.withoutShadow&&(this.shadow=null);var T=a.util.createCanvasElement(),E=this.getBoundingRect(!0,!0),I=this.shadow,D,N={x:0,y:0},L,F,O;I&&(L=I.blur,I.nonScaling?D={scaleX:1,scaleY:1}:D=this.getObjectScaling(),N.x=2*Math.round(S(I.offsetX)+L)*S(D.scaleX),N.y=2*Math.round(S(I.offsetY)+L)*S(D.scaleY)),F=E.width+N.x,O=E.height+N.y,T.width=Math.ceil(F),T.height=Math.ceil(O);var V=new a.StaticCanvas(T,{enableRetinaScaling:!1,renderOnAddRemove:!1,skipOffscreen:!1});m.format==="jpeg"&&(V.backgroundColor="#fff"),this.setPositionByOrigin(new a.Point(V.width/2,V.height/2),"center","center");var re=this.canvas;V.add(this);var J=V.toCanvasElement(x||1,m);return this.shadow=w,this.set("canvas",re),_&&(this.group=_),this.set(b).setCoords(),V._objects=[],V.dispose(),V=null,J},toDataURL:function(m){return m||(m={}),a.util.toDataURL(this.toCanvasElement(m),m.format||"png",m.quality||1)},isType:function(m){return arguments.length>1?Array.from(arguments).includes(this.type):this.type===m},complexity:function(){return 1},toJSON:function(m){return this.toObject(m)},rotate:function(m){var y=(this.originX!=="center"||this.originY!=="center")&&this.centeredRotation;return y&&this._setOriginToCenter(),this.set("angle",m),y&&this._resetOrigin(),this},centerH:function(){return this.canvas&&this.canvas.centerObjectH(this),this},viewportCenterH:function(){return this.canvas&&this.canvas.viewportCenterObjectH(this),this},centerV:function(){return this.canvas&&this.canvas.centerObjectV(this),this},viewportCenterV:function(){return this.canvas&&this.canvas.viewportCenterObjectV(this),this},center:function(){return this.canvas&&this.canvas.centerObject(this),this},viewportCenter:function(){return this.canvas&&this.canvas.viewportCenterObject(this),this},getLocalPointer:function(m,y){y=y||this.canvas.getPointer(m);var b=new a.Point(y.x,y.y),_=this._getLeftTopCoords();return this.angle&&(b=a.util.rotatePoint(b,_,s(-this.angle))),{x:b.x-_.x,y:b.y-_.y}},_setupCompositeOperation:function(m){this.globalCompositeOperation&&(m.globalCompositeOperation=this.globalCompositeOperation)},dispose:function(){a.runningAnimations&&a.runningAnimations.cancelByTarget(this)}}),a.util.createAccessors&&a.util.createAccessors(a.Object),u(a.Object.prototype,a.Observable),a.Object.NUM_FRACTION_DIGITS=2,a.Object.ENLIVEN_PROPS=["clipPath"],a.Object._fromObject=function(m,y,b,_){var w=a[m];y=d(y,!0),a.util.enlivenPatterns([y.fill,y.stroke],function(S){typeof S[0]<"u"&&(y.fill=S[0]),typeof S[1]<"u"&&(y.stroke=S[1]),a.util.enlivenObjectEnlivables(y,y,function(){var x=_?new w(y[_],y):new w(y);b&&b(x)})})},a.Object.__uid=0)}(e),function(){var o=t.util.degreesToRadians,a={left:-.5,center:0,right:.5},u={top:-.5,center:0,bottom:.5};t.util.object.extend(t.Object.prototype,{translateToGivenOrigin:function(d,c,f,s,h){var v=d.x,m=d.y,y,b,_;return typeof c=="string"?c=a[c]:c-=.5,typeof s=="string"?s=a[s]:s-=.5,y=s-c,typeof f=="string"?f=u[f]:f-=.5,typeof h=="string"?h=u[h]:h-=.5,b=h-f,(y||b)&&(_=this._getTransformedDimensions(),v=d.x+y*_.x,m=d.y+b*_.y),new t.Point(v,m)},translateToCenterPoint:function(d,c,f){var s=this.translateToGivenOrigin(d,c,f,"center","center");return this.angle?t.util.rotatePoint(s,d,o(this.angle)):s},translateToOriginPoint:function(d,c,f){var s=this.translateToGivenOrigin(d,"center","center",c,f);return this.angle?t.util.rotatePoint(s,d,o(this.angle)):s},getCenterPoint:function(){var d=new t.Point(this.left,this.top);return this.translateToCenterPoint(d,this.originX,this.originY)},getPointByOrigin:function(d,c){var f=this.getCenterPoint();return this.translateToOriginPoint(f,d,c)},toLocalPoint:function(d,c,f){var s=this.getCenterPoint(),h,v;return typeof c<"u"&&typeof f<"u"?h=this.translateToGivenOrigin(s,"center","center",c,f):h=new t.Point(this.left,this.top),v=new t.Point(d.x,d.y),this.angle&&(v=t.util.rotatePoint(v,s,-o(this.angle))),v.subtractEquals(h)},setPositionByOrigin:function(d,c,f){var s=this.translateToCenterPoint(d,c,f),h=this.translateToOriginPoint(s,this.originX,this.originY);this.set("left",h.x),this.set("top",h.y)},adjustPosition:function(d){var c=o(this.angle),f=this.getScaledWidth(),s=t.util.cos(c)*f,h=t.util.sin(c)*f,v,m;typeof this.originX=="string"?v=a[this.originX]:v=this.originX-.5,typeof d=="string"?m=a[d]:m=d-.5,this.left+=s*(m-v),this.top+=h*(m-v),this.setCoords(),this.originX=d},_setOriginToCenter:function(){this._originalOriginX=this.originX,this._originalOriginY=this.originY;var d=this.getCenterPoint();this.originX="center",this.originY="center",this.left=d.x,this.top=d.y},_resetOrigin:function(){var d=this.translateToOriginPoint(this.getCenterPoint(),this._originalOriginX,this._originalOriginY);this.originX=this._originalOriginX,this.originY=this._originalOriginY,this.left=d.x,this.top=d.y,this._originalOriginX=null,this._originalOriginY=null},_getLeftTopCoords:function(){return this.translateToOriginPoint(this.getCenterPoint(),"left","top")}})}(),function(){function o(f){return[new t.Point(f.tl.x,f.tl.y),new t.Point(f.tr.x,f.tr.y),new t.Point(f.br.x,f.br.y),new t.Point(f.bl.x,f.bl.y)]}var a=t.util,u=a.degreesToRadians,d=a.multiplyTransformMatrices,c=a.transformPoint;a.object.extend(t.Object.prototype,{oCoords:null,aCoords:null,lineCoords:null,ownMatrixCache:null,matrixCache:null,controls:{},_getCoords:function(f,s){return s?f?this.calcACoords():this.calcLineCoords():((!this.aCoords||!this.lineCoords)&&this.setCoords(!0),f?this.aCoords:this.lineCoords)},getCoords:function(f,s){return o(this._getCoords(f,s))},intersectsWithRect:function(f,s,h,v){var m=this.getCoords(h,v),y=t.Intersection.intersectPolygonRectangle(m,f,s);return y.status==="Intersection"},intersectsWithObject:function(f,s,h){var v=t.Intersection.intersectPolygonPolygon(this.getCoords(s,h),f.getCoords(s,h));return v.status==="Intersection"||f.isContainedWithinObject(this,s,h)||this.isContainedWithinObject(f,s,h)},isContainedWithinObject:function(f,s,h){for(var v=this.getCoords(s,h),m=s?f.aCoords:f.lineCoords,y=0,b=f._getImageLines(m);y<4;y++)if(!f.containsPoint(v[y],b))return!1;return!0},isContainedWithinRect:function(f,s,h,v){var m=this.getBoundingRect(h,v);return m.left>=f.x&&m.left+m.width<=s.x&&m.top>=f.y&&m.top+m.height<=s.y},containsPoint:function(f,y,h,v){var m=this._getCoords(h,v),y=y||this._getImageLines(m),b=this._findCrossPoints(f,y);return b!==0&&b%2===1},isOnScreen:function(f){if(!this.canvas)return!1;var s=this.canvas.vptCoords.tl,h=this.canvas.vptCoords.br,v=this.getCoords(!0,f);return v.some(function(m){return m.x<=h.x&&m.x>=s.x&&m.y<=h.y&&m.y>=s.y})||this.intersectsWithRect(s,h,!0,f)?!0:this._containsCenterOfCanvas(s,h,f)},_containsCenterOfCanvas:function(f,s,h){var v={x:(f.x+s.x)/2,y:(f.y+s.y)/2};return!!this.containsPoint(v,null,!0,h)},isPartiallyOnScreen:function(f){if(!this.canvas)return!1;var s=this.canvas.vptCoords.tl,h=this.canvas.vptCoords.br;if(this.intersectsWithRect(s,h,!0,f))return!0;var v=this.getCoords(!0,f).every(function(m){return(m.x>=h.x||m.x<=s.x)&&(m.y>=h.y||m.y<=s.y)});return v&&this._containsCenterOfCanvas(s,h,f)},_getImageLines:function(f){var s={topline:{o:f.tl,d:f.tr},rightline:{o:f.tr,d:f.br},bottomline:{o:f.br,d:f.bl},leftline:{o:f.bl,d:f.tl}};return s},_findCrossPoints:function(f,s){var h,v,m,y,b,_=0,w;for(var S in s)if(w=s[S],!(w.o.y<f.y&&w.d.y<f.y)&&!(w.o.y>=f.y&&w.d.y>=f.y)&&(w.o.x===w.d.x&&w.o.x>=f.x?b=w.o.x:(h=0,v=(w.d.y-w.o.y)/(w.d.x-w.o.x),m=f.y-h*f.x,y=w.o.y-v*w.o.x,b=-(m-y)/(h-v)),b>=f.x&&(_+=1),_===2))break;return _},getBoundingRect:function(f,s){var h=this.getCoords(f,s);return a.makeBoundingBoxFromPoints(h)},getScaledWidth:function(){return this._getTransformedDimensions().x},getScaledHeight:function(){return this._getTransformedDimensions().y},_constrainScale:function(f){return Math.abs(f)<this.minScaleLimit?f<0?-this.minScaleLimit:this.minScaleLimit:f===0?1e-4:f},scale:function(f){return this._set("scaleX",f),this._set("scaleY",f),this.setCoords()},scaleToWidth:function(f,s){var h=this.getBoundingRect(s).width/this.getScaledWidth();return this.scale(f/this.width/h)},scaleToHeight:function(f,s){var h=this.getBoundingRect(s).height/this.getScaledHeight();return this.scale(f/this.height/h)},calcLineCoords:function(){var f=this.getViewportTransform(),s=this.padding,h=u(this.angle),v=a.cos(h),m=a.sin(h),y=v*s,b=m*s,_=y+b,w=y-b,S=this.calcACoords(),x={tl:c(S.tl,f),tr:c(S.tr,f),bl:c(S.bl,f),br:c(S.br,f)};return s&&(x.tl.x-=w,x.tl.y-=_,x.tr.x+=_,x.tr.y-=w,x.bl.x-=_,x.bl.y+=w,x.br.x+=w,x.br.y+=_),x},calcOCoords:function(){var f=this._calcRotateMatrix(),s=this._calcTranslateMatrix(),h=this.getViewportTransform(),v=d(h,s),m=d(v,f),m=d(m,[1/h[0],0,0,1/h[3],0,0]),y=this._calculateCurrentDimensions(),b={};return this.forEachControl(function(_,w,S){b[w]=_.positionHandler(y,m,S)}),b},calcACoords:function(){var f=this._calcRotateMatrix(),s=this._calcTranslateMatrix(),h=d(s,f),v=this._getTransformedDimensions(),m=v.x/2,y=v.y/2;return{tl:c({x:-m,y:-y},h),tr:c({x:m,y:-y},h),bl:c({x:-m,y},h),br:c({x:m,y},h)}},setCoords:function(f){return this.aCoords=this.calcACoords(),this.lineCoords=this.group?this.aCoords:this.calcLineCoords(),f?this:(this.oCoords=this.calcOCoords(),this._setCornerCoords&&this._setCornerCoords(),this)},_calcRotateMatrix:function(){return a.calcRotateMatrix(this)},_calcTranslateMatrix:function(){var f=this.getCenterPoint();return[1,0,0,1,f.x,f.y]},transformMatrixKey:function(f){var s="_",h="";return!f&&this.group&&(h=this.group.transformMatrixKey(f)+s),h+this.top+s+this.left+s+this.scaleX+s+this.scaleY+s+this.skewX+s+this.skewY+s+this.angle+s+this.originX+s+this.originY+s+this.width+s+this.height+s+this.strokeWidth+this.flipX+this.flipY},calcTransformMatrix:function(f){var s=this.calcOwnMatrix();if(f||!this.group)return s;var h=this.transformMatrixKey(f),v=this.matrixCache||(this.matrixCache={});return v.key===h?v.value:(this.group&&(s=d(this.group.calcTransformMatrix(!1),s)),v.key=h,v.value=s,s)},calcOwnMatrix:function(){var f=this.transformMatrixKey(!0),s=this.ownMatrixCache||(this.ownMatrixCache={});if(s.key===f)return s.value;var h=this._calcTranslateMatrix(),v={angle:this.angle,translateX:h[4],translateY:h[5],scaleX:this.scaleX,scaleY:this.scaleY,skewX:this.skewX,skewY:this.skewY,flipX:this.flipX,flipY:this.flipY};return s.key=f,s.value=a.composeMatrix(v),s.value},_getNonTransformedDimensions:function(){var f=this.strokeWidth,s=this.width+f,h=this.height+f;return{x:s,y:h}},_getTransformedDimensions:function(f,s){typeof f>"u"&&(f=this.skewX),typeof s>"u"&&(s=this.skewY);var h,v,m,y=f===0&&s===0;if(this.strokeUniform?(v=this.width,m=this.height):(h=this._getNonTransformedDimensions(),v=h.x,m=h.y),y)return this._finalizeDimensions(v*this.scaleX,m*this.scaleY);var b=a.sizeAfterTransform(v,m,{scaleX:this.scaleX,scaleY:this.scaleY,skewX:f,skewY:s});return this._finalizeDimensions(b.x,b.y)},_finalizeDimensions:function(f,s){return this.strokeUniform?{x:f+this.strokeWidth,y:s+this.strokeWidth}:{x:f,y:s}},_calculateCurrentDimensions:function(){var f=this.getViewportTransform(),s=this._getTransformedDimensions(),h=c(s,f,!0);return h.scalarAdd(2*this.padding)}})}(),t.util.object.extend(t.Object.prototype,{sendToBack:function(){return this.group?t.StaticCanvas.prototype.sendToBack.call(this.group,this):this.canvas&&this.canvas.sendToBack(this),this},bringToFront:function(){return this.group?t.StaticCanvas.prototype.bringToFront.call(this.group,this):this.canvas&&this.canvas.bringToFront(this),this},sendBackwards:function(o){return this.group?t.StaticCanvas.prototype.sendBackwards.call(this.group,this,o):this.canvas&&this.canvas.sendBackwards(this,o),this},bringForward:function(o){return this.group?t.StaticCanvas.prototype.bringForward.call(this.group,this,o):this.canvas&&this.canvas.bringForward(this,o),this},moveTo:function(o){return this.group&&this.group.type!=="activeSelection"?t.StaticCanvas.prototype.moveTo.call(this.group,this,o):this.canvas&&this.canvas.moveTo(this,o),this}}),function(){function o(u,d){if(d){if(d.toLive)return u+": url(#SVGID_"+d.id+"); ";var c=new t.Color(d),f=u+": "+c.toRgb()+"; ",s=c.getAlpha();return s!==1&&(f+=u+"-opacity: "+s.toString()+"; "),f}else return u+": none; "}var a=t.util.toFixed;t.util.object.extend(t.Object.prototype,{getSvgStyles:function(u){var d=this.fillRule?this.fillRule:"nonzero",c=this.strokeWidth?this.strokeWidth:"0",f=this.strokeDashArray?this.strokeDashArray.join(" "):"none",s=this.strokeDashOffset?this.strokeDashOffset:"0",h=this.strokeLineCap?this.strokeLineCap:"butt",v=this.strokeLineJoin?this.strokeLineJoin:"miter",m=this.strokeMiterLimit?this.strokeMiterLimit:"4",y=typeof this.opacity<"u"?this.opacity:"1",b=this.visible?"":" visibility: hidden;",_=u?"":this.getSvgFilter(),w=o("fill",this.fill),S=o("stroke",this.stroke);return[S,"stroke-width: ",c,"; ","stroke-dasharray: ",f,"; ","stroke-linecap: ",h,"; ","stroke-dashoffset: ",s,"; ","stroke-linejoin: ",v,"; ","stroke-miterlimit: ",m,"; ",w,"fill-rule: ",d,"; ","opacity: ",y,";",_,b].join("")},getSvgSpanStyles:function(u,d){var c="; ",s=u.fontFamily?"font-family: "+(u.fontFamily.indexOf("'")===-1&&u.fontFamily.indexOf('"')===-1?"'"+u.fontFamily+"'":u.fontFamily)+c:"",f=u.strokeWidth?"stroke-width: "+u.strokeWidth+c:"",s=s,h=u.fontSize?"font-size: "+u.fontSize+"px"+c:"",v=u.fontStyle?"font-style: "+u.fontStyle+c:"",m=u.fontWeight?"font-weight: "+u.fontWeight+c:"",y=u.fill?o("fill",u.fill):"",b=u.stroke?o("stroke",u.stroke):"",_=this.getSvgTextDecoration(u),w=u.deltaY?"baseline-shift: "+-u.deltaY+"; ":"";return _&&(_="text-decoration: "+_+c),[b,f,s,h,v,m,_,y,w,d?"white-space: pre; ":""].join("")},getSvgTextDecoration:function(u){return["overline","underline","line-through"].filter(function(d){return u[d.replace("-","")]}).join(" ")},getSvgFilter:function(){return this.shadow?"filter: url(#SVGID_"+this.shadow.id+");":""},getSvgCommons:function(){return[this.id?'id="'+this.id+'" ':"",this.clipPath?'clip-path="url(#'+this.clipPath.clipPathId+')" ':""].join("")},getSvgTransform:function(u,d){var c=u?this.calcTransformMatrix():this.calcOwnMatrix(),f='transform="'+t.util.matrixToSVG(c);return f+(d||"")+'" '},_setSVGBg:function(u){if(this.backgroundColor){var d=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(this.backgroundColor),' x="',a(-this.width/2,d),'" y="',a(-this.height/2,d),'" width="',a(this.width,d),'" height="',a(this.height,d),`"></rect>
-`)}},toSVG:function(u){return this._createBaseSVGMarkup(this._toSVG(u),{reviver:u})},toClipPathSVG:function(u){return"	"+this._createBaseClipPathSVGMarkup(this._toSVG(u),{reviver:u})},_createBaseClipPathSVGMarkup:function(u,d){d=d||{};var c=d.reviver,f=d.additionalTransform||"",s=[this.getSvgTransform(!0,f),this.getSvgCommons()].join(""),h=u.indexOf("COMMON_PARTS");return u[h]=s,c?c(u.join("")):u.join("")},_createBaseSVGMarkup:function(u,d){d=d||{};var c=d.noStyle,f=d.reviver,s=c?"":'style="'+this.getSvgStyles()+'" ',h=d.withShadow?'style="'+this.getSvgFilter()+'" ':"",v=this.clipPath,m=this.strokeUniform?'vector-effect="non-scaling-stroke" ':"",y=v&&v.absolutePositioned,b=this.stroke,_=this.fill,w=this.shadow,S,x=[],T,E=u.indexOf("COMMON_PARTS"),I=d.additionalTransform;return v&&(v.clipPathId="CLIPPATH_"+t.Object.__uid++,T='<clipPath id="'+v.clipPathId+`" >
+`)},sendToBack:function(m){if(!m)return this;var y=this._activeObject,b,_,w;if(m===y&&m.type==="activeSelection")for(w=y._objects,b=w.length;b--;)_=w[b],u(this._objects,_),this._objects.unshift(_);else u(this._objects,m),this._objects.unshift(m);return this.renderOnAddRemove&&this.requestRenderAll(),this},bringToFront:function(m){if(!m)return this;var y=this._activeObject,b,_,w;if(m===y&&m.type==="activeSelection")for(w=y._objects,b=0;b<w.length;b++)_=w[b],u(this._objects,_),this._objects.push(_);else u(this._objects,m),this._objects.push(m);return this.renderOnAddRemove&&this.requestRenderAll(),this},sendBackwards:function(m,y){if(!m)return this;var b=this._activeObject,_,w,S,x,k,E=0;if(m===b&&m.type==="activeSelection")for(k=b._objects,_=0;_<k.length;_++)w=k[_],S=this._objects.indexOf(w),S>0+E&&(x=S-1,u(this._objects,w),this._objects.splice(x,0,w)),E++;else S=this._objects.indexOf(m),S!==0&&(x=this._findNewLowerIndex(m,S,y),u(this._objects,m),this._objects.splice(x,0,m));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewLowerIndex:function(m,y,b){var _,w;if(b)for(_=y,w=y-1;w>=0;--w){var S=m.intersectsWithObject(this._objects[w])||m.isContainedWithinObject(this._objects[w])||this._objects[w].isContainedWithinObject(m);if(S){_=w;break}}else _=y-1;return _},bringForward:function(m,y){if(!m)return this;var b=this._activeObject,_,w,S,x,k,E=0;if(m===b&&m.type==="activeSelection")for(k=b._objects,_=k.length;_--;)w=k[_],S=this._objects.indexOf(w),S<this._objects.length-1-E&&(x=S+1,u(this._objects,w),this._objects.splice(x,0,w)),E++;else S=this._objects.indexOf(m),S!==this._objects.length-1&&(x=this._findNewUpperIndex(m,S,y),u(this._objects,m),this._objects.splice(x,0,m));return this.renderOnAddRemove&&this.requestRenderAll(),this},_findNewUpperIndex:function(m,y,b){var _,w,S;if(b)for(_=y,w=y+1,S=this._objects.length;w<S;++w){var x=m.intersectsWithObject(this._objects[w])||m.isContainedWithinObject(this._objects[w])||this._objects[w].isContainedWithinObject(m);if(x){_=w;break}}else _=y+1;return _},moveTo:function(m,y){return u(this._objects,m),this._objects.splice(y,0,m),this.renderOnAddRemove&&this.requestRenderAll()},dispose:function(){return this.isRendering&&(t.util.cancelAnimFrame(this.isRendering),this.isRendering=0),this.forEachObject(function(m){m.dispose&&m.dispose()}),this._objects=[],this.backgroundImage&&this.backgroundImage.dispose&&this.backgroundImage.dispose(),this.backgroundImage=null,this.overlayImage&&this.overlayImage.dispose&&this.overlayImage.dispose(),this.overlayImage=null,this._iTextInstances=null,this.contextContainer=null,this.lowerCanvasEl.classList.remove("lower-canvas"),t.util.setStyle(this.lowerCanvasEl,this._originalCanvasStyle),delete this._originalCanvasStyle,this.lowerCanvasEl.setAttribute("width",this.width),this.lowerCanvasEl.setAttribute("height",this.height),t.util.cleanUpJsdomNode(this.lowerCanvasEl),this.lowerCanvasEl=void 0,this},toString:function(){return"#<fabric.Canvas ("+this.complexity()+"): { objects: "+this._objects.length+" }>"}}),o(t.StaticCanvas.prototype,t.Observable),o(t.StaticCanvas.prototype,t.Collection),o(t.StaticCanvas.prototype,t.DataURLExporter),o(t.StaticCanvas,{EMPTY_JSON:'{"objects": [], "background": "white"}',supports:function(m){var y=h();if(!y||!y.getContext)return null;var b=y.getContext("2d");if(!b)return null;switch(m){case"setLineDash":return typeof b.setLineDash<"u";default:return null}}}),t.StaticCanvas.prototype.toJSON=t.StaticCanvas.prototype.toObject,t.isLikelyNode&&(t.StaticCanvas.prototype.createPNGStream=function(){var m=s(this.lowerCanvasEl);return m&&m.createPNGStream()},t.StaticCanvas.prototype.createJPEGStream=function(m){var y=s(this.lowerCanvasEl);return y&&y.createJPEGStream(m)})}(),t.BaseBrush=t.util.createClass({color:"rgb(0, 0, 0)",width:1,shadow:null,strokeLineCap:"round",strokeLineJoin:"round",strokeMiterLimit:10,strokeDashArray:null,limitedToCanvasSize:!1,_setBrushStyles:function(o){o.strokeStyle=this.color,o.lineWidth=this.width,o.lineCap=this.strokeLineCap,o.miterLimit=this.strokeMiterLimit,o.lineJoin=this.strokeLineJoin,o.setLineDash(this.strokeDashArray||[])},_saveAndTransform:function(o){var a=this.canvas.viewportTransform;o.save(),o.transform(a[0],a[1],a[2],a[3],a[4],a[5])},_setShadow:function(){if(!!this.shadow){var o=this.canvas,a=this.shadow,u=o.contextTop,d=o.getZoom();o&&o._isRetinaScaling()&&(d*=t.devicePixelRatio),u.shadowColor=a.color,u.shadowBlur=a.blur*d,u.shadowOffsetX=a.offsetX*d,u.shadowOffsetY=a.offsetY*d}},needsFullRender:function(){var o=new t.Color(this.color);return o.getAlpha()<1||!!this.shadow},_resetShadow:function(){var o=this.canvas.contextTop;o.shadowColor="",o.shadowBlur=o.shadowOffsetX=o.shadowOffsetY=0},_isOutSideCanvas:function(o){return o.x<0||o.x>this.canvas.getWidth()||o.y<0||o.y>this.canvas.getHeight()}}),function(){t.PencilBrush=t.util.createClass(t.BaseBrush,{decimate:.4,drawStraightLine:!1,straightLineKey:"shiftKey",initialize:function(o){this.canvas=o,this._points=[]},needsFullRender:function(){return this.callSuper("needsFullRender")||this._hasStraightLine},_drawSegment:function(o,a,u){var d=a.midPointFrom(u);return o.quadraticCurveTo(a.x,a.y,d.x,d.y),d},onMouseDown:function(o,a){!this.canvas._isMainEvent(a.e)||(this.drawStraightLine=a.e[this.straightLineKey],this._prepareForDrawing(o),this._captureDrawingPath(o),this._render())},onMouseMove:function(o,a){if(!!this.canvas._isMainEvent(a.e)&&(this.drawStraightLine=a.e[this.straightLineKey],!(this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o))&&this._captureDrawingPath(o)&&this._points.length>1))if(this.needsFullRender())this.canvas.clearContext(this.canvas.contextTop),this._render();else{var u=this._points,d=u.length,c=this.canvas.contextTop;this._saveAndTransform(c),this.oldEnd&&(c.beginPath(),c.moveTo(this.oldEnd.x,this.oldEnd.y)),this.oldEnd=this._drawSegment(c,u[d-2],u[d-1],!0),c.stroke(),c.restore()}},onMouseUp:function(o){return this.canvas._isMainEvent(o.e)?(this.drawStraightLine=!1,this.oldEnd=void 0,this._finalizeAndAddPath(),!1):!0},_prepareForDrawing:function(o){var a=new t.Point(o.x,o.y);this._reset(),this._addPoint(a),this.canvas.contextTop.moveTo(a.x,a.y)},_addPoint:function(o){return this._points.length>1&&o.eq(this._points[this._points.length-1])?!1:(this.drawStraightLine&&this._points.length>1&&(this._hasStraightLine=!0,this._points.pop()),this._points.push(o),!0)},_reset:function(){this._points=[],this._setBrushStyles(this.canvas.contextTop),this._setShadow(),this._hasStraightLine=!1},_captureDrawingPath:function(o){var a=new t.Point(o.x,o.y);return this._addPoint(a)},_render:function(o){var a,u,d=this._points[0],c=this._points[1];if(o=o||this.canvas.contextTop,this._saveAndTransform(o),o.beginPath(),this._points.length===2&&d.x===c.x&&d.y===c.y){var f=this.width/1e3;d=new t.Point(d.x,d.y),c=new t.Point(c.x,c.y),d.x-=f,c.x+=f}for(o.moveTo(d.x,d.y),a=1,u=this._points.length;a<u;a++)this._drawSegment(o,d,c),d=this._points[a],c=this._points[a+1];o.lineTo(d.x,d.y),o.stroke(),o.restore()},convertPointsToSVGPath:function(o){var a=this.width/1e3;return t.util.getSmoothPathFromPoints(o,a)},_isEmptySVGPath:function(o){var a=t.util.joinPath(o);return a==="M 0 0 Q 0 0 0 0 L 0 0"},createPath:function(o){var a=new t.Path(o,{fill:null,stroke:this.color,strokeWidth:this.width,strokeLineCap:this.strokeLineCap,strokeMiterLimit:this.strokeMiterLimit,strokeLineJoin:this.strokeLineJoin,strokeDashArray:this.strokeDashArray});return this.shadow&&(this.shadow.affectStroke=!0,a.shadow=new t.Shadow(this.shadow)),a},decimatePoints:function(o,a){if(o.length<=2)return o;var u=this.canvas.getZoom(),d=Math.pow(a/u,2),c,f=o.length-1,s=o[0],h=[s],v;for(c=1;c<f-1;c++)v=Math.pow(s.x-o[c].x,2)+Math.pow(s.y-o[c].y,2),v>=d&&(s=o[c],h.push(s));return h.push(o[f]),h},_finalizeAndAddPath:function(){var o=this.canvas.contextTop;o.closePath(),this.decimate&&(this._points=this.decimatePoints(this._points,this.decimate));var a=this.convertPointsToSVGPath(this._points);if(this._isEmptySVGPath(a)){this.canvas.requestRenderAll();return}var u=this.createPath(a);this.canvas.clearContext(this.canvas.contextTop),this.canvas.fire("before:path:created",{path:u}),this.canvas.add(u),this.canvas.requestRenderAll(),u.setCoords(),this._resetShadow(),this.canvas.fire("path:created",{path:u})}})}(),t.CircleBrush=t.util.createClass(t.BaseBrush,{width:10,initialize:function(o){this.canvas=o,this.points=[]},drawDot:function(o){var a=this.addPoint(o),u=this.canvas.contextTop;this._saveAndTransform(u),this.dot(u,a),u.restore()},dot:function(o,a){o.fillStyle=a.fill,o.beginPath(),o.arc(a.x,a.y,a.radius,0,Math.PI*2,!1),o.closePath(),o.fill()},onMouseDown:function(o){this.points.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.drawDot(o)},_render:function(){var o=this.canvas.contextTop,a,u,d=this.points;for(this._saveAndTransform(o),a=0,u=d.length;a<u;a++)this.dot(o,d[a]);o.restore()},onMouseMove:function(o){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o)||(this.needsFullRender()?(this.canvas.clearContext(this.canvas.contextTop),this.addPoint(o),this._render()):this.drawDot(o))},onMouseUp:function(){var o=this.canvas.renderOnAddRemove,a,u;this.canvas.renderOnAddRemove=!1;var d=[];for(a=0,u=this.points.length;a<u;a++){var c=this.points[a],f=new t.Circle({radius:c.radius,left:c.x,top:c.y,originX:"center",originY:"center",fill:c.fill});this.shadow&&(f.shadow=new t.Shadow(this.shadow)),d.push(f)}var s=new t.Group(d);s.canvas=this.canvas,this.canvas.fire("before:path:created",{path:s}),this.canvas.add(s),this.canvas.fire("path:created",{path:s}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=o,this.canvas.requestRenderAll()},addPoint:function(o){var a=new t.Point(o.x,o.y),u=t.util.getRandomInt(Math.max(0,this.width-20),this.width+20)/2,d=new t.Color(this.color).setAlpha(t.util.getRandomInt(0,100)/100).toRgba();return a.radius=u,a.fill=d,this.points.push(a),a}}),t.SprayBrush=t.util.createClass(t.BaseBrush,{width:10,density:20,dotWidth:1,dotWidthVariance:1,randomOpacity:!1,optimizeOverlapping:!0,initialize:function(o){this.canvas=o,this.sprayChunks=[]},onMouseDown:function(o){this.sprayChunks.length=0,this.canvas.clearContext(this.canvas.contextTop),this._setShadow(),this.addSprayChunk(o),this.render(this.sprayChunkPoints)},onMouseMove:function(o){this.limitedToCanvasSize===!0&&this._isOutSideCanvas(o)||(this.addSprayChunk(o),this.render(this.sprayChunkPoints))},onMouseUp:function(){var o=this.canvas.renderOnAddRemove;this.canvas.renderOnAddRemove=!1;for(var a=[],u=0,d=this.sprayChunks.length;u<d;u++)for(var c=this.sprayChunks[u],f=0,s=c.length;f<s;f++){var h=new t.Rect({width:c[f].width,height:c[f].width,left:c[f].x+1,top:c[f].y+1,originX:"center",originY:"center",fill:this.color});a.push(h)}this.optimizeOverlapping&&(a=this._getOptimizedRects(a));var v=new t.Group(a);this.shadow&&v.set("shadow",new t.Shadow(this.shadow)),this.canvas.fire("before:path:created",{path:v}),this.canvas.add(v),this.canvas.fire("path:created",{path:v}),this.canvas.clearContext(this.canvas.contextTop),this._resetShadow(),this.canvas.renderOnAddRemove=o,this.canvas.requestRenderAll()},_getOptimizedRects:function(o){var a={},u,d,c;for(d=0,c=o.length;d<c;d++)u=o[d].left+""+o[d].top,a[u]||(a[u]=o[d]);var f=[];for(u in a)f.push(a[u]);return f},render:function(o){var a=this.canvas.contextTop,u,d;for(a.fillStyle=this.color,this._saveAndTransform(a),u=0,d=o.length;u<d;u++){var c=o[u];typeof c.opacity<"u"&&(a.globalAlpha=c.opacity),a.fillRect(c.x,c.y,c.width,c.width)}a.restore()},_render:function(){var o=this.canvas.contextTop,a,u;for(o.fillStyle=this.color,this._saveAndTransform(o),a=0,u=this.sprayChunks.length;a<u;a++)this.render(this.sprayChunks[a]);o.restore()},addSprayChunk:function(o){this.sprayChunkPoints=[];var a,u,d,c=this.width/2,f;for(f=0;f<this.density;f++){a=t.util.getRandomInt(o.x-c,o.x+c),u=t.util.getRandomInt(o.y-c,o.y+c),this.dotWidthVariance?d=t.util.getRandomInt(Math.max(1,this.dotWidth-this.dotWidthVariance),this.dotWidth+this.dotWidthVariance):d=this.dotWidth;var s=new t.Point(a,u);s.width=d,this.randomOpacity&&(s.opacity=t.util.getRandomInt(0,100)/100),this.sprayChunkPoints.push(s)}this.sprayChunks.push(this.sprayChunkPoints)}}),t.PatternBrush=t.util.createClass(t.PencilBrush,{getPatternSrc:function(){var o=20,a=5,u=t.util.createCanvasElement(),d=u.getContext("2d");return u.width=u.height=o+a,d.fillStyle=this.color,d.beginPath(),d.arc(o/2,o/2,o/2,0,Math.PI*2,!1),d.closePath(),d.fill(),u},getPatternSrcFunction:function(){return String(this.getPatternSrc).replace("this.color",'"'+this.color+'"')},getPattern:function(o){return o.createPattern(this.source||this.getPatternSrc(),"repeat")},_setBrushStyles:function(o){this.callSuper("_setBrushStyles",o),o.strokeStyle=this.getPattern(o)},createPath:function(o){var a=this.callSuper("createPath",o),u=a._getLeftTopCoords().scalarAdd(a.strokeWidth/2);return a.stroke=new t.Pattern({source:this.source||this.getPatternSrcFunction(),offsetX:-u.x,offsetY:-u.y}),a}}),function(){var o=t.util.getPointer,a=t.util.degreesToRadians,u=t.util.isTouchEvent;t.Canvas=t.util.createClass(t.StaticCanvas,{initialize:function(c,f){f||(f={}),this.renderAndResetBound=this.renderAndReset.bind(this),this.requestRenderAllBound=this.requestRenderAll.bind(this),this._initStatic(c,f),this._initInteractive(),this._createCacheCanvas()},uniformScaling:!0,uniScaleKey:"shiftKey",centeredScaling:!1,centeredRotation:!1,centeredKey:"altKey",altActionKey:"shiftKey",interactive:!0,selection:!0,selectionKey:"shiftKey",altSelectionKey:null,selectionColor:"rgba(100, 100, 255, 0.3)",selectionDashArray:[],selectionBorderColor:"rgba(255, 255, 255, 0.3)",selectionLineWidth:1,selectionFullyContained:!1,hoverCursor:"move",moveCursor:"move",defaultCursor:"default",freeDrawingCursor:"crosshair",notAllowedCursor:"not-allowed",containerClass:"canvas-container",perPixelTargetFind:!1,targetFindTolerance:0,skipTargetFind:!1,isDrawingMode:!1,preserveObjectStacking:!1,snapAngle:0,snapThreshold:null,stopContextMenu:!1,fireRightClick:!1,fireMiddleClick:!1,targets:[],enablePointerEvents:!1,_hoveredTarget:null,_hoveredTargets:[],_initInteractive:function(){this._currentTransform=null,this._groupSelector=null,this._initWrapperElement(),this._createUpperCanvas(),this._initEventListeners(),this._initRetinaScaling(),this.freeDrawingBrush=t.PencilBrush&&new t.PencilBrush(this),this.calcOffset()},_chooseObjectsToRender:function(){var c=this.getActiveObjects(),f,s,h;if(c.length>0&&!this.preserveObjectStacking){s=[],h=[];for(var v=0,m=this._objects.length;v<m;v++)f=this._objects[v],c.indexOf(f)===-1?s.push(f):h.push(f);c.length>1&&(this._activeObject._objects=h),s.push.apply(s,h)}else s=this._objects;return s},renderAll:function(){this.contextTopDirty&&!this._groupSelector&&!this.isDrawingMode&&(this.clearContext(this.contextTop),this.contextTopDirty=!1),this.hasLostContext&&(this.renderTopLayer(this.contextTop),this.hasLostContext=!1);var c=this.contextContainer;return this.renderCanvas(c,this._chooseObjectsToRender()),this},renderTopLayer:function(c){c.save(),this.isDrawingMode&&this._isCurrentlyDrawing&&(this.freeDrawingBrush&&this.freeDrawingBrush._render(),this.contextTopDirty=!0),this.selection&&this._groupSelector&&(this._drawSelection(c),this.contextTopDirty=!0),c.restore()},renderTop:function(){var c=this.contextTop;return this.clearContext(c),this.renderTopLayer(c),this.fire("after:render"),this},_normalizePointer:function(c,f){var s=c.calcTransformMatrix(),h=t.util.invertTransform(s),v=this.restorePointerVpt(f);return t.util.transformPoint(v,h)},isTargetTransparent:function(c,f,s){if(c.shouldCache()&&c._cacheCanvas&&c!==this._activeObject){var h=this._normalizePointer(c,{x:f,y:s}),v=Math.max(c.cacheTranslationX+h.x*c.zoomX,0),m=Math.max(c.cacheTranslationY+h.y*c.zoomY,0),w=t.util.isTransparent(c._cacheContext,Math.round(v),Math.round(m),this.targetFindTolerance);return w}var y=this.contextCache,b=c.selectionBackgroundColor,_=this.viewportTransform;c.selectionBackgroundColor="",this.clearContext(y),y.save(),y.transform(_[0],_[1],_[2],_[3],_[4],_[5]),c.render(y),y.restore(),c.selectionBackgroundColor=b;var w=t.util.isTransparent(y,f,s,this.targetFindTolerance);return w},_isSelectionKeyPressed:function(c){var f=!1;return Array.isArray(this.selectionKey)?f=!!this.selectionKey.find(function(s){return c[s]===!0}):f=c[this.selectionKey],f},_shouldClearSelection:function(c,f){var s=this.getActiveObjects(),h=this._activeObject;return!f||f&&h&&s.length>1&&s.indexOf(f)===-1&&h!==f&&!this._isSelectionKeyPressed(c)||f&&!f.evented||f&&!f.selectable&&h&&h!==f},_shouldCenterTransform:function(c,f,s){if(!!c){var h;return f==="scale"||f==="scaleX"||f==="scaleY"||f==="resizing"?h=this.centeredScaling||c.centeredScaling:f==="rotate"&&(h=this.centeredRotation||c.centeredRotation),h?!s:s}},_getOriginFromCorner:function(c,f){var s={x:c.originX,y:c.originY};return f==="ml"||f==="tl"||f==="bl"?s.x="right":(f==="mr"||f==="tr"||f==="br")&&(s.x="left"),f==="tl"||f==="mt"||f==="tr"?s.y="bottom":(f==="bl"||f==="mb"||f==="br")&&(s.y="top"),s},_getActionFromCorner:function(c,f,s,h){if(!f||!c)return"drag";var v=h.controls[f];return v.getActionName(s,v,h)},_setupCurrentTransform:function(c,f,s){if(!!f){var h=this.getPointer(c),v=f.__corner,m=f.controls[v],y=s&&v?m.getActionHandler(c,f,m):t.controlsUtils.dragHandler,b=this._getActionFromCorner(s,v,c,f),_=this._getOriginFromCorner(f,v),w=c[this.centeredKey],S={target:f,action:b,actionHandler:y,corner:v,scaleX:f.scaleX,scaleY:f.scaleY,skewX:f.skewX,skewY:f.skewY,offsetX:h.x-f.left,offsetY:h.y-f.top,originX:_.x,originY:_.y,ex:h.x,ey:h.y,lastX:h.x,lastY:h.y,theta:a(f.angle),width:f.width*f.scaleX,shiftKey:c.shiftKey,altKey:w,original:t.util.saveObjectTransform(f)};this._shouldCenterTransform(f,b,w)&&(S.originX="center",S.originY="center"),S.original.originX=_.x,S.original.originY=_.y,this._currentTransform=S,this._beforeTransform(c)}},setCursor:function(c){this.upperCanvasEl.style.cursor=c},_drawSelection:function(c){var f=this._groupSelector,s=new t.Point(f.ex,f.ey),h=t.util.transformPoint(s,this.viewportTransform),v=new t.Point(f.ex+f.left,f.ey+f.top),m=t.util.transformPoint(v,this.viewportTransform),y=Math.min(h.x,m.x),b=Math.min(h.y,m.y),_=Math.max(h.x,m.x),w=Math.max(h.y,m.y),S=this.selectionLineWidth/2;this.selectionColor&&(c.fillStyle=this.selectionColor,c.fillRect(y,b,_-y,w-b)),!(!this.selectionLineWidth||!this.selectionBorderColor)&&(c.lineWidth=this.selectionLineWidth,c.strokeStyle=this.selectionBorderColor,y+=S,b+=S,_-=S,w-=S,t.Object.prototype._setLineDash.call(this,c,this.selectionDashArray),c.strokeRect(y,b,_-y,w-b))},findTarget:function(c,f){if(!this.skipTargetFind){var s=!0,h=this.getPointer(c,s),v=this._activeObject,m=this.getActiveObjects(),y,b,_=u(c),w=m.length>1&&!f||m.length===1;if(this.targets=[],w&&v._findTargetCorner(h,_)||m.length>1&&!f&&v===this._searchPossibleTargets([v],h))return v;if(m.length===1&&v===this._searchPossibleTargets([v],h))if(this.preserveObjectStacking)y=v,b=this.targets,this.targets=[];else return v;var S=this._searchPossibleTargets(this._objects,h);return c[this.altSelectionKey]&&S&&y&&S!==y&&(S=y,this.targets=b),S}},_checkTarget:function(c,f,s){if(f&&f.visible&&f.evented&&f.containsPoint(c))if((this.perPixelTargetFind||f.perPixelTargetFind)&&!f.isEditing){var h=this.isTargetTransparent(f,s.x,s.y);if(!h)return!0}else return!0},_searchPossibleTargets:function(c,f){for(var s,h=c.length,v;h--;){var m=c[h],y=m.group?this._normalizePointer(m.group,f):f;if(this._checkTarget(y,m,f)){s=c[h],s.subTargetCheck&&s instanceof t.Group&&(v=this._searchPossibleTargets(s._objects,f),v&&this.targets.push(v));break}}return s},restorePointerVpt:function(c){return t.util.transformPoint(c,t.util.invertTransform(this.viewportTransform))},getPointer:function(c,f){if(this._absolutePointer&&!f)return this._absolutePointer;if(this._pointer&&f)return this._pointer;var s=o(c),h=this.upperCanvasEl,v=h.getBoundingClientRect(),m=v.width||0,y=v.height||0,b;(!m||!y)&&("top"in v&&"bottom"in v&&(y=Math.abs(v.top-v.bottom)),"right"in v&&"left"in v&&(m=Math.abs(v.right-v.left))),this.calcOffset(),s.x=s.x-this._offset.left,s.y=s.y-this._offset.top,f||(s=this.restorePointerVpt(s));var _=this.getRetinaScaling();return _!==1&&(s.x/=_,s.y/=_),m===0||y===0?b={width:1,height:1}:b={width:h.width/m,height:h.height/y},{x:s.x*b.width,y:s.y*b.height}},_createUpperCanvas:function(){var c=this.lowerCanvasEl.className.replace(/\s*lower-canvas\s*/,""),f=this.lowerCanvasEl,s=this.upperCanvasEl;s?s.className="":(s=this._createCanvasElement(),this.upperCanvasEl=s),t.util.addClass(s,"upper-canvas "+c),this.wrapperEl.appendChild(s),this._copyCanvasStyle(f,s),this._applyCanvasStyle(s),this.contextTop=s.getContext("2d")},getTopContext:function(){return this.contextTop},_createCacheCanvas:function(){this.cacheCanvasEl=this._createCanvasElement(),this.cacheCanvasEl.setAttribute("width",this.width),this.cacheCanvasEl.setAttribute("height",this.height),this.contextCache=this.cacheCanvasEl.getContext("2d")},_initWrapperElement:function(){this.wrapperEl=t.util.wrapElement(this.lowerCanvasEl,"div",{class:this.containerClass}),t.util.setStyle(this.wrapperEl,{width:this.width+"px",height:this.height+"px",position:"relative"}),t.util.makeElementUnselectable(this.wrapperEl)},_applyCanvasStyle:function(c){var f=this.width||c.width,s=this.height||c.height;t.util.setStyle(c,{position:"absolute",width:f+"px",height:s+"px",left:0,top:0,"touch-action":this.allowTouchScrolling?"manipulation":"none","-ms-touch-action":this.allowTouchScrolling?"manipulation":"none"}),c.width=f,c.height=s,t.util.makeElementUnselectable(c)},_copyCanvasStyle:function(c,f){f.style.cssText=c.style.cssText},getSelectionContext:function(){return this.contextTop},getSelectionElement:function(){return this.upperCanvasEl},getActiveObject:function(){return this._activeObject},getActiveObjects:function(){var c=this._activeObject;return c?c.type==="activeSelection"&&c._objects?c._objects.slice(0):[c]:[]},_onObjectRemoved:function(c){c===this._activeObject&&(this.fire("before:selection:cleared",{target:c}),this._discardActiveObject(),this.fire("selection:cleared",{target:c}),c.fire("deselected")),c===this._hoveredTarget&&(this._hoveredTarget=null,this._hoveredTargets=[]),this.callSuper("_onObjectRemoved",c)},_fireSelectionEvents:function(c,f){var s=!1,h=this.getActiveObjects(),v=[],m=[];c.forEach(function(y){h.indexOf(y)===-1&&(s=!0,y.fire("deselected",{e:f,target:y}),m.push(y))}),h.forEach(function(y){c.indexOf(y)===-1&&(s=!0,y.fire("selected",{e:f,target:y}),v.push(y))}),c.length>0&&h.length>0?s&&this.fire("selection:updated",{e:f,selected:v,deselected:m}):h.length>0?this.fire("selection:created",{e:f,selected:v}):c.length>0&&this.fire("selection:cleared",{e:f,deselected:m})},setActiveObject:function(c,f){var s=this.getActiveObjects();return this._setActiveObject(c,f),this._fireSelectionEvents(s,f),this},_setActiveObject:function(c,f){return this._activeObject===c||!this._discardActiveObject(f,c)||c.onSelect({e:f})?!1:(this._activeObject=c,!0)},_discardActiveObject:function(c,f){var s=this._activeObject;if(s){if(s.onDeselect({e:c,object:f}))return!1;this._activeObject=null}return!0},discardActiveObject:function(c){var f=this.getActiveObjects(),s=this.getActiveObject();return f.length&&this.fire("before:selection:cleared",{target:s,e:c}),this._discardActiveObject(c),this._fireSelectionEvents(f,c),this},dispose:function(){var c=this.wrapperEl;return this.removeListeners(),c.removeChild(this.upperCanvasEl),c.removeChild(this.lowerCanvasEl),this.contextCache=null,this.contextTop=null,["upperCanvasEl","cacheCanvasEl"].forEach(function(f){t.util.cleanUpJsdomNode(this[f]),this[f]=void 0}.bind(this)),c.parentNode&&c.parentNode.replaceChild(this.lowerCanvasEl,this.wrapperEl),delete this.wrapperEl,t.StaticCanvas.prototype.dispose.call(this),this},clear:function(){return this.discardActiveObject(),this.clearContext(this.contextTop),this.callSuper("clear")},drawControls:function(c){var f=this._activeObject;f&&f._renderControls(c)},_toObject:function(c,f,s){var h=this._realizeGroupTransformOnObject(c),v=this.callSuper("_toObject",c,f,s);return this._unwindGroupTransformOnObject(c,h),v},_realizeGroupTransformOnObject:function(c){if(c.group&&c.group.type==="activeSelection"&&this._activeObject===c.group){var f=["angle","flipX","flipY","left","scaleX","scaleY","skewX","skewY","top"],s={};return f.forEach(function(h){s[h]=c[h]}),t.util.addTransformToObject(c,this._activeObject.calcOwnMatrix()),s}else return null},_unwindGroupTransformOnObject:function(c,f){f&&c.set(f)},_setSVGObject:function(c,f,s){var h=this._realizeGroupTransformOnObject(f);this.callSuper("_setSVGObject",c,f,s),this._unwindGroupTransformOnObject(f,h)},setViewportTransform:function(c){this.renderOnAddRemove&&this._activeObject&&this._activeObject.isEditing&&this._activeObject.clearContextTop(),t.StaticCanvas.prototype.setViewportTransform.call(this,c)}});for(var d in t.StaticCanvas)d!=="prototype"&&(t.Canvas[d]=t.StaticCanvas[d])}(),function(){var o=t.util.addListener,a=t.util.removeListener,u=3,d=2,c=1,f={passive:!1};function s(h,v){return h.button&&h.button===v-1}t.util.object.extend(t.Canvas.prototype,{mainTouchId:null,_initEventListeners:function(){this.removeListeners(),this._bindEvents(),this.addOrRemove(o,"add")},_getEventPrefix:function(){return this.enablePointerEvents?"pointer":"mouse"},addOrRemove:function(h,v){var m=this.upperCanvasEl,y=this._getEventPrefix();h(t.window,"resize",this._onResize),h(m,y+"down",this._onMouseDown),h(m,y+"move",this._onMouseMove,f),h(m,y+"out",this._onMouseOut),h(m,y+"enter",this._onMouseEnter),h(m,"wheel",this._onMouseWheel),h(m,"contextmenu",this._onContextMenu),h(m,"dblclick",this._onDoubleClick),h(m,"dragover",this._onDragOver),h(m,"dragenter",this._onDragEnter),h(m,"dragleave",this._onDragLeave),h(m,"drop",this._onDrop),this.enablePointerEvents||h(m,"touchstart",this._onTouchStart,f),typeof eventjs<"u"&&v in eventjs&&(eventjs[v](m,"gesture",this._onGesture),eventjs[v](m,"drag",this._onDrag),eventjs[v](m,"orientation",this._onOrientationChange),eventjs[v](m,"shake",this._onShake),eventjs[v](m,"longpress",this._onLongPress))},removeListeners:function(){this.addOrRemove(a,"remove");var h=this._getEventPrefix();a(t.document,h+"up",this._onMouseUp),a(t.document,"touchend",this._onTouchEnd,f),a(t.document,h+"move",this._onMouseMove,f),a(t.document,"touchmove",this._onMouseMove,f)},_bindEvents:function(){this.eventsBound||(this._onMouseDown=this._onMouseDown.bind(this),this._onTouchStart=this._onTouchStart.bind(this),this._onMouseMove=this._onMouseMove.bind(this),this._onMouseUp=this._onMouseUp.bind(this),this._onTouchEnd=this._onTouchEnd.bind(this),this._onResize=this._onResize.bind(this),this._onGesture=this._onGesture.bind(this),this._onDrag=this._onDrag.bind(this),this._onShake=this._onShake.bind(this),this._onLongPress=this._onLongPress.bind(this),this._onOrientationChange=this._onOrientationChange.bind(this),this._onMouseWheel=this._onMouseWheel.bind(this),this._onMouseOut=this._onMouseOut.bind(this),this._onMouseEnter=this._onMouseEnter.bind(this),this._onContextMenu=this._onContextMenu.bind(this),this._onDoubleClick=this._onDoubleClick.bind(this),this._onDragOver=this._onDragOver.bind(this),this._onDragEnter=this._simpleEventHandler.bind(this,"dragenter"),this._onDragLeave=this._simpleEventHandler.bind(this,"dragleave"),this._onDrop=this._onDrop.bind(this),this.eventsBound=!0)},_onGesture:function(h,v){this.__onTransformGesture&&this.__onTransformGesture(h,v)},_onDrag:function(h,v){this.__onDrag&&this.__onDrag(h,v)},_onMouseWheel:function(h){this.__onMouseWheel(h)},_onMouseOut:function(h){var v=this._hoveredTarget;this.fire("mouse:out",{target:v,e:h}),this._hoveredTarget=null,v&&v.fire("mouseout",{e:h});var m=this;this._hoveredTargets.forEach(function(y){m.fire("mouse:out",{target:v,e:h}),y&&v.fire("mouseout",{e:h})}),this._hoveredTargets=[]},_onMouseEnter:function(h){!this._currentTransform&&!this.findTarget(h)&&(this.fire("mouse:over",{target:null,e:h}),this._hoveredTarget=null,this._hoveredTargets=[])},_onOrientationChange:function(h,v){this.__onOrientationChange&&this.__onOrientationChange(h,v)},_onShake:function(h,v){this.__onShake&&this.__onShake(h,v)},_onLongPress:function(h,v){this.__onLongPress&&this.__onLongPress(h,v)},_onDragOver:function(h){h.preventDefault();var v=this._simpleEventHandler("dragover",h);this._fireEnterLeaveEvents(v,h)},_onDrop:function(h){return this._simpleEventHandler("drop:before",h),this._simpleEventHandler("drop",h)},_onContextMenu:function(h){return this.stopContextMenu&&(h.stopPropagation(),h.preventDefault()),!1},_onDoubleClick:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"dblclick"),this._resetTransformEventData(h)},getPointerId:function(h){var v=h.changedTouches;return v?v[0]&&v[0].identifier:this.enablePointerEvents?h.pointerId:-1},_isMainEvent:function(h){return h.isPrimary===!0?!0:h.isPrimary===!1?!1:h.type==="touchend"&&h.touches.length===0?!0:h.changedTouches?h.changedTouches[0].identifier===this.mainTouchId:!0},_onTouchStart:function(h){h.preventDefault(),this.mainTouchId===null&&(this.mainTouchId=this.getPointerId(h)),this.__onMouseDown(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();o(t.document,"touchend",this._onTouchEnd,f),o(t.document,"touchmove",this._onMouseMove,f),a(v,m+"down",this._onMouseDown)},_onMouseDown:function(h){this.__onMouseDown(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();a(v,m+"move",this._onMouseMove,f),o(t.document,m+"up",this._onMouseUp),o(t.document,m+"move",this._onMouseMove,f)},_onTouchEnd:function(h){if(!(h.touches.length>0)){this.__onMouseUp(h),this._resetTransformEventData(),this.mainTouchId=null;var v=this._getEventPrefix();a(t.document,"touchend",this._onTouchEnd,f),a(t.document,"touchmove",this._onMouseMove,f);var m=this;this._willAddMouseDown&&clearTimeout(this._willAddMouseDown),this._willAddMouseDown=setTimeout(function(){o(m.upperCanvasEl,v+"down",m._onMouseDown),m._willAddMouseDown=0},400)}},_onMouseUp:function(h){this.__onMouseUp(h),this._resetTransformEventData();var v=this.upperCanvasEl,m=this._getEventPrefix();this._isMainEvent(h)&&(a(t.document,m+"up",this._onMouseUp),a(t.document,m+"move",this._onMouseMove,f),o(v,m+"move",this._onMouseMove,f))},_onMouseMove:function(h){!this.allowTouchScrolling&&h.preventDefault&&h.preventDefault(),this.__onMouseMove(h)},_onResize:function(){this.calcOffset()},_shouldRender:function(h){var v=this._activeObject;return!!v!=!!h||v&&h&&v!==h?!0:(v&&v.isEditing,!1)},__onMouseUp:function(h){var v,m=this._currentTransform,y=this._groupSelector,b=!1,_=!y||y.left===0&&y.top===0;if(this._cacheTransformEventData(h),v=this._target,this._handleEvent(h,"up:before"),s(h,u)){this.fireRightClick&&this._handleEvent(h,"up",u,_);return}if(s(h,d)){this.fireMiddleClick&&this._handleEvent(h,"up",d,_),this._resetTransformEventData();return}if(this.isDrawingMode&&this._isCurrentlyDrawing){this._onMouseUpInDrawingMode(h);return}if(!!this._isMainEvent(h)){if(m&&(this._finalizeCurrentTransform(h),b=m.actionPerformed),!_){var w=v===this._activeObject;this._maybeGroupObjects(h),b||(b=this._shouldRender(v)||!w&&v===this._activeObject)}var S,x;if(v){if(S=v._findTargetCorner(this.getPointer(h,!0),t.util.isTouchEvent(h)),v.selectable&&v!==this._activeObject&&v.activeOn==="up")this.setActiveObject(v,h),b=!0;else{var k=v.controls[S],E=k&&k.getMouseUpHandler(h,v,k);E&&(x=this.getPointer(h),E(h,m,x.x,x.y))}v.isMoving=!1}if(m&&(m.target!==v||m.corner!==S)){var I=m.target&&m.target.controls[m.corner],D=I&&I.getMouseUpHandler(h,v,k);x=x||this.getPointer(h),D&&D(h,m,x.x,x.y)}this._setCursorFromEvent(h,v),this._handleEvent(h,"up",c,_),this._groupSelector=null,this._currentTransform=null,v&&(v.__corner=0),b?this.requestRenderAll():_||this.renderTop()}},_simpleEventHandler:function(h,v){var m=this.findTarget(v),y=this.targets,b={e:v,target:m,subTargets:y};if(this.fire(h,b),m&&m.fire(h,b),!y)return m;for(var _=0;_<y.length;_++)y[_].fire(h,b);return m},_handleEvent:function(h,v,m,y){var b=this._target,_=this.targets||[],w={e:h,target:b,subTargets:_,button:m||c,isClick:y||!1,pointer:this._pointer,absolutePointer:this._absolutePointer,transform:this._currentTransform};v==="up"&&(w.currentTarget=this.findTarget(h),w.currentSubTargets=this.targets),this.fire("mouse:"+v,w),b&&b.fire("mouse"+v,w);for(var S=0;S<_.length;S++)_[S].fire("mouse"+v,w)},_finalizeCurrentTransform:function(h){var v=this._currentTransform,m=v.target,y={e:h,target:m,transform:v,action:v.action};m._scaling&&(m._scaling=!1),m.setCoords(),(v.actionPerformed||this.stateful&&m.hasStateChanged())&&this._fire("modified",y)},_onMouseDownInDrawingMode:function(h){this._isCurrentlyDrawing=!0,this.getActiveObject()&&this.discardActiveObject(h).requestRenderAll();var v=this.getPointer(h);this.freeDrawingBrush.onMouseDown(v,{e:h,pointer:v}),this._handleEvent(h,"down")},_onMouseMoveInDrawingMode:function(h){if(this._isCurrentlyDrawing){var v=this.getPointer(h);this.freeDrawingBrush.onMouseMove(v,{e:h,pointer:v})}this.setCursor(this.freeDrawingCursor),this._handleEvent(h,"move")},_onMouseUpInDrawingMode:function(h){var v=this.getPointer(h);this._isCurrentlyDrawing=this.freeDrawingBrush.onMouseUp({e:h,pointer:v}),this._handleEvent(h,"up")},__onMouseDown:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"down:before");var v=this._target;if(s(h,u)){this.fireRightClick&&this._handleEvent(h,"down",u);return}if(s(h,d)){this.fireMiddleClick&&this._handleEvent(h,"down",d);return}if(this.isDrawingMode){this._onMouseDownInDrawingMode(h);return}if(!!this._isMainEvent(h)&&!this._currentTransform){var m=this._pointer;this._previousPointer=m;var y=this._shouldRender(v),b=this._shouldGroup(h,v);if(this._shouldClearSelection(h,v)?this.discardActiveObject(h):b&&(this._handleGrouping(h,v),v=this._activeObject),this.selection&&(!v||!v.selectable&&!v.isEditing&&v!==this._activeObject)&&(this._groupSelector={ex:this._absolutePointer.x,ey:this._absolutePointer.y,top:0,left:0}),v){var _=v===this._activeObject;v.selectable&&v.activeOn==="down"&&this.setActiveObject(v,h);var w=v._findTargetCorner(this.getPointer(h,!0),t.util.isTouchEvent(h));if(v.__corner=w,v===this._activeObject&&(w||!b)){this._setupCurrentTransform(h,v,_);var S=v.controls[w],m=this.getPointer(h),x=S&&S.getMouseDownHandler(h,v,S);x&&x(h,this._currentTransform,m.x,m.y)}}this._handleEvent(h,"down"),(y||b)&&this.requestRenderAll()}},_resetTransformEventData:function(){this._target=null,this._pointer=null,this._absolutePointer=null},_cacheTransformEventData:function(h){this._resetTransformEventData(),this._pointer=this.getPointer(h,!0),this._absolutePointer=this.restorePointerVpt(this._pointer),this._target=this._currentTransform?this._currentTransform.target:this.findTarget(h)||null},_beforeTransform:function(h){var v=this._currentTransform;this.stateful&&v.target.saveState(),this.fire("before:transform",{e:h,transform:v})},__onMouseMove:function(h){this._handleEvent(h,"move:before"),this._cacheTransformEventData(h);var v,m;if(this.isDrawingMode){this._onMouseMoveInDrawingMode(h);return}if(!!this._isMainEvent(h)){var y=this._groupSelector;y?(m=this._absolutePointer,y.left=m.x-y.ex,y.top=m.y-y.ey,this.renderTop()):this._currentTransform?this._transformObject(h):(v=this.findTarget(h)||null,this._setCursorFromEvent(h,v),this._fireOverOutEvents(v,h)),this._handleEvent(h,"move"),this._resetTransformEventData()}},_fireOverOutEvents:function(h,v){var m=this._hoveredTarget,y=this._hoveredTargets,b=this.targets,_=Math.max(y.length,b.length);this.fireSyntheticInOutEvents(h,v,{oldTarget:m,evtOut:"mouseout",canvasEvtOut:"mouse:out",evtIn:"mouseover",canvasEvtIn:"mouse:over"});for(var w=0;w<_;w++)this.fireSyntheticInOutEvents(b[w],v,{oldTarget:y[w],evtOut:"mouseout",evtIn:"mouseover"});this._hoveredTarget=h,this._hoveredTargets=this.targets.concat()},_fireEnterLeaveEvents:function(h,v){var m=this._draggedoverTarget,y=this._hoveredTargets,b=this.targets,_=Math.max(y.length,b.length);this.fireSyntheticInOutEvents(h,v,{oldTarget:m,evtOut:"dragleave",evtIn:"dragenter"});for(var w=0;w<_;w++)this.fireSyntheticInOutEvents(b[w],v,{oldTarget:y[w],evtOut:"dragleave",evtIn:"dragenter"});this._draggedoverTarget=h},fireSyntheticInOutEvents:function(h,v,m){var y,b,_=m.oldTarget,w,S,x=_!==h,k=m.canvasEvtIn,E=m.canvasEvtOut;x&&(y={e:v,target:h,previousTarget:_},b={e:v,target:_,nextTarget:h}),S=h&&x,w=_&&x,w&&(E&&this.fire(E,b),_.fire(m.evtOut,b)),S&&(k&&this.fire(k,y),h.fire(m.evtIn,y))},__onMouseWheel:function(h){this._cacheTransformEventData(h),this._handleEvent(h,"wheel"),this._resetTransformEventData()},_transformObject:function(h){var v=this.getPointer(h),m=this._currentTransform;m.reset=!1,m.shiftKey=h.shiftKey,m.altKey=h[this.centeredKey],this._performTransformAction(h,m,v),m.actionPerformed&&this.requestRenderAll()},_performTransformAction:function(h,v,m){var y=m.x,b=m.y,_=v.action,w=!1,S=v.actionHandler;S&&(w=S(h,v,y,b)),_==="drag"&&w&&(v.target.isMoving=!0,this.setCursor(v.target.moveCursor||this.moveCursor)),v.actionPerformed=v.actionPerformed||w},_fire:t.controlsUtils.fireEvent,_setCursorFromEvent:function(h,v){if(!v)return this.setCursor(this.defaultCursor),!1;var m=v.hoverCursor||this.hoverCursor,y=this._activeObject&&this._activeObject.type==="activeSelection"?this._activeObject:null,b=(!y||!y.contains(v))&&v._findTargetCorner(this.getPointer(h,!0));b?this.setCursor(this.getCornerCursor(b,v,h)):(v.subTargetCheck&&this.targets.concat().reverse().map(function(_){m=_.hoverCursor||m}),this.setCursor(m))},getCornerCursor:function(h,v,m){var y=v.controls[h];return y.cursorStyleHandler(m,y,v)}})}(),function(){var o=Math.min,a=Math.max;t.util.object.extend(t.Canvas.prototype,{_shouldGroup:function(u,d){var c=this._activeObject;return c&&this._isSelectionKeyPressed(u)&&d&&d.selectable&&this.selection&&(c!==d||c.type==="activeSelection")&&!d.onSelect({e:u})},_handleGrouping:function(u,d){var c=this._activeObject;c.__corner||d===c&&(d=this.findTarget(u,!0),!d||!d.selectable)||(c&&c.type==="activeSelection"?this._updateActiveSelection(d,u):this._createActiveSelection(d,u))},_updateActiveSelection:function(u,d){var c=this._activeObject,f=c._objects.slice(0);c.contains(u)?(c.removeWithUpdate(u),this._hoveredTarget=u,this._hoveredTargets=this.targets.concat(),c.size()===1&&this._setActiveObject(c.item(0),d)):(c.addWithUpdate(u),this._hoveredTarget=c,this._hoveredTargets=this.targets.concat()),this._fireSelectionEvents(f,d)},_createActiveSelection:function(u,d){var c=this.getActiveObjects(),f=this._createGroup(u);this._hoveredTarget=f,this._setActiveObject(f,d),this._fireSelectionEvents(c,d)},_createGroup:function(u){var d=this._objects,c=d.indexOf(this._activeObject)<d.indexOf(u),f=c?[this._activeObject,u]:[u,this._activeObject];return this._activeObject.isEditing&&this._activeObject.exitEditing(),new t.ActiveSelection(f,{canvas:this})},_groupSelectedObjects:function(u){var d=this._collectObjects(u),c;d.length===1?this.setActiveObject(d[0],u):d.length>1&&(c=new t.ActiveSelection(d.reverse(),{canvas:this}),this.setActiveObject(c,u))},_collectObjects:function(u){for(var d=[],c,f=this._groupSelector.ex,s=this._groupSelector.ey,h=f+this._groupSelector.left,v=s+this._groupSelector.top,m=new t.Point(o(f,h),o(s,v)),y=new t.Point(a(f,h),a(s,v)),b=!this.selectionFullyContained,_=f===h&&s===v,w=this._objects.length;w--&&(c=this._objects[w],!(!(!c||!c.selectable||!c.visible)&&(b&&c.intersectsWithRect(m,y,!0)||c.isContainedWithinRect(m,y,!0)||b&&c.containsPoint(m,null,!0)||b&&c.containsPoint(y,null,!0))&&(d.push(c),_))););return d.length>1&&(d=d.filter(function(S){return!S.onSelect({e:u})})),d},_maybeGroupObjects:function(u){this.selection&&this._groupSelector&&this._groupSelectedObjects(u),this.setCursor(this.defaultCursor),this._groupSelector=null}})}(),function(){t.util.object.extend(t.StaticCanvas.prototype,{toDataURL:function(o){o||(o={});var a=o.format||"png",u=o.quality||1,d=(o.multiplier||1)*(o.enableRetinaScaling?this.getRetinaScaling():1),c=this.toCanvasElement(d,o);return t.util.toDataURL(c,a,u)},toCanvasElement:function(o,a){o=o||1,a=a||{};var u=(a.width||this.width)*o,d=(a.height||this.height)*o,c=this.getZoom(),f=this.width,s=this.height,h=c*o,v=this.viewportTransform,m=(v[4]-(a.left||0))*o,y=(v[5]-(a.top||0))*o,b=this.interactive,_=[h,0,0,h,m,y],w=this.enableRetinaScaling,S=t.util.createCanvasElement(),x=this.contextTop;return S.width=u,S.height=d,this.contextTop=null,this.enableRetinaScaling=!1,this.interactive=!1,this.viewportTransform=_,this.width=u,this.height=d,this.calcViewportBoundaries(),this.renderCanvas(S.getContext("2d"),this._objects),this.viewportTransform=v,this.width=f,this.height=s,this.calcViewportBoundaries(),this.interactive=b,this.enableRetinaScaling=w,this.contextTop=x,S}})}(),t.util.object.extend(t.StaticCanvas.prototype,{loadFromJSON:function(o,a,u){if(!!o){var d=typeof o=="string"?JSON.parse(o):t.util.object.clone(o),c=this,f=d.clipPath,s=this.renderOnAddRemove;return this.renderOnAddRemove=!1,delete d.clipPath,this._enlivenObjects(d.objects,function(h){c.clear(),c._setBgOverlay(d,function(){f?c._enlivenObjects([f],function(v){c.clipPath=v[0],c.__setupCanvas.call(c,d,h,s,a)}):c.__setupCanvas.call(c,d,h,s,a)})},u),this}},__setupCanvas:function(o,a,u,d){var c=this;a.forEach(function(f,s){c.insertAt(f,s)}),this.renderOnAddRemove=u,delete o.objects,delete o.backgroundImage,delete o.overlayImage,delete o.background,delete o.overlay,this._setOptions(o),this.renderAll(),d&&d()},_setBgOverlay:function(o,a){var u={backgroundColor:!1,overlayColor:!1,backgroundImage:!1,overlayImage:!1};if(!o.backgroundImage&&!o.overlayImage&&!o.background&&!o.overlay){a&&a();return}var d=function(){u.backgroundImage&&u.overlayImage&&u.backgroundColor&&u.overlayColor&&a&&a()};this.__setBgOverlay("backgroundImage",o.backgroundImage,u,d),this.__setBgOverlay("overlayImage",o.overlayImage,u,d),this.__setBgOverlay("backgroundColor",o.background,u,d),this.__setBgOverlay("overlayColor",o.overlay,u,d)},__setBgOverlay:function(o,a,u,d){var c=this;if(!a){u[o]=!0,d&&d();return}o==="backgroundImage"||o==="overlayImage"?t.util.enlivenObjects([a],function(f){c[o]=f[0],u[o]=!0,d&&d()}):this["set"+t.util.string.capitalize(o,!0)](a,function(){u[o]=!0,d&&d()})},_enlivenObjects:function(o,a,u){if(!o||o.length===0){a&&a([]);return}t.util.enlivenObjects(o,function(d){a&&a(d)},null,u)},_toDataURL:function(o,a){this.clone(function(u){a(u.toDataURL(o))})},_toDataURLWithMultiplier:function(o,a,u){this.clone(function(d){u(d.toDataURLWithMultiplier(o,a))})},clone:function(o,a){var u=JSON.stringify(this.toJSON(a));this.cloneWithoutData(function(d){d.loadFromJSON(u,function(){o&&o(d)})})},cloneWithoutData:function(o){var a=t.util.createCanvasElement();a.width=this.width,a.height=this.height;var u=new t.Canvas(a);this.backgroundImage?(u.setBackgroundImage(this.backgroundImage.src,function(){u.renderAll(),o&&o(u)}),u.backgroundImageOpacity=this.backgroundImageOpacity,u.backgroundImageStretch=this.backgroundImageStretch):o&&o(u)}}),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.util.object.clone,c=a.util.toFixed,f=a.util.string.capitalize,s=a.util.degreesToRadians,h=!a.isLikelyNode,v=2;a.Object||(a.Object=a.util.createClass(a.CommonMethods,{type:"object",originX:"left",originY:"top",top:0,left:0,width:0,height:0,scaleX:1,scaleY:1,flipX:!1,flipY:!1,opacity:1,angle:0,skewX:0,skewY:0,cornerSize:13,touchCornerSize:24,transparentCorners:!0,hoverCursor:null,moveCursor:null,padding:0,borderColor:"rgb(178,204,255)",borderDashArray:null,cornerColor:"rgb(178,204,255)",cornerStrokeColor:null,cornerStyle:"rect",cornerDashArray:null,centeredScaling:!1,centeredRotation:!0,fill:"rgb(0,0,0)",fillRule:"nonzero",globalCompositeOperation:"source-over",backgroundColor:"",selectionBackgroundColor:"",stroke:null,strokeWidth:1,strokeDashArray:null,strokeDashOffset:0,strokeLineCap:"butt",strokeLineJoin:"miter",strokeMiterLimit:4,shadow:null,borderOpacityWhenMoving:.4,borderScaleFactor:1,minScaleLimit:0,selectable:!0,evented:!0,visible:!0,hasControls:!0,hasBorders:!0,perPixelTargetFind:!1,includeDefaultValues:!0,lockMovementX:!1,lockMovementY:!1,lockRotation:!1,lockScalingX:!1,lockScalingY:!1,lockSkewingX:!1,lockSkewingY:!1,lockScalingFlip:!1,excludeFromExport:!1,objectCaching:h,statefullCache:!1,noScaleCache:!0,strokeUniform:!1,dirty:!0,__corner:0,paintFirst:"fill",activeOn:"down",stateProperties:"top left width height scaleX scaleY flipX flipY originX originY transformMatrix stroke strokeWidth strokeDashArray strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit angle opacity fill globalCompositeOperation shadow visible backgroundColor skewX skewY fillRule paintFirst clipPath strokeUniform".split(" "),cacheProperties:"fill stroke strokeWidth strokeDashArray width height paintFirst strokeUniform strokeLineCap strokeDashOffset strokeLineJoin strokeMiterLimit backgroundColor clipPath".split(" "),colorProperties:"fill stroke backgroundColor".split(" "),clipPath:void 0,inverted:!1,absolutePositioned:!1,initialize:function(m){m&&this.setOptions(m)},_createCacheCanvas:function(){this._cacheProperties={},this._cacheCanvas=a.util.createCanvasElement(),this._cacheContext=this._cacheCanvas.getContext("2d"),this._updateCacheCanvas(),this.dirty=!0},_limitCacheSize:function(m){var y=a.perfLimitSizeTotal,b=m.width,_=m.height,w=a.maxCacheSideLimit,S=a.minCacheSideLimit;if(b<=w&&_<=w&&b*_<=y)return b<S&&(m.width=S),_<S&&(m.height=S),m;var x=b/_,k=a.util.limitDimsByArea(x,y),E=a.util.capValue,I=E(S,k.x,w),D=E(S,k.y,w);return b>I&&(m.zoomX/=b/I,m.width=I,m.capped=!0),_>D&&(m.zoomY/=_/D,m.height=D,m.capped=!0),m},_getCacheCanvasDimensions:function(){var m=this.getTotalObjectScaling(),y=this._getTransformedDimensions(0,0),b=y.x*m.scaleX/this.scaleX,_=y.y*m.scaleY/this.scaleY;return{width:b+v,height:_+v,zoomX:m.scaleX,zoomY:m.scaleY,x:b,y:_}},_updateCacheCanvas:function(){var m=this.canvas;if(this.noScaleCache&&m&&m._currentTransform){var y=m._currentTransform.target,b=m._currentTransform.action;if(this===y&&b.slice&&b.slice(0,5)==="scale")return!1}var _=this._cacheCanvas,w=this._limitCacheSize(this._getCacheCanvasDimensions()),S=a.minCacheSideLimit,x=w.width,k=w.height,E,I,D=w.zoomX,N=w.zoomY,L=x!==this.cacheWidth||k!==this.cacheHeight,F=this.zoomX!==D||this.zoomY!==N,O=L||F,V=0,re=0,J=!1;if(L){var ue=this._cacheCanvas.width,M=this._cacheCanvas.height,H=x>ue||k>M,R=(x<ue*.9||k<M*.9)&&ue>S&&M>S;J=H||R,H&&!w.capped&&(x>S||k>S)&&(V=x*.1,re=k*.1)}return this instanceof a.Text&&this.path&&(O=!0,J=!0,V+=this.getHeightOfLine(0)*this.zoomX,re+=this.getHeightOfLine(0)*this.zoomY),O?(J?(_.width=Math.ceil(x+V),_.height=Math.ceil(k+re)):(this._cacheContext.setTransform(1,0,0,1,0,0),this._cacheContext.clearRect(0,0,_.width,_.height)),E=w.x/2,I=w.y/2,this.cacheTranslationX=Math.round(_.width/2-E)+E,this.cacheTranslationY=Math.round(_.height/2-I)+I,this.cacheWidth=x,this.cacheHeight=k,this._cacheContext.translate(this.cacheTranslationX,this.cacheTranslationY),this._cacheContext.scale(D,N),this.zoomX=D,this.zoomY=N,!0):!1},setOptions:function(m){this._setOptions(m),this._initGradient(m.fill,"fill"),this._initGradient(m.stroke,"stroke"),this._initPattern(m.fill,"fill"),this._initPattern(m.stroke,"stroke")},transform:function(m){var y=this.group&&!this.group._transformDone||this.group&&this.canvas&&m===this.canvas.contextTop,b=this.calcTransformMatrix(!y);m.transform(b[0],b[1],b[2],b[3],b[4],b[5])},toObject:function(m){var y=a.Object.NUM_FRACTION_DIGITS,b={type:this.type,version:a.version,originX:this.originX,originY:this.originY,left:c(this.left,y),top:c(this.top,y),width:c(this.width,y),height:c(this.height,y),fill:this.fill&&this.fill.toObject?this.fill.toObject():this.fill,stroke:this.stroke&&this.stroke.toObject?this.stroke.toObject():this.stroke,strokeWidth:c(this.strokeWidth,y),strokeDashArray:this.strokeDashArray?this.strokeDashArray.concat():this.strokeDashArray,strokeLineCap:this.strokeLineCap,strokeDashOffset:this.strokeDashOffset,strokeLineJoin:this.strokeLineJoin,strokeUniform:this.strokeUniform,strokeMiterLimit:c(this.strokeMiterLimit,y),scaleX:c(this.scaleX,y),scaleY:c(this.scaleY,y),angle:c(this.angle,y),flipX:this.flipX,flipY:this.flipY,opacity:c(this.opacity,y),shadow:this.shadow&&this.shadow.toObject?this.shadow.toObject():this.shadow,visible:this.visible,backgroundColor:this.backgroundColor,fillRule:this.fillRule,paintFirst:this.paintFirst,globalCompositeOperation:this.globalCompositeOperation,skewX:c(this.skewX,y),skewY:c(this.skewY,y)};return this.clipPath&&!this.clipPath.excludeFromExport&&(b.clipPath=this.clipPath.toObject(m),b.clipPath.inverted=this.clipPath.inverted,b.clipPath.absolutePositioned=this.clipPath.absolutePositioned),a.util.populateWithProperties(this,b,m),this.includeDefaultValues||(b=this._removeDefaultValues(b)),b},toDatalessObject:function(m){return this.toObject(m)},_removeDefaultValues:function(m){var y=a.util.getKlass(m.type).prototype,b=y.stateProperties;return b.forEach(function(_){_==="left"||_==="top"||(m[_]===y[_]&&delete m[_],Array.isArray(m[_])&&Array.isArray(y[_])&&m[_].length===0&&y[_].length===0&&delete m[_])}),m},toString:function(){return"#<fabric."+f(this.type)+">"},getObjectScaling:function(){if(!this.group)return{scaleX:this.scaleX,scaleY:this.scaleY};var m=a.util.qrDecompose(this.calcTransformMatrix());return{scaleX:Math.abs(m.scaleX),scaleY:Math.abs(m.scaleY)}},getTotalObjectScaling:function(){var m=this.getObjectScaling(),y=m.scaleX,b=m.scaleY;if(this.canvas){var _=this.canvas.getZoom(),w=this.canvas.getRetinaScaling();y*=_*w,b*=_*w}return{scaleX:y,scaleY:b}},getObjectOpacity:function(){var m=this.opacity;return this.group&&(m*=this.group.getObjectOpacity()),m},_set:function(m,y){var b=m==="scaleX"||m==="scaleY",_=this[m]!==y,w=!1;return b&&(y=this._constrainScale(y)),m==="scaleX"&&y<0?(this.flipX=!this.flipX,y*=-1):m==="scaleY"&&y<0?(this.flipY=!this.flipY,y*=-1):m==="shadow"&&y&&!(y instanceof a.Shadow)?y=new a.Shadow(y):m==="dirty"&&this.group&&this.group.set("dirty",y),this[m]=y,_&&(w=this.group&&this.group.isOnACache(),this.cacheProperties.indexOf(m)>-1?(this.dirty=!0,w&&this.group.set("dirty",!0)):w&&this.stateProperties.indexOf(m)>-1&&this.group.set("dirty",!0)),this},setOnGroup:function(){},getViewportTransform:function(){return this.canvas&&this.canvas.viewportTransform?this.canvas.viewportTransform:a.iMatrix.concat()},isNotVisible:function(){return this.opacity===0||!this.width&&!this.height&&this.strokeWidth===0||!this.visible},render:function(m){this.isNotVisible()||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(m.save(),this._setupCompositeOperation(m),this.drawSelectionBackground(m),this.transform(m),this._setOpacity(m),this._setShadow(m,this),this.shouldCache()?(this.renderCache(),this.drawCacheOnCanvas(m)):(this._removeCacheCanvas(),this.dirty=!1,this.drawObject(m),this.objectCaching&&this.statefullCache&&this.saveState({propertySet:"cacheProperties"})),m.restore())},renderCache:function(m){m=m||{},(!this._cacheCanvas||!this._cacheContext)&&this._createCacheCanvas(),this.isCacheDirty()&&(this.statefullCache&&this.saveState({propertySet:"cacheProperties"}),this.drawObject(this._cacheContext,m.forClipping),this.dirty=!1)},_removeCacheCanvas:function(){this._cacheCanvas=null,this._cacheContext=null,this.cacheWidth=0,this.cacheHeight=0},hasStroke:function(){return this.stroke&&this.stroke!=="transparent"&&this.strokeWidth!==0},hasFill:function(){return this.fill&&this.fill!=="transparent"},needsItsOwnCache:function(){return!!(this.paintFirst==="stroke"&&this.hasFill()&&this.hasStroke()&&typeof this.shadow=="object"||this.clipPath)},shouldCache:function(){return this.ownCaching=this.needsItsOwnCache()||this.objectCaching&&(!this.group||!this.group.isOnACache()),this.ownCaching},willDrawShadow:function(){return!!this.shadow&&(this.shadow.offsetX!==0||this.shadow.offsetY!==0)},drawClipPathOnCache:function(m,y){if(m.save(),y.inverted?m.globalCompositeOperation="destination-out":m.globalCompositeOperation="destination-in",y.absolutePositioned){var b=a.util.invertTransform(this.calcTransformMatrix());m.transform(b[0],b[1],b[2],b[3],b[4],b[5])}y.transform(m),m.scale(1/y.zoomX,1/y.zoomY),m.drawImage(y._cacheCanvas,-y.cacheTranslationX,-y.cacheTranslationY),m.restore()},drawObject:function(m,y){var b=this.fill,_=this.stroke;y?(this.fill="black",this.stroke="",this._setClippingProperties(m)):this._renderBackground(m),this._render(m),this._drawClipPath(m,this.clipPath),this.fill=b,this.stroke=_},_drawClipPath:function(m,y){!y||(y.canvas=this.canvas,y.shouldCache(),y._transformDone=!0,y.renderCache({forClipping:!0}),this.drawClipPathOnCache(m,y))},drawCacheOnCanvas:function(m){m.scale(1/this.zoomX,1/this.zoomY),m.drawImage(this._cacheCanvas,-this.cacheTranslationX,-this.cacheTranslationY)},isCacheDirty:function(m){if(this.isNotVisible())return!1;if(this._cacheCanvas&&this._cacheContext&&!m&&this._updateCacheCanvas())return!0;if(this.dirty||this.clipPath&&this.clipPath.absolutePositioned||this.statefullCache&&this.hasStateChanged("cacheProperties")){if(this._cacheCanvas&&this._cacheContext&&!m){var y=this.cacheWidth/this.zoomX,b=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-y/2,-b/2,y,b)}return!0}return!1},_renderBackground:function(m){if(!!this.backgroundColor){var y=this._getNonTransformedDimensions();m.fillStyle=this.backgroundColor,m.fillRect(-y.x/2,-y.y/2,y.x,y.y),this._removeShadow(m)}},_setOpacity:function(m){this.group&&!this.group._transformDone?m.globalAlpha=this.getObjectOpacity():m.globalAlpha*=this.opacity},_setStrokeStyles:function(m,y){var b=y.stroke;b&&(m.lineWidth=y.strokeWidth,m.lineCap=y.strokeLineCap,m.lineDashOffset=y.strokeDashOffset,m.lineJoin=y.strokeLineJoin,m.miterLimit=y.strokeMiterLimit,b.toLive?b.gradientUnits==="percentage"||b.gradientTransform||b.patternTransform?this._applyPatternForTransformedGradient(m,b):(m.strokeStyle=b.toLive(m,this),this._applyPatternGradientTransform(m,b)):m.strokeStyle=y.stroke)},_setFillStyles:function(m,y){var b=y.fill;b&&(b.toLive?(m.fillStyle=b.toLive(m,this),this._applyPatternGradientTransform(m,y.fill)):m.fillStyle=b)},_setClippingProperties:function(m){m.globalAlpha=1,m.strokeStyle="transparent",m.fillStyle="#000000"},_setLineDash:function(m,y){!y||y.length===0||(1&y.length&&y.push.apply(y,y),m.setLineDash(y))},_renderControls:function(m,y){var b=this.getViewportTransform(),_=this.calcTransformMatrix(),w,S,x;y=y||{},S=typeof y.hasBorders<"u"?y.hasBorders:this.hasBorders,x=typeof y.hasControls<"u"?y.hasControls:this.hasControls,_=a.util.multiplyTransformMatrices(b,_),w=a.util.qrDecompose(_),m.save(),m.translate(w.translateX,w.translateY),m.lineWidth=1*this.borderScaleFactor,this.group||(m.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1),this.flipX&&(w.angle-=180),m.rotate(s(this.group?w.angle:this.angle)),y.forActiveSelection||this.group?S&&this.drawBordersInGroup(m,w,y):S&&this.drawBorders(m,y),x&&this.drawControls(m,y),m.restore()},_setShadow:function(m){if(!!this.shadow){var y=this.shadow,b=this.canvas,_,w=b&&b.viewportTransform[0]||1,S=b&&b.viewportTransform[3]||1;y.nonScaling?_={scaleX:1,scaleY:1}:_=this.getObjectScaling(),b&&b._isRetinaScaling()&&(w*=a.devicePixelRatio,S*=a.devicePixelRatio),m.shadowColor=y.color,m.shadowBlur=y.blur*a.browserShadowBlurConstant*(w+S)*(_.scaleX+_.scaleY)/4,m.shadowOffsetX=y.offsetX*w*_.scaleX,m.shadowOffsetY=y.offsetY*S*_.scaleY}},_removeShadow:function(m){!this.shadow||(m.shadowColor="",m.shadowBlur=m.shadowOffsetX=m.shadowOffsetY=0)},_applyPatternGradientTransform:function(m,y){if(!y||!y.toLive)return{offsetX:0,offsetY:0};var b=y.gradientTransform||y.patternTransform,_=-this.width/2+y.offsetX||0,w=-this.height/2+y.offsetY||0;return y.gradientUnits==="percentage"?m.transform(this.width,0,0,this.height,_,w):m.transform(1,0,0,1,_,w),b&&m.transform(b[0],b[1],b[2],b[3],b[4],b[5]),{offsetX:_,offsetY:w}},_renderPaintInOrder:function(m){this.paintFirst==="stroke"?(this._renderStroke(m),this._renderFill(m)):(this._renderFill(m),this._renderStroke(m))},_render:function(){},_renderFill:function(m){!this.fill||(m.save(),this._setFillStyles(m,this),this.fillRule==="evenodd"?m.fill("evenodd"):m.fill(),m.restore())},_renderStroke:function(m){if(!(!this.stroke||this.strokeWidth===0)){if(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(m),m.save(),this.strokeUniform&&this.group){var y=this.getObjectScaling();m.scale(1/y.scaleX,1/y.scaleY)}else this.strokeUniform&&m.scale(1/this.scaleX,1/this.scaleY);this._setLineDash(m,this.strokeDashArray),this._setStrokeStyles(m,this),m.stroke(),m.restore()}},_applyPatternForTransformedGradient:function(m,y){var b=this._limitCacheSize(this._getCacheCanvasDimensions()),_=a.util.createCanvasElement(),w,S=this.canvas.getRetinaScaling(),x=b.x/this.scaleX/S,k=b.y/this.scaleY/S;_.width=x,_.height=k,w=_.getContext("2d"),w.beginPath(),w.moveTo(0,0),w.lineTo(x,0),w.lineTo(x,k),w.lineTo(0,k),w.closePath(),w.translate(x/2,k/2),w.scale(b.zoomX/this.scaleX/S,b.zoomY/this.scaleY/S),this._applyPatternGradientTransform(w,y),w.fillStyle=y.toLive(m),w.fill(),m.translate(-this.width/2-this.strokeWidth/2,-this.height/2-this.strokeWidth/2),m.scale(S*this.scaleX/b.zoomX,S*this.scaleY/b.zoomY),m.strokeStyle=w.createPattern(_,"no-repeat")},_findCenterFromElement:function(){return{x:this.left+this.width/2,y:this.top+this.height/2}},_assignTransformMatrixProps:function(){if(this.transformMatrix){var m=a.util.qrDecompose(this.transformMatrix);this.flipX=!1,this.flipY=!1,this.set("scaleX",m.scaleX),this.set("scaleY",m.scaleY),this.angle=m.angle,this.skewX=m.skewX,this.skewY=0}},_removeTransformMatrix:function(m){var y=this._findCenterFromElement();this.transformMatrix&&(this._assignTransformMatrixProps(),y=a.util.transformPoint(y,this.transformMatrix)),this.transformMatrix=null,m&&(this.scaleX*=m.scaleX,this.scaleY*=m.scaleY,this.cropX=m.cropX,this.cropY=m.cropY,y.x+=m.offsetLeft,y.y+=m.offsetTop,this.width=m.width,this.height=m.height),this.setPositionByOrigin(y,"center","center")},clone:function(m,y){var b=this.toObject(y);this.constructor.fromObject?this.constructor.fromObject(b,m):a.Object._fromObject("Object",b,m)},cloneAsImage:function(m,y){var b=this.toCanvasElement(y);return m&&m(new a.Image(b)),this},toCanvasElement:function(m){m||(m={});var y=a.util,b=y.saveObjectTransform(this),_=this.group,w=this.shadow,S=Math.abs,x=(m.multiplier||1)*(m.enableRetinaScaling?a.devicePixelRatio:1);delete this.group,m.withoutTransform&&y.resetObjectTransform(this),m.withoutShadow&&(this.shadow=null);var k=a.util.createCanvasElement(),E=this.getBoundingRect(!0,!0),I=this.shadow,D,N={x:0,y:0},L,F,O;I&&(L=I.blur,I.nonScaling?D={scaleX:1,scaleY:1}:D=this.getObjectScaling(),N.x=2*Math.round(S(I.offsetX)+L)*S(D.scaleX),N.y=2*Math.round(S(I.offsetY)+L)*S(D.scaleY)),F=E.width+N.x,O=E.height+N.y,k.width=Math.ceil(F),k.height=Math.ceil(O);var V=new a.StaticCanvas(k,{enableRetinaScaling:!1,renderOnAddRemove:!1,skipOffscreen:!1});m.format==="jpeg"&&(V.backgroundColor="#fff"),this.setPositionByOrigin(new a.Point(V.width/2,V.height/2),"center","center");var re=this.canvas;V.add(this);var J=V.toCanvasElement(x||1,m);return this.shadow=w,this.set("canvas",re),_&&(this.group=_),this.set(b).setCoords(),V._objects=[],V.dispose(),V=null,J},toDataURL:function(m){return m||(m={}),a.util.toDataURL(this.toCanvasElement(m),m.format||"png",m.quality||1)},isType:function(m){return arguments.length>1?Array.from(arguments).includes(this.type):this.type===m},complexity:function(){return 1},toJSON:function(m){return this.toObject(m)},rotate:function(m){var y=(this.originX!=="center"||this.originY!=="center")&&this.centeredRotation;return y&&this._setOriginToCenter(),this.set("angle",m),y&&this._resetOrigin(),this},centerH:function(){return this.canvas&&this.canvas.centerObjectH(this),this},viewportCenterH:function(){return this.canvas&&this.canvas.viewportCenterObjectH(this),this},centerV:function(){return this.canvas&&this.canvas.centerObjectV(this),this},viewportCenterV:function(){return this.canvas&&this.canvas.viewportCenterObjectV(this),this},center:function(){return this.canvas&&this.canvas.centerObject(this),this},viewportCenter:function(){return this.canvas&&this.canvas.viewportCenterObject(this),this},getLocalPointer:function(m,y){y=y||this.canvas.getPointer(m);var b=new a.Point(y.x,y.y),_=this._getLeftTopCoords();return this.angle&&(b=a.util.rotatePoint(b,_,s(-this.angle))),{x:b.x-_.x,y:b.y-_.y}},_setupCompositeOperation:function(m){this.globalCompositeOperation&&(m.globalCompositeOperation=this.globalCompositeOperation)},dispose:function(){a.runningAnimations&&a.runningAnimations.cancelByTarget(this)}}),a.util.createAccessors&&a.util.createAccessors(a.Object),u(a.Object.prototype,a.Observable),a.Object.NUM_FRACTION_DIGITS=2,a.Object.ENLIVEN_PROPS=["clipPath"],a.Object._fromObject=function(m,y,b,_){var w=a[m];y=d(y,!0),a.util.enlivenPatterns([y.fill,y.stroke],function(S){typeof S[0]<"u"&&(y.fill=S[0]),typeof S[1]<"u"&&(y.stroke=S[1]),a.util.enlivenObjectEnlivables(y,y,function(){var x=_?new w(y[_],y):new w(y);b&&b(x)})})},a.Object.__uid=0)}(e),function(){var o=t.util.degreesToRadians,a={left:-.5,center:0,right:.5},u={top:-.5,center:0,bottom:.5};t.util.object.extend(t.Object.prototype,{translateToGivenOrigin:function(d,c,f,s,h){var v=d.x,m=d.y,y,b,_;return typeof c=="string"?c=a[c]:c-=.5,typeof s=="string"?s=a[s]:s-=.5,y=s-c,typeof f=="string"?f=u[f]:f-=.5,typeof h=="string"?h=u[h]:h-=.5,b=h-f,(y||b)&&(_=this._getTransformedDimensions(),v=d.x+y*_.x,m=d.y+b*_.y),new t.Point(v,m)},translateToCenterPoint:function(d,c,f){var s=this.translateToGivenOrigin(d,c,f,"center","center");return this.angle?t.util.rotatePoint(s,d,o(this.angle)):s},translateToOriginPoint:function(d,c,f){var s=this.translateToGivenOrigin(d,"center","center",c,f);return this.angle?t.util.rotatePoint(s,d,o(this.angle)):s},getCenterPoint:function(){var d=new t.Point(this.left,this.top);return this.translateToCenterPoint(d,this.originX,this.originY)},getPointByOrigin:function(d,c){var f=this.getCenterPoint();return this.translateToOriginPoint(f,d,c)},toLocalPoint:function(d,c,f){var s=this.getCenterPoint(),h,v;return typeof c<"u"&&typeof f<"u"?h=this.translateToGivenOrigin(s,"center","center",c,f):h=new t.Point(this.left,this.top),v=new t.Point(d.x,d.y),this.angle&&(v=t.util.rotatePoint(v,s,-o(this.angle))),v.subtractEquals(h)},setPositionByOrigin:function(d,c,f){var s=this.translateToCenterPoint(d,c,f),h=this.translateToOriginPoint(s,this.originX,this.originY);this.set("left",h.x),this.set("top",h.y)},adjustPosition:function(d){var c=o(this.angle),f=this.getScaledWidth(),s=t.util.cos(c)*f,h=t.util.sin(c)*f,v,m;typeof this.originX=="string"?v=a[this.originX]:v=this.originX-.5,typeof d=="string"?m=a[d]:m=d-.5,this.left+=s*(m-v),this.top+=h*(m-v),this.setCoords(),this.originX=d},_setOriginToCenter:function(){this._originalOriginX=this.originX,this._originalOriginY=this.originY;var d=this.getCenterPoint();this.originX="center",this.originY="center",this.left=d.x,this.top=d.y},_resetOrigin:function(){var d=this.translateToOriginPoint(this.getCenterPoint(),this._originalOriginX,this._originalOriginY);this.originX=this._originalOriginX,this.originY=this._originalOriginY,this.left=d.x,this.top=d.y,this._originalOriginX=null,this._originalOriginY=null},_getLeftTopCoords:function(){return this.translateToOriginPoint(this.getCenterPoint(),"left","top")}})}(),function(){function o(f){return[new t.Point(f.tl.x,f.tl.y),new t.Point(f.tr.x,f.tr.y),new t.Point(f.br.x,f.br.y),new t.Point(f.bl.x,f.bl.y)]}var a=t.util,u=a.degreesToRadians,d=a.multiplyTransformMatrices,c=a.transformPoint;a.object.extend(t.Object.prototype,{oCoords:null,aCoords:null,lineCoords:null,ownMatrixCache:null,matrixCache:null,controls:{},_getCoords:function(f,s){return s?f?this.calcACoords():this.calcLineCoords():((!this.aCoords||!this.lineCoords)&&this.setCoords(!0),f?this.aCoords:this.lineCoords)},getCoords:function(f,s){return o(this._getCoords(f,s))},intersectsWithRect:function(f,s,h,v){var m=this.getCoords(h,v),y=t.Intersection.intersectPolygonRectangle(m,f,s);return y.status==="Intersection"},intersectsWithObject:function(f,s,h){var v=t.Intersection.intersectPolygonPolygon(this.getCoords(s,h),f.getCoords(s,h));return v.status==="Intersection"||f.isContainedWithinObject(this,s,h)||this.isContainedWithinObject(f,s,h)},isContainedWithinObject:function(f,s,h){for(var v=this.getCoords(s,h),m=s?f.aCoords:f.lineCoords,y=0,b=f._getImageLines(m);y<4;y++)if(!f.containsPoint(v[y],b))return!1;return!0},isContainedWithinRect:function(f,s,h,v){var m=this.getBoundingRect(h,v);return m.left>=f.x&&m.left+m.width<=s.x&&m.top>=f.y&&m.top+m.height<=s.y},containsPoint:function(f,y,h,v){var m=this._getCoords(h,v),y=y||this._getImageLines(m),b=this._findCrossPoints(f,y);return b!==0&&b%2===1},isOnScreen:function(f){if(!this.canvas)return!1;var s=this.canvas.vptCoords.tl,h=this.canvas.vptCoords.br,v=this.getCoords(!0,f);return v.some(function(m){return m.x<=h.x&&m.x>=s.x&&m.y<=h.y&&m.y>=s.y})||this.intersectsWithRect(s,h,!0,f)?!0:this._containsCenterOfCanvas(s,h,f)},_containsCenterOfCanvas:function(f,s,h){var v={x:(f.x+s.x)/2,y:(f.y+s.y)/2};return!!this.containsPoint(v,null,!0,h)},isPartiallyOnScreen:function(f){if(!this.canvas)return!1;var s=this.canvas.vptCoords.tl,h=this.canvas.vptCoords.br;if(this.intersectsWithRect(s,h,!0,f))return!0;var v=this.getCoords(!0,f).every(function(m){return(m.x>=h.x||m.x<=s.x)&&(m.y>=h.y||m.y<=s.y)});return v&&this._containsCenterOfCanvas(s,h,f)},_getImageLines:function(f){var s={topline:{o:f.tl,d:f.tr},rightline:{o:f.tr,d:f.br},bottomline:{o:f.br,d:f.bl},leftline:{o:f.bl,d:f.tl}};return s},_findCrossPoints:function(f,s){var h,v,m,y,b,_=0,w;for(var S in s)if(w=s[S],!(w.o.y<f.y&&w.d.y<f.y)&&!(w.o.y>=f.y&&w.d.y>=f.y)&&(w.o.x===w.d.x&&w.o.x>=f.x?b=w.o.x:(h=0,v=(w.d.y-w.o.y)/(w.d.x-w.o.x),m=f.y-h*f.x,y=w.o.y-v*w.o.x,b=-(m-y)/(h-v)),b>=f.x&&(_+=1),_===2))break;return _},getBoundingRect:function(f,s){var h=this.getCoords(f,s);return a.makeBoundingBoxFromPoints(h)},getScaledWidth:function(){return this._getTransformedDimensions().x},getScaledHeight:function(){return this._getTransformedDimensions().y},_constrainScale:function(f){return Math.abs(f)<this.minScaleLimit?f<0?-this.minScaleLimit:this.minScaleLimit:f===0?1e-4:f},scale:function(f){return this._set("scaleX",f),this._set("scaleY",f),this.setCoords()},scaleToWidth:function(f,s){var h=this.getBoundingRect(s).width/this.getScaledWidth();return this.scale(f/this.width/h)},scaleToHeight:function(f,s){var h=this.getBoundingRect(s).height/this.getScaledHeight();return this.scale(f/this.height/h)},calcLineCoords:function(){var f=this.getViewportTransform(),s=this.padding,h=u(this.angle),v=a.cos(h),m=a.sin(h),y=v*s,b=m*s,_=y+b,w=y-b,S=this.calcACoords(),x={tl:c(S.tl,f),tr:c(S.tr,f),bl:c(S.bl,f),br:c(S.br,f)};return s&&(x.tl.x-=w,x.tl.y-=_,x.tr.x+=_,x.tr.y-=w,x.bl.x-=_,x.bl.y+=w,x.br.x+=w,x.br.y+=_),x},calcOCoords:function(){var f=this._calcRotateMatrix(),s=this._calcTranslateMatrix(),h=this.getViewportTransform(),v=d(h,s),m=d(v,f),m=d(m,[1/h[0],0,0,1/h[3],0,0]),y=this._calculateCurrentDimensions(),b={};return this.forEachControl(function(_,w,S){b[w]=_.positionHandler(y,m,S)}),b},calcACoords:function(){var f=this._calcRotateMatrix(),s=this._calcTranslateMatrix(),h=d(s,f),v=this._getTransformedDimensions(),m=v.x/2,y=v.y/2;return{tl:c({x:-m,y:-y},h),tr:c({x:m,y:-y},h),bl:c({x:-m,y},h),br:c({x:m,y},h)}},setCoords:function(f){return this.aCoords=this.calcACoords(),this.lineCoords=this.group?this.aCoords:this.calcLineCoords(),f?this:(this.oCoords=this.calcOCoords(),this._setCornerCoords&&this._setCornerCoords(),this)},_calcRotateMatrix:function(){return a.calcRotateMatrix(this)},_calcTranslateMatrix:function(){var f=this.getCenterPoint();return[1,0,0,1,f.x,f.y]},transformMatrixKey:function(f){var s="_",h="";return!f&&this.group&&(h=this.group.transformMatrixKey(f)+s),h+this.top+s+this.left+s+this.scaleX+s+this.scaleY+s+this.skewX+s+this.skewY+s+this.angle+s+this.originX+s+this.originY+s+this.width+s+this.height+s+this.strokeWidth+this.flipX+this.flipY},calcTransformMatrix:function(f){var s=this.calcOwnMatrix();if(f||!this.group)return s;var h=this.transformMatrixKey(f),v=this.matrixCache||(this.matrixCache={});return v.key===h?v.value:(this.group&&(s=d(this.group.calcTransformMatrix(!1),s)),v.key=h,v.value=s,s)},calcOwnMatrix:function(){var f=this.transformMatrixKey(!0),s=this.ownMatrixCache||(this.ownMatrixCache={});if(s.key===f)return s.value;var h=this._calcTranslateMatrix(),v={angle:this.angle,translateX:h[4],translateY:h[5],scaleX:this.scaleX,scaleY:this.scaleY,skewX:this.skewX,skewY:this.skewY,flipX:this.flipX,flipY:this.flipY};return s.key=f,s.value=a.composeMatrix(v),s.value},_getNonTransformedDimensions:function(){var f=this.strokeWidth,s=this.width+f,h=this.height+f;return{x:s,y:h}},_getTransformedDimensions:function(f,s){typeof f>"u"&&(f=this.skewX),typeof s>"u"&&(s=this.skewY);var h,v,m,y=f===0&&s===0;if(this.strokeUniform?(v=this.width,m=this.height):(h=this._getNonTransformedDimensions(),v=h.x,m=h.y),y)return this._finalizeDimensions(v*this.scaleX,m*this.scaleY);var b=a.sizeAfterTransform(v,m,{scaleX:this.scaleX,scaleY:this.scaleY,skewX:f,skewY:s});return this._finalizeDimensions(b.x,b.y)},_finalizeDimensions:function(f,s){return this.strokeUniform?{x:f+this.strokeWidth,y:s+this.strokeWidth}:{x:f,y:s}},_calculateCurrentDimensions:function(){var f=this.getViewportTransform(),s=this._getTransformedDimensions(),h=c(s,f,!0);return h.scalarAdd(2*this.padding)}})}(),t.util.object.extend(t.Object.prototype,{sendToBack:function(){return this.group?t.StaticCanvas.prototype.sendToBack.call(this.group,this):this.canvas&&this.canvas.sendToBack(this),this},bringToFront:function(){return this.group?t.StaticCanvas.prototype.bringToFront.call(this.group,this):this.canvas&&this.canvas.bringToFront(this),this},sendBackwards:function(o){return this.group?t.StaticCanvas.prototype.sendBackwards.call(this.group,this,o):this.canvas&&this.canvas.sendBackwards(this,o),this},bringForward:function(o){return this.group?t.StaticCanvas.prototype.bringForward.call(this.group,this,o):this.canvas&&this.canvas.bringForward(this,o),this},moveTo:function(o){return this.group&&this.group.type!=="activeSelection"?t.StaticCanvas.prototype.moveTo.call(this.group,this,o):this.canvas&&this.canvas.moveTo(this,o),this}}),function(){function o(u,d){if(d){if(d.toLive)return u+": url(#SVGID_"+d.id+"); ";var c=new t.Color(d),f=u+": "+c.toRgb()+"; ",s=c.getAlpha();return s!==1&&(f+=u+"-opacity: "+s.toString()+"; "),f}else return u+": none; "}var a=t.util.toFixed;t.util.object.extend(t.Object.prototype,{getSvgStyles:function(u){var d=this.fillRule?this.fillRule:"nonzero",c=this.strokeWidth?this.strokeWidth:"0",f=this.strokeDashArray?this.strokeDashArray.join(" "):"none",s=this.strokeDashOffset?this.strokeDashOffset:"0",h=this.strokeLineCap?this.strokeLineCap:"butt",v=this.strokeLineJoin?this.strokeLineJoin:"miter",m=this.strokeMiterLimit?this.strokeMiterLimit:"4",y=typeof this.opacity<"u"?this.opacity:"1",b=this.visible?"":" visibility: hidden;",_=u?"":this.getSvgFilter(),w=o("fill",this.fill),S=o("stroke",this.stroke);return[S,"stroke-width: ",c,"; ","stroke-dasharray: ",f,"; ","stroke-linecap: ",h,"; ","stroke-dashoffset: ",s,"; ","stroke-linejoin: ",v,"; ","stroke-miterlimit: ",m,"; ",w,"fill-rule: ",d,"; ","opacity: ",y,";",_,b].join("")},getSvgSpanStyles:function(u,d){var c="; ",s=u.fontFamily?"font-family: "+(u.fontFamily.indexOf("'")===-1&&u.fontFamily.indexOf('"')===-1?"'"+u.fontFamily+"'":u.fontFamily)+c:"",f=u.strokeWidth?"stroke-width: "+u.strokeWidth+c:"",s=s,h=u.fontSize?"font-size: "+u.fontSize+"px"+c:"",v=u.fontStyle?"font-style: "+u.fontStyle+c:"",m=u.fontWeight?"font-weight: "+u.fontWeight+c:"",y=u.fill?o("fill",u.fill):"",b=u.stroke?o("stroke",u.stroke):"",_=this.getSvgTextDecoration(u),w=u.deltaY?"baseline-shift: "+-u.deltaY+"; ":"";return _&&(_="text-decoration: "+_+c),[b,f,s,h,v,m,_,y,w,d?"white-space: pre; ":""].join("")},getSvgTextDecoration:function(u){return["overline","underline","line-through"].filter(function(d){return u[d.replace("-","")]}).join(" ")},getSvgFilter:function(){return this.shadow?"filter: url(#SVGID_"+this.shadow.id+");":""},getSvgCommons:function(){return[this.id?'id="'+this.id+'" ':"",this.clipPath?'clip-path="url(#'+this.clipPath.clipPathId+')" ':""].join("")},getSvgTransform:function(u,d){var c=u?this.calcTransformMatrix():this.calcOwnMatrix(),f='transform="'+t.util.matrixToSVG(c);return f+(d||"")+'" '},_setSVGBg:function(u){if(this.backgroundColor){var d=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(this.backgroundColor),' x="',a(-this.width/2,d),'" y="',a(-this.height/2,d),'" width="',a(this.width,d),'" height="',a(this.height,d),`"></rect>
+`)}},toSVG:function(u){return this._createBaseSVGMarkup(this._toSVG(u),{reviver:u})},toClipPathSVG:function(u){return"	"+this._createBaseClipPathSVGMarkup(this._toSVG(u),{reviver:u})},_createBaseClipPathSVGMarkup:function(u,d){d=d||{};var c=d.reviver,f=d.additionalTransform||"",s=[this.getSvgTransform(!0,f),this.getSvgCommons()].join(""),h=u.indexOf("COMMON_PARTS");return u[h]=s,c?c(u.join("")):u.join("")},_createBaseSVGMarkup:function(u,d){d=d||{};var c=d.noStyle,f=d.reviver,s=c?"":'style="'+this.getSvgStyles()+'" ',h=d.withShadow?'style="'+this.getSvgFilter()+'" ':"",v=this.clipPath,m=this.strokeUniform?'vector-effect="non-scaling-stroke" ':"",y=v&&v.absolutePositioned,b=this.stroke,_=this.fill,w=this.shadow,S,x=[],k,E=u.indexOf("COMMON_PARTS"),I=d.additionalTransform;return v&&(v.clipPathId="CLIPPATH_"+t.Object.__uid++,k='<clipPath id="'+v.clipPathId+`" >
 `+v.toClipPathSVG(f)+`</clipPath>
 `),y&&x.push("<g ",h,this.getSvgCommons(),` >
 `),x.push("<g ",this.getSvgTransform(!1),y?"":h+this.getSvgCommons(),` >
-`),S=[s,m,c?"":this.addPaintOrder()," ",I?'transform="'+I+'" ':""].join(""),u[E]=S,_&&_.toLive&&x.push(_.toSVG(this)),b&&b.toLive&&x.push(b.toSVG(this)),w&&x.push(w.toSVG(this)),v&&x.push(T),x.push(u.join("")),x.push(`</g>
+`),S=[s,m,c?"":this.addPaintOrder()," ",I?'transform="'+I+'" ':""].join(""),u[E]=S,_&&_.toLive&&x.push(_.toSVG(this)),b&&b.toLive&&x.push(b.toSVG(this)),w&&x.push(w.toSVG(this)),v&&x.push(k),x.push(u.join("")),x.push(`</g>
 `),y&&x.push(`</g>
 `),f?f(x.join("")):x.join("")},addPaintOrder:function(){return this.paintFirst!=="fill"?' paint-order="'+this.paintFirst+'" ':""}})}(),function(){var o=t.util.object.extend,a="stateProperties";function u(c,f,s){var h={},v=!0;s.forEach(function(m){h[m]=c[m]}),o(c[f],h,v)}function d(c,f,s){if(c===f)return!0;if(Array.isArray(c)){if(!Array.isArray(f)||c.length!==f.length)return!1;for(var h=0,v=c.length;h<v;h++)if(!d(c[h],f[h]))return!1;return!0}else if(c&&typeof c=="object"){var m=Object.keys(c),y;if(!f||typeof f!="object"||!s&&m.length!==Object.keys(f).length)return!1;for(var h=0,v=m.length;h<v;h++)if(y=m[h],!(y==="canvas"||y==="group")&&!d(c[y],f[y]))return!1;return!0}}t.util.object.extend(t.Object.prototype,{hasStateChanged:function(c){c=c||a;var f="_"+c;return Object.keys(this[f]).length<this[c].length?!0:!d(this[f],this,!0)},saveState:function(c){var f=c&&c.propertySet||a,s="_"+f;return this[s]?(u(this,s,this[f]),c&&c.stateProperties&&u(this,s,c.stateProperties),this):this.setupState(c)},setupState:function(c){c=c||{};var f=c.propertySet||a;return c.propertySet=f,this["_"+f]={},this.saveState(c),this}})}(),function(){var o=t.util.degreesToRadians;t.util.object.extend(t.Object.prototype,{_findTargetCorner:function(a,u){if(!this.hasControls||this.group||!this.canvas||this.canvas._activeObject!==this)return!1;var d=a.x,c=a.y,f,s,h=Object.keys(this.oCoords),v=h.length-1,m;for(this.__corner=0;v>=0;v--)if(m=h[v],!!this.isControlVisible(m)&&(s=this._getImageLines(u?this.oCoords[m].touchCorner:this.oCoords[m].corner),f=this._findCrossPoints({x:d,y:c},s),f!==0&&f%2===1))return this.__corner=m,m;return!1},forEachControl:function(a){for(var u in this.controls)a(this.controls[u],u,this)},_setCornerCoords:function(){var a=this.oCoords;for(var u in a){var d=this.controls[u];a[u].corner=d.calcCornerCoords(this.angle,this.cornerSize,a[u].x,a[u].y,!1),a[u].touchCorner=d.calcCornerCoords(this.angle,this.touchCornerSize,a[u].x,a[u].y,!0)}},drawSelectionBackground:function(a){if(!this.selectionBackgroundColor||this.canvas&&!this.canvas.interactive||this.canvas&&this.canvas._activeObject!==this)return this;a.save();var u=this.getCenterPoint(),d=this._calculateCurrentDimensions(),c=this.canvas.viewportTransform;return a.translate(u.x,u.y),a.scale(1/c[0],1/c[3]),a.rotate(o(this.angle)),a.fillStyle=this.selectionBackgroundColor,a.fillRect(-d.x/2,-d.y/2,d.x,d.y),a.restore(),this},drawBorders:function(a,u){u=u||{};var d=this._calculateCurrentDimensions(),c=this.borderScaleFactor,f=d.x+c,s=d.y+c,h=typeof u.hasControls<"u"?u.hasControls:this.hasControls,v=!1;return a.save(),a.strokeStyle=u.borderColor||this.borderColor,this._setLineDash(a,u.borderDashArray||this.borderDashArray),a.strokeRect(-f/2,-s/2,f,s),h&&(a.beginPath(),this.forEachControl(function(m,y,b){m.withConnection&&m.getVisibility(b,y)&&(v=!0,a.moveTo(m.x*f,m.y*s),a.lineTo(m.x*f+m.offsetX,m.y*s+m.offsetY))}),v&&a.stroke()),a.restore(),this},drawBordersInGroup:function(a,u,d){d=d||{};var c=t.util.sizeAfterTransform(this.width,this.height,u),f=this.strokeWidth,s=this.strokeUniform,h=this.borderScaleFactor,v=c.x+f*(s?this.canvas.getZoom():u.scaleX)+h,m=c.y+f*(s?this.canvas.getZoom():u.scaleY)+h;return a.save(),this._setLineDash(a,d.borderDashArray||this.borderDashArray),a.strokeStyle=d.borderColor||this.borderColor,a.strokeRect(-v/2,-m/2,v,m),a.restore(),this},drawControls:function(a,u){u=u||{},a.save();var d=this.canvas.getRetinaScaling(),c,f;return a.setTransform(d,0,0,d,0,0),a.strokeStyle=a.fillStyle=u.cornerColor||this.cornerColor,this.transparentCorners||(a.strokeStyle=u.cornerStrokeColor||this.cornerStrokeColor),this._setLineDash(a,u.cornerDashArray||this.cornerDashArray),this.setCoords(),this.group&&(c=this.group.calcTransformMatrix()),this.forEachControl(function(s,h,v){f=v.oCoords[h],s.getVisibility(v,h)&&(c&&(f=t.util.transformPoint(f,c)),s.render(a,f.x,f.y,u,v))}),a.restore(),this},isControlVisible:function(a){return this.controls[a]&&this.controls[a].getVisibility(this,a)},setControlVisible:function(a,u){return this._controlsVisibility||(this._controlsVisibility={}),this._controlsVisibility[a]=u,this},setControlsVisibility:function(a){a||(a={});for(var u in a)this.setControlVisible(u,a[u]);return this},onDeselect:function(){},onSelect:function(){}})}(),t.util.object.extend(t.StaticCanvas.prototype,{FX_DURATION:500,fxCenterObjectH:function(o,a){a=a||{};var u=function(){},d=a.onComplete||u,c=a.onChange||u,f=this;return t.util.animate({target:this,startValue:o.left,endValue:this.getCenterPoint().x,duration:this.FX_DURATION,onChange:function(s){o.set("left",s),f.requestRenderAll(),c()},onComplete:function(){o.setCoords(),d()}})},fxCenterObjectV:function(o,a){a=a||{};var u=function(){},d=a.onComplete||u,c=a.onChange||u,f=this;return t.util.animate({target:this,startValue:o.top,endValue:this.getCenterPoint().y,duration:this.FX_DURATION,onChange:function(s){o.set("top",s),f.requestRenderAll(),c()},onComplete:function(){o.setCoords(),d()}})},fxRemove:function(o,a){a=a||{};var u=function(){},d=a.onComplete||u,c=a.onChange||u,f=this;return t.util.animate({target:this,startValue:o.opacity,endValue:0,duration:this.FX_DURATION,onChange:function(s){o.set("opacity",s),f.requestRenderAll(),c()},onComplete:function(){f.remove(o),d()}})}}),t.util.object.extend(t.Object.prototype,{animate:function(){if(arguments[0]&&typeof arguments[0]=="object"){var o=[],a,u,d=[];for(a in arguments[0])o.push(a);for(var c=0,f=o.length;c<f;c++)a=o[c],u=c!==f-1,d.push(this._animate(a,arguments[0][a],arguments[1],u));return d}else return this._animate.apply(this,arguments)},_animate:function(o,a,u,d){var c=this,f;a=a.toString(),u?u=t.util.object.clone(u):u={},~o.indexOf(".")&&(f=o.split("."));var s=c.colorProperties.indexOf(o)>-1||f&&c.colorProperties.indexOf(f[1])>-1,h=f?this.get(f[0])[f[1]]:this.get(o);"from"in u||(u.from=h),s||(~a.indexOf("=")?a=h+parseFloat(a.replace("=","")):a=parseFloat(a));var v={target:this,startValue:u.from,endValue:a,byValue:u.by,easing:u.easing,duration:u.duration,abort:u.abort&&function(m,y,b){return u.abort.call(c,m,y,b)},onChange:function(m,y,b){f?c[f[0]][f[1]]=m:c.set(o,m),!d&&u.onChange&&u.onChange(m,y,b)},onComplete:function(m,y,b){d||(c.setCoords(),u.onComplete&&u.onComplete(m,y,b))}};return s?t.util.animateColor(v.startValue,v.endValue,v.duration,v):t.util.animate(v)}}),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.util.object.clone,c={x1:1,x2:1,y1:1,y2:1};if(a.Line){a.warn("fabric.Line is already defined");return}a.Line=a.util.createClass(a.Object,{type:"line",x1:0,y1:0,x2:0,y2:0,cacheProperties:a.Object.prototype.cacheProperties.concat("x1","x2","y1","y2"),initialize:function(s,h){s||(s=[0,0,0,0]),this.callSuper("initialize",h),this.set("x1",s[0]),this.set("y1",s[1]),this.set("x2",s[2]),this.set("y2",s[3]),this._setWidthHeight(h)},_setWidthHeight:function(s){s||(s={}),this.width=Math.abs(this.x2-this.x1),this.height=Math.abs(this.y2-this.y1),this.left="left"in s?s.left:this._getLeftToOriginX(),this.top="top"in s?s.top:this._getTopToOriginY()},_set:function(s,h){return this.callSuper("_set",s,h),typeof c[s]<"u"&&this._setWidthHeight(),this},_getLeftToOriginX:f({origin:"originX",axis1:"x1",axis2:"x2",dimension:"width"},{nearest:"left",center:"center",farthest:"right"}),_getTopToOriginY:f({origin:"originY",axis1:"y1",axis2:"y2",dimension:"height"},{nearest:"top",center:"center",farthest:"bottom"}),_render:function(s){s.beginPath();var h=this.calcLinePoints();s.moveTo(h.x1,h.y1),s.lineTo(h.x2,h.y2),s.lineWidth=this.strokeWidth;var v=s.strokeStyle;s.strokeStyle=this.stroke||s.fillStyle,this.stroke&&this._renderStroke(s),s.strokeStyle=v},_findCenterFromElement:function(){return{x:(this.x1+this.x2)/2,y:(this.y1+this.y2)/2}},toObject:function(s){return u(this.callSuper("toObject",s),this.calcLinePoints())},_getNonTransformedDimensions:function(){var s=this.callSuper("_getNonTransformedDimensions");return this.strokeLineCap==="butt"&&(this.width===0&&(s.y-=this.strokeWidth),this.height===0&&(s.x-=this.strokeWidth)),s},calcLinePoints:function(){var s=this.x1<=this.x2?-1:1,h=this.y1<=this.y2?-1:1,v=s*this.width*.5,m=h*this.height*.5,y=s*this.width*-.5,b=h*this.height*-.5;return{x1:v,x2:y,y1:m,y2:b}},_toSVG:function(){var s=this.calcLinePoints();return["<line ","COMMON_PARTS",'x1="',s.x1,'" y1="',s.y1,'" x2="',s.x2,'" y2="',s.y2,`" />
 `]}}),a.Line.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("x1 y1 x2 y2".split(" ")),a.Line.fromElement=function(s,h,v){v=v||{};var m=a.parseAttributes(s,a.Line.ATTRIBUTE_NAMES),y=[m.x1||0,m.y1||0,m.x2||0,m.y2||0];h(new a.Line(y,u(m,v)))},a.Line.fromObject=function(s,h){function v(y){delete y.points,h&&h(y)}var m=d(s,!0);m.points=[s.x1,s.y1,s.x2,s.y2],a.Object._fromObject("Line",m,v,"points")};function f(s,h){var v=s.origin,m=s.axis1,y=s.axis2,b=s.dimension,_=h.nearest,w=h.center,S=h.farthest;return function(){switch(this.get(v)){case _:return Math.min(this.get(m),this.get(y));case w:return Math.min(this.get(m),this.get(y))+.5*this.get(b);case S:return Math.max(this.get(m),this.get(y))}}}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.degreesToRadians;if(a.Circle){a.warn("fabric.Circle is already defined.");return}a.Circle=a.util.createClass(a.Object,{type:"circle",radius:0,startAngle:0,endAngle:360,cacheProperties:a.Object.prototype.cacheProperties.concat("radius","startAngle","endAngle"),_set:function(c,f){return this.callSuper("_set",c,f),c==="radius"&&this.setRadius(f),this},toObject:function(c){return this.callSuper("toObject",["radius","startAngle","endAngle"].concat(c))},_toSVG:function(){var c,f=0,s=0,h=(this.endAngle-this.startAngle)%360;if(h===0)c=["<circle ","COMMON_PARTS",'cx="'+f+'" cy="'+s+'" ','r="',this.radius,`" />
@@ -121,15 +121,15 @@ https://github.com/nodeca/pako/blob/main/LICENSE
 `]}return c},_render:function(c){c.beginPath(),c.arc(0,0,this.radius,u(this.startAngle),u(this.endAngle),!1),this._renderPaintInOrder(c)},getRadiusX:function(){return this.get("radius")*this.get("scaleX")},getRadiusY:function(){return this.get("radius")*this.get("scaleY")},setRadius:function(c){return this.radius=c,this.set("width",c*2).set("height",c*2)}}),a.Circle.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("cx cy r".split(" ")),a.Circle.fromElement=function(c,f){var s=a.parseAttributes(c,a.Circle.ATTRIBUTE_NAMES);if(!d(s))throw new Error("value of `r` attribute is required and can not be negative");s.left=(s.left||0)-s.radius,s.top=(s.top||0)-s.radius,f(new a.Circle(s))};function d(c){return"radius"in c&&c.radius>=0}a.Circle.fromObject=function(c,f){a.Object._fromObject("Circle",c,f)}}(e),function(o){var a=o.fabric||(o.fabric={});if(a.Triangle){a.warn("fabric.Triangle is already defined");return}a.Triangle=a.util.createClass(a.Object,{type:"triangle",width:100,height:100,_render:function(u){var d=this.width/2,c=this.height/2;u.beginPath(),u.moveTo(-d,c),u.lineTo(0,-c),u.lineTo(d,c),u.closePath(),this._renderPaintInOrder(u)},_toSVG:function(){var u=this.width/2,d=this.height/2,c=[-u+" "+d,"0 "+-d,u+" "+d].join(",");return["<polygon ","COMMON_PARTS",'points="',c,'" />']}}),a.Triangle.fromObject=function(u,d){return a.Object._fromObject("Triangle",u,d)}}(e),function(o){var a=o.fabric||(o.fabric={}),u=Math.PI*2;if(a.Ellipse){a.warn("fabric.Ellipse is already defined.");return}a.Ellipse=a.util.createClass(a.Object,{type:"ellipse",rx:0,ry:0,cacheProperties:a.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(d){this.callSuper("initialize",d),this.set("rx",d&&d.rx||0),this.set("ry",d&&d.ry||0)},_set:function(d,c){switch(this.callSuper("_set",d,c),d){case"rx":this.rx=c,this.set("width",c*2);break;case"ry":this.ry=c,this.set("height",c*2);break}return this},getRx:function(){return this.get("rx")*this.get("scaleX")},getRy:function(){return this.get("ry")*this.get("scaleY")},toObject:function(d){return this.callSuper("toObject",["rx","ry"].concat(d))},_toSVG:function(){return["<ellipse ","COMMON_PARTS",'cx="0" cy="0" ','rx="',this.rx,'" ry="',this.ry,`" />
 `]},_render:function(d){d.beginPath(),d.save(),d.transform(1,0,0,this.ry/this.rx,0,0),d.arc(0,0,this.rx,0,u,!1),d.restore(),this._renderPaintInOrder(d)}}),a.Ellipse.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("cx cy rx ry".split(" ")),a.Ellipse.fromElement=function(d,c){var f=a.parseAttributes(d,a.Ellipse.ATTRIBUTE_NAMES);f.left=(f.left||0)-f.rx,f.top=(f.top||0)-f.ry,c(new a.Ellipse(f))},a.Ellipse.fromObject=function(d,c){a.Object._fromObject("Ellipse",d,c)}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend;if(a.Rect){a.warn("fabric.Rect is already defined");return}a.Rect=a.util.createClass(a.Object,{stateProperties:a.Object.prototype.stateProperties.concat("rx","ry"),type:"rect",rx:0,ry:0,cacheProperties:a.Object.prototype.cacheProperties.concat("rx","ry"),initialize:function(d){this.callSuper("initialize",d),this._initRxRy()},_initRxRy:function(){this.rx&&!this.ry?this.ry=this.rx:this.ry&&!this.rx&&(this.rx=this.ry)},_render:function(d){var c=this.rx?Math.min(this.rx,this.width/2):0,f=this.ry?Math.min(this.ry,this.height/2):0,s=this.width,h=this.height,v=-this.width/2,m=-this.height/2,y=c!==0||f!==0,b=1-.5522847498;d.beginPath(),d.moveTo(v+c,m),d.lineTo(v+s-c,m),y&&d.bezierCurveTo(v+s-b*c,m,v+s,m+b*f,v+s,m+f),d.lineTo(v+s,m+h-f),y&&d.bezierCurveTo(v+s,m+h-b*f,v+s-b*c,m+h,v+s-c,m+h),d.lineTo(v+c,m+h),y&&d.bezierCurveTo(v+b*c,m+h,v,m+h-b*f,v,m+h-f),d.lineTo(v,m+f),y&&d.bezierCurveTo(v,m+b*f,v+b*c,m,v+c,m),d.closePath(),this._renderPaintInOrder(d)},toObject:function(d){return this.callSuper("toObject",["rx","ry"].concat(d))},_toSVG:function(){var d=-this.width/2,c=-this.height/2;return["<rect ","COMMON_PARTS",'x="',d,'" y="',c,'" rx="',this.rx,'" ry="',this.ry,'" width="',this.width,'" height="',this.height,`" />
 `]}}),a.Rect.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("x y rx ry width height".split(" ")),a.Rect.fromElement=function(d,c,f){if(!d)return c(null);f=f||{};var s=a.parseAttributes(d,a.Rect.ATTRIBUTE_NAMES);s.left=s.left||0,s.top=s.top||0,s.height=s.height||0,s.width=s.width||0;var h=new a.Rect(u(f?a.util.object.clone(f):{},s));h.visible=h.visible&&h.width>0&&h.height>0,c(h)},a.Rect.fromObject=function(d,c){return a.Object._fromObject("Rect",d,c)}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.util.array.min,c=a.util.array.max,f=a.util.toFixed,s=a.util.projectStrokeOnPoints;if(a.Polyline){a.warn("fabric.Polyline is already defined");return}a.Polyline=a.util.createClass(a.Object,{type:"polyline",points:null,exactBoundingBox:!1,cacheProperties:a.Object.prototype.cacheProperties.concat("points"),initialize:function(h,v){v=v||{},this.points=h||[],this.callSuper("initialize",v),this._setPositionDimensions(v)},_projectStrokeOnPoints:function(){return s(this.points,this,!0)},_setPositionDimensions:function(h){var v=this._calcDimensions(h),m,y=this.exactBoundingBox?this.strokeWidth:0;this.width=v.width-y,this.height=v.height-y,h.fromSVG||(m=this.translateToGivenOrigin({x:v.left-this.strokeWidth/2+y/2,y:v.top-this.strokeWidth/2+y/2},"left","top",this.originX,this.originY)),typeof h.left>"u"&&(this.left=h.fromSVG?v.left:m.x),typeof h.top>"u"&&(this.top=h.fromSVG?v.top:m.y),this.pathOffset={x:v.left+this.width/2+y/2,y:v.top+this.height/2+y/2}},_calcDimensions:function(){var h=this.exactBoundingBox?this._projectStrokeOnPoints():this.points,v=d(h,"x")||0,m=d(h,"y")||0,y=c(h,"x")||0,b=c(h,"y")||0,_=y-v,w=b-m;return{left:v,top:m,width:_,height:w}},toObject:function(h){return u(this.callSuper("toObject",h),{points:this.points.concat()})},_toSVG:function(){for(var h=[],v=this.pathOffset.x,m=this.pathOffset.y,y=a.Object.NUM_FRACTION_DIGITS,b=0,_=this.points.length;b<_;b++)h.push(f(this.points[b].x-v,y),",",f(this.points[b].y-m,y)," ");return["<"+this.type+" ","COMMON_PARTS",'points="',h.join(""),`" />
-`]},commonRender:function(h){var v,m=this.points.length,y=this.pathOffset.x,b=this.pathOffset.y;if(!m||isNaN(this.points[m-1].y))return!1;h.beginPath(),h.moveTo(this.points[0].x-y,this.points[0].y-b);for(var _=0;_<m;_++)v=this.points[_],h.lineTo(v.x-y,v.y-b);return!0},_render:function(h){!this.commonRender(h)||this._renderPaintInOrder(h)},complexity:function(){return this.get("points").length}}),a.Polyline.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(),a.Polyline.fromElementGenerator=function(h){return function(v,m,y){if(!v)return m(null);y||(y={});var b=a.parsePointsAttribute(v.getAttribute("points")),_=a.parseAttributes(v,a[h].ATTRIBUTE_NAMES);_.fromSVG=!0,m(new a[h](b,u(_,y)))}},a.Polyline.fromElement=a.Polyline.fromElementGenerator("Polyline"),a.Polyline.fromObject=function(h,v){return a.Object._fromObject("Polyline",h,v,"points")}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.projectStrokeOnPoints;if(a.Polygon){a.warn("fabric.Polygon is already defined");return}a.Polygon=a.util.createClass(a.Polyline,{type:"polygon",_projectStrokeOnPoints:function(){return u(this.points,this)},_render:function(d){!this.commonRender(d)||(d.closePath(),this._renderPaintInOrder(d))}}),a.Polygon.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(),a.Polygon.fromElement=a.Polyline.fromElementGenerator("Polygon"),a.Polygon.fromObject=function(d,c){a.Object._fromObject("Polygon",d,c,"points")}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.array.min,d=a.util.array.max,c=a.util.object.extend,f=a.util.object.clone,s=a.util.toFixed;if(a.Path){a.warn("fabric.Path is already defined");return}a.Path=a.util.createClass(a.Object,{type:"path",path:null,cacheProperties:a.Object.prototype.cacheProperties.concat("path","fillRule"),stateProperties:a.Object.prototype.stateProperties.concat("path"),initialize:function(h,v){v=f(v||{}),delete v.path,this.callSuper("initialize",v),this._setPath(h||[],v)},_setPath:function(h,v){this.path=a.util.makePathSimpler(Array.isArray(h)?h:a.util.parsePath(h)),a.Polyline.prototype._setPositionDimensions.call(this,v||{})},_renderPathCommands:function(h){var v,m=0,y=0,b=0,_=0,w=0,S=0,x=-this.pathOffset.x,T=-this.pathOffset.y;h.beginPath();for(var E=0,I=this.path.length;E<I;++E)switch(v=this.path[E],v[0]){case"L":b=v[1],_=v[2],h.lineTo(b+x,_+T);break;case"M":b=v[1],_=v[2],m=b,y=_,h.moveTo(b+x,_+T);break;case"C":b=v[5],_=v[6],w=v[3],S=v[4],h.bezierCurveTo(v[1]+x,v[2]+T,w+x,S+T,b+x,_+T);break;case"Q":h.quadraticCurveTo(v[1]+x,v[2]+T,v[3]+x,v[4]+T),b=v[3],_=v[4],w=v[1],S=v[2];break;case"z":case"Z":b=m,_=y,h.closePath();break}},_render:function(h){this._renderPathCommands(h),this._renderPaintInOrder(h)},toString:function(){return"#<fabric.Path ("+this.complexity()+'): { "top": '+this.top+', "left": '+this.left+" }>"},toObject:function(h){return c(this.callSuper("toObject",h),{path:this.path.map(function(v){return v.slice()})})},toDatalessObject:function(h){var v=this.toObject(["sourcePath"].concat(h));return v.sourcePath&&delete v.path,v},_toSVG:function(){var h=a.util.joinPath(this.path);return["<path ","COMMON_PARTS",'d="',h,'" stroke-linecap="round" ',`/>
-`]},_getOffsetTransform:function(){var h=a.Object.NUM_FRACTION_DIGITS;return" translate("+s(-this.pathOffset.x,h)+", "+s(-this.pathOffset.y,h)+")"},toClipPathSVG:function(h){var v=this._getOffsetTransform();return"	"+this._createBaseClipPathSVGMarkup(this._toSVG(),{reviver:h,additionalTransform:v})},toSVG:function(h){var v=this._getOffsetTransform();return this._createBaseSVGMarkup(this._toSVG(),{reviver:h,additionalTransform:v})},complexity:function(){return this.path.length},_calcDimensions:function(){for(var h=[],v=[],m,y=0,b=0,_=0,w=0,S,x=0,T=this.path.length;x<T;++x){switch(m=this.path[x],m[0]){case"L":_=m[1],w=m[2],S=[];break;case"M":_=m[1],w=m[2],y=_,b=w,S=[];break;case"C":S=a.util.getBoundsOfCurve(_,w,m[1],m[2],m[3],m[4],m[5],m[6]),_=m[5],w=m[6];break;case"Q":S=a.util.getBoundsOfCurve(_,w,m[1],m[2],m[1],m[2],m[3],m[4]),_=m[3],w=m[4];break;case"z":case"Z":_=y,w=b;break}S.forEach(function(O){h.push(O.x),v.push(O.y)}),h.push(_),v.push(w)}var E=u(h)||0,I=u(v)||0,D=d(h)||0,N=d(v)||0,L=D-E,F=N-I;return{left:E,top:I,width:L,height:F}}}),a.Path.fromObject=function(h,v){if(typeof h.sourcePath=="string"){var m=h.sourcePath;a.loadSVGFromURL(m,function(y){var b=y[0];b.setOptions(h),h.clipPath?a.util.enlivenObjects([h.clipPath],function(_){b.clipPath=_[0],v&&v(b)}):v&&v(b)})}else a.Object._fromObject("Path",h,v,"path")},a.Path.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(["d"]),a.Path.fromElement=function(h,v,m){var y=a.parseAttributes(h,a.Path.ATTRIBUTE_NAMES);y.fromSVG=!0,v(new a.Path(y.d,c(y,m)))}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.array.min,d=a.util.array.max;a.Group||(a.Group=a.util.createClass(a.Object,a.Collection,{type:"group",strokeWidth:0,subTargetCheck:!1,cacheProperties:[],useSetOnGroup:!1,initialize:function(c,f,s){f=f||{},this._objects=[],s&&this.callSuper("initialize",f),this._objects=c||[];for(var h=this._objects.length;h--;)this._objects[h].group=this;if(s)this._updateObjectsACoords();else{var v=f&&f.centerPoint;f.originX!==void 0&&(this.originX=f.originX),f.originY!==void 0&&(this.originY=f.originY),v||this._calcBounds(),this._updateObjectsCoords(v),delete f.centerPoint,this.callSuper("initialize",f)}this.setCoords()},_updateObjectsACoords:function(){for(var c=!0,f=this._objects.length;f--;)this._objects[f].setCoords(c)},_updateObjectsCoords:function(f){for(var f=f||this.getCenterPoint(),s=this._objects.length;s--;)this._updateObjectCoords(this._objects[s],f)},_updateObjectCoords:function(c,f){var s=c.left,h=c.top,v=!0;c.set({left:s-f.x,top:h-f.y}),c.group=this,c.setCoords(v)},toString:function(){return"#<fabric.Group: ("+this.complexity()+")>"},addWithUpdate:function(c){var f=!!this.group;return this._restoreObjectsState(),a.util.resetObjectTransform(this),c&&(f&&a.util.removeTransformFromObject(c,this.group.calcTransformMatrix()),this._objects.push(c),c.group=this,c._set("canvas",this.canvas)),this._calcBounds(),this._updateObjectsCoords(),this.dirty=!0,f?this.group.addWithUpdate():this.setCoords(),this},removeWithUpdate:function(c){return this._restoreObjectsState(),a.util.resetObjectTransform(this),this.remove(c),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},_onObjectAdded:function(c){this.dirty=!0,c.group=this,c._set("canvas",this.canvas)},_onObjectRemoved:function(c){this.dirty=!0,delete c.group},_set:function(c,f){var s=this._objects.length;if(this.useSetOnGroup)for(;s--;)this._objects[s].setOnGroup(c,f);if(c==="canvas")for(;s--;)this._objects[s]._set(c,f);a.Object.prototype._set.call(this,c,f)},toObject:function(c){var f=this.includeDefaultValues,s=this._objects.filter(function(v){return!v.excludeFromExport}).map(function(v){var m=v.includeDefaultValues;v.includeDefaultValues=f;var y=v.toObject(c);return v.includeDefaultValues=m,y}),h=a.Object.prototype.toObject.call(this,c);return h.objects=s,h},toDatalessObject:function(c){var f,s=this.sourcePath;if(s)f=s;else{var h=this.includeDefaultValues;f=this._objects.map(function(m){var y=m.includeDefaultValues;m.includeDefaultValues=h;var b=m.toDatalessObject(c);return m.includeDefaultValues=y,b})}var v=a.Object.prototype.toDatalessObject.call(this,c);return v.objects=f,v},render:function(c){this._transformDone=!0,this.callSuper("render",c),this._transformDone=!1},shouldCache:function(){var c=a.Object.prototype.shouldCache.call(this);if(c){for(var f=0,s=this._objects.length;f<s;f++)if(this._objects[f].willDrawShadow())return this.ownCaching=!1,!1}return c},willDrawShadow:function(){if(a.Object.prototype.willDrawShadow.call(this))return!0;for(var c=0,f=this._objects.length;c<f;c++)if(this._objects[c].willDrawShadow())return!0;return!1},isOnACache:function(){return this.ownCaching||this.group&&this.group.isOnACache()},drawObject:function(c){for(var f=0,s=this._objects.length;f<s;f++)this._objects[f].render(c);this._drawClipPath(c,this.clipPath)},isCacheDirty:function(c){if(this.callSuper("isCacheDirty",c))return!0;if(!this.statefullCache)return!1;for(var f=0,s=this._objects.length;f<s;f++)if(this._objects[f].isCacheDirty(!0)){if(this._cacheCanvas){var h=this.cacheWidth/this.zoomX,v=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-h/2,-v/2,h,v)}return!0}return!1},_restoreObjectsState:function(){var c=this.calcOwnMatrix();return this._objects.forEach(function(f){a.util.addTransformToObject(f,c),delete f.group,f.setCoords()}),this},destroy:function(){return this._objects.forEach(function(c){c.set("dirty",!0)}),this._restoreObjectsState()},dispose:function(){this.callSuper("dispose"),this.forEachObject(function(c){c.dispose&&c.dispose()}),this._objects=[]},toActiveSelection:function(){if(!!this.canvas){var c=this._objects,f=this.canvas;this._objects=[];var s=this.toObject();delete s.objects;var h=new a.ActiveSelection([]);return h.set(s),h.type="activeSelection",f.remove(this),c.forEach(function(v){v.group=h,v.dirty=!0,f.add(v)}),h.canvas=f,h._objects=c,f._activeObject=h,h.setCoords(),h}},ungroupOnCanvas:function(){return this._restoreObjectsState()},setObjectsCoords:function(){var c=!0;return this.forEachObject(function(f){f.setCoords(c)}),this},_calcBounds:function(c){for(var f=[],s=[],h,v,m,y=["tr","br","bl","tl"],b=0,_=this._objects.length,w,S=y.length;b<_;++b){for(h=this._objects[b],m=h.calcACoords(),w=0;w<S;w++)v=y[w],f.push(m[v].x),s.push(m[v].y);h.aCoords=m}this._getBounds(f,s,c)},_getBounds:function(c,f,s){var h=new a.Point(u(c),u(f)),v=new a.Point(d(c),d(f)),m=h.y||0,y=h.x||0,b=v.x-h.x||0,_=v.y-h.y||0;this.width=b,this.height=_,s||this.setPositionByOrigin({x:y,y:m},"left","top")},_toSVG:function(c){for(var f=["<g ","COMMON_PARTS",` >
+`]},commonRender:function(h){var v,m=this.points.length,y=this.pathOffset.x,b=this.pathOffset.y;if(!m||isNaN(this.points[m-1].y))return!1;h.beginPath(),h.moveTo(this.points[0].x-y,this.points[0].y-b);for(var _=0;_<m;_++)v=this.points[_],h.lineTo(v.x-y,v.y-b);return!0},_render:function(h){!this.commonRender(h)||this._renderPaintInOrder(h)},complexity:function(){return this.get("points").length}}),a.Polyline.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(),a.Polyline.fromElementGenerator=function(h){return function(v,m,y){if(!v)return m(null);y||(y={});var b=a.parsePointsAttribute(v.getAttribute("points")),_=a.parseAttributes(v,a[h].ATTRIBUTE_NAMES);_.fromSVG=!0,m(new a[h](b,u(_,y)))}},a.Polyline.fromElement=a.Polyline.fromElementGenerator("Polyline"),a.Polyline.fromObject=function(h,v){return a.Object._fromObject("Polyline",h,v,"points")}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.projectStrokeOnPoints;if(a.Polygon){a.warn("fabric.Polygon is already defined");return}a.Polygon=a.util.createClass(a.Polyline,{type:"polygon",_projectStrokeOnPoints:function(){return u(this.points,this)},_render:function(d){!this.commonRender(d)||(d.closePath(),this._renderPaintInOrder(d))}}),a.Polygon.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(),a.Polygon.fromElement=a.Polyline.fromElementGenerator("Polygon"),a.Polygon.fromObject=function(d,c){a.Object._fromObject("Polygon",d,c,"points")}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.array.min,d=a.util.array.max,c=a.util.object.extend,f=a.util.object.clone,s=a.util.toFixed;if(a.Path){a.warn("fabric.Path is already defined");return}a.Path=a.util.createClass(a.Object,{type:"path",path:null,cacheProperties:a.Object.prototype.cacheProperties.concat("path","fillRule"),stateProperties:a.Object.prototype.stateProperties.concat("path"),initialize:function(h,v){v=f(v||{}),delete v.path,this.callSuper("initialize",v),this._setPath(h||[],v)},_setPath:function(h,v){this.path=a.util.makePathSimpler(Array.isArray(h)?h:a.util.parsePath(h)),a.Polyline.prototype._setPositionDimensions.call(this,v||{})},_renderPathCommands:function(h){var v,m=0,y=0,b=0,_=0,w=0,S=0,x=-this.pathOffset.x,k=-this.pathOffset.y;h.beginPath();for(var E=0,I=this.path.length;E<I;++E)switch(v=this.path[E],v[0]){case"L":b=v[1],_=v[2],h.lineTo(b+x,_+k);break;case"M":b=v[1],_=v[2],m=b,y=_,h.moveTo(b+x,_+k);break;case"C":b=v[5],_=v[6],w=v[3],S=v[4],h.bezierCurveTo(v[1]+x,v[2]+k,w+x,S+k,b+x,_+k);break;case"Q":h.quadraticCurveTo(v[1]+x,v[2]+k,v[3]+x,v[4]+k),b=v[3],_=v[4],w=v[1],S=v[2];break;case"z":case"Z":b=m,_=y,h.closePath();break}},_render:function(h){this._renderPathCommands(h),this._renderPaintInOrder(h)},toString:function(){return"#<fabric.Path ("+this.complexity()+'): { "top": '+this.top+', "left": '+this.left+" }>"},toObject:function(h){return c(this.callSuper("toObject",h),{path:this.path.map(function(v){return v.slice()})})},toDatalessObject:function(h){var v=this.toObject(["sourcePath"].concat(h));return v.sourcePath&&delete v.path,v},_toSVG:function(){var h=a.util.joinPath(this.path);return["<path ","COMMON_PARTS",'d="',h,'" stroke-linecap="round" ',`/>
+`]},_getOffsetTransform:function(){var h=a.Object.NUM_FRACTION_DIGITS;return" translate("+s(-this.pathOffset.x,h)+", "+s(-this.pathOffset.y,h)+")"},toClipPathSVG:function(h){var v=this._getOffsetTransform();return"	"+this._createBaseClipPathSVGMarkup(this._toSVG(),{reviver:h,additionalTransform:v})},toSVG:function(h){var v=this._getOffsetTransform();return this._createBaseSVGMarkup(this._toSVG(),{reviver:h,additionalTransform:v})},complexity:function(){return this.path.length},_calcDimensions:function(){for(var h=[],v=[],m,y=0,b=0,_=0,w=0,S,x=0,k=this.path.length;x<k;++x){switch(m=this.path[x],m[0]){case"L":_=m[1],w=m[2],S=[];break;case"M":_=m[1],w=m[2],y=_,b=w,S=[];break;case"C":S=a.util.getBoundsOfCurve(_,w,m[1],m[2],m[3],m[4],m[5],m[6]),_=m[5],w=m[6];break;case"Q":S=a.util.getBoundsOfCurve(_,w,m[1],m[2],m[1],m[2],m[3],m[4]),_=m[3],w=m[4];break;case"z":case"Z":_=y,w=b;break}S.forEach(function(O){h.push(O.x),v.push(O.y)}),h.push(_),v.push(w)}var E=u(h)||0,I=u(v)||0,D=d(h)||0,N=d(v)||0,L=D-E,F=N-I;return{left:E,top:I,width:L,height:F}}}),a.Path.fromObject=function(h,v){if(typeof h.sourcePath=="string"){var m=h.sourcePath;a.loadSVGFromURL(m,function(y){var b=y[0];b.setOptions(h),h.clipPath?a.util.enlivenObjects([h.clipPath],function(_){b.clipPath=_[0],v&&v(b)}):v&&v(b)})}else a.Object._fromObject("Path",h,v,"path")},a.Path.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat(["d"]),a.Path.fromElement=function(h,v,m){var y=a.parseAttributes(h,a.Path.ATTRIBUTE_NAMES);y.fromSVG=!0,v(new a.Path(y.d,c(y,m)))}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.array.min,d=a.util.array.max;a.Group||(a.Group=a.util.createClass(a.Object,a.Collection,{type:"group",strokeWidth:0,subTargetCheck:!1,cacheProperties:[],useSetOnGroup:!1,initialize:function(c,f,s){f=f||{},this._objects=[],s&&this.callSuper("initialize",f),this._objects=c||[];for(var h=this._objects.length;h--;)this._objects[h].group=this;if(s)this._updateObjectsACoords();else{var v=f&&f.centerPoint;f.originX!==void 0&&(this.originX=f.originX),f.originY!==void 0&&(this.originY=f.originY),v||this._calcBounds(),this._updateObjectsCoords(v),delete f.centerPoint,this.callSuper("initialize",f)}this.setCoords()},_updateObjectsACoords:function(){for(var c=!0,f=this._objects.length;f--;)this._objects[f].setCoords(c)},_updateObjectsCoords:function(f){for(var f=f||this.getCenterPoint(),s=this._objects.length;s--;)this._updateObjectCoords(this._objects[s],f)},_updateObjectCoords:function(c,f){var s=c.left,h=c.top,v=!0;c.set({left:s-f.x,top:h-f.y}),c.group=this,c.setCoords(v)},toString:function(){return"#<fabric.Group: ("+this.complexity()+")>"},addWithUpdate:function(c){var f=!!this.group;return this._restoreObjectsState(),a.util.resetObjectTransform(this),c&&(f&&a.util.removeTransformFromObject(c,this.group.calcTransformMatrix()),this._objects.push(c),c.group=this,c._set("canvas",this.canvas)),this._calcBounds(),this._updateObjectsCoords(),this.dirty=!0,f?this.group.addWithUpdate():this.setCoords(),this},removeWithUpdate:function(c){return this._restoreObjectsState(),a.util.resetObjectTransform(this),this.remove(c),this._calcBounds(),this._updateObjectsCoords(),this.setCoords(),this.dirty=!0,this},_onObjectAdded:function(c){this.dirty=!0,c.group=this,c._set("canvas",this.canvas)},_onObjectRemoved:function(c){this.dirty=!0,delete c.group},_set:function(c,f){var s=this._objects.length;if(this.useSetOnGroup)for(;s--;)this._objects[s].setOnGroup(c,f);if(c==="canvas")for(;s--;)this._objects[s]._set(c,f);a.Object.prototype._set.call(this,c,f)},toObject:function(c){var f=this.includeDefaultValues,s=this._objects.filter(function(v){return!v.excludeFromExport}).map(function(v){var m=v.includeDefaultValues;v.includeDefaultValues=f;var y=v.toObject(c);return v.includeDefaultValues=m,y}),h=a.Object.prototype.toObject.call(this,c);return h.objects=s,h},toDatalessObject:function(c){var f,s=this.sourcePath;if(s)f=s;else{var h=this.includeDefaultValues;f=this._objects.map(function(m){var y=m.includeDefaultValues;m.includeDefaultValues=h;var b=m.toDatalessObject(c);return m.includeDefaultValues=y,b})}var v=a.Object.prototype.toDatalessObject.call(this,c);return v.objects=f,v},render:function(c){this._transformDone=!0,this.callSuper("render",c),this._transformDone=!1},shouldCache:function(){var c=a.Object.prototype.shouldCache.call(this);if(c){for(var f=0,s=this._objects.length;f<s;f++)if(this._objects[f].willDrawShadow())return this.ownCaching=!1,!1}return c},willDrawShadow:function(){if(a.Object.prototype.willDrawShadow.call(this))return!0;for(var c=0,f=this._objects.length;c<f;c++)if(this._objects[c].willDrawShadow())return!0;return!1},isOnACache:function(){return this.ownCaching||this.group&&this.group.isOnACache()},drawObject:function(c){for(var f=0,s=this._objects.length;f<s;f++)this._objects[f].render(c);this._drawClipPath(c,this.clipPath)},isCacheDirty:function(c){if(this.callSuper("isCacheDirty",c))return!0;if(!this.statefullCache)return!1;for(var f=0,s=this._objects.length;f<s;f++)if(this._objects[f].isCacheDirty(!0)){if(this._cacheCanvas){var h=this.cacheWidth/this.zoomX,v=this.cacheHeight/this.zoomY;this._cacheContext.clearRect(-h/2,-v/2,h,v)}return!0}return!1},_restoreObjectsState:function(){var c=this.calcOwnMatrix();return this._objects.forEach(function(f){a.util.addTransformToObject(f,c),delete f.group,f.setCoords()}),this},destroy:function(){return this._objects.forEach(function(c){c.set("dirty",!0)}),this._restoreObjectsState()},dispose:function(){this.callSuper("dispose"),this.forEachObject(function(c){c.dispose&&c.dispose()}),this._objects=[]},toActiveSelection:function(){if(!!this.canvas){var c=this._objects,f=this.canvas;this._objects=[];var s=this.toObject();delete s.objects;var h=new a.ActiveSelection([]);return h.set(s),h.type="activeSelection",f.remove(this),c.forEach(function(v){v.group=h,v.dirty=!0,f.add(v)}),h.canvas=f,h._objects=c,f._activeObject=h,h.setCoords(),h}},ungroupOnCanvas:function(){return this._restoreObjectsState()},setObjectsCoords:function(){var c=!0;return this.forEachObject(function(f){f.setCoords(c)}),this},_calcBounds:function(c){for(var f=[],s=[],h,v,m,y=["tr","br","bl","tl"],b=0,_=this._objects.length,w,S=y.length;b<_;++b){for(h=this._objects[b],m=h.calcACoords(),w=0;w<S;w++)v=y[w],f.push(m[v].x),s.push(m[v].y);h.aCoords=m}this._getBounds(f,s,c)},_getBounds:function(c,f,s){var h=new a.Point(u(c),u(f)),v=new a.Point(d(c),d(f)),m=h.y||0,y=h.x||0,b=v.x-h.x||0,_=v.y-h.y||0;this.width=b,this.height=_,s||this.setPositionByOrigin({x:y,y:m},"left","top")},_toSVG:function(c){for(var f=["<g ","COMMON_PARTS",` >
 `],s=0,h=this._objects.length;s<h;s++)f.push("		",this._objects[s].toSVG(c));return f.push(`</g>
 `),f},getSvgStyles:function(){var c=typeof this.opacity<"u"&&this.opacity!==1?"opacity: "+this.opacity+";":"",f=this.visible?"":" visibility: hidden;";return[c,this.getSvgFilter(),f].join("")},toClipPathSVG:function(c){for(var f=[],s=0,h=this._objects.length;s<h;s++)f.push("	",this._objects[s].toClipPathSVG(c));return this._createBaseClipPathSVGMarkup(f,{reviver:c})}}),a.Group.fromObject=function(c,f){var s=c.objects,h=a.util.object.clone(c,!0);if(delete h.objects,typeof s=="string"){a.loadSVGFromURL(s,function(v){var m=a.util.groupSVGElements(v,c,s),y=h.clipPath;delete h.clipPath,m.set(h),y?a.util.enlivenObjects([y],function(b){m.clipPath=b[0],f&&f(m)}):f&&f(m)});return}a.util.enlivenObjects(s,function(v){a.util.enlivenObjectEnlivables(c,h,function(){f&&f(new a.Group(v,h,!0))})})})}(e),function(o){var a=o.fabric||(o.fabric={});a.ActiveSelection||(a.ActiveSelection=a.util.createClass(a.Group,{type:"activeSelection",initialize:function(u,d){d=d||{},this._objects=u||[];for(var c=this._objects.length;c--;)this._objects[c].group=this;d.originX&&(this.originX=d.originX),d.originY&&(this.originY=d.originY),this._calcBounds(),this._updateObjectsCoords(),a.Object.prototype.initialize.call(this,d),this.setCoords()},toGroup:function(){var u=this._objects.concat();this._objects=[];var d=a.Object.prototype.toObject.call(this),c=new a.Group([]);if(delete d.type,c.set(d),u.forEach(function(s){s.canvas.remove(s),s.group=c}),c._objects=u,!this.canvas)return c;var f=this.canvas;return f.add(c),f._activeObject=c,c.setCoords(),c},onDeselect:function(){return this.destroy(),!1},toString:function(){return"#<fabric.ActiveSelection: ("+this.complexity()+")>"},shouldCache:function(){return!1},isOnACache:function(){return!1},_renderControls:function(u,d,c){u.save(),u.globalAlpha=this.isMoving?this.borderOpacityWhenMoving:1,this.callSuper("_renderControls",u,d),c=c||{},typeof c.hasControls>"u"&&(c.hasControls=!1),c.forActiveSelection=!0;for(var f=0,s=this._objects.length;f<s;f++)this._objects[f]._renderControls(u,c);u.restore()}}),a.ActiveSelection.fromObject=function(u,d){a.util.enlivenObjects(u.objects,function(c){delete u.objects,d&&d(new a.ActiveSelection(c,u,!0))})})}(e),function(o){var a=t.util.object.extend;if(o.fabric||(o.fabric={}),o.fabric.Image){t.warn("fabric.Image is already defined.");return}t.Image=t.util.createClass(t.Object,{type:"image",strokeWidth:0,srcFromAttribute:!1,_lastScaleX:1,_lastScaleY:1,_filterScalingX:1,_filterScalingY:1,minimumScaleTrigger:.5,stateProperties:t.Object.prototype.stateProperties.concat("cropX","cropY"),cacheProperties:t.Object.prototype.cacheProperties.concat("cropX","cropY"),cacheKey:"",cropX:0,cropY:0,imageSmoothing:!0,initialize:function(u,d){d||(d={}),this.filters=[],this.cacheKey="texture"+t.Object.__uid++,this.callSuper("initialize",d),this._initElement(u,d)},getElement:function(){return this._element||{}},setElement:function(u,d){return this.removeTexture(this.cacheKey),this.removeTexture(this.cacheKey+"_filtered"),this._element=u,this._originalElement=u,this._initConfig(d),this.filters.length!==0&&this.applyFilters(),this.resizeFilter&&this.applyResizeFilters(),this},removeTexture:function(u){var d=t.filterBackend;d&&d.evictCachesForKey&&d.evictCachesForKey(u)},dispose:function(){this.callSuper("dispose"),this.removeTexture(this.cacheKey),this.removeTexture(this.cacheKey+"_filtered"),this._cacheContext=void 0,["_originalElement","_element","_filteredEl","_cacheCanvas"].forEach(function(u){t.util.cleanUpJsdomNode(this[u]),this[u]=void 0}.bind(this))},getCrossOrigin:function(){return this._originalElement&&(this._originalElement.crossOrigin||null)},getOriginalSize:function(){var u=this.getElement();return{width:u.naturalWidth||u.width,height:u.naturalHeight||u.height}},_stroke:function(u){if(!(!this.stroke||this.strokeWidth===0)){var d=this.width/2,c=this.height/2;u.beginPath(),u.moveTo(-d,-c),u.lineTo(d,-c),u.lineTo(d,c),u.lineTo(-d,c),u.lineTo(-d,-c),u.closePath()}},toObject:function(u){var d=[];this.filters.forEach(function(f){f&&d.push(f.toObject())});var c=a(this.callSuper("toObject",["cropX","cropY"].concat(u)),{src:this.getSrc(),crossOrigin:this.getCrossOrigin(),filters:d});return this.resizeFilter&&(c.resizeFilter=this.resizeFilter.toObject()),c},hasCrop:function(){return this.cropX||this.cropY||this.width<this._element.width||this.height<this._element.height},_toSVG:function(){var u=[],d=[],c,f=this._element,s=-this.width/2,h=-this.height/2,v="",m="";if(!f)return[];if(this.hasCrop()){var y=t.Object.__uid++;u.push('<clipPath id="imageCrop_'+y+`">
 `,'	<rect x="'+s+'" y="'+h+'" width="'+this.width+'" height="'+this.height+`" />
 `,`</clipPath>
 `),v=' clip-path="url(#imageCrop_'+y+')" '}if(this.imageSmoothing||(m='" image-rendering="optimizeSpeed'),d.push("	<image ","COMMON_PARTS",'xlink:href="',this.getSvgSrc(!0),'" x="',s-this.cropX,'" y="',h-this.cropY,'" width="',f.width||f.naturalWidth,'" height="',f.height||f.height,m,'"',v,`></image>
 `),this.stroke||this.strokeDashArray){var b=this.fill;this.fill=null,c=["	<rect ",'x="',s,'" y="',h,'" width="',this.width,'" height="',this.height,'" style="',this.getSvgStyles(),`"/>
-`],this.fill=b}return this.paintFirst!=="fill"?u=u.concat(c,d):u=u.concat(d,c),u},getSrc:function(u){var d=u?this._element:this._originalElement;return d?d.toDataURL?d.toDataURL():this.srcFromAttribute?d.getAttribute("src"):d.src:this.src||""},setSrc:function(u,d,c){return t.util.loadImage(u,function(f,s){this.setElement(f,c),this._setWidthHeight(),d&&d(this,s)},this,c&&c.crossOrigin),this},toString:function(){return'#<fabric.Image: { src: "'+this.getSrc()+'" }>'},applyResizeFilters:function(){var u=this.resizeFilter,d=this.minimumScaleTrigger,c=this.getTotalObjectScaling(),f=c.scaleX,s=c.scaleY,h=this._filteredEl||this._originalElement;if(this.group&&this.set("dirty",!0),!u||f>d&&s>d){this._element=h,this._filterScalingX=1,this._filterScalingY=1,this._lastScaleX=f,this._lastScaleY=s;return}t.filterBackend||(t.filterBackend=t.initFilterBackend());var v=t.util.createCanvasElement(),m=this._filteredEl?this.cacheKey+"_filtered":this.cacheKey,y=h.width,b=h.height;v.width=y,v.height=b,this._element=v,this._lastScaleX=u.scaleX=f,this._lastScaleY=u.scaleY=s,t.filterBackend.applyFilters([u],h,y,b,this._element,m),this._filterScalingX=v.width/this._originalElement.width,this._filterScalingY=v.height/this._originalElement.height},applyFilters:function(u){if(u=u||this.filters||[],u=u.filter(function(h){return h&&!h.isNeutralState()}),this.set("dirty",!0),this.removeTexture(this.cacheKey+"_filtered"),u.length===0)return this._element=this._originalElement,this._filteredEl=null,this._filterScalingX=1,this._filterScalingY=1,this;var d=this._originalElement,c=d.naturalWidth||d.width,f=d.naturalHeight||d.height;if(this._element===this._originalElement){var s=t.util.createCanvasElement();s.width=c,s.height=f,this._element=s,this._filteredEl=s}else this._element=this._filteredEl,this._filteredEl.getContext("2d").clearRect(0,0,c,f),this._lastScaleX=1,this._lastScaleY=1;return t.filterBackend||(t.filterBackend=t.initFilterBackend()),t.filterBackend.applyFilters(u,this._originalElement,c,f,this._element,this.cacheKey),(this._originalElement.width!==this._element.width||this._originalElement.height!==this._element.height)&&(this._filterScalingX=this._element.width/this._originalElement.width,this._filterScalingY=this._element.height/this._originalElement.height),this},_render:function(u){t.util.setImageSmoothing(u,this.imageSmoothing),this.isMoving!==!0&&this.resizeFilter&&this._needsResize()&&this.applyResizeFilters(),this._stroke(u),this._renderPaintInOrder(u)},drawCacheOnCanvas:function(u){t.util.setImageSmoothing(u,this.imageSmoothing),t.Object.prototype.drawCacheOnCanvas.call(this,u)},shouldCache:function(){return this.needsItsOwnCache()},_renderFill:function(u){var d=this._element;if(!!d){var c=this._filterScalingX,f=this._filterScalingY,s=this.width,h=this.height,v=Math.min,m=Math.max,y=m(this.cropX,0),b=m(this.cropY,0),_=d.naturalWidth||d.width,w=d.naturalHeight||d.height,S=y*c,x=b*f,T=v(s*c,_-S),E=v(h*f,w-x),I=-s/2,D=-h/2,N=v(s,_/c-y),L=v(h,w/f-b);d&&u.drawImage(d,S,x,T,E,I,D,N,L)}},_needsResize:function(){var u=this.getTotalObjectScaling();return u.scaleX!==this._lastScaleX||u.scaleY!==this._lastScaleY},_resetWidthHeight:function(){this.set(this.getOriginalSize())},_initElement:function(u,d){this.setElement(t.util.getById(u),d),t.util.addClass(this.getElement(),t.Image.CSS_CANVAS)},_initConfig:function(u){u||(u={}),this.setOptions(u),this._setWidthHeight(u)},_initFilters:function(u,d){u&&u.length?t.util.enlivenObjects(u,function(c){d&&d(c)},"fabric.Image.filters"):d&&d()},_setWidthHeight:function(u){u||(u={});var d=this.getElement();this.width=u.width||d.naturalWidth||d.width||0,this.height=u.height||d.naturalHeight||d.height||0},parsePreserveAspectRatioAttribute:function(){var u=t.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio||""),d=this._element.width,c=this._element.height,f=1,s=1,h=0,v=0,m=0,y=0,b,_=this.width,w=this.height,S={width:_,height:w};return u&&(u.alignX!=="none"||u.alignY!=="none")?(u.meetOrSlice==="meet"&&(f=s=t.util.findScaleToFit(this._element,S),b=(_-d*f)/2,u.alignX==="Min"&&(h=-b),u.alignX==="Max"&&(h=b),b=(w-c*s)/2,u.alignY==="Min"&&(v=-b),u.alignY==="Max"&&(v=b)),u.meetOrSlice==="slice"&&(f=s=t.util.findScaleToCover(this._element,S),b=d-_/f,u.alignX==="Mid"&&(m=b/2),u.alignX==="Max"&&(m=b),b=c-w/s,u.alignY==="Mid"&&(y=b/2),u.alignY==="Max"&&(y=b),d=_/f,c=w/s)):(f=_/d,s=w/c),{width:d,height:c,scaleX:f,scaleY:s,offsetLeft:h,offsetTop:v,cropX:m,cropY:y}}}),t.Image.CSS_CANVAS="canvas-img",t.Image.prototype.getSvgSrc=t.Image.prototype.getSrc,t.Image.fromObject=function(u,d){var c=t.util.object.clone(u);t.util.loadImage(c.src,function(f,s){if(s){d&&d(null,!0);return}t.Image.prototype._initFilters.call(c,c.filters,function(h){c.filters=h||[],t.Image.prototype._initFilters.call(c,[c.resizeFilter],function(v){c.resizeFilter=v[0],t.util.enlivenObjectEnlivables(c,c,function(){var m=new t.Image(f,c);d(m,!1)})})})},null,c.crossOrigin)},t.Image.fromURL=function(u,d,c){t.util.loadImage(u,function(f,s){d&&d(new t.Image(f,c),s)},null,c&&c.crossOrigin)},t.Image.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("x y width height preserveAspectRatio xlink:href crossOrigin image-rendering".split(" ")),t.Image.fromElement=function(u,d,c){var f=t.parseAttributes(u,t.Image.ATTRIBUTE_NAMES);t.Image.fromURL(f["xlink:href"],d,a(c?t.util.object.clone(c):{},f))}}(e),t.util.object.extend(t.Object.prototype,{_getAngleValueForStraighten:function(){var o=this.angle%360;return o>0?Math.round((o-1)/90)*90:Math.round(o/90)*90},straighten:function(){return this.rotate(this._getAngleValueForStraighten())},fxStraighten:function(o){o=o||{};var a=function(){},u=o.onComplete||a,d=o.onChange||a,c=this;return t.util.animate({target:this,startValue:this.get("angle"),endValue:this._getAngleValueForStraighten(),duration:this.FX_DURATION,onChange:function(f){c.rotate(f),d()},onComplete:function(){c.setCoords(),u()}})}}),t.util.object.extend(t.StaticCanvas.prototype,{straightenObject:function(o){return o.straighten(),this.requestRenderAll(),this},fxStraightenObject:function(o){return o.fxStraighten({onChange:this.requestRenderAllBound})}}),function(){function o(u,d){var c="precision "+d+` float;
+`],this.fill=b}return this.paintFirst!=="fill"?u=u.concat(c,d):u=u.concat(d,c),u},getSrc:function(u){var d=u?this._element:this._originalElement;return d?d.toDataURL?d.toDataURL():this.srcFromAttribute?d.getAttribute("src"):d.src:this.src||""},setSrc:function(u,d,c){return t.util.loadImage(u,function(f,s){this.setElement(f,c),this._setWidthHeight(),d&&d(this,s)},this,c&&c.crossOrigin),this},toString:function(){return'#<fabric.Image: { src: "'+this.getSrc()+'" }>'},applyResizeFilters:function(){var u=this.resizeFilter,d=this.minimumScaleTrigger,c=this.getTotalObjectScaling(),f=c.scaleX,s=c.scaleY,h=this._filteredEl||this._originalElement;if(this.group&&this.set("dirty",!0),!u||f>d&&s>d){this._element=h,this._filterScalingX=1,this._filterScalingY=1,this._lastScaleX=f,this._lastScaleY=s;return}t.filterBackend||(t.filterBackend=t.initFilterBackend());var v=t.util.createCanvasElement(),m=this._filteredEl?this.cacheKey+"_filtered":this.cacheKey,y=h.width,b=h.height;v.width=y,v.height=b,this._element=v,this._lastScaleX=u.scaleX=f,this._lastScaleY=u.scaleY=s,t.filterBackend.applyFilters([u],h,y,b,this._element,m),this._filterScalingX=v.width/this._originalElement.width,this._filterScalingY=v.height/this._originalElement.height},applyFilters:function(u){if(u=u||this.filters||[],u=u.filter(function(h){return h&&!h.isNeutralState()}),this.set("dirty",!0),this.removeTexture(this.cacheKey+"_filtered"),u.length===0)return this._element=this._originalElement,this._filteredEl=null,this._filterScalingX=1,this._filterScalingY=1,this;var d=this._originalElement,c=d.naturalWidth||d.width,f=d.naturalHeight||d.height;if(this._element===this._originalElement){var s=t.util.createCanvasElement();s.width=c,s.height=f,this._element=s,this._filteredEl=s}else this._element=this._filteredEl,this._filteredEl.getContext("2d").clearRect(0,0,c,f),this._lastScaleX=1,this._lastScaleY=1;return t.filterBackend||(t.filterBackend=t.initFilterBackend()),t.filterBackend.applyFilters(u,this._originalElement,c,f,this._element,this.cacheKey),(this._originalElement.width!==this._element.width||this._originalElement.height!==this._element.height)&&(this._filterScalingX=this._element.width/this._originalElement.width,this._filterScalingY=this._element.height/this._originalElement.height),this},_render:function(u){t.util.setImageSmoothing(u,this.imageSmoothing),this.isMoving!==!0&&this.resizeFilter&&this._needsResize()&&this.applyResizeFilters(),this._stroke(u),this._renderPaintInOrder(u)},drawCacheOnCanvas:function(u){t.util.setImageSmoothing(u,this.imageSmoothing),t.Object.prototype.drawCacheOnCanvas.call(this,u)},shouldCache:function(){return this.needsItsOwnCache()},_renderFill:function(u){var d=this._element;if(!!d){var c=this._filterScalingX,f=this._filterScalingY,s=this.width,h=this.height,v=Math.min,m=Math.max,y=m(this.cropX,0),b=m(this.cropY,0),_=d.naturalWidth||d.width,w=d.naturalHeight||d.height,S=y*c,x=b*f,k=v(s*c,_-S),E=v(h*f,w-x),I=-s/2,D=-h/2,N=v(s,_/c-y),L=v(h,w/f-b);d&&u.drawImage(d,S,x,k,E,I,D,N,L)}},_needsResize:function(){var u=this.getTotalObjectScaling();return u.scaleX!==this._lastScaleX||u.scaleY!==this._lastScaleY},_resetWidthHeight:function(){this.set(this.getOriginalSize())},_initElement:function(u,d){this.setElement(t.util.getById(u),d),t.util.addClass(this.getElement(),t.Image.CSS_CANVAS)},_initConfig:function(u){u||(u={}),this.setOptions(u),this._setWidthHeight(u)},_initFilters:function(u,d){u&&u.length?t.util.enlivenObjects(u,function(c){d&&d(c)},"fabric.Image.filters"):d&&d()},_setWidthHeight:function(u){u||(u={});var d=this.getElement();this.width=u.width||d.naturalWidth||d.width||0,this.height=u.height||d.naturalHeight||d.height||0},parsePreserveAspectRatioAttribute:function(){var u=t.util.parsePreserveAspectRatioAttribute(this.preserveAspectRatio||""),d=this._element.width,c=this._element.height,f=1,s=1,h=0,v=0,m=0,y=0,b,_=this.width,w=this.height,S={width:_,height:w};return u&&(u.alignX!=="none"||u.alignY!=="none")?(u.meetOrSlice==="meet"&&(f=s=t.util.findScaleToFit(this._element,S),b=(_-d*f)/2,u.alignX==="Min"&&(h=-b),u.alignX==="Max"&&(h=b),b=(w-c*s)/2,u.alignY==="Min"&&(v=-b),u.alignY==="Max"&&(v=b)),u.meetOrSlice==="slice"&&(f=s=t.util.findScaleToCover(this._element,S),b=d-_/f,u.alignX==="Mid"&&(m=b/2),u.alignX==="Max"&&(m=b),b=c-w/s,u.alignY==="Mid"&&(y=b/2),u.alignY==="Max"&&(y=b),d=_/f,c=w/s)):(f=_/d,s=w/c),{width:d,height:c,scaleX:f,scaleY:s,offsetLeft:h,offsetTop:v,cropX:m,cropY:y}}}),t.Image.CSS_CANVAS="canvas-img",t.Image.prototype.getSvgSrc=t.Image.prototype.getSrc,t.Image.fromObject=function(u,d){var c=t.util.object.clone(u);t.util.loadImage(c.src,function(f,s){if(s){d&&d(null,!0);return}t.Image.prototype._initFilters.call(c,c.filters,function(h){c.filters=h||[],t.Image.prototype._initFilters.call(c,[c.resizeFilter],function(v){c.resizeFilter=v[0],t.util.enlivenObjectEnlivables(c,c,function(){var m=new t.Image(f,c);d(m,!1)})})})},null,c.crossOrigin)},t.Image.fromURL=function(u,d,c){t.util.loadImage(u,function(f,s){d&&d(new t.Image(f,c),s)},null,c&&c.crossOrigin)},t.Image.ATTRIBUTE_NAMES=t.SHARED_ATTRIBUTES.concat("x y width height preserveAspectRatio xlink:href crossOrigin image-rendering".split(" ")),t.Image.fromElement=function(u,d,c){var f=t.parseAttributes(u,t.Image.ATTRIBUTE_NAMES);t.Image.fromURL(f["xlink:href"],d,a(c?t.util.object.clone(c):{},f))}}(e),t.util.object.extend(t.Object.prototype,{_getAngleValueForStraighten:function(){var o=this.angle%360;return o>0?Math.round((o-1)/90)*90:Math.round(o/90)*90},straighten:function(){return this.rotate(this._getAngleValueForStraighten())},fxStraighten:function(o){o=o||{};var a=function(){},u=o.onComplete||a,d=o.onChange||a,c=this;return t.util.animate({target:this,startValue:this.get("angle"),endValue:this._getAngleValueForStraighten(),duration:this.FX_DURATION,onChange:function(f){c.rotate(f),d()},onComplete:function(){c.setCoords(),u()}})}}),t.util.object.extend(t.StaticCanvas.prototype,{straightenObject:function(o){return o.straighten(),this.requestRenderAll(),this},fxStraightenObject:function(o){return o.fxStraighten({onChange:this.requestRenderAllBound})}}),function(){function o(u,d){var c="precision "+d+` float;
 void main(){}`,f=u.createShader(u.FRAGMENT_SHADER);return u.shaderSource(f,c),u.compileShader(f),!!u.getShaderParameter(f,u.COMPILE_STATUS)}t.isWebglSupported=function(u){if(t.isLikelyNode)return!1;u=u||t.WebglFilterBackend.prototype.tileSize;var d=document.createElement("canvas"),c=d.getContext("webgl")||d.getContext("experimental-webgl"),f=!1;if(c){t.maxTextureSize=c.getParameter(c.MAX_TEXTURE_SIZE),f=t.maxTextureSize>=u;for(var s=["highp","mediump","lowp"],h=0;h<3;h++)if(o(c,s[h])){t.webGlPrecision=s[h];break}}return this.isSupported=f,f},t.WebglFilterBackend=a;function a(u){u&&u.tileSize&&(this.tileSize=u.tileSize),this.setupGLContext(this.tileSize,this.tileSize),this.captureGPUInfo()}a.prototype={tileSize:2048,resources:{},setupGLContext:function(u,d){this.dispose(),this.createWebGLCanvas(u,d),this.aPosition=new Float32Array([0,0,0,1,1,0,1,1]),this.chooseFastestCopyGLTo2DMethod(u,d)},chooseFastestCopyGLTo2DMethod:function(u,d){var c=typeof window.performance<"u",f;try{new ImageData(1,1),f=!0}catch{f=!1}var s=typeof ArrayBuffer<"u",h=typeof Uint8ClampedArray<"u";if(!!(c&&f&&s&&h)){var v=t.util.createCanvasElement(),m=new ArrayBuffer(u*d*4);if(t.forceGLPutImageData){this.imageBuffer=m,this.copyGLTo2D=g;return}var y={imageBuffer:m,destinationWidth:u,destinationHeight:d,targetCanvas:v},b,_,w;v.width=u,v.height=d,b=window.performance.now(),l.call(y,this.gl,y),_=window.performance.now()-b,b=window.performance.now(),g.call(y,this.gl,y),w=window.performance.now()-b,_>w?(this.imageBuffer=m,this.copyGLTo2D=g):this.copyGLTo2D=l}},createWebGLCanvas:function(u,d){var c=t.util.createCanvasElement();c.width=u,c.height=d;var f={alpha:!0,premultipliedAlpha:!1,depth:!1,stencil:!1,antialias:!1},s=c.getContext("webgl",f);s||(s=c.getContext("experimental-webgl",f)),s&&(s.clearColor(0,0,0,0),this.canvas=c,this.gl=s)},applyFilters:function(u,d,c,f,s,h){var v=this.gl,m;h&&(m=this.getCachedTexture(h,d));var y={originalWidth:d.width||d.originalWidth,originalHeight:d.height||d.originalHeight,sourceWidth:c,sourceHeight:f,destinationWidth:c,destinationHeight:f,context:v,sourceTexture:this.createTexture(v,c,f,!m&&d),targetTexture:this.createTexture(v,c,f),originalTexture:m||this.createTexture(v,c,f,!m&&d),passes:u.length,webgl:!0,aPosition:this.aPosition,programCache:this.programCache,pass:0,filterBackend:this,targetCanvas:s},b=v.createFramebuffer();return v.bindFramebuffer(v.FRAMEBUFFER,b),u.forEach(function(_){_&&_.applyTo(y)}),i(y),this.copyGLTo2D(v,y),v.bindTexture(v.TEXTURE_2D,null),v.deleteTexture(y.sourceTexture),v.deleteTexture(y.targetTexture),v.deleteFramebuffer(b),s.getContext("2d").setTransform(1,0,0,1,0,0),y},dispose:function(){this.canvas&&(this.canvas=null,this.gl=null),this.clearWebGLCaches()},clearWebGLCaches:function(){this.programCache={},this.textureCache={}},createTexture:function(u,d,c,f){var s=u.createTexture();return u.bindTexture(u.TEXTURE_2D,s),u.texParameteri(u.TEXTURE_2D,u.TEXTURE_MAG_FILTER,u.NEAREST),u.texParameteri(u.TEXTURE_2D,u.TEXTURE_MIN_FILTER,u.NEAREST),u.texParameteri(u.TEXTURE_2D,u.TEXTURE_WRAP_S,u.CLAMP_TO_EDGE),u.texParameteri(u.TEXTURE_2D,u.TEXTURE_WRAP_T,u.CLAMP_TO_EDGE),f?u.texImage2D(u.TEXTURE_2D,0,u.RGBA,u.RGBA,u.UNSIGNED_BYTE,f):u.texImage2D(u.TEXTURE_2D,0,u.RGBA,d,c,0,u.RGBA,u.UNSIGNED_BYTE,null),s},getCachedTexture:function(u,d){if(this.textureCache[u])return this.textureCache[u];var c=this.createTexture(this.gl,d.width,d.height,d);return this.textureCache[u]=c,c},evictCachesForKey:function(u){this.textureCache[u]&&(this.gl.deleteTexture(this.textureCache[u]),delete this.textureCache[u])},copyGLTo2D:l,captureGPUInfo:function(){if(this.gpuInfo)return this.gpuInfo;var u=this.gl,d={renderer:"",vendor:""};if(!u)return d;var c=u.getExtension("WEBGL_debug_renderer_info");if(c){var f=u.getParameter(c.UNMASKED_RENDERER_WEBGL),s=u.getParameter(c.UNMASKED_VENDOR_WEBGL);f&&(d.renderer=f.toLowerCase()),s&&(d.vendor=s.toLowerCase())}return this.gpuInfo=d,d}}}();function i(o){var a=o.targetCanvas,u=a.width,d=a.height,c=o.destinationWidth,f=o.destinationHeight;(u!==c||d!==f)&&(a.width=c,a.height=f)}function l(o,a){var u=o.canvas,d=a.targetCanvas,c=d.getContext("2d");c.translate(0,d.height),c.scale(1,-1);var f=u.height-d.height;c.drawImage(u,0,f,d.width,d.height,0,0,d.width,d.height)}function g(o,a){var u=a.targetCanvas,d=u.getContext("2d"),c=a.destinationWidth,f=a.destinationHeight,s=c*f*4,h=new Uint8Array(this.imageBuffer,0,s),v=new Uint8ClampedArray(this.imageBuffer,0,s);o.readPixels(0,0,c,f,o.RGBA,o.UNSIGNED_BYTE,h);var m=new ImageData(v,c,f);d.putImageData(m,0,0)}(function(){var o=function(){};t.Canvas2dFilterBackend=a;function a(){}a.prototype={evictCachesForKey:o,dispose:o,clearWebGLCaches:o,resources:{},applyFilters:function(u,d,c,f,s){var h=s.getContext("2d");h.drawImage(d,0,0,c,f);var v=h.getImageData(0,0,c,f),m=h.getImageData(0,0,c,f),y={sourceWidth:c,sourceHeight:f,imageData:v,originalEl:d,originalImageData:m,canvasEl:s,ctx:h,filterBackend:this};return u.forEach(function(b){b.applyTo(y)}),(y.imageData.width!==c||y.imageData.height!==f)&&(s.width=y.imageData.width,s.height=y.imageData.height),h.putImageData(y.imageData,0,0),y}}})(),t.Image=t.Image||{},t.Image.filters=t.Image.filters||{},t.Image.filters.BaseFilter=t.util.createClass({type:"BaseFilter",vertexSource:`attribute vec2 aPosition;
 varying vec2 vTexCoord;
 void main() {
@@ -286,7 +286,7 @@ color.rgb += texture2D(uTexture, vTexCoord + matrixPos).rgb * uMatrix[int(h * 9.
 float alpha = texture2D(uTexture, vTexCoord).a;
 gl_FragColor = color;
 gl_FragColor.a = alpha;
-}`},retrieveShader:function(f){var s=Math.sqrt(this.matrix.length),h=this.type+"_"+s+"_"+(this.opaque?1:0),v=this.fragmentSource[h];return f.programCache.hasOwnProperty(h)||(f.programCache[h]=this.createProgram(f.context,v)),f.programCache[h]},applyTo2d:function(f){var s=f.imageData,h=s.data,v=this.matrix,m=Math.round(Math.sqrt(v.length)),y=Math.floor(m/2),b=s.width,_=s.height,w=f.ctx.createImageData(b,_),S=w.data,x=this.opaque?1:0,T,E,I,D,N,L,F,O,V,re,J,ue,M;for(J=0;J<_;J++)for(re=0;re<b;re++){for(N=(J*b+re)*4,T=0,E=0,I=0,D=0,M=0;M<m;M++)for(ue=0;ue<m;ue++)F=J+M-y,L=re+ue-y,!(F<0||F>=_||L<0||L>=b)&&(O=(F*b+L)*4,V=v[M*m+ue],T+=h[O]*V,E+=h[O+1]*V,I+=h[O+2]*V,x||(D+=h[O+3]*V));S[N]=T,S[N+1]=E,S[N+2]=I,x?S[N+3]=h[N+3]:S[N+3]=D}f.imageData=w},getUniformLocations:function(f,s){return{uMatrix:f.getUniformLocation(s,"uMatrix"),uOpaque:f.getUniformLocation(s,"uOpaque"),uHalfSize:f.getUniformLocation(s,"uHalfSize"),uSize:f.getUniformLocation(s,"uSize")}},sendUniformData:function(f,s){f.uniform1fv(s.uMatrix,this.matrix)},toObject:function(){return u(this.callSuper("toObject"),{opaque:this.opaque,matrix:this.matrix})}}),a.Image.filters.Convolute.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Grayscale=d(u.BaseFilter,{type:"Grayscale",fragmentSource:{average:`precision highp float;
+}`},retrieveShader:function(f){var s=Math.sqrt(this.matrix.length),h=this.type+"_"+s+"_"+(this.opaque?1:0),v=this.fragmentSource[h];return f.programCache.hasOwnProperty(h)||(f.programCache[h]=this.createProgram(f.context,v)),f.programCache[h]},applyTo2d:function(f){var s=f.imageData,h=s.data,v=this.matrix,m=Math.round(Math.sqrt(v.length)),y=Math.floor(m/2),b=s.width,_=s.height,w=f.ctx.createImageData(b,_),S=w.data,x=this.opaque?1:0,k,E,I,D,N,L,F,O,V,re,J,ue,M;for(J=0;J<_;J++)for(re=0;re<b;re++){for(N=(J*b+re)*4,k=0,E=0,I=0,D=0,M=0;M<m;M++)for(ue=0;ue<m;ue++)F=J+M-y,L=re+ue-y,!(F<0||F>=_||L<0||L>=b)&&(O=(F*b+L)*4,V=v[M*m+ue],k+=h[O]*V,E+=h[O+1]*V,I+=h[O+2]*V,x||(D+=h[O+3]*V));S[N]=k,S[N+1]=E,S[N+2]=I,x?S[N+3]=h[N+3]:S[N+3]=D}f.imageData=w},getUniformLocations:function(f,s){return{uMatrix:f.getUniformLocation(s,"uMatrix"),uOpaque:f.getUniformLocation(s,"uOpaque"),uHalfSize:f.getUniformLocation(s,"uHalfSize"),uSize:f.getUniformLocation(s,"uSize")}},sendUniformData:function(f,s){f.uniform1fv(s.uMatrix,this.matrix)},toObject:function(){return u(this.callSuper("toObject"),{opaque:this.opaque,matrix:this.matrix})}}),a.Image.filters.Convolute.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Grayscale=d(u.BaseFilter,{type:"Grayscale",fragmentSource:{average:`precision highp float;
 uniform sampler2D uTexture;
 varying vec2 vTexCoord;
 void main() {
@@ -349,7 +349,7 @@ float fposY = float(posY);
 vec2 squareCoords = vec2(fposX * blockW, fposY * blockH);
 vec4 color = texture2D(uTexture, squareCoords);
 gl_FragColor = color;
-}`,applyTo2d:function(c){var f=c.imageData,s=f.data,h=f.height,v=f.width,m,y,b,_,w,S,x,T,E,I,D;for(y=0;y<h;y+=this.blocksize)for(b=0;b<v;b+=this.blocksize)for(m=y*4*v+b*4,_=s[m],w=s[m+1],S=s[m+2],x=s[m+3],I=Math.min(y+this.blocksize,h),D=Math.min(b+this.blocksize,v),T=y;T<I;T++)for(E=b;E<D;E++)m=T*4*v+E*4,s[m]=_,s[m+1]=w,s[m+2]=S,s[m+3]=x},isNeutralState:function(){return this.blocksize===1},getUniformLocations:function(c,f){return{uBlocksize:c.getUniformLocation(f,"uBlocksize"),uStepW:c.getUniformLocation(f,"uStepW"),uStepH:c.getUniformLocation(f,"uStepH")}},sendUniformData:function(c,f){c.uniform1f(f.uBlocksize,this.blocksize)}}),a.Image.filters.Pixelate.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.Image.filters,c=a.util.createClass;d.RemoveColor=c(d.BaseFilter,{type:"RemoveColor",color:"#FFFFFF",fragmentSource:`precision highp float;
+}`,applyTo2d:function(c){var f=c.imageData,s=f.data,h=f.height,v=f.width,m,y,b,_,w,S,x,k,E,I,D;for(y=0;y<h;y+=this.blocksize)for(b=0;b<v;b+=this.blocksize)for(m=y*4*v+b*4,_=s[m],w=s[m+1],S=s[m+2],x=s[m+3],I=Math.min(y+this.blocksize,h),D=Math.min(b+this.blocksize,v),k=y;k<I;k++)for(E=b;E<D;E++)m=k*4*v+E*4,s[m]=_,s[m+1]=w,s[m+2]=S,s[m+3]=x},isNeutralState:function(){return this.blocksize===1},getUniformLocations:function(c,f){return{uBlocksize:c.getUniformLocation(f,"uBlocksize"),uStepW:c.getUniformLocation(f,"uStepW"),uStepH:c.getUniformLocation(f,"uStepH")}},sendUniformData:function(c,f){c.uniform1f(f.uBlocksize,this.blocksize)}}),a.Image.filters.Pixelate.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.extend,d=a.Image.filters,c=a.util.createClass;d.RemoveColor=c(d.BaseFilter,{type:"RemoveColor",color:"#FFFFFF",fragmentSource:`precision highp float;
 uniform sampler2D uTexture;
 uniform vec4 uLow;
 uniform vec4 uHigh;
@@ -393,7 +393,7 @@ vec4 color = texture2D(uTexture, vTexCoord);
 gl_FragColor = color;
 if (color.a > 0.0) {
 `+this.fragmentSource[c]+`}
-}`},retrieveShader:function(c){var f=this.type+"_"+this.mode,s;return c.programCache.hasOwnProperty(f)||(s=this.buildSource(this.mode),c.programCache[f]=this.createProgram(c.context,s)),c.programCache[f]},applyTo2d:function(c){var f=c.imageData,s=f.data,h=s.length,v,m,y,b,_,w,S,x=1-this.alpha;S=new a.Color(this.color).getSource(),v=S[0]*this.alpha,m=S[1]*this.alpha,y=S[2]*this.alpha;for(var T=0;T<h;T+=4)switch(b=s[T],_=s[T+1],w=s[T+2],this.mode){case"multiply":s[T]=b*v/255,s[T+1]=_*m/255,s[T+2]=w*y/255;break;case"screen":s[T]=255-(255-b)*(255-v)/255,s[T+1]=255-(255-_)*(255-m)/255,s[T+2]=255-(255-w)*(255-y)/255;break;case"add":s[T]=b+v,s[T+1]=_+m,s[T+2]=w+y;break;case"diff":case"difference":s[T]=Math.abs(b-v),s[T+1]=Math.abs(_-m),s[T+2]=Math.abs(w-y);break;case"subtract":s[T]=b-v,s[T+1]=_-m,s[T+2]=w-y;break;case"darken":s[T]=Math.min(b,v),s[T+1]=Math.min(_,m),s[T+2]=Math.min(w,y);break;case"lighten":s[T]=Math.max(b,v),s[T+1]=Math.max(_,m),s[T+2]=Math.max(w,y);break;case"overlay":s[T]=v<128?2*b*v/255:255-2*(255-b)*(255-v)/255,s[T+1]=m<128?2*_*m/255:255-2*(255-_)*(255-m)/255,s[T+2]=y<128?2*w*y/255:255-2*(255-w)*(255-y)/255;break;case"exclusion":s[T]=v+b-2*v*b/255,s[T+1]=m+_-2*m*_/255,s[T+2]=y+w-2*y*w/255;break;case"tint":s[T]=v+b*x,s[T+1]=m+_*x,s[T+2]=y+w*x}},getUniformLocations:function(c,f){return{uColor:c.getUniformLocation(f,"uColor")}},sendUniformData:function(c,f){var s=new a.Color(this.color).getSource();s[0]=this.alpha*s[0]/255,s[1]=this.alpha*s[1]/255,s[2]=this.alpha*s[2]/255,s[3]=this.alpha,c.uniform4fv(f.uColor,s)},toObject:function(){return{type:this.type,color:this.color,mode:this.mode,alpha:this.alpha}}}),a.Image.filters.BlendColor.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric,u=a.Image.filters,d=a.util.createClass;u.BlendImage=d(u.BaseFilter,{type:"BlendImage",image:null,mode:"multiply",alpha:1,vertexSource:`attribute vec2 aPosition;
+}`},retrieveShader:function(c){var f=this.type+"_"+this.mode,s;return c.programCache.hasOwnProperty(f)||(s=this.buildSource(this.mode),c.programCache[f]=this.createProgram(c.context,s)),c.programCache[f]},applyTo2d:function(c){var f=c.imageData,s=f.data,h=s.length,v,m,y,b,_,w,S,x=1-this.alpha;S=new a.Color(this.color).getSource(),v=S[0]*this.alpha,m=S[1]*this.alpha,y=S[2]*this.alpha;for(var k=0;k<h;k+=4)switch(b=s[k],_=s[k+1],w=s[k+2],this.mode){case"multiply":s[k]=b*v/255,s[k+1]=_*m/255,s[k+2]=w*y/255;break;case"screen":s[k]=255-(255-b)*(255-v)/255,s[k+1]=255-(255-_)*(255-m)/255,s[k+2]=255-(255-w)*(255-y)/255;break;case"add":s[k]=b+v,s[k+1]=_+m,s[k+2]=w+y;break;case"diff":case"difference":s[k]=Math.abs(b-v),s[k+1]=Math.abs(_-m),s[k+2]=Math.abs(w-y);break;case"subtract":s[k]=b-v,s[k+1]=_-m,s[k+2]=w-y;break;case"darken":s[k]=Math.min(b,v),s[k+1]=Math.min(_,m),s[k+2]=Math.min(w,y);break;case"lighten":s[k]=Math.max(b,v),s[k+1]=Math.max(_,m),s[k+2]=Math.max(w,y);break;case"overlay":s[k]=v<128?2*b*v/255:255-2*(255-b)*(255-v)/255,s[k+1]=m<128?2*_*m/255:255-2*(255-_)*(255-m)/255,s[k+2]=y<128?2*w*y/255:255-2*(255-w)*(255-y)/255;break;case"exclusion":s[k]=v+b-2*v*b/255,s[k+1]=m+_-2*m*_/255,s[k+2]=y+w-2*y*w/255;break;case"tint":s[k]=v+b*x,s[k+1]=m+_*x,s[k+2]=y+w*x}},getUniformLocations:function(c,f){return{uColor:c.getUniformLocation(f,"uColor")}},sendUniformData:function(c,f){var s=new a.Color(this.color).getSource();s[0]=this.alpha*s[0]/255,s[1]=this.alpha*s[1]/255,s[2]=this.alpha*s[2]/255,s[3]=this.alpha,c.uniform4fv(f.uColor,s)},toObject:function(){return{type:this.type,color:this.color,mode:this.mode,alpha:this.alpha}}}),a.Image.filters.BlendColor.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric,u=a.Image.filters,d=a.util.createClass;u.BlendImage=d(u.BaseFilter,{type:"BlendImage",image:null,mode:"multiply",alpha:1,vertexSource:`attribute vec2 aPosition;
 varying vec2 vTexCoord;
 varying vec2 vTexCoord2;
 uniform mat3 uTransformMatrix;
@@ -423,19 +423,19 @@ vec4 color = texture2D(uTexture, vTexCoord);
 vec4 color2 = texture2D(uImage, vTexCoord2);
 color.a = color2.a;
 gl_FragColor = color;
-}`},retrieveShader:function(c){var f=this.type+"_"+this.mode,s=this.fragmentSource[this.mode];return c.programCache.hasOwnProperty(f)||(c.programCache[f]=this.createProgram(c.context,s)),c.programCache[f]},applyToWebGL:function(c){var f=c.context,s=this.createTexture(c.filterBackend,this.image);this.bindAdditionalTexture(f,s,f.TEXTURE1),this.callSuper("applyToWebGL",c),this.unbindAdditionalTexture(f,f.TEXTURE1)},createTexture:function(c,f){return c.getCachedTexture(f.cacheKey,f._element)},calculateMatrix:function(){var c=this.image,f=c._element.width,s=c._element.height;return[1/c.scaleX,0,0,0,1/c.scaleY,0,-c.left/f,-c.top/s,1]},applyTo2d:function(c){var f=c.imageData,s=c.filterBackend.resources,h=f.data,v=h.length,m=f.width,y=f.height,b,_,w,S,x,T,E,I,D,N,L=this.image,F;s.blendImage||(s.blendImage=a.util.createCanvasElement()),D=s.blendImage,N=D.getContext("2d"),D.width!==m||D.height!==y?(D.width=m,D.height=y):N.clearRect(0,0,m,y),N.setTransform(L.scaleX,0,0,L.scaleY,L.left,L.top),N.drawImage(L._element,0,0,m,y),F=N.getImageData(0,0,m,y).data;for(var O=0;O<v;O+=4)switch(x=h[O],T=h[O+1],E=h[O+2],I=h[O+3],b=F[O],_=F[O+1],w=F[O+2],S=F[O+3],this.mode){case"multiply":h[O]=x*b/255,h[O+1]=T*_/255,h[O+2]=E*w/255,h[O+3]=I*S/255;break;case"mask":h[O+3]=S;break}},getUniformLocations:function(c,f){return{uTransformMatrix:c.getUniformLocation(f,"uTransformMatrix"),uImage:c.getUniformLocation(f,"uImage")}},sendUniformData:function(c,f){var s=this.calculateMatrix();c.uniform1i(f.uImage,1),c.uniformMatrix3fv(f.uTransformMatrix,!1,s)},toObject:function(){return{type:this.type,image:this.image&&this.image.toObject(),mode:this.mode,alpha:this.alpha}}}),a.Image.filters.BlendImage.fromObject=function(c,f){a.Image.fromObject(c.image,function(s){var h=a.util.object.clone(c);h.image=s,f(new a.Image.filters.BlendImage(h))})}}(e),function(o){var a=o.fabric||(o.fabric={}),u=Math.pow,d=Math.floor,c=Math.sqrt,f=Math.abs,s=Math.round,h=Math.sin,v=Math.ceil,m=a.Image.filters,y=a.util.createClass;m.Resize=y(m.BaseFilter,{type:"Resize",resizeType:"hermite",scaleX:1,scaleY:1,lanczosLobes:3,getUniformLocations:function(b,_){return{uDelta:b.getUniformLocation(_,"uDelta"),uTaps:b.getUniformLocation(_,"uTaps")}},sendUniformData:function(b,_){b.uniform2fv(_.uDelta,this.horizontal?[1/this.width,0]:[0,1/this.height]),b.uniform1fv(_.uTaps,this.taps)},retrieveShader:function(b){var _=this.getFilterWindow(),w=this.type+"_"+_;if(!b.programCache.hasOwnProperty(w)){var S=this.generateShader(_);b.programCache[w]=this.createProgram(b.context,S)}return b.programCache[w]},getFilterWindow:function(){var b=this.tempScale;return Math.ceil(this.lanczosLobes/b)},getTaps:function(){for(var b=this.lanczosCreate(this.lanczosLobes),_=this.tempScale,w=this.getFilterWindow(),S=new Array(w),x=1;x<=w;x++)S[x-1]=b(x*_);return S},generateShader:function(S){for(var _=new Array(S),w=this.fragmentSourceTOP,S,x=1;x<=S;x++)_[x-1]=x+".0 * uDelta";return w+="uniform float uTaps["+S+`];
+}`},retrieveShader:function(c){var f=this.type+"_"+this.mode,s=this.fragmentSource[this.mode];return c.programCache.hasOwnProperty(f)||(c.programCache[f]=this.createProgram(c.context,s)),c.programCache[f]},applyToWebGL:function(c){var f=c.context,s=this.createTexture(c.filterBackend,this.image);this.bindAdditionalTexture(f,s,f.TEXTURE1),this.callSuper("applyToWebGL",c),this.unbindAdditionalTexture(f,f.TEXTURE1)},createTexture:function(c,f){return c.getCachedTexture(f.cacheKey,f._element)},calculateMatrix:function(){var c=this.image,f=c._element.width,s=c._element.height;return[1/c.scaleX,0,0,0,1/c.scaleY,0,-c.left/f,-c.top/s,1]},applyTo2d:function(c){var f=c.imageData,s=c.filterBackend.resources,h=f.data,v=h.length,m=f.width,y=f.height,b,_,w,S,x,k,E,I,D,N,L=this.image,F;s.blendImage||(s.blendImage=a.util.createCanvasElement()),D=s.blendImage,N=D.getContext("2d"),D.width!==m||D.height!==y?(D.width=m,D.height=y):N.clearRect(0,0,m,y),N.setTransform(L.scaleX,0,0,L.scaleY,L.left,L.top),N.drawImage(L._element,0,0,m,y),F=N.getImageData(0,0,m,y).data;for(var O=0;O<v;O+=4)switch(x=h[O],k=h[O+1],E=h[O+2],I=h[O+3],b=F[O],_=F[O+1],w=F[O+2],S=F[O+3],this.mode){case"multiply":h[O]=x*b/255,h[O+1]=k*_/255,h[O+2]=E*w/255,h[O+3]=I*S/255;break;case"mask":h[O+3]=S;break}},getUniformLocations:function(c,f){return{uTransformMatrix:c.getUniformLocation(f,"uTransformMatrix"),uImage:c.getUniformLocation(f,"uImage")}},sendUniformData:function(c,f){var s=this.calculateMatrix();c.uniform1i(f.uImage,1),c.uniformMatrix3fv(f.uTransformMatrix,!1,s)},toObject:function(){return{type:this.type,image:this.image&&this.image.toObject(),mode:this.mode,alpha:this.alpha}}}),a.Image.filters.BlendImage.fromObject=function(c,f){a.Image.fromObject(c.image,function(s){var h=a.util.object.clone(c);h.image=s,f(new a.Image.filters.BlendImage(h))})}}(e),function(o){var a=o.fabric||(o.fabric={}),u=Math.pow,d=Math.floor,c=Math.sqrt,f=Math.abs,s=Math.round,h=Math.sin,v=Math.ceil,m=a.Image.filters,y=a.util.createClass;m.Resize=y(m.BaseFilter,{type:"Resize",resizeType:"hermite",scaleX:1,scaleY:1,lanczosLobes:3,getUniformLocations:function(b,_){return{uDelta:b.getUniformLocation(_,"uDelta"),uTaps:b.getUniformLocation(_,"uTaps")}},sendUniformData:function(b,_){b.uniform2fv(_.uDelta,this.horizontal?[1/this.width,0]:[0,1/this.height]),b.uniform1fv(_.uTaps,this.taps)},retrieveShader:function(b){var _=this.getFilterWindow(),w=this.type+"_"+_;if(!b.programCache.hasOwnProperty(w)){var S=this.generateShader(_);b.programCache[w]=this.createProgram(b.context,S)}return b.programCache[w]},getFilterWindow:function(){var b=this.tempScale;return Math.ceil(this.lanczosLobes/b)},getTaps:function(){for(var b=this.lanczosCreate(this.lanczosLobes),_=this.tempScale,w=this.getFilterWindow(),S=new Array(w),x=1;x<=w;x++)S[x-1]=b(x*_);return S},generateShader:function(S){for(var _=new Array(S),w=this.fragmentSourceTOP,S,x=1;x<=S;x++)_[x-1]=x+".0 * uDelta";return w+="uniform float uTaps["+S+`];
 `,w+=`void main() {
 `,w+=`  vec4 color = texture2D(uTexture, vTexCoord);
 `,w+=`  float sum = 1.0;
-`,_.forEach(function(T,E){w+="  color += texture2D(uTexture, vTexCoord + "+T+") * uTaps["+E+`];
-`,w+="  color += texture2D(uTexture, vTexCoord - "+T+") * uTaps["+E+`];
+`,_.forEach(function(k,E){w+="  color += texture2D(uTexture, vTexCoord + "+k+") * uTaps["+E+`];
+`,w+="  color += texture2D(uTexture, vTexCoord - "+k+") * uTaps["+E+`];
 `,w+="  sum += 2.0 * uTaps["+E+`];
 `}),w+=`  gl_FragColor = color / sum;
 `,w+="}",w},fragmentSourceTOP:`precision highp float;
 uniform sampler2D uTexture;
 uniform vec2 uDelta;
 varying vec2 vTexCoord;
-`,applyTo:function(b){b.webgl?(b.passes++,this.width=b.sourceWidth,this.horizontal=!0,this.dW=Math.round(this.width*this.scaleX),this.dH=b.sourceHeight,this.tempScale=this.dW/this.width,this.taps=this.getTaps(),b.destinationWidth=this.dW,this._setupFrameBuffer(b),this.applyToWebGL(b),this._swapTextures(b),b.sourceWidth=b.destinationWidth,this.height=b.sourceHeight,this.horizontal=!1,this.dH=Math.round(this.height*this.scaleY),this.tempScale=this.dH/this.height,this.taps=this.getTaps(),b.destinationHeight=this.dH,this._setupFrameBuffer(b),this.applyToWebGL(b),this._swapTextures(b),b.sourceHeight=b.destinationHeight):this.applyTo2d(b)},isNeutralState:function(){return this.scaleX===1&&this.scaleY===1},lanczosCreate:function(b){return function(_){if(_>=b||_<=-b)return 0;if(_<11920929e-14&&_>-11920929e-14)return 1;_*=Math.PI;var w=_/b;return h(_)/_*h(w)/w}},applyTo2d:function(b){var _=b.imageData,w=this.scaleX,S=this.scaleY;this.rcpScaleX=1/w,this.rcpScaleY=1/S;var x=_.width,T=_.height,E=s(x*w),I=s(T*S),D;this.resizeType==="sliceHack"?D=this.sliceByTwo(b,x,T,E,I):this.resizeType==="hermite"?D=this.hermiteFastResize(b,x,T,E,I):this.resizeType==="bilinear"?D=this.bilinearFiltering(b,x,T,E,I):this.resizeType==="lanczos"&&(D=this.lanczosResize(b,x,T,E,I)),b.imageData=D},sliceByTwo:function(b,_,w,S,x){var T=b.imageData,E=.5,I=!1,D=!1,N=_*E,L=w*E,F=a.filterBackend.resources,O,V,re=0,J=0,ue=_,M=0;for(F.sliceByTwo||(F.sliceByTwo=document.createElement("canvas")),O=F.sliceByTwo,(O.width<_*1.5||O.height<w)&&(O.width=_*1.5,O.height=w),V=O.getContext("2d"),V.clearRect(0,0,_*1.5,w),V.putImageData(T,0,0),S=d(S),x=d(x);!I||!D;)_=N,w=L,S<d(N*E)?N=d(N*E):(N=S,I=!0),x<d(L*E)?L=d(L*E):(L=x,D=!0),V.drawImage(O,re,J,_,w,ue,M,N,L),re=ue,J=M,M+=L;return V.getImageData(re,J,S,x)},lanczosResize:function(b,_,w,S,x){function T(R){var P,Q,j,A,q,G,te,fe,pe,ve,H;for(M.x=(R+.5)*L,U.x=d(M.x),P=0;P<x;P++){for(M.y=(P+.5)*F,U.y=d(M.y),q=0,G=0,te=0,fe=0,pe=0,Q=U.x-re;Q<=U.x+re;Q++)if(!(Q<0||Q>=_)){ve=d(1e3*f(Q-M.x)),ue[ve]||(ue[ve]={});for(var W=U.y-J;W<=U.y+J;W++)W<0||W>=w||(H=d(1e3*f(W-M.y)),ue[ve][H]||(ue[ve][H]=N(c(u(ve*O,2)+u(H*V,2))/1e3)),j=ue[ve][H],j>0&&(A=(W*_+Q)*4,q+=j,G+=j*E[A],te+=j*E[A+1],fe+=j*E[A+2],pe+=j*E[A+3]))}A=(P*S+R)*4,D[A]=G/q,D[A+1]=te/q,D[A+2]=fe/q,D[A+3]=pe/q}return++R<S?T(R):I}var E=b.imageData.data,I=b.ctx.createImageData(S,x),D=I.data,N=this.lanczosCreate(this.lanczosLobes),L=this.rcpScaleX,F=this.rcpScaleY,O=2/this.rcpScaleX,V=2/this.rcpScaleY,re=v(L*this.lanczosLobes/2),J=v(F*this.lanczosLobes/2),ue={},M={},U={};return T(0)},bilinearFiltering:function(b,_,w,S,x){var T,E,I,D,N,L,F,O,V,re,J,ue,M=0,U,R=this.rcpScaleX,P=this.rcpScaleY,Q=4*(_-1),j=b.imageData,A=j.data,q=b.ctx.createImageData(S,x),G=q.data;for(F=0;F<x;F++)for(O=0;O<S;O++)for(N=d(R*O),L=d(P*F),V=R*O-N,re=P*F-L,U=4*(L*_+N),J=0;J<4;J++)T=A[U+J],E=A[U+4+J],I=A[U+Q+J],D=A[U+Q+4+J],ue=T*(1-V)*(1-re)+E*V*(1-re)+I*re*(1-V)+D*V*re,G[M++]=ue;return q},hermiteFastResize:function(b,_,w,S,x){for(var T=this.rcpScaleX,E=this.rcpScaleY,I=v(T/2),D=v(E/2),N=b.imageData,L=N.data,F=b.ctx.createImageData(S,x),O=F.data,V=0;V<x;V++)for(var re=0;re<S;re++){for(var J=(re+V*S)*4,ue=0,M=0,U=0,R=0,P=0,Q=0,j=0,A=(V+.5)*E,q=d(V*E);q<(V+1)*E;q++)for(var G=f(A-(q+.5))/D,te=(re+.5)*T,fe=G*G,pe=d(re*T);pe<(re+1)*T;pe++){var ve=f(te-(pe+.5))/I,H=c(fe+ve*ve);H>1&&H<-1||(ue=2*H*H*H-3*H*H+1,ue>0&&(ve=4*(pe+q*_),j+=ue*L[ve+3],U+=ue,L[ve+3]<255&&(ue=ue*L[ve+3]/250),R+=ue*L[ve],P+=ue*L[ve+1],Q+=ue*L[ve+2],M+=ue))}O[J]=R/M,O[J+1]=P/M,O[J+2]=Q/M,O[J+3]=j/U}return F},toObject:function(){return{type:this.type,scaleX:this.scaleX,scaleY:this.scaleY,resizeType:this.resizeType,lanczosLobes:this.lanczosLobes}}}),a.Image.filters.Resize.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Contrast=d(u.BaseFilter,{type:"Contrast",fragmentSource:`precision highp float;
+`,applyTo:function(b){b.webgl?(b.passes++,this.width=b.sourceWidth,this.horizontal=!0,this.dW=Math.round(this.width*this.scaleX),this.dH=b.sourceHeight,this.tempScale=this.dW/this.width,this.taps=this.getTaps(),b.destinationWidth=this.dW,this._setupFrameBuffer(b),this.applyToWebGL(b),this._swapTextures(b),b.sourceWidth=b.destinationWidth,this.height=b.sourceHeight,this.horizontal=!1,this.dH=Math.round(this.height*this.scaleY),this.tempScale=this.dH/this.height,this.taps=this.getTaps(),b.destinationHeight=this.dH,this._setupFrameBuffer(b),this.applyToWebGL(b),this._swapTextures(b),b.sourceHeight=b.destinationHeight):this.applyTo2d(b)},isNeutralState:function(){return this.scaleX===1&&this.scaleY===1},lanczosCreate:function(b){return function(_){if(_>=b||_<=-b)return 0;if(_<11920929e-14&&_>-11920929e-14)return 1;_*=Math.PI;var w=_/b;return h(_)/_*h(w)/w}},applyTo2d:function(b){var _=b.imageData,w=this.scaleX,S=this.scaleY;this.rcpScaleX=1/w,this.rcpScaleY=1/S;var x=_.width,k=_.height,E=s(x*w),I=s(k*S),D;this.resizeType==="sliceHack"?D=this.sliceByTwo(b,x,k,E,I):this.resizeType==="hermite"?D=this.hermiteFastResize(b,x,k,E,I):this.resizeType==="bilinear"?D=this.bilinearFiltering(b,x,k,E,I):this.resizeType==="lanczos"&&(D=this.lanczosResize(b,x,k,E,I)),b.imageData=D},sliceByTwo:function(b,_,w,S,x){var k=b.imageData,E=.5,I=!1,D=!1,N=_*E,L=w*E,F=a.filterBackend.resources,O,V,re=0,J=0,ue=_,M=0;for(F.sliceByTwo||(F.sliceByTwo=document.createElement("canvas")),O=F.sliceByTwo,(O.width<_*1.5||O.height<w)&&(O.width=_*1.5,O.height=w),V=O.getContext("2d"),V.clearRect(0,0,_*1.5,w),V.putImageData(k,0,0),S=d(S),x=d(x);!I||!D;)_=N,w=L,S<d(N*E)?N=d(N*E):(N=S,I=!0),x<d(L*E)?L=d(L*E):(L=x,D=!0),V.drawImage(O,re,J,_,w,ue,M,N,L),re=ue,J=M,M+=L;return V.getImageData(re,J,S,x)},lanczosResize:function(b,_,w,S,x){function k(R){var P,Q,j,A,K,q,te,fe,pe,ve,U;for(M.x=(R+.5)*L,H.x=d(M.x),P=0;P<x;P++){for(M.y=(P+.5)*F,H.y=d(M.y),K=0,q=0,te=0,fe=0,pe=0,Q=H.x-re;Q<=H.x+re;Q++)if(!(Q<0||Q>=_)){ve=d(1e3*f(Q-M.x)),ue[ve]||(ue[ve]={});for(var X=H.y-J;X<=H.y+J;X++)X<0||X>=w||(U=d(1e3*f(X-M.y)),ue[ve][U]||(ue[ve][U]=N(c(u(ve*O,2)+u(U*V,2))/1e3)),j=ue[ve][U],j>0&&(A=(X*_+Q)*4,K+=j,q+=j*E[A],te+=j*E[A+1],fe+=j*E[A+2],pe+=j*E[A+3]))}A=(P*S+R)*4,D[A]=q/K,D[A+1]=te/K,D[A+2]=fe/K,D[A+3]=pe/K}return++R<S?k(R):I}var E=b.imageData.data,I=b.ctx.createImageData(S,x),D=I.data,N=this.lanczosCreate(this.lanczosLobes),L=this.rcpScaleX,F=this.rcpScaleY,O=2/this.rcpScaleX,V=2/this.rcpScaleY,re=v(L*this.lanczosLobes/2),J=v(F*this.lanczosLobes/2),ue={},M={},H={};return k(0)},bilinearFiltering:function(b,_,w,S,x){var k,E,I,D,N,L,F,O,V,re,J,ue,M=0,H,R=this.rcpScaleX,P=this.rcpScaleY,Q=4*(_-1),j=b.imageData,A=j.data,K=b.ctx.createImageData(S,x),q=K.data;for(F=0;F<x;F++)for(O=0;O<S;O++)for(N=d(R*O),L=d(P*F),V=R*O-N,re=P*F-L,H=4*(L*_+N),J=0;J<4;J++)k=A[H+J],E=A[H+4+J],I=A[H+Q+J],D=A[H+Q+4+J],ue=k*(1-V)*(1-re)+E*V*(1-re)+I*re*(1-V)+D*V*re,q[M++]=ue;return K},hermiteFastResize:function(b,_,w,S,x){for(var k=this.rcpScaleX,E=this.rcpScaleY,I=v(k/2),D=v(E/2),N=b.imageData,L=N.data,F=b.ctx.createImageData(S,x),O=F.data,V=0;V<x;V++)for(var re=0;re<S;re++){for(var J=(re+V*S)*4,ue=0,M=0,H=0,R=0,P=0,Q=0,j=0,A=(V+.5)*E,K=d(V*E);K<(V+1)*E;K++)for(var q=f(A-(K+.5))/D,te=(re+.5)*k,fe=q*q,pe=d(re*k);pe<(re+1)*k;pe++){var ve=f(te-(pe+.5))/I,U=c(fe+ve*ve);U>1&&U<-1||(ue=2*U*U*U-3*U*U+1,ue>0&&(ve=4*(pe+K*_),j+=ue*L[ve+3],H+=ue,L[ve+3]<255&&(ue=ue*L[ve+3]/250),R+=ue*L[ve],P+=ue*L[ve+1],Q+=ue*L[ve+2],M+=ue))}O[J]=R/M,O[J+1]=P/M,O[J+2]=Q/M,O[J+3]=j/H}return F},toObject:function(){return{type:this.type,scaleX:this.scaleX,scaleY:this.scaleY,resizeType:this.resizeType,lanczosLobes:this.lanczosLobes}}}),a.Image.filters.Resize.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Contrast=d(u.BaseFilter,{type:"Contrast",fragmentSource:`precision highp float;
 uniform sampler2D uTexture;
 uniform float uContrast;
 varying vec2 vTexCoord;
@@ -489,7 +489,7 @@ color += texture2D(uTexture, vTexCoord + uDelta * percent) * weight;
 total += weight;
 }
 gl_FragColor = color / total;
-}`,blur:0,mainParameter:"blur",applyTo:function(c){c.webgl?(this.aspectRatio=c.sourceWidth/c.sourceHeight,c.passes++,this._setupFrameBuffer(c),this.horizontal=!0,this.applyToWebGL(c),this._swapTextures(c),this._setupFrameBuffer(c),this.horizontal=!1,this.applyToWebGL(c),this._swapTextures(c)):this.applyTo2d(c)},applyTo2d:function(c){c.imageData=this.simpleBlur(c)},simpleBlur:function(c){var f=c.filterBackend.resources,s,h,v=c.imageData.width,m=c.imageData.height;f.blurLayer1||(f.blurLayer1=a.util.createCanvasElement(),f.blurLayer2=a.util.createCanvasElement()),s=f.blurLayer1,h=f.blurLayer2,(s.width!==v||s.height!==m)&&(h.width=s.width=v,h.height=s.height=m);var y=s.getContext("2d"),b=h.getContext("2d"),_=15,w,S,x,T,E=this.blur*.06*.5;for(y.putImageData(c.imageData,0,0),b.clearRect(0,0,v,m),T=-_;T<=_;T++)w=(Math.random()-.5)/4,S=T/_,x=E*S*v+w,b.globalAlpha=1-Math.abs(S),b.drawImage(s,x,w),y.drawImage(h,0,0),b.globalAlpha=1,b.clearRect(0,0,h.width,h.height);for(T=-_;T<=_;T++)w=(Math.random()-.5)/4,S=T/_,x=E*S*m+w,b.globalAlpha=1-Math.abs(S),b.drawImage(s,w,x),y.drawImage(h,0,0),b.globalAlpha=1,b.clearRect(0,0,h.width,h.height);c.ctx.drawImage(s,0,0);var I=c.ctx.getImageData(0,0,s.width,s.height);return y.globalAlpha=1,y.clearRect(0,0,s.width,s.height),I},getUniformLocations:function(c,f){return{delta:c.getUniformLocation(f,"uDelta")}},sendUniformData:function(c,f){var s=this.chooseRightDelta();c.uniform2fv(f.delta,s)},chooseRightDelta:function(){var c=1,f=[0,0],s;return this.horizontal?this.aspectRatio>1&&(c=1/this.aspectRatio):this.aspectRatio<1&&(c=this.aspectRatio),s=c*this.blur*.12,this.horizontal?f[0]=s:f[1]=s,f}}),u.Blur.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Gamma=d(u.BaseFilter,{type:"Gamma",fragmentSource:`precision highp float;
+}`,blur:0,mainParameter:"blur",applyTo:function(c){c.webgl?(this.aspectRatio=c.sourceWidth/c.sourceHeight,c.passes++,this._setupFrameBuffer(c),this.horizontal=!0,this.applyToWebGL(c),this._swapTextures(c),this._setupFrameBuffer(c),this.horizontal=!1,this.applyToWebGL(c),this._swapTextures(c)):this.applyTo2d(c)},applyTo2d:function(c){c.imageData=this.simpleBlur(c)},simpleBlur:function(c){var f=c.filterBackend.resources,s,h,v=c.imageData.width,m=c.imageData.height;f.blurLayer1||(f.blurLayer1=a.util.createCanvasElement(),f.blurLayer2=a.util.createCanvasElement()),s=f.blurLayer1,h=f.blurLayer2,(s.width!==v||s.height!==m)&&(h.width=s.width=v,h.height=s.height=m);var y=s.getContext("2d"),b=h.getContext("2d"),_=15,w,S,x,k,E=this.blur*.06*.5;for(y.putImageData(c.imageData,0,0),b.clearRect(0,0,v,m),k=-_;k<=_;k++)w=(Math.random()-.5)/4,S=k/_,x=E*S*v+w,b.globalAlpha=1-Math.abs(S),b.drawImage(s,x,w),y.drawImage(h,0,0),b.globalAlpha=1,b.clearRect(0,0,h.width,h.height);for(k=-_;k<=_;k++)w=(Math.random()-.5)/4,S=k/_,x=E*S*m+w,b.globalAlpha=1-Math.abs(S),b.drawImage(s,w,x),y.drawImage(h,0,0),b.globalAlpha=1,b.clearRect(0,0,h.width,h.height);c.ctx.drawImage(s,0,0);var I=c.ctx.getImageData(0,0,s.width,s.height);return y.globalAlpha=1,y.clearRect(0,0,s.width,s.height),I},getUniformLocations:function(c,f){return{delta:c.getUniformLocation(f,"uDelta")}},sendUniformData:function(c,f){var s=this.chooseRightDelta();c.uniform2fv(f.delta,s)},chooseRightDelta:function(){var c=1,f=[0,0],s;return this.horizontal?this.aspectRatio>1&&(c=1/this.aspectRatio):this.aspectRatio<1&&(c=this.aspectRatio),s=c*this.blur*.12,this.horizontal?f[0]=s:f[1]=s,f}}),u.Blur.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Gamma=d(u.BaseFilter,{type:"Gamma",fragmentSource:`precision highp float;
 uniform sampler2D uTexture;
 uniform vec3 uGamma;
 varying vec2 vTexCoord;
@@ -501,14 +501,14 @@ color.g = pow(color.g, correction.g);
 color.b = pow(color.b, correction.b);
 gl_FragColor = color;
 gl_FragColor.rgb *= color.a;
-}`,gamma:[1,1,1],mainParameter:"gamma",initialize:function(c){this.gamma=[1,1,1],u.BaseFilter.prototype.initialize.call(this,c)},applyTo2d:function(c){var f=c.imageData,s=f.data,h=this.gamma,v=s.length,m=1/h[0],y=1/h[1],b=1/h[2],_;for(this.rVals||(this.rVals=new Uint8Array(256),this.gVals=new Uint8Array(256),this.bVals=new Uint8Array(256)),_=0,v=256;_<v;_++)this.rVals[_]=Math.pow(_/255,m)*255,this.gVals[_]=Math.pow(_/255,y)*255,this.bVals[_]=Math.pow(_/255,b)*255;for(_=0,v=s.length;_<v;_+=4)s[_]=this.rVals[s[_]],s[_+1]=this.gVals[s[_+1]],s[_+2]=this.bVals[s[_+2]]},getUniformLocations:function(c,f){return{uGamma:c.getUniformLocation(f,"uGamma")}},sendUniformData:function(c,f){c.uniform3fv(f.uGamma,this.gamma)}}),a.Image.filters.Gamma.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Composed=d(u.BaseFilter,{type:"Composed",subFilters:[],initialize:function(c){this.callSuper("initialize",c),this.subFilters=this.subFilters.slice(0)},applyTo:function(c){c.passes+=this.subFilters.length-1,this.subFilters.forEach(function(f){f.applyTo(c)})},toObject:function(){return a.util.object.extend(this.callSuper("toObject"),{subFilters:this.subFilters.map(function(c){return c.toObject()})})},isNeutralState:function(){return!this.subFilters.some(function(c){return!c.isNeutralState()})}}),a.Image.filters.Composed.fromObject=function(c,f){var s=c.subFilters||[],h=s.map(function(m){return new a.Image.filters[m.type](m)}),v=new a.Image.filters.Composed({subFilters:h});return f&&f(v),v}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.HueRotation=d(u.ColorMatrix,{type:"HueRotation",rotation:0,mainParameter:"rotation",calculateMatrix:function(){var c=this.rotation*Math.PI,f=a.util.cos(c),s=a.util.sin(c),h=1/3,v=Math.sqrt(h)*s,m=1-f;this.matrix=[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],this.matrix[0]=f+m/3,this.matrix[1]=h*m-v,this.matrix[2]=h*m+v,this.matrix[5]=h*m+v,this.matrix[6]=f+h*m,this.matrix[7]=h*m-v,this.matrix[10]=h*m-v,this.matrix[11]=h*m+v,this.matrix[12]=f+h*m},isNeutralState:function(c){return this.calculateMatrix(),u.BaseFilter.prototype.isNeutralState.call(this,c)},applyTo:function(c){this.calculateMatrix(),u.BaseFilter.prototype.applyTo.call(this,c)}}),a.Image.filters.HueRotation.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.clone;if(a.Text){a.warn("fabric.Text is already defined");return}var d="fontFamily fontWeight fontSize text underline overline linethrough textAlign fontStyle lineHeight textBackgroundColor charSpacing styles direction path pathStartOffset pathSide pathAlign".split(" ");a.Text=a.util.createClass(a.Object,{_dimensionAffectingProps:["fontSize","fontWeight","fontFamily","fontStyle","lineHeight","text","charSpacing","textAlign","styles","path","pathStartOffset","pathSide","pathAlign"],_reNewline:/\r?\n/,_reSpacesAndTabs:/[ \t\r]/g,_reSpaceAndTab:/[ \t\r]/,_reWords:/\S+/g,type:"text",fontSize:40,fontWeight:"normal",fontFamily:"Times New Roman",underline:!1,overline:!1,linethrough:!1,textAlign:"left",fontStyle:"normal",lineHeight:1.16,superscript:{size:.6,baseline:-.35},subscript:{size:.6,baseline:.11},textBackgroundColor:"",stateProperties:a.Object.prototype.stateProperties.concat(d),cacheProperties:a.Object.prototype.cacheProperties.concat(d),stroke:null,shadow:null,path:null,pathStartOffset:0,pathSide:"left",pathAlign:"baseline",_fontSizeFraction:.222,offsets:{underline:.1,linethrough:-.315,overline:-.88},_fontSizeMult:1.13,charSpacing:0,styles:null,_measuringContext:null,deltaY:0,direction:"ltr",_styleProperties:["stroke","strokeWidth","fill","fontFamily","fontSize","fontWeight","fontStyle","underline","overline","linethrough","deltaY","textBackgroundColor"],__charBounds:[],CACHE_FONT_SIZE:400,MIN_TEXT_WIDTH:2,initialize:function(c,f){this.styles=f?f.styles||{}:{},this.text=c,this.__skipDimension=!0,this.callSuper("initialize",f),this.path&&this.setPathInfo(),this.__skipDimension=!1,this.initDimensions(),this.setCoords(),this.setupState({propertySet:"_dimensionAffectingProps"})},setPathInfo:function(){var c=this.path;c&&(c.segmentsInfo=a.util.getPathSegmentsInfo(c.path))},getMeasuringContext:function(){return a._measuringContext||(a._measuringContext=this.canvas&&this.canvas.contextCache||a.util.createCanvasElement().getContext("2d")),a._measuringContext},_splitText:function(){var c=this._splitTextIntoLines(this.text);return this.textLines=c.lines,this._textLines=c.graphemeLines,this._unwrappedTextLines=c._unwrappedLines,this._text=c.graphemeText,c},initDimensions:function(){this.__skipDimension||(this._splitText(),this._clearCache(),this.path?(this.width=this.path.width,this.height=this.path.height):(this.width=this.calcTextWidth()||this.cursorWidth||this.MIN_TEXT_WIDTH,this.height=this.calcTextHeight()),this.textAlign.indexOf("justify")!==-1&&this.enlargeSpaces(),this.saveState({propertySet:"_dimensionAffectingProps"}))},enlargeSpaces:function(){for(var c,f,s,h,v,m,y,b=0,_=this._textLines.length;b<_;b++)if(!(this.textAlign!=="justify"&&(b===_-1||this.isEndOfWrapping(b)))&&(h=0,v=this._textLines[b],f=this.getLineWidth(b),f<this.width&&(y=this.textLines[b].match(this._reSpacesAndTabs)))){s=y.length,c=(this.width-f)/s;for(var w=0,S=v.length;w<=S;w++)m=this.__charBounds[b][w],this._reSpaceAndTab.test(v[w])?(m.width+=c,m.kernedWidth+=c,m.left+=h,h+=c):m.left+=h}},isEndOfWrapping:function(c){return c===this._textLines.length-1},missingNewlineOffset:function(){return 1},toString:function(){return"#<fabric.Text ("+this.complexity()+'): { "text": "'+this.text+'", "fontFamily": "'+this.fontFamily+'" }>'},_getCacheCanvasDimensions:function(){var c=this.callSuper("_getCacheCanvasDimensions"),f=this.fontSize;return c.width+=f*c.zoomX,c.height+=f*c.zoomY,c},_render:function(c){var f=this.path;f&&!f.isNotVisible()&&f._render(c),this._setTextStyles(c),this._renderTextLinesBackground(c),this._renderTextDecoration(c,"underline"),this._renderText(c),this._renderTextDecoration(c,"overline"),this._renderTextDecoration(c,"linethrough")},_renderText:function(c){this.paintFirst==="stroke"?(this._renderTextStroke(c),this._renderTextFill(c)):(this._renderTextFill(c),this._renderTextStroke(c))},_setTextStyles:function(c,f,s){if(c.textBaseline="alphabetical",this.path)switch(this.pathAlign){case"center":c.textBaseline="middle";break;case"ascender":c.textBaseline="top";break;case"descender":c.textBaseline="bottom";break}c.font=this._getFontDeclaration(f,s)},calcTextWidth:function(){for(var c=this.getLineWidth(0),f=1,s=this._textLines.length;f<s;f++){var h=this.getLineWidth(f);h>c&&(c=h)}return c},_renderTextLine:function(c,f,s,h,v,m){this._renderChars(c,f,s,h,v,m)},_renderTextLinesBackground:function(c){if(!(!this.textBackgroundColor&&!this.styleHas("textBackgroundColor"))){for(var f,s,h=c.fillStyle,v,m,y=this._getLeftOffset(),b=this._getTopOffset(),_=0,w=0,S,x,T=this.path,E,I=0,D=this._textLines.length;I<D;I++){if(f=this.getHeightOfLine(I),!this.textBackgroundColor&&!this.styleHas("textBackgroundColor",I)){b+=f;continue}v=this._textLines[I],s=this._getLineLeftOffset(I),w=0,_=0,m=this.getValueOfPropertyAt(I,0,"textBackgroundColor");for(var N=0,L=v.length;N<L;N++)S=this.__charBounds[I][N],x=this.getValueOfPropertyAt(I,N,"textBackgroundColor"),T?(c.save(),c.translate(S.renderLeft,S.renderTop),c.rotate(S.angle),c.fillStyle=x,x&&c.fillRect(-S.width/2,-f/this.lineHeight*(1-this._fontSizeFraction),S.width,f/this.lineHeight),c.restore()):x!==m?(E=y+s+_,this.direction==="rtl"&&(E=this.width-E-w),c.fillStyle=m,m&&c.fillRect(E,b,w,f/this.lineHeight),_=S.left,w=S.width,m=x):w+=S.kernedWidth;x&&!T&&(E=y+s+_,this.direction==="rtl"&&(E=this.width-E-w),c.fillStyle=x,c.fillRect(E,b,w,f/this.lineHeight)),b+=f}c.fillStyle=h,this._removeShadow(c)}},getFontCache:function(c){var f=c.fontFamily.toLowerCase();a.charWidthsCache[f]||(a.charWidthsCache[f]={});var s=a.charWidthsCache[f],h=c.fontStyle.toLowerCase()+"_"+(c.fontWeight+"").toLowerCase();return s[h]||(s[h]={}),s[h]},_measureChar:function(c,f,s,h){var v=this.getFontCache(f),m=this._getFontDeclaration(f),y=this._getFontDeclaration(h),b=s+c,_=m===y,w,S,x,T=f.fontSize/this.CACHE_FONT_SIZE,E;if(s&&v[s]!==void 0&&(x=v[s]),v[c]!==void 0&&(E=w=v[c]),_&&v[b]!==void 0&&(S=v[b],E=S-x),w===void 0||x===void 0||S===void 0){var I=this.getMeasuringContext();this._setTextStyles(I,f,!0)}return w===void 0&&(E=w=I.measureText(c).width,v[c]=w),x===void 0&&_&&s&&(x=I.measureText(s).width,v[s]=x),_&&S===void 0&&(S=I.measureText(b).width,v[b]=S,E=S-x),{width:w*T,kernedWidth:E*T}},getHeightOfChar:function(c,f){return this.getValueOfPropertyAt(c,f,"fontSize")},measureLine:function(c){var f=this._measureLine(c);return this.charSpacing!==0&&(f.width-=this._getWidthOfCharSpacing()),f.width<0&&(f.width=0),f},_measureLine:function(c){var f=0,s,h,v=this._textLines[c],m,y,b=0,_=new Array(v.length),w=0,S,x,T=this.path,E=this.pathSide==="right";for(this.__charBounds[c]=_,s=0;s<v.length;s++)h=v[s],y=this._getGraphemeBox(h,c,s,m),_[s]=y,f+=y.kernedWidth,m=h;if(_[s]={left:y?y.left+y.width:0,width:0,kernedWidth:0,height:this.fontSize},T){switch(x=T.segmentsInfo[T.segmentsInfo.length-1].length,S=a.util.getPointOnPath(T.path,0,T.segmentsInfo),S.x+=T.pathOffset.x,S.y+=T.pathOffset.y,this.textAlign){case"left":w=E?x-f:0;break;case"center":w=(x-f)/2;break;case"right":w=E?0:x-f;break}for(w+=this.pathStartOffset*(E?-1:1),s=E?v.length-1:0;E?s>=0:s<v.length;E?s--:s++)y=_[s],w>x?w%=x:w<0&&(w+=x),this._setGraphemeOnPath(w,y,S),w+=y.kernedWidth}return{width:f,numOfSpaces:b}},_setGraphemeOnPath:function(c,f,s){var h=c+f.kernedWidth/2,v=this.path,m=a.util.getPointOnPath(v.path,h,v.segmentsInfo);f.renderLeft=m.x-s.x,f.renderTop=m.y-s.y,f.angle=m.angle+(this.pathSide==="right"?Math.PI:0)},_getGraphemeBox:function(c,f,s,h,v){var m=this.getCompleteStyleDeclaration(f,s),y=h?this.getCompleteStyleDeclaration(f,s-1):{},b=this._measureChar(c,m,h,y),_=b.kernedWidth,w=b.width,S;this.charSpacing!==0&&(S=this._getWidthOfCharSpacing(),w+=S,_+=S);var x={width:w,left:0,height:m.fontSize,kernedWidth:_,deltaY:m.deltaY};if(s>0&&!v){var T=this.__charBounds[f][s-1];x.left=T.left+T.width+b.kernedWidth-b.width}return x},getHeightOfLine:function(c){if(this.__lineHeights[c])return this.__lineHeights[c];for(var f=this._textLines[c],s=this.getHeightOfChar(c,0),h=1,v=f.length;h<v;h++)s=Math.max(this.getHeightOfChar(c,h),s);return this.__lineHeights[c]=s*this.lineHeight*this._fontSizeMult},calcTextHeight:function(){for(var c,f=0,s=0,h=this._textLines.length;s<h;s++)c=this.getHeightOfLine(s),f+=s===h-1?c/this.lineHeight:c;return f},_getLeftOffset:function(){return this.direction==="ltr"?-this.width/2:this.width/2},_getTopOffset:function(){return-this.height/2},_renderTextCommon:function(c,f){c.save();for(var s=0,h=this._getLeftOffset(),v=this._getTopOffset(),m=0,y=this._textLines.length;m<y;m++){var b=this.getHeightOfLine(m),_=b/this.lineHeight,w=this._getLineLeftOffset(m);this._renderTextLine(f,c,this._textLines[m],h+w,v+s+_,m),s+=b}c.restore()},_renderTextFill:function(c){!this.fill&&!this.styleHas("fill")||this._renderTextCommon(c,"fillText")},_renderTextStroke:function(c){(!this.stroke||this.strokeWidth===0)&&this.isEmptyStyles()||(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(c),c.save(),this._setLineDash(c,this.strokeDashArray),c.beginPath(),this._renderTextCommon(c,"strokeText"),c.closePath(),c.restore())},_renderChars:function(c,f,s,h,v,m){var y=this.getHeightOfLine(m),b=this.textAlign.indexOf("justify")!==-1,_,w,S="",x,T=0,E,I=this.path,D=!b&&this.charSpacing===0&&this.isEmptyStyles(m)&&!I,N=this.direction==="ltr",L=this.direction==="ltr"?1:-1,F,O=f.canvas.getAttribute("dir");if(f.save(),O!==this.direction&&(f.canvas.setAttribute("dir",N?"ltr":"rtl"),f.direction=N?"ltr":"rtl",f.textAlign=N?"left":"right"),v-=y*this._fontSizeFraction/this.lineHeight,D){this._renderChar(c,f,m,0,s.join(""),h,v,y),f.restore();return}for(var V=0,re=s.length-1;V<=re;V++)E=V===re||this.charSpacing||I,S+=s[V],x=this.__charBounds[m][V],T===0?(h+=L*(x.kernedWidth-x.width),T+=x.width):T+=x.kernedWidth,b&&!E&&this._reSpaceAndTab.test(s[V])&&(E=!0),E||(_=_||this.getCompleteStyleDeclaration(m,V),w=this.getCompleteStyleDeclaration(m,V+1),E=a.util.hasStyleChanged(_,w,!1)),E&&(I?(f.save(),f.translate(x.renderLeft,x.renderTop),f.rotate(x.angle),this._renderChar(c,f,m,V,S,-T/2,0,y),f.restore()):(F=h,this._renderChar(c,f,m,V,S,F,v,y)),S="",_=w,h+=L*T,T=0);f.restore()},_applyPatternGradientTransformText:function(c){var f=a.util.createCanvasElement(),s,h=this.width+this.strokeWidth,v=this.height+this.strokeWidth;return f.width=h,f.height=v,s=f.getContext("2d"),s.beginPath(),s.moveTo(0,0),s.lineTo(h,0),s.lineTo(h,v),s.lineTo(0,v),s.closePath(),s.translate(h/2,v/2),s.fillStyle=c.toLive(s),this._applyPatternGradientTransform(s,c),s.fill(),s.createPattern(f,"no-repeat")},handleFiller:function(c,f,s){var h,v;return s.toLive?s.gradientUnits==="percentage"||s.gradientTransform||s.patternTransform?(h=-this.width/2,v=-this.height/2,c.translate(h,v),c[f]=this._applyPatternGradientTransformText(s),{offsetX:h,offsetY:v}):(c[f]=s.toLive(c,this),this._applyPatternGradientTransform(c,s)):(c[f]=s,{offsetX:0,offsetY:0})},_setStrokeStyles:function(c,f){return c.lineWidth=f.strokeWidth,c.lineCap=this.strokeLineCap,c.lineDashOffset=this.strokeDashOffset,c.lineJoin=this.strokeLineJoin,c.miterLimit=this.strokeMiterLimit,this.handleFiller(c,"strokeStyle",f.stroke)},_setFillStyles:function(c,f){return this.handleFiller(c,"fillStyle",f.fill)},_renderChar:function(c,f,s,h,v,m,y){var b=this._getStyleDeclaration(s,h),_=this.getCompleteStyleDeclaration(s,h),w=c==="fillText"&&_.fill,S=c==="strokeText"&&_.stroke&&_.strokeWidth,x,T;!S&&!w||(f.save(),w&&(x=this._setFillStyles(f,_)),S&&(T=this._setStrokeStyles(f,_)),f.font=this._getFontDeclaration(_),b&&b.textBackgroundColor&&this._removeShadow(f),b&&b.deltaY&&(y+=b.deltaY),w&&f.fillText(v,m-x.offsetX,y-x.offsetY),S&&f.strokeText(v,m-T.offsetX,y-T.offsetY),f.restore())},setSuperscript:function(c,f){return this._setScript(c,f,this.superscript)},setSubscript:function(c,f){return this._setScript(c,f,this.subscript)},_setScript:function(c,f,s){var h=this.get2DCursorLocation(c,!0),v=this.getValueOfPropertyAt(h.lineIndex,h.charIndex,"fontSize"),m=this.getValueOfPropertyAt(h.lineIndex,h.charIndex,"deltaY"),y={fontSize:v*s.size,deltaY:m+v*s.baseline};return this.setSelectionStyles(y,c,f),this},_getLineLeftOffset:function(c){var f=this.getLineWidth(c),s=this.width-f,h=this.textAlign,v=this.direction,y,m=0,y=this.isEndOfWrapping(c);return h==="justify"||h==="justify-center"&&!y||h==="justify-right"&&!y||h==="justify-left"&&!y?0:(h==="center"&&(m=s/2),h==="right"&&(m=s),h==="justify-center"&&(m=s/2),h==="justify-right"&&(m=s),v==="rtl"&&(m-=s),m)},_clearCache:function(){this.__lineWidths=[],this.__lineHeights=[],this.__charBounds=[]},_shouldClearDimensionCache:function(){var c=this._forceClearCache;return c||(c=this.hasStateChanged("_dimensionAffectingProps")),c&&(this.dirty=!0,this._forceClearCache=!1),c},getLineWidth:function(c){if(this.__lineWidths[c]!==void 0)return this.__lineWidths[c];var f=this.measureLine(c),s=f.width;return this.__lineWidths[c]=s,s},_getWidthOfCharSpacing:function(){return this.charSpacing!==0?this.fontSize*this.charSpacing/1e3:0},getValueOfPropertyAt:function(c,f,s){var h=this._getStyleDeclaration(c,f);return h&&typeof h[s]<"u"?h[s]:this[s]},_renderTextDecoration:function(c,f){if(!(!this[f]&&!this.styleHas(f))){for(var s,h,v,m,y,b,_,w,S=this._getLeftOffset(),x=this._getTopOffset(),T,E,I,D,N,L,F,O,V=this.path,re=this._getWidthOfCharSpacing(),J=this.offsets[f],ue=0,M=this._textLines.length;ue<M;ue++){if(s=this.getHeightOfLine(ue),!this[f]&&!this.styleHas(f,ue)){x+=s;continue}_=this._textLines[ue],L=s/this.lineHeight,m=this._getLineLeftOffset(ue),E=0,I=0,w=this.getValueOfPropertyAt(ue,0,f),O=this.getValueOfPropertyAt(ue,0,"fill"),T=x+L*(1-this._fontSizeFraction),h=this.getHeightOfChar(ue,0),y=this.getValueOfPropertyAt(ue,0,"deltaY");for(var U=0,R=_.length;U<R;U++)if(D=this.__charBounds[ue][U],N=this.getValueOfPropertyAt(ue,U,f),F=this.getValueOfPropertyAt(ue,U,"fill"),v=this.getHeightOfChar(ue,U),b=this.getValueOfPropertyAt(ue,U,"deltaY"),V&&N&&F)c.save(),c.fillStyle=O,c.translate(D.renderLeft,D.renderTop),c.rotate(D.angle),c.fillRect(-D.kernedWidth/2,J*v+b,D.kernedWidth,this.fontSize/15),c.restore();else if((N!==w||F!==O||v!==h||b!==y)&&I>0){var P=S+m+E;this.direction==="rtl"&&(P=this.width-P-I),w&&O&&(c.fillStyle=O,c.fillRect(P,T+J*h+y,I,this.fontSize/15)),E=D.left,I=D.width,w=N,O=F,h=v,y=b}else I+=D.kernedWidth;var P=S+m+E;this.direction==="rtl"&&(P=this.width-P-I),c.fillStyle=F,N&&F&&c.fillRect(P,T+J*h+y,I-re,this.fontSize/15),x+=s}this._removeShadow(c)}},_getFontDeclaration:function(c,f){var s=c||this,h=this.fontFamily,v=a.Text.genericFonts.indexOf(h.toLowerCase())>-1,m=h===void 0||h.indexOf("'")>-1||h.indexOf(",")>-1||h.indexOf('"')>-1||v?s.fontFamily:'"'+s.fontFamily+'"';return[a.isLikelyNode?s.fontWeight:s.fontStyle,a.isLikelyNode?s.fontStyle:s.fontWeight,f?this.CACHE_FONT_SIZE+"px":s.fontSize+"px",m].join(" ")},render:function(c){!this.visible||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(this._shouldClearDimensionCache()&&this.initDimensions(),this.callSuper("render",c))},_splitTextIntoLines:function(c){for(var f=c.split(this._reNewline),s=new Array(f.length),h=[`
-`],v=[],m=0;m<f.length;m++)s[m]=a.util.string.graphemeSplit(f[m]),v=v.concat(s[m],h);return v.pop(),{_unwrappedLines:s,lines:f,graphemeText:v,graphemeLines:s}},toObject:function(c){var f=d.concat(c),s=this.callSuper("toObject",f);return s.styles=a.util.stylesToArray(this.styles,this.text),s.path&&(s.path=this.path.toObject()),s},set:function(c,f){this.callSuper("set",c,f);var s=!1,h=!1;if(typeof c=="object")for(var v in c)v==="path"&&this.setPathInfo(),s=s||this._dimensionAffectingProps.indexOf(v)!==-1,h=h||v==="path";else s=this._dimensionAffectingProps.indexOf(c)!==-1,h=c==="path";return h&&this.setPathInfo(),s&&(this.initDimensions(),this.setCoords()),this},complexity:function(){return 1}}),a.Text.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("x y dx dy font-family font-style font-weight font-size letter-spacing text-decoration text-anchor".split(" ")),a.Text.DEFAULT_SVG_FONT_SIZE=16,a.Text.fromElement=function(c,f,s){if(!c)return f(null);var h=a.parseAttributes(c,a.Text.ATTRIBUTE_NAMES),v=h.textAnchor||"left";if(s=a.util.object.extend(s?u(s):{},h),s.top=s.top||0,s.left=s.left||0,h.textDecoration){var m=h.textDecoration;m.indexOf("underline")!==-1&&(s.underline=!0),m.indexOf("overline")!==-1&&(s.overline=!0),m.indexOf("line-through")!==-1&&(s.linethrough=!0),delete s.textDecoration}"dx"in h&&(s.left+=h.dx),"dy"in h&&(s.top+=h.dy),"fontSize"in s||(s.fontSize=a.Text.DEFAULT_SVG_FONT_SIZE);var y="";"textContent"in c?y=c.textContent:"firstChild"in c&&c.firstChild!==null&&"data"in c.firstChild&&c.firstChild.data!==null&&(y=c.firstChild.data),y=y.replace(/^\s+|\s+$|\n+/g,"").replace(/\s+/g," ");var b=s.strokeWidth;s.strokeWidth=0;var _=new a.Text(y,s),w=_.getScaledHeight()/_.height,S=(_.height+_.strokeWidth)*_.lineHeight-_.height,x=S*w,T=_.getScaledHeight()+x,E=0;v==="center"&&(E=_.getScaledWidth()/2),v==="right"&&(E=_.getScaledWidth()),_.set({left:_.left-E,top:_.top-(T-_.fontSize*(.07+_._fontSizeFraction))/_.lineHeight,strokeWidth:typeof b<"u"?b:1}),f(_)},a.Text.fromObject=function(c,f){var s=u(c),h=c.path;return delete s.path,a.Object._fromObject("Text",s,function(v){v.styles=a.util.stylesFromArray(c.styles,c.text),h?a.Object._fromObject("Path",h,function(m){v.set("path",m),f(v)},"path"):f(v)},"text")},a.Text.genericFonts=["sans-serif","serif","cursive","fantasy","monospace"],a.util.createAccessors&&a.util.createAccessors(a.Text)}(e),function(){t.util.object.extend(t.Text.prototype,{isEmptyStyles:function(o){if(!this.styles||typeof o<"u"&&!this.styles[o])return!0;var a=typeof o>"u"?this.styles:{line:this.styles[o]};for(var u in a)for(var d in a[u])for(var c in a[u][d])return!1;return!0},styleHas:function(o,a){if(!this.styles||!o||o===""||typeof a<"u"&&!this.styles[a])return!1;var u=typeof a>"u"?this.styles:{0:this.styles[a]};for(var d in u)for(var c in u[d])if(typeof u[d][c][o]<"u")return!0;return!1},cleanStyle:function(o){if(!this.styles||!o||o==="")return!1;var a=this.styles,u=0,d,c,f=!0,s=0,h;for(var v in a){d=0;for(var m in a[v]){var h=a[v][m],y=h.hasOwnProperty(o);u++,y?(c?h[o]!==c&&(f=!1):c=h[o],h[o]===this[o]&&delete h[o]):f=!1,Object.keys(h).length!==0?d++:delete a[v][m]}d===0&&delete a[v]}for(var b=0;b<this._textLines.length;b++)s+=this._textLines[b].length;f&&u===s&&(this[o]=c,this.removeStyle(o))},removeStyle:function(o){if(!(!this.styles||!o||o==="")){var a=this.styles,u,d,c;for(d in a){u=a[d];for(c in u)delete u[c][o],Object.keys(u[c]).length===0&&delete u[c];Object.keys(u).length===0&&delete a[d]}}},_extendStyles:function(o,a){var u=this.get2DCursorLocation(o);this._getLineStyle(u.lineIndex)||this._setLineStyle(u.lineIndex),this._getStyleDeclaration(u.lineIndex,u.charIndex)||this._setStyleDeclaration(u.lineIndex,u.charIndex,{}),t.util.object.extend(this._getStyleDeclaration(u.lineIndex,u.charIndex),a)},get2DCursorLocation:function(o,a){typeof o>"u"&&(o=this.selectionStart);for(var u=a?this._unwrappedTextLines:this._textLines,d=u.length,c=0;c<d;c++){if(o<=u[c].length)return{lineIndex:c,charIndex:o};o-=u[c].length+this.missingNewlineOffset(c)}return{lineIndex:c-1,charIndex:u[c-1].length<o?u[c-1].length:o}},getSelectionStyles:function(o,a,u){typeof o>"u"&&(o=this.selectionStart||0),typeof a>"u"&&(a=this.selectionEnd||o);for(var d=[],c=o;c<a;c++)d.push(this.getStyleAtPosition(c,u));return d},getStyleAtPosition:function(o,a){var u=this.get2DCursorLocation(o),d=a?this.getCompleteStyleDeclaration(u.lineIndex,u.charIndex):this._getStyleDeclaration(u.lineIndex,u.charIndex);return d||{}},setSelectionStyles:function(o,a,u){typeof a>"u"&&(a=this.selectionStart||0),typeof u>"u"&&(u=this.selectionEnd||a);for(var d=a;d<u;d++)this._extendStyles(d,o);return this._forceClearCache=!0,this},_getStyleDeclaration:function(o,a){var u=this.styles&&this.styles[o];return u?u[a]:null},getCompleteStyleDeclaration:function(o,a){for(var u=this._getStyleDeclaration(o,a)||{},d={},c,f=0;f<this._styleProperties.length;f++)c=this._styleProperties[f],d[c]=typeof u[c]>"u"?this[c]:u[c];return d},_setStyleDeclaration:function(o,a,u){this.styles[o][a]=u},_deleteStyleDeclaration:function(o,a){delete this.styles[o][a]},_getLineStyle:function(o){return!!this.styles[o]},_setLineStyle:function(o){this.styles[o]={}},_deleteLineStyle:function(o){delete this.styles[o]}})}(),function(){function o(a){a.textDecoration&&(a.textDecoration.indexOf("underline")>-1&&(a.underline=!0),a.textDecoration.indexOf("line-through")>-1&&(a.linethrough=!0),a.textDecoration.indexOf("overline")>-1&&(a.overline=!0),delete a.textDecoration)}t.IText=t.util.createClass(t.Text,t.Observable,{type:"i-text",selectionStart:0,selectionEnd:0,selectionColor:"rgba(17,119,255,0.3)",isEditing:!1,editable:!0,editingBorderColor:"rgba(102,153,255,0.25)",cursorWidth:2,cursorColor:"",cursorDelay:1e3,cursorDuration:600,caching:!0,hiddenTextareaContainer:null,_reSpace:/\s|\n/,_currentCursorOpacity:0,_selectionDirection:null,_abortCursorAnimation:!1,__widthOfSpace:[],inCompositionMode:!1,initialize:function(a,u){this.callSuper("initialize",a,u),this.initBehavior()},setSelectionStart:function(a){a=Math.max(a,0),this._updateAndFire("selectionStart",a)},setSelectionEnd:function(a){a=Math.min(a,this.text.length),this._updateAndFire("selectionEnd",a)},_updateAndFire:function(a,u){this[a]!==u&&(this._fireSelectionChanged(),this[a]=u),this._updateTextarea()},_fireSelectionChanged:function(){this.fire("selection:changed"),this.canvas&&this.canvas.fire("text:selection:changed",{target:this})},initDimensions:function(){this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this.callSuper("initDimensions")},render:function(a){this.clearContextTop(),this.callSuper("render",a),this.cursorOffsetCache={},this.renderCursorOrSelection()},_render:function(a){this.callSuper("_render",a)},clearContextTop:function(a){if(!(!this.isEditing||!this.canvas||!this.canvas.contextTop)){var u=this.canvas.contextTop,d=this.canvas.viewportTransform;u.save(),u.transform(d[0],d[1],d[2],d[3],d[4],d[5]),this.transform(u),this._clearTextArea(u),a||u.restore()}},renderCursorOrSelection:function(){if(!(!this.isEditing||!this.canvas||!this.canvas.contextTop)){var a=this._getCursorBoundaries(),u=this.canvas.contextTop;this.clearContextTop(!0),this.selectionStart===this.selectionEnd?this.renderCursor(a,u):this.renderSelection(a,u),u.restore()}},_clearTextArea:function(a){var u=this.width+4,d=this.height+4;a.clearRect(-u/2,-d/2,u,d)},_getCursorBoundaries:function(a){typeof a>"u"&&(a=this.selectionStart);var u=this._getLeftOffset(),d=this._getTopOffset(),c=this._getCursorBoundariesOffsets(a);return{left:u,top:d,leftOffset:c.left,topOffset:c.top}},_getCursorBoundariesOffsets:function(a){if(this.cursorOffsetCache&&"top"in this.cursorOffsetCache)return this.cursorOffsetCache;var u,d,c,f=0,s=0,h,v=this.get2DCursorLocation(a);c=v.charIndex,d=v.lineIndex;for(var m=0;m<d;m++)f+=this.getHeightOfLine(m);u=this._getLineLeftOffset(d);var y=this.__charBounds[d][c];return y&&(s=y.left),this.charSpacing!==0&&c===this._textLines[d].length&&(s-=this._getWidthOfCharSpacing()),h={top:f,left:u+(s>0?s:0)},this.direction==="rtl"&&(h.left*=-1),this.cursorOffsetCache=h,this.cursorOffsetCache},renderCursor:function(a,u){var d=this.get2DCursorLocation(),c=d.lineIndex,f=d.charIndex>0?d.charIndex-1:0,s=this.getValueOfPropertyAt(c,f,"fontSize"),h=this.scaleX*this.canvas.getZoom(),v=this.cursorWidth/h,m=a.topOffset,y=this.getValueOfPropertyAt(c,f,"deltaY");m+=(1-this._fontSizeFraction)*this.getHeightOfLine(c)/this.lineHeight-s*(1-this._fontSizeFraction),this.inCompositionMode&&this.renderSelection(a,u),u.fillStyle=this.cursorColor||this.getValueOfPropertyAt(c,f,"fill"),u.globalAlpha=this.__isMousedown?1:this._currentCursorOpacity,u.fillRect(a.left+a.leftOffset-v/2,m+a.top+y,v,s)},renderSelection:function(a,u){for(var d=this.inCompositionMode?this.hiddenTextarea.selectionStart:this.selectionStart,c=this.inCompositionMode?this.hiddenTextarea.selectionEnd:this.selectionEnd,f=this.textAlign.indexOf("justify")!==-1,s=this.get2DCursorLocation(d),h=this.get2DCursorLocation(c),v=s.lineIndex,m=h.lineIndex,y=s.charIndex<0?0:s.charIndex,b=h.charIndex<0?0:h.charIndex,_=v;_<=m;_++){var w=this._getLineLeftOffset(_)||0,S=this.getHeightOfLine(_),x=0,T=0,E=0;if(_===v&&(T=this.__charBounds[v][y].left),_>=v&&_<m)E=f&&!this.isEndOfWrapping(_)?this.width:this.getLineWidth(_)||5;else if(_===m)if(b===0)E=this.__charBounds[m][b].left;else{var I=this._getWidthOfCharSpacing();E=this.__charBounds[m][b-1].left+this.__charBounds[m][b-1].width-I}x=S,(this.lineHeight<1||_===m&&this.lineHeight>1)&&(S/=this.lineHeight);var D=a.left+w+T,N=E-T,L=S,F=0;this.inCompositionMode?(u.fillStyle=this.compositionColor||"black",L=1,F=S):u.fillStyle=this.selectionColor,this.direction==="rtl"&&(D=this.width-D-N),u.fillRect(D,a.top+a.topOffset+F,N,L),a.topOffset+=x}},getCurrentCharFontSize:function(){var a=this._getCurrentCharIndex();return this.getValueOfPropertyAt(a.l,a.c,"fontSize")},getCurrentCharColor:function(){var a=this._getCurrentCharIndex();return this.getValueOfPropertyAt(a.l,a.c,"fill")},_getCurrentCharIndex:function(){var a=this.get2DCursorLocation(this.selectionStart,!0),u=a.charIndex>0?a.charIndex-1:0;return{l:a.lineIndex,c:u}}}),t.IText.fromObject=function(a,u){var d=t.util.stylesFromArray(a.styles,a.text),c=Object.assign({},a,{styles:d});if(o(c),c.styles)for(var f in c.styles)for(var s in c.styles[f])o(c.styles[f][s]);t.Object._fromObject("IText",c,u,"text")}}(),function(){var o=t.util.object.clone;t.util.object.extend(t.IText.prototype,{initBehavior:function(){this.initAddedHandler(),this.initRemovedHandler(),this.initCursorSelectionHandlers(),this.initDoubleClickSimulation(),this.mouseMoveHandler=this.mouseMoveHandler.bind(this)},onDeselect:function(){this.isEditing&&this.exitEditing(),this.selected=!1},initAddedHandler:function(){var a=this;this.on("added",function(){var u=a.canvas;u&&(u._hasITextHandlers||(u._hasITextHandlers=!0,a._initCanvasHandlers(u)),u._iTextInstances=u._iTextInstances||[],u._iTextInstances.push(a))})},initRemovedHandler:function(){var a=this;this.on("removed",function(){var u=a.canvas;u&&(u._iTextInstances=u._iTextInstances||[],t.util.removeFromArray(u._iTextInstances,a),u._iTextInstances.length===0&&(u._hasITextHandlers=!1,a._removeCanvasHandlers(u)))})},_initCanvasHandlers:function(a){a._mouseUpITextHandler=function(){a._iTextInstances&&a._iTextInstances.forEach(function(u){u.__isMousedown=!1})},a.on("mouse:up",a._mouseUpITextHandler)},_removeCanvasHandlers:function(a){a.off("mouse:up",a._mouseUpITextHandler)},_tick:function(){this._currentTickState=this._animateCursor(this,1,this.cursorDuration,"_onTickComplete")},_animateCursor:function(a,u,d,c){var f;return f={isAborted:!1,abort:function(){this.isAborted=!0}},a.animate("_currentCursorOpacity",u,{duration:d,onComplete:function(){f.isAborted||a[c]()},onChange:function(){a.canvas&&a.selectionStart===a.selectionEnd&&a.renderCursorOrSelection()},abort:function(){return f.isAborted}}),f},_onTickComplete:function(){var a=this;this._cursorTimeout1&&clearTimeout(this._cursorTimeout1),this._cursorTimeout1=setTimeout(function(){a._currentTickCompleteState=a._animateCursor(a,0,this.cursorDuration/2,"_tick")},100)},initDelayedCursor:function(a){var u=this,d=a?0:this.cursorDelay;this.abortCursorAnimation(),this._currentCursorOpacity=1,this._cursorTimeout2=setTimeout(function(){u._tick()},d)},abortCursorAnimation:function(){var a=this._currentTickState||this._currentTickCompleteState,u=this.canvas;this._currentTickState&&this._currentTickState.abort(),this._currentTickCompleteState&&this._currentTickCompleteState.abort(),clearTimeout(this._cursorTimeout1),clearTimeout(this._cursorTimeout2),this._currentCursorOpacity=0,a&&u&&u.clearContext(u.contextTop||u.contextContainer)},selectAll:function(){return this.selectionStart=0,this.selectionEnd=this._text.length,this._fireSelectionChanged(),this._updateTextarea(),this},getSelectedText:function(){return this._text.slice(this.selectionStart,this.selectionEnd).join("")},findWordBoundaryLeft:function(a){var u=0,d=a-1;if(this._reSpace.test(this._text[d]))for(;this._reSpace.test(this._text[d]);)u++,d--;for(;/\S/.test(this._text[d])&&d>-1;)u++,d--;return a-u},findWordBoundaryRight:function(a){var u=0,d=a;if(this._reSpace.test(this._text[d]))for(;this._reSpace.test(this._text[d]);)u++,d++;for(;/\S/.test(this._text[d])&&d<this._text.length;)u++,d++;return a+u},findLineBoundaryLeft:function(a){for(var u=0,d=a-1;!/\n/.test(this._text[d])&&d>-1;)u++,d--;return a-u},findLineBoundaryRight:function(a){for(var u=0,d=a;!/\n/.test(this._text[d])&&d<this._text.length;)u++,d++;return a+u},searchWordBoundary:function(a,u){for(var d=this._text,c=this._reSpace.test(d[a])?a-1:a,f=d[c],s=t.reNonWord;!s.test(f)&&c>0&&c<d.length;)c+=u,f=d[c];return s.test(f)&&(c+=u===1?0:1),c},selectWord:function(a){a=a||this.selectionStart;var u=this.searchWordBoundary(a,-1),d=this.searchWordBoundary(a,1);this.selectionStart=u,this.selectionEnd=d,this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()},selectLine:function(a){a=a||this.selectionStart;var u=this.findLineBoundaryLeft(a),d=this.findLineBoundaryRight(a);return this.selectionStart=u,this.selectionEnd=d,this._fireSelectionChanged(),this._updateTextarea(),this},enterEditing:function(a){if(!(this.isEditing||!this.editable))return this.canvas&&(this.canvas.calcOffset(),this.exitEditingOnOthers(this.canvas)),this.isEditing=!0,this.initHiddenTextarea(a),this.hiddenTextarea.focus(),this.hiddenTextarea.value=this.text,this._updateTextarea(),this._saveEditingProps(),this._setEditingProps(),this._textBeforeEdit=this.text,this._tick(),this.fire("editing:entered"),this._fireSelectionChanged(),this.canvas?(this.canvas.fire("text:editing:entered",{target:this}),this.initMouseMoveHandler(),this.canvas.requestRenderAll(),this):this},exitEditingOnOthers:function(a){a._iTextInstances&&a._iTextInstances.forEach(function(u){u.selected=!1,u.isEditing&&u.exitEditing()})},initMouseMoveHandler:function(){this.canvas.on("mouse:move",this.mouseMoveHandler)},mouseMoveHandler:function(a){if(!(!this.__isMousedown||!this.isEditing)){document.activeElement!==this.hiddenTextarea&&this.hiddenTextarea.focus();var u=this.getSelectionStartFromPointer(a.e),d=this.selectionStart,c=this.selectionEnd;(u!==this.__selectionStartOnMouseDown||d===c)&&(d===u||c===u)||(u>this.__selectionStartOnMouseDown?(this.selectionStart=this.__selectionStartOnMouseDown,this.selectionEnd=u):(this.selectionStart=u,this.selectionEnd=this.__selectionStartOnMouseDown),(this.selectionStart!==d||this.selectionEnd!==c)&&(this.restartCursorIfNeeded(),this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()))}},_setEditingProps:function(){this.hoverCursor="text",this.canvas&&(this.canvas.defaultCursor=this.canvas.moveCursor="text"),this.borderColor=this.editingBorderColor,this.hasControls=this.selectable=!1,this.lockMovementX=this.lockMovementY=!0},fromStringToGraphemeSelection:function(a,u,d){var c=d.slice(0,a),f=t.util.string.graphemeSplit(c).length;if(a===u)return{selectionStart:f,selectionEnd:f};var s=d.slice(a,u),h=t.util.string.graphemeSplit(s).length;return{selectionStart:f,selectionEnd:f+h}},fromGraphemeToStringSelection:function(a,u,d){var c=d.slice(0,a),f=c.join("").length;if(a===u)return{selectionStart:f,selectionEnd:f};var s=d.slice(a,u),h=s.join("").length;return{selectionStart:f,selectionEnd:f+h}},_updateTextarea:function(){if(this.cursorOffsetCache={},!!this.hiddenTextarea){if(!this.inCompositionMode){var a=this.fromGraphemeToStringSelection(this.selectionStart,this.selectionEnd,this._text);this.hiddenTextarea.selectionStart=a.selectionStart,this.hiddenTextarea.selectionEnd=a.selectionEnd}this.updateTextareaPosition()}},updateFromTextArea:function(){if(!!this.hiddenTextarea){this.cursorOffsetCache={},this.text=this.hiddenTextarea.value,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords());var a=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value);this.selectionEnd=this.selectionStart=a.selectionEnd,this.inCompositionMode||(this.selectionStart=a.selectionStart),this.updateTextareaPosition()}},updateTextareaPosition:function(){if(this.selectionStart===this.selectionEnd){var a=this._calcTextareaPosition();this.hiddenTextarea.style.left=a.left,this.hiddenTextarea.style.top=a.top}},_calcTextareaPosition:function(){if(!this.canvas)return{x:1,y:1};var a=this.inCompositionMode?this.compositionStart:this.selectionStart,u=this._getCursorBoundaries(a),d=this.get2DCursorLocation(a),c=d.lineIndex,f=d.charIndex,s=this.getValueOfPropertyAt(c,f,"fontSize")*this.lineHeight,h=u.leftOffset,v=this.calcTransformMatrix(),m={x:u.left+h,y:u.top+u.topOffset+s},y=this.canvas.getRetinaScaling(),b=this.canvas.upperCanvasEl,_=b.width/y,w=b.height/y,S=_-s,x=w-s,T=b.clientWidth/_,E=b.clientHeight/w;return m=t.util.transformPoint(m,v),m=t.util.transformPoint(m,this.canvas.viewportTransform),m.x*=T,m.y*=E,m.x<0&&(m.x=0),m.x>S&&(m.x=S),m.y<0&&(m.y=0),m.y>x&&(m.y=x),m.x+=this.canvas._offset.left,m.y+=this.canvas._offset.top,{left:m.x+"px",top:m.y+"px",fontSize:s+"px",charHeight:s}},_saveEditingProps:function(){this._savedProps={hasControls:this.hasControls,borderColor:this.borderColor,lockMovementX:this.lockMovementX,lockMovementY:this.lockMovementY,hoverCursor:this.hoverCursor,selectable:this.selectable,defaultCursor:this.canvas&&this.canvas.defaultCursor,moveCursor:this.canvas&&this.canvas.moveCursor}},_restoreEditingProps:function(){!this._savedProps||(this.hoverCursor=this._savedProps.hoverCursor,this.hasControls=this._savedProps.hasControls,this.borderColor=this._savedProps.borderColor,this.selectable=this._savedProps.selectable,this.lockMovementX=this._savedProps.lockMovementX,this.lockMovementY=this._savedProps.lockMovementY,this.canvas&&(this.canvas.defaultCursor=this._savedProps.defaultCursor,this.canvas.moveCursor=this._savedProps.moveCursor))},exitEditing:function(){var a=this._textBeforeEdit!==this.text,u=this.hiddenTextarea;return this.selected=!1,this.isEditing=!1,this.selectionEnd=this.selectionStart,u&&(u.blur&&u.blur(),u.parentNode&&u.parentNode.removeChild(u)),this.hiddenTextarea=null,this.abortCursorAnimation(),this._restoreEditingProps(),this._currentCursorOpacity=0,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this.fire("editing:exited"),a&&this.fire("modified"),this.canvas&&(this.canvas.off("mouse:move",this.mouseMoveHandler),this.canvas.fire("text:editing:exited",{target:this}),a&&this.canvas.fire("object:modified",{target:this})),this},_removeExtraneousStyles:function(){for(var a in this.styles)this._textLines[a]||delete this.styles[a]},removeStyleFromTo:function(a,u){var d=this.get2DCursorLocation(a,!0),c=this.get2DCursorLocation(u,!0),f=d.lineIndex,s=d.charIndex,h=c.lineIndex,v=c.charIndex,m,y;if(f!==h){if(this.styles[f])for(m=s;m<this._unwrappedTextLines[f].length;m++)delete this.styles[f][m];if(this.styles[h])for(m=v;m<this._unwrappedTextLines[h].length;m++)y=this.styles[h][m],y&&(this.styles[f]||(this.styles[f]={}),this.styles[f][s+m-v]=y);for(m=f+1;m<=h;m++)delete this.styles[m];this.shiftLineStyles(h,f-h)}else if(this.styles[f]){y=this.styles[f];var b=v-s,_,w;for(m=s;m<v;m++)delete y[m];for(w in this.styles[f])_=parseInt(w,10),_>=v&&(y[_-b]=y[w],delete y[w])}},shiftLineStyles:function(a,u){var d=o(this.styles);for(var c in this.styles){var f=parseInt(c,10);f>a&&(this.styles[f+u]=d[f],d[f-u]||delete this.styles[f])}},restartCursorIfNeeded:function(){(!this._currentTickState||this._currentTickState.isAborted||!this._currentTickCompleteState||this._currentTickCompleteState.isAborted)&&this.initDelayedCursor()},insertNewlineStyleObject:function(a,u,d,c){var f,s={},h=!1,v=this._unwrappedTextLines[a].length===u;d||(d=1),this.shiftLineStyles(a,d),this.styles[a]&&(f=this.styles[a][u===0?u:u-1]);for(var m in this.styles[a]){var y=parseInt(m,10);y>=u&&(h=!0,s[y-u]=this.styles[a][m],v&&u===0||delete this.styles[a][m])}var b=!1;for(h&&!v&&(this.styles[a+d]=s,b=!0),b&&d--;d>0;)c&&c[d-1]?this.styles[a+d]={0:o(c[d-1])}:f?this.styles[a+d]={0:o(f)}:delete this.styles[a+d],d--;this._forceClearCache=!0},insertCharStyleObject:function(a,u,d,c){this.styles||(this.styles={});var f=this.styles[a],s=f?o(f):{};d||(d=1);for(var h in s){var v=parseInt(h,10);v>=u&&(f[v+d]=s[v],s[v-d]||delete f[v])}if(this._forceClearCache=!0,c){for(;d--;)!Object.keys(c[d]).length||(this.styles[a]||(this.styles[a]={}),this.styles[a][u+d]=o(c[d]));return}if(!!f)for(var m=f[u?u-1:1];m&&d--;)this.styles[a][u+d]=o(m)},insertNewStyleBlock:function(a,u,d){for(var c=this.get2DCursorLocation(u,!0),f=[0],s=0,h=0;h<a.length;h++)a[h]===`
+}`,gamma:[1,1,1],mainParameter:"gamma",initialize:function(c){this.gamma=[1,1,1],u.BaseFilter.prototype.initialize.call(this,c)},applyTo2d:function(c){var f=c.imageData,s=f.data,h=this.gamma,v=s.length,m=1/h[0],y=1/h[1],b=1/h[2],_;for(this.rVals||(this.rVals=new Uint8Array(256),this.gVals=new Uint8Array(256),this.bVals=new Uint8Array(256)),_=0,v=256;_<v;_++)this.rVals[_]=Math.pow(_/255,m)*255,this.gVals[_]=Math.pow(_/255,y)*255,this.bVals[_]=Math.pow(_/255,b)*255;for(_=0,v=s.length;_<v;_+=4)s[_]=this.rVals[s[_]],s[_+1]=this.gVals[s[_+1]],s[_+2]=this.bVals[s[_+2]]},getUniformLocations:function(c,f){return{uGamma:c.getUniformLocation(f,"uGamma")}},sendUniformData:function(c,f){c.uniform3fv(f.uGamma,this.gamma)}}),a.Image.filters.Gamma.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.Composed=d(u.BaseFilter,{type:"Composed",subFilters:[],initialize:function(c){this.callSuper("initialize",c),this.subFilters=this.subFilters.slice(0)},applyTo:function(c){c.passes+=this.subFilters.length-1,this.subFilters.forEach(function(f){f.applyTo(c)})},toObject:function(){return a.util.object.extend(this.callSuper("toObject"),{subFilters:this.subFilters.map(function(c){return c.toObject()})})},isNeutralState:function(){return!this.subFilters.some(function(c){return!c.isNeutralState()})}}),a.Image.filters.Composed.fromObject=function(c,f){var s=c.subFilters||[],h=s.map(function(m){return new a.Image.filters[m.type](m)}),v=new a.Image.filters.Composed({subFilters:h});return f&&f(v),v}}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.Image.filters,d=a.util.createClass;u.HueRotation=d(u.ColorMatrix,{type:"HueRotation",rotation:0,mainParameter:"rotation",calculateMatrix:function(){var c=this.rotation*Math.PI,f=a.util.cos(c),s=a.util.sin(c),h=1/3,v=Math.sqrt(h)*s,m=1-f;this.matrix=[1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0],this.matrix[0]=f+m/3,this.matrix[1]=h*m-v,this.matrix[2]=h*m+v,this.matrix[5]=h*m+v,this.matrix[6]=f+h*m,this.matrix[7]=h*m-v,this.matrix[10]=h*m-v,this.matrix[11]=h*m+v,this.matrix[12]=f+h*m},isNeutralState:function(c){return this.calculateMatrix(),u.BaseFilter.prototype.isNeutralState.call(this,c)},applyTo:function(c){this.calculateMatrix(),u.BaseFilter.prototype.applyTo.call(this,c)}}),a.Image.filters.HueRotation.fromObject=a.Image.filters.BaseFilter.fromObject}(e),function(o){var a=o.fabric||(o.fabric={}),u=a.util.object.clone;if(a.Text){a.warn("fabric.Text is already defined");return}var d="fontFamily fontWeight fontSize text underline overline linethrough textAlign fontStyle lineHeight textBackgroundColor charSpacing styles direction path pathStartOffset pathSide pathAlign".split(" ");a.Text=a.util.createClass(a.Object,{_dimensionAffectingProps:["fontSize","fontWeight","fontFamily","fontStyle","lineHeight","text","charSpacing","textAlign","styles","path","pathStartOffset","pathSide","pathAlign"],_reNewline:/\r?\n/,_reSpacesAndTabs:/[ \t\r]/g,_reSpaceAndTab:/[ \t\r]/,_reWords:/\S+/g,type:"text",fontSize:40,fontWeight:"normal",fontFamily:"Times New Roman",underline:!1,overline:!1,linethrough:!1,textAlign:"left",fontStyle:"normal",lineHeight:1.16,superscript:{size:.6,baseline:-.35},subscript:{size:.6,baseline:.11},textBackgroundColor:"",stateProperties:a.Object.prototype.stateProperties.concat(d),cacheProperties:a.Object.prototype.cacheProperties.concat(d),stroke:null,shadow:null,path:null,pathStartOffset:0,pathSide:"left",pathAlign:"baseline",_fontSizeFraction:.222,offsets:{underline:.1,linethrough:-.315,overline:-.88},_fontSizeMult:1.13,charSpacing:0,styles:null,_measuringContext:null,deltaY:0,direction:"ltr",_styleProperties:["stroke","strokeWidth","fill","fontFamily","fontSize","fontWeight","fontStyle","underline","overline","linethrough","deltaY","textBackgroundColor"],__charBounds:[],CACHE_FONT_SIZE:400,MIN_TEXT_WIDTH:2,initialize:function(c,f){this.styles=f?f.styles||{}:{},this.text=c,this.__skipDimension=!0,this.callSuper("initialize",f),this.path&&this.setPathInfo(),this.__skipDimension=!1,this.initDimensions(),this.setCoords(),this.setupState({propertySet:"_dimensionAffectingProps"})},setPathInfo:function(){var c=this.path;c&&(c.segmentsInfo=a.util.getPathSegmentsInfo(c.path))},getMeasuringContext:function(){return a._measuringContext||(a._measuringContext=this.canvas&&this.canvas.contextCache||a.util.createCanvasElement().getContext("2d")),a._measuringContext},_splitText:function(){var c=this._splitTextIntoLines(this.text);return this.textLines=c.lines,this._textLines=c.graphemeLines,this._unwrappedTextLines=c._unwrappedLines,this._text=c.graphemeText,c},initDimensions:function(){this.__skipDimension||(this._splitText(),this._clearCache(),this.path?(this.width=this.path.width,this.height=this.path.height):(this.width=this.calcTextWidth()||this.cursorWidth||this.MIN_TEXT_WIDTH,this.height=this.calcTextHeight()),this.textAlign.indexOf("justify")!==-1&&this.enlargeSpaces(),this.saveState({propertySet:"_dimensionAffectingProps"}))},enlargeSpaces:function(){for(var c,f,s,h,v,m,y,b=0,_=this._textLines.length;b<_;b++)if(!(this.textAlign!=="justify"&&(b===_-1||this.isEndOfWrapping(b)))&&(h=0,v=this._textLines[b],f=this.getLineWidth(b),f<this.width&&(y=this.textLines[b].match(this._reSpacesAndTabs)))){s=y.length,c=(this.width-f)/s;for(var w=0,S=v.length;w<=S;w++)m=this.__charBounds[b][w],this._reSpaceAndTab.test(v[w])?(m.width+=c,m.kernedWidth+=c,m.left+=h,h+=c):m.left+=h}},isEndOfWrapping:function(c){return c===this._textLines.length-1},missingNewlineOffset:function(){return 1},toString:function(){return"#<fabric.Text ("+this.complexity()+'): { "text": "'+this.text+'", "fontFamily": "'+this.fontFamily+'" }>'},_getCacheCanvasDimensions:function(){var c=this.callSuper("_getCacheCanvasDimensions"),f=this.fontSize;return c.width+=f*c.zoomX,c.height+=f*c.zoomY,c},_render:function(c){var f=this.path;f&&!f.isNotVisible()&&f._render(c),this._setTextStyles(c),this._renderTextLinesBackground(c),this._renderTextDecoration(c,"underline"),this._renderText(c),this._renderTextDecoration(c,"overline"),this._renderTextDecoration(c,"linethrough")},_renderText:function(c){this.paintFirst==="stroke"?(this._renderTextStroke(c),this._renderTextFill(c)):(this._renderTextFill(c),this._renderTextStroke(c))},_setTextStyles:function(c,f,s){if(c.textBaseline="alphabetical",this.path)switch(this.pathAlign){case"center":c.textBaseline="middle";break;case"ascender":c.textBaseline="top";break;case"descender":c.textBaseline="bottom";break}c.font=this._getFontDeclaration(f,s)},calcTextWidth:function(){for(var c=this.getLineWidth(0),f=1,s=this._textLines.length;f<s;f++){var h=this.getLineWidth(f);h>c&&(c=h)}return c},_renderTextLine:function(c,f,s,h,v,m){this._renderChars(c,f,s,h,v,m)},_renderTextLinesBackground:function(c){if(!(!this.textBackgroundColor&&!this.styleHas("textBackgroundColor"))){for(var f,s,h=c.fillStyle,v,m,y=this._getLeftOffset(),b=this._getTopOffset(),_=0,w=0,S,x,k=this.path,E,I=0,D=this._textLines.length;I<D;I++){if(f=this.getHeightOfLine(I),!this.textBackgroundColor&&!this.styleHas("textBackgroundColor",I)){b+=f;continue}v=this._textLines[I],s=this._getLineLeftOffset(I),w=0,_=0,m=this.getValueOfPropertyAt(I,0,"textBackgroundColor");for(var N=0,L=v.length;N<L;N++)S=this.__charBounds[I][N],x=this.getValueOfPropertyAt(I,N,"textBackgroundColor"),k?(c.save(),c.translate(S.renderLeft,S.renderTop),c.rotate(S.angle),c.fillStyle=x,x&&c.fillRect(-S.width/2,-f/this.lineHeight*(1-this._fontSizeFraction),S.width,f/this.lineHeight),c.restore()):x!==m?(E=y+s+_,this.direction==="rtl"&&(E=this.width-E-w),c.fillStyle=m,m&&c.fillRect(E,b,w,f/this.lineHeight),_=S.left,w=S.width,m=x):w+=S.kernedWidth;x&&!k&&(E=y+s+_,this.direction==="rtl"&&(E=this.width-E-w),c.fillStyle=x,c.fillRect(E,b,w,f/this.lineHeight)),b+=f}c.fillStyle=h,this._removeShadow(c)}},getFontCache:function(c){var f=c.fontFamily.toLowerCase();a.charWidthsCache[f]||(a.charWidthsCache[f]={});var s=a.charWidthsCache[f],h=c.fontStyle.toLowerCase()+"_"+(c.fontWeight+"").toLowerCase();return s[h]||(s[h]={}),s[h]},_measureChar:function(c,f,s,h){var v=this.getFontCache(f),m=this._getFontDeclaration(f),y=this._getFontDeclaration(h),b=s+c,_=m===y,w,S,x,k=f.fontSize/this.CACHE_FONT_SIZE,E;if(s&&v[s]!==void 0&&(x=v[s]),v[c]!==void 0&&(E=w=v[c]),_&&v[b]!==void 0&&(S=v[b],E=S-x),w===void 0||x===void 0||S===void 0){var I=this.getMeasuringContext();this._setTextStyles(I,f,!0)}return w===void 0&&(E=w=I.measureText(c).width,v[c]=w),x===void 0&&_&&s&&(x=I.measureText(s).width,v[s]=x),_&&S===void 0&&(S=I.measureText(b).width,v[b]=S,E=S-x),{width:w*k,kernedWidth:E*k}},getHeightOfChar:function(c,f){return this.getValueOfPropertyAt(c,f,"fontSize")},measureLine:function(c){var f=this._measureLine(c);return this.charSpacing!==0&&(f.width-=this._getWidthOfCharSpacing()),f.width<0&&(f.width=0),f},_measureLine:function(c){var f=0,s,h,v=this._textLines[c],m,y,b=0,_=new Array(v.length),w=0,S,x,k=this.path,E=this.pathSide==="right";for(this.__charBounds[c]=_,s=0;s<v.length;s++)h=v[s],y=this._getGraphemeBox(h,c,s,m),_[s]=y,f+=y.kernedWidth,m=h;if(_[s]={left:y?y.left+y.width:0,width:0,kernedWidth:0,height:this.fontSize},k){switch(x=k.segmentsInfo[k.segmentsInfo.length-1].length,S=a.util.getPointOnPath(k.path,0,k.segmentsInfo),S.x+=k.pathOffset.x,S.y+=k.pathOffset.y,this.textAlign){case"left":w=E?x-f:0;break;case"center":w=(x-f)/2;break;case"right":w=E?0:x-f;break}for(w+=this.pathStartOffset*(E?-1:1),s=E?v.length-1:0;E?s>=0:s<v.length;E?s--:s++)y=_[s],w>x?w%=x:w<0&&(w+=x),this._setGraphemeOnPath(w,y,S),w+=y.kernedWidth}return{width:f,numOfSpaces:b}},_setGraphemeOnPath:function(c,f,s){var h=c+f.kernedWidth/2,v=this.path,m=a.util.getPointOnPath(v.path,h,v.segmentsInfo);f.renderLeft=m.x-s.x,f.renderTop=m.y-s.y,f.angle=m.angle+(this.pathSide==="right"?Math.PI:0)},_getGraphemeBox:function(c,f,s,h,v){var m=this.getCompleteStyleDeclaration(f,s),y=h?this.getCompleteStyleDeclaration(f,s-1):{},b=this._measureChar(c,m,h,y),_=b.kernedWidth,w=b.width,S;this.charSpacing!==0&&(S=this._getWidthOfCharSpacing(),w+=S,_+=S);var x={width:w,left:0,height:m.fontSize,kernedWidth:_,deltaY:m.deltaY};if(s>0&&!v){var k=this.__charBounds[f][s-1];x.left=k.left+k.width+b.kernedWidth-b.width}return x},getHeightOfLine:function(c){if(this.__lineHeights[c])return this.__lineHeights[c];for(var f=this._textLines[c],s=this.getHeightOfChar(c,0),h=1,v=f.length;h<v;h++)s=Math.max(this.getHeightOfChar(c,h),s);return this.__lineHeights[c]=s*this.lineHeight*this._fontSizeMult},calcTextHeight:function(){for(var c,f=0,s=0,h=this._textLines.length;s<h;s++)c=this.getHeightOfLine(s),f+=s===h-1?c/this.lineHeight:c;return f},_getLeftOffset:function(){return this.direction==="ltr"?-this.width/2:this.width/2},_getTopOffset:function(){return-this.height/2},_renderTextCommon:function(c,f){c.save();for(var s=0,h=this._getLeftOffset(),v=this._getTopOffset(),m=0,y=this._textLines.length;m<y;m++){var b=this.getHeightOfLine(m),_=b/this.lineHeight,w=this._getLineLeftOffset(m);this._renderTextLine(f,c,this._textLines[m],h+w,v+s+_,m),s+=b}c.restore()},_renderTextFill:function(c){!this.fill&&!this.styleHas("fill")||this._renderTextCommon(c,"fillText")},_renderTextStroke:function(c){(!this.stroke||this.strokeWidth===0)&&this.isEmptyStyles()||(this.shadow&&!this.shadow.affectStroke&&this._removeShadow(c),c.save(),this._setLineDash(c,this.strokeDashArray),c.beginPath(),this._renderTextCommon(c,"strokeText"),c.closePath(),c.restore())},_renderChars:function(c,f,s,h,v,m){var y=this.getHeightOfLine(m),b=this.textAlign.indexOf("justify")!==-1,_,w,S="",x,k=0,E,I=this.path,D=!b&&this.charSpacing===0&&this.isEmptyStyles(m)&&!I,N=this.direction==="ltr",L=this.direction==="ltr"?1:-1,F,O=f.canvas.getAttribute("dir");if(f.save(),O!==this.direction&&(f.canvas.setAttribute("dir",N?"ltr":"rtl"),f.direction=N?"ltr":"rtl",f.textAlign=N?"left":"right"),v-=y*this._fontSizeFraction/this.lineHeight,D){this._renderChar(c,f,m,0,s.join(""),h,v,y),f.restore();return}for(var V=0,re=s.length-1;V<=re;V++)E=V===re||this.charSpacing||I,S+=s[V],x=this.__charBounds[m][V],k===0?(h+=L*(x.kernedWidth-x.width),k+=x.width):k+=x.kernedWidth,b&&!E&&this._reSpaceAndTab.test(s[V])&&(E=!0),E||(_=_||this.getCompleteStyleDeclaration(m,V),w=this.getCompleteStyleDeclaration(m,V+1),E=a.util.hasStyleChanged(_,w,!1)),E&&(I?(f.save(),f.translate(x.renderLeft,x.renderTop),f.rotate(x.angle),this._renderChar(c,f,m,V,S,-k/2,0,y),f.restore()):(F=h,this._renderChar(c,f,m,V,S,F,v,y)),S="",_=w,h+=L*k,k=0);f.restore()},_applyPatternGradientTransformText:function(c){var f=a.util.createCanvasElement(),s,h=this.width+this.strokeWidth,v=this.height+this.strokeWidth;return f.width=h,f.height=v,s=f.getContext("2d"),s.beginPath(),s.moveTo(0,0),s.lineTo(h,0),s.lineTo(h,v),s.lineTo(0,v),s.closePath(),s.translate(h/2,v/2),s.fillStyle=c.toLive(s),this._applyPatternGradientTransform(s,c),s.fill(),s.createPattern(f,"no-repeat")},handleFiller:function(c,f,s){var h,v;return s.toLive?s.gradientUnits==="percentage"||s.gradientTransform||s.patternTransform?(h=-this.width/2,v=-this.height/2,c.translate(h,v),c[f]=this._applyPatternGradientTransformText(s),{offsetX:h,offsetY:v}):(c[f]=s.toLive(c,this),this._applyPatternGradientTransform(c,s)):(c[f]=s,{offsetX:0,offsetY:0})},_setStrokeStyles:function(c,f){return c.lineWidth=f.strokeWidth,c.lineCap=this.strokeLineCap,c.lineDashOffset=this.strokeDashOffset,c.lineJoin=this.strokeLineJoin,c.miterLimit=this.strokeMiterLimit,this.handleFiller(c,"strokeStyle",f.stroke)},_setFillStyles:function(c,f){return this.handleFiller(c,"fillStyle",f.fill)},_renderChar:function(c,f,s,h,v,m,y){var b=this._getStyleDeclaration(s,h),_=this.getCompleteStyleDeclaration(s,h),w=c==="fillText"&&_.fill,S=c==="strokeText"&&_.stroke&&_.strokeWidth,x,k;!S&&!w||(f.save(),w&&(x=this._setFillStyles(f,_)),S&&(k=this._setStrokeStyles(f,_)),f.font=this._getFontDeclaration(_),b&&b.textBackgroundColor&&this._removeShadow(f),b&&b.deltaY&&(y+=b.deltaY),w&&f.fillText(v,m-x.offsetX,y-x.offsetY),S&&f.strokeText(v,m-k.offsetX,y-k.offsetY),f.restore())},setSuperscript:function(c,f){return this._setScript(c,f,this.superscript)},setSubscript:function(c,f){return this._setScript(c,f,this.subscript)},_setScript:function(c,f,s){var h=this.get2DCursorLocation(c,!0),v=this.getValueOfPropertyAt(h.lineIndex,h.charIndex,"fontSize"),m=this.getValueOfPropertyAt(h.lineIndex,h.charIndex,"deltaY"),y={fontSize:v*s.size,deltaY:m+v*s.baseline};return this.setSelectionStyles(y,c,f),this},_getLineLeftOffset:function(c){var f=this.getLineWidth(c),s=this.width-f,h=this.textAlign,v=this.direction,y,m=0,y=this.isEndOfWrapping(c);return h==="justify"||h==="justify-center"&&!y||h==="justify-right"&&!y||h==="justify-left"&&!y?0:(h==="center"&&(m=s/2),h==="right"&&(m=s),h==="justify-center"&&(m=s/2),h==="justify-right"&&(m=s),v==="rtl"&&(m-=s),m)},_clearCache:function(){this.__lineWidths=[],this.__lineHeights=[],this.__charBounds=[]},_shouldClearDimensionCache:function(){var c=this._forceClearCache;return c||(c=this.hasStateChanged("_dimensionAffectingProps")),c&&(this.dirty=!0,this._forceClearCache=!1),c},getLineWidth:function(c){if(this.__lineWidths[c]!==void 0)return this.__lineWidths[c];var f=this.measureLine(c),s=f.width;return this.__lineWidths[c]=s,s},_getWidthOfCharSpacing:function(){return this.charSpacing!==0?this.fontSize*this.charSpacing/1e3:0},getValueOfPropertyAt:function(c,f,s){var h=this._getStyleDeclaration(c,f);return h&&typeof h[s]<"u"?h[s]:this[s]},_renderTextDecoration:function(c,f){if(!(!this[f]&&!this.styleHas(f))){for(var s,h,v,m,y,b,_,w,S=this._getLeftOffset(),x=this._getTopOffset(),k,E,I,D,N,L,F,O,V=this.path,re=this._getWidthOfCharSpacing(),J=this.offsets[f],ue=0,M=this._textLines.length;ue<M;ue++){if(s=this.getHeightOfLine(ue),!this[f]&&!this.styleHas(f,ue)){x+=s;continue}_=this._textLines[ue],L=s/this.lineHeight,m=this._getLineLeftOffset(ue),E=0,I=0,w=this.getValueOfPropertyAt(ue,0,f),O=this.getValueOfPropertyAt(ue,0,"fill"),k=x+L*(1-this._fontSizeFraction),h=this.getHeightOfChar(ue,0),y=this.getValueOfPropertyAt(ue,0,"deltaY");for(var H=0,R=_.length;H<R;H++)if(D=this.__charBounds[ue][H],N=this.getValueOfPropertyAt(ue,H,f),F=this.getValueOfPropertyAt(ue,H,"fill"),v=this.getHeightOfChar(ue,H),b=this.getValueOfPropertyAt(ue,H,"deltaY"),V&&N&&F)c.save(),c.fillStyle=O,c.translate(D.renderLeft,D.renderTop),c.rotate(D.angle),c.fillRect(-D.kernedWidth/2,J*v+b,D.kernedWidth,this.fontSize/15),c.restore();else if((N!==w||F!==O||v!==h||b!==y)&&I>0){var P=S+m+E;this.direction==="rtl"&&(P=this.width-P-I),w&&O&&(c.fillStyle=O,c.fillRect(P,k+J*h+y,I,this.fontSize/15)),E=D.left,I=D.width,w=N,O=F,h=v,y=b}else I+=D.kernedWidth;var P=S+m+E;this.direction==="rtl"&&(P=this.width-P-I),c.fillStyle=F,N&&F&&c.fillRect(P,k+J*h+y,I-re,this.fontSize/15),x+=s}this._removeShadow(c)}},_getFontDeclaration:function(c,f){var s=c||this,h=this.fontFamily,v=a.Text.genericFonts.indexOf(h.toLowerCase())>-1,m=h===void 0||h.indexOf("'")>-1||h.indexOf(",")>-1||h.indexOf('"')>-1||v?s.fontFamily:'"'+s.fontFamily+'"';return[a.isLikelyNode?s.fontWeight:s.fontStyle,a.isLikelyNode?s.fontStyle:s.fontWeight,f?this.CACHE_FONT_SIZE+"px":s.fontSize+"px",m].join(" ")},render:function(c){!this.visible||this.canvas&&this.canvas.skipOffscreen&&!this.group&&!this.isOnScreen()||(this._shouldClearDimensionCache()&&this.initDimensions(),this.callSuper("render",c))},_splitTextIntoLines:function(c){for(var f=c.split(this._reNewline),s=new Array(f.length),h=[`
+`],v=[],m=0;m<f.length;m++)s[m]=a.util.string.graphemeSplit(f[m]),v=v.concat(s[m],h);return v.pop(),{_unwrappedLines:s,lines:f,graphemeText:v,graphemeLines:s}},toObject:function(c){var f=d.concat(c),s=this.callSuper("toObject",f);return s.styles=a.util.stylesToArray(this.styles,this.text),s.path&&(s.path=this.path.toObject()),s},set:function(c,f){this.callSuper("set",c,f);var s=!1,h=!1;if(typeof c=="object")for(var v in c)v==="path"&&this.setPathInfo(),s=s||this._dimensionAffectingProps.indexOf(v)!==-1,h=h||v==="path";else s=this._dimensionAffectingProps.indexOf(c)!==-1,h=c==="path";return h&&this.setPathInfo(),s&&(this.initDimensions(),this.setCoords()),this},complexity:function(){return 1}}),a.Text.ATTRIBUTE_NAMES=a.SHARED_ATTRIBUTES.concat("x y dx dy font-family font-style font-weight font-size letter-spacing text-decoration text-anchor".split(" ")),a.Text.DEFAULT_SVG_FONT_SIZE=16,a.Text.fromElement=function(c,f,s){if(!c)return f(null);var h=a.parseAttributes(c,a.Text.ATTRIBUTE_NAMES),v=h.textAnchor||"left";if(s=a.util.object.extend(s?u(s):{},h),s.top=s.top||0,s.left=s.left||0,h.textDecoration){var m=h.textDecoration;m.indexOf("underline")!==-1&&(s.underline=!0),m.indexOf("overline")!==-1&&(s.overline=!0),m.indexOf("line-through")!==-1&&(s.linethrough=!0),delete s.textDecoration}"dx"in h&&(s.left+=h.dx),"dy"in h&&(s.top+=h.dy),"fontSize"in s||(s.fontSize=a.Text.DEFAULT_SVG_FONT_SIZE);var y="";"textContent"in c?y=c.textContent:"firstChild"in c&&c.firstChild!==null&&"data"in c.firstChild&&c.firstChild.data!==null&&(y=c.firstChild.data),y=y.replace(/^\s+|\s+$|\n+/g,"").replace(/\s+/g," ");var b=s.strokeWidth;s.strokeWidth=0;var _=new a.Text(y,s),w=_.getScaledHeight()/_.height,S=(_.height+_.strokeWidth)*_.lineHeight-_.height,x=S*w,k=_.getScaledHeight()+x,E=0;v==="center"&&(E=_.getScaledWidth()/2),v==="right"&&(E=_.getScaledWidth()),_.set({left:_.left-E,top:_.top-(k-_.fontSize*(.07+_._fontSizeFraction))/_.lineHeight,strokeWidth:typeof b<"u"?b:1}),f(_)},a.Text.fromObject=function(c,f){var s=u(c),h=c.path;return delete s.path,a.Object._fromObject("Text",s,function(v){v.styles=a.util.stylesFromArray(c.styles,c.text),h?a.Object._fromObject("Path",h,function(m){v.set("path",m),f(v)},"path"):f(v)},"text")},a.Text.genericFonts=["sans-serif","serif","cursive","fantasy","monospace"],a.util.createAccessors&&a.util.createAccessors(a.Text)}(e),function(){t.util.object.extend(t.Text.prototype,{isEmptyStyles:function(o){if(!this.styles||typeof o<"u"&&!this.styles[o])return!0;var a=typeof o>"u"?this.styles:{line:this.styles[o]};for(var u in a)for(var d in a[u])for(var c in a[u][d])return!1;return!0},styleHas:function(o,a){if(!this.styles||!o||o===""||typeof a<"u"&&!this.styles[a])return!1;var u=typeof a>"u"?this.styles:{0:this.styles[a]};for(var d in u)for(var c in u[d])if(typeof u[d][c][o]<"u")return!0;return!1},cleanStyle:function(o){if(!this.styles||!o||o==="")return!1;var a=this.styles,u=0,d,c,f=!0,s=0,h;for(var v in a){d=0;for(var m in a[v]){var h=a[v][m],y=h.hasOwnProperty(o);u++,y?(c?h[o]!==c&&(f=!1):c=h[o],h[o]===this[o]&&delete h[o]):f=!1,Object.keys(h).length!==0?d++:delete a[v][m]}d===0&&delete a[v]}for(var b=0;b<this._textLines.length;b++)s+=this._textLines[b].length;f&&u===s&&(this[o]=c,this.removeStyle(o))},removeStyle:function(o){if(!(!this.styles||!o||o==="")){var a=this.styles,u,d,c;for(d in a){u=a[d];for(c in u)delete u[c][o],Object.keys(u[c]).length===0&&delete u[c];Object.keys(u).length===0&&delete a[d]}}},_extendStyles:function(o,a){var u=this.get2DCursorLocation(o);this._getLineStyle(u.lineIndex)||this._setLineStyle(u.lineIndex),this._getStyleDeclaration(u.lineIndex,u.charIndex)||this._setStyleDeclaration(u.lineIndex,u.charIndex,{}),t.util.object.extend(this._getStyleDeclaration(u.lineIndex,u.charIndex),a)},get2DCursorLocation:function(o,a){typeof o>"u"&&(o=this.selectionStart);for(var u=a?this._unwrappedTextLines:this._textLines,d=u.length,c=0;c<d;c++){if(o<=u[c].length)return{lineIndex:c,charIndex:o};o-=u[c].length+this.missingNewlineOffset(c)}return{lineIndex:c-1,charIndex:u[c-1].length<o?u[c-1].length:o}},getSelectionStyles:function(o,a,u){typeof o>"u"&&(o=this.selectionStart||0),typeof a>"u"&&(a=this.selectionEnd||o);for(var d=[],c=o;c<a;c++)d.push(this.getStyleAtPosition(c,u));return d},getStyleAtPosition:function(o,a){var u=this.get2DCursorLocation(o),d=a?this.getCompleteStyleDeclaration(u.lineIndex,u.charIndex):this._getStyleDeclaration(u.lineIndex,u.charIndex);return d||{}},setSelectionStyles:function(o,a,u){typeof a>"u"&&(a=this.selectionStart||0),typeof u>"u"&&(u=this.selectionEnd||a);for(var d=a;d<u;d++)this._extendStyles(d,o);return this._forceClearCache=!0,this},_getStyleDeclaration:function(o,a){var u=this.styles&&this.styles[o];return u?u[a]:null},getCompleteStyleDeclaration:function(o,a){for(var u=this._getStyleDeclaration(o,a)||{},d={},c,f=0;f<this._styleProperties.length;f++)c=this._styleProperties[f],d[c]=typeof u[c]>"u"?this[c]:u[c];return d},_setStyleDeclaration:function(o,a,u){this.styles[o][a]=u},_deleteStyleDeclaration:function(o,a){delete this.styles[o][a]},_getLineStyle:function(o){return!!this.styles[o]},_setLineStyle:function(o){this.styles[o]={}},_deleteLineStyle:function(o){delete this.styles[o]}})}(),function(){function o(a){a.textDecoration&&(a.textDecoration.indexOf("underline")>-1&&(a.underline=!0),a.textDecoration.indexOf("line-through")>-1&&(a.linethrough=!0),a.textDecoration.indexOf("overline")>-1&&(a.overline=!0),delete a.textDecoration)}t.IText=t.util.createClass(t.Text,t.Observable,{type:"i-text",selectionStart:0,selectionEnd:0,selectionColor:"rgba(17,119,255,0.3)",isEditing:!1,editable:!0,editingBorderColor:"rgba(102,153,255,0.25)",cursorWidth:2,cursorColor:"",cursorDelay:1e3,cursorDuration:600,caching:!0,hiddenTextareaContainer:null,_reSpace:/\s|\n/,_currentCursorOpacity:0,_selectionDirection:null,_abortCursorAnimation:!1,__widthOfSpace:[],inCompositionMode:!1,initialize:function(a,u){this.callSuper("initialize",a,u),this.initBehavior()},setSelectionStart:function(a){a=Math.max(a,0),this._updateAndFire("selectionStart",a)},setSelectionEnd:function(a){a=Math.min(a,this.text.length),this._updateAndFire("selectionEnd",a)},_updateAndFire:function(a,u){this[a]!==u&&(this._fireSelectionChanged(),this[a]=u),this._updateTextarea()},_fireSelectionChanged:function(){this.fire("selection:changed"),this.canvas&&this.canvas.fire("text:selection:changed",{target:this})},initDimensions:function(){this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this.callSuper("initDimensions")},render:function(a){this.clearContextTop(),this.callSuper("render",a),this.cursorOffsetCache={},this.renderCursorOrSelection()},_render:function(a){this.callSuper("_render",a)},clearContextTop:function(a){if(!(!this.isEditing||!this.canvas||!this.canvas.contextTop)){var u=this.canvas.contextTop,d=this.canvas.viewportTransform;u.save(),u.transform(d[0],d[1],d[2],d[3],d[4],d[5]),this.transform(u),this._clearTextArea(u),a||u.restore()}},renderCursorOrSelection:function(){if(!(!this.isEditing||!this.canvas||!this.canvas.contextTop)){var a=this._getCursorBoundaries(),u=this.canvas.contextTop;this.clearContextTop(!0),this.selectionStart===this.selectionEnd?this.renderCursor(a,u):this.renderSelection(a,u),u.restore()}},_clearTextArea:function(a){var u=this.width+4,d=this.height+4;a.clearRect(-u/2,-d/2,u,d)},_getCursorBoundaries:function(a){typeof a>"u"&&(a=this.selectionStart);var u=this._getLeftOffset(),d=this._getTopOffset(),c=this._getCursorBoundariesOffsets(a);return{left:u,top:d,leftOffset:c.left,topOffset:c.top}},_getCursorBoundariesOffsets:function(a){if(this.cursorOffsetCache&&"top"in this.cursorOffsetCache)return this.cursorOffsetCache;var u,d,c,f=0,s=0,h,v=this.get2DCursorLocation(a);c=v.charIndex,d=v.lineIndex;for(var m=0;m<d;m++)f+=this.getHeightOfLine(m);u=this._getLineLeftOffset(d);var y=this.__charBounds[d][c];return y&&(s=y.left),this.charSpacing!==0&&c===this._textLines[d].length&&(s-=this._getWidthOfCharSpacing()),h={top:f,left:u+(s>0?s:0)},this.direction==="rtl"&&(h.left*=-1),this.cursorOffsetCache=h,this.cursorOffsetCache},renderCursor:function(a,u){var d=this.get2DCursorLocation(),c=d.lineIndex,f=d.charIndex>0?d.charIndex-1:0,s=this.getValueOfPropertyAt(c,f,"fontSize"),h=this.scaleX*this.canvas.getZoom(),v=this.cursorWidth/h,m=a.topOffset,y=this.getValueOfPropertyAt(c,f,"deltaY");m+=(1-this._fontSizeFraction)*this.getHeightOfLine(c)/this.lineHeight-s*(1-this._fontSizeFraction),this.inCompositionMode&&this.renderSelection(a,u),u.fillStyle=this.cursorColor||this.getValueOfPropertyAt(c,f,"fill"),u.globalAlpha=this.__isMousedown?1:this._currentCursorOpacity,u.fillRect(a.left+a.leftOffset-v/2,m+a.top+y,v,s)},renderSelection:function(a,u){for(var d=this.inCompositionMode?this.hiddenTextarea.selectionStart:this.selectionStart,c=this.inCompositionMode?this.hiddenTextarea.selectionEnd:this.selectionEnd,f=this.textAlign.indexOf("justify")!==-1,s=this.get2DCursorLocation(d),h=this.get2DCursorLocation(c),v=s.lineIndex,m=h.lineIndex,y=s.charIndex<0?0:s.charIndex,b=h.charIndex<0?0:h.charIndex,_=v;_<=m;_++){var w=this._getLineLeftOffset(_)||0,S=this.getHeightOfLine(_),x=0,k=0,E=0;if(_===v&&(k=this.__charBounds[v][y].left),_>=v&&_<m)E=f&&!this.isEndOfWrapping(_)?this.width:this.getLineWidth(_)||5;else if(_===m)if(b===0)E=this.__charBounds[m][b].left;else{var I=this._getWidthOfCharSpacing();E=this.__charBounds[m][b-1].left+this.__charBounds[m][b-1].width-I}x=S,(this.lineHeight<1||_===m&&this.lineHeight>1)&&(S/=this.lineHeight);var D=a.left+w+k,N=E-k,L=S,F=0;this.inCompositionMode?(u.fillStyle=this.compositionColor||"black",L=1,F=S):u.fillStyle=this.selectionColor,this.direction==="rtl"&&(D=this.width-D-N),u.fillRect(D,a.top+a.topOffset+F,N,L),a.topOffset+=x}},getCurrentCharFontSize:function(){var a=this._getCurrentCharIndex();return this.getValueOfPropertyAt(a.l,a.c,"fontSize")},getCurrentCharColor:function(){var a=this._getCurrentCharIndex();return this.getValueOfPropertyAt(a.l,a.c,"fill")},_getCurrentCharIndex:function(){var a=this.get2DCursorLocation(this.selectionStart,!0),u=a.charIndex>0?a.charIndex-1:0;return{l:a.lineIndex,c:u}}}),t.IText.fromObject=function(a,u){var d=t.util.stylesFromArray(a.styles,a.text),c=Object.assign({},a,{styles:d});if(o(c),c.styles)for(var f in c.styles)for(var s in c.styles[f])o(c.styles[f][s]);t.Object._fromObject("IText",c,u,"text")}}(),function(){var o=t.util.object.clone;t.util.object.extend(t.IText.prototype,{initBehavior:function(){this.initAddedHandler(),this.initRemovedHandler(),this.initCursorSelectionHandlers(),this.initDoubleClickSimulation(),this.mouseMoveHandler=this.mouseMoveHandler.bind(this)},onDeselect:function(){this.isEditing&&this.exitEditing(),this.selected=!1},initAddedHandler:function(){var a=this;this.on("added",function(){var u=a.canvas;u&&(u._hasITextHandlers||(u._hasITextHandlers=!0,a._initCanvasHandlers(u)),u._iTextInstances=u._iTextInstances||[],u._iTextInstances.push(a))})},initRemovedHandler:function(){var a=this;this.on("removed",function(){var u=a.canvas;u&&(u._iTextInstances=u._iTextInstances||[],t.util.removeFromArray(u._iTextInstances,a),u._iTextInstances.length===0&&(u._hasITextHandlers=!1,a._removeCanvasHandlers(u)))})},_initCanvasHandlers:function(a){a._mouseUpITextHandler=function(){a._iTextInstances&&a._iTextInstances.forEach(function(u){u.__isMousedown=!1})},a.on("mouse:up",a._mouseUpITextHandler)},_removeCanvasHandlers:function(a){a.off("mouse:up",a._mouseUpITextHandler)},_tick:function(){this._currentTickState=this._animateCursor(this,1,this.cursorDuration,"_onTickComplete")},_animateCursor:function(a,u,d,c){var f;return f={isAborted:!1,abort:function(){this.isAborted=!0}},a.animate("_currentCursorOpacity",u,{duration:d,onComplete:function(){f.isAborted||a[c]()},onChange:function(){a.canvas&&a.selectionStart===a.selectionEnd&&a.renderCursorOrSelection()},abort:function(){return f.isAborted}}),f},_onTickComplete:function(){var a=this;this._cursorTimeout1&&clearTimeout(this._cursorTimeout1),this._cursorTimeout1=setTimeout(function(){a._currentTickCompleteState=a._animateCursor(a,0,this.cursorDuration/2,"_tick")},100)},initDelayedCursor:function(a){var u=this,d=a?0:this.cursorDelay;this.abortCursorAnimation(),this._currentCursorOpacity=1,this._cursorTimeout2=setTimeout(function(){u._tick()},d)},abortCursorAnimation:function(){var a=this._currentTickState||this._currentTickCompleteState,u=this.canvas;this._currentTickState&&this._currentTickState.abort(),this._currentTickCompleteState&&this._currentTickCompleteState.abort(),clearTimeout(this._cursorTimeout1),clearTimeout(this._cursorTimeout2),this._currentCursorOpacity=0,a&&u&&u.clearContext(u.contextTop||u.contextContainer)},selectAll:function(){return this.selectionStart=0,this.selectionEnd=this._text.length,this._fireSelectionChanged(),this._updateTextarea(),this},getSelectedText:function(){return this._text.slice(this.selectionStart,this.selectionEnd).join("")},findWordBoundaryLeft:function(a){var u=0,d=a-1;if(this._reSpace.test(this._text[d]))for(;this._reSpace.test(this._text[d]);)u++,d--;for(;/\S/.test(this._text[d])&&d>-1;)u++,d--;return a-u},findWordBoundaryRight:function(a){var u=0,d=a;if(this._reSpace.test(this._text[d]))for(;this._reSpace.test(this._text[d]);)u++,d++;for(;/\S/.test(this._text[d])&&d<this._text.length;)u++,d++;return a+u},findLineBoundaryLeft:function(a){for(var u=0,d=a-1;!/\n/.test(this._text[d])&&d>-1;)u++,d--;return a-u},findLineBoundaryRight:function(a){for(var u=0,d=a;!/\n/.test(this._text[d])&&d<this._text.length;)u++,d++;return a+u},searchWordBoundary:function(a,u){for(var d=this._text,c=this._reSpace.test(d[a])?a-1:a,f=d[c],s=t.reNonWord;!s.test(f)&&c>0&&c<d.length;)c+=u,f=d[c];return s.test(f)&&(c+=u===1?0:1),c},selectWord:function(a){a=a||this.selectionStart;var u=this.searchWordBoundary(a,-1),d=this.searchWordBoundary(a,1);this.selectionStart=u,this.selectionEnd=d,this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()},selectLine:function(a){a=a||this.selectionStart;var u=this.findLineBoundaryLeft(a),d=this.findLineBoundaryRight(a);return this.selectionStart=u,this.selectionEnd=d,this._fireSelectionChanged(),this._updateTextarea(),this},enterEditing:function(a){if(!(this.isEditing||!this.editable))return this.canvas&&(this.canvas.calcOffset(),this.exitEditingOnOthers(this.canvas)),this.isEditing=!0,this.initHiddenTextarea(a),this.hiddenTextarea.focus(),this.hiddenTextarea.value=this.text,this._updateTextarea(),this._saveEditingProps(),this._setEditingProps(),this._textBeforeEdit=this.text,this._tick(),this.fire("editing:entered"),this._fireSelectionChanged(),this.canvas?(this.canvas.fire("text:editing:entered",{target:this}),this.initMouseMoveHandler(),this.canvas.requestRenderAll(),this):this},exitEditingOnOthers:function(a){a._iTextInstances&&a._iTextInstances.forEach(function(u){u.selected=!1,u.isEditing&&u.exitEditing()})},initMouseMoveHandler:function(){this.canvas.on("mouse:move",this.mouseMoveHandler)},mouseMoveHandler:function(a){if(!(!this.__isMousedown||!this.isEditing)){document.activeElement!==this.hiddenTextarea&&this.hiddenTextarea.focus();var u=this.getSelectionStartFromPointer(a.e),d=this.selectionStart,c=this.selectionEnd;(u!==this.__selectionStartOnMouseDown||d===c)&&(d===u||c===u)||(u>this.__selectionStartOnMouseDown?(this.selectionStart=this.__selectionStartOnMouseDown,this.selectionEnd=u):(this.selectionStart=u,this.selectionEnd=this.__selectionStartOnMouseDown),(this.selectionStart!==d||this.selectionEnd!==c)&&(this.restartCursorIfNeeded(),this._fireSelectionChanged(),this._updateTextarea(),this.renderCursorOrSelection()))}},_setEditingProps:function(){this.hoverCursor="text",this.canvas&&(this.canvas.defaultCursor=this.canvas.moveCursor="text"),this.borderColor=this.editingBorderColor,this.hasControls=this.selectable=!1,this.lockMovementX=this.lockMovementY=!0},fromStringToGraphemeSelection:function(a,u,d){var c=d.slice(0,a),f=t.util.string.graphemeSplit(c).length;if(a===u)return{selectionStart:f,selectionEnd:f};var s=d.slice(a,u),h=t.util.string.graphemeSplit(s).length;return{selectionStart:f,selectionEnd:f+h}},fromGraphemeToStringSelection:function(a,u,d){var c=d.slice(0,a),f=c.join("").length;if(a===u)return{selectionStart:f,selectionEnd:f};var s=d.slice(a,u),h=s.join("").length;return{selectionStart:f,selectionEnd:f+h}},_updateTextarea:function(){if(this.cursorOffsetCache={},!!this.hiddenTextarea){if(!this.inCompositionMode){var a=this.fromGraphemeToStringSelection(this.selectionStart,this.selectionEnd,this._text);this.hiddenTextarea.selectionStart=a.selectionStart,this.hiddenTextarea.selectionEnd=a.selectionEnd}this.updateTextareaPosition()}},updateFromTextArea:function(){if(!!this.hiddenTextarea){this.cursorOffsetCache={},this.text=this.hiddenTextarea.value,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords());var a=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value);this.selectionEnd=this.selectionStart=a.selectionEnd,this.inCompositionMode||(this.selectionStart=a.selectionStart),this.updateTextareaPosition()}},updateTextareaPosition:function(){if(this.selectionStart===this.selectionEnd){var a=this._calcTextareaPosition();this.hiddenTextarea.style.left=a.left,this.hiddenTextarea.style.top=a.top}},_calcTextareaPosition:function(){if(!this.canvas)return{x:1,y:1};var a=this.inCompositionMode?this.compositionStart:this.selectionStart,u=this._getCursorBoundaries(a),d=this.get2DCursorLocation(a),c=d.lineIndex,f=d.charIndex,s=this.getValueOfPropertyAt(c,f,"fontSize")*this.lineHeight,h=u.leftOffset,v=this.calcTransformMatrix(),m={x:u.left+h,y:u.top+u.topOffset+s},y=this.canvas.getRetinaScaling(),b=this.canvas.upperCanvasEl,_=b.width/y,w=b.height/y,S=_-s,x=w-s,k=b.clientWidth/_,E=b.clientHeight/w;return m=t.util.transformPoint(m,v),m=t.util.transformPoint(m,this.canvas.viewportTransform),m.x*=k,m.y*=E,m.x<0&&(m.x=0),m.x>S&&(m.x=S),m.y<0&&(m.y=0),m.y>x&&(m.y=x),m.x+=this.canvas._offset.left,m.y+=this.canvas._offset.top,{left:m.x+"px",top:m.y+"px",fontSize:s+"px",charHeight:s}},_saveEditingProps:function(){this._savedProps={hasControls:this.hasControls,borderColor:this.borderColor,lockMovementX:this.lockMovementX,lockMovementY:this.lockMovementY,hoverCursor:this.hoverCursor,selectable:this.selectable,defaultCursor:this.canvas&&this.canvas.defaultCursor,moveCursor:this.canvas&&this.canvas.moveCursor}},_restoreEditingProps:function(){!this._savedProps||(this.hoverCursor=this._savedProps.hoverCursor,this.hasControls=this._savedProps.hasControls,this.borderColor=this._savedProps.borderColor,this.selectable=this._savedProps.selectable,this.lockMovementX=this._savedProps.lockMovementX,this.lockMovementY=this._savedProps.lockMovementY,this.canvas&&(this.canvas.defaultCursor=this._savedProps.defaultCursor,this.canvas.moveCursor=this._savedProps.moveCursor))},exitEditing:function(){var a=this._textBeforeEdit!==this.text,u=this.hiddenTextarea;return this.selected=!1,this.isEditing=!1,this.selectionEnd=this.selectionStart,u&&(u.blur&&u.blur(),u.parentNode&&u.parentNode.removeChild(u)),this.hiddenTextarea=null,this.abortCursorAnimation(),this._restoreEditingProps(),this._currentCursorOpacity=0,this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this.fire("editing:exited"),a&&this.fire("modified"),this.canvas&&(this.canvas.off("mouse:move",this.mouseMoveHandler),this.canvas.fire("text:editing:exited",{target:this}),a&&this.canvas.fire("object:modified",{target:this})),this},_removeExtraneousStyles:function(){for(var a in this.styles)this._textLines[a]||delete this.styles[a]},removeStyleFromTo:function(a,u){var d=this.get2DCursorLocation(a,!0),c=this.get2DCursorLocation(u,!0),f=d.lineIndex,s=d.charIndex,h=c.lineIndex,v=c.charIndex,m,y;if(f!==h){if(this.styles[f])for(m=s;m<this._unwrappedTextLines[f].length;m++)delete this.styles[f][m];if(this.styles[h])for(m=v;m<this._unwrappedTextLines[h].length;m++)y=this.styles[h][m],y&&(this.styles[f]||(this.styles[f]={}),this.styles[f][s+m-v]=y);for(m=f+1;m<=h;m++)delete this.styles[m];this.shiftLineStyles(h,f-h)}else if(this.styles[f]){y=this.styles[f];var b=v-s,_,w;for(m=s;m<v;m++)delete y[m];for(w in this.styles[f])_=parseInt(w,10),_>=v&&(y[_-b]=y[w],delete y[w])}},shiftLineStyles:function(a,u){var d=o(this.styles);for(var c in this.styles){var f=parseInt(c,10);f>a&&(this.styles[f+u]=d[f],d[f-u]||delete this.styles[f])}},restartCursorIfNeeded:function(){(!this._currentTickState||this._currentTickState.isAborted||!this._currentTickCompleteState||this._currentTickCompleteState.isAborted)&&this.initDelayedCursor()},insertNewlineStyleObject:function(a,u,d,c){var f,s={},h=!1,v=this._unwrappedTextLines[a].length===u;d||(d=1),this.shiftLineStyles(a,d),this.styles[a]&&(f=this.styles[a][u===0?u:u-1]);for(var m in this.styles[a]){var y=parseInt(m,10);y>=u&&(h=!0,s[y-u]=this.styles[a][m],v&&u===0||delete this.styles[a][m])}var b=!1;for(h&&!v&&(this.styles[a+d]=s,b=!0),b&&d--;d>0;)c&&c[d-1]?this.styles[a+d]={0:o(c[d-1])}:f?this.styles[a+d]={0:o(f)}:delete this.styles[a+d],d--;this._forceClearCache=!0},insertCharStyleObject:function(a,u,d,c){this.styles||(this.styles={});var f=this.styles[a],s=f?o(f):{};d||(d=1);for(var h in s){var v=parseInt(h,10);v>=u&&(f[v+d]=s[v],s[v-d]||delete f[v])}if(this._forceClearCache=!0,c){for(;d--;)!Object.keys(c[d]).length||(this.styles[a]||(this.styles[a]={}),this.styles[a][u+d]=o(c[d]));return}if(!!f)for(var m=f[u?u-1:1];m&&d--;)this.styles[a][u+d]=o(m)},insertNewStyleBlock:function(a,u,d){for(var c=this.get2DCursorLocation(u,!0),f=[0],s=0,h=0;h<a.length;h++)a[h]===`
 `?(s++,f[s]=0):f[s]++;f[0]>0&&(this.insertCharStyleObject(c.lineIndex,c.charIndex,f[0],d),d=d&&d.slice(f[0]+1)),s&&this.insertNewlineStyleObject(c.lineIndex,c.charIndex+f[0],s);for(var h=1;h<s;h++)f[h]>0?this.insertCharStyleObject(c.lineIndex+h,0,f[h],d):d&&this.styles[c.lineIndex+h]&&d[0]&&(this.styles[c.lineIndex+h][0]=d[0]),d=d&&d.slice(f[h]+1);f[h]>0&&this.insertCharStyleObject(c.lineIndex+h,0,f[h],d)},setSelectionStartEndWithShift:function(a,u,d){d<=a?(u===a?this._selectionDirection="left":this._selectionDirection==="right"&&(this._selectionDirection="left",this.selectionEnd=a),this.selectionStart=d):d>a&&d<u?this._selectionDirection==="right"?this.selectionEnd=d:this.selectionStart=d:(u===a?this._selectionDirection="right":this._selectionDirection==="left"&&(this._selectionDirection="right",this.selectionStart=u),this.selectionEnd=d)},setSelectionInBoundaries:function(){var a=this.text.length;this.selectionStart>a?this.selectionStart=a:this.selectionStart<0&&(this.selectionStart=0),this.selectionEnd>a?this.selectionEnd=a:this.selectionEnd<0&&(this.selectionEnd=0)}})}(),t.util.object.extend(t.IText.prototype,{initDoubleClickSimulation:function(){this.__lastClickTime=+new Date,this.__lastLastClickTime=+new Date,this.__lastPointer={},this.on("mousedown",this.onMouseDown)},onMouseDown:function(o){if(!!this.canvas){this.__newClickTime=+new Date;var a=o.pointer;this.isTripleClick(a)&&(this.fire("tripleclick",o),this._stopEvent(o.e)),this.__lastLastClickTime=this.__lastClickTime,this.__lastClickTime=this.__newClickTime,this.__lastPointer=a,this.__lastIsEditing=this.isEditing,this.__lastSelected=this.selected}},isTripleClick:function(o){return this.__newClickTime-this.__lastClickTime<500&&this.__lastClickTime-this.__lastLastClickTime<500&&this.__lastPointer.x===o.x&&this.__lastPointer.y===o.y},_stopEvent:function(o){o.preventDefault&&o.preventDefault(),o.stopPropagation&&o.stopPropagation()},initCursorSelectionHandlers:function(){this.initMousedownHandler(),this.initMouseupHandler(),this.initClicks()},doubleClickHandler:function(o){!this.isEditing||this.selectWord(this.getSelectionStartFromPointer(o.e))},tripleClickHandler:function(o){!this.isEditing||this.selectLine(this.getSelectionStartFromPointer(o.e))},initClicks:function(){this.on("mousedblclick",this.doubleClickHandler),this.on("tripleclick",this.tripleClickHandler)},_mouseDownHandler:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.__isMousedown=!0,this.selected&&(this.inCompositionMode=!1,this.setCursorByClick(o.e)),this.isEditing&&(this.__selectionStartOnMouseDown=this.selectionStart,this.selectionStart===this.selectionEnd&&this.abortCursorAnimation(),this.renderCursorOrSelection()))},_mouseDownHandlerBefore:function(o){!this.canvas||!this.editable||o.e.button&&o.e.button!==1||(this.selected=this===this.canvas._activeObject)},initMousedownHandler:function(){this.on("mousedown",this._mouseDownHandler),this.on("mousedown:before",this._mouseDownHandlerBefore)},initMouseupHandler:function(){this.on("mouseup",this.mouseUpHandler)},mouseUpHandler:function(o){if(this.__isMousedown=!1,!(!this.editable||this.group||o.transform&&o.transform.actionPerformed||o.e.button&&o.e.button!==1)){if(this.canvas){var a=this.canvas._activeObject;if(a&&a!==this)return}this.__lastSelected&&!this.__corner?(this.selected=!1,this.__lastSelected=!1,this.enterEditing(o.e),this.selectionStart===this.selectionEnd?this.initDelayedCursor(!0):this.renderCursorOrSelection()):this.selected=!0}},setCursorByClick:function(o){var a=this.getSelectionStartFromPointer(o),u=this.selectionStart,d=this.selectionEnd;o.shiftKey?this.setSelectionStartEndWithShift(u,d,a):(this.selectionStart=a,this.selectionEnd=a),this.isEditing&&(this._fireSelectionChanged(),this._updateTextarea())},getSelectionStartFromPointer:function(o){for(var a=this.getLocalPointer(o),u=0,d=0,c=0,f=0,s=0,h,v,m=0,y=this._textLines.length;m<y&&c<=a.y;m++)c+=this.getHeightOfLine(m)*this.scaleY,s=m,m>0&&(f+=this._textLines[m-1].length+this.missingNewlineOffset(m-1));h=this._getLineLeftOffset(s),d=h*this.scaleX,v=this._textLines[s],this.direction==="rtl"&&(a.x=this.width*this.scaleX-a.x+d);for(var b=0,_=v.length;b<_&&(u=d,d+=this.__charBounds[s][b].kernedWidth*this.scaleX,d<=a.x);b++)f++;return this._getNewSelectionStartFromOffset(a,u,d,f,_)},_getNewSelectionStartFromOffset:function(o,a,u,d,c){var f=o.x-a,s=u-o.x,h=s>f||s<0?0:1,v=d+h;return this.flipX&&(v=c-v),v>this._text.length&&(v=this._text.length),v}}),t.util.object.extend(t.IText.prototype,{initHiddenTextarea:function(){this.hiddenTextarea=t.document.createElement("textarea"),this.hiddenTextarea.setAttribute("autocapitalize","off"),this.hiddenTextarea.setAttribute("autocorrect","off"),this.hiddenTextarea.setAttribute("autocomplete","off"),this.hiddenTextarea.setAttribute("spellcheck","false"),this.hiddenTextarea.setAttribute("data-fabric-hiddentextarea",""),this.hiddenTextarea.setAttribute("wrap","off");var o=this._calcTextareaPosition();this.hiddenTextarea.style.cssText="position: absolute; top: "+o.top+"; left: "+o.left+"; z-index: -999; opacity: 0; width: 1px; height: 1px; font-size: 1px; padding\uFF70top: "+o.fontSize+";",this.hiddenTextareaContainer?this.hiddenTextareaContainer.appendChild(this.hiddenTextarea):t.document.body.appendChild(this.hiddenTextarea),t.util.addListener(this.hiddenTextarea,"keydown",this.onKeyDown.bind(this)),t.util.addListener(this.hiddenTextarea,"keyup",this.onKeyUp.bind(this)),t.util.addListener(this.hiddenTextarea,"input",this.onInput.bind(this)),t.util.addListener(this.hiddenTextarea,"copy",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"cut",this.copy.bind(this)),t.util.addListener(this.hiddenTextarea,"paste",this.paste.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionstart",this.onCompositionStart.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionupdate",this.onCompositionUpdate.bind(this)),t.util.addListener(this.hiddenTextarea,"compositionend",this.onCompositionEnd.bind(this)),!this._clickHandlerInitialized&&this.canvas&&(t.util.addListener(this.canvas.upperCanvasEl,"click",this.onClick.bind(this)),this._clickHandlerInitialized=!0)},keysMap:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorRight",36:"moveCursorLeft",37:"moveCursorLeft",38:"moveCursorUp",39:"moveCursorRight",40:"moveCursorDown"},keysMapRtl:{9:"exitEditing",27:"exitEditing",33:"moveCursorUp",34:"moveCursorDown",35:"moveCursorLeft",36:"moveCursorRight",37:"moveCursorRight",38:"moveCursorUp",39:"moveCursorLeft",40:"moveCursorDown"},ctrlKeysMapUp:{67:"copy",88:"cut"},ctrlKeysMapDown:{65:"selectAll"},onClick:function(){this.hiddenTextarea&&this.hiddenTextarea.focus()},onKeyDown:function(o){if(!!this.isEditing){var a=this.direction==="rtl"?this.keysMapRtl:this.keysMap;if(o.keyCode in a)this[a[o.keyCode]](o);else if(o.keyCode in this.ctrlKeysMapDown&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapDown[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),o.keyCode>=33&&o.keyCode<=40?(this.inCompositionMode=!1,this.clearContextTop(),this.renderCursorOrSelection()):this.canvas&&this.canvas.requestRenderAll()}},onKeyUp:function(o){if(!this.isEditing||this._copyDone||this.inCompositionMode){this._copyDone=!1;return}if(o.keyCode in this.ctrlKeysMapUp&&(o.ctrlKey||o.metaKey))this[this.ctrlKeysMapUp[o.keyCode]](o);else return;o.stopImmediatePropagation(),o.preventDefault(),this.canvas&&this.canvas.requestRenderAll()},onInput:function(o){var a=this.fromPaste;if(this.fromPaste=!1,o&&o.stopPropagation(),!!this.isEditing){var u=this._splitTextIntoLines(this.hiddenTextarea.value).graphemeText,d=this._text.length,c=u.length,f,s,h=c-d,v=this.selectionStart,m=this.selectionEnd,y=v!==m,b,_,w;if(this.hiddenTextarea.value===""){this.styles={},this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll());return}var S=this.fromStringToGraphemeSelection(this.hiddenTextarea.selectionStart,this.hiddenTextarea.selectionEnd,this.hiddenTextarea.value),x=v>S.selectionStart;y?(f=this._text.slice(v,m),h+=m-v):c<d&&(x?f=this._text.slice(m+h,m):f=this._text.slice(v,v-h)),s=u.slice(S.selectionEnd-h,S.selectionEnd),f&&f.length&&(s.length&&(b=this.getSelectionStyles(v,v+1,!1),b=s.map(function(){return b[0]})),y?(_=v,w=m):x?(_=m-f.length,w=m):(_=m,w=m+f.length),this.removeStyleFromTo(_,w)),s.length&&(a&&s.join("")===t.copiedText&&!t.disableStyleCopyPaste&&(b=t.copiedTextStyle),this.insertNewStyleBlock(s,v,b)),this.updateFromTextArea(),this.fire("changed"),this.canvas&&(this.canvas.fire("text:changed",{target:this}),this.canvas.requestRenderAll())}},onCompositionStart:function(){this.inCompositionMode=!0},onCompositionEnd:function(){this.inCompositionMode=!1},onCompositionUpdate:function(o){this.compositionStart=o.target.selectionStart,this.compositionEnd=o.target.selectionEnd,this.updateTextareaPosition()},copy:function(){this.selectionStart!==this.selectionEnd&&(t.copiedText=this.getSelectedText(),t.disableStyleCopyPaste?t.copiedTextStyle=null:t.copiedTextStyle=this.getSelectionStyles(this.selectionStart,this.selectionEnd,!0),this._copyDone=!0)},paste:function(){this.fromPaste=!0},_getClipboardData:function(o){return o&&o.clipboardData||t.window.clipboardData},_getWidthBeforeCursor:function(o,a){var u=this._getLineLeftOffset(o),d;return a>0&&(d=this.__charBounds[o][a-1],u+=d.left+d.width),u},getDownCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===this._textLines.length-1||o.metaKey||o.keyCode===34)return this._text.length-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c+1,s),v=this._textLines[c].slice(f);return v.length+h+1+this.missingNewlineOffset(c)},_getSelectionForOffset:function(o,a){return o.shiftKey&&this.selectionStart!==this.selectionEnd&&a?this.selectionEnd:this.selectionStart},getUpCursorOffset:function(o,a){var u=this._getSelectionForOffset(o,a),d=this.get2DCursorLocation(u),c=d.lineIndex;if(c===0||o.metaKey||o.keyCode===33)return-u;var f=d.charIndex,s=this._getWidthBeforeCursor(c,f),h=this._getIndexOnLine(c-1,s),v=this._textLines[c].slice(0,f),m=this.missingNewlineOffset(c-1);return-this._textLines[c-1].length+h-v.length+(1-m)},_getIndexOnLine:function(o,a){for(var u=this._textLines[o],d=this._getLineLeftOffset(o),c=d,f=0,s,h,v=0,m=u.length;v<m;v++)if(s=this.__charBounds[o][v].width,c+=s,c>a){h=!0;var y=c-s,b=c,_=Math.abs(y-a),w=Math.abs(b-a);f=w<_?v:v-1;break}return h||(f=u.length-1),f},moveCursorDown:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorUpOrDown("Down",o)},moveCursorUp:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorUpOrDown("Up",o)},_moveCursorUpOrDown:function(o,a){var u="get"+o+"CursorOffset",d=this[u](a,this._selectionDirection==="right");a.shiftKey?this.moveCursorWithShift(d):this.moveCursorWithoutShift(d),d!==0&&(this.setSelectionInBoundaries(),this.abortCursorAnimation(),this._currentCursorOpacity=1,this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorWithShift:function(o){var a=this._selectionDirection==="left"?this.selectionStart+o:this.selectionEnd+o;return this.setSelectionStartEndWithShift(this.selectionStart,this.selectionEnd,a),o!==0},moveCursorWithoutShift:function(o){return o<0?(this.selectionStart+=o,this.selectionEnd=this.selectionStart):(this.selectionEnd+=o,this.selectionStart=this.selectionEnd),o!==0},moveCursorLeft:function(o){this.selectionStart===0&&this.selectionEnd===0||this._moveCursorLeftOrRight("Left",o)},_move:function(o,a,u){var d;if(o.altKey)d=this["findWordBoundary"+u](this[a]);else if(o.metaKey||o.keyCode===35||o.keyCode===36)d=this["findLineBoundary"+u](this[a]);else return this[a]+=u==="Left"?-1:1,!0;if(typeof d<"u"&&this[a]!==d)return this[a]=d,!0},_moveLeft:function(o,a){return this._move(o,a,"Left")},_moveRight:function(o,a){return this._move(o,a,"Right")},moveCursorLeftWithoutShift:function(o){var a=!0;return this._selectionDirection="left",this.selectionEnd===this.selectionStart&&this.selectionStart!==0&&(a=this._moveLeft(o,"selectionStart")),this.selectionEnd=this.selectionStart,a},moveCursorLeftWithShift:function(o){if(this._selectionDirection==="right"&&this.selectionStart!==this.selectionEnd)return this._moveLeft(o,"selectionEnd");if(this.selectionStart!==0)return this._selectionDirection="left",this._moveLeft(o,"selectionStart")},moveCursorRight:function(o){this.selectionStart>=this._text.length&&this.selectionEnd>=this._text.length||this._moveCursorLeftOrRight("Right",o)},_moveCursorLeftOrRight:function(o,a){var u="moveCursor"+o+"With";this._currentCursorOpacity=1,a.shiftKey?u+="Shift":u+="outShift",this[u](a)&&(this.abortCursorAnimation(),this.initDelayedCursor(),this._fireSelectionChanged(),this._updateTextarea())},moveCursorRightWithShift:function(o){if(this._selectionDirection==="left"&&this.selectionStart!==this.selectionEnd)return this._moveRight(o,"selectionStart");if(this.selectionEnd!==this._text.length)return this._selectionDirection="right",this._moveRight(o,"selectionEnd")},moveCursorRightWithoutShift:function(o){var a=!0;return this._selectionDirection="right",this.selectionStart===this.selectionEnd?(a=this._moveRight(o,"selectionStart"),this.selectionEnd=this.selectionStart):this.selectionStart=this.selectionEnd,a},removeChars:function(o,a){typeof a>"u"&&(a=o+1),this.removeStyleFromTo(o,a),this._text.splice(o,a-o),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()},insertChars:function(o,a,u,d){typeof d>"u"&&(d=u),d>u&&this.removeStyleFromTo(u,d);var c=t.util.string.graphemeSplit(o);this.insertNewStyleBlock(c,u,a),this._text=[].concat(this._text.slice(0,u),c,this._text.slice(d)),this.text=this._text.join(""),this.set("dirty",!0),this._shouldClearDimensionCache()&&(this.initDimensions(),this.setCoords()),this._removeExtraneousStyles()}}),function(){var o=t.util.toFixed,a=/  +/g;t.util.object.extend(t.Text.prototype,{_toSVG:function(){var u=this._getSVGLeftTopOffsets(),d=this._getSVGTextAndBg(u.textTop,u.textLeft);return this._wrapSVGTextAndBg(d)},toSVG:function(u){return this._createBaseSVGMarkup(this._toSVG(),{reviver:u,noStyle:!0,withShadow:!0})},_getSVGLeftTopOffsets:function(){return{textLeft:-this.width/2,textTop:-this.height/2,lineTop:this.getHeightOfLine(0)}},_wrapSVGTextAndBg:function(u){var d=!0,c=this.getSvgTextDecoration(this);return[u.textBgRects.join(""),'		<text xml:space="preserve" ',this.fontFamily?'font-family="'+this.fontFamily.replace(/"/g,"'")+'" ':"",this.fontSize?'font-size="'+this.fontSize+'" ':"",this.fontStyle?'font-style="'+this.fontStyle+'" ':"",this.fontWeight?'font-weight="'+this.fontWeight+'" ':"",c?'text-decoration="'+c+'" ':"",'style="',this.getSvgStyles(d),'"',this.addPaintOrder()," >",u.textSpans.join(""),`</text>
-`]},_getSVGTextAndBg:function(u,d){var c=[],f=[],s=u,h;this._setSVGBg(f);for(var v=0,m=this._textLines.length;v<m;v++)h=this._getLineLeftOffset(v),(this.textBackgroundColor||this.styleHas("textBackgroundColor",v))&&this._setSVGTextLineBg(f,v,d+h,s),this._setSVGTextLineText(c,v,d+h,s),s+=this.getHeightOfLine(v);return{textSpans:c,textBgRects:f}},_createTextCharSpan:function(u,d,c,f){var s=u!==u.trim()||u.match(a),h=this.getSvgSpanStyles(d,s),v=h?'style="'+h+'"':"",m=d.deltaY,y="",b=t.Object.NUM_FRACTION_DIGITS;return m&&(y=' dy="'+o(m,b)+'" '),['<tspan x="',o(c,b),'" y="',o(f,b),'" ',y,v,">",t.util.string.escapeXml(u),"</tspan>"].join("")},_setSVGTextLineText:function(u,d,c,f){var s=this.getHeightOfLine(d),h=this.textAlign.indexOf("justify")!==-1,v,m,y="",b,_,w=0,S=this._textLines[d],x;f+=s*(1-this._fontSizeFraction)/this.lineHeight;for(var T=0,E=S.length-1;T<=E;T++)x=T===E||this.charSpacing,y+=S[T],b=this.__charBounds[d][T],w===0?(c+=b.kernedWidth-b.width,w+=b.width):w+=b.kernedWidth,h&&!x&&this._reSpaceAndTab.test(S[T])&&(x=!0),x||(v=v||this.getCompleteStyleDeclaration(d,T),m=this.getCompleteStyleDeclaration(d,T+1),x=t.util.hasStyleChanged(v,m,!0)),x&&(_=this._getStyleDeclaration(d,T)||{},u.push(this._createTextCharSpan(y,_,c,f)),y="",v=m,c+=w,w=0)},_pushTextBgRect:function(u,d,c,f,s,h){var v=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(d),' x="',o(c,v),'" y="',o(f,v),'" width="',o(s,v),'" height="',o(h,v),`"></rect>
+`]},_getSVGTextAndBg:function(u,d){var c=[],f=[],s=u,h;this._setSVGBg(f);for(var v=0,m=this._textLines.length;v<m;v++)h=this._getLineLeftOffset(v),(this.textBackgroundColor||this.styleHas("textBackgroundColor",v))&&this._setSVGTextLineBg(f,v,d+h,s),this._setSVGTextLineText(c,v,d+h,s),s+=this.getHeightOfLine(v);return{textSpans:c,textBgRects:f}},_createTextCharSpan:function(u,d,c,f){var s=u!==u.trim()||u.match(a),h=this.getSvgSpanStyles(d,s),v=h?'style="'+h+'"':"",m=d.deltaY,y="",b=t.Object.NUM_FRACTION_DIGITS;return m&&(y=' dy="'+o(m,b)+'" '),['<tspan x="',o(c,b),'" y="',o(f,b),'" ',y,v,">",t.util.string.escapeXml(u),"</tspan>"].join("")},_setSVGTextLineText:function(u,d,c,f){var s=this.getHeightOfLine(d),h=this.textAlign.indexOf("justify")!==-1,v,m,y="",b,_,w=0,S=this._textLines[d],x;f+=s*(1-this._fontSizeFraction)/this.lineHeight;for(var k=0,E=S.length-1;k<=E;k++)x=k===E||this.charSpacing,y+=S[k],b=this.__charBounds[d][k],w===0?(c+=b.kernedWidth-b.width,w+=b.width):w+=b.kernedWidth,h&&!x&&this._reSpaceAndTab.test(S[k])&&(x=!0),x||(v=v||this.getCompleteStyleDeclaration(d,k),m=this.getCompleteStyleDeclaration(d,k+1),x=t.util.hasStyleChanged(v,m,!0)),x&&(_=this._getStyleDeclaration(d,k)||{},u.push(this._createTextCharSpan(y,_,c,f)),y="",v=m,c+=w,w=0)},_pushTextBgRect:function(u,d,c,f,s,h){var v=t.Object.NUM_FRACTION_DIGITS;u.push("		<rect ",this._getFillAttributes(d),' x="',o(c,v),'" y="',o(f,v),'" width="',o(s,v),'" height="',o(h,v),`"></rect>
 `)},_setSVGTextLineBg:function(u,d,c,f){for(var s=this._textLines[d],h=this.getHeightOfLine(d)/this.lineHeight,v=0,m=0,y,b,_=this.getValueOfPropertyAt(d,0,"textBackgroundColor"),w=0,S=s.length;w<S;w++)y=this.__charBounds[d][w],b=this.getValueOfPropertyAt(d,w,"textBackgroundColor"),b!==_?(_&&this._pushTextBgRect(u,_,c+m,f,v,h),m=y.left,v=y.width,_=b):v+=y.kernedWidth;b&&this._pushTextBgRect(u,b,c+m,f,v,h)},_getFillAttributes:function(u){var d=u&&typeof u=="string"?new t.Color(u):"";return!d||!d.getSource()||d.getAlpha()===1?'fill="'+u+'"':'opacity="'+d.getAlpha()+'" fill="'+d.setAlpha(1).toRgb()+'"'},_getSVGLineTopOffset:function(u){for(var d=0,c=0,f=0;f<u;f++)d+=this.getHeightOfLine(f);return c=this.getHeightOfLine(f),{lineTop:d,offset:(this._fontSizeMult-this._fontSizeFraction)*c/(this.lineHeight*this._fontSizeMult)}},getSvgStyles:function(u){var d=t.Object.prototype.getSvgStyles.call(this,u);return d+" white-space: pre;"}})}(),function(o){var a=o.fabric||(o.fabric={});a.Textbox=a.util.createClass(a.IText,a.Observable,{type:"textbox",minWidth:20,dynamicMinWidth:2,__cachedLines:null,lockScalingFlip:!0,noScaleCache:!1,_dimensionAffectingProps:a.Text.prototype._dimensionAffectingProps.concat("width"),_wordJoiners:/[ \t\r]/,splitByGrapheme:!1,initDimensions:function(){this.__skipDimension||(this.isEditing&&this.initDelayedCursor(),this.clearContextTop(),this._clearCache(),this.dynamicMinWidth=0,this._styleMap=this._generateStyleMap(this._splitText()),this.dynamicMinWidth>this.width&&this._set("width",this.dynamicMinWidth),this.textAlign.indexOf("justify")!==-1&&this.enlargeSpaces(),this.height=this.calcTextHeight(),this.saveState({propertySet:"_dimensionAffectingProps"}))},_generateStyleMap:function(u){for(var d=0,c=0,f=0,s={},h=0;h<u.graphemeLines.length;h++)u.graphemeText[f]===`
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c=this._styleMap[u];u=c.line,d=c.offset+d,delete this.styles[u][d]},_getLineStyle:function(u){var d=this._styleMap[u];return!!this.styles[d.line]},_setLineStyle:function(u){var d=this._styleMap[u];this.styles[d.line]={}},_wrapText:function(u,d){var c=[],f;for(this.isWrapping=!0,f=0;f<u.length;f++)c=c.concat(this._wrapLine(u[f],f,d));return this.isWrapping=!1,c},_measureWord:function(u,d,c){var f=0,s,h=!0;c=c||0;for(var v=0,m=u.length;v<m;v++){var y=this._getGraphemeBox(u[v],d,v+c,s,h);f+=y.kernedWidth,s=u[v]}return f},_wrapLine:function(u,d,c,D){var s=0,h=this.splitByGrapheme,v=[],m=[],y=h?a.util.string.graphemeSplit(u):u.split(this._wordJoiners),b="",_=0,w=h?"":" ",S=0,x=0,T=0,E=!0,I=this._getWidthOfCharSpacing(),D=D||0;y.length===0&&y.push([]),c-=D;for(var N=0;N<y.length;N++)b=h?y[N]:a.util.string.graphemeSplit(y[N]),S=this._measureWord(b,d,_),_+=b.length,s+=x+S-I,s>c&&!E?(v.push(m),m=[],s=S,E=!0):s+=I,!E&&!h&&m.push(w),m=m.concat(b),x=h?0:this._measureWord([w],d,_),_++,E=!1,S>T&&(T=S);return N&&v.push(m),T+D>this.dynamicMinWidth&&(this.dynamicMinWidth=T-I+D),v},isEndOfWrapping:function(u){return!this._styleMap[u+1]||this._styleMap[u+1].line!==this._styleMap[u].line},missingNewlineOffset:function(u){return this.splitByGrapheme?this.isEndOfWrapping(u)?1:0:1},_splitTextIntoLines:function(u){for(var d=a.Text.prototype._splitTextIntoLines.call(this,u),c=this._wrapText(d.lines,this.width),f=new Array(c.length),s=0;s<c.length;s++)f[s]=c[s].join("");return d.lines=f,d.graphemeLines=c,d},getMinWidth:function(){return Math.max(this.minWidth,this.dynamicMinWidth)},_removeExtraneousStyles:function(){var u={};for(var d in this._styleMap)this._textLines[d]&&(u[this._styleMap[d].line]=1);for(var d in this.styles)u[d]||delete this.styles[d]},toObject:function(u){return this.callSuper("toObject",["minWidth","splitByGrapheme"].concat(u))}}),a.Textbox.fromObject=function(u,d){var c=a.util.stylesFromArray(u.styles,u.text),f=Object.assign({},u,{styles:c});return a.Object._fromObject("Textbox",f,d,"text")}}(e),function(){var o=t.controlsUtils,a=o.scaleSkewCursorStyleHandler,u=o.scaleCursorStyleHandler,d=o.scalingEqually,c=o.scalingYOrSkewingX,f=o.scalingXOrSkewingY,s=o.scaleOrSkewActionName,h=t.Object.prototype.controls;if(h.ml=new t.Control({x:-.5,y:0,cursorStyleHandler:a,actionHandler:f,getActionName:s}),h.mr=new t.Control({x:.5,y:0,cursorStyleHandler:a,actionHandler:f,getActionName:s}),h.mb=new t.Control({x:0,y:.5,cursorStyleHandler:a,actionHandler:c,getActionName:s}),h.mt=new t.Control({x:0,y:-.5,cursorStyleHandler:a,actionHandler:c,getActionName:s}),h.tl=new t.Control({x:-.5,y:-.5,cursorStyleHandler:u,actionHandler:d}),h.tr=new t.Control({x:.5,y:-.5,cursorStyleHandler:u,actionHandler:d}),h.bl=new 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ed(e.bl_tree,R2),e.bi_buf=0,e.bi_valid=0,F2(e)},N2=(e,t,n,r)=>{En(e,(gW<<1)+(r?1:0),3),z2(e),vl(e,n),vl(e,~n),n&&e.pending_buf.set(e.window.subarray(t,t+n),e.pending),e.pending+=n},PW=e=>{En(e,k2<<1,3),Tr(e,bm,Gr),SW(e)},IW=(e,t,n,r)=>{let i,l,g=0;e.level>0?(e.strm.data_type===mW&&(e.strm.data_type=OW(e)),Oh(e,e.l_desc),Oh(e,e.d_desc),g=kW(e),i=e.opt_len+3+7>>>3,l=e.static_len+3+7>>>3,l<=i&&(i=l)):i=l=n+5,n+4<=i&&t!==-1?N2(e,t,n,r):e.strategy===pW||l===i?(En(e,(k2<<1)+(r?1:0),3),J0(e,Gr,Rs)):(En(e,(vW<<1)+(r?1:0),3),EW(e,e.l_desc.max_code+1,e.d_desc.max_code+1,g+1),J0(e,e.dyn_ltree,e.dyn_dtree)),F2(e),r&&z2(e)},MW=(e,t,n)=>(e.pending_buf[e.sym_buf+e.sym_next++]=t,e.pending_buf[e.sym_buf+e.sym_next++]=t>>8,e.pending_buf[e.sym_buf+e.sym_next++]=n,t===0?e.dyn_ltree[n*2]++:(e.matches++,t--,e.dyn_ltree[(gl[n]+Vl+1)*2]++,e.dyn_dtree[D2(t)*2]++),e.sym_next===e.sym_end);var LW=AW,RW=N2,DW=IW,$W=MW,BW=PW,FW={_tr_init:LW,_tr_stored_block:RW,_tr_flush_block:DW,_tr_tally:$W,_tr_align:BW};const 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version"},qo={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:UW,_tr_stored_block:Ah,_tr_flush_block:HW,_tr_tally:Bi,_tr_align:WW}=FW,{Z_NO_FLUSH:Fi,Z_PARTIAL_FLUSH:YW,Z_FULL_FLUSH:XW,Z_FINISH:qn,Z_BLOCK:ny,Z_OK:rn,Z_STREAM_END:ry,Z_STREAM_ERROR:Pr,Z_DATA_ERROR:KW,Z_BUF_ERROR:nd,Z_DEFAULT_COMPRESSION:GW,Z_FILTERED:qW,Z_HUFFMAN_ONLY:_u,Z_RLE:ZW,Z_FIXED:JW,Z_DEFAULT_STRATEGY:QW,Z_UNKNOWN:eY,Z_DEFLATED:lf}=qo,tY=9,nY=15,rY=8,iY=29,aY=256,Ph=aY+1+iY,oY=30,sY=19,lY=2*Ph+1,uY=15,lt=3,Li=258,Ir=Li+lt+1,cY=32,$o=42,wm=57,Ih=69,Mh=73,Lh=91,Rh=103,wa=113,ms=666,wn=1,Zo=2,Ba=3,Jo=4,fY=3,Ca=(e,t)=>(e.msg=$a[t],t),iy=e=>e*2-(e>4?9:0),Pi=e=>{let t=e.length;for(;--t>=0;)e[t]=0},dY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let hY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,zi=hY;const $n=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},jn=(e,t)=>{HW(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,$n(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},us=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Dh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=yl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Zt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},j2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Ir?e.strstart-(e.w_size-Ir):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Li;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Li-(f-r),r=f-Li,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Bo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Ir)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),dY(e),r+=t),e.strm.avail_in===0)break;if(n=Dh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=zi(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=zi(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Ir&&e.strm.avail_in!==0)},V2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==qn||t===Fi||r!==i+e.strm.avail_in)))break;g=t===qn&&r===i+e.strm.avail_in?1:0,Ah(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,$n(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Dh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?Jo:t!==Fi&&t!==qn&&e.strm.avail_in===0&&e.strstart===e.block_start?Zo:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Dh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===qn)&&t!==Fi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===qn&&e.strm.avail_in===0&&r===i?1:0,Ah(e,e.block_start,r,g),e.block_start+=r,$n(e.strm)),g?Ba:wn)},rd=(e,t)=>{let n,r;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Fi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n)),e.match_length>=lt)if(r=Bi(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=zi(e,e.ins_h,e.window[e.strstart+1]);else r=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},ao=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Ir){if(Bo(e),e.lookahead<Ir&&t===Fi)return wn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Ir&&(e.match_length=j2(e,n),e.match_length<=5&&(e.strategy===qW||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=Bi(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=zi(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(jn(e,!1),e.strm.avail_out===0))return wn}else if(e.match_available){if(r=Bi(e,0,e.window[e.strstart-1]),r&&jn(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return wn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=Bi(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},pY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Li){if(Bo(e),e.lookahead<=Li&&t===Fi)return wn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Li;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Li-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=Bi(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo},mY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Bo(e),e.lookahead===0)){if(t===Fi)return wn;break}if(e.match_length=0,n=Bi(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(jn(e,!1),e.strm.avail_out===0))return wn}return e.insert=0,t===qn?(jn(e,!0),e.strm.avail_out===0?Ba:Jo):e.sym_next&&(jn(e,!1),e.strm.avail_out===0)?wn:Zo};function wr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const gs=[new wr(0,0,0,0,V2),new wr(4,4,8,4,rd),new wr(4,5,16,8,rd),new wr(4,6,32,32,rd),new wr(4,4,16,16,ao),new wr(8,16,32,32,ao),new wr(8,16,128,128,ao),new wr(8,32,128,256,ao),new wr(32,128,258,1024,ao),new wr(32,258,258,4096,ao)],gY=e=>{e.window_size=2*e.w_size,Pi(e.head),e.max_lazy_match=gs[e.level].max_lazy,e.good_match=gs[e.level].good_length,e.nice_match=gs[e.level].nice_length,e.max_chain_length=gs[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function vY(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=lf,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(lY*2),this.dyn_dtree=new Uint16Array((2*oY+1)*2),this.bl_tree=new Uint16Array((2*sY+1)*2),Pi(this.dyn_ltree),Pi(this.dyn_dtree),Pi(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(uY+1),this.heap=new Uint16Array(2*Ph+1),Pi(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(2*Ph+1),Pi(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Ul=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.status!==$o&&t.status!==wm&&t.status!==Ih&&t.status!==Mh&&t.status!==Lh&&t.status!==Rh&&t.status!==wa&&t.status!==ms?1:0},U2=e=>{if(Ul(e))return Ca(e,Pr);e.total_in=e.total_out=0,e.data_type=eY;const t=e.state;return t.pending=0,t.pending_out=0,t.wrap<0&&(t.wrap=-t.wrap),t.status=t.wrap===2?wm:t.wrap?$o:wa,e.adler=t.wrap===2?0:1,t.last_flush=-2,UW(t),rn},H2=e=>{const t=U2(e);return t===rn&&gY(e.state),t},yY=(e,t)=>Ul(e)||e.state.wrap!==2?Pr:(e.state.gzhead=t,rn),W2=(e,t,n,r,i,l)=>{if(!e)return Pr;let g=1;if(t===GW&&(t=6),r<0?(g=0,r=-r):r>15&&(g=2,r-=16),i<1||i>tY||n!==lf||r<8||r>15||t<0||t>9||l<0||l>JW||r===8&&g!==1)return Ca(e,Pr);r===8&&(r=9);const o=new vY;return e.state=o,o.strm=e,o.status=$o,o.wrap=g,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=i+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+lt-1)/lt),o.window=new Uint8Array(o.w_size*2),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<i+6,o.pending_buf_size=o.lit_bufsize*4,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=(o.lit_bufsize-1)*3,o.level=t,o.strategy=l,o.method=n,H2(e)},bY=(e,t)=>W2(e,t,lf,nY,rY,QW),_Y=(e,t)=>{if(Ul(e)||t>ny||t<0)return e?Ca(e,Pr):Pr;const n=e.state;if(!e.output||e.avail_in!==0&&!e.input||n.status===ms&&t!==qn)return Ca(e,e.avail_out===0?nd:Pr);const r=n.last_flush;if(n.last_flush=t,n.pending!==0){if($n(e),e.avail_out===0)return n.last_flush=-1,rn}else if(e.avail_in===0&&iy(t)<=iy(r)&&t!==qn)return Ca(e,nd);if(n.status===ms&&e.avail_in!==0)return 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D[l++]=f[T++];while(--w);T=l-x,E=D}}}else if(T+=c-w,w<S){S-=w;do D[l++]=f[T++];while(--w);T=l-x,E=D}for(;S>2;)D[l++]=E[T++],D[l++]=E[T++],D[l++]=E[T++],S-=3;S&&(D[l++]=E[T++],S>1&&(D[l++]=E[T++]))}else{T=l-x;do D[l++]=D[T++],D[l++]=D[T++],D[l++]=D[T++],S-=3;while(S>2);S&&(D[l++]=D[T++],S>1&&(D[l++]=D[T++]))}}else if((w&64)===0){_=m[(_&65535)+(s&(1<<w)-1)];continue n}else{t.msg="invalid distance code",N.mode=wu;break e}break}}else if((w&64)===0){_=v[(_&65535)+(s&(1<<w)-1)];continue t}else if(w&32){N.mode=nX;break e}else{t.msg="invalid literal/length code",N.mode=wu;break e}break}}while(r<i&&l<o);S=h>>3,r-=S,h-=S<<3,s&=(1<<h)-1,t.next_in=r,t.next_out=l,t.avail_in=r<i?5+(i-r):5-(r-i),t.avail_out=l<o?257+(o-l):257-(l-o),N.hold=s,N.bits=h};const oo=15,ay=852,oy=592,sy=0,id=1,ly=2,iX=new Uint16Array([3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258,0,0]),aX=new Uint8Array([16,16,16,16,16,16,16,16,17,17,17,17,18,18,18,18,19,19,19,19,20,20,20,20,21,21,21,21,16,72,78]),oX=new Uint16Array([1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577,0,0]),sX=new Uint8Array([16,16,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,64,64]),lX=(e,t,n,r,i,l,g,o)=>{const a=o.bits;let u=0,d=0,c=0,f=0,s=0,h=0,v=0,m=0,y=0,b=0,_,w,S,x,T,E=null,I;const D=new Uint16Array(oo+1),N=new Uint16Array(oo+1);let L=null,F,O,V;for(u=0;u<=oo;u++)D[u]=0;for(d=0;d<r;d++)D[t[n+d]]++;for(s=a,f=oo;f>=1&&D[f]===0;f--);if(s>f&&(s=f),f===0)return i[l++]=1<<24|64<<16|0,i[l++]=1<<24|64<<16|0,o.bits=1,0;for(c=1;c<f&&D[c]===0;c++);for(s<c&&(s=c),m=1,u=1;u<=oo;u++)if(m<<=1,m-=D[u],m<0)return-1;if(m>0&&(e===sy||f!==1))return-1;for(N[1]=0,u=1;u<oo;u++)N[u+1]=N[u]+D[u];for(d=0;d<r;d++)t[n+d]!==0&&(g[N[t[n+d]]++]=d);if(e===sy?(E=L=g,I=20):e===id?(E=iX,L=aX,I=257):(E=oX,L=sX,I=0),b=0,d=0,u=c,T=l,h=s,v=0,S=-1,y=1<<s,x=y-1,e===id&&y>ay||e===ly&&y>oy)return 1;for(;;){F=u-v,g[d]+1<I?(O=0,V=g[d]):g[d]>=I?(O=L[g[d]-I],V=E[g[d]-I]):(O=32+64,V=0),_=1<<u-v,w=1<<h,c=w;do w-=_,i[T+(b>>v)+w]=F<<24|O<<16|V|0;while(w!==0);for(_=1<<u-1;b&_;)_>>=1;if(_!==0?(b&=_-1,b+=_):b=0,d++,--D[u]===0){if(u===f)break;u=t[n+g[d]]}if(u>s&&(b&x)!==S){for(v===0&&(v=s),T+=c,h=u-v,m=1<<h;h+v<f&&(m-=D[h+v],!(m<=0));)h++,m<<=1;if(y+=1<<h,e===id&&y>ay||e===ly&&y>oy)return 1;S=b&x,i[S]=s<<24|h<<16|T-l|0}}return b!==0&&(i[T+b]=u-v<<24|64<<16|0),o.bits=s,0};var $s=lX;const uX=0,G2=1,q2=2,{Z_FINISH:uy,Z_BLOCK:cX,Z_TREES:Cu,Z_OK:Fa,Z_STREAM_END:fX,Z_NEED_DICT:dX,Z_STREAM_ERROR:nr,Z_DATA_ERROR:Z2,Z_MEM_ERROR:J2,Z_BUF_ERROR:hX,Z_DEFLATED:cy}=qo,cf=16180,fy=16181,dy=16182,hy=16183,py=16184,my=16185,gy=16186,vy=16187,yy=16188,by=16189,Oc=16190,Ur=16191,ad=16192,_y=16193,od=16194,wy=16195,Cy=16196,Sy=16197,xy=16198,Su=16199,xu=16200,Ty=16201,ky=16202,Ey=16203,Oy=16204,Ay=16205,sd=16206,Py=16207,Iy=16208,Bt=16209,Q2=16210,eC=16211,pX=852,mX=592,gX=15,vX=gX,My=e=>(e>>>24&255)+(e>>>8&65280)+((e&65280)<<8)+((e&255)<<24);function yX(){this.strm=null,this.mode=0,this.last=!1,this.wrap=0,this.havedict=!1,this.flags=0,this.dmax=0,this.check=0,this.total=0,this.head=null,this.wbits=0,this.wsize=0,this.whave=0,this.wnext=0,this.window=null,this.hold=0,this.bits=0,this.length=0,this.offset=0,this.extra=0,this.lencode=null,this.distcode=null,this.lenbits=0,this.distbits=0,this.ncode=0,this.nlen=0,this.ndist=0,this.have=0,this.next=null,this.lens=new Uint16Array(320),this.work=new Uint16Array(288),this.lendyn=null,this.distdyn=null,this.sane=0,this.back=0,this.was=0}const Ga=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.mode<cf||t.mode>eC?1:0},tC=e=>{if(Ga(e))return nr;const t=e.state;return e.total_in=e.total_out=t.total=0,e.msg="",t.wrap&&(e.adler=t.wrap&1),t.mode=cf,t.last=0,t.havedict=0,t.flags=-1,t.dmax=32768,t.head=null,t.hold=0,t.bits=0,t.lencode=t.lendyn=new Int32Array(pX),t.distcode=t.distdyn=new Int32Array(mX),t.sane=1,t.back=-1,Fa},nC=e=>{if(Ga(e))return nr;const t=e.state;return t.wsize=0,t.whave=0,t.wnext=0,tC(e)},rC=(e,t)=>{let n;if(Ga(e))return nr;const r=e.state;return t<0?(n=0,t=-t):(n=(t>>4)+5,t<48&&(t&=15)),t&&(t<8||t>15)?nr:(r.window!==null&&r.wbits!==t&&(r.window=null),r.wrap=n,r.wbits=t,nC(e))},iC=(e,t)=>{if(!e)return nr;const n=new yX;e.state=n,n.strm=e,n.window=null,n.mode=cf;const r=rC(e,t);return r!==Fa&&(e.state=null),r},bX=e=>iC(e,vX);let Ly=!0,ld,ud;const _X=e=>{if(Ly){ld=new Int32Array(512),ud=new Int32Array(32);let t=0;for(;t<144;)e.lens[t++]=8;for(;t<256;)e.lens[t++]=9;for(;t<280;)e.lens[t++]=7;for(;t<288;)e.lens[t++]=8;for($s(G2,e.lens,0,288,ld,0,e.work,{bits:9}),t=0;t<32;)e.lens[t++]=5;$s(q2,e.lens,0,32,ud,0,e.work,{bits:5}),Ly=!1}e.lencode=ld,e.lenbits=9,e.distcode=ud,e.distbits=5},aC=(e,t,n,r)=>{let i;const l=e.state;return l.window===null&&(l.wsize=1<<l.wbits,l.wnext=0,l.whave=0,l.window=new Uint8Array(l.wsize)),r>=l.wsize?(l.window.set(t.subarray(n-l.wsize,n),0),l.wnext=0,l.whave=l.wsize):(i=l.wsize-l.wnext,i>r&&(i=r),l.window.set(t.subarray(n-r,n-r+i),l.wnext),r-=i,r?(l.window.set(t.subarray(n-r,n),0),l.wnext=r,l.whave=l.wsize):(l.wnext+=i,l.wnext===l.wsize&&(l.wnext=0),l.whave<l.wsize&&(l.whave+=i))),0},wX=(e,t)=>{let n,r,i,l,g,o,a,u,d,c,f,s,h,v,m=0,y,b,_,w,S,x,T,E;const I=new Uint8Array(4);let D,N;const L=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]);if(Ga(e)||!e.output||!e.input&&e.avail_in!==0)return nr;n=e.state,n.mode===Ur&&(n.mode=ad),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,c=o,f=a,E=Fa;e:for(;;)switch(n.mode){case cf:if(n.wrap===0){n.mode=ad;break}for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&2&&u===35615){n.wbits===0&&(n.wbits=15),n.check=0,I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0),u=0,d=0,n.mode=fy;break}if(n.head&&(n.head.done=!1),!(n.wrap&1)||(((u&255)<<8)+(u>>8))%31){e.msg="incorrect header check",n.mode=Bt;break}if((u&15)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(u>>>=4,d-=4,T=(u&15)+8,n.wbits===0&&(n.wbits=T),T>15||T>n.wbits){e.msg="invalid window size",n.mode=Bt;break}n.dmax=1<<n.wbits,n.flags=0,e.adler=n.check=1,n.mode=u&512?by:Ur,u=0,d=0;break;case fy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.flags=u,(n.flags&255)!==cy){e.msg="unknown compression method",n.mode=Bt;break}if(n.flags&57344){e.msg="unknown header flags set",n.mode=Bt;break}n.head&&(n.head.text=u>>8&1),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=dy;case dy:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.time=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,I[2]=u>>>16&255,I[3]=u>>>24&255,n.check=Zt(n.check,I,4,0)),u=0,d=0,n.mode=hy;case hy:for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.head&&(n.head.xflags=u&255,n.head.os=u>>8),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0,n.mode=py;case py:if(n.flags&1024){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length=u,n.head&&(n.head.extra_len=u),n.flags&512&&n.wrap&4&&(I[0]=u&255,I[1]=u>>>8&255,n.check=Zt(n.check,I,2,0)),u=0,d=0}else n.head&&(n.head.extra=null);n.mode=my;case my:if(n.flags&1024&&(s=n.length,s>o&&(s=o),s&&(n.head&&(T=n.head.extra_len-n.length,n.head.extra||(n.head.extra=new Uint8Array(n.head.extra_len)),n.head.extra.set(r.subarray(l,l+s),T)),n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,n.length-=s),n.length))break e;n.length=0,n.mode=gy;case gy:if(n.flags&2048){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.name+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.name=null);n.length=0,n.mode=vy;case vy:if(n.flags&4096){if(o===0)break e;s=0;do T=r[l+s++],n.head&&T&&n.length<65536&&(n.head.comment+=String.fromCharCode(T));while(T&&s<o);if(n.flags&512&&n.wrap&4&&(n.check=Zt(n.check,r,s,l)),o-=s,l+=s,T)break e}else n.head&&(n.head.comment=null);n.mode=yy;case yy:if(n.flags&512){for(;d<16;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.check&65535)){e.msg="header crc mismatch",n.mode=Bt;break}u=0,d=0}n.head&&(n.head.hcrc=n.flags>>9&1,n.head.done=!0),e.adler=n.check=0,n.mode=Ur;break;case by:for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}e.adler=n.check=My(u),u=0,d=0,n.mode=Oc;case Oc:if(n.havedict===0)return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,dX;e.adler=n.check=1,n.mode=Ur;case Ur:if(t===cX||t===Cu)break e;case ad:if(n.last){u>>>=d&7,d-=d&7,n.mode=sd;break}for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}switch(n.last=u&1,u>>>=1,d-=1,u&3){case 0:n.mode=_y;break;case 1:if(_X(n),n.mode=Su,t===Cu){u>>>=2,d-=2;break e}break;case 2:n.mode=Cy;break;case 3:e.msg="invalid block type",n.mode=Bt}u>>>=2,d-=2;break;case _y:for(u>>>=d&7,d-=d&7;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((u&65535)!==(u>>>16^65535)){e.msg="invalid stored block lengths",n.mode=Bt;break}if(n.length=u&65535,u=0,d=0,n.mode=od,t===Cu)break e;case od:n.mode=wy;case wy:if(s=n.length,s){if(s>o&&(s=o),s>a&&(s=a),s===0)break e;i.set(r.subarray(l,l+s),g),o-=s,l+=s,a-=s,g+=s,n.length-=s;break}n.mode=Ur;break;case Cy:for(;d<14;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.nlen=(u&31)+257,u>>>=5,d-=5,n.ndist=(u&31)+1,u>>>=5,d-=5,n.ncode=(u&15)+4,u>>>=4,d-=4,n.nlen>286||n.ndist>30){e.msg="too many length or distance symbols",n.mode=Bt;break}n.have=0,n.mode=Sy;case Sy:for(;n.have<n.ncode;){for(;d<3;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.lens[L[n.have++]]=u&7,u>>>=3,d-=3}for(;n.have<19;)n.lens[L[n.have++]]=0;if(n.lencode=n.lendyn,n.lenbits=7,D={bits:n.lenbits},E=$s(uX,n.lens,0,19,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid code lengths set",n.mode=Bt;break}n.have=0,n.mode=xy;case xy:for(;n.have<n.nlen+n.ndist;){for(;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(_<16)u>>>=y,d-=y,n.lens[n.have++]=_;else{if(_===16){for(N=y+2;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(u>>>=y,d-=y,n.have===0){e.msg="invalid bit length repeat",n.mode=Bt;break}T=n.lens[n.have-1],s=3+(u&3),u>>>=2,d-=2}else if(_===17){for(N=y+3;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=3+(u&7),u>>>=3,d-=3}else{for(N=y+7;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=y,d-=y,T=0,s=11+(u&127),u>>>=7,d-=7}if(n.have+s>n.nlen+n.ndist){e.msg="invalid bit length repeat",n.mode=Bt;break}for(;s--;)n.lens[n.have++]=T}}if(n.mode===Bt)break;if(n.lens[256]===0){e.msg="invalid code -- missing end-of-block",n.mode=Bt;break}if(n.lenbits=9,D={bits:n.lenbits},E=$s(G2,n.lens,0,n.nlen,n.lencode,0,n.work,D),n.lenbits=D.bits,E){e.msg="invalid literal/lengths set",n.mode=Bt;break}if(n.distbits=6,n.distcode=n.distdyn,D={bits:n.distbits},E=$s(q2,n.lens,n.nlen,n.ndist,n.distcode,0,n.work,D),n.distbits=D.bits,E){e.msg="invalid distances set",n.mode=Bt;break}if(n.mode=Su,t===Cu)break e;case Su:n.mode=xu;case xu:if(o>=6&&a>=258){e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,rX(e,f),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,n.mode===Ur&&(n.back=-1);break}for(n.back=0;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(b&&(b&240)===0){for(w=y,S=b,x=_;m=n.lencode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,n.length=_,b===0){n.mode=Ay;break}if(b&32){n.back=-1,n.mode=Ur;break}if(b&64){e.msg="invalid literal/length code",n.mode=Bt;break}n.extra=b&15,n.mode=Ty;case Ty:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=ky;case ky:for(;m=n.distcode[u&(1<<n.distbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((b&240)===0){for(w=y,S=b,x=_;m=n.distcode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,b&64){e.msg="invalid distance code",n.mode=Bt;break}n.offset=_,n.extra=b&15,n.mode=Ey;case Ey:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.offset+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=Bt;break}n.mode=Oy;case Oy:if(a===0)break e;if(s=f-a,n.offset>s){if(s=n.offset-s,s>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=Bt;break}s>n.wnext?(s-=n.wnext,h=n.wsize-s):h=n.wnext-s,s>n.length&&(s=n.length),v=n.window}else v=i,h=g-n.offset,s=n.length;s>a&&(s=a),a-=s,n.length-=s;do i[g++]=v[h++];while(--s);n.length===0&&(n.mode=xu);break;case Ay:if(a===0)break e;i[g++]=n.length,a--,n.mode=xu;break;case sd:if(n.wrap){for(;d<32;){if(o===0)break 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Uint8Array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,3,7]),$2=new Uint8Array([16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15]),RW=512,Jr=new Array((bl+2)*2);es(Jr);const zs=new Array(Eo*2);es(zs);const _l=new Array(RW);es(_l);const wl=new Array(IW-PW+1);es(wl);const km=new Array(Cm);es(km);const Ic=new Array(Eo);es(Ic);function id(e,t,n,r,i){this.static_tree=e,this.extra_bits=t,this.extra_base=n,this.elems=r,this.max_length=i,this.has_stree=e&&e.length}let B2,F2,z2;function ad(e,t){this.dyn_tree=e,this.max_code=0,this.stat_desc=t}const N2=e=>e<256?_l[e]:_l[256+(e>>>7)],Cl=(e,t)=>{e.pending_buf[e.pending++]=t&255,e.pending_buf[e.pending++]=t>>>8&255},An=(e,t,n)=>{e.bi_valid>rd-n?(e.bi_buf|=t<<e.bi_valid&65535,Cl(e,e.bi_buf),e.bi_buf=t>>rd-e.bi_valid,e.bi_valid+=n-rd):(e.bi_buf|=t<<e.bi_valid&65535,e.bi_valid+=n)},Or=(e,t,n)=>{An(e,n[t*2],n[t*2+1])},j2=(e,t)=>{let n=0;do n|=e&1,e>>>=1,n<<=1;while(--t>0);return n>>>1},DW=e=>{e.bi_valid===16?(Cl(e,e.bi_buf),e.bi_buf=0,e.bi_valid=0):e.bi_valid>=8&&(e.pending_buf[e.pending++]=e.bi_buf&255,e.bi_buf>>=8,e.bi_valid-=8)},$W=(e,t)=>{const n=t.dyn_tree,r=t.max_code,i=t.stat_desc.static_tree,l=t.stat_desc.has_stree,g=t.stat_desc.extra_bits,o=t.stat_desc.extra_base,a=t.stat_desc.max_length;let u,d,c,f,s,h,v=0;for(f=0;f<=ka;f++)e.bl_count[f]=0;for(n[e.heap[e.heap_max]*2+1]=0,u=e.heap_max+1;u<M2;u++)d=e.heap[u],f=n[n[d*2+1]*2+1]+1,f>a&&(f=a,v++),n[d*2+1]=f,!(d>r)&&(e.bl_count[f]++,s=0,d>=o&&(s=g[d-o]),h=n[d*2],e.opt_len+=h*(f+s),l&&(e.static_len+=h*(i[d*2+1]+s)));if(v!==0){do{for(f=a-1;e.bl_count[f]===0;)f--;e.bl_count[f]--,e.bl_count[f+1]+=2,e.bl_count[a]--,v-=2}while(v>0);for(f=a;f!==0;f--)for(d=e.bl_count[f];d!==0;)c=e.heap[--u],!(c>r)&&(n[c*2+1]!==f&&(e.opt_len+=(f-n[c*2+1])*n[c*2],n[c*2+1]=f),d--)}},V2=(e,t,n)=>{const r=new Array(ka+1);let i=0,l,g;for(l=1;l<=ka;l++)i=i+n[l-1]<<1,r[l]=i;for(g=0;g<=t;g++){let o=e[g*2+1];o!==0&&(e[g*2]=j2(r[o]++,o))}},BW=()=>{let e,t,n,r,i;const l=new Array(ka+1);for(n=0,r=0;r<Cm-1;r++)for(km[r]=n,e=0;e<1<<Mh[r];e++)wl[n++]=r;for(wl[n-1]=r,i=0,r=0;r<16;r++)for(Ic[r]=i,e=0;e<1<<Ju[r];e++)_l[i++]=r;for(i>>=7;r<Eo;r++)for(Ic[r]=i<<7,e=0;e<1<<Ju[r]-7;e++)_l[256+i++]=r;for(t=0;t<=ka;t++)l[t]=0;for(e=0;e<=143;)Jr[e*2+1]=8,e++,l[8]++;for(;e<=255;)Jr[e*2+1]=9,e++,l[9]++;for(;e<=279;)Jr[e*2+1]=7,e++,l[7]++;for(;e<=287;)Jr[e*2+1]=8,e++,l[8]++;for(V2(Jr,bl+1,l),e=0;e<Eo;e++)zs[e*2+1]=5,zs[e*2]=j2(e,5);B2=new id(Jr,Mh,Xl+1,bl,ka),F2=new id(zs,Ju,0,Eo,ka),z2=new id(new Array(0),LW,0,Sm,MW)},U2=e=>{let t;for(t=0;t<bl;t++)e.dyn_ltree[t*2]=0;for(t=0;t<Eo;t++)e.dyn_dtree[t*2]=0;for(t=0;t<Sm;t++)e.bl_tree[t*2]=0;e.dyn_ltree[xm*2]=1,e.opt_len=e.static_len=0,e.sym_next=e.matches=0},H2=e=>{e.bi_valid>8?Cl(e,e.bi_buf):e.bi_valid>0&&(e.pending_buf[e.pending++]=e.bi_buf),e.bi_buf=0,e.bi_valid=0},ny=(e,t,n,r)=>{const i=t*2,l=n*2;return e[i]<e[l]||e[i]===e[l]&&r[t]<=r[n]},od=(e,t,n)=>{const r=e.heap[n];let i=n<<1;for(;i<=e.heap_len&&(i<e.heap_len&&ny(t,e.heap[i+1],e.heap[i],e.depth)&&i++,!ny(t,r,e.heap[i],e.depth));)e.heap[n]=e.heap[i],n=i,i<<=1;e.heap[n]=r},ry=(e,t,n)=>{let r,i,l=0,g,o;if(e.sym_next!==0)do r=e.pending_buf[e.sym_buf+l++]&255,r+=(e.pending_buf[e.sym_buf+l++]&255)<<8,i=e.pending_buf[e.sym_buf+l++],r===0?Or(e,i,t):(g=wl[i],Or(e,g+Xl+1,t),o=Mh[g],o!==0&&(i-=km[g],An(e,i,o)),r--,g=N2(r),Or(e,g,n),o=Ju[g],o!==0&&(r-=Ic[g],An(e,r,o)));while(l<e.sym_next);Or(e,xm,t)},Lh=(e,t)=>{const n=t.dyn_tree,r=t.stat_desc.static_tree,i=t.stat_desc.has_stree,l=t.stat_desc.elems;let g,o,a=-1,u;for(e.heap_len=0,e.heap_max=M2,g=0;g<l;g++)n[g*2]!==0?(e.heap[++e.heap_len]=a=g,e.depth[g]=0):n[g*2+1]=0;for(;e.heap_len<2;)u=e.heap[++e.heap_len]=a<2?++a:0,n[u*2]=1,e.depth[u]=0,e.opt_len--,i&&(e.static_len-=r[u*2+1]);for(t.max_code=a,g=e.heap_len>>1;g>=1;g--)od(e,n,g);u=l;do g=e.heap[1],e.heap[1]=e.heap[e.heap_len--],od(e,n,1),o=e.heap[1],e.heap[--e.heap_max]=g,e.heap[--e.heap_max]=o,n[u*2]=n[g*2]+n[o*2],e.depth[u]=(e.depth[g]>=e.depth[o]?e.depth[g]:e.depth[o])+1,n[g*2+1]=n[o*2+1]=u,e.heap[1]=u++,od(e,n,1);while(e.heap_len>=2);e.heap[--e.heap_max]=e.heap[1],$W(e,t),V2(n,a,e.bl_count)},iy=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),t[(n+1)*2+1]=65535,r=0;r<=n;r++)l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)&&(o<u?e.bl_tree[l*2]+=o:l!==0?(l!==i&&e.bl_tree[l*2]++,e.bl_tree[L2*2]++):o<=10?e.bl_tree[R2*2]++:e.bl_tree[D2*2]++,o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4))},ay=(e,t,n)=>{let r,i=-1,l,g=t[0*2+1],o=0,a=7,u=4;for(g===0&&(a=138,u=3),r=0;r<=n;r++)if(l=g,g=t[(r+1)*2+1],!(++o<a&&l===g)){if(o<u)do Or(e,l,e.bl_tree);while(--o!==0);else l!==0?(l!==i&&(Or(e,l,e.bl_tree),o--),Or(e,L2,e.bl_tree),An(e,o-3,2)):o<=10?(Or(e,R2,e.bl_tree),An(e,o-3,3)):(Or(e,D2,e.bl_tree),An(e,o-11,7));o=0,i=l,g===0?(a=138,u=3):l===g?(a=6,u=3):(a=7,u=4)}},FW=e=>{let t;for(iy(e,e.dyn_ltree,e.l_desc.max_code),iy(e,e.dyn_dtree,e.d_desc.max_code),Lh(e,e.bl_desc),t=Sm-1;t>=3&&e.bl_tree[$2[t]*2+1]===0;t--);return e.opt_len+=3*(t+1)+5+5+4,t},zW=(e,t,n,r)=>{let i;for(An(e,t-257,5),An(e,n-1,5),An(e,r-4,4),i=0;i<r;i++)An(e,e.bl_tree[$2[i]*2+1],3);ay(e,e.dyn_ltree,t-1),ay(e,e.dyn_dtree,n-1)},NW=e=>{let t=4093624447,n;for(n=0;n<=31;n++,t>>>=1)if(t&1&&e.dyn_ltree[n*2]!==0)return ey;if(e.dyn_ltree[9*2]!==0||e.dyn_ltree[10*2]!==0||e.dyn_ltree[13*2]!==0)return ty;for(n=32;n<Xl;n++)if(e.dyn_ltree[n*2]!==0)return ty;return ey};let oy=!1;const jW=e=>{oy||(BW(),oy=!0),e.l_desc=new ad(e.dyn_ltree,B2),e.d_desc=new ad(e.dyn_dtree,F2),e.bl_desc=new 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ZW=(e,t,n,r)=>{let i=e&65535|0,l=e>>>16&65535|0,g=0;for(;n!==0;){g=n>2e3?2e3:n,n-=g;do i=i+t[r++]|0,l=l+i|0;while(--g);i%=65521,l%=65521}return i|l<<16|0};var Sl=ZW;const JW=()=>{let e,t=[];for(var n=0;n<256;n++){e=n;for(var r=0;r<8;r++)e=e&1?3988292384^e>>>1:e>>>1;t[n]=e}return t},QW=new Uint32Array(JW()),eY=(e,t,n,r)=>{const i=QW,l=r+n;e^=-1;for(let g=r;g<l;g++)e=e>>>8^i[(e^t[g])&255];return e^-1};var Jt=eY,ja={2:"need dictionary",1:"stream end",0:"","-1":"file error","-2":"stream error","-3":"data error","-4":"insufficient memory","-5":"buffer error","-6":"incompatible version"},ts={Z_NO_FLUSH:0,Z_PARTIAL_FLUSH:1,Z_SYNC_FLUSH:2,Z_FULL_FLUSH:3,Z_FINISH:4,Z_BLOCK:5,Z_TREES:6,Z_OK:0,Z_STREAM_END:1,Z_NEED_DICT:2,Z_ERRNO:-1,Z_STREAM_ERROR:-2,Z_DATA_ERROR:-3,Z_MEM_ERROR:-4,Z_BUF_ERROR:-5,Z_NO_COMPRESSION:0,Z_BEST_SPEED:1,Z_BEST_COMPRESSION:9,Z_DEFAULT_COMPRESSION:-1,Z_FILTERED:1,Z_HUFFMAN_ONLY:2,Z_RLE:3,Z_FIXED:4,Z_DEFAULT_STRATEGY:0,Z_BINARY:0,Z_TEXT:1,Z_UNKNOWN:2,Z_DEFLATED:8};const{_tr_init:tY,_tr_stored_block:Rh,_tr_flush_block:nY,_tr_tally:ji,_tr_align:rY}=qW,{Z_NO_FLUSH:Vi,Z_PARTIAL_FLUSH:iY,Z_FULL_FLUSH:aY,Z_FINISH:Qn,Z_BLOCK:sy,Z_OK:an,Z_STREAM_END:ly,Z_STREAM_ERROR:Lr,Z_DATA_ERROR:oY,Z_BUF_ERROR:sd,Z_DEFAULT_COMPRESSION:sY,Z_FILTERED:lY,Z_HUFFMAN_ONLY:ku,Z_RLE:uY,Z_FIXED:cY,Z_DEFAULT_STRATEGY:fY,Z_UNKNOWN:dY,Z_DEFLATED:hf}=ts,hY=9,pY=15,mY=8,gY=29,vY=256,Dh=vY+1+gY,yY=30,bY=19,_Y=2*Dh+1,wY=15,lt=3,Bi=258,Rr=Bi+lt+1,CY=32,jo=42,Tm=57,$h=69,Bh=73,Fh=91,zh=103,Ta=113,_s=666,xn=1,ns=2,Va=3,rs=4,SY=3,Ea=(e,t)=>(e.msg=ja[t],t),uy=e=>e*2-(e>4?9:0),Ri=e=>{let t=e.length;for(;--t>=0;)e[t]=0},xY=e=>{let t,n,r,i=e.w_size;t=e.hash_size,r=t;do n=e.head[--r],e.head[r]=n>=i?n-i:0;while(--t);t=i,r=t;do n=e.prev[--r],e.prev[r]=n>=i?n-i:0;while(--t)};let kY=(e,t,n)=>(t<<e.hash_shift^n)&e.hash_mask,Ui=kY;const Fn=e=>{const t=e.state;let n=t.pending;n>e.avail_out&&(n=e.avail_out),n!==0&&(e.output.set(t.pending_buf.subarray(t.pending_out,t.pending_out+n),e.next_out),e.next_out+=n,t.pending_out+=n,e.total_out+=n,e.avail_out-=n,t.pending-=n,t.pending===0&&(t.pending_out=0))},Un=(e,t)=>{nY(e,e.block_start>=0?e.block_start:-1,e.strstart-e.block_start,t),e.block_start=e.strstart,Fn(e.strm)},mt=(e,t)=>{e.pending_buf[e.pending++]=t},ps=(e,t)=>{e.pending_buf[e.pending++]=t>>>8&255,e.pending_buf[e.pending++]=t&255},Nh=(e,t,n,r)=>{let i=e.avail_in;return i>r&&(i=r),i===0?0:(e.avail_in-=i,t.set(e.input.subarray(e.next_in,e.next_in+i),n),e.state.wrap===1?e.adler=Sl(e.adler,t,i,n):e.state.wrap===2&&(e.adler=Jt(e.adler,t,i,n)),e.next_in+=i,e.total_in+=i,i)},Y2=(e,t)=>{let n=e.max_chain_length,r=e.strstart,i,l,g=e.prev_length,o=e.nice_match;const a=e.strstart>e.w_size-Rr?e.strstart-(e.w_size-Rr):0,u=e.window,d=e.w_mask,c=e.prev,f=e.strstart+Bi;let s=u[r+g-1],h=u[r+g];e.prev_length>=e.good_match&&(n>>=2),o>e.lookahead&&(o=e.lookahead);do if(i=t,!(u[i+g]!==h||u[i+g-1]!==s||u[i]!==u[r]||u[++i]!==u[r+1])){r+=2,i++;do;while(u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&u[++r]===u[++i]&&r<f);if(l=Bi-(f-r),r=f-Bi,l>g){if(e.match_start=t,g=l,l>=o)break;s=u[r+g-1],h=u[r+g]}}while((t=c[t&d])>a&&--n!==0);return g<=e.lookahead?g:e.lookahead},Vo=e=>{const t=e.w_size;let n,r,i;do{if(r=e.window_size-e.lookahead-e.strstart,e.strstart>=t+(t-Rr)&&(e.window.set(e.window.subarray(t,t+t-r),0),e.match_start-=t,e.strstart-=t,e.block_start-=t,e.insert>e.strstart&&(e.insert=e.strstart),xY(e),r+=t),e.strm.avail_in===0)break;if(n=Nh(e.strm,e.window,e.strstart+e.lookahead,r),e.lookahead+=n,e.lookahead+e.insert>=lt)for(i=e.strstart-e.insert,e.ins_h=e.window[i],e.ins_h=Ui(e,e.ins_h,e.window[i+1]);e.insert&&(e.ins_h=Ui(e,e.ins_h,e.window[i+lt-1]),e.prev[i&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=i,i++,e.insert--,!(e.lookahead+e.insert<lt)););}while(e.lookahead<Rr&&e.strm.avail_in!==0)},X2=(e,t)=>{let n=e.pending_buf_size-5>e.w_size?e.w_size:e.pending_buf_size-5,r,i,l,g=0,o=e.strm.avail_in;do{if(r=65535,l=e.bi_valid+42>>3,e.strm.avail_out<l||(l=e.strm.avail_out-l,i=e.strstart-e.block_start,r>i+e.strm.avail_in&&(r=i+e.strm.avail_in),r>l&&(r=l),r<n&&(r===0&&t!==Qn||t===Vi||r!==i+e.strm.avail_in)))break;g=t===Qn&&r===i+e.strm.avail_in?1:0,Rh(e,0,0,g),e.pending_buf[e.pending-4]=r,e.pending_buf[e.pending-3]=r>>8,e.pending_buf[e.pending-2]=~r,e.pending_buf[e.pending-1]=~r>>8,Fn(e.strm),i&&(i>r&&(i=r),e.strm.output.set(e.window.subarray(e.block_start,e.block_start+i),e.strm.next_out),e.strm.next_out+=i,e.strm.avail_out-=i,e.strm.total_out+=i,e.block_start+=i,r-=i),r&&(Nh(e.strm,e.strm.output,e.strm.next_out,r),e.strm.next_out+=r,e.strm.avail_out-=r,e.strm.total_out+=r)}while(g===0);return o-=e.strm.avail_in,o&&(o>=e.w_size?(e.matches=2,e.window.set(e.strm.input.subarray(e.strm.next_in-e.w_size,e.strm.next_in),0),e.strstart=e.w_size,e.insert=e.strstart):(e.window_size-e.strstart<=o&&(e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,e.insert>e.strstart&&(e.insert=e.strstart)),e.window.set(e.strm.input.subarray(e.strm.next_in-o,e.strm.next_in),e.strstart),e.strstart+=o,e.insert+=o>e.w_size-e.insert?e.w_size-e.insert:o),e.block_start=e.strstart),e.high_water<e.strstart&&(e.high_water=e.strstart),g?rs:t!==Vi&&t!==Qn&&e.strm.avail_in===0&&e.strstart===e.block_start?ns:(l=e.window_size-e.strstart,e.strm.avail_in>l&&e.block_start>=e.w_size&&(e.block_start-=e.w_size,e.strstart-=e.w_size,e.window.set(e.window.subarray(e.w_size,e.w_size+e.strstart),0),e.matches<2&&e.matches++,l+=e.w_size,e.insert>e.strstart&&(e.insert=e.strstart)),l>e.strm.avail_in&&(l=e.strm.avail_in),l&&(Nh(e.strm,e.window,e.strstart,l),e.strstart+=l,e.insert+=l>e.w_size-e.insert?e.w_size-e.insert:l),e.high_water<e.strstart&&(e.high_water=e.strstart),l=e.bi_valid+42>>3,l=e.pending_buf_size-l>65535?65535:e.pending_buf_size-l,n=l>e.w_size?e.w_size:l,i=e.strstart-e.block_start,(i>=n||(i||t===Qn)&&t!==Vi&&e.strm.avail_in===0&&i<=l)&&(r=i>l?l:i,g=t===Qn&&e.strm.avail_in===0&&r===i?1:0,Rh(e,e.block_start,r,g),e.block_start+=r,Fn(e.strm)),g?Va:xn)},ld=(e,t)=>{let n,r;for(;;){if(e.lookahead<Rr){if(Vo(e),e.lookahead<Rr&&t===Vi)return xn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Ui(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),n!==0&&e.strstart-n<=e.w_size-Rr&&(e.match_length=Y2(e,n)),e.match_length>=lt)if(r=ji(e,e.strstart-e.match_start,e.match_length-lt),e.lookahead-=e.match_length,e.match_length<=e.max_lazy_match&&e.lookahead>=lt){e.match_length--;do e.strstart++,e.ins_h=Ui(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart;while(--e.match_length!==0);e.strstart++}else e.strstart+=e.match_length,e.match_length=0,e.ins_h=e.window[e.strstart],e.ins_h=Ui(e,e.ins_h,e.window[e.strstart+1]);else r=ji(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++;if(r&&(Un(e,!1),e.strm.avail_out===0))return xn}return e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Qn?(Un(e,!0),e.strm.avail_out===0?Va:rs):e.sym_next&&(Un(e,!1),e.strm.avail_out===0)?xn:ns},co=(e,t)=>{let n,r,i;for(;;){if(e.lookahead<Rr){if(Vo(e),e.lookahead<Rr&&t===Vi)return xn;if(e.lookahead===0)break}if(n=0,e.lookahead>=lt&&(e.ins_h=Ui(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart),e.prev_length=e.match_length,e.prev_match=e.match_start,e.match_length=lt-1,n!==0&&e.prev_length<e.max_lazy_match&&e.strstart-n<=e.w_size-Rr&&(e.match_length=Y2(e,n),e.match_length<=5&&(e.strategy===lY||e.match_length===lt&&e.strstart-e.match_start>4096)&&(e.match_length=lt-1)),e.prev_length>=lt&&e.match_length<=e.prev_length){i=e.strstart+e.lookahead-lt,r=ji(e,e.strstart-1-e.prev_match,e.prev_length-lt),e.lookahead-=e.prev_length-1,e.prev_length-=2;do++e.strstart<=i&&(e.ins_h=Ui(e,e.ins_h,e.window[e.strstart+lt-1]),n=e.prev[e.strstart&e.w_mask]=e.head[e.ins_h],e.head[e.ins_h]=e.strstart);while(--e.prev_length!==0);if(e.match_available=0,e.match_length=lt-1,e.strstart++,r&&(Un(e,!1),e.strm.avail_out===0))return xn}else if(e.match_available){if(r=ji(e,0,e.window[e.strstart-1]),r&&Un(e,!1),e.strstart++,e.lookahead--,e.strm.avail_out===0)return xn}else e.match_available=1,e.strstart++,e.lookahead--}return e.match_available&&(r=ji(e,0,e.window[e.strstart-1]),e.match_available=0),e.insert=e.strstart<lt-1?e.strstart:lt-1,t===Qn?(Un(e,!0),e.strm.avail_out===0?Va:rs):e.sym_next&&(Un(e,!1),e.strm.avail_out===0)?xn:ns},TY=(e,t)=>{let n,r,i,l;const g=e.window;for(;;){if(e.lookahead<=Bi){if(Vo(e),e.lookahead<=Bi&&t===Vi)return xn;if(e.lookahead===0)break}if(e.match_length=0,e.lookahead>=lt&&e.strstart>0&&(i=e.strstart-1,r=g[i],r===g[++i]&&r===g[++i]&&r===g[++i])){l=e.strstart+Bi;do;while(r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&r===g[++i]&&i<l);e.match_length=Bi-(l-i),e.match_length>e.lookahead&&(e.match_length=e.lookahead)}if(e.match_length>=lt?(n=ji(e,1,e.match_length-lt),e.lookahead-=e.match_length,e.strstart+=e.match_length,e.match_length=0):(n=ji(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++),n&&(Un(e,!1),e.strm.avail_out===0))return xn}return e.insert=0,t===Qn?(Un(e,!0),e.strm.avail_out===0?Va:rs):e.sym_next&&(Un(e,!1),e.strm.avail_out===0)?xn:ns},EY=(e,t)=>{let n;for(;;){if(e.lookahead===0&&(Vo(e),e.lookahead===0)){if(t===Vi)return xn;break}if(e.match_length=0,n=ji(e,0,e.window[e.strstart]),e.lookahead--,e.strstart++,n&&(Un(e,!1),e.strm.avail_out===0))return xn}return e.insert=0,t===Qn?(Un(e,!0),e.strm.avail_out===0?Va:rs):e.sym_next&&(Un(e,!1),e.strm.avail_out===0)?xn:ns};function xr(e,t,n,r,i){this.good_length=e,this.max_lazy=t,this.nice_length=n,this.max_chain=r,this.func=i}const ws=[new xr(0,0,0,0,X2),new xr(4,4,8,4,ld),new xr(4,5,16,8,ld),new xr(4,6,32,32,ld),new xr(4,4,16,16,co),new xr(8,16,32,32,co),new xr(8,16,128,128,co),new xr(8,32,128,256,co),new xr(32,128,258,1024,co),new xr(32,258,258,4096,co)],OY=e=>{e.window_size=2*e.w_size,Ri(e.head),e.max_lazy_match=ws[e.level].max_lazy,e.good_match=ws[e.level].good_length,e.nice_match=ws[e.level].nice_length,e.max_chain_length=ws[e.level].max_chain,e.strstart=0,e.block_start=0,e.lookahead=0,e.insert=0,e.match_length=e.prev_length=lt-1,e.match_available=0,e.ins_h=0};function AY(){this.strm=null,this.status=0,this.pending_buf=null,this.pending_buf_size=0,this.pending_out=0,this.pending=0,this.wrap=0,this.gzhead=null,this.gzindex=0,this.method=hf,this.last_flush=-1,this.w_size=0,this.w_bits=0,this.w_mask=0,this.window=null,this.window_size=0,this.prev=null,this.head=null,this.ins_h=0,this.hash_size=0,this.hash_bits=0,this.hash_mask=0,this.hash_shift=0,this.block_start=0,this.match_length=0,this.prev_match=0,this.match_available=0,this.strstart=0,this.match_start=0,this.lookahead=0,this.prev_length=0,this.max_chain_length=0,this.max_lazy_match=0,this.level=0,this.strategy=0,this.good_match=0,this.nice_match=0,this.dyn_ltree=new Uint16Array(_Y*2),this.dyn_dtree=new Uint16Array((2*yY+1)*2),this.bl_tree=new Uint16Array((2*bY+1)*2),Ri(this.dyn_ltree),Ri(this.dyn_dtree),Ri(this.bl_tree),this.l_desc=null,this.d_desc=null,this.bl_desc=null,this.bl_count=new Uint16Array(wY+1),this.heap=new Uint16Array(2*Dh+1),Ri(this.heap),this.heap_len=0,this.heap_max=0,this.depth=new Uint16Array(2*Dh+1),Ri(this.depth),this.sym_buf=0,this.lit_bufsize=0,this.sym_next=0,this.sym_end=0,this.opt_len=0,this.static_len=0,this.matches=0,this.insert=0,this.bi_buf=0,this.bi_valid=0}const Kl=e=>{if(!e)return 1;const t=e.state;return!t||t.strm!==e||t.status!==jo&&t.status!==Tm&&t.status!==$h&&t.status!==Bh&&t.status!==Fh&&t.status!==zh&&t.status!==Ta&&t.status!==_s?1:0},K2=e=>{if(Kl(e))return Ea(e,Lr);e.total_in=e.total_out=0,e.data_type=dY;const t=e.state;return t.pending=0,t.pending_out=0,t.wrap<0&&(t.wrap=-t.wrap),t.status=t.wrap===2?Tm:t.wrap?jo:Ta,e.adler=t.wrap===2?0:1,t.last_flush=-2,tY(t),an},G2=e=>{const t=K2(e);return t===an&&OY(e.state),t},PY=(e,t)=>Kl(e)||e.state.wrap!==2?Lr:(e.state.gzhead=t,an),q2=(e,t,n,r,i,l)=>{if(!e)return Lr;let g=1;if(t===sY&&(t=6),r<0?(g=0,r=-r):r>15&&(g=2,r-=16),i<1||i>hY||n!==hf||r<8||r>15||t<0||t>9||l<0||l>cY||r===8&&g!==1)return Ea(e,Lr);r===8&&(r=9);const o=new AY;return e.state=o,o.strm=e,o.status=jo,o.wrap=g,o.gzhead=null,o.w_bits=r,o.w_size=1<<o.w_bits,o.w_mask=o.w_size-1,o.hash_bits=i+7,o.hash_size=1<<o.hash_bits,o.hash_mask=o.hash_size-1,o.hash_shift=~~((o.hash_bits+lt-1)/lt),o.window=new Uint8Array(o.w_size*2),o.head=new Uint16Array(o.hash_size),o.prev=new Uint16Array(o.w_size),o.lit_bufsize=1<<i+6,o.pending_buf_size=o.lit_bufsize*4,o.pending_buf=new Uint8Array(o.pending_buf_size),o.sym_buf=o.lit_bufsize,o.sym_end=(o.lit_bufsize-1)*3,o.level=t,o.strategy=l,o.method=n,G2(e)},IY=(e,t)=>q2(e,t,hf,pY,mY,fY),MY=(e,t)=>{if(Kl(e)||t>sy||t<0)return e?Ea(e,Lr):Lr;const n=e.state;if(!e.output||e.avail_in!==0&&!e.input||n.status===_s&&t!==Qn)return Ea(e,e.avail_out===0?sd:Lr);const r=n.last_flush;if(n.last_flush=t,n.pending!==0){if(Fn(e),e.avail_out===0)return n.last_flush=-1,an}else if(e.avail_in===0&&uy(t)<=uy(r)&&t!==Qn)return Ea(e,sd);if(n.status===_s&&e.avail_in!==0)return Ea(e,sd);if(n.status===jo&&n.wrap===0&&(n.status=Ta),n.status===jo){let i=hf+(n.w_bits-8<<4)<<8,l=-1;if(n.strategy>=ku||n.level<2?l=0:n.level<6?l=1:n.level===6?l=2:l=3,i|=l<<6,n.strstart!==0&&(i|=CY),i+=31-i%31,ps(n,i),n.strstart!==0&&(ps(n,e.adler>>>16),ps(n,e.adler&65535)),e.adler=1,n.status=Ta,Fn(e),n.pending!==0)return n.last_flush=-1,an}if(n.status===Tm){if(e.adler=0,mt(n,31),mt(n,139),mt(n,8),n.gzhead)mt(n,(n.gzhead.text?1:0)+(n.gzhead.hcrc?2:0)+(n.gzhead.extra?4:0)+(n.gzhead.name?8:0)+(n.gzhead.comment?16:0)),mt(n,n.gzhead.time&255),mt(n,n.gzhead.time>>8&255),mt(n,n.gzhead.time>>16&255),mt(n,n.gzhead.time>>24&255),mt(n,n.level===9?2:n.strategy>=ku||n.level<2?4:0),mt(n,n.gzhead.os&255),n.gzhead.extra&&n.gzhead.extra.length&&(mt(n,n.gzhead.extra.length&255),mt(n,n.gzhead.extra.length>>8&255)),n.gzhead.hcrc&&(e.adler=Jt(e.adler,n.pending_buf,n.pending,0)),n.gzindex=0,n.status=$h;else if(mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,0),mt(n,n.level===9?2:n.strategy>=ku||n.level<2?4:0),mt(n,SY),n.status=Ta,Fn(e),n.pending!==0)return n.last_flush=-1,an}if(n.status===$h){if(n.gzhead.extra){let i=n.pending,l=(n.gzhead.extra.length&65535)-n.gzindex;for(;n.pending+l>n.pending_buf_size;){let o=n.pending_buf_size-n.pending;if(n.pending_buf.set(n.gzhead.extra.subarray(n.gzindex,n.gzindex+o),n.pending),n.pending=n.pending_buf_size,n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex+=o,Fn(e),n.pending!==0)return n.last_flush=-1,an;i=0,l-=o}let g=new Uint8Array(n.gzhead.extra);n.pending_buf.set(g.subarray(n.gzindex,n.gzindex+l),n.pending),n.pending+=l,n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Bh}if(n.status===Bh){if(n.gzhead.name){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i)),Fn(e),n.pending!==0)return n.last_flush=-1,an;i=0}n.gzindex<n.gzhead.name.length?l=n.gzhead.name.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i)),n.gzindex=0}n.status=Fh}if(n.status===Fh){if(n.gzhead.comment){let i=n.pending,l;do{if(n.pending===n.pending_buf_size){if(n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i)),Fn(e),n.pending!==0)return n.last_flush=-1,an;i=0}n.gzindex<n.gzhead.comment.length?l=n.gzhead.comment.charCodeAt(n.gzindex++)&255:l=0,mt(n,l)}while(l!==0);n.gzhead.hcrc&&n.pending>i&&(e.adler=Jt(e.adler,n.pending_buf,n.pending-i,i))}n.status=zh}if(n.status===zh){if(n.gzhead.hcrc){if(n.pending+2>n.pending_buf_size&&(Fn(e),n.pending!==0))return n.last_flush=-1,an;mt(n,e.adler&255),mt(n,e.adler>>8&255),e.adler=0}if(n.status=Ta,Fn(e),n.pending!==0)return n.last_flush=-1,an}if(e.avail_in!==0||n.lookahead!==0||t!==Vi&&n.status!==_s){let 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Au:if(o>=6&&a>=258){e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,mX(e,f),g=e.next_out,i=e.output,a=e.avail_out,l=e.next_in,r=e.input,o=e.avail_in,u=n.hold,d=n.bits,n.mode===Yr&&(n.back=-1);break}for(n.back=0;m=n.lencode[u&(1<<n.lenbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(b&&(b&240)===0){for(w=y,S=b,x=_;m=n.lencode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,n.length=_,b===0){n.mode=Ry;break}if(b&32){n.back=-1,n.mode=Yr;break}if(b&64){e.msg="invalid literal/length code",n.mode=zt;break}n.extra=b&15,n.mode=Py;case Py:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.length+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}n.was=n.length,n.mode=Iy;case Iy:for(;m=n.distcode[u&(1<<n.distbits)-1],y=m>>>24,b=m>>>16&255,_=m&65535,!(y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if((b&240)===0){for(w=y,S=b,x=_;m=n.distcode[x+((u&(1<<w+S)-1)>>w)],y=m>>>24,b=m>>>16&255,_=m&65535,!(w+y<=d);){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}u>>>=w,d-=w,n.back+=w}if(u>>>=y,d-=y,n.back+=y,b&64){e.msg="invalid distance code",n.mode=zt;break}n.offset=_,n.extra=b&15,n.mode=My;case My:if(n.extra){for(N=n.extra;d<N;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}n.offset+=u&(1<<n.extra)-1,u>>>=n.extra,d-=n.extra,n.back+=n.extra}if(n.offset>n.dmax){e.msg="invalid distance too far back",n.mode=zt;break}n.mode=Ly;case Ly:if(a===0)break e;if(s=f-a,n.offset>s){if(s=n.offset-s,s>n.whave&&n.sane){e.msg="invalid distance too far back",n.mode=zt;break}s>n.wnext?(s-=n.wnext,h=n.wsize-s):h=n.wnext-s,s>n.length&&(s=n.length),v=n.window}else v=i,h=g-n.offset,s=n.length;s>a&&(s=a),a-=s,n.length-=s;do i[g++]=v[h++];while(--s);n.length===0&&(n.mode=Au);break;case Ry:if(a===0)break e;i[g++]=n.length,a--,n.mode=Au;break;case dd:if(n.wrap){for(;d<32;){if(o===0)break e;o--,u|=r[l++]<<d,d+=8}if(f-=a,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Jt(n.check,i,f,g-f):Sl(n.check,i,f,g-f)),f=a,n.wrap&4&&(n.flags?u:By(u))!==n.check){e.msg="incorrect data check",n.mode=zt;break}u=0,d=0}n.mode=Dy;case Dy:if(n.wrap&&n.flags){for(;d<32;){if(o===0)break e;o--,u+=r[l++]<<d,d+=8}if(n.wrap&4&&u!==(n.total&4294967295)){e.msg="incorrect length check",n.mode=zt;break}u=0,d=0}n.mode=$y;case $y:E=SX;break e;case zt:E=nC;break e;case iC:return rC;case aC:default:return ar}return e.next_out=g,e.avail_out=a,e.next_in=l,e.avail_in=o,n.hold=u,n.bits=d,(n.wsize||f!==e.avail_out&&n.mode<zt&&(n.mode<dd||t!==py))&&cC(e,e.output,e.next_out,f-e.avail_out),c-=e.avail_in,f-=e.avail_out,e.total_in+=c,e.total_out+=f,n.total+=f,n.wrap&4&&f&&(e.adler=n.check=n.flags?Jt(n.check,i,f,e.next_out-f):Sl(n.check,i,f,e.next_out-f)),e.data_type=n.bits+(n.last?64:0)+(n.mode===Yr?128:0)+(n.mode===Ou||n.mode===fd?256:0),(c===0&&f===0||t===py)&&E===Ua&&(E=kX),E},RX=e=>{if(eo(e))return ar;let t=e.state;return t.window&&(t.window=null),e.state=null,Ua},DX=(e,t)=>{if(eo(e))return ar;const n=e.state;return(n.wrap&2)===0?ar:(n.head=t,t.done=!1,Ua)},$X=(e,t)=>{const n=t.length;let r,i,l;return eo(e)||(r=e.state,r.wrap!==0&&r.mode!==Lc)?ar:r.mode===Lc&&(i=1,i=Sl(i,t,n,0),i!==r.check)?nC:(l=cC(e,t,n,n),l?(r.mode=iC,rC):(r.havedict=1,Ua))};var BX=sC,FX=lC,zX=oC,NX=IX,jX=uC,VX=LX,UX=RX,HX=DX,WX=$X,YX="pako inflate (from Nodeca 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d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;mask-size:100% 100%;-webkit-mask:url("data:image/svg+xml;utf8,%3Csvg class='icon' width='200' height='200' viewBox='0 0 1024 1024' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill='currentColor' d='M406.656 706.944L195.84 496.256a32 32 0 10-45.248 45.248l256 256 512-512a32 32 0 00-45.248-45.248L406.592 706.944z'%3E%3C/path%3E%3C/svg%3E") no-repeat;-webkit-mask-size:100% 100%;transform:translateY(-50%);width:12px;height:12px}.el-select-dropdown .el-scrollbar.is-empty .el-select-dropdown__list{padding:0}.el-select-dropdown .el-select-dropdown__item.is-disabled:hover{background-color:unset}.el-select-dropdown .el-select-dropdown__item.is-disabled.selected{color:var(--el-text-color-disabled)}.el-select-dropdown__empty{padding:10px 0;margin:0;text-align:center;color:var(--el-text-color-secondary);font-size:var(--el-select-font-size)}.el-select-dropdown__wrap{max-height:274px}.el-select-dropdown__list{list-style:none;padding:6px 0;margin:0;box-sizing:border-box}.el-select{--el-select-border-color-hover:var(--el-border-color-hover);--el-select-disabled-border:var(--el-disabled-border-color);--el-select-font-size:var(--el-font-size-base);--el-select-close-hover-color:var(--el-text-color-secondary);--el-select-input-color:var(--el-text-color-placeholder);--el-select-multiple-input-color:var(--el-text-color-regular);--el-select-input-focus-border-color:var(--el-color-primary);--el-select-input-font-size:14px}.el-select{display:inline-block;position:relative;vertical-align:middle;line-height:32px}.el-select__popper.el-popper{background:var(--el-bg-color-overlay);border:1px solid var(--el-border-color-light);box-shadow:var(--el-box-shadow-light)}.el-select__popper.el-popper .el-popper__arrow:before{border:1px solid var(--el-border-color-light)}.el-select__popper.el-popper[data-popper-placement^=top] 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   <body>

From e11d3ddef04df4988755a864aae471ef3aed28ca Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Wed, 8 Jul 2026 06:08:19 -0300
Subject: [PATCH 290/292] sd: sync with master-767-885f01a (#2310)

* sd: minor API path handling cleanup

* sd: sync with master-749-b11c95a

* sd: use original API parameters at the internal C++ API

* sd: split_mode and auto_fit backend support

* sd: sync with master-758-c674225

* sd: sync with master-765-bb84971

* sd: sync with master-767-885f01a
---
 Makefile                                      |   2 +-
 expose.h                                      |  10 +-
 koboldcpp.py                                  |  42 +-
 otherarch/sdcpp/examples/cli/main.cpp         |   3 +-
 otherarch/sdcpp/examples/common/common.cpp    | 102 ++--
 otherarch/sdcpp/examples/common/common.h      |  17 +-
 otherarch/sdcpp/include/stable-diffusion.h    |  24 +-
 otherarch/sdcpp/sdtype_adapter.cpp            |  76 ++-
 .../sdcpp/src/conditioning/conditioner.hpp    | 222 ++++++++-
 otherarch/sdcpp/src/convert.cpp               | 345 ++++++++++---
 otherarch/sdcpp/src/core/backend_fit.cpp      | 390 +++++++++++++++
 otherarch/sdcpp/src/core/backend_fit.h        |  23 +
 otherarch/sdcpp/src/core/ggml_extend.hpp      | 369 +++++++++++++-
 .../sdcpp/src/core/ggml_extend_backend.cpp    | 144 +++++-
 .../sdcpp/src/core/ggml_extend_backend.h      |  15 +
 .../sdcpp/src/core/layer_split_partition.cpp  | 257 ++++++++++
 .../sdcpp/src/core/layer_split_partition.h    |  44 ++
 otherarch/sdcpp/src/core/util.cpp             |  26 +
 otherarch/sdcpp/src/kcpp_sd_extensions.h      |   2 -
 .../sdcpp/src/model/diffusion/control.hpp     |   3 +-
 otherarch/sdcpp/src/model/diffusion/flux.hpp  |  22 +-
 .../sdcpp/src/model/diffusion/qwen_image.hpp  |  17 +-
 otherarch/sdcpp/src/model/vae/vae.hpp         |   6 +-
 otherarch/sdcpp/src/model_io/gguf_io.cpp      | 115 +++++
 otherarch/sdcpp/src/model_io/gguf_io.h        |  28 ++
 .../sdcpp/src/model_io/safetensors_io.cpp     | 112 ++++-
 otherarch/sdcpp/src/model_io/safetensors_io.h |  23 +
 .../sdcpp/src/model_io/streaming_writer.h     |  26 +
 otherarch/sdcpp/src/model_io/tensor_storage.h |  19 +
 otherarch/sdcpp/src/model_loader.cpp          |  75 ++-
 otherarch/sdcpp/src/model_loader.h            |   6 +-
 otherarch/sdcpp/src/model_manager.cpp         | 124 ++++-
 otherarch/sdcpp/src/model_manager.h           |  22 +-
 otherarch/sdcpp/src/name_conversion.cpp       |  59 +++
 otherarch/sdcpp/src/runtime/denoiser.hpp      | 219 +++++++++
 otherarch/sdcpp/src/runtime/imatrix.cpp       |   4 +
 otherarch/sdcpp/src/stable-diffusion.cpp      | 465 ++++++++++++++----
 otherarch/sdcpp/src/upscaler.cpp              |   1 +
 otherarch/sdcpp/src/weight_manager.h          |   4 +
 39 files changed, 3124 insertions(+), 339 deletions(-)
 create mode 100644 otherarch/sdcpp/src/core/backend_fit.cpp
 create mode 100644 otherarch/sdcpp/src/core/backend_fit.h
 create mode 100644 otherarch/sdcpp/src/core/layer_split_partition.cpp
 create mode 100644 otherarch/sdcpp/src/core/layer_split_partition.h
 create mode 100644 otherarch/sdcpp/src/model_io/streaming_writer.h

diff --git a/Makefile b/Makefile
index ae2351e5b..717c81de9 100644
--- a/Makefile
+++ b/Makefile
@@ -703,7 +703,7 @@ budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 chat.o: common/chat.cpp common/chat.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/minit2i.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/sefi_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/imatrix.cpp src/runtime/imatrix.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
+SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/backend_fit.cpp src/core/backend_fit.h src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/layer_split_partition.cpp src/core/layer_split_partition.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/diffusion/anima.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/minit2i.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/sefi_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/streaming_writer.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/imatrix.cpp src/runtime/imatrix.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
 SDCPP_MAIN_BASENAMES := examples/cli/image_metadata.cpp examples/cli/image_metadata.h examples/cli/main.cpp examples/cli/msf_gif.h examples/common/common.cpp examples/common/common.h examples/common/log.cpp examples/common/log.h examples/common/media_io.cpp examples/common/media_io.h examples/common/resource_owners.hpp src/tokenizers/vocab/clip_merges.hpp src/tokenizers/vocab/gemma2_merges.hpp src/tokenizers/vocab/gemma2_vocab.hpp src/tokenizers/vocab/gemma_merges.hpp src/tokenizers/vocab/gemma_vocab.hpp src/tokenizers/vocab/gpt_oss_merges.hpp src/tokenizers/vocab/gpt_oss_vocab.hpp src/tokenizers/vocab/mistral_merges.hpp src/tokenizers/vocab/mistral_vocab.hpp src/tokenizers/vocab/qwen_merges.hpp src/tokenizers/vocab/t5.hpp src/tokenizers/vocab/umt5.hpp src/tokenizers/vocab/vocab.cpp src/convert.cpp src/version.cpp
 
diff --git a/expose.h b/expose.h
index f753a2ec2..202427586 100644
--- a/expose.h
+++ b/expose.h
@@ -186,14 +186,12 @@ struct sd_load_model_inputs
 {
     const char * model_filename = nullptr;
     const char * executable_path = nullptr;
-    const int kcpp_main_device = -1;
+    const char * backend = nullptr;
     const int threads = 0;
     const int quant = 0;
     const bool flash_attention = false;
-    const bool offload_cpu = false;
+    const char * params_backend = nullptr;
     const bool use_mmap = false;
-    const int kcpp_vae_device = -1;
-    const int kcpp_clip_device = -1;
     const bool diffusion_conv_direct = false;
     const bool vae_conv_direct = false;
     const bool taesd = false;
@@ -211,8 +209,10 @@ struct sd_load_model_inputs
     const char * upscaler_filename = nullptr;
     const int img_hard_limit = 0;
     const int img_soft_limit = 0;
-    const float max_vram = 0.f;
+    const char * max_vram = nullptr;
+    const char * split_mode = nullptr;
     const bool stream_layers = false;
+    const bool auto_fit = false;
     const char * devices_override = nullptr;
     const bool quiet = false;
     const int debugmode = 0;
diff --git a/koboldcpp.py b/koboldcpp.py
index b6ba4a3a7..bfc16ee10 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -378,14 +378,12 @@ class generation_outputs(ctypes.Structure):
 class sd_load_model_inputs(ctypes.Structure):
     _fields_ = [("model_filename", ctypes.c_char_p),
                 ("executable_path", ctypes.c_char_p),
-                ("kcpp_main_device", ctypes.c_int),
+                ("backend", ctypes.c_char_p),
                 ("threads", ctypes.c_int),
                 ("quant", ctypes.c_int),
                 ("flash_attention", ctypes.c_bool),
-                ("offload_cpu", ctypes.c_bool),
+                ("params_backend", ctypes.c_char_p),
                 ("use_mmap", ctypes.c_bool),
-                ("kcpp_vae_device", ctypes.c_int),
-                ("kcpp_clip_device", ctypes.c_int),
                 ("diffusion_conv_direct", ctypes.c_bool),
                 ("vae_conv_direct", ctypes.c_bool),
                 ("taesd", ctypes.c_bool),
@@ -403,8 +401,10 @@ class sd_load_model_inputs(ctypes.Structure):
                 ("upscaler_filename", ctypes.c_char_p),
                 ("img_hard_limit", ctypes.c_int),
                 ("img_soft_limit", ctypes.c_int),
-                ("max_vram", ctypes.c_float),
+                ("max_vram", ctypes.c_char_p),
+                ("split_mode", ctypes.c_char_p),
                 ("stream_layers", ctypes.c_bool),
+                ("auto_fit", ctypes.c_bool),
                 ("devices_override", ctypes.c_char_p),
                 ("quiet", ctypes.c_bool),
                 ("debugmode", ctypes.c_int)]
@@ -2487,6 +2487,21 @@ def sd_resolve_device(name, default_=-1):
         name = str(max(name, -2))
     return sd_get_device_number(name)
 
+def sd_get_device_override(deviceid, module=''):
+    '''formats a device id and a module name in sd.cpp --backend syntax'''
+    global cached_sd_info
+    devices = cached_sd_info.get('devices', [])
+    device_name = ''
+    if deviceid <= -2:
+        device_name = "CPU"
+    elif deviceid >= 0 and deviceid < len(devices):
+        device_name = devices[deviceid]['name']
+    if device_name and module:
+        result = module + '=' + device_name
+    else:
+        result = device_name
+    return result;
+
 def sd_load_model(model_filename,vae_filename,t5xxl_filename,clip1_filename,clip2_filename,photomaker_filename,upscaler_filename,audio_vae_filename):
     global args
     inputs = sd_load_model_inputs()
@@ -2501,10 +2516,14 @@ def sd_load_model(model_filename,vae_filename,t5xxl_filename,clip1_filename,clip
     inputs.threads = thds
     inputs.quant = args.sdquant
     inputs.flash_attention = args.sdflashattention
-    inputs.offload_cpu = args.sdoffloadcpu
+    inputs.params_backend = b'CPU' if args.sdoffloadcpu else b''
     inputs.use_mmap = args.usemmap
-    inputs.kcpp_vae_device = sd_resolve_device(args.sdvaedevice, default_sdvaedevice)
-    inputs.kcpp_clip_device = sd_resolve_device(args.sdclipdevice, default_sdclipdevice)
+    backends = [
+        sd_get_device_override(sd_resolve_device(args.sdmaingpu, 'main')),
+        sd_get_device_override(sd_resolve_device(args.sdclipdevice, default_sdclipdevice), 'CLIP'),
+        sd_get_device_override(sd_resolve_device(args.sdvaedevice, default_sdvaedevice), 'VAE'),
+    ]
+    inputs.backend = ','.join([b for b in backends if b]).encode("UTF-8")
     sdconvdirect = sd_convdirect_option(args.sdconvdirect)
     inputs.diffusion_conv_direct = sdconvdirect == 'full'
     inputs.vae_conv_direct = sdconvdirect in ['vaeonly', 'full']
@@ -2517,7 +2536,7 @@ def sd_load_model(model_filename,vae_filename,t5xxl_filename,clip1_filename,clip
     inputs.clip2_filename = clip2_filename.encode("UTF-8")
     inputs.photomaker_filename = photomaker_filename.encode("UTF-8")
     inputs.upscaler_filename = upscaler_filename.encode("UTF-8")
-    inputs.max_vram = (args.sdvramlimit/1024.0) if args.sdvramlimit > 0 else 0
+    inputs.max_vram = str((args.sdvramlimit/1024.0) if args.sdvramlimit > 0 else '').encode('UTF-8')
     inputs.stream_layers = False
 
     lora_filenames, lora_multipliers = prepare_initial_lora_multipliers()
@@ -2535,7 +2554,6 @@ def sd_load_model(model_filename,vae_filename,t5xxl_filename,clip1_filename,clip
     inputs.img_hard_limit = args.sdclamped
     inputs.img_soft_limit = args.sdclampedsoft
     inputs = set_backend_props(inputs)
-    inputs.kcpp_main_device = sd_resolve_device(args.sdmaingpu, 'main')
     ret = handle.sd_load_model(inputs)
     return ret
 
@@ -6293,8 +6311,6 @@ Change Mode<br>
                 response_body = (json.dumps([{"name":name,"label":name} for name in cached_sd_info.get('available_schedulers', [])]).encode())
         elif clean_path.endswith('/sdapi/v1/latent-upscale-modes'):
            response_body = (json.dumps([]).encode())
-        elif clean_path.endswith('/sdapi/v1/upscalers'):
-           response_body = (json.dumps([]).encode())
 
         #vits compatible
         elif clean_path=='/voice/check':
@@ -11867,8 +11883,8 @@ def kcpp_main_process(launch_args, g_memory=None, gui_launcher=False):
             friendlysdmodelname = os.path.basename(imgmodel)
             friendlysdmodelname = os.path.splitext(friendlysdmodelname)[0]
             friendlysdmodelname = sanitize_string(friendlysdmodelname)
-            loadok = sd_load_model(imgmodel,imgvae,imgt5xxl,imgclip1,imgclip2,imgphotomaker,imgupscaler,imgaudiovae)
             cached_sd_info = sd_get_info()
+            loadok = sd_load_model(imgmodel,imgvae,imgt5xxl,imgclip1,imgclip2,imgphotomaker,imgupscaler,imgaudiovae)
             print("Load Image Model OK: " + str(loadok))
             if not loadok:
                 exitcounter = 999
diff --git a/otherarch/sdcpp/examples/cli/main.cpp b/otherarch/sdcpp/examples/cli/main.cpp
index 7892d5213..29cb391b6 100644
--- a/otherarch/sdcpp/examples/cli/main.cpp
+++ b/otherarch/sdcpp/examples/cli/main.cpp
@@ -653,7 +653,8 @@ int main(int argc, const char* argv[]) {
                                                cli_params.output_path.c_str(),
                                                ctx_params.wtype,
                                                ctx_params.tensor_type_rules.c_str(),
-                                               cli_params.convert_name);
+                                               cli_params.convert_name,
+                                               ctx_params.n_threads);
         if (!success) {
             LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                       ctx_params.model_path.c_str(),
diff --git a/otherarch/sdcpp/examples/common/common.cpp b/otherarch/sdcpp/examples/common/common.cpp
index ac1cf32f6..1dfb6aa70 100644
--- a/otherarch/sdcpp/examples/common/common.cpp
+++ b/otherarch/sdcpp/examples/common/common.cpp
@@ -443,6 +443,12 @@ ArgOptions SDContextParams::get_options() {
          "weight type per tensor pattern (example: \"^vae\\.=f16,model\\.=q8_0\")",
          (int)',',
          &tensor_type_rules},
+        {"",
+         "--model-args",
+         "extra model args, key=value list. Supports chroma_use_dit_mask, chroma_use_t5_mask, "
+         "chroma_t5_mask_pad, qwen_image_zero_cond_t",
+         (int)',',
+         &model_args},
         {"",
          "--photo-maker",
          "path to PHOTOMAKER model",
@@ -468,6 +474,13 @@ ArgOptions SDContextParams::get_options() {
          "parameter backend assignment, e.g. disk, cpu, or diffusion=disk,clip=cpu",
          (int)',',
          &params_backend},
+        {"",
+         "--split-mode",
+         "weight distribution for modules assigned multiple devices (--backend \"diffusion=cuda0&cuda1\"): "
+         "layer (whole transformer blocks per device, default) or row (matmul rows split across devices, CUDA only). "
+         "Accepts a single mode or per-module assignments, e.g. row or diffusion=row,te=layer",
+         (int)',',
+         &split_mode},
         {"",
          "--rpc-servers",
          "comma-separated list of RPC servers to connect to for offloading, in the format host:port, e.g. localhost:50052,192.168.1.3:50052",
@@ -486,10 +499,6 @@ ArgOptions SDContextParams::get_options() {
          "number of threads to use during computation (default: -1). "
          "If threads <= 0, then threads will be set to the number of CPU physical cores",
          &n_threads},
-        {"",
-         "--chroma-t5-mask-pad",
-         "t5 mask pad size of chroma",
-         &chroma_t5_mask_pad},
     };
 
     options.bool_options = {
@@ -501,6 +510,12 @@ ArgOptions SDContextParams::get_options() {
          "--eager-load",
          "load all params into the params backend at model-load time instead of lazily on first use (defaults to false)",
          true, &eager_load},
+        {"",
+         "--auto-fit",
+         "pick the diffusion/te/vae device placements automatically from the model size and the per-device "
+         "memory budgets (--max-vram; defaults to free memory minus a small margin). Overrides --backend and "
+         "--params-backend; may split modules across GPUs (--split-mode still selects layer or row)",
+         true, &auto_fit},
         {"",
          "--force-sdxl-vae-conv-scale",
          "force use of conv scale on sdxl vae",
@@ -541,30 +556,6 @@ ArgOptions SDContextParams::get_options() {
          "--vae-conv-direct",
          "use ggml_conv2d_direct in the vae model",
          true, &vae_conv_direct},
-        {"",
-         "--circular",
-         "enable circular padding for convolutions",
-         true, &circular},
-        {"",
-         "--circularx",
-         "enable circular RoPE wrapping on x-axis (width) only",
-         true, &circular_x},
-        {"",
-         "--circulary",
-         "enable circular RoPE wrapping on y-axis (height) only",
-         true, &circular_y},
-        {"",
-         "--chroma-disable-dit-mask",
-         "disable dit mask for chroma",
-         false, &chroma_use_dit_mask},
-        {"",
-         "--qwen-image-zero-cond-t",
-         "enable zero_cond_t for qwen image",
-         true, &qwen_image_zero_cond_t},
-        {"",
-         "--chroma-enable-t5-mask",
-         "enable t5 mask for chroma",
-         true, &chroma_use_t5_mask},
     };
 
     auto on_type_arg = [&](int argc, const char** argv, int index) {
@@ -663,6 +654,18 @@ ArgOptions SDContextParams::get_options() {
          "but it usually offers faster inference speed and, in some cases, lower memory usage. "
          "The at_runtime mode, on the other hand, is exactly the opposite.",
          on_lora_apply_mode_arg},
+        {"",
+         "--list-devices",
+         "list available ggml backend devices (one 'name<TAB>description' per line) and exit; "
+         "the names are the device names accepted by --backend and --params-backend",
+         [](int /*argc*/, const char** /*argv*/, int /*index*/) {
+             size_t device_list_size = sd_list_devices(nullptr, 0);
+             std::vector<char> devices(device_list_size + 1);
+             sd_list_devices(devices.data(), devices.size());
+             fputs(devices.data(), stdout);
+             std::exit(0);
+             return 0;
+         }},
     };
 
     return options;
@@ -818,6 +821,9 @@ std::string SDContextParams::to_string() const {
         << "  eager_load: " << (eager_load ? "true" : "false") << ",\n"
         << "  backend: \"" << backend << "\",\n"
         << "  params_backend: \"" << params_backend << "\",\n"
+        << "  split_mode: \"" << split_mode << "\",\n"
+        << "  model_args: \"" << model_args << "\",\n"
+        << "  auto_fit: " << (auto_fit ? "true" : "false") << ",\n"
         << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
         << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
         << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
@@ -826,13 +832,6 @@ std::string SDContextParams::to_string() const {
         << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
         << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
         << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
-        << "  circular: " << (circular ? "true" : "false") << ",\n"
-        << "  circular_x: " << (circular_x ? "true" : "false") << ",\n"
-        << "  circular_y: " << (circular_y ? "true" : "false") << ",\n"
-        << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
-        << "  qwen_image_zero_cond_t: " << (qwen_image_zero_cond_t ? "true" : "false") << ",\n"
-        << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
-        << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
         << "  prediction: " << sd_prediction_name(prediction) << ",\n"
         << "  lora_apply_mode: " << sd_lora_apply_mode_name(lora_apply_mode) << ",\n"
         << "  force_sdxl_vae_conv_scale: " << (force_sdxl_vae_conv_scale ? "true" : "false") << "\n"
@@ -885,20 +884,17 @@ sd_ctx_params_t SDContextParams::to_sd_ctx_params_t(bool taesd_preview) {
     sd_ctx_params.tae_preview_only                = taesd_preview;
     sd_ctx_params.diffusion_conv_direct           = diffusion_conv_direct;
     sd_ctx_params.vae_conv_direct                 = vae_conv_direct;
-    sd_ctx_params.circular_x                      = circular || circular_x;
-    sd_ctx_params.circular_y                      = circular || circular_y;
     sd_ctx_params.force_sdxl_vae_conv_scale       = force_sdxl_vae_conv_scale;
-    sd_ctx_params.chroma_use_dit_mask             = chroma_use_dit_mask;
-    sd_ctx_params.chroma_use_t5_mask              = chroma_use_t5_mask;
-    sd_ctx_params.chroma_t5_mask_pad              = chroma_t5_mask_pad;
-    sd_ctx_params.qwen_image_zero_cond_t          = qwen_image_zero_cond_t;
     sd_ctx_params.vae_format                      = str_to_vae_format(vae_format);
     sd_ctx_params.max_vram                        = max_vram.c_str();
     sd_ctx_params.stream_layers                   = stream_layers;
     sd_ctx_params.eager_load                      = eager_load;
     sd_ctx_params.backend                         = effective_backend.c_str();
     sd_ctx_params.params_backend                  = effective_params_backend.c_str();
+    sd_ctx_params.split_mode                      = split_mode.c_str();
+    sd_ctx_params.auto_fit                        = auto_fit;
     sd_ctx_params.rpc_servers                     = rpc_servers.c_str();
+    sd_ctx_params.model_args                      = model_args.empty() ? nullptr : model_args.c_str();
     return sd_ctx_params;
 }
 
@@ -1077,7 +1073,7 @@ ArgOptions SDGenerationParams::get_options() {
          &sample_params.guidance.slg.layer_end},
         {"",
          "--eta",
-         "noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)",
+         "noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a, dpm++2m_sde and dpm++2m_sde_bt)",
          &sample_params.eta},
         {"",
          "--flow-shift",
@@ -1109,7 +1105,7 @@ ArgOptions SDGenerationParams::get_options() {
          &high_noise_sample_params.guidance.slg.layer_end},
         {"",
          "--high-noise-eta",
-         "(high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)",
+         "(high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde, dpm++2s_a, dpm++2m_sde and dpm++2m_sde_bt)",
          &high_noise_sample_params.eta},
         {"",
          "--strength",
@@ -1160,6 +1156,18 @@ ArgOptions SDGenerationParams::get_options() {
          "disable auto resize of ref images",
          false,
          &auto_resize_ref_image},
+        {"",
+         "--circular",
+         "enable circular padding on both axes for tileable output",
+         true, &circular},
+        {"",
+         "--circularx",
+         "enable circular padding on x-axis (width) only",
+         true, &circular_x},
+        {"",
+         "--circulary",
+         "enable circular padding on y-axis (height) only",
+         true, &circular_y},
         {"",
          "--disable-image-metadata",
          "do not embed generation metadata on image files",
@@ -1480,12 +1488,12 @@ ArgOptions SDGenerationParams::get_options() {
          on_seed_arg},
         {"",
          "--sampling-method",
-         "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
+         "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, dpm++2m_sde_bt, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
          "(default: euler for Flux/SD3/Wan, euler_a otherwise)",
          on_sample_method_arg},
         {"",
          "--high-noise-sampling-method",
-         "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
+         "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, dpm++2m_sde, dpm++2m_sde_bt, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp]"
          " default: euler for Flux/SD3/Wan, euler_a otherwise",
          on_high_noise_sample_method_arg},
         {"",
@@ -2446,6 +2454,8 @@ sd_img_gen_params_t SDGenerationParams::to_sd_img_gen_params_t() {
     params.hires.upscale_tile_size   = hires_upscale_tile_size;
     params.hires.custom_sigmas       = hires_custom_sigmas.empty() ? nullptr : hires_custom_sigmas.data();
     params.hires.custom_sigmas_count = static_cast<int>(hires_custom_sigmas.size());
+    params.circular_x                = circular || circular_x;
+    params.circular_y                = circular || circular_y;
     return params;
 }
 
@@ -2511,6 +2521,8 @@ sd_vid_gen_params_t SDGenerationParams::to_sd_vid_gen_params_t() {
     params.hires.upscale_tile_size   = hires_upscale_tile_size;
     params.hires.custom_sigmas       = hires_custom_sigmas.empty() ? nullptr : hires_custom_sigmas.data();
     params.hires.custom_sigmas_count = static_cast<int>(hires_custom_sigmas.size());
+    params.circular_x                = circular || circular_x;
+    params.circular_y                = circular || circular_y;
     return params;
 }
 
diff --git a/otherarch/sdcpp/examples/common/common.h b/otherarch/sdcpp/examples/common/common.h
index 941fa3317..3a5b107bc 100644
--- a/otherarch/sdcpp/examples/common/common.h
+++ b/otherarch/sdcpp/examples/common/common.h
@@ -151,6 +151,9 @@ struct SDContextParams {
     bool eager_load             = false;
     std::string backend;
     std::string params_backend;
+    std::string split_mode;
+    std::string model_args;
+    bool auto_fit = false;
     std::string rpc_servers;
     std::string effective_backend;
     std::string effective_params_backend;
@@ -163,16 +166,6 @@ struct SDContextParams {
     bool diffusion_conv_direct = false;
     bool vae_conv_direct       = false;
 
-    bool circular   = false;
-    bool circular_x = false;
-    bool circular_y = false;
-
-    bool chroma_use_dit_mask = true;
-    bool chroma_use_t5_mask  = false;
-    int chroma_t5_mask_pad   = 1;
-
-    bool qwen_image_zero_cond_t = false;
-
     prediction_t prediction           = PREDICTION_COUNT;
     lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
 
@@ -244,6 +237,10 @@ struct SDGenerationParams {
     int upscale_repeats   = 1;
     int upscale_tile_size = 128;
 
+    bool circular   = false;
+    bool circular_x = false;
+    bool circular_y = false;
+
     bool hires_enabled         = false;
     std::string hires_upscaler = "Latent";
     std::string hires_upscaler_model_path;
diff --git a/otherarch/sdcpp/include/stable-diffusion.h b/otherarch/sdcpp/include/stable-diffusion.h
index d5bace723..3fc6f64b7 100644
--- a/otherarch/sdcpp/include/stable-diffusion.h
+++ b/otherarch/sdcpp/include/stable-diffusion.h
@@ -54,6 +54,8 @@ enum sample_method_t {
     EULER_CFG_PP_SAMPLE_METHOD,
     EULER_A_CFG_PP_SAMPLE_METHOD,
     EULER_GE_SAMPLE_METHOD,
+    DPMPP2M_SDE_SAMPLE_METHOD,
+    DPMPP2M_SDE_BT_SAMPLE_METHOD,
     SAMPLE_METHOD_COUNT
 };
 
@@ -214,20 +216,17 @@ typedef struct {
     bool tae_preview_only;
     bool diffusion_conv_direct;
     bool vae_conv_direct;
-    bool circular_x;
-    bool circular_y;
     bool force_sdxl_vae_conv_scale;
-    bool chroma_use_dit_mask;
-    bool chroma_use_t5_mask;
-    int chroma_t5_mask_pad;
-    bool qwen_image_zero_cond_t;
     enum sd_vae_format_t vae_format;
     const char* max_vram;  // GiB budget or backend assignment spec for graph-cut segmented param offload (0 = disabled, -1 = auto)
     bool stream_layers;  // Enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram)
     bool eager_load;  // Load all params into the params backend at model-load time instead of lazily on first use
     const char* backend;
     const char* params_backend;
+    const char* split_mode;  // weight distribution for multi-device modules: layer (default) or row, or per-module assignments e.g. "diffusion=row"
+    bool auto_fit;
     const char* rpc_servers;
+    const char* model_args;
 } sd_ctx_params_t;
 
 typedef struct {
@@ -381,6 +380,8 @@ typedef struct {
     sd_cache_params_t cache;
     sd_hires_params_t hires;
     int qwen_image_layers;
+    bool circular_x;
+    bool circular_y;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -407,6 +408,8 @@ typedef struct {
     sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
     sd_hires_params_t hires;
+    bool circular_x;
+    bool circular_y;
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
@@ -518,7 +521,8 @@ SD_API bool convert_with_components(const char* model_path,
                                     const char* output_path,
                                     enum sd_type_t output_type,
                                     const char* tensor_type_rules,
-                                    bool convert_name);
+                                    bool convert_name,
+                                    int n_threads);
 
 SD_API bool preprocess_canny(sd_image_t image,
                              float high_threshold,
@@ -535,6 +539,12 @@ SD_API void disable_imatrix_collection(void);
 SD_API const char* sd_commit(void);
 SD_API const char* sd_version(void);
 
+// List available ggml backend devices, one `name<TAB>description` per line.
+// The names are the device names accepted by the --backend / --params-backend
+// assignment specs. Returns the number of bytes required, excluding the null
+// terminator. Passing nullptr or buffer_size 0 only queries the required size.
+SD_API size_t sd_list_devices(char* buffer, size_t buffer_size);
+
 // for C API, caller needs to call free_sd_images to free the memory after use
 // This helps avoid CRT problems on Windows when memory is allocated in the library but freed in the caller, which may use a different CRT.
 SD_API void free_sd_images(sd_image_t* result_images, int num_images);
diff --git a/otherarch/sdcpp/sdtype_adapter.cpp b/otherarch/sdcpp/sdtype_adapter.cpp
index 00c0a6dd7..4757a39b5 100644
--- a/otherarch/sdcpp/sdtype_adapter.cpp
+++ b/otherarch/sdcpp/sdtype_adapter.cpp
@@ -270,35 +270,6 @@ std::string load_gpt_oss_vocab_json()
     return load_embd_file(cache, "embd_res/gpt_oss_vocab_json.embd");
 }
 
-static std::string get_device_override(int value, const char * module = nullptr)
-{
-    std::string device_name;
-    if (value <= -2) {
-        device_name = "CPU";
-    } else if (value >= 0) {
-        size_t gpu_index = static_cast<size_t>(value);
-        if (gpu_index >= ggml_backend_dev_count()) {
-            printf("\nWARNING: device %zu doesn't exist, falling back to default for %s\n",
-                   gpu_index,
-                   module ? module : "the main device");
-        } else {
-            auto dev = ggml_backend_dev_get(gpu_index);
-            device_name = ggml_backend_dev_name(dev);
-        }
-    }
-    std::string result;
-    if (device_name == "") {
-        result = ""; // no override: sdcpp will use the main device
-    } else if (module) {
-        printf("Selecting %s as %s image generation device\n", device_name.c_str(), module);
-        result = std::string{","} + module + "=" + device_name;
-    } else {
-        printf("Selecting %s as the main image generation device\n", device_name.c_str());
-        result = device_name;
-    }
-    return result;
-}
-
 bool sdtype_load_model(const sd_load_model_inputs inputs) {
     sd_is_quiet = inputs.quiet;
     set_sd_quiet(sd_is_quiet);
@@ -323,7 +294,9 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     cfg_square_limit = inputs.img_soft_limit;
     printf("\nImageGen Init - Load Model: %s\n",inputs.model_filename);
 
-    std::string backends = get_device_override(inputs.kcpp_main_device);
+    std::string backend = inputs.backend ? inputs.backend : "";
+    std::string params_backend = inputs.params_backend ? inputs.params_backend : "";
+    std::string split_mode = inputs.split_mode ? inputs.split_mode : "";
 
     int lora_apply_mode = LORA_APPLY_AT_RUNTIME;
     bool lora_dynamic = false;
@@ -402,20 +375,33 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     {
         printf("Conv2D Direct for VAE model is enabled\n");
     }
-    if (inputs.use_mmap && inputs.offload_cpu) {
+    if(backend != "")
+    {
+        printf("Backend assignment: \"%s\"\n", backend.c_str());
+    }
+    if (inputs.use_mmap && params_backend == "CPU") {
         printf("Offloading weights to system RAM with mmap\n");
         if (!lora_dynamic && inputs.lora_len > 0) {
             printf("Note: static LoRAs can reduce mmap memory savings!\n");
         }
-    } else if (inputs.offload_cpu) {
+    } else if (inputs.params_backend == "CPU") {
         printf("Offloading weights to system RAM\n");
     } else if (inputs.use_mmap) {
         printf("Using mmap for I/O\n");
     }
+    if(inputs.auto_fit) {
+        printf("Using auto-fit");
+    }
+    if(params_backend != "" && params_backend != "CPU") {
+        printf("Parameters backend assignment: \"%s\"\n", params_backend.c_str());
+    }
+    if(split_mode != "") {
+        printf("Using split mode: \"%s\"\n", split_mode.c_str());
+    }
     std::string max_vram;
-    if(inputs.max_vram != 0.f) {
-        printf("Using max VRAM = %0.2f GB\n", inputs.max_vram);
-        max_vram = std::to_string(inputs.max_vram);
+    if(inputs.max_vram && *inputs.max_vram) {
+        max_vram = inputs.max_vram;
+        printf("Using max VRAM = %s GB\n", max_vram.c_str());
     }
     if(inputs.quant > 0)
     {
@@ -479,21 +465,15 @@ bool sdtype_load_model(const sd_load_model_inputs inputs) {
     params.diffusion_flash_attn = sd_params->diffusion_flash_attn;
     params.diffusion_conv_direct = sd_params->diffusion_conv_direct;
     params.vae_conv_direct = sd_params->vae_conv_direct;
-    params.chroma_use_dit_mask = true;
+    params.model_args = "chroma_use_dit_mask=true";
     params.max_vram = max_vram.c_str();
     params.stream_layers = inputs.stream_layers;
     params.eager_load = true; //kcpp should preload everything
     params.enable_mmap = inputs.use_mmap;
-    params.params_backend = inputs.offload_cpu ? "CPU" : "";
-    backends += get_device_override(inputs.kcpp_vae_device, "VAE");
-    backends += get_device_override(inputs.kcpp_clip_device, "CLIP");
-    if (backends.rfind(",", 0) == 0) {
-        backends = "auto" + backends;
-    }
-    params.backend = backends.c_str();
-    if (inputs.debugmode==1) {
-        printf("\nSetting sd backend list to \"%s\", params backend list to \"%s\"", params.backend, params.params_backend);
-    }
+    params.backend = backend.c_str();
+    params.params_backend = params_backend.c_str();
+    params.split_mode = split_mode.c_str();
+    params.auto_fit = inputs.auto_fit;
     params.lora_apply_mode = (lora_apply_mode_t)lora_apply_mode;
 
     // also switches flash attn for the vae and conditioner
@@ -1030,8 +1010,6 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         sd_params->sample_method = sd_get_default_sample_method(sd_ctx);
     }
 
-    SetCircularAxesAll(sd_ctx, inputs.circular_x, inputs.circular_y);
-
     sd_params->cache_mode    = inputs.cache_mode ? inputs.cache_mode : "";
     sd_params->cache_options = inputs.cache_options ? inputs.cache_options : "";
 
@@ -1289,6 +1267,8 @@ sd_generation_outputs sdtype_generate(const sd_generation_inputs inputs)
         params.vae_tiling_params.temporal_tiling = true;
     }
     parse_cache_options(params.cache, sd_params->cache_mode, sd_params->cache_options);
+    params.circular_x = inputs.circular_x;
+    params.circular_y = inputs.circular_y;
 
     LoraMap lora_map = sd_params->lora_map;
     if (sd_params->lora_dynamic) {
diff --git a/otherarch/sdcpp/src/conditioning/conditioner.hpp b/otherarch/sdcpp/src/conditioning/conditioner.hpp
index d63303a82..5d4ad7fbb 100644
--- a/otherarch/sdcpp/src/conditioning/conditioner.hpp
+++ b/otherarch/sdcpp/src/conditioning/conditioner.hpp
@@ -6,6 +6,7 @@
 #include <optional>
 
 #include "core/tensor_ggml.hpp"
+#include "core/util.h"
 #include "model/te/clip.hpp"
 #include "model/te/llm.hpp"
 #include "model/te/t5.hpp"
@@ -116,6 +117,10 @@ public:
     virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors)           = 0;
     virtual void set_max_graph_vram_bytes(size_t max_vram_bytes) {}
     virtual void set_stream_layers_enabled(bool enabled) {}
+    virtual void set_runtime_backends(const std::vector<ggml_backend_t>& backends) {}
+    virtual void set_graph_cut_layer_split_enabled(bool enabled) {}
+    virtual void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) {}
+    virtual void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {}
     virtual void set_flash_attention_enabled(bool enabled) = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
     virtual void runner_done() {}
@@ -178,6 +183,27 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        text_model->set_runtime_backends(backends);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        text_model->set_graph_cut_layer_split_enabled(enabled);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        text_model->set_graph_cut_layer_split_backend_vram_limits(limits);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         text_model->set_flash_attention_enabled(enabled);
         if (sd_version_is_sdxl(version)) {
@@ -635,6 +661,48 @@ struct SD3CLIPEmbedder : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (clip_l) {
+            clip_l->set_runtime_backends(backends);
+        }
+        if (clip_g) {
+            clip_g->set_runtime_backends(backends);
+        }
+        if (t5) {
+            t5->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (clip_g) {
+            clip_g->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (clip_g) {
+            clip_g->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         if (clip_l) {
             clip_l->set_flash_attention_enabled(enabled);
@@ -994,6 +1062,39 @@ struct FluxCLIPEmbedder : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (clip_l) {
+            clip_l->set_runtime_backends(backends);
+        }
+        if (t5) {
+            t5->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (clip_l) {
+            clip_l->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         if (clip_l) {
             clip_l->set_flash_attention_enabled(enabled);
@@ -1191,8 +1292,27 @@ struct T5CLIPEmbedder : public Conditioner {
                    bool use_mask                                       = false,
                    int mask_pad                                        = 0,
                    bool is_umt5                                        = false,
-                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                   const char* model_args                              = nullptr)
         : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
+        for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+            if (key == "chroma_use_t5_mask") {
+                bool parsed = false;
+                if (parse_strict_bool(value, parsed)) {
+                    this->use_mask = parsed;
+                } else {
+                    LOG_WARN("ignoring invalid Chroma T5 model arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "chroma_t5_mask_pad") {
+                int parsed = 0;
+                if (parse_strict_int(value, parsed)) {
+                    this->mask_pad = parsed;
+                } else {
+                    LOG_WARN("ignoring invalid Chroma T5 model arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+
         bool use_t5 = false;
         for (auto pair : tensor_storage_map) {
             if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
@@ -1226,6 +1346,30 @@ struct T5CLIPEmbedder : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (t5) {
+            t5->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         if (t5) {
             t5->set_flash_attention_enabled(enabled);
@@ -1418,6 +1562,30 @@ struct MiniT2IConditioner : public Conditioner {
         }
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        if (t5) {
+            t5->set_runtime_backends(backends);
+        }
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (t5) {
+            t5->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         if (t5) {
             t5->set_flash_attention_enabled(enabled);
@@ -1502,6 +1670,22 @@ struct AnimaConditioner : public Conditioner {
         llm->set_stream_layers_enabled(enabled);
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        llm->set_runtime_backends(backends);
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        llm->set_graph_cut_layer_split_enabled(enabled);
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        llm->set_graph_cut_layer_split_backend_vram_limits(limits);
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, "text_encoders.llm");
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         llm->set_flash_attention_enabled(enabled);
     }
@@ -1647,6 +1831,26 @@ struct LLMEmbedder : public Conditioner {
         llm->set_stream_layers_enabled(enabled);
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        llm->set_runtime_backends(backends);
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        if (llm) {
+            llm->set_graph_cut_layer_split_enabled(enabled);
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        if (llm) {
+            llm->set_graph_cut_layer_split_backend_vram_limits(limits);
+        }
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, "text_encoders.llm");
+    }
+
     void set_flash_attention_enabled(bool enabled) override {
         llm->set_flash_attention_enabled(enabled);
     }
@@ -2316,6 +2520,22 @@ struct LTXAVEmbedder : public Conditioner {
         projector->set_max_graph_vram_bytes(max_vram_bytes);
     }
 
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) override {
+        llm->set_runtime_backends(backends);
+    }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) override {
+        llm->set_graph_cut_layer_split_enabled(enabled);
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) override {
+        llm->set_graph_cut_layer_split_backend_vram_limits(limits);
+    }
+
+    void get_layer_split_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, "text_encoders.llm");
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         llm->set_weight_adapter(adapter);
         projector->set_weight_adapter(adapter);
diff --git a/otherarch/sdcpp/src/convert.cpp b/otherarch/sdcpp/src/convert.cpp
index 0b7fe2cfb..8e94a940c 100644
--- a/otherarch/sdcpp/src/convert.cpp
+++ b/otherarch/sdcpp/src/convert.cpp
@@ -1,14 +1,33 @@
+#include <algorithm>
+#include <condition_variable>
+#include <cstdint>
 #include <cstring>
+#include <exception>
+#include <fstream>
+#include <memory>
 #include <mutex>
 #include <regex>
+#include <string>
+#include <thread>
 #include <vector>
 
+#include "core/util.h"
 #include "model_io/gguf_io.h"
 #include "model_io/safetensors_io.h"
+#include "model_io/streaming_writer.h"
 #include "model_loader.h"
-#include "util.h"
 
-#include "ggml_extend_backend.h"
+struct TensorExportInfo {
+    TensorStorage storage;
+    ggml_type type;
+};
+
+struct TensorExportJob {
+    TensorExportInfo info;
+    std::vector<uint8_t> data;
+    std::string error;
+    bool success = false;
+};
 
 static ggml_type get_export_tensor_type(ModelLoader& model_loader,
                                         const TensorStorage& tensor_storage,
@@ -33,47 +52,262 @@ static ggml_type get_export_tensor_type(ModelLoader& model_loader,
     return tensor_type;
 }
 
-static bool load_tensors_for_export(ModelLoader& model_loader,
-                                    ggml_context* ggml_ctx,
-                                    ggml_type type,
-                                    const TensorTypeRules& tensor_type_rules,
-                                    std::vector<TensorWriteInfo>& tensors) {
-    std::mutex tensor_mutex;
-    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
-        const std::string& name = tensor_storage.name;
-        ggml_type tensor_type   = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+static bool collect_tensors_for_export(ModelLoader& model_loader,
+                                       ggml_type type,
+                                       const TensorTypeRules& tensor_type_rules,
+                                       std::vector<TensorExportInfo>& tensors) {
+    tensors.clear();
+    tensors.reserve(model_loader.get_tensor_storage_map().size());
+    for (const auto& kv : model_loader.get_tensor_storage_map()) {
+        const TensorStorage& tensor_storage = kv.second;
+        TensorExportInfo info;
+        info.storage = tensor_storage;
+        info.type    = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+        tensors.push_back(std::move(info));
+    }
+    LOG_INFO("collected %zu tensors for export", tensors.size());
+    return true;
+}
 
-        std::lock_guard<std::mutex> lock(tensor_mutex);
-        ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
-        if (tensor == nullptr) {
-            LOG_ERROR("ggml_new_tensor failed");
+static size_t export_tensor_nbytes(const TensorExportInfo& info) {
+    TensorStorage output_storage = info.storage;
+    output_storage.type          = info.type;
+    return static_cast<size_t>(output_storage.nbytes());
+}
+
+static TensorWritePlan tensor_write_plan_from_export_info(const TensorExportInfo& info) {
+    TensorWritePlan plan;
+    plan.name   = info.storage.name;
+    plan.type   = info.type;
+    plan.n_dims = info.storage.n_dims;
+    for (int i = 0; i < SD_MAX_DIMS; i++) {
+        plan.ne[i] = info.storage.ne[i];
+    }
+    return plan;
+}
+
+static std::vector<TensorWritePlan> tensor_write_plans_from_export_infos(const std::vector<TensorExportInfo>& tensors) {
+    std::vector<TensorWritePlan> plans;
+    plans.reserve(tensors.size());
+    for (const TensorExportInfo& info : tensors) {
+        plans.push_back(tensor_write_plan_from_export_info(info));
+    }
+    return plans;
+}
+
+static bool preallocate_output_file(const std::string& output_path, uint64_t file_size, std::string* error) {
+    if (file_size == 0) {
+        return true;
+    }
+
+    std::fstream file(output_path, std::ios::binary | std::ios::in | std::ios::out);
+    if (!file.is_open()) {
+        if (error != nullptr) {
+            *error = "failed to open output file '" + output_path + "' for preallocation";
+        }
+        return false;
+    }
+
+    // This portable fallback sets the final file size. A platform-specific
+    // posix_fallocate/ftruncate path can replace it later.
+    file.seekp(static_cast<std::streamoff>(file_size - 1), std::ios::beg);
+    file.put('\0');
+    file.flush();
+    if (!file) {
+        if (error != nullptr) {
+            *error = "failed to preallocate output file '" + output_path + "'";
+        }
+        return false;
+    }
+    return true;
+}
+
+static bool load_tensor_for_export(ModelLoader& model_loader, TensorExportJob& job) {
+    size_t mem_size = 1 * 1024 * 1024;
+    mem_size += ggml_tensor_overhead();
+    TensorStorage output_storage = job.info.storage;
+    output_storage.type          = job.info.type;
+    mem_size += static_cast<size_t>(output_storage.nbytes());
+
+    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
+    if (ggml_ctx == nullptr) {
+        job.error = "ggml_init failed for tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+
+    ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, job.info.type, job.info.storage.n_dims, job.info.storage.ne);
+    if (tensor == nullptr) {
+        ggml_free(ggml_ctx);
+        job.error = "ggml_new_tensor failed for tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+    ggml_set_name(tensor, job.info.storage.name.c_str());
+
+    const size_t tensor_nbytes = ggml_nbytes(tensor);
+    if (tensor_nbytes > 0 && !model_loader.load_tensor(job.info.storage, tensor)) {
+        ggml_free(ggml_ctx);
+        job.error = "failed to load tensor '" + job.info.storage.name + "'";
+        return false;
+    }
+
+    job.data.resize(tensor_nbytes);
+    if (tensor_nbytes > 0) {
+        memcpy(job.data.data(), tensor->data, tensor_nbytes);
+    }
+    ggml_free(ggml_ctx);
+    return true;
+}
+
+static bool stream_tensor_data(ModelLoader& model_loader,
+                               const std::string& output_path,
+                               const std::vector<TensorExportInfo>& tensors,
+                               const StreamingModelWriter& writer,
+                               int n_threads,
+                               std::string* error) {
+    n_threads = n_threads > 0 ? n_threads : sd_get_num_physical_cores();
+    n_threads = std::max(1, n_threads);
+    LOG_INFO("streaming convert with %d threads", n_threads);
+
+    int64_t start_time       = ggml_time_ms();
+    uint64_t bytes_written   = 0;
+    size_t tensors_written   = 0;
+    size_t next_tensor_index = 0;
+    bool failed              = false;
+    std::string failure;
+
+    const size_t memory_budget = 1024ull * 1024ull * 1024ull;
+    size_t reserved_bytes      = 0;
+
+    std::mutex work_mutex;
+    std::mutex progress_mutex;
+    std::condition_variable memory_cv;
+    std::vector<std::thread> workers;
+    workers.reserve(n_threads);
+
+    auto reserve_memory = [&](size_t bytes) -> bool {
+        std::unique_lock<std::mutex> lock(work_mutex);
+        memory_cv.wait(lock, [&]() {
+            return failed || reserved_bytes == 0 || reserved_bytes + bytes <= memory_budget;
+        });
+        if (failed) {
             return false;
         }
-        ggml_set_name(tensor, name.c_str());
-
-        if (!tensor->data) {
-            GGML_ASSERT(ggml_nelements(tensor) == 0);
-            // Avoid crashing writers by setting a dummy pointer for zero-sized tensors.
-            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
-            tensor->data = ggml_get_mem_buffer(ggml_ctx);
-        }
-
-        TensorWriteInfo write_info;
-        write_info.tensor = tensor;
-        write_info.n_dims = tensor_storage.n_dims;
-        for (int i = 0; i < tensor_storage.n_dims; ++i) {
-            write_info.ne[i] = tensor_storage.ne[i];
-        }
-
-        *dst_tensor = tensor;
-        tensors.push_back(std::move(write_info));
-
+        reserved_bytes += bytes;
         return true;
     };
 
-    bool success = model_loader.load_tensors(on_new_tensor_cb);
-    LOG_INFO("load tensors done");
-    return success;
+    auto release_memory = [&](size_t bytes) {
+        {
+            std::lock_guard<std::mutex> lock(work_mutex);
+            reserved_bytes -= std::min(reserved_bytes, bytes);
+        }
+        memory_cv.notify_all();
+    };
+
+    auto fail = [&](const std::string& message) {
+        {
+            std::lock_guard<std::mutex> lock(work_mutex);
+            if (!failed) {
+                failed  = true;
+                failure = message;
+            }
+        }
+        memory_cv.notify_all();
+    };
+
+    for (int worker = 0; worker < n_threads; worker++) {
+        workers.emplace_back([&]() {
+            std::fstream output_file(output_path, std::ios::binary | std::ios::in | std::ios::out);
+            if (!output_file.is_open()) {
+                fail("failed to open output file '" + output_path + "' for tensor writing");
+                return;
+            }
+
+            while (true) {
+                size_t tensor_index = 0;
+                {
+                    std::lock_guard<std::mutex> lock(work_mutex);
+                    if (failed || next_tensor_index >= tensors.size()) {
+                        return;
+                    }
+                    tensor_index = next_tensor_index++;
+                }
+
+                const size_t tensor_bytes = export_tensor_nbytes(tensors[tensor_index]);
+                if (!reserve_memory(tensor_bytes)) {
+                    return;
+                }
+
+                TensorExportJob job;
+                job.info = tensors[tensor_index];
+                try {
+                    job.success = load_tensor_for_export(model_loader, job);
+                } catch (const std::exception& e) {
+                    job.error   = e.what();
+                    job.success = false;
+                }
+
+                if (!job.success) {
+                    release_memory(tensor_bytes);
+                    fail(job.error.empty() ? "streaming conversion failed" : job.error);
+                    return;
+                }
+
+                std::string write_error;
+                if (!writer.write_tensor(output_file,
+                                         tensor_index,
+                                         job.data.empty() ? nullptr : job.data.data(),
+                                         job.data.size(),
+                                         &write_error)) {
+                    release_memory(tensor_bytes);
+                    fail(write_error.empty() ? "streaming conversion write failed" : write_error);
+                    return;
+                }
+
+                {
+                    std::lock_guard<std::mutex> lock(progress_mutex);
+                    bytes_written += job.data.size();
+                    tensors_written++;
+                    float elapsed_seconds = (ggml_time_ms() - start_time) / 1000.0f;
+                    pretty_bytes_progress(static_cast<int>(tensors_written),
+                                          static_cast<int>(tensors.size()),
+                                          bytes_written,
+                                          elapsed_seconds);
+                }
+                release_memory(tensor_bytes);
+            }
+        });
+    }
+
+    for (auto& worker : workers) {
+        worker.join();
+    }
+    printf("\n");
+    if (failed) {
+        if (error != nullptr) {
+            *error = failure;
+        }
+        return false;
+    }
+    LOG_INFO("streaming conversion completed, taking %.2fs", (ggml_time_ms() - start_time) / 1000.f);
+    return true;
+}
+
+static bool write_model_file_streaming(ModelLoader& model_loader,
+                                       const std::string& output_path,
+                                       const std::vector<TensorExportInfo>& tensors,
+                                       StreamingModelWriter& writer,
+                                       int n_threads,
+                                       std::string* error) {
+    std::vector<TensorWritePlan> plans = tensor_write_plans_from_export_infos(tensors);
+    if (!writer.write_metadata(output_path, plans, error)) {
+        return false;
+    }
+    if (!preallocate_output_file(output_path, writer.file_size(), error)) {
+        return false;
+    }
+    model_loader.process_model_files(false, false);
+    return stream_tensor_data(model_loader, output_path, tensors, writer, n_threads, error);
 }
 
 static bool init_convert_path(ModelLoader& model_loader, const char* path, const char* prefix, bool& loaded_any) {
@@ -91,42 +325,29 @@ static bool init_convert_path(ModelLoader& model_loader, const char* path, const
 static bool export_loaded_model(ModelLoader& model_loader,
                                 const char* output_path,
                                 sd_type_t output_type,
-                                const char* tensor_type_rules) {
+                                const char* tensor_type_rules,
+                                int n_threads) {
     ggml_type type             = sd_type_to_ggml_type(output_type);
     bool output_is_safetensors = ends_with(output_path, ".safetensors");
     TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
 
-    auto backend    = sd_backend_cpu_init();
-    size_t mem_size = 1 * 1024 * 1024;  // for padding
-    mem_size += model_loader.get_tensor_storage_map().size() * ggml_tensor_overhead();
-    mem_size += model_loader.get_params_mem_size(backend, type);
-    LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
-    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
-
-    if (ggml_ctx == nullptr) {
-        LOG_ERROR("ggml_init failed for converter");
-        ggml_backend_free(backend);
-        return false;
-    }
-
-    std::vector<TensorWriteInfo> tensors;
-    bool success = load_tensors_for_export(model_loader, ggml_ctx, type, type_rules, tensors);
-    ggml_backend_free(backend);
-
+    std::vector<TensorExportInfo> tensors;
+    bool success = collect_tensors_for_export(model_loader, type, type_rules, tensors);
     std::string error;
     if (success) {
+        std::unique_ptr<StreamingModelWriter> writer;
         if (output_is_safetensors) {
-            success = write_safetensors_file(output_path, tensors, &error);
+            writer = std::make_unique<SafetensorsStreamingWriter>();
         } else {
-            success = write_gguf_file(output_path, tensors, &error);
+            writer = std::make_unique<GGUFStreamingWriter>();
         }
+        success = write_model_file_streaming(model_loader, output_path, tensors, *writer, n_threads, &error);
     }
 
     if (!success && !error.empty()) {
         LOG_ERROR("%s", error.c_str());
     }
 
-    ggml_free(ggml_ctx);
     return success;
 }
 
@@ -139,7 +360,8 @@ bool convert_with_components(const char* model_path,
                              const char* output_path,
                              sd_type_t output_type,
                              const char* tensor_type_rules,
-                             bool convert_name) {
+                             bool convert_name,
+                             int n_threads) {
     ModelLoader model_loader;
     bool loaded_any = false;
 
@@ -161,7 +383,7 @@ bool convert_with_components(const char* model_path,
         model_loader.convert_tensors_name();
     }
 
-    return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules);
+    return export_loaded_model(model_loader, output_path, output_type, tensor_type_rules, n_threads);
 }
 
 bool convert(const char* input_path,
@@ -179,5 +401,6 @@ bool convert(const char* input_path,
                                    output_path,
                                    output_type,
                                    tensor_type_rules,
-                                   convert_name);
+                                   convert_name,
+                                   0);
 }
diff --git a/otherarch/sdcpp/src/core/backend_fit.cpp b/otherarch/sdcpp/src/core/backend_fit.cpp
new file mode 100644
index 000000000..460194740
--- /dev/null
+++ b/otherarch/sdcpp/src/core/backend_fit.cpp
@@ -0,0 +1,390 @@
+#include "backend_fit.h"
+
+#include <algorithm>
+#include <cctype>
+#include <cstdint>
+#include <utility>
+#include <vector>
+
+#include "core/ggml_extend_backend.h"
+#include "core/util.h"
+#include "ggml-backend.h"
+
+namespace sd::backend_fit {
+    namespace {
+
+        constexpr int64_t MiB = 1024ll * 1024;
+
+        enum class ComponentKind {
+            DIT         = 0,
+            VAE         = 1,
+            CONDITIONER = 2,
+        };
+
+        struct Component {
+            ComponentKind kind;
+            const char* name;
+            int64_t params_bytes  = 0;
+            int64_t reserve_bytes = 0;
+            bool splittable       = false;
+        };
+
+        struct Device {
+            ggml_backend_dev_t dev = nullptr;
+            std::string name;
+            std::string description;
+            int64_t free_bytes   = 0;
+            int64_t total_bytes  = 0;
+            int64_t budget_bytes = 0;
+        };
+
+        struct Decision {
+            ComponentKind kind;
+            bool on_cpu = false;
+            std::vector<size_t> device_idxs;
+        };
+
+        struct Plan {
+            bool valid      = false;
+            bool time_share = false;
+            std::vector<Decision> decisions;
+        };
+
+        bool classify_tensor(const std::string& name, ComponentKind& out) {
+            auto contains = [&](const char* s) { return name.find(s) != std::string::npos; };
+
+            if (contains("model.diffusion_model.") || contains("unet.")) {
+                out = ComponentKind::DIT;
+                return true;
+            }
+            if (contains("first_stage_model.") ||
+                name.rfind("vae.", 0) == 0 ||
+                name.rfind("tae.", 0) == 0) {
+                out = ComponentKind::VAE;
+                return true;
+            }
+            if (contains("text_encoders") ||
+                contains("cond_stage_model") ||
+                contains("te.text_model.") ||
+                contains("conditioner") ||
+                name.rfind("text_encoder.", 0) == 0 ||
+                name.rfind("text_embedding_projection.", 0) == 0 ||
+                contains(".aggregate_embed.")) {
+                out = ComponentKind::CONDITIONER;
+                return true;
+            }
+            return false;
+        }
+
+        std::vector<Component> estimate_components(ModelLoader& loader, ggml_type override_wtype) {
+            const auto& storage = loader.get_tensor_storage_map();
+
+            int64_t bytes[3] = {0, 0, 0};
+            for (const auto& [name, ts_const] : storage) {
+                TensorStorage ts = ts_const;
+                if (is_unused_tensor(ts.name)) {
+                    continue;
+                }
+                ComponentKind kind;
+                if (!classify_tensor(ts.name, kind)) {
+                    continue;
+                }
+                if (override_wtype != GGML_TYPE_COUNT &&
+                    loader.tensor_should_be_converted(ts, override_wtype)) {
+                    ts.type = override_wtype;
+                } else if (ts.expected_type != GGML_TYPE_COUNT && ts.expected_type != ts.type) {
+                    ts.type = ts.expected_type;
+                }
+                bytes[int(kind)] += (int64_t)ts.nbytes() + 64;
+            }
+
+            std::vector<Component> out;
+            out.push_back({ComponentKind::DIT, "DiT", bytes[int(ComponentKind::DIT)], 2048 * MiB, true});
+            out.push_back({ComponentKind::VAE, "VAE", bytes[int(ComponentKind::VAE)], 1024 * MiB, false});
+            out.push_back({ComponentKind::CONDITIONER, "Conditioner", bytes[int(ComponentKind::CONDITIONER)], 2048 * MiB, true});
+            return out;
+        }
+
+        std::vector<Device> enumerate_gpu_devices(const sd::ggml_graph_cut::MaxVramAssignment& budgets) {
+            std::vector<Device> out;
+            for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
+                ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+                if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) {
+                    continue;
+                }
+                Device d;
+                d.dev             = dev;
+                d.name            = ggml_backend_dev_name(dev);
+                d.description     = ggml_backend_dev_description(dev);
+                size_t free_bytes = 0, total_bytes = 0;
+                ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
+                d.free_bytes  = (int64_t)free_bytes;
+                d.total_bytes = (int64_t)total_bytes;
+
+                std::string budget_key = d.name;
+                std::transform(budget_key.begin(), budget_key.end(), budget_key.begin(),
+                               [](unsigned char c) { return (char)std::tolower(c); });
+                float gib = budgets.default_gib;
+                auto it   = budgets.backend_gib.find(budget_key);
+                if (it != budgets.backend_gib.end()) {
+                    gib = it->second;
+                }
+                if (gib > 0.f) {
+                    d.budget_bytes = std::min<int64_t>((int64_t)(gib * 1024.0 * 1024.0 * 1024.0), d.free_bytes);
+                } else if (gib < 0.f) {
+                    d.budget_bytes = d.free_bytes + (int64_t)(gib * 1024.0 * 1024.0 * 1024.0);
+                } else {
+                    d.budget_bytes = d.free_bytes - 512 * MiB;
+                }
+                d.budget_bytes = std::max<int64_t>(d.budget_bytes, 0);
+                out.push_back(d);
+            }
+            return out;
+        }
+
+        Plan compute_plan(const std::vector<Component>& components, const std::vector<Device>& devices) {
+            Plan plan;
+            if (devices.empty()) {
+                return plan;
+            }
+
+            std::vector<size_t> order(components.size());
+            for (size_t i = 0; i < order.size(); i++) {
+                order[i] = i;
+            }
+            std::sort(order.begin(), order.end(), [&](size_t a, size_t b) {
+                return components[a].params_bytes > components[b].params_bytes;
+            });
+
+            {
+                std::vector<int64_t> params_sum(devices.size(), 0);
+                std::vector<int64_t> max_reserve(devices.size(), 0);
+                std::vector<Decision> decisions(components.size());
+                bool ok = true;
+                for (size_t ci : order) {
+                    const Component& comp = components[ci];
+                    decisions[ci].kind    = comp.kind;
+                    if (comp.params_bytes == 0) {
+                        continue;
+                    }
+                    int best = -1;
+                    for (size_t di = 0; di < devices.size(); di++) {
+                        int64_t need = params_sum[di] + comp.params_bytes + std::max(max_reserve[di], comp.reserve_bytes);
+                        if (need <= devices[di].budget_bytes &&
+                            (best < 0 || devices[di].budget_bytes - params_sum[di] > devices[best].budget_bytes - params_sum[best])) {
+                            best = (int)di;
+                        }
+                    }
+                    if (best < 0) {
+                        ok = false;
+                        break;
+                    }
+                    params_sum[best] += comp.params_bytes;
+                    max_reserve[best] = std::max(max_reserve[best], comp.reserve_bytes);
+                    decisions[ci].device_idxs.push_back((size_t)best);
+                }
+                if (ok) {
+                    plan.valid      = true;
+                    plan.time_share = false;
+                    plan.decisions  = std::move(decisions);
+                    return plan;
+                }
+            }
+
+            plan.decisions.assign(components.size(), {});
+            for (size_t ci : order) {
+                const Component& comp = components[ci];
+                Decision& decision    = plan.decisions[ci];
+                decision.kind         = comp.kind;
+                if (comp.params_bytes == 0) {
+                    continue;
+                }
+                int best = -1;
+                for (size_t di = 0; di < devices.size(); di++) {
+                    if (comp.params_bytes + comp.reserve_bytes <= devices[di].budget_bytes &&
+                        (best < 0 || devices[di].budget_bytes > devices[best].budget_bytes)) {
+                        best = (int)di;
+                    }
+                }
+                if (best >= 0) {
+                    decision.device_idxs.push_back((size_t)best);
+                    continue;
+                }
+                if (comp.splittable && devices.size() > 1) {
+                    int64_t capacity = 0;
+                    for (const Device& d : devices) {
+                        capacity += std::max<int64_t>(d.budget_bytes - comp.reserve_bytes, 0);
+                    }
+                    if (comp.params_bytes <= capacity) {
+                        std::vector<size_t> idxs(devices.size());
+                        for (size_t i = 0; i < idxs.size(); i++) {
+                            idxs[i] = i;
+                        }
+                        std::sort(idxs.begin(), idxs.end(), [&](size_t a, size_t b) {
+                            return devices[a].budget_bytes > devices[b].budget_bytes;
+                        });
+                        decision.device_idxs = std::move(idxs);
+                        continue;
+                    }
+                }
+                decision.on_cpu = true;
+            }
+            plan.valid      = true;
+            plan.time_share = true;
+            return plan;
+        }
+
+        void print_plan(const Plan& plan,
+                        const std::vector<Component>& components,
+                        const std::vector<Device>& devices) {
+            LOG_INFO("auto-fit plan%s:", plan.time_share ? " (time-share: params load per phase and free after)" : "");
+            LOG_INFO("  devices:");
+            for (const Device& d : devices) {
+                LOG_INFO("    %-12s %-32s free %6lld MiB, budget %6lld MiB",
+                         d.name.c_str(), d.description.c_str(),
+                         (long long)(d.free_bytes / MiB), (long long)(d.budget_bytes / MiB));
+            }
+            LOG_INFO("  components:");
+            for (size_t ci = 0; ci < components.size(); ci++) {
+                const Component& comp    = components[ci];
+                const Decision& decision = plan.decisions[ci];
+                std::string target;
+                if (comp.params_bytes == 0) {
+                    target = "(not present)";
+                } else if (decision.on_cpu) {
+                    target = "CPU";
+                } else {
+                    for (size_t k = 0; k < decision.device_idxs.size(); k++) {
+                        if (k > 0) {
+                            target += " & ";
+                        }
+                        target += devices[decision.device_idxs[k]].name;
+                    }
+                    if (decision.device_idxs.size() > 1) {
+                        target += " (split)";
+                    }
+                }
+                LOG_INFO("    %-12s params %6lld MiB, compute reserve %5lld MiB -> %s",
+                         comp.name,
+                         (long long)(comp.params_bytes / MiB),
+                         (long long)(comp.reserve_bytes / MiB),
+                         target.c_str());
+            }
+        }
+
+        void append_assignment(std::string& spec, const char* key, const std::string& value) {
+            if (!spec.empty()) {
+                spec += ",";
+            }
+            spec += key;
+            spec += "=";
+            spec += value;
+        }
+
+        void append_component_decision(const std::vector<Component>& components,
+                                       const std::vector<Device>& devices,
+                                       const Plan& plan,
+                                       ComponentKind kind,
+                                       const char* module_key,
+                                       std::string& runtime_spec,
+                                       std::string& params_spec) {
+            for (size_t ci = 0; ci < components.size(); ci++) {
+                if (components[ci].kind != kind || components[ci].params_bytes == 0) {
+                    continue;
+                }
+                const Decision& decision = plan.decisions[ci];
+                if (decision.on_cpu) {
+                    append_assignment(runtime_spec, module_key, "cpu");
+                    return;
+                }
+                if (decision.device_idxs.empty()) {
+                    return;
+                }
+                std::string device_list;
+                for (size_t k = 0; k < decision.device_idxs.size(); k++) {
+                    if (k > 0) {
+                        device_list += "&";
+                    }
+                    device_list += devices[decision.device_idxs[k]].name;
+                }
+                append_assignment(runtime_spec, module_key, device_list);
+                if (plan.time_share) {
+                    append_assignment(params_spec, module_key, "disk");
+                }
+                return;
+            }
+        }
+
+    }  // namespace
+
+    bool derive_backend_specs(ModelLoader& loader,
+                              ggml_type override_wtype,
+                              sd::ggml_graph_cut::MaxVramAssignment& budgets,
+                              std::string& runtime_spec,
+                              std::string& params_spec) {
+        if (!runtime_spec.empty() || !params_spec.empty()) {
+            LOG_WARN("--auto-fit is enabled; ignoring --backend / --params-backend");
+        }
+
+        {
+            std::string error;
+            if (!budgets.canonicalize_backend_keys(&error)) {
+                LOG_ERROR("%s", error.c_str());
+                return false;
+            }
+        }
+
+        auto components = estimate_components(loader, override_wtype);
+        auto devices    = enumerate_gpu_devices(budgets);
+        auto plan       = compute_plan(components, devices);
+        if (!plan.valid) {
+            LOG_WARN("auto-fit: no usable GPU devices; using the default backend");
+            runtime_spec.clear();
+            params_spec.clear();
+            return true;
+        }
+
+        print_plan(plan, components, devices);
+
+        std::string derived_runtime_spec;
+        std::string derived_params_spec;
+        append_component_decision(components, devices, plan, ComponentKind::DIT, "diffusion", derived_runtime_spec, derived_params_spec);
+        append_component_decision(components, devices, plan, ComponentKind::CONDITIONER, "te", derived_runtime_spec, derived_params_spec);
+        append_component_decision(components, devices, plan, ComponentKind::VAE, "vae", derived_runtime_spec, derived_params_spec);
+
+        runtime_spec = std::move(derived_runtime_spec);
+        params_spec  = std::move(derived_params_spec);
+
+        LOG_INFO("auto-fit: --backend \"%s\"%s%s%s",
+                 runtime_spec.empty() ? "(default)" : runtime_spec.c_str(),
+                 params_spec.empty() ? "" : " --params-backend \"",
+                 params_spec.c_str(),
+                 params_spec.empty() ? "" : "\"");
+        return true;
+    }
+
+    bool prepare_vae_decode_retry_tiling(sd_tiling_params_t& tiling_params, bool prefer_temporal_tiling) {
+        if (prefer_temporal_tiling) {
+            if (tiling_params.temporal_tiling) {
+                return false;
+            }
+            tiling_params.temporal_tiling = true;
+        } else {
+            if (tiling_params.enabled) {
+                return false;
+            }
+            tiling_params.enabled = true;
+            if (tiling_params.tile_size_x <= 0) {
+                tiling_params.tile_size_x = 256;
+            }
+            if (tiling_params.tile_size_y <= 0) {
+                tiling_params.tile_size_y = 256;
+            }
+        }
+
+        LOG_WARN("auto-fit: VAE decode failed (likely out of memory); retrying with %s tiling",
+                 tiling_params.temporal_tiling ? "temporal" : "spatial");
+        return true;
+    }
+
+}  // namespace sd::backend_fit
diff --git a/otherarch/sdcpp/src/core/backend_fit.h b/otherarch/sdcpp/src/core/backend_fit.h
new file mode 100644
index 000000000..9ef298b3b
--- /dev/null
+++ b/otherarch/sdcpp/src/core/backend_fit.h
@@ -0,0 +1,23 @@
+#ifndef __SD_BACKEND_FIT_H__
+#define __SD_BACKEND_FIT_H__
+
+#include <string>
+
+#include "core/ggml_graph_cut.h"
+#include "model_loader.h"
+#include "stable-diffusion.h"
+
+namespace sd::backend_fit {
+
+    bool derive_backend_specs(ModelLoader& loader,
+                              ggml_type override_wtype,
+                              sd::ggml_graph_cut::MaxVramAssignment& budgets,
+                              std::string& runtime_spec,
+                              std::string& params_spec);
+
+    bool prepare_vae_decode_retry_tiling(sd_tiling_params_t& tiling_params,
+                                         bool prefer_temporal_tiling);
+
+}  // namespace sd::backend_fit
+
+#endif  // __SD_BACKEND_FIT_H__
diff --git a/otherarch/sdcpp/src/core/ggml_extend.hpp b/otherarch/sdcpp/src/core/ggml_extend.hpp
index 48a7ad740..3adae6a57 100644
--- a/otherarch/sdcpp/src/core/ggml_extend.hpp
+++ b/otherarch/sdcpp/src/core/ggml_extend.hpp
@@ -21,10 +21,12 @@
 #include <sstream>
 #include <string>
 #include <unordered_map>
+#include <unordered_set>
 #include <vector>
 
 #include "core/ggml_extend_backend.h"
 #include "core/ggml_graph_cut.h"
+#include "core/layer_split_partition.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "ggml.h"
@@ -1745,6 +1747,13 @@ protected:
     size_t max_graph_vram_bytes           = 0;
     bool stream_layers_enabled            = false;
     size_t observed_max_effective_budget_ = 0;
+    bool graph_cut_layer_split_enabled    = false;
+    std::vector<size_t> graph_cut_layer_split_backend_vram_limits_;
+
+    std::vector<ggml_backend_t> extra_runtime_backends;  // borrowed (SDBackendManager-owned)
+    ggml_backend_sched_t sched             = nullptr;    // owned, multi-device only
+    ggml_backend_t cpu_fallback_backend    = nullptr;    // owned, sched requires a trailing CPU backend
+    bool multi_device_eval_callback_warned = false;
 
     std::shared_ptr<WeightAdapter> weight_adapter = nullptr;
     std::weak_ptr<RunnerWeightManager> weight_manager;
@@ -1771,6 +1780,9 @@ protected:
 
     sd::ggml_graph_cut::PlanCache graph_cut_plan_cache_;
     std::unordered_set<const ggml_tensor*> params_tensor_set_;
+    std::unordered_map<const ggml_tensor*, ggml_backend_t> graph_cut_layer_split_assignments_;
+    std::unordered_map<const ggml_tensor*, ggml_backend_t> graph_cut_layer_split_node_assignments_;
+    bool graph_cut_layer_split_primary_notice_logged_ = false;
 
     template <typename T>
     static sd::Tensor<T> take_or_empty(std::optional<sd::Tensor<T>> tensor) {
@@ -1869,6 +1881,20 @@ protected:
         params_tensor_set_dirty_ = false;
     }
 
+    ggml_tensor* canonical_param_tensor(ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return nullptr;
+        }
+        if (params_tensor_set_.find(tensor) != params_tensor_set_.end()) {
+            return tensor;
+        }
+        if (tensor->view_src != nullptr &&
+            params_tensor_set_.find(tensor->view_src) != params_tensor_set_.end()) {
+            return tensor->view_src;
+        }
+        return nullptr;
+    }
+
     std::vector<ggml_tensor*> collect_used_param_tensors(ggml_cgraph* gf) {
         std::vector<ggml_tensor*> used_params;
         rebuild_params_tensor_set();
@@ -1881,12 +1907,8 @@ protected:
         seen_params.reserve(static_cast<size_t>(n_leafs));
         for (int i = 0; i < n_leafs; ++i) {
             ggml_tensor* leaf       = sd::ggml_graph_cut::leaf_tensor(gf, i);
-            ggml_tensor* param_leaf = leaf;
-            if (param_leaf != nullptr && params_tensor_set_.find(param_leaf) == params_tensor_set_.end()) {
-                param_leaf = param_leaf->view_src;
-            }
+            ggml_tensor* param_leaf = canonical_param_tensor(leaf);
             if (param_leaf != nullptr &&
-                params_tensor_set_.find(param_leaf) != params_tensor_set_.end() &&
                 seen_params.insert(param_leaf).second) {
                 used_params.push_back(param_leaf);
             }
@@ -2013,7 +2035,127 @@ protected:
         return true;
     }
 
+    // Pass explicit buffer types: synthesized defaults can make CUDA devices
+    // report supporting each other's buffers and skip a required copy.
+    bool ensure_sched(ggml_cgraph* gf) {
+        if (sched != nullptr) {
+            return true;
+        }
+        std::vector<ggml_backend_t> backends;
+        backends.reserve(extra_runtime_backends.size() + 2);
+        backends.push_back(runtime_backend);
+        for (ggml_backend_t backend : extra_runtime_backends) {
+            backends.push_back(backend);
+        }
+        if (cpu_fallback_backend == nullptr && !sd_backend_is_cpu(runtime_backend)) {
+            cpu_fallback_backend = sd_backend_cpu_init();
+        }
+        if (cpu_fallback_backend != nullptr) {
+            backends.push_back(cpu_fallback_backend);
+        }
+
+        std::vector<ggml_backend_buffer_type_t> bufts;
+        bufts.reserve(backends.size());
+        ggml_backend_dev_t main_dev = ggml_backend_get_device(runtime_backend);
+        for (ggml_backend_t backend : backends) {
+            ggml_backend_buffer_type_t buft = nullptr;
+            if (backend == cpu_fallback_backend && main_dev != nullptr) {
+                buft = ggml_backend_dev_host_buffer_type(main_dev);
+            }
+            if (buft == nullptr) {
+                buft = ggml_backend_get_default_buffer_type(backend);
+            }
+            bufts.push_back(buft);
+        }
+
+        size_t graph_size = MAX_GRAPH_SIZE;
+        if (gf != nullptr) {
+            graph_size = std::max<size_t>(graph_size, (size_t)ggml_graph_n_nodes(gf));
+        }
+        sched = ggml_backend_sched_new(backends.data(),
+                                       bufts.data(),
+                                       (int)backends.size(),
+                                       graph_size,
+                                       /*parallel=*/false,
+                                       /*op_offload=*/false);
+        if (sched == nullptr) {
+            LOG_ERROR("%s: failed to create backend sched", get_desc().c_str());
+            return false;
+        }
+        return true;
+    }
+
+    ggml_backend_t backend_for_weight(const ggml_tensor* tensor) const {
+        if (tensor == nullptr || tensor->buffer == nullptr) {
+            return nullptr;
+        }
+        if (ggml_backend_buffer_get_usage(tensor->buffer) != GGML_BACKEND_BUFFER_USAGE_WEIGHTS ||
+            ggml_backend_buffer_is_host(tensor->buffer)) {
+            return nullptr;
+        }
+        ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
+        if (dev == nullptr) {
+            return nullptr;
+        }
+        if (ggml_backend_get_device(runtime_backend) == dev) {
+            return runtime_backend;
+        }
+        for (ggml_backend_t backend : extra_runtime_backends) {
+            if (ggml_backend_get_device(backend) == dev) {
+                return backend;
+            }
+        }
+        return nullptr;
+    }
+
+    // Weightless ops have no scheduler anchor, so pin them to the most recent
+    // weight device. Views must stay unpinned or cross-device copies can be
+    // skipped for their consumers.
+    void pin_multi_device_nodes(ggml_cgraph* gf) {
+        if (sched == nullptr || gf == nullptr) {
+            return;
+        }
+        ggml_backend_t current = runtime_backend;
+        const int n_nodes      = ggml_graph_n_nodes(gf);
+        for (int i = 0; i < n_nodes; i++) {
+            ggml_tensor* node    = ggml_graph_node(gf, i);
+            auto node_assignment = graph_cut_layer_split_node_assignments_.find(node);
+            if (node_assignment != graph_cut_layer_split_node_assignments_.end()) {
+                current = node_assignment->second;
+            }
+            for (int s = 0; s < GGML_MAX_SRC; s++) {
+                ggml_backend_t weight_backend = backend_for_weight(node->src[s]);
+                if (weight_backend != nullptr) {
+                    if (node_assignment == graph_cut_layer_split_node_assignments_.end()) {
+                        current = weight_backend;
+                    }
+                }
+            }
+            if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE ||
+                node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) {
+                continue;
+            }
+            if (ggml_backend_supports_op(current, node)) {
+                ggml_backend_sched_set_tensor_backend(sched, node, current);
+            }
+        }
+    }
+
+    bool is_multi_device() const {
+        return !extra_runtime_backends.empty();
+    }
+
     bool alloc_compute_buffer(ggml_cgraph* gf) {
+        if (is_multi_device()) {
+            // The sched replaces the gallocr. Do NOT ggml_backend_sched_reserve
+            // the graph here: reserve runs split_graph, which rewires the
+            // graph's src pointers to sched-internal copy tensors, and the
+            // later ggml_backend_sched_alloc_graph would split the already
+            // rewired graph, silently corrupting every cross-backend input. A
+            // graph must be split at most once; the alloc in execute_graph
+            // performs the real allocation.
+            return ensure_sched(gf);
+        }
         if (compute_allocr != nullptr) {
             return true;
         }
@@ -2229,12 +2371,14 @@ protected:
                plan.valid &&
                max_graph_vram_bytes > 0 &&
                plan.segments.size() > 1 &&
-               !sd_backend_is_cpu(runtime_backend);
+               !sd_backend_is_cpu(runtime_backend) &&
+               !is_multi_device();
     }
 
     bool can_attempt_graph_cut_segmented_compute() const {
         return max_graph_vram_bytes > 0 &&
-               !sd_backend_is_cpu(runtime_backend);
+               !sd_backend_is_cpu(runtime_backend) &&
+               !is_multi_device();
     }
 
     bool resolve_graph_cut_plan(ggml_cgraph* gf,
@@ -2314,6 +2458,123 @@ protected:
         return true;
     }
 
+    bool resolve_graph_cut_layer_split_plan(ggml_cgraph* gf,
+                                            GraphCutPlan* plan_out) {
+        GGML_ASSERT(plan_out != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        *plan_out = sd::ggml_graph_cut::resolve_plan(runtime_backend,
+                                                     gf,
+                                                     &graph_cut_plan_cache_,
+                                                     0,
+                                                     params_tensor_set_,
+                                                     get_desc().c_str());
+        return true;
+    }
+
+    bool assign_graph_cut_layer_split_backends(ggml_cgraph* gf) {
+        graph_cut_layer_split_node_assignments_.clear();
+        if (!graph_cut_layer_split_enabled) {
+            return true;
+        }
+        if (!is_multi_device()) {
+            LOG_ERROR("%s graph-cut layer split requires multiple runtime backends", get_desc().c_str());
+            return false;
+        }
+
+        GraphCutPlan plan;
+        if (!resolve_graph_cut_layer_split_plan(gf, &plan)) {
+            return false;
+        }
+        if (!plan.valid || !plan.has_cuts || plan.segments.size() <= 1) {
+            auto manager = weight_manager.lock();
+            if (manager == nullptr) {
+                LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
+                return false;
+            }
+            std::vector<ggml_tensor*> graph_params = collect_used_param_tensors(gf);
+            if (!graph_params.empty() &&
+                !manager->assign_compute_backend(graph_params, runtime_backend)) {
+                LOG_ERROR("%s graph-cut layer split failed to assign unmarked graph params to %s",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(runtime_backend).c_str());
+                return false;
+            }
+            for (ggml_tensor* param : graph_params) {
+                if (param != nullptr) {
+                    graph_cut_layer_split_assignments_[param] = runtime_backend;
+                }
+            }
+            const int n_nodes = ggml_graph_n_nodes(gf);
+            for (int i = 0; i < n_nodes; i++) {
+                ggml_tensor* node = ggml_graph_node(gf, i);
+                if (node != nullptr) {
+                    graph_cut_layer_split_node_assignments_[node] = runtime_backend;
+                }
+            }
+            if (!graph_cut_layer_split_primary_notice_logged_) {
+                LOG_WARN("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
+                         get_desc().c_str(),
+                         sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
+                         graph_params.size());
+                graph_cut_layer_split_primary_notice_logged_ = true;
+            } else {
+                LOG_DEBUG("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
+                          graph_params.size());
+            }
+            return true;
+        }
+
+        std::vector<ggml_backend_t> split_backends;
+        split_backends.reserve(extra_runtime_backends.size() + 1);
+        split_backends.push_back(runtime_backend);
+        for (ggml_backend_t backend : extra_runtime_backends) {
+            if (backend != nullptr) {
+                split_backends.push_back(backend);
+            }
+        }
+
+        auto manager = weight_manager.lock();
+        if (manager == nullptr) {
+            LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
+            return false;
+        }
+
+        sd::GraphCutLayerSplitAssignment assignment;
+        auto canonicalize_param = [this](ggml_tensor* tensor) {
+            return canonical_param_tensor(tensor);
+        };
+        if (!sd::partition_graph_cut_layer_split(get_desc().c_str(),
+                                                 gf,
+                                                 plan,
+                                                 split_backends,
+                                                 graph_cut_layer_split_backend_vram_limits_,
+                                                 max_graph_vram_bytes,
+                                                 graph_cut_layer_split_assignments_,
+                                                 canonicalize_param,
+                                                 &assignment)) {
+            return false;
+        }
+
+        for (size_t i = 0; i < split_backends.size(); i++) {
+            if (assignment.tensors_by_backend[i].empty()) {
+                continue;
+            }
+            if (!manager->assign_compute_backend(assignment.tensors_by_backend[i], split_backends[i])) {
+                LOG_ERROR("%s graph-cut layer split failed to assign params to %s",
+                          get_desc().c_str(),
+                          sd::layer_split_backend_device_display_name(split_backends[i]).c_str());
+                return false;
+            }
+        }
+
+        graph_cut_layer_split_node_assignments_ = std::move(assignment.node_assignments);
+        sd::log_graph_cut_layer_split_assignment(get_desc().c_str(), split_backends, assignment);
+
+        return true;
+    }
+
     struct PersistentExternalBinding {
         ggml_backend_buffer_t buffer = nullptr;
         void* data                   = nullptr;
@@ -2490,7 +2751,14 @@ protected:
         };
         ComputeBufferGuard compute_buffer_guard(this, free_compute_buffer);
 
-        if (!ggml_gallocr_alloc_graph(compute_allocr, gf)) {
+        if (is_multi_device()) {
+            ggml_backend_sched_reset(sched);
+            pin_multi_device_nodes(gf);  // reset clears the pins; re-apply before alloc
+            if (!ggml_backend_sched_alloc_graph(sched, gf)) {
+                LOG_ERROR("%s sched alloc compute graph failed", get_desc().c_str());
+                return std::nullopt;
+            }
+        } else if (!ggml_gallocr_alloc_graph(compute_allocr, gf)) {
             LOG_ERROR("%s alloc compute graph failed", get_desc().c_str());
             return std::nullopt;
         }
@@ -2499,11 +2767,27 @@ protected:
         if (sd_backend_is_cpu(runtime_backend)) {
             sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
         }
+        if (cpu_fallback_backend != nullptr) {
+            sd_backend_cpu_set_n_threads(cpu_fallback_backend, n_threads);
+        }
 
-        ggml_status status = sd_backend_graph_compute_with_eval_callback(runtime_backend,
-                                                                         gf,
-                                                                         sd_get_backend_eval_callback(),
-                                                                         sd_get_backend_eval_callback_data());
+        ggml_status status;
+        if (is_multi_device()) {
+            if (sd_get_backend_eval_callback() != nullptr && !multi_device_eval_callback_warned) {
+                LOG_WARN("%s: eval callback is not supported with multiple runtime backends; ignoring",
+                         get_desc().c_str());
+                multi_device_eval_callback_warned = true;
+            }
+            status = ggml_backend_sched_graph_compute(sched, gf);
+            if (status == GGML_STATUS_SUCCESS) {
+                ggml_backend_sched_synchronize(sched);
+            }
+        } else {
+            status = sd_backend_graph_compute_with_eval_callback(runtime_backend,
+                                                                 gf,
+                                                                 sd_get_backend_eval_callback(),
+                                                                 sd_get_backend_eval_callback_data());
+        }
         if (status != GGML_STATUS_SUCCESS) {
             LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
             return std::nullopt;
@@ -2680,6 +2964,10 @@ public:
         free_params_ctx();
         free_compute_ctx();
         free_cache_ctx_and_buffer();
+        if (cpu_fallback_backend != nullptr) {
+            ggml_backend_free(cpu_fallback_backend);
+            cpu_fallback_backend = nullptr;
+        }
     }
 
     virtual GGMLRunnerContext get_context() {
@@ -2720,10 +3008,20 @@ public:
             ggml_gallocr_free(compute_allocr);
             compute_allocr = nullptr;
         }
+        if (sched != nullptr) {
+            ggml_backend_sched_free(sched);
+            sched = nullptr;
+        }
     }
 
     // do copy after alloc graph
     void set_backend_tensor_data(ggml_tensor* tensor, const void* data) {
+        if (is_multi_device()) {
+            // The sched only assigns a backend (and thus a buffer) to tensors
+            // that participate in the graph; flag standalone data tensors as
+            // inputs so they get one.
+            ggml_set_input(tensor);
+        }
         backend_tensor_data_map[tensor] = data;
     }
 
@@ -2814,6 +3112,11 @@ public:
         GGML_ASSERT(gf != nullptr);
         rebuild_params_tensor_set();
 
+        if (!assign_graph_cut_layer_split_backends(gf)) {
+            free_compute_ctx();
+            return std::nullopt;
+        }
+
         if (can_attempt_graph_cut_segmented_compute()) {
             GraphCutPlan plan;
             if (!resolve_graph_cut_plan(gf, &plan)) {
@@ -2859,8 +3162,50 @@ public:
     }
 
     void set_stream_layers_enabled(bool enabled) {
+        if (enabled && is_multi_device()) {
+            LOG_WARN("%s: --stream-layers is not supported with multiple runtime backends; ignoring",
+                     get_desc().c_str());
+            return;
+        }
         stream_layers_enabled = enabled;
     }
+
+    void set_graph_cut_layer_split_enabled(bool enabled) {
+        graph_cut_layer_split_enabled = enabled;
+        if (!enabled) {
+            graph_cut_layer_split_assignments_.clear();
+            graph_cut_layer_split_node_assignments_.clear();
+            graph_cut_layer_split_primary_notice_logged_ = false;
+        }
+    }
+
+    void set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) {
+        graph_cut_layer_split_backend_vram_limits_ = limits;
+        graph_cut_layer_split_assignments_.clear();
+        graph_cut_layer_split_node_assignments_.clear();
+        graph_cut_layer_split_primary_notice_logged_ = false;
+    }
+
+    void set_runtime_backends(const std::vector<ggml_backend_t>& backends) {
+        extra_runtime_backends.clear();
+        for (ggml_backend_t backend : backends) {
+            if (backend == nullptr || backend == runtime_backend) {
+                continue;
+            }
+            if (std::find(extra_runtime_backends.begin(), extra_runtime_backends.end(), backend) ==
+                extra_runtime_backends.end()) {
+                extra_runtime_backends.push_back(backend);
+            }
+        }
+        graph_cut_layer_split_assignments_.clear();
+        graph_cut_layer_split_node_assignments_.clear();
+        graph_cut_layer_split_primary_notice_logged_ = false;
+        if (is_multi_device() && stream_layers_enabled) {
+            LOG_WARN("%s: --stream-layers is not supported with multiple runtime backends; ignoring",
+                     get_desc().c_str());
+            stream_layers_enabled = false;
+        }
+    }
 };
 
 class GGMLBlock {
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
index f29bdb696..c66bb63f0 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.cpp
@@ -665,12 +665,52 @@ SDBackendManager::~SDBackendManager() {
 
 void SDBackendManager::reset() {
     backends_.clear();
-    runtime_assignment_ = {};
-    params_assignment_  = {};
+    runtime_assignment_    = {};
+    params_assignment_     = {};
+    split_mode_assignment_ = {};
+}
+
+static std::vector<std::string> split_device_list(const std::string& value) {
+    std::vector<std::string> names;
+    for (const std::string& raw : split_copy(value, '&')) {
+        const std::string name = trim_copy(raw);
+        if (!name.empty()) {
+            names.push_back(name);
+        }
+    }
+    return names;
+}
+
+static std::string primary_device_name(const std::string& value) {
+    std::vector<std::string> names = split_device_list(value);
+    return names.empty() ? std::string() : names.front();
 }
 
 ggml_backend_t SDBackendManager::runtime_backend(SDBackendModule module) {
-    return init_cached_backend(runtime_assignment_.get(module));
+    return init_cached_backend(primary_device_name(runtime_assignment_.get(module)));
+}
+
+std::vector<ggml_backend_t> SDBackendManager::runtime_backends(SDBackendModule module) {
+    std::vector<ggml_backend_t> backends;
+    for (const std::string& name : split_device_list(runtime_assignment_.get(module))) {
+        ggml_backend_t backend = init_cached_backend(name);
+        if (backend == nullptr) {
+            LOG_ERROR("failed to initialize backend '%s' for module %s",
+                      name.c_str(),
+                      sd_backend_module_name(module));
+            continue;
+        }
+        if (std::find(backends.begin(), backends.end(), backend) == backends.end()) {
+            backends.push_back(backend);
+        }
+    }
+    if (backends.empty()) {
+        ggml_backend_t backend = runtime_backend(module);
+        if (backend != nullptr) {
+            backends.push_back(backend);
+        }
+    }
+    return backends;
 }
 
 ggml_backend_t SDBackendManager::params_backend(SDBackendModule module) {
@@ -696,6 +736,10 @@ bool SDBackendManager::params_backend_is_disk(SDBackendModule module) const {
     return is_disk_backend_token(params_assignment_.get(module));
 }
 
+bool SDBackendManager::params_backend_follows_runtime(SDBackendModule module) const {
+    return params_assignment_.get(module).empty();
+}
+
 bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule module) {
     ggml_backend_t backend = runtime_backend(module);
     if (backend == nullptr) {
@@ -715,6 +759,7 @@ bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule modu
 
 bool SDBackendManager::init(const char* backend_spec,
                             const char* params_backend_spec,
+                            const char* split_mode_spec,
                             std::string* error) {
     reset();
 
@@ -724,12 +769,53 @@ bool SDBackendManager::init(const char* backend_spec,
     if (!sd_parse_backend_assignment(SAFE_STR(params_backend_spec), &params_assignment_, error)) {
         return false;
     }
+    if (!sd_parse_backend_assignment(SAFE_STR(split_mode_spec), &split_mode_assignment_, error)) {
+        return false;
+    }
 
     return validate(error);
 }
 
+SDSplitMode SDBackendManager::split_mode(SDBackendModule module) const {
+    return lower_copy(trim_copy(split_mode_assignment_.get(module))) == "row" ? SDSplitMode::ROW
+                                                                              : SDSplitMode::LAYER;
+}
+
+ggml_backend_buffer_type_t SDBackendManager::split_buffer_type(ggml_backend_t backend,
+                                                               const std::vector<float>& tensor_split) {
+    if (backend == nullptr) {
+        return nullptr;
+    }
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    if (dev == nullptr) {
+        return nullptr;
+    }
+    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+    if (reg == nullptr) {
+        return nullptr;
+    }
+    auto fn = (ggml_backend_split_buffer_type_t)ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
+    if (fn == nullptr) {
+        return nullptr;
+    }
+    int main_device        = -1;
+    const size_t dev_count = ggml_backend_reg_dev_count(reg);
+    for (size_t i = 0; i < dev_count; ++i) {
+        if (ggml_backend_reg_dev_get(reg, i) == dev) {
+            main_device = (int)i;
+            break;
+        }
+    }
+    if (main_device < 0) {
+        return nullptr;
+    }
+    std::vector<float> padded_split(std::max<size_t>(tensor_split.size(), 64), 0.0f);
+    std::copy(tensor_split.begin(), tensor_split.end(), padded_split.begin());
+    return fn(main_device, padded_split.data());
+}
+
 bool SDBackendManager::validate(std::string* error) const {
-    auto validate_runtime_name = [&](const std::string& name) -> bool {
+    auto validate_single_runtime_name = [&](const std::string& name) -> bool {
         if (is_default_backend_token(name)) {
             return true;
         }
@@ -747,15 +833,56 @@ bool SDBackendManager::validate(std::string* error) const {
         }
         return false;
     };
+    auto validate_runtime_name = [&](const std::string& name) -> bool {
+        if (name.find('&') == std::string::npos) {
+            return validate_single_runtime_name(name);
+        }
+        std::vector<std::string> names = split_device_list(name);
+        if (names.empty()) {
+            if (error != nullptr) {
+                *error = "invalid backend device list '" + name + "'";
+            }
+            return false;
+        }
+        for (const std::string& entry : names) {
+            if (is_default_backend_token(entry)) {
+                if (error != nullptr) {
+                    *error = "default backend token is not allowed in a device list '" + name + "'";
+                }
+                return false;
+            }
+            if (!validate_single_runtime_name(entry)) {
+                return false;
+            }
+        }
+        return true;
+    };
     auto validate_params_name = [&](const std::string& name) -> bool {
         if (is_disk_backend_token(name)) {
             return true;
         }
-        return validate_runtime_name(name);
+        if (name.find('&') != std::string::npos) {
+            if (error != nullptr) {
+                *error = "params_backend does not accept device lists ('" + name + "')";
+            }
+            return false;
+        }
+        return validate_single_runtime_name(name);
+    };
+    auto validate_split_mode_name = [&](const std::string& name) -> bool {
+        const std::string lower = lower_copy(trim_copy(name));
+        if (lower.empty() || lower == "layer" || lower == "row") {
+            return true;
+        }
+        if (error != nullptr) {
+            *error = "invalid split mode '" + name + "' (expected layer or row)";
+        }
+        return false;
     };
 
     if (!validate_runtime_name(runtime_assignment_.default_name) ||
-        !validate_params_name(params_assignment_.default_name)) {
+        !validate_params_name(params_assignment_.default_name) ||
+        !validate_split_mode_name(split_mode_assignment_.default_name)) {
         return false;
     }
     for (const auto& kv : runtime_assignment_.module_names) {
@@ -768,6 +895,11 @@ bool SDBackendManager::validate(std::string* error) const {
             return false;
         }
     }
+    for (const auto& kv : split_mode_assignment_.module_names) {
+        if (!validate_split_mode_name(kv.second)) {
+            return false;
+        }
+    }
     return true;
 }
 
diff --git a/otherarch/sdcpp/src/core/ggml_extend_backend.h b/otherarch/sdcpp/src/core/ggml_extend_backend.h
index 19b71d432..1f3bc8b3a 100644
--- a/otherarch/sdcpp/src/core/ggml_extend_backend.h
+++ b/otherarch/sdcpp/src/core/ggml_extend_backend.h
@@ -6,6 +6,7 @@
 #include <memory>
 #include <string>
 #include <unordered_map>
+#include <vector>
 
 #include "ggml-backend.h"
 #include "ggml.h"
@@ -37,10 +38,16 @@ struct SDBackendHandleDeleter {
 
 using SDBackendHandle = std::unique_ptr<struct ggml_backend, SDBackendHandleDeleter>;
 
+enum class SDSplitMode {
+    LAYER,
+    ROW,
+};
+
 class SDBackendManager {
 private:
     SDBackendAssignment runtime_assignment_;
     SDBackendAssignment params_assignment_;
+    SDBackendAssignment split_mode_assignment_;
     std::unordered_map<std::string, SDBackendHandle> backends_;
 
 public:
@@ -52,15 +59,23 @@ public:
 
     bool init(const char* backend_spec,
               const char* params_backend_spec,
+              const char* split_mode_spec,
               std::string* error);
     void reset();
 
     ggml_backend_t runtime_backend(SDBackendModule module);
     ggml_backend_t params_backend(SDBackendModule module);
 
+    std::vector<ggml_backend_t> runtime_backends(SDBackendModule module);
+
+    SDSplitMode split_mode(SDBackendModule module) const;
+    ggml_backend_buffer_type_t split_buffer_type(ggml_backend_t backend,
+                                                 const std::vector<float>& tensor_split);
+
     bool runtime_backend_is_cpu(SDBackendModule module);
     bool params_backend_is_cpu(SDBackendModule module);
     bool params_backend_is_disk(SDBackendModule module) const;
+    bool params_backend_follows_runtime(SDBackendModule module) const;
     bool runtime_backend_supports_host_buffer(SDBackendModule module);
 
 private:
diff --git a/otherarch/sdcpp/src/core/layer_split_partition.cpp b/otherarch/sdcpp/src/core/layer_split_partition.cpp
new file mode 100644
index 000000000..654b3056b
--- /dev/null
+++ b/otherarch/sdcpp/src/core/layer_split_partition.cpp
@@ -0,0 +1,257 @@
+#include "core/layer_split_partition.h"
+
+#include <algorithm>
+#include <cstdlib>
+#include <cstring>
+#include <limits>
+#include <unordered_set>
+#include <utility>
+
+#include "core/util.h"
+
+namespace sd {
+
+    static bool layer_split_path_segment_starts_at(const std::string& name, size_t pos) {
+        return pos == 0 || name[pos - 1] == '.';
+    }
+
+    static bool layer_split_has_path_segment(const std::string& name, const char* segment) {
+        size_t pos = name.find(segment);
+        while (pos != std::string::npos) {
+            if (layer_split_path_segment_starts_at(name, pos)) {
+                return true;
+            }
+            pos = name.find(segment, pos + 1);
+        }
+        return false;
+    }
+
+    int layer_split_tensor_block_index(const std::string& name) {
+        static const char* unet_block_segments[] = {"input_blocks.", "output_blocks.", "middle_block.",
+                                                    "down_blocks.", "up_blocks.", "mid_block."};
+        for (const char* segment : unet_block_segments) {
+            if (layer_split_has_path_segment(name, segment)) {
+                return -1;
+            }
+        }
+
+        static const char* block_keywords[] = {"transformer_blocks.", "joint_blocks.", "double_blocks.",
+                                               "single_blocks.", "blocks.", "block.", "layers."};
+        for (const char* keyword : block_keywords) {
+            size_t pos = name.find(keyword);
+            while (pos != std::string::npos) {
+                if (!layer_split_path_segment_starts_at(name, pos)) {
+                    pos = name.find(keyword, pos + 1);
+                    continue;
+                }
+                pos += std::strlen(keyword);
+                size_t end = pos;
+                while (end < name.size() && name[end] >= '0' && name[end] <= '9') {
+                    end++;
+                }
+                if (end > pos && (end == name.size() || name[end] == '.')) {
+                    return std::atoi(name.substr(pos, end - pos).c_str());
+                }
+                break;
+            }
+        }
+        return -1;
+    }
+
+    std::string layer_split_backend_device_display_name(ggml_backend_t backend) {
+        ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+        const char* name       = dev != nullptr ? ggml_backend_dev_name(dev) : ggml_backend_name(backend);
+        return name != nullptr ? name : "unknown";
+    }
+
+    static size_t graph_cut_layer_split_backend_vram_limit(const std::vector<size_t>& backend_vram_limits,
+                                                           size_t backend_index,
+                                                           size_t primary_backend_vram_limit) {
+        if (backend_index < backend_vram_limits.size()) {
+            return backend_vram_limits[backend_index];
+        }
+        return backend_index == 0 ? primary_backend_vram_limit : 0;
+    }
+
+    static std::vector<int64_t> graph_cut_layer_split_backend_capacities(const std::vector<ggml_backend_t>& backends,
+                                                                         const std::vector<size_t>& backend_vram_limits,
+                                                                         size_t primary_backend_vram_limit) {
+        std::vector<int64_t> capacities(backends.size(), std::numeric_limits<int64_t>::max() / 4);
+        constexpr int64_t compute_headroom_bytes = 2ll * 1024 * 1024 * 1024;
+        for (size_t i = 0; i < backends.size(); i++) {
+            ggml_backend_dev_t dev = ggml_backend_get_device(backends[i]);
+            size_t free_bytes = 0, total_bytes = 0;
+            if (dev != nullptr) {
+                ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
+            }
+            if (free_bytes > 0) {
+                capacities[i] = std::max<int64_t>((int64_t)free_bytes - compute_headroom_bytes, 0);
+            }
+            size_t limit_bytes = graph_cut_layer_split_backend_vram_limit(backend_vram_limits,
+                                                                          i,
+                                                                          primary_backend_vram_limit);
+            if (limit_bytes > 0) {
+                capacities[i] = std::min<int64_t>(capacities[i], (int64_t)limit_bytes);
+            }
+        }
+        return capacities;
+    }
+
+    bool partition_graph_cut_layer_split(const char* desc,
+                                         ggml_cgraph* gf,
+                                         const sd::ggml_graph_cut::Plan& plan,
+                                         const std::vector<ggml_backend_t>& split_backends,
+                                         const std::vector<size_t>& backend_vram_limits,
+                                         size_t primary_backend_vram_limit,
+                                         std::unordered_map<const ggml_tensor*, ggml_backend_t>& param_assignments,
+                                         const std::function<ggml_tensor*(ggml_tensor*)>& canonical_param_tensor,
+                                         GraphCutLayerSplitAssignment* assignment_out) {
+        GGML_ASSERT(gf != nullptr);
+        GGML_ASSERT(assignment_out != nullptr);
+        GGML_ASSERT(canonical_param_tensor != nullptr);
+        GGML_ASSERT(!split_backends.empty());
+
+        GraphCutLayerSplitAssignment assignment;
+        assignment.segment_count = plan.segments.size();
+        assignment.tensors_by_backend.resize(split_backends.size());
+        assignment.bytes_by_backend.resize(split_backends.size(), 0);
+        assignment.first_segment_by_backend.resize(split_backends.size(), plan.segments.size());
+        assignment.last_segment_by_backend.resize(split_backends.size(), 0);
+
+        std::vector<std::vector<ggml_tensor*>> segment_params(plan.segments.size());
+        std::vector<int64_t> segment_param_bytes(plan.segments.size(), 0);
+        std::unordered_set<ggml_tensor*> seen_params;
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            std::vector<ggml_tensor*> params = sd::ggml_graph_cut::param_tensors(gf, plan.segments[seg_idx]);
+            for (ggml_tensor* raw_param : params) {
+                ggml_tensor* param = canonical_param_tensor(raw_param);
+                if (param == nullptr || !seen_params.insert(param).second) {
+                    continue;
+                }
+                segment_params[seg_idx].push_back(param);
+                segment_param_bytes[seg_idx] += (int64_t)ggml_nbytes(param);
+            }
+        }
+
+        int64_t total_param_bytes = 0;
+        for (int64_t bytes : segment_param_bytes) {
+            total_param_bytes += bytes;
+        }
+        if (total_param_bytes <= 0) {
+            LOG_ERROR("%s graph-cut layer split found no graph params to assign", desc);
+            return false;
+        }
+
+        std::vector<int64_t> backend_capacities = graph_cut_layer_split_backend_capacities(split_backends,
+                                                                                           backend_vram_limits,
+                                                                                           primary_backend_vram_limit);
+
+        std::vector<ggml_backend_t> backend_by_segment(plan.segments.size(), split_backends[0]);
+        size_t current_backend = 0;
+        int64_t current_used   = 0;
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            int64_t bytes = segment_param_bytes[seg_idx];
+            while (current_backend + 1 < split_backends.size() &&
+                   bytes > 0 &&
+                   current_used + bytes > backend_capacities[current_backend]) {
+                current_backend++;
+                current_used = 0;
+            }
+            if (bytes > 0 && current_used + bytes > backend_capacities[current_backend]) {
+                LOG_ERROR("%s graph-cut layer split: segment %zu needs %.1f MB on %s, but only %.1f MB is available under current VRAM limits",
+                          desc,
+                          seg_idx,
+                          (current_used + bytes) / (1024.0 * 1024.0),
+                          layer_split_backend_device_display_name(split_backends[current_backend]).c_str(),
+                          backend_capacities[current_backend] / (1024.0 * 1024.0));
+                return false;
+            }
+            current_used += bytes;
+            backend_by_segment[seg_idx] = split_backends[current_backend];
+
+            for (ggml_tensor* param : segment_params[seg_idx]) {
+                ggml_backend_t target_backend = split_backends[current_backend];
+                auto assigned_it              = param_assignments.find(param);
+                if (assigned_it == param_assignments.end()) {
+                    param_assignments[param]            = target_backend;
+                    assignment.has_new_param_assignment = true;
+                } else {
+                    target_backend = assigned_it->second;
+                }
+
+                auto backend_it = std::find(split_backends.begin(), split_backends.end(), target_backend);
+                if (backend_it == split_backends.end()) {
+                    LOG_ERROR("%s graph-cut layer split tensor '%s' is assigned to an unavailable backend",
+                              desc,
+                              ggml_get_name(param));
+                    return false;
+                }
+                size_t backend_idx                               = (size_t)std::distance(split_backends.begin(), backend_it);
+                assignment.first_segment_by_backend[backend_idx] = std::min(assignment.first_segment_by_backend[backend_idx], seg_idx);
+                assignment.last_segment_by_backend[backend_idx]  = std::max(assignment.last_segment_by_backend[backend_idx], seg_idx + 1);
+                assignment.tensors_by_backend[backend_idx].push_back(param);
+                assignment.bytes_by_backend[backend_idx] += (int64_t)ggml_nbytes(param);
+            }
+        }
+
+        const int n_nodes = ggml_graph_n_nodes(gf);
+        for (size_t seg_idx = 0; seg_idx < plan.segments.size(); seg_idx++) {
+            ggml_backend_t backend = backend_by_segment[seg_idx];
+            const auto& segment    = plan.segments[seg_idx];
+            for (int node_index : segment.internal_node_indices) {
+                if (node_index < 0 || node_index >= n_nodes) {
+                    continue;
+                }
+                ggml_tensor* node = ggml_graph_node(gf, node_index);
+                if (node != nullptr) {
+                    assignment.node_assignments[node] = backend;
+                }
+            }
+            for (int node_index : segment.output_node_indices) {
+                if (node_index < 0 || node_index >= n_nodes) {
+                    continue;
+                }
+                ggml_tensor* node = ggml_graph_node(gf, node_index);
+                if (node != nullptr) {
+                    assignment.node_assignments[node] = backend;
+                }
+            }
+        }
+
+        *assignment_out = std::move(assignment);
+        return true;
+    }
+
+    void log_graph_cut_layer_split_assignment(const char* desc,
+                                              const std::vector<ggml_backend_t>& split_backends,
+                                              const GraphCutLayerSplitAssignment& assignment) {
+        for (size_t i = 0; i < split_backends.size(); i++) {
+            if (i >= assignment.tensors_by_backend.size() ||
+                assignment.tensors_by_backend[i].empty()) {
+                continue;
+            }
+            size_t first_segment = assignment.first_segment_by_backend[i] == assignment.segment_count
+                                       ? 0
+                                       : assignment.first_segment_by_backend[i];
+            size_t last_segment  = assignment.last_segment_by_backend[i];
+            if (assignment.has_new_param_assignment) {
+                LOG_INFO("%s graph-cut layer split: %s <- segments [%zu, %zu), %zu tensors, %.1f MB",
+                         desc,
+                         layer_split_backend_device_display_name(split_backends[i]).c_str(),
+                         first_segment,
+                         last_segment,
+                         assignment.tensors_by_backend[i].size(),
+                         assignment.bytes_by_backend[i] / (1024.0 * 1024.0));
+            } else {
+                LOG_DEBUG("%s graph-cut layer split: %s <- segments [%zu, %zu), %zu tensors, %.1f MB",
+                          desc,
+                          layer_split_backend_device_display_name(split_backends[i]).c_str(),
+                          first_segment,
+                          last_segment,
+                          assignment.tensors_by_backend[i].size(),
+                          assignment.bytes_by_backend[i] / (1024.0 * 1024.0));
+            }
+        }
+    }
+
+}  // namespace sd
diff --git a/otherarch/sdcpp/src/core/layer_split_partition.h b/otherarch/sdcpp/src/core/layer_split_partition.h
new file mode 100644
index 000000000..9450b379e
--- /dev/null
+++ b/otherarch/sdcpp/src/core/layer_split_partition.h
@@ -0,0 +1,44 @@
+#ifndef __SD_CORE_LAYER_SPLIT_PARTITION_H__
+#define __SD_CORE_LAYER_SPLIT_PARTITION_H__
+
+#include <cstdint>
+#include <functional>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#include "core/ggml_graph_cut.h"
+
+namespace sd {
+
+    struct GraphCutLayerSplitAssignment {
+        std::vector<std::vector<ggml_tensor*>> tensors_by_backend;
+        std::vector<int64_t> bytes_by_backend;
+        std::vector<size_t> first_segment_by_backend;
+        std::vector<size_t> last_segment_by_backend;
+        std::unordered_map<const ggml_tensor*, ggml_backend_t> node_assignments;
+        size_t segment_count          = 0;
+        bool has_new_param_assignment = false;
+    };
+
+    std::string layer_split_backend_device_display_name(ggml_backend_t backend);
+    int layer_split_tensor_block_index(const std::string& name);
+    bool partition_graph_cut_layer_split(const char* desc,
+                                         ggml_cgraph* gf,
+                                         const sd::ggml_graph_cut::Plan& plan,
+                                         const std::vector<ggml_backend_t>& split_backends,
+                                         const std::vector<size_t>& backend_vram_limits,
+                                         size_t primary_backend_vram_limit,
+                                         std::unordered_map<const ggml_tensor*, ggml_backend_t>& param_assignments,
+                                         const std::function<ggml_tensor*(ggml_tensor*)>& canonical_param_tensor,
+                                         GraphCutLayerSplitAssignment* assignment_out);
+    void log_graph_cut_layer_split_assignment(const char* desc,
+                                              const std::vector<ggml_backend_t>& split_backends,
+                                              const GraphCutLayerSplitAssignment& assignment);
+
+}  // namespace sd
+
+#endif  // __SD_CORE_LAYER_SPLIT_PARTITION_H__
diff --git a/otherarch/sdcpp/src/core/util.cpp b/otherarch/sdcpp/src/core/util.cpp
index cf1460cb9..4672b139f 100644
--- a/otherarch/sdcpp/src/core/util.cpp
+++ b/otherarch/sdcpp/src/core/util.cpp
@@ -4,6 +4,8 @@
 #include <cmath>
 #include <codecvt>
 #include <cstdarg>
+#include <cstdlib>
+#include <cstring>
 #include <exception>
 #include <filesystem>
 #include <fstream>
@@ -29,6 +31,7 @@
 #include <unistd.h>
 #endif
 
+#include "ggml-backend.h"
 #include "ggml.h"
 #include "stable-diffusion.h"
 
@@ -1042,3 +1045,26 @@ std::vector<std::pair<std::string, float>> split_quotation_attention(
     }
     return result;
 }
+
+size_t sd_list_devices(char* buffer, size_t buffer_size) {
+    if (ggml_backend_dev_count() == 0) {
+        // dynamic-backend builds discover their backend modules at runtime
+        ggml_backend_load_all();
+    }
+
+    std::ostringstream oss;
+    for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        const char* name       = ggml_backend_dev_name(dev);
+        const char* desc       = ggml_backend_dev_description(dev);
+        oss << (name ? name : "") << '\t' << (desc ? desc : "") << '\n';
+    }
+
+    std::string devices = oss.str();
+    if (buffer != nullptr && buffer_size > 0) {
+        size_t copy_size = std::min(devices.size(), buffer_size - 1);
+        memcpy(buffer, devices.data(), copy_size);
+        buffer[copy_size] = '\0';
+    }
+    return devices.size();
+}
diff --git a/otherarch/sdcpp/src/kcpp_sd_extensions.h b/otherarch/sdcpp/src/kcpp_sd_extensions.h
index 0e0c6795a..516d4273b 100644
--- a/otherarch/sdcpp/src/kcpp_sd_extensions.h
+++ b/otherarch/sdcpp/src/kcpp_sd_extensions.h
@@ -27,8 +27,6 @@ namespace kcpp_sd {
 
     model_info get_model_info(sd_ctx_t* ctx);
 
-    void SetCircularAxesAll(sd_ctx_t* ctx, bool circular_x, bool circular_y);
-
     void set_lora_cache(sd_ctx_t *ctx, bool enable);
 
     void apply_loras(sd_ctx_t *ctx, const std::vector<sd_lora_t>& lora_specs);
diff --git a/otherarch/sdcpp/src/model/diffusion/control.hpp b/otherarch/sdcpp/src/model/diffusion/control.hpp
index eeb8f5109..bf3c7e435 100644
--- a/otherarch/sdcpp/src/model/diffusion/control.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/control.hpp
@@ -5,7 +5,8 @@
 #include "model_loader.h"
 #include "model_manager.h"
 
-#define CONTROL_NET_GRAPH_SIZE 1536
+// Match main UNet's MAX_GRAPH_SIZE so SDXL ControlNet (transformer_depth={1,2,10}) fits.
+#define CONTROL_NET_GRAPH_SIZE MAX_GRAPH_SIZE
 
 /*
     =================================== ControlNet ===================================
diff --git a/otherarch/sdcpp/src/model/diffusion/flux.hpp b/otherarch/sdcpp/src/model/diffusion/flux.hpp
index e3cd6ba7c..e5d795f74 100644
--- a/otherarch/sdcpp/src/model/diffusion/flux.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/flux.hpp
@@ -4,6 +4,7 @@
 #include <memory>
 #include <vector>
 
+#include "core/util.h"
 #include "model/adapter/pulid.hpp"
 #include "model/common/rope.hpp"
 #include "model/diffusion/dit.hpp"
@@ -1400,18 +1401,28 @@ namespace Flux {
         std::vector<float> dct_vec;
         sd::Tensor<float> guidance_tensor;
         SDVersion version;
-        bool use_mask = false;
+        bool use_mask = true;
 
         FluxRunner(ggml_backend_t backend,
                    const String2TensorStorage& tensor_storage_map      = {},
                    const std::string prefix                            = "",
                    SDVersion version                                   = VERSION_FLUX,
-                   bool use_mask                                       = false,
-                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                   std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                   const char* model_args                              = nullptr)
             : DiffusionModelRunner(backend, prefix, weight_manager),
               config(FluxConfig::detect_from_weights(tensor_storage_map, prefix, version)),
-              version(version),
-              use_mask(use_mask) {
+              version(version) {
+            for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+                if (key == "chroma_use_dit_mask") {
+                    bool parsed = true;
+                    if (parse_strict_bool(value, parsed)) {
+                        use_mask = parsed;
+                    } else {
+                        LOG_WARN("ignoring invalid Chroma DiT model arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
+
             if (config.is_chroma) {
                 LOG_INFO("Using pruned modulation (Chroma)");
             }
@@ -1718,7 +1729,6 @@ namespace Flux {
                                                                             tensor_storage_map,
                                                                             "model.diffusion_model",
                                                                             VERSION_FLUX2,
-                                                                            false,
                                                                             model_manager);
 
             if (!model_manager->register_runner_params("Flux test",
diff --git a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
index 5dabbb176..52edc0d56 100644
--- a/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
+++ b/otherarch/sdcpp/src/model/diffusion/qwen_image.hpp
@@ -3,6 +3,7 @@
 
 #include <memory>
 
+#include "core/util.h"
 #include "model/common/block.hpp"
 #include "model/diffusion/dit.hpp"
 #include "model/diffusion/flux.hpp"
@@ -566,12 +567,21 @@ namespace Qwen {
                         const String2TensorStorage& tensor_storage_map      = {},
                         const std::string prefix                            = "",
                         SDVersion version                                   = VERSION_QWEN_IMAGE,
-                        bool zero_cond_t                                    = false,
-                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr)
+                        std::shared_ptr<RunnerWeightManager> weight_manager = nullptr,
+                        const char* model_args                              = nullptr)
             : DiffusionModelRunner(backend, prefix, weight_manager),
               config(QwenImageConfig::detect_from_weights(tensor_storage_map, prefix)),
               version(version) {
-            config.zero_cond_t = config.zero_cond_t || zero_cond_t;
+            for (const auto& [key, value] : parse_key_value_args(model_args, "model arg")) {
+                if (key == "qwen_image_zero_cond_t") {
+                    bool parsed = false;
+                    if (parse_strict_bool(value, parsed)) {
+                        config.zero_cond_t = config.zero_cond_t || parsed;
+                    } else {
+                        LOG_WARN("ignoring invalid Qwen Image model arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
             if (version == VERSION_QWEN_IMAGE_LAYERED) {
                 config.use_additional_t_cond = true;
             }
@@ -775,7 +785,6 @@ namespace Qwen {
                                                                                             tensor_storage_map,
                                                                                             "model.diffusion_model",
                                                                                             VERSION_QWEN_IMAGE,
-                                                                                            false,
                                                                                             model_manager);
 
             if (!model_manager->register_runner_params("Qwen image test",
diff --git a/otherarch/sdcpp/src/model/vae/vae.hpp b/otherarch/sdcpp/src/model/vae/vae.hpp
index 8b8c46ded..b97aecee0 100644
--- a/otherarch/sdcpp/src/model/vae/vae.hpp
+++ b/otherarch/sdcpp/src/model/vae/vae.hpp
@@ -133,7 +133,11 @@ public:
             int64_t H              = input.shape()[1] / scale_factor;
             float tile_overlap;
             int tile_size_x, tile_size_y;
-            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, W, H, 1.30539f);
+            // Image VAE encode is more sensitive to tile boundary context than decode.
+            // Keep the smaller legacy factor for video VAEs, but default image encode
+            // tiles to 64 latent pixels so a 512px SD image is encoded as one tile.
+            const float encode_tile_factor = (sd_version_is_wan(version) || sd_version_is_ltxav(version)) ? 1.30539f : 2.0f;
+            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, W, H, encode_tile_factor);
             LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
             output = tiled_compute(input,
                                    n_threads,
diff --git a/otherarch/sdcpp/src/model_io/gguf_io.cpp b/otherarch/sdcpp/src/model_io/gguf_io.cpp
index c701d01f3..cd22312d5 100644
--- a/otherarch/sdcpp/src/model_io/gguf_io.cpp
+++ b/otherarch/sdcpp/src/model_io/gguf_io.cpp
@@ -1,7 +1,10 @@
 #include "gguf_io.h"
 
+#include <algorithm>
 #include <cstdint>
+#include <cstdio>
 #include <fstream>
+#include <ostream>
 #include <string>
 #include <vector>
 
@@ -121,3 +124,115 @@ bool write_gguf_file(const std::string& file_path,
     gguf_free(gguf_ctx);
     return success;
 }
+
+GGUFStreamingWriter::~GGUFStreamingWriter() {
+    close();
+}
+
+bool GGUFStreamingWriter::write_metadata(const std::string& file_path,
+                                         const std::vector<TensorWritePlan>& tensors,
+                                         std::string* error) {
+    close();
+    tensors_   = tensors;
+    file_size_ = 0;
+
+    size_t meta_mem = 1 * 1024 * 1024 + tensors.size() * ggml_tensor_overhead();
+    meta_ctx_       = ggml_init({meta_mem, nullptr, true});
+    if (meta_ctx_ == nullptr) {
+        set_error(error, "ggml_init failed for GGUF metadata");
+        return false;
+    }
+
+    gguf_ctx_ = gguf_init_empty();
+    if (gguf_ctx_ == nullptr) {
+        set_error(error, "gguf_init_empty failed");
+        close();
+        return false;
+    }
+
+    for (const TensorWritePlan& plan : tensors) {
+        ggml_tensor* tensor = ggml_new_tensor(meta_ctx_, plan.type, plan.n_dims, plan.ne);
+        if (tensor == nullptr) {
+            set_error(error, "ggml_new_tensor failed for tensor '" + plan.name + "'");
+            close();
+            return false;
+        }
+        ggml_set_name(tensor, plan.name.c_str());
+        gguf_add_tensor(gguf_ctx_, tensor);
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    FILE* file = fopen(file_path.c_str(), "wb+");
+    if (file == nullptr) {
+        set_error(error, "failed to open output file '" + file_path + "'");
+        close();
+        return false;
+    }
+
+    // ggml exposes GGUF metadata writing through FILE* only. Keep FILE usage
+    // isolated here; tensor data is written through std::fstream by the shared
+    // streaming pipeline.
+    if (!gguf_write_to_file_ptr(gguf_ctx_, file, true)) {
+        fclose(file);
+        set_error(error, "failed to write GGUF metadata to '" + file_path + "'");
+        close();
+        return false;
+    }
+    fclose(file);
+
+    const uint64_t data_start = gguf_get_meta_size(gguf_ctx_);
+    tensor_offsets_.resize(tensors.size());
+    file_size_ = data_start;
+    for (size_t i = 0; i < tensors.size(); i++) {
+        tensor_offsets_[i] = data_start + gguf_get_tensor_offset(gguf_ctx_, static_cast<int64_t>(i));
+        file_size_         = std::max(file_size_, tensor_offsets_[i] + tensors[i].nbytes());
+    }
+    return true;
+}
+
+bool GGUFStreamingWriter::write_tensor(std::ostream& output,
+                                       size_t tensor_index,
+                                       const uint8_t* data,
+                                       size_t size,
+                                       std::string* error) const {
+    if (tensor_index >= tensors_.size() || tensor_index >= tensor_offsets_.size()) {
+        set_error(error, "invalid GGUF tensor index");
+        return false;
+    }
+    const TensorWritePlan& plan = tensors_[tensor_index];
+    if (size != plan.nbytes()) {
+        set_error(error, "size mismatch while writing tensor '" + plan.name + "'");
+        return false;
+    }
+    output.seekp(static_cast<std::streamoff>(tensor_offsets_[tensor_index]), std::ios::beg);
+    if (!output) {
+        set_error(error, "failed to seek output for tensor '" + plan.name + "'");
+        return false;
+    }
+    if (size > 0) {
+        output.write(reinterpret_cast<const char*>(data), static_cast<std::streamsize>(size));
+    }
+    if (!output) {
+        set_error(error, "failed to write tensor '" + plan.name + "'");
+        return false;
+    }
+    return true;
+}
+
+uint64_t GGUFStreamingWriter::file_size() const {
+    return file_size_;
+}
+
+void GGUFStreamingWriter::close() {
+    tensor_offsets_.clear();
+    tensors_.clear();
+    file_size_ = 0;
+    if (gguf_ctx_ != nullptr) {
+        gguf_free(gguf_ctx_);
+        gguf_ctx_ = nullptr;
+    }
+    if (meta_ctx_ != nullptr) {
+        ggml_free(meta_ctx_);
+        meta_ctx_ = nullptr;
+    }
+}
diff --git a/otherarch/sdcpp/src/model_io/gguf_io.h b/otherarch/sdcpp/src/model_io/gguf_io.h
index 81c981145..3a4ae8200 100644
--- a/otherarch/sdcpp/src/model_io/gguf_io.h
+++ b/otherarch/sdcpp/src/model_io/gguf_io.h
@@ -4,8 +4,12 @@
 #include <string>
 #include <vector>
 
+#include "streaming_writer.h"
 #include "tensor_storage.h"
 
+struct ggml_context;
+struct gguf_context;
+
 bool is_gguf_file(const std::string& file_path);
 bool read_gguf_file(const std::string& file_path,
                     std::vector<TensorStorage>& tensor_storages,
@@ -14,4 +18,28 @@ bool write_gguf_file(const std::string& file_path,
                      const std::vector<TensorWriteInfo>& tensors,
                      std::string* error = nullptr);
 
+class GGUFStreamingWriter : public StreamingModelWriter {
+public:
+    GGUFStreamingWriter() = default;
+    ~GGUFStreamingWriter();
+
+    bool write_metadata(const std::string& file_path,
+                        const std::vector<TensorWritePlan>& tensors,
+                        std::string* error = nullptr) override;
+    bool write_tensor(std::ostream& output,
+                      size_t tensor_index,
+                      const uint8_t* data,
+                      size_t size,
+                      std::string* error = nullptr) const override;
+    uint64_t file_size() const override;
+    void close();
+
+private:
+    std::vector<TensorWritePlan> tensors_;
+    std::vector<uint64_t> tensor_offsets_;
+    uint64_t file_size_     = 0;
+    ggml_context* meta_ctx_ = nullptr;
+    gguf_context* gguf_ctx_ = nullptr;
+};
+
 #endif  // __SD_MODEL_IO_GGUF_IO_H__
diff --git a/otherarch/sdcpp/src/model_io/safetensors_io.cpp b/otherarch/sdcpp/src/model_io/safetensors_io.cpp
index 39131dbd8..197dc5f54 100644
--- a/otherarch/sdcpp/src/model_io/safetensors_io.cpp
+++ b/otherarch/sdcpp/src/model_io/safetensors_io.cpp
@@ -1,8 +1,10 @@
 #include "safetensors_io.h"
 
+#include <algorithm>
 #include <cstdint>
 #include <exception>
 #include <fstream>
+#include <ostream>
 #include <string>
 #include <vector>
 
@@ -41,7 +43,7 @@ bool is_safetensors_file(const std::string& file_path) {
     }
 
     size_t header_size_ = model_io::read_u64(header_size_buf);
-    if (header_size_ >= file_size_ || header_size_ <= 2) {
+    if (header_size_ > file_size_ - ST_HEADER_SIZE_LEN || header_size_ <= 2) {
         return false;
     }
 
@@ -112,10 +114,11 @@ bool read_safetensors_file(const std::string& file_path,
     }
 
     size_t header_size_ = model_io::read_u64(header_size_buf);
-    if (header_size_ >= file_size_) {
+    if (header_size_ > file_size_ - ST_HEADER_SIZE_LEN) {
         set_error(error, "invalid safetensor file '" + file_path + "'");
         return false;
     }
+    const size_t data_start = ST_HEADER_SIZE_LEN + header_size_;
 
     // read header
     std::vector<char> header_buf;
@@ -154,6 +157,10 @@ bool read_safetensors_file(const std::string& file_path,
 
         size_t begin = tensor_info["data_offsets"][0].get<size_t>();
         size_t end   = tensor_info["data_offsets"][1].get<size_t>();
+        if (begin > end || end > file_size_ - data_start) {
+            set_error(error, "data offsets out of bounds for tensor '" + name + "'");
+            return false;
+        }
 
         ggml_type type = safetensors_dtype_to_ggml_type(dtype);
         if (type == GGML_TYPE_COUNT) {
@@ -185,7 +192,7 @@ bool read_safetensors_file(const std::string& file_path,
             n_dims = 1;
         }
 
-        TensorStorage tensor_storage(name, type, ne, n_dims, 0, ST_HEADER_SIZE_LEN + header_size_ + begin);
+        TensorStorage tensor_storage(name, type, ne, n_dims, 0, data_start + begin);
         tensor_storage.reverse_ne();
 
         size_t tensor_data_size = end - begin;
@@ -314,3 +321,102 @@ bool write_safetensors_file(const std::string& file_path,
 
     return true;
 }
+
+bool SafetensorsStreamingWriter::write_metadata(const std::string& file_path,
+                                                const std::vector<TensorWritePlan>& tensors,
+                                                std::string* error) {
+    file_path_ = file_path;
+    tensors_   = tensors;
+    tensor_offsets_.clear();
+    data_start_ = 0;
+    file_size_  = 0;
+
+    nlohmann::ordered_json header = nlohmann::ordered_json::object();
+    uint64_t data_offset          = 0;
+    tensor_offsets_.resize(tensors.size());
+    for (size_t i = 0; i < tensors.size(); i++) {
+        const TensorWritePlan& plan = tensors[i];
+        std::string dtype;
+        if (!ggml_type_to_safetensors_dtype(plan.type, &dtype)) {
+            set_error(error,
+                      "unsupported safetensors dtype '" + std::string(ggml_type_name(plan.type)) +
+                          "' for tensor '" + plan.name + "'");
+            return false;
+        }
+
+        nlohmann::ordered_json json_tensor_info = nlohmann::ordered_json::object();
+        json_tensor_info["dtype"]               = dtype;
+
+        nlohmann::ordered_json shape = nlohmann::ordered_json::array();
+        for (int j = 0; j < plan.n_dims; ++j) {
+            shape.push_back(plan.ne[plan.n_dims - 1 - j]);
+        }
+        json_tensor_info["shape"] = shape;
+
+        nlohmann::ordered_json data_offsets = nlohmann::ordered_json::array();
+        data_offsets.push_back(data_offset);
+        data_offsets.push_back(data_offset + plan.nbytes());
+        json_tensor_info["data_offsets"] = data_offsets;
+
+        header[plan.name]  = json_tensor_info;
+        tensor_offsets_[i] = data_offset;
+        data_offset += plan.nbytes();
+    }
+
+    const std::string header_str = header.dump();
+    data_start_                  = ST_HEADER_SIZE_LEN + header_str.size();
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    std::ofstream file(file_path, std::ios::binary | std::ios::trunc);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "' for writing");
+        return false;
+    }
+
+    uint8_t header_size[ST_HEADER_SIZE_LEN];
+    for (int i = 0; i < static_cast<int>(ST_HEADER_SIZE_LEN); ++i) {
+        header_size[i] = static_cast<uint8_t>((header_str.size() >> (8 * i)) & 0xFF);
+    }
+    file.write(reinterpret_cast<const char*>(header_size), sizeof(header_size));
+    file.write(header_str.data(), static_cast<std::streamsize>(header_str.size()));
+    if (!file) {
+        set_error(error, "failed to write safetensors header to '" + file_path + "'");
+        return false;
+    }
+
+    file_size_ = data_start_ + data_offset;
+    return true;
+}
+
+bool SafetensorsStreamingWriter::write_tensor(std::ostream& output,
+                                              size_t tensor_index,
+                                              const uint8_t* data,
+                                              size_t size,
+                                              std::string* error) const {
+    if (tensor_index >= tensors_.size() || tensor_index >= tensor_offsets_.size()) {
+        set_error(error, "invalid safetensors tensor index");
+        return false;
+    }
+    const TensorWritePlan& plan = tensors_[tensor_index];
+    if (size != plan.nbytes()) {
+        set_error(error, "size mismatch while writing tensor '" + plan.name + "'");
+        return false;
+    }
+    output.seekp(static_cast<std::streamoff>(data_start_ + tensor_offsets_[tensor_index]), std::ios::beg);
+    if (!output) {
+        set_error(error, "failed to seek output for tensor '" + plan.name + "'");
+        return false;
+    }
+    if (size > 0) {
+        output.write(reinterpret_cast<const char*>(data), static_cast<std::streamsize>(size));
+    }
+    if (!output) {
+        set_error(error, "failed to write tensor '" + plan.name + "' to '" + file_path_ + "'");
+        return false;
+    }
+    return true;
+}
+
+uint64_t SafetensorsStreamingWriter::file_size() const {
+    return file_size_;
+}
diff --git a/otherarch/sdcpp/src/model_io/safetensors_io.h b/otherarch/sdcpp/src/model_io/safetensors_io.h
index 08a1bc1f3..b4938ee18 100644
--- a/otherarch/sdcpp/src/model_io/safetensors_io.h
+++ b/otherarch/sdcpp/src/model_io/safetensors_io.h
@@ -4,6 +4,7 @@
 #include <string>
 #include <vector>
 
+#include "streaming_writer.h"
 #include "tensor_storage.h"
 
 bool is_safetensors_file(const std::string& file_path);
@@ -14,4 +15,26 @@ bool write_safetensors_file(const std::string& file_path,
                             const std::vector<TensorWriteInfo>& tensors,
                             std::string* error = nullptr);
 
+class SafetensorsStreamingWriter : public StreamingModelWriter {
+public:
+    SafetensorsStreamingWriter() = default;
+
+    bool write_metadata(const std::string& file_path,
+                        const std::vector<TensorWritePlan>& tensors,
+                        std::string* error = nullptr) override;
+    bool write_tensor(std::ostream& output,
+                      size_t tensor_index,
+                      const uint8_t* data,
+                      size_t size,
+                      std::string* error = nullptr) const override;
+    uint64_t file_size() const override;
+
+private:
+    std::string file_path_;
+    std::vector<TensorWritePlan> tensors_;
+    std::vector<uint64_t> tensor_offsets_;
+    uint64_t data_start_ = 0;
+    uint64_t file_size_  = 0;
+};
+
 #endif  // __SD_MODEL_IO_SAFETENSORS_IO_H__
diff --git a/otherarch/sdcpp/src/model_io/streaming_writer.h b/otherarch/sdcpp/src/model_io/streaming_writer.h
new file mode 100644
index 000000000..06f65c59b
--- /dev/null
+++ b/otherarch/sdcpp/src/model_io/streaming_writer.h
@@ -0,0 +1,26 @@
+#ifndef __SD_MODEL_IO_STREAMING_WRITER_H__
+#define __SD_MODEL_IO_STREAMING_WRITER_H__
+
+#include <cstdint>
+#include <iosfwd>
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+class StreamingModelWriter {
+public:
+    virtual ~StreamingModelWriter() = default;
+
+    virtual bool write_metadata(const std::string& file_path,
+                                const std::vector<TensorWritePlan>& tensors,
+                                std::string* error = nullptr)     = 0;
+    virtual bool write_tensor(std::ostream& output,
+                              size_t tensor_index,
+                              const uint8_t* data,
+                              size_t size,
+                              std::string* error = nullptr) const = 0;
+    virtual uint64_t file_size() const                            = 0;
+};
+
+#endif  // __SD_MODEL_IO_STREAMING_WRITER_H__
diff --git a/otherarch/sdcpp/src/model_io/tensor_storage.h b/otherarch/sdcpp/src/model_io/tensor_storage.h
index c0cf079c5..307535a53 100644
--- a/otherarch/sdcpp/src/model_io/tensor_storage.h
+++ b/otherarch/sdcpp/src/model_io/tensor_storage.h
@@ -127,6 +127,25 @@ struct TensorWriteInfo {
     ggml_tensor* tensor     = nullptr;
 };
 
+struct TensorWritePlan {
+    std::string name;
+    ggml_type type          = GGML_TYPE_F32;
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+
+    int64_t nelements() const {
+        int64_t n = 1;
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            n *= ne[i];
+        }
+        return n;
+    }
+
+    uint64_t nbytes() const {
+        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
+    }
+};
+
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
 
 #endif  // __SD_TENSOR_STORAGE_H__
diff --git a/otherarch/sdcpp/src/model_loader.cpp b/otherarch/sdcpp/src/model_loader.cpp
index fda1f3e82..6aa9afa8d 100644
--- a/otherarch/sdcpp/src/model_loader.cpp
+++ b/otherarch/sdcpp/src/model_loader.cpp
@@ -971,7 +971,8 @@ std::vector<MmapTensorStore> ModelLoader::mmap_tensors(std::map<std::string, ggm
 
 bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                                bool enable_mmap,
-                               const std::set<std::string>* target_tensor_names) {
+                               const std::set<std::string>* target_tensor_names,
+                               bool log_progress) {
     process_model_files(enable_mmap, false);
 
     std::atomic<int64_t> read_time_ms(0);
@@ -1240,7 +1241,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
             }
             size_t curr_num       = total_tensors_processed + current_idx;
             float elapsed_seconds = (ggml_time_ms() - t_start) / 1000.0f;
-            if (total_tensors_to_process > 0) {
+            if (log_progress && total_tensors_to_process > 0) {
                 pretty_bytes_progress(static_cast<int>(curr_num),
                                       static_cast<int>(total_tensors_to_process),
                                       bytes_processed.load(),
@@ -1258,27 +1259,81 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
             break;
         }
         total_tensors_processed += tensors_to_process.size();
-        if (total_tensors_to_process > 0) {
+        if (log_progress && total_tensors_to_process > 0) {
             pretty_bytes_progress(static_cast<int>(total_tensors_processed),
                                   static_cast<int>(total_tensors_to_process),
                                   bytes_processed.load(),
                                   (ggml_time_ms() - t_start) / 1000.0f);
         }
-        if (total_tensors_processed < total_tensors_to_process && total_tensors_to_process > 0) {
+        if (log_progress && total_tensors_processed < total_tensors_to_process && total_tensors_to_process > 0) {
             printf("\n");
         }
     }
 
     int64_t end_time = ggml_time_ms();
-    LOG_INFO("loading tensors completed, taking %.2fs (read: %.2fs, memcpy: %.2fs, convert: %.2fs, copy_to_backend: %.2fs)",
-             (end_time - start_time) / 1000.f,
-             (read_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (memcpy_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (convert_time_ms.load() / (float)last_n_threads) / 1000.f,
-             (copy_to_backend_time_ms.load() / (float)last_n_threads) / 1000.f);
+    if (log_progress) {
+        LOG_INFO("loading tensors completed, taking %.2fs (read: %.2fs, memcpy: %.2fs, convert: %.2fs, copy_to_backend: %.2fs)",
+                 (end_time - start_time) / 1000.f,
+                 (read_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (memcpy_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (convert_time_ms.load() / (float)last_n_threads) / 1000.f,
+                 (copy_to_backend_time_ms.load() / (float)last_n_threads) / 1000.f);
+    }
     return success;
 }
 
+bool ModelLoader::load_tensor(const TensorStorage& tensor_storage, ggml_tensor* dst_tensor) {
+    if (dst_tensor == nullptr || dst_tensor->data == nullptr) {
+        LOG_ERROR("load tensor failed: null destination for '%s'", tensor_storage.name.c_str());
+        return false;
+    }
+
+    bool loaded = false;
+    std::set<std::string> target_tensor_names{tensor_storage.name};
+    auto on_new_tensor_cb = [&](const TensorStorage& current_tensor_storage, ggml_tensor** out_tensor) -> bool {
+        *out_tensor = nullptr;
+        if (current_tensor_storage.name != tensor_storage.name) {
+            return true;
+        }
+
+        if (current_tensor_storage.file_index != tensor_storage.file_index ||
+            current_tensor_storage.offset != tensor_storage.offset ||
+            current_tensor_storage.index_in_zip != tensor_storage.index_in_zip) {
+            LOG_ERROR("load tensor failed: storage mismatch for '%s'", tensor_storage.name.c_str());
+            return false;
+        }
+
+        if (current_tensor_storage.n_dims != tensor_storage.n_dims ||
+            current_tensor_storage.nelements() != tensor_storage.nelements()) {
+            LOG_ERROR("load tensor failed: metadata changed for '%s'", tensor_storage.name.c_str());
+            return false;
+        }
+
+        for (int i = 0; i < current_tensor_storage.n_dims; i++) {
+            if (current_tensor_storage.ne[i] != dst_tensor->ne[i]) {
+                LOG_ERROR("load tensor failed: shape mismatch for '%s'", tensor_storage.name.c_str());
+                return false;
+            }
+        }
+
+        *out_tensor = dst_tensor;
+        loaded      = true;
+        return true;
+    };
+
+    if (!load_tensors(on_new_tensor_cb, false, &target_tensor_names, false)) {
+        LOG_ERROR("load tensor failed: '%s'", tensor_storage.name.c_str());
+        return false;
+    }
+
+    if (!loaded) {
+        LOG_ERROR("load tensor failed: tensor '%s' not found", tensor_storage.name.c_str());
+        return false;
+    }
+
+    return true;
+}
+
 bool ModelLoader::load_float_tensor(const std::string& name,
                                     std::vector<float>& data,
                                     int n_threads,
diff --git a/otherarch/sdcpp/src/model_loader.h b/otherarch/sdcpp/src/model_loader.h
index 0d894a1a2..1973aaaec 100644
--- a/otherarch/sdcpp/src/model_loader.h
+++ b/otherarch/sdcpp/src/model_loader.h
@@ -27,6 +27,8 @@ struct MmapTensorStore {
     std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
 };
 
+bool is_unused_tensor(const std::string& name);
+
 class ModelLoader {
 protected:
     SDVersion version_ = VERSION_COUNT;
@@ -68,7 +70,8 @@ public:
                                               bool writable                        = true);
     bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb,
                       bool use_mmap                                    = false,
-                      const std::set<std::string>* target_tensor_names = nullptr);
+                      const std::set<std::string>* target_tensor_names = nullptr,
+                      bool log_progress                                = true);
     bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
                       bool use_mmap                        = false);
@@ -76,6 +79,7 @@ public:
                            std::vector<float>& data,
                            int n_threads = 0,
                            bool use_mmap = false);
+    bool load_tensor(const TensorStorage& tensor_storage, ggml_tensor* dst_tensor);
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;
diff --git a/otherarch/sdcpp/src/model_manager.cpp b/otherarch/sdcpp/src/model_manager.cpp
index 7095ec6a9..3a98bd545 100644
--- a/otherarch/sdcpp/src/model_manager.cpp
+++ b/otherarch/sdcpp/src/model_manager.cpp
@@ -100,12 +100,42 @@ size_t estimate_tensors_size(const std::map<std::string, ggml_tensor*>& tensors)
     return size;
 }
 
+void ModelManager::set_split_buffer_type(ggml_backend_t compute_backend, ggml_backend_buffer_type_t split_buft) {
+    if (compute_backend == nullptr) {
+        return;
+    }
+    if (split_buft == nullptr) {
+        split_buffer_types_.erase(compute_backend);
+        return;
+    }
+    split_buffer_types_[compute_backend] = split_buft;
+}
+
+bool ModelManager::tensor_shape_supports_split_buffer(const ggml_tensor* tensor) {
+    return tensor != nullptr &&
+           tensor->view_src == nullptr &&
+           ggml_is_contiguous(tensor) &&
+           ggml_n_dims(tensor) == 2 &&
+           tensor->ne[0] >= 256 &&
+           tensor->ne[1] >= 256;
+}
+
+ggml_backend_buffer_type_t ModelManager::split_buffer_type_for(const TensorState& state) const {
+    if (!state.allow_split_buffer || !tensor_shape_supports_split_buffer(state.tensor)) {
+        return nullptr;
+    }
+    auto it = split_buffer_types_.find(state.compute_backend);
+    return it != split_buffer_types_.end() ? it->second : nullptr;
+}
+
 bool ModelManager::register_param_tensors(const std::string& desc,
                                           std::map<std::string, ggml_tensor*> tensors,
                                           ResidencyMode residency_mode,
                                           ggml_backend_t compute_backend,
                                           ggml_backend_t params_backend,
-                                          size_t* registered_tensor_size) {
+                                          size_t* registered_tensor_size,
+                                          bool allow_split_buffer,
+                                          bool params_follow_compute_backend) {
     if (desc.empty()) {
         LOG_ERROR("model manager tensor desc is empty");
         return false;
@@ -129,13 +159,15 @@ bool ModelManager::register_param_tensors(const std::string& desc,
         }
         ggml_set_name(tensor, name.c_str());
 
-        auto state             = std::make_unique<TensorState>();
-        state->name            = name;
-        state->tensor          = tensor;
-        state->desc            = desc;
-        state->residency_mode  = residency_mode;
-        state->compute_backend = compute_backend;
-        state->params_backend  = params_backend;
+        auto state                           = std::make_unique<TensorState>();
+        state->name                          = name;
+        state->tensor                        = tensor;
+        state->desc                          = desc;
+        state->residency_mode                = residency_mode;
+        state->compute_backend               = compute_backend;
+        state->params_backend                = params_backend;
+        state->allow_split_buffer            = allow_split_buffer;
+        state->params_follow_compute_backend = params_follow_compute_backend;
         new_states.push_back(std::move(state));
     }
 
@@ -237,7 +269,7 @@ bool ModelManager::load_tensors_to_params_backend(const std::vector<TensorState*
 }
 
 bool ModelManager::stage_tensors_to_compute_backend(const std::vector<TensorState*>& states) {
-    std::map<ggml_backend_t, std::vector<TensorState*>> states_by_compute_backend;
+    std::map<std::pair<ggml_backend_t, ggml_backend_buffer_type_t>, std::vector<TensorState*>> states_by_staging_target;
     for (TensorState* state : states) {
         if (state == nullptr || should_ignore(*state) || is_optional_missing_tensor(state->name)) {
             continue;
@@ -257,11 +289,16 @@ bool ModelManager::stage_tensors_to_compute_backend(const std::vector<TensorStat
             LOG_ERROR("model manager tensor '%s' is not loaded to params backend", state->name.c_str());
             return false;
         }
-        states_by_compute_backend[state->compute_backend].push_back(state);
+        ggml_backend_buffer_type_t staging_buft = split_buffer_type_for(*state);
+        if (staging_buft == nullptr) {
+            staging_buft = ggml_backend_get_default_buffer_type(state->compute_backend);
+        }
+        states_by_staging_target[{state->compute_backend, staging_buft}].push_back(state);
     }
 
-    for (const auto& pair : states_by_compute_backend) {
-        ggml_backend_t compute_backend          = pair.first;
+    for (const auto& pair : states_by_staging_target) {
+        ggml_backend_t compute_backend          = pair.first.first;
+        ggml_backend_buffer_type_t staging_buft = pair.first.second;
         const std::vector<TensorState*>& states = pair.second;
         if (states.empty()) {
             continue;
@@ -285,7 +322,7 @@ bool ModelManager::stage_tensors_to_compute_backend(const std::vector<TensorStat
             staged_tensors.push_back({state, staging_tensor});
         }
 
-        ggml_backend_buffer_t compute_buffer = ggml_backend_alloc_ctx_tensors(staging_ctx, compute_backend);
+        ggml_backend_buffer_t compute_buffer = ggml_backend_alloc_ctx_tensors_from_buft(staging_ctx, staging_buft);
         if (compute_buffer == nullptr) {
             LOG_ERROR("model manager alloc compute params backend buffer failed, num_tensors = %zu",
                       staged_tensors.size());
@@ -350,6 +387,17 @@ bool ModelManager::apply_loras_to_params(const std::vector<TensorState*>& states
             LOG_ERROR("model manager compute backend is null for lora target tensor '%s'", state->name.c_str());
             return false;
         }
+        if (state->tensor->buffer != nullptr &&
+            ggml_backend_buffer_get_type(state->tensor->buffer) == split_buffer_type_for(*state)) {
+            if (!warned_split_lora_skip_) {
+                LOG_WARN(
+                    "model manager skipping direct lora application to row-split tensors "
+                    "(use --lora-apply-mode at_runtime with row split)");
+                warned_split_lora_skip_ = true;
+            }
+            state->applied_lora_epoch = current_lora_epoch_;
+            continue;
+        }
         if (state->tensor->data == nullptr) {
             LOG_ERROR("model manager lora target tensor '%s' is not prepared", state->name.c_str());
             return false;
@@ -694,6 +742,8 @@ ggml_backend_buffer_type_t ModelManager::params_buffer_type_for(const TensorStat
         if (compute_dev != nullptr) {
             params_buft = ggml_backend_dev_host_buffer_type(compute_dev);
         }
+    } else if (state.params_backend == state.compute_backend) {
+        params_buft = split_buffer_type_for(state);
     }
     if (params_buft == nullptr) {
         params_buft = ggml_backend_get_default_buffer_type(state.params_backend);
@@ -871,6 +921,54 @@ bool ModelManager::resolve_required_tensor_states(const std::vector<ggml_tensor*
     return true;
 }
 
+bool ModelManager::assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                          ggml_backend_t compute_backend) {
+    if (tensors.empty()) {
+        return true;
+    }
+    if (compute_backend == nullptr) {
+        LOG_ERROR("model manager cannot assign tensors to a null compute backend");
+        return false;
+    }
+
+    std::vector<TensorState*> required_states;
+    if (!resolve_required_tensor_states(tensors, required_states)) {
+        return false;
+    }
+
+    for (TensorState* state : required_states) {
+        if (state == nullptr || state->tensor == nullptr) {
+            continue;
+        }
+
+        const bool params_follow_compute = state->params_follow_compute_backend ||
+                                           state->residency_mode == ResidencyMode::Disk;
+        const bool compute_changes = state->compute_backend != compute_backend;
+        const bool params_changes  = params_follow_compute && state->params_backend != compute_backend;
+        if (!compute_changes && !params_changes) {
+            continue;
+        }
+
+        if (state->active_prepare_count > 0 || state->staged_to_compute_backend) {
+            LOG_ERROR("model manager cannot move active tensor '%s' to another compute backend",
+                      state->name.c_str());
+            return false;
+        }
+        if (params_changes && state->loaded_to_params_backend) {
+            LOG_ERROR("model manager cannot move loaded tensor '%s' to another params backend",
+                      state->name.c_str());
+            return false;
+        }
+
+        state->compute_backend = compute_backend;
+        if (params_follow_compute) {
+            state->params_backend = compute_backend;
+        }
+    }
+
+    return true;
+}
+
 bool ModelManager::prepare_params(const std::vector<ggml_tensor*>& tensors) {
     if (tensors.empty()) {
         return true;
diff --git a/otherarch/sdcpp/src/model_manager.h b/otherarch/sdcpp/src/model_manager.h
index 9225e3ea6..d80032614 100644
--- a/otherarch/sdcpp/src/model_manager.h
+++ b/otherarch/sdcpp/src/model_manager.h
@@ -33,10 +33,12 @@ private:
         ggml_tensor* tensor = nullptr;
         std::string desc;
 
-        ResidencyMode residency_mode   = ResidencyMode::ParamBackend;
-        ggml_backend_t compute_backend = nullptr;
-        ggml_backend_t params_backend  = nullptr;
-        bool metadata_validated        = false;
+        ResidencyMode residency_mode       = ResidencyMode::ParamBackend;
+        ggml_backend_t compute_backend     = nullptr;
+        ggml_backend_t params_backend      = nullptr;
+        bool allow_split_buffer            = false;
+        bool params_follow_compute_backend = false;
+        bool metadata_validated            = false;
 
         int active_prepare_count = 0;
 
@@ -63,6 +65,8 @@ private:
     std::map<std::string, TensorState*> tensor_states_by_name_;
     std::vector<std::unique_ptr<ParamsStorageBlock>> params_storage_blocks_;
     std::vector<std::unique_ptr<ComputeStagingBlock>> compute_staging_blocks_;
+    std::map<ggml_backend_t, ggml_backend_buffer_type_t> split_buffer_types_;
+    bool warned_split_lora_skip_ = false;
     std::set<std::string> common_ignore_tensors_;
     std::vector<LoraSpec> loras_;
     SDVersion lora_version_      = VERSION_COUNT;
@@ -91,6 +95,7 @@ private:
     bool stage_tensors_to_compute_backend(const std::vector<TensorState*>& states);
 
     ggml_backend_buffer_type_t params_buffer_type_for(const TensorState& state) const;
+    ggml_backend_buffer_type_t split_buffer_type_for(const TensorState& state) const;
     void release_compute_staging_blocks(bool force                                            = false,
                                         const std::unordered_set<TensorState*>* target_states = nullptr);
     void release_params_storage_blocks(bool force                                            = false,
@@ -114,6 +119,9 @@ public:
     void set_writable_mmap(bool writable_mmap) { writable_mmap_ = writable_mmap; }
     void set_common_ignore_tensors(std::set<std::string> ignore_tensors);
     void set_loras(std::vector<LoraSpec> loras, SDVersion version);
+    void set_split_buffer_type(ggml_backend_t compute_backend, ggml_backend_buffer_type_t split_buft);
+
+    static bool tensor_shape_supports_split_buffer(const ggml_tensor* tensor);
 
     std::set<std::string> tensor_names() const;
 
@@ -122,7 +130,9 @@ public:
                                 ResidencyMode residency_mode,
                                 ggml_backend_t compute_backend,
                                 ggml_backend_t params_backend,
-                                size_t* registered_tensor_size = nullptr);
+                                size_t* registered_tensor_size     = nullptr,
+                                bool allow_split_buffer            = false,
+                                bool params_follow_compute_backend = false);
 
     template <typename Runner>
     bool register_runner_params(const std::string& desc,
@@ -162,6 +172,8 @@ public:
     bool validate_registered_tensors();
     bool load_all_params_eagerly();
 
+    bool assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                ggml_backend_t compute_backend) override;
     bool prepare_params(const std::vector<ggml_tensor*>& tensors) override;
     void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) override;
     void release_params_backend_params(const std::vector<ggml_tensor*>& tensors) override;
diff --git a/otherarch/sdcpp/src/name_conversion.cpp b/otherarch/sdcpp/src/name_conversion.cpp
index 64b7c681d..2f9a4d184 100644
--- a/otherarch/sdcpp/src/name_conversion.cpp
+++ b/otherarch/sdcpp/src/name_conversion.cpp
@@ -736,6 +736,62 @@ std::string convert_diffusers_dit_to_original_krea2(std::string name) {
     return name;
 }
 
+// Convert a diffusers-format ControlNet tensor name to the original (LDM/lllyasviel) layout
+// declared by ControlNetBlock. Reuses the UNet down/mid conversion for the shared encoder
+// (down_blocks, mid_block, time_embedding, add_embedding, conv_in) and adds the ControlNet-only
+// mappings: input_hint_block, zero_convs, middle_block_out.
+std::string convert_diffusers_controlnet_to_original_sdxl(std::string name) {
+    name = convert_diffusers_unet_to_original_sdxl(std::move(name));
+
+    static const std::vector<std::pair<std::string, std::string>> prefix_map = {
+        {"controlnet_cond_embedding.conv_in.", "input_hint_block.0."},
+        {"controlnet_cond_embedding.blocks.0.", "input_hint_block.2."},
+        {"controlnet_cond_embedding.blocks.1.", "input_hint_block.4."},
+        {"controlnet_cond_embedding.blocks.2.", "input_hint_block.6."},
+        {"controlnet_cond_embedding.blocks.3.", "input_hint_block.8."},
+        {"controlnet_cond_embedding.blocks.4.", "input_hint_block.10."},
+        {"controlnet_cond_embedding.blocks.5.", "input_hint_block.12."},
+        {"controlnet_cond_embedding.conv_out.", "input_hint_block.14."},
+        {"controlnet_mid_block.", "middle_block_out.0."},
+    };
+    for (const auto& p : prefix_map) {
+        if (starts_with(name, p.first)) {
+            return p.second + name.substr(p.first.size());
+        }
+    }
+
+    static const std::string controlnet_down_prefix = "controlnet_down_blocks.";
+    if (starts_with(name, controlnet_down_prefix)) {
+        size_t rest_start = controlnet_down_prefix.size();
+        size_t dot        = name.find('.', rest_start);
+        if (dot != std::string::npos) {
+            std::string idx = name.substr(rest_start, dot - rest_start);
+            return "zero_convs." + idx + ".0" + name.substr(dot);
+        }
+    }
+
+    return name;
+}
+
+static bool is_diffusers_controlnet_name(const std::string& name) {
+    static const std::vector<std::string> heads = {
+        "controlnet_cond_embedding.",
+        "controlnet_down_blocks.",
+        "controlnet_mid_block.",
+        "down_blocks.",
+        "mid_block.",
+        "time_embedding.",
+        "add_embedding.",
+        "conv_in.",
+    };
+    for (const auto& h : heads) {
+        if (starts_with(name, h)) {
+            return true;
+        }
+    }
+    return false;
+}
+
 std::string convert_diffusion_model_name(std::string name, std::string prefix, SDVersion version) {
     if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
         name = convert_diffusers_unet_to_original_sd1(name);
@@ -1338,6 +1394,9 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
                 name = name.substr(pos + 1);
             }
         }
+        if (sd_version_is_sdxl(version) && is_diffusers_controlnet_name(name)) {
+            name = convert_diffusers_controlnet_to_original_sdxl(name);
+        }
     }
 
     if (is_lora) {
diff --git a/otherarch/sdcpp/src/runtime/denoiser.hpp b/otherarch/sdcpp/src/runtime/denoiser.hpp
index 812eebe61..115175b14 100644
--- a/otherarch/sdcpp/src/runtime/denoiser.hpp
+++ b/otherarch/sdcpp/src/runtime/denoiser.hpp
@@ -4,7 +4,10 @@
 #include <algorithm>
 #include <cctype>
 #include <cmath>
+#include <cstring>
 #include <functional>
+#include <limits>
+#include <map>
 #include <string>
 #include <utility>
 
@@ -1013,6 +1016,7 @@ struct Denoiser {
                                             const sd::Tensor<float>& latent)         = 0;
     virtual sd::Tensor<float> inverse_noise_scaling(float sigma,
                                                     const sd::Tensor<float>& latent) = 0;
+    virtual float noise_level_to_sigma(float noise_level)                            = 0;
 
     virtual std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) {
         auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
@@ -1160,6 +1164,10 @@ struct CompVisDenoiser : public Denoiser {
         SD_UNUSED(sigma);
         return latent;
     }
+
+    float noise_level_to_sigma(float noise_level) {
+        return noise_level / (1.0f - noise_level);
+    }
 };
 
 struct CompVisVDenoiser : public CompVisDenoiser {
@@ -1247,6 +1255,10 @@ struct DiscreteFlowDenoiser : public Denoiser {
     sd::Tensor<float> inverse_noise_scaling(float sigma, const sd::Tensor<float>& latent) override {
         return latent * (1.0f / (1.0f - sigma));
     }
+
+    float noise_level_to_sigma(float noise_level) {
+        return noise_level;
+    }
 };
 
 struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
@@ -1384,6 +1396,11 @@ struct MiniT2IFlowDenoiser : public Denoiser {
         return latent;
     }
 
+    float noise_level_to_sigma(float noise_level) {
+        SD_UNUSED(noise_level);
+        return 1.0f;
+    }
+
     std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) override {
         SD_UNUSED(image_seq_len);
         SD_UNUSED(scheduler_type);
@@ -1814,6 +1831,204 @@ static sd::Tensor<float> sample_dpmpp_2m_v2(denoise_cb_t model,
     return x;
 }
 
+// DPM-Solver++(2M) SDE, midpoint variant. Ref: Lu et al. arXiv:2211.01095;
+// k-diffusion sample_dpmpp_2m_sde.
+static sd::Tensor<float> sample_dpmpp_2m_sde(denoise_cb_t model,
+                                             sd::Tensor<float> x,
+                                             const std::vector<float>& sigmas,
+                                             std::shared_ptr<RNG> rng,
+                                             float eta) {
+    sd::Tensor<float> old_denoised;
+    bool have_old_denoised = false;
+    float h_last           = 0.f;
+
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        auto denoised_opt = model(x, sigmas[i], i + 1);
+        if (denoised_opt.pred.empty()) {
+            return {};
+        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+
+        if (sigmas[i + 1] == 0.f) {
+            x = denoised;
+        } else {
+            float t     = -std::log(sigmas[i]);
+            float s     = -std::log(sigmas[i + 1]);
+            float h     = s - t;
+            float eta_h = eta * h;
+            float a     = sigmas[i + 1] / sigmas[i] * std::exp(-eta_h);
+            float b     = -std::expm1(-h - eta_h);
+
+            x = a * x + b * denoised;
+
+            if (have_old_denoised) {
+                float r = h_last / h;
+                x += (0.5f * b / r) * (denoised - old_denoised);
+            }
+            if (eta > 0.f) {
+                x += sd::Tensor<float>::randn_like(x, rng) * (sigmas[i + 1] * std::sqrt(-std::expm1(-2.f * eta_h)));
+            }
+            h_last = h;
+        }
+        old_denoised      = denoised;
+        have_old_denoised = true;
+    }
+    return x;
+}
+
+// Seeded Brownian tree providing deterministic, step-count-stable Gaussian
+// increments for stochastic samplers. Constructed once per generation; each
+// call returns unit-variance noise for interval [sigma_a, sigma_b].
+// Reference: torchsde BrownianTree; k-diffusion BatchedBrownianTree.
+class BrownianTreeNoiseSampler {
+public:
+    BrownianTreeNoiseSampler(const sd::Tensor<float>& x_template,
+                             double sigma_min,
+                             double sigma_max,
+                             uint64_t seed)
+        : t_min_(sigma_min),
+          t_max_(sigma_max),
+          shape_(x_template.shape()),
+          root_seed_(mix64(seed, 0x9E3779B97F4A7C15ULL)) {
+        auto rng = std::make_shared<STDDefaultRNG>();
+        rng->manual_seed(mix64(seed, 0xBF58476D1CE4E5B9ULL));
+        w_at_tmax_ = sd::Tensor<float>::randn(shape_, rng) * std::sqrt(static_cast<float>(t_max_ - t_min_));
+    }
+
+    sd::Tensor<float> operator()(double sigma_a, double sigma_b) {
+        double a   = clamp(std::min(sigma_a, sigma_b));
+        double b   = clamp(std::max(sigma_a, sigma_b));
+        auto dW    = w(b) - w(a);
+        float span = static_cast<float>(std::max(std::abs(sigma_b - sigma_a), 1e-12));
+        return dW * (1.0f / std::sqrt(span));
+    }
+
+private:
+    static constexpr int kMaxDepth = 24;
+
+    static uint64_t mix64(uint64_t v, uint64_t salt) {
+        uint64_t z = v + salt;
+        z          = (z ^ (z >> 30)) * 0xBF58476D1CE4E5B9ULL;
+        z          = (z ^ (z >> 27)) * 0x94D049BB133111EBULL;
+        return z ^ (z >> 31);
+    }
+
+    double clamp(double t) const {
+        return std::min(std::max(t, t_min_), t_max_);
+    }
+
+    sd::Tensor<float> w(double t) {
+        auto it = cache_.find(t);
+        if (it != cache_.end()) {
+            return it->second;
+        }
+        sd::Tensor<float> zero = sd::Tensor<float>::zeros(shape_);
+        sd::Tensor<float> out  = bridge(t_min_, t_max_, zero, w_at_tmax_, t, root_seed_, kMaxDepth);
+        cache_.emplace(t, out);
+        return out;
+    }
+
+    sd::Tensor<float> bridge(double a,
+                             double c,
+                             const sd::Tensor<float>& w_a,
+                             const sd::Tensor<float>& w_c,
+                             double t,
+                             uint64_t node_seed,
+                             int depth) {
+        if (depth <= 0 || c - a < 1e-9) {
+            float alpha = (c > a) ? static_cast<float>((t - a) / (c - a)) : 0.5f;
+            return (1.0f - alpha) * w_a + alpha * w_c;
+        }
+        double m       = 0.5 * (a + c);
+        double std_dev = std::sqrt((c - m) * (m - a) / (c - a));
+        auto rng       = std::make_shared<STDDefaultRNG>();
+        rng->manual_seed(node_seed);
+        auto z   = sd::Tensor<float>::randn(shape_, rng);
+        auto w_m = 0.5f * (w_a + w_c) + static_cast<float>(std_dev) * z;
+        if (t == m) {
+            return w_m;
+        }
+        if (t < m) {
+            return bridge(a, m, w_a, w_m, t, mix64(node_seed, 1), depth - 1);
+        }
+        return bridge(m, c, w_m, w_c, t, mix64(node_seed, 2), depth - 1);
+    }
+
+    double t_min_;
+    double t_max_;
+    std::vector<int64_t> shape_;
+    uint64_t root_seed_;
+    sd::Tensor<float> w_at_tmax_;
+    std::map<double, sd::Tensor<float>> cache_;
+};
+
+// DPM-Solver++(2M) SDE, midpoint variant, with step-count-stable Brownian-tree
+// noise. Same trajectory shape at any step count for a given seed. Aliased in
+// k-diffusion / ComfyUI as sample_dpmpp_2m_sde_gpu.
+// Ref: Lu et al. arXiv:2211.01095; torchsde BrownianTree.
+static sd::Tensor<float> sample_dpmpp_2m_sde_bt(denoise_cb_t model,
+                                                sd::Tensor<float> x,
+                                                const std::vector<float>& sigmas,
+                                                std::shared_ptr<RNG> rng,
+                                                float eta) {
+    double sigma_max = 0.0;
+    double sigma_min = std::numeric_limits<double>::infinity();
+    for (float s : sigmas) {
+        if (s > 0.0f) {
+            sigma_max = std::max(sigma_max, static_cast<double>(s));
+            sigma_min = std::min(sigma_min, static_cast<double>(s));
+        }
+    }
+    if (sigma_max <= sigma_min) {
+        return x;
+    }
+    uint64_t tree_seed = 0;
+    {
+        auto draw = rng->randn(2);
+        std::memcpy(&tree_seed, draw.data(), sizeof(tree_seed));
+    }
+    BrownianTreeNoiseSampler noise_sampler(x, sigma_min, sigma_max, tree_seed);
+
+    sd::Tensor<float> old_denoised;
+    bool have_old_denoised = false;
+    float h_last           = 0.f;
+
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        auto denoised_opt = model(x, sigmas[i], i + 1);
+        if (denoised_opt.pred.empty()) {
+            return {};
+        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+
+        if (sigmas[i + 1] == 0.f) {
+            x = denoised;
+        } else {
+            float t     = -std::log(sigmas[i]);
+            float s     = -std::log(sigmas[i + 1]);
+            float h     = s - t;
+            float eta_h = eta * h;
+            float a     = sigmas[i + 1] / sigmas[i] * std::exp(-eta_h);
+            float b     = -std::expm1(-h - eta_h);
+
+            x = a * x + b * denoised;
+
+            if (have_old_denoised) {
+                float r = h_last / h;
+                x += (0.5f * b / r) * (denoised - old_denoised);
+            }
+            if (eta > 0.f) {
+                x += noise_sampler(sigmas[i], sigmas[i + 1]) * (sigmas[i + 1] * std::sqrt(-std::expm1(-2.f * eta_h)));
+            }
+            h_last = h;
+        }
+        old_denoised      = denoised;
+        have_old_denoised = true;
+    }
+    return x;
+}
+
 using SamplerExtraArgs = KeyValueArgs;
 
 static sd::Tensor<float> sample_lcm(denoise_cb_t model,
@@ -2490,6 +2705,10 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
             return sample_res_2s(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
         case ER_SDE_SAMPLE_METHOD:
             return sample_er_sde(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
+        case DPMPP2M_SDE_SAMPLE_METHOD:
+            return sample_dpmpp_2m_sde(model, std::move(x), sigmas, rng, eta);
+        case DPMPP2M_SDE_BT_SAMPLE_METHOD:
+            return sample_dpmpp_2m_sde_bt(model, std::move(x), sigmas, rng, eta);
         case DDIM_TRAILING_SAMPLE_METHOD:
             // DDIM is equivalent to Euler Ancestral with the Simple scheduler
             return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
diff --git a/otherarch/sdcpp/src/runtime/imatrix.cpp b/otherarch/sdcpp/src/runtime/imatrix.cpp
index 313eadc68..a6f3bca95 100644
--- a/otherarch/sdcpp/src/runtime/imatrix.cpp
+++ b/otherarch/sdcpp/src/runtime/imatrix.cpp
@@ -287,6 +287,10 @@ bool IMatrixCollector::load_imatrix(const char* fname) {
         if (e.values.empty()) {
             e.values.resize(nval, 0);
             e.counts.resize(nval, 0);
+        } else if (e.values.size() != (size_t)nval) {
+            LOG_ERROR("inconsistent size for a repeated entry (%d vs %d)\n", (int)e.values.size(), nval);
+            stats_ = {};
+            return false;
         }
 
         std::vector<float> tmp(nval);
diff --git a/otherarch/sdcpp/src/stable-diffusion.cpp b/otherarch/sdcpp/src/stable-diffusion.cpp
index a0ee643b3..f9f4e54e8 100644
--- a/otherarch/sdcpp/src/stable-diffusion.cpp
+++ b/otherarch/sdcpp/src/stable-diffusion.cpp
@@ -4,11 +4,14 @@
 #include <filesystem>
 #include <limits>
 #include <set>
+#include <type_traits>
 #include <unordered_set>
+#include <utility>
 #include <vector>
 
 #include "core/ggml_extend.hpp"
 #include "core/ggml_graph_cut.h"
+#include "core/layer_split_partition.h"
 
 #include "core/rng.hpp"
 #include "core/rng_mt19937.hpp"
@@ -19,6 +22,7 @@
 #include "stable-diffusion.h"
 
 #include "conditioning/conditioner.hpp"
+#include "core/backend_fit.h"
 #include "extensions/generation_extension.h"
 #include "model/adapter/lora.hpp"
 #include "model/diffusion/anima.hpp"
@@ -55,11 +59,11 @@
 #include "name_conversion.h"
 #include "runtime/latent-preview.h"
 
+#include <atomic>
+
 const char* sd_vae_format_name(enum sd_vae_format_t format);
 static SDVersion sd_vae_format_to_version(enum sd_vae_format_t format, SDVersion fallback);
 
-#include <atomic>
-
 const char* model_version_to_str[] = {
     "SD 1.x",
     "SD 1.x Inpaint",
@@ -172,6 +176,13 @@ static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
 
 /*=============================================== StableDiffusionGGML ================================================*/
 
+template <typename T, typename = void>
+struct has_set_runtime_backends : std::false_type {};
+template <typename T>
+struct has_set_runtime_backends<T,
+                                std::void_t<decltype(std::declval<T&>().set_runtime_backends(
+                                    std::declval<const std::vector<ggml_backend_t>&>()))>> : std::true_type {};
+
 static_assert(std::atomic<sd_cancel_mode_t>::is_always_lock_free,
               "sd_cancel_mode_t must be lock-free");
 
@@ -212,6 +223,8 @@ public:
     bool eager_load    = false;
     std::string backend_spec;
     std::string params_backend_spec;
+    std::string split_mode_spec;
+    bool auto_fit_enabled = false;
 
     bool is_using_v_parameterization     = false;
     bool is_using_edm_v_parameterization = false;
@@ -259,6 +272,15 @@ public:
         return max_vram_assignment.bytes_for_backend(backend_for(module));
     }
 
+    std::vector<size_t> layer_split_vram_limits_for_backends(const std::vector<ggml_backend_t>& backends) {
+        std::vector<size_t> limits;
+        limits.reserve(backends.size());
+        for (ggml_backend_t backend : backends) {
+            limits.push_back(max_vram_assignment.bytes_for_backend(backend));
+        }
+        return limits;
+    }
+
     bool ensure_backend_pair(SDBackendModule module) {
         if (backend_for(module) == nullptr) {
             return false;
@@ -279,18 +301,208 @@ public:
         if (model_manager == nullptr) {
             return true;
         }
+        ModelManager::ResidencyMode residency_mode =
+            backend_manager.params_backend_is_disk(module) ? ModelManager::ResidencyMode::Disk : ModelManager::ResidencyMode::ParamBackend;
+
+        std::vector<ggml_backend_t> module_backends = backend_manager.runtime_backends(module);
+        if (module_backends.size() > 1) {
+            if constexpr (has_set_runtime_backends<T>::value) {
+                if (module == SDBackendModule::DIFFUSION || module == SDBackendModule::TE) {
+                    if (backend_manager.split_mode(module) == SDSplitMode::ROW) {
+                        return register_row_split_runner_params(desc,
+                                                                model,
+                                                                module,
+                                                                module_backends,
+                                                                std::move(group_tensors),
+                                                                residency_mode,
+                                                                params_mem_size);
+                    }
+                    return register_layer_split_runner_params(desc,
+                                                              model,
+                                                              module,
+                                                              module_backends,
+                                                              std::move(group_tensors),
+                                                              residency_mode,
+                                                              params_mem_size);
+                }
+            }
+            LOG_WARN("%s module does not support multiple runtime backends; using %s",
+                     sd_backend_module_name(module),
+                     sd::layer_split_backend_device_display_name(module_backends[0]).c_str());
+        }
         return model_manager->register_param_tensors(desc,
                                                      std::move(group_tensors),
-                                                     backend_manager.params_backend_is_disk(module) ? ModelManager::ResidencyMode::Disk : ModelManager::ResidencyMode::ParamBackend,
+                                                     residency_mode,
                                                      backend_for(module),
                                                      params_backend_for(module),
                                                      params_mem_size);
     }
 
+    template <typename T>
+    bool register_row_split_runner_params(const std::string& desc,
+                                          const std::shared_ptr<T>& model,
+                                          SDBackendModule module,
+                                          const std::vector<ggml_backend_t>& module_backends,
+                                          std::map<std::string, ggml_tensor*> group_tensors,
+                                          ModelManager::ResidencyMode residency_mode,
+                                          size_t* params_mem_size) {
+        ggml_backend_t main_backend = module_backends[0];
+
+        auto fall_back_to_layer_split = [&](const char* reason) {
+            LOG_WARN("%s: row split unavailable (%s); falling back to layer split", desc.c_str(), reason);
+            return register_layer_split_runner_params(desc,
+                                                      model,
+                                                      module,
+                                                      module_backends,
+                                                      std::move(group_tensors),
+                                                      residency_mode,
+                                                      params_mem_size);
+        };
+
+        ggml_backend_dev_t main_dev = ggml_backend_get_device(main_backend);
+        ggml_backend_reg_t reg      = main_dev != nullptr ? ggml_backend_dev_backend_reg(main_dev) : nullptr;
+        if (reg == nullptr) {
+            return fall_back_to_layer_split("no backend registry");
+        }
+        const size_t reg_dev_count = ggml_backend_reg_dev_count(reg);
+        std::vector<float> tensor_split(reg_dev_count, 0.0f);
+        constexpr int64_t compute_headroom_bytes = 2ll * 1024 * 1024 * 1024;
+        for (ggml_backend_t backend : module_backends) {
+            ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+            int reg_index          = -1;
+            for (size_t i = 0; i < reg_dev_count; i++) {
+                if (ggml_backend_reg_dev_get(reg, i) == dev) {
+                    reg_index = (int)i;
+                    break;
+                }
+            }
+            if (reg_index < 0) {
+                return fall_back_to_layer_split("devices span different backend registries");
+            }
+            size_t free_bytes = 0, total_bytes = 0;
+            ggml_backend_dev_memory(dev, &free_bytes, &total_bytes);
+            int64_t usable_bytes    = std::max<int64_t>((int64_t)free_bytes - compute_headroom_bytes,
+                                                     (int64_t)free_bytes / 8);
+            tensor_split[reg_index] = usable_bytes > 0 ? (float)((double)usable_bytes / (1024.0 * 1024.0)) : 1.0f;
+        }
+
+        ggml_backend_buffer_type_t split_buft = backend_manager.split_buffer_type(main_backend, tensor_split);
+        if (split_buft == nullptr) {
+            return fall_back_to_layer_split("backend has no split buffer type");
+        }
+        model_manager->set_split_buffer_type(main_backend, split_buft);
+
+        std::map<std::string, ggml_tensor*> split_tensors;
+        if constexpr (std::is_base_of_v<Conditioner, T>) {
+            model->get_layer_split_param_tensors(split_tensors);
+        } else {
+            split_tensors = group_tensors;
+        }
+
+        std::map<std::string, ggml_tensor*> row_split_map;
+        std::map<std::string, ggml_tensor*> regular_map;
+        size_t row_split_bytes = 0;
+        for (const auto& kv : group_tensors) {
+            if (split_tensors.count(kv.first) != 0 &&
+                sd::layer_split_tensor_block_index(kv.first) >= 0 &&
+                ModelManager::tensor_shape_supports_split_buffer(kv.second)) {
+                row_split_map[kv.first] = kv.second;
+                row_split_bytes += ggml_nbytes(kv.second);
+            } else {
+                regular_map[kv.first] = kv.second;
+            }
+        }
+        if (row_split_map.empty()) {
+            return fall_back_to_layer_split("no row-splittable transformer block weights found");
+        }
+
+        LOG_INFO("%s row split: %zu tensors (%.1f MB) split across %zu devices (main %s)",
+                 desc.c_str(),
+                 row_split_map.size(),
+                 row_split_bytes / (1024.f * 1024.f),
+                 module_backends.size(),
+                 sd::layer_split_backend_device_display_name(main_backend).c_str());
+
+        if (!model_manager->register_param_tensors(desc,
+                                                   std::move(row_split_map),
+                                                   residency_mode,
+                                                   main_backend,
+                                                   params_backend_for(module),
+                                                   params_mem_size,
+                                                   /*allow_split_buffer=*/true)) {
+            return false;
+        }
+        return model_manager->register_param_tensors(desc,
+                                                     std::move(regular_map),
+                                                     residency_mode,
+                                                     main_backend,
+                                                     params_backend_for(module),
+                                                     params_mem_size);
+    }
+
+    // Register graph-cut layer-split tensors on the primary backend first.
+    // The first real graph assigns each param tensor to a runtime backend
+    // before weights are loaded or staged.
+    template <typename T>
+    bool register_layer_split_runner_params(const std::string& desc,
+                                            const std::shared_ptr<T>& model,
+                                            SDBackendModule module,
+                                            const std::vector<ggml_backend_t>& module_backends,
+                                            std::map<std::string, ggml_tensor*> group_tensors,
+                                            ModelManager::ResidencyMode residency_mode,
+                                            size_t* params_mem_size) {
+        bool has_cpu_device = false;
+        for (ggml_backend_t backend : module_backends) {
+            has_cpu_device = has_cpu_device || sd_backend_is_cpu(backend);
+        }
+        if (has_cpu_device) {
+            // The scheduler reserves the CPU slot for its fallback backend, and
+            // CPU weight participation is what --params-backend <module>=cpu is
+            // for; a CPU device in a split list is almost certainly a mistake.
+            LOG_WARN(
+                "%s: layer split across a CPU device is not supported; using %s "
+                "(use --params-backend %s=cpu to keep weights in RAM)",
+                desc.c_str(),
+                sd::layer_split_backend_device_display_name(module_backends[0]).c_str(),
+                sd_backend_module_name(module));
+            return model_manager->register_param_tensors(desc,
+                                                         std::move(group_tensors),
+                                                         residency_mode,
+                                                         module_backends[0],
+                                                         params_backend_for(module),
+                                                         params_mem_size);
+        }
+
+        model->set_runtime_backends(module_backends);
+        model->set_graph_cut_layer_split_backend_vram_limits(layer_split_vram_limits_for_backends(module_backends));
+        model->set_graph_cut_layer_split_enabled(true);
+        const bool params_follow_runtime = backend_manager.params_backend_follows_runtime(module) ||
+                                           backend_manager.params_backend_is_disk(module);
+        ggml_backend_t initial_params_backend = params_follow_runtime ? module_backends[0] : params_backend_for(module);
+        if (initial_params_backend == nullptr) {
+            return false;
+        }
+
+        LOG_INFO("%s graph-cut layer split: deferring %zu tensors across %zu runtime backends until first graph",
+                 desc.c_str(),
+                 group_tensors.size(),
+                 module_backends.size());
+
+        return model_manager->register_param_tensors(desc,
+                                                     std::move(group_tensors),
+                                                     residency_mode,
+                                                     module_backends[0],
+                                                     initial_params_backend,
+                                                     params_mem_size,
+                                                     false,
+                                                     params_follow_runtime);
+    }
+
     bool init_backend() {
         std::string error;
         if (!backend_manager.init(backend_spec.c_str(),
                                   params_backend_spec.c_str(),
+                                  split_mode_spec.c_str(),
                                   &error)) {
             LOG_ERROR("backend config failed: %s", error.c_str());
             return false;
@@ -298,6 +510,26 @@ public:
         return ensure_backend_pair(SDBackendModule::DIFFUSION);
     }
 
+    bool row_split_active() {
+        for (SDBackendModule module : {SDBackendModule::DIFFUSION, SDBackendModule::TE}) {
+            if (backend_manager.split_mode(module) == SDSplitMode::ROW &&
+                backend_manager.runtime_backends(module).size() > 1) {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    bool graph_cut_layer_split_active() {
+        for (SDBackendModule module : {SDBackendModule::DIFFUSION, SDBackendModule::TE}) {
+            if (backend_manager.split_mode(module) == SDSplitMode::LAYER &&
+                backend_manager.runtime_backends(module).size() > 1) {
+                return true;
+            }
+        }
+        return false;
+    }
+
     std::shared_ptr<RNG> get_rng(rng_type_t rng_type) {
         if (rng_type == STD_DEFAULT_RNG) {
             return std::make_shared<STDDefaultRNG>();
@@ -359,6 +591,8 @@ public:
         eager_load          = sd_ctx_params->eager_load;
         backend_spec        = SAFE_STR(sd_ctx_params->backend);
         params_backend_spec = SAFE_STR(sd_ctx_params->params_backend);
+        split_mode_spec     = SAFE_STR(sd_ctx_params->split_mode);
+        auto_fit_enabled    = sd_ctx_params->auto_fit;
         max_vram_assignment.reset(0.f);
         {
             std::string error;
@@ -384,21 +618,6 @@ public:
 
         ggml_log_set(ggml_log_callback_default, nullptr);
 
-        if (!init_backend()) {
-            return false;
-        }
-        {
-            std::string error;
-            if (!max_vram_assignment.canonicalize_backend_keys(&error)) {
-                LOG_ERROR("%s", error.c_str());
-                return false;
-            }
-        }
-        if (stream_layers && !backend_manager.params_backend_is_cpu(SDBackendModule::DIFFUSION)) {
-            LOG_WARN("--stream-layers has no effect unless diffusion params backend is cpu; ignoring");
-            stream_layers = false;
-        }
-
         std::string clip_vision_fixed     = SAFE_STR(sd_ctx_params->clip_vision_path);
         std::string clipg_path_fixed      = SAFE_STR(sd_ctx_params->clip_g_path);
         std::string clipl_path_fixed      = SAFE_STR(sd_ctx_params->clip_l_path);
@@ -788,6 +1007,35 @@ public:
             model_loader.set_wtype_override(wtype, tensor_type_rules);
         }
 
+        if (auto_fit_enabled) {
+            if (!sd::backend_fit::derive_backend_specs(model_loader,
+                                                       wtype,
+                                                       max_vram_assignment,
+                                                       backend_spec,
+                                                       params_backend_spec)) {
+                return false;
+            }
+        }
+
+        if (!init_backend()) {
+            return false;
+        }
+        {
+            std::string error;
+            if (!max_vram_assignment.canonicalize_backend_keys(&error)) {
+                LOG_ERROR("%s", error.c_str());
+                return false;
+            }
+        }
+        if (stream_layers && !backend_manager.params_backend_is_cpu(SDBackendModule::DIFFUSION)) {
+            LOG_WARN("--stream-layers has no effect unless diffusion params backend is cpu; ignoring");
+            stream_layers = false;
+        }
+        if (eager_load && graph_cut_layer_split_active()) {
+            LOG_WARN("--eager-load is not supported with graph-cut layer split; weights will be prepared lazily");
+            eager_load = false;
+        }
+
         std::map<ggml_type, uint32_t> wtype_stat                 = model_loader.get_wtype_stat();
         std::map<ggml_type, uint32_t> conditioner_wtype_stat     = model_loader.get_conditioner_wtype_stat();
         std::map<ggml_type, uint32_t> diffusion_model_wtype_stat = model_loader.get_diffusion_model_wtype_stat();
@@ -829,12 +1077,17 @@ public:
             // Avoid full-model LoRA merge buffers on constrained setups.
             const bool params_offloaded      = params_backend_for(SDBackendModule::DIFFUSION) != backend_for(SDBackendModule::DIFFUSION);
             const bool streaming_constrained = stream_layers || params_offloaded;
-            if (have_quantized_weight || streaming_constrained) {
+            if (have_quantized_weight || streaming_constrained || row_split_active()) {
                 apply_lora_immediately = false;
             } else {
                 apply_lora_immediately = true;
             }
         } else if (sd_ctx_params->lora_apply_mode == LORA_APPLY_IMMEDIATELY) {
+            if (row_split_active()) {
+                LOG_WARN(
+                    "row-split tensors do not support the immediately LoRA apply mode; "
+                    "LoRAs will not be applied to them (use --lora-apply-mode at_runtime)");
+            }
             apply_lora_immediately = true;
         } else {
             apply_lora_immediately = false;
@@ -860,10 +1113,6 @@ public:
             use_tae          = true;
         }
 
-        if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {
-            LOG_INFO("Using circular padding for convolutions");
-        }
-
         {
             if (!ensure_backend_pair(SDBackendModule::TE) ||
                 !ensure_backend_pair(SDBackendModule::DIFFUSION)) {
@@ -922,10 +1171,11 @@ public:
                 if (is_chroma) {
                     cond_stage_model = std::make_shared<T5CLIPEmbedder>(backend_for(SDBackendModule::TE),
                                                                         tensor_storage_map,
-                                                                        sd_ctx_params->chroma_use_t5_mask,
-                                                                        sd_ctx_params->chroma_t5_mask_pad,
                                                                         false,
-                                                                        model_manager);
+                                                                        1,
+                                                                        false,
+                                                                        model_manager,
+                                                                        sd_ctx_params->model_args);
                 } else if (version == VERSION_OVIS_IMAGE) {
                     cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                      tensor_storage_map,
@@ -942,8 +1192,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (sd_version_is_flux2(version) || sd_version_is_sefi_image(version)) {
                 bool is_chroma   = false;
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
@@ -956,8 +1206,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (sd_version_is_ltxav(version)) {
                 cond_stage_model = std::make_shared<LTXAVEmbedder>(backend_for(SDBackendModule::TE),
                                                                    tensor_storage_map,
@@ -1015,8 +1265,8 @@ public:
                                                                           tensor_storage_map,
                                                                           "model.diffusion_model",
                                                                           version,
-                                                                          sd_ctx_params->qwen_image_zero_cond_t,
-                                                                          model_manager);
+                                                                          model_manager,
+                                                                          sd_ctx_params->model_args);
             } else if (sd_version_is_longcat(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(backend_for(SDBackendModule::TE),
                                                                  tensor_storage_map,
@@ -1028,8 +1278,8 @@ public:
                                                                      tensor_storage_map,
                                                                      "model.diffusion_model",
                                                                      version,
-                                                                     sd_ctx_params->chroma_use_dit_mask,
-                                                                     model_manager);
+                                                                     model_manager,
+                                                                     sd_ctx_params->model_args);
             } else if (version == VERSION_HIDREAM_O1) {
                 cond_stage_model = std::make_shared<HiDreamO1::HiDreamO1Conditioner>(backend_for(SDBackendModule::TE),
                                                                                      tensor_storage_map,
@@ -1368,16 +1618,6 @@ public:
                     high_noise_diffusion_model->set_flash_attention_enabled(true);
                 }
             }
-
-            diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            if (high_noise_diffusion_model) {
-                high_noise_diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            }
-            if (control_net) {
-                control_net->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
-            }
-            circular_x = sd_ctx_params->circular_x;
-            circular_y = sd_ctx_params->circular_y;
         }
 
         LOG_DEBUG("validating model metadata");
@@ -2718,7 +2958,16 @@ public:
         }
         auto latents = first_stage_model->diffusion_to_vae_latents(x);
         first_stage_model->set_temporal_tiling_enabled(vae_tiling_params.temporal_tiling);
-        return first_stage_model->decode(n_threads, latents, vae_tiling_params, decode_video, circular_x, circular_y);
+        auto decoded = first_stage_model->decode(n_threads, latents, vae_tiling_params, decode_video, circular_x, circular_y);
+        if (decoded.empty() && auto_fit_enabled) {
+            bool prefer_temporal_tiling = decode_video && std::dynamic_pointer_cast<LTXVideoVAE>(first_stage_model) != nullptr;
+            if (sd::backend_fit::prepare_vae_decode_retry_tiling(vae_tiling_params, prefer_temporal_tiling)) {
+                first_stage_model->free_compute_buffer();
+                first_stage_model->set_temporal_tiling_enabled(vae_tiling_params.temporal_tiling);
+                decoded = first_stage_model->decode(n_threads, latents, vae_tiling_params, decode_video, circular_x, circular_y);
+            }
+        }
+        return decoded;
     }
 
     sd::Tensor<float> normalize_ltx_video_latents(const sd::Tensor<float>& x) {
@@ -2762,22 +3011,6 @@ public:
         return !!flow_denoiser;
     }
 
-    //added for kcpp
-    void SetCircularAxesAll(bool circular_x, bool circular_y)
-    {
-        diffusion_model->set_circular_axes(circular_x, circular_y);
-        if (high_noise_diffusion_model) {
-            high_noise_diffusion_model->set_circular_axes(circular_x, circular_y);
-        }
-        if (control_net) {
-            control_net->set_circular_axes(circular_x, circular_y);
-        }
-        if (first_stage_model) {
-            first_stage_model->set_circular_axes(circular_x, circular_y);
-        }
-    }
-    //end added for kcpp
-
 };
 
 /*================================================= SD API ==================================================*/
@@ -2842,6 +3075,8 @@ const char* sample_method_to_str[] = {
     "euler_cfg_pp",
     "euler_a_cfg_pp",
     "euler_ge",
+    "dpm++2m_sde",
+    "dpm++2m_sde_bt",
 };
 
 const char* sd_sample_method_name(enum sample_method_t sample_method) {
@@ -3083,15 +3318,13 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->eager_load           = false;
     sd_ctx_params->enable_mmap          = false;
     sd_ctx_params->diffusion_flash_attn = false;
-    sd_ctx_params->circular_x           = false;
-    sd_ctx_params->circular_y           = false;
-    sd_ctx_params->chroma_use_dit_mask  = true;
-    sd_ctx_params->chroma_use_t5_mask   = false;
-    sd_ctx_params->chroma_t5_mask_pad   = 1;
     sd_ctx_params->vae_format           = SD_VAE_FORMAT_AUTO;
     sd_ctx_params->backend              = nullptr;
     sd_ctx_params->params_backend       = nullptr;
+    sd_ctx_params->split_mode           = nullptr;
+    sd_ctx_params->auto_fit             = false;
     sd_ctx_params->rpc_servers          = nullptr;
+    sd_ctx_params->model_args           = nullptr;
     sd_ctx_params->pulid_weights_path   = nullptr;
 }
 
@@ -3130,13 +3363,11 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "eager_load: %s\n"
              "backend: %s\n"
              "params_backend: %s\n"
+             "split_mode: %s\n"
+             "model_args: %s\n"
+             "auto_fit: %s\n"
              "flash_attn: %s\n"
              "diffusion_flash_attn: %s\n"
-             "circular_x: %s\n"
-             "circular_y: %s\n"
-             "chroma_use_dit_mask: %s\n"
-             "chroma_use_t5_mask: %s\n"
-             "chroma_t5_mask_pad: %d\n"
              "vae_format: %s\n",
              SAFE_STR(sd_ctx_params->model_path),
              SAFE_STR(sd_ctx_params->clip_l_path),
@@ -3166,13 +3397,11 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              BOOL_STR(sd_ctx_params->eager_load),
              SAFE_STR(sd_ctx_params->backend),
              SAFE_STR(sd_ctx_params->params_backend),
+             SAFE_STR(sd_ctx_params->split_mode),
+             SAFE_STR(sd_ctx_params->model_args),
+             BOOL_STR(sd_ctx_params->auto_fit),
              BOOL_STR(sd_ctx_params->flash_attn),
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
-             BOOL_STR(sd_ctx_params->circular_x),
-             BOOL_STR(sd_ctx_params->circular_y),
-             BOOL_STR(sd_ctx_params->chroma_use_dit_mask),
-             BOOL_STR(sd_ctx_params->chroma_use_t5_mask),
-             sd_ctx_params->chroma_t5_mask_pad,
              sd_vae_format_name(sd_ctx_params->vae_format));
 
     return buf;
@@ -3250,6 +3479,8 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->batch_count       = 1;
     sd_img_gen_params->control_strength  = 0.9f;
     sd_img_gen_params->qwen_image_layers = 3;
+    sd_img_gen_params->circular_x        = false;
+    sd_img_gen_params->circular_y        = false;
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
     sd_img_gen_params->pulid_params      = {nullptr, 1.0f};
     sd_img_gen_params->vae_tiling_params = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
@@ -3283,6 +3514,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "control_strength: %.2f\n"
              "photo maker: {style_strength = %.2f, id_images_count = %d, id_embed_path = %s}\n"
              "VAE tiling: %s (temporal=%s, extra_tiling_args=%s)\n"
+             "circular_x: %s\n"
+             "circular_y: %s\n"
              "hires: {enabled=%s, upscaler=%s, model_path=%s, scale=%.2f, target=%dx%d, steps=%d, denoising_strength=%.2f}\n",
              SAFE_STR(sd_img_gen_params->prompt),
              SAFE_STR(sd_img_gen_params->negative_prompt),
@@ -3304,6 +3537,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              BOOL_STR(sd_img_gen_params->vae_tiling_params.enabled),
              BOOL_STR(sd_img_gen_params->vae_tiling_params.temporal_tiling),
              SAFE_STR(sd_img_gen_params->vae_tiling_params.extra_tiling_args),
+             BOOL_STR(sd_img_gen_params->circular_x),
+             BOOL_STR(sd_img_gen_params->circular_y),
              BOOL_STR(sd_img_gen_params->hires.enabled),
              sd_hires_upscaler_name(sd_img_gen_params->hires.upscaler),
              SAFE_STR(sd_img_gen_params->hires.model_path),
@@ -3353,6 +3588,8 @@ void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
     sd_vid_gen_params->hires.upscale_tile_size               = 128;
     sd_vid_gen_params->hires.custom_sigmas                   = nullptr;
     sd_vid_gen_params->hires.custom_sigmas_count             = 0;
+    sd_vid_gen_params->circular_x                            = false;
+    sd_vid_gen_params->circular_y                            = false;
     sd_cache_params_init(&sd_vid_gen_params->cache);
 }
 
@@ -3609,6 +3846,8 @@ static float resolve_eta(sd_ctx_t* sd_ctx,
             case DPMPP2S_A_SAMPLE_METHOD:
             case ER_SDE_SAMPLE_METHOD:
             case EULER_A_CFG_PP_SAMPLE_METHOD:
+            case DPMPP2M_SDE_SAMPLE_METHOD:
+            case DPMPP2M_SDE_BT_SAMPLE_METHOD:
                 return 1.0f;
             default:;
         }
@@ -4298,6 +4537,25 @@ struct CircularAxesState {
     bool circular_y = false;
 };
 
+static void apply_circular_axes_to_diffusion(sd_ctx_t* sd_ctx, bool circular_x, bool circular_y) {
+    sd_ctx->sd->circular_x = circular_x;
+    sd_ctx->sd->circular_y = circular_y;
+    if (sd_ctx->sd->diffusion_model) {
+        sd_ctx->sd->diffusion_model->set_circular_axes(circular_x, circular_y);
+    }
+    if (sd_ctx->sd->high_noise_diffusion_model) {
+        sd_ctx->sd->high_noise_diffusion_model->set_circular_axes(circular_x, circular_y);
+    }
+    if (sd_ctx->sd->control_net) {
+        sd_ctx->sd->control_net->set_circular_axes(circular_x, circular_y);
+    }
+    if (circular_x || circular_y) {
+        LOG_INFO("Using circular padding for convolutions (x=%s, y=%s)",
+                 circular_x ? "true" : "false",
+                 circular_y ? "true" : "false");
+    }
+}
+
 static CircularAxesState configure_image_vae_axes(sd_ctx_t* sd_ctx,
                                                   const sd_img_gen_params_t* sd_img_gen_params,
                                                   const GenerationRequest& request) {
@@ -4408,13 +4666,56 @@ static std::optional<ImageGenerationLatents> prepare_image_generation_latents(sd
         LOG_INFO("IMG2IMG");
 
         if (request->strength < 1.f) {
-            size_t t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
-            if (t_enc == static_cast<size_t>(plan->sample_steps)) {
-                t_enc--;
+            bool strength_as_noise_level = false;
+            bool force_first_sigma       = false;
+            for (const auto& [key, value] : parse_key_value_args(sd_img_gen_params->sample_params.extra_sample_args, "img2img arg")) {
+                if (key == "strength_as_noise_level") {
+                    if (!parse_strict_bool(value, strength_as_noise_level)) {
+                        LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                } else if (key == "force_first_sigma") {
+                    if (!parse_strict_bool(value, force_first_sigma)) {
+                        LOG_WARN("ignoring invalid img2img sample arg '%s=%s'", key.c_str(), value.c_str());
+                    }
+                }
+            }
+
+            size_t t_enc;
+            float target_sigma = -1;
+            if (!strength_as_noise_level) {
+                t_enc = static_cast<size_t>(plan->sample_steps * request->strength);
+                if (t_enc == static_cast<size_t>(plan->sample_steps)) {
+                    t_enc--;
+                }
+            } else {
+                LOG_DEBUG("Interpreting denoise strength as relative noise level");
+                // assume x_noised = K * (x * (1-noise_level) + noise * noise_level) = K * lerp(x, noise, noise_level)
+                // K = 1, noise_level = sigma for flow models
+                // K = 1+sigma, noise_level=sigma/(1+sigma) for diffusion models
+                float target_noise_level = request->strength;
+                target_sigma             = sd_ctx->sd->denoiser->noise_level_to_sigma(target_noise_level);
+                size_t start_index       = 0;
+                for (size_t i = 0; i < plan->sigmas.size(); ++i) {
+                    if (plan->sigmas[i] <= target_sigma) {
+                        start_index = i;
+                        break;
+                    }
+                }
+
+                if (start_index >= plan->sigmas.size() - 1) {
+                    start_index = plan->sigmas.size() - 2;  // Leave at least 1 step
+                }
+                t_enc = plan->sample_steps - start_index - 1;
             }
             LOG_INFO("target t_enc is %zu steps", t_enc);
             std::vector<float> sigma_sched;
             sigma_sched.assign(plan->sigmas.begin() + plan->sample_steps - t_enc - 1, plan->sigmas.end());
+
+            if (target_sigma > 0 && force_first_sigma && strength_as_noise_level) {
+                LOG_DEBUG("force_first_sigma to %.4f (from %.4f)", target_sigma, sigma_sched[0]);
+                sigma_sched[0] = target_sigma;
+            }
+
             plan->sigmas       = std::move(sigma_sched);
             plan->sample_steps = static_cast<int>(plan->sigmas.size() - 1);
         }
@@ -5011,6 +5312,7 @@ SD_API bool generate_image(sd_ctx_t* sd_ctx,
     sd_ctx->sd->sampler_rng->manual_seed(request.seed);
     sd_ctx->sd->set_flow_shift(sd_img_gen_params->sample_params.flow_shift);
     sd_ctx->sd->apply_loras(sd_img_gen_params->loras, sd_img_gen_params->lora_count);
+    apply_circular_axes_to_diffusion(sd_ctx, sd_img_gen_params->circular_x, sd_img_gen_params->circular_y);
 
     ImageVaeAxesGuard axes_guard(sd_ctx, sd_img_gen_params, request);
 
@@ -5900,6 +6202,7 @@ SD_API bool generate_video(sd_ctx_t* sd_ctx,
     }
     int64_t t0                    = ggml_time_ms();
     sd_ctx->sd->vae_tiling_params = sd_vid_gen_params->vae_tiling_params;
+    apply_circular_axes_to_diffusion(sd_ctx, sd_vid_gen_params->circular_x, sd_vid_gen_params->circular_y);
     GenerationRequest request(sd_ctx, sd_vid_gen_params);
     bool has_input_audio = sd_vid_gen_params->input_audio != nullptr &&
                            sd_vid_gen_params->input_audio->data != nullptr &&
@@ -6327,10 +6630,6 @@ namespace kcpp_sd {
         return res;
     }
 
-    void SetCircularAxesAll(sd_ctx_t* ctx, bool circular_x, bool circular_y) {
-        ctx->sd->SetCircularAxesAll(circular_x, circular_y);
-    }
-
     void set_lora_cache(sd_ctx_t *ctx, bool enable) {
         ctx->sd->kcpp_lora_cache_populate = enable;
     }
diff --git a/otherarch/sdcpp/src/upscaler.cpp b/otherarch/sdcpp/src/upscaler.cpp
index 88a8a6336..dbb99af36 100644
--- a/otherarch/sdcpp/src/upscaler.cpp
+++ b/otherarch/sdcpp/src/upscaler.cpp
@@ -46,6 +46,7 @@ bool UpscalerGGML::load_from_file(const std::string& esrgan_path,
     std::string error;
     if (!backend_manager.init(backend_spec.c_str(),
                               params_backend_spec.c_str(),
+                              /*split_mode_spec=*/nullptr,
                               &error)) {
         LOG_ERROR("upscaler backend config failed: %s", error.c_str());
         return false;
diff --git a/otherarch/sdcpp/src/weight_manager.h b/otherarch/sdcpp/src/weight_manager.h
index 28d6cf5c4..82f6d03e4 100644
--- a/otherarch/sdcpp/src/weight_manager.h
+++ b/otherarch/sdcpp/src/weight_manager.h
@@ -3,10 +3,14 @@
 
 #include <vector>
 
+#include "ggml-backend.h"
+
 struct ggml_tensor;
 
 struct RunnerWeightManager {
     virtual ~RunnerWeightManager()                                                        = default;
+    virtual bool assign_compute_backend(const std::vector<ggml_tensor*>& tensors,
+                                        ggml_backend_t compute_backend)                   = 0;
     virtual bool prepare_params(const std::vector<ggml_tensor*>& tensors)                 = 0;
     virtual void release_compute_backend_params(const std::vector<ggml_tensor*>& tensors) = 0;
     virtual void release_params_backend_params(const std::vector<ggml_tensor*>& tensors)  = 0;

From dc8f2b2cd1c950ae5a492ce2e40a44532642c364 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Wed, 8 Jul 2026 17:26:47 +0800
Subject: [PATCH 291/292] updated cmake

---
 CMakeLists.txt | 2 ++
 koboldcpp.py   | 2 +-
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bfced02f9..b3f782aaf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -537,6 +537,8 @@ add_library(sdtype_adapter
             otherarch/sdcpp/src/tokenizers/gpt_oss_tokenizer.cpp
             otherarch/sdcpp/src/tokenizers/tokenizer.cpp
             otherarch/sdcpp/src/tokenizers/tokenize_util.cpp
+            otherarch/sdcpp/src/core/backend_fit.cpp
+            otherarch/sdcpp/src/core/layer_split_partition.cpp
             otherarch/sdcpp/src/core/ggml_extend_backend.cpp)
 target_include_directories(sdtype_adapter PUBLIC . ./ggml/include ./ggml/src ./ggml/src/ggml-cpu ./include ./otherarch ./otherarch/tools ./vendor/stb ./vendor/nlohmann ./vendor ./otherarch/sdcpp ./otherarch/sdcpp/include ./otherarch/sdcpp/src ./otherarch/sdcpp/examples ./tools ./common)
 target_compile_features(sdtype_adapter PUBLIC cxx_std_17) # don't bump
diff --git a/koboldcpp.py b/koboldcpp.py
index bfc16ee10..68d2d2857 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -2500,7 +2500,7 @@ def sd_get_device_override(deviceid, module=''):
         result = module + '=' + device_name
     else:
         result = device_name
-    return result;
+    return result
 
 def sd_load_model(model_filename,vae_filename,t5xxl_filename,clip1_filename,clip2_filename,photomaker_filename,upscaler_filename,audio_vae_filename):
     global args

From 3f0bab3a3b9b420210d607341998054726ba9751 Mon Sep 17 00:00:00 2001
From: Concedo <39025047+LostRuins@users.noreply.github.com>
Date: Fri, 10 Jul 2026 15:18:17 +0800
Subject: [PATCH 292/292] fixed default gen amt not actually increased (+1
 squashed commits)

Squashed commits:

[8e2417d83] fixed default gen amt not actually increased
---
 koboldcpp.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/koboldcpp.py b/koboldcpp.py
index 68d2d2857..2c9862e1c 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -78,7 +78,7 @@ dry_seq_break_max = 128
 extra_images_max = 4 # for kontext/qwen img
 
 # global vars
-KcppVersion = "1.117"
+KcppVersion = "1.117.1"
 showdebug = True
 kcpp_instance = None #global running instance
 global_memory = {"tunnel_url": "", "restart_target":"", "input_to_exit":False, "load_complete":False, "restart_override_base_config":"", "last_active_timestamp":datetime.now(), "triggered_sleeping":False, "current_model":"initial_model", "base_config":"", "swapReqType": None, "autoswapmode": False}
@@ -12362,7 +12362,7 @@ if __name__ == '__main__':
     advparser.add_argument("--chatcompletionsadapter", metavar=('[filename]'), help="Select an optional ChatCompletions Adapter JSON file to force custom instruct tags.", default="AutoGuess")
     advparser.add_argument("--cli", help="Does not launch KoboldCpp HTTP server. Instead, enables KoboldCpp from the command line, accepting interactive console input and displaying responses to the terminal.", action='store_true')
     advparser.add_argument("--debugmode", help="Shows additional debug info in the terminal. Levels: -1 (Horde-quiet, suppresses non-essential prints; auto-applied when Horde args are set), 0 (default, normal output), 1 (verbose: extra slot/cache info, larger print buffers, retains horde-debug prefix). Passing the flag without a value implies 1.", nargs='?', const=1, type=int, default=0)
-    advparser.add_argument("--defaultgenamt", help="How many tokens to generate by default, if not specified. Must be smaller than context size. Usually, your frontend GUI will override this.", type=check_range(int,64,16384), default=default_genlen)
+    advparser.add_argument("--defaultgenamt", help="How many tokens to generate by default, if not specified. Must be smaller than context size. Usually, your frontend GUI will override this.", type=check_range(int,64,32768), default=default_genlen)
     advparser.add_argument("--device", "-dev", metavar=('<dev1,dev2,..>'), help="Set llama.cpp compatible device selection override. Comma separated. Overrides normal device choices.", default="")
     advparser.add_argument("--downloaddir", metavar=('[directory]'), help="Specify a directory that models will be downloaded to or searched from, if unset uses the working directory.", default="")
     advparser.add_argument("--draftamount","--draft-max","--draft-n","--spec-draft-n-max", metavar=('[tokens]'), help="How many tokens to draft per chunk before verifying results", type=int, default=default_draft_amount)